Almond as a model to explore epigenetic signatures ...

Almond as a model to explore epigenetic signatures associated with aging in perennial

plants

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

By

Katherine Mary D’Amico Willman, MS

Graduate Program in Translational Plant Sciences

The Ohio State University

2021

Dissertation Committee

Dr. Jonathan Fresnedo Ramirez, Advisor

Dr. Andrew Michel, Co-Advisor

Dr. Eric Stockinger

Dr. Leah McHale

Dr. Tea Meulia

Dr. Chad Niederhuth

Copyrighted by

Katherine M. D. Willman

2021

ii

Abstract

All organisms age; however, our knowledge of aging and the exhibition of the process

itself varies dramatically by species. While less is known about the aging process in

plants compared to other species, perennial plant species do age, resulting in reduced

physiological competence and leading to the development of aging-related disorders and

phenotypes. The effects of aging are of particular relevance to productive perennials,

such as fruit and nut crops, whose production represents a key economic sector in the US

and whose supply chain provides healthy food to consumers. Almond, an economically

important perennial nut-crop grown almost exclusively in the US, exhibits an aging-

related disorder called non-infectious bud-failure (BF). BF-exhibition makes almond an

ideal perennial system to test aging-related hypotheses and develop aging models since

this process is directly linked to the exhibition of a disorder with a quantifiable phenotype

that affects economic relevance in a primarily clonally propagated crop. Currently, little

information is available to track either age or BF-potential in almond germplasm. The

aim of this work is to test the hypotheses that quantifiable, differential genomic features

like telomere length, expression of the telomerase-associated gene, TERT, and epigenetic

signatures like DNA methylation in almond are associated with age and with BF-

exhibition. The research herein explores these features using quantitative PCR and

bisulfite and enzymatic sequencing approaches to test their suitability as potential

iii

biomarkers of age and BF-potential in almond. To test putative epigenetic signatures

associated with BF-potential, DNA methylome profiling was performed, utilizing a

unique monozygotic twin almond germplasm collection with sets of twins discordant for

BF-exhibition. In an effort to identify potential biomarkers of age in almond, DNA

methylation profiles were generated for ~70 almond breeding selections from three

distinct age cohorts. Finally, telomere length and TERT expression were measured over

two years of sampling almond breeding selections of known age. Results from this

collective work revealed a pattern of hypermethylation and a pattern of decreased

telomere length and TERT expression as trees age. Further, DNA demethylation was

found to be associated with BF-exhibition in the two sets of twin almonds analyzed. In

methylation studies, specific regions of the genome were identified with differential

methylation profiles associated with either age or BF-status. These regions and their

associated genetic features (i.e., genes, micro RNAs, etc.) are of interest to develop

biomarkers in almond and investigate the underlying mechanisms that contribute to BF-

development and regulate the aging process in this species. Taken together, the results

from this work represent a foundation upon which future work can be done to validate the

differential methylation and telomeric patterns observed in these studies. The suitability

of these patterns as potential biomarkers of age and/or BF-potential can also be assessed

in future studies, as well as the applicability of extending this approach to other perennial

crops. Expanding our knowledge of the aging process in perennial plants will benefit

breeders, growers, and consumers by ensuring a resilient supply chain of these crops and

will enhance our understanding of this complex process in plants.

iv

Dedication

To Bryan, I miss you always and hope you found some peace

To Matthew, the love and light of my life

v

Acknowledgments

When reflecting on my time as a PhD student, one word continues to come to mind:

unexpected. Nothing about my PhD has gone the way I expected, from loss and grief, to

love and marriage, from oaks to almonds, to a global pandemic, but through it all I have

had the unwavering support of my family, friends, and OSU colleagues for whom I am

eternally grateful. I first want to thank my advisor, Jonathan, for accepting me into his

lab, letting me take ownership of this crazy almond project, and for the hours of

stimulating and fun conversation. I genuinely appreciate all of his guidance, mentoring,

and encouragement. I also want to thank all my lab mates, past and present, including

David, Caterina, Beth, Cheri, Debbie, Elizabeth, and Daniel. It was truly a joy to be a

member of the Fresnedo Ramirez lab these past four years. A special thanks to Elizabeth

for being an incredible undergraduate mentee, lab manager, and friend. The work I

present in this document would not have been possible without her help.

I also want to thank my co-advisor, Andy, for all his advice and support

throughout my PhD program, as well as my student advisory committee for their

guidance. I would like to thank Leah for her comfort and kind words during a difficult

time. I would also like to thank Chad for teaching me so much about epigenetics and for

contributing his expertise to these projects. Additionally, a big thank you to the entire

Plant Pathology and Horticulture and Crop Science departments. Completing a degree is

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not possible without the fiscal associates, custodians, grant managers, greenhouse staff,

librarians, etc. who do so much to keep our work running smoothly. I would also like to

thank the staff, past and present, of the Center for Applied Plant Sciences for their

support, especially Mike Sovic for his bioinformatics and sequencing advice. I cannot

thank Courtney Price enough for everything she has done to guide the TPS program and

its students. She is an amazing graduate program coordinator, and I am happy to call her

my friend.

Finally, I would like to thank all my friends and family. I have been so fortunate

to meet such an amazing group of people during my PhD program. To my first and best

friend at OSU, Vivian, I will cherish our friendship, always. I am also so grateful that

living and working in Wooster has kept me so close to family. To my sister, my best

friend from birth and my constant confidante, you are my lifeline. And to my parents, I

am able to reach high because you are my foundation. And finally, to Matt, meeting you

and building our life together is the happiest, most wonderful thing to come from the past

six years.

vii

Vita

2008 B.S. Biology, John Carroll University

2012 M.S. Conservation Biology, SUNY College of

Environmental Science and Forestry

2012-2015 Molecular Biologist, USDA Agricultural Research Service

2015-2019 Translational Plant Sciences Graduate Fellow

2019-2021 USDA NIFA AFRI Education and Workforce

Development Predoctoral Fellow

Publications

D’Amico-Willman, K.M., Anderson, E.S., Gradziel, T.M., and Fresnedo Ramirez, J.

(2021) Telomere length and Telomerase Reverse Transcriptase (TERT) expression

are associated with age in almond (Prunus dulcis [Mill.] D.A.Webb). Plants 10.

Conrad, A.O., McPherson, B.A., Lopez-Nicora, H., D’Amico, K.M., Wood, D.L., and

Bonello, P. (2019). Disease incidence and spatial distribution of host resistance in

a coast live oak/sudden oak death pathosystem. Forest Ecology and Management

433, 618-624.

Chakravarthy, S., Butcher, B., Liu, Y., D’Amico, K., Coster, M., and Filiatrault, M.

(2017). Virulence of Pseudomonas syringae is modulated through the catabolite

repression control protein Crc. Molecular Plant-Microbe Interactions 30, 283-

294.

D’Amico, K., and Filiatrault, M. (2017). The conserved hypothetical protein

PSPTO_3957 is essential for virulence in the plant pathogen Pseudomonas

syringae pv. tomato DC3000. FEMS Microbiology Letters 364.

viii

Butcher, B., Chakravarthy, S., D’Amico, K., Stoos, K.B., and Filiatrault, M. (2016).

Disruption of the carA gene in Pseudomonas syringae results in reduced fitness

and alters motility. BMC Microbiology 12, 194-209.

D’Amico, K.M., Horton, T.R., Maynard, C.A., Stehman, S.V., Oakes, A.D. and Powell,

W.A. (2015). Assessing Ectomycorrhizal Associations on Transgenic American

Chestnut Compared to the Wild Type, a Conventionally-Bred Hybrid, and Related

Fagaceae Species. Applied and Environmental Microbiology 81, 100-108.

Park, S.H., Bao, Z., Butcher, B.G., D’Amico, K., Xu, Y., Stodghill, P., Schneider, D.J.,

Cartinhour, S., and Filiatrault, M.J. (2014). Analysis of the small RNA spf in the

plant pathogen Pseudomonas syringae pv. tomato strain DC3000. Microbiology

160 941-953.

Park, S.H., Butcher, B.G., Anderson, Z., Pellegrini, N., Bao, Z., D’Amico, K., and

Filiatrault, M.J. (2013). Analysis of the small RNA P16/RgsA in the plant

pathogen Pseudomonas syringae pv. tomato strain DC3000. Microbiology 159,

296-306.

D’Amico, K.M. (2012). Assessing Ectomycorrhizal Associations on Chestnut:

Comparing Transgenic, Wild-type, a Conventionally-bred Hybrid and Related

Fagaceae Species. Master’s Thesis. SUNY College of Environmental Science and

Forestry.

Fields of Study

Major Field: Graduate Program in Translational Plant Sciences

ix

Table of Contents

Abstract ............................................................................................................................... ii

Dedication .......................................................................................................................... iv

Acknowledgments............................................................................................................... v

Vita .................................................................................................................................... vii

List of Tables ................................................................................................................... xiv

List of Figures ................................................................................................................. xxii

Chapter 1 Introduction ..................................................................................................... 1

Almond (Prunus dulcis [Mill.] D.A. Webb) ................................................................... 1

Biology and production............................................................................................... 1

Meristem development, shoot development, and dormancy ...................................... 5

Non-infectious bud-failure history, research, and mitigation ................................... 10

Plant Epigenetics – DNA Methylation ......................................................................... 16

Mechanisms of DNA methylation ............................................................................ 16

Stress-induced DNA methylation and implications .................................................. 20

Methods for measuring DNA methylation ............................................................... 26

Plant Telomere Biology ................................................................................................ 31

Telomere function and mechanisms of maintenance ................................................ 31

Methods for measuring telomere length ................................................................... 33

Aging in Perennial Plants.............................................................................................. 35

Biological predictors of age ...................................................................................... 35

x

Aging-induced disorder development in plants ........................................................ 40

Dissertation Objectives ................................................................................................. 42

References ..................................................................................................................... 44

Chapter 2 Relative telomere length and telomerase reverse transcriptase (TERT)

expression are associated with age in almond (Prunus dulcis [Mill.] D.A. Webb) .......... 72

Katherine M. D’Amico-Willman1,2, Elizabeth S. Anderson3, Thomas M. Gradziel4,

Jonathan Fresnedo-Ramírez1,2, * .................................................................................... 72

Abstract ......................................................................................................................... 73

Introduction ................................................................................................................... 73

Materials and Methods .................................................................................................. 77

Plant Material ............................................................................................................ 77

DNA and RNA Extraction ........................................................................................ 77

Monochrome Multiplex Quantitative PCR (MMQPCR) to Measure Relative

Telomere Lengths ..................................................................................................... 80

cDNA Synthesis and Quantitative Reverse Transcriptase PCR (qRT-PCR) to

Measure Relative Expression of TERT .................................................................... 81

Statistical Analysis .................................................................................................... 82

Results ........................................................................................................................... 83

Association of Relative Telomere Length and Age in Almond ................................ 83

TERT Gene Expression Patterns Associated with Age in Almond .......................... 84

Discussion ..................................................................................................................... 84

Quantitative PCR Approaches Suggest an Association between Relative Telomere

Length and Age in Almond Leaf and Bud Tissues ................................................... 86

TERT Expression Measured by qRT-PCR is Putatively Associated with Age in

Almond Accessions .................................................................................................. 88

Acknowledgments......................................................................................................... 90

References ..................................................................................................................... 91

Chapter 3 Hypermethylation is associated with increased age in almond (Prunus dulcis

[Mill.] D.A.Webb) accessions ........................................................................................ 102

Abstract ....................................................................................................................... 102

Introduction ................................................................................................................. 103

Materials and Methods ................................................................................................ 105

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Plant Material .......................................................................................................... 105

DNA Extraction ...................................................................................................... 106

Enzymatic Methyl-Seq Library Preparation and Illumina Sequencing .................. 107

Processing and Alignment of Enzymatic Methyl-Seq Libraries ............................. 108

Weighted Genome-wide Methylation Analysis of Age-Cohorts ............................ 108

Differential Methylation Analysis of Age-Cohorts ................................................ 109

Classification and annotation of differentially methylated regions ........................ 109

Annotation of unknown protein sequences ............................................................. 112

Results ......................................................................................................................... 112

Genome-wide methylation analysis in almond accessions representing three age-

cohorts ..................................................................................................................... 112

Identification and classification of differentially methylated regions (DMRs)

between age cohorts ................................................................................................ 114

Classification of DMRs as hyper- or hypomethylated in the age cohort comparisons

................................................................................................................................. 115

Annotation of hyper- and hypomethylated differentially methylated regions (DMRs)

................................................................................................................................. 116

Annotation of genes associated with 17 hypermethylated DMRs identified across the

three age cohort age-contrasts ................................................................................. 117

Discussion ................................................................................................................... 118

DNA hypermethylation in the CG and CHH contexts is associated with increased

age in almond .......................................................................................................... 119

Differentially methylated regions (DMRs) in the CG and CHG contexts are enriched

on specific chromosomes in the almond genome ................................................... 121

DMRs as potential biomarkers of age in almond.................................................... 123

Acknowledgements ..................................................................................................... 127

References ................................................................................................................... 128

Chapter 4 Integrated analysis of the methylome and transcriptome of twin almonds

(Prunus dulcis [Mill.] D.A. Webb) reveals genomic features associated with non-

infectious bud-failure ...................................................................................................... 153

Abstract ....................................................................................................................... 153

Introduction ................................................................................................................. 155

Materials and Methods ................................................................................................ 158

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Plant Material .......................................................................................................... 158

DNA Extraction ...................................................................................................... 159

RNA Extraction ...................................................................................................... 160

Whole Genome Bisulfite Sequencing Library Preparation and Illumina Sequencing

................................................................................................................................. 160

Processing and Alignment of Bisulfite-Sequencing Libraries ................................ 161

Identification of Differentially Methylated Regions (DMRs) and Permutation Tests

................................................................................................................................. 162

Annotation of Genes Associated with Shared DMRs and Enrichment Analysis ... 164

mRNA Sequencing Library Preparation and Illumina Sequencing ........................ 165

Processing and Alignment of mRNA Sequencing Libraries .................................. 166

Identifying Differentially Expressed Genes and Integrating Expression Data with

DMR-Associated Genes.......................................................................................... 166

In silico Analysis of a Hypothetical Protein Identified in mRNA Sequencing ...... 167

Results ......................................................................................................................... 168

Contrasting genome-wide DNA methylation status in the ‘Stukey’ twins ............. 168

Identifying regions of differential methylation associated with bud-failure status 169

Annotation of DMR-associated genes identified in the ‘Stukey’ twins ................. 171

Transcriptomic analysis of ‘Stukey’ twin pairs ...................................................... 172

Differentially expressed transcripts associated with bud-failure exhibition ........... 172

Discussion ................................................................................................................... 173

A monozygotic (MZ) twin-based design enables identification of methylomic

signatures associated with BF ................................................................................. 174

Bud-failure exhibition is associated with genome-wide DNA hypomethylation in

almond..................................................................................................................... 174

Genes associated with DMRs are involved in meristem development, DNA

methylation, dormancy, and response to heat stress ............................................... 177

Patterns of differential expression identified in genes related to cell wall

maintenance and metal ion transport ...................................................................... 178

Ethylene responsive factor (ERF) transcription factor family binding sites are

enriched in DMRs ................................................................................................... 179

Acknowledgements ..................................................................................................... 181

References ................................................................................................................... 182

Chapter 5 Prospectives and Conclusions ..................................................................... 224

Overview of research findings in this dissertation ...................................................... 224

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The effect of heat stress on the almond methylome ................................................... 225

Profiling DNA methylation during almond development .......................................... 227

Transgenerational inheritance of DNA methylation in almond .................................. 228

Inducing DNA demethylation in the almond genome ................................................ 229

Phenotyping approaches to characterize non-infectious bud-failure .......................... 230

Developing biomarkers of age in perennial plant species .......................................... 232

Conclusions ................................................................................................................. 233

References ................................................................................................................... 235

Complete Bibliography ................................................................................................... 239

Appendix A: Supplemental Tables and Files.................................................................. 280

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List of Tables

Table 1.1 Number of chromosomes and estimated genome size of select members of the

Rosaceae family (adapted from Jung et al., 2019). ............................................... 71

Table 2.1 Sampling scheme for 2018 and 2019 almond age cohort collections. ............ 100

Table 2.2 Oligos used for all Monochrome Multiplex Quantitative PCR (MMQPCR) and

quantitative reverse transcriptase PCR (qRT-PCR) studies. ............................... 101

Table 3.1 Pairwise comparison of least squared means of weighted percent methylation in

the CG, CHG, and CHH contexts for each chromosome in the ‘Nonpareil’ almond

genome. Age cohort contrasts include the 2 – 11, 7 – 11, and 2 – 7-year contrasts.

Significant contrasts are represented in bold with an alpha = 0.05. ................... 144

Table 3.2 Number of identified differentially methylated regions (DMRs) in each

methylation-context when comparing the three age cohorts. DMRs were identified

with a threshold of p 0.0001. DMRs are classified as hypermethylated if the

percent methylation in that region is greater in the older age cohort compared to

the younger age cohort within each contrast. DMRs are classified as

hypomethylated if the percent methylation in that region is lesser in the older age

cohort compared to the younger age cohort within each contrast. Hypermethylated

DMR values in bold represent those with a cumulative binomial probability <

110-6. ................................................................................................................. 145

Table 3.3 Number of occurrences of overlap when comparing differentially methylated

regions (DMRs) identified in each contrast to those identified in the other

contrasts. The number of overlaps means the number of times a DMR in a

particular age-contrast (e.g. 11-2) overlaps the genomic region of a DMR in one

of the other age-contrasts (e.g. 11-7). The overall comparison indicates the

number of DMRs occurring in overlapping genomic regions in all three contrasts.

xv

DMRs are classified as either hyper- or hypomethylated in each methylation

context. ................................................................................................................ 146

Table 3.4 Genomic coordinates and length in base pairs for the 17 overlapping DMRs

occurring in each of the three age-contrasts. ...................................................... 147

Table 3.5 Annotation of hyper- and hypomethylated differentially methylated regions

(DMRs) in each methylation context and for each age-contrast. The ‘Nonpareil’

genome annotation was used to classify the DMRs into four categories: gene,

exon, five prime untranslated region (5’ UTR), and three prime untranslated

region (3’ UTR). The percentages under each classification represent the

percentage of DMRs from each methylation-context and contrast in each of the

four categories. .................................................................................................... 148

Table 3.6 Annotation of genes associated with 17 hypermethylated differentially

methylated regions (DMRs) occurring in all three age cohort contrasts. The

chromosome (chr) and genomic coordinates (start and end) of each gene are listed

along with the gene identification from the ‘Nonpareil’ genome annotation.

Protein identifiers from InterPro and Pfam databases are also included as well as

gene ontology (GO) terms associated with the gene. ......................................... 149

Table 3.7 Characterization of eight unknown proteins associated with the 17 shared DMR

sequences identified among the three age-contrasts. The unknown protein ID

corresponds to the unknown proteins associated with the shared DMR sequences.

The putative motifs identified within each protein sequence include the position

of the motif within the sequence and the e-value. The number of amino acids and

estimated molecular weight (kDa) are also included for each protein sequence.

Finally, the predicted localization of each protein is provided as well as the

calculated probability of this prediction. ............................................................ 151

Table 4.1 Combined sequencing results from Illumina MiSeq and NextSeq for ‘Stukey’

libraries. Library refers to the combined technical replicate libraries for each

‘Stukey’ individual. The associated SRA Biosample number is listed to access

raw data for this sample on the NCBI SRA repository. Total reads are the number

of reads produced in the MiSeq and NextSeq sequencing runs, and the depth of

xvi

coverage represents the coverage based on the size of the ‘Nonpareil’ genome

(~250 Mbp). Total aligned reads are the number of reads that aligned to the

‘Nonpareil’ almond genome and mapping efficiency is the percentage of total

reads that properly aligned. Coverage aligned represents the depth of coverage for

aligned reads based on the size of the ‘Nonpareil’ almond genome (~250 Mbp).

Finally, conversion efficiency was calculated based on the conversion rate of the

‘Nonpareil’ chloroplast genome, representing a fully unmethylated sequence. . 206

Table 4.2 Percent cytosine methylation in each methylation context (C = cytosine; G =

guanine; H = adenine, thymine, or cytosine) calculated for combined technical

replicate libraries for each ‘Stukey’ individual (individuals exhibiting BF are

represented in bold)............................................................................................. 207

Table 4.3 Contingency tables (a), (b), and (c) contain the number of observed methylated

and unmethylated cytosines in the CG, CHG, and CHH contexts, respectively, for

the BF and no-BF twins in each twin pair (Chi-squared statistic, p-value, and

effect size for effect size for each comparison is included in the table subtitles and

the Pearson’s residual for each count is included in the parentheses in each table).

............................................................................................................................. 208

Table 4.4 Number of DMRs in each methylation context for each ‘Stukey’ twin pair.

DMRs are classified by proximity relative to a gene: upstream (within 2,000 bp)

of a gene, intragenic, or downstream (within 2,000 bp) of a gene (significant

permutation tests are represented in bold). ......................................................... 210

Table 4.5 Number of identified DMRs per twin pair that are associated with the same

gene, are in the same proximity class relative to that gene, and are in the same

context in both ‘Stukey’ twin pair 1 and ‘Stukey’ twin pair 2 (significant

permutation tests are represented in bold). Values in parentheses represent the

number of DMRs from each twin pair that have overlapping genomic coordinates.

............................................................................................................................. 211

Table 4.6 Protein sequences in the UniProtKB Reviewed (Swiss-Prot) database

significantly aligning to genes associated with DMRs both hypermethylated and

hypomethylated in BF twins compared to No-BF twins (e-value 0.0001) ...... 212

xvii

Table 4.7 GO terms assigned to DMR-associated genes for each GO category: biological

process (a), molecular function (b), and cellular component (c). Significantly

enriched GO terms are represented in bold (alpha = 0.1). .................................. 215

Table 4.8 List of the significantly enriched (adjusted p-value < 0.05) transcription factor

family binding sites in the shared DMRs. ........................................................... 221

Table 4.9 Sequencing results from an RNASeq experiment performed on two ‘Stukey’

twin pairs displaying divergent bud-failure exhibition. ...................................... 222

Table 4.10 Annotation of significantly differentially expressed genes in the BF twins

compared to the no-BF twins (p-value < 0.1). .................................................... 223

Table A. 1 Data for the relative telomere length estimation of almond age cohort samples

collected in 2018. Included are the age of the individual in years, the calculated

T/S ratio for each individual, the calculated relative telomere length based on the

T/S ratio, and the calculated z-score for each relative telomere length. ............. 280

Table A. 2 Raw Cq values produced from the technical replicate wells in qPCR using

either the primer for the telomere amplicon or the primer for the PP2A amplicon

for each almond individual from the classified age. ........................................... 281

Table A. 3 Data for the relative telomere length estimation of almond age cohort leaf

samples collected in 2019. Included are the age of the individual in years, the

calculated T/S ratio for each individual, the calculated relative telomere length

based on the T/S ratio, the calculated z-score for each relative telomere length,

and the raw Cq values produced from the technical replicate wells in qPCR using

either the primer for the telomere amplicon or the primer for the PP2A amplicon.

............................................................................................................................. 283



for each almond individual leaf sample from the classified age. ........................ 284

Table A. 5 Data for the relative telomere length estimation of almond age cohort bud

samples collected in 2019. Included are the age of the individual in years, the

calculated T/S ratio for each individual, the calculated relative telomere length

xviii

based on the T/S ratio, the calculated z-score for each relative telomere length,

and the raw Cq values produced from the technical replicate wells in qPCR using

either the primer for the telomere amplicon or the primer for the PP2A amplicon.

............................................................................................................................. 285



for each almond individual bud sample from the classified age. ........................ 286

Table A. 7 Data for relative expression of TERT in almond age cohort samples collected

in 2018. Included are the age of each sample in years, the relative expression of

TERT in each sample calculated from Cq values for TERT and a reference gene,

RPII, the log2 expression for each sample, and raw Cq values for the technical

replicates for both the RPII amplicon and the TERT amplicon. ......................... 288

Table A. 8 Data for relative expression of TERT in almond age cohort samples collected

in 2019. Included are the age of each sample in years, the relative expression of

TERT in each sample calculated from Cq values for TERT and a reference gene,

RPII, the log2 expression for each sample, and raw Cq values for the technical

replicates for both the RPII amplicon and the TERT amplicon. ......................... 289

Table A. 9 Table containing sequencing statistics, conversion efficiencies, total percent

methylation, and percent methylation within each context (CG, CHG, CHH) for

almond accessions presented in this study. ......................................................... 291

Table A. 10 Number of CG hypermethylated DMR-associated genes associated with each

biological process (A), molecular function (B), and cellular component (C) gene

ontology (GO) terms for each of the three age-contrasts (i.e., 11 – 2 year, 11 – 7

year, and 7 – 2 year). Values in each column represent the number of DMR-

associated genes that are associated with each GO term. Black squares indicate no

genes associated with that contrast were assigned the particular GO term. ....... 295

Table A. 11 Number of CG hypomethylated DMR-associated genes associated with each

biological process (A), molecular function (B), and cellular component gene

ontology (GO) terms for each of the three age-contrasts (i.e. 11 – 2 year, 11 – 7


xix



Table A. 12 Number of CHG hypermethylated DMR-associated genes associated with

each biological process (A), molecular function (B), and cellular component gene





Table A. 13 Number of CHG hypomethylated DMR-associated genes associated with






Table A. 14 Number of CHH hypermethylated DMR-associated genes associated with






Table A. 15 Number of CHH hypomethylated DMR-associated genes associated with






Table A. 16 Genomic coordinates for CG hypermethylated (increased methylation in BF

twins compared to no-BF twins) DMRs identified in each twin pair and associated

with the same gene. Coordinates include scaffold number in the 'Nonpareil'

almond reference genome followed by start and end genomic positions. .......... 322

xx

Table A. 17 Genomic coordinates for CG hypomethylated (decreased methylation in BF

twins compared to no-BF twins) DMRs identified in each twin pair and associated

with the same gene. Coordinates include scaffold number in the 'Nonpareil'


Table A. 18 Genomic coordinates for CHG hypermethylated (increased methylation in

BF twins compared to no-BF twins) DMRs identified in each twin pair and

associated with the same gene. Coordinates include scaffold number in the

'Nonpareil' almond reference genome followed by start and end genomic

positions. ............................................................................................................. 326

Table A. 19 Genomic coordinates for CHG hypomethylated (decreased methylation in




positions. ............................................................................................................. 327

Table A. 20 Genomic coordinates for CHH hypermethylated (increased methylation in




positions. ............................................................................................................. 328

Table A. 21 Genomic coordinates for CHH hypomethylated (decreased methylation in




positions. ............................................................................................................. 329

Table A. 22 Genomic coordinates for CG DMRs identified in each twin pair and

associated with the same gene. Coordinates include scaffold number in 'Nonpareil'


Table A. 23 Genomic coordinates for all shared CHG DMRs identified in each twin pair

and associated with the same gene. Coordinates include scaffold number in

xxi


positions. ............................................................................................................. 335

Table A. 24 Genomic coordinates for all shared CHH DMRs identified in each twin pair

and associated with the same gene. Coordinates include scaffold number in


positions. ............................................................................................................. 337

xxii

List of Figures

Figure 1.1 Percentage of global almond production in each country or region 2019/20

(adapted from Almond Board of California, 2020). ............................................. 66

Figure 1.2 Shoot apical meristem (SAM) (A) zones including the central zone (yellow),

peripheral zone (purple), and rib zone (green), and (B) layers including the tunica

(L1 and L2 Cell layer – green and purple) and corpus (L3 Cell layers –pink) (from

Sharma & Fletcher, 2002). .................................................................................... 67

Figure 1.3 Timeline of almond growth and dormancy cycles in the California Central

Valley. ................................................................................................................... 68

Figure 1.4 Characteristic noninfectious bud-failure signs in an almond cultivar: including

bud death and erratic branching patterns. (Photo taken by K.M. D’Amico-

Willman; Davis, CA) ............................................................................................ 69

Figure 1.5 Acreage planted of the almond cultivar ‘Carmel’ from 1988 – 2017. High

incidence of bud-failure exhibition led to a dramatic decrease in planting in the

mid-2000’s. (Source: California Department of Agriculture, 2020) .................... 70

Figure 2.1 Image of the almond cultivar Nonpareil (photo taken by K. D’Amico-Willman

in May 2018). ........................................................................................................ 96

Figure 2.2 Boxplots depicting the calculated z-score of the T/S ratio for leaf samples

within the age cohorts tested. (A) Age cohort collected in 2018. (B) Age cohort

collected in 2019. Significant differences in z-scores between age cohorts based

on ANOVA followed by post hoc Fisher’s least significant difference (LSD) (α =

0.1) are denoted by letter groupings where differing letters indicate significant

differences following means separation analysis (ANOVA 2018 p-value = 0.1077;

ANOVA 2019 p-value = 0.06548). Bold dots represent outliers within each age

cohort. ................................................................................................................... 97

xxiii

Figure 2.3 Boxplot depicting calculated z-score for the T/S ratio for bud samples within

the age cohorts collected in 2019. Significant differences in z-scores between ages

cohorts based on ANOVA followed by post hoc Fisher’s LSD (α = 0.05) are

denoted by letter groupings where differing letters indicate significant differences

following means separation analysis (ANOVA p-value = 0.067). Bold dots

represent outliers within each age cohort.............................................................. 98

Figure 2.4 Normalized expression of TERT for almond samples within the age cohorts

tested. (A) Age cohort collected in 2018. (B) Age cohort collected in 2019.

Significant differences in relative expression between age cohorts based on

ANOVA followed by post hoc Tukey’s HSD (alpha = 0.1) are denoted by the

letter groupings where differing letters indicate significant differences following

means separation analysis (ANOVA 2018 p-value = 0.09087; ANOVA 2019 p-

value = 0.1414). .................................................................................................... 99

Figure 3.1 Proportion of weighted genome-wide methylation in the CG (a), CHG (b), and

CHH (c) methylation-contexts for each age cohort (2, 7, and 11 years-old). Letter

groups represent significant differences based on pairwise comparisons using

least squared means (alpha = 0.05). .................................................................... 135

Figure 3.2 Boxplots depicting the proportion of weighted methylation in each age cohort

(2 years old – red; 7 years old – grey; 11 years old – yellow) across the three

methylation contexts: (a) CG, (b) CHG, and (c) CHH. The black dots represent

outliers................................................................................................................. 136

Figure 3.3 Distribution of lengths in base pairs of differentially methylated regions

(DMRs) identified in each age contrast and methylation context. Panels a-c show

distribution of DMRs identified in the CG context, panels d-f show distribution in

the CHG context, and panels g-l show distribution in the CHH context. The

values listed next to the methylation context indicate the age-contrast (11 – 2 year,

11 – 7 year, and 7 – 2 year). ................................................................................ 137

Figure 3.4 Dot plots representing the number of significant (p < 0.0001) differentially

methylated regions (DMRs) identified in each of the contrasts (11 – 2 years: red;

xxiv

11 – 7 years: grey; 7 – 2 years: yellow) in each methylation-context: (a) CG, (b)

CHG, and (c) CHH. ............................................................................................ 138

Figure 3.5 Circos plots depicting individual hyper- (red) and hypo- (blue) methylated

differentially methylated regions (DMRs) identified in each contrast and

methylation-context. The outer ring of each plot gives the approximate location of

the individual DMRs on each of the eight ‘Nonpareil’ chromosomes represented

by red and blue dots. The middle ring of each plot represents enrichment of

hypermethylated DMRs across each chromosome, and the innermost ring of each

plot represents enrichment of hypomethylated DMRs across each chromosome.

Panel a shows the distribution of DMRs in the CG context, panel b shows

distribution of DMRs in the CHG context, and panel c shows distribution of

DMRs in the CHH context. ................................................................................. 139

Figure 3.6 Heatmaps displaying average percent DNA methylation across cytosines in the

11-year, 7-year, and 2-year age cohorts within the genomic range of 13

overlapping differentially methylated regions (DMRs) in the CG context

identified in the three age- contrasts. The regions correspond to CGDMR1-13 (a-

m; see Table S2) and the values to the right of each heatmap represent the

genomic position of each cytosine on the respective chromosome. ................... 140

Figure 3.7 Heatmaps displaying average percent DNA methylation across cytosines in the

11-year, 7-year, and 2-year age cohorts within the genomic range of 3 overlapping

differentially methylated regions (DMRs) in the CHG context identified in the

three age- contrasts. The regions correspond to CHGDMR1-3 (a-c; see Table S2)

and the values to the right of each heatmap represent the genomic position of each

cytosine on the respective chromosome. ............................................................ 142

Figure 3.8 Heatmap displaying average percent DNA methylation across cytosines in the

11-year, 7-year, and 2-year age cohorts within the genomic range of the

overlapping differentially methylated region (DMR) in the CHH context

identified in the three age-contrast. The regions correspond to CHHDMR1 (see

Table S2). ............................................................................................................ 143

xxv

Figure 4.1 Monozygotic twin almond ‘Stukey’ trees discordant for BF-exhibition; BF

twin (left). ........................................................................................................... 194

Figure 4.2 Percent methylation in the CG context across each of the eight ‘Nonpareil’

genome scaffolds representing the eight almond chromosomes. Chromosome

number is listed above each plot. The solid lines represent bud-failure (BF)

individuals and the dashed lines represent no-BF individuals. The gold lines

represent ‘Stukey’ twin pair 1, and the blue lines represent ‘Stukey’ twin pair 2.

............................................................................................................................. 195

Figure 4.3 Percent methylation in the CHG context across each of the eight ‘Nonpareil’



individuals, and the dashed lines represent no-BF individuals. The gold lines


............................................................................................................................. 196

Figure 4.4 Percent methylation in the CHH context across each of the eight ‘Nonpareil’



individuals, and the dashed lines represent no-BF individuals. The gold lines


............................................................................................................................. 197

Figure 4.5 Heatmap displaying percent methylated cytosines in each twin pair for the

DMRs in each methylation context: (a) CG methylation, (b) CHG methylation,

and (c) CHH methylation. ‘Stukey’ twins 1a and 2a exhibit BF while ‘Stukey’

twins 1b and 2b are BF-free. The Venn diagram represents the total number of

significant DMRs in both ‘Stukey’ twin pairs as well as the number of regions

shared between the pairs. Panel (a) represents the CG context, panel (b) represents

the CHG context, and panel (c) represents the CHH context. ............................ 198

Figure 4.6 Distribution of length of all DMRs found in each twin pair in all methylation

contexts. .............................................................................................................. 199

xxvi

Figure 4.7 Differential cytosine methylation in each DMR and expression of genes

associated with the DMR in almond twin pairs discordant for BF-exhibition. The

heatmap in red and blue represents the difference in percent methylation in the BF

twin compared to the no-BF twin for every significant shared DMR in each

context. The heatmaps in purple and yellow represent the differential expression

in the BF twins compared to the no-BF twins for the genes associated with each

DMR. Panel (a) represents the CG context DMRs, panel (b) represents CHG

context DMRs, and panel (c) represents CHH context DMRs. .......................... 200



heatmap in red and blue represents the difference in percent methylation in the BF

twin compared to the no-BF twin for every significant shared DMR in each

proximity class. The heatmaps in purple and yellow represent the differential

expression in the BF twins compared to the no-BF twins for the genes associated

with each DMR. Panel (a) represents the DMRs upstream (within 2,000 bp) of a

gene, panel (b) represents the intragenic DMRs, and panel (c) represents the

DMRs downstream (within 2,000 bp) of a gene. ................................................ 201

Figure 4.9 Linear regression of percent methylation within shared regions with significant

differential cytosine methylation in no-BF ‘Stukey’ twins (a, c, e) and BF

‘Stukey’ twins (b, d, f). Panels a and b represent methylation in DMRs upstream

(within 2,000 bp) of a gene, panels c and d represent methylation in intragenic

DMRs, and panels e and f represent methylation in DMRs downstream (within

2,000 bp) of a gene. Each circle represents the percent methylation in each twin in

a single region that is significantly differentially methylated in both ‘Stukey’ twin

pairs. Red circles represent DMRs in the CG context, green triangles represent

DMRs in the CHG context, and blue squares represent DMRs in the CHH

context. ................................................................................................................ 202

Figure 4.10 Number of transcription factor binding sites identified in the shared DMRs in

all methylation contexts. ..................................................................................... 203

xxvii

Figure 4.11 Log2 fold change by normalized count means of genes observed in bud-

failure compared to no-bud-failure ‘Stukey’ twins. Red points represent those

defined as significantly differentially expressed in each contrast (p-value < 0.1).

............................................................................................................................. 204

Figure 4.12 Principal component analysis of differential gene expression data showing

separation by ‘Stukey’ twin pair and BF condition. ........................................... 205

1

Chapter 1 Introduction

Almond (Prunus dulcis [Mill.] D.A. Webb)

Biology and production

Almond (Prunus dulcis [Mill.] D.A. Webb) is a member of the Rosaceae family which

includes other related Prunus species such as peach (P. persica [L.] Batsch), sweet cherry

(P. avium L.), and apricot (P. armeniaca L.). Almond is diploid and its genome contains

a total of 16 chromosomes (Corredor et al., 2004) (Table 1.1). The almond genome is

relatively small, ~240 Mb (approximately twice as large as Arabidopsis thaliana (L.),

Heynh.), making genetic analyses in this species more feasible and simpler in comparison

to those with large genome sizes like gymnosperms (Jung et al., 2019; Soundararajan et

al., 2019) (Table 1.1). As a member of the Prunus genus, almond produces drupe or stone

fruits; however, unlike other Rosaceous crops, the edible seed or kernel is the commercial

part of the almond fruit, classifying almond as a nut crop. The domesticated almond

kernel is unique because of its sweetness (hence the species name dulcis), and this flavor

profile is an important trait in modern almond breeding and production efforts (Socias i

Company & Gradziel, 2017). The native range of almond is in central Asia where its wild

relatives are still present (Kester et al., 1991; Browicz & Zohary, 1996; Ladizinsky,

1999), and almond tends to grow best in Mediterranean climates where most modern-day

cultivation occurs (i.e. California’s Central Valley) (Kester & Asay, 1975).

2

Almond represents the oldest domesticated and cultivated nut crop, with evidence

of domestication dating back to as early as the 4th millennium B.C. (Spiegel-Roy, 1986).

Wild almond trees typically produce kernels high in cyanide and thus, with a bitter,

undesirable flavor, but some individual trees produce a sweet kernel resulting from a

genetic mutation and were identified by early domesticators and selected for cultivation

(Socias i Company & Gradziel, 2017; Sánchez-Pérez et al., 2019). In addition to the

flavor, almonds were a preferred food source because of their high fat content, noted

medicinal properties, size, and portability (Albala, 2009; Socias i Company & Gradziel,

2017). Early cultivation of almond occurred in two major stages: Asiatic and

Mediterranean, following movement of almond along major trade routes in Asia and

Europe including the old Silk Road (Socias i Company & Gradziel, 2017). In fact, traces

of almond were identified in the tomb of King Tutankahamun, suggesting almond

cultivation in the Mediterranean as early as the 2nd millennium B.C. (Zohary et al., 2012).

Asian and European cultivation resulted in development of local and regional cultivars

and landraces for specific horticultural and culinary characteristics (Socias i Company &

Gradziel, 2017).

Following early establishment of almond in Asia and Europe, new world

cultivation began in North America following colonization by Spanish missionaries

(Socias i Company et al., 2012). Attempts to grow almond in North America outside of

the Central Valley in California mostly failed; however, serious attempts to establish

almond orchards in the US began in the mid-1800s, with the first documented orchard in

the Sacramento Valley in 1843 (Micke, 1996). The foundation for the US almond

industry was set in 1879 when A. T. Hatch made four selections from an orchard of over

3

2,000 seedlings that he named ‘Nonpareil’, ‘IXL’, ‘Ne Plus Ultra’, and ‘La Prima’

(Micke, 1996). In 1923, a cooperative almond breeding program was established in

Davis, CA by the USDA and University of California, which is now the oldest

continuous breeding program for almond, releasing cultivars such as ‘Winters’,

‘Sweetheart’, ‘Avalon’, and ‘Morely’ (Socias i Company & Gradziel, 2017).

The US almond industry now represents approximately 78% of the world’s

production and contributes greatly to the US economy, with the value of production in

2017 equaling ~$5.6 billion (USDA NASS, 2019; Almond Board of California, 2020)

(Fig. 1.1). In 2020, approximately 500,000 hectares of almond were planted in the US

producing ~1.4 billion kilograms of almonds for domestic consumption and export

(Almond Board of California, 2020). The cultivar, ‘Nonpareil’, is still the most planted

cultivar representing 41% of US production, followed by ‘Monterey’, ‘Butte/Padre’,

‘Independence’, and ‘Carmel’ (Almond Board of California, 2020). Further, almonds are

the most consumed nut in the US, far exceeding any other nut crop (Almond Board of

California, 2020), and are used in a variety of commercial applications including

confectionaries, bakery, and alternative-milks and creamers (Socias i Company et al.,

2012; Socias i Company & Gradziel, 2017). Driving the consumption of almonds are the

health benefits afforded by the chemical composition of the kernel (Socias i Company &

Gradziel, 2017). Almond kernels are high in oleic and linoleic acids, phytosterols,

proteins (globulins & albumins), tocopherols (antioxidants, including vitamin E), and

dietary fiber, all contributing to the myriad of health benefits including reduced

cholesterol, heart disease, obesity, and diabetes among others (Socias i Company &

Gradziel, 2017).

4

The success of the almond industry in the US is due in part to technological

advances and mechanization of production (Micke, 1996; Socias i Company et al., 2012).

In certain almond growing regions, including the native range in Central Asia, almonds

are still produced via traditional methods of seedling establishment and rain-fed irrigation

(Micke, 1996). However, almond production in places like Europe and the US relies

heavily on irrigation systems in orchards and on clonal propagation by bud-wood grafting

onto established rootstocks (Micke, 1996; Socias i Company & Gradziel, 2017). Almond

is primarily self-incompatible (Socias i Company, 1990; Socias i Company et al., 2010),

meaning inbred lines cannot be created following establishment of desirable cultivars, as

self-fertilization does not produce viable offspring. Thus, clonal propagation is used to

maintain desirable characteristics (e.g., nut quality and growth habit) in select cultivars

while sexual reproduction and seedling establishment are reserved for breeding programs

(Micke, 1996). Most productive almond cultivars are self-incompatible, including

‘Nonpareil’, meaning that orchards must include selections solely for cross-pollination,

making self-compatibility a highly-sought after trait for most almond breeding efforts

(Socias i Company, 1990; Socias i Company, 2002; Ortega & Dicenta, 2003; Socias i

Company et al., 2010). A new almond cultivar called ‘Independence’ was recently

developed that is self-compatible, meaning it can self-pollinate without the need for

pollinator cultivars (Zaiger et al., 2009). ‘Independence’ is reported to come from a (P.

dulcis P. persica) P. dulcis cross and has a flavor profile similar to ‘Nonpareil’

(Zaiger et al., 2009).

Once cultivars are selected for orchard establishment, almond trees must be

continually managed throughout their commercial lifespans to maintain productivity

5

either by pruning or other cultural practices like fertilization and irrigation (Micke, 1996;

Socias i Company & Gradziel, 2017). Once a tree is planted, training and pruning are

required for the first three growing seasons in order to establish the tree’s shape by

defining the primary scaffolds or branches to anchor the tree for its remaining

commercial life (Socias i Company & Gradziel, 2017). Once a tree reaches maturity (~4

years after planting), pruning is only required to facilitate cultural practices like

harvesting and spraying, as research has shown that pruning mature almond does not

contribute to increased or maintained yield in orchards (Socias i Company & Gradziel,

2017). The average commercial lifespan of an almond tree in an orchard is ~20 years

following the three to four-year juvenile period when the tree is not productive (Boriss &

Brunke, 2005). Almond tree planting is thus cyclical with trees planted continuously to

maintain productivity as the orchard ages (Micke, 1996).

Meristem development, shoot development, and dormancy

As almond is primarily clonally propagated via bud-wood grafting, vegetative meristem

development is key to sustained almond production. Vegetative meristem or shoot apical

meristem (SAM) is defined as meristematic tissue resulting in organ initiation and above-

ground plant growth as opposed to root apical meristem which produces new root growth

(Kerstetter & Hake, 1997). SAM, which is formed during embryogenesis, is pluripotent

giving rise to cells that will form organs, including leaves and flowers, and axillary and

adventitious buds (Carles & Fletcher, 2003). SAM itself is comprised of three zones of

cells: the peripheral zone, the central zone, and the rib zone (Kerstetter & Hake, 1997;

Sharma & Fletcher, 2002; Carles & Fletcher, 2003) (Fig. 1.2a). The peripheral zone cells

become lateral organs including leaves while the rib zone cells become the interior of the

6

stem (Sharma & Fletcher, 2002). The central zone cells are akin to stem cells in humans,

remaining undifferentiated and pluripotent (Sharma & Fletcher, 2002). The SAM is

further classified into two cell layers: the tunica (cell layers L1 and L2) and the corpus

(cell layers L3), which are differentiated based on planal growth patterns where the tunica

grows in a single plane and the corpus grows in all planes (Sharma & Fletcher, 2002;

Carles & Fletcher, 2003) (Fig. 1.2b). The upper tunica layer (L1) contributes to formation

of the epidermis in plant organs and the L2 layer contributes to development of

mesophyll and germline cells (Sharma & Fletcher, 2002; Carles & Fletcher, 2003). The

corpus (L3) is involved in development of vascular tissues in the stem and leaves

(Kerstetter & Hake, 1997; Sharma & Fletcher, 2002).

Regions of vegetative meristematic tissue, or buds, are typically classified into

three types: apical, axillary, and adventitious. Apical buds are located at the actively

growing tips of plant branches and exhibit apical dominance over axillary buds,

inhibiting growth of these buds through hormonal cues (Cline, 1991). Axillary buds form

in the axials of leaves and are only reactivated following disruption of apical dominance

from the SAM, typically from pruning or herbivory, after which the axillary buds

transition to apical buds (Cline, 1991; Kerstetter & Hake, 1997). Finally, adventitious

buds form in other locations in a plant, sometimes representing former axillary buds

covered by bark, and remain dormant similarly to axillary buds unless released from

apical dominance (Stone & Stone, 1943; Cline, 1991; Kerstetter & Hake, 1997). Shoots

that grow from adventitious buds are often referred to as epicormic, and their initiation

and growth can result from a number of factors including environmental stimuli and

hormonal signals (Kerr, 2001; Gordon et al., 2006b; Meier et al., 2012). As epicormic

7

branching is generally considered undesirable in fruit tree production due to issues of

crowding out or shading by excessive growth (Tymoszuk et al., 1981; Day et al., 1989;

Myers, 1993), most research related to epicormic buds and meristem formation in Prunus

is focused on factors influencing initiation of epicormic buds including timing of pruning

and water stress (Gordon et al., 2006b; Costes et al., 2014; Negrón et al., 2014).

In addition to epicormic branches, almond (along with other fruit tree species

including peach) produces two types of shoots from axillary buds: proleptic and sylleptic

(Negrón et al., 2014; Prats-Llinàs et al., 2019). Proleptic shoots are produced from

axillary buds following a period of dormancy and are considered preformed, meaning the

number of nodes is already established within the bud and growth is just the result of cell

elongation between nodes (Wilson, 2000; Gordon et al., 2006a; DeJong et al., 2012).

Almond spurs are short, fruit-bearing proleptic shoots that form laterally on existing

proleptic shoots and are typically viable for 3-5 years, producing anywhere from 1-5

fruits (Micke, 1996; Lampinen et al., 2011). Spurs always contain vegetative apical buds

but also have the ability to produce up to 5 flower buds per growing season, though not

all of these flower buds produce fruit (Micke, 1996). Sylleptic shoots, however, are

shoots that form from axillary buds without a dormancy period and are considered both

preformed and neoformed. This means sylleptic shoots have a predetermined number of

nodes but can continue to grow, adding neoformed nodes as long as environmental

conditions remain favorable (Wilson, 2000; Dejong et al., 2012; Prats-Llinàs et al.,

2019). Epicormic shoots from adventitious buds (former axillary buds), as described

above, are completely neoformed and tend to have lower numbers of flower buds

compared to long, proleptic shoots (Gordon et al., 2006b; Prats-Llinàs et al., 2019).

8

As a perennial species, almond goes through cycles of growth, dormancy, and

reproduction. Dormancy, which represents a reduction of meristematic growth, occurs in

response to either environmental cues or internal stimuli (Anderson et al., 2001) and can

be classified based on regulation as: ecodormancy, paradormancy, or endodormancy

(Lang et al., 1987). Ecodormancy is regulated by environmental factors like water stress

or temperature, paradormancy is regulated by internal factors outside the structure (or

bud) being affected (e.g. apical dominance), and endodormancy is regulated by factors

coming from the structure (or bud) itself as a response to things like photoperiod or

chilling (Lang et al., 1987).

In almond, a cycle of dormancy occurs each season starting in early winter

(November – mid-December) with endodormancy resulting from response to cold

temperatures (Lang et al., 1987; Micke, 1996) (Fig. 1.3). Following the accumulation of

chilling hours, endodormancy is broken and almond trees enter ecodormancy (which is

then regulated by accumulation of heat units) (Freeman & Martin, 1981; Micke, 1996;

Alonso et al., 2005) (Fig. 1.3). Among the Prunus species, almond has the lowest chilling

requirement, meaning it requires accumulation of the fewest number of hours below a

critical temperature to break endodormancy (Egea et al., 2003; Alonso et al., 2005;

Benmoussa et al., 2017). Additionally, almond has a low heat requirement to release

ecodormancy in comparison to other Prunus species, though the number of chilling hours

and growing degree hours can vary dramatically by cultivar (Egea et al., 2003; Alonso et

al., 2005; Benmoussa et al., 2017). Following the break from ecodormancy, almond

flowers and then experiences a period of active vegetative growth lasting until late spring

(Micke, 1996) (Fig. 1.3). Vegetative buds form in early summer, followed by a period of

9

summer dormancy resulting from a combination of ecodormancy, caused by high

summer temperatures, and paradormancy, induced by apical dominance (Micke, 1996;

Balachowski et al., 2016). As temperatures begin to drop in the fall, endodormancy is

induced and the cycle begins again (Fig. 1.3).

In addition to environmental cues regulating dormancy, several genetic

mechanisms controlling these processes have been examined in Prunus species. In an

effort to identify genes or gene families involved in dormancy regulation, researchers

have utilized RNA sequencing to characterize the transcriptome in almond at various

stages of bud dormancy (Barros et al., 2012; Xu et al., 2014; Castède et al., 2015;

Falavigna et al., 2019; Yu et al., 2020; Prudencio et al., 2020). Initial results revealed a

link between the gene family Dormancy Associated MADS-BOX (DAM) and dormancy

and flowering time in almond (Bielenberg et al., 2008; Yamane et al., 2011; Xu et al.,

2014; Zhu et al., 2015; Prudencio et al., 2018a; Falavigna et al., 2019; Yu et al., 2020).

This family of genes is homologous to the Short Vegetative Phase (SVP) genes

characterized in Arabidopsis (Gregis et al., 2013; Falavigna et al., 2019). Later, C-repeat

binding factor (CBF) genes were found to be regulators of dormancy pathways in

response to chilling (Barros et al., 2012). The CBF family regulates DAM genes among

others to control dormancy processes (Barros et al., 2012; Zhao et al., 2018). Further

work has identified other genetic factors involved in regulating dormancy in almond,

including epigenetic mechanisms like DNA methylation and histone modifications

(Rothkegel et al., 2017; Prudencio et al., 2018c; Zhu et al., 2020). The interest in genetic

components of dormancy in almond is focused primarily on flowering time since this is a

particularly vulnerable time for the industry due to the risk of spring frosts (Micke, 1996;

10

Socias i Company & Gradziel, 2017). Late-blooming cultivars like ‘Butte’, ‘Texas’,

‘Lauranne’, and ‘Penta’ have been developed through almond breeding efforts in

attempts to mitigate this risk (Socias i Company & Gradziel, 2017; Prudencio et al.,

2018b).

Non-infectious bud-failure history, research, and mitigation

Non-infectious bud-failure (BF) is a genetic disorder of almond first described in 1944

(Wilson & Stout, 1944) but observed in the cultivars ‘Nonpareil’ and ‘Peerless’

beginning in the early 1900s (Wilson & Stout, 1944; Micke, 1996) (Fig. 1.4). The

disorder occurs when vegetative buds formed in the previous season fail to emerge in the

spring (Wilson & Schein, 1956). Typically, basal buds of BF shoots are less affected and

following the death of terminal buds, the unaffected buds break dormancy resulting in

lateral shoot growth and creating a “witch’s broom” like branching pattern that has led

some to refer to BF as ‘crazytop’ (Wilson & Schein, 1956; Kester, 1969; Hellali et al.,

1978). Affected buds tend to occur on shoots formed in warmer temperatures later in the

growing season (i.e. sylleptic shoots), while buds on proleptic and epicormic shoots seem

to be less affected (Micke, 1996). In addition to failed buds and erratic branching, rough

bark has also been described as a sign of the disorder in almond (Wilson & Schein, 1956;

Kester & Jones, 1970a). Since vegetative buds emerge after flowering, the season’s nut

set is not affected in the first year of BF-exhibition, but growth and subsequent floral bud

development is limited in subsequent years, sometimes causing dramatic yield losses

(Kester et al., 1969; Micke, 1996). While BF was initially hypothesized to be a viral

disease, no study to date has identified an infectious agent leading to development of

11

bud-failure (and thus fulfilling Koch’s postulates) (Wilson & Schein, 1956; Fenton et al.,

1988b).

Exhibition of BF became a serious threat to the almond industry beginning in the

1940s when the cultivar ‘Jordanolo’ began to exhibit the disorder (Wilson, 1950, 1952;

Wilson & Schein, 1956). Despite its popularity and promise as a productive cultivar,

‘Jordanolo’ was eventually abandoned due to the severe incidence of BF (Wilson, 1950).

This disorder is still a threat to the almond industry and cultivar development, with the

development and subsequent near abandonment of ‘Carmel’ in the early 2000s due to

widespread BF-exhibition among clones (Kester et al., 2004; California Department of

Food and Agriculture, 2020) (Fig. 1.5). Further, the unknown cause and lack of screening

methods make BF a threat to any future almond breeding and production efforts (Kester

& Jones, 1970a).

Early studies were undertaken to address four major aspects of the disorder: (1)

the morphology of affected buds, (2) inheritance patterns of BF, (3) variability in BF-

exhibition among almond cultivars, and (4) the influence of environment on BF

incidence. Morphological analysis was performed in two studies utilizing dormant buds

from almond clones with no known history of BF-exhibition and from clones with a

history of BF symptoms (Saikia et al., 1966; Hellali et al., 1978). Results from these

studies revealed that following initial vegetative bud development in May and into the

summer, BF and no-BF buds are nearly identical, with the only noted difference being

smaller, wider buds on BF trees (Saikia et al., 1966; Hellali et al., 1978). The size

difference became more pronounced in the winter months (Oct – Jan) when BF buds

12

were observed to be significantly smaller than no-BF buds (Saikia et al., 1966; Hellali et

al., 1978).

Examination of meristematic tissue by microscopy and Azure B staining revealed

meristematic abnormalities beginning in August in BF buds, where the tunica cells began

to collapse and there was an accumulation of tannins (Hellali et al., 1978). This was

followed by compression and distortion of dividing corpus cells in the early fall (Hellali

et al., 1978). A complementary study examining accumulation of abscisic acid (ABA)

and gibberellins (GA) in BF buds observed a large increase in ABA content in early fall

compared to no-BF buds, corresponding to the time when internal degradation begins to

occur (Hellali et al., 1978, 1979). While no difference in GA content was detected, BF

buds were found to be smaller in size and had a lower water content compared to the no-

BF buds (Hellali et al., 1979). Together, these results suggest that BF is initiated in the

summer after which internal symptoms become apparent involving a breakdown of the

meristematic tissue throughout the fall (Schein, 1952; Saikia et al., 1966; Hellali et al.,

1978, 1979).

Exhibition of BF is non-reversible and has been shown to be transmissible by

both vegetative propagation and sexual reproduction (Kester, 1967a). Several studies

have been conducted to examine inheritance patterns of BF following sexual

reproduction by performing crosses with parents of known BF-status and monitoring

symptom development in the progeny (Kester, 1967a,b, 1969; Kester & Jones, 1970b).

Results from this work show that severity of BF-exhibition in seedling progeny produced

from these crosses is directly proportional to the severity of BF in the parents (Kester,

1967a,b, 1969; Kester & Jones, 1970b). Further, vegetative propagation from progeny

13

result in a subsequent generation of clones with even greater levels of BF-exhibition

beginning at increasingly earlier ages (Kester, 1967a,b, 1969; Kester & Jones, 1970b).

These results were further corroborated in a study with the cultivar ‘Carmel’ where

subsequent generations of clones showed increasingly greater levels of BF-exhibition at

earlier ages (Kester et al., 2004). The association between a parent’s (i.e., sexual

reproduction) or source’s (i.e., vegetative propagation) BF-potential and the incidence of

BF in subsequent generations is of particular concern for almond breeders and producers

as there is currently no method to identify a tree’s BF-potential, it can only be inferred

based on parentage or source history and age (Kester, 1967b; Kester & Jones, 1970b;

Micke, 1996).

While BF is known to be inherited in almond, different cultivars do vary in the

level and severity of BF-exhibition in their clones (Kester et al., 1969; Micke, 1996). The

cultivar ‘Nonpareil’ has generally been observed to have moderate prevalence and

severity of BF-exhibition in orchards, while the cultivars ‘Jordanolo’ and ‘Carmel’ were

abandoned due to the high prevalence and severity in their exhibition (Kester et al., 1969;

Micke, 1996). BF-exhibition has not been observed in some modern productive cultivars

including ‘Monterey’ and ‘Butte/Padre’ (Micke, 1996), nor has BF been observed in

peach, a closely related Prunus species that can readily hybridize with almond (Socias i

Company & Gradziel, 2017). Since peach is not known to exhibit BF, crosses have been

performed to produce peach almond hybrids in an effort to uncover the genetic

components contributing to BF-development (Kester, 1978; T. M. Gradziel, pers.

comm.). Results from these crosses show a 50% segregation of the BF-phenotype in the

14

F1 hybrid generation, suggesting that a single locus in the almond parent may contribute

to the BF-genotype (Kester, 1978; T. M. Gradziel, pers. comm.).

The variability of BF-exhibition observed in different almond cultivars is

hypothesized to be due, in part, to environmental conditions based on the location of the

orchards (Kester, 1969; Micke, 1996; Kester et al., 2004). Research has shown that

‘Nonpareil’ clones planted in orchards in regions of California experiencing the highest

temperatures in July (during almond summer dormancy) had the highest percent of BF-

exhibition over a seven-year period (Kester & Asay, R. N., 1978). More recently, work in

‘Carmel’ demonstrated a positive linear correlation between the average day temperature

in the previous June and the yearly change in the percent of trees showing BF from 1991

– 1998 (Kester et al., 2004). In a controlled, growth chamber experiment, shoots of no-

BF ‘Nonpareil’ were grown at high temperatures after which they exhibited severe,

internal BF-symptoms in the meristematic tissues compared to the shoots grown at

normal temperatures that remained BF-free (Hellali & Kester, 1979). Additionally, this

study also showed an increase in ABA levels in those shoots grown in the high-

temperature growth chamber compared to the control (Hellali & Kester, 1979).

Collectively, these results all suggest a strong environmental component impacts the

exhibition of the BF-phenotype in almond, namely heat stress during a pivotal point in

bud development and dormancy.

Based on the characteristics of BF in almond, it has been long-hypothesized to be

a genetic disorder (Wilson & Schein, 1956); however, more recent work suggests this

disorder may have an epigenetic component involving differential DNA methylation

(Fresnedo-Ramírez et al., 2017). Results from this study support the association between

15

BF-exhibition and clonal age in a cultivar-specific manner and demonstrate an

association between methylation and BF-status in almond (Fresnedo-Ramírez et al.,

2017). The method employed in this study (methylation sensitive amplified fragment

length polymorphism) provides a genome-level profile of DNA methylation but does not

allow for a more precise analysis of locus-specific methylation status, leaving open the

questions of where in the genome does differential methylation occur and what genes or

gene features are affected?

Despite this lack of understanding of BF-development and biomarkers to screen

for BF-potential, efforts have been made to develop short and long-term mitigation

strategies to lessen the impact of BF on the almond industry (Kester et al., 1969; Micke,

1996; Gradziel et al., 2019). Short-term mitigation strategies involve managing BF on a

tree-by-tree basis and include tree removal and replacement or topworking, which

involves pruning affected branches and grafting established no-BF shoots onto the

mature, affected tree to prolong its productivity (Micke, 1996). The mitigation strategy

will depend in part on the age at which the tree first shows BF-symptoms and the severity

of those symptoms. Growers could possibly opt to remove trees that show BF at a young

age (~3-5 years old), prune and topwork trees that show less severe symptoms or exhibit

BF later in the productive lives, and finally, a grower may choose to do nothing if the tree

is near the end of its commercial lifespan (Micke, 1996).

More long-term mitigation strategies are focused on avoiding the use of high BF-

potential germplasm for breeding and propagation purposes by evaluating each clone

based on its location and pedigree (Kester et al., 1969; Micke, 1996). Modeling studies

have also been undertaken to account for a variety of factors influencing BF-potential in

16

an effort to establish more precise selection procedures when choosing germplasm

sources for propagation and breeding (Fenton et al., 1988a). Finally, a recent study tested

the use of basal epicormic or adventitious buds, representing ontogenetically young,

meristematic tissue, for use in propagation (Meier et al., 2012; Gradziel et al., 2019).

Since BF has been shown in several studies to be associated with advanced age,

particularly of the propagation source, Gradziel et al. (2019) proposes using basal

epicormic buds with lower BF-potential to establish commercial propagation stocks and

circumvent the impact of the advanced ontogenetic age of current propagation sources.

While this method could serve as an effective, long-term management strategy for BF in

almond, the question still remains as to the mechanisms underlying BF development in

this species.

Plant Epigenetics – DNA Methylation

Mechanisms of DNA methylation

The term “epigenetics” was first coined by Conrad Waddington in 1942 in a paper

focused on the connections between genotype and phenotype, particularly when

considering developmental processes and phenotypic plasticity (Waddington, 2012).

Later, Waddington conducted an experiment in Drosophila where he showed that an

acquired phenotype resulting from exposure to environmental stimuli could be inherited

(Waddington, 1956). We now consider this to be epigenetic inheritance and have since

defined the field of epigenetics as the study of stable and potentially heritable changes in

phenotype that occur without changes to the underlying DNA sequence (Waddington,

1956; Goldberg et al., 2007; Noble, 2015). These epigenetic phenomena include things

17

like paramutation, position effect variegation, and genomic imprinting (Goldberg et al.,

2007; Li & Sasaki, 2011; Elgin & Reuter, 2013; Hollick, 2017).

The molecular mechanisms underlying epigenetic phenomena are now being

uncovered including DNA methylation, histone modification, and expression of non-

coding RNAs (Goldberg et al., 2007). While the remainder of this review and dissertation

will focus on DNA methylation, it should be noted that epigenetic phenomena result from

a complex interaction of several mechanisms which are interdependent; thus, the bud-

failure phenotype in almond likely results not from one mechanism (i.e. DNA

methylation), but from a network of underlying, connected mechanisms (Goldberg et al.,

2007).

DNA methylation occurs when a methyl group is added to the fifth carbon of the

heterocyclic aromatic ring forming 5-methylcytosine and was first discovered by Rollin

Hotchkiss in 1948 (Hotchkiss, 1948; Moore et al., 2013). In plants, DNA methylation can

occur in one of three contexts: CG, CHG, and CHH, where H represents either a cytosine,

adenine, or thymine (Zhang et al., 2018). DNA methylation serves a variety of functions

in the plant genome, including silencing transposable elements and regulating gene

expression (Zhang et al., 2018).

The processes involved in regulating DNA methylation in plants include

establishment, maintenance, and removal of methylation at specific sites in the genome

(Moore et al., 2013; Zhang et al., 2018). DNA methylation is established de novo in

plants through a process known as RNA-directed DNA methylation (RdDM) (Law &

Jacobsen, 2010; Zhang & Zhu, 2011; Zhang et al., 2013; Matzke & Mosher, 2014). The

RdDM pathway is initiated through small interfering RNA (siRNA) production via either

18

RNA Polymerase IV (POL IV) or POL II and subsequent cleavage by a DICER-LIKE

PROTEIN (DCL2, DCL3, or DCL4) (Law & Jacobsen, 2010; Zhang et al., 2013; Matzke

& Mosher, 2014). Following this, siRNAs bind to ARGONAUTE 4 (AGO4) or AGO6

and pair to complementary scaffold RNAs produced by POL V (Law & Jacobsen, 2010;

Zhang et al., 2013; Matzke & Mosher, 2014). DNA methylation is then catalyzed by an

interaction between AGO4 and DOMAINS REARRANGED METHYLASE 2 (DRM2)

(Law & Jacobsen, 2010; Zhang et al., 2013; Matzke & Mosher, 2014). The process of

RdDM establishes new sites of DNA methylation in the plant genome in all methylation

contexts (Zhang et al., 2018).

Once DNA methylation is established, maintenance occurs through various

mechanisms depending on the context of methylation (Zhang et al., 2018). In the CG

context, methylation is maintained following replication by METHYLTRANSFERASE 1

(MET1) which recognizes hemi-methylated DNA (Kankel et al., 2003; Law & Jacobsen,

2010). DNA methylation is maintained in the CHG context by CHROMOMETHYLASE

3 (CMT3) which also binds to the histone mark H3K9me2 (Jackson et al., 2004; Du et

al., 2012; Stroud et al., 2014; Li et al., 2018a). Conversely, the methyltransferase

SUPPRESOR OF VARIEGATION 3-9 HOMOLOGE PROTEIN 4 (SUVH-4) binds

CHG methylation, recruiting the protein to methylatedH3K9 (Jackson et al., 2004; Du et

al., 2012; Stroud et al., 2014; Li et al., 2018a). This creates a positive feedback loop

between H3K9 and CHG methylation, where the presence of one reinforces the presence

of the other (Jackson et al., 2004; Du et al., 2012; Stroud et al., 2014; Li et al., 2018a).

Finally, CHH methylation is maintained by DRM2 via RdDM and by CMT2 in regions

where RdDM does not occur (Stroud et al., 2014).

19

The removal of methylated cytosines in plants can occur either by passive

demethylation or active demethylation (Zhang et al., 2018). Passive demethylation occurs

following rounds of cellular replication where methylation is not maintained at a

particular site, diluting methylation over time (Zhu, 2009; Zhang et al., 2018). Active

demethylation occurs in plants via a base-excision repair pathway catalyzed by the DNA

glycosylase enzymes, REPRESSOR OF SILENCING (ROS1), TRANSCRIPTIONAL

ACTIVATOR DEMETER (DME), DEMETER-LIKE PROTEIN 2 (DML2), and DML3

(Gong et al., 2002; Zhu, 2009; Li et al., 2018b; Parrilla-Doblas et al., 2019). DNA

demethylation through enzymatic pathways acts as a regulatory mechanism for

hypermethylation in the genome, particularly around regions containing methylated

transposons (Zhu, 2009; Tang et al., 2016; Li et al., 2018b; Parrilla-Doblas et al., 2019).

Further work has shown that DNA methylation and demethylation pathways form a

feedback loop in which the expression of genes associated with one pathway influences

expression of the other, leading to methylation stability in the genome (Lei et al., 2015;

Zhang et al., 2018).

Dynamic patterns of DNA methylation have been shown in several systems to

influence observed phenotypes (Elhamamsy, 2016). Interestingly, this is observed in

almond in relation to observed self-incompatibility resulting from activation of Sf -RNase

(Fernández i Martí et al., 2014). It was found that when the Sf allele is methylated, its

expression is inhibited and the trees are self-compatible (Fernández i Martí et al., 2014).

However, when methylation is absent from the Sf allele, the trees show the typical self-

incompatibility phenotype (Fernández i Martí et al., 2014). These results suggest a

20

potential use for so called epialleles in plant breeding to produce plants with desirable

phenotypes like self-compatibility in almond (Quadrana et al., 2014).

Stress-induced DNA methylation and implications

Alterations in the DNA methylation patterns in plant genomes can occur for a variety of

reasons including as a response to biotic or abiotic stressors (Zogli & Libault, 2017;

Zhang et al., 2018; Alonso et al., 2019). Several studies have documented changes to the

plant methylome in response to exposure to a pathogen, including in both annual and

perennial plants (Zogli & Libault, 2017; Zhang et al., 2018; Alonso et al., 2019).

Hypomethylation mutants of the model plant, Arabidopsis, showed increased resistance

to the plant pathogen, Pseudomonas syringae pv. tomato DC3000 (Pst) (Dowen et al.,

2012). Further, an RNA sequencing experiment showed that those Arabidopsis mutants

exposed to the pathogen had a number of genes that were differentially expressed in

comparison to the un-exposed control, and those genes were associated with changes in

DNA methylation via their transcriptional networks (Dowen et al., 2012). Finally, a

global methylation profiling of wild-type Arabidopsis following infection with Pst

showed dynamic DNA methylation patterns at five days post infection at a number of loci

that are correlated with changes in gene expression (Dowen et al., 2012). Taken together,

these results show how important DNA methylation is in regulating defense-responses,

likely in an effort to modulate expression of defense-related genes when infections are

not present (Dowen et al., 2012). Additionally, this study shows the dynamic response

plants have once infection occurs, including not only changes in expression of defense-

related genes but also changes in patterns of DNA methylation across the genome

(Dowen et al., 2012).

21

Patterns of differential methylation were also observed in studies examining the

epigenome of Arabidopsis and soybean (Glycine max L.) after infection with the beet cyst

nematode (Heterodera schachtii A.Schmidt) and soybean cyst nematode (H. glycines

Ichinohe), respectively (Rambani et al., 2015; Hewezi et al., 2017). Results in the study

on Arabidopsis showed genome-wide hypomethylation patterns in response to nematode

infection (Hewezi et al., 2017). Similar results were observed in the soybean study where

hypomethylation was induced at a much greater rate compared to hypermethylation in the

genomes of infected plants (Rambani et al., 2015). A study in ash (Fraxinus excelsior L.)

revealed differential methylation patterns in clones showing high susceptibility to ash

dieback (ADB), a fungal disease, compared to clones showing low susceptibility (Sollars

& Buggs, 2018). When examining specific genes associated with susceptibility to ADB,

low susceptibility clones showed hypomethylation in those regions while high

susceptibility clones tend to be hypermethylated (Sollars & Buggs, 2018). Collectively,

the results from these studies suggest that DNA hypomethylation tends to be associated

with increased disease resistance, and that part of the defense response in plants involves

demethylation of genomic regions associated with defense-related genes (Zogli &

Libault, 2017; Alonso et al., 2019).

DNA methylation has also been implicated in plant response to abiotic stressors

including temperature, drought, salt, and UV radiation (Zhang et al., 2018; Chang et al.,

2020; Liu & He, 2020). Studies have been conducted on the impact of drought on DNA

methylation profiles in a number of plant species including Arabidopsis, rice (Oryza

sativa L.), poplar (Populus trichocarpa Torr. & A.Gray ex. Hook.), pea (Pisum sativum

L.), and barley (Hordeum vulgare L.) (Labra et al., 2002; Raj et al., 2011; Liang et al.,

22

2014; Wang et al., 2016; Chwialkowska et al., 2016; Ganguly et al., 2017). In rice, a

comparison of DNA methylation profiles generated from a drought-tolerant and a

drought-sensitive line under control conditions showed regions of differential hyper- and

hypomethylation associated with the drought tolerance, with DMR-associated genes

involved in processes related to drought stress tolerance (Wang et al., 2016). Each rice

line was also grown under drought conditions, and when comparing the methylome of the

drought-treated plants to those in the control treatment, more DMRs were detected in the

drought sensitive line compared to the drought tolerant line (Wang et al., 2016). Results

from the study in rice suggest that the drought tolerant line has a more stable methylome

compared to the drought sensitive line when exposed to drought stress, and subsequent

transcriptome analysis showed a correlation between these methylation patterns and

genes implicated in drought response (Wang et al., 2016).

Similarly, in studies in poplar and barley where DNA methylation profiles were

generated from plants under control conditions and drought stress, both hyper- and

hypomethylated regions of differential methylation were identified (Liang et al., 2014;

Chwialkowska et al., 2016). However, in pea root tissues, drought stress was associated

with genome-wide hypermethylation (Labra et al., 2002). A study in tobacco also showed

hypermethylation following osmotic stress; however, this experiment was performed

using a cell suspension rather than whole plants (Nagata et al., 1992; Kovar˘ik et al.,

1997). As in plant defense, it is clear that plants experience a dynamic, epigenetic

response to abiotic stress such as drought, and changes in DNA methylation patterns

likely affect expression of genes associated with these responses (Chang et al., 2020; Liu

& He, 2020).

23

In addition to studies on drought stress response, heat stress has been a focus of

research on epigenetic responses to abiotic stimuli (Liu et al., 2015; He et al., 2021). This

focus is due in part to increasing global temperatures occurring and predicted as a result

of climate change and an interest in developing resilient crops able to withstand these

perturbations (Liu et al., 2015; Saraswat et al., 2017). Several studies have been

conducted to analyze epigenetic changes resulting from exposure to heat stress that show

complex alterations similar to what is seen in response to other stressors (Liu et al.,

2015). In Arabidopsis, exposure to heat stress was found to upregulate genes involved in

the RdDM pathway, suggesting that heat stress prompts stabilization of methylation

levels throughout the genome (Naydenov et al., 2015). Additionally, methylation levels

increased in the promoter region of key stress response genes in wildtype Arabidopsis in

the presence of and following exposure to heat stress in comparison to a mutant line with

defective POL IV and POL V subunits (Naydenov et al., 2015). Interestingly, another

study in Arabidopsis showed increased genome-wide methylation in the progeny of

plants (parents) exposed to heat stress prior to crossing, suggesting that heat stress not

only induces hypermethylation but that this effect can be inherited at least to the first-

generation progeny (Boyko et al., 2010).

Research exploring epigenetic phenomena in response to heat stress has extended

into perennials including trees (Bräutigam et al., 2013; Amaral et al., 2020). A study in

cork oak (Quercus suber L.), where plants were artificially exposed to heat stress in a

controlled greenhouse setting, showed an increase in global methylation levels at high

temperatures (Correia et al., 2013). A landscape-level study in valley oak (Quercus

lobata Née) revealed a significant correlation between what are described as single-

24

methylation variants, akin to single nucleotide polymorphisms, and maximum

temperature (Gugger et al., 2016). Results from this study suggest that DNA methylation

may play a role in adaptation to climate and plant plasticity in response to temperature

changes (Gugger et al., 2016).

While results from studies on plant response to stress show a clear effect on or of

DNA methylation patterns, the question remains as to whether these changes are heritable

and how inheritance might influence a progeny’s response to the same stress (Avramova,

2015; Lämke & Bäurle, 2017; Sudan et al., 2018). Research in this area examines “stress

memory” or the ability of progeny to withstand the same stress that was previously

applied to their parents (Crisp et al., 2016; Lämke & Bäurle, 2017; Zheng et al., 2017). In

these studies, researchers aim to address questions like: are epigenetic alterations

resulting from exposure to stress stably inherited, and do these alterations extend some

benefit to subsequent generations when exposed to the same stress?

Two terms have been established to define the inheritance of epigenetic

modifications following stress: intergenerational and transgenerational (Lämke & Bäurle,

2017; Liu & He, 2020). Intergenerational refers to inheritance of profiles by the direct

progeny, while transgenerational refers to inheritance by at least two stress-free

generations (Lämke & Bäurle, 2017; Liu & He, 2020). Recent work in scots pine (Pinus

sylvestris L.) demonstrates intergenerational inheritance of “stress memory” using

offspring from trees either exposed to drought conditions or control conditions (Bose et

al., 2020). Results from this work showed that those offspring from the drought-exposed

parents performed better under drought conditions than those offspring from the control

parents, despite finding no significant genetic differentiation among the offspring (Bose

25

et al., 2020). The hypothesis is that this “stress memory” passed to the immediate

offspring may provide an adaptive advantage when those individuals are exposed to the

same stress as their progenitors (Crisp et al., 2016). The question remains as to how

durable this “priming” is in subsequent generations which is particularly relevant for

perennial species.

Despite the advantages imposed on the offspring in intergenerational inheritance

of “stress memory”, it is unclear whether the advantages continue for subsequent

generations via transgenerational inheritance or if the costs outweigh any potential

benefits (Crisp et al., 2016). A study in Arabidopsis showed that changes in DNA

methylation following hyperosmotic stress were inherited by first generation progeny of

exposed parents, but these changes were lost after one generation of no stress (Wibowo et

al., 2016; Ganguly et al., 2017). Methylation patterns were also shown to be transmitted

through the female germline (Wibowo et al., 2016). In rice, however, epi-mutations

observed following drought stress were maintained for the 11 generations examined in

the study, leading the authors to conclude that these epimutations are not only stable but

could contribute to long-term adaptation in rice (Zheng et al., 2017). Inheritance of

epigenetic alterations like changes in DNA methylation has implications for plant

phenotypic plasticity, evolution, and population structuring (Agrawal, 2011; Benayoun et

al., 2015; Quadrana & Colot, 2016; Wibowo et al., 2018).

While it is clear from the research that DNA methylation and epigenetic

phenomena are influenced by and influence responses to biotic and abiotic stressors,

many questions regarding the inheritance of these modifications remain unanswered

(Crisp et al., 2016). However, many researchers are motivated by the potential

26

applications of inter- and transgenerational inheritance of epigenetic alteration for plant

breeding and crop improvement efforts in the future (Saraswat et al., 2017; Varotto et al.,

2020). This work is driven in particular by the unknowns surrounding climate change and

the impacts of climate perturbations on agricultural production worldwide (Saraswat et

al., 2017; Varotto et al., 2020).

Methods for measuring DNA methylation

A variety of sequencing, PCR, and chromatography-based methods are currently

available to profile DNA methylation in plant genomes (Parle-Mcdermott & Harrison,

2011; Yong et al., 2016). Whole genome bisulfite sequencing (WGBS) is considered the

“gold standard” for measuring genome-wide methylation at the single nucleotide level

(Frommer et al., 1992; Grunau et al., 2001). This approach couples bisulfite treatment

with high-throughput sequencing (HTS) technology to produce a genome-wide profile of

DNA methylation (Yong et al., 2016). Prior to sequencing library preparation, DNA is

bisulfite treated, deaminating unmethylated cytosines to uracils while leaving 5-

methylcytosines intact (Frommer et al., 1992; Huang et al., 2010). Subsequent PCR

following bisulfite treatment converts uracils to thymines, producing thymine-cytosine

mismatches upon read alignment (Grunau et al., 2001; Huang et al., 2010). Once the

samples are bisulfite treated, sequencing libraries can be prepared through a variety of

kit-based or alternative methods, and libraires can be indexed for multiplexing prior to

sequencing on any HTS platform (Yong et al., 2016).

Limitations to WGBS include severe DNA degradation (up to 90%) at the

bisulfite treatment step, greatly reducing the amount of input DNA for library preparation

(Raizis et al., 1995; Tanaka & Okamoto, 2007). Additionally, incomplete conversion of

27

unmethylated cytosines to uracils can bias the results and lead to overestimates of

genome-wide methylation levels (Olova et al., 2018). Conversion efficiency can be

calculated either based on the amount of methylation detected in the chloroplast genome,

which is assumed to be completely unmethylated (Fojtová et al., 2001), or by spiking in

methylated or unmethylated control DNA prior to bisulfite treatment (Warnecke et al.,

2002). Another limitation of WGBS is low mapping efficiency following sequencing,

particularly in species with limited or less-developed genomic resources (Olova et al.,

2018). To overcome this issue, greater sequencing depth is required to ensure adequate

coverage of cytosines following alignment (Ziller et al., 2015).

While WGBS is the standard method to profile genome-wide methylation at the

single nucleotide level, a number of other methods are also available to profile DNA

methylation (Parle-Mcdermott & Harrison, 2011; Yong et al., 2016). Recently, an

enzymatic methylation sequencing approach was developed that couples conversion of

unmethylated cytosines to uracils by enzymatic reactions with HTS platforms (Vaisvila et

al., 2020). The enzymes TET2 and APOBEC catalyze the reactions necessary to convert

unmethylated cytosines without losing the integrity of the DNA sample (Vaisvila et al.,

2020). This method shows promise as an alternative to bisulfite treatment for profiling

DNA methylation at single-bases (Vaisvila et al., 2020; Feng et al., 2020). One limitation

of both the enzymatic methylation sequencing and WGBS is the cost and low throughput

nature of the approaches (Olova et al., 2018; Zhou et al., 2019). Reduced representation

bisulfite sequencing (RRBS) is a method that allows more targeted methylation profiling

by first performing a digestion and size selection on DNA samples prior to bisulfite

conversion and library preparation (Schmidt et al., 2017; Paun et al., 2019). Enzymes are

28

selected based on their sensitivity to the presence of 5-methylcytosine to enrich for

regions of the genome likely to contain high levels of methylation (Wang et al., 2013;

Schmidt et al., 2017). This approach can be applied in a more high-throughput manner

and thus, is desirable when a researcher would like to profile many samples at a reduced

cost (Wang et al., 2013; Schmidt et al., 2017; Paun et al., 2019). A modification of

RRBS was recently developed called epi-genotype by sequencing (epiGBS) where

methylation profiling via RRBS is coupled with a GBS approach to identify not only

epialleles but also potential single-nucleotide polymorphisms (Gurp et al., 2016).

Non-sequencing-based methods are also available to profile methylation levels

including chromatography, array-based, and PCR-based methods (Fraga & Esteller,

2002). Chromatography methods are used to quantify global DNA methylation levels and

are desirable because they are high-throughput and relatively inexpensive compared to

sequencing-based methods (Fraga & Esteller, 2002; Kurdyukov & Bullock, 2016). The

limitation to these approaches, however, is the loss of precision in determining where in

the genome differential methylation occurs when comparing samples (Fraga & Esteller,

2002; Kurdyukov & Bullock, 2016). Available chromatography methods include high

performance liquid chromatography (HPLC), capillary electrophoresis (CE), and liquid

chromatography-mass spectrometry (LC-MS) (Ramsahoye, 2002; Stach et al., 2003;

Song et al., 2005; Armstrong et al., 2011). One limitation to HPLC methods is that they

require large amounts of input DNA (up to 50 g), which can be difficult to obtain in

some plant species or with limited available tissue; however, alternative HPLC methods

are being developed that require lower DNA inputs (Armstrong et al., 2011; Yotani et al.,

2018). Both CE and LC-MS methods are more sensitive and thus require less input DNA

29

but can be time-consuming and require optimization for each tissue-type and species

(Stach et al., 2003; Song et al., 2005).

PCR-based methods are also available to profile and validate DNA methylation,

including methylation-sensitive amplified fragment length polymorphism (MS-AFLP)

and chop-PCR (Hernández et al., 2013; Šestáková et al., 2019). In MS-AFLP,

isoschizomers with different methylation sensitivities are used to perform digests on

DNA samples from the same individual (Xu et al., 2000). Following digestion and

adaptor ligation, PCR amplification is performed, and fragmentation patterns are

visualized to determine differences between the banding patterns of the two enzymes (Xu

et al., 2000). The difference in banding patterns between the isoschizomers provides

distinct methylation profiles for the individual tested (Xu et al., 2000). To analyze

methylation patterns at targeted loci, chop-PCR also utilizes methylation sensitive

restriction enzymes that cannot cut when methylated cytosines are present at the cut-site

(Dasgupta & Chaudhuri, 2019). Digests are performed followed by PCR with primers

specific to the regions of interest (Dasgupta & Chaudhuri, 2019). If methylation is

present at the region, the section of DNA will be maintained and amplicons produced via

PCR; however, if the region in unmethylated, the enzyme will cut and there will be no

template for PCR (Dasgupta & Chaudhuri, 2019). Visualization of the PCR product

compared to control reactions with no enzyme validate the presence or absence of DNA

methylation at the site (Dasgupta & Chaudhuri, 2019).

Several pipelines for analyzing methylation data, particularly those generated

through HTS approaches, have been developed (Krueger et al., 2012; Wreczycka et al.,

2017). These pipelines generally include the same initial steps, which are quality control

30

of sequencing data, aligning reads to the genome, and calling methylated and

unmethylated cytosines (Krueger et al., 2012). The first hurdle in the analysis of

methylation data compared to other types of sequencing data is alignment to the reference

genome. Due to the nature of methylation sequencing, which involves conversion of

unmethylated cytosines to uracils, traditional alignment algorithms will not work because

of the number of mismatches between the sequenced reads and the genome and the

resulting low complexity of the libraries (Krueger et al., 2012; Wreczycka et al., 2017).

Therefore, alignment algorithms have been adapted to work with converted and

unconverted versions of the genome (Krueger et al., 2012).

In these pipelines, two versions of the genome are created, one with a cytosine-to-

thymine conversion and one with a guanine-to-adenine conversion (Krueger et al., 2012).

This conversion is repeated for each read, and the pair of reads are aligned to each

version of the genome (Krueger et al., 2012). The best alignment is chosen from the four

possible alignments (Krueger et al., 2012). The software package Bismark (Krueger &

Andrews, 2011) is commonly used to perform this type of alignment, relying on bowtie2

(Langmead & Salzberg, 2012) with modifications included to generate the converted

version of the genome. Resulting mapping efficiencies have a tendency to be lower than

in traditional sequencing (i.e., RNA-seq or whole genome sequencing), due primarily to

mismatches and the low complexity of the methylation data (Krueger et al., 2012; Sun et

al., 2018).

Following alignment, methylation calls are generated based on the number of

reads associated with each cytosine and the methylation status of those reads (Krueger &

Andrews, 2011). Ideally, all reads associated with a cytosine are either methylated or

31

unmethylated; however, issues like incomplete conversion or a mixture of cell types can

result in noncorresponding reads for a single cytosine (Krueger et al., 2012). For this

reason, methylation is typically presented as a weighted percentage, taking into account

the total number of reads for each cytosine and the number of reads reported as

methylated (Krueger et al., 2012; Schultz et al., 2012). After methylation is called across

the genome for each library, these call or count files are used as input to software

packages designed to identify significantly differentially methylated regions (DMRs)

between two or more treatments (Krueger et al., 2012; Wreczycka et al., 2017). Several

software packages are available to call DMRs that rely on different statistical models

(Wreczycka et al., 2017). Common approaches used to identify DMRs include t-

tests/linear regression, logistic regression, and beta binomial regression (Wreczycka et

al., 2017). Selection of the appropriate model depends on the experimental design

including number of replicates and presence of covariates (Wreczycka et al., 2017).

Plant Telomere Biology

Telomere function and mechanisms of maintenance

Telomeres are nucleoproteins that cap the end of chromosomes, preventing premature

instability of genomic material and cellular senescence due to the end replication problem

(Blackburn, 2000; Hemann et al., 2001; Watson & Riha, 2010, 2011). The telomeric cap

is comprised of repeating, heptanucleotide telomeric DNA sequence and telomere

binding proteins (Kuchar, 2006; Procházková Schrumpfová et al., 2016). In most plants,

including Arabidopsis and Rosaceous species, the telomeric repeat sequence is

TTTAGGG (Fuchs et al., 1995); however, several exceptions exist including in members

of the Alliaceae (onion) (Pich et al., 1996) and the Solanaceae family (Sykorova et al.,

32

2003), as well as members of the order Asparagales (Fajkus et al., 2005). The total

average length of the telomeric DNA sequence can vary considerably between and even

within species, with the shortest telomeric sequence in plants reported in Physcomitrella

patens (Hedw.) Bruch & Schimp. at 500 bp and the longest in Nicotiana sylvestris Speg.

& Comes at 200 kb (Procházková Schrumpfová et al., 2016).

The associated proteins that bind to telomeric DNA are involved in stability and

maintenance of the telomeric sequence and include double-stranded DNA-associated

proteins, single-stranded DNA-associated proteins, and telomerase (Kuchar, 2006;

Procházková Schrumpfová et al., 2016). The double-stranded DNA-associated proteins

include Myb-like proteins with a Myb domain at either the C- or N-terminus (Kuchar,

2006; Procházková Schrumpfová et al., 2016). In addition to the observed function in

maintaining telomere length in several plant species, N-terminus Myb-like proteins have

also been found to be epigenetic regulators involved in controlling H3K27 methylation

(Procházková Schrumpfová et al., 2016; Zhou et al., 2016). In fact, several genes

involved in epigenetic regulation in plants, including DNA methyltransferases, play a

role in maintaining telomere lengths as has been shown in Arabidopsis (Ogrocká et al.,

2014; Vega-Vaquero et al., 2016; Xie & Shippen, 2018).

The specialized reverse transcriptase telomerase is likely the most well-studied of

the proteins involved in regulating telomere length, due in part to its potential human-

health applications (Fossel, 1998; Boccardi & Paolisso, 2014; Nagpal & Agarwal, 2020).

In animals, telomeres shorten over mitotic cellular divisions due to decreased activity or a

lack of telomerase; however, malignant cells seem to have increased telomerase activity,

thought to contribute to their longevity (Boccardi & Paolisso, 2014; Nagpal & Agarwal,

33

2020). Telomerase and its function in maintaining telomeres is conserved in plants;

however, the nature of telomerase expression and thus modulation of telomere length in

plants seems to differ from what is observed in animals (Procházková Schrumpfová et

al., 2019).

In plants, telomerase is made up of the catalytic subunit TERT (Oguchi et al.,

1999) and an RNA subunit that serves as a template for reverse transcription

(Procházková Schrumpfová et al., 2016, 2019). Expression of the TERT gene in

Arabidopsis was shown to be a marker of telomerase activity, and when TERT is

disrupted, there is a slow shortening of telomeres over multiple generations (Fitzgerald et

al., 1999). Telomerase activity and subsequent telomere shortening in plants appears to

be a much more dynamic and plastic process compared to that in animals (Fitzgerald et

al., 1996). Several studies have documented declining telomerase activity and telomere

length following cell differentiation and division in plants, but simultaneously observe

high levels of telomerase activity in meristematic and reproductive tissues (Fitzgerald et

al., 1996; Kilian et al., 1998; Zachová et al., 2013; Jurečková et al., 2017). Interestingly,

a study monitoring telomerase activity in cauliflower found that the DNA binding

requirement seen in other species was more relaxed, suggesting some plant telomerases

may be able to initiate telomere elongation on broken chromosomes or those missing

large segments of existing telomeric sequence (Fitzgerald et al., 1996).

Methods for measuring telomere length

Several methods have been developed to measure telomere lengths in plants and other

organisms (Aubert et al., 2012; Montpetit et al., 2014; Lai et al., 2018). Terminal

restriction fragment (TRF) analysis using Southern blot is considered the “gold standard”

34

of telomere length measurement (Kimura et al., 2010; Aubert et al., 2012). The TRF

method relies on an enzymatic digestion of genomic DNA that preserves the telomeric

sequence followed by resolution via gel electrophoresis (Kimura et al., 2010). The

telomere fragments are then detected by Southern blot using a labeled probe, and both the

length and intensity of the resulting smear are used to calculate telomere length in the

original sample (Kimura et al., 2010). The limitations of TRF analysis include low

sensitivity in detecting short telomeres and a high genomic DNA input requirement,

precluding this method from samples with low tissue availability (Aubert et al., 2012).

Recently, more sensitive methods have been developed to detect and measure

telomere lengths including quantitative PCR (qPCR)-based methods and in silico

approaches utilizing whole-genome sequencing data (Aubert et al., 2012; Lee et al.,

2017; Lin et al., 2019). The initial qPCR method was developed in 2002 and works by

amplifying telomeric sequence (T) using primers specially designed to avoid the

formation of primer dimers but still anneal to the repetitive sequence (Cawthon, 2002). A

single copy gene (S) is selected and amplified from the same sample to serve as a control

sequence (Cawthon, 2002). A relative T/S ratio is then calculated based on the cycle of

quantification (Cq) values detected for each primer pair reaction (Cawthon, 2002). This

T/S ratio was found to correlate with the telomere length calculated based on TRF

analysis, suggesting that qPCR is a robust method for measuring relative telomere lengths

(Cawthon, 2002; Lin et al., 2019). A modified version of this protocol called

monochrome multiplex qPCR (MMQPCR) was subsequently developed that allows

multiplexing of the T and S reactions in a single well to reduce technical variation

produced when separate reactions are prepared (Cawthon, 2009). Quantitative PCR-based

35

methods are increasingly used to measure telomere lengths, especially in clinical settings

where high throughput processing is necessary and samples may be limited (Montpetit et

al., 2014; Lin et al., 2019). The MMQPCR method has also been effectively

implemented in plant systems to produce telomere length measurements comparable to

the TRF method (Vaquero-Sedas & Vega-Palas, 2014).

Finally, tools to measure telomere lengths using whole-genome sequencing data

have recently been developed that show strong correlations with wet lab-based methods

(Lee et al., 2017). These in silico approaches typically require an input telomeric repeat

sequence (i.e., TTTAGGG for most plants) and a haploid chromosome number (Lee et

al., 2017). Additionally, the algorithms used to detect telomeric repeats in the aligned

sequencing reads account for possible issues like interstitial telomeric sequence that

might confound telomere length measurements (Lee et al., 2017). A limitation of these

methods is that they require whole-genome sequencing data aligned to a high-quality

reference genome. Availability of this type of genomic resource is limited for certain

species due to high-cost and/or biological issues preventing generation of high-quality

genome (Lee et al., 2017). However, this method could be used to address questions

related to telomere length using the abundance of whole-genome sequencing data already

available in public repositories.

Aging in Perennial Plants

Biological predictors of age

The ability to predict biological age (as opposed to chronological age) has been a focus of

aging research in humans for decades as biological age is associated with the

development of a myriad of conditions and disorders (e.g., Alzheimer’s, diabetes, cancer,

36

etc.) (Freude et al., 2010; Jylhävä et al., 2017). The rationale for establishing biological

predictors of age is that chronological age alone does not necessarily associate with the

development of certain disorders, and predisposition or likelihood of experiencing age-

related effects is better predicted by other factors (Freude et al., 2010; Jylhävä et al.,

2017). Efforts to uncover predictors of biological age in humans have led to several

proposed “biological clock” models including the use of telomere length, DNA

methylation, and protein glycosylation (Runov et al., 2015; Benayoun et al., 2015;

Jylhävä et al., 2017; Xiao et al., 2019). These models have been well-studied in humans

for their suitability to predict prevalence and severity of a wide-range of conditions,

including more recently, COVID-19 disease (Hillary et al., 2020; Lauc & Sinclair, 2020).

To date, little effort has been made to apply biological predictors of age or

develop “biological clock” models in plants despite the potential suitability for long-lived

perennials or clonally propagated species (Thomas, 2013; Brutovská et al., 2013). This is

due in part to complications in applying models of aging developed in animals to plants,

particularly those plants with multiple reproductive cycles or that can be clonally

propagated (Thomas, 2002; Van Dijk, 2009; Brutovská et al., 2013; Salguero-Gomez,

2018). Another issue in considering and developing models of aging in plants is that of

scale, as aging is often considered in the scope of a human lifetime which can be an order

of magnitude shorter than that of long-lived plants (Thomas, 2002, 2013; Ally et al.,

2010). With these caveats in mind, several published studies have attempted to uncover

patterns in plant aging and develop biomarkers of biological age in plants similar to those

developed in humans (Thomas, 2013; Brutovská et al., 2013).

37

One model of aging applied to plants is that of somatic DNA mutation

accumulation, where irreversible cell and tissue damage occurs as a result of the

accumulation of deleterious mutations over time (Brutovská et al., 2013; Jylhävä et al.,

2017; Schoen & Schultz, 2019). As in humans, the accumulation of mutations is thought

to occur as a result of a breakdown in the repair machinery in cells, where the energy cost

of repair is no longer worth the expense, and DNA mutations are allowed to accumulate

(Jylhävä et al., 2017; Schoen & Schultz, 2019). Several studies have addressed the

accumulation of somatic mutations in plants; however, many of these have focused on

annual plants with only one reproductive cycle where aging is synonymous with

senescence (Brutovská et al., 2013; Dubrovina & Kiselev, 2016; Schoen & Schultz,

2019). In Arabidopsis, somatic mutations were shown to accumulate at a rate of 7 x 10-9

base substitutions per generation (Ossowski et al., 2010), and the rate of point mutations

in individual plants increased substantially throughout development in studies where ages

ranged from 2-days-old to 12-weeks-old (Kovalchuk et al., 2000; Boyko et al., 2006;

Golubov et al., 2010; Kiselev et al., 2015). In long-lived perennials, however, the somatic

mutation rate has been found to be lower than that of annuals, suggesting these species

differ in their ability to maintain genomic integrity (Schoen & Schultz, 2019). For

example, a study profiling fixed-somatic mutations in the canopy of a 234-year-old oak

(Quercus robur L.) found a low number of mutations relative to what has been observed

in annual species (Schmid-Siegert et al., 2017). This trend has been observed in other

long-lived perennials, where the somatic mutation rate is lower than what is documented

in annual species (Orr et al., 2020; Hofmeister et al., 2020).

38

Another model of aging applied in plants is telomere length shortening, where the

nucleoprotein caps of chromosomes shorten over time, eventually leading to genome

instability and cell death (Watson & Riha, 2011; Sanders & Newman, 2013; Marioni et

al., 2016). Telomere length reduction has been demonstrated as a biomarker of aging and

predictor of several diseases and disorders, including cancers, in humans and is one of the

most well-studied biomarkers of age in plants (Watson & Riha, 2011; Sanders &

Newman, 2013; Marioni et al., 2016). Unlike animals, however, the relationship between

telomere length and age in plants is not as clear (Watson & Riha, 2011). Previous studies

in both Ginkgo biloba L. and Panax ginseng C.A.Mey showed a pattern of increased

telomere length with increased age (Liu et al., 2007; Liang et al., 2015). Work in apple

(Malus domestica Borkh.) and Prunus yedoensis Matsum, both members of the

Rosaceae family, showed no change in telomere lengths with increased plant age over a

five-year timespan (Moriguchi et al., 2007). In bristlecone pine (Pinus longaeva

D.K.Bailey), a long-lived perennial gymnosperm, telomere lengths measured in needle

and root tissues between 0–3500 years old showed a cyclical pattern of lengthening and

shortening with age (Flanary & Kletetschka, 2005). Further, when analyzing telomere

length in relation to tissue differentiation, studies in both barley and Scots pine showed

telomere shortening from embryo development to leaf or needle formation (Kilian et al.,

1995; Aronen & Ryynänen, 2012). Similarly, in silver birch (Betula pendula Roth),

telomeres shorten when plants are grown in tissue culture conditions compared to those

grown outdoors, suggesting abiotic stressors may also induce telomere shortening

(Aronen & Ryynänen, 2014). Taken together, results from these studies and others

suggest that a model of aging in plants that employs telomere length as a predictor will

39

likely be more complex than existing models developed for animals (Watson & Riha,

2011; Brutovská et al., 2013).

Finally, the model of aging related to the level of epigenetic alterations in the

genome has been more recently applied in plants showing not only a pattern associated

with age but also a heritable component to epigenetic changes that occur during a plant’s

lifetime (Ay et al., 2014; Quadrana & Colot, 2016; Dubrovina & Kiselev, 2016; Wibowo

et al., 2018; Xiao et al., 2019). In Arabidopsis, a measurement of methylation at two loci

in the genome revealed a decrease in methylation with increased age from 1 to 12 weeks

(Ogneva et al., 2016). This same study also showed a decrease in expression of key

methyltransferase genes, which are responsible for maintaining context-specific

methylation, with increased plant age (Ogneva et al., 2016). A similar pattern was

observed in other species including chicory (Cichorium intybus L.), Acacia mangium

Willd., chestnut (Castanea sativa Mill.), and giant sequoia (Sequoiadendron giganteum

(Lindl.) J.Buchh.), where methylation levels in juvenile tissues are higher compared to

levels in mature tissues (Demeulemeester et al., 1999; Baurens et al., 2004; Hasbún et al.,

2007; Monteuuis et al., 2008, 2009). However, several studies observed the opposite

trend, where juvenile tissues show lower levels of methylation in comparison to mature

tissues including in Monterey pine (Pinus radiata D.Don), peach, eucalyptus (Eucalyptus

urophylla E. grandis), coast redwood (Sequoia sempervirens (D.Don) Endl.), and Moso

bamboo (Phyllostachys heterocycle (Carrière) J.Houz.) (Bitonti et al., 2002; Fraga et al.,

2002; Valledor et al., 2010; Mankessi et al., 2011; Guo et al., 2011; Huang et al., 2012;

Yuan et al., 2014; Zhang et al., 2021). Results from these studies suggest that, similar to

telomere length, age-related changes in DNA methylation in plants do not follow the

40

same patterns previously seen in animals (Dubrovina & Kiselev, 2016). Thus, models of

plant aging that include DNA methylation will likely be complex and may need to take

additional factors into account (Dubrovina & Kiselev, 2016). Despite the seemingly

contradictory results in studies focused on the association between age and DNA

methylation in plants, it is clear that alterations in DNA methylation occurring over time

have impacts on processes including gene expression (Dubrovina & Kiselev, 2016).

Aging-induced disorder development in plants

While several studies have implicated aging in the development of disorders in humans,

research on the development of aging-induced disorders in plants is very limited

(Thomas, 2002; Gensous et al., 2017). The impact of aging on plants is particularly

important when considering productive perennials such as fruit and nut crops (Munné-

Bosch, 2007). Studies focused on perennial plant aging often ask the question: do these

species actually age, and if so, how does this aging occur? (Munné-Bosch, 2007, 2008).

Work addressing the questions surrounding perennial plant aging have shown that

perennials do age, and some of the impacts of this process can mirror what is observed in

other species (Munné-Bosch, 2007, 2008, 2020).

It has been noted in several studies that as perennials age, particularly long-lived

perennials, there is a reduction in photosynthetic capacity and reproductive success

(Munné-Bosch, 2007, 2020). There are several proposed theories for why this occurs,

including the size of the plant and the accumulation of oxidative stress, which could be

related to either somatic mutations or to changes in epigenetic profiles including DNA

methylation (Van Dijk, 2009; Ally et al., 2010). Two studies focused on aging in the

perennial shrub, romerina (Cistus clusii Dunal), showed markers of oxidative stress and

41

changes in abscisic acid with increased age (Munné-Bosch & Alegre, 2002; Munné-

Bosch & Lalueza, 2007). Accumulation of oxidative stress including reactive oxygen

species (ROS), also known as the “free radical theory” of aging, is well-studied in

animals and could be one explanation for the decrease in plant photosynthetic capacity as

plants age (Beckman & Ames, 1998; Munné-Bosch, 2007). This theory was tested in the

long-lived perennial herb, Borderea pyrenaica (Bubani et Bordère ex Gren.) Miegev.,

and no signs of age-dependent oxidative stress were found, suggesting accumulation of

oxidative stress may not occur universally in all perennials (Morales et al., 2013). The

presence of ROS has also been found to be involved in cell wall lignification processes,

which are of particularly relevance to perennial plant aging as most tree structures

become lignified as the tree ages (Thomas, 2013; Lee et al., 2018). Antioxidants such as

superoxide dismutases play a role in regulating ROS accumulation in plants, including

Rosaceous species (Li et al., 2021), however activity of these enzymes over time is still

not well understood. Additional work in other productive perennials, including fruit and

nut trees, would provide further insights into how oxidative stress impacts these species

and what impact that stress might have on production.

In almond, as was described above, non-infectious bud-failure is known to be

associated with age (Kester et al., 2004). The underlying mechanisms contributing to

development of this disorder, however, remain unknown (Kester et al., 2004). As a

clonally propagated crop, the biological ages of almond individuals are difficult to

determine (Salguero-Gomez, 2018). Further, DNA methylation has been shown to be

associated with development of the disorder (Fresnedo-Ramírez et al., 2017). The

exhibition of an aging-related disorder and its impact on production is likely not isolated

42

to almond, and the possibility exists for the exhibition of aging-related disorders in other

productive perennials. Therefore, it is necessary to develop biomarkers of age in almond

(applying either the aging model of telomere length or epigenetic alterations), and to

determine what, if any, impact changes in DNA methylation profiles might have on the

exhibition of bud-failure in this species.

Dissertation Objectives

1. Assess the suitability of telomere length and telomerase reverse transcriptase (TERT)

expression as biomarkers of age in almond.

a. Measure telomere length in cohorts of almond breeding selections of distinct

age over a two-year period using both leaf and bud tissue.

b. Measure TERT expression in cohorts of almond breeding selections of

distinct age over a two-year period using leaf tissue.

2. Profile DNA methylation in cohorts of almond breeding selections of distinct age to

determine the association of age and methylation in almond genomes.

a. Measure weighted genome-wide methylation in each of the three methylation

contexts in 70 almond methylomes representing three distinct age cohorts.

b. Identify regions of the genome exhibiting differential methylation patterns

(DMRs) when comparing the cohorts and annotate the identified regions to

determine what genetic components are associated with the differential

methylation patterns.

3. Profile DNA methylation in two sets of monozygotic twin almonds discordant for

non-infectious bud-failure exhibition.

43

a. Measure weighted genome-wide methylation in each of the three methylation

contexts in two sets of twin almonds to determine patterns of hypo- or

hypermethylation associated with non-infectious bud-failure exhibition.

b. Identify regions of the genome exhibiting differential methylation patterns

(DMRs) when comparing individuals within each twin pair.

c. Determine the proximity of each identified DMR to the closest gene

(upstream, intragenic, or downstream) and identify DMRs from each twin

pair comparison that are associated with the same gene.

d. Annotate genes associated with DMRs to identify possible processes or

mechanisms underlying non-infectious bud-failure exhibition in almond.

e. Profile gene expression in two sets of almond twins to determine patterns of

differential expression associated with non-infectious bud-failure exhibition

and with differentially methylated regions identified in objective 3b.

44

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Figure 1.1 Percentage of global almond production in each country or region 2019/20

(adapted from Almond Board of California, 2020).

United States78%

European Union8%

Australia7%

All Others2%

Tunisia1%

Iran1%

Chile1%

Turkey1%

Morocco1%

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Figure 1.2 Shoot apical meristem (SAM) (A) zones including the central zone (yellow),

peripheral zone (purple), and rib zone (green), and (B) layers including the tunica (L1 and

L2 Cell layer – green and purple) and corpus (L3 Cell layers –pink) (from Sharma &

Fletcher, 2002).

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Figure 1.3 Timeline of almond growth and dormancy cycles in the California Central

Valley.

Ecodormancy

(Dec – Feb)

Flowering

(mid-Feb to mid-March)

Active Vegetative

Growth

(March –April)

Flower & Vegetative Bud Development (mid-May to

June)

Summer Ecodormancy &

Paradormancy

(July – Sept)

Endodormancy

(Oct – Dec)

• Flowering occurs first, followed by vegetative bud burst

• Vegetative growth follows expansion and division of merstimatic cells to form shoot, leaf, and bud tissues

• Buds are held in ecodormany until accumulation of growing degree days

• Ecodormancy is consider broken once ~50% of flowers are in anthesis

• Flower and vegetative buds differentiate• Outer budscales become brown and

harden

• Summer dormancy is a combination of ecodormancyand paradormancy, induced in part by increased temperatures and apical dominance

• Buds enter endodormancy when temperatures begin to drop in the fall

• Buds are released from endodormancy following accumulation of chilling hours

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Figure 1.4 Characteristic noninfectious bud-failure signs in an almond cultivar:

including bud death and erratic branching patterns. (Photo taken by K.M.

D’Amico-Willman; Davis, CA)

70

Figure 1.5 Acreage planted of the almond cultivar ‘Carmel’ from 1988 – 2017. High

incidence of bud-failure exhibition led to a dramatic decrease in planting in the mid-

2000’s. (Source: California Department of Agriculture, 2020)

0

1,000

2,000

3,000

4,000

5,000

1988 1993 1998 2003 2008 2013 2018

Ace

rgae

Pla

nte

d

'Carmel' Production by Acreage in California

71

Table 1.1 Number of chromosomes and estimated genome size of select members of

the Rosaceae family (adapted from Jung et al., 2019).

Species Name Chromosome Number Haploid Genome Size

Fragaria ananassa 2n=8x=56 240 Mb

Malus domestica 2n=34 750 Mb

Prunus armeniaca 2n=16 240 Mb

Prunus avium 2n=16 338 Mb

Prunus dulcis 2n=16 240 Mb

Prunus persica 2n=2x=16 265 Mb

Pyrus communis 2n=34 577 Mb

Rubus occidentalis 2n=14 240 Mb

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Chapter 2 Relative telomere length and telomerase reverse transcriptase (TERT)

expression are associated with age in almond (Prunus dulcis [Mill.] D.A. Webb)

Katherine M. D’Amico-Willman1,2, Elizabeth S. Anderson3, Thomas M. Gradziel4,

Jonathan Fresnedo-Ramírez1,2, *

1 Department of Horticulture and Crop Science, Ohio Agricultural Research and

Development Center, The Ohio State University, Wooster, OH 44691, USA

2 Center for Applied Plant Sciences, The Ohio State University, Columbus, OH 43210,

USA

3 Department of Biology, College of Wooster, Wooster, OH 44691

4 Department of Plant Sciences, University of California, Davis, CA 95616, USA

* Author to whom correspondence should be addressed

Published in Plants:

D’Amico-Willman KM, Anderson ES, Gradziel TM, Fresnedo-Ramírez J. 2021.

Relative telomere length and telomerase reverse transcriptase (TERT) expression are

associated with age in almond (Prunus dulcis [Mill] D.A.Webb). 10: 189.

Author Contributions:

Conceptualization, K.M.D.-W., E.S.A., J.F.-R., and T.M.G.; methodology, K.M.D.-W.,

T.M.G., E.S.A., J.F.-R.; validation, K.M.D.-W. and E.S.A.; formal analysis, K.M.D.-W.

and J.F.-R.; investigation, K.M.D.-W. and E.S.A.; resources, T.M.G.; data curation,

K.M.D.-W. and J.F.-R.; writing—original draft preparation, K.M.D.-W. and E.S.A.;

writing—reviewing and editing, K.M.D.-W., E.S.A., and J.F.-R.; visualization, K.M.D.-

W.; supervision, J.F.-R. and T.M.G.; project administration, J.F.-R.; funding acquisition,

73

K.M.D.-W. and J.F.-R. All authors have read and agreed to the published version of this

manuscript.

Abstract

While all organisms age, our understanding of how aging occurs varies among species.

The aging process in perennial plants is not well-defined yet can have implications on

production and yield of valuable fruit and nut crops. Almond exhibits an age-related

disorder known as non-infectious bud-failure (BF) that affects vegetative bud

development, indirectly affecting kernel yield. This species and disorder present an

opportunity to address aging in a commercially relevant and vegetatively propagated

perennial crop. The hypothesis tested in this study was that relative telomere length

and/or telomerase reverse transcriptase (TERT) expression can serve as biomarkers of

aging in almond. Relative telomere lengths and expression of TERT, a subunit of the

enzyme telomerase, were measured via qPCR methods using bud and leaf samples

collected from distinct age cohorts over a two-year period. Results from this work show a

marginal but significant association between both relative telomere length and TERT

expression, and age, suggesting that as almonds age, telomeres shorten and TERT

expression decreases. This work provides information on potential biomarkers of

perennial plant aging, contributing to our knowledge of this process. In addition, these

results provide opportunities to address BF in almond breeding and nursery propagation.

Introduction

The current concept and study of aging is centered primarily around mammals with

research focused on circumventing deleterious impacts on health (Kirkwood, 2005;

Sanders & Newman, 2013). However, all eukaryotic organisms exhibit signals of aging,

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resulting in the deterioration of key biological processes and subsequent decrease in

health, performance, and fitness of individuals. Perennial plants represent a unique model

to address the aging process and its impact since these species undergo cycles of

dormancy and growth, and they maintain the ability to reproduce for multiple years. The

aging process of perennial plants is relevant due to the longevity and economic

importance of perennial crops such as fruit and nut trees (Munné-Bosch, 2007; Brutovská

et al., 2013; Thomas, 2013). Individual trees can remain productive in orchards for

decades; however, aging in plants and its implications for growth and reproduction are

neglected areas of research with potential consequences for production, management,

conservation, and breeding.

The lack of understanding of aging in perennials is partly due to the complexity in

measuring and conceptualizing age in perennial plant species since chronologic and

ontogenetic age are inversely related (i.e., newly emerged tissues are the youngest

chronologically but the oldest ontogenetically) (Poethig, 2003). Chronologic age can be

defined as the amount of time since tissue/organ formation (e.g., human skin cells

replenish every few days, meaning each cell is typically a day or two days old), while

ontogenetic age refers more to developmental time and allows for the accumulation of

mutations or chromosomal alterations (e.g., two-day old skin cells at age six compared to

two-day old skin cells at age 60). Agriculturally relevant perennials are often vegetatively

propagated (i.e., cloned), blurring the distinction between ontogenetic and chronologic

age, and tend to be grown under intensive management. The difficulty in determining age

in perennials creates a need to identify biomarkers in these species that enable

ontogenetic age estimation.

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Almond (Prunus dulcis [Mill.] D.A.Webb; Fig. 2.1) is an economically relevant,

Rosaceous crop subject to intense horticultural management to maintain maximum nut

production. In California, the almond industry is estimated to contribute ~$11 billion to

the state’s gross domestic product annually (Almond Board of California, 2020). Top-

producing almond cultivars, some of which were seedlings first obtained more than 100

years ago, are produced for commercial orchards via vegetative propagation (Wickson,

1914; Micke, 1996). As orchards age (after 20–25 years), trees are replaced with “new”

clones, typically of the same cultivar, to maintain high levels of production and

homogeneity in quality (Micke, 1996).

Almond exhibits an age-related disorder known as non-infectious bud-failure

(BF) affecting vegetative bud development in the spring (Wickson, 1914; Kester & Jones,

1970). Genotypes exhibiting this disorder show characteristic dieback at the top of the

canopy, and severe levels of BF can result in up to 50% yield loss (Gradziel et al., 2013).

Empirical evidence shows that BF is associated with age (Kester et al., 2004); however,

as almonds are produced primarily through vegetative propagation rather than by seed,

their true ontogenetic age and thus susceptibility to BF can be difficult to assess (Micke,

1996). Biomarkers indicative of age would be valuable to growers, breeders, and

producers to screen germplasm. Thus, the almond represents a potential model species for

the study of aging in perennials due to its economic relevance, the abundance of available

germplasm and breeding programs, and the exhibition of an age-related disorder.

Several biomarkers of aging have been studied in animals including protein

glycation (Bilova et al., 2017), DNA methylation (Dubrovina et al., 2016), and telomere

length (Sanders & Newman, 2013; Runov et al., 2015; Marioni et al., 2016). Telomeres

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are nucleoproteins that cap the end of chromosomes, preventing premature instability of

genomic material and cellular senescence (Watson & Riha, 2011). Telomeres tend to

shorten over mitotic cellular divisions due to decreased levels of telomerase, an enzyme

that supports telomere replication during the S-phase of the cell cycle (Nelson et al.,

2014). This progressive shortening is proposed as a marker of aging in mammalian cells

and is linked to physiological deterioration and some age-related disorders (Watson &

Rhia, 2011; Sanders & Newman, 2013; Aviv & Shay, 2018). Given that telomerase

activity modulates telomere length, expression of genes involved in the telomerase

biosynthetic pathway could also serve as biomarkers for aging (Fitzgerald et al., 1996;

De la Fossel, 1998; Anchelin et al., 2011; Boccardi & Paolisso, 2014; Torre-Espinosa et

al., 2020). Telomerase reverse transcriptase (TERT) is the catalytic subunit of the

telomerase enzyme (Oguchi et al., 1999) and the RNA subunit functions as the template

for reverse transcription (Procházková Schrumpfová et al., 2019). Expression of TERT is

shown to affect telomerase activity (Jurečjivá et al., 2017; Sweetlove & Gutierrez, 2019).

This study tests the hypothesis that relative telomere length and TERT expression

in almond are associated with ontogenetic age and can thus be used to differentiate age

cohorts and serve as biomarkers of aging in this species. Both relative telomere length

and TERT expression show promise as diagnostic biomarkers since they can be measured

in a high-throughput manner by applying a variety of informative methods (Cawthon,

2009; Montpetit et al., 2014; Nersisyan & Arakelyan, 2015). These approaches build on

previous research examining the relationship between telomere lengths and age in

perennial plants (Flanary & Kletetschka, 2005; Moriguchi et al., 2007; Liu et al., 2007;

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Liang et al., 2015). The goal of this work is to advance our understanding and provide a

model for the study of aging and its implications in perennial plant species.

Materials and Methods

Plant Material

Leaf samples for this study were collected in May 2018 and 2019 from almond breeding

selections located at the Wolfskill Experimental Orchards (Almond Breeding Program,

University of California—Davis, Winters, CA, USA). Leaf tissue was harvested from the

upper canopy of a total of 36 unique individuals representing distinct age cohorts (Table

2.1). Vegetative buds were sampled in May 2019 from the upper canopy stem segments

of the 18 unique individuals used for leaf sample collection (Table 2.1). Samples were

immediately frozen on ice and stored at −20 °C until shipment overnight on dry ice to the

Ohio Agricultural Research and Development Center (OARDC—Wooster, OH, USA).

Samples were stored at −20 °C until processing, and all subsequent experimental

procedures were conducted at the OARDC.

DNA and RNA Extraction

DNA was extracted from the leaf samples using the Omega E-Z 96® Plant DNA Kit

(Omega Bio-tek, Norcross, GA, USA) with slight modification. Briefly, 100 mg of leaf

material was weighed in 2.0 mL tubes containing two 1.6 mm steel beads and kept frozen

in liquid nitrogen. Samples were ground in a 2000 Geno/Grinder (SPEX SamplePrep,

Metuchen, NJ, USA) in two 48-well cryo-blocks frozen in liquid nitrogen. Following a

65 °C incubation, samples were incubated on ice for 20 min, treated with 10 μL of RNase

solution (2.5 μL RNase [Omega Bio-tek, Norcross, GA, USA] + 7.5 μL TE pH 8),

equilibrated through addition of 150 μL equilibration buffer (3 M NaOH), incubated at

78

room temperature for four minutes, and centrifuged at 4400 rpm for two minutes prior to

the addition of SP3 buffer. Concentration and quality were analyzed using a NanoDrop

1000 spectrophotometer and a Qubit 4 Fluorometer with a dsDNA HS Assay Kit

(ThermoFisher Scientific, Waltham, MA, USA).

DNA was extracted from bud samples following a modified version of the

protocol outlined in Vilanova et al. (2016). Briefly, 5 buds from each sample were

ground in a 2 mL microfuge tube with one 3.2 mm steel bead using a 2000

Geno/Grinder (SPEX SamplePrep, Metuchen, NJ, USA) set at 200 strokes per minute

for 5 min. Finely ground tissues were added to 1 mL of extraction buffer (2% w/v CTAB;

2% w/v PVP-40; 20 mmol/L EDTA; 100 mmol/L Tris-HCl [pH 8.0]; 1.4 mol/L NaCl),

14 μL beta-mercaptoethanol, and 2 μL RNase (10 mg/mL). The solution was incubated at

65 °C for 30 min and on ice for 5 min followed by a phase separation with 700 μL

chloroform:isoamyl alcohol (24:1). The aqueous phase (~800 μL) was recovered, and

480 μL binding buffer (2.5 mol/L NaCl; 20% w/v PEG 8000) was added followed by

720 μL 100% ice-cold ethanol.

Silica matrix buffer was prepared by adding 10 g silicon dioxide to 50 mL ultra-

pure water prior to incubation and centrifugation steps. Silica matrix buffer (20 μL) was

added to each sample, and samples were gently mixed for 5 min. Samples were spun for

10 s and supernatant was removed. To resuspend the remaining mucilaginous material

(but not the pellet), 500 μL cold 70% ethanol was used and supernatant was removed.

Another 500 μL cold 70% ethanol was added to resuspend the silica pellet, the tubes were

spun for 5 s, and the supernatant was removed. The pellet was allowed to dry at room

temperature for 5 min and was resuspended in 100 μL elution buffer (10 mmol/L Tris

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HCl [pH 8.0]; 1 mmol/L EDTA [pH 8.0]) followed by a 5 min incubation at 65 °C.

Samples were centrifuged at 14,000 rpm for 10 min at room temperature and 90 μL of

supernatant was transferred to a new tube. DNA concentration was assessed by

fluorometry using a Qubit 4 and Qubit 1X dsDNA HS Assay Kit (ThermoFisher

Scientific, Waltham, MA, USA).

RNA was extracted from leaf tissue following the protocol outlined in Gambino et

al. (2008) with slight modifications. Briefly, leaf material was ground in liquid nitrogen

using a mortar and pestle, and 150 mg of tissue was weighed into a 2.0 mL microfuge

tube frozen in liquid nitrogen. To extract RNA, 900 μL CTAB extraction buffer (2%

CTAB, 2.5% PVP-40, 2 mol/L NaCl, 100 mmol/L Tris-HCl pH 8.0, 25 mmol/L EDTA

pH 8.0, 2% beta-mercaptoethanol added before use) was added to each tube and samples

were incubated at 65 °C for ten minutes. Following incubation, two phase separations

were performed using an equal volume of chloroform:isoamyl alcohol (24:1). RNA was

precipitated in 3 mol/L lithium chloride and incubated on ice for 30 min, and samples

were pelleted by centrifugation at 21,000× g for 15 min. Pellets were then resuspended in

500 μL pre-warmed SSTE buffer (10 mmol/L Tris-HCl pH 8.0, 1 mmol/L EDTA pH 8.0,

1% SDS, 1 mol/L NaCl) followed by a phase separation with an equal volume of

chloroform:isoamyl alcohol (24:1). A final precipitation was performed using 0.7 volume

chilled 100% isopropanol. RNA was pelleted and washed with 70% ethanol before being

resuspended in 30 μL nuclease-free water. A DNase treatment was performed using

DNA-free DNA Removal Kit (ThermoFisher Scientific) according to the

manufacturer’s instructions. All materials used for extraction were nuclease-free and

cleaned with RNaseZap RNase decontamination wipes (ThermoFisher Scientific) prior

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to use. All centrifugation steps were performed at 4 °C. RNA quality and concentration

were assessed using a NanoDrop 1000 spectrophotometer and a Qubit 4 fluorometer

with an RNA HS Assay Kit (ThermoFisher Scientific).

Monochrome Multiplex Quantitative PCR (MMQPCR) to Measure Relative Telomere

Lengths

MMQPCR was conducted following the protocol outlined in Vaquero–Sedas & Vega–

Palas (2014) with minimal modifications. Primer sequences for genes used in this study

are shown in Table 2.2, including primers for the single copy gene, PP2A, and for the

telomere sequence [52,53]. Oligos were synthesized by MilliporeSigma (Burlington,

MA) and resuspended to a concentration of 100 μmol/L upon arrival. Standard curves

were created for each primer pair by pooling six aliquots of DNA isolated from a single

clone of the almond cultivar Nonpareil, and performing successive dilutions to 20 ng/μL,

10 ng/μL, 1 ng/μL, 0.5 ng/μL, and 0.25 ng/μL. Reactions were carried out in triplicate for

each primer by concentration combination.

Isolated DNA from the age cohort samples was diluted to 20 ng/μL. Multiplex

reactions were carried out in sextuplicate for each replicate within the age cohorts in a 10

μL volume using QuantaBio PerfeCTa SYBR® Green SuperMix (Quanta Biosciences,

Beverly, MA, USA) (2×), forward and reverse primers (100 nmol/L each), and 20 ng

template DNA according to the manufacturer’s instructions. Reactions were performed in

a Bio Rad C1000 Touch Thermal Cycler (Bio Rad Laboratories, Hercules, CA, USA)

using the following program: initial denaturation at 95 °C for 3 min followed by 2 cycles

of incubation at 94 °C for 15 s and annealing at 49 °C for 15 s; telomere and PP2A

81

amplicons were generated following 35 cycles at 95 °C for 30 s, 59 °C for 1 min, 72 °C

for 30 s, 84 °C for 15 s, and 85 °C for 15 s; final incubation at 72 °C for 1 min. Melting

curve analysis was performed at a temperature range of 74–85 °C for both primer pairs to

ensure no non-specific amplification.

cDNA Synthesis and Quantitative Reverse Transcriptase PCR (qRT-PCR) to Measure

Relative Expression of TERT

Reactions were carried out in a 20 μL volume using the Verso™ cDNA Synthesis Kit

(ThermoFisher Scientific). One reaction was prepared for each age cohort sample

according to the manufacturer’s instructions. Reactions were performed in an MJ

Research PTC-200 thermal cycler using the following program: 42 °C for 30 min

followed by 95 °C for 2 min. To quantify expression of TERT in age cohort individuals,

qRT-PCR was performed in triplicate for each sample. The gene RPII from peach was

used as a reference (Tong et al., 2009; Bastias et al., 2020), and the sequence for the

TERT gene was derived from the ‘Texas’ genome

(https://www.rosaceae.org/analysis/295) using the homologous peach gene sequence as a

reference (Alioto et al., 2020). Primer sequences are shown in Table 2.2, and all oligos

were synthesized by MilliporeSigma (Burlington, MA, USA) and resuspended to a

concentration of 100 μmol/L upon arrival.

To generate cDNA from the age cohort samples, 100 ng of RNA was used as

input in the Verso cDNA Synthesis Kit (ThermoFisher Scientific) according to the

manufacturer’s instructions. To test for relative expression of TERT, reactions were

carried out in triplicate for each biological replicate within the age cohorts in a 10 μL

82

volume using QuantaBio PerfeCTa SYBR® Green SuperMix (Quanta Biosciences) (1×),

forward and reverse primers (100 nmol/L), and cDNA (1 μL) according to the

manufacturer’s instructions. Reactions were performed in Bio Rad C1000 Touch Thermal

Cycler (Bio Rad Laboratories) using the following program: initial denaturation at 95 °C

for 3 min followed by 40 cycles at 95 °C for 15 s and 55 °C for 45 s. Melt curves were

generated at a temperature range of 74–85 °C for both primer pairs to ensure no non-

specific amplification.

Statistical Analysis

Using the standard curve generated with PP2A (S) and telomere (T) primers for a

reference almond sample, relative T/S ratios were calculated for each individual sample

based on Cq values for the telomere and PP2A products (Vaquero-Sedas & Vega-Palas,

2014). Z-scores were calculated from the T/S ratios as recommended in Verhulst (2020)

for each replicate within the age cohorts. Normality and homogeneity of variance were

confirmed using Shapiro–Wilks and Bartlett tests. Analysis of variance (ANOVA) was

performed for each age cohort followed by post hoc Fisher’s LSD and pairwise t-tests.

Gene expression data were analyzed according to guidelines in Bustin et al. (2009), first

by normalizing TERT expression to that of the reference gene, RPII. Following

normalization, data were log-transformed, and normality and homogeneity of variance

were confirmed using Shapiro-Wilks and Bartlett tests. ANOVA was performed for each

age cohort followed by post hoc analysis with Tukey’s HSD. Letter groupings indicate

significant means separation following significant ANOVA results. Shared letters

indicate that means did not significantly differ between groups, while different letters

indicate a significant difference between means when comparing groups. All analyses

83

were performed using R v. 3.6.1 and plots were generated using ggplot2 v. 3.3.0.

Calculated T/S ratios, relative telomere lengths, relative TERT expression and log-

transformed TERT expression as well as raw Cq values for each individual are listed in

Appendix Tables 1-8. All R code used to perform analyses is reported in Appendix File

1. Analyses were performed using the Ohio Supercomputer Center resources (Ohio

Supercomputer Center, 1987).

Results

Association of Relative Telomere Length and Age in Almond

Relative telomere lengths were generated for the almond individuals within each of the

age cohorts collected in 2018 (1, 5, 9, and 14 years) using leaf tissue and in 2019 (2, 7,

and 11 years old) using leaf and bud tissue following the monochrome multiplex

quantitative PCR (MMQPCR) approach. Normality of residuals and homogeneity of

variance of relative telomere lengths were confirmed using Shapiro–Wilks (2018: p-value

= 0.2578, n = 4–6; 2019 (leaf): p-value = 0.4682, n = 3; 2019 (bud): p-value = 0.0402, n

= 5–7) and Bartlett (2018: p-value = 0.1408; 2019 (leaf): p-value = 0.4613; 2019 (bud):

p-value = 0.1076) tests. ANOVA results based on leaf tissue analysis for the linear

model, z-score ~ age, were marginally significant in both 2018 and 2019, and subsequent

post hoc Fisher’s least significant difference (LSD) and pairwise t-tests revealed

significant differences between ages 1 and 14 years and 5 and 14 years (Fig. 2.2a) in the

2018 cohorts, and between ages 2 and 11 years old (Fig. 2.2b) in the 2019 cohorts. The

ANOVA result based on the bud tissue analysis for the linear model, z-score ~ age, was

significant at alpha = 0.1, and subsequent post hoc Fisher’s LSD and pairwise t-tests

showed significant differences between ages 2 and 11 years old (Fig. 2.3). Both bud and

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leaf tissue showed similar patterns in decreased relative telomere length with increased

age.

TERT Gene Expression Patterns Associated with Age in Almond

Normalized expression of TERT was measured for almond samples among the age

cohorts collected in 2018 and 2019 for this study using PP2A as the reference gene.

Normality of residuals and homogeneity of variance were confirmed using Shapiro–

Wilks (2018: p-value = 0.694, n = 2–3; 2019: p-value = 0.09456, n = 4) and Bartlett

(2018: p-value = 0.6976; 2019: p-value = 0.3579) tests. ANOVA results comparing the

average log (expression) values for each age cohort revealed significant differences

between cohorts in both 2018 and 2019. Post hoc analysis with Tukey’s HSD revealed

significant differences in TERT expression between ages 1 and 14 years old in the 2018

cohorts (Fig. 2.4a) and between ages 2 and 11 years old in the 2019 age cohorts (Fig.

2.4b).

Discussion

Almond, an economically valuable nut crop, exhibits an aging-related disorder known as

non-infectious bud-failure that negatively impacts vegetative development and,

ultimately, yield. As a clonally propagated crop, tracking age and thus susceptibility to

bud-failure is difficult, making biomarkers of age a valuable resource to circumvent the

impacts of aging-related disorders in almond germplasm. Relative telomere length is used

as a biomarker of age and development of age-related disorders in mammals, but the

association between relative telomere length and age in plants is not well-defined

(Watson & Riha, 2011; Procházková Schrumpfová et al., 2019). Over mitotic cell

divisions, telomeres eventually reach a critical minimum length at which point the cell

85

senesces and dies due to genome instability resulting from single stranded DNA at the

ends of chromosomes (Hemann et al., 2001). Plant chromosomes also contain telomeres

with similar functions. While the relationship between telomeres and the aging process is

not as clearly defined in plants as in animals, previous work shows associations between

relative telomere length and various stages of plant development (Watson & Riha, 2011;

Zachová et al., 2013; Procházková Schrumpfová et al., 2019) suggesting relative

telomere length could be a suitable biomarker of age in plants. Additionally, telomerase

activity modulates telomere length, and thus expression of genes such as TERT, which

are involved in telomerase synthesis, could also serve as biomarkers of age. In

Arabidopsis, increased TERT expression is linked to proportional increases in telomerase

activity and telomere length (Fitzgerald et al., 1999; Zangi et al., 2019), which are in turn

linked to age. Since TERT is tied to telomere length in both plants and animals, its

expression may also serve as an indicator of age in plants (Watson & Riha, 2011).

The present study tests the hypothesis that relative telomere length and/or TERT

expression are associated with ontogenetic age in almond. To test this, a qPCR approach

was utilized to measure relative telomere length and estimate TERT expression in sets of

almond accessions of known chronological age. Leaf and bud samples were collected

from three and four sets of age cohorts over two years to test for an association between

relative telomere length and individual age using the MMQPCR method as well as

between TERT expression and age using qRT-PCR.

86

Quantitative PCR Approaches Suggest an Association between Relative Telomere Length

and Age in Almond Leaf and Bud Tissues

A pattern of decreased relative telomere length with increased age was shown utilizing

MMQPCR and almond leaf and bud samples collected from different almond age cohorts

in 2018 and 2019. The association demonstrated in this study adds to the growing body of

knowledge regarding the complex relationship between telomere length and plant aging.

Previous studies in both Ginkgo biloba and Panax ginseng showed a pattern of increased

telomere length with increased age, suggesting plants do not follow the same patterns of

telomere shortening as seen in mammals (Liu et al., 2007; Liang et al., 2015). Work in

apple (Malus domestica) and Prunus yedoensis, both members of the Rosaceae family

like almond, show no change in telomere lengths with increased plant age over a five-

year timespan (Moriguchi et al., 2007). In bristlecone pine (Pinus longaeva), a long-lived

perennial gymnosperm, telomere lengths measured in needle and root tissues between 0–

3500 years old showed a cyclical pattern of lengthening and shortening with age (Flanary

& Kletetschka, 2005). Further, when analyzing telomere length in relation to tissue

differentiation, studies in both barley (Hordeum vulgare) and Scots pine (Pinus

sylvestris) showed telomere shortening from embryo development to leaf or needle

formation (Kilian et al., 1995; Aronon & Ryynänen, 2012). Similarly, in silver birch

(Betula pendula), telomeres shorten when plants are grown in tissue culture conditions

compared to those grown outdoors, suggesting abiotic stressors may also induce telomere

shortening (Aronon & Ryynänen, 2014).

87

The results in almond suggest a pattern closest to what was observed in

bristlecone pine where telomere lengths shorten and lengthen throughout an individual’s

lifetime. This pattern could be unique to gymnosperms, however, and needs to be further

characterized in angiosperms including Rosaceous species. While the commercial

lifespan of productive almond clones is typically less than 30 years, almond seedlings can

live more than 150 years (Micke, 1996). In this study, the maximum age tested via qPCR

was 14 years old, suggesting that a wider age range of trees and a larger sample size

could produce a more refined model of telomere length patterns over time.

Current almond cultivars may also be ontogenetically old. Nonpareil, the most

relevant US cultivar representing ~40% of acreage, was first described almost 140 years

ago and has been propagated by budding since (Wickson, 1914; Almond Board of

California, 2020). The ontogenetic age of a cultivar may be a factor to consider in the

onset of aging-related disorders like bud-failure in almond. Additionally, it would be

interesting to track the change in telomere length following clonal propagation (through

budding) during which plants experience a rejuvenation process, reverting to a juvenile

state for a short period of time (Bonga, 1982). Interestingly, both bud and leaf tissue

showed similar patterns of decreased relative telomere length with increasing age in this

study. The bud tissue utilized in this study was excised from stems containing the leaves

that were also sampled. Based on their close proximity, it is possible that the telomere

profile of the bud would be reflected in the associated leaf tissues. It would be useful to

profile telomere lengths of buds throughout the tree to see if similar patterns of relative

telomere length were obtained. Further, Gradziel et al. (2019) found that propagating

almond from basal epicormic buds, potentially representing ontogenetically young

88

meristematic tissue, seemed to alleviate BF in resulting clones. Testing telomere lengths

in epicormic tissues could present another avenue to both track aging in almond and

develop biomarkers to predict BF potential in almond.

TERT Expression Measured by qRT-PCR is Putatively Associated with Age in Almond

Accessions

To test the hypothesis that TERT expression can serve as a biomarker of ontogenetic age

in almond, expression patterns were tested in cohorts representing either three or four

distinct ages over two years. Results from this work showed a consistent pattern of

marginally significant, decreased expression with increased ontogenetic age. Telomerase

was shown to be a modulator of longevity in humans and other mammals, but work

describing telomerase patterns in plants is limited (Fitzgerald et al., 1996; Boccardi &

Paolisso, 2014).

A comprehensive study examining telomerase protein activity in carrot (Daucus

carota), cauliflower (Brassica oleracea), soybean (Glycine max), Arabidopsis thaliana,

and rice (Oryza sativa) demonstrated that, like telomere lengths, protein activity tends to

be highest in undifferentiated tissues like meristematic tissues and is lower in

differentiated tissues such as leaves (Fitzgerald et al., 1996). This result was supported by

further work in barley and maize showing little activity in differentiated tissues (Kilian et

al., 1995). These studies were all performed in annuals or biennials, however, suggesting

that telomerase activity does in fact decrease with increased plant age in these crops.

Work in perennials including bristlecone pine, P. ginseng, and G. biloba showed an

association between telomerase activity and age, suggesting patterns unique to perennial

89

plant species (Flanary & Kletetschka, 2005; Liu et al., 2007; Song et al., 2011). A study

in almond could be performed using a wider age range and larger sample size to elucidate

the effect of age on telomerase activity, similar to what was referenced above for

telomere length measurements. Additionally, many of the studies performed in other

plants examining patterns of telomerase activity focused on protein activity rather than

gene expression. A future study will be necessary in almond to examine the telomerase

protein activity, potentially by Western blot or other proteomics approaches, to

corroborate the association between TERT expression and protein activity.

While a pattern was established in plants demonstrating a direct relationship

between telomerase activity and telomere length, regulation of telomerase is still not well

understood in the plant kingdom (Fitzgerald et al., 1996; Zachová et al., 2013; Jurečková

et al., 2017). Interestingly, work in Arabidopsis has shown a link between DNA

methylation and telomere length, suggesting that this epigenetic mark likely has a role in

regulating telomere lengths potentially by modulating telomerase activity (Ogrocká et al.,

2014; Vega-Vaquero et al., 2016; Lee & Cho, 2019). A study is ongoing in almond to

analyze DNA methylation patterns in a set of almond accessions representing three

distinct age cohorts to determine what, if any, impact age has on methylation profiles.

Despite the limited age range and small sample size used in this study, a

consistent pattern of both decreased relative telomere length and decreased TERT

expression with increased age was observed over two years of sampling, regardless of

whether the sample was taken from actively growing tissue like buds or a more static

tissue in terms of cell division, like leaves. These results provide a basis for future study

and exploration into the utility of relative telomere length measurement and/or TERT

90

expression or telomerase activity as biomarkers of aging in almond. Developing a robust

biomarker to track aging in almond, a primarily clonally propagated crop, would allow

growers, producers, and breeders to screen germplasm to eliminate selections or clones

with a high susceptibility to age-related disorders due to advanced ontogenetic age.

Acknowledgments

We would like to acknowledge Matthew Willman for his assistance with the statistical

analyses and Cheri Nemes for her assistance with wet lab portions of this project. We

would like to thank Daniel Williams for editing later versions of this manuscript. We

would also like to acknowledge the Ohio Supercomputer Center.

91

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Figure 2.1 Image of the almond cultivar Nonpareil (photo taken by K. D’Amico-

Willman in May 2018).

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Figure 2.2 Boxplots depicting the calculated z-score of the T/S ratio for leaf samples

within the age cohorts tested. (A) Age cohort collected in 2018. (B) Age cohort collected

in 2019. Significant differences in z-scores between age cohorts based on ANOVA

followed by post hoc Fisher’s least significant difference (LSD) (α = 0.1) are denoted by

letter groupings where differing letters indicate significant differences following means

separation analysis (ANOVA 2018 p-value = 0.1077; ANOVA 2019 p-value = 0.06548).

Bold dots represent outliers within each age cohort.

a

a

ab

b

−3

−2

−1

0

1

2

1 5 9 14

Chronological Age (Years)

Zsco

re R

ela

tive

Te

lom

ere

Le

ng

th

A

a

ab

b

−1

0

1

2 7 11


Zscore

Rela

tive

Te

lom

ere

Le

ng

th

B

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Figure 2.3 Boxplot depicting calculated z-score for the T/S ratio for bud samples within

the age cohorts collected in 2019. Significant differences in z-scores between ages

cohorts based on ANOVA followed by post hoc Fisher’s LSD (α = 0.05) are denoted by

letter groupings where differing letters indicate significant differences following means

separation analysis (ANOVA p-value = 0.067). Bold dots represent outliers within each

age cohort.

a ab b

−10

0

10

20

2 7 11


Zsco

re R

ela

tive

Te

lom

ere

Le

ng

th

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Figure 2.4 Normalized expression of TERT for almond samples within the age cohorts

tested. (A) Age cohort collected in 2018. (B) Age cohort collected in 2019. Significant

differences in relative expression between age cohorts based on ANOVA followed by

post hoc Tukey’s HSD (alpha = 0.1) are denoted by the letter groupings where differing

letters indicate significant differences following means separation analysis (ANOVA

2018 p-value = 0.09087; ANOVA 2019 p-value = 0.1414).

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Table 2.1 Sampling scheme for 2018 and 2019 almond age cohort collections.

Sampling Year Age (Years) Leaf Sample Size Bud Sample Size

2018 1 6 N/A

2018 5 6 N/A

2018 9 6 N/A

2018 14 6 N/A

2019 2 4 6

2019 7 4 7

2019 11 4 5

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Table 2.2 Oligos used for all Monochrome Multiplex Quantitative PCR (MMQPCR) and

quantitative reverse transcriptase PCR (qRT-PCR) studies.

Oligo Name Oligo Sequence (5′–3′)

PP2A

Forward CGGCGGCGGGCGGCGCGGGCAGGATAGACATTGGAGGGTTCGGCTCGCAA

PP2A

Reverse CGGCGGCGGGCGGCGCGGGACCACTGCATGCAAAGGGACCCAAGCTTAT

Telomere

Forward CCCCGGTTTTGGGTTTTGGGTTTTGGGTTTTGGGT

Telomere

Reverse GGGGCCCTAATCCCTAATCCCTAATCCCTAATCCCT

TERT

Forward GCATCAGAGAAGGGTCAGATT

TERT

Reverse CTCTGGCTCCTTGAATCGTATAG

RPII

Forward TGAAGCATACACCTATGATGATGAAG

RPII Reverse CTTTGACAGCACCAGTAGATTCC

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Chapter 3 Hypermethylation is associated with increased age in almond (Prunus

dulcis [Mill.] D.A.Webb) accessions

Abstract

Almond (Prunus dulcis [Mill.] D.A.Webb) is a perennial crop produced primarily by

vegetative propagation and exhibits an age-related disorder known as non-infectious bud-

failure. As a vegetatively propagated crop, determining age and thus susceptibility of

bud-failure exhibition in almond clones is difficult. The focus of this study is to profile

changes in DNA methylation occurring with increased age in almond breeding

germplasm in an effort to identify possible biomarkers of age that can be used to assess

the potential individuals have to develop aging-related disorders in this productive

species. To profile DNA methylation in almond germplasm, 70 methylomes were

generated from almond individuals representing three age cohorts (11, 7, and 2-years old)

using an enzymatic methyl-seq approach followed by analysis to call differentially

methylated regions (DMRs) within these cohorts. Weighted chromosome-level

methylation analysis reveals hypermethylation in 11-year-old almond breeding selections

when compared to 2-year-old selections in the CG and CHH contexts. A total of 17

consensus DMRs were identified in all age-contrasts, and one of these DMRs contains

the sequence for miR156, a microRNA with known involvement in regulating the

juvenile-to-adult transition. Almond shows a pattern of hypermethylation with increased

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age, and this increase in methylation may be involved in regulating the vegetative

transition in almond. The identified DMRs could function as putative biomarkers of age

in almond following validation in additional age groups.

Introduction

The study of aging has centered primarily around mammalian systems with a focus on

humans (Kirkwood, 2005; Ferrucci et al., 2020); however, the aging process has also

been shown to impact plants with emphasis placed on long-lived perennials (Munné-

Bosch, 2007; Thomas, 2013; Brutovská et al., 2013; Woo et al., 2018). These impacts

can include things like diminished growth, reduced flower and fruit production, and the

development of aging-related disorders (Kester & Jones, 1970; Van Dijk, 2009). In

perennial plants and in other organisms such as humans, causal mechanisms underlying

the development of age-related phenotypes include genetic alterations such as somatic

mutations or differential epigenetic marks (Jaligot et al., 2000; Dubrovina & Kiselev,

2016; Ogneva et al., 2016; Xiao et al., 2019; Wang et al., 2020). In fact, DNA

methylation in particular has been proposed as a biomarker of aging in many systems,

serving as a biological “clock” that can be used to track aging and predict aging outcomes

(Runov et al., 2015; Jylhävä et al., 2017; Xiao et al., 2019).

Studying epigenetic alterations like differential DNA methylation associated with

advanced age in perennial plant systems can (1) provide a means to track aging in these

systems and (2) lead to an increased understanding of the development of age-related

disorders or degeneration of important physiological processes. This information is

valuable to agricultural industries that rely on sustained production of perennial crops,

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including fruit and nut trees. Almond (Prunus dulcis [Mill.] D.A. Webb) is an example of

a perennial nut crop that is negatively affected by the aging process through the

exhibition of non-infectious bud-failure, an aging associated disorder (Kester & Jones,

1970; Micke, 1996; Kester et al., 2004). Additionally, almond trees are primarily

produced by clonal propagation for orchard establishment, meaning age, and thus

susceptibility to age-related impacts is difficult to determine (Ally et al., 2010; de Witte

& Stöcklin, 2010; Salguero-Gomez, 2018). A means to track aging, particularly in crops

like almond which are produced by clonal propagation or shown to exhibit age-related

disorders, benefits growers, producers, and consumers by helping to protect the supply

chain of these valuable commodities.

Profiling genome-wide DNA methylation is one approach to quantify differential

epigenetic marks in an effort to model alterations associated with advanced age. Whole-

genome enzymatic methylation sequencing is equivalent to the “gold standard” bisulfite

sequencing approach to profile the methylome at the nucleotide level (Feng et al., 2020).

Utilizing this approach provides information on both genome-wide methylation in each

context (CG, CHG, and CHH [H = A, T, or C]) and allows for the identification of

differentially methylated regions (DMRs), pin-pointing regions of the genome showing

dynamic patterns of methylation associated with increased age (Vaisvila et al., 2020;

Feng et al., 2020). Identification of specific regions of the genome showing changes in

methylation associated with aging provides both the opportunity to develop biomarkers to

track aging and information on those genes or genic regions that might contribute to the

development of age-related phenotypes (Xiao et al., 2019).

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In this study, we utilize almond breeding germplasm grown from seed following

pedigreed crosses as part of the almond breeding program at the University of California,

Davis. The individuals used in this study are, since grown from seed, of known age and

thus particularly useful to generate models to track aging in this species where clonal

propagation is standard. The goal of this study was to examine DNA methylation patterns

in the genome of a productive perennial crop by performing an exhaustive methylome

profiling of 70 almond individuals from three cohorts aged 2, 7, and 11-years. The

hypothesis is that the almond breeding selection cohorts will exhibit, on average,

divergent DNA methylation profiles associated with age. Our overall aim is to identify

variability in the almond methylome that could enable model development to track aging

in this clonally propagated crop and provide targets (i.e., differentially methylated

regions) for further investigation into mechanisms influencing age-related phenotypes

such as non-infectious bud-failure or the juvenile-to-adult transition. This work

additionally serves as a model to explore aging and its impacts in other important

perennial crops.


Plant Material

Almond leaf samples were collected in May 2019 from the canopy of 30 distinct breeding

selections planted in 2008, in 2012, and in 2017, totaling 90 individuals sampled. These

selections represent three almond age cohorts aged 11, 7, and 2-years at the time of

sampling. Almond breeding germplasm sampled for this study is maintained at the

Wolfskill Experimental Orchards (Almond Breeding Program, University of California –

Davis, Winters, CA). The pedigree of each sample collected was also documented,

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including both the female and male parents of each individual. Leaf samples were

collected in the field and immediately stored on ice and then at -20 C until shipping.

Samples were shipped on ice to the Ohio Agricultural Research and Development Center

(OARDC; The Ohio State University, Wooster, OH, USA) and immediately stored at -20

C until sample processing.

DNA Extraction

High-quality DNA was extracted from leaves following a modified version of the

protocol outlined in Vilanova et al., 2020. Briefly, samples were ground to a fine powder

with a mortar and pestle in liquid nitrogen, and 50 mg of the ground material was added

to 1 mL of extraction buffer (2% w/v CTAB; 2% w/v PVP-40; 20 mM EDTA; 100 mM

Tris-HCl [pH 8.0]; 1.4 M NaCl), 14 L beta-mercaptoethanol, and 2 L RNase (10

mg/mL). The solution was incubated at 65 C for 30 mins and on ice for 5 mins followed

by a phase separation with 700 L chloroform:isoamyl alcohol (24:1). The aqueous phase

(~800 L) was recovered, and 480 L binding buffer (2.5 M NaCl; 20% w/v PEG 8000)

was added followed by 720 L 100% ice-cold ethanol.

A silica matrix buffer was prepared by adding 10 g silicon dioxide to 50 mL ultra-

pure water prior to incubation and centrifugation steps. Silica matrix buffer (20 L) was

added to each sample, and samples were gently mixed for 5 mins. Samples were spun for

10 secs and the supernatant was removed. To resuspend the remaining mucilaginous

material (but not the pellet), 500 L cold 70% ethanol was used, and the supernatant was

removed. Another 500 L cold 70% ethanol was added to resuspend the silica pellet, the

tubes were spun for 5 secs, and the supernatant was removed. The pellet was allowed to

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dry at room temperature for 5 mins and was resuspended in 100 L elution buffer (10

mM Trish HCl [pH 8.0]; 1 mM EDTA [pH 8.0]) followed by a 5 min incubation at 65 C.

Samples were centrifuged at 14,000 rpm for 10 mins at room temperature and 90 L of

supernatant was transferred to a new tube. DNA concentration was assessed by


Scientific, Waltham, MA, USA).

Enzymatic Methyl-Seq Library Preparation and Illumina Sequencing

Whole-genome enzymatic methyl-seq libraries were prepared using the NEBNext

Enzymatic Methyl-seq kit (New England BioLabs Inc., Ipswich, MA, USA) following

the protocol for standard insert libraries (370-420 basepairs). Each sample was prepared

using 100 ng input DNA in 48 L TE buffer (1 mM Tris-HCl; 0.1 mM EDTA; pH 8.0)

with 1 L spikes of both the CpG unmethylated Lambda and CpG methylated pUC19

control DNA provided in the kit. The samples were sonicated using a Covaris S220

focused-ultrasonicator in microTUBE AFA Fiber Pre-Slit Snap-Cap 616 mm tubes

(Covaris, Woburn, MA, USA) with the following program parameters: peak incident

power (W) = 140; duty factor = 10%; cycles per burst = 200; treatment time (s) = 80.

Following library preparation, library concentration and quality were assessed by


Scientific) and by electrophoresis using a TapeStation (Agilent, Santa Clara, CA, USA).

Library concentration was further quantified by qPCR using the NEBNext Library

Quant Kit for Illumina (New England BioLabs Inc.). Libraries were equimolarly

pooled in batches of ~15 (five libraries per age cohort) and cleaned using an equal

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volume of NEBNext Sample Purification Beads (New England BioLabs Inc.). The

library pools were eluted in 25 L TE buffer (1 mM Tris-HCl; 0.1 mM EDTA; pH 8.0),

and concentration and quality were assessed by fluorometry and electrophoresis as above.

Library pools were sequenced on two lanes of the Illumina HiSeq4000 platform to

generate 150-bp paired-end reads.

Processing and Alignment of Enzymatic Methyl-Seq Libraries

Methyl-Seq read quality was initially assessed using FastQC v. 0.11.7 (Andrews, 2010)

and reads were trimmed using TrimGalore v. 0.6.6 and Cutadapt v. 2.10 with default

parameters (Krueger, 2016). Forward read fastq and reverse read fastq files from the two

HiSeq4000 lanes were combined for each library to produce single fastq files for both

read one and read two. Reads were aligned to the ‘Nonpareil’ v. 2.0 almond reference

genome, deduplicated, and methylation calls were generated using Bismark v. 0.22.3

(Krueger & Andrews, 2011) with default parameters in paired-end mode. To test

conversion efficiency, reads were also aligned to both the Lambda and pUC19 nucleotide

sequence fasta files provided by NEB (https://www.neb.com/tools-and-

resources/interactive-tools/dna-sequences-and-maps-tool). All analyses were performed

using the Ohio Supercomputer Center computing resources (Ohio Supercomputer Center,

1987).

Weighted Genome-wide Methylation Analysis of Age-Cohorts

Weighted genome-wide percent methylation values were calculated for each individual

within each cohort by taking the total number of methylated reads at each cytosine and

dividing this by the total number of reads (methylated + unmethylated) at each cytosine.

Weighted values were calculated for each methylation context. These values were used as

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input to R v. 4.0.2 (R Core Team, 2020) to perform beta regression using the package

betareg v. 3.1-3 (Cribari-Neto & Zeileis, 2010). Pairwise comparison of least squared

means were completed by the functions emmeans() and cld() from the R packages

emmeans v. 1.5.2-1 and multcomp v. 1.4-14 with an alpha = 0.05 and Sidak adjustment

(Hothorn et al., 2008). The R package ggplot2 v. 3.3.2 was used to create plots for

weighted percent methylation within each methylation-context (Wickham, 2016). Files

were then subset by chromosome (chr1 – chr8), and weighted percent methylation values

were calculated for all individuals by chromosome using the same formula as above for

each methylation context. These values were used as input in R v. 4.0.2 (R Core Team,

2020) to perform beta regression and subsequent pairwise comparison of least squared

means as performed above for the genome-wide weighted percent methylation values.

Differential Methylation Analysis of Age-Cohorts

Coverage files for each methylation context produced by Bismark were prepared for

input into the R package DSS (Dispersion Shrinkage for Sequencing Data) v. 2.38.0 (Wu

et al., 2013; Feng et al., 2014; Park & Wu, 2016). The functions DMLtest() and

callDMR() were used with a significance p.threshold set to 0.0001 to identify

differentially methylated regions (DMRs) through pairwise comparisons between the

three age cohorts. Comparisons were made relative to the oldest cohort in each DMR test

(i.e. 11-year old cohort relative to 2-year old cohort).

Classification and annotation of differentially methylated regions

Following identification of DMRs in each age-contrast (11 – 2 year; 11 – 7 year; 7 – 2

year) and methylation-context, DMRs were further characterized based on the

directionality of differential methylation. Hypermethylated DMRs are those that show

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increased methylation in the oldest cohort in each contrast, and hypomethylated DMRs

are those that show decreased methylation in the oldest cohort in each contrast. The

cumulative binomial probability of identifying an equal or greater number of

hypermethylated DMRs in each age-contrast by methylation-context was calculated using

the R base package stats command pbinom() where x = the number of hypermethylated

DMRs in each age-contrast by methylation-context, size = the total number of DMRs

identified in each age-contrast/methylation-context, p = 0.5, and lower.tail = FALSE.

To visualize enrichment of DMRs across the eight chromosomes in the ‘Nonpareil’

genome, circos plots were generated with one track depicting each DMR classified as

either hyper- or hypomethylated and two additional tracks depicting DMR enrichment

across the genome. To create the circos plots, the R package circlize v. 0.41.2 (Gu et al.,

2014) was used along with the bed files for all hyper- and hypomethylated DMRs in each

methylation-context for all age-contrasts. The command circos.genomicRainfall() was

used to create the first track with dots representing each individual DMR (red –

hypermethylated, blue – hypomethylated) and positioned based on the number of DMRs

occurring in that location. The command circos.genomicDensity() was used to create the

two additional tracks representing enrichment of hyper- and hypomethylated DMRs on

each chromosome where the taller the peak, the higher the number of DMRs occurring in

the specific region (Gu et al., 2014).

Following classification into hyper- and hypomethylated regions, bed files were

generated for these DMRs using genomic coordinates. These bed files were used as input

along with the ‘Nonpareil’ genome annotation file into the R v. 4.0.2 (R Core Team,

2020) packages GenomicRanges v. 1.40.0 (Lawrence et al., 2013) and genomation v.

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1.20.0 (Akalin et al., 2015) to prepare a GRanges object and annotate DMRs using the

command annotateWithFeatures(). Initial annotation of DMRs by features includes the

percentage of DMRs overlapping at least one of four features: gene, exon, 5’ untranslated

region (UTR), and 3’ UTR. The DMRs were further annotated, and gene ontology (GO)

enrichment was performed using the software HOMER v. 4.11 (Heinz et al., 2010) and

the R package topGO v. 2.40.0 (Alexa A, 2020). Initially, all DMRs were annotated by

assigning the gene with the closest transcriptional start site to each DMR using

annotatePeaks.pl -noann with the ‘Nonpareil’ genome and genome annotation files. This

produced a list of gene identifiers from the genome annotation file that are associated

with each DMR. The GO terms assigned to each DMR-associated gene were used as

input along with the ‘Nonpareil’ genomic annotation file to determine enrichment of GO

terms in each age-contrast DMR-associated gene set. The DMR-associated genes in each

methylation-context were classified based on biological process, molecular function, and

cellular component GO term to produce tables depicting the number of DMR-associated

genes assigned to each descriptor.

The DMRs were then further classified based on the occurrence of overlapping

genomic regions among DMRs when comparing age-contrasts. The bed files generated

above were used as input in the bedtools v. 2.29.2 (Quinlan & Hall, 2010) command

intersect -wao to identify overlaps in DMRs from each of the age-contrasts (i.e. 11-7

contrast compared to 11-2 contrast). Finally, genomic regions were identified that contain

significant DMRs in all three age-contrasts using bedtools intersect. These overlapping

DMRs were annotated using the annotatePeaks.pl script as above to find DMR-

associated genes as well as GO terms, Pfam identifiers, and Interpro identifiers associated

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with each gene. The genomic DMR sequence was extracted from the ‘Nonpareil’ genome

fasta file, and individual DMR fasta files were searched against the miRbase database v.

22.1 (Kozomara et al., 2019) to identify any putative microRNAs (miRNAs) within those

regions. Searches were performed by sequence using an e-value cutoff of 10 and the

Prunus persica (L.) Batsch species filter.

Annotation of unknown protein sequences

Genes coding for proteins with unknown function and associated with the DMRs shared

across the three age-contrasts were interrogated using in silico approaches to characterize

the proteins. Several programs were used to annotate these protein sequences and

determine additional information about their putative functions. The program ProtParam

was used to characterize protein properties including molecular weight (Gasteiger et al.,

2005). To predict subcellular localization, the program YLoc was used (Briesemeister et

al., 2010a,b). Finally, the Motif tool on the GenomeNet website

(https://www.genome.jp/tools/motif/) was used to search a protein query against several

databases to identify putative alignments (Marchler-Bauer et al., 2013; Sigrist et al.,

2013; Finn et al., 2014).

Results

Genome-wide methylation analysis in almond accessions representing three age-cohorts

Following DNA isolation, library preparation, and Illumina sequencing, a total of 21

almond breeding selections were used for subsequent analysis in the 2-year-old age

cohort, 25 in the 7-year-old age cohort, and 24 in the 11-year-old age cohort. Sequencing

results show aligned coverage for almond accessions ranged from 3.85 – 50.41X with an

https://www.genome.jp/tools/motif/

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average mapping rate of 49.8 % (Appendix Table 9). Conversion rate ranged from 98.4 –

99.95% based on alignment to the Lambda reference sequence file (Appendix Table 9).

Analysis of weighted genome-wide percent methylation within all methylation-

contexts (CG, CHG, and CHH) revealed a significant increase in weighted methylation in

the 11-year-old age cohort compared to the 2-year-old in the CG (p-value = 0.0105) and

CHH (p-value = 0.0399) contexts, respectively (Fig. 3.1a, c). There was also a significant

increase in CG methylation in the 11-year-old age cohort compared to the 7-year-old age

cohort (p-value = 0.0115; Fig. 3.1a). There was not a significant difference in weighted

genome-wide methylation in the CHG context when comparing age cohorts (Fig. 3.1b) or

between the 2-year-old and 7-year-old age cohorts in any methylation context (Fig. 3.1).

To further analyze weighted methylation in these samples, methylation data for

each individual was processed per chromosome, and weighted methylation was analyzed

at the chromosome level for each methylation-context. (Fig. 3.2a-c). Pairwise

comparisons of DNA methylation within each chromosome revealed significant

differences in cytosine methylation on distinct chromosomes for each methylation-

context (Table 3.1a-c). In the CG context, both the 2 – 11 year and the 7 – 11-year age-

contrasts were significant on chromosomes 1, 3, 5, 7, and 8 (Table 3.1). In the CHG

context, both the 2 – 11 year and the 7 – 11-year age-contrasts were significant on

chromosome 5, the 7 – 11-year age-contrast was significant on chromosome 7, and the 2

– 11 year age-contrast was significant on chromosome 8 (Table 3.1). Finally, in the CHH

context, the 2 – 11-year age-contrast was significant on chromosomes 5, 7, and 8 (Table

3.1). Overall, significant differences in chromosome-level DNA methylation between age

cohorts tend to occur on chromosomes 5, 7, and 8.

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Identification and classification of differentially methylated regions (DMRs) between age

cohorts

DMRs were identified based on comparisons between the age cohorts in each

methylation context. Most DMRs identified are in the CG context, followed by CHH and

CHG, respectively (Table 3.2). These DMRs were further classified as hyper- and

hypomethylated based on the amount of methylation in the older cohort compared to the

younger. Hypermethylated DMRs have a higher amount of methylation in the older

cohort, while hypomethylated DMRs have a higher amount of methylation in the younger

cohort for each comparison. In the CG context, 96%, 94%, and 64% of the identified

DMRs were hypermethylated in the 11 – 2 year, 11 – 7 year, and 7 – 2-year age-

contrasts, respectively (Table 3.2). In the CHG context, 68%, 52%, and 64% of DMRs

were hypermethylated in the 11 – 2 year, 11 – 7 year, and 7 – 2-year age-contrasts,

respectively (Table 3.2). Finally, in the CHH context, 82%, 38%, and 82% of DMRs

were hypermethylated in the 11 – 2 year, 11 – 7 year, and 7 – 2-year age-contrasts,

respectively (Table 3.2). The cumulative binomial probability of the occurrence of

hypermethylated DMRs was less than 110-6 for all age-contrasts except 11 – 7 year in

the CHG and CHH contexts, suggesting there are more hypermethylated DMRs than

would be expected given an equal probability of hyper- and hypomethylated DMRs in the

genome. Identified DMRs ranged in length from 51-4824 base pairs with an average

length of 195 base pairs, a median length of 134 base pairs, and a mode of 69 base pairs

(Fig. 3.3a-l). The average length of a gene in the ‘Nonpareil’ genome is 2,912 bp, so most

of the identified DMRs are much shorter than the average gene.

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The distribution of CG-context DMRs showed a similar pattern across all

chromosomes, where the 11 – 2 year age-contrast has the highest number of DMRs per

chromosome, followed by the 11 – 7 year and 7 – 2 year age-contrasts (Fig. 3.4a). In the

CHG and CHH contexts, the distribution of DMRs showed greater variability, with the

11 – 7 year age-contrast typically showing the lowest number of DMRs across all

chromosomes, while the 11 – 2 and 11 – 7 year age-contrasts oscillate in number of

DMRs occurring on each chromosome across the genome (Fig. 3.4b,c).

Classification of DMRs as hyper- or hypomethylated in the age cohort comparisons

Using the classifications of hyper- and hypomethylated, DMRs were plotted across the

eight chromosomes of the ‘Nonpareil’ genome revealing unique distributions based on

both methylation-context and age-contrast, as well as indicating DMR enrichment in

specific chromosomes (Fig. 3.5). In the CG context, DMR enrichment occurs in the 11 –

2 year age-contrast, with predominantly hypermethylated DMRs, though enrichment of

hypomethylated DMRs appears on chromosome 5 (Fig. 3.5a). The CHG context

represents the lowest overall enrichment of DMRs compared to the other methylation-

contexts, with regions throughout the genome showing slight enrichment of DMRs (Fig.

3.5b). Finally, DMRs in the CHH context show similar patterns in enrichment for both

the 7 – 2 year and 11 – 2 year age-contrasts, with evident DMR enrichment occurring on

chromosomes 3 and 8 (Fig. 3.5c). The 11 – 7 year age-contrast in the CHH methylation-

context is the only contrast to have a higher number of hypomethylated DMRs compared

to hypermethylated (Table 3.2; Fig. 3.5c).

Following classification of DMRs as either hyper- or hypomethylated in each age-

contrast, DMRs were compared among age-contrasts to identify overlapping genomic

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regions. This analysis revealed several overlapping DMRs from distinct age-contrasts.

The highest number of overlaps occurred in CG context hypermethylated DMRs,

particularly when comparing the 11 – 2 by 11 – 7 and 11 – 2 by 7 – 2 age-contrasts

(Table 3.3). Interestingly, the 11 – 7 by 7 – 2 age-contrast revealed very few overlaps in

hypermethylated DMRs, and no overlapping hypomethylated DMRs (Table 3.3). Finally,

a comparison was performed to identify DMRs with overlapping genomic regions among

all three age-contrasts, showing the 11 – 2 age-contrast contains DMRs that share a

genomic region with the overlapping DMRs in the 11 – 7 by 7 – 2 comparison (Table

3.3). This final analysis revealed 17 overlapping DMRs among the three age-contrasts,

meaning these DMRs share overlapping genomic coordinates (Table 3.4). These 17

DMRs are the longest (ranging from 106 – 1,504 base pairs) in the 11 – 2 age-contrast,

followed by the 11 – 7 (56 – 713 base pairs) and 7 – 2 (52 – 1,037 base pairs) age

contrasts (Table 3.4). Analysis of the average percent methylation of cytosines within the

genomic regions identified in the 11 – 7 age-contrasts shows that in the majority of these

regions, cytosines become more methylated with increased age across the three age

cohorts (Fig. 3.6a-m, 3.7a-c, 3.8).

Annotation of hyper- and hypomethylated differentially methylated regions (DMRs)

An annotation was performed classifying all hyper- and hypomethylated DMRs in each

methylation-context into four categories (gene, exon, 5’ untranslated region [UTR], and

3’ UTR) based on their association with features in the ‘Nonpareil’ genome annotation.

CG context DMRs generally tended to have higher associations with genes and exons

compared to the other methylation contexts, while CHG DMRs tended to have higher

117

associations with 5’ UTRs and CHH DMRs with 3’ UTRs compared to the other contexts

(Table 3.5).

Identified DMRs were then annotated using the ‘Nonpareil’ genome annotation

file to determine the closest gene associated with each DMR. Enrichment analysis was

performed for both hyper- and hypomethylated DMR-associated genes in all methylation

contexts for each age-contrast, revealing a suite of biological process, molecular function,

and cellular component gene ontology (GO) terms associated with each contrast

(Appendix Tables 10-15). Comparing annotations between age-contrasts identified GO

terms unique to each age-contrast in each methylation-context and degree of methylation

(i.e. hyper or hypo). For example, a subset of genes associated with hypermethylated

DMRs in the CG context from all three age-contrasts were assigned the molecular

function GO terms transmembrane transporter activity, protein serine/threonine kinase

activity, and DNA-binding transcription factor activity (Table S10b).

Annotation of genes associated with 17 hypermethylated DMRs identified across the

three age cohort age-contrasts

Of the DMRs identified in each age-contrast, 17 hypermethylated DMRs were found to

share genomic regions in all three age-contrasts, meaning these regions showed

consistent significant increases in methylation in the older age cohort relative to the

younger in each age-contrast. The 17 DMRs were annotated using the ‘Nonpareil’

genome annotation to identify the closest associated gene. In total, eight previously

annotated genes including FAR1-RELATED SEQUENCE 5 (FRS5), a receptor-interacting

serine/threonine-protein kinase 4 (Ripk4), and dCTP pyrophosphatase 1 (Dctpp1) were

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identified as associated with nine of the DMRs (Table 3.6). One CG DMR and one CHG

DMR are both associated with the gene tryptophan aminotransferase-related protein 3

(TAR3) (Table 3.6). The remaining eight DMRs are associated with genes of unknown

function (Table 3.6). These eight unknown protein sequences were used as input into

three programs to determine properties including predicted motifs, localization, and

weight. Two of these unknown proteins contained transposase_24 motifs, and four are

predicted to be localized to the nucleus (Table 3.7).

In addition to identifying genes associated with these 17 shared DMRs, the DMR

genomic sequences (Appendix File 2) were searched against the miRBase database using

the Prunus persica species filter to identify any potential miRNAs within these regions.

Results from this analysis showed that two of the 17 DMRs contain miRNA sequence

including CGDMR8 and CHGDMR1. The miRNA identified in CGDMR8 is ppe-

miR6276 and ppe-miR156 in CHGDMR1.

Discussion

Perennial plant aging and the impacts of this process, particularly on productive fruit and

nut crops, is a neglected area of research with potential applications for agricultural

production and crop improvement. The ability to track age in clonally propagated crops

could aid in the mitigation of age-related disorders like non-infectious bud-failure (Kester

et al., 2004) and, more broadly, in overcoming the decrease in plant performance

resulting from intense production systems that affect orchard/vineyard longevity.

Biomarkers of age in these species, however, are lacking. The aim of this study was to

test the hypothesis that, on average, almond breeding selection cohorts will exhibit

divergent DNA methylation profiles associated with age. The long-terms goals of this

119

work are to develop biomarkers of age in almond, a clonally-propagated crop, and to

further our understanding of the aging process in perennial species. To address this,

whole-genome DNA methylation profiles were generated for ~70 almond individuals

from three distinct age cohorts, and comparisons were made between cohorts to identify

regions of interest for further study into their involvement in the aging process and utility

as biomarkers of age in almond.

DNA hypermethylation in the CG and CHH contexts is associated with increased age in

almond

The DNA methylation profiles generated for individuals in the three age cohorts (11, 7,

and 2-years old) were compared at the whole-genome and chromosome level, which

showed that hypermethylation in the CG and CHH methylation-contexts is associated

with increased age in almond. Further, the probability of identifying the number of

hypermethylated DMRs observed in this study was very low for most age-contrasts,

suggesting there was a disproportionately high number of hypermethylated DMRs

identified compared to hypomethylated DMRs. This result supports previous work

theorizing an increase in total genomic DNA methylation with increased age in plants

(Dubrovina & Kiselev, 2016). Previous studies have also shown that genome-wide

hypermethylation can result in a high number of identified hypermethylated DMRs in

subsequent analyses, as was reported in several species including Monterey pine (Pinus

radiata D.Don), peach (P. persica), and coast redwood (Sequoia sempervirens [D.Don]

Endl.) (Bitonti et al., 2002; Fraga et al., 2002b; Huang et al., 2012).

120

DNA methylation has been proposed as a “biological clock” capable of predicting

the true, ontogenetic age of an individual due to observed patterns of increased

methylation with increased age in a variety of species (Runov et al., 2015). Results in this

study suggest that almond fits this pattern of hypermethylation, and thus DNA

methylation may serve as a biomarker of age in this species. Whole-genome

hypermethylation with increased age represents an opportunity to develop high-

throughput screening methods that do not require whole-genome sequencing. These

methods could include high-performance liquid chromatography or capillary

electrophoresis (Stach et al., 2003; Armstrong et al., 2011).

Epigenetic regulation by DNA methylation, histone modifications, and chromatin

remodeling has also been shown to modulate the juvenile-to-adult phase transition in

plants, including in gymnosperms like Monterey pine and in angiosperms such as peach

(Bitonti et al., 2002; Fraga et al., 2002a; Xu et al., 2018). The juvenile period in almond

is approximately 3-4 years; thus, differential patterns of methylation observed in this

study between the 2-year cohort and the 7- and 11-year cohorts could be associated with

the juvenile-to-adult transition as has been documented in other plants (Dubrovina &

Kiselev, 2016). Patterns of differential methylation identified in this study and associated

with specific regions of the genome further demonstrate the potential involvement of

DNA methylation in regulating this transition in almond. Further investigation is needed

focusing on the involvement of DNA-methylation in the juvenile-to-adult transition in

almond and other perennial species, including those with available transgenic germplasm

exhibiting reduced juvenility, such as apple (Flachowsky et al., 2011; Kumar et al.,

2020).

121

Differentially methylated regions (DMRs) in the CG and CHG contexts are enriched on

specific chromosomes in the almond genome

Following identification of DMRs in the three age-contrasts, these regions were plotted

across the almond genome showing enrichment of DMRs on specific chromosomes,

particularly in the CG and CHH methylation-contexts. These so called “hotspots” of

differential DNA methylation could suggest loci in these regions are prone to epigenetic

alterations including methylation. Transposable elements (TEs) tend to be heavily

methylated and have been reported to have involvement in developmental processes in

almond, such as the juvenile-to-adult transition (Han et al., 2018; Corso-Díaz et al., 2020;

Wyler et al., 2020), suggesting the “hotspots” identified in almond in this study could

contain TE sequences. This type of pattern has been documented in other species such as

rice, where a study on salt tolerance identified DMRs that tended to cluster on specific

chromosomes and were typically associated with TEs on these chromosomes (Ferreira et

al., 2019).

In this study, hypermethylated DMRs were enriched on chromosome 8 in the CG

context, and on chromosomes 3 and 8 in the CHH context. As increased levels of

methylation tend to occur in regions rich in TEs, it is possible that regions enriched in

DMRs contain TEs which become increasingly methylated with age. Interestingly, a

study in Brachypodium distachyon (L.) P.Beauv. found that DMRs were highly

correlated with genetic diversity as classified by presence of single nucleotide

polymorphisms (SNPs) throughout the genome (Eichten et al., 2016). This genetic

diversity was found to be related to the presence of TEs at these sites, potentially

122

contributing to the formation of SNPs as well as leading to differential levels of

methylation between the lines tested (Eichten et al., 2016). Given the heterozygosity and

diversity in almond germplasm, it may be relevant to compare regions enriched in DMRs

from the age-contrasts to SNP data in almond to test for a correlation between increased

methylation and genetic diversity, particularly for traits associated with growth and

development and length of juvenility.

To identify genetic components involved in the juvenile-to-adult transition in

Prunus, a study utilizing two almond peach interspecific populations found a single

QTL on chromosome 6 associated with juvenility period, defined as the number of years

to first fruit (Donoso et al., 2016). However, this QTL only explained ~13% of the

variability in time to first fruit in these populations, suggesting there may be additional

regions influencing this trait (Donoso et al., 2016), as has been observed in other species

such as citrus (Raga et al., 2012). Additional work is necessary to explore the genetic

variability associated with juvenility in almond to see if chromosomes enriched in DMRs

based on age are associated with variation in these traits among almond populations.

Further, the TE landscape of regions enriched in DMRs could be characterized to

determine if there is an abundance of TEs at these locations, potentially explaining the

high number of DMRs observed in these regions. A recent study in almond characterized

the TE landscape in the ‘Texas’ cultivar and compared the distribution of TEs in the

genome to that of peach (Alioto et al., 2020). This study revealed not only an increased

involvement of TEs in trait diversity in almond compared to peach, but also showed

enrichment of TEs on almond chromosomes 3 and 8 (Alioto et al., 2020).

123

DMRs as potential biomarkers of age in almond

The DMRs identified in this study represent those regions in the genome that showed

either an increase or a decrease in cytosine methylation with increased age in almond.

The results herein suggest that DMRs tend to be hypermethylated in the age-contrasts,

meaning there is an increase in methylation in these regions with increased age in each

age-contrast. This pattern fits with the weighted genome-wide methylation patterns

showing significant increases in methylation in the CG and CHH contexts between the 2

and 11-year old age cohorts. Unique, hypermethylated DMRs were identified in each

age-contrast, providing information on DNA methylation dynamics associated with age.

Regions that show increased methylation in each age contrast are of particular interest

due to their potential suitability as biomarkers of age, since these regions show

incremental increases in methylation from 2-to-7 years and again from 7-to-11 years old.

Within the DMRs identified in each of the age-contrasts, 17 hypermethylated

DMRs were identified with overlapping genomic regions in all three contrasts. Once

these regions are validated via DNA methylation profiling in additional almond cohorts

of known age, a predictive model could be developed considering cytosine methylation

level. This model could be applied to clonal germplasm to predict ontogenetic age,

providing a basis to screen germplasm for susceptibility to undesirable, age-related

phenotypes. These tools may have implications for germplasm management in breeding,

production (orchard), propagation (nursery), and conservation (repository) settings.

The genetic features associated with these specific DMRs could also have

involvement in developmental processes, including the juvenile-to-adult transition. To

annotate these DMRs, genes were identified with transcriptional start sites close to or

124

overlapping the DMR. Of the 17 DMRs, nine were found to be associated with eight

annotated genes. These genes include FRS5, a FAR1-related protein in the FAR1 gene

family which is involved in light perception and was demonstrated to have involvement

in plant development and regulation of aging processes in Arabidopsis (Lin & Wang,

2004; Ma & Li, 2018; Xie et al., 2020). The gene TAR3 was found to be associated with

two of the 17 identified DMRs, one in the CG context and one in the CHG context. This

gene is part of a gene family known as TRYPTOPHAN AMINOTRANSFERASE (TAR),

whose members are a component of one of the major auxin biosynthetic pathways

(Hofmann, 2011). The TAR genes are involved in the first step of the pathway, in which

tryptophan is converted to indole-3-pyruvic acid, which is subsequently converted to

auxin (Brumos et al., 2014). Auxin is a well-known regulator of plant development and

senescence processes (Ljung, 2013; Khan et al., 2014; Mueller-Roeber & Balazadeh,

2014). While tryptophan-independent pathways for auxin biosynthesis are present in

plants, TAR genes are involved in the primary biosynthetic pathway, and disruption of

TAR3 expression could impede auxin production (Hofmann, 2011). These genes and the

others identified represent interesting targets for future study on their potential

involvement in aging processes in almond, including in the vegetative transition.

Additionally, eight proteins of unknown function were identified as associated with the

overlapping DMRs. Characterization of these proteins could reveal novel genes with

potential functions in plant development and aging in almond.

In addition to identifying nearby genes associated with these DMRs, microRNAs

(miRNAs) were also surveyed in these regions. Interestingly, two of the DMRs were

found to contain miRNA sequences, including one with the sequence for ppe-miR156, a

125

well-characterized miRNA known to be a major regulator of development and phase

transition in plants (Wu et al., 2009). Previous studies have shown that miR156 regulates

vegetative phase transition in plants by targeting SQUAMOSA-PROMOTER BINDING

PROTEIN-LIKE (SPL) genes, inhibiting their expression during juvenility (Wu et al.,

2009; Jia et al., 2017). As plants age, miR156 expression is repressed, allowing activation

of target genes and inducing the transition to adult (Wu et al., 2009). In fact, studies in

both Arabidopsis and maize show that mutants overexpressing miR156 experience

prolonged juvenility (Wu & Poethig, 2006; Chuck et al., 2007). A recent study in

Arabidopsis showed that light perception via FAR1-family genes may also interact with

miR156 in regulating plant development (Xie et al., 2020).

Previous work has shown 11 putative members of the miR156 family in peach,

suggesting that the miRNA identified in this study could be one of several in this family

in almond (Luo et al., 2013); work has shown that the function of miR156 in the aging

pathway is conserved in Prunus species (Bastías et al., 2016). It has also been proposed

that miR156 is regulated by epigenetic modifications (Xu et al., 2018), including DNA

methylation, however more work is needed to disentangle epigenetic regulation of

miR156 expression as well as to characterize and identify targets of miR156 in almond.

Additionally, expression of miR156 has been altered through transgenic approaches in

other crops to delay flowering, leading to increased plant biomass or abiotic stress

tolerance (Zheng et al., 2016; Kang et al., 2020). Manipulation of miR156 in Prunus

could lead to potential applications for crop improvement, including decreased juvenility,

which could greatly decrease breeding cycles for these crops. Results in this study

identified a hypermethylated DMR overlapping miR156 and associated with increased

126

age, suggesting cytosine methylation could be one mode of regulation for this miRNA.

Induced DNA methylation using gene-editing techniques could provide a potential

avenue for manipulation of miR156 in almond or other Prunus species, not only for crop

improvement applications, but also to further our understanding of this miRNA in plant

development and aging in these crops. Since almond is recalcitrant to tissue culture

methods, the application of gene-editing techniques in this species would first require

optimization of methods allowing propagation of modified material.

The second miRNA sequence identified was ppe-miR6276, which was first

identified as a novel miRNA in Japanese apricot (P. mume Sieb. et Zucc), and found to

have homology with a miRNA sequence in peach (Gao et al., 2012). This miRNA is

currently uncharacterized and could provide an interesting target for further investigation

in almond and other Prunus species to determine its potential role in the plant aging

process.

The study of aging in perennial plants is limited despite the potential applications

for agriculture and plant production, particularly for fruit and nut crops. Results from this

work show that DNA hypermethylation is associated with age in almond, and identifies

specific genomic regions that could serve as putative biomarkers of age for this species.

Biomarkers of age are valuable for clonally propagated crops, whose ontogenetic age can

be difficult to determine, to screen and select germplasm with low potential for

developing age-related disorders. Further, the DMRs identified in this study can be used

to guide future studies aimed at increasing our understanding of plant aging and

vegetative phase transition in perennials. Perennial plants, including almond, are known

for having long juvenile periods which can inhibit breeding and improvement efforts.

127

Epigenetic regulators of phase transition such as DNA methylation could provide another

tool for developing perennial crops with shorter juvenile periods, dramatically shortening

breeding cycles for these species.

Acknowledgements

We would like to acknowledge Matthew Willman for his assistance with the statistical

analysis and preparation of the scripts used to perform the computational analyses for this

manuscript. We would also like to acknowledge the Ohio Supercomputer Center for

access to computing resources and the Translational Plant Sciences Graduate Program for

the fellowship for KMDW. This work was supported by The Ohio State University

CFAES-SEEDS program grant # 2019-125, the Almond Board of California Grant

HORT35, the U.S. Department of Health and Human Services National Institutes of

Health - National Cancer Institute - Cancer Center Support Grant (CCSG)

P30CA016058, the USDA National Institute of Food and Agriculture AFRI-EWD

Predoctoral Fellowship 2019-67011-29558.

128

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135

Figure 3.1 Proportion of weighted genome-wide methylation in the CG (a), CHG (b),

and CHH (c) methylation-contexts for each age cohort (2, 7, and 11 years-old). Letter

groups represent significant differences based on pairwise comparisons using least

squared means (alpha = 0.05).

136

Figure 3.2 Boxplots depicting the proportion of weighted methylation in each age cohort

(2 years old – red; 7 years old – grey; 11 years old – yellow) across the three methylation

contexts: (a) CG, (b) CHG, and (c) CHH. The black dots represent outliers.

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Figure 3.3 Distribution of lengths in base pairs of differentially methylated regions

(DMRs) identified in each age contrast and methylation context. Panels a-c show

distribution of DMRs identified in the CG context, panels d-f show distribution in the

CHG context, and panels g-l show distribution in the CHH context. The values listed next

to the methylation context indicate the age-contrast (11 – 2 year, 11 – 7 year, and 7 – 2

year).

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138

Figure 3.4 Dot plots representing the number of significant (p < 0.0001) differentially

methylated regions (DMRs) identified in each of the contrasts (11 – 2 years: red; 11 – 7

years: grey; 7 – 2 years: yellow) in each methylation-context: (a) CG, (b) CHG, and (c)

CHH.

139

Figure 3.5 Circos plots depicting individual hyper- (red) and hypo- (blue) methylated

differentially methylated regions (DMRs) identified in each contrast and methylation-

context. The outer ring of each plot gives the approximate location of the individual

DMRs on each of the eight ‘Nonpareil’ chromosomes represented by red and blue dots.

The middle ring of each plot represents enrichment of hypermethylated DMRs across

each chromosome, and the innermost ring of each plot represents enrichment of

hypomethylated DMRs across each chromosome. Panel a shows the distribution of

DMRs in the CG context, panel b shows distribution of DMRs in the CHG context, and

panel c shows distribution of DMRs in the CHH context.

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140

Figure 3.6 Heatmaps displaying average percent DNA methylation across cytosines in

the 11-year, 7-year, and 2-year age cohorts within the genomic range of 13 overlapping

differentially methylated regions (DMRs) in the CG context identified in the three age-

contrasts. The regions correspond to CGDMR1-13 (a-m; see Table S2) and the values to

the right of each heatmap represent the genomic position of each cytosine on the

respective chromosome.

(continued)

11 7 2 11 7 2 11 7 2 a b cchr6 chr8 chr8

11 7 2 11 7 2 11 7 2 d e fchr1 chr8 chr8

141

(Figure 3.6 continued)

11 7 2 11 7 2 11 7 2 g h ichr6 chr6 chr2

11 7 2 11 7 2 11 7 2 j k lchr7 chr8 chr1

11 7 2 m chr7

142

Figure 3.7 Heatmaps displaying average percent DNA methylation across cytosines in

the 11-year, 7-year, and 2-year age cohorts within the genomic range of 3 overlapping

differentially methylated regions (DMRs) in the CHG context identified in the three age-

contrasts. The regions correspond to CHGDMR1-3 (a-c; see Table S2) and the values to

the right of each heatmap represent the genomic position of each cytosine on the

respective chromosome.

11 7 2 11 7 2 11 7 2 a b cchr3 chr8 chr8

143

Figure 3.8 Heatmap displaying average percent DNA methylation across cytosines in the

11-year, 7-year, and 2-year age cohorts within the genomic range of the overlapping

differentially methylated region (DMR) in the CHH context identified in the three age-

contrast. The regions correspond to CHHDMR1 (see Table S2).

11 7 2 chr1

144

Table 3.1 Pairwise comparison of least squared means of weighted percent methylation

in the CG, CHG, and CHH contexts for each chromosome in the ‘Nonpareil’ almond

genome. Age cohort contrasts include the 2 – 11, 7 – 11, and 2 – 7-year contrasts.

Significant contrasts are represented in bold with an alpha = 0.05.

Chromosome Age-

Contrast

CG context

p-value

CHG context

p-value

CHH context

p-value

Chr1

11-2 0.0235 0.5329 0.0595

11-7 0.0422 0.4425 0.7068

7-2 0.9436 0.9949 0.2780

Chr2

11-2 0.9973 0.3587 0.1320

11-7 0.7356 0.9063 0.6008

7-2 0.7924 0.5948 0.5698

Chr3

11-2 0.0020 0.2137 0.0138

11-7 0.0046 0.1647 0.3543

7-2 0.9267 0.9980 0.2883

Chr4

11-2 0.6659 0.9917 0.0972

11-7 0.5390 0.8626 0.6030

7-2 0.9867 0.8071 0.4789

Chr5

11-2 <.0001 0.0056 0.0020

11-7 <.0001 0.0049 0.1935

7-2 0.8706 0.9954 0.1873

Chr6

11-2 0.3472 0.9922 0.0675

11-7 0.1645 0.7924 0.5824

7-2 0.9342 0.8679 0.4042

Chr7

11-2 0.0003 0.0658 0.0956

11-7 0.0002 0.0263 0.7183

7-2 0.9963 0.9676 0.3716

Chr8

11-2 0.0004 0.0163 0.0055

11-7 0.0295 0.1837 0.4246

7-2 0.3440 0.5279 0.1329

145

Table 3.2 Number of identified differentially methylated regions (DMRs) in each

methylation-context when comparing the three age cohorts. DMRs were identified with a

threshold of p 0.0001. DMRs are classified as hypermethylated if the percent

methylation in that region is greater in the older age cohort compared to the younger age

cohort within each contrast. DMRs are classified as hypomethylated if the percent

methylation in that region is lesser in the older age cohort compared to the younger age

cohort within each contrast. Hypermethylated DMR values in bold represent those with a

cumulative binomial probability < 110-6.

Methylation

Context Contrast

Number of

DMRs

Hypermethylated

DMRs

Hypomethylated

DMRs

CG

11-2 6479 6232 247

11-7 2105 1983 122

7-2 1211 985 226

CHG

11-2 1129 763 366

11-7 485 252 233

7-2 813 524 289

CHH

11-2 2059 1690 369

11-7 567 216 351

7-2 2259 1849 410

146

Table 3.3 Number of occurrences of overlap when comparing differentially methylated

regions (DMRs) identified in each contrast to those identified in the other contrasts. The

number of overlaps means the number of times a DMR in a particular age-contrast (e.g.

11-2) overlaps the genomic region of a DMR in one of the other age-contrasts (e.g. 11-7).

The overall comparison indicates the number of DMRs occurring in overlapping genomic

regions in all three contrasts. DMRs are classified as either hyper- or hypomethylated in

each methylation context.

Methylation

Context

Hyper

or Hypo 11-2 11-7 11-2 7-2 11-7 7-2 11-2 11-7 7-2

CG Hyper 677 646 13 13

Hypo 37 58 0 0

CHG Hyper 75 161 3 3

Hypo 74 62 0 0

CHH Hyper 95 607 1 1

Hypo 78 146 0 0

147

Table 3.4 Genomic coordinates and length in base pairs for the 17 overlapping DMRs occurring in each of the three age-contrasts.

11-2 11-7 7-2

DMR_ID Chr Start End Length

(bp) Start End

Length

(bp) Start End

Length

(bp)

CGDMR1 6 11486222 11486807 585 11486637 11486807 170 11486637 11486807 170

CGDMR2 8 21310028 21310528 500 21310028 21310149 121 21310028 21310403 375

CGDMR3 8 14689409 14689818 409 14689430 14689807 377 14689477 14689547 70

CGDMR4 1 17183093 17183455 362 17183249 17183343 94 17183249 17183315 66

CGDMR5 8 7039851 7040044 193 7039954 7040044 90 7039899 7040044 145

CGDMR6 8 5018558 5018792 234 5018650 5018792 142 5018740 5018792 52

CGDMR7 6 3438057 3438262 205 3438026 3438089 63 3438057 3438174 117

CGDMR8 6 5119949 5120234 285 5119949 5120126 177 5119949 5120126 177

CGDMR9 2 12423793 12423952 159 12423835 12423931 96 12423896 12423952 56

CGDMR10 7 7738726 7739046 320 7738980 7739046 66 7738980 7739046 66

CGDMR11 8 7305381 7305503 122 7305386 7305503 117 7305381 7305503 122

CGDMR12 1 36985701 36985875 174 36985796 36985875 79 36985796 36985875 79

CGDMR13 1 1104987 1105093 106 1104987 1105093 106 1104987 1105093 106

CHGDMR1 3 20615159 20615610 451 20615337 20615502 165 20615337 20615391 54

CHGDMR2 8 5017982 5018361 379 5018000 5018056 56 5018000 5018056 56

CHGDMR3 8 7206115 7206361 246 7206170 7206242 72 7206170 7206231 61

CHHDMR1 1 34468252 34469756 1504 34469043 34469756 713 34468083 34469120 1037

148

Table 3.5 Annotation of hyper- and hypomethylated differentially methylated regions

(DMRs) in each methylation context and for each age-contrast. The ‘Nonpareil’ genome

annotation was used to classify the DMRs into four categories: gene, exon, five prime

untranslated region (5’ UTR), and three prime untranslated region (3’ UTR). The

percentages under each classification represent the percentage of DMRs from each

methylation-context and contrast in each of the four categories.

Methylation

Context Hyper/Hypo Contrast %Gene %Exon %5’ UTR %3’ UTR

CG

Hyper

11 – 2 28.0 20.6 0.91 0.96

11 – 7 34.2 25.1 1.51 1.46

7 – 2 27.8 21.4 0.81 1.32

Hypo

11 – 2 39.7 31.2 7.29 1.21

11 – 7 22.1 14.8 0.82 3.28

7 – 2 29.2 22.1 2.65 0.88

CHG

Hyper

11 – 2 27.7 20.1 2.49 1.70

11 – 7 24.6 18.3 3.17 1.98

7 – 2 24.8 17.2 1.91 0.76

Hypo

11 – 2 23.5 17.8 3.28 1.37

11 – 7 27.9 18.5 3.00 2.58

7 – 2 19.4 13.8 2.77 0

CHH

Hyper

11 – 2 18.0 9.41 1.60 2.13

11 – 7 19.0 12.0 0.46 1.39

7 – 2 17.9 9.20 1.35 2.16

Hypo

11 – 2 27.4 14.6 3.25 1.63

11 – 7 24.5 14.0 3.99 1.99

7 – 2 24.4 14.2 1.22 1.22

149

Table 3.6 Annotation of genes associated with 17 hypermethylated differentially methylated regions (DMRs) occurring in all three

age cohort contrasts. The chromosome (chr) and genomic coordinates (start and end) of each gene are listed along with the gene

identification from the ‘Nonpareil’ genome annotation. Protein identifiers from InterPro and Pfam databases are also included as well

as gene ontology (GO) terms associated with the gene.

Methylation

Context Chr Start End GeneID InterPro Pfam GO

CG

chr6 11481648 11484599 BGLU12: Beta-glucosidase IPR001360 PF00232 GO:0004553

GO:0005975

chr6 5122822 5124703 At3g47570: Probable LRR receptor-like

serine/threonine-protein kinase

IPR001245

IPR001611

IPR013210

PF00560

PF07714

PF08263

GO:0004672

GO:0005515

GO:0006468

chr8 14686870 14697541 Protein of unknown function1

chr1 36988003 36989001 ESD4: Ubiquitin-like-specific protease IPR003653 PF02902 GO:0006508

GO:0008234

chr1 17181984 17183843 Protein of unknown function2 IPR004252 PF03004



chr6 3439053 3439763 Protein of unknown function5 IPR009769 PF07059

chr8 7299785 7304401 PS1: FHA domain-containing protein IPR000253

IPR002716

PF00498

PF13638 GO:0005515

chr8 21331964 21333852 Dctpp1: dCTP pyrophosphatase 1 IPR025984 PF12643 GO:0009143

GO:0047429


chr8 5019298 5021435 TAR3: Tryptophan aminotransferase-

related protein 3 IPR006948 PF04864 GO:0016846

chr1 1101269 1101943 Protein of unknown function7 (continued)

150

(Table 3.6 Continued)

CHG

chr8 7202236 7205963 FRS5: Protein FAR1-RELATED

SEQUENCE 5

IPR004330

IPR007527

IPR018289

PF03101

PF04434

PF10551

PF13561

GO:0008270

chr3 20612169 20614252 Ripk4: Receptor-interacting

serine/threonine-protein kinase 4

IPR020683

IPR026961

PF12796

PF13962

chr8 5019298 5021435 TAR3: Tryptophan aminotransferase-

related protein 3 IPR006948 PF04864 GO:0016846

CHH chr1 34468253 34469756 Protein of unknown function8

151

Table 3.7 Characterization of eight unknown proteins associated with the 17 shared DMR sequences identified among the three age-

contrasts. The unknown protein ID corresponds to the unknown proteins associated with the shared DMR sequences. The putative

motifs identified within each protein sequence include the position of the motif within the sequence and the e-value. The number of

amino acids and estimated molecular weight (kDa) are also included for each protein sequence. Finally, the predicted localization of

each protein is provided as well as the calculated probability of this prediction.

Protein ID Putative Motifs (amino acid position; e-value)

Number of

Amino

Acids

Estimated

Molecular

Weight

(kDa)

Predicted

Location

(probability)

unknown

protein1 none found 1,122 122,388.22

chloroplast

(98.98%)

unknown

protein2 o transposase_24 (213-319; 8e-7) 438 48,372.76 nucleus (50.2%)

unknown

protein3

o transposase_24 (186-312; 5e-11)

o AbiJ N-terminal domain 4 (114-226; 0.014) 610 69,422.01 nucleus (90.87%)

unknown

protein4 none found 100 11,346.45

mitochondrion

(89.43%)

unknown

protein5

o DUF1336 (15-106; 5e-9)

o DUF924 (22-89; 0.0093) 124 14,296.34

cytoplasm

(93.95%)

unknown

protein6 none found 339 38,963.80 nucleus (97.83%)

unknown

protein7 none found 86 9,893.82

secreted pathway

(85.92%)

(continued)

152

o

o

unknown

protein8

o GIT coiled-coil Rho guanine nucleotide exchange factor

(77-109; 0.0019)

o bZIP transcription factor (76-106; 0.0033)

o Fungal N-terminal domain of STAND proteins (54-111;

0.0084)

o Uncharacterised protein family (UPF0242) N-terminus

(54-121; 0.014)

o Leucine-rich repeats of kinetochore protein Cenp-

F/LEK1 (62-119; 0.028)

o DUF4514 (72-146; 0.019)

o Axonemal dynein light chain (47-115; 0.038)

o RNA polymerase II transcription mediator complex

subunit 9 (59-117; 0.067)

o EvpB/VC_A0108, tail sheath N-terminal domain (86-

141; 0.17)

o Centrosome localisation domain of PPC89 (76-111; 0.3)

o Septum formation initiator (71-105; 0.47)

158 17,972.46 nucleus (99.25%)

(Table 3.7 continued)

153

Chapter 4 Integrated analysis of the methylome and transcriptome of twin almonds

(Prunus dulcis [Mill.] D.A. Webb) reveals genomic features associated with non-

infectious bud-failure

Abstract

Almond (Prunus dulcis [Mill.] D.A.Webb) exhibits an age-related disorder called non-

infectious bud-failure (BF) affecting vegetative bud development and nut yield. The

underlying cause of BF remains unknown but is hypothesized to be associated with

heritable epigenetic mechanisms. To address this disorder and its epigenetic components,

we utilized a monozygotic twin study model profiling genome-wide DNA methylation

and gene expression in two sets of twin almonds discordant for BF-exhibition. Analysis

of DNA methylation patterns show that BF-exhibition and methylation, namely

hypomethylation, are not independent phenomena. Transcriptomic data generated from

the twin pairs also shows genome-wide differential gene expression associated with BF-

exhibition. After identifying differentially methylated regions (DMRs) in each twin pair,

a comparison revealed 170 shared DMRs between the two twin pairs. These DMRs and

the associated genetic components may play a role in BF-exhibition. A subset of 52

shared DMRs are in close proximity to genes involved in meristem maintenance, cell

cycle regulation, and response to heat stress. Annotation of specific genes included

involvement in processes like cell wall development, calcium ion signaling, and DNA

154

methylation. Results of this work support the hypothesis that BF-exhibition is associated

with hypomethylation in almond and identified DMRs and differentially expressed genes

can serve as potential biomarkers to assess BF-potential in almond germplasm. Our

results contribute to an understanding of the contribution of epigenetic disorders in

agricultural performance and biological fitness of perennials.

155

Introduction

Plant diseases and disorders are responsible for reductions in performance and fitness of

individuals and can be particularly devastating in crop production systems. While plant

diseases are typically attributed to pathogens, several disorders of plants have abiotic or

non-pathogenic origins (Kennelly et al., 2012). Non-pathogenic disorders in plants can

result from issues like nutrient deficiencies or unfavorable climatic conditions (Kennelly

et al., 2012), but these disorders can also result from genetic abnormalities leading to

genome instability (Tremblay et al., 1999; Henry et al., 2010). Information on the

development of (epi)genetic disorders in plants is currently lacking, but there is evidence

that genetic abnormalities, including chromatin modifications, can occur with increased

plant age and result in undesirable phenotypes (Watson and Riha, 2011; Dubrovina and

Kiselev, 2016).

Almond (Prunus dulcis [Mill.] D.A.Webb) is an economically-important nut-crop

that exhibits an age-related disorder called non-infectious bud-failure (BF) (Kester and

Jones, 1970; Almond Board of California, 2019). Exhibition of this disorder leads to

repression of vegetative meristem development in the spring, indirectly decreasing yield

(Wilson and Schein, 1956; Kester and Jones, 1970). BF is transmitted to both vegetative

propagules and sexual progeny without a pathogenic origin or epidemiological pattern of

dispersion, and the disorder is non-reversible, meaning a tree will always show the

phenotype following initial exhibition (Fenton et al., 1988). The severity of exhibition in

progeny, though not immediate, is proportional to severity in the parents (Kester et al.,

2004). Cultivars and breeding selections exhibiting this disorder show characteristic

156

dieback at the top of the canopy (Fig. 4.1), and severe levels of BF can be detrimental to

productive orchards with up to 50% yield loss in some cultivars (Gradziel et al., 2013).

For example, clones of the cultivar ‘Nonpareil’, representing ~40% of U.S. almond

production (Almond Board of California, 2019), exhibit BF, making this disorder a threat

to the U.S. agricultural economy. Further, BF led to abandonment of promising almond

cultivars including ‘Jordanolo’ (Wilson and Schein, 1956). BF-exhibition has also been

observed in other almond producing countries including Australia (M.G. Wirthensohn,

pers. comm.), Iran (B. Shiran, pers. comm), and Spain (P.J. Martínez-García, pers.

comm.). Our current understanding of BF is limited; to date, no biomarkers to screen for

early onset and prevent this disorder have been identified or described.

Given the sexual and asexual transmission along with the environmental cues

associated with BF, epigenetic mechanisms may play a role in its exhibition in almond

(Kester et al., 2004). DNA methylation is the most widely studied chromatin mark in

plants and has been implicated in phenotypic variation in several species including

almond (He et al., 2011; Fernández i Martí et al., 2014; Elhamamsy, 2016; Fresnedo-

Ramírez et al., 2017). Cytosine methylation occurs in three contexts in plants: CG, CHG,

and CHH (H = A, T, or G) and can impact gene expression due to inhibition of

transcription factors or transcription machinery binding (Zhang et al., 2010; He et al.,

2011). Alterations in DNA methylation have been shown to be associated with changes in

plant development, including in meristematic tissue, and can be induced as a response to

biotic or abiotic stress (Zhang et al., 2010; Bej and Basak, 2017; Seymour and Becker,

2017; Dolzblasz et al., 2018; Alonso et al., 2019). Changes in plant DNA methylation

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can also be inherited (Richards, 2006; Holeski et al., 2012; Li et al., 2014; Tricker, 2015;

Köhler and Springer, 2017), though inheritance of epialleles may not follow the same

patterns found in other heritable genetic components (Kakutani, 2002).

Discordant monozygotic (MZ) twin-based studies are an effective tool to

precisely address, describe, and quantify DNA methylation mechanisms affecting

exhibition of traits within the same genotype. Such studies have been widely used in

humans and livestock to identify chromatin marks associated with various disorders

(Field and Suttle, 1979; Petronis, 2006; Bell and Spector, 2011; Svendsen et al., 2016).

To our knowledge, this framework has not yet been applied in plants despite its potential

and suitability in several outcrossing, perennial systems. Almond is an obligated

outcrosser (Ushijima et al., 2003) and recalcitrant to inbreeding (Alonso Segura and

Socias i Company, 2007; Martínez-García et al., 2012); however, almond can produce

fruit with multiple embryos due to post-fertilization embryo splitting resulting in MZ

twin almond genotypes (Martínez-Gómez et al., 2002; Martínez-Gómez and Gradziel,

2003). Post-fertilization embryo splitting, often called a double-seed trait, is well known

and characterized in the cultivar ‘Nonpareil’ (Martínez-Gómez et al., 2002; Martínez-

Gómez and Gradziel, 2003). Currently, almond germplasm derived from MZ twin pairs,

known commonly as the ‘Stukey’ collection, is available at the University of California,

Davis (Martínez-Gómez et al., 2002), and several twin pairs now exhibit BF. Twin pairs

discordant for BF-exhibition in this collection allow for the implementation of a MZ

twin-based study model to analyze potential chromatin alterations associated with the

disorder.

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Fresnedo-Ramírez et al. (2017) postulated that DNA methylation interacts with

genotype and chronological age in BF-exhibition in almond. However, DNA methylation

signatures identified in the prior study were anonymous and qualitative (e.g., presence vs.

absence of bands), limiting a precise contrast and characterization of the features

influenced by methylation and the role they might play in BF development. Differential

DNA methylation signatures associated with BF-status between isogenic individuals may

suggest the methylome plays a role in BF-exhibition. Thus, identifying and quantifying

these signatures utilizing a MZ twin-based design and an annotated genome will enhance

elucidation of the role of DNA methylation in BF-exhibition in almond. In this study, we

tested this hypothesis by profiling DNA methylation and gene expression patterns in two

sets of MZ twin almonds with discordant BF-exhibition. To our knowledge, this is the

first study performed in an outcrossing plant species utilizing a twin-based model and a

bisulfite sequencing approach to address an age-related disorder. The DNA methylation

profiles generated will aid in further interrogation and subsequent design of strategies to

monitor and mitigate BF in almond germplasm.


Plant Material

The ‘Stukey’ almond twins are maintained at the Wolfskill Experimental Orchards

(Almond Breeding Program, University of California – Davis, Winters, CA) and are

comprised of eleven pairs of maternal half-sib (i.e., the father is unknown) MZ twin trees

grown from polyembryonic seed from the cultivar ‘Nonpareil’ (Martínez-Gómez et al.,

2002; Martínez-Gómez and Gradziel, 2003). The seeds were planted in 2000, and the

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trees are monitored annually for BF-exhibition. Two pairs discordant for BF-exhibition

before May 2017 were selected for profiling in this study. Leaf samples were collected

for DNA extraction in May 2017 from the canopy of each tree and immediately stored in

desiccation beads until lyophilization. Samples were shipped on ice to the Ohio

Agricultural Research and Development Center (OARDC; The Ohio State University,

Wooster, OH, USA) and immediately processed for lyophilization. Following

lyophilization, samples were stored at -20 °C until DNA extraction. Leaf material from

the same two ‘Stukey’ pairs still showing diverged BF-exhibition was collected in April

2018 for RNA extraction. Leaf samples were harvested from two locations within the

canopy of each tree and immediately stored on ice. Samples were shipped on dry ice to

the OARDC and stored at -45 °C until sample processing.

DNA Extraction

High-quality DNA was extracted from leaves following a modified version of the

protocol outlined in Lodhi et. al (1994). Briefly, samples were ground with a mortar and

pestle in liquid nitrogen, and 100 mg of tissue was added to 1 mL of CTAB buffer

(20mM EDTA, 100mM Tris-HCl pH 8.0, 1.4M NaCl, 2.0% CTAB w/v, 2% beta-

mercaptoethanol v/v). Phase separation was performed using chloroform:isoamyl alcohol

(24:1 v/v) and DNA was ethanol precipitated. DNA was RNase (ThermoFisher Scientific

Waltham, MA) treated according to manufacturer’s instructions, and another phase

separation and precipitation was performed. DNA quality and concentration were

assessed by gel electrophoresis (1% agarose, 100 Volts, 45 minutes), fluorometry using a

Qubit 4, and spectrophotometry using a NanoDrop 1000 (ThermoFisher Scientific).

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Two independent DNA extractions representing technical replicates were performed for

each ‘Stukey’ individual.

RNA Extraction

High-quality RNA was isolated from leaves following a modified version of the protocol

outlined in Gambino et. al (2008). Briefly, leaf tissue was ground in liquid nitrogen using

mortar and pestle pre-treated with RNaseZap Wipes (ThermoFisher Scientific,

Waltham, MA). Approximately 150 mg of ground material was added to 900 mL CTAB

extraction buffer (2% CTAB, 2.5% PVP-40, 2M NaCl, 100 mM Tris-HCl pH 8.0, 25 mM

EDTA pH 8.0, 2% beta-mercaptoethanol). Phase separation was performed using

chloroform:isoamyl alcohol (24:1), followed by precipitation with 3M lithium chloride.

The pellet was resuspended in 500 µL SSTE buffer (10 mM Tris-HCl pH 8.0, 1 mM

EDTA pH 8.0, 1% SDS, 1M NaCl) and an additional phase separation was performed.

RNA was precipitated with isopropanol and resuspended in 30 µL RNase-free water.

RNA was DNase-treated using the DNA-free DNA Removal Kit (ThermoFisher

Scientific) according to the manufacturer’s instructions. RNA quality and concentration

were assessed by fluorometry using a Qubit 4 RNA HS kit and by electrophoresis using

a TapeStation (Agilent, Santa Clara, CA).

Whole Genome Bisulfite Sequencing Library Preparation and Illumina Sequencing

Whole-genome bisulfite libraries were prepared using the Pico Methyl-Seq Library Prep

Kit (Zymo Research, Irvine, CA) following the protocol for standard insert libraries (370-

420 basepairs). Eight sequencing libraries were prepared, and each library was uniquely

barcoded for multiplexing. The kit provides barcodes for multiplexing six libraries, so

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two compatible barcodes were synthesized by MilliporeSigma (St. Louis, MO). Library

concentration and quality were assessed by fluorometry using a Qubit 4 and

electrophoresis using a TapeStation (Agilent). Libraries were equimolarly pooled prior to

sequencing. Bisulfite libraries were sequenced on one lane of the Illumina® MiSeq

platform v. 3 to generate 100 bp single-end reads and re-sequenced on one lane of the

Illumina® NextSeq 550 platform to generate 75-bp single-end reads.

Processing and Alignment of Bisulfite-Sequencing Libraries

Bisulfite sequencing reads were assessed using FastQC v. 0.11.7 (Andrews, 2010) and

trimmed using TrimGalore v. 0.6.0 and Cutadapt v. 2.1 with default parameters and an

additional 10 base pair 3’ and 5’ prime clip (Krueger, 2016). Reads from the MiSeq and

NextSeq runs were then combined into single fasta files for each library. Reads were

aligned to the ‘Nonpareil’ almond reference genome, and methylation calls were

generated using Bismark v. 0.21.0 (Krueger and Andrews, 2011). Genome coverage,

mapping efficiency, and genome-wide methylation levels for each methylation context

(CHG, CHH, and CG) were calculated for each ‘Stukey’ individual by combining data

from the two technical replicate libraries.

The reference genome used in this study corresponds to the gene space of the

almond cultivar ‘Nonpareil’ developed using a combination of Illumina and Hi-C data

resulting in eight main scaffolds (N90 = 13.96 Mb) (Fresnedo-Ramírez, 2020). This

assembly was functionally annotated using short-read and long-read (Oxford Nanopore,

OX4 4DQ, United Kingdom) RNA-seq data and the MAKER pipeline, representing

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28,637 gene models. The ‘Nonpareil’ plastid genome was produced by aligning reads to

the sequence of the plastid genome NC_034696.1 reported in NCBI.

Plots displaying percent methylation for each twin across each of the eight

almond chromosomes were generated using the R package ggplot2 v. 3.3.2 (Wicham

2016). To test for lack of independence between methylation status and bud-failure

exhibition, a Pearson’s Chi-squared test with Yates’s continuity correction was conducted

using the chisq.test() function in R to analyze the number of methylated and

unmethylated cytosines detected in each twin pair. An effect size, phi, was calculated for

each Pearson’s Chi-squared test using the function ES.chisq.assoc() from the package

powerAnalysis v. 0.2.1 in R. Finally, conversion efficiencies were calculated for each

library by aligning reads to the ‘Nonpareil’ almond chloroplast genome and calculating

the percent of methylated cytosines observed. Observed methylation in the chloroplast

genome represent non-converted, unmethylated cytosines since all chloroplast cytosines

are unmethylated (Fojtová et al., 2001). All analyses were performed using the Ohio

Supercomputer Center computing resources (Ohio Supercomputer Center, 1987).

Identification of Differentially Methylated Regions (DMRs) and Permutation Tests

Differentially methylated loci (DML) were identified for each twin pair and methylation

context using the R package DSS (Dispersion Shrinkage for Sequencing data) v. 2.36.0

function DMLtest() with smoothing (Wu et al., 2013; Feng et al., 2014; Wu et al., 2015;

Park and Wu, 2016). Identified DMLs were then used as input in the function callDMR()

to identify differentially methylated regions (DMRs) comprised of several DMLs with a

p-value threshold set to 0.01 for each twin pair and methylation context. DMR-gene

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associations were defined by a DMR’s proximity to a nearby gene and classified as either

upstream, downstream, or intragenic. Bed files were generated containing the genomic

coordinates of almond genes throughout the genome and of all identified DMRs in each

twin pair. These files were input to the window function in bedtools v. 2.29.2 to identify

all DMRs within 2,000 base pairs upstream or downstream of a gene, including

intragenic DMRs (Quinlan and Hall, 2010). The observed frequency of DMR-gene

associations in each proximity class for each twin pair and methylation context

combination were analyzed using a two-tailed permutation test with 1,000 iterations.

Bedtools v 2.29.2 shuffle function was used to generate a null set of DMR coordinates for

each iteration based on observed DMRs and the current almond genome assembly. A

histogram of DMR lengths was generated using the R package ggplot2 v. 3.3.2

(Wickham, 2016).

DMRs were considered shared between the two twin pairs if they were associated

with the same gene in the same proximity class relative to that gene. The observed

frequency of shared DMRs in each methylation context and proximity class were

analyzed using a two-tailed permutation test with 1,000 iterations. The null DMR sets

generated for each twin pair in the previous permutation were compared to determine

frequency of shared DMRs within each set using the definition of shared DMR presented

above. Venn diagrams were constructed using the R package VennDiagram v. 1.6.20

displaying the number of identified DMRs in each methylation context in each twin pair

and the number of shared DMRs (Chen and Boutros, 2011). A heatmap representing the

degree of percent methylation within each shared DMR was generated for each twin in

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each methylation context and in each proximity class using the R package

ComplexHeatmap v. 2.2.0 (Gu et al., 2016).

DMRs were further classified based on the percent methylation difference for

each twin pair, and only those with the same magnitude of methylation difference

(positive or negative) between the BF and no-BF twin in each twin pair were maintained.

Shared DMRs with a negative methylation difference were classified as hypomethylated

in the BF twins relative to the no-BF twins, and shared DMRs with a positive methylation

difference were classified as hypermethylated. Linear regression was performed in R v.

4.0.2 using lm() to test for significant correlation between the percent methylation of

shared DMRs for the BF twins and for the no-BF twins (R Core Team, 2020). An

adjusted R2 and p-value were calculated for each regression.

Annotation of Genes Associated with Shared DMRs and Enrichment Analysis

To obtain a fasta file containing the genomic sequence for genes associated with DMRs

in each methylation context and proximity class, samtools v. 1.9 faidx was used with both

the current almond genome and genomic coordinates for each gene (Li et al., 2009; Li,

2011). Fasta files were queried against a UniProtKB Reviewed (Swiss-Prot) database

using BLAST+ v. 2.4.0 with the routine blastx and an e-value cutoff of 1x10-6 to obtain a

list of gene entry identifiers (Camacho et al., 2009; The UniProt Consortium, 2019).

Identifiers were uploaded to the Retrieve/ID mapping tool on the UniProt website

(https://www.uniprot.org/uploadlists) with options from: UniProtKB AC/ID and to:

UniProtKB to obtain gene names and ontology (GO) terms. To perform enrichment

analysis of GO terms, a reference map was generated for genes in the current almond

https://www.uniprot.org/uploadlists

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genome using the same method described above. The R package clusterProfiler v. 3.16.1

function enricher() was used with the DMR-associated gene list and almond GO term

reference map, a p-value cutoff of 0.1, and a Benjamini-Hochberg correction (Yu et al.,

2012). Both the list of significantly enriched GO terms identified using enricher() and the

list of all GO terms present in the DMR-associated gene list were annotated using the

term() and termLabel() functions in the R package rols v. 2.16.4 (Gatto, 2020).

To identify putative transcription factor binding sites within shared DMR

sequences, fasta files containing genomic sequence for all shared DMRs were used as

input in the Transcription Factor Prediction tool on the Plant Transcription Factor

Database (PlantRegMap/PlantTFDB v 5.0;

https://planttfdb.cbi.pku.edu.cn/prediction.php) (Jin et al., 2017; Tian et al., 2019).

Enrichment analysis was based on the genome-wide frequency of transcription factor

binding sites for each family in a reference panel generated using the ‘Nonpareil’

reference genome. To test for significant enrichment, a Fisher’s exact test was performed

for each family in R v. 4.0.2 with a Bonferroni correction (R Core Team, 2020).

mRNA Sequencing Library Preparation and Illumina Sequencing

Ribosomal RNA was depleted from 1 mg of DNase-treated RNA from two technical

replicates of each ‘Stukey’ sample using the Invitrogen RiboMinus Plant Kit for RNA-

Seq (ThermoFisher Scientific) according to the manufacturer’s instructions. RNA

concentration was determined by fluorometry using a Qubit™ 4 RNA HS kit. To prepare

mRNA sequencing libraries, 100 ng of rRNA-depleted RNA from each technical

replicate was used as input in the NEBNext Ultra II Directional RNA Library Prep with

http://planttfdb.cbi.pku.edu.cn/prediction.php

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Sample Purification Beads kit (New England BioLabs Inc., Ipswich, MA) following

instructions provided in Section 5 in the kit manual. Each library was uniquely barcoded

using NEBNext Multiplex Oligos for Illumina® (Index Primers Set 1) (New England

BioLabs Inc.). Library concentration and quality were determined by fluorometry using a

Qubit™ 4 and electrophoresis using a TapeStation (Agilent). Libraries were then

equimolarly pooled and sequenced on one lane of the Illumina® NextSeq 550 platform to

generate 75-bp single-end reads.

Processing and Alignment of mRNA Sequencing Libraries

mRNA sequencing reads were assessed using FastQC v. 0.11.7 (Andrews, 2010). The

reference genome was prepared using STAR v. 2.6.0a with --runMode genomeGenerate

and --genomeSAindexNbases 12 (Dobin et al., 2013). Reads were aligned to the prepared

genome using STAR v. 2.6.0a with the following commands: --alignIntronMin 60 --

alignIntronMax 6000 --outFilterScoreMinOverLread 0.3 --

outFilterMatchNminOverLread 0.3. Gene counts were tallied for each library using

Subread v. 1.5.0 featureCounts with the reference ‘Nonpareil’ gff file (Liao et al., 2013).

Processing and alignment pipeline were executed on the Ohio Supercomputer Center

computing clusters (Ohio Supercomputer Center, 1987).

Identifying Differentially Expressed Genes and Integrating Expression Data with DMR-

Associated Genes

Differentially expressed genes were identified using a significance cutoff of adjusted p-

value < 0.1 in the R package DESeq2 v. 1.26.0 after fitting the model,

~Condition+TwinPair (Love et al., 2014). Shrunken log2 fold changes were generated for

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the condition contrast using the ‘ashr’ shrinkage estimator (Stephens, 2017). Count data

were transformed using the varianceStabilizingTransformation() function prior to

principal component analysis (Anders and Huber, 2010). Heatmaps were generated using

the R package ComplexHeatmap v. 2.2.0 (Gu et al., 2016).

In silico Analysis of a Hypothetical Protein Identified in mRNA Sequencing

Nucleotide and amino acid sequences for an unidentified protein on chromosome 6 of the

‘Nonpareil’ almond genome with ~3 log2fold increase can be found in Appendix File 3.

A blastp was performed on the amino acid sequence using the UniprotKB reference

proteome plus Swiss-Prot with the default parameters (The Uniprot Consortium, 2019).

Protein motifs were determined within the sequence using ExPASy ScanProsite (release

2020_02) (Castro et al., 2006; Sigrist et al., 2013). The program SVMProt: Protein

Functional Family Prediction was used to predict protein functional families using

support vector machine and k-nearest neighbors algorithms (Cai et al., 2003). The

program ProtParam was used to characterize protein properties including predicted

charge, molecular weight, and stability (Gasteiger et al., 2005). To predict subcellular

localization, the program YLoc was used (Briesemeister, Rahnenführer, et al., 2010a;

Briesemeister, Rahnenüfhrer, et al., 2010b). TargetP-2.0 was used to identify putative

peptides (Almagro Armenteros et al., 2019). Finally, the Motif tool on the GenomeNet

website (https://www.genome.jp/tools/motif/) was used to search a protein query against

several databases to identify putative alignments (Marchler-Bauer et al., 2013; Sigrist et

al., 2013; Finn et al., 2014)

https://www.genome.jp/tools/motif/

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Results

Contrasting genome-wide DNA methylation status in the ‘Stukey’ twins

Whole-genome bisulfite sequencing was performed on four ‘Stukey’ individuals

representing two MZ twin pairs discordant for BF-exhibition. Twin 1a exhibits BF while

twin 1b is BF-free (i.e., no-BF), and in twin pair two, twin 2a exhibits BF while twin 2b

is BF-free. Genome-wide sequencing depth ranged from 70-110X for each individual,

and mapping efficiencies following alignment to the ‘Nonpareil’ reference genome

ranged 36.5-39.1% representing 25-43X genome-wide coverage (Table 4.1). Non-

methylated cytosine conversion efficiencies were >98% for each individual.

CG methylation is the most abundant genome-wide methylation context for all

twins, followed by CHG and CHH, respectively (Table 4.2). Percent methylation

calculations show that in both twin pairs, methylation is higher in the no-BF twin for the

CHG and CHH contexts (Table 4.2). A lack of independence between BF-exhibition

(represented as a binomial presence/absence) and DNA methylation in each methylation

context was demonstrated by Chi-squared (2) analysis with Yates’s continuity correction

(Table 4.3a-c; p-value < 2.2´10-16). While results showed a lack of independence between

methylation and BF-status in the CG context, there is an opposite relationship in twin pair

1 compared to twin pair 2 (Table 4.3a). Genome-wide cytosine methylation in each

context is shown across each of the eight scaffolds of the current ‘Nonpareil’ genome

assembly (representing the eight chromosomes of the almond genome) with evident

regions of higher overall methylation (Fig. 4.2, Fig. 4.3, Fig. 4.4).

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Identifying regions of differential methylation associated with bud-failure status

Differentially methylated regions (DMRs) were identified and compared between the BF

and no-BF twins in both twin pair one and twin pair two. Significant DMRs were

identified in both twin pairs based on percent cytosine methylation comparisons in the

three methylation contexts in the respective regions (p-value < 0.01) (Fig. 4.5). The

DMRs identified in each twin pair methylation context combination were further

classified based on their proximity to a gene. The DMRs were classified as either within

2,000 base pairs upstream, within 2,000 base pairs downstream, or intragenic (Table 4.4).

Results of the two-tailed permutation test in the CG context show the frequency of DMRs

in the upstream and downstream proximity class are significantly higher, and the

frequency of DMRs in the intragenic class is significantly lower than in null DMR sets

randomly distributed throughout the almond genome (Table 4.4). Permutation testing

shows that significantly more DMRs occur in the upstream class for both the CHG and

CHH context, significantly fewer DMRs occur in the intragenic class for both contexts,

and the frequency of downstream DMRs in both contexts do not exhibit significant

deviance from a random frequency distribution of DMRs along the genome (Table 4.4).

Of the 1,007 significant DMRs identified in the CG context in twin pair one and

the 1,154 significant DMRs identified in the CG context in twin pair two, 82 DMRs were

associated with the same gene in the same proximity class (Appendix Tables 16-24, Fig.

4.5a). Of the 646 significant DMRs in the CHG context in twin pair one and the 838

significant DMRs in the CHG context in twin pair two, 34 were associated with the same

gene in the same proximity class (Appendix Tables 16-24, Fig. 4.5b). And of the 382

significant DMRs in the CHH context in twin pair one and the 533 significant DMRs in

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the CHH context in twin pair two, 54 were associated with the same gene in the same

proximity class (Appendix Tables 16-24, Fig. 4.5c). Patterns of percent methylation

within each gene-associated DMR are similar for BF twins and no-BF twins (Fig.

4.5a,b,c). The DMRs range in length from 51 – 1,460 bp, the average length of the DMRs

in all contexts is ~173 bp, the median DMR length is 128 bp, and the most frequent DMR

length is 77 bp (Fig. 4.6).

The genomic coordinates of the DMRs associated with the same gene from each

twin pair were compared, and 121 of the 170 DMRs had overlapping genomic

coordinates. The DMRs associated with the same gene and proximity class were further

classified as either hyper- or hypomethylated in the BF twins compared to the no-BF

twins in each methylation context and proximity class. Of these 170 DMRs, 29 were

hypermethylated and 68 were hypomethylated. The remaining 73 DMRs were classified

as either hyper- or hypomethylated in one twin pair and had the opposite classification in

the other twin pair. Comparing percent methylation difference by methylation context in

hyper- and hypomethylated-DMRs associated with the same gene shows that most DMRs

in the CG context are hypermethylated in BF twins, while DMRs in the CHG and CHH

contexts tend to be hypomethylated in BF twins (Fig. 4.7a,b,c). Comparing percent

methylation by proximity class for the same set of DMRs shows that all intragenic DMRs

are hypermethylated in BF twins, while DMRs upstream and downstream are both hyper-

and hypomethylated in BF twins (Fig. 4.8a,b,c). Permutation testing revealed

significantly more shared DMRs in all methylation contexts and proximity classes with

the exception of intragenic CHG DMRs when comparing to null DMR sets (Table 4.5).

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The magnitude of methylation difference in each DMR associated with the same

gene and proximity class showed a significant linear relationship when comparing

percent methylation for each DMR in no-BF twin 1b to the corresponding DMR in no-BF

twin 2b in the upstream and downstream proximity classes (Fig. 4.9a – upstream, Fig.

4.9c – intragenic, Fig. 4.9e – downstream). A significant linear relationship was also

shown when comparing percent methylation for each DMR in BF twin 1a to the

corresponding DMR in BF twin 2a for each proximity class (Fig. 4.9b – upstream, Fig.

4.9d – intragenic, Fig. 4.9f – downstream).

Annotation of DMR-associated genes identified in the ‘Stukey’ twins

The genomic sequence of 15 genes associated with hypermethylated DMRs significantly

aligned to previously identified genes in the UniProtKB database (Table 4.6). And the

genomic sequence of 37 genes associated with hypomethylated DMRs significantly

aligned to previously identified genes (Table 4.6). Gene ontology (GO) enrichment

analysis revealed significant enrichment in the biological process GO terms: regulation of

timing from vegetative to reproductive phase, protein sumoylation, and glucosinate

metabolic processes (Table 4.7a). Enrichment analysis also revealed enrichment of the

molecular function GO term, protein tag (Table 4.7b). GO terms assigned to all DMR-

associated genes are also reported (Table 4.7a, 4.7b, 4.7c).

A total of 92,188 transcription factor binding sites representing 43 transcription

factor families were identified in the hyper- and hypomethylated DMRs associated with

the same gene and proximity class using the PlantTFDB (Fig. 4.10). Significantly

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enriched transcription factor family binding sites in the DMRs include ERF, Dof, and

BBR/BPC (Table 4.8).

Transcriptomic analysis of ‘Stukey’ twin pairs

Sequencing was performed to characterize the transcriptome of each ‘Stukey’ twin (Table

4.9). Of the 25,392 annotated, expressed transcripts detected in the ‘Stukey’ twin pairs

(out of the 27,487 annotated transcripts in the ‘Nonpareil’ genome), 16 were found to be

significantly differentially expressed with three downregulated in the BF twins and 13

upregulated in the BF twins compared to the no-BF twins (Table 4.10, Fig. 4.11).

Annotation of these genes revealed involvement in processes such as cell-wall synthesis

and metal-ion transport. Principal component analysis of expressed gene counts shows

Principal Component (PC) 1 explaining 49% of variance by the separation of twin pairs

and PC 2 explaining 45% of variance by the separation of BF condition (Fig. 4.12).

Differentially expressed transcripts associated with bud-failure exhibition

Expression analysis of DMR-associated genes in each proximity class and methylation

context revealed one significantly differentially expressed gene, CNGC1, associated with

CG hypomethylated intragenic DMRs in both twin pairs. Expression patterns

(Log2FoldChange) of DMR-associated genes in both twin pairs do not show a consistent

pattern of up- or downregulation separated either by methylation context (Fig. 4.7a,b,c)

or proximity class (Fig. 4.8a,b,c).

Interestingly, an uncharacterized protein of 72 amino acids in length showing the

highest log2fold change (2.97, Table 4.10) is located on chromosome 6 in the ‘Nonpareil’

genome. Results using several in silico approaches to further analyze this protein suggest

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it is related to plant development and most likely localized in the nucleus. Three putative

motifs sites were identified within the amino acid sequence including one aspartic acid-

rich region (19-67) and two Casein kinase II phosphorylation sites (12-15 and 45-48).

The protein (C332H463N77O129S3) is composed mostly of aspartic acid (27.8%) and glycine

(11.1%) and is predicted to have an overall negative charge with an estimated molecular

weight of 7693 kDa. The aliphatic index is predicted to be 59.86 and the average

hydropathicity is -0.544. Results from Motif analysis on GenomeNet produced 14 motif

alignments, all to entries in the NCBI-CDD database. The most significant alignment (32

amino acids) was to a Cwf15/Cwf15 cell cycle protein from Schizosaccharomyces pombe

and S. cerevisiae (e-value = 0.028, score = 31.4).

Discussion

The goal of this study was to test the hypothesis that DNA methylation signatures are

associated with bud-failure (BF) exhibition in almond, and to identify and quantify

signatures that may have a role in BF-exhibition, emphasizing the almond genome gene

space. Previous work representing a first approach to deciphering the methylome

landscape in almond showed a lack of independence between BF and DNA methylation;

however, genes or genetic regions influenced by DNA methylation that may have a role

in BF-exhibition were not elucidated (Fresnedo-Ramírez et al., 2017). The current study

represents the first comprehensive scrutiny of the methylome landscape in almond with

the purpose of interrogating a nonpathogenic disorder.

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A monozygotic (MZ) twin-based design enables identification of methylomic signatures

associated with BF

A MZ twin-based model (Bell and Spector, 2011; Tan et al., 2015) was used in this study

to profile DNA methylation in two twin almond pairs discordant for BF-exhibition. To

our knowledge, this is the first time this study model had been implemented in a plant

species. Using this model, methylation differences were distinguished between each twin

pair independently and then compared those differences to find shared divergence

between the two twin pairs. The existence of MZ twins in almond and other plant species

is documented (Ensign, 1919; Kenworthy et al., 1973; Litz et al., 1982; Martínez-Gómez

and Gradziel, 2003; Aleza et al., 2010) and is particularly useful in systems like almond

where the production of isogenic or inbred germplasm is complex, such as in obligated

outcrossers with systems of self-incompatibility, or when the number of individuals

available for sampling or resources are limited (Ushijima et al., 2003). MZ twins are also

advantageous over clonally propagated individuals because confounding variables

impacting epigenetic patterns, such as chronologic versus ontogenetic ages, are not a

factor (Gatsuk et al., 1980; Dubrovina and Kiselev, 2016).

Bud-failure exhibition is associated with genome-wide DNA hypomethylation in almond

Patterns discerned from the ‘Stukey’ twin pairs revealed a significant lack of

independence between BF-exhibition and DNA methylation, and suggest a putative

association between the BF phenotype and non-CG (i.e. CHG and CHH)

hypomethylation. This finding supports the hypothesis that epigenetics and chromatin-

based mechanisms contribute to the BF disorder and that DNA methylation patterns may

175

inform BF-exhibition (Fresnedo-Ramírez et al., 2017). Bisulfite sequencing enabled

quantification of percent methylation in each context (CG, CHG, and CHH) showing that

observed methylation falls within levels typically seen in other angiosperms including

peach (Niederhuth et al., 2016; Bartels et al., 2018). Observed genome-wide methylation

patterns show higher levels of methylation in conserved regions on each chromosome,

consistent with patterns in centromeric and pericentromeric regions in other plant species

(Fig. 4.1, Fig. 4.2, Fig. 4.3) (Lister et al., 2008; Yan et al., 2010).

There was significant hypomethylation in non-CG contexts in the BF twins, while

patterns of hyper- and hypomethylation in the CG context were not as clear. Non-CG

methylation is prevalent among plant genomes and can be highly variable, impacting

transposition and expression of transposons, early and late-stage plant development, and

response to stressors (Kenchanmane Raju et al., 2019). Levels of genome-wide CHH

methylation were found to vary seasonally in cotton, resulting in developmental changes

and impacting fiber production due to alterations in gene expression (Jin et al., 2013). In

the African oil palm, hypomethylation of transposons in the CHG context is associated

with the ‘mantling’ phenotype, resulting in desiccated fruit often not observed until the

sixth year of growth and causing dramatically reduced oil yields in tissue culture-derived

germplasm (Ong-Abdullah et al., 2015). Like mantling in the African oil palm, BF-

exhibition in almond can take several years to appear in afflicted trees.

DNA demethylation occurs actively and passively in plants throughout the aging

process or after exposure to abiotic and biotic stressors (Liu et al., 2015; Parrilla-Doblas

et al., 2019; Liu and Lang, 2020). For example, a study in Quercus robur L.

176

demonstrated genome-wide hypomethylation in seeds with advanced age and after

exposure to heat stress, resulting in reduced viability (Michalak et al., 2015). BF-

exhibition was previously linked to prolonged heat stress in almond (Hellali and Kester,

1979) which could have implications in triggering activation of DNA demethylation

mechanisms (Centomani et al., 2015; Liu et al., 2015; Naydenov et al., 2015; Harkess,

2018; Liu et al., 2018). Heat-induced hypomethylation can also result in heritable

alterations to DNA methylation profiles including reactivation of transposable elements,

as has been demonstrated in Arabidopsis (Lang-Mladek et al., 2010; Ito et al., 2011;

Sanchez and Paszkowski, 2014; Liu et al., 2015; Tricker, 2015; Yang et al., 2020),

though abiotic stressors do not always induce these heritable marks (Ganguly et al.,

2017). Since BF-exhibition can be transmitted by vegetative propagation and sexual

reproduction, epigenetic inheritance through chromatin modifications, such as changes in

DNA methylation patterns, could serve as a mode of transmission (Kester et al., 1975;

Kester et al., 2004). A pedigree study could also be performed to test the

inheritance/transmission of differentially methylated regions (DMRs) identified in this

study or other chromatin patterns found to be associated with BF-exhibition.

More than 70% of the identified DMRs were classified into one of three

proximity classes relative to a gene. Of these DMR-gene associations, permutation

testing revealed significantly more DMRs in the up- and downstream proximity classes

and significantly fewer in the intragenic proximity class relative to a gene. This result

supports previous findings showing gene body methylation in plants tends to be stable

over time (and even across multiple generations [Luo et al., 2020]), while methylation

177

occurring up- or downstream of genes tends to exhibit greater dynamism, particularly in

response to stressors (Bewick and Schmitz, 2017; Picard and Gehring, 2017; Ito et al.,

2019).

Genes associated with DMRs are involved in meristem development, DNA methylation,

dormancy, and response to heat stress

Of the DMR-associated genes, 52 showed significant similarity to previously annotated

genes in related species. These genes are significantly enriched in biological processes

including protein sumoylation and vegetative meristem development and transition.

Identified DMR-associated genes are also involved in meristem maintenance, cell-wall

biogenesis, dormancy, and cell-cycle regulation including a probable pectin

methylesterase (PME), tesmin (TCX5), a ribosomal protein (RPL27), an agamous-like

MADS-box (AGL14), and maintenance of meristems (MAIN). These genes and processes

represent interesting targets for further functional analysis in almond to explore what, if

any, impact they might have on BF development. Available antibodies for the DMR-

associated genes could also be used to identify their regulatory functions or to look for

active protein expression in key tissues such as meristems.

Empirical evidence suggests a relationship between dormancy processes in the

summer (i.e., summer dormancy) and BF-exhibition in almond (Micke, 1996; Kester et

al., 2004). Progression of BF results in necrosis of vegetative axillary buds following

summer dormancy and occurs by degradation of tunica cells followed by an overgrowth

and subsequent collapse of corpus tissues in the meristem (Hellali et al., 1978).

Meristems are complex structures with orchestrated growth cycles dependent on internal

178

and external cues (Barton, 2010; Paul et al., 2014; Lloret et al., 2018). Growth and

development of meristematic tissues, including dormancy processes, can be regulated by

chromatin marks including DNA methylation and histone modifications (Bitonti et al.,

2002; Conde et al., 2017; Le Gac et al., 2018; Conde et al., 2019). In poplar, changes in

DNA methylation following exposure to heat stress in the previous summer were still

present in meristems following winter dormancy, suggesting an epigenetic “memory” (Le

Gac et al., 2018). Results herein suggest dysregulation of cell wall biogenesis pathways

in almond meristematic tissues could contribute to disruption of dormancy initiation,

leading to the BF phenotype, and that this alteration could be initiated by abiotic stress

(i.e., heat) and maintained in the meristem. DMR-associated genes involved in dormancy

or meristem maintenance provide targets for further study to elucidate their involvement

in summer dormancy and putative role in the onset of BF in almond (Micke, 1996; Kester

et al., 2004; Santamaría et al., 2009; Ríos et al., 2014; Lloret et al., 2018; Fadón et al.,

2020).

Patterns of differential expression identified in genes related to cell wall maintenance

and metal ion transport

Callose synthase family gene, CSLG2 was identified as significantly differentially

expressed in this study. CSL family members are shown in other plant species to be

involved in synthesis of non-cellulosic polysaccharides such as hemicellulose (Richmond

and Somerville, 2000; Farrokhi et al., 2006). The DMR-associated gene encoding cyclic

nucleotide-gated ion channel 1 (CNGC1) was identified as both significantly upregulated

in BF twins and as containing an intragenic, shared DMR hypomethylated in BF twins

179

(Table 4.6; Table 4.10). Disruption of CNGC1 in A. thaliana resulted in a reduction of

calcium ion accumulation in cells (Ma et al., 2006). Proper regulation of calcium uptake

in plants is vital to several developmental processes including meristem organization and

cell wall biogenesis (Hepler, 2005; Li et al., 2019). Finally, the putative gene coding for

an uncharacterized protein was found to have the highest log2 fold change. Data produced

in silico surveying various databases and algorithms suggest this putative genomic

feature may be shared among other members of the Rosaceae. A highly similar (94.3%)

protein of unknown function is reported in the peach gene annotation, and the protein

identified in this study also shows a high degree of similarity (>70%) with undescribed

proteins in Malus and Pyrus. The three genes represent interesting targets for further

study to explore their involvement in BF-exhibition.

Ethylene responsive factor (ERF) transcription factor family binding sites are enriched in

DMRs

Transcription factors (TF) can be sensitive to the presence of DNA methylation at their

binding sites, potentially impacting regulation of target genes (Domcke et al., 2015;

Héberlé and Bardet, 2019). TF families enriched in DMRs include those with functions

related to cell cycle regulation, hormone signaling, plant organ identity, and meristem

development (as shown in Table 4.8). The ERF family represents approximately 25% of

the TF binding sites identified. The ERF TF family is involved in stress response and

hormone signaling, and members have been shown to regulate growth and development

in plants (Licausi et al., 2013; Xie et al., 2019). In pear, another Rosaceous crop, an ERF

family TF, which is itself regulated by chromatin marks, was found to be involved in

180

regulating budbreak by activating expression of genes associated with cell division in the

apex (Anh Tuan et al., 2016). Results from this work and other studies in apple and

poplar support the role of ERF family TFs in dormancy initiation and release in woody

perennials (Wisniewski et al., 2015; Busov et al., 2016).

The association between bud-failure status and DNA methylation signatures in

almond was assessed utilizing a monozygotic twin model with two sets of twin almonds

and a whole-genome bisulfite sequencing approach to provide the first comprehensive

survey of the almond methylome. The goal was to determine whether the methylome has

a role in bud-failure exhibition. Results from this work support the hypothesis that

genome-wide hypomethylation is associated with bud-failure exhibition. The approach

utilized in this study allowed identification of several differentially methylated regions

between bud-failure and bud-failure-free twins, providing targets for further

investigation. Relevant genomic features identified in this study include genes and

transcription factors involved in meristem maintenance, cell cycle control, dormancy, and

response to heat stress which might be impacted by chromatin alterations and contribute

to the bud-failure phenotype. These potential targets can be investigated to evaluate their

suitability as biomarkers for bud-failure exhibition potential. The availability of

biomarkers to screen almond germplasm for bud-failure susceptibility would be valuable

to breeders, producers, and growers to avoid transmission of the disorder to progeny

through breeding and propagation. Elucidation of the mechanisms underlying bud failure

development would also improve our understanding of this threatening disorder and

181

provide a basis for addressing and mitigating similar identified or undescribed disorders

in other plants.

Acknowledgements

We would like to acknowledge Cheri Nemes for her assistance with wet lab portions of

this project. We would also like to acknowledge Andrew Michel and Eric Stockinger for

providing edits to later versions of this manuscript. To the Ohio Supercomputer Center

for access to computing resources and the Translational Plant Sciences Graduate Program

for the fellowship for KMDW. This work was supported by The Ohio State University

CFAES-SEEDS program grant # 2019-125, the Almond Board of California Grant

HORT35, the U.S. Department of Health and Human Services National Institutes of

Health - National Cancer Institute - Cancer Center Support Grant (CCSG)

P30CA016058, the USDA National Institute of Food and Agriculture AFRI-EWD

Predoctoral Fellowship 2019-67011-29558.

182

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194

Figure 4.1 Monozygotic twin almond ‘Stukey’ trees discordant for BF-exhibition; BF

twin (left).

195

Figure 4.2 Percent methylation in the CG context across each of the eight ‘Nonpareil’ genome scaffolds representing the eight

almond chromosomes. Chromosome number is listed above each plot. The solid lines represent bud-failure (BF) individuals and

the dashed lines represent no-BF individuals. The gold lines represent ‘Stukey’ twin pair 1, and the blue lines represent ‘Stukey’

twin pair 2.

20

40

60

80

0 10 20 30

Genomic Position (Mbp)

% M

ethy

lati

on

Chr 1

20

40

60

80

0 5 10 15 20


% M

ethy

lati

on

Chr 2

20

40

60

80

0 5 10 15


% M

ethy

lati

on

Chr 3

20

40

60

80

0 5 10 15


% M

ethy

lati

on

Chr 4

20

40

60

80

0 5 10 15


% M

ethy

lati

on

Chr 5

25

50

75

0 5 10 15

Genomic Position (Mbp)%

Met

hy

lati

on

Chr 6

20

40

60

0 5 10 15


% M

ethy

lati

on

Chr 7

20

40

60

0 5 10


% M

ethy

lati

on

Chr 8

196

Figure 4.3 Percent methylation in the CHG context across each of the eight ‘Nonpareil’ genome scaffolds representing the eight

almond chromosomes. Chromosome number is listed above each plot. The solid lines represent bud-failure (BF) individuals, and


twin pair 2.

0

20

40

60

0 10 20 30


% M

ethy

lati

on

Chr 1

0

20

40

60

0 5 10 15 20


% M

ethy

lati

on

Chr 2

0

20

40

0 5 10 15


% M

ethy

lati

on

Chr 3

0

20

40

60

0 5 10 15


% M

ethy

lati

on

Chr 4

0

20

40

60

0 5 10 15


% M

ethy

lati

on

Chr 5

0

20

40

60

0 5 10 15


Met

hy

lati

on

Chr 6

0

20

40

0 5 10 15


% M

ethy

lati

on

Chr 7

0

10

20

30

40

0 5 10


% M

ethy

lati

on

Chr 8

197

Figure 4.4 Percent methylation in the CHH context across each of the eight ‘Nonpareil’ genome scaffolds representing the eight

almond chromosomes. Chromosome number is listed above each plot. The solid lines represent bud-failure (BF) individuals, and


twin pair 2.

2

3

4

5

0 10 20 30


% M

ethy

lati

on

Chr 1

2

3

4

5

0 5 10 15 20


% M

ethy

lati

on

Chr 2

2

3

4

5

0 5 10 15


% M

ethy

lati

on

Chr 3

2

3

4

5

0 5 10 15


% M

ethy

lati

on

Chr 4

2

3

4

0 5 10 15


% M

ethy

lati

on

Chr 5

2

3

4

5

0 5 10 15


Met

hy

lati

on

Chr 6

2

3

4

5

0 5 10 15


% M

ethy

lati

on

Chr 7

2

3

4

5

0 5 10


% M

ethy

lati

on

Chr 8

198

Figure 4.5 Heatmap displaying percent methylated cytosines in each twin pair for the

DMRs in each methylation context: (a) CG methylation, (b) CHG methylation, and (c)

CHH methylation. ‘Stukey’ twins 1a and 2a exhibit BF while ‘Stukey’ twins 1b and 2b

are BF-free. The Venn diagram represents the total number of significant DMRs in both

‘Stukey’ twin pairs as well as the number of regions shared between the pairs. Panel (a)

represents the CG context, panel (b) represents the CHG context, and panel (c) represents

the CHH context.

Stu

key1a

Stu

key1b

Stu

key2a

Stu

key2b

% CG DNA Methylation

0

50

100

Stu

key1a

Stu

key1b

Stu

key2a

Stu

key2b

% CHG DNA Methylation

0

50

100

Stu

key1

a

Stu

key1

b

Stu

key2

a

Stu

key2

b

% CHH DNA Methylation

0

50

100

1072965 82

Stukey2Stukey1

804612 34

Stukey2Stukey1

479328 54

Stukey2Stukey1

a

b

c

199

Figure 4.6 Distribution of length of all DMRs found in each twin pair in all methylation

contexts.

0

100

200

300

400

0 500 1000 1500

DMR Length (basepairs)

Co

un

t

200



heatmap in red and blue represents the difference in percent methylation in the BF twin

compared to the no-BF twin for every significant shared DMR in each context. The

heatmaps in purple and yellow represent the differential expression in the BF twins

compared to the no-BF twins for the genes associated with each DMR. Panel (a)

represents the CG context DMRs, panel (b) represents CHG context DMRs, and panel (c)

represents CHH context DMRs.

Stu

key

Tw

inp

air

1

Stu

key

Tw

inp

air

2

Methylation Diff

−50%

−100%

0

50%

Log2FoldChange

−0.002

−0.004

0

0.002

0.004

a

Stu

key

Tw

inp

air

1

Stu

key

Tw

inp

air

2

Methylation Diff

−50%

0

50%

Log2FoldChange

−0.01

−0.02

−0.03

0

0.01

b

Stu

key

Tw

inpai

r 1

Stu

key

Tw

inpai

r 2

Methylation Diff

−20%

−40%

0

20%

40%

Log2FoldChange

−0.005

0

0.005

0.01

0.015

c

201



heatmap in red and blue represents the difference in percent methylation in the BF twin

compared to the no-BF twin for every significant shared DMR in each proximity class.

The heatmaps in purple and yellow represent the differential expression in the BF twins

compared to the no-BF twins for the genes associated with each DMR. Panel (a)

represents the DMRs upstream (within 2,000 bp) of a gene, panel (b) represents the

intragenic DMRs, and panel (c) represents the DMRs downstream (within 2,000 bp) of a

gene.

Stu

key

Tw

inp

air

1

Stu

key

Tw

inp

air

2

Methylation Diff

−50%

−100%

100%

50%

0

Log2FoldChange

−0.005

0

0.005

0.01

0.015

a

Stu

key

Tw

inp

air

1

Stu

key

Tw

inp

air

2

Methylation Diff

−50%

−100%

0

50%

Log2FoldChange

−0.002

−0.004

0

0.002

0.004

b

Stu

key

Tw

inpai

r 1

Stu

key

Tw

inpai

r 2

Methylation Diff

−50%

−100%

50%

0

Log2FoldChange

−0.01

−0.02

−0.03

0

0.01

c

202

Figure 4.9 Linear regression of percent methylation within shared regions with

significant differential cytosine methylation in no-BF ‘Stukey’ twins (a, c, e) and BF

‘Stukey’ twins (b, d, f). Panels a and b represent methylation in DMRs upstream (within

2,000 bp) of a gene, panels c and d represent methylation in intragenic DMRs, and panels

e and f represent methylation in DMRs downstream (within 2,000 bp) of a gene. Each

circle represents the percent methylation in each twin in a single region that is

significantly differentially methylated in both ‘Stukey’ twin pairs. Red circles represent

DMRs in the CG context, green triangles represent DMRs in the CHG context, and blue

squares represent DMRs in the CHH context.

40

60

80

100

20 40 60 80 100% Methylation Twin 1b

% M

ethyla

tion

Tw

in 2

bContext

CG

CHG

CHH

Adj R2 = 0.5761 p−value < 0.001

a

20

40

60

80

100

25 50 75 100% Methylation Twin 1a

% M

eth

yla

tio

n T

win

2a

Context

CG

CHG

CHH

Adj R2 = 0.6782 p−value < 0.001

b

40

60

80

100

50 60 70 80 90% Methylation Twin 1b

% M

ethy

lati

on

Tw

in 2

b

Context

CG

CHH

Adj R2 = −0.1262 p−value = 0.7573

c

0

25

50

75

100


% M

ethy

lati

on

Tw

in 2

a

Context

CG

CHH

Adj R2 = 0.6583 p−value < 0.01

d

20

40

60

80

100

40 60 80 100% Methylation Twin 1b

% M

eth

yla

tio

n T

win

2b

Context

CG

CHG

CHH

Adj R2 = 0.6371 p−value < 0.001

e

25

50

75

100


% M

ethyla

tion

Tw

in 2

a

Context

CG

CHG

CHH

Adj R2 = 0.812 p−value < 0.001

f

203

Figure 4.10 Number of transcription factor binding sites identified in the shared DMRs

in all methylation contexts.

0

5000

10000

15000

20000E

RF

Do

fM

YB

NA

CC

2H

2M

IKC

_M

AD

SB

BR

−B

PC

bH

LH

WR

KY

bZ

IPT

CP

B3

LB

DG

AT

AA

P2

MY

B_

rela

ted

G2

−li

ke

GR

AS

Tri

hel

ixH

SF

HD

−Z

IPN

in−

like

BE

S1

E2F

/DP

SB

PR

AV

AR

FC

PP

C3

HZ

F−

HD

GeB

PA

RR

−B

WO

XS

RS

NF

−Y

BE

ILC

AM

TA

FA

R1

S1

Fa−

like

LF

YG

RF

YA

BB

YV

OZ

Transcription Factor Family

Num

ber

of

Bin

din

g S

ites

204

Figure 4.11 Log2 fold change by normalized count means of genes observed in bud-

failure compared to no-bud-failure ‘Stukey’ twins. Red points represent those defined as

significantly differentially expressed in each contrast (p-value < 0.1).

−2

−1

0

1

2

3

1e+02 1e+04 1e+06mean of normalized counts

log

fo

ld c

han

ge

q−value

Significant

NA

205

Figure 4.12 Principal component analysis of differential gene expression data showing

separation by ‘Stukey’ twin pair and BF condition.

−10

0

10

20

−20 −10 0 10PC1: 49% variance

PC

2:

45%

var

ian

ce

TwinPair

1

2

Condition

BF

no−BF

206

Table 4.1 Combined sequencing results from Illumina MiSeq and NextSeq for ‘Stukey’ libraries. Library refers to the combined

technical replicate libraries for each ‘Stukey’ individual. The associated SRA Biosample number is listed to access raw data for this

sample on the NCBI SRA repository. Total reads are the number of reads produced in the MiSeq and NextSeq sequencing runs, and

the depth of coverage represents the coverage based on the size of the ‘Nonpareil’ genome (~250 Mbp). Total aligned reads are the

number of reads that aligned to the ‘Nonpareil’ almond genome and mapping efficiency is the percentage of total reads that properly

aligned. Coverage aligned represents the depth of coverage for aligned reads based on the size of the ‘Nonpareil’ almond genome

(~250 Mbp). Finally, conversion efficiency was calculated based on the conversion rate of the ‘Nonpareil’ chloroplast genome,

representing a fully unmethylated sequence.

Library

SRA

Biosample

Number

Total Reads

(Depth)

Total

Aligned

Reads

Mapping

Efficiency

Coverage

Aligned

Conversion

Efficiency

‘Stukey’ 1a

SAMN1640- 3998

116,099,290 (104X)

42,474,875 36.7% 38X 98.8%

‘Stukey’ 1b SAMN1640- 3999 123,115,678

(110X) 47,998,414 39.1% 43X 98.8%

‘Stukey’ 2a

SAMN1640- 4000

112,172,115 (100X)

43,716,467 38.9% 39X 98.9%

‘Stukey’ 2b

SAMN1640- 4001

78,347,132 (70X)

28,476,583 36.5% 25X 98.7%

207

Table 4.2 Percent cytosine methylation in each methylation context (C = cytosine; G =

guanine; H = adenine, thymine, or cytosine) calculated for combined technical replicate

libraries for each ‘Stukey’ individual (individuals exhibiting BF are represented in bold).

‘Stukey’

Twin BF-

Exhibition %CG

Methylation %CHG

Methylation %CHH

Methylation

1a Yes 39.3% 18.8% 2.7%

1b No 37.2% 19.6% 3.1%

2a Yes 37.2% 16.8% 2.6%

2b No 41.7% 21.1% 3.1%

208

Table 4.3 Contingency tables (a), (b), and (c) contain the number of observed methylated

and unmethylated cytosines in the CG, CHG, and CHH contexts, respectively, for the BF

and no-BF twins in each twin pair (Chi-squared statistic, p-value, and effect size for

effect size for each comparison is included in the table subtitles and the Pearson’s

residual for each count is included in the parentheses in each table).

a. CG Methylation Contingency Tables

Twin pair 1: 2 = 69317, df = 1, p-value < 2.2e-16, = 0.03

Bud-Failure Status

Methylation Status Bud-Failure No Bud-Failure

Methylated 19329120 (144.973) 17300854 (-148.071) Unmethylated 29823230 (-113.616) 29816340 (116.044)


Bud-Failure Status


Methylated 16613797 (-196.478) 12140156 (243.834) Unmethylated 28264980 (156.598) 16999037 (-194.342)

b. CHG Methylation Contingency Tables


Bud-Failure Status




Bud-Failure Status



(continued)

209


c. CHH Methylation Contingency Tables


Bud-Failure Status




Bud-Failure Status



210

Table 4.4 Number of DMRs in each methylation context for each ‘Stukey’ twin pair.

DMRs are classified by proximity relative to a gene: upstream (within 2,000 bp) of a

gene, intragenic, or downstream (within 2,000 bp) of a gene (significant permutation tests

are represented in bold).

Methylation

Context

Twin

pair Proximity

Number of

DMRs

Permutation

Result

p-

value

CG

1

Upstream 441 more < 0.001

Intragenic 154 less < 0.001

Downstream 339 more < 0.001

2



Downstream 368 more < 0.001

CHG

1



Downstream 191 more 0.086

2




CHH

1




2




211

Table 4.5 Number of identified DMRs per twin pair that are associated with the same

gene, are in the same proximity class relative to that gene, and are in the same context in

both ‘Stukey’ twin pair 1 and ‘Stukey’ twin pair 2 (significant permutation tests are

represented in bold). Values in parentheses represent the number of DMRs from each

twin pair that have overlapping genomic coordinates.

Methylation

Context Proximity

Number of DMRs

Per Twin Pair

Permutation Result p-value

CG

Upstream 42 (36) more < 0.001

Intragenic 10 (6) more 0.006

Downstream 30 (24) more < 0.001

CHG


Intragenic 2 (1) more 0.179


CHH


Intragenic 3 (2) more < 0.001


212

Table 4.6 Protein sequences in the UniProtKB Reviewed (Swiss-Prot) database significantly aligning to genes associated with

DMRs both hypermethylated and hypomethylated in BF twins compared to No-BF twins (e-value 0.0001)

Methylation

Level

Proximity Context Identifier Entry Name Protein Name

Hyper Upstream CG Q05609 CTR1_ARATH Serine/threonine-protein kinase CTR1

Hyper Upstream CG & CHG Q9SZD1 TCX5_ARATH Protein tesmin/TSO1-like CXC 5 TCX5

Hyper Upstream CG A9YF60 SGR_CAPAN Protein STAY-GREEN homolog,

chloroplastic SGR

Hyper Upstream CHG Q9FLP6 TCX5_ARATH Small ubiquitin-related modifier 2 SUMO2

Hyper Upstream CHG Q9XIM8 SUMO2_ARATH Pentatricopeptide repeat-containing protein

At2g15980

Hyper Upstream CHH Q40392 PP155_ARATH TMV resistance protein N

Hyper Upstream CHH P34284 TMVRN_NICGU F-box/LRR-repeat protein fbxl-1

Hyper Upstream CHH Q07346 FBXL_CAEEL Glutamate decarboxylase GAD

Hyper Downstream CG Q9ZW20 TRNHD_ARATH Tropinone reductase homolog At2g29370

Hyper Downstream CG Q38838 AGL14_ARATH Agamous-like MADS-box protein

AGL14/XAANTAL 2

Hyper Downstream CHG Q94A09 TRM9_ARATH TRM9

Hyper Downstream CHG P55857 SUMO1_ORYSJ Small ubiquitin-related modifier 1

SUMO1/SMT3

Hyper Downstream CHH A0A075F7E9 LERK1_ORYSI G-type lectin S-receptor-like

serine/threonine-protein kinase LECRK1

Hyper Downstream CHH P24465 C71A1_PERAE Cytochrome P450 71A1 CYP71A1

Hypo Upstream CG Q9LK37 RLF24_ARATH Protein RALF-like 24 RALFL24

Hypo Upstream CG & CHH Q9SND9 Y3028_ARATH Uncharacterized acetyltransferase

At3g50280

Hypo Upstream CG Q653H7 ARFR_ORYSJ Auxin response factor 18 ARF18 (continued)

213

Hypo Upstream CG Q9LMT7 MAIN_ARATH Protein MAINTENANCE OF MERISTEMS

Hypo Upstream CG Q8RWR2 LRP1B_ARATH La-related protein 1B LARP1B

Hypo Upstream CG Q9SD53 Y3720_ARATH UPF0481 protein At3g47200

Hypo Upstream CG Q9TL56 NU5C_CARCG NAD(P)H-quinone oxidoreductase subunit

5, chloroplastic ndhF

Hypo Upstream CG Q5RJC5 C3H67_ARATH Zinc finger CCCH domain-containing

protein 67 At5g63260

Hypo Upstream CG F4JCQ3 DG783_ARATH Putative DNA glycosylase At3g47830

Hypo Upstream CG Q9LN86 DRE1F_ARATH Dehydration-responsive element-binding

protein 1F DREB1F

Hypo Upstream CHG Q3E6Y3 Y1869_ARATH Uncharacterized protein At1g28695

Hypo Upstream CHG P30155 RK27_TOBAC RPL27

Hypo Upstream CHG F4KE63 SYVM2_ARATH Valine RNA ligase,

chloroplastic/mitochondrial EMB2247

Hypo Upstream CHH Q8LFD1 LPP3_ARATH Putative lipid phosphate phosphatase 3,

chloroplastic LPP3

Hypo Upstream CHH Q8H0Z6 IP5P3_ARATH Type IV inositol polyphosphate 5-

phosphatase 3 5PTase3

Hypo Upstream CHH Q8GYA6 PS13B_ARATH 26S proteasome non-ATPase regulatory

subunit 13 homolog B RPN9B

Hypo Intragenic CG O50048 MDL2_PRUSE (R)-mandelonitrile lyase 2 MDL2

Hypo Intragenic CG O65717 CNGC1_ARATH Cyclic nucleotide-gated ion channel 1

CNGC1

Hypo Intragenic CG Q6NMR9 GDL84_ARATH GDSL esterase/lipase At5g45920

Hypo Intragenic &

Upstream CG & CHG Q9LES4 L2HDH_ARATH

L-2-hydroxyglutarate dehydrogenase,

mitochondrial L2HGDH

Hypo Intragenic CHH P42731 PABP2_ARATH Polyadenylate-binding protein 2 PAB2 (continued)


214

Hypo Downstream CG Q06077 ABRB_ABRPR Abrin-b

Hypo Downstream CG Q0WUQ1 BAG1_ARATH BAG family molecular chaperone regulator

1

Hypo Downstream CG Q9LY17 PME50_ARATH Probable pectinesterase 50 AtPME50

Hypo Downstream CG O81832 Y4729_ARATH G-type lectin S-receptor-like

serine/threonine-protein kinase At4g27290

Hypo Downstream CG P83332 TLP1_PRUPE Thaumatin-like protein 1 PpAZ44

Hypo Downstream CG Q8W453 DIRL1_ARATH Putative lipid-transfer protein DIR1

Hypo Downstream CG Q9FJP6 PUB38_ARATH U-box domain-containing protein 38 PUB38

Hypo Downstream CG Q9FJ93 DRE1D_ARATH Dehydration-responsive element-binding

protein 1D DREB1D

Hypo Downstream CHG O23393 BIA1_ARATH BAHD acyltransferase BIA1 ABS1

Hypo Downstream CHG O23254 GLYC4_ARATH Serine hydroxymethyltransferase 4 SHMT4

Hypo Downstream CHG Q93WI9 HD3A_ORYSJ Protein HEADING DATE 3A HD3A

Hypo Downstream CHG Q94CD1 HHT1_ARATH Omega-hydroxypalmitate O-feruloyl

transferase HHT1 ASFT

Hypo Downstream CHH Q8LSN3 FYPP_PEA Phytochrome-associated serine/threonine-

protein phosphatase FyPP

Hypo Downstream CHH Q9C516 XLG3_ARATH Extra-large guanine nucleotide-binding

protein 3 XGL3


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Table 4.7 GO terms assigned to DMR-associated genes for each GO category: biological

process (a), molecular function (b), and cellular component (c). Significantly enriched

GO terms are represented in bold (alpha = 0.1).

(a) Biological Process Number of

Genes

regulation of timing of transition from vegetative to reproductive phase

3

defense response 3

ubiquitin-dependent protein catabolic process 2

protein sumoylation 2

response to ethylene 2

regulation of transcription, DNA-templated 2

multicellular organism development 2 flower development 2

vegetative to reproductive phase transition of meristem 2

glucosinolate metabolic process 2

brassinosteroid mediated signaling pathway 1

regulation of flavonoid biosynthetic process 1

regulation of brassinosteroid biosynthetic process 1

response to absence of light 1

brassinosteroid metabolic process 1

brassinosteroid homeostasis 1

response to brassinosteroid 1

cell-cell signaling 1

calcium-mediated signaling 1

SCF-dependent proteasomal ubiquitin-dependent protein catabolic process

1

lipid transport 1

systemic acquired resistance 1

systemic acquired resistance, salicylic acid mediated signaling pathway

1

protein ubiquitination 1

signal transduction 1

plant-type hypersensitive response 1

circadian rhythm 1

tetrahydrofolate interconversion 1 glycine metabolic process 1

glycine biosynthetic process from serine 1

one-carbon metabolic process 1

L-serine catabolic process 1

folic acid metabolic process 1

cellular response to tetrahydrofolate 1

(continued)

216

response to cadmium ion 1

tetrahydrofolate metabolic process 1

negative regulation of ethylene-activated signaling pathway 1

protein autophosphorylation 1 regulation of post-embryonic root development 1 sugar mediated signaling pathway 1 response to hypoxia 1 response to fructose 1 ethylene-activated signaling pathway 1 regulation of stem cell division 1 gibberellin biosynthetic process 1 response to sucrose 1 tRNA wobble uridine modification 1 regulation of nuclear-transcribed mRNA poly(A) tail shortening 1 regulation of nuclear-transcribed mRNA catabolic process, deadenylation- dependent decay

1

regulation of translational initiation 1 translational initiation 1 viral process 1 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay

1

negative regulation of translation 1 short-day photoperiodism 1 regulation of flower development 1 cell differentiation 1 inflorescence development 1 short-day photoperiodism, flowering 1 suberin biosynthetic process 1 cell wall pectin biosynthetic process 1 auxin-activated signaling pathway 1 cytoplasm protein quality control by the ubiquitin-proteasome system

1

cell wall modification 1 pectin catabolic process 1 translation 1 fruit ripening 1 lipid catabolic process 1 recognition of pollen 1 protein phosphorylation 1 glutamate metabolic process 1 valyl-tRNA aminoacylation 1 embryo development ending in seed dormancy 1 tRNA aminoacylation for protein translation 1 phospholipid metabolic process 1


(continued)

217

leaf senescence 1 ATP synthesis coupled electron transport 1 phosphatidylinositol dephosphorylation 1 chlorophyll catabolic process 1 abscisic acid-activated signaling pathway 1 base-excision repair 1 protein catabolic process 1 proteasome assembly 1 regulation of root development 1 response to bacterium 1 adenylate cyclase-modulating G protein-coupled receptor signaling pathway

1

positive regulation of transcription by RNA polymerase II 1 regulation of root meristem growth 1 maintenance of floral meristem identity 1 regulation of auxin polar transport 1 meristem development 1 regulation of growth 1 regulation of gibberellin biosynthetic process 1 response to freezing 1 response to heat 1 regulation of unidimensional cell growth 1 response to water deprivation 1

(b) Molecular Function Number of

Genes ATP binding 5

metal ion binding 4

protein serine/threonine kinase activity 3

mRNA binding 3

carbohydrate binding 3

DNA-binding transcription factor activity 3

DNA binding 3

calmodulin binding 3

transferase activity, transferring acyl groups other than amino-acyl groups 2

ubiquitin-like protein ligase binding 2

protein tag 2

zinc ion binding 2

pyridoxal phosphate binding 2

RNA polymerase II transcription regulatory region sequence-specific DNA binding

2

sequence-specific DNA binding 2

hormone activity 1

(continued)


218

protein self-association 1

fatty acid binding 1

transmembrane receptor protein serine/threonine kinase binding 1

ubiquitin-protein transferase activity 1

phosphoprotein phosphatase activity 1

ADP binding 1

NAD+ nucleotidase, cyclic ADP-ribose generating 1

NAD(P)+ nucleosidase activity 1

cobalt ion binding 1

amino acid binding 1

glycine hydroxymethyltransferase activity 1

serine binding 1

protein serine/threonine/tyrosine kinase activity 1 protein kinase activity 1 tRNA methyltransferase activity 1

tRNA (uracil) methyltransferase activity 1

translation initiation factor activity 1

mRNA 3'-UTR binding 1

poly(U) RNA binding 1

RNA binding 1

poly(A) binding 1

toxin activity 1

rRNA N-glycosylase activity 1

oxidoreductase activity, acting on CH-OH group of donors 1

flavin adenine dinucleotide binding 1

mandelonitrile lyase activity 1

oxidoreductase activity 1

phosphatidylethanolamine binding 1

hydroxycinnamoyltransferase activity 1

omega-hydroxypalmitate O-sinapoyl transferase activity 1

chaperone binding 1

pectinesterase activity 1

carboxylic ester hydrolase activity 1

aspartyl esterase activity 1

cAMP binding 1

ion channel activity 1

cGMP binding 1

structural constituent of ribosome 1

monooxygenase activity 1

iron ion binding 1

heme binding 1

oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen

1

(continued)


219

hydrolase activity, acting on ester bonds 1

glutamate decarboxylase activity 1

valine-tRNA ligase activity 1

aminoacyl-tRNA editing activity 1

phosphatidate phosphatase activity 1

FAD binding 1

2-hydroxyglutarate dehydrogenase activity 1

(S)-2-hydroxy-acid oxidase activity 1

quinone binding 1

NADH dehydrogenase (ubiquinone) activity 1

phosphatidylinositol-4,5-bisphosphate 5-phosphatase activity 1

phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase activity 1

hydrolase activity, acting on glycosyl bonds 1

structural molecule activity 1

GTP binding 1

GTPase activity 1 G protein-coupled receptor binding 1 G-protein beta/gamma-subunit complex binding 1

DNA-binding transcription factor activity, RNA polymerase II-specific 1

protein dimerization activity 1

transcription factor binding 1

transcription regulatory region sequence-specific DNA binding 1

transaminase activity 1

DNA-binding transcription factor activity 1

(c) Cellular Component Number of Genes

nucleus 16

cytoplasm 10

cytosol 7

plasma membrane 6

integral component of membrane 5

plasmodesma 3

mitochondrion 3

chloroplast 3

apoplast 2

endoplasmic reticulum membrane 2

extracellular region 2

SCF ubiquitin ligase complex 1

endoplasmic reticulum 1

secretory vesicle 1 ribonucleoprotein complex 1

aleurone grain 1

(continued)


220

vacuole 1

peroxisome 1

cell wall 1

ribosome 1

chloroplast stroma 1

integral component of plasma membrane 1

chloroplast membrane 1

polysome 1

chloroplast thylakoid membrane 1

proteasome complex 1

proteasome regulatory particle, lid subcomplex 1 heterotrimeric G-protein complex 1


221

Table 4.8 List of the significantly enriched (adjusted p-value < 0.05) transcription factor

family binding sites in the shared DMRs.

Transcription

Factor Family

Binding sites in

DMRs Description

ERF 23,245 Hormone regulation; stress response1

Dof 7,629 Metabolism; phytohormone response;

photoperiodic regulation2

BBR/BPC 4,014 Flower development; stem cell size;

seed development3

bHLH 3,886 Phytochrome signaling4

TCP 3,231 Branching; flower development; leaf

development; cell cycle regulation5

B3 2,518 Vegetative and reproductive

development6,7

LBD 2,272 Leaf, flower, and root development;

auxin response8

GATA 2,245

Nitrogen metabolism; stress response;

hormonal signaling9; meristem

development10

AP2 2,061 Flower development11

GRAS 1,209 Gibberellin signaling; root patterning;

light signaling; nodule formation12

BES1 523 Brassinosteroid regulation13

E2F/DP 499 Cell cycle regulation14

RAV 464 Regulation of flowering; gibberellin

regulation15

C3H 353 Plant organ identity; embryogenesis;

leaf senescence16

GeBP 233 Leaf primordia development17

ARR-B 229 Cytokinin regulation; shoot

development18

GRF 80 Leaf and stem development; flowering;

plant longevity19

1Xie et al., 2019; 2Yanagisawa, 2016;3Theune et al., 2019; 4Duek and Fankhauser, 2005; 5Danisman, 2016; 6Peng and Weselake, 2013; 7Swaminathan et al., 2008; 8Majer and

Hochholdinger, 2011; 9Gupta et al., 2017; 10Zhao et al., 2004; 11Okamuro et al., 1997; 12Hofmann, 2016; 13Song et al., 2018; 14Vandepoele et al., 2005; 15Matías-Hernández et

al., 2014; 16Jiang et al., 2014; 17Curaba et al., 2003; 18Xie et al., 2018; 19Omidbakhshfard

et al., 2015

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Table 4.9 Sequencing results from an RNA-seq experiment performed on two ‘Stukey’

twin pairs displaying divergent bud-failure exhibition.

Library SRA Biosample

Number

Total Reads

Generated

Total Reads

Aligned

Mapping

Efficiency

‘Stukey’ 1a SAMN1640-3998 84,640,142 56,935,045 67.3%

‘Stukey’ 1b SAMN1640-3999 103,419,251 69,651,011 67.3%

‘Stukey’ 2a SAMN1640-4000 99,578,512 66,298,129 66.6%

‘Stukey’ 2b SAMN1640-4001 85,576,260 60,312,161 70.5%

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Table 4.10 Annotation of significantly differentially expressed genes in the BF twins

compared to the no-BF twins (p-value < 0.1).

Log2

Fold

Change

p-

adjusted Protein ID Gene Name

2.97 4.66E-10 Undescribed protein

2.34 0.011 Oligopeptide transporter 1 OPT1 (At5g55930)

1.94 2.04E-04 ABC transporter C family

member 5

ABCC5/MRP5

(At1g04120)

1.14 0.058 Putative mediator of RNA

polymerase II

1.06 0.058 Rust resistance kinase Lr10 LRK10

1.05 0.058 Cyclic nucleotide-gated ion

channel 1 CNGC1 (At5g53130)

1.05 0.058 Cellulose synthase-like protein G2 CSLG2

0.92 0.058 Early nodulin-like protein 2 AT4G27520

0.81 0.079 Cytochrome P450 82A3 CYP82A3

0.68 0.090 Protein SAR DEFICIENT 1 SARD1 (At1g73805)

0.64 0.090 Beta-glucosidase 12 BGLU12 (OsI_16288)

0.47 0.100 Methionine gamma-lyase MGL (At1g64660)

0.32 0.090 DMR6-LIKE OXYGENASE 1 DLO1/SAG108

(At4g10500)

-0.59 0.090 Polygalacturonase PGLR_PRUPE

-0.81 0.058 GDSL esterase/lipase At2g04570

-0.93 0.058 Proline-rich protein 4-like

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Chapter 5 Prospectives and Conclusions

Overview of research findings in this dissertation

The primary focus of this dissertation is exploring epigenetic modifications underlying

the development of an aging-related disorder in almond, and more broadly, how those

modifications are associated with increased age. This research fits into a body of work

characterizing the impacts of perennial plant aging in an effort to enhance our

understanding of this process and to develop direct applications for crop improvement.

Following review of the literature, the first research chapter describes the relationship

between age, telomere length, and expression of a previously described telomerase-

associated gene (TERT) in distinct age cohorts of almond breeding selections. Results

from this work show decreased telomere length and TERT expression with increased age

over two years of sampling almond leaf tissue (D’Amico-Willman et al., 2021). This

pattern is reflected in one year of sampling bud tissue from the same age cohorts

(D’Amico-Willman et al., 2021).

The second research chapter characterizes the methylome of 70 almond breeding

selections from three distinct age cohorts, showing a pattern of genome-wide

hypermethylation with increased age. Regions of differential methylation in these

selections were identified and annotated to explore those genomic features impacted by

changes in DNA methylation and to discern pathways whose expression patterns might

be regulated by those changes. This annotation includes the microRNA, miR156, which

is a well-known regulator of the juvenile-to-adult phase transition in plants. The final

research chapter focuses specifically on non-infectious bud-failure in almond by

characterizing the methylome of two pairs of monozygotic twin almonds discordant for

bud-failure exhibition. Results from this work show a pattern of hypomethylation

associated with bud-failure exhibition in almond and identify specific genes associated

225

with regions of differential methylation. These identified genes represent targets for

future study into biomarkers of bud-failure potential in almond and into the molecular

mechanisms that underlie bud-failure development.

Taken together, the results from these research chapters support the models of

aging developed in other species, including decreased telomere length and increased

DNA methylation (Runov et al., 2015). Further, the dysregulation of methylation

observed in non-infectious bud-failure trees reflects what is documented in other crops

such as in oil palm (Elaeis guineensis Jacq.) exhibiting the mantling phenotype (Ong-

Abdullah et al., 2015), and demonstrates that this disorder likely occurs as a result of

interacting factors including aging and exposure to abiotic stress. Several questions

remain, however, and several more were generated as a result of the analyses presented in

this document, including questions related to heat stress response, metabolomic profiles,

chromatin structure, and transgenerational heritability of DNA methylation. Addressing

these questions would build on the knowledge generated in this dissertation and on the

existing work described in the review of the literature. In the following paragraphs,

several potential future projects are outlined which would add to the growing body of

knowledge on bud-failure development in almond and aging in perennial plants. These

projects consider aspects such as the role of abiotic stressors, developmental profiling,

inheritance of epigenetic marks, epigenome editing, and phenotyping approaches to

dissect aging related disorders such as noninfectious bud-failure.

The effect of heat stress on the almond methylome

The first proposed project explores the impact of heat stress on the almond methylome.

Previous work in almond revealed impacts of artificial heat stress on both the internal

structure of vegetative buds and on specific hormone profiles in an attempt to link heat

stress to bud-failure development (Hellali & Kester, 1979). We now have the tools

available to develop a robust profiling of almond responses to heat stress, including

genetic resources like the sequenced ‘Nonpareil’ genome and methodology developed in

this dissertation to profile DNA methylation in almond. A project could then be

developed to test the hypothesis that exposure to heat stress leads to a dysregulation of

DNA methylation in the almond genome and results in the non-infectious bud-failure

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phenotype. The methodology employed in this project could include both a controlled,

growth chamber experiment as well as a field-based, long-term experiment including

multiple, commercially relevant cultivars.

The growth chamber experiment would involve acquiring propagated clones, all

from the same propagation source for each cultivar, and exposing a set of those clones to

heat stress within a controlled growth chamber. Similar experiments have been conducted

in other species including Arabidopsis, Brassica rapa L., maize, the perennial herb

Ranunculus kuepferi Greuter & Burdet, Monterey pine (Pinus radiata D.Don), and poplar

(Populus euramericana (Dode) Guinier) to determine the effects of heat or abiotic

stress on methylation profiles (Sanchez & Paszkowski, 2014; Liu et al., 2018; Le Gac et

al., 2018; Qian et al., 2019; Syngelaki et al., 2020; Lamelas et al., 2020). Both bud and

leaf tissue samples could be collected at either one or several time points to compare the

methylomes of clones under heat stress to those under optimal temperature conditions. A

parallel field-based experiment could be conducted where a set of propagated clones from

the same source are grown in field plots in different regions of California representing a

range of maximum summer temperatures. In this experiment, leaf and bud samples could

again be collected across this range to profile DNA methylation either at one or several

time points. The field-based experiment also represents an opportunity to establish a

long-term study where these trees could be revisited over several years. This type of

project might also be coupled with the cultivar and breeding selection trials supported by

the Almond Board of California.

Results from this work would provide information on changes in DNA

methylation in response to heat stress in almond and if/how these patterns differ based on

genetic background (i.e. cultivar). Comparisons could be made between DNA

methylation patterns observed following heat stress to those in almond trees exhibiting

bud-failure to look for commonality either in regions of differential methylation or

downstream pathways. This work would have further implications for issues like climate

change and the anticipated increase in maximum temperatures expected in almond

growing regions (Luedeling et al., 2009; Mera et al., 2015; Parker & Abatzoglou, 2018).

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Profiling DNA methylation during almond development

In addition to monitoring alterations in DNA methylation patterns in response to heat

stress, it would also be interesting to profile DNA methylation over developmental time

in almond. Chapter 3 of this dissertation shows hypermethylation associated with age in

almond; however, this study was conducted using almond breeding selections

representing different ages at a single timepoint. A potential study could be performed to

profile DNA methylation changes in the same individuals over developmental time to test

the hypothesis that the methylome is dynamic in early development in almond and that

this dynamism contributes to regulating developmental pathways. This project would

involve acquiring seed from a single cross using commercially relevant cultivars and

establishing greenhouse plantings to profile DNA methylation. Select seed from the cross

could also be sampled to establish baseline DNA methylation profiles prior to planting.

Seedlings could be grown in the greenhouse and sampled at several time points for DNA

methylation profiling, including multiple tissue types (i.e., leaf, bud, root, flowers). In

addition to planting seeds in the greenhouse, individual breeding selections could be

monitored and sampled over time in the field to assess DNA methylation profiles in a

natural environment and over a potentially longer time period.

Results from this study would contribute to a more basic understanding of

epigenetic modifications that occur during almond development. The ability to profile

several tissue-types including root tissue in the greenhouse, which can be difficult to

acquire as commercial almond is usually grafted onto peach or Prunus hybrid rootstock,

would also provide a unique perspective on how epigenetic alterations like DNA

methylation influence and potentially regulate developmental processes in almond. One

limitation of the greenhouse study is that, due to logistics, it would likely only be feasible

to run this experiment for a limited time (possibly 1-2 years). The field-based portion of

this study could account for longer developmental time; however, it might be difficult to

sample as frequently and other confounding factors would need to be considered (i.e.,

environmental conditions).

The data generated from these projects would provide valuable insight into

processes regulating development in almond, which is currently lacking. Time course

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studies have also been employed in other systems to analyze patterns of epigenetic

change, including in perennials like poplar (Populus tomentosa Carrière) and sweet

orange (Citrus sinensis (L.) Osbeck.) (Song et al., 2013; Huang et al., 2019). Both

studies show changes in the methylome correlated with altered expression of genes

involved in maintenance or resetting of DNA methylation and associated with

developmental processes like fruit and flower development (Song et al., 2013; Huang et

al., 2019). While the proposed study focuses on DNA methylation, it would also be

possible to extend this analysis into other epigenetic marks including histone

modifications or chromatin structure using the same sampling scheme and tissues.

Results from this additional profiling could be integrated with the DNA methylation

analysis, creating a broad picture of the epigenetic landscape over developmental time in

almond.

Transgenerational inheritance of DNA methylation in almond

Exploring heritability of methylation in almond is another relevant future project that

could be pursued, particularly in light of the heritability of non-infectious bud-failure and

the possibilities for epigenetic crop improvement (Köhler & Springer, 2017). To date,

few studies have addressed heritability of DNA methylation in perennial species, in part

because developing multiple generations for profiling is a time-consuming process due to

their long juvenile period. In almond, it would be possible to take advantage of the

breeding germplasm at UC Davis to select pedigrees of breeding selections for profiling

to ensure relatedness among individuals and test the hypothesis that DNA methylation

patterns are stably heritable in almond across multiple generations.

These pedigrees could be further extended by performing select crosses using

almond accessions with divergent methylomes and profiling the progeny to determine

which methylation profiles are preferentially inherited. The proposed project would

utilize lineages of almond breeding selections with a minimum of three generations

available for profiling. Ideally, two parents would be selected for DNA methylation

profiling whose progeny (F1) are also available to sample and sequence. Select F1

individuals would be used to perform crosses with both full-siblings and unrelated

individuals to create an F2 generation that would also be sequenced. DNA methylation

229

profiles over three generations could then be compared to analyze inheritance patterns

and determine which DNA signatures are passed to subsequent generations. This type of

project could also provide an interesting framework to implement unsupervised machine

learning and artificial neural network techniques to unveil hidden patterns of inheritance

of methylomic signatures (Harfouche et al., 2019).

The proposed sampling scheme would produce germplasm suitable to study

transgenerational rather than intergenerational inheritance, providing a robust analysis of

inheritance patterns in almond. Several studies have profiled both intergenerational and

transgenerational inheritance in various plant species; however, this work has primarily

been performed in annual species like Arabidopsis and rice (Ou et al., 2012; Zhong et al.,

2013). This study would contribute to an understanding of transgenerational inheritance

in a productive perennial with implications for breeding efforts and remediating

deleterious phenotypes like non-infectious bud-failure.

Inducing DNA demethylation in the almond genome

A final proposed methylation study in almond involves artificial demethylation to

observe the induced phenotypic changes occurring as a result. This study tests the

hypothesis that induced DNA demethylation results in vegetative bud morphology

observed in trees exhibiting non-infectious bud-failure. Previous work in plants has

utilized the chemical 5-azacytidine, which induces DNA demethylation, to monitor the

effects of demethylation on various phenotypes including biotic and abiotic resistance

(Griffin et al., 2016; Ogneva et al., 2019; Fan et al., 2020; Browne et al., 2020). In a

recent study on valley oak (Quercus lobata Née), seedlings were treated with 5-

azacytidine, and the authors observed genome-wide decreases in methylation in all

contexts as well as reductions in growth of treated trees (Browne et al., 2020). To

perform a similar study in almond, seeds could be harvested following a planned cross

utilizing two parents of cultivars with documented low bud-failure potential, two parents

of cultivars with documented high bud-failure potential, and an intermediary cross (i.e.,

one parent with high and one parent with low bud-failure potential).

The seeds could be planted in a common greenhouse, and following germination

and establishment (~6 months), samples could be collected from all seedlings (leaf and

230

bud), and a subset of seedlings treated with a foliar application of 5-azacytidine. Control

seedlings would be sprayed using a formulation that does not contain 5-azacytidine.

Following this, plants could be monitored for alterations in phenotypes including changes

in growth or any other physiological measurements (i.e., photosynthetic capacity, etc.).

Samples of newly formed bud and leaf tissue could also be collected to profile DNA

methylation with comparisons including treated and control seedlings from each

individual cross, treated seedlings from all crosses, as well as to baseline DNA

methylation levels prior to treatment.

Interesting results from this project would include observing phenotypes of non-

infectious bud-failure in seedlings derived from the low bud-failure-potential cross and

treated with 5-azacytidine. It would also be interesting to observe how buds develop in

each of the three crosses, comparing bud morphology across all control seedlings and

across all treated seedlings from each cross. Dissection of newly formed bud tissue in

treated seedlings could also reveal disruption in meristem cell layers as was seen in

previous work analyzing buds harvested from bud-failure-affected trees (Hellali et al.,

1978). An artificial demethylation study in almond provides an opportunity to study

demethylation in a controlled environment, eliminating confounding factors like abiotic

or biotic stress, and allows analysis of phenotypes in full sibling seedlings, reducing the

potential impacts of genetic variability outside of epigenetic alterations.

Phenotyping approaches to characterize non-infectious bud-failure

In addition to studies profiling DNA methylation, projects could be developed examining

other aspects of non-infectious bud-failure development in almond such as metabolomic

and anatomical characterizations. Previous work on bud-failure development in almond

revealed changes in abscisic acid (ABA) levels associated with the disorder; however,

since this study was published, little work has been done to characterize the metabolome

of affected buds (Hellali et al., 1978; Hellali & Kester, 1979). A potential study could

include profiling the metabolome of buds from multiple cultivars exhibiting bud-failure

and comparing those profiles to trees that are bud-failure free. This study could focus on

specific hormones like ABA and gibberellins (GA) in a targeted approach to test

hypotheses based on previous work including that ABA levels will be higher in buds

231

from trees exhibiting bud-failure compared to those that are bud-failure free, and that GA

levels will not differ when comparing buds from trees with bud-failure to those that are

bud-failure free. An untargeted metabolomic approach could be used to capture a broader

picture of the metabolomic profile, generating new hypotheses and potential projects

based on comparing profiles from bud-failure and bud-failure-free trees.

A recent study describes characterization of metabolites in almond buds related to

endodormancy release in an effort to develop biomarkers (Guillamón et al., 2020),

suggesting this approach could work well to profile the metabolome associated with bud-

failure and even potentially produce additional biomarkers to predict development of the

disorder. This work could also be integrated with the methylation profiling described

above to examine potential underlying epigenetic modifications impacting metabolite

production.

As non-infectious bud-failure represents a failure of vegetative bud development,

previous work has been done to describe the anatomical features of failing buds at

different time points (Hellali et al., 1978); however, this work was performed more than

40 years ago using a dissecting microscope, and microscopy capabilities have since

improved dramatically. Scanning electron microscopy (SEM) is an approach previously

used to visualize morphology of meristematic tissues in other plant species (Routier-

Kierzkowska & Kwiatkowska, 2008; Jerominek et al., 2014; Haberman et al., 2017) and

could be used to more precisely characterize the morphology of bud-failure-affected buds

in almond. This approach could be applied in a study using field-sampled buds from trees

exhibiting bud-failure compared to those that are bud-failure free. Additionally, this

approach could be used to examine buds from heat-stressed trees in the experiment

utilizing clones in a controlled growth chamber setting.

Monitoring changes in meristem structure in a time-course study could also

provide insight into how buds fail and help to identify the cell layers within the meristem

corpus and/or tunica that show initial signs of degradation. This type of anatomical study

could also be used to inform future studies utilizing single-cell sequencing approaches. In

a recent study published in maize, single-cell analysis was used to examine the

transcriptome of meristematic cells (Satterlee et al., 2020). A similar approach could be

232

applied in almond to examine DNA methylation or other epigenetic marks at the single

cell level in the shoot apical meristem. Additionally, it might be possible to utilize an

approach such as in situ genome sequencing where information at the nucleotide level is

integrated with 3D chromatin structure data at the single-cell level, providing a more

complete analysis of the epigenetic landscape at a given time point or in a specific cell

type (Payne et al., 2020).

Developing biomarkers of age in perennial plant species

A primary aim of the research presented herein is to provide a foundation for developing

biomarkers of age in almond that could be extended to other perennial plant species. The

results presented suggest that DNA methylation could serve as a putative biomarker of

age as patterns of hypermethylation were found with increased age. Interestingly,

methylation patterns at 17 distinct loci showed consecutive, significant increases in

methylation at each age transition (2-to-7 years and 7-to-11 years), providing specific

genomic targets for developing these biomarkers. However, biomarker validation is

necessary to confirm the suitability of these regions as predictors of age in almond.

Therefore, a project could be developed to test these regions in additional almond trees of

known age and pedigree with a wider age range represented (i.e., beyond 11-years-old).

Based on results from this work, a model could be developed taking methylation status

into account at these particular regions as well as other potential factors such as pedigree

to predict age of sexual and clonal progeny.

In humans, an age “clock” was developed based on a multivariate model using the

methylation status of 353 CG loci (Horvath, 2013). A similar type of model could be

developed in almond using the methylation status either of individual methylated

cytosines or of regions exhibiting differential methylation patterns. This model could then

be employed to predict age of clonally propagated germplasm to determine an

appropriate ontogenetic age for those individuals and to assess their potential to develop

negative phenotypes such as bud-failure. Currently, no such model exists for plants; thus,

this would represent a novel avenue of research in the field of plant aging, with practical

applications for crop management and improvement for perennial crops.

233

Conclusions

The studies proposed herein would build on the body of knowledge presented in this

dissertation both in regard to non-infectious bud-failure development in almond and more

generally, to the implementation of epigenetic profiling in crop improvement and

development of models for perennial plant aging.

The translational goal of the work on non-infectious bud-failure is to develop

biomarkers that can be used to screen almond germplasm for bud-failure potential. To

date, there are no biomarkers or predictors of bud-failure development, leaving the

almond industry to depend solely on pedigree information to infer bud-failure potential

and pruning techniques to mitigate the effects of bud-failure exhibition. Presented in this

dissertation is a first analysis to identify specific regions of the genome showing

differential levels of methylation in trees exhibiting bud-failure. However, in order to

fully develop and implement this type of biomarker, further work is necessary to validate

the regions identified in this study in other germplasm, particularly in other cultivars

known to exhibit bud-failure such as ‘Carmel’. In addition to validating or developing

cultivar specific biomarkers for bud-failure, a high-throughput screening method would

need to be developed to quickly screen hundreds or thousands of individual trees for the

presence of the biomarker(s). This method would need to use tissue easily sampled by

growers or producers (i.e., leaf), and would require an inexpensive, targeted methylation

assay such as the multiplex biomarker panel described in Lam et al. (2020). These

biomarkers would give breeders, producers, and growers increased certainty in their

germplasm and security in their financial investments. Additionally, this work helps to

ensure sustained production of almond, protecting the valuable supply chain and

maintaining consumer access to a healthy food option.

Perennial crops like almond represent valuable commodities often neglected in

basic research despite their suitability to serve as models in studies on plant aging and

response and adaptation to biotic and abiotic stress (Rankenberg et al., 2021). Perennial

plant species, however, represent a unique opportunity to develop the field of plant

gerontology outside of the work focused on annuals. In comparison with annual species,

perennials can be profiled across distinct geographical regions over multiple growing

234

seasons. Additionally, their phenology is well-established and ample germplasm is

available through various breeding programs to develop studies on these species.

Our current knowledge on perennial plant aging is lacking, and even the concept

of aging in perennials remains unclear (Munné-Bosch, 2008). Aging in plants is often

considered synonymous with senescence, since in annuals, these processes occur

simultaneously. Perennials can go through a senescence process each year, but this

process is organ-specific (i.e., leaves) and is distinct from the aging process affecting

pluripotent meristematic tissues. Whether and how perennials experience whole-plant

senescence is still debated (Munné-Bosch, 2008, 2020). Developing better models of

plant aging not only satisfies scientific curiosity in this complex and fascinating field, but

will lead to increased understanding of how aging contributes to the development of both

advantageous and deleterious phenotypes in productive crops. This work is particularly

relevant given the ever-changing and uncertain stressors perennial plants face as they age,

including introduced pests and pathogens as well as the environmental perturbations

resulting from a changing climate. How to incorporate models of plant aging into facets

of plant research and production will continue to develop as we expand our knowledge of

this topic.

Recent advances in omics technologies and in our understanding of epigenetic

regulation in plants is leading to a revolution in how we consider crop improvement,

particularly in light of phenomena like climate change (Varotto et al., 2020).

Incorporating epigenetics into crop improvement strategies and platforms via methods

like induced demethylation or even gene-editing (Lee et al., 2020) provides a new tool to

ensure sustained and resilient production of our most valuable crop species. However,

efforts to introduce these methods into perennial crop production lags far behind that of

annual crops, in part due to a lack of understanding of epigenetic mechanisms in these

species. Continued research like the projects proposed in this chapter would contribute to

our understanding of basic epigenetic mechanisms in a valuable, perennial crop which

could be extended to address other issues in almond, such as leaf-failing recently

observed in the cultivar ‘Monterey’ (Milliron et al., 2020), and towards improvement

efforts in other Rosaceous crops (Gogorcena et al., 2020).

235

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Appendix A: Supplemental Tables and Files

Table A. 1 Data for the relative telomere length estimation of almond age cohort samples

collected in 2018. Included are the age of the individual in years, the calculated T/S ratio

for each individual, the calculated relative telomere length based on the T/S ratio, and the

calculated z-score for each relative telomere length.

Age (years) T/S ratio relativeTeloLength zScore_TeloLength

14 -0.49 -0.70 -2.77

14 0.16 0.24 -1.07

14 0.71 1.03 0.36

14 0.38 0.55 -0.51

14 0.75 1.08 0.45

9 0.27 0.40 -0.79

9 0.33 0.48 -0.63

9 0.48 0.70 -0.24

9 0.48 0.70 -0.24

5 0.54 0.78 -0.09

5 1.41 2.02 2.17

5 0.51 0.73 -0.18

5 0.58 0.83 0.01

5 0.66 0.95 0.21

5 0.76 1.10 0.48

1 0.27 0.40 -0.79

1 0.86 1.24 0.75

1 1.16 1.68 1.54

1 0.68 0.99 0.28

1 0.90 1.30 0.85

1 0.65 0.94 0.19

281

Table A. 2 Raw Cq values produced from the technical replicate wells in qPCR using either the primer for the telomere

amplicon or the primer for the PP2A amplicon for each almond individual from the classified age.

(continued)

Age (years) TeloCq_Rep1 TeloCq_Rep2 TeloCq_Rep3 TeloCq_Rep4 TeloCq_Rep5 TeloCq_Rep6

14 17.89 17.94 18.20 18.37 18.49 18.18

14 14.09 16.23 18.75 14.70 19.00 16.51

14 18.41 14.70 15.22 18.94 14.22 17.31

14 13.27 13.75 14.21 16.79 16.08 N/A

14 17.38 14.13 14.20 13.79 13.49 13.65

9 14.23 14.05 14.04 16.10 14.55 14.41

9 15.70 15.31 15.78 15.29 14.33 14.20

9 14.49 14.79 14.89 14.58 14.75 15.22

9 14.25 14.18 14.18 14.21 14.18 14.32

5 13.47 13.21 13.62 13.04 13.34 13.27

5 15.74 12.86 13.87 14.11 14.01 14.31

5 14.46 16.52 13.58 14.80 14.18 14.13

5 14.46 16.52 13.58 14.80 14.18 14.13

5 13.91 13.43 13.72 13.26 13.49 13.56

5 12.74 12.99 13.58 13.16 13.00 18.19

1 13.75 13.45 13.97 14.10 13.58 14.32

1 12.99 13.58 12.96 12.85 N/A N/A

1 20.41 19.23 20.77 19.44 20.79 20.15

1 13.17 15.80 14.01 16.83 14.22 13.42

1 15.58 14.59 14.33 13.70 18.45 N/A

1 12.91 14.83 12.91 12.94 13.56 N/A

282

(Table A.2 continued)

PP2ACq_Rep1 PP2ACq_Rep2 PP2ACq_Rep3 PP2ACq_Rep4 PP2ACq_Rep5 PP2ACq_Rep6

18.31 18.01 21.13 20.24 20.74 21.01

17.57 18.55 19.07 18.67 19.20 18.58

17.51 14.92 21.82 18.18 18.28 23.77

21.84 16.85 15.47 17.00 18.28 N/A

17.33 21.62 22.10 20.99 20.22 20.44

19.42 23.17 22.30 19.25 20.07 21.89

24.66 23.34 N/A 27.52 19.87 20.20

16.73 21.21 21.23 17.30 21.16 22.23

19.71 19.68 20.13 19.38 19.30 19.79

19.59 22.04 23.91 21.60 23.28 21.94

22.56 18.50 18.60 18.83 18.73 20.52

23.48 19.52 23.47 15.70 16.09 N/A

23.48 19.52 23.47 15.70 16.09 N/A

20.39 19.26 20.04 19.15 19.50 21.01

18.91 18.17 17.62 17.60 18.28 20.41

19.06 17.82 19.88 19.08 19.48 18.97

21.88 24.76 21.55 21.41 N/A N/A

29.24 26.10 30.51 27.10 30.36 28.52

22.73 26.51 27.78 23.14 N/A N/A

25.15 24.07 22.45 20.28 27.81 N/A

20.39 17.07 20.16 19.06 15.20 N/A

283

Table A. 3 Data for the relative telomere length estimation of almond age cohort leaf

samples collected in 2019. Included are the age of the individual in years, the calculated

T/S ratio for each individual, the calculated relative telomere length based on the T/S

ratio, the calculated z-score for each relative telomere length, and the raw Cq values

produced from the technical replicate wells in qPCR using either the primer for the

telomere amplicon or the primer for the PP2A amplicon.


2 1.94 2.78 0.86

2 0.93 1.33 -0.59

2 1.47 2.10 0.18

7 0.96 1.37 -0.55

7 0.99 1.41 -0.51

7 0.66 0.95 -0.97

11 0.76 1.09 -0.83

11 0.77 1.11 -0.81

11 0.17 0.25 -1.67

284

Table A. 4 Raw Cq values produced from the technical replicate wells in qPCR using either the primer for the telomere amplicon or

the primer for the PP2A amplicon for each almond individual leaf sample from the classified age.

Age (years) TeloCq_Rep1 TeloCq_Rep2 TeloCq_Rep3 TeloCq_Rep4 TeloCq_Rep5 TeloCq_Rep6 PP2ACq_Rep1

2 15.77 17.97 18.33 19.13 19.33 n/a 21.49

2 13.51 13.42 14.36 14.22 15.31 15.88 18.13

2 15.11 16.25 17.00 15.55 16.83 n/a 22.59

7 16.23 17.28 19.11 21.50 19.23 19.37 21.89

7 19.18 18.79 20.71 24.15 19.92 n/a 26.45

7 24.14 20.21 23.23 19.64 19.62 n/a 29.65

11 16.44 17.25 17.70 22.66 22.00 18.28 23.40

11 18.27 21.42 22.37 20.07 19.13 19.29 26.85

11 17.70 18.04 18.27 19.29 22.54 18.69 34.46

Age (years) PP2ACq_Rep2 PP2ACq_Rep3 PP2ACq_Rep4 PP2ACq_Rep5 PP2ACq_Rep6

2 24.44 24.40 25.36 26.05 n/a

2 19.52 20.98 19.61 20.38 20.78

2 23.47 23.46 22.61 22.35 n/a

7 22.84 27.19 29.24 27.78 25.12

7 25.05 28.93 27.01 n/a n/a

7 34.64 28.68 26.88 n/a n/a

11 23.13 24.36 34.39 25.95 n/a

11 30.90 32.68 28.30 27.45 27.88

11 29.60 30.69 30.52 29.55 n/a

285

Table A. 5 Data for the relative telomere length estimation of almond age cohort bud

samples collected in 2019. Included are the age of the individual in years, the calculated

T/S ratio for each individual, the calculated relative telomere length based on the T/S

ratio, the calculated z-score for each relative telomere length, and the raw Cq values

produced from the technical replicate wells in qPCR using either the primer for the

telomere amplicon or the primer for the PP2A amplicon.


2 3.88589374 5.570538103 5.247081148

2 13.3921252 19.19798865 18.87453169

2 2.2738748 3.259663551 2.936206596

2 3.51609083 5.040415219 4.716958263

2 0.67809726 0.972071518 0.648614563

2 1.96560757 2.817753808 2.494296852

7 5.08929065 7.295641452 6.972184497

7 0.36461136 0.522680647 0.199223692

7 -0.1717277 -0.246176442 -0.569633398

7 2.7074387 3.881189612 3.557732656

7 1.54037715 2.208174021 1.884717065

7 -0.8122108 -1.1643271 -1.487784056

7 1.57670774 2.26025495 1.936797995

11 -0.7472457 -1.071197726 -1.394654681

11 -6.2162948 -8.911233693 -9.234690648

11 4.22102429 6.050957174 5.727500218

11 -8.4900828 -12.17077279 -12.49422974

11 1.57742136 2.261277936 1.93782098

286

Table A. 6 Raw Cq values produced from the technical replicate wells in qPCR using either the primer for the telomere amplicon or

the primer for the PP2A amplicon for each almond individual bud sample from the classified age.

(continued)

Age

(years)

TeloCq_Rep

1

TeloCq_Rep

2

TeloCq_Rep

3

TeloCq_Rep

4

TeloCq_Rep

5

PP2ACq_Rep

1

PP2ACq_Rep

2

2 14.12 14.43 13.99 14.22 14.32 23.82 22.24

2 14.79 14.92 14.96 14.62 14.51 24.57 24.47

2 13.92 14.53 14.64 14.39 15.03 22.23 23.04

2 15.03 15.47 15.10 15.12 15.46 22.91 22.77

2 15.37 15.42 15.22 15.58 15.19 22.25 13.30

2 14.73 14.49 14.76 14.59 14.96 21.30 20.34

7 14.75 14.60 14.46 13.98 13.95 23.31 22.19

7 14.15 14.34 14.28 14.59 14.32 22.08 23.57

7 16.58 17.50 16.61 16.96 16.62 1.49 31.95

7 15.39 15.07 14.81 14.56 14.50 20.94 21.37

7 14.20 14.38 14.16 14.25 14.52 20.58 18.27

7 15.06 14.65 14.94 15.21 14.96 30.37 21.90

7 14.46 14.68 14.50 14.45 14.37 20.90 19.83

11 14.77 14.86 15.37 14.94 14.72 25.90 27.74

11 14.36 14.61 14.69 N/A N/A 25.14 25.39

11 14.50 14.44 14.69 14.97 N/A 24.11 21.48

11 15.10 15.48 15.32 N/A N/A 23.47 24.43

11 15.35 14.23 14.32 14.59 14.41 19.81 18.82

287


Age (years) PP2ACq_Rep3 PP2ACq_Rep4 PP2ACq_Rep5

2 22.87 23.48 23.10

2 23.12 25.01 22.62

2 22.38 22.48 21.95

2 22.42 27.12 24.18

2 31.79 19.40 30.17

2 21.67 21.29 20.79

7 24.28 23.15 22.94

7 22.93 22.07 25.18

7 34.00 33.15 33.35

7 22.37 20.93 22.12

7 21.39 19.59 19.54

7 24.67 26.33 24.53

7 19.43 20.14 21.23

11 33.13 23.22 28.70

11 27.89 N/A N/A

11 23.53 22.60 N/A

11 23.69 N/A N/A

11 21.44 20.24 20.94

288

Table A. 7 Data for relative expression of TERT in almond age cohort samples collected in 2018. Included are the age of each sample

in years, the relative expression of TERT in each sample calculated from Cq values for TERT and a reference gene, RPII, the log2

expression for each sample, and raw Cq values for the technical replicates for both the RPII amplicon and the TERT amplicon.

Age (years) RelativeTERTExpression Log2Expression TERTCq_Rep1 TERTCq_Rep2 TERTCq_Rep2

5 2.25 0.81 35.5 37.0 N/A

5 1.04 0.04 34.8 39.0 N/A

5 0.90 -0.11 31.2 29.9 30.6

1 1.47 0.38 34.0 34.1 34.1

1 3.36 1.21 33.9 33.6 33.6

1 1.48 0.39 33.9 33.6 34.5

14 0.51 -0.67 36.3 37.2 N/A

14 0.71 -0.345 36.3 37.5 37.5

14 1.04 0.04 35.9 36.5 N/A

9 0.93 -0.07 31.2 31.0 31.4

9 0.78 -0.25 31.2 31.2 30.5

RPIICq_Rep1 RPIICq_Rep2 RPIICq_Rep3

32.5 29.0 N/A

30.8 31.3 N/A

26.5 26.4 N/A

30.6 31.7 31.2

32.3 31.8 31.8

30.7 31.1 31.4

32.3 32.2 32.4

32.7 33.3 33.3

33.1 32.4 32.7

26.9 26.7 28.8

289

Table A. 8 Data for relative expression of TERT in almond age cohort samples collected in 2019. Included are the age of each sample

in years, the relative expression of TERT in each sample calculated from Cq values for TERT and a reference gene, RPII, the log2

expression for each sample, and raw Cq values for the technical replicates for both the RPII amplicon and the TERT amplicon.

Age (years) RelativeTERTExpression Log2Expression TERTCq_Rep1 TERTCq_Rep2 TERTCq_Rep3

11 1.24 0.21 36.2 35.2 35.1

11 0.29 -1.24 34.5 34.9 34.8

11 0.41 -0.89 34.9 37.2 38.3

11 1.79 0.58 35.0 36.5 N/A

7 1.79 0.58 30.2 31.4 31.0

7 1.50 0.40 36.3 35.2 N/A

7 1.22 0.20 36.2 36.7 N/A

7 0.62 -0.48 36.0 36.0 N/A

2 1.78 0.58 35.8 36.9 N/A

2 2.47 0.90 35.3 N/A N/A

2 2.47 0.90 35.0 36.2 35.4

2 1.10 0.098 35.2 34.3 35.8

(continued)

290


Age (years) RPIICq_Rep1 RPIICq_Rep2 RPIICq_Rep3

11 34.2 31.3 33.6

11 32.8 32.8 32.3

11 33.4 33.7 34.1

11 35.2 34.9 34.7

7 30.4 30.5 30.4

7 36.8 35.5 N/A

7 34.4 34.1 34.4

7 34.4 33.1 33.5

2 34.0 37.2 N/A

2 34.5 34.0 35.2

2 35.1 33.3 34.0

2 33.3 33.5 34.0

291

Table A. 9 Table containing sequencing statistics, conversion efficiencies, total percent methylation, and percent methylation within

each context (CG, CHG, CHH) for almond accessions presented in this study.

samp_ID age num_reads cov_seq num_reads_align cov_align per_align per_CG per_CHG per_meth per_lambda per_puc19

51 2 31297380 18.8 13749685 8.2 0.439 0.512 0.211 0.116 0.002 0.976

55 2 26341622 15.8 12252616 7.4 0.465 0.487 0.228 0.103 0.003 0.942

56 2 28016948 16.8 12329600 7.4 0.44 0.515 0.224 0.112 0.003 0.981

46 2 32861503 19.72 17661490 10.6 0.537 0.475 0.229 0.108 0.003 0.967

45 2 44230441 26.54 23808875 14.3 0.538 0.479 0.227 0.111 0.001 0.975

49 2 48170341 28.9 25501433 15.3 0.529 0.409 0.182 0.098 0.001 0.909

52 2 41430789 24.86 20859398 12.5 0.503 0.508 0.225 0.115 0.002 0.976

54 2 48531019 29.12 23620182 14.2 0.487 0.441 0.202 0.095 0.002 0.921

57 2 37613337 22.57 20384490 12.2 0.542 0.417 0.176 0.092 0.002 0.802

35 2 50541320 30.32 24043007 14.4 0.476 0.435 0.201 0.096 0.004 0.904

39 2 52674838 31.6 25747370 15.5 0.489 0.49 0.235 0.11 0.002 0.978

41 2 41279612 24.8 20377533 12.2 0.494 0.514 0.226 0.113 0.002 1

42 2 16534210 9.9 6438416 3.86 0.389 0.556 0.278 0.139 0.002 0.967

44 2 49540541 29.7 26723876 16.0 0.539 0.456 0.193 0.104 0.001 0.968

40 2 20246774 12.1 9225312 5.54 0.456 0.431 0.192 0.1 0.002 0.975

38 2 23618228 14.2 12251103 7.35 0.519 0.501 0.227 0.116 0.002 0.956

36 2 34890738 20.9 17723383 10.6 0.508 0.527 0.244 0.121 0.002 0.981

37 2 28345188 17 12636660 7.58 0.446 0.437 0.2 0.095 0.003 0.985

47 2 98319896 59 50290060 30.2 0.511 0.503 0.236 0.116 0.002 0.972

48 2 55093356 33.1 27736869 16.6 0.503 0.524 0.248 0.121 0.002 0.984

50 2 33033951 19.8 16544985 9.93 0.501 0.506 0.227 0.115 0.003 0.977

124 7 35055353 21 15277723 9.2 0.436 0.528 0.231 0.116 0.001 0.987

(continued)

292


126 7 28593912 17.2 11983479 7.2 0.419 0.377 0.126 0.077 0.002 0.906

132 7 29585255 17.8 15054356 9 0.509 0.433 0.167 0.092 0.002 0.973

133 7 30210415 18.1 15329804 9.2 0.507 0.469 0.194 0.1 0.001 0.972

135 7 44613939 26.8 21668740 13 0.486 0.472 0.218 0.112 0.003 0.97

136 7 17422158 10.5 7932753 4.8 0.455 0.503 0.238 0.117 0.002 0.973

144 7 36246652 21.7 17346965 10.4 0.479 0.491 0.216 0.11 0.001 0.978

145 7 21126080 12.7 9442150 5.7 0.447 0.532 0.24 0.12 0.002 0.983

146 7 29914559 17.9 14792824 8.9 0.495 0.491 0.228 0.109 0.002 0.966

150 7 35719010 21.43 18468179 11.1 0.517 0.515 0.236 0.116 0.002 0.973

147 7 47988999 28.79 26576252 16.0 0.554 0.514 0.235 0.118 0.001 0.936

130 7 45786720 27.47 21973306 13.2 0.48 0.502 0.224 0.11 0.0008 0.967

140 7 40648845 24.39 17395055 10.4 0.428 0.429 0.204 0.106 0.001 0.973

143 7 47930901 28.76 24235561 14.5 0.506 0.527 0.243 0.12 0.001 0.975

148 7 48362108 29.02 23725211 14.2 0.491 0.478 0.201 0.107 0.001 0.973

134 7 23974597 14.4 13022640 7.81 0.543 0.502 0.232 0.115 0.002 0.978

137 7 50634207 30.4 27310455 16.4 0.539 0.492 0.225 0.113 0.002 0.978

139 7 79328288 47.6 39787558 23.9 0.502 0.425 0.206 0.102 0.002 0.976

131 7 63570292 38.1 33958542 20.4 0.534 0.407 0.175 0.092 0.003 0.923

127 7 13002485 7.8 6411422 3.85 0.493 0.432 0.205 0.108 0.002 0.982

122 7 40529732 24.3 19104709 11.5 0.471 0.537 0.266 0.13 0.002 0.98

123 7 71971846 43.2 38491179 23.2 0.535 0.51 0.24 0.119 0.002 0.975

125 7 49197657 29.5 24866398 14.9 0.505 0.511 0.227 0.115 0.002 0.979


(continued)

293


128 7 32918109 19.8 16682419 10.0 0.507 0.52 0.243 0.118 0.003 0.979

129 7 29512271 17.7 14920804 8.95 0.506 0.503 0.256 0.135 0.016 0.971

61 11 43894112 26.3 20244951 12.1 0.461 0.51 0.227 0.116 0.002 0.978

76 11 40616818 24.4 18502909 11.1 0.456 0.513 0.213 0.112 0.001 0.984

77 11 42005872 25.2 20771654 12.5 0.494 0.49 0.213 0.11 0.001 0.987

82 11 33498595 20.1 16231217 9.7 0.485 0.479 0.195 0.11 0.001 0.971

83 11 37716302 22.6 17936056 10.8 0.476 0.496 0.21 0.111 0.003 0.976

84 11 29776754 17.9 14797650 8.9 0.497 0.558 0.244 0.121 0.002 0.988

63 11 33537550 20.12 17079402 10.3 0.509 0.484 0.239 0.127 0.0011 0.983

62 11 32601035 19.56 18217095 10.9 0.559 0.547 0.235 0.124 0.0005 0.981

67 11 42773275 25.66 20980483 12.6 0.491 0.517 0.224 0.113 0.001 0.973

68 11 43876467 26.33 22474208 13.5 0.512 0.508 0.23 0.114 0.001 0.979

87 11 46860369 28.12 24753547 14.9 0.528 0.549 0.263 0.123 0.002 0.979

89 11 33877345 20.33 16613073 9.97 0.49 0.515 0.218 0.113 0.0009 0.992

90 11 37995583 22.8 19222328 11.5 0.506 0.522 0.229 0.117 0.001 0.973

75 11 62939431 37.8 34541436 20.7 0.549 0.509 0.224 0.115 0.004 0.965

78 11 45447750 27.3 24392741 14.6 0.537 0.515 0.228 0.115 0.001 0.981

69 11 41923186 25.2 21990000 13.2 0.525 0.458 0.207 0.105 0.002 0.977

74 11 29962426 18 16477632 9.89 0.55 0.529 0.217 0.116 0.002 0.979

85 11 152581225 91.5 84021777 50.4 0.551 0.487 0.224 0.112 0.002 0.975

64 11 32586742 19.6 17646055 10.6 0.542 0.567 0.266 0.128 0.001 0.975

72 11 77215051 46.3 38735265 23.2 0.502 0.529 0.242 0.12 0.001 0.982


(continued)

294


79 11 14657373 8.8 7194411 4.32 0.491 0.498 0.23 0.113 0.002 0.986

80 11 56648577 34 28375628 17.0 0.501 0.504 0.225 0.112 0.002 0.972

81 11 35066675 21 17503781 10.5 0.499 0.521 0.23 0.117 0.003 0.978

(continued)


295

Table A. 10 Number of CG hypermethylated DMR-associated genes associated with

each biological process (A), molecular function (B), and cellular component (C) gene

ontology (GO) terms for each of the three age-contrasts (i.e., 11 – 2 year, 11 – 7 year, and

7 – 2 year). Values in each column represent the number of DMR-associated genes that

are associated with each GO term. Black squares indicate no genes associated with that

contrast were assigned the particular GO term.

A. Biological Process GO Term 11-2 11-7 7-2

proteolysis 37 36 8

protein phosphorylation 29 28 7

obsolete oxidation-reduction process 28 22 4

transmembrane transport 18 16 4

regulation of transcription, DNA-templated 9 13 2

carbohydrate metabolic process 10 11 4

steroid biosynthetic process 5 N/A N/A

tRNA aminoacylation for protein translation 3 N/A N/A

signal transduction 1 8 N/A

translation 2 8 N/A

biosynthetic process 1 7 N/A

transcription, DNA-templated 1 7 1

DNA topological change N/A 6 N/A

cation transport 2 4 N/A

endoplasmic reticulum to Golgi vesicle-mediated transport N/A 3 N/A

nitrogen compound metabolic process N/A 3 1

proteolysis involved in cellular protein catabolic process N/A 3 N/A

tRNA processing 3 3 N/A

ATP synthesis coupled proton transport N/A 2 N/A

attachment of GPI anchor to protein N/A 2 N/A

cell redox homeostasis N/A 2 N/A

cell wall modification N/A 2 N/A

defense response N/A 2 N/A

fatty acid biosynthetic process N/A 2 N/A

histone lysine methylation 2 N/A N/A

lipid metabolic process 1 2 N/A

metal ion transport N/A 2 N/A

nucleotide-excision repair 2 N/A N/A

proteasome-mediated ubiquitin-dependent protein catabolic

process 2 N/A

recognition of pollen 2

translational initiation 2

cell wall macromolecule catabolic process 1

cellular glucan metabolic process 1

cellular modified amino acid biosynthetic process 1 1

cellulose biosynthetic process 1 N/A 1

(continued)

296

chitin catabolic process 1

dephosphorylation 1

DNA replication 1

DNA-templated transcription, termination 1 N/A

drug transmembrane transport 1

endoplasmic reticulum organization 1

fatty acid beta-oxidation 2 1

glutamine biosynthetic process 1

intermembrane lipid transfer 1

intracellular protein transport 1

intracellular transport 1

ion transport 1

malate transport 1 N/A

mitochondrial pyruvate transmembrane transport 1

mRNA processing 1

nucleobase-containing compound metabolic process 1

nucleotide transmembrane transport 1

nucleotide transport 1

nucleosome assembly 1

phosphatidylserine biosynthetic process 1

photosynthesis 1 N/A

photosynthesis, light reaction 1

photosynthetic electron transport in photosystem II 1

phytochromobilin biosynthetic process 1

potassium ion transport 1

protein folding 2 1

protein kinase C-activating G protein-coupled receptor signaling

pathway 1 N/A

protein neddylation 1

protein ubiquitination 1 1

proton transmembrane transport 1 N/A

pseudouridine synthesis 1

pyrimidine nucleotide-sugar transmembrane transport 1

regulation of translational fidelity 1 N/A

response to auxin 1

response to hormone 1

RNA modification 1

RNA processing 1

rRNA processing 1

sucrose metabolic process 1 1

translation termination 1

(continued)


297

terpenoid biosynthetic process 1 N/A

trehalose biosynthetic process 1 N/A

tRNA wobble uridine modification 1

B. Molecular Function GO Term 11-2 11-7 7-2

ATP binding 41 33 11

cysteine-type peptidase activity 36 32 8

hydrolase activity, acting on ester bonds 26 28 4

protein binding 35 27 10

protein kinase activity 18 26 5

DNA binding 13 20 5

zinc ion binding 20 20 7

ADP binding 3 18

nucleic acid binding 18 16 3

oxidoreductase activity 3 13 1

transferase activity, transferring hexosyl groups 11 3

catalytic activity 4 9

DNA-binding transcription factor activity 7 8 1

structural constituent of ribosome 2 8

DNA topoisomerase activity 6

transmembrane transporter activity 3 6 1

hydrolase activity, hydrolyzing O-glycosyl compounds 6 5 2

polygalacturonase activity 4 5 2

RNA binding 1 5 1

sequence-specific DNA binding 1 5

transferase activity, transferring acyl groups other than amino-acyl

groups 3 5 1

acetylglucosaminyltransferase activity 1 4

aminoacyl-tRNA ligase activity 3

DNA-directed 5'-3' RNA polymerase activity 1 4 1

heme binding 10 4

iron ion binding 10 4

lyase activity 4 4

magnesium ion binding 4 4

nucleotide binding 3

oxidoreductase activity, acting on paired donors, with

incorporation or reduction of molecular oxygen 9 4

phosphatidylinositol binding 4

protein dimerization activity 4 4 1

RNA-DNA hybrid ribonuclease activity 10 4 2

solute:proton antiporter activity 1 4


(continued)

298

terpene synthase activity 4 4

GTP binding 4 3 1

GTPase activity 3 1

hydrolase activity 1 3

oxidoreductase activity, acting on the CH-OH group of donors,

NAD or NADP as acceptor 5 3

threonine-type endopeptidase activity 3

3-beta-hydroxy-delta5-steroid dehydrogenase activity 5

ATPase activity 7 2 1

calcium ion binding 1 2

copper ion binding 1 2

damaged DNA binding 2

electron transfer activity 2

enzyme inhibitor activity 2 2

histone-lysine N-methyltransferase activity 2

metal ion binding 2

methyltransferase activity 2 2

nickel cation binding 2

nutrient reservoir activity 2 1

O-methyltransferase activity 1 2

obsolete coenzyme binding 2 2

oxidoreductase activity, acting on NAD(P)H, oxygen as acceptor 1 2


peroxidase activity 2 2

protein disulfide oxidoreductase activity 2

protein serine/threonine kinase activity 11 2 2

proton transmembrane transporter activity 2

serine-type carboxypeptidase activity 2

serine-type endopeptidase activity 2 2

transcription factor binding 2

transferase activity, transferring acyl groups 2 1


2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase activity 1

3'-5' exonuclease activity 1

acetyl-CoA carboxylase activity 1

acyl-CoA oxidase activity 2 1

antioxidant activity 1

antiporter activity 1 1

ATPase-coupled transmembrane transporter activity 5 1 1

ATP:ADP antiporter activity 1

cation transmembrane transporter activity 1


(continued)

299

cellulose synthase (UDP-forming) activity 1 1

channel activity 1 1

chitin binding 1

chitinase activity 1


diacylglycerol kinase activity 1

DNA-directed DNA polymerase activity 1

electron transporter, transferring electrons within the cyclic

electron transport pathway of photosynthesis activity 1

endonuclease activity 1

endopeptidase inhibitor activity 1 1

FAD binding 1

glutamate-ammonia ligase activity 1

glutamate-cysteine ligase activity 1 1

glycerol-3-phosphate O-acyltransferase activity 1

GTPase activator activity 1 1

helicase activity 1

inorganic diphosphatase activity 1

intramolecular lyase activity 1

intramolecular transferase activity, phosphotransferases 1


lipid binding 1

lipid transfer activity 1

iron-sulfur cluster binding 1

malate dehydrogenase (decarboxylating) (NAD+) activity 1

metal ion transmembrane transporter activity 1

metallopeptidase activity 1

NAD binding 1

NEDD8 activating enzyme activity 1

nucleoside transmembrane transporter activity 1

oxidoreductase activity, acting on single donors with

incorporation of molecular oxygen, incorporation of two atoms of

oxygen

1

oxidoreductase activity, acting on the CH-CH group of donors 2 1

oxidoreductase activity, acting on the CH-CH group of donors,

iron-sulfur protein as acceptor 1

peptide-methionine (S)-S-oxide reductase activity 4 1

peptidyl-prolyl cis-trans isomerase activity 1

phospho-N-acetylmuramoyl-pentapeptide-transferase activity 1

polysaccharide binding 1

protein tyrosine/serine/threonine phosphatase activity 1


(continued)

300

pseudouridine synthase activity 1

pyridoxal phosphate binding 1 1

pyrimidine nucleotide-sugar transmembrane transporter activity 1

pyrophosphate hydrolysis-driven proton transmembrane

transporter activity 1

racemase activity, acting on amino acids and derivatives 1

strictosidine synthase activity 1

sucrose synthase activity 1 1

translation release factor activity 1

ubiquitin-like modifier activating enzyme activity 1

ubiquitin-protein transferase activity 1 1

unfolded protein binding 2 1

uridylyltransferase activity 1

xyloglucan:xyloglucosyl transferase activity 1

C. Cellular Component GO Term 11-2 11-7 7-2

integral component of membrane 16 18 4

membrane 10 16 4

nucleus 5 9 2

ribosome 2 8

endoplasmic reticulum 2


proteasome core complex 3

cytoplasm 2

GPI-anchor transamidase complex 2

mitochondrial proton-transporting ATP synthase complex,

coupling factor F(o) 2

nucleolus 2

nucleosome 2

acetyl-CoA carboxylase complex 1

apoplast 1

cell wall 1

chromosome 1

elongator holoenzyme complex 1

Golgi membrane 1

Las1 complex 1

mitochondrial inner membrane 1

nuclear pore 1

peroxisome 2 1

photosystem I 1

photosystem I reaction center 1


301

Table A. 11 Number of CG hypomethylated DMR-associated genes associated with each

biological process (A), molecular function (B), and cellular component gene ontology

(GO) terms for each of the three age-contrasts (i.e. 11 – 2 year, 11 – 7 year, and 7 – 2

year). Values in each column represent the number of DMR-associated genes that are

associated with each GO term. Black squares indicate no genes associated with that




proteolysis 4 7

cell redox homeostasis 3 1


cellulose biosynthetic process 2 1



translational elongation 2

transmembrane transport 2 1

tRNA aminoacylation for protein translation 2 1


ATP synthesis coupled proton transport 1

biosynthetic process 1 1


carboxylic acid metabolic process 1

cell wall modification 1

defense response 1

detection of visible light 1

dolichol-linked oligosaccharide biosynthetic process 1 1

glutaminyl-tRNA aminoacylation 1

Golgi vesicle transport 1 1

histone lysine methylation 1


photosynthesis 1

protein-chromophore linkage 1


proteolysis involved in cellular protein catabolic process 1

response to oxidative stress 1

transcription, DNA-templated 1

translation 1 2 3

trehalose biosynthetic process 1 1

tRNA aminoacylation 1

tRNA aminoacylation for protein translation 1

(continued)

302



ATP binding 8 4 9


cysteine-type peptidase activity 4 7

heme binding 1 2 7

DNA binding 1 2 6

iron ion binding 1 2 6


incorporation or reduction of molecular oxygen 1 2 6

transferase activity, transferring hexosyl groups 1 1 5


catalytic activity 4 3 4


hydrolase activity, hydrolyzing O-glycosyl compounds 4

electron transfer activity 3 1


oxidoreductase activity 1 3



structural constituent of ribosome 1 2 3

acetylglucosaminyltransferase activity 2

ADP binding 1 1 2

carbohydrate binding 2 1

GTP binding 1 2


RNA-DNA hybrid ribonuclease activity 2

translation elongation factor activity 2

transmembrane transporter activity 1 2

actin binding 1

aminoacyl-tRNA ligase activity 2 1 1

ATPase activity 1 1

ATPase-coupled transmembrane transporter activity 1 1

calcium ion binding 1

carbon-sulfur lyase activity 1

carbonate dehydratase activity 1 1

carboxy-lyase activity 1

cellulose synthase (UDP-forming) activity 2 1

channel activity 1

copper ion binding 1

DNA-directed 5'-3' RNA polymerase activity 1


(continued)

303

double-stranded DNA binding 1

endopeptidase activity 1

enzyme inhibitor activity 1


galactose binding 1

glutamine-tRNA ligase activity 1

GTPase activity 1


hydrolase activity 1

hydrolase activity, acting on ester bonds 1

kinase activity 1

Lys48-specific deubiquitinase activity 1

lyase activity 1 2


manganese ion binding 1

metal ion binding 1

metallopeptidase activity 1


nucleotide binding 2 1 1

obsolete coenzyme binding 1




oxygen

1 1


peroxidase activity 1

phospho-N-acetylmuramoyl-pentapeptide-transferase activity 1

phospholipid bidning 1

phosphorelay sensor kinase activity 1

polygalacturonase activity 1 1 1

protein dimerization activity 1 1

protein-containing complex binding 1

proton-transporting ATP synthase activity, rotational mechanism 1

RNA binding 1 1

serine-type endopeptidase activity 1


thiol-dependent ubiquitin-specific protease activity 1



groups 1 2


(continued)


304

ubiquitin-ubiquitin ligase activity 1

uridylyltransferase activity 1 1


membrane 6 2 6


ribosome 1 2 3

cytoplasm 2

nucleus 2 1 2

eukaryotic translation initiation factor 3 complex 1

mitochondrial proton-transporting ATP synthase complex,

catalytic sector F(1) 1

mitochondrion 1 1

photosystem II 1


proteasome core complex, alpha-subunit complex 1

ubiquitin ligase complex 1


305

Table A. 12 Number of CHG hypermethylated DMR-associated genes associated with


ontology (GO) terms for each of the three age-contrasts (i.e. 11 – 2 year, 11 – 7 year, and





proteolysis 16 16 4



carbohydrate metabolic process 4 4



positive regulation of stomatal complex development 3

biosynthetic process 1 2 1

fatty acid biosynthetic process 2

nitrogen compound metabolic process 2 1


response to auxin 2

signal transduction 5 2 1

translational termination 2 1

cation transport 1

cell population proliferation 1


cell wall modification 1 1

DNA topological change 1


glycerolipid biosynthetic process 1


intracellular signal transduction 1 1

ion transport 1

lipid metabolic process 1 1

mistmatch repair 1

nucleoside metabolic process 1 1


potassium ion transport 1

recognition of pollen 1

response to oxidative stress 1 2

terpenoid biosynthetic process 1

transcription, DNA-templated 1 1

translation 2 1 1

vesicle-mediated transport 1

(continued)

306




ATP binding 10 7 8



ADP binding 5 5 1


hydrolase activity, hydrolyzing O-glycosyl compounds 4

catalytic activity 3 2

copper ion binding 3

DNA binding 3 2 6

RNA-DNA hybrid ribonuclease activity 3 1


actin binding 2 1

endopeptidase inhibitor activity 2 2

GTP binding 2

heme binding 2 5 2


hydrolase activity, acting on glycosyl bonds 2

iron ion binding 2 4

nickel cation binding 2


incorporation or reduction of molecular oxygen 2 4

polygalacturonase activity 2


RNA binding 2


serine-type endopeptidase activity 2 1


translation release factor activity 2 1


2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase activity 1

acid phosphatase activity 1

alpha-amylase activity 1


ATP-dependent peptidase activity 1

ATPase activity 1


diacylglycerol O-acyltransferase activity 1



(continued)

307



double-stranded DNA binding 1


endonuclease activity 1



growth factor activity 1

GTPase activator activity 1 1

hydrolase activity 1 2


ionotropic glutamate receptor activity 1

lyase activity 1

magnesium ion binding 1

malonyl-CoA decarboxylase activity 1

metal ion binding 1 2

mismatched DNA binding 1

NAD binding 1

nutrient reservoir activity 1



oxidoreductase activity, acting on the aldehyde or oxo group of

donors, NAD or NADP as acceptor 1


NAD or NADP as acceptor 1

pectinesterase activity 1 1

peptide-methionine (S)-S-oxide reductase activity 1 1

peroxidase activity 1 2

phosphotransferase activity, alcohol group as acceptor 1


protein disulfide oxidoreductase activity 1 1

protein phosphatase regulator activity 1 1


ribonuclease activity 1

serine-type carboxypeptidase activity 1 2

solute:proton antiporter activity 1

strictosidine synthase activity 1

terpene synthase activity 1



groups 1 2 2

(continued)


308


ubiquitin-like modifier activating enzyme activity 1 1


membrane 5 4 1


AP-5 adaptor complex 1

COPI vesicle coat 1

extracellular region 1

membrane coat 1

nucleus 2 1 4

protein phosphatase type 2A complex 1 1

proton-transporting two-sector ATPase complex, catalytic domain 1

ribosome 2 1 1

membrane 4


309

Table A. 13 Number of CHG hypomethylated DMR-associated genes associated with









proteolysis 5 8 8



cysteinyl-tRNA aminoacylation 2

phospholipid biosynthetic process 2

response to auxin 2 1


amine metabolic process 1 1

ATP metabolic process 1

autophagy 1

base-excision repair 1

cell redox homeostasis 1

defense response 1

drug transmembrane transport 1 1

G protein-coupled receptor signaling pathway 1


lipid biosynthetic process 1 1

magnesium ion transport 1

metal ion transport 1 1

mistmatch repair 1



organic substance metabolic process 1

proton transmembrane transport 1

purine nucleobase biosynthetic process 1 1

regulation of cyclin-dependent protein serine/threonine kinase

activity 1


rRNA processing 1

steroid biosynthetic process 1


translation 1 2 2

translational elongation 1 1

trehalose biosynthetic process 1

310

tRNA processing 1



ATP binding 10 5 10




DNA binding 5 5 1



heme binding 3 2 5





groups 3



cysteine-tRNA ligase activity 2


hydrolase activity 2 2 1

hydrolase activity, hydrolyzing O-glycosyl compounds 2 1



methyltransferase activity 2

nucleotide binding 2

phosphatidylserine decarboxylase activity 2

sequence-specific DNA binding 2 1 1

serine-type endopeptidase activity 2


acid phosphatase activity 1 2

ADP binding 1 1 2

aminopeptidase activity 1

ATPase activity 1


calcium-dependent phospholipid binding 1 1

carbohydrate binding 1 1

carbon-sulfur lyase activity 1

chromatin binding 1


(continued)


311

DNA-3-methyladenine glycosylase activity 1


electron transfer activity 1

endopeptidase inhibitor activity 1

enzyme inhibitor activity 1


glycopeptide alpha-N-acetylgalactosaminidase activity 1

GTPase activity 1

hydrolase activity, acting on ester bonds 1 1

intramolecular transferase activity, phosphotransferases 1

ionotropic glutamate receptor activity 1 1

lyase activity 1 2


magnesium ion transmembrane transporter activity 1

mismatched DNA binding 1



phospholipid binding 1 1

phosphoribosylamine-glycine ligase activity 1 1


primary amine oxidase activity 1 1



protein kinase binding 1

protein serine/threonine kinase activity 1 1

quinone binding 1 1

RNA-DNA hybrid ribonuclease activity 1 1 2

RNA binding 1

serine-type carboxypeptidase activity 1



transferase activity, transferring glycosyl groups 1


translation elongation factor activity 1 1

transmembrane transporter activity 1 3

tRNA dihydrouridine synthase activity 1

ubiquitin protein ligase binding 1



cytoplasm 3


(continued)

312

membrane 1 2 3

nucleus 1 1 3

ribosome 1 2 2

anchored component of plasma membrane 1


313

Table A. 14 Number of CHH hypermethylated DMR-associated genes associated with








proteolysis 13 6 21



translation 1 2 8


signal transduction 3 1 6


defense response 1 3

tRNA aminoacylation for protein translation 1 3

cation transport 2


cell wall modification 1 2


exocytosis 2

fatty acid beta-oxidation 2

histone lysine methylation 2


nitrate assimilation 3 2

positive regulation of Notch signaling pathway 2


pyrimidine nucleotide biosynthetic process 2



biosynthetic process 1 1

carboxylic acid metabolic process 1

cell wall biogenesis 1


cellular amino acid metabolic process 1

cellular glucan metabolic process 1

cellulose biosynthetic process 1

cellulose microfibril organization 1 1


DNA-templated transcription, initiation 1


endocytosis 1

314

fatty acid biosynthetic process 1 1

glutathione biosynthetic process 1

glycolytic process 1 1

glycyl-tRNA aminoacylation 1

guanosine tetraphosphate metabolic process 1


intracellular protein transport 1 1

intracellular signal transduction 1

ion transport 1 1

lipid catabolic process 2 1

malate transport 1

mannose metabolic process 2 1

mRNA export from nucleus 2 1



phosphatidylinositol metabolic process 1 1

phospholipid biosynthetic process 1

photosynthesis 1

proteasome assembly 2 1

protein kinase C-activating G protein-coupled receptor signaling

pathway 1

protein neddylation 1

protein peptidyl-prolyl isomerization 1

proteolysis involved in cellular protein catabolic process 1


regulation of systemic acquired resistance 1

RNA processing 1


retrograde vesicle-mediated transport, Golgi to endoplasmic

reticulum

1

ribosome biogenesis 1 1

rRNA processing 1

sulfate transport 1

terpenoid biosynthetic process 1

transcription by RNA polymerase II 1

transcription initiation from RNA polymerase II promoter 1 1 1

translational elongation 1

tryptophan catabolic process to kynurenine 1

tryptophan metabolic process 1

ubiquitin-dependent protein catabolic process 1 1

valyl-tRNA aminoacylation 1

(continued)


315

vesicle docking involved in exocytosis 1

vesicle-mediated transport 1 1



ATP binding 21 10 37



DNA binding 8 1 19

ADP binding 5 3 16








RNA binding 1 6

acetylglucosaminyltransferase activity 2 1 5

RNA-DNA hybrid ribonuclease activity 4 5

3-hydroxyisobutyryl-CoA hydrolase activity 4

heme binding 2 1 4


nucleotide binding 1 4



polysaccharide binding 1 2 4

sequence-specific DNA binding 2 1 4


aminoacyl-tRNA ligase activity 1 3

ATPase activity 5 1 3




GTPase activity 3

hydrolase activity 3

oxidoreductase activity, acting on the CH-CH group of donors 3

serine-type endopeptidase activity 1 1 3

transferase activity, transferring hexosyl groups 4 1 3

ubiquitin-like modifier activating enzyme activity 1 3

(continued)


316

acyl-CoA oxidase activity 2



GTP binding 5 1 2

helicase activity 2




methyltransferase activity 2

molybdenum ion binding 3 2

nutrient reservoir activity 2


pectinesterase activity 1 2

protein disulfide oxidoreductase activity 1 2

solute:proton antiporter activity 2

transferase activity, transferring glycosyl groups 2


1-deoxy-D-xylulose-5-phosphate synthase activity 1


alpha-amylase activity 1

alpha-mannosidase activity 2 1


arylformamidase activity 1

ATPase-coupled transmembrane transporter activity 1

calcium-dependent phospholipid binding 1

carboxy-lyase activity 1

cellulose synthase (UDP-forming) activity 1

channel activity 1 1

chitin binding 1


chromatin binding 2 1


cysteine-type endopeptidase inhibitor activity 1

diacylglycerol kinase activity 1

DNA topoisomerase type II (double strand cut, ATP-hydrolyzing)

activity 1


endopeptidase activity 1


galactoside 2-alpha-L-fucosyltransferase activity 1

glutathione synthase activity 1

(continued)


317

glycine-tRNA ligase activity 1

glycopeptide alpha-N-acetylgalactosaminidase activity 1

heat shock protein binding 1

ion channel activity 1 1



mannosyl-glycoprotein endo-beta-N-acetylglucosaminidase

activity 1

microtubule minus-end binding 1

NEDD8 activating enzyme activity 1

O-methyltransferase activity 1

obsolete coenzyme binding 1 1

peptidyl-prolyl cis-trans isomerase activity 1


phosphatidylinositol binding 1

phosphatidylinositol phosphate kinase activity 1 1

phosphatidylserine decarboxylase activity 1


potassium ion binding 1 1

protein serine/threonine kinase activity 1

proton-transporting ATPase activity, rotational mechanism 1


pyruvate kinase activity 1 1

ribonuclease T2 activity 1

serine-type carboxypeptidase activity 1 1

serine-type exopeptidase activity 1

serine-type peptidase activity 1

sulfate transmembrane transporter activity 1

TBP-class protein binding 1


thiamine pyrophosphate binding 1

thiolester hydrolase activity 1



groups 1 2 1

translation elongation factor activity 1


tRNA binding 1

tryptophan synthase activity 1

unfolded protein binding 1

valine-tRNA ligase activity 1


(continued)

318

xyloglucan:xyloglucosyl transferase activity 1



membrane 10 4 14

ribosome 1 2 8

nucleus 1 7

cytoplasm 4

chloroplast 2

chromosome 2

exocyst 2

peroxisome 2

anchored component of membrane 1 1

apoplast 1

cell wall 1


extrinsic component of membrane 1

Golgi transport complex 1

nucleolus 1

nucleosome 1

photosystem II 1

photosystem II oxygen evolving complex 1


proteasome core complex, alpha-subunit complex 1

proteasome regulatory particle, lid subcomplex 2 1

proton-transporting two-sector ATPase complex, catalytic domain 1

transcription factor TFIIA complex 1 1

(continued)

(Appendix Table 14 continued)


319

Table A. 15 Number of CHH hypomethylated DMR-associated genes associated with








regulation of transcription, DNA-templated 6 9

proteolysis 2 5 4

transmembrane transport 2 5



translation 1 3


base-excision repair 1 2



response to hormone 2

response to oxidative stress 2 2

cell redox homeostasis 1



defense response 1

DNA repair 1


exocytosis 1


intracellular protein transport 1 1


metal ion transport 1 1 1

nitrogen compound metabolic process 1

phytochromobilin biosynthetic process 1

protein folding 1

rRNA processing 1


steroid biosynthetic process 1

sucrose metabolic process 1

superoxide metabolic process 1

translational initiation 1


ATP binding 3 14 3

(continued)

320

DNA binding 5 13 2



zinc ion binding 6 2


RNA binding 5


DNA-binding transcription factor activity 5 4


ATPase activity 3

ATPase-coupled transmembrane transporter activity 3


structural constituent of ribosome 1 3


ADP binding 1 2

DNA-3-methyladenine glycosylase activity 1 2

glutathione peroxidase activity 2 2

heme binding 2

hydrolase activity 1 2 2

hydrolase activity, acting on ester bonds 2 3

iron ion binding 2

nucleic acid binding 2 4


incorporation or reduction of molecular oxygen

2


pyridoxal phosphate binding 2


3-beta-hydroxy-delta5-steroid dehydrogenase activity 1

acetylglucosaminyltransferase activity 1 1 2

acid phosphatase activity 1

carbohydrate binding 1


chitin binding 1






intramolecular lyase activity 1



(continued)


321

metal ion transmembrane transporter activity 1 1


microtubule minus-end binding 1

O-methyltransferase activity 1

obsolete coenzyme binding 1


oxidoreductase activity, acting on the CH-OH group of donors,

NAD or NADP as acceptor

1



oxygen

1


iron-sulfur protein as acceptor 1

polysaccharide binding 1 1


sucrose synthase activity 1

superoxide dismutase activity 1



groups 1




integral component of membrane 5

ribosome 1 3

membrane 2 2 4

nucleus 2 1

cytoplasm 1

exocyst 1


322

Table A. 16 Genomic coordinates for CG hypermethylated (increased methylation in BF

twins compared to no-BF twins) DMRs identified in each twin pair and associated with

the same gene. Coordinates include scaffold number in the 'Nonpareil' almond reference

genome followed by start and end genomic positions.

Scaffold_Twinpair1 Start_Twinpair1 End_Twinpair1

Scaffold_1 3419653 3419752

Scaffold_1 8059989 8060276

Scaffold_2 18128474 18128620

Scaffold_7 11904989 11905049

Scaffold_8 6245472 6245538


Scaffold_1 3419277 3419771

Scaffold_1 8059989 8060276

Scaffold_2 18128260 18128390

Scaffold_7 11904846 11904989

Scaffold_8 6245472 6245538

323

Table A. 17 Genomic coordinates for CG hypomethylated (decreased methylation in BF

twins compared to no-BF twins) DMRs identified in each twin pair and associated with

the same gene. Coordinates include scaffold number in the 'Nonpareil' almond reference

genome followed by start and end genomic positions.


Scaffold_1 11884461 11884811

Scaffold_1 15917452 15917574

Scaffold_1 20726710 20726864

Scaffold_1 25363504 25363612

Scaffold_1 8564186 8564249

Scaffold_1 8933829 8933947

Scaffold_1 29913157 29913520

Scaffold_1 29913157 29913520

Scaffold_1 11884461 11884811

Scaffold_1 15917452 15917574

Scaffold_2 16212723 16212785

Scaffold_2 18967168 18967288

Scaffold_2 18966307 18967105

Scaffold_3 13774533 13774879

Scaffold_3 15850588 15850648

Scaffold_4 12981996 12982334

Scaffold_4 7317194 7317317

Scaffold_4 15608941 15609210

Scaffold_4 7317194 7317317

Scaffold_4 8230345 8230738

Scaffold_5 10998025 10998269

Scaffold_5 597044 597114

Scaffold_5 10170509 10170710

Scaffold_5 9317289 9317394

Scaffold_6 13025959 13026053

Scaffold_6 5482642 5482727

Scaffold_6 800907 801252

Scaffold_6 9339285 9339513

Scaffold_6 2906640 2906739

Scaffold_6 2906640 2906739

Scaffold_6 2906640 2906739

Scaffold_6 13025959 13026053

Scaffold_6 4949523 4949907

324

Scaffold_7 3382055 3382636

Scaffold_7 1164446 1164513

Scaffold_7 7242481 7242824

Scaffold_8 10028830 10029044

Scaffold_8 10980855 10981083

Scaffold_8 6480510 6480854

Scaffold_8 10028830 10029044

Scaffold_8 10975444 10975556

Scaffold_8 10980855 10981083

Scaffold_8 6480510 6480854


Scaffold_1 11884793 11885010

Scaffold_1 15917452 15917574

Scaffold_1 20726736 20726864

Scaffold_1 25363505 25363756

Scaffold_1 8564098 8564249

Scaffold_1 8932194 8932408

Scaffold_1 29913157 29913269

Scaffold_1 29913349 29913520

Scaffold_1 11884793 11885010

Scaffold_1 15917452 15917574

Scaffold_2 16212723 16212838

Scaffold_2 18967078 18967288

Scaffold_2 18967078 18967288

Scaffold_3 13772397 13772452

Scaffold_3 15850588 15850648

Scaffold_4 12981893 12982334

Scaffold_4 7317194 7317317

Scaffold_4 15608941 15609383

Scaffold_4 7317194 7317317

Scaffold_4 8230657 8230738

Scaffold_5 10998190 10998269

Scaffold_5 596925 597115

Scaffold_5 10173025 10173112


(continued)

325

Scaffold_5 9317185 9317505

Scaffold_6 13025921 13026053

Scaffold_6 5481728 5482066

Scaffold_6 800907 801147

Scaffold_6 9339285 9339513

Scaffold_6 2906640 2906953

Scaffold_6 2907470 2907550

Scaffold_6 2905695 2906297

Scaffold_6 13025921 13026053

Scaffold_6 4949523 4949907

Scaffold_7 3382055 3382501

Scaffold_7 1162910 1163088

Scaffold_7 7242481 7242768

Scaffold_8 10028900 10029017

Scaffold_8 10980834 10981136

Scaffold_8 6480510 6480854

Scaffold_8 10028900 10029017

Scaffold_8 10974761 10975128

Scaffold_8 10980834 10981136

Scaffold_8 6480510 6480854


326

Table A. 18 Genomic coordinates for CHG hypermethylated (increased methylation in

BF twins compared to no-BF twins) DMRs identified in each twin pair and associated

with the same gene. Coordinates include scaffold number in the 'Nonpareil' almond

reference genome followed by start and end genomic positions.


Scaffold_1 8059746 8060077

Scaffold_1 4964340 4964455

Scaffold_4 6600104 6600281

Scaffold_4 6600104 6600281

Scaffold_5 5549592 5549887

Scaffold_5 9118290 9118528

Scaffold_7 7447805 7447905


Scaffold_1 8060219 8060286

Scaffold_1 4963966 4964023

Scaffold_4 6599702 6599778

Scaffold_4 6599290 6599651

Scaffold_5 5549889 5550233

Scaffold_5 9118412 9118483

Scaffold_7 7447905 7448002

327

Table A. 19 Genomic coordinates for CHG hypomethylated (decreased methylation in





Scaffold_3 8140284 8141197

Scaffold_4 12982231 12982335

Scaffold_4 3842627 3842792

Scaffold_4 6149929 6150020

Scaffold_5 10452544 10452667

Scaffold_6 2905673 2905728

Scaffold_7 3382094 3382308

Scaffold_1 19246437 19246534

Scaffold_1 25391309 25391550

Scaffold_2 121095 121310


Scaffold_3 8141033 8141199

Scaffold_4 12982868 12982970

Scaffold_4 3842778 3843036

Scaffold_4 6149929 6150020

Scaffold_5 10452662 10453755

Scaffold_6 2905673 2905882

Scaffold_7 3381836 3382156

Scaffold_1 19246503 19246556

Scaffold_1 25391309 25391550

Scaffold_2 121262 121364

328

Table A. 20 Genomic coordinates for CHH hypermethylated (increased methylation in





Scaffold_1 2140020 2140090

Scaffold_1 22622072 22622175

Scaffold_1 35411377 35411614

Scaffold_1 8189707 8189777

Scaffold_2 1328127 1328224

Scaffold_4 16280111 16280595

Scaffold_5 862690 862805

Scaffold_8 4938307 4938600

Scaffold_1 35411377 35411614

Scaffold_2 11655183 11655520

Scaffold_4 7910059 7910413

Scaffold_8 4938307 4938600


Scaffold_1 2139004 2139218

Scaffold_1 22621920 22622170

Scaffold_1 35411541 35411651

Scaffold_1 8189519 8189569

Scaffold_2 1328551 1328626

Scaffold_4 16279081 16279200

Scaffold_5 862748 862866

Scaffold_8 4938099 4938155

Scaffold_1 35411541 35411651

Scaffold_2 11655173 11655457

Scaffold_4 7910555 7910967

Scaffold_8 4938099 4938155

329

Table A. 21 Genomic coordinates for CHH hypomethylated (decreased methylation in





Scaffold_1 8933739 8933838

Scaffold_1 12942419 12942547

Scaffold_5 9682862 9683084

Scaffold_7 2298460 2298518

Scaffold_8 1925515 1925718


Scaffold_1 8933426 8933648

Scaffold_1 12942412 12942538

Scaffold_5 9682643 9682974

Scaffold_7 2298463 2298716

Scaffold_8 1925402 1925659

330

Table A. 22 Genomic coordinates for CG DMRs identified in each twin pair and

associated with the same gene. Coordinates include scaffold number in 'Nonpareil'

almond reference genome followed by start and end genomic positions.


Scaffold_1 11884461 11884811

Scaffold_1 15917452 15917574

Scaffold_1 20726710 20726864

Scaffold_1 25363504 25363612

Scaffold_1 32880472 32880690

Scaffold_1 3419653 3419752

Scaffold_1 8059989 8060276

Scaffold_1 8564186 8564249

Scaffold_1 8933829 8933947

Scaffold_1 1673137 1673199

Scaffold_1 29913157 29913520

Scaffold_1 29913157 29913520

Scaffold_1 30123171 30123232

Scaffold_1 11884461 11884811

Scaffold_1 15917452 15917574

Scaffold_2 16212723 16212785

Scaffold_2 3304147 3304437

Scaffold_2 18128474 18128620

Scaffold_2 18128984 18129083

Scaffold_2 18967168 18967288

Scaffold_2 18966307 18967105

Scaffold_2 3304147 3304437

Scaffold_3 13774533 13774879

Scaffold_3 15850588 15850648

Scaffold_3 454216 454315

Scaffold_3 454216 454315

Scaffold_4 12981996 12982334

Scaffold_4 2275376 2275458

Scaffold_4 4646516 4646633

Scaffold_4 4646516 4646633

Scaffold_4 6735391 6735493

Scaffold_4 7317194 7317317

Scaffold_4 15608941 15609210

Scaffold_4 6735391 6735493

(continued)

331

Scaffold_4 7317194 7317317

Scaffold_4 8230345 8230738

Scaffold_5 10998025 10998269

Scaffold_5 14907545 14907606

Scaffold_5 597044 597114

Scaffold_5 9770872 9770965

Scaffold_5 10170509 10170710

Scaffold_5 14907545 14907606

Scaffold_5 9317289 9317394

Scaffold_5 9813110 9813170

Scaffold_6 13025959 13026053

Scaffold_6 5482642 5482727

Scaffold_6 800907 801252

Scaffold_6 9339285 9339513

Scaffold_6 2906640 2906739

Scaffold_6 2906640 2906739

Scaffold_6 2906640 2906739

Scaffold_6 13025959 13026053

Scaffold_6 4949523 4949907

Scaffold_7 11904989 11905049

Scaffold_7 1219287 1219428

Scaffold_7 3382055 3382636

Scaffold_7 1164446 1164513

Scaffold_7 7242481 7242824

Scaffold_8 10028830 10029044

Scaffold_8 10980855 10981083

Scaffold_8 3393987 3394258

Scaffold_8 6245472 6245538

Scaffold_8 6480510 6480854

Scaffold_8 6874777 6874853

Scaffold_8 8602495 8602548

Scaffold_8 10028830 10029044

Scaffold_8 10975444 10975556

Scaffold_8 10980855 10981083

Scaffold_8 12891092 12891397

Scaffold_8 6245472 6245538

Scaffold_8 6480510 6480854


(continued)

332

Scaffold_8 6874777 6874853


Scaffold_1 11884793 11885010

Scaffold_1 15917452 15917574

Scaffold_1 20726736 20726864

Scaffold_1 25363505 25363756

Scaffold_1 32880690 32881152

Scaffold_1 3419277 3419771

Scaffold_1 8059989 8060276

Scaffold_1 8564098 8564249

Scaffold_1 8932194 8932408

Scaffold_1 1673047 1673139

Scaffold_1 29913157 29913269

Scaffold_1 29913349 29913520

Scaffold_1 30123172 30123232

Scaffold_1 11884793 11885010

Scaffold_1 15917452 15917574

Scaffold_2 16212723 16212838

Scaffold_2 3304342 3304437

Scaffold_2 18128260 18128390

Scaffold_2 18128260 18128390

Scaffold_2 18967078 18967288

Scaffold_2 18967078 18967288

Scaffold_2 3304342 3304437

Scaffold_3 13772397 13772452

Scaffold_3 15850588 15850648

Scaffold_3 453821 454315

Scaffold_3 453505 453736

Scaffold_4 12981893 12982334

Scaffold_4 2275004 2275231

Scaffold_4 4645041 4645266

Scaffold_4 4644811 4644889

Scaffold_4 6735391 6735493

Scaffold_4 7317194 7317317

Scaffold_4 15608941 15609383

Scaffold_4 6735391 6735493


(continued)

333

Scaffold_4 7317194 7317317

Scaffold_4 8230657 8230738

Scaffold_5 10998190 10998269

Scaffold_5 14907545 14907606

Scaffold_5 596925 597115

Scaffold_5 9771288 9771381

Scaffold_5 10173025 10173112

Scaffold_5 14907545 14907606

Scaffold_5 9317185 9317505

Scaffold_5 9812319 9812400

Scaffold_6 13025921 13026053

Scaffold_6 5481728 5482066

Scaffold_6 800907 801147

Scaffold_6 9339285 9339513

Scaffold_6 2906640 2906953

Scaffold_6 2907470 2907550

Scaffold_6 2905695 2906297

Scaffold_6 13025921 13026053

Scaffold_6 4949523 4949907

Scaffold_7 11904846 11904989

Scaffold_7 1219342 1219428

Scaffold_7 3382055 3382501

Scaffold_7 1162910 1163088

Scaffold_7 7242481 7242768

Scaffold_8 10028900 10029017

Scaffold_8 10980834 10981136

Scaffold_8 3393960 3394010

Scaffold_8 6245472 6245538

Scaffold_8 6480510 6480854

Scaffold_8 6874777 6874901

Scaffold_8 8602452 8602549

Scaffold_8 10028900 10029017

Scaffold_8 10974761 10975128

Scaffold_8 10980834 10981136

Scaffold_8 12891304 12891418

Scaffold_8 6245472 6245538

Scaffold_8 6480510 6480854


(continued)

334

Scaffold_8 6874777 6874901


335

Table A. 23 Genomic coordinates for all shared CHG DMRs identified in each twin pair

and associated with the same gene. Coordinates include scaffold number in 'Nonpareil'



Scaffold_1 8059746 8060077

Scaffold_1 19246437 19246534

Scaffold_1 25391309 25391550

Scaffold_1 29053934 29053988

Scaffold_1 4964340 4964455

Scaffold_2 121095 121310

Scaffold_3 3422937 3423106

Scaffold_3 8140284 8141197

Scaffold_4 12982231 12982335

Scaffold_4 3842627 3842792

Scaffold_4 6149929 6150020

Scaffold_4 6600104 6600281

Scaffold_4 7117732 7118094

Scaffold_4 7117732 7118094

Scaffold_4 6600104 6600281

Scaffold_4 6600104 6600281

Scaffold_4 7117732 7118094

Scaffold_4 7117732 7118094

Scaffold_5 10452544 10452667

Scaffold_5 5549592 5549887

Scaffold_5 14614175 14614254

Scaffold_5 9118290 9118528

Scaffold_6 2905673 2905728

Scaffold_6 5842466 5842610

Scaffold_6 8280724 8280781

Scaffold_7 2283024 2283314

Scaffold_7 3382094 3382308

Scaffold_7 7447805 7447905

Scaffold_7 2283024 2283314

Scaffold_8 2553435 2553800


Scaffold_1 8060219 8060286

Scaffold_1 19246503 19246556

Scaffold_1 25391309 25391550

(continued)

336

Scaffold_1 29053551 29053651

Scaffold_1 4963966 4964023

Scaffold_2 121262 121364

Scaffold_3 3422935 3423106

Scaffold_3 8141033 8141199

Scaffold_4 12982868 12982970

Scaffold_4 3842778 3843036

Scaffold_4 6149929 6150020

Scaffold_4 6599702 6599778

Scaffold_4 7117732 7118068

Scaffold_4 7115317 7115534

Scaffold_4 6599290 6599651

Scaffold_4 6599702 6599778

Scaffold_4 7118617 7118985

Scaffold_4 7117732 7118068

Scaffold_5 10452662 10453755

Scaffold_5 5549889 5550233

Scaffold_5 14613240 14613334

Scaffold_5 9118412 9118483

Scaffold_6 2905673 2905882

Scaffold_6 5841737 5841916

Scaffold_6 8280724 8280779

Scaffold_7 2283024 2283084

Scaffold_7 3381836 3382156

Scaffold_7 7447905 7448002

Scaffold_7 2283024 2283084

Scaffold_8 2552780 2552891


337

Table A. 24 Genomic coordinates for all shared CHH DMRs identified in each twin pair

and associated with the same gene. Coordinates include scaffold number in 'Nonpareil'



Scaffold_1 1042904 1042997

Scaffold_1 10858835 10858902

Scaffold_1 2140020 2140090

Scaffold_1 22622072 22622175

Scaffold_1 27827614 27827671

Scaffold_1 27827614 27827671

Scaffold_1 27827227 27827453

Scaffold_1 27827227 27827453

Scaffold_1 27827614 27827671

Scaffold_1 33067850 33068113

Scaffold_1 34577665 34577780

Scaffold_1 35411377 35411614

Scaffold_1 4474523 4474738

Scaffold_1 8189707 8189777

Scaffold_1 8933739 8933838

Scaffold_1 34577665 34577780

Scaffold_1 12942419 12942547

Scaffold_1 35411377 35411614

Scaffold_1 4474523 4474738

Scaffold_2 1328127 1328224

Scaffold_2 4181614 4181671

Scaffold_2 4181614 4181671

Scaffold_2 11655755 11655873

Scaffold_3 17094563 17094659

Scaffold_3 4787220 4787382

Scaffold_3 11748087 11748362

Scaffold_4 16280111 16280595

Scaffold_4 9092984 9093051

Scaffold_4 7588812 7588909

Scaffold_4 7910059 7910413

Scaffold_4 7910059 7910413

Scaffold_4 9116076 9116160

Scaffold_5 11651796 11652001

Scaffold_5 3334753 3334999

(continued)

338

Scaffold_5 862690 862805

Scaffold_5 9682862 9683084

Scaffold_5 13879149 13879208

Scaffold_7 2298460 2298518

Scaffold_7 7781678 7781747

Scaffold_8 1925515 1925718

Scaffold_8 4938307 4938600

Scaffold_8 6164429 6164558


Scaffold_1 1042785 1042982

Scaffold_1 10858672 10858992

Scaffold_1 2139004 2139218

Scaffold_1 22621920 22622170

Scaffold_1 27827614 27827671

Scaffold_1 27827227 27827403

Scaffold_1 27827614 27827671

Scaffold_1 27827227 27827403

Scaffold_1 27827227 27827403

Scaffold_1 33067815 33068092

Scaffold_1 34577489 34577786

Scaffold_1 35411541 35411651

Scaffold_1 4474670 4474770

Scaffold_1 8189519 8189569

Scaffold_1 8933426 8933648

Scaffold_1 34577489 34577786

Scaffold_1 12942412 12942538

Scaffold_1 35411541 35411651

Scaffold_1 4474670 4474770

Scaffold_2 1328551 1328626

Scaffold_2 4181840 4181940

Scaffold_2 4181604 4181696

Scaffold_2 11655173 11655457

Scaffold_3 17094574 17094627

Scaffold_3 4787220 4787301

Scaffold_3 11748202 11748308

Scaffold_4 16279081 16279200

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339

Scaffold_4 9092902 9093051

Scaffold_4 7589128 7589354

Scaffold_4 7910555 7910967

Scaffold_4 7910140 7910211

Scaffold_4 9116094 9116225

Scaffold_5 11651019 11651115

Scaffold_5 3334967 3335027

Scaffold_5 862748 862866

Scaffold_5 9682643 9682974

Scaffold_5 13879218 13879290

Scaffold_7 2298463 2298716

Scaffold_7 7781662 7781770

Scaffold_8 1925402 1925659

Scaffold_8 4938099 4938155

Scaffold_8 6164424 6164493


340

File A.1 R script to perform analysis described in Chapter 2.

#Analysis of relative telomere lengths generated by monochrome multiplex qPCR comparing distinct almond age cohorts collected in 2018 and 2019 #Load required packages require(agricolae) require(ggplot2) require(wesanderson) require(multipanelfigure) require(grid) #Read in relative telomere length data from file 'TelomereData.csv'; file headers: Year (year planted), Replicate (biological replicate number), T.S (estimated T/S ratio genearted from standard curve using telomere and PP2A Cq values) telo_data <- read.csv("TelomereData.csv") #Convert variable 'Replicate' to a factor for analysis telo_data$Replicate <- as.factor(telo_data$Replicate) #Calculate the average T/S ratio for the reference almond sample to use to calculate relative telomere lengths for each almond indivdiual referenceAverage <- mean(c(0.4758955, 0.8377413, 0.779102)) #Create a relative telomere length variable (rTL) telo_data$rTL <- telo_data$T.S/referenceAverage #Create a z-score variable (zscore_rTL) by calculating the z-score for each almond sample telo_data$zscore_rTL <- (telo_data$rTL - mean(telo_data$rTL)/sd(telo_data$rTL)) #Create a variable called age listed the chronological age of each almond in years; make this variable a factor #Example is for data collected in 2019 telo_data$age <- c(2,2,2,7,7,7,11,11,11) telo_data$age <- as.factor(telo_data$age) #Perform Shapiro-Wilk normality test on zscore_rTL assess test normality assumption

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shapiro.test(telo_data$zscore_rTL) #Perform Bartlett test to assess homogeneity of variance assumption bartlett.test(zscore_rTL ~ age, data = telo_data) #Regress zscore_rTL on almond age fit <- lm(telo_data$zscore_rTL ~ telo_data$age) #Perform ANOVA to generate p-value on linear model anova(fit) #Perform post-hoc Fisher's LSD for means separation LSD_output <- LSD.test(fit6, "telo_data$age", alpha = 0.1, p.adj = "none") LSD_output #Create coords variable to display letter groupings based on means separation results #Example is for data collected in 2019 coords <- data.frame("x" = c(1,2,3), "y" = c(1.5,0,-0.5), "sig" = c("a", "ab", "b")) #Create boxplot displaying the zscore of relative telomere lenght (zscore_rTL) for each almond age cohort, including the means separation groupings #Example is for data collected in 2019 Telo19 <- ggplot(telo_data, aes(x=age, y=zscore_rTL, fill = age)) + geom_boxplot() + labs(x = "Chronological Age (Years)", y = "Zscore Relative Telomere Length") + theme_bw()+ theme(legend.position = "none", text = element_text(size = 12)) + geom_text(data = coords, mapping = aes(x=x, y=y, label = sig, size = 12), inherit.aes = FALSE) + scale_fill_manual(values=wes_palette(name="Royal2")) #Create a multipanel figure (figure1) combining boxplot produced with 2018 data and boxplot produced with 2019 data figure1 <- multi_panel_figure(width = 180, height = 180, columns = 1, rows = 2) figure1 %<>% fill_panel(Telo18) figure1 %<>% fill_panel(Telo19)

(continued)

(File A.1 continued)

342

(File A.1 continued) figure1 ##Analysis was repeated for data generated for bud tissue collected in 2019. #Analysis of relative TERT expression data generated via qRT-PCR comparing distinct almond age cohorts collected in 2018 and 2019 #Load required packages require(ggplot2) require(agricolae) require(multipanelfigure) require(grid) #Read in relative expression data from file 'TERTExpression.csv'; file headers: Chronological.Age (age of each almond accession in years), Expression (normalized relative TERT expression) TERT_data <- read.csv('TERTExpression.csv') #Convert variable 'Chronological.Age' to a factor TERT_data$Chronological.Age <- as.factor(TERT_data$Chronological.Age) #Perform a log2 transformation on 'Expression' data variable TERT_data$logExpression <- log(TERT_data$Expression) #Omit any missing data values (n/a's) TERT_data <- na.omit(TERT_data) #Perform Shapiro-Wilk normality test on logExpression to assess test normality assumption shapiro.test(TERT_data$logExpression) #Perform Bartlett test to assess homogeneity of variance assumption bartlett.test(logExpression ~ Chronological.Age, data = TERT_data) #Regress log-transformed Expression on Chronological.Age fit_tert <- lm(log(TERT_data$Expression) ~ TERT_data$Chronological.Age) #Perform ANOVA to generate p-value on linear model

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(File A.1 continued) anova(fit_tert) Perform post-hoc Tukey's HSD for means separation HSD_tert <- HSD.test(fit_tert, "TERT_data$Chronological.Age", alpha = 0.1) HSD_tert #Create coords variable to display letter groupings based on means separation results #Example is for data collected in 2018 tert_coords <- data.frame("x" = c(1,2,3,4), "y" = c(1.5,1,0.25,0.25), "sig" = c("a", "ab", "ab", "b")) #Create boxplot displaying the zscore of relative telomere lenght (zscore_rTL) for each almond age cohort, including the means separation groupings #Example is for data collected in 2018 TERT2018 <- ggplot(TERT_data, aes(x=Chronological.Age, y=logExpression, fill = Chronological.Age)) + geom_boxplot() + labs(x = "Chronological Age (Years)", y = "Relative TERT Expression") + theme_bw()+ theme(legend.position = "none", text = element_text(size = 12)) + geom_text(data = tert_coords, mapping = aes(x=x, y=y, label = sig, size = 12), inherit.aes = FALSE) + scale_fill_manual(values=wes_palette(name="Royal2")) #Create a multipanel figure (figure2) combining boxplot produced with 2018 data and boxplot produced with 2019 data figure2 <- multi_panel_figure(width = 180, height = 180, columns = 1, rows = 2) figure2 %<>% fill_panel(TERT2018) figure2 %<>% fill_panel(TERT2019) figure2

344

File A.2 Genomic sequence of 17 differentially methylated regions (DMRs) shared

across the three age-contrats (11 - 2 year, 11 - 7 year, and 7 - 2 year).

>CGDMR1 CGCCGTTAAATATAATTTTCACGACGTTATGGAAAACTACTCTGTCTCTTATTGAAAACTACTACGTCTTTAAATACATGAGAAGACGACGTTATTGAAAACTACTACGTCTCTTATTTACAAAAGACCGTCGTGAAATAAGTTTTCCACTTCGATTGGTCTAAAAAAGATGACGTAGATATAGCTGTCTGTGTCATTATTTACGACGTATGTATTTATGTTGTTGACGTTGAAATGCGAAACAACGTCGGTGGCATTTATATAGTCGACGTTGTATTTATATGTATTCAGTACTCACGACGTACTTATTAATGTAGACGACGTTGAACTTCTAAACAACGTCAATATTTACGACGCATTTAATAAACCACGTCGGTTCTAAATTAATGTTCAGATATTTAATCTCATTTTTAGACGTCGGTATTATGGGATTGGAGTCGTGTTATTTTGGATTACATGGCTGTTCTTAATCCTTCCCCGACGTGATATCCTGGATAATTGCTTCACAATAGCGTCGTGGTTCTTCATTGTTACGTTGCTTTAAGACGTCGGTAAATATGCTGAATAAATGGTTAACCATAACGTC >CGDMR2 CGCGCGGCTATACTATTTGTAAATATAAAAAGCCCAGCCCACAGAGCAGGTACCACGTGTCAAAGAGGTACAGCCGGGCGGCTGTACTATTTTTTTAAAAGATAACACGAGTATAGCCGCGCGGCTATACTATTGCTTGGAAAAAAAAAAAAAAAAAAAAAAGAGAATCTGGTATAGCCCGGCCCAGGCGCCATGTGCGCCACGTGGCAAACAAGTAGAGCCGAACGGCTATACTATTTTTAAAAGCAATTAAGGGAAAAATGAGTATAGCCGCACGGCTATACTATTTCTTTAAAAAAGAAAAAGAAAAAAAGGATCTGGTACAGCCCGGCCCAGGCGCCATGTGCGCCACGTGGCAAGCAAGTAGAGCCGAACGGCTACTCTATTTTTAAAAAGCAATTAAAGGAAAACTGAGTATAGCCGCACGGCTATACTATTCCTTGAAAGAAAAAAAAAATCTGGTTCAGCCCGTGCGCGCCACGTGGCAAACAAGTAGAGCCG >CGDMR3 CGATCGATTTGGTGGAGAAACGAAGGAGAATTTCGAGCTGGAAGTTCGGGTGGCTTTGCAGGAACCTCCGTCGGAAAACATGGTTTTCCGGCCAACTCAGAGCGGCCCCGGGGTTGAGATCTGTCAGGTTGTGACGGCGACACGAAGGCGGACCCGTAGGTACCAACGGCGGAGATCGGCTCGGGCCTGTGGTGGCCGGGCCGTCCCTTGGAAGGCGAAGGGTCGCACGACCCCAAATCGCGCGAAGGAGAGAGAGGGAGGAGAGAGAAAGTGACGGGGAGAAGAGAGAGAGGGGGTATAAAATCTGACTTTTTGGCCAAATTACCATTTTGCCCTTCGCGGTTTTTAGACCATAACTTCTTCGTTACTGCTCCGATTCGGGCCTACTCCGTGTCTACGAACTCCTTTCG >CGDMR4 CGTCAACGACTCTTCGTCGAGATCCTCCAAGTTATAGTTCGTCTGCAATCAATTAATTAGATACGTTAAGTAATAGAAATATACACAATTTTAAAGAAAAATAATGAAAATGTAAGGTACATACCGACAACTGGCCGCGAACCTCCGCCTTGATCTCGTCAGGCATGACCCTCCAAGACTTCCATTGCATCGGGCAATGGGTCCGCACGACGTGGCCAATGTCGTGGGCCAAGGAGCTATGCAACTCCGCCGTCGGTGCAGCCCGATGGCGCTCGTCGTATCCGATGCTAATACGACTGTTGGTCACCCGGGTGACCTTCGCCGTCTTCAGCTGACGACACGGTCCCCGGGTGTTCTTCTTCG >CGDMR5 GCGGGGTGCCCCTGATTTTTCAGGGTAAAGTTGGGTGTACTCTTTCTTCGAGTTTGACCAATTAAATTAATTAAAATTAATATAATAGAAAATTATTTCCCTGCGAAACTGCCGCAGCTTCTTCTATTTTCCTGCGAGTTTGACACCCGCCGCTTCTTCTCTATTTTCCTGCGAAACTGCCGCAGCTGGAGGCG >CGDMR6 CGCTCGTAAGGAGTCCTATTGAAACCAAGAAATATAGGCCTGTCTGCCATCCACACCAGAATAAATGAAGTTTTTTTTACTCTAACCCTTTTCGAAATAATTACAGTTTTGCCATTGATAATGTTTTGATCACCAAATCTTCGTTACAACTCCGTTTCAAGCCTACCGCGTGTCTACAAATTCGTCTTAGTACCACCTACCTAAAAATACCAATCGTGTTCCCAAAATCCTTCCG >CGDMR7 CGTCGACTATATAAATGCCACCGACGTTGTTTCGCATTTCAACGTCAACAACATAAATACATACGTCGTAAATAATGACACAGACAGCTATATCTACGTCATCTTTTTTAGACAAATCGAAGTGGAAAACTTATTTCACGACGGTCGTTTGTAAATAAGAGACGTAGTAGTTTTCAATAACGTCGTCTTCTCATGTATTTAAAGAC >CGDMR8 CGTTTTGCCCTCATTCGCAGCAGCAGGCAATCGATTAATGAGATGGCTTGCATAAGTAATCGCCTCAGCCCAAAACGCCTTGCCTAAACCAGCATTAGACAACATACACCGAACTTTCTCAAGCAAAGTACGGTTCATGCGCTCTGCC

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345

(File A.2 continued) ACCCCATTCTGTTGCGGTGTCTCCCTAACCGTGAAGTGCCTCACAATACCCTCATCTTGACAAACTTTCAAGAAAGGATCAGACTTATATTCACCACCATTGTCTGATCTGAGAGTCTTGATCTTTCGACCGCTTTGC >CGDMR9 CGTTCCTCGTCAAGTATTAGGGTTAAGGGTGACTGTGGGTATCGCAAGAGAGAAGAAAGGGAGTTTGAAGGTGAGAAGGATGAGAGTAACTATGACTGGGTATCGCAAGAGAGAAGAGAGGGAGTCTGAAATATTCACGAGGGAGTGAAATTTTTCAGCG >CGDMR10 GTGCAGCTTTTAATGGGAAACCTGAATATGGCATACCTCCCGAGCCATTAACCGGAGAAGAAGTGCTGCATATGGTTGAAAATGGTGACAGAGTTTGTTGGAAGAAGAAATCAATATTCTTTGATCTCGAGTATTGGAAATACCTTCCTGTGAGGCATGCCCTAGATGTTATGCATATTGAGAAGAATGTTTGCGATAGTATCATTGGTACATTGCTGGAGATCCCTGGAAAAAATAAAGATGGGATTGCTGCTCGATTAGATTTATTGAACATGGGGGTCAAAACTGATTTGCAACCCGAGTATGGAGAAAGACGTACTC >CGDMR11 CGGCCCGATAAGTAATATATTTATTTTTAAGTAGTTTATAATATAATATAACCATGTATGATTATAGGGTTTTAATGTCCTCAAACTTCGAGTTGTATTTTGGTCCCTCAACTAAATTATTCG >CGDMR12 CGTAGTTGTATGACGTCACAAGGAGGCCGGAGTAATTTTCGGGGCATTTGTAGGAATTTCCTACTATTTATTACCTATTATCAAGGATTTTTCCACGAAAATAGCCAACCCTAATTGCAAAAACACACCTGGCGCAATCTCAGCAGGTAACCTTTCAGCACTTGCACAAGTGTAC >CGDMR13 CGAGGGTTTGTCTATATTTCAATATAGAGTATTCAAATAAGCATGTAAAAATCTACTGATGACTGTGCTTGTATAACTGAACAAATATGTAAAGATATAAATGTACG >CHGDMR1 CAGTAACAAAAAAAAGGGAGAAGGCTTCCACTGAAAAGAAAACAAACGGAAGAAGAGTAAAAAAAAAAGGAAAGTGGCGTCAGCCACTTTGTTTCAAAAGGAAGAAGAAAGCGTCAGCCACTTTGTTTCAAAAGGAAGAAGAAATTCAAAGAAGAAAAAAAAAGAGAAGAGAGTGGCTGATGCCATTTTGCTGAAATTGGAAGAAAAGCAAAGCAAAGGACTTGGTGTTGCAGATCAAGAAGAAGAAAAAGAAAAGTCAAAACAAGGACAAAAAAGAACAAGGTGGCTGACGCCACTTTTGGAGTTGGAAGAAAAAAAGGGACACAAGAGACTTGCTGTTGCAGAGAAAAAATGAGGAAAAGGACAAAAAAAAAAGTCTGAAACCTCTTGGGTGTTGCGGCAGTTTTTCTCTCTTTCTTCTGGCTTTCTTTTCTTCTTGTTTGCAAAGGCTG >CHGDMR2 CTGGCTTGTGCTTTTTGGCAGCTATCTGGTATTGGGTGTATTGGGATCTAGAAATATTTATTGATGAACATACAGGAAAACCCTCTTTGAATTTGCCTAAGATCTTTGGAATTCATTTATTTCTATCCGGGGTGGCTTGCTTTGGTTTTGGTGCATTTCATGTAACAGGATTGTATGGTCCTGGAAGGTTCTACCTCCTATTTTTTATGAAGAGAATGAATCTTTTTATGGAAGGATCAGAAAAAAATGGGTCCGGACCTCCTGCGAGAATGATTTGGAAGATCCAAAACCAAAAATAGTGGTATTTGCTAGCAACAACATAGTTAGTAAGAGGGATCTTGAACTAAGAAATAGATTCTAGAAGCTAAAAAAGGGTATCC >CHGDMR3 GCACCTTCTATAGTGCACCAGGTGCATCCATGCATCATTTGTCATTTATAATTGACAGGACCCAACCCAATTTCCACTTTGAAATTCGAGCCAAGTCCTGCGCGTGTCCGACACCTGGCGAATGTCGGGCACAATTGTCCTTTTTACCCTTCTTACTTCAATTCTTCTTTAAAATTGCCTTAGACTTCTGCCGAAAATTCGGCAGAGTCTCCCCTGTATTTTTGACTTATCCCAAAATTTTCACCTG >CHHDMR1 GTTTGGCGTCCATCCTTTGTATCCCAAAATCGTCATCTCACAGTTAATGATTCTGTGATGATGAATGATGCTACTGCTGTCACAGTAGCTAGGAATTTCATTATTCCAATGGATGAAATGCTGTTGACAGGGAGGTCTGAAGAAGAGGCTATTGAGGACTCAATGGCTTTTAGCATTCAGAGTGCTGCTTCTGTTTCTAACATGGCTGATCGTTTGCGTGCTAGAGCAAACGAGGTTCAGAAGCTAACAACGAAAAATTCGTCTCTCCAAAGAATACTTCATGAGTCTCAAAAAGAGGTTGAGAAACTTAAAGGAGAGAATAATTCCTTGTTGAAACTGGTGAGTTCGTATTCTGTTGATACACAGAGGA

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346

(File A.2 continued) AGCTAGACATGCTGCAGGTCTCAAATGAAAGAATTTTGGGAGACCACGAGAGGCTCATGGCTAGGCTTAAGAAGCGCCGTCCTCTTCCTTCAGAGGCTTCCAGAACATAATGTAATTTTATAGATTTTACAGGGCCTGCACCTTCATTGCAGGTGGAAAAATCTATCTGTTGTATGTTCATTTGCTGTTATAATAATTGTACATGTTCTTAAACTTGCATCTGTGGTTTTTACGTCTTTTCAAAATGACGGTTTGGAACCTTGTGCCTTATAGGTTCAAATAACCACATCAAATCT CTCATATTTCCATGCATATGAGCCCAGAGCTTTTGGTCTGGGTTAAACCCAAACACATAATTAATTCGCCATTTTCAAATGGAGAGCATTAACTACAATGTACCCACAACTTCAAGTTTTAGGATCTCTCATATATTTGGATCCATGGGCTTCCGGCCCAGATATAACAAAATATGTGGGGAGCCTCAATTCATTATTTGAGGTTTATATTGATATTATCCATTTCGCGGTGTATTCTTAACAACCGGAATTCACAAAATATATTTCTTCCTTGAGGTGTCGATTATAACAGAA TCGAACTTCATTAAATTCATCATCTTCTTATGCCAAAGAAATATGTGGCATATCACAATTTGCAATAATACCTCAAGGGTTGTCCATTTAATTGTTGGAACTTCAGGTTCTCAATACTGTTAGATTTTGAACTTCGGGCCAAAATCACATATTCTCATGGTATGGACATTTTTACAATTTTCTGTACATATTTTGGGACTTCAAGTCCTTACATAATTGTCCATATTTTGAGGAACTTCTGGCATCTCATTTAATTGCTCATCCATGAGTGTAAGGAACTGCAGGTTCCCTTTTGTATATAGTGACAGTTTACCCAAAATGGTTAATATTTATGCATACGTCACTATTCATGTGAATAGTACTATTCATCAATCATGAATACATATCTATTCATATGTGCAGTACATTTGCCAGTACAGTTATTATTCATGTGTACGGCACTTTTAACCAATACGGTACTGTTACATCATTAAGGAAACCCAGGTCCTTATTCACATGTCAGGGATCAAGGACCCTTAAGTCCGATCACATGTTTACAAATATAG

347

File A.3 Nucleotide and protein sequence of unidentified protein identified as most

significantly differentially expressed between the twin pairs in Chapter 4.

>Scaffold_6_UnidentifiedProtein TCATGCAGCTGGAGCATAATCATAACCACCATCATCATCGTCATCGTCATCCCCTTCCATGGCTGCTGGAGCATAGTCAT AGTCTGAATCATCGTCGTCGCCGTCGCCTTCTATACATGCTGCCAGGGCAAATATATCATCACCGTTGAAGTCGTCGACT CCATCGCCATGCACCTCGTACCATGAGGCACTGATGATCAACTTGGAAAGGGGAAACAT >Scaffold_6_UnidentifiedProtein MFPLSKLIISASWYEVHGDGVDDFNGDDIFALAACIEGDGDDDDSDYDYAPAAMEGDDDDDDDGGYDYAPAA

Almond as a model to explore epigenetic signatures ...

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