i Non-invasive assessment of paediatric cystic fibrosis liver ...

i

Non-invasive assessment of paediatric cystic fibrosis liver disease and the role of

microRNAs in disease mechanism

Diego Calvopina

Bachelor of Science

Master of Biotechnology

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2018

Faculty of Medicine

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Abstract

Cystic fibrosis (CF)-associated liver disease (CFLD) is a hepatobiliary complication of CF

responsible for significant morbidity and mortality in children with the disease. It is estimated

that ~30% of children with CF develop severe liver abnormalities during the first decade of

life. However, the real prevalence of CFLD could be higher, as shown by autopsies

performed in adults with CF. The reason why only a subset of CF children develop liver

disease is not fully elucidated. Current methods to diagnose CFLD are non-specific and the

gold standard to assess disease severity relies on an invasive liver biopsy which can be

associated with significant complications. New current trialled methods for the diagnosis of

CFLD focus on biological and physical properties to indirectly assess the degree of fibrosis

in the liver. Development of new non-invasive diagnostic modalities is necessary to more

accurately detect and monitor liver disease progression and thus to improve the quality of

life of children with CF. In this thesis, Supersonic shearwave elastography (SSWE) and

serum microRNAs (miRNAs) were assessed as novel non-invasive methods for the

diagnosis and monitoring of liver fibrosis in children with CF. Furthermore, the role of

miRNAs were explored by identifying their mRNA targets and their potential involvement in

the mechanism of fibrosis development. Chapter 3 demonstrates the utility of SSWE to

detect CFLD in children with CF by providing real time measurement of liver stiffness and

the capacity of SSWE to assess liver stiffness over time. Chapter 4 stablishes a serum

miRNA panel capable of discriminating liver disease in CF children outperforming current

proposed biomarkers. Moreover, this work demonstrates that serum levels of a single

miRNA can discriminate liver severity in children with CFLD. miRNAs reported in Chapter 3

were further investigated in Chapters 4 and 5 where miRNA targets were identified and

validated, and their role explored in the liver’s fibrogenic response using liver cultured cells.

Overall, this thesis demonstrated that SSWE and serum miRNAs are capable of diagnosing

CFLD and stratify liver disease severity in children with CF. This work has also identified

miRNA targets and their potential regulatory role in liver cells involved in fibrogenesis which

could lead to the discovery of novel therapeutic interventions for CFLD and other fibrosing

chronic liver diseases.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published

or written by another person except where due reference has been made in the text. I have

clearly stated the contribution by others to jointly-authored works that I have included in my

thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical

assistance, survey design, data analysis, significant technical procedures, professional

editorial advice, financial support and any other original research work used or reported in

my thesis. The content of my thesis is the result of work I have carried out since the

commencement of my higher degree by research candidature and does not include a

substantial part of work that has been submitted to qualify for the award of any other degree

or diploma in any university or other tertiary institution. I have clearly stated which parts of

my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library

and, subject to the policy and procedures of The University of Queensland, the thesis be

made available for research and study in accordance with the Copyright Act 1968 unless a

period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright

holder(s) of that material. Where appropriate I have obtained copyright permission from the

copyright holder to reproduce material in this thesis and have sought permission from co-

authors for any jointly authored works included in the thesis.

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Publications included in this thesis

Calvopina DA, Coleman MA, Lewindon PJ, Ramm GA. Function and Regulation of

MicroRNAs and Their Potential as Biomarkers in Paediatric Liver Disease. International

Journal of Molecular Sciences 2016; 17:1795.

Calvopina DA, Chatfield MD, Weis A, Coleman MA, Fernandez-Rojo MA, Noble C, Ramm

LE, Leung DH, Lewindon PJ, Ramm GA. MicroRNA Sequencing Identifies a Serum

MicroRNA Panel, Which Combined With Aspartate Aminotransferase to Platelet Ratio Index

Can Detect and Monitor Liver Disease in Pediatric Cystic Fibrosis. Hepatology 2018;

68:2301-2316.

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Submitted manuscripts included in this thesis

No manuscripts submitted for publication

Other publications during candidature

Articles under review:

Genz B, Coleman MA, Irvine KM, Kutasovic JR, Miranda M, Calvopina DA, Weis A,

Cloonan N, Robinson H, Hill MM, Al-Ejeh F, Ramm GA. Overexpression of miRNA-25-3p

inhibits Notch1 signalling and TGF-β-induced collagen expression in hepatic stellate cells.

Scientific reports. Under review.

Lewindon PJ, Puertolas-Lopez M, Ramm LE, Noble C, Pereira T, Wixey J, Hartel G,

Calvopina DA, Leung DH, Ramm GA. Utility of transient elastography combined with APRI

for liver disease detection and staging hepatic fibrosis in pediatric Cystic Fibrosis.

Hepatology Communications. Under review.

Patent:

A Complete Patent application (Australia and US only) was filed on April 18 to protect the

intellectual property associated with this thesis, entitled ‘DETECTION OF LIVER DISEASE

(CF LIVER DISEASE)’. Patent number: AU2018202717; Inventors: Diego Calvopina, Grant

A. Ramm.

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Contributions by others to the thesis

Prof. Grant A. Ramm conceived the idea and contributed with the design and analysis of all

experiments. A/Prof. Peter J. Lewindon conceived the idea of Chapter 3 and assisted with

study design, enrolment of patients and collection of tissue specimens for Chapters 4 and

5. Dr. Miranda A. Coleman and Dr. Manuel A. Fernandez-Rojo provided critical feedback

and proofread this thesis.

In Chapter 3, Charlton Noble helped with patient enrolment and performed liver stiffness

scans using SSWE. Anna Weis and Louise Ramm assisted with the collection of clinical

data. Dr. Gunter Hartel assisted with the more advanced statistical analyses. Dr. Leesa

Wockner performed sample size calculation.

In Chapter 4, Dr. Daniel Leung provided advice on study design. Anna Weis and Louise

Ramm assisted with the collection of clinical data. Charlton Noble helped with patient

enrolment and collection of tissue specimens. Dr. Mark Chatfield performed the more

advanced statistical analysis. Dr. Leesa Wockner performed sample size calculation.

Quantification of cDNA libraries, template preparation, sequencing and sequence data

acquisition and analysis was performed by the Genomics Research Centre (GFC; Brisbane-

Australia).

In Chapter 5, library preparation and sequencing of enriched BMOL samples was performed

by the Centre for Brain Genomics of the Queensland Brain Institute (QBI; The University of

Queensland, St. Lucia, Brisbane). Library preparation and sequencing of enriched LX2

samples was performed by the IMB Sequencing Facility (ISF) of the Institute for Molecular

Bioscience (IMB; The University of Queensland, St. Lucia, Australia). Initial sequencing

analysis (e.g. de-multiplexing, conversion, quality control, trimming and alignment) was

performed by members of the Clinical Genomics Group (QIMR Berghofer, Queensland-

Australia).

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In Chapter 6, Dr. Michael Pearen and Diem Hoang-Le performed and analysed western blot

analysis. Functional assays were performed with advice and support of Dr. Michael Pearen,

Dr. Berit Genz and Anna Weis.

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Statement of parts of the thesis submitted to qualify for the award of another degree

No works submitted towards another degree have been included in this thesis.

Research Involving Human or Animal Subjects

This study was approved by the Human Research Ethics Committee of the Children’s Health

Services Queensland (Queensland Children’s Hospital; Brisbane-Australia) under the

reference number HREC/10/QRCH/87 and the Human Research Ethics Committee of the

QIMR-Berghofer Research Institute under the reference number P1083.

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Acknowledgements

This is the end of a life-long journey in which many people have been involved in one way

or the other. I cannot name all, but I am grateful for every one of you.

I would like to start by thanking my supervisor Prof. Grant A. Ramm and co-supervisors

A/Prof. Peter J. Lewindon, Dr. Miranda A. Coleman and Dr. Manuel A. Fernandez-Rojo for

your guidance during these past four years. A special thanks to Grant, whose support from

day one made this all possible.

My lab members, which were always available for discussing project related matters or

taking a break. Special thanks to Michael Pearen, Berit Genz and Anna Weis who have

been my biggest help especially when experiments stopped working.

A part of this thesis involved patient enrolment, and it would have not been possible without

the help of the Department of Gastroenterology and Hepatology staff at the Queensland

Children’s Hospital; in particular Charlton Noble who spent many hours and lunch breaks

scanning patients. Of course, without the support of the children and their families none of

this would have been possible, thank you.

To my parents and siblings, this has been a common goal and I am infinitely happy to share

it with you. I am aware of all the sacrifices and effort and I would not be who I am today

without your encouragement. Lastly but not least, to my partner Anna Weis, I have been

lucky enough to have found someone that shares my craziness and who doesn’t mind

spending hours talking about science. These four years were easier just by having you by

my side and I am looking forward to whatever tomorrow brings.

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Financial support

This research was supported by The University of Queensland International Scholarship

(UQI) tuition free award and the Australian Liver Foundation (ALF) Pitcher Partners PhD

Scholarship for Paediatric Liver Disease Research.

Keywords

microRNA, serum, miRNA-sequencing, cystic fibrosis liver disease, Supersonic, transient

elastography, biotin pulldown assay, diagnostic, hepatic stellate cells, liver progenitor cells.

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Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 110307, Gastroenterology and Hepatology, 60%

ANZSRC code: 060106, Cellular Interactions, 10%

ANZSRC code: 060111, Signal Transduction, 10%

ANZSRC code: 060405, Gene Expression, 20%

Fields of Research (FoR) Classification

FoR code: 1103, Clinical Sciences, 60%

FoR code: 0604, Genetics, 20%

FoR code: 0601, Biochemistry and Cell Biology, 20%

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Table of Content

CHAPTER 1 Introduction and literature review …… .................................................. 1

1.1 Chapter contribution ............................................................................................. 2

1.2 INTRODUCTION .................................................................................................. 3

1.3 Cystic fibrosis ....................................................................................................... 4

1.4 Epidemiology of cystic fibrosis liver disease (CFLD) ............................................. 6

1.5 Pathogenesis of CFLD ......................................................................................... 6

1.6 Mechanisms of fibrosis in CFLD ........................................................................... 8

1.7 Ductular reaction .................................................................................................. 9

1.8 Manifestations of CFLD ...................................................................................... 10

1.9 Detection of CFLD .............................................................................................. 11

1.10 Liver biopsy ........................................................................................................ 14

1.11 Imaging techniques ............................................................................................ 17

1.12 Liver enzymes .................................................................................................... 18

1.13 Novel non-invasive methods of liver disease detection ....................................... 18

1.14 HYPOTHESES AND AIMS ................................................................................. 27

CHAPTER 2 Materials and Methods .......................................................................... 29

2.1 Chapter contribution ........................................................................................... 30

2.2 Patients .............................................................................................................. 31

2.3 In vitro studies .................................................................................................... 34

2.4 Nucleic Acid studies ........................................................................................... 43

2.5 Statistical and data analysis ............................................................................... 55

CHAPTER 3 Assessment of paediatric CFLD using Supersonic shearwave

elastography ................................................................................................................ 63

3.1 INTRODUCTION ................................................................................................ 64

3.2 RESULTS .......................................................................................................... 67

3.3 DISCUSSION ..................................................................................................... 79

3.4 Summary and future directions ........................................................................... 83

CHAPTER 4 Investigation of a serum microRNA signature for the discrimination

of liver disease in CF and monitoring liver disease severity in paediatric CFLD ...... 85

4.1 Chapter contribution ........................................................................................... 86

4.2 INTRODUCTION ................................................................................................ 87

4.3 RESULTS .......................................................................................................... 89

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4.4 DISCUSSION ................................................................................................... 133

4.5 Summary and future directions ......................................................................... 138

CHAPTER 5 Identification of microRNA targets in HSCs and LPCs ..................... 139

5.1 INTRODUCTION .............................................................................................. 140

5.2 RESULTS ........................................................................................................ 143

5.3 DISCUSSION ................................................................................................... 166


CHAPTER 6 Validation of miRNA targets and the role of microRNAs in HSC and

LPC biology .............................................................................................................. 172

6.1 INTRODUCTION .............................................................................................. 173

6.2 RESULTS ........................................................................................................ 176

6.3 DISCUSSION ................................................................................................... 216


CHAPTER 7 General discussion .............................................................................. 229

7.1 Chapter contribution ......................................................................................... 230

CHAPTER 8 References ........................................................................................... 238

CHAPTER 9 Appendices ………………………………………………………………….268

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List of Figures

Figure 1.1. Classification of CFTR mutations. ..................................................................... 5

Figure 3.1. Liver stiffness measurements (LSM) in healthy controls, CFnoLD and CFLD

children............................................................................................................................. 70

Figure 3.2. Diagnostic assessment of SSWE for the discrimination of CFLD. ................... 71

Figure 3.3. CART decision tree for predicting liver disease in children with CF. ............... 73

Figure 3.4. ROC curves for assessment of hepatic fibrosis severity in CFLD. .................. 74

Figure 4.1. miRNA library distribution. .............................................................................. 92

Figure 4.2. Relative serum miRNA expression levels in CFLD, CFnoLD and Control groups.

....................................................................................................................................... 118

Figure 4.3.Relative serum miRNA expression and APRI levels in CFLD F0 fibrosis vs.

CFnoLD. ......................................................................................................................... 120

Figure 4.4. ROC curves for the discrimination of CFLD vs CFnoLD................................ 122

Figure 4.5. Association between relative serum miRNA expression and hepatic fibrosis

staging in CFLD. ............................................................................................................. 124

Figure 4.6. ROC curves for assessment of CFLD severity. ............................................. 127

Figure 4.7. Scatterplots of serum miRNAs versus lung function. ................................... 132

Figure 5.1. BMOL pulldown assay for let7g-5p putative target identification. .................. 149

Figure 5.2. BMOL pulldown assay for miR-142-3p putative target identification. ............ 150

Figure 5.3. BMOL pulldown assay for miR-34a-5p putative target identification. ............ 151

Figure 5.4. BMOL pulldown assay for miR-365a-3p putative target identification. .......... 152

Figure 5.5. LX2 pulldown assay for let7g-5p putative target identification. ...................... 153

Figure 5.6. LX2 pulldown assay for miR-142-3p putative target identification. ................ 154

Figure 5.7. LX2 pulldown assay for miR-34a-5p putative target identification. ................ 155

Figure 5.8. LX2 pulldown assay for miR-365a-3p putative target identification. .............. 156

Figure 5.9. Comparison of pulldown experimental assay and bioinformatics predicted

targets. ........................................................................................................................... 157

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Figure 6.1. qRT-PCR assessment of putative let7g-5p targets in BMOL cells. ............... 177

Figure 6.2. qRT-PCR assessment of putative miR-34a-5p targets in BMOL cells. .......... 178

Figure 6.3. qRT-PCR assessment of putative miR-365a-3p targets in BMOL cells. ........ 179

Figure 6.4. qRT-PCR assessment of differentiation markers on BMOL cells transfected with

let7g-5p. ......................................................................................................................... 181


miR-34a-5p. ................................................................................................................... 182


miR-365a-3p................................................................................................................... 183

Figure 6.7. Western blot assessment of cholangiocyte and hepatocyte proteins in BMOL

cells transiently transfected with let7g-5p. ...................................................................... 184

Figure 6.8. BMOL functional assays for let7g-5p. ........................................................... 185

Figure 6.9. BMOL functional assays for miR-34a-5p. ..................................................... 186

Figure 6.10. BMOL functional assays for miR-365a-3p................................................... 187

Figure 6.11. qRT-PCR assessment of putative let7g-5p targets in LX2 cells. ................. 189

Figure 6.12. qRT-PCR assessment of putative miR-142-3p targets in LX2 . ................... 190

Figure 6.13. qRT-PCR assessment of putative miR-34a-5p targets in LX2 cells. ........... 191

Figure 6.14. qRT-PCR assessment of putative miR-365a-3p targets in LX2 cells........... 192

Figure 6.15. qRT-PCR assessment of HSC activation and quiescence markers on LX2 cells

transfected with let7g-5p. ............................................................................................... 194


transfected with miR-142-3p. .......................................................................................... 196


transfected with miR-34a-5p. .......................................................................................... 198


transfected with miR-365a-3p. ........................................................................................ 200

Figure 6.19. Transduction of LX2 cells using lentivirus miRNA constructs. ..................... 202

Figure 6.20. LX2 functional assays for let7g-5p. ............................................................. 203

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Figure 6.21. LX2 functional assays for miR-142-3p. ....................................................... 204

Figure 6.22. LX2 functional assays for miR-34a-5p. ....................................................... 205

Figure 6.23. LX2 functional assays for miR-365a-3p. ..................................................... 206

Figure 6.24. qRT-PCR assessment of HSC activation markers on LX2 cells transduced with

let7g-5p. ......................................................................................................................... 207

Figure 6.25. qRT-PCR assessment of HSC activation markers on LX2 cells transduced with

miR-365a-3p................................................................................................................... 208

Figure 6.26. Let7g-5p pulldown putative targets interacting with genes associated with HSC

activation. ....................................................................................................................... 211

Figure 6.27. miR-365a-3p pulldown putative targets interacting with genes associated with

HSC activation. ............................................................................................................... 212

Figure 6.28. Dual luciferase assay of validated LX2 let7g-5p targets. ............................. 214

Figure 6.29. Dual luciferase assay of validated LX2 miR-365a-3p targets. ..................... 215

Figure 6.30. Potential mechanistic role of let7g-5p and miR-365a-3p in the synthesis of

collagen. ......................................................................................................................... 225

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List of Tables

Table 1.1. Bile acid toxic effects. ........................................................................................ 9

Table 1.2. CFLD classification. ......................................................................................... 11

Table 1.3. Current accepted CFLD definitions. ................................................................. 13

Table 1.4. Clinical value of liver biopsy in paediatric chronic liver diseases. ..................... 14

Table 1.5. miRNA target identification parameters. ........................................................... 25

Table 2.1. miRNA mimics. ................................................................................................ 36

Table 2.2. Lenti-miRNA viral particles used for LX2 transduction. .................................... 37

Table 2.3. Biotinylated miRNA duplexes. .......................................................................... 39

Table 2.4.Primary antibodies used for Western blot analysis. ........................................... 41

Table 2.5. miRNA 3’UTR target clones used during luciferase assay. .............................. 43

Table 2.6. miRNA primer sequences. ............................................................................... 51

Table 2.7. BMOL self-designed RNA primer sequences. .................................................. 53

Table 2.8.LX2 self-designed RNA primer sequences. ...................................................... 54

Table 2.9.Commercial RNA primer sequences. ................................................................ 55

Table 3.1. Patient characteristics. ..................................................................................... 68

Table 3.2. Assessment of LSM progression. .................................................................... 76

Table 3.3. Yearly LSM follow-up. ...................................................................................... 77

Table 3.4. Correlations of LSM with patient demographics, liver related serum enzymes and

APRI. ................................................................................................................................ 78

Table 4.1. Patient characteristics ...................................................................................... 90

Table 4.2. miRNA sequence summary. ............................................................................ 93

Table 4.3. Differentially expressed miRNAs ...................................................................... 94

Table 4.4.Differentially expressed miRNAs in CFLD vs CFnoLD children. ...................... 116

Table 4.5. Logistic regression model for discriminating liver disease in CF..................... 123

Table 4.6. Differentially expressed miRNAs between fibrotic stages in CFLD children. .. 126

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Table 4.7. Diagnostic test accuracy to predict severity in CFLD. .................................... 128

Table 4.8. Correlations of circulatory miRNA levels with liver related serum enzymes and

APRI. .............................................................................................................................. 130

Table 5.1. BMOL RNA-sequence summary. ................................................................... 144

Table 5.2. LX2 RNA-sequence summary........................................................................ 146

Table 5.3. BMOL RNA putative mRNA targets summary. ............................................... 148

Table 5.4. LX2 RNA putative mRNA targets summary. ................................................... 152

Table 5.5. Gene set enrichment analysis (IPA). .............................................................. 158

Table 5.6. Selected putative miRNA targets in BMOL cells. ........................................... 161

Table 5.7. Selected putative miRNA targets in LX2 cells. ............................................... 163

Table 6.1. Let7g-5p putative targets mediating HSC activation markers. ........................ 209

Table 6.2. miR-365a-3p putative targets mediating HSC activation markers. ................. 210

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List of Abbreviations

%FEV1p Percent predicted forced expiratory volume in one second

∆∆CT Comparative CT method

95% CI 95% confidence interval

A Adenine

ABC ATP-binding cassette

Acvr2a Activin A receptor 2A

Adam17 ADAM Metallopeptidase domain 17

AGO Argonaute

AIH Autoimmune hepatitis

Akt3 AKT serine/threonine kinase 3

ALP Alkaline phosphatase

ALT Alanine amino transferase

AMOT Angiomotin

ANOVA One-way analysis of variance

Appl2 Adaptor protein phosphotyrosine zipper 2

APRI Aspartate aminotransferase (AST) to platelet ratio index

APS Ammonium persulfate

ARFI Acoustic radiation force impulse

AS Alagille syndrome

AST Aspartate amino transferase

ATL A-tailing mix

AUC Area under the curve

AUROC Area under the receiver operating characteristic curve

BA Biliary atresia

BBB Bead binding buffer

BL Base line

BMI Body mass index

BMOL Bipotential murine oval cell line

bp Base pairs

BWB Bead washing buffer

CART Classification and Regression tree modelling

CD109 CD109 molecule;

CD248 Endosialin

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CF Cystic fibrosis

CFLD Cystic fibrosis-associated liver disease

CFnoLD CF with no liver disease

CFTR Cystic fibrosis transmembrane conductance regulator

CI Confidence interval

COL4A1 Collagen type IV α-1

COL4A2 Collagen type IV α-2

COL4A5 Collagen type IV α-5;

Cq Quantification cycle

Crkl CRK like proto-oncogene

CSNK1G2 Casein kinase 1 γ-2

c-statistic Concordance statistic

CTA A-tailing control

CTE End repair control

CTL Ligation control

Cx3cr1 C-X3-C Motif chemokine receptor 1

DDX5 DEAD-Box helicase 5

DR The ductular reaction

Dvl3 Dishevelled segment polarity protein 3

E Young’s modulus

ECM Extracellular matrix

EFSUMB European Federation of Societies for Ultrasound in Medicine and Biology

ELB Elution buffer

Ep300 E1A Binding protein P300

EP300 E1A Binding protein P300

ESPGHAN European Society for Paediatric Gastroenterology, Hepatology and Nutrition

Ets1 ETS proto-oncogene 1

F Phenylalanine

F0 No fibrosis

F1 Portal fibrosis only (minimal scaring)

F2 Portal fibrosis with rare fibrous septa

F3 Portal fibrosis with numerous fibrous septa

F4 Cirrhosis

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FBC Focal biliary cirrhosis

FBS Foetal bovine serum

FC Fold change

FDR False discovery rate

FIB-4 Fibrosis 4

FIC1 Familial intrahepatic cholestasis type 1

FPF Fragment prime finish mix

FSA First strand synthesis act D

FU Follow-up

Fzd2 Frizzled class receptor 2

GATA4 GATA binding protein 4

GEO Gene Expression Omnibus

GGT γ-glutamyl transferase

GLI3 Glioma-associated oncogene family zinc finger 3

GRC Genomics Research Centre

GSEA Gene set enrichment analysis

HCC Hepatocellular carcinoma

Hnf4α Hepatocyte nuclear factor 4α

HNRNPA1 Heterogenous nuclear ribonucleoprotein A1

HSC Hepatic stellate cells

IDT Integrated DNA technologies

IL1RAP Interleukin 1 receptor accessory protein

IMB Institute for Molecular Bioscience

IPA Ingenuity Pathway Analysis

IQR Interquartile range

IRC Inter-run calibrator

ISPs Ion Sphere Particles

Itga2 Integrin α-2

K Rate constant

kPa Kilopascals

LATS1 Large tumor supressor kinase 1

Lenti miR LX2 cells transduced with miRNA of interest

Lenti NegCtrl LX2 cells transduced with miRNA negative control

LIG Ligation mix

logCPM Log-counts per million

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logFC Log-fold change

LPC Liver progenitor cells

LSM Liver stiffness measurement

LT-β Lymphotoxin-β

LX2 Lieming Xu-2 human hepatic stellate cell line

M Median

M value Expression stability value

m/s meters/second

MAP3K1 Mitogen activated protein kinase kinase kinase 1

MAP3K9 Mitogen activated protein kinase kinase kinase 9

miRISC miRNA-induced silencing complex

miRNA microRNA

MMP-1 Matrix metalloproteinase-1

MOI Multiplicity of infection

MYD88 Myeloid differentiation primary response 88

n Number

NA Not available

NaCl Sodium chloride

NAFLD Non-alcoholic fatty liver disease

Nfkβ1 Nuclear factor Kappa β-1

NGS Next-generation sequencing

NOX2 NADPH oxidase

NR1D1 Nuclear receptor-1 group D-1

nt Nucleotides

NUPR1 Nuclear protein 1

ORF Open reading frame

P P-value

PAMPs Pathogen associated molecular patterns

PBS Phosphate buffered saline

PD Pulldown

PH Prolyl hydroxylase

PHOX Phagocytic NADPH oxidase

PHT Portal hypertension

PILBDs Paucity of interlobular bile ducts

PMM PCR master mix

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PPC PCR primer cocktail

pre-miRNA Precursor miRNA

pri-miRNAs Primary miRNAs

Prob Probability

pSWE Point shear-wave elastography

Ptch1 Patched 1

PTCH1 Patched 1

Ptpn11 Protein tyrosine phosphatase 11

QBI Queensland Brain Institute

QC Quality control

qRT-PCR Reverse transcription-quantitative polymerase chain reaction

RBP RNA purification beads

RBP RNA binding proteins

RFX5 Regulatory factor X5

RFXAP Regulatory factor X associated protein

RISC RNA-induced silencing complex

RNases Ribonucleases

ROC Receiver operating characteristics

Rock1 Rho associated coiled-coil containing protein kinase 1

ROCK1 Rho associated coiled-coil containing protein kinase 1

ROI Region of interest

ROS Reactive oxygen species

rs Spearman's correlation coefficient

RSB Resuspension buffer

RT Reverse transcription

S Small

SAV1 Salvador family WW domain 1

SD Standard deviation

SEM Standard error of mean

SMAD3 Mothers against decapentaplegic homolog 3

SNPs Single nucleotide polymorphisms

SP1 Specificity protein 1

SSM Second strand marking master mix

SSWE Supersonic shearwave elastography

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STL Stop ligation buffer

TE Transient elastography

TEAD1 TEA Domain transcription factor 1

TEM Transmission electron microscopy

TEMED Tetramethylethylenediamine

TGF- β1 Transforming growth factor-β1

TGFβR1 Transforming growth factor-β receptor 1

TGFβRI TGFβ-receptor 1

TIMP-1 Tissue inhibitor of metalloproteinase 1

TIMP-4 Tissue inhibitor of metalloproteinase 4

TLR4 Toll like receptor 4

TNFα Tumour necrosis-α

TWEAK TNF-related weak inducer of apoptosis

UDCA Ursodeoxycholic acid

UTR Untranslated region

V Volts

VCL Vinculin

WD Wilson disease

WFUMB World Federation for Ultrasound in Medicine and Biology

WHO World Health Organization

WWC1 WW and C2 domain containing 1

XL Extra Large

Y Year

1

CHAPTER 1

Introduction and literature review ……

2

1.1 Chapter contribution

The microRNA sections on this chapter (1.13.2.2; 1.13.3;1.13.4; 1.13.5 and 1.13.6)

have been published in the following review article:


MicroRNAs and Their Potential as Biomarkers in Paediatric Liver Disease.

International Journal of Molecular Sciences 2016; 17:1795.

Diego A. Calvopina researched the content, wrote the manuscript and designed

tables and figures.

Miranda A. Coleman, Peter J. Lewindon and Grant A. Ramm edited, revised and

approved the content of the literature review.

3

1.2 INTRODUCTION

Chronic liver disease remains a growing burden for the health care system. Over the

past two decades a concerted research effort has taken place internationally to better

understand the aetiology and mechanisms associated with the development of

paediatric liver diseases. One driving force has been that liver disease with onset

during childhood is a precursor of chronic liver disease in adults. Prevention as well

as better clinical management of these liver diseases at an early stage can expect to

have major long-term impacts not just for individual patient care but also the whole

community.

In the US alone, approximately 15000 children are hospitalized for liver disease every

year (1). However, the relative lack of epidemiological research studies in children

masks the true prevalence of chronic liver disease which is likely underestimated.

Paediatric liver disease has an important impact on health care costs, leads to

premature death and impacts on quality of life in affected children. Paediatric liver

diseases are often diagnosed late, mainly due to the lack of symptoms during early

stages. The diagnosis is challenging when symptoms are non-specific such as loss of

appetite, abdominal pain or fatigue. Recent advances in the availability, accuracy and

affordability of molecular techniques and genetic testing have resulted in more routine

use in the clinic improving diagnosis and disease management. This has also lead to

a renewed focus on hereditary diseases such as cystic fibrosis (CF)-associated liver

disease (CFLD).

This literature review will discuss the epidemiology and pathogenesis of CFLD as well

as a brief overview of possible mechanisms by which hepatic injury occurs in some

patients with CF. There remain significant challenges in the diagnosis and assessment

of disease severity in paediatric CFLD, thus the following review will also discuss

current research approaches to provide better, novel diagnostic methods for CFLD

detection which will provide a preface to the research conducted in my PhD project.

4

1.3 Cystic fibrosis

Cystic fibrosis (CF) is the most common, lethal, inherited autosomal recessive disorder

in Caucasian populations, widespread especially in western countries. CF is caused

by a mutation on chromosome 7 encoding the cystic fibrosis transmembrane

conductance regulator (CFTR) protein (2). CFTR is expressed on the apical

membrane of epithelial cells in multiple organs including lungs, sweat glands, vas

deferens, pancreas, liver and intestines. To date more than 2000 mutations have been

described (3), the most common being a deletion of phenylalanine (F) at position 508

(∆F508), which encodes a binding domain of CFTR.

CFTR is part of the ATP-binding cassette (ABC) transporters which function as a

cAMP-dependent chloride channel (4), maintaining pH levels and diluting fluid

secretions (5). Defects in CFTR result in the inability of the organ duct lumen to hydrate

macromolecules which precipitate into concentrated, viscous secretions. As a

consequence of this precipitation, ductular fluid flow is reduced and properties of

secretions change, resulting in ductular or glandular plugging that culminates in

exocrine organ damage and the manifestations of CF (6, 7). CFTR mutations are

classified into six groups depending on the predicted functional consequences of the

mutation. Class I mutations produce non-functional proteins due to defective

biosynthesis. Class II mutations (includes ∆F508) result in a misfolded CFTR that is

intracellularly degraded. Class III mutations disrupt channel activation by preventing

hydrolysis of ATP at a binding domain of CFTR. Class IV mutations produce a

defective anion conduction protein due to abnormal splicing or promoter mutations.

Class V mutations are characterized by a reduction in functional CFTR proteins on the

apical surface. Class VI mutations produce a functional but unstable CFTR protein

due to a truncation at the C-terminus of CFTR (8) (Figure 1.1)

5

Figure 1.1. Classification of CFTR mutations.

Mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR). Classes I-III produce non-functional chloride channels (most

severe form of the disease), whereas classes IV-VI produce chloride channels with decreased activity (adapted from (9))

6

1.4 Epidemiology of cystic fibrosis liver disease (CFLD)

During recent decades the median life span of patients with CF has dramatically

improved, thanks to improved management of respiratory complications associated

with CF. In western countries the prevalence of CF is expected to increase by 50% in

2025, corresponding to 20% and 75% in children and adults respectively (10). Life

expectancy in CF has improved from 34.0 years during the period 1991-1995 to a

mean of 47.7 years in 2016 (11) and is now expected to exceed 50 years for infants

born after the year 2000 (12). This improvement in life expectancy has exposed a rise

in non-respiratory complications of CF such as CFLD, which is the leading cause of

non-respiratory mortality associated with CF. According to the World Health

Organization (WHO), CF affects 1 out of 2500 newborns (13), 15.6% of which develop

clinically significant liver abnormalities and 5% that develop significant morbidity

culminating in cirrhosis and death or transplantation (14). CFLD usually develops

during the first decade of life and presenting with significant clinical manifestation

during the second decade.

1.5 Pathogenesis of CFLD

CFTR is expressed on the apical membrane of cholangiocytes and gall bladder

epithelium (15). It regulates fluid and electrolyte content and therefore controls

intrahepatic biliary ductal secretion by increasing chloride transport and bile flow.

Malfunction or absence of CFTR affects chloride channels, decreasing bile flow.

Thickened secretions produce blockage of bile ducts which leads to acute and chronic

periductal inflammation, hepatic and cholangiocyte injury, bile duct proliferation and

depletion of hepatic antioxidants, which results in fibrosis of portal tracts.

CFLD is characterized by non-uniform portal tract abnormalities including focal biliary

fibrosis, which in some patients can lead to focal biliary cirrhosis (FBC) and eventually

multilobular biliary cirrhosis. Hepatic stellate cells (HSC) play an important role in

7

wound healing associated with liver regeneration as the main cells responsible for the

synthesis of fibrotic tissue in the liver following activation and transformation into

myofibroblast-like cells. In the early stages of liver injury HSCs are activated, probably

by increased levels of hydrophobic bile acids, causing damage to hepatocytes which

secrete HSC-stimulating growth factors and cytokines (16-18). As a result,

extracellular matrix (ECM) composition changes, enriching fibril-forming collagens

which increases ECM density and matrix stiffness. Activated HSCs generate scar

tissue as part of the wound healing process, which leads to fibrogenesis and

eventually to liver disease. As fibrosis advances, collagenous bands are formed

containing large numbers of activated HSCs that impede portal blood flow. Failure to

degrade fibril-forming collagens and scar matrix is an important determinant in fibrosis

progression. When liver injury persists, HSC activation and excessive collagen

deposition leads to FBC and in some cases multilobular cirrhosis (17).

An alternative hypothesis (and potentially additive mechanism) for the pathogenesis

of CFLD suggests that other factor such as CF-specific bacterial colonisation,

infections, constant exposure to antibiotic treatment and increased permeability of

intestines of CF patients contribute to the development of CFLD (19). This increased

permeability together with increased presence of an adverse CF microbiota leads to

the absorption of pathogen associated molecular patterns (PAMPs) to the portal

circulation, stimulating inflammation and fibrosis (20). Inflammation has been well

documented to participate in liver fibrogenesis (21, 22) and studies have shown

increased permeability in CF patients by several methods (23-25). Similarly, it has

been proposed that the leaky gut phenomenon is also responsible for the development

of fibrosis and portal hypertension (26), as shown by increased intestinal inflammatory

lesions and slower small bowel transit in cirrhotic CF patients (19). Altered permeability

(leaky gut) was present in >95% of CFLD patients and more important, the faecal

microbiome composition of these patients was significantly different compared to

patients that did not have liver disease (19).

8

Several risk factors have been associated with the development of CFLD. These risk

factors include the most severe CFTR mutations classes I-III, male sex, history of

meconium ileus or carriers of the SERPINA1 Z allele (27-29).

1.6 Mechanisms of fibrosis in CFLD

The accumulation of bile acids due to cholestasis and plugging of bile ducts has a toxic

effect on different liver cell types, including HSCs. Bile acids are a product of

cholesterol metabolism generated in hepatocytes that are secreted into bile ducts

where they are stabilised by bicarbonate ions and water from cholangiocytes as they

transit from the liver into the gall bladder. Their main function is to aid in the

solubilisation and absorption of lipids and lipid-soluble vitamins, after which bile acids

are recycled into the liver through the portal venous system. Bile acids are polar due

to their role in lipid absorption and digestion, which makes them toxic to hepatocellular

membranes (30). Bile toxicity is determined by its polarity; the more hydrophobic the

bile acid, the more toxic compared to hydrophilic bile acids (30).

Bile acid composition is altered in CFLD children and plays a role in the progression

of liver injury (16). Correlation exists between serum cholic acids and staging of

hepatic fibrosis in CFLD patients. Ursodeoxycholic acid (UDCA), a hydrophilic bile

acid, is increased in CF children with no liver disease (CFnoLD) compared to CFLD,

suggesting a protective function in the liver (16). In contrast, the hydrophobic bile acid

taurocholate has been shown to be significantly increased in bile and serum of CFLD

children (31). As with others bile acids, taurocholate increases the expression of

inflammatory genes in the liver, stimulating cytokine secretion and HSC migration (31-

34). Hepatocellular injury produced by bile acid toxicity results in the activation of

quiescent HSCs into collagen-producing myofibroblasts. Bile acid toxic effects are

listed in Table 1.1.

9

Table 1.1. Bile acid toxic effects.

Toxicity effect Activation mechanism on HSC

Apoptosis Degradation of cellular components to

apoptotic bodies phagocyte by Kupffer

cells and HSCs (35-37)

Reactive oxygen species (ROS) Lipid peroxidation metabolites

produced by impaired electron

transport in mitochondria of liver cells

(38, 39)

Phagocytic NADPH oxidase (PHOX) Catalyses production of ROS in

Kupffer cells, which produce tumour

necrosis-α (TNF-α) that activates

HSCs (40, 41)

NADPH oxidase (NOX2) Induce oxidative stress directly in

HSCs (40, 42)

Necrosis At high concentrations, hydrophobic

bile acids have detergent action and

cell necrosis is predominant (43)

1.7 Ductular reaction

During acute injury of the liver, hepatocyte-mediated liver regeneration predominates

(44). In the case of severe or chronic injury, bipotential adult stem-like cells termed

liver progenitor cells (LPCs) are activated in periportal regions of the liver or the Canals

of Hering (45). These progenitor cells have the capacity to differentiate into biliary

epithelial cells and hepatocytes. The ductular reaction (DR) refers to the phenotypic

reaction of LPCs that differentiates into biliary epithelia (46). During chronic liver

injury, the disorganised DR affects liver architecture producing bile duct hyperplasia

or proliferation. Furthermore, the DR not only manifests as a proliferation of bile ducts,

10

but also with infiltration of inflammatory cells in portal areas which contribute to

fibrogenesis and progression of liver injury (47). LPCs are found in close association

with other liver cells including HSCs and macrophages (48) which incorporates the

LPC niche. Cellular interactions occurring in this niche are driven via different

signalling pathways involving Wnt, Notch or Hedgehog (49, 50).

A clear correlation exists between the DR and the severity of fibrosis across various

liver pathologies including biliary atresia, chronic hepatitis C, non-alcoholic

steatohepatitis and CFLD among others (51-55). Animal model studies have shown

that reduced numbers of LPCs are associated with a reduction in liver fibrosis (56). In

contrast, increased numbers of LPCs are reflected in an increase in liver fibrosis (57).

In fact, proinflammatory and profibrogenic factors released by LPCs and cells of the

DR are hypothesised to interact with HSCs playing an important role in fibrogenesis

(58).

1.8 Manifestations of CFLD

Different definitions and classifications have been used for the diagnosis of CFLD,

including the use of clinical assessment and/or biochemical signs of liver disease.

However, the variability in definitions between different centres and indeed countries

makes CFLD classification challenging. In 2007, the Cystic Fibrosis Foundation in the

United States proposed a classification system based on phenotypic characterization

of liver involvement, separating cirrhosis with or without portal hypertension from other

liver diseases in an attempt to standardised international reporting data (59) (see

Table 1.2).

11

Table 1.2. CFLD classification.

1.9 Detection of CFLD

The reasons why only some children with CF develop clinically significant liver disease

are unclear. Even though only 5-10% of patients will develop portal hypertension and

cirrhosis, autopsy studies report significant liver damage and fibrosis in 72% by the

end of life (60). Most children in the CF clinic will develop blood test liver abnormalities

during their follow up and many will develop abnormal ultrasound echotexture (61). In

other conditions, persistence of abnormal liver tests and abnormal ultrasound would

constitute evidence of liver disease that might expect over time to lead to liver scarring

and liver failure. This is less certain in CFLD where there are other temporary causes

of these blood and ultrasound changes (nutritional and drug related) that will not lead

to serious liver scarring. Hence, the challenge is not just the definition of liver disease

prior to the manifestation of its most advanced stages of portal hypertension or

cirrhosis, but how to detect and discriminate those children with CF who are going to

•Multilobular cirrhosis. Multiple regenerative nodules and diffuse involvement of the liver

•Portal hypertension without cirrhosis. May be present prior to the onset of cirrhosis

•Complications of cirrhosis with portal hypertension. Including splenomegaly, esophageal or gastric varices, ascites, malnutrition

CF cirrhosis with or without portal hypertension

•Focal biliary cirrhosis (FBC). Pathognomonic sign of CFLD. Rarely, progresses to multilobular cirrhosis

•Elevation of aminotransferases or liver enzymes (AST, ALT or GGT). Intermittent elevation in CF patients. It cannot predict development of fibrosis

•Hepatic steatosis. Most common hepatic finding in CF patients

Liver involvement without cirrhosis or portal hypertension

•Cholangiopathy. Abnormalities in intrahepatic bile ducts

•Cholestasis. Earliest manifestation of liver involvement in CF patients

•Gallbladder involvement. Abnormalities such as microgallbladder or gallbladder distension and dysfunction have been reported in CF patients

Biliary tract involvement

12

develop clinically significant and life changing liver scarring from those who will not,

and to detect it early enough to permit and develop interventions to improve later

outcomes. Therefore, early detection of CFLD is crucial for better clinical management

and in identifying patients at risk of developing cirrhosis and portal hypertension (59).

However, paediatric liver diseases are often missed or diagnosed late, mainly due to

the lack of symptoms in early stages of the disease. CFLD is frequently asymptomatic

and shows a wide spectrum of manifestations. The lack of a uniform clinical definition

and classification for CFLD only contributes to an already challenging identification.

Currently, there are two accepted definitions for CFLD. In North America, CFLD is

classified by the presence of advanced liver damage with cirrhosis/portal hypertension

or liver abnormalities such as persistent elevation of liver enzymes, steatosis, fibrosis,

cholangiopathy and/or abnormal ultrasound findings (59) (Table 1.3). In Europe,

following the guidelines and recommendations by the European Society for Paediatric

Gastroenterology, Hepatology and Nutrition (ESPGHAN), CFLD should be considered

when at least two of the following conditions are present after the exclusion of

steatosis: abnormal physical examination defined by the presence of hepatomegaly

and/or splenomegaly, persistent abnormalities of liver function test defined as increase

levels of transaminases (AST and ALT) and GGT above 1.5 times the upper normal

limit over 12 months and evidence of liver involvement by ultrasonographic means or

portal hypertension (62) (Table 1.3).

13

Table 1.3. Current accepted CFLD definitions.

North America* (CFLD classification)

Europe** (considered with at least two of the following characteristics)

CFLD with cirrhosis/portal hypertension. Based on:

Clinical exam

Imaging

Histology

Laparoscopy

Hepatomegaly

Increased liver span relative to age or 2cm below mid-clavicular line

Prominent left lobe in epigastrium in cases of multilobular cirrhosis

Splenomegaly

Confirmed by ultrasound

Liver involvement without cirrhosis/portal hypertension. Consisting of at least one of the following:

Persistent elevation AST, ALT, GGT>2 times upper limit or intermittent elevation above laboratory values

Steatosis (histologic determination)

Fibrosis (histologic determination)

Cholangiopathy (based on ultrasound, MRI, CT)

Ultrasound abnormalities not consistent with cirrhosis

Abnormal liver function tests (above upper normal limit over 12 months)

Increase transaminases levels (AST and ALT)

Increase GGT levels

Preclinical. No evidence of liver disease.

Liver involvement by ultrasonographic means

Increased and/or heterogeneous echogenicity

Irregular margins

Nodularity

Portal hypertension

Increased thickness of lesser omentum

Splenorenal anastomosis

Ascites * Adapted from Flass et al. (59). ** Adapted from Debray et al. (62).

Both definitions use non-specific diagnostic criteria and prioritise advanced liver

disease and cirrhosis against early phases of the development of the disease that

could lead to epidemiological confusion, as discussed by Debray et al. (60) and thus,

a common definition of CFLD and a better and standardized method for CFLD

diagnosis are necessary.

14

1.10 Liver biopsy

Liver biopsy is considered the ‘gold standard’ to assess disease severity, in particular

fibrosis, and stratify paediatric liver disease. Since its introduction in the decade of

1950 (63) several improvements such as dual-pass liver biopsy which increased the

predictive value of liver biopsy in CFLD (64) have been implemented, however, there

are still major limitations in its use. When combined with histopathological techniques

such as transmission electron microscopy (TEM) or immunohistochemistry, liver

biopsy can be a powerful tool for assessing liver diseases. Constant improvements

understanding the aetiology, molecular basis and treatment of paediatric liver diseases

have determined the indications in which liver biopsy is advised. In children these

indications include forms of cholestasis, autoimmune hepatitis, metabolic liver

disorders or viral hepatitis as summarised in Table 1.4.

Table 1.4. Clinical value of liver biopsy in paediatric chronic liver diseases.

Disease Value of liver biopsy

Histopathological findings

Comments

Biliary atresia (BA) Diagnostic Ductular reaction, bile plugs, portal-tract expansion by

oedema and fibrosis

90% diagnostic accuracy

(experienced pathologist) (65).

Α1-antitrypsin disorder

Diagnostic Granular cytoplasmic bodies

stained with periodic acid-Schiff stain after diastase

digestion

Not required for diagnostic.

Unclear if helpfully reflect liver

disease severity (66)

15

Disease Value of liver biopsy

Histopathological findings

Comments

Alagille syndrome (AS)

Diagnostic Paucity of interlobular bile ducts (PILBDs)

PILBDs may not be present in

young infants (67)

Familial intrahepatic

cholestasis type 1 (FIC1)

Diagnostic Canalicular cholestasis and hepatocellular

ballooning

TEM of sample containing

coarsely and loosely granular

canalicular content (68)

Cystic fibrosis liver disease (CFLD)

Diagnostic Steatosis or focal biliary cirrhosis

Lesions focally distributed

Non-alcoholic fatty liver disease

(NAFLD)

Diagnostic Increased inflammation in

portal tracts, steatosis with less

Mallory hyaline bodies

Liver biopsy required for

definitive diagnosis. It

excludes other diseases if

advanced disease is suspected (69)

Autoimmune hepatitis (AIH)

Diagnostic Dense mononuclear and

plasma cell filtration, interface

hepatitis, parenchymal

collapse

Liver biopsy necessary to

confirm diagnosis and mandatory to

document absence of

inflammation(70)

Wilson disease (WD)

Diagnostic Liver copper content ≥250µg/g

(dry tissue)

Sampling error in paediatric patients can render test as

unreliable in patients with cirrhosis and

evidence of WD (71)

Viral hepatitis (hepatitis B and

hepatitis C)

Diagnostic

Prognostic

Grade of inflammation and stage of fibrosis

Liver biopsy recommended for compensated liver

disease before initiating therapy

for hepatitis B (72)

16

The standard represents 1/50000th of the entire liver. It is estimated that 11 complete

portal tracts and a diameter of between 1.2 and 1.8 mm with a length from 7 to 9 cm

is required to adequately diagnose chronic hepatopathies (73, 74). The small size of

the biopsy sample and the focal nature of liver diseases, especially CFLD, can

misrepresent the overall liver histology. In these cases, a dual pass liver biopsy can

improve the detection of fibrosis as has been reported in paediatric CFLD (64).

Liver biopsy needs special consideration when performed in children. Lack of patient

cooperation requires the use of general anaesthesia in an operating room for the

procedure, which makes liver biopsy more complex and expensive than in adults. Inter

and intra-observation variation is a limiting factor in the interpretation of liver biopsy

(75), which highlights the need for experienced pathologists. Inexperience accounts

for 25% of all misdiagnosed cases (76).

Complications of biopsy can be categorised as either major or minor. Minor

complications include pain, subcapsular bleeding without transfusion, infection, bile

leak or haemobilia and arteriovenous fistula. Major complications include bleeding,

haemobilia (requiring transfusion, surgery or intensive care management),

pneumothorax or haemothorax and death (77). Minor complications generally occur

during the first two hours after biopsy in 60% of cases, whereas major complications

occur within the first 24 hours in 96% of cases (78).

Bleeding in which haemodynamic repercussion or transfusion is required, occurs in 1

of 10000 biopsies in adults (79). In children bleeding has been reported in 2.8% of

cases (80), with this incidence increased to 15% in children with oncological disease

(81). A recent study reports an increased risk of bleeding in patients with focal lesions

and administrated with low molecular weight heparin (82). Moreover the same study

shows that routine ultrasonography after liver biopsy revealed unsuspected

haemorrhage in 2.6% of the cases (82).

17

Pain is the most common informed complication after biopsy. Pain is reported at the

site of puncture or as referred pain in the right shoulder. It affects 84% of adult

patients; however no studies have been conducted in children (83). As a less common

complication, only one case of arteriovenous fistula with a fatal outcome has been

shown after transplantation (84). Similarly, there is only one case (0.2%) of

pneumothorax in a paediatric study (80). Cases of organ perforation in adults are low

with only 0.07% of all cases (85). Rare complications include bile leak and haemobilia

with an incidence of 0.6% in children (80). Systemic or local infections are both a

possible complication, although extremely rare. The rate of death from liver biopsy

was reported in one study to be ~0.6% (80), however recent studies have not reported

any deaths (82, 86).

1.11 Imaging techniques

Additional non-invasive modalities such as imaging techniques are commonly utilised

in the diagnosis of CFLD. Ultrasound is routinely used during clinical assessment and

considered in the CFLD diagnostic criteria (Table 1.3). However, ultrasound has poor

sensitivity and specificity for staging fibrosis (87). It can detect cirrhosis but results are

unreliable for differentiating early stages of liver fibrosis from steatosis, which is

sonographically similar to focal biliary cirrhosis. In fact, children with normal

ultrasound can have advanced fibrosis (87). Non-concordance has been reported

between liver function tests and ultrasound scores (88, 89). Despite these limitations,

ultrasound has proven to be a useful diagnostic tool for complications of portal

hypertension such as ascites, gallstones, bile duct stones or splenomegaly (87).

Similarly, magnetic resonance imaging especially when combined with magnetic

resonance cholangiography, can detect intra and extrahepatic bile duct alteration,

periportal fibrosis and evaluate parenchyma (90). Magnetic resonance

cholangiography alone can detect cholangiopathy in CF patients more reliably than

ultrasound (91) however the presence of cholangiopathy does not correlate with the

presence of, nor progression to, more advanced liver fibrosis. Furthermore, newer

18

‘next generation’ magnetic resonance imaging can also be used for determining fat

content or steatosis of patients (92), which remains of academic interest only.

1.12 Liver enzymes

Aspartate amino transferase (AST), alanine amino transferase (ALT) and γ-glutamyl

transferase (GGT) are widely used as biochemical markers of liver function. However,

in CF their elevation is intermittent and does not always correlates with histological

findings, meaning these markers are not reliable enough for detection of fibrosis or its

progression. Changes in the levels of these biochemical markers can be due to

infection, medication and furthermore, children with advanced fibrosis can show

normal biochemistry. According to the guidelines provided by the Cystic Fibrosis

Foundation, CFLD must be suspected in any case where liver enzymes are elevated

by more than 1.5 times the upper limit on two subsequent occasions (93).

1.13 Novel non-invasive methods of liver disease detection

Due to the issues outlined above associated with the use of liver biopsy and the lack

of sensitivity and specificity of existing modalities to diagnose CFLD, current

investigations are focussed on biological and physical properties of the liver to

indirectly assess the degree of fibrosis. Liver biological properties are surveyed

through the study of circulatory biomarkers (either serum or plasma), whereas physical

properties are assessed by measuring liver stiffness via the use of modified ultrasound

techniques such as elastographic imaging.

1.13.1 Elastographic imaging

Elastographic imaging relies on the principle of transient elastography (TE) to measure

liver stiffness which increases with the progression of fibrosis and cirrhosis. TE uses

19

low frequency elastic waves that are transmitted via an ultrasound a probe applied

against the body wall to measure liver stiffness. The velocity of the wave propagation

is proportional to the collagen fibres in the liver; thus the more collagen, the higher the

stiffness, the faster the waves are propagated and higher the median transient

elasticity value (94). Liver stiffness correlates with liver fibrosis in different liver disease

aetiologies such as primary biliary cirrhosis (95), NAFLD (96) or cholangitis (97). No

TE method directly images the elastic properties of the liver, thus, to observe liver

deformation in response to the elastic waves applied, an existing modality such as

ultrasound is used to display an elasticity image or elastogram. Therefore, TE shows

the tissue response to mechanical excitation producing a quantitative image

corresponding to the stiffness of the liver. Differences of elasticity of soft tissues are

expressed by elastic moduli such as the Young’s modulus (E), which represents liver

resistance to be deformed by mechanical stimuli. Young’s modulus is expressed in

kilopascals (kPa) and is expressed by E=3PC2, where P represents the tissue density

(~ 1000 kg/m3) and C the shear wave velocity (m/s) (98).

Currently, there are three main ultrasound-based elastography methods

commercialised: transient elastography (TE, Fibroscan ®), acoustic radiation force

impulse (ARFI, VTQ) and Supersonic shearwave elastography (SSWE).

1.13.1.1 Fibroscan

Fibroscan measures the velocity of propagation of an elastic shearwave through the

liver. The shearwave is produced by a piston vibrator placed in the thoracic wall

between the intercostal ribs. The speed of the wave is measured by a pulse-echo

ultrasound one dimensional probe and is proportional to the liver stiffness. The area

of the liver assessed is measured at a depth of 25-65mm in an area of 1x4cm, 100

times of the area assessed by liver biopsy (99). Fibroscan allows multiple readings

from different areas which decreases sampling error. Measurements are considered

to be accurate when the ratio between successful measurements and total

measurements is ≥60% and the interquartile range (IQR) is within 30% of the median

20

(100). In most cases the successful rate is affected by the lack of short and direct path

to the liver where the penetration is limited. Furthermore, accuracy is decreased by

patient characteristics such as narrow rib interspace or patients with obesity or ascites

(101). The loss of accuracy due to obese patients has been partially overcome by the

introduction of the XL (Extra Large) probe obtaining 61% of readings that were

unreliable using the M (Median) probe (102). Similar studies have been conducted in

children using the S (Small) and M probes based on thorax circumference (103).

However, special consideration must be taken as using different probes introduces

measurement bias as the technical characteristics are variable between probes and

therefore not necessarily comparable (104).

Fibroscan has been successfully used to demonstrate increase liver stiffness in

several diseases including CFLD, biliary atresia or non-alcoholic fatty liver disease

among others (105-109).

Fibroscan is efficient at distinguishing cirrhotic patients from non-cirrhotic as high

elastography values normally are indicative of cirrhosis. However, its values have

been shown to be elevated by extrahepatic cholestasis independent of liver stiffness

due to fibrosis in patients with biliary atresia suggesting that caution needs to be

exercised in some patients with cholestatic liver disease (110).

1.13.1.2 Acoustic radiation force impulse (ARFI)

As the name implies, ARFI uses short duration acoustic pulse waves of less than 1

millisecond as a mechanism to stimulate a force pulse through the liver. ARFI is

integrated into a standard ultrasound machine which facilitates a complete deep

ultrasound examination of the liver which informs about the mechanical properties in

vivo. Having ARFI integrated to an ultrasound machine gives the operator the

opportunity to choose the region of interest (ROI) where stiffness is measured,

avoiding heterogeneous and vascular regions which decreases unreliable

21

examinations compared to Fibroscan (111). The shear wave speed is expressed in

meters/second (m/s) and quantified in a ROI of 1x0.5 cm (112).

Correlation between Fibroscan and ARFI fibrosis indices have been reported in adult

patients with CFLD during a prospective study assessing CFLD simultaneously by

Fibroscan and ARFI (113). As with studies using Fibroscan, only a few ARFI studies

have been conducted on paediatric CFLD patients (114, 115). Despite the evidence

suggested by these studies on the feasibility of ARFI to assess CFLD, correlation

between elastrography values and histological findings are not available in children.

1.13.1.3 Supersonic shearwave elastography (SSWE)

SSWE is a two dimensional study in which the shearwave is generated by a focalized

ultrasound pulse. The push pulses create a real time plane shear wave at different

frequencies ranging from 60 to 600Hz which allows a synchronous evaluation of

several shear waves in a wide frequency range that propagates across the tissue (116,

117). Similar to ARFI, SSWE is integrated into an ultrasound system which allows real

time assessment, detection of focal lesions and choice of ROI. The speed of the pulse

transmitted through the liver is estimated by Doppler-like acquisition in real time (118,

119). The estimated elasticity is colour coded creating a 2D quantitative liver map

expressed in kPa and m/s. The real time acquisition means that ROI can be selected

and adjusted to represent a better average physiological condition and therefore

fibrotic stage. Additionally, SSWE uses shear waves with greater bandwidths which

should also improve the differentiation between intermediate liver fibrotic stages (116).

Accuracy of measurements with the SSWE is dependent in the operator and patient

characteristics such as obesity that decrease the rate of reliable examinations.

Furthermore, a recent study in obese adult patients showed increased reliable

measurements in experienced operators compared to novice operators (120).

22

1.13.2 Circulatory biomarkers

1.13.2.1 Serum biomarkers

The development of fibrosis in all chronic liver diseases occurs via an imbalance

between collagen deposition/synthesis and degradation (121). Evaluation of the

molecules involved in fibrogenesis has been of significant interest in recent years both

from a mechanistic viewpoint but also in the search for better diagnostic biomarkers.

Collagen type IV (COL-IV), tissue inhibitor of metalloproteinase 1 (TIMP-1), prolyl

hydroxylase (PH) or matrix metalloproteinase-1 (MMP-1) are but a few such

biomarkers that have been proposed as promising candidates for the early detection

of CFLD (122, 123). TIMP-4 and endoglin have also been described as potential

biomarkers for CFLD. Increased expression of TIMP-4 and endoglin correlates with

fibrosis staging and interestingly, in combination with TE measurements, diagnosis

can be improved (123). Similarly, the aspartate aminotransferase (AST) to platelet

ratio index (APRI) and the fibrosis 4 (FIB-4) indices are readily available via routine

blood tests and are simple serum biomarkers that have been shown to be significantly

increased in CFLD children and are capable of discriminating liver disease in CF

children (124).

1.13.2.2 MicroRNA (miRNA)

miRNAs are short interfering RNAs which catalytically silence gene expression at a

posttranscriptional level. They constitute the most abundant class of endogenous

small non-coding RNA with approximately 50000 copies per cell in the liver (125).

Since Ambros’ discovery in 1993 (126), miRNAs have been extensively investigated

due to their role in RNA-induced silencing. In 2000, the miRNA let-7 was identified

(127). In contrast to previous miRNAs described, let-7 was shown to be widely

conserved across different animal species (128). This breakthrough discovery started

23

an intensive search for novel miRNAs conserved over different species including

humans (125, 129-131).

1.13.3 miRNA biogenesis

Mature miRNAs are single stranded RNAs of about 17-24 nucleotides (nt) which

interact with RNA-induced silencing complex (RISC) in the cytoplasm of eukaryotic

cells (132). miRNAs are encoded by specific genes that are transcribed by RNA Pol

II into polyadenylated and capped stem-loop transcripts termed primary miRNAs (pri-

miRNAs) (133, 134). Most miRNA genes encode a single miRNA, however, some

miRNAs are encoded in clusters and can include up to six miRNAs with a similar

sequence (135). A minority of miRNAs are located in introns of coding genes which

form miRNA precursors during splicing (136). miRNA biogenesis is regulated at a

transcriptional and post-transcriptional level and single nucleotide polymorphisms

(SNPs) in miRNA genes can modulate activity and function (137).

pri-miRNA undergoes a process of maturation in the nucleus, mediated by a complex

called microprocessor. The microprocessor is formed by RNase III endonuclease

Drosha and RNA-binding protein DGCR8 (138, 139). Drosha cuts the stem loop of

the pri-miRNA, releasing a hairpin shaped RNA of 60-70nt with a two nucleotide 3’

overhang named precursor miRNA (pre-miRNA) (140).

Once Drosha has processed pre-miRNA, it is exported to the cytoplasm by a complex

formed of exportin 5 and GTP-binding nuclear protein RanGTP (141, 142). In the

cytoplasm, pre-miRNA is processed by the RNase III endonuclease called Dicer (143,

144). Dicer recognizes the 5’ phosphate and 3’ overhang at the base of the stem loop

and cuts both RNA strands, liberating a mature miRNA with 5’ phosphate and two

nucleotide 3’ overhang on each end of the double stranded RNA (145, 146). The

mature miRNA processed by Dicer consists of a guide strand, which is antisense to

the target sense strand on mRNA, and an unstable passenger strand.

24

The recently formed mature miRNA is loaded onto a protein called argonaute (AGO)

which is a family of four members (147). This interaction forms an effector complex

known as RNA-induced silencing complex (RISC) (148, 149). AGO proteins present a

PAZ domain in the N-terminal lobe which binds to single stranded and duplex RNA

(150, 151).

Assembly of RISC involves two steps: the binding of miRNA duplex and its unwinding

(152). In humans there is no strict RNA sorting system as in other species such as

Drosophila (153). RNA binds to any of the four AGO proteins with preference of small

RNA duplexes with central mismatches between nucleotides in position 8-11 (154,

155). Once the RNA duplex is bound to an AGO protein, the passenger strand is

removed to generate the mature and functional RISC complex (148). This process is

mediated by AGO2 which has helicase and endonuclease activity (156). The guide

strand presents mismatches at positions 2-8nt and 12-15nt that promote the unwinding

of the duplex (154, 157).

1.13.4 miRNA target identification

Identification of the target mRNA by the miRNA within the RISC complex is essential.

Watson-Crick base pairing of the nucleotides on the 5’ end of the miRNA and the target

mRNA take place during this interaction (158-160). The region in which mRNA and

miRNA hybridization occurs is termed the “seed” sequence (158, 161) and consists of

a minimum of six base pairs (bp) which correspond to nucleotides 2-7 from the 5’ end

of the miRNA (3’UTR of the mRNA). The seed sequence can be up to 8bp in length,

based on the homology at position eight or the presence of an adenine (A) at

nucleotide position one of the target mRNA (161).

However, seed matching alone is not enough to identify validated targets (162). The

context in which seed matching occurs plays an important role in determining miRNA

25

targets in the canonical 3’UTR or different segments of the mRNA. Grimson et al. (162)

described the characteristics of the most common miRNA targets (Table 1.5). These

parameters have been implemented in computational tools and algorithms which

predict miRNA targets.

Table 1.5. miRNA target identification parameters.

1.13.5 Circulatory miRNA as biomarkers

miRNAs finely adjust rather than completely repress gene expression (163). It is

estimated that most of the genes expressed in mammals (164) are to some extent

regulated by the 2654 mature miRNAs that have been identified to date (165). Many

biological pathways are regulated by miRNAs including proliferation, differentiation,

apoptosis, cell cycle regulation and development. Furthermore, the roles of miRNAs

have been described in several diseases, including cancers (166-169), coronary

diseases (170-172), autoimmune diseases (173-175) or viral infections such as viral

hepatitis (176-179). miRNAs are predominantly present intracellularly, however, it is

possible to find miRNA in extracellular environments such as serum, plasma, semen,

cerebrospinal fluid or urine (180-187). Circulatory miRNAs are relatively stable,

especially compared with most extracellular RNAs. miRNAs are packed in vesicles or

Parameters for the

identification of miRNA

targets

Closely spaced miRNAs often act synergistically

Watson-Crick pairing at nucleotide 12-17nt in addition to seed match enhanced miRNA targeting

Effective targets reside within locally AU-rich context

Effective targets reside in 3'UTR strand but not close to stop codon

Effective sites preferentially reside near both ends of the 3'UTR

26

in association with RNA binding proteins to prevent digestion by ribonucleases

(RNases) (188).

Two hypotheses have been proposed regarding the origin of miRNAs in the circulation.

First, cells evolved to selectively release miRNA in paracrine or endocrine routes to

mediate cell-cell signalling (189, 190). Second, miRNAs are released in a non-

selective manner after cell death, which correlates to the increased levels of miRNAs

in blood after toxicity in certain organs (191-193). Regardless of their origin, it is clear

that the presence of miRNAs in readily accessible body fluids makes them attractive

biomarker candidates. The ideal biomarker should meet stringent criteria, such as

being disease specific, detected in a non-invasive manner, be an indicator of disease

at an early stage of disease, or responsive through the progression of the disease or

treatment. miRNAs are disease specific, tissue specific, stable in circulation and

capable of distinguishing between healthy and diseased individuals, making

circulating miRNAs attractive biomarkers (194).

1.13.6 Circulatory miRNAs as biomarkers in paediatric liver disease

Several studies have explored the potential targets and mechanistic role of miRNAs

in chronic liver diseases, including those affecting children (reviewed elsewhere

(195)). These studies used liver tissue or animal models to identify miRNA expression

profiles in the context of a specific disease. Once miRNAs of interest had been

selected, liver and biliary cell lines were used to identify potential target genes and

their effect on protein synthesis, mRNA expression or their role in different cellular

functions and pathways. These studies not only show the importance of gene

regulation through miRNAs in liver disease, but also the complex mechanisms that

drive fibrosis in which miRNAs also play an important role. For instance, miR-122 has

been the subject of extensive investigation in liver disease. miR-122 knockdown

experiments in mice demonstrated important hepatopathological effects of miR-122

as mice aged including hepatocyte proliferation, imbalance in cellular differentiation

and ductular reaction (196). Similarly, multiple studies have shown increased serum

27

levels of miR-122 corresponding to different liver aetiologies, suggesting that

upregulation of miR-122 in serum may be a global marker of liver injury rather than

disease-specific (193, 197-199). These evident roles for miRNAs in physiological and

pathological pathways and their reflection in the circulation could be utilised for the

non-invasive detection of CFLD.

Few studies have explored the utility of circulatory miRNAs as biomarkers for CFLD.

Sera from children diagnosed with CFLD, cystic fibrosis but no liver disease (CFnoLD)

and healthy control children were collected to identify a circulating miRNA signature in

CF children using a limited miRNA PCR array (200). In this pilot study, 84 miRNAs

were evaluated by PCR array and validated by qRT-PCR. Analysis revealed

upregulation of serum miR-122 in the CFLD group compared to both CFnoLD and

control groups. Furthermore, miR-21 and miR-25 were elevated in sera from CFnoLD

compared to both CFLD and control groups. Therefore, a panel consisting of miR122,

miR-21 and miR-25 showed the potential for early diagnosis of CFLD demonstrating

the potential utility of serum miRNAs to diagnose liver disease in CF children.

1.14 HYPOTHESES AND AIMS

There is no consensus about the definition and classification of CFLD. Currently,

annual clinical reviews, physical examination, biochemical markers, imaging

techniques and liver biopsy (not widely employed) are used to assess liver

abnormalities in CF patients. While of some use in the detection of CFLD, these

approaches are insensitive and non-specific in the detection and monitoring of fibrosis

severity. Moreover, the health risks and discomfort associated with liver biopsy,

reinforces the need for a better non-invasive diagnostic method. This non-invasive

method should be able to determine CF patients at risk of cirrhosis prior its

development and to identify patients with a compromised liver who present without

showing any biochemical or imaging signs of liver disease.

28

Therefore, the hypotheses of this thesis are:

1. That serum miRNAs and Supersonic shearwave elastography (SSWE) can be

used as non-invasive methods for the diagnosis and assessment of liver fibrosis

in children with CF.

2. That miRNAs differentially expressed in children with CFLD regulate the

expression of genes in hepatic stellate cells (HSC) and/or liver progenitor cells

(LPC) involved in the development of fibrosis and ultimately progression to

cirrhosis.

Hence, the aims proposed for this thesis are:

1. To evaluate the clinical utility and feasibility of Supersonic ShearWave

Elastography as a screening tool for both the early detection of liver disease

and monitoring liver disease progression in children with CF.

2. To identify novel and previously described circulatory miRNAs which are

differentially expressed among children with CFLD versus those with

CFnoLD

3. To identify downstream targets of differentially expressed miRNAs involved

in fibrogenesis and disease progression to cirrhosis and their potential role

in CFLD

29

CHAPTER 2

Materials and Methods………

30


Segments of the material and methods described in this chapter (in sections 2.2.1;

2.4.1; 2.4.3; 2.4.5; 2.5.1; 2.5.3; 2.5.4 and 2.5.6) have been published in the following

research article:

Calvopina DA, Chatfield MD, Weis A, Coleman MA, Fernandez-Rojo MA, Noble C,

Ramm LE, Leung DH, Lewindon PJ, Ramm GA. MicroRNA Sequencing Identifies a

Serum MicroRNA Panel, Which Combined With Aspartate Aminotransferase to

Platelet Ratio Index Can Detect and Monitor Liver Disease in Pediatric Cystic Fibrosis.

Hepatology 2018; 68:2301-2316.

Diego A. Calvopina allocated patients into their respective cohorts (under the

supervision of Peter J. Lewindon and Grant A. Ramm), extracted miRNA from serum

samples, constructed miRNA libraries, performed miRNA reverse transcription and

miRNA qRT-PCR, collected, analysed and interpreted data and wrote the research

article.

Mark D. Chatfield performed the more advanced statistical analyses (e.g. logistic

regression, concordance statistic and Obuchowski method). Grant A. Ramm

conceived the idea for the study. Anna Weis and Louise E. Ramm collected clinical

data. Peter J. Lewindon assisted in study design and collection of tissue specimens

and blood samples. Charlton Noble helped enrol patients and collected tissue

specimens and blood samples. Miranda A. Coleman, Manuel A. Fernandez-Rojo and

Daniel H. Leung provided advice on study design.

31

2.2 Patients

2.2.1 Patient recruitment and characteristics

This study was approved by the Human Research Ethics Committees of the QIMR

Berghofer Medical Research Institute (Brisbane, Australia) and the Children’s Health

Services Queensland (Brisbane, Australia) under the reference number

HREC/10/QRCH/87. Parental informed consent was obtained for every patient prior

to data collection.

All children were prospectively evaluated for this study while attending the Queensland

Children’s Hospital (Brisbane, Australia) and allocated in one of three study cohorts

(CFLD, CFnoLD, Controls), as described below. Children with CFLD were selected by

the presence of at least two of the following three signs or symptoms: hepatomegaly

± splenomegaly; persistent elevation (> six months) of serum alanine

aminotransferase (ALT >1.5x upper limit of normal) and abnormal ultrasound scan

(showing abnormal echogenicity or nodularity suggesting cirrhosis as has previously

been described) (60, 62, 64).

In the serum miRNA biomarker study, archival serum samples were accessed and all

of these patients had percutaneous dual-pass liver biopsies to assess CFLD and stage

liver fibrosis, as previously described (64). Liver histology was staged for hepatic

fibrosis by two independent and blinded pathologists based on the METAVIR scoring

system as follows: F0: no fibrosis; F1: portal fibrosis only (minimal scaring); F2: portal

fibrosis with rare fibrous septa; F3: portal fibrosis with numerous fibrous septa and F4:

cirrhosis. The higher fibrosis stage was used if fibrosis stages between the two

biopsies were not the same.

In the Supersonic ShearWave elastography study, patients were prospectively

enrolled with CFLD children subcategorised in no/mild/moderate fibrosis (equivalent

to METAVIR score F0-F2) or severe fibrosis/cirrhosis (equivalent to METAVIR score

32

F3-4). In the absence of liver biopsy (as biopsy is currently not routinely performed in

these patients), severe fibrosis/cirrhosis was designated by two separate approaches:

(i) clinical assessment based on the presence of portal hypertension and ultrasound

scan showing unequivocal macronodularity (60, 64) or (ii) APRI cut-offs based on a

previous study (124), where children with an APRI score >0.462 were classified as

having severe fibrosis/cirrhosis.

Children with CF but no signs of liver disease by clinical, biochemical or

ultrasonographic means in the two years prior to sampling or in annual clinical follow-

up performed over the duration of the study were selected as CFnoLD. A group of

non-CF and non-liver disease paediatric control patients were enrolled as the healthy

controls while attending the hospital for minor routine procedures such as:

orthopaedic, plastic, otolaryngology and burns consultations.

APRI and FIB-4 scores were calculated as described previously (124), according to

the following equations:

APRI =

AST (U L)⁄Upper limit of normal AST (U L)⁄ × 100

Platelet Count (109 L)⁄

FIB − 4 =Age (years) × AST (U L)⁄

Platelet Count (109 L)⁄ × (√ALT [U L]⁄ )

An AST value of 40U/L was used as upper limit of normal.

2.2.2 Liver stiffness measurement (LSM)

LSM was performed according to the procedures recommended by the World

Federation for Ultrasound in Medicine and Biology (WFUMB) and the European

33

Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) (201, 202).

LSM was performed using the Supersonic shearwave elastography (SSWE; Aixplorer,

Supersonic Imagine SA, Aix-en-Provence, France). All examinations were performed

by a single trained and accredited operator (Charlton Noble, Paediatric Hepatology

& Liver Transplant Coordinator, Queensland Children’s Hospital, with >3 years of

elastography experience) using the convex abdominal SC6-1 paediatric transducer

probe for all patients. Evaluation was performed in dual mode (including grey-scale

ultrasound and elastogram displayed in real time). Children were non-fasting while

scanned as the procedure was performed opportunistically during scheduled visits to

the CF and outpatients clinics. A minimum of three measurements were obtained with

patients holding their breath. Children were placed in the supine position with their

right arm extended under the head for optimal intercostal access. During the

procedure, the operator chose the best segment in the right upper lobe of the liver and

the region of interest (ROI) placed 1-2cm below Glisson’s capsule of the liver avoiding

artefacts such as rib shadowing, gallbladder and large vessels. Once the optimal ROI

was selected, it was fixed and liver elasticity measured. Successful SSWE was defined

by a stiffness measure >1Kpa as described by others (119, 203, 204).

2.2.3 LSM progression

Liver stiffness progression was evaluated using SSWE in children with CF with more

than one LSM taken in no less than a 12 month period. Following a similar strategy as

performed using Fibroscan®-based transient elastography (105), LSM progression

was expressed as the absolute difference between LSM at Baseline (BL) and LSM at

Follow-up (FU); relative progression (%) expressed as (FU – BL)/BL*100; rate of

progression (kPa/year) expressed as (FU – BL)/years between the two

measurements; and relative rate of progression (%/year) expressed as (FU – BL)/-

BL/time. BL represents the first LSM for each patient. FU represents the last

measurement available for each patient.

34

2.3 In vitro studies

2.3.1 Cell culture

The immortalised murine oval cell line, bipotential murine oval (BMOL), were cultured

as described previously (205) in Williams’ E medium (Thermo Fisher, Massachusetts,

USA) containing 2mM glutamine (GlutaMAX; Thermo Fisher), 1% penicillin-

streptomycin (Life Technologies, Carlsbad, CA, USA), epidermal growth factor (10

ng/mL; Thermo Fisher), insulin-like growth factor II (30 ng/mL; GroPep Bioreagents,

Adelaide, SA, Australia), insulin (10µg/mL; Life Technologies) and 10% foetal bovine

serum (FBS; Life Technologies). The Lieming Xu-2 (LX2) human hepatic stellate cell

(HSC) line, were cultured as previously described (206) in high glucose Dulbecco’s

modified eagle medium (DMEM; Sigma Aldrich, St. Louis, Missouri, USA) containing

2mM glutamine (GlutaMAX; Thermo Fisher), 1% penicillin-streptomycin (Life

Technologies) and 2% FBS (Life Technologies). Both cell lines were grown in a 5%

CO2 atmosphere at 37°C.

2.3.2 Transient miRNA transfection and transfection efficiency

Cells were transfected in either 6 well-plates (Corning Inc., Corning, New York, USA)

or 10cm-plates (Corning Inc.) depending on the application. BMOL cells were

transfected at 70-90% confluency using Lipofectamine 2000 reagent (Thermo Fisher)

as suggested by the manufacturer. Transfection reagent mix was prepared fresh

containing the following components for 6-well plate transfection: 495.5µL Opti-MEM

medium (Life Technologies), 2.5µL lipofectamine 2000 and 2µL of 10µM of selected

miRNA mimic. For the 10cm-plate transfection, the mix was prepared as follows: 1mL

Opti-MEM medium, 40µL lipofectamine 2000 and 5µL of 10µM selected miRNA mimic.

LX2 cells were transfected at 70-90% confluency using Lipofectamine LTX reagent

(Thermo Fisher) to minimise cell death due to toxicity observed when using

Lipofectamine 2000. Transfection reagent mix was prepared fresh containing the

following components for 6-well plate transfection: 500µL Opti-MEM medium (Life

35

Technologies), 14µL plus reagent, 14µL lipofectamine LTX and 2µL of 10µM of

selected miRNA mimic. For 10cm-plate transfection: 1mL Opti-MEM medium, 40µL of

PLUS reagent, 40µL of lipofectamine LTX and 5µL of 10µM selected miRNA mimic.

For all cases, transfection mix was vortexed for 15 seconds and incubated for 10

minutes at room temperature. Cells were washed twice with phosphate buffered saline

(PBS) and 1.5mL of fresh medium added to the 6-well plates and 4mL to the 10-cm

plates. Transfection mix was then evenly distributed dropwise for each well/plate.

Transfection was optimised for each cell line and efficiency measured using the

AccuTarget fluorescein-labelled miRNA mimic negative control #1 (Bioneer

Corporation, Daedeok, Daejeon, Republic of Korea). Uptake of fluorescein-labelled

miRNA mimic was assessed after 24 hours through cell counting performed on EVOS

fluorescence cell imaging system (Thermo Fisher). For both cell lines the average

transfection efficiency was >50%. miRNA mimics used are shown in Table 2.1.

36

Table 2.1. miRNA mimics.

Name Product number Supplier

AccuTarget fluorescein-labelled miRNA mimic

negative control #1

SMC-4002 Bioneer Corporation

MISSION miRNA negative control 2

HMC0003 Sigma Aldrich

miScript Inhibitor negative control

1027271 Qiagen

MISSION miRNA mimic hsa-let-7g

HMI0019 Sigma Aldrich

MISSION miRNA mimic hsa-miR34a-5p


MISSION miRNA mimic hsa-miR365a-3p


Syn-hsa-miR-142-3p miScript miRNA Mimic

MSY0000434 Qiagen

Selected miRNA mimics used for the different studies in this thesis.

2.3.3 miRNA transduction

Mission Lenti-miRNA Constructs (Sigma-Aldrich) expressing let-7g-5p, miR-142-3p,

miR-34a-5p and miR-365a-3p were used to induce stable expression in LX2 cells.

Lentiviral constructs used are shown in Table 2.2. Transduction was performed as

follows: 150000 cells per well were seeded into 6-well pates. The following day

lentivirus was thawed on ice; in the meantime, cell media was replaced by fresh total

media without antibiotics and with hexadimethrine bromidepolybrene (polybrene,

Sigma-Aldrich) at a final concentration of 8μg/mL. Following polybrene addition, viral

particles were added to the cells at a multiplicity of infection (MOI) of 10, gently swirled

and centrifuged at 2500 rpm for 30 minutes at 30°C before incubating the cells for 24

hours at 37°C and 5% CO2. After 24 hours media was replaced and cells incubated

for an additional 42 hours. Following this second incubation, transduced cells were

selected by selection media with 1μg/mL of puromycin (Thermo Fisher). miRNA stable

expression in LX2 cells was assessed by qRT-PCR.

37

Table 2.2. Lenti-miRNA viral particles used for LX2 transduction.

Name Product number Accession number

MISSION Lenti microRNA Negative Control 2

NCLMIR002 NA

MISSION Lenti microRNA

let7g-5p

HLMIR0019 MIMAT0000414


miR-34a-5p



miR-365a-3p


Lenti-miRNA viral particles purchased from Sigma-Aldrich. Accession number based on miRBase V20 Mature ID Database.

2.3.4 Biotin pulldown assay to identify miRNA targets

In order to detect miRNA-mRNA interactions and identify potential miRNA targets, an

experimental pulldown method was used (207). Buffers and wash solutions were

made following the instructions in the pulldown protocol (207). BMOL were seeded in

10-cm plates at a density of 80000 cells, whereas, LX2 were seeded in 10cm-plates

at a density of 1200000 cells which resulted in 80% confluency after 24 hours. Seeded

cells were transfected (as described in section 2.3.2) with each selected biotinylated

miRNA duplex at a concentration of 20pmol per 10cm-plate and incubated for 24 hours

at 37°C and 5% CO2. The number of plates needed for each cell line was optimised

to obtain sufficient yield of enriched mRNA. For BMOL cells four 10cm-plates were

used per replicate, whereas, for LX2 sixteen 10cm-plates were used per replicate as

LX2 cells showed a lower RNA yield. On the day of transfection, streptavidin magnetic

beads (Dynabeads MyOne Streptavidin C1; Thermo Fisher) were washed and blocked

in preparation for the next day. Beads were resuspended by vortexing and 25µL

beads/plate transferred to a 2mL Eppendorf LoBind tube (Sigma Aldrich). LoBind

tubes were placed for two minutes in a magnetic rack and the supernatant discarded.

Beads were resuspended with 100µL of wash buffer for three times and resuspended

for two times in 100µL Solution A, followed by a final wash with 100µL Solution B. After

38

this final wash, beads were resuspended in 200µL of freshly made bead blocking

solution and incubated on a rotating mixer at 4°C overnight.

On the second day, transfected cells were washed with PBS and fresh medium added.

Cells were lifted from the plate using a cell scrapper (Corning Inc.) and transferred to

a 15mL falcon tube (Corning Inc.) pooling the cells from the 10cm-plates that constitute

a replicate together (i.e. four plates for BMOL and sixteen for LX2) into a final volume

of 8mL. Harvested cells were centrifuged at 1000rpm for five minutes at room

temperature, after which the supernatant was discarded and cells washed with 5mL

PBS and centrifuged. Supernatant was discarded and cells resuspended in 250µL of

freshly made lysis buffer, incubate for five minutes on dry ice and allowed to thaw at

room temperature for better lysis of cells. Lysate was transferred into a 2mL LoBind

tube and centrifuged at 13000rmp at 4°C for two minutes after which the clear

supernatant was transferred to a clean LoBind tube. A 10µL aliquot of the lysate was

kept on ice to be used as a control as it reflects changes undergone by the transfected

cells that otherwise would not be considered. Clear supernatant had sodium chloride

(NaCl) added to a final concentration of 1M and kept on ice.

In the meantime, streptavidin beads blocked on day one were prepared by being

washed two times with 100µL wash buffer, after which they were resuspended in

300µL of wash buffer. Next, the 300µL resuspended streptavidin beads were added

to the clear supernatant + NaCl and incubated on a rotating mixer at room temperature

for 30 minutes. Cell supernatant + streptavidin beads mix were washed four times in

300µL of wash buffer and resuspended in 100µL of nuclease free water which then

contained the target mRNAs. Subsequently, target mRNAs captured on streptavidin

beads and the control RNA were purified using the RNA clean-up protocol from the

RNeasy kit (Qiagen, Venio, The Netherlands) and eluted twice in a total of 50µL

nuclease free water. Finally, purified RNA targets and controls were concentrated by

centrifuging them at 5000rpm for two minutes at room temperature using an Amicon

Ultra-0.5 Centrifugal filter (Merck PTY LTD., Kenilworth, NJ, USA). Enriched targeted

mRNA and control RNA were quantified using NanodropTM 2000/2000c (Thermo

Fisher) and RNA 6000 Pico chip (Agilent Technologies, Santa Clara, USA) in the 2100

39

Bioanalyzer instrument (Agilent Technologies) using the manufacturer’s protocol. All

buffers and solutions were prepared fresh as indicated in the pulldown protocol (207)

Design of miRNA duplexes was performed as indicated on the pulldown assay

protocol (207) with biotin attached to the 3’ OH of the mature strand via a C6 linker, a

two nucleotide overhang at the 3’ end of each strand and two mismatches in the

passenger strand to favour incorporation into the RISC complex. Synthetic biotinylated

miRNA duplexes were synthesised by Integrated DNA technologies (IDT; Coralville,

Iowa, United States) and sequences are shown in Table 2.3.

Table 2.3. Biotinylated miRNA duplexes.

Name Sequence (5’– 3’) Accession number

hsa-miR-365a-3p

PO4-UGUAGUGUUUCCUACUUUAUGGAAG-OH-C6-Biotin

PO4-AUAACGAUUUUUAGGGUCAUUAAG-OH

MIMAT0000710

hsa-Let-7g-5p

PO4-UGAGGUAGUAGUUUGUACAGUUAG-OH-C6-Biotin

PO4-AACUCUACAAACUACUACCUUAAG-OH

MIMAT0000414

hsa-miR-142-3p

PO4- UGUAGUGUUUCCUACUUUAUGGAAG-OH-C6-Biotin

PO4-UCCACAAAGUAGGAAACACUAUAAG-OH

MIMAT0000434

hsa-miR-34a-5p

PO4- UGGCAGUGUCUUAGCUGGUUGUAG-OH-C6-Biotin

PO4-ACAACUAGCUAAGACACUGCUAAG-OH

MIMAT0000255

Sequences of the synthetic biotinylated miRNA duplexes. Bold shows nucleotide overhangs. Italics and underline shows mismatches of the passenger strand. Accession number is based on miRBase database v21.

40

2.3.5 Protein extraction and western blot

BMOL cells in 6-well plates were placed on ice, washed twice with cold PBS. Following

PBS washes, 100µL of RIPA buffer was added in each well and a cell scrapper used

to lyse cells. BMOL lysates were collected into cooled Eppendorf tubes and spun at

8000 x g for five minutes at 4°C after which the supernatant was transferred to new

cooled Eppendorf tubes and stored at -20°C until needed. Extracted protein was

quantified using the Pierce BCA protein assay (Thermo Fisher) as suggested by the

manufacturer. Western blot was performed using 10ng of cell protein extract mixed

with 1X Loading Buffer and boiled at 95°C and spun down before loading the samples

into 10% SDS-PAGE gels. Gels were prepared as follow: separating gel comprised of

2.5mL acrylamide, 1.5mL of 5x separating buffer, 3.5mL of milliQ water, 25µL of 10%

ammonium persulfate (APS) and 7.5µL of tetramethylethylenediamine (TEMED),

whereas, the stacking gel was prepared using 0.5mL of acrylamide/bis-acrylamide

(29:1), 0.5mL of 10x stacking buffer, 4mL of milliQ water, 15µL of 10% APS and 12µL

of TEMED. Proteins on the gels were separated by electrophoresis at 100volts (V) for

25 minutes until the separating gel was reached, followed by 200V for additional 40

minutes. Transfer to Immobilion-FL polyvinyl difluoride membranes (Millipore,

Burlington, Massachusetts, USA) was performed at 100V for 1 hour at room

temperature. Membranes were blocked in TBST LI-COR Odyssey blocking buffer (LI-

COR Bioscience, Lincoln, NE, USA) for 30 minutes and probed overnight at 4°C with

antibodies diluted in TBST LI-COR Odyssey blocking buffer. Primary antibodies used

are shown on Table 2.4. Blots were then incubated with either, anti-mouse 680RD or

anti-rabbit 800CW secondary antibodies at 1:20000 in TBST LI-COR Odyssey

blocking buffer and localization detected with the LI-COR Odyssey CLx Infrared

Imaging System (LI-COR Bioscience). Band quantification was performed using

Image Studio Lite version 5.2 (LI-COR Bioscience).

41

Table 2.4.Primary antibodies used for Western blot analysis.

Name Product number Dilution Supplier

Anti-Ck19 AB15463 1:2000 Abcam

Anti-acetylated

α-tubulin

T7451 1:1000 Sigma-Aldrich

Anti-Hnf4α SC6556 1:1000 Santa Cruz

Anti-β actin A5441 1:4000 Sigma-Aldrich

Primary antibodies used for western blot analysis.

2.3.6 Proliferation assay

Proliferation was performed using the IncuCyte Zoom live-cell imaging system (Essen

Bioscience, Ann Arbor, Michigan, USA). BMOL cells at 80% confluency in 10-cm

plates (Corning Inc.) were transfected with miRNA mimics of interest or miRNA

negative control as described in section 2.3.2. At 24 hours post-transfection, 12000

transiently-transfected BMOL cells were re-seeded into 12-well plates (Corning, Inc.)

in triplicates for each miRNA and incubated in the IncuCyte Zoom system. Remaining

cells were lysed, miRNA extracted and quantified by qRT-PCR as described in

sections 2.4.1 and 2.4.5. LX2 stably-transduced cells expressing either the miRNA

of interest or the negative control were seeded into 12-well plates (Corning, Inc.) in

triplicates at a density of 20000 cells per well and incubated in the IncuCyte Zoom

system. Cell growth in BMOL and LX2 was measured at three hour intervals for up to

five days and data collected using the IncuCyte Zoom 2016A software (Essen

Bioscience).

2.3.7 Migration assay

Migration was performed using the IncuCyte Zoom live-cell imaging system (Essen

Bioscience). BMOL cells at 80% confluency in 10-cm plates (Corning Inc.) were

transiently-transfected with miRNA mimics of interest or miRNA negative control as

described in section 2.3.2. At six hours post-transfection, 40000 transfected BMOL

42

cells were re-seeded into 96-well Image Lock Plate (Essen Bioscience) in 12

replicates. Remaining cells were lysed, miRNA extracted and quantified by qRT-PCR

as described in sections 2.4.1 and 2.4.5. LX2 stably-transduced cells expressing

either the miRNA of interest or the negative control were seeded into 96-well Image

Lock Plate (Essen Bioscience) in 12 replicates at a density of 55000 cells per well.

Both BMOL and LX2 seeded cells in the 96-well Image Lock Plates were incubated

for 18 hours at 37°C and 5% CO2 after which they were scratched using the Scratch

Wound Maker (Essen Bioscience) following manufacturers’ instructions and incubated

in the IncuCyte Zoom system. Cell migration of BMOLs and LX2s were measured at

one hour intervals for 24 hours and data collected using the IncuCyte Zoom 2016A

software (Essen Bioscience). Only wells showing clean, defined scratch wounds were

assessed by the IncuCyte imaging and selected for further analysis.

2.3.8 Dual luciferase assay

Luciferase reporter assays were used for miRNA target mRNA validation. LX2 cells

were co-transfected in 6-well plates (see section 2.3.2) with 50ng of miRNA 3’UTR

target clone and either miRNA mimic or miRNA negative control to a final

concentration of 20nM. At 48 hours post-transfection, luciferase activity was assayed

using the Luc-Pair Duo-Luciferase Assay Kit 2.0 (GeneCopoeia, Rockville, MD, USA)

following manufacturers’ instructions and detected on a Synergy H4 Hybrid Multi-Mode

Microplate Reader (BioTek, Winooski, Vermont, USA). Luciferase activity was

normalised to Renilla activity in each well. Assays were performed in duplicates and

independently repeated three times. miRNA 3’UTR target clones purchased from

GeneCopoeia are listed in Table 2.5.

43

Table 2.5. miRNA 3’UTR target clones used during luciferase assay.

Name Gene accession number

Product number 3’UTR length (bp)

NUPR1 miRNA 3’UTR target clone

NM_001042483.1 HmiT007248-MT06 342

SP1 miRNA 3’UTR target clone

NM_138473.2 HmiT017640a-MT06 2740

SP1 miRNA 3’UTR target clone

NM_138473.2 HmiT017640b-MT06 2734

DDX5 miRNA 3’UTR target clone

NM_004396.4 HmiT088758-MT06 1706

RFXAP miRNA 3’UTR target clone

NM_000538.3 HmiT088759-MT06 1859

RFX5 miRNA 3’UTR target clone

NM_000449.3 HmiT016246-MT06 1580

GATA4 miRNA 3’UTR target clone

NM_001308093.1 HmiT088508-MT06 1527

EP300 miRNA 3’UTR target clone

NM_001429.3 HmiT088760-MT06 1120

CD109 miRNA 3’UTR target clone

NM_133493.2 HmiT002808a-MT06 2562

CD109 miRNA 3’UTR target clone

NM_133493.2 HmiT002808b-MT06 2208

pEZX-MT06 empty vector

NA CmiT000001-MT06 NA

miRNA 3’UTR target clone vectors purchased from GeneCopoeia. 3’UTR sequence was cloned into a pEZX-MT06 vector expressing luciferase and renilla as internal control. Gene accession number is based on NCBI Reference Sequence Database. Bp= base pairs.

2.4 Nucleic Acid studies

2.4.1 Small RNA extraction

miRNAs were extracted from serum samples and subjected to next-generation

sequencing (NGS) with subsequent validation by qRT-PCR in order to identify the

miRNA signature. Serum samples were centrifuged to pellet any debris. Haemolysis

of serum samples was assessed by spectrophotometry measuring the absorbance of

free haemoglobin at 414nm. Samples with an absorbance greater than 0.2 were

44

excluded from the NGS discovery phase (208). Small RNA was extracted from 200µL

serum using the Plasma/Serum RNA Purification Mini Kit (Norgen Biotek Corporation,

Thorold, Canada) following manufacturers’ instructions as follows: 200µL of serum

was combined to 600µL of lysis buffer A (10µL β-mercaptoethanol/1mL lysis buffer A)

and mixed, after which 800µL of ethanol (96-100%) was added. The mixture was

transferred into a micro spin column (650µL) and centrifuged for two minutes at 3300

x g. The flow-through was discarded and the process repeated until the entire mixture

was passed through the column. The spin column was then washed three times with

400µL of the kit manufacturer’s wash solution A by centrifuging the column for 30

seconds at 3300 x g and discarding the flowthrough after each wash. The column was

spun empty in a new collection tube for two minutes at 13000 x g. The spin column

was transferred into a new Eppendorf tube and 20µL of RNase-free water applied and

let stand for two minutes at room temperature after which it was centrifuged for one

minute at 400 x g, followed by two minutes at 5800 x g. In order to increase the yield

of RNA, the elution from the previous step was added back to the spin column and re-

eluted. NanodropTM 2000/2000c (Thermo Fisher) was used to assess the presence of

inhibitors that could interfere in downstream applications, while the presence and

relative abundance of small RNA was assessed using the RNA 6000 Pico chip (Agilent

Technologies) in the 2100 Bioanalyzer instrument (Agilent Technologies).

2.4.2 RNA extraction

RNA isolated from cultured BMOL or LX2 cells was used to assess the role of miRNAs

in LPCs and HSCs biology. RNA was extracted using the RNeasy Mini Kit (Qiagen)

following manufacturers’ instructions as follows: medium was completely aspirated,

350µL of buffer RLT added and cells harvested using a cell scrapper. An equal volume

(e.g. 350µL) of 70% ethanol was added to the cell lysate, mixed and the total volume

transferred to an RNeasy Mini spin column and centrifuged for 15 seconds at 8000 x

g. The flowthrough was discarded and the 700µL of buffer RW1 added to the column

and centrifuged for 15 seconds at 8000 x g. After discarding the flowthrough, the spin

column was washed two times with 500µL of buffer RPE and centrifuged for 15

45

seconds at 8000 x g and two minutes at 8000 x g respectively discarding the

flowthrough after each wash. The column was spun empty in a new collection tube for

one minute at 8000 x g. Then, the spin column was transferred into a new Eppendorf

tube and 20µL of RNase-free water applied and centrifuged for one minute at 8000 x

g to elute RNA. NanodropTM 2000/2000c (Thermo Fisher) was used to quantify and

assess the quality of extracted RNA.

2.4.3 Small RNA library construction and NGS

cDNA libraries were prepared from miRNA isolated from serum samples (see section

2.4.1), followed by NGS to identify a circulating miRNA signature in CFLD and CFnoLD

children. An equal RNA input of 5µL from every sample was pooled into its respective

library following a common strategy when working with circulatory miRNAs (200, 209).

Libraries were prepared using the Ion Total RNA-Seq Kit v2 protocol (Thermo Fisher,

Revision E). For the hybridization and ligation of small RNAs with the adaptor, 5µL of

extracted RNA from each sample was added to its corresponding pool and

concentrated to a final volume of 3µL with the Savant DNA 120 SpeedVac

Concentrator (Thermo Fisher). The resultant 3µL of concentrated small RNA was

combined with 5µL of hybridization master mix containing 3µL of hybridization solution

and 2µL of Ion adaptor mix v2 and incubated at 65°C for 10 minutes and 16°C for five

minutes. After incubation time, 12µL of ligation master mix formed by 10µL of 2X

ligation buffer and 2µL of ligation enzyme mix was added to the hybridization reaction

and incubated at 16°C for 16 hours.

Reverse transcription (RT) of the ligated small RNA was performed using a S1000

Thermal Cycler (Biorad, California, USA), as follows: 16µL of RT master mix

(containing 2µL of nuclease-free water, 4µL of 10X RT buffer, 2µL of 2.5 mM dNTP

mix and 8µL of ion RT primer v2) was added to the 20µL of ligated small RNA. RT

reaction was then incubated at 70°C for 10 minutes, snap-cooled on ice, added 40µL

of 10X Superscript III Enzyme Mix and incubated at 42°C for 30 minutes. Following

46

RT reaction, cDNA was purified and size selected using the Magnetic Bead Cleanup

Module and eluted in 12µL of nuclease-free water.

Amplification of the obtained cDNA was performed by adding 46µL of PCR mix

(containing 45µL of Platinum PCR SuperMix High Fidelity and 1µL of Ion Xpress RNA

3’ Barcode Primer) and 1µL of Ion Xpress RNA-seq Barcode BC primer (choose from

BC01-BC16 for each library) to 6µL of purified cDNA. PCR reaction was then placed

in a S1000 Thermal Cycler (Biorad) and amplified using the following program: 94°C

for two minutes; (94°C for 30 seconds, 50°C for 30 seconds and 68°C for 30 seconds)

for 2 cycles; (94°C for three seconds, 62°C for 30 seconds and 68°C for 30 seconds)

for 14 cycles and 68°C for five minutes. The amplified cDNA was then purified and

size selected using the Magnetic Bead Cleanup Module and eluted in 15µL of

nuclease-free water.

Yield and size distribution of amplified cDNA libraries was assessed by Agilent 2100

Bioanalyzer instrument with the High Sensitivity DNA Kit (Agilent Technologies) as

described in the Ion Total RNA-Seq Kit v2 protocol. A total of 1µL of the cDNA

barcoded library was used and smear analysis performed to determine size

distribution. The ratio of mRNA ligation in the total ligation products was calculated

using the following formula: [Area (94-114bp)] ÷ [Area (50-300bp)]. The molar

concentration for each library was determined using the size range 50-300bp and

diluted to a final concentration of 100pM as suggested in the protocol. Equimolar

volumes of the diluted libraries were combined for the following steps.

Pooled diluted libraries were used to generate template-positive Ion Sphere Particles

(ISPs) coated with clonally amplified cDNA. Emulsion PCR (emPCR) was performed

using the Ion OneTouch 2 System (Thermo Fisher) following the instructions of the Ion

PI Hi-Q OT2 200 Kit (Revision C). Enrichment of the template-positive Ion PI ISPs was

performed using the Ion OneTouch ES as detailed in the Ion PI Hi-Q OT2 200 Kit

protocol.

47

miRNA sequencing was performed on the Ion Proton System (Thermo Fisher) using

the Ion PI Chip v3 (Thermo Fisher) following the Ion PI Hi-Q Sequencing 200 Kit

protocol (Revision C). Sequencing primer and Control Ion Spheres were added to the

enriched template-positive ISPs from the previous step. After annealing, sequencing

polymerase was added and a final volume of 55µL loaded into the Ion PI Chip.

Quantification of cDNA libraries, template preparation and sequencing was performed

by the Genomics Research Centre (GRC) (Brisbane, Australia).

2.4.4 RNA library construction and RNA-sequencing

RNA libraries were constructed from enriched RNA and control RNA samples obtained

during the biotin pulldown assay using the TruSeq Stranded mRNA Library Prep Kit

(Illumina, San Diego, Cal, USA) following manufacturers’ protocol described in

Document # 1000000040498 v00. For all libraries an RNA input of 100ng was used.

Purification of polyA RNA was performed by placing 50µL of each RNA input and 50µL

of RNA purification beads into individual wells of the RNA purification beads (RBP)

plate. Samples were mixed by shaking the plate at 1000rpm for one minute and

incubated at 65°C for five minutes followed by one minute on ice. Next, the RBP plate

was incubated at room temperature for five minutes and placed on a magnetic stand

to remove supernatant and washed with 200µL of Bead washing buffer (BWB) and

50µL of Elution buffer (ELB), after which it was placed on an S1000 (Biorad) 80°C pre-

heated thermal cycler for two minutes and snapped on ice. Purified polyA mRNA was

then fragmented by adding 50µL of bead binding buffer (BBB) incubated at 65°C for

five minutes followed by one minute on ice followed by additional five minutes

incubation at room temperature. The plate was then placed on a magnetic stand and

washed with 200µL of BWB and 19.5µL of fragment prime finish mix (FPF), centrifuged

at 280 x g for one minute and placed on a S1000 (Biorad) 100°C pre-heated thermal

cycler for eight minutes at 94°C and held at 4°C. After fragmentation, cleaved RNA

fragments were reverse transcribed into first strand cDNA by transferring 17µL

fragmented RNA to a cDNA plate and adding 8µL of first strand synthesis act D (FSA)

+ SuperScript II mixture, mixed thoroughly and centrifuged at 280 x g for one minute

48

and placed on a S1000 (Biorad) 100°C pre-heated thermal cycler for 10 minutes at

25°C; 15 minutes for 42°C; 70°C for 15 minutes and held at 4°C. Next, during the

synthesis of second strand cDNA, RNA template was removed and replaced by the

complementary cDNA strand by adding 5µL of resuspension buffer (RSB) + end repair

control (CTE) to each sample, followed by 20µL of second strand marking master mix

(SSM), centrifuged at 280 x g for one minute and placed on S1000 (Biorad) 16°C pre-

heated thermal cycler for one hour at 16°C and brought to room temperature. Two-

stranded cDNA was then purified by adding 90µL of AMPure XP beads, incubated for

15 minutes at room temperature and centrifuged at 280 x g for one minute before the

supernatant was discarded. AMPure XP beads were then washed twice with 200µL

80% ethanol and air-dried for 15 minutes, after which 17.5µL of resuspension buffer

(RSB) was added, placed on a magnetic stand and the supernatant transferred to a

clean plate. One adenine (A) nucleotide was then added to the 3’ ends of purified

cDNA to prevent them from ligating to each other by adding 2.5µL of RSB + A-tailing

control (CTA), centrifuged at 600 x g for five seconds, followed by the addition of

12.5µL of A-tailing mix (ATL) and incubated for 30 minutes at 37°C, 70°C for five

minutes and placed on ice for one minute. Barcode multiplex adapters were then

added to the double stranded cDNA fragments by adding 2.5µL of RSB + ligation

control (CTL); 2.5µL ligation mix (LIG); 2.5µL RNA adapter, centrifuged at 280 x g for

one minute and incubated at 30°C for ten minutes. Then, 5µL of stop ligation buffer

(STL) were added and centrifuged at 280 x g for one minute after which ligated

fragments were cleaned up using the AMPure XP Beads. Following adapter ligation,

cDNA fragments with adapter molecules were enriched and amplified by adding 5µL

of PCR primer cocktail (PPC) and 25µL of PCR master mix (PMM) to the cDNA

fragments on a PCR plate on ice and placed on S1000 (Biorad) 100°C pre-heated

thermal cycler for 30 seconds at 98°C; 15 cycles of: 98°C for 10 seconds, 60°C for 30

seconds, 72°C for 30 seconds; followed by 72°C for five minutes and held at 4°C.

cDNA amplification was performed by adding 25µL of PMM and placed on S1000

(Biorad) 100°C pre-heated thermal cycler for 30 seconds at 98°C; 15 cycles of: 98°C

for 10 seconds, 60°C for 30 seconds, 72°C for 30 seconds; followed by 72°C for five

minutes and held at 4°C. Amplified cDNA was cleaned up using the AMPure XP

Beads.

49

Quantification and yield of DNA libraries was assessed by Agilent 2100 Bioanalyzer

instrument with the High Sensitivity DNA Kit (Agilent Technologies) and libraries

normalized and pooled for cluster generation. Clustered cDNA libraries were

sequenced on the NextSeq500 (Illumina Inc.), 150 cycle (2x75bpp) High Output Run

following the setup recommended by the manufacturer. Library preparation and

sequencing of enriched BMOL samples was performed by the Centre for Brain

Genomics of the Queensland Brain Institute (QBI; The University of Queensland, St.

Lucia, Australia). Library preparation and sequencing of enriched LX2 samples was

performed by the IMB Sequencing Facility (ISF) of the Institute for Molecular

Bioscience (IMB; The University of Queensland, St. Lucia, Australia).

2.4.5 miRNA Reverse transcription (RT) and miRNA qRT-PCR Validation

Following NGS, miRNA candidates were selected for individual validation in 124

individual patient samples by qRT-PCR. Similarly to the small library preparation, a

fixed RNA volume input of 4µL per sample was used for the RT reaction due to

limitations in quantifying circulatory miRNAs. Extracted RNA was reverse transcribed

using the miScript II RT Kit (Qiagen) following manufacturer’s indication as follows:

4µL of extracted RNA from each sample was added to 16µL RT master mix (comprised

of 4µL miScript HiSpec Buffer, 2µL 10X Nucleics Mix, 2µL miScript Reverse

Transcriptase Mix and 8µL of nuclease-free water). The RT reaction mix was then

incubated at 37°C for 60 minutes and 95°C for five minutes using a S1000 Thermal

Cycler (Biorad). cDNA was diluted 1:10 in nuclease-free water, split into aliquots for

qRT-PCR and stored at -80°C.

qRT-PCR assay was performed using the miScript SYBR Green PCR Kit (Qiagen) as

follows: 2µL of cDNA template was added to 8µL of qRT-PCR master mix (containing

5µL of 2X QuantiTect SYBR Green PCR Master Mix, 1µL of 10X miScript Universal

Primer, 1µL of 5µM miRNA primer and 1µL of nuclease-free water) and added in

triplicates for each sample to LightCycler 384-multiwell plates (Roche, Basel,

Switzerland). PCR amplification was performed in the CFX384 Touch Real-Time PCR

50

Detection System (Biorad) with the following cycling parameters: PCR initial activation

step at 95°C for 15 minutes; (denaturing step at 95°C for 15 seconds, annealing step

at 60°C for one minute and extension step at 95°C for 15 seconds [fluorescence data

collected]) for 40 cycles. Melting curve analysis was performed by increasing the

temperature of the plate from 55°C to 90°C with fluorescence measurements obtained

every one degree increment. The analysis confirmed the production of a single

amplicon in every run. A 1:10 cDNA inter-run calibrator (IRC) was made by pooling

RNA from Huh7 cultured cells (hepatocyte lineage) and run in triplicate to correct for

inter-plate variation.

miRNA primer design was performed using the miRprimer algorithm which generates

a number of putative primers with a score assigned based on its predicted

performance (210). Briefly, the miRprimer algorithm designs the primers with a melting

temperature of 59°C, using the assigned score to calculate secondary structures.

Finally, primers are combined and a new score assigned based on the scores of each

primer included and the score for the propensity of primer formation. Primers were

synthesised by Integrated DNA technologies (IDT). miRNA primer sequences are

shown in Table 2.6.

51

Table 2.6. miRNA primer sequences.

Name Sequence (5’-3’) Accession number

hsa-miR-122-5p TGGAGTGTGACAATGGTGT MIMAT0000421

hsa-miR-25-3p CATTGCACTTGTCTCGGT MIMAT0000081

hsa-miR-199a-3p CAGACAGTAGTCTGCACATTG MIMAT0000232

hsa-miR-365a-3p GCAGTAATGCCCCTAAAAATCC MIMAT0000710

hsa-miR-18a-5p GTAAGGTGCATCTAGTGCAG MIMAT0000072

hsa-miR-126-5p CGCAGCATTATTACTTTTGGT MIMAT0000444

hsa-miR-142-3p CGCAGTGTAGTGTTTCCT MIMAT0000434

hsa-Let-7g-5p CGCAGTGAGGTAGTAGTTTG MIMAT0000414

hsa-miR-103a-3p GCAGAGCAGCATTGTACAG MIMAT0000101

hsa-miR-34a-5p GCAGTGGCAGTGTCTTAG MIMAT0000255

hsa-miR-484 GCAGTCAGGCTCAGTCCCCT MI0002468

hsa-miR-19b-3p AGTGTGCAAATCCATGCAA MIMAT0000074

hsa-miR-93-5p GCAAAGTGCTGTTCGTG MIMAT0000093

hsa-miR-20a-5p CGCAGTAAAGTGCTTATAGTG MIMAT0000075

hsa-Let-7i-5p GCAGTGAGGTAGTAGTTTGTG MIMAT0000415

hsa-Let-7b-5p CAGTGAGGTAGTAGGTTGTGT MIMAT0000063

hsa-Let-7d-5p CGCAGAGAGGTAGTAGGTTG MIMAT0000065

hsa-miR-27a-3p GCAGTTCACAGTGGCTAAG MIMAT0000084

hsa-miR-146a-5p GCAGTGAGAACTGAATTCCA MIMAT0000449

Sequences of the primers used for miRNA qRT-PCR validation. Accession number is based on miRBase database v21.

2.4.6 RNA reverse transcription (RT) and RNA qRT-PCR Validation

RNA was reverse transcribed using the Sensifast cDNA Synthesis Kit (Bioline;

Meridian Bioscience, Cincinnati, Ohio, USA) following manufacturer’s indication as

follows: 500ng of BMOL extracted RNA and 1000ng of extracted RNA for LX2 were

used as RNA template input and added to 20µL RT master mix (comprised of 4µL 5x

TransAmp Buffer and 1µL of Reverse Transcriptase). The RT reaction mix was then

incubated at 25°C for 10 minutes followed by 42°C for 15 minutes and 85°C for five

52

minutes using a S1000 Thermal Cycler (Biorad). cDNA was diluted 1:10 in nuclease-

free water, split into aliquots for qRT-PCR and stored at -80°C.

qRT-PCR assay was performed using the Platinum SYBR Green qPCR SuperMix-

UDG (ThermoFisher) as follows: 3µL of cDNA template from the previous step was

added to 12µL of qRT-PCR master mix (containing 7.5µL of Platinum SYBR Green,

1.5µL of RNA primer mix (forward and reverse primers at a 5µM concentration each)

and 3µL of nuclease-free water) and added in triplicates for each sample to LightCycler

384-multiwell plates (Roche). PCR amplification was performed in the CFX384 Touch

Real-Time PCR Detection System (Biorad) with the following settings: 50°C for two

minutes; 95°C for two minutes followed by 40 cycles of 95°C for three seconds and

60°C for 30 seconds. Melting curve analysis was performed by increasing the

temperature of the plate from 55°C to 90°C with fluorescence measurements obtained

every one degree increment. The analysis confirmed the production of a single

amplicon in every run. A 1:10 cDNA inter-run calibrator (IRC) was made by pooling

RNA from Huh7 cultured cells (hepatocyte lineage) and run in triplicate to correct for

inter-plate variation. Most RNA primers were self-designed and synthesised by

Integrated DNA technologies (IDT); however, three were purchased from Qiagen, a

commercial supplier. Self-designed RNA and commercial RNA primer sequences are

shown in Table 2.7, Table 2.8 and Table 2.9.

53

Table 2.7. BMOL self-designed RNA primer sequences.

Gene Forward primer (5’- 3’) Reverse primer (5’- 3’)

Itga2 TGTCACGATTCCCCTCATGA TGCAGTCATAGCCAACAGCAA

Crkl CCTGGACACTACCACCTTAATCG TCTTCTGCTGTAGGTAAGTTGGG

Ptch1 GTTCTGGACGGTGCTGTGTC GCCAGGACGGCAAAGAAG

Rock1 TGTTGTGGTAAGCAGCAAAAA CTTGGGTTACAGGTCGGACA

Ep300 AGCACACACAGCTGATCCAGAGAA

CGCTGGCACTTGTGAGCATGTAAA

Nfkβ1 AGCAGGATGCTGAGGATTCTG GGCAACTCTGTCCTGCACCTA

Hnf4α GGCCACCGGCAAACACTAC CACACATTGTCGGCTAAACCTG

Notch1 CAGCTTGCACAACCAGACAGAC ACGGAGTACGGCCCATGTT

Dvl3 GTCACCTTGGCGGACTTTAAG AAGCAGGGTAGCTTGGCATTG

Appl2 TTGATAGGCTTTGCCACAGGA TCCACCTGAATGCTCTGAACCA

Akt3 TGGGTTCAGAAGAGGGGAGAA AGGGGATAAGGTAAGTCCACATC

Ptpn11 CTGAAAGAGAAGAATGGAGATG CTTTTCCAGACAAGTGACC

Acvr2a GCGTTCGCCGTCTTTCTTATC GTTGTTTCTGTCTCTTTCCCAAT

Ets1 AGTCTTGTCAGTCCTTTATCAGC TCGCACACAAAGCGGTAT

Adam17 CAGCAGCACTCCATAAGGAAA TTTGTAAAAGCGTTCGGTA

Fzd2 CCGACGGCTCTATGTTCTTC TAGCAGCCGGACAGAAAGAT

Cx3cr1 CAGCATCGACCGGTACCTT GCTGCACTGTCCGGTTGTT

Hprt GCCCCAAAATGGTTAAGGTTGC AACAAAGTCTGGCCTGTATCCAAC

Ggt1 ACAGAAGGCACTGACGTATCACC GCTCATGTTGCGGATCACCT

Tat TCGGGACGGGCTGGTGAA GGGTGCGCTGAAGGATGCTC

Sequences of the self-designed primers used for miRNA target qRT-PCR validation in BMOL

cells.

54

Table 2.8.LX2 self-designed RNA primer sequences.


VCL AGCGATACCACAACTCCCATCAA CAAATACCTCTTCCCTGTTAGGCG

TLR4 AGACCTGTCCCTGAACCCTAT TTCTAAACCAGCCAGACCTTG

MYD88 CGGCAACTGGAACAGACAAACTATC

ATCAGAGC=ACAACCACCACCATCC

NR1D1 TTGCTCCATCGTCCGCATCAATC ATCCGCTGCTTCTCTCGTTTGG

CD248 TCAAATCTCTGTGCCTGCTGCC ATGTGTAGGGCTGATGGGTGAG

AMOT TTGTTTCCAGAGCCCAGCAGAT CAGTCTTGCCACCTTCTCATAGCA

LATS1 GGCAAACATCTCTTCACATTCCACC

ATCTTCGGGTCCTCGGCAAAGT

TEAD1 CCATTCCAGGGTTTGAGCCT GCTTGGTTGTGCCAATGGAG

SMAD3 TGTCGTCCATCCTGCCTTTCA TTCTCGCACCATTTCTCCTCCT

IL1RAP CAACTACAGCACAGCCCATTCA CGGAACCACAGCACATCTTTCT

PTCH1 AACTGGAACGAGGACAAAGCG CGTGGTGGTGAAGGAAAGCAC

GLI3 GCAACACTACCTCAAAGCGGGAA TGAAGACTGACCACCAGGGCT

NOTCH3 GGGTTCAGGTACCGCTTCTC ATCAGTGCCGTTGAGCCATCTG

MAP3K1 CGTTCTGTCAATGGTCTGTGCT TTCCGCTAAACTGTGGCAAGGA

MAP3K9 TTGCTACCCGCCCATTCAGTT ACGATAGACCTTCCCAAAGCCC

TGF-βR1 GCCTTGGTCCTGTGGAACTG GGTCCTCTTCATTTGGCACTCG

COL4A1 ACCATAGAGAGGAGCGAGATGTT

C CTCATACAGACTTGGCAGCGG

COL4A5 CCCGTGGTTTGGATGGTCCC TTGTCTGGCTGTGGCGTGTAA

WWC1 ACGCTGTGTCTGCTCTGTTGGA TTCCTCGGCTACTGCCTCATTCT

TGF- β1 TCCTGGCGATACCTCAGCAACC TCCACGGCTCAACCACTGCC

SAV1 TACCAGCCACAGCAAACTGAAAG TCGTGCGTAAACCTGAAGCCA

CSNK1G2

CCGCAGTGGCTTCGTGTT CAGGTCGGTGTGGACGGTG

COL4A2 GAGCCTGGATTGGTCGGTTTC AGTCCTCTGTTGCCTTGCTGT

ACTA2 CTATCAGGGGGCACCACTATGTA GCTCCGGAGGGGCAATGA

TIMP3 ACCTGCCTTGCTTTGTGACTTCC GGCGTAGTGTTTGGACTGGTAG

C

55


COL1A1 CGATGGCTGCACGAGTCACA AAGCCGAATTCCTGGTCTGG

COL1A2 TGCTAAAGGAGAAAGAGGAGCCA CCATCACCACGACTTCCAGC

PPAR-γ GGCTTCATGACAAGGGAGTTTC AACTCAAACTTGGGCTCCATAAA

G

CDH1 CTGGTTCAGATCAAATCCAACA CTTCAGCCATCCTGTTTCTCTT

GAPDH GACACCCACTCCTCCACCTTTGA GTCCACCACCCTGTTGCTGTAG

Sequences of the self-designed primers used for miRNA target qRT-PCR validation in LX2

cells.

Table 2.9.Commercial RNA primer sequences.

Gene Species Product number Supplier

EP300 Human QT00094500 Qiagen

ROCK1 Human QT00034972 Qiagen

HNRNPA1 Human QT00089859 Qiagen

Sequences of the commercial primers used for miRNA target qRT-PCR validation.

2.5 Statistical and data analysis

2.5.1 Validation of reference miRNAs

geNorm v3.0 (211) and NormFinder v20 (212) were used to analyse the expression

stability of candidate reference miRNAs selected from the differential expression

analysis performed in the NGS part of the study. For both programs, quantification

cycle (Cq) values were transformed into relative quantification data applying the 2-∆∆CT

equation, by subtracting the highest Cq value from the Cq value of every measured

miRNA. By doing so, all data input represented relative expression to the least

expressed miRNA. geNorm provides a ranking of the tested miRNAs based on

expression stability (M value) which represents the mean pairwise variation of one of

the candidates when compared to the other candidates. The lowest M value

represents the most stable candidate. The stability ranking is generated by stepwise

exclusion of the candidate with the highest M value, after which the M value is

recalculated with the remaining candidates until the two most stable candidates are

56

selected. Normfinder uses an inter- and intra-group variation approach to calculate the

stability value for each of the candidates. It ranks the miRNA candidates according to

stability expression and experimental design. NormFinder ranks the candidates with

lowest stability highest.

2.5.2 qRT-PCR data analysis

Cq values were determined using the CFX Manager Software v3.1 (Biorad) with the

linear regression setting. Relative quantities of miRNA or RNA expression were

calculated using the comparative CT method (∆∆CT method). In order to correct for

differences in the quantities of cDNA used as template for qRT-PCR reactions,

reference endogenous miRNA and RNA controls were used to normalize expression

data. In the miRNA assay miR-93-5p, miR-20a-5p and let-7i-5p were used as the most

stable reference miRNAs, whereas Hprt and GADPH were used to normalise RNA

expression in BMOL and LX2 cell lines respectively. The amount of target, normalized

to the endogenous reference controls is given by the following formula:

2-∆∆CT

The mean and standard deviation of the triplicate Cq values were estimated, after

which the ∆CT values were calculated by:

∆CT=Cq target – Cq reference

The ∆∆CT was then calculated by:

∆∆CT=∆CT test sample - ∆CT calibrator sample

57

The ∆∆CT values were then incorporated into the fold-difference by using:

2-∆∆CT

Normalized relative quantities were then subject to statistical analysis.

2.5.3 miRNA-Sequence data acquisition and analysis

NGS data was analysed using the Torrent Suite v5.0.4 (Thermo Fisher) with the

default settings. The miRBase miRNA hairpin database (miRBase v21) was used as

the alignment reference following the recommendations of manufacturer (Thermo

Fisher). Cutadapt was used to remove adapter sequences and initial alignment was

carried out using Tophat2. Unmapped sequences were converted from bam to fastq

files using bam2fastq after which were aligned using Bowtie2. Finally, Bam files were

merged with Picard using the MergeSamFiles module.

mirPRo software pipeline (213) was used to perform quality control (QC), alignment

and small RNA counting, as well as prediction of potentially novel miRNAs. Briefly,

mirPRo extract mature and precursor miRNA sequences annotated by miRBase21

database for human miRNA and mapped these mature miRNAs to their corresponding

hairpins using Novoalign. Only perfect mappings are selected and positioned to their

corresponding hairpins for downstream analysis. Raw read quality filtering was

performed by FASTX-Toolkit and adapter sequence trimmed using

mirpro_findAdapter. Clean reads were collapsed and expression numbers counted

after which they were mapped to the known pre-miRNAs (hairpin sequences) using

Novoalign. mirPRo uses Novoalign to mapped final clean sequence reads to the

reference genome sequences for cataloguing clean reads in terms of genome

annotation. Clean reads are then categorized and counted in different features

annotated in gene annotation GTF files. Differential expression analysis was

performed in R v3.3.2 (214). Results from the separate raw count results were

58

combined (mixture of precursor and mature miRNAs), however, no difference was

observed in counts between precursor and mature miRNAs so these last ones were

used for the analysis. Initial threshold filtering was performed on raw count data as

follows: miRNA with two or fewer non-zero counts were selected as noise, with 10

counts selected as noise threshold. Any miRNA with a maximum count of less than

the noise threshold was excluded from the analysis. The miRNA read counts identified

by the mirPRo pipeline were normalized for compositional bias in sequenced libraries

and library size using the DESeq2 v1.15.28 package (215). Next, differential

expression of miRNA read counts was performed using DESeq2. Pair-wise

comparisons were generated between the different conditions of interest. Sequence

data acquisition and analysis was performed by the Genomics Research Centre

(GRC) (Brisbane, Australia).

2.5.4 Data deposition

Raw NGS of the miRNA-sequencing data used in this study are available from the

Gene Expression Omnibus (GEO) under accession number GSE111754.

2.5.5 RNA-sequencing data acquisition and analyses

RNA sequencing data output was de-multiplexed and converted from BCL to FASTQ

file format using the bcl2fastq Conversion Software v2.20 (Illumina Inc.). Sequencing

quality control (QC) was performed using RNA-SeQC v.1.1.8 (216). Converted

sequencing reads were processed as follows: Cutadapt v1.11 (217) was used to trim

adapters and aligned with STAR v2.5.2a (218), using the GRCh37 database as

alignment reference and assembled with Ensembl (release 70) features including

gene, transcripts and exon. Gene expression was estimated using RSEM v1.2.30

(219). Differential expression analysis was performed in R v3.3.2 (214), using the

edgeR Bioconductor package (220). Pair-wise comparisons were generated between

controls and enriched pulldown RNA for each miRNA duplex and cell line used. De-

59

multiplexing, conversion, QC, trimming and alignment was performed by members of

the Clinical Genomics Group (QIMR Berghofer, Queensland, Australia).

2.5.6 miRNA-sequencing statistical analysis

All statistical analyses were performed in GraphPad Prism v7 (GraphPad Software,

San Diego, USA) and STATA v15 (Stata Corp LLC, Texas, USA), with P values <0.05

considered statistically significant. Sample size calculation was based on a previous

study (200). Limited work has been undertaken in miRNA-sequencing statistical

design, however, in this study the statistical design is based on RNA-sequencing

studies using a method developed by Hart et al.(221). This method bases its

calculations on a negative binomial distribution (NB) that accounts for both biological

and technical variability. It was determined that a minimum of 10 samples per group

were required to detect a fold change of two or greater, reading depth of 20X, 80% of

power and 5% error.

Normalised qRT-PCR data was transformed to log base 2 and analysed using one-

way analysis of variance (ANOVA) and Tukey post-hoc test to compare the differences

between all of the groups. Correlation analyses were performed using Spearman rank

correlations. Receiver operating characteristics (ROC) analysis was used to assess

the ability of selected miRNAs to distinguish between CFnoLD and CFLD children.

miRNA panel calculations to differentiate study groups were based on stepwise logistic

regression using the lowest Bayesian information criterion for model selection and

Liu’s method to determine the optimal cut-point. The probability that a patient has

CFLD using the proposed miRNAs can be determined based on the coefficient

estimated by the logistic regression analysis based on the following equation:

ln (pᵢ

1-pᵢ) = Intercept + ∑ Slopek x ∆CPmiRNAk

k

k=1

60

Where ln (pᵢ

1-pᵢ) is the “odds” of the event occurring, i.e., the probability of an event

divided by the probability of no event; 𝑘 is the number of miRNAs in the panel; intercept

is the 𝑦- Intercept value and Slopek is the slope of the regression corresponding to the

𝑘th miRNA (∆CPmiRNAk).

According to the stepwise logistic regression derived diagnostic panel shown in this

thesis consisting of let-7g-5p, miR-365a-3p, miR-142-3p and log_APRI, the probability

of a patient having CFLD can be determined based on the following coefficients:

ln (𝑝ᵢ

1−𝑝ᵢ) = 5.82 + (-0.792 × ∆CT let-7g-5p) + (1.411 × ∆CT miR-365a-3p) + (-0.861 X

∆CT miR-142-3p) + (5.209 X log_APRI)

To obtain the 𝑝𝑖 , the probability of the ith subject having an outcome uses the following

formula where function is the Intercept + ∑ Slopek x ∆CPmiRNAkkk=1 , as shown in the

equation above:

𝑝𝑖 =1

1 + exp (−𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛)

The probability 𝑝𝑖 ranges from 0 to 1.

Concordance statistic (c-statistic) (222) and the Obuchowski method (223) were used

as predictive discriminators of METAVIR scoring based fibrosis staging in CFLD

children.

61

2.5.7 Liver stiffness statistical analysis

All statistical analyses were performed in GraphPad Prism v7 (GraphPad Software,

San Diego, USA) and JMP Pro version 14.1.0 (SAS Institute Inc., North Carolina,

USA), with P values <0.05 considered statistically significant. All LSM measurements

were expressed as median with interquartile range (IQR) (224). Sample size

calculation was performed based on a previous study (225) on an Acuson S2000

device (Siemens Medical Solutions, Mountain View, CA, USA) as it uses acoustic

radiation force impulse to create the mechanical pulse-wave, similar to SSWE. Based

on this study, standard deviation of the stiffness measurement in the right lobe of the

liver is 0.15; hence, sample size was calculated to detect differences between 0.05

and 0.35 meters/second (m/s) assuming that the analysis will be a one-way ANOVA.

It was determined that a minimum of 34 samples per group are required to detect a

difference of 0.1m/s in at least one of the three enrolled patient groups with 80% power

and 5% significance level.

LSM differences between the study groups were analysed using Mann-Whitney test

or Kruskal-Wallis and Dunn’s post-hoc test as appropriate. Correlation between LSM

and clinical characteristics and liver biochemistries were performed using Spearman

rank correlations. Receiver operating characteristics (ROC) analysis and

Classification and Regression tree (CART) modelling were used to assess the

performance of SSWE to discriminate liver disease alone, or in combination with APRI,

in CF children. During CART analysis the dataset is divided based on ranges of the

LSM and APRI as predictor variables into high and low risk groups. CART was

performed using 5-fold cross-validation and a minimum split size of 10 to reduce over

fitting. Area under the curve (AUC) was performed using ROC curve in the CART

model to assess its diagnostic performance.

62

2.5.8 Network and pathway analyses

Predicted miRNA-mRNA interactions were obtained using the TargetScan algorithm

v7.2 (226). Enriched mRNA obtained from the pulldown assays were compared with

targets predicted by TargetScan using Ingenuity Pathway Analysis (IPA; Qiagen).

Putative targets were selected based on significance (P<0.05) and fold change (FC)

≥1.5. The Functional analysis tool on IPA was used to identify the most representative

biological functions represented by enriched RNA captured during the pulldown assay.

63

CHAPTER 3

Assessment of paediatric CFLD using Supersonic

shearwave elastography....

64

3.1 INTRODUCTION

The implementation of respiratory and microbial management, nutrition optimization,

cystic fibrosis transmembrane conductance regulator (CFTR) modulators and

mucoactive drugs over the past several decades has increased the life expectancy of

cystic fibrosis (CF) patients from 34.0 years during 1991-1995 to 47.7 years in 2016

(11) and is expected to exceed 50 years of age in patients born after the year 2000

(12). Simultaneously, the number of patients with CF in western countries is expected

to increase by 50% in 2025, corresponding to 20% and 75% in children and adults

respectively (10). This improved life expectancy and increase in the prevalence of CF

has drawn attention to non-respiratory complications such as cystic fibrosis-

associated liver disease (CFLD), currently, the leading cause of non-respiratory

mortality and morbidity associated with CF. It has been proposed that during CFLD,

abnormal CFTR function leads to inspissation of viscid bile in the biliary tree which

results in biliary obstruction, hepatocyte and cholangiocyte injury and periductal

inflammation producing fibrosis and cirrhosis (227).

According to the World Health Organization (WHO), CF affects 1 out of 2500 newborns

(13), 15.6% of whom are diagnosed with severe liver abnormalities and 5% die due to

end stage liver disease prior to the advent of liver transplantation (228). The true

prevalence of CFLD, however, is likely to be higher as an autopsy study reported that

up to 70% of CF patients showed significant focal biliary cirrhosis (60). Indeed,

advanced fibrosis of the liver including cirrhosis and the complications of portal

hypertension are reported in 5- 10% of children during the paediatric years (229).

Similarly, the impact of CFLD on mortality has been underestimated as demonstrated

by studies reporting increased all-cause childhood mortality in those with CFLD (64,

230). With an increasing CF population, diagnosis and monitoring of non-pulmonary

complications such as CFLD will be required to better manage patient care and

treatment.

65

Paediatric liver diseases are often missed or diagnosed late, mainly due to the lack of

symptoms at early stages of the disease. CFLD is frequently asymptomatic and shows

a wide spectrum of manifestations. The lack of a uniform clinical definition and

classification for CFLD only contributes to an already challenging diagnosis. Currently,

liver biopsy is the gold standard method to assess and stratify the severity of liver

damage in CFLD prior to the advent of cirrhosis and portal hypertension. However,

liver biopsy has limitations as the specimen collected represents 1/50,000th (231) of

the entire liver which limits it reliability given the sampling limitations and the known

focal distribution of CFLD lesions. Modifications such as the use of dual-pass liver

biopsy can improve the assessment of liver fibrosis in CFLD as demonstrated by

Lewindon et al. (64). Despite these improvements, liver biopsy is not popular as it is

invasive with the potential for severe complications including bleeding, pneumothorax,

haemothorax and death, and management pathways are not well elucidated (77).

Non-invasive diagnostic modalities to diagnose CFLD and stratify disease severity are

paramount to identify children with CF at risk of developing liver disease, for

surveillance and screening for complications, or predict the most appropriate timing

for liver transplantation and effectively evaluate emerging therapies. The utility of

serum biomarkers such as aspartate aminotransferase to platelet ratio index (APRI)

(124) or circulatory miRNAs (232) to detect CFLD has recently been demonstrated.

Over the past decade, non-invasive imaging techniques such as ultrasound-based

elastography have been developed and extensively assessed for detection of hepatic

fibrosis in adult chronic liver diseases (94, 233). The principle behind elastography is

complex (reviewed elsewhere (234)); briefly, it is based on a directional mechanical

excitation such as a pulse-wave applied to the liver causing a deformation or change

of shape to assess liver stiffness. Due to the liver’s elastic restoring forces, the tissue

acts against the change of shape and propagates a shearwave that travels through

the liver and back to the probe where it can be evaluated. Elastography uses

ultrasound to detect the deformation in the liver in response to the applied force and

measures the velocity of propagation of the shearwave in the tissue. The velocity of

the wave is proportional to liver stiffness, propagating faster in a stiffer tissue which

66

changes with the presence of hepatic stiffening content such as blood congestion, bile

retention in biliary obstruction and most significantly, of fibrosis (202).

Several studies have shown the ability of transient elastography (TE; Fibroscan®,

Echosens) to effectively discriminate fibrosing liver disease, not only in adults but also

in children with CF (106, 235-237). Similar studies have been conducted using

acoustic radiation force impulse (ARFI)-based techniques or point shear-wave

elastography (pSWE; Virtual Touch Quantification (VTQ). Siemens), showing

reliability as a non-invasive tool to diagnose liver fibrosis in CFLD in children and adults

(113-115, 238, 239). However, no studies have been conducted for utility in CFLD of

using 2-D ShearWave elastography (Supersonic, Aixplorer). Supersonic ShearWave

elastography (SSWE) uses acoustic radiation force to create a supersonic moving

mechanical excitation that produces tissue displacement. SSWE is integrated into an

ultrasound allowing real-time visual imaging to avoid artefacts influencing the

measurements, but also generates a real-time coloured map displaying the elasticity

values at any given time in the region of interest. These characteristics, and the

generation of a wider frequency excitation band offer distinct advantages compared to

TE and pSWE (117) including, as proposed by some, the detection of intermediate

liver fibrotic stages (204). The hypothesis for this chapter is that Supersonic

ShearWave elastography (SSWE) can be used as a non-invasive method for the

diagnosis and assessment of liver fibrosis in children with CF. Hence, the proposed

aim is to evaluate the clinical utility and feasibility of SSWE as a screening tool for the

detection of paediatric CFLD. For this chapter, I have enrolled patients and measured

liver stiffness in children with either CFLD, CFnoLD or healthy controls using SSWE

in order to prospectively evaluate its utility as a non-invasive tool for the detection of

CFLD, as well as to determine normal liver stiffness values compared to CF children

without signs of liver disease.

67

3.2 RESULTS

3.2.1 Patient characteristics

Between 2015 and 2018, a total of 125 children were prospectively enrolled and

evaluated for this study, comprising the following: 29 healthy paediatric controls, 41

children with CFnoLD and 55 children with CFLD. All children were scanned using

SSWE to assess their LSM, with 100% success rate. CFLD children were older than

children with CFnoLD and controls (P=0.0556). There were more males with CFLD

enrolled in this study (P=0.0072). Children with CFLD had increased body mass index

(BMI, P=0.0421), aspartate aminotransferase (AST, P=0.0086), alanine

aminotransferase (ALT, P=0.0109), γ-glutamyl transpeptidase (GGT, P=0.0022) and

APRI (P=0.0049), when compared to CFnoLD children. APRI was calculated in 55 CF

children (CFLD, n=36; CFnoLD, n=19) with available liver biochemistries and platelet

counts within a sixty day timeframe from the day of the SSWE scan. Patient

characteristics are shown in Table 3.1.

68

Table 3.1. Patient characteristics.

Controls

(n=29)

CFnoLD

(n=41)

CFLD

(n=55)

CFLD vs CFnoLD

P-value

Sex

Male, n (%)

Female, n (%)

21 (72.4)

8 (27.6)

19 (46.3)

22 (53.7)

30 (54.5)

25 (45.5)

0.5363a

Age (years)

Mean ± SD

IQR

7.72 ± 4.99

4 - 13

8.63 ± 3.94

6 - 12

10.27 ± 4.21

8 – 14

0.0556b

BMI (Kg/m2)

Geometric mean ± geometric SD

IQR

NA 16.42 ± 1.11

15 - 17

17.38 ± 1.15

16 - 19

0.0421c

Aspartate aminotransferase, AST (U/L)


IQR

NA 40.10 ± 1.38

33 - 52

61.49 ± 2.06

37 - 91

0.0086c

Alanine aminotransferase, ALT (U/L)


IQR

NA 38.00 ± 1.83

24 - 67

62.68 ± 2.14

36 - 100

0.0109c

γ-Glutamyl transpeptidase, γGT (U/L)


IQR

NA 14.67 ± 1.85

10 - 19

28.50 ± 2.31

14 - 59

0.0022c

Alkaline phosphatase, ALP (U/L)


IQR

NA 257.5 ± 1.40

195 - 332

239.3 ± 1.61

205 - 296

0.7657c

AST to platelet ratio index , APRI (arbitrary units)


IQR

NA 0.31 ± 1.62

0.21 – 0.42

0.56 ± 2.28

0.34 – 0.82

0.0049c

69

Controls

(n=29)

CFnoLD

(n=41)

CFLD

(n=55)

CFLD vs CFnoLD

P-value

Diagnostic criteria, n (%)

Clinical hepatomegaly

Present NA 1 (2.4) 22 (40.0)

Persistent elevation of ALT

Present NA 28 (68.3) 52 (94.5)

Abnormal ultrasound

Present NA 2 (4.9) 53 (96.4)

CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; NA= data not available; SD= standard deviation; a= Fisher’s exact test; b= Student t test; c= Mann-Whitney Test; IQR=interquartile range.

3.2.2 CFLD children showed higher liver stiffness values compared to CFnoLD

and healthy controls

CFLD children showed significantly higher median LSM (8.1kPa, IQR= 6.7 – 11.9)

compared to both CFnoLD (6.2kPa, IQR= 5.6 – 7.0; P<0.0001) and controls (5.3kPa,

IQR= 4.9 – 5.8; P<0.0001). LSM was also increased in CFnoLD compared to controls

(P=0.0192) (Figure 3.1).

70

Figure 3.1. Liver stiffness measurements (LSM) in healthy controls, CFnoLD and CFLD children.

SSWE liver stiffness measurements (LSM) of children with CFLD versus CFnoLD and Controls. Representative SSWE image and analysis performed in children with either (A) CFnoLD showing an LSM = 5.1kPa, or (B) CFLD with an LSM = 40.5kPa. Images show real-time stiffness colour map which is homogeneous in the region of interest (circle). (C) Liver stiffness values expressed in kPa for healthy controls, CFnoLD and CFLD children. Lines represent median ± 95%CI. * P<.05, **P<.01 and ***P<.001 by Kruskal-Wallis with Dunn’s post-hoc test. LSM= Liver stiffness measurement; 95%CI= 95% confidence interval; kPa= Kilopascal; CFnoLD = cystic fibrosis no liver disease; CFLD = cystic fibrosis-associated liver disease.

3.2.3 SSWE and APRI can discriminate children with CFLD

Receiver operating characteristics (ROC) analysis revealed that SSWE demonstrated

good diagnostic accuracy for CFLD (AUC= 0.79, P<0.0001) (Figure 3.2.A). Youden’s

index determined 6.85kPa as the optimal cut-point to distinguish CFLD children with

a sensitivity of 75% and specificity of 71% (Figure 3.2.B). In this model, a LSM

71

increase of 1kPa detected by SSWE is associated with 1.7-fold (CI: 1.3 – 2.4) increase

odds of having liver disease in paediatric CF.

Figure 3.2. Diagnostic assessment of SSWE for the discrimination of CFLD.

(A) ROC curve of SSWE (AUC= 0.79, P<0.0001). (B) LSM optimal cut-point of 6.85kPa for CFLD discrimination (sensitivity= 75%; specificity= 71%). (C) ROC curve analysis in CART model combining LSM and APRI showed improve diagnostic accuracy for CFLD (AUC= 0.84, sensitivity=67%, specificity=88%). (D) Scatterplot illustrates cut-points determined by CART modelling used to discriminate CFLD from CFnoLD children. Blue dots represent CFnoLD. Red dots represent CFLD

Several studies have shown the use of APRI as a non-invasive biomarker for the

detection of CFLD (124, 232). ROC curve analysis of APRI for detection of liver

disease in this cohort (i.e., CFLD vs CFnoLD) showed an AUC= 0.74 (P=0.0040),

similar to previous reports (124). Therefore, classification and regression tree (CART)

modelling was used to assess the combined performance of SSWE and APRI to

72

predict liver disease in CF children. CART modelling showed that a LSM ≥8.1kPa

(Figure 3.2D) was associated with an 89% probability of CFLD, whereas a LSM

<6.5kPa was associated with 23% probability of CFLD. Children with a LSM <8.1kPa,

have less probability (42%) of CFLD compared to children with LSM ≥8.1kPa. In the

<8.1kPa group, patients with APRI <0.44 (or missing APRI values) reduced the

probability of CFLD to 34% compared to 80% probability for those with APRI ≥0.44.

Whereas in the LSM ≥8.1kPa group, patients with APRI ≥0.96 (or missing APRI

values) have an increased probability of CFLD (97%) compared to 81% probability for

those with APRI <0.96 (Figure 3.3) ROC curve analysis combining SSWE LSM and

APRI resulted in an improved AUC= 0.84 (sensitivity=67%, specificity=88%)

compared to the AUC of LSM alone (Figure 3.2.C-D). SSWE combined with APRI

showed 14.8 greater odds of discriminating liver disease in paediatric CF.

73

Figure 3.3. CART decision tree for predicting liver disease in children with CF.

Recursive partition analysis (using CART) revealed the best cut-points for LSM and APRI that discriminate liver disease in children with CF. All 96 CF patients had LSM, however, only 55 had APRI available within sixty days of the time of SSWE (CFLD, n=37 and CFnoLD, n=18). The term “Missing” represents children for whom APRI is not available. Table (top left corner) shows the number of patients classified in each assigned cut-point (leaf). Red colour represents CFLD children. Blue colour represents CFnoLD children. LSM= Liver stiffness measurement; kPa= Kilopascal; APRI= Aspartate aminotransferase (AST) to platelet ratio index; Prob= probability; CFnoLD = cystic fibrosis no liver disease; CFLD = cystic fibrosis-associated liver disease.

3.2.4 SSWE for assessing hepatic fibrosis severity in CFLD

Children with CFLD and severe fibrosis/cirrhosis were identified via two separate

approaches, using (i) clinical assessment or (ii) published APRI cut-offs for F3/F4

fibrosis (as described in Chapter 2, section 2.2.1). (i) Using the clinical assessment

of fibrosis severity approach, in the 55 CFLD children, 17 were classified with severe

(F3/F4) fibrosis, whereas 38 were classified with no/mild/moderate (F0-F2) fibrosis.

SSWE demonstrated excellent diagnostic accuracy in the ROC curve analysis for the

detection of severe fibrosis/cirrhosis with an AUC= 0.95 (P<0.0001), with sensitivity=

88% and specificity= 87% at an LSM cut-point of 9.05kPa, as determined by Youden’s

index (Figure 3.4.A). No improvement was observed in the diagnostic performance

74

by combining LSM and APRI. (ii) Using the published APRI cut-off for fibrosis severity

approach, 37 had APRI available from which 21 were classified as severe fibrosis and

16 as no/mild/moderate fibrosis. ROC curve analysis yielded an AUC= 0.65

(P=0.1144) with a sensitivity= 88% and specificity= 25% with a cut-point of 6.3kPa

(Figure 3.4.B). Thus, when fibrosis severity is diagnosed based on clinical

assessment, an increase of 1kPa in LSM detected by SSWE is associated with 2.2-

fold (C= 1.4 – 3.3) increase odds of having severe liver disease. This is compared to

a 1.1-fold (CI=1.0 – 1.2) increase odds when severe fibrosis/cirrhosis is classified

according to APRI score.

Figure 3.4. ROC curves for assessment of hepatic fibrosis severity in CFLD.

(A) ROC curve of SSWE based on clinical assessment to distinguish between mild/moderate fibrosis (n= 38) vs. severe fibrosis/cirrhosis (n=17); AUC= 0.95 (P<0.0001). (B) ROC curve of SSWE based on APRI score for severe fibrosis/cirrhosis (124), to distinguish between mild/moderate fibrosis (n= 16) vs. severe fibrosis/cirrhosis (n= 21); AUC= 0.65 (P=0.1144).

3.2.5 The utility of SSWE in assessing LSM progression in paediatric CF

CF children with no less than 12 months between SSWE measurements were

considered for LSM progression assessment. A total of 35 children with CF had at

least one additional LSM during the three year duration of the study, distributed as

follows: 22 children with CFLD (one year, n= 18; 2 years, n= 11; 3 years, n= 4) and 13

children with CFnoLD (one year, n= 8; two years, n= 7, three years, n= 0). None of the

CFnoLD children showed clinical evidence of CFLD development over the three year

75

duration of the study. In children with CF (CFLD and CFnoLD considered together) the

median time period between SSWE scans was 2 years (IQR= 1-2); the median LSM

progression was 0.9kPa (IQR= 0.5 – 2.4) which represents an LSM difference of

13.9% (IQR= 8.9 – 28.3); the median rate of progression was 0.7kPa/year (IQR= 0.4

– 1.3) representing a relative progression of 10%/year (IQR= 5 – 18) (Table 3.2.). For

CFnoLD children the median time period between the readings was 2 years (IQR= 1-

2); the median LSM progression was 0.9kPa (IQR= 0.6 – 1.3); which represents an

LSM difference of 15.7% (IQR= 9.8 – 21.7); the median rate of progression was

0.5kPa/year (IQR= 0.4 – 1.1) representing a relative progression of 9%/year (IQR= 8

– 14.5) (Table 3.2). For CFLD children the median time period between the readings

was 2 years (IQR= 1-2); the median LSM progression was 1.1kPa (IQR= 0.5 – 2.8)

which represents an LSM difference of 13.3% (IQR= 6.5 – 34.2); the median rate of

progression was 0.8kPa/year (IQR= 0.4 – 2.4) representing a relative progression of

11%/year (IQR= 3.8 – 21.3) (Table 3.2).

76

Table 3.2. Assessment of LSM progression.

All CF (n= 35) CFnoLD (n= 13)

CFLD (n=22) CFnoLD vs CFLD

P-value

Time period, y

median (IQR)

2

(1 – 2)

2

(1 – 2)

2

(1 – 2)

0.5904

Progression, kPa

median (IQR)

0.9

(0.5 – 2.4)

0.9

(0.6 – 1.3)

1.1

( 0.5 – 2.8)

0.3654

Relative progression,

%

median (IQR)

13.9

(8.9 – 28.3)

15.7

(9.8 – 21.7)

13.3

(6.5 – 34.2)

0.9397

Rate of progression,

kPa/y

median (IQR)

0.7

(0.4 – 1.3)

0.5

( 0.4 – 1.1)

0.8

(0.4 – 2.4) 0.3735

Relative progression,

%

median (IQR)

10.0

(5.0 – 18.0)

9.0

(8 – 14.5)

11.0

(3.8 – 21.3)

0.8069

CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; IQR= interquartile range; y= year. All comparisons performed with Mann-Whitney test.

CFLD children showed higher progression, rate of progression and relative rate of

progression in LSM assessment compared to CFnoLD, although these comparisons

were not statistically significant (Table 3.2). Comparison between the first (BL) and

last (FU) LSM measurement within each group, showed no significant difference in

either CFnoLD (P=0.5358) or CFLD (P=0.5414) (Table 3.3). When compared the

77

initial LSM with the follow-up measurement for every time period, no significant

difference was found for CFnoLD at one year time period (P= 0.6320) or at two year

time period (P=0.5994) (Table 3.3). In the CFLD group, there was no significant

difference at one year time period (P=0.5414) or two year time period (P=0.8879),

however, for the three year time period LSM was significantly higher in the follow-up

LSM compared to baseline (P=0.0274) (Table 3.3).

Table 3.3. Yearly LSM follow-up.

CFnoLD CFLD

median BL

median FU

P-value median BL

median FU

P-value

BL vs. Last 5.9

(n=13) 6.3

(n=13) 0.5358

8 (n=22)

7.5 (n=22)

0.5414

BL vs. one year 5.9

(n=13) 6.4

(n=8) 0.6320

8 (n=22)

7.5 (n=18)

0.3920

BL vs two years 5.9

(n=13) 6.3

(n=7) 0.5994

8 (n=22)

6.1 (n=11)

0.8879

BL vs. three years NA NA NA 8

(n=22) 18.8 (n=4)

0.0274

Comparison of the initial LSM with each follow-up measurement during the duration of the study. BL= first LSM; Last= last available LSM; FU= follow-up; CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; NA= not available. All comparisons were performed with Mann-Whitney test.

3.2.6 Correlation of SSWE with patient demographics and liver enzymes

LSM values were positively correlated with age (rs= 0.478, P<0.0001), GGT (rs=

0.554, P<0.0001) and APRI (rs= 0.383, P=0.004) in all CF children (Table 3.4). When

correlations were assessed in CFnoLD children, only age showed a significant positive

correlation with LSM (rs= 0.527, P=0.0004) (Table 3.4). In contrast, age (rs= 0.320,

P=0.017), GGT (rs= 0.570, P=0.0002) and APRI (rs=0.327, P=0.049) showed a

significant positive correlation with LSM in CFLD children. (Table 3.4).

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Table 3.4. Correlations of LSM with patient demographics, liver related serum enzymes and APRI.

Study group Age BMI AST ALT GGT ALP APRI

All CF

rs 0.478 0.177 0.149 0.130 0.554 0.015 0.383

P-value

<0.0001 0.076 0.268 0.328 <0.0001 0.906 0.004

CFnoLD

rs 0.527 -0.127 -0.208 -0.150 0.197 -0.080 0.192

P-value

0.0004 0.428 0.378 0.505 0.380 0.723 0.447

CFLD

rs 0.320 0.131 0.040 -0.003 0.570 0.126 0.327

P-value

0.017 0.341 0.813 0.987 0.0002 0.463 0.049

CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; rs= Spearman's correlation coefficient; BMI= body mass index; AST= aspartate-aminotransferase; ALT= alanine aminotransferase; GGT= γ-glutamyl transpeptidase; ALP= alkaline phosphatase; APRI= aspartate aminotransferase to platelet ratio.

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3.3 DISCUSSION

Over the past 10-20 years, improvements made in CF patient management such as

nutrition optimization, better understanding and strategies in antibiotic treatment,

multidisciplinary care and specially improvements in pulmonary complications have

increased life expectancy (11). Most of these advances have had a direct impact on

the respiratory complications of CF. However, non-pulmonary complications such as

CFLD, have a reported incidence of between 20-40% (61, 240), and is responsible for

approximately 2.8% of overall deaths in the CF population (62), with little advances

made in the early diagnosis or treatment of those at risk of severe liver abnormalities.

The identification of children at risk of developing CFLD with subsequent progression

to cirrhosis and portal hypertension is of great clinical importance for better patient

management. Diagnosis of CFLD is currently based on non-specific and insensitive

clinical, imaging and liver enzyme assessment (62). While dual-pass liver biopsy has

been shown to improve diagnostic accuracy to both monitor severity and the

progression of hepatic fibrosis in CFLD (64), it is not widely utilised and is an invasive

procedure with inherent risks (77) and thus it is not practical for monitoring disease

progression. Therefore, the development of non-invasive methods to assess hepatic

fibrosis is a clinical priority in CF. Ultrasound based elastography, has emerged as a

promising tool suitable for indirectly evaluating hepatic fibrosis by measuring liver

stiffness. There are three principal ultrasound-based elastography methods including

transient elastography (TE, Fibroscan), pSWE (ARFI, VTQ) and 2DSWE

(Supersonic). TE has been available for a longer period of time and therefore it has

been assessed and validated in a number of adult and paediatric chronic liver

diseases, including CFLD (235, 236). In this chapter, I have evaluated the utility of

Supersonic shearwave elastography (SSWE) to diagnose paediatric CFLD. It was

hypothesised that children with CFLD will have more liver fibrosis when compared to

those with CFnoLD and non-CF controls, leading to a higher liver stiffness. Therefore,

the difference in liver stiffness could be detected with SSWE and used to diagnose

and monitor CFLD.

In this study SSWE-derived LSM was successfully performed in 100% of subjects

scanned, similar to previous studies in paediatric patients (204, 241). Measurements

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were well tolerated even in children < one year old. SSWE confirmed increased liver

stiffness in CFLD compared to both CFnoLD and healthy controls. The most

appropriate case definition of CFLD remains a topic of ongoing discussion in paediatric

hepatology. This thesis uses the definitions and guidelines recommended by the

European Society for Paediatric Gastroenterology, Hepatology and Nutrition

(ESPGHAN) to categorise CFLD. These guidelines state that CFLD should be

considered when at least two of the following conditions are present: abnormal

physical examination (hepatomegaly ± splenomegaly), persistent abnormalities of liver

function tests and ultrasonographic evidence of liver involvement (including increased

and/or heterogeneous echogenicity, nodularity) or portal hypertension (62). By using

these criteria the risk of ignoring important aspects of the disease as well as over

interpreting others such as elevation of serum transaminases, which are relatively

common among the CF population are reduced. Others have proposed that CFLD

should be defined as the presence of cirrhosis/portal hypertension or intermittent

elevation of liver enzymes, steatosis, fibrosis, cholangiopathy or ultrasound

abnormalities (59). Both definitions use non-specific diagnostic criteria and prioritise

advanced liver disease/cirrhosis against early manifestations of the disease or

fibrogenic processes that could potentially lead to misclassification. There are

limitations in the current ‘best practise’ guidelines for the definition of CFLD, thus these

limitations in case definition in the cohorts used in this thesis need to be considered

when assessing the clinical utility of the findings thorough the entire thesis.

Several studies have evaluated LSM using SSWE in healthy control children

demonstrating median values ranging from 5.5kPa to 7.4kPa (204, 241, 242), similar

to the median LSM of 5.3kPa in this study. No previous studies have investigated the

utility of SSWE in CF subjects. However, comparative elastography studies using

SSWE and Fibroscan-TE have been performed in other chronic liver diseases showing

no major differences in stiffness measurements using these two techniques (243-245).

Several TE studies reported higher LSM in CFLD compared to CFnoLD children. In

this study, CFnoLD had a median LSM of 6.2kPa; similar to that reported for CFnoLD

by other groups using TE (106, 236, 237).The median LSM for CFLD using SSWE

reported here was of 8.1kPa, again similar to the median LSM of 8.2kPa from Klotter

81

et al. (106). However, other TE studies report much higher median LSM of between

14 – 15.1 kPa (236, 237). These differences are due to differences in study design.

TE studies with lower median kPa values for CFLD (106, 236), comparable to the

current study, used the European Society for Paediatric Gastroenterology, Hepatology

and Nutrition (ESPGHAN) guidelines to classify CFLD (62) which does not require the

presence of portal hypertension. Others define CFLD based on the presence of at

least one of the following: increased ALT, prescription of ursodeoxycholic acid,

abnormal ultrasound findings or portal hypertension (237).

SSWE demonstrated good clinical utility to discriminate liver disease in CF children

with an AUROC of 0.79 (Figure 3.2.A). Fibroscan-TE studies, however, have

demonstrated much better results with AUROC for CFLD ranging between 0.91 - 0.96.

This appears to be principally due to the classification used to define CFLD in these

patient cohorts including more patients with portal hypertension and cirrhosis (i.e.,

classifying CFLD as severe having liver abnormalities, rather than a continuum of

disease progression), as well as the inclusion of young adults up to 25 years of age.

Of interest, the suggested cut-points to distinguish CFLD in these TE studies are

between 6-7 kPa, which we confirmed in our SSWE study with a cut-point of 6.85 kPa

(Figure 3.2.B). We have previously shown the ability of APRI to differentiate CFLD

children (124). In the present study the diagnostic performance for CFLD detection

was improved when SSWE-derived LSM was combined with APRI using CART

modelling, resulting in an AUC = 0.84 (Figure 3.2.C), increasing the odds of detecting

liver disease by 15-fold.

Diagnosis of CFLD severity is a significant clinical need with major implications in the

clinical management and quality of life of children with CFLD. In the absence of liver

biopsy in this study, children with CFLD were classified as having severe

fibrosis/cirrhosis or mild/moderate fibrosis using two different approaches: firstly, using

clinical assessment i.e., ± PHT or ultrasound-based cirrhosis, and secondly based on

previously reported APRI cut-off for severe fibrosis/cirrhosis (124). Using these

classifications, 17 children were classified with severe fibrosis/cirrhosis and 38 with

mild/moderate fibrosis via clinical assessment, whereas 21 and 16 children were

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classified with severe fibrosis/cirrhosis and mild/moderate fibrosis, respectively, by

APRI cut-off (where APRI was available). Using SSWE the diagnostic accuracy for

discriminating severe fibrosis/cirrhosis on clinical assessment was excellent with an

AUC=0.95 (sensitivity= 88% and specificity= 87%) at a proposed cut-point of 9.05 kPa

(Figure 3.4.A). Others have reported similar values for SSWE when compared to liver

biopsy to assess hepatic fibrosis severity in a variety of chronic liver diseases (204,

246). Franchi-Abella et al. (204) measured liver stiffness in children with chronic liver

disease including patients after liver transplantation, biliary atresia and cholestasis,

with an AUC= 0.97 (sensitivity= 91.7% and specificity= 95.7; cut-point of 9.49 kPa) for

severe fibrosis. Similarly, a meta-analysis of 12 studies with 550 children with chronic

liver disease demonstrated an overall AUC=0.91 (sensitivity= 81% and specificity=

91%) with a cut-point of 9.4 kPa to predict significant liver disease (246). Taken

together, these results confirm the strength of SSWE in detecting advanced fibrosis in

children with CFLD, comparable to its use in other liver diseases. When hepatic

fibrosis severity was assessed based on APRI, the diagnostic performance of SSWE

for severe fibrosis/cirrhosis was sub-optimal with an AUC= 0.65 (sensitivity= 88% and

specificity= 25% with a cut-point of 6.3kPa) (Figure 3.4.B). These differences may be

explained by the relatively small number of children with CF where APRI data was

available at the time of SSWE performance. Furthermore, when classifying patients

via clinical assessment, children with the most severe form of the disease are more

likely to be grouped together than when APRI was used to classify patients. This is

reflected on the higher cut-point suggested for the clinical assessment classification

(9.05kPa), compared to lower cut-point suggested for the APRI-based classification

(6.3 kPa).

Monitoring the development and evolution of disease severity in CF children is also of

great clinical importance as it provides information on patients likely to develop CFLD

as well as those with worsening fibrosis. In the present study, LSM increased in all CF

children over a median follow-up of two years, representing an average increase of

10% per year (Table 3.2). Assessed by study groups, LSM increased in children with

CFnoLD by 0.5 kPa per year, representing a 9% increase in LSM/year. In children with

CFLD, LSM increased at a rate of 0.8 kPa per year, representing an 11% increase in

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LSM/year (Table 3.2). No significant difference was observed in progression of liver

stiffness between children with CFLD or CFnoLD, although there was a significant

increase in LSM in CFLD comparing year 3 follow-up to baseline LSM (Table 3.3),

which supports the potential utility of SSWE-derived LSM in monitoring fibrosis

progression in paediatric CFLD. Other studies have however demonstrated

differences in TE-derived LSM between CFLD and CFnoLD over time. Gominon et al.

(105) did not classify CFLD at the initial TE-LSM scan in children with CF and

examined progression of liver stiffness in only those that developed CFLD over the

subsequent 3.5 years. Others have shown increased TE-LSM progression in CFLD

compared to CFnoLD after a 4-5 year follow-up (106). A limitation of our study was

the relatively short follow-up time and extending this period up to 5 years may reveal

similar observations to the observations of others.

3.4 Summary and future directions

In this chapter, I have shown the potential utility of the non-invasive ultrasound-based

SSWE for the detection of CFLD in children with CF, providing real time measurement

of liver stiffness for the discrimination of liver disease severity. SSWE showed very

good diagnostic accuracy for CFLD which was improved when used in combination

with APRI. An important finding from this study lies in the assessment of LSM

progression in CFLD over time. These findings can provide guidance as a bedside tool

for clinicians in the implementation of strategies to assess the presence of CFLD or in

anticipating progression to advanced fibrosis and liver transplantation.

The study design was selected based on replicating as close as possible a typical

case during annual review in the CF clinic as it informs the real applicability of SSWE

as a bedside tool. Although some factors such as fasting status, or free-breathing have

been reported to influence LSM (201), this study showed that SSWE is still robust

enough to detect important clinical differences. Likewise, when combining APRI and

LSM, children with missing APRI values were included in the model to prevent bias

when interpreting the data. This replicates real life clinical situations where SSWE but

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not APRI may be available. This is particularly important as it demonstrates the real

utility and applicability of the technique with no need of special preparations or

cooperation from younger children.

In the future, this study could benefit from validation in a larger cohort with biopsy

validated fibrotic staging to better assess the capability of SSWE to discriminate

between intermediate degrees of fibrosis. Similarly, the combination of SSWE and

serum biomarkers is particularly exciting as it seeks to improve the diagnostic

accuracy. SSWE has proved to be a good discriminator of CFLD with a better

diagnostic performance compared to current accepted methods although not in

routinely clinical practise, such as APRI. Despite outperforming APRI, SSWE probably

lacks the diagnostic accuracy to be utilised as a stand-alone test for CFLD detection

or assessing fibrosis severity, therefore, in the next chapter I will explore the potential

of circulatory miRNAs as CFLD biomarkers.

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CHAPTER 4

Investigation of a serum microRNA signature for the

discrimination of liver disease in CF and monitoring

liver disease severity in paediatric CFLD

86


Segments of the introduction, results and discussion of this chapter have been

published in the following research article:

Calvopina DA, Chatfield MD, Weis A, Coleman MA, Fernandez-Rojo MA, Noble C,

Ramm LE, Leung DH, Lewindon PJ, Ramm GA. MicroRNA Sequencing Identifies a

Serum MicroRNA Panel, Which Combined With Aspartate Aminotransferase to

Platelet Ratio Index Can Detect and Monitor Liver Disease in Pediatric Cystic Fibrosis.

Hepatology 2018; 68:2301-2316.

Diego A. Calvopina allocated patients into their respective cohorts (under the

supervision of Peter J. Lewindon and Grant A. Ramm), extracted miRNA from serum

samples, constructed miRNA libraries, performed miRNA reverse transcription and

miRNA qRT-PCR, collected, analysed and interpreted data and wrote the research

article.

Mark D. Chatfield performed the more advanced statistical analyses (e.g. logistic

regression, concordance statistic and Obuchowski method). Grant A. Ramm

conceived the idea for the study. Anna Weis and Louise E. Ramm collected clinical

data. Peter J. Lewindon assisted in study design and collection of tissue specimens

and blood samples. Charlton Noble helped enrol patients and collected tissue

specimens and blood samples. Miranda A. Coleman, Manuel A. Fernandez-Rojo and

Daniel H. Leung provided advice on study design.

87

4.2 INTRODUCTION

Non-invasive diagnostic modalities to diagnose CFLD and stratify disease severity are

paramount to identify children with CF at risk of developing liver disease, for

surveillance and screening for complications, or to predict the most appropriate timing

for liver transplantation. Current non-invasive candidates for CFLD assessment focus

on biological (serum biomarkers) and physiological properties (transient

elastrography) to indirectly assess the degree of fibrosis in the liver. Despite the

potential for these non-invasive techniques, limited studies have been undertaken in

paediatric CF populations. Increase expression of tissue inhibitor metalloproteinase 4

(TiIMP-4) and endoglin have been described in serum of adult CFLD patients (123),

while the aspartate aminotransferase (AST) to platelet ratio index (APRI) and the

fibrosis-4 (FIB-4) index have been proposed as biomarkers for diagnosis and

assessment of severity of CFLD in children (124). During the past decade, microRNAs

(miRNAs) have been considered as potential biomarkers for several chronic diseases

as they are disease- and tissue-specific, accessible in body fluids and stable in

circulation which makes them attractive circulatory biomarkers (194).

miRNAs are short interfering RNAs which catalytically silence gene expression at a

post-transcriptional level. They constitute the most abundant class of endogenous,

small, non-coding RNA with approximately 50000 copies per cell in the liver (125, 247).

miRNAs are involved in the regulation of all biological and pathological processes of

every cell type and their altered expression has been associated with a broader

fibrogenic response or liver damage (248). Furthermore, the expression profiles of

miRNAs also seem to be specific when compared between liver diseases of different

aetiologies. miRNAs predominantly exist intracellularly, however, it is possible to find

miRNAs in extracellular environments such as in serum, plasma, semen,

cerebrospinal fluid and urine (180-183, 185, 187) making them attractive biomarker

candidates.

The hypothesis for this chapter is that there is an alteration in the circulating miRNA

signature of children with CFLD that can be used to diagnose liver disease and assess

88

the progression of fibrosis severity among the CF population. Hence, the proposed

aim is to identify novel and previously described circulatory miRNAs that are

differentially expressed among CFLD and CF but no liver disease (CFnoLD) children.

In this chapter, I have used next-generation sequencing (NGS) to identify differentially

expressed serum miRNAs in children with liver biopsy validated CFLD vs. CFnoLD

children and healthy controls. Selected miRNA candidates were then validated by

qRT-PCR in an extended patient cohort. Furthermore, statistical analysis was

performed to assess the diagnostic performance of validated differentially expressed

miRNAs, alone or in combination with APRI, in the detection of liver disease in CF

children.

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4.3 RESULTS

4.3.1 Patient characteristics

Between 1997 and 2016, a total of 124 children were prospectively evaluated, their

sera collected and distributed as follows: 40 controls, 40 CFnoLD and 44 CFLD. From

the 44 biopsy-proven CFLD children, fibrosis staging was as follows: F0, n=14; F1,

n=8; F2, n=8; F3, n=8; F4, n=6. There was no significant difference in age or sex

between the CFLD and CFnoLD cohorts. The CFLD group had significantly higher

serum aspartate aminotransferase (AST, p<0.0001), alanine aminotransferase (ALT,

p<0.0001), γ-glutamyl transpeptidase (GGT, p<0.0001) and alkaline phosphatase

(ALP, p=0.0003) vs. CFnoLD. APRI was significantly higher in CFLD compared to

CFnoLD (p<0.0001). While FIB-4 was increased in CFLD, this did not reach statistical

significance (p=0.6709). Patient characteristics are shown in Table 4.1.

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Table 4.1. Patient characteristics

Controls

(n=40)

CFnoLD

(n=40)

CFLD

(n=44)

CFLD vs CFnoLD

P-value

Sex

Male, n (%)

Female, n (%)

24 (60)

16 (40)

19 (47.5)

21 (52.5)

21 (47.7)

23 (52.3)

>0.9999a

Age (years)

Mean ± SD

IQR

9.03 ± 3.01

7 - 11

11.23 ± 3.61

8 - 14

9.66 ± 4.07

6 – 13

0.0666b

Aspartate aminotransferase, AST (U/L)

Mean ± SD

IQR

NA 25.55 ± 9.53

18 - 32

44.38 ± 17.91

29 - 56

<0.0001b

Alanine aminotransferase, ALT (U/L)

Mean ± SD

IQR

NA 22.25 ± 10.53

15 - 28

49.05 ± 27.24

28 - 68

<0.0001b

γ-Glutamyl transpeptidase, γGT (U/L)

Mean ± SD

IQR

NA 12.00 ± 4.58

8 - 15

35.54 ± 33

13 - 44

<0.0001b

Alkaline phosphatase, ALP (U/L)

Mean ± SD

IQR

NA 212.7 ± 80.15

156 - 266

295 ± 109.2

218 - 365

0.0003b

91

Controls

(n=40)

CFnoLD

(n=40)

CFLD

(n=44)

CFLD vs CFnoLD

P-value

AST to platelet ratio index , APRI (arbitrary units)


IQR

NA 0.20 ± 1.48

0.16 – 0.25

0.40 ± 2.05

0.25 – 0.52

<0.0001c

Fibrosis-4 index, FIB-4 (arbitrary units)


IQR

NA 0.18 ± 1.56

0.13 – 0.26

0.22 ± 2.26

0.14 – 0.28

0.6709c

CFTR genotype (%)

ΔF508 NA 65 75

Other NA 35 25

Diagnostic criteria, n (%)

Clinical hepatomegaly

Present NA 0 (0) 27 (61.3)

Persistent elevation of ALT

Present NA 12 (30) 32 (72.7)

Abnormal ultrasound

Present NA 6 (15) 35 (79.5)

CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; NA= data not available; SD= standard deviation; a= Fisher’s exact test; b= Student t test; c= Mann-Whitney Test; IQR=interquartile range.

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4.3.2 Next-generation sequencing (NGS) revealed a circulatory miRNA

signature in CFLD children

Small RNA libraries were constructed from an initial subset of 90 serum samples

corresponding to the three study cohorts: controls (n=30), CFnoLD (n=30) and CFLD

(n=30). Following RNA extraction, samples were pooled together to create three

libraries per study cohort, a design adapted from previous studies (249, 250). Each

library contained extracted RNA of 10 serum samples. The libraries corresponding to

the CFLD cohort were further sub-divided according to liver biopsy fibrosis staging as

follows: no fibrosis (F0, n=10), mild to moderate fibrosis (F1-2, n=10) and advanced

fibrosis-cirrhosis (F3-4, n=10) (Figure 4.1).

Figure 4.1. miRNA library distribution.

Controls consisted of paediatric patients with no CF or liver diseases; CFnoLD consists of CF paediatric patients with no signs of liver disease. CFLD consists of CF paediatric patients with signs of liver disease categorized in no fibrosis (F0), mild fibrosis (F1-2) and advanced fibrosis/cirrhosis (F3-4).

In order to identify differentially expressed circulatory miRNAs, the nine libraries were

sequenced using the Ion Proton System producing 64,658,545 total reads, with

Controls

Library 1.1 (n=10)

Library 1.2 (n=10)

Library 1.3 (n=10)

CFnoLD

Library 2.1 (n=10)

Library 2.2 (n=10)

Library 2.3 (n=10)

CFLD

Library 3.1 F0 (n=10)

Library 3.2 F1-2 (n=10)

Library 3.3 F3-4 (n=10)

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average read lengths between 17-23 nucleotides (Table 4.2), the expected size for

miRNAs.

Table 4.2. miRNA sequence summary.

Library Number of reads Number of

bases Phred (≥Q20)

Control 1 4,301,615 224,702,541 216,458,153

Control 2 4,987,003 255,396,641 242,380,662

Control 3 5,241,708 269,660,791 257,464,638

CFnoLD 1 4,887,864 256,643,536 245,565,056

CFnoLD 2 5,275,420 250,121,205 235,918,877

CFnoLD 3 3,324,667 156,565,113 149,733,646

CFLD 1 2,348,026 114,431,120 110,016,466

CFLD 2 2,571,561 134,331,880 129,660,436

CFLD 3 19,745,706 1,036,283,169 992,314,962

Sequencing summary of the miRNA libraries used for this study. Phred represents the number of bases with a base call accuracy of over 99%.

During processing of the sequencing data, significantly low counts mapped from the

CFnoLD library 3 were noticed. Following principal component analysis this library was

confirmed to be an outlier and excluded from the analysis, however this did not

decrease statistical power based of the sample size calculation. Analysis of the

remaining libraries identified 659 miRNAs with a minimum average count of 10.

miRNAs were then selected for further validation by qRT-PCR based on the NGS

differential analysis, miRNA scatter plots clustering and previous literature reporting

the functions of these miRNAs related to liver function or fibrosis. Similarly, stable

expressed miRNAs were selected to identify endogenous miRNAs necessary to qRT-

PCR data. A list of the most relevant miRNAs identified can be found in Table 4.3.

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Table 4.3. Differentially expressed miRNAs

DIFFERENTIALLY EXPRESSED miRNAs

miRNA

CFLD vs Controls CFLD vs. CFnoLD

miRNA cluster

Previously published description of role in liver disease

logFC P value LogFC P value

miR-122-5p 2.80 0.015 1.93 0.071 122/3591 • 1.5x FC tissue expression associated with increased GGT (251)

miR-132-3p 1.70 0.018 1.46 0.036 • 2x FC expression in cholestatic livers vs no cholestasis (251)

miR-365a-3p 1.44 0.080 2.59 0.004

miR-365b-3p 1.44 0.080 2.59 0.004

miR-454-5p -1.21 0.096 -1.26 0.080 • Decreased expression of α-SMA and inhibit activation Of TGFB1 treated HSCs by targeting Smad4 (252)

• Increased expression in cirrhotic liver. Negative correlation with CYP3A activity (253)

miR-664a-5p -1.27 0.099 -1.62 0.042 • Increased expression in HCC. Regulates MAT1A. Decrease MAT1A correlates with poor prognosis (254)

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miRNA


miRNA cluster



miR-181b-5p -1.34 0.073 -1.26 0.074 • Increased expression in cirrhotic liver. Negative correlation with CYP3A activity (253)

• Promotes HSC activation targeting p27. Activates HSCs via PTEN/AKT pathway

• Increased in serum of chronic hepatitis B. Associated with fibrosis F4 (255)

• Increased expression in plasma of chronic hepatitis B cirrhosis vs chronic hepatitis B (256)

• Increased in HCC. Induces apoptosis and inhibits angiogenesis, cell invasion and migration by targeting TIMP3. TGF-B promotes upregulation of miR-181b (257)

• Induce proliferation and migration of activated HSCs (258)

miR-551a -1.42 0.053 -1.72 0.023

miR-550a-5p -1.76 0.031 -1.38 0.083

miR-96-5p -2.35 0.007 -1.76 0.034 183/96/182 • Increased expression in HCC liver. Targets EphrinA5. Acts as tumour suppressor (259)

• Increased expression in HCC cells promotes tumour growth by targeting MMP2 (PI3/AKT)

• ANT2 impair HCC progression by decreasing expression of miR-96 (260)

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miRNA


miRNA cluster



miR-126-5p n.a. n.s. 4.89 0.034 • Increased in liver fibrosis (tissue). Increase expression inhibits activation and migration of HSC by targeting CPK.

• Increased expression targets NFkB inhibitor-α leading to NFKB activation

• Regulates VCAM-1 and PI3K/AKT/mTOR pathways. Anti-inflammatory mechanism in colon tumorigenesis (261)

miR-574-3p 2.61 0.102 n.a. n.s.

miR-200b-3p 2.54 0.012 n.a. n.s. 200 • Highly implicated in EMT

• Targets ZEB1/2 which repress E-cadherin (enhance EMT) (262)

Increased regulation accelerates proliferation and migration of HSCs through PI3K/AKT (258)

• Aberrant expression of miR-200 cluster associated with hepatic fibrosis

• miR-200b targets FOG2 and activates PI3K/AKT pathway that leads to proliferation and migration of HSCs

• miR-200b enhanced expression of MMP-2 which increase HSCs migration (263)

miR-26a-5p n.a. n.s. 3.998 0.065

miR-142-3p n.a. n.s. 3.509 0.035

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miRNA


miRNA cluster



miR-194-5p n.a. n.s. 2.631 0.063 • Increased expression in HSCs inhibits cell migration and decrease α-SMA and collagen (262)

• Decrease expression in HSC isolated from fibrotic livers. Targets c-Myb and Rac1 inhibiting HSC activation and ECM production (261)

• Increased expression in bile of patients who develop acute cellular rejection after liver transplantation showing liver damage (264)

• Decrease expression in HCC tissue correlates with vascular invasion

• Increase expression in HCC cells suppresses migration and invasiveness

• Targets TRIM23 having restrictive function on NFKB signalling in HCC cells

• HNF1α decrease transcription of miR-194 (265)

miR-4429 n.a. n.s. -1.517 0.081

miR-10b-5p n.a. n.s. 2.251 0.030

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miRNA


miRNA cluster



miR-20a-5p n.a. n.s. 5.32 0.020 17/92 • Cluster 17/92 involved in regulation of fibrosis in rodent and human livers.

• Increased expression in plasma associated to HCV infection and fibrosis.

• Increased expression in HCV patients correlate with progression of fibrosis (178)

• Increased expression in gall bladder carcinoma (GCC) promotes invasion and proliferation • Targets Smad7 (inhibitor of TGF-B) (266)

• 17/92 cluster highly expressed in cholangiocarcinoma cells vs normal biliary epithelial cells

• Expression of 17/92 cluster regulated by IL-6/STAT3 suggesting IL-6/STAT-3-miR17-92/PTEN is crucial in cholangiocarcinogenesis and tumour progression (267)

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miRNA


miRNA cluster



let-7g-5p n.a. n.s. 4.56 0.047 • Regulates EMT by targeting K-Ras and HMGA2A in HCC cells

• Negatively regulates Bcl-xL inducing apoptosis in HCC

• Inhibits cell migration in HCC by targeting collagen I α-2

• Re-expression in HCC cells blocks EMT by decreasing regulation of K-Ras/ERK1/2 pathways (268)

• Involved in suppression of HCC migration. Decreased in regulated in metastatic HCC

• Increased expression in HCC cells. Decrease cell motility by degrading Col1A2 interfering in cell migration (269)

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miRNA


miRNA cluster



miR-144-3p n.a. n.s. 4.48 0.010 144/451 • Decrease expression in fibrotic liver and correlated to TGF-B1 expression. May regulate TGF-B1 induced HSCs activation

• Act as tumour suppressor in HCC by targeting Akt3 (258)

• Decrease expression in HCC liver tissue

• Increased expression of miR-144 inhibit HCC metastasis, invasion and EMT by targeting Smad4 (270)

• Increase expression reduce cell proliferation, increase apoptosis and supresses migration and invasion of HCC cells

• miR-144 targets E2F3. Expression of miR-144 negatively correlated with E2F3 in HCC tissue (271)

101


miRNA


miRNA cluster



miR-15b-5p n.a. n.s. 4.24 0.036 15/107 • Increase expression inhibits proliferation and induces apoptosis of HSCs via Bcl-2 and cyclinD1 (262)

• Increased expression in serum of fatty liver disease patients.

• Increased expression of miR-15b leads to decrease of cell proliferation

• miR-15b can induce cell apoptosis by targeting BCL2 gene (272)

• Increased expression in HCC cells inhibits proliferation by targeting Bcl-2 (273)

• Regulates apoptosis by inhibiting anti-apoptotic genes e.g. Bcl-2

• Considered as redoximiRs: miRNAs that response to osmotic stress

• Targets IKKB: Inhibitor of NFk-B. Protects liver against TNF-α induced apoptosis (274)

102


miRNA


miRNA cluster



miR-17-5p n.a. n.s. 4.102 0.031 17/92 • Inter miRNA correlation with miR-106a in liver tissue

• Control of TGF-B by regulating c-myc (251)

• Activates Wnt/B-catenin which activates HSCs by inhibiting WIF1 expression

• Increased miR-17 lead to increase α-SMA in HSCs (contributes to HSCs activation)

• Decreased miR-17 suppressed CCl4 induced liver fibrosis

• miR-17 promotes HSCs proliferation and activation via Smad7 (275)

• Increase expression in rat fibrotic livers

• Inhibition of miR-17 suppressed TGF-B1 induced HSCs proliferation

• miR-17 associated with TGF-B1 induced expression of CollagenI and α-SMA in HSCs

• Increase expression in serum of cirrhotic patients vs healthy controls (276)

• miR17/92 cluster highly expressed in cholangiocarcinoma cells vs normal biliary epithelial cells


103


miRNA


miRNA cluster



miR-26b-5p n.a. n.s. 4.085 0.041 • Decrease expression in serum of PBC patients (277)

miR-18a-5p n.a. n.s. 4.077 0.015 17/92 Increased expression in serum exosomes of HCC vs chronic hepatitis B / liver cirrhosis (278)



miR-16-5p n.a. n.s. 3.847 0.049 15/107 • Increased expression inhibits proliferation and induces apoptosis of HSCs via Bcl-2 and cyclinD1 (262)

• Increased expression in serum of fatty liver disease patients (272)

• Increased expression inhibits HGF and Smad7 in liver cells infected with HCV contributing

in development of liver fibrosis (279)

miR-27b-3p n.a. n.s. 3.402 0.027 • Decreased expression in cholestatic livers vs no cholestatic livers (251)

miR-335-5p n.a. n.s. 3.159 0.045 • Inhibit HSCs migration by decreasing Tenascing-C expression (252)

miR-20b-5p n.a. n.s. 3.093 0.043 106a/363

104


miRNA


miRNA cluster



miR-30b-5p n.a. n.s. 2.826 0.046 30b/30d

miR-130a-3p n.a. n.s. 2.610 0.020 • Increased expression in rat fibrotic liver. Targets PPAR-γ and enhance activation of HSCs (280)

miR-18b-5p n.a. n.s. 2.453 0.001 106a/363

miR-143-3p n.a. n.s. 2.318 0.044 • 1.5x FC tissue expression associated with increased GGT (251)

miR-25-3p n.a. n.s. -2.007 0.043

miR-146b-5p n.a. n.s. -2.169 0.058 • Decreased expression in serum of biopsy proven NAFLD vs healthy controls (281)

105


miRNA


miRNA cluster



miR-155-5p n.a. n.s. -2.821 0.052 • Increased expression in cirrhotic liver. Negative correlation with CYP3A activity (253)

• Increased expression promotes liver fibrosis and inflammation from alcohol induced steatosis

• miR-155 targets PPRE and PPAR-α which decreased MCP1 production (282)

• Decreased expression attenuates steatosis and fibrosis but not liver injury in MCD mouse model (regulation depends on model and genes involved)

• Fibrotic mechanism by miR-155 targeting caspase3, Smad3, PDGF and C/EBPB

• miR-155 has negative regulatory role of inflammatory pathways in NASH (283)

• Decreased expression in activated HSCs, sera and liver of cirrhotic patients

• Targets simultaneously TCF4 and AGTR1 which regulate EMT and ERK1pathway (284)

miR-5100 n.a. n.s. -2.869 0.033

miR-489-3p n.a. n.s. -3.332 0.029

miR-302d-3p n.a. n.s. -3.675 0.032

106


miRNA


miRNA cluster



miR-27a-3p n.a. n.s. 4.040 0.054 • Increase expression in tissue, plasma and adipose tissue of obese-related cancer patients

• miR-27a promoted HCC proliferation by targeting FOXO1 promoting G1/S cell cycle

transition by decreasing p21 and p27 and increasing cyclin D1 (285)

miR-30d-5p n.a. n.s. 3.883 0.074 30b/30d

miR-30e-5p n.a. n.s. 3.814 0.063 miR-39 • Decreased expression in sera HCC patients vs chronic liver disease patients (286)

miR-148a-3p n.a. n.s 3.324 0.061 148/152 • Inter miRNA correlation with miR-101 (tissue)

• Decreased expression in cholestatic livers vs no cholestatic livers (251)

• Potent inducer of hepatocytic differentiation

• Decreased expression in HCC. Correlated with phenotypic differentiation of tumours measured by expression levels of hepatocytic markers

• Targets IKK-α which inhibits NUMB/NOTCH signalling (287)

Increased expression in bile of patients who develop acute cellular rejection after liver transplantation showing liver damage(264)

107


miRNA


miRNA cluster



miR-19a-3p n.a. n.s. 3.004 0.061 17/92 • Control of TGF-B by regulating c-myc (tissue) (251)

• Increased expression in HCC cells promotes tumour growth by targeting MMP2 (PI3/AKT)

• ANT2 impair HCC progression by decreasing expression of miR-19a (260)


• Expression of 17/92 cluster regulated by IL-6/STAT3 suggesting IL-6/STAT-3-miR17-92/PTEN is crucial in cholangiocarcinogenesis and tumour progression

• Increased expression of miR-19a enhances tumour cell proliferation, colony formation and invasiveness in cholangiocarcinoma cells

• miR-19a targets PTEN (267)

108


miRNA


miRNA cluster



miR-199a-3p n.a. n.s. 2.788 0.059 • miR-199a suppresses proteins that respond to unfolded protein stress (due bile accumulation) e.g. GRP78

• miR-199a modulates ER stress by negatively regulation of IRE1α pathway

• Increased expression in bile acid stimulated cultured hepatocytes and bile duct ligated mice

• Increased expression of miR-199a disrupts ER stress and prevent hepatocytes from bile acid induced cell death (288)

Increased expression in quiescent HSCs and delivered by exosomes to activated HSCs • Targets CCN2 (or CTGF) which decrease expression of Collagen1(α1) and α-SMA (289)

miR-199a expression is induced by TGF-B, promoting activation of pulmonary fibroblasts into myofibroblasts • Key effector of TGF-B signalling in lung fibroblasts by targeting Cav1

• Aberrant miR-199a expression found in mouse models of liver fibrosis. Possible role in general mechanism of liver process (290)

miR-199b-3p n.a. n.s. 2.787 0.059

109


miRNA


miRNA cluster



miR-101-3p n.a. n.s. 2.718 0.055 • Inter miRNA correlation with miR-148a (tissue) (251)

• Increased expression in serum supress liver fibrosis by targeting TGF-B pathway (261)

• Decreased expression of miR-101 associated with increased expression of ZEB1 and decreased expression of E-cadherin. Loss of miR-101 promotes hepatocyte EMT

• Exogenous expression of miR-101 supressed mesenchymal phenotype and metastasis of HCC by increasing regulation of epithelial genes and repression of mesenchymal ones (291)

• Decreased expression of miR-101 in fibrotic liver, activated HSCs and injured hepatocytes

• Targets TBRI (TGF-B receptor) and KLF6 suppressing TGF-B pathway in hepatocytes and HSCs

• miR-101 promotes transformation from activated to quiescent HSCs by suppressing proliferation, migration, loss of activation markers and gain of specific quiescent markers (292)

miR-99a-5p n.a. n.s. 2.702 0.069

110


miRNA


miRNA cluster



miR-139-5p n.a. n.s. 2.622 0.067 • Decreased expression in livers of advanced PBC

• Increased expression in lymphocyte-derived hepatocytes (293)

• Decreased expression in HCC tissues and cell lines

• Inhibits EMT, migration and invasion in HCC cell lines by targeting ZEB1/2 (294)

miR-376c-3p n.a. n.s. 2.535 0.074

miR-106a-5p n.a. n.s. 2.174 0.051 106a/363 • Inter miRNA correlation with miR-148a (tissue) (251)

miR-4508 n.a. n.s. -1.747 0.081

111


miRNA


miRNA cluster



miR-92a-1-5p n.a. n.s. -1.820 0.059 17/92 • Cluster 17/92 involved in regulation of fibrosis in rodent and human livers.

• Increased expression in plasma associated to HCV infection and fibrosis.

• miR-92a may serve as biomarker for early detection for benign lesions before neoplastic lesions

• Expression of miR-92a inversely correlated with fibrotic stage (178)


• Expression of 17/92 cluster is regulated by IL-6/STAT3 suggesting IL-6/STAT-3-miR17-92/PTEN is crucial in cholangiocarcinogenesis and tumour progression

• Increased expression of miR-92a enhances tumour cell proliferation, colony formation and invasiveness in cholangiocarcinoma cells

• miR-92a targets PTEN (267)

miR-3667-5p n.a. n.s. -1.886 0.059

miR-30c-5p n.a. n.s. -2.462 0.074 miR-39

112


miRNA


miRNA cluster



miR-34a-5p n.a. n.s. -2.702 0.079 • 1.5x FC tissue expression associated with increased GGT

• 2xFC expression in cholestatic livers vs no cholestasis (251)

• Increased expression in serum of NAFLD patients (272)

miR-7-5p n.a. n.s. -2.796 0.055 • miR-7 targets PDX-1 which activates Ngn-3 (expressed in proliferating cholangiocytes)

• Targets Ngn-3 which decreased cholangiocyte proliferation and collagen deposition (295)

113


miRNA


miRNA cluster



miR-183-5p n.a. n.s. -2.826 0.064 183/96/182 • Increased expression confers oncogenic function in HCC cell dissemination

• FOXO1 is the only target of the 183/96/182 cluster (3 members). It is correlated with poor

prognosis

• Increased regulation of the cluster members are correlated with metastatic features. Cluster is downstream effector of Wnt pathway through CTNNB1. CTNNB1-TCF produce translation of cluster that targets FOXO1 and enhance metastasis (296)

• Inhibits TGF-B1 induced apoptosis in HCC cells by targeting PDCD4 (297)

miR-21-5p 2.020 0.139 n.a. n.s. • 2xFC expression in cholestatic livers vs no cholestasis (251)

Differentially expressed miRNAs between controls, CFnoLD and CFLD. logFC: logarithmic fold change; FC: fold change, miR: microRNA; n.s.: not significant; n.a.: not available; GGT: gamma-glutamyl transferase; α-SMA: α-smooth muscle actin; TGFB1: transforming growth factor β-1; HSCs: hepatic stellate cells; Smad4: mother against decapentaplegic homolog 4; CYP3A: cytochrome P450 family 3 subfamily A; HCC: hepatocellular carcinoma; MAT1A: methionine adenosyltransferase 1A; p27: cyclin dependent kinase inhibitor 1B; TIMP3: metallopeptidase inhibitor 3; MMP2: matrix metallopeptidase 2; ANT2: solute carrier family 25 member 5; CPK: phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 α; NFKβ: nuclear factor kappa β; VCAM-1: vascular cell adhesion molecule 1; EMT: epithelial-mesenchymal transition: ZEB1/2: zinc finger E-box binding homeobox1/2; FOG2: zinc finger protein, fork family member 2; c-Myb: MYB proto-oncogene transcription factor: Rac1: Rac family small GTPase 1; ECM: extracellular matrix; TRIM23: tripartite motif containing 23; HNF1α: hepatocyte nuclear factor 1 α; HCV: hepatitis C virus; Smad7: mother against decapentaplegic homolog 7; K-Ras: KRAS proto-oncogene GTPase; HMGA2A: high mobility group AT-Hook 2; Bcl-xl: protein phosphatase 1 regulatory subunit 52; Col1A2: collagen type 1 α 2; Akt3: AKT serine/threonine kinase 3; E2F3: E2F transcription factor 3; Bcl2: protein phosphatase 1 regulatory subunit 50; IKKB: inhibitor of nuclear factor kappa B kinase subunit β; TNFα: tumor necrosis factor; c-myc: myc proto-oncogene BHLH transcription factor; CCl4: C-C motif chemokine ligand 4; PBC: primary biliary cirrhosis; HGF:

114

hepatocyte growth factor; PPAR-γ: peroxisome proliferator activated receptor γ; NAFLD: non-alcoholic fatty liver disease; MCP1: C-C motif chemokine ligand 2; MCD: methionine-choline deficient; PDGF: platelet derived growth factor B; CEBPB: Inteleukin 6- dependent DNA-binding protein; NASH: non-alcoholic steatohepatitis; TCF4: transcription factor 4; AGTR1: angiotensin II receptor type 1; ERK1: mitogen-activated protein kinase 3; p21: cyclin dependent kinase inhibitor 1A; PTEN: phosphatase and tensin homolog; GRP78: heat shock protein family A (HSP70) member 5; ER: endoplasmic reticulum; IRE1α: endoplasmic reticulum 2 nucleus signalling 1; CCN2: cellular communication network factor 2; Cav-1: caveolin 1; KLF6: Kruppel like factor 6; PDX-1: pancreatic and duodenal homeo-box 1; Ngn-3: neurogenin 3; FOXO1: forkhead-box O1; CTNNB1: catenin β1; PDCD4: program cell death 4.

115

Based on miRNA-seq and differential expression analysis miR-122-5p; miR-25-3p;

miR-199a-3p; miR-365a-3p; miR-18a-5p; miR-126-5p; miR-142-3p; let-7g-5p; miR-

103a-3p; miR-34a-5p and miR-484 were selected as potential biomarkers of CFLD,

while, miR-19b-3p; miR-93-5p; miR-20a-5p; let-7i-5p; let-7b-5p; let-7d-5p; miR-27a-

3p and miR-146a-5p were selected as potential endogenous reference miRNAs.

An independent subset of 30 samples (n=10/patient cohort) were used to measure the

expression of potential endogenous reference miRNAs. geNorm and NormFinder

algorithms determined an optimal panel of miR-93-5p; miR-20a-5p and let-7i-5p to be

used as endogenous reference miRNA for data normalization.

4.3.3 Circulatory miRNA signature confirmation by qRT-PCR

The expression of differentially expressed miRNA candidates was then confirmed in

the entire cohort of 124 patients (Table 4.4).

116

Table 4.4.Differentially expressed miRNAs in CFLD vs CFnoLD children.

CFnoLD (n=40)

CFLD (n=44) P-value AUC

miR-19b-3p 1.65 (0.79) 1.83 (0.75) 0.652 0.58

let-7g-5p -0.12 (1.07) -0.98 (1.41) 0.003 0.71

miR-103a-3p -0.59 (1.05) -0.82 (1.11) 0.513 0.57

miR-34a-5p -6.62 (2.33) -5.25 (1.60) 0.004 0.71

miR-484 -1.16 (0.90) -1.08 (1.03) 0.904 0.51

miR-122-5p -0.50 (1.97) 1.14 (2.12) 0.002 0.70

miR-365a-3p -5.02 (1.25) -3.91 (1.24) <0.001 0.74

miR-18a-5p -2.27 (0.59) -2.24 (0.68) 0.974 0.55

miR-199a-3p -1.75 (1.20) -1.58 (1.16) 0.786 0.53

miR-126-5p -2.36 (1.25) -1.61 (1.36) 0.055 0.65

miR-142-3p -3.09 (1.18) -3.74 (1.44) 0.005 0.62

miR-25-3p 1.03 (0.58) 1.19 (0.97) 0.674 0.55

Data correspond to qRT-PCR derived log 2 base scale relative expression of miRNAs. Data are represented as mean (standard deviation). CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis-associated liver disease; AUC= area under the curve.

Serum levels of miR-122-5p were significantly elevated in CFLD compared to both

CFnoLD (fold change [FC] =3.1, P=0.0015) and Controls (FC=7.5, P<0.0001), and

also in CFnoLD compared to Controls (FC=2.4, P=0.022) (Figure 4.2.A). Expression

levels of serum miR-34a-5p were also elevated in CFLD compared to both CFnoLD

and Controls (FC=2.6, P=0.0042 and FC=2.9, P=0.0011, respectively) (Figure 4.2.B).

Similarly, circulatory miR-365a-3p was overexpressed in CFLD when compared to

both CFnoLD and Controls (FC=2.2, P=0.0002 and FC=2.2, P=0.0001, respectively)

(Figure 4.2.C). In contrast, expression levels of let-7g-5p were downregulated in

CFLD compared to both CFnoLD and Controls (FC=-1.8, P=0.0034 and FC=-2.1,

P=0.0003, respectively) (Figure 4.2.D), while expression levels of miR-142-3p were

elevated in CFnoLD compared to CFLD (FC=1.6, P=0.0047) (Figure 4.2.E). miR-126-

5p was significantly downregulated in CFnoLD compared to Controls (FC=-1.7,

P=0.042) and increased in CFLD compared to CFnoLD however this did not reach

statistical significance (FC=1.7, P=0.0549).

117

Serum miR-18a-5p levels were upregulated in both CFnoLD and CFLD compared to

controls (FC=1.7, P<0.0001 and FC=1.7, P<0.0001, respectively) (Figure 4.2.F).

118

Figure 4.2. Relative serum miRNA expression levels in CFLD, CFnoLD and Control groups.

Log2 relative expression of (A) miR-122-5p, (B) miR-34a-5p, (C) miR-365a-3p, (D) let-7g-5p, (E) miR-142-3p and (F) miR-18a-5p. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by ANOVA with Tukey post-hoc test. miR = microRNA; SEM = standard error of mean; CFnoLD = cystic fibrosis no liver disease; CFLD = cystic fibrosis-associated liver disease.

119

4.3.4 Circulatory miRNA expression in CFLD F0 fibrosis staging

Fourteen children within the CFLD cohort had F0 fibrosis on dual-pass liver biopsy.

Despite the absence of overt histological collagen deposition these patients were

classified as having liver disease based on clinical assessment (as defined in Chapter

2, section 2.2.1). A sub-analysis was performed on these patients with F0 fibrosis to

determine whether they were distinct from children classified as CFnoLD. CFLD

subjects with F0 fibrosis showed significant higher serum levels of miR-122-5p

(FC=4.9, P=0.001), miR-365a-3p (FC=2.2, P=0.009) and APRI (FC=2.0, P<0.0003)

compared to CFnoLD children (Figure 4.3.A-C), while miR-142-3p (FC=-2.95,

P0.0006) showed significant decreased expression in F0 compared to CFnoLD

(Figure 4.3.D). Moreover, ROC curve analysis revealed the ability of miR-122-5p

(area under the curve [AUC] =0.76, P=0.004), miR-142-3p (AUC=0.75, P=0.006) and

APRI (AUC=0.86, P=0.0006) to discriminate F0 from CFnoLD children (Figure 4.3.E).

120

Figure 4.3.Relative serum miRNA expression and APRI levels in CFLD F0 fibrosis vs. CFnoLD.

Relative expression of (A) log2 miR-122-5p, (B) log2 miR-365a-3p, (C) APRI, (D) log2 miR-142-3p and (E) ROC curves of miR-122-5p (AUC=0.76), miR-142-3p (AUC=0.75) and APRI (AUC=0.86) . Lines represent mean ± SEM for miRNAs, or geometric mean and geometric standard deviation for APRI. * P<.05, **P<.01 and ***P<.001 by two-tailed T test for miRNAs and Mann-Whitney test for APRI. miR = microRNA; SEM = standard error of mean; CFnoLD = cystic fibrosis no liver disease; F0= no fibrosis based on METAVIR score.

121

4.3.5 Validated miRNA candidates can discriminate liver disease in children

with CF

miR-365a-3p demonstrated the best diagnostic accuracy for liver disease (AUC=0.74;

sensitivity=70%; specificity=67%; P=0.0001), while miR-122-5p (AUC=0.70;

sensitivity=70%; specificity=63%; P=0.0001), miR-34a-5p (AUC=0.71;

sensitivity=71%; specificity=64.%; P=0.0014) and let-7g-5p (AUC=0.71;

sensitivity=70%; specificity=65%; P=0.0010) showed similar differential diagnostic

performance (Figure 4.4.A).

Next, to derive a panel of miRNAs that could diagnose liver disease in CF children,

stepwise logistic regression analysis was performed using subsets of up to four

miRNAs (794 models in total). Model selection, based on Bayesian information

criterion, resulted in a model including miR-365a-3p, miR-142-3p and let-7g-5p

yielding an AUC=0.87 (P<0.0001) (Figure 4.4.B). In order to assess the performance

of this selected miRNA panel to diagnose liver disease in CF children, the statistical

model evaluation method 5-fold internal cross validation was performed within the

same cohort of CFLD and CFnoLD children. The result of the cross-validation showed

AUC=0.79 with a sensitivity of 63% and specificity of 77% across the different

validation sets.

122

Figure 4.4. ROC curves for the discrimination of CFLD vs CFnoLD.

A) ROC curves of APRI (AUC = 0.80, n=75), miR-122-5p (AUC = 0.70, n=84), let-7g-5p (AUC = 0.71, n=84), miR-34a-5p (AUC = 0.71, n=84) and miR-365-3p (AUC = 0.74, n=84). (B) Logistic regression model based on the combination of miR-365a-3p, miR-142-3p and let-7g-5p with the addition of log(APRI) (AUC = 0.91, n=75) and without the addition of log(APRI) (AUC = 0.87, n=84) to detect liver disease in CF. (C) miRNA panel cut point of 1.41*miR-365a-3p-0.86*miR-142-3p-0.79*let-7g-5p+5.21*log10(APRI) = -5.81 based on Liu’s method discriminates CFLD from CFnoLD.

4.3.6 Addition of APRI to miRNA panel for differential diagnosis of CFLD

The utility of APRI to detect liver disease in children with CF has been previously

shown (124) , thus APRI was calculated on CF children with available biochemistries

and platelet counts (CFLD, n=36 and CFnoLD, n=36). In this cohort of CFLD vs.

CFnoLD children, APRI demonstrated an AUC=0.80 (P<0.0001) with a sensitivity of

79% and specificity of 80%, recapitulating the results obtained in previous reports

(Figure 4.4.A) (124). Logistic regression analysis combining APRI with the three

123

miRNA panel demonstrated a markedly improved diagnostic accuracy with an

AUC=0.91. A cut-point of -5.81 was optimal for liver disease discrimination

demonstrating a sensitivity of 83% and specificity of 92% (see logistic regression

equation in Table 4.5) (Figure 4.4.C). Performance evaluation of the new proposed

CFLD panel using 5-fold cross validation resulted in an AUC=0.86 with a sensitivity of

73% and specificity of 76%. Using this three miRNA + APRI panel with a cut-point of

-5.81 predicted a 21 times greater odds of children with CF having CFLD, compared

to the ~6 times greater odds when APRI is used alone, as previously described (124).

Table 4.5. Logistic regression model for discriminating liver disease in CF.

Risk factor Coefficient 95% CI P-value

let-7g-5p -0.792 -1.429 to -0.154 0.015

miR-365a-3p 1.411 0.534 to 2.289 0.002

miR-142-3p -0.861 -1.584 to -0.138 0.020

log10APRI 5.209 1.502 to 8.915 0.006

(Intercept) 5.821

Logistic regression model for discriminating liver disease in CF. CI= confidence interval.

4.3.7 Serum miRNAs reflect hepatic fibrosis severity in CFLD

The expression of serum miRNA candidates was analysed to assess the potential

association with hepatic fibrosis severity in CFLD. Expression levels of miR-19b-3p

(FC=1.6, P=0.0465), miR-18a-5p (FC=1.8, P=0.0011) and miR-142-3p (FC=2.6,

P=0.0244) were all increased in serum from CFLD patients with severe

fibrosis/cirrhosis (F3/F4) compared to CFLD children with no fibrosis (F0). Expression

of miR-142-3p was increased in CFLD with mild/moderate fibrosis (F1/F2) compared

to F0 fibrosis (FC=2.5, P=0.026), whereas expression of miR25-3p was significantly

lower (FC=-1.9, P=0.023) in F1/F2 vs. F0 fibrosis. A significant positive correlation

between both miR-19b-3p (rs=0.35, P=0.021) and miR-18a-5p (rs=0.56, P<0.0001)

and increasing fibrosis severity was observed. miRNA expression levels and

correlations with hepatic fibrosis stage are depicted in Figure 4.5.

124

Figure 4.5. Association between relative serum miRNA expression and hepatic fibrosis staging in CFLD.

Scatter plots show log2 relative expression of (A) miR-19b-3p, (B) miR-18a-5p, (C) miR-142-3p, (D) miR-25-3p vs. hepatic fibrosis severity. Correlation between log2 miRNA expression levels and hepatic fibrosis stage for (E) miR-19b-3p and (F) miR-18a-5p. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by ANOVA with Tukey post-hoc test. miR = microRNA; SEM = standard error of mean; rs = Spearman's correlation coefficient; CFnoLD = cystic fibrosis no liver disease; CFLD = cystic fibrosis-associated liver disease; F0 = no fibrosis; F1-2 = mild/moderate fibrosis; F3-4 = severe fibrosis/cirrhosis (fibrosis staging based on METAVIR score).

125

4.3.8 Association between serum miR-18a-5p levels and hepatic fibrosis stage

in CFLD

From the validated differentially expressed miRNAs only miR-18a-5p demonstrated

potential for discriminating the severity of liver disease in CF children (Table 4.6). ROC

curve analysis to determine the ability of miR-18a-5p to distinguish liver disease

severity within CFLD yielded the following: F0 vs. any fibrosis/cirrhosis (F1-4) exhibited

an AUC=0.78 (P=0.003; sensitivity=76%; specificity=64%), while F0-F1 vs. F2-F4

showed an AUC=0.76 (P=0.003; sensitivity=86%; specificity=59%) and F0-F2 vs. F3-

4 demonstrated an AUC=0.82 (P=0.004; sensitivity=92%; specificity=73%) (Figure

4.6).

126

Table 4.6. Differentially expressed miRNAs between fibrotic stages in CFLD children.

F0-2 (n=30) F3-4 (n=14) P-value AUC C-statistic Obuchowski (Lambert et al.)

Obuchowski

miR-19b-3p 1.68 (0.85) 2.15 (0.33) 0.054 0.67 0.64 0.68 0.63

let-7g-5p -0.96 (1.56) -1.01 (1.07) 0.92 0.57 0.60 0.63 0.61

miR-103a-3p -0.80 (1.22) -0.86 (0.86) 0.88 0.54 0.53 0.54 0.51

miR-34a-5p -5.40 (1.72) -4.94 (1.34) 0.39 0.60 0.55 0.56 0.53

miR-484 -0.92 (1.04) -1.41 (0.94) 0.14 0.65 0.57 0.60 0.55

miR-122-5p 1.17 (2.33) 1.07 (1.66) 0.89 0.51 0.55 0.55 0.56

miR-365a-3p -3.93 (1.41) -3.86 (0.83) 0.87 0.51 0.52 0.53 0.53

miR-18a-5p -2.44 (0.73) -1.82 (0.26) 0.004 0.82 0.74 0.79 0.74

miR-199a-3p -1.60 (1.26) -1.53 (0.94) 0.86 0.52 0.51 0.51 0.53

miR-126-5p -1.72 (1.55) -1.37 (0.81) 0.43 0.64 0.57 0.59 0.54

miR-142-3p -3.95 (1.62) -3.28 (0.85) 0.15 0.62 0.60 0.63 0.63

miR-25-3p 1.21 (1.16) 1.13 (0.30) 0.80 0.53 0.51 0.50 0.55

APRI 0.31 (1.70) 0.65 (2.20) 0.002 0.77 0.64 0.68 0.59

FIB-4 0.17 (1.80) 0.35 (2.61) 0.007 0.70 0.66 0.69 0.63

Data correspond to log 2 base scale relative expression. Data are represented as mean (standard deviation) for miRNAs and geometric mean (geometric standard deviation) for APRI and FIB-4. F0-2= not severe fibrosis; F3-4= severe fibrosis/cirrhosis; AUC= area under the curve; C-statistic= concordance statistic; Obuchowski (Lambert et al.)= Obuchowski measure using penalties proportional to the difference in stages apart; Obuchowski= Obuchowski measure with the penalties used in this study prioritizing the differences between contiguous fibrotic stages.

127

Figure 4.6. ROC curves for assessment of CFLD severity.

(A) ROC curves of miR-18a-5p (n=44) to distinguish between F0 vs. F1-4 (AUC = 0.78); F0-1 vs. F2-4 (AUC = 0.76); F0-2 vs. F3-4 (AUC = 0.82). (B) ROC curves of APRI (n=36) to distinguish between F0 vs. F1-4 (AUC = 0.53); F0-1 vs. F2-4 (AUC = 0.69); F0-2 vs. F3-4 (AUC = 0.77).

Concordance statistic (c-statistic) (222) and the Obuchowski method (223) were used

to assess the discriminative potential of miR-18a-5p to identify hepatic fibrosis staging

in CFLD. C-statistic shows the proportion of all pairs of subjects in which predictions

and outcomes are concordant and expresses the probability of correctly ranking two

random patients from different stages. The Obuchowski method was used as

multinomial version of AUROC based on the fibrosis stages categorised by the

METAVIR scoring system using a weighting scheme based on fibrosis stage

distribution and a penalty proportional to the difference of the fibrosis stages. Pairwise

comparison of the Obuchowski measure was weighted based on fibrosis stage

distribution of the study cohort including the following penalties between METAVIR

categories: 0 between no fibrosis (F0) and cirrhosis (F4); 0.5 between mild fibrosis

(F1) and severe fibrosis (F3) and moderate fibrosis (F2) and cirrhosis (F4); and 1 for

all other combinations. This scheme prioritizes the differences between contiguous

fibrosis stages that are normally misclassified in comparison to distant fibrosis stages

(e.g. F0 to F4) that can be detected by other effective means such as imaging.

In this model, miR-18a-5p demonstrated a c-statistic = 0.74, while APRI had a c-

statistic = 0.64; in addition, miR-18a-5p had an Obuchowski measure = 0.74, whereas

APRI had an Obuchowski measure = 0.59 (Table 4.7).

128

Table 4.7. Diagnostic test accuracy to predict severity in CFLD.

Biomarkers performance to predict disease severity in CFLD

AUC (Pvalue)

miR-18a-5p

(n=44)

APRI (n=36)

F0 vs F1-4

0.78 (0.003)

0.53 (0.804)

F0-1 vs F2-4

0.76 (0.003)

0.69 (0.054)

F0-2 vs F3-4

0.82 (0.004)

0.77 (0.002)

Concordance statistic (c-statistic)

miR-18a-5p

(n=44)

APRI (n=36)

0.74

0.64

Obuchowski measure

miR-18a-5p

(n=44)

APRI (n=36)

0.74

0.59

AUC, c-statistic and Obuchowski measure for miR-18a-5p (n=44) and APRI (n=36) to assess their performance in discriminating fibrosis stages in CFLD. Fibrosis stages based on METAVIR scoring system. AUC=area under the curve; F0= no fibrosis; F1-4= any fibrosis/cirrhosis; F0-1= no fibrosis/mild fibrosis; F2-4= moderate fibrosis/cirrhosis; F0-2= not severe fibrosis; F3-4= severe fibrosis/cirrhosis.

Stepwise ordinal logistic regression analysis was performed to evaluate the

performance of selected miRNAs + APRI in predicting CFLD severity, however, only

miR-18a-5p demonstrated a statistically significant result (P=0.001). One unit of

increased log2 relative expression is associated with 7-fold increased odds of having

advanced fibrosis. When APRI was added to miR-18a-5p as a predictor of CFLD

severity in combined logistic regression, there was no improvement in its performance

with an Obuchowski measure of 0.71 for both miR-18a-5p alone and combined with

APRI.

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4.3.9 Serum miRNA expression levels correlate with liver enzymes

In CFnoLD children, AST and GGT showed significant positive correlation with miR-

365a-3p and miR-34a-5p respectively, while significant negative correlations were

observed between GGT with miR-19b-3p, miR-103a-3p and miR-484; and ALP with

miR-34a-5p (Table 4.8). In CFLD children, significant positive correlations were

observed for AST with miR-34a-5p; ALT with miR-122-5p, miR-34a-5p and miR-19b-

3p; and ALP with miR-19b-3p (Table 4.8). Similar correlations were observed between

APRI and these miRNAs which is expected as APRI is based on AST and platelet

count. In fact, when compared to APRI as a biomarker for liver disease progression,

miR-365a-3p showed a positive correlation in CFnoLD children, whereas, miR-34a-5p

was positively correlated in CFLD patients (Table 4.8).

130

Table 4.8. Correlations of circulatory miRNA levels with liver related serum enzymes and APRI.

miR-122-5p miR-34a-5p miR-365a-3p miR-19b-3p miR-103a-3p miR-484

CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD

AST

rs

95%CI

P

0.197

-0.130 - 0.487

0.221

0.312

-0.025-0.584

0.060

0.016

-0.308-0.339

0.918

0.398

0.065-0.651

0.017

0.362

0.047-0.611

0.021

0.164

-0.178-0.417

0.332

-0.147

-0.446-0.181

0.362

0.230

-0.114-0.552

0.171

-0.117

-0.425

0.478

0.243

-0.097-0.533

0.147

0.144

-0.189

0.382

0.186

-0.157-0.489

0.271

ALT

rs

95%CI

P

0.184

-0.144-0.476

0.255

0.508

0.224-0.712

<0.001

-0.089

-0.401-0.241

0.588

0.457

0.151-0.683

0.004

0.161

-0.167-0.457

0.321

0.272

-0.051-0.545

0.088

-.034

-0.351-0.288

0.831

0.163

-0.165-0.459

0.313

0.005

-0.320-0.328

0.978

0.263

-0.063-0.537

0.102

-0.119

-0.426-0.214

0.473

0.279

-0.044-0.550

0.080

GGT

rs

95%CI

P

0.134

-0.193-0.435

0.407

0.175

-0.167-0.479

0.300

0.372

0.054-0.621

0.019

0.121

-0.231-0.445

0.489

0.003

-0.316-0.323

0.980

0.060

-0.278-0.385

0.772

-0.316

-0.577-0.004

0.047

0.371

0.044-0.626

0.024

-0.338

-0.597--0.016

0.035

0.131

-0.212-0.444

0.442

-0.451

-0.676-0.148

0.004

0.055

-0.283-0.381

0.746

131

miR-122-5p miR-34a-5p miR-365a-3p miR-19b-3p miR-103a-3p miR-484

CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD CFnoLD CFLD

ALP

rs

95%CI

P

-0.016

-0.334-0.305

0.920

0.044

-0.293-0.371

0.795

-0.347

-0.603--0.026

0.030

0.154

-0.918-0.471

0.376

-0.052

-0.366-0.272

0.747

0.019

-0.315-0.350

0.909

-0.277

-0.549-0.046

0.083

0.348

0.017-0.610

0.035

-0.088

-0.401-0.243

0.593

0.008

-0.326-0.339

0.963

-0.112

-0.421-0.219

0.496

-0.156

-0.465-0.186

0.356

APRI

rs

95%CI

P

0.100

-0.231-0.411

0.542

0.244

-0.101-0.537

0.151

-0.030

-0.355-0.301

0.857

0.367

0.022-0.633

0.033

0.397

0.083-0.639

0.012

0.056

-0.286-0.386

0.745

-0.052

-0.370-0.275

0.749

0.179

-0.168-0.487

0.295

0.011

-0.319-0.338

0.948

0.029

-0.312-0.363

0.867

0.156

-0.182-0.461

0.351

0.030

-0.311-0.363

0.863

miR= microRNA; CFnoLD= cystic fibrosis no liver disease; CFLD= cystic fibrosis liver disease; AST= aspartate aminotransferase; ALT= alanine aminotransferase; GGT= γ-glutamyl transpeptidase; ALP= alkaline phosphatase; APRI= aspartate aminotransferase to platelet ratio; rs= Spearman's correlation coefficient; 95%CI= 95% confidence interval; P= p-value

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Next, the association between circulatory miRNAs and lung function, represented by

percent predicted forced expiratory volume in one second (%FEV1p) was analysed.

No correlations were found between miR-18a-5p and %FEV1p in either CFLD or

CFnoLD, suggesting that the altered serum levels of miR-18a-5p seen in CFLD may

be liver-specific, although this required further investigation which is beyond the scope

of the current study. CFnoLD children showed significant positive correlation with miR-

103a-3p (rs=0.401, P=0.017) (Figure 4.7.A), and significant negative correlation with

miR-34a-5p (rs=-0.442, P=0.008) (Figure 4.7.B). No correlations were found between

circulatory miRNAs and %FEV1p in CFLD children, however, a significant negative

correlation was found with let-7g-5p (rs=-0.280, P=0.023) in CF children with and

without liver disease (Figure 4.7.C).

Figure 4.7. Scatterplots of serum miRNAs versus lung function.

(A) Spearman rank correlation of log2 miR-103a-3p and %FEV1p in CFnoLD children (rs= 0.401) (B) Spearman rank correlation of log2 miR-34a-5p and %FEV1p in CFnoLD children (rs= -0.442) (C) Spearman rank correlation of log2 let-7g-5p and %FEV1p in CF children with and without liver disease (rs=-0.280). miR= microRNA; rs= Spearman's correlation coefficient; P= p-value; %FEV1p= percent predicted forced expiratory volume in one second.

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4.4 DISCUSSION

The prevalence of liver disease in children with CF ranges between 20-40% (61, 240)

depending on definitions and tools employed. The reasons why only select CF patients

develop clinically significant liver damage remain unclear. CFLD can be asymptomatic

with multiple manifestations making classification difficult. The lack of a uniform clinical

definition hinders the diagnosis of CFLD in children as it relies on non-specific clinical,

biochemical and imaging assessments. Fibrosis markers including collagen IV, prolyl

hydroxylase and tissue inhibitor of metalloproteinase 1 has been shown to have some

discriminatory potential in diagnosing CFLD (122). Similarly, APRI seems to predict

CFLD and severe fibrosis (124), although the relative diagnostic accuracy of APRI and

other modalities are less than ideal. The potential utility of circulatory miRNA for

discriminating liver disease in CF children and disease severity has recently been

assessed using a limited miRNA array screen (200). In this chapter, I have used serum

miRNA-Seq with PCR-validation to assess all novel and known miRNAs in children

with liver biopsy-validated CFLD to demonstrate differential miRNA expression

compared to children with CFnoLD and healthy Controls, as well as to assess utility in

predicting hepatic fibrosis severity. It was reasoned that a potential serum miRNA

signature may change during the development and progression of CFLD. Therefore,

it was hypothesised that if serum miRNAs were differentially expressed in CFLD

compared to CFnoLD children then they could be used to identify paediatric patients

at highest risk for the development of CFLD.

Following miRNA-Seq, qRT-PCR validation on serum of 124 children confirmed

increased expression of miR-122-5p, miR-34a-5p and miR-365a-3p in CFLD vs.

CFnoLD while let-7g-5p and miR-142-3p expression was decreased. The most

abundant miRNA in the liver is miR-122 which accounts for more than half of the total

hepatic miRNA content (247). Multiple studies have reported increased circulatory

levels of miR-122 in paediatric chronic liver diseases such as biliary atresia, CFLD,

non-alcoholic fatty liver disease (NAFLD) and hepatitis B (200, 298-300). This shows

increased circulatory miR-122 levels in children with CFLD when compared to both

CFnoLD and healthy controls. Over expression of miR-122 in CFnoLD compared to

134

healthy controls was also found. It is possible that altered circulatory miR-122

expression is an early marker of liver injury and thus detected before the existing

clinical modalities used in this study to classify children with CFLD. Autopsy studies

have reported that up to 70% of CF patients show focal biliary cirrhosis (60), which

suggests that some of the CFnoLD children included in this study may have liver

damage that may manifest as an overexpression of miR-122 before liver disease

detection by the traditional diagnostic methods. Overexpression of miR-34a-5p and

miR-365a-3p was also found in serum of CFLD compared to CFnoLD children.

Increased serum levels of miR-34a have been reported in chronic liver diseases such

as hepatitis C and NAFLD (197, 301). miR-34a has been associated with liver

regeneration, having a direct repressor effect on hepatocyte proliferation and

senescence in alcoholic liver injury and in animal models of partial hepatectomy (302,

303). Moreover, a significant increase of miR-34a expression has been reported in

liver disease, including cholestasis (251). Based on previous findings (31), a role for

the hydrophobic bile acid taurocholate in the abnormal development of bile ducts, a

process known as the ductular reaction, in CFLD has been proposed (55). Key

components of the Notch signalling pathway such as Notch1, Notch2 and Jagged1

are direct targets of miR-34a (304). There is supporting evidence that hepatic stellate

cells and macrophages can influence the differentiation of liver progenitor cells into

reactive biliary cells and bile ducts via the Notch pathway (50), therefore suggesting a

potential role of miR-34a in the manifestation of CFLD. A potential role of miR-365 has

been implicated in paediatric chronic liver disease including biliary atresia (305) and

increased levels of circulating miR-365a-3p have been found in the plasma of children

with hepatitis B, suggesting its potential as a biomarker for disease progression (299,

306). However, further mechanistic studies are required to fully elucidate the role of

miR-34a-5p and miR-365a-3p in the pathogenesis of these paediatric liver diseases.

A decreased expression of serum let-7g-5p and miR-142-3p in CFLD compared to

CFnoLD children was also found. Both let-7g-5p and miR-142-3p expression are

downregulated in hepatocellular carcinoma (HCC) (269, 307) and are involved in

suppressing HCC cell migration, motility and invasion (269, 308). Downregulated miR-

142-3p has also been found in activated hepatic stellate cells (HSC) controlling cell

135

viability and cell growth by targeting TGFβ-receptor 1 (TGFβRI) (309). Based on the

functions that these miRNAs play in HCC and in HSCs, it is possible that their

decreased expression in CFLD children may play a role in preventing fibrosis.

Functional studies are required to establish the origin and potential role that these

differentially expressed miRNAs may play in CFLD and in the development of fibrosis.

Serum miR-365a-3p, miR-34a-5p and let-7g-5p individually demonstrated moderate

clinical utility for liver disease detection in CF with AUROCs ranging between 0.71-

0.74 (Table 4.4). However, using stepwise logistic regression analysis I identified a

miRNA panel consisting of miR-365a-3p, miR-142-3p and let-7g-5p with the ability to

discriminate liver disease in CF children with an AUC=0.87. Recently, it was reported

that APRI can differentiate liver disease in CF children with an AUC=0.75, as well as

severe CFLD (F3-F4) from mild to moderate CFLD (F0-F2) with an AUC=0.81 (124).

When APRI was combined with the proposed miRNA panel a marked improvement in

discriminating CFLD from CFnoLD was observed with an AUC=0.91. Performance

evaluation of the selected miRNA panel using 5-fold cross validation resulted in an

AUC=0.79 for the three miRNA panel alone and an AUC=0.86 when the miRNA panel

was combined with APRI, thus validating significant discriminatory capacity. The new

proposed three miRNA + APRI panel outperforms APRI alone in predicting CFLD even

in 5-fold cross validation evaluation. This study represents one of the largest biopsy-

proven CFLD cohorts with internal cross-validation methodology that confirms the

clinical utility of the combined three miRNA + APRI biomarker panel.

There is a clinical need for the continuous assessment of CFLD to monitor disease

progression in order to provide anticipatory guidance or identify patients in need of

future liver transplantation. In this study, significant differences in the expression of

miR-19b-3p, miR-25-3p, miR-18a-5p and miR-142-3p between varying METAVIR

fibrosis stages were found. Moreover, a significant positive correlation between miR-

19b-3p and miR-18a-5p with fibrosis advancement was identified. Specifically, miR-

18a-5p discriminated severe fibrosis (F3-4) from mild to moderate fibrosis (F0-2)

(Table 4.6). miR-18-5p was able to discriminate liver disease severity in CFLD children

when comparing F0 vs. F1-4 (AUC=0.78), F0-1 vs. F2-4 (AUC=0.76) and F0-2 vs. F3-

136

4 (AUC=0.82).This data suggests that miR-18a-5p was able to significantly

differentiate CFLD severity in all three cases and its diagnostic performance was better

than previously published results for APRI in stratifying disease progression (Table

4.7). ROC curve analysis dichotomizes the observations as it works on the assumption

of a binary system; however, fibrosis staging in liver disease is based on an ordinal

scale subject to the distribution of the study cohort, thus I further analysed this result

using the Obuchowski measure to assess the diagnostic accuracy of miR-18a-5p, as

recommended by Lambert et al. (310). In this model the assessment of miR-18a-5p to

discriminate severity in CFLD resulted in an Obuchowski measure of 0.74 which was

superior to APRI with an Obuchowski measure of 0.59. Lambert et al. suggests using

penalties proportional to the difference in METAVIR units between stages. Applying

this penalty scheme, miR-18a-5p remains superior to APRI in discriminating disease

severity with an Obuchowski measure of 0.79 vs. 0.68 respectively (Table 4.6).

Logistic regression with stepwise variable selection was used to select a linear

combination of miRNAs to discriminate CFLD severity, however, only miR-18a-5p was

significant in this model.

It has been suggested that children with CFLD appear to have a better pulmonary

prognosis than children with CFnoLD (311). In this study, there were no confounding

effects of worsening lung function as measured by %FEV1p, on the levels of serum

miRNAs in CFLD. No correlation was found between serum miR-18a-5p and %FEV1p

in CF children with or without liver disease, suggesting that the altered serum levels

of miR-18a-5p seen in CFLD may be liver-specific and thus reflecting hepatic fibrosis,

although this required further investigation.

As mentioned in Chapter 3, there is a lack of consensus regarding the classification of

patients having CFLD with two different guidelines commonly used to define CFLD

(59, 62). This impacts the categorization of CF children into the study cohorts defined

here (i.e. CFnoLD and CFLD). However, the observations of this study are robust for

several reasons. Firstly, this study used dual-pass liver biopsy to stage hepatic fibrosis

in all children classified as CFLD, as previously reported (64). Fibrosis staging

revealed 14 children without histological evidence of fibrosis (F0 fibrosis). Thus

137

secondly, a sub-analysis on this group vs CFnoLD subjects to assess whether this

group was potentially misclassified based on the current clinical criteria was

performed. This analysis showed that serum miR-122-5p, miR-365a-3p and APRI

were all significantly increased, while miR-142-3p was significantly decreased in CFLD

F0 fibrosis when compared to CFnoLD, which mirror the observations for the collective

CFLD cohort. These findings support previous observations (64, 124) which suggest

the majority of subjects classified as CFLD were correctly classified, even though in

some there was an absence of overt histological fibrosis. Finally, the clinical utility of

these observations lies in the use of the 3 miRNA panel + APRI to identify liver disease

in children with CF, who may potentially be at risk of the development of severe

complications such as cirrhosis and portal hypertension, with miR-18-5p used in the

stratification of liver disease severity.

During this study, discrepancies were found between the miRNAs patterns expressed

in CFLD children reported previously by Cook et al (200). These kinds of discrepancies

have been reported by others (298, 301) and may be due to methodological

differences. In this study, I used NGS to select potential miRNA candidates by pooling

small RNA from serum of selected subjects using a study design to minimise loss of

power (249). Subsequent validation was performed by qRT-PCR in individual subjects

using a panel of endogenous reference miRNAs for data normalization. By using

geNorm and NormFinder algorithms I selected miR-93-5p, miR-20a-5p and let-7i-5p

as the most suitable combination of endogenous miRNAs to normalize miRNA

expression data. miR-93 and let-7i have been previously described as stable

references for normalization of serum miRNAs (312, 313), while miR-20a expression

in plasma has been associated to hepatitis C and liver disease progression (178).

However, in this cohort miR-20a was among the most stable miRNAs across all

groups. These differences in the study design, added to the differences in sample

processing including RNA extraction methods and qRT-PCR reagents may be the

reasons for the observed discrepancies and highlights the need for standardized

methodological designs to guarantee reproducibility and clinical utility.

138


In this chapter, I have demonstrated a distinctive serum miRNA expression profile in

children with CFLD compared to CF children with no evidence of liver disease.

Moreover, I have shown differential circulatory miRNA expression in CFLD between

different stages of fibrosis. This particular circulatory miRNA expression profile is

important as it can be used to diagnose and assess CFLD. I have demonstrated that

a combination of circulatory levels of miR-365a-3p, miR-142-3p and let-7g-5p, when

used in combination with the free readily available APRI biochemical index has

excellent diagnostic utility for the early diagnosis of CFLD. Additionally, circulatory

expression of miR-18a-5p was able to stratify fibrosis severity in children with CFLD.

Current diagnostic methods for CFLD are non-specific, insensitive and are

controversial as the definition of CFLD is not standardized. Assessment of CFLD still

relies on liver biopsy, an invasive procedure; associate with severe complications and

with an important impact on health care costs. The development of non-invasive

methods to identify and stratify CFLD, especially at an early stage where timely

intervention is critical, is paramount. miRNA expression has the potential to be used

as a novel serum biomarker for the diagnosis of CFLD, especially when combined with

other non-invasive methods currently in development such as transient elastography.

The miRNA panel proposed in this chapter requires further independent validation in

a larger study cohort. Future studies could investigate the origin of the circulatory

miRNAs described here. The development of a reliable CFLD animal model could be

useful not only to determine the origin of the circulatory miRNAs, but also to investigate

the role they play in the development of CFLD, reveal future therapeutic strategies and

may offer further insights into CFLD pathogenesis. Based on the results presented

here, the next chapters will focus on the identification of the genes being targeted by

these miRNAs and the potential role they play in the development of hepatic disease

and liver fibrosis.

139

CHAPTER 5

Identification of microRNA targets in HSCs and LPCs…

140

5.1 INTRODUCTION

Since their discovery in 1993 (126), small microRNAs (miRNAs) have been

extensively studied due to their role in controlling gene expression. With the

description of let-7, a widely conserved miRNA in several species (128), research in

the miRNA field grew attempting to identify novel miRNAs, their functions and relation

to physiological and pathological processes. miRNAs play an important role regulating

more than a third of the human mRNA described in the genome (314). In order to

exert their function, miRNAs bind to a four family member protein called Argonaute

(AGO) (147) to form the ribonucleoprotein effector complex known as RNA-induced

silencing complex (RISC) (148). In the RISC complex, miRNAs bind to complementary

sequences in target mRNAs guiding the effect of proteins that form part of RISC and

thus silence gene expression.

The liver is known for its remarkable regenerative capacity. During acute liver injury,

uninjured hepatocytes increase in size, enter the cell cycle and proliferate, causing an

augmentation in the volume of the remaining liver lobes producing a compensatory

hypertrophy (315). During chronic injury, as observed in CFLD, continuous insult to

the liver produces a significant degree of hepatocyte damage/loss that impairs

hepatocyte-mediated regeneration. In this scenario, bipotential liver progenitor (stem)

cells (LPC) are induced to proliferate and differentiate into either hepatocytes or

cholangiocytes to facilitate hepatic regeneration. LPCs are estimated to become active

with a 50% loss of hepatocytes in the liver (316). It has been suggested that LPCs

originate in the canals of Hering (317, 318) which forms a canalicular link between

hepatocytes and the biliary tree. In chronic injury, a process known as the ductular

reaction occurs leading LPCs to expand and differentiate into cholangiocytes and form

reactive bile ducts. The ductular reaction is present in a broad range of chronic liver

diseases including non-alcoholic fatty liver disease (NAFLD) (47), cholestatic diseases

(319) and viral hepatitis (320). Moreover, different studies have shown the direct

correlation between hepatic fibrosis severity and the ductular reaction in different

aetiologies including CFLD (52, 55, 320, 321). Expansion and differentiation of LPCs

occur in close association with other hepatic non-parenchymal cells in the LPC niche

141

including hepatic stellate cells (HSCs), macrophages and extracellular matrix (48).

HSCs are liver mesenchymal cells that account for approximately 10% of the liver cell

population (322). In the absence of injury, HSCs exhibit a quiescent and non-

proliferative phenotype were the presence of vitamin A lipid droplets is their main

feature storing 50-80% of the total vitamin A in the body (323). Following liver injury,

HSCs are transformed from a quiescent to an activated myofibroblast-like phenotype

which are contractile, proliferative and motile (324) and characterized as being the

major source of extracellular matrix (ECM) including fibrillary collagens. During chronic

liver injury, persistent activation of HSCs results in a widespread and disorganised

formation of scar tissue typical of liver fibrosis. It has been proposed that crosstalk

between cells of the LPC niche in chronic liver disease ultimately leads to fibrosis

(325).

miRNAs subtly regulate cell processes by, in most cases, repressing the expression

of mRNA targets by interacting with the 3’ untranslated region (UTR) (314). miRNAs

can influence a vast number of gene transcripts with more than one miRNA acting

over each specific mRNA (161) which creates a complex regulatory network

influencing fundamental biological processes including those in the liver (reviewed

elsewhere (248)). Thus, the identification of miRNA targets is vital to understand not

only the contribution of specific miRNAs to cellular homeostasis and disease, but more

importantly to increase our understanding of the origin and progression of human

pathologies to enable the identification of potential novel therapeutic avenues.

For this chapter, the hypothesis is that miRNAs differentially expressed in CFLD

(reported in chapter 4) play a role in regulating liver disease development in CF.

Hence; the proposed aim is to identify downstream targets of these miRNAs that

contribute to the development of fibrogenesis and disease progression to cirrhosis in

CFLD. Consequently, I have identified potential targets of let-7g-5p, miR-142-3p, miR-

34a-5p and miR-365a-3p in the LPC line bipotential murine oval cells (BMOL) and in

the Lieming Xu-2 (LX2) human HSC line. BMOL cells are non-tumorigenic progenitor

cells (205) commonly known as oval cells in rodents due to their ovoid shape. LX2

cells are human HSCs, that contrary to LX1, are immortalised without sustained

142

expression of T antigen (206). Identification of potential miRNA-mRNA target

interactions was performed using a pulldown experimental method (207) as described

in Chapter 2, section 2.3.4.

143

5.2 RESULTS

5.2.1 RNA-sequencing assessment of pulldown enriched mRNA targets

In order to identify potential putative targets of miRNAs selected based on their

differential expression in serum of CFLD children (as shown in Chapter 4), an

experimental pulldown method using biotinylated synthetic miRNA duplexes was used

(207). This method involves the transient transfection of biotinylated miRNA duplexes

of either miR-365a-3p, Let-7g-5p, miR-142-3p or miR-34a-5p into BMOL and LX2 cells

and analysis of the captured target mRNA using RNA-sequencing. Expression

profiling between pulldown enriched mRNA and total RNA lysate used as controls was

then performed for each cell line and synthetic miRNA duplex. Samples were

sequenced using the Illumina NextSeq500 platform, processed and aligned to the

GRCh37 database as alignment reference. RNA-sequencing summary for BMOL and

LX2 cells are shown in Table 5.1 and Table 5.2 respectively. Two libraries

corresponding to Control 3 in BMOL transfected with miR-365a-3p and Control 2 in

LX2 transfected with miR-142-3p showed low mapping rates of 67% and 60%

respectively and therefore, were excluded from further analysis.

144

Table 5.1. BMOL RNA-sequence summary.

miRNA duplex

Type Read length Mapped reads

Mapping rate Exonic rate Intronic rate Intergenic

rate

Let7g-5p

Control 1 126 35356903 0.95 0.74 0.07 0.20

Control 2 126 19813039 0.94 0.81 0.07 0.12

Control 3 126 47871621 0.97 0.77 0.05 0.18

Pulldown 1 126 18622332 0.97 0.82 0.06 0.12

Pulldown 2 126 13095894 0.96 0.85 0.06 0.10

Pulldown 3 126 984579 0.96 0.77 0.05 0.18

miR-142-3p

Control 1 126 14423430 0.96 0.79 0.06 0.15

Control 2 126 23082527 0.95 0.79 0.06 0.15

Control 3 126 43635610 0.96 0.78 0.06 0.16

Pulldown 1 126 15755319 0.93 0.83 0.05 0.11

Pulldown 2 126 20615207 0.96 0.82 0.06 0.13

Pulldown 3 126 17001883 0.97 0.81 0.06 0.14

miR-34a-5p

Control 1 126 16798867 0.96 0.78 0.06 0.17

Control 2 126 21397989 0.96 0.82 0.06 0.12

Control 3 126 28805761 0.97 0.79 0.05 0.16

Pulldown 1 126 20054820 0.97 0.81 0.05 0.15

Pulldown 2 126 20704566 0.96 0.84 0.05 0.11

Pulldown 3 126 34866887 0.97 0.81 0.05 0.14

145

miRNA duplex



rate

miR-365a-3p

Control 1 126 27036783 0.95 0.77 0.06 0.17

Control 2 126 26150898 0.95 0.76 0.06 0.18

Control 3 126 23686794 0.67 0.75 0.05 0.20

Pulldown 1 126 22707323 0.96 0.78 0.06 0.17

Pulldown 2 126 20128354 0.96 0.77 0.06 0.18

Pulldown 3 126 26862309 0.97 0.79 0.05 0.16

RNA-sequencing summary for BMOL controls and pulldown enriched libraries. miRNA duplex shows the synthetic biotinylated miRNA duplex transfected into BMOL cells. Mapping rate shows the proportion of total reads mapped to the reference genome. Exonic rate shows the ratio of mapped exonic regions and mapped unique reads. Intronic rate shows the ratio of mapped intron regions and mapped unique reads. Intergenic rate shows the ratio of reads mapped to intergenic regions and mapped unique reads.

146

Table 5.2. LX2 RNA-sequence summary.

miRNA duplex



rate

Let7g-5p

Control 1 76 39820358 0.97 0.92 0.06 0.02

Control 2 76 22445582 0.96 0.92 0.06 0.02

Control 3 76 31094339 0.97 0.92 0.06 0.03

Pulldown 1 76 30490634 0.94 0.95 0.04 0.01

Pulldown 2 76 32653576 0.96 0.95 0.04 0.01

Pulldown 3 76 23148839 0.97 0.94 0.05 0.01

miR-142-3p

Control 1 76 34178434 0.96 0.92 0.06 0.02

Control 2 76 15434608 0.60 0.92 0.06 0.02

Control 3 76 25767656 0.97 0.92 0.06 0.02

Pulldown 1 76 30188056 0.92 0.92 0.06 0.02

Pulldown 2 76 37474651 0.96 0.92 0.06 0.02

Pulldown 3 76 38985085 0.96 0.92 0.06 0.02

miR-34a-5p

Control 1 76 27693000 0.97 0.93 0.05 0.02

Control 2 76 29134184 0.97 0.92 0.06 0.02

Control 3 76 15817783 0.96 0.93 0.06 0.01

Pulldown 1 76 20087815 0.96 0.93 0.05 0.02

Pulldown 2 76 12440017 0.98 0.94 0.05 0.01

Pulldown 3 76 27165611 0.96 0.94 0.05 0.01

147

miRNA duplex



rate

miR-365a-3p

Control 1 76 30038835 0.95 0.91 0.06 0.03

Control 2 76 20574744 0.96 0.93 0.06 0.02

Control 3 76 29896857 0.94 0.92 0.06 0.02

Pulldown 1 76 42301660 0.96 0.91 0.07 0.02

Pulldown 2 76 15369241 0.96 0.91 0.07 0.02

Pulldown 3 76 31085415 0.95 0.91 0.07 0.02

RNA-sequencing summary for LX2 controls and pulldown enriched libraries. miRNA duplex shows the synthetic biotinylated miRNA duplex transfected into LX2 cells. Mapping rate shows the proportion of total reads mapped to the reference genome. Exonic rate shows the ratio of mapped exonic regions and mapped unique reads. Intronic rate shows the ratio of mapped intron regions and mapped unique reads. Intergenic rate shows the ratio of reads mapped to intergenic regions and mapped unique reads.

148

5.2.2 Identification of putative miRNA targets using pulldown method in BMOL

cells

Endogenous targets for selected miRNAs in BMOL cells were identified by analysing

their expression profile comparing enriched pulldown fraction and total cell lysate

obtained simultaneously. False discovery rate (FDR) was calculated to correct for

multiple testing and targets with a fold change (FC) ≥1.5 were considered as

significantly enriched (Table 5.3).

Table 5.3. BMOL RNA putative mRNA targets summary.

miRNA duplex Identified mRNAs Quality filtered

mRNAs

Putative mRNA targets (≥1.5 FC,

5%FDR)

let7g-5p 38215 9945 254

miR-142-3p 38215 11250 32

miR-34a-5p 38215 11259 243

miR-365a-3p 38215 11105 1130

Summary of BMOL putative mRNA targets identified by differential expression analysis between enriched pulldown fraction and total cell lysate. mRNAs were considered significantly enriched with 5% FDR and ≥1.5 FC. FC= fold change. FDR= false discovery rate.

5.2.2.1 BMOL putative targets of let7g-5p

Hierarchical cluster analysis was performed between let7g-5p transfected BMOL cells

and the control fractions where similarity is represented by the distance between the

samples (Figure 5.1.A). As observed in the clustering dendrogram, two libraries

corresponding to pulldown 3 and control 2 samples showed less similarity with their

corresponding replicates. RNA-sequencing analysis identified 38215 mRNAs in

pulldown and control samples. Differential expression analysis between the pulldown

and control fractions performed in 9945 mRNAs after quality processing showed 254

mRNAs significantly enriched in the biotin fraction compared to the total cell lysate at

a 5% FDR and ≥1.5 FC (Table 5.3; Figure 5.1.B ).

149

Figure 5.1. BMOL pulldown assay for let7g-5p putative target identification.

Identification of putative mRNA targets of let7g-5p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene) MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.2.2 BMOL putative targets of miR-142-3p

Hierarchical cluster analysis performed between miR-142-3p transfected BMOL cells

and the control fractions showed the control 1 and pulldown 1 have less similarity than

their corresponding replicates (Figure 5.2.A). RNA-sequencing analysis identified

38215 mRNAs in pulldown and control samples. Differential expression analysis

between the pulldown and control fractions performed in 11250 mRNAs after quality

processing showed 32 mRNAs significantly enriched in the biotin fraction compared

to the total cell lysate at a 5% FDR and ≥1.5FC (Table 5.3; Figure 5.2.B).

150

Figure 5.2. BMOL pulldown assay for miR-142-3p putative target identification.

Identification of putative mRNA targets of miR-142-3p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene). MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.2.3 BMOL putative targets of miR-34a-5p

Hierarchical cluster analysis performed between miR-34a-5p transfected BMOL cells

and the control fractions showed that control 2 and pulldown 3 samples have less

similarity with their corresponding replicates (Figure 5.3.A). RNA sequencing

identified 38215 mRNAs in control and pulldown samples. Differential expression

analysis between the pulldown and control fractions performed in 11259 mRNAs after

quality processing showed 243 mRNAs significantly enriched in the biotin fraction

compared to the total cell lysate at a 5% FDR and ≥1.5FC (Table 5.3; Figure 5.3.B).

151

Figure 5.3. BMOL pulldown assay for miR-34a-5p putative target identification.

Identification of putative mRNA targets of miR-34a-5p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene). MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.2.4 BMOL putative targets of miR-365a-3p

Hierarchical cluster analysis performed between miR-365a-3p transfected BMOL cells

and the control fractions showed pulldown 3 sample shared less similarity with the

other pulldown replicates (Figure 5.4.A). RNA sequencing identified 38215 mRNAs in

control and pulldown samples. Differential expression analysis between the pulldown

and control fractions performed in the remaining 11105 mRNAs after quality

processing showed 1130 mRNAs significantly enriched in the biotin fraction compared

to the total cell lysate at a 5% FDR and ≥1.5FC (Table 5.3; Figure 5.4.B).

152

Figure 5.4. BMOL pulldown assay for miR-365a-3p putative target identification.


5.2.3 Identification of putative miRNA targets using pulldown method in LX2

cells

Endogenous targets for selected miRNAs in LX2 cells were identified by analysing

their expression profile comparing enriched pulldown fraction and total cell lysate

obtained simultaneously as shown with BMOL cells. (Table 5.4).

Table 5.4. LX2 RNA putative mRNA targets summary.

miRNA duplex Identified mRNAs Quality filtered

mRNAs

Putative mRNA targets (≥1.5 FC,

5%FDR)

let7g-5p 56764 11887 1016

miR-142-3p 56764 12673 624

miR-34a-5p 56764 11942 683

miR-365a-3p 56764 12338 2361

Summary of LX2 putative mRNA targets identified by differential expression analysis between enriched pulldown fraction and total cell lysate. mRNAs were considered significantly enriched with 5% FDR and ≥1.5 FC. FC= fold change. FDR= false discovery rate.

153

5.2.3.1 LX2 putative targets of let7g-5p

Hierarchical cluster analysis performed between let7g-5p transfected LX2 cells and

the control fractions showed high similarity in control and pulldown samples (Figure

5.5.A). RNA-sequencing analysis identified 56764 mRNAs in pulldown and control

samples. Differential expression analysis between the pulldown and control fractions

performed in 11887 mRNAs after quality processing showed 1016 mRNAs

significantly enriched in the biotin fraction compared to the total cell lysate at a 5%

FDR and ≥1.5FC (Table 5.4; Figure 5.5.B).

Figure 5.5. LX2 pulldown assay for let7g-5p putative target identification.

Identification of putative mRNA targets of let7g-5p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene). MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.3.2 LX2 putative targets of miR-142-3p

Hierarchical cluster analysis performed between miR-142-3p transfected LX2 cells

and the control fractions showed high similarity in control and pulldown samples

(Figure 5.6.A). RNA-sequencing analysis identified 56764 mRNAs in pulldown and

control samples. Differential expression analysis between the pulldown and control

fractions performed in 12673 mRNAs after quality processing showed 624 mRNAs

154

significantly enriched in the biotin fraction compared to the total cell lysate at a 5%

FDR and ≥1.5FC (Table 5.4; Figure 5.6.B ).

Figure 5.6. LX2 pulldown assay for miR-142-3p putative target identification.

Identification of putative mRNA targets of miR-142-3p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene). MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.3.3 LX2 putative targets of miR-34a-5p

Hierarchical cluster analysis performed between miR-34a-5p transfected LX2 cells

and the control fractions showed a slight decrease in the similarity of pulldown sample

1 compared to the remaining pulldown biological replicates (Figure 5.7.A). RNA-

sequencing analysis identified 56764 mRNAs in pulldown and control samples.

Differential expression analysis between the pulldown and control fractions performed

in 11942 mRNAs after quality processing showed 683 mRNAs significantly enriched

in the biotin fraction compared to the total cell lysate at a 5% FDR and ≥1.5FC (Table

5.4; Figure 5.7.B).

155

Figure 5.7. LX2 pulldown assay for miR-34a-5p putative target identification.

Identification of putative mRNA targets of miR-34a-5p by biotin pulldown assay. (A) Clustering dendogram of biotin enriched mRNA and total lysate controls. Vertical distance represents similarity. (B) MA plot (“M”=log-fold change; “A”= counts for each gene) MA plot confirmed the presence of pulldown enriched mRNAs with a fold change ≥1.5 and a minimum expression intensity of ≥ 4 million counts. Red dots showed genes with 5% FDR. PD= pulldown; logFC= log-fold change; logCPM= log- counts per million.

5.2.3.4 LX2 putative targets of miR-365a-3p

Hierarchical cluster analysis performed between miR-365a-3p transfected LX2 cells

and the control fractions showed that control 3 and pulldown 1 samples shared less

similarity with their corresponding biological replicate samples (Figure 5.8.A). RNA-

sequencing analysis identified 56764 mRNAs in pulldown and control samples.

Differential expression analysis between the pulldown and control fractions performed

in 12338 mRNAs after quality processing showed 2361 mRNAs significantly enriched

in the biotin fraction compared to the total cell lysate at a 5% FDR and ≥1.5FC (Table

5.4; Figure 5.8.B).

156

Figure 5.8. LX2 pulldown assay for miR-365a-3p putative target identification.


5.2.4 Assessment of enriched putative pulldown targets compared to target

prediction

There are no experimental studies validating targets of different miRNAs on a large

scale, however, there are computational algorithms that predict miRNA targets based

on known parameters of miRNA-mRNA interaction such as pairing with the seed

region, location of binding sites or nucleotide composition (195). The TargetScan tool

(161) was used to compared predicted targets of let-7g-5p, miR-142-3p, miR-34a-5p

and miR-365a-3p with enriched putative targets identified from the biotin pulldown

assay in BMOL and LX2 cells together, as TargetSCan does not discriminate based

on cell type. An overlap of 125 genes (P=0.0013) for let7g-5p; 67 genes (P=0.0121)

for miR-142-3p; 56 genes (P=0.0121) for miR-34a-5p and 96 genes (P<0.0001) for

miR-365a-3p was observed between the biotin pulldown experimental method and the

TargetScan predicted targets (Figure 5.9.A-D).

157

Figure 5.9. Comparison of pulldown experimental assay and bioinformatics predicted targets.

Venn diagrams showing the overlap of genes between (A) TargetScan predicted targets of let7g-5p and biotinylated let7g-5p pulldown predicted targets in BMOL and LX2 cells (B) TargetScan predicted targets of miR-142-3p and biotinylated miR-142-3p pulldown predicted targets in BMOL and LX2 cells (C) TargetScan predicted targets of miR-34a-5p and biotinylated miR-34a-3p pulldown predicted targets in BMOL and LX2 cells (D) TargetScan predicted targets of miR-34a-5p and biotinylated miR-34a-3p pulldown predicted targets in BMOL and LX2 cells. In all cases the overlap was significantly more than expected by chance when assessed by Fisher’s exact test.

5.2.5 Involvement of pulldown experimental predicted targets in cellular

functions and pathways

In order to infer the potential regulatory roles of let-7g-5p, miR-142-3p, miR-34a-5p

and miR-365a-3p on BMOL and LX2 cell phenotype, gene set enrichment analysis

(GSEA) was performed using Ingenuity Pathway Analysis (IPA). GSEA analysis

revealed the involvement of let-7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p in

158

key molecular and cellular processes involving a significant number of putative targets

in both BMOL and LX2 cell lines (Table 5.5).

Table 5.5. Gene set enrichment analysis (IPA).

miRNA duplex

Cell type

Molecular and cellular functions P-value Targets

(n)

let7g-5p BMOL

Gene Expression 2.94x10-

9 80

Cell Morphology 5.84x10-

8 92

Cellular Movement 2.15x10-

7 77

Cell Death and Survival 4.36x10-

6 101

Cellular Assembly and Organization 4.53x10-

6 68

miR-142-3p

BMOL


6 11

Cellular Development 2.17x10-

6 15

Cellular Function and Maintenance 2.17x10-

6 15

Cellular Growth and Proliferation 2.17x10-

6 15


4 11

miR-34a-5p

BMOL


5 82


5 48


5 78

Cell Cycle 6.88x10-

5 34


4 42

159

miRNA duplex

Cell type


(n)

miR-365a-3p

BMOL

RNA Post-Transcriptional Modification 1.28x10-

15 49


9 168

Cell Cycle 7.66x10-

9 136

Protein Synthesis 5.56x10-

7 80


6 219

let7g-5p LX2


8 208


4 180

Amino Acid Metabolism 2.28x10-

4 4

Small Molecule Biochemistry 2.28x10-

4 17


4 35

miR-142-3p

LX2


7 125


7 130


6 102

Post-Translational Modification 2.16x10-

6 51


6 140

160

miRNA duplex

Cell type


(n)

miR-34a-5p

LX2


17 190


9 189


9 156


8 225

Cell Cycle 7.48x10-

8 128

miR-365a-3p

LX2


18 98


11 459

Cell Cycle 6.59x10-

10 332

DNA Replication, Recombination, and Repair

9.94x10-

10 238


9 449

Functional enrichment analysis of biotin pulldown determined miRNA putative targets.

Furthermore, enrichment analysis was performed in IPA to identify and validate

putative enriched pulldown miRNA targets within pathways known to play an important

role in BMOL and LX2 cells during fibrogenesis such as proliferation, migration, cell-

cell interaction, expansion, differentiation or activation. These potential putative

targets were selected based on 5% FDR, FC>1.5 and/or documented importance in

key cell-specific processes. Enrichment analysis for miR-142-3p in BMOL cells was

not preformed due to the low number of identified enriched targets (32) compared to

the 624 identified for LX2. Potential miRNA targets selected for validation in BMOL

and LX2 cells are shown in Table 5.6 and Table 5.7. respectively.

161

Table 5.6. Selected putative miRNA targets in BMOL cells.

miRNA duplex

Gene Log Fold change

False discovery

rate Pathway/Role

let7g-5p

Itga2 1.62 9.61x10-5 HGF/ Reverts myofibroblast phenotype in LPC

Crkl 0.86 0.0223 PDGF/ Induce chemotaxis in HSC

Ptch1 0.60 0.0411 SHH/ LPC proliferation and differentiation

Rock1 1.34 2.66x10-14 VEGF/ Matrix deposition and degradation in HSC

Ep300 1.27 2.26x10-8 LTX-β/ LPC proliferation

TGF- β1/ Induces myofibroblast phenotype in LPC

Nfkβ1 0.47 0.0193 TWEAK/ LPC expansion

Hnf4α 0.35 0.011 Hepatocyte differentiation

miR-34a-5p

Itga2 0.79 8.48x10-4 HGF/ Reverts myofibroblast phenotype in LPC

Crkl 1.26 3.94x10-8 PDGF/ Induce chemotaxis in HSC

Notch1 0.75 1.98x10-5 NOTCH/ LPC differentiation

Ep300 0.70 3.84x10-6 LTX-β/ LPC proliferation

TGF- β1/ Induces myofibroblast phenotype in LPC

Nfkβ1 0.43 4.96x10-4 TWEAK/ LPC expansion

Dvl3 0.65 1.71x10-4 WNT-β catenin/ LPC differentiation

Appl2 0.60 7.05x10-7 WNT-β catenin/ LPC differentiation

162

miRNA duplex


False discovery

rate Pathway/Role

miR-365a-3p

Akt3 0.93 7.65 x10-6 EGF/ Reverts myofibroblast phenotype in LPC

Nfkβ1 0.82 2.15 x10-6 TWEAK/ LPC expansion

Dvl31 0.52 0.0275 WNT-β catenin/ LPC differentiation

Ptpn11 0.87 5.31 x10-10 LTX-β/ LPC proliferation

PDGF/ Induce chemotaxis in HSC

Acvr2a 0.59 0.0283 TGF- β1/ Induces myofibroblast phenotype in LPC

Ets1 0.59 0.0003 HGF/ Reverts myofibroblast phenotype in LPC

Adam17 0.54 0.0245 NOTCH/ LPC differentiation

Fzd2 0.51 0.0298 WNT-β catenin/ LPC differentiation

Cx3cr1 1.16 0.0010 Mediates adhesion and migration in HSC

Putative enriched targets in BMOL cells selected for follow-up. Targets were selected based on 5%FDR, FC>1.5 and/or documented role in BMOL cellular processes. LPC= liver progenitor cell; HSC= hepatic stellate cell; Itga2= Integrin α-2; Crkl= CRK like proto-oncogene; Ptch1= Patched 1; Rock1= Rho associated coiled-coil containing protein kinase 1; Ep300= E1A Binding protein P300; Nfkβ1= Nuclear factor Kappa β-1; Hnf4α= Hepatocyte nuclear factor 4α; Dvl3= Dishevelled segment polarity protein 3; Appl2= Adaptor protein phosphotyrosine zipper 2; Akt3= AKT serine/threonine kinase 3; Ptpn11= Protein tyrosine phosphatase 11; Acvr2a= Activin A receptor 2A; Ets1= ETS proto-oncogene 1; Adam17= ADAM Metallopeptidase domain 17; Fzd2= Frizzled class receptor 2; Cx3cr1= C-X3-C Motif chemokine receptor 1; HGF= Hepatocyte growth factor; PDGF= platelet-derived growth factor; SHH= Sonic hedgehog; VEGF= Vascular endotelial growth factor; LTX-β= Lymphotoxin- β; TGF- β1= Transforming growth factor-β1; TWEAK= TNF related weak inducer of apoptosis; EGF= Epidermal growth factor.

163

Table 5.7. Selected putative miRNA targets in LX2 cells.

miRNA duplex


False discovery

rate Pathway/Role

let7g-5p

EP300 0.87 4.94x10-13 WNT-β catenin/ HSC activation

TGF- β1/ HSC activation

ROCK1 2.24 2.02x10-60 VEGF/ HSC proliferation

VCL 0.71 5.40x10-11 VEGF/ HSC proliferation

TLR4 0.89 2.89x10-5 HSC activation

MYD88 0.80 3.03x10-11 HSC activation

NR1D1 0.94 4.39x10-11 HSC activation

CD248 0.94 1.95x10-6 HSC proliferation

164

miRNA duplex


False discovery

rate Pathway/Role

miR-142-3p

AMOT 1.36 5.10x10-5 HIPPO/ HSC activation

LATS1 0.99 4.04x10-10 HIPPO/ HSC activation

TEAD1 0.86 2.07x10-16 HIPPO/ HSC activation

SMAD3 0.61 4.73x10-10 HIPPO/ HSC activation

TGF- β1/ HSC activation

IL1RAP 0.65 0.0010 IL-1/ HSC survival

PTCH1 0.77 0.0110 SHH/ HSC activation

GLI3 0.68 2.52x10-5 SHH/ HSC activation

NOTCH3 1.16 0.0007 NOTCH/ HSC activation

MAP3K1 0.94 0.0030 SAP-JNK/ HSC activation

MAP3K9 0.91 0.0050 SAP-JNK/ HSC activation

TGFβR1 1.03 1.68x10-22 TGF- β1/ HSC activation

COL4A1 0.63 4.18x10-8 Collagen 4 predominantly in healthy liver

COL4A5 0.76 0.0115 Collagen 4 predominantly in healthy liver

miR-34a-5p

AMOT 1.28 0.0158 HIPPO/ HSC activation

WWC1 1.05 0.0152 HIPPO/ HSC activation

TGF- β1 1.18 0.0008 TGF- β1/ HSC activation

165

miRNA duplex

Gene Log Fold change False discovery

rate Pathway/Role

miR-365a-3p

WWC1 1.81 9.46x10-10 HIPPO/ HSC activation

AMOT 0.72 0.0179 HIPPO/ HSC activation

LATS1 1.06 0.0003 HIPPO/ HSC activation

SAV1 0.68 0.0276 HIPPO/ HSC activation

CSNK1G2 0.97 0.0002 WNT-β catenin/ HSC activation

HNRNPA1 2.36 3.69x10-11 HSC activation



COL4A5 1.35 0.0006 Collagen 4 predominantly in healthy liver

Putative enriched targets in LX2 cells selected for follow-up. Targets were selected based on 5%FDR, FC>1.5 and/or documented role in LX2 cellular processes. HSC= hepatic stellate cell; EP300= E1A Binding protein P300; ROCK1= Rho associated coiled-coil containing protein kinase 1; VCL= Vinculin; TLR4= Toll like receptor 4; MYD88= Myeloid differentiation primary response 88; NR1D1= Nuclear receptor-1 group D-1; CD248= Endosialin; AMOT= Angiomotin; LATS1= Large tumor supressor kinase 1; TEAD1= TEA Domain transcription factor 1; SMAD3= Mothers against decapentaplegic homolog 3; IL1RAP= Interleukin 1 receptor accessory protein; PTCH1= Patched 1; GLI3= Glioma-associated oncogene family zinc finger 3; MAP3K1= Mitogen activated protein kinase kinase kinase 1; MAP3K9= Mitogen activated protein kinase kinase kinase 9; TGFβR1= Transforming growth factor-β receptor 1; COL4A1= Collagen type IV α-1; COL4A5= Collagen type IV α-5; WWC1= WW and C2 domain containing 1; TGF- β1= Transforming growth factor-β1; SAV1= Salvador family WW domain 1; CSNK1G2= Casein kinase 1 γ-2; HNRNPA1= Heterogeneous nuclear ribonucleoprotein A1; COL4A2= Collagen type IV α-2; VEGF= Vascular endotelial growth factor; IL-1= Interleukin 1;

SHH= Sonic hedgehog; SAP-JNK= Stress-activated protein kinases/ Jun amino-terminal kinase.

166

5.3 DISCUSSION

microRNAs have gained great interest in recent years due to their role as regulators

of gene expression. Two decades on from their discovery, the effector mechanisms of

miRNAs that regulate mRNA expression are not entirely elucidated. miRNAs control

gene expression at a post-transcriptional level by binding to complementary

sequences in mRNA targets. Understanding how this binding occurs in chronic liver

disease is not only fundamental to explore miRNA regulatory effects, but also to infer

the biological functions of miRNAs that contribute to hepatic fibrosis initiation and

progression, and to the identification of potential anti-fibrotic therapies.

As part of this process, miRNAs need to form the miRNA-induced silencing complex

(miRISC) which guides the RISC complex to the target mRNA, mediating its regulation

(326). In animals most of the miRNA-mRNA interactions occur in the mRNA 3’UTR

region. Some miRNA targets show perfect complementarity to the six nucleotides

corresponding to position 2-7 at the 3’UTR end of the mRNA. This region is known as

the seed sequence (158, 327) and can include up to eight nucleotides in length based

on the homology at position eight or if the nucleotide in position one correspond to an

adenine (A) (161, 328). Binding sites in mRNA targets with perfect complementarity

with the eight nucleotides of the seed sequence are known as canonical sites (161).

Marginal sites (complementarity with the six nucleotide seed sequence) and non-

canonical site interactions produce a milder regulatory effect than the ones observed

in canonical sites (314). However, sequence complementarity alone cannot explain

miRNA-mRNA binding interactions. Additional features such as target site

accessibility, nucleotide composition, multiple binding sites, RNA binding proteins,

miRISC regulation or target interactions influence regulation efficacy.

The majority of miRNA target sites are located in 3’UTR regions, however, there is

enough experimental evidence showing binding sites in the 5’UTR and open reading

frame (ORF) regions (329, 330) as well. It is thought that the reduced frequency of

5’UTR and ORFs as miRNA binding sites is due to the displacement of the miRISC

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complex from the translation machinery (132). The fact that a great number of

conserved binding sites are at least at a 15 nucleotide distance from the stop codon

and different than the middle of long UTRs support this explanation as it avoids

ribosome interference (162). The nucleotide composition is as important as the

location of the binding site within the UTR region. Sites rich in AU content perform best

as they provide a more accessible UTR context favourable to the miRISC complex

(331).

Co-expression of binding sites for the same or different miRNAs can be found in

miRNA targets. Binding site studies of the miRNAs lin-4 and lin-14 3’UTRs revealed

that multiple sites can function cooperatively with effects similar to what would be

expected if each site contributes independently, multiplying the response from each

binding site by itself (332). When two binding sites are within 40nt but no closer than

8nt, the increased repression surpasses the expected independent contribution of

each site (333). By this mechanism, miRNA regulatory effects can be enhanced and

become more sensitive to changes in miRNA expression levels. However, when a site

overlap is present, the effects can be either enhanced or repressed (333, 334).

External factors can also influence the target binding capacity independently of the

site length or composition. RNA binding proteins (RBP) antagonize miRNA regulation

by interacting with mRNA AU-rich sequences protecting them from degradation (335).

This mechanism can be observed in ELAV1 preventing the repression of CAT1 by

miR-122 (336) or DND1 which reduces the interaction between miR-430 and TDRD7

(337). Members of the miRISC complex are also subject to regulation, enhancing or

repressing its functions. LIN41 and MEI-P26 members of the TRIM-NHL family, target

Argonaute (AGO) for proteosome-mediated degradation which reduce AGO levels

promoting miRISC instability by decreasing the association of miRNA and the RISC

complex (338, 339). In contrast, other members of the TRIM-NHL family such as

NHL2 and TRIM32 positively regulate miRISC function. Interaction of NHL2 has been

studied in C. elegans (340) showing stimulatory effects when it associates with AGO

and the helicase CGH-1 (341). Studies on TRIM32 have shown to enhance the

repression mediated by let-7 miRNA in mouse neural progenitor cells, although the

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process is yet to be determined (342). Other mechanisms such as phosphorylation

can also influence miRISC performance. Human AGO2 can be phosphorylated at

multiple positions including the region that serves as a binding pocket to the miRNA

which results in a reduced ability to bind to miRNA and form the miRISC complex

(343).

Interactions between the miRISC complex members can also have an effect on

miRNA regulatory functions. For instance, increased expression of AGO results in the

upregulation of mature miRNA levels (339), while loss of AGO reduces miRNA

abundance (145), suggesting that the presence of AGO in the cell is a limiting factor.

Similarly, interaction between miRNA and mRNA also has a regulatory effect as

extensive pairing between miRNA and its targets produce 3’ miRNA end trimming and

the addition of non-templated uridines which can lead to miRNA destabilization (344,

345). Interestingly, miRNA activity can be repressed by interaction that does not

necessarily produce changes in miRNA levels or the miRISC complex. Several studies

have shown that competition between common targets for different miRNAs can

influence the potency of miRNA regulation (346-348). Target decoys have also been

identified where several transcripts present target sites for specific miRNAs by which

they cross-regulate each other and titrate the activity of the miRNA where they share

a similar target site, thus functioning as a sink of miRNA activity (349, 350).

Given the number of mRNAs that are the subject of miRNA regulation, the interest in

identifying miRNA targets to determine their function in cells and organs has grown

exponentially. Genome-wide computational methods were developed to predict

potential miRNA targets. Most of these target prediction algorithms focus on canonical

sites or sequence complementarity with the seed sequence (161). Experimental data

continue to expand our knowledge on target identification such as the preference for

AU-rich regions which increase the prediction accuracy of potential target sites (314),

however, this new information is not always incorporated to the existent algorithms.

As discussed above, miRNA-mRNA target interaction is complex and subject to

multiple factors that can affect its efficiency. These factors, including mismatches, G:U

wobbles and bulges affects the performance of computational methods to predict

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miRNA targets with studies ranging in their false positive rate between 24-70% (163,

351, 352). Additionally, target prediction programs normally produce different lists of

predicted targets due to alignment artefacts, different source databases for 3’UTR and

miRNA sequences, or their own configuration settings used in each program.

Moreover, these computational algorithms do not discriminate for cell gene expression

specificity, which adds a layer of difficulty when exploring the role that miRNAs play in

different hepatic cell populations during liver fibrosis. Therefore, in this chapter I have

characterised the mRNA targets of let-7g-5p, miR-142-3p, miR-34a-5p and miR-365a-

3p identified in Chapter 4 using an experimental co-purification method of bound

biotinylated miRNA and mRNA (207) in the LPC line BMOL and the human HSC line

LX2. This miRNA pulldown protocol is a modification of a biochemical approach to

enrich miRNA targets developed by Orom et al. (353) which permits enrichment of

mRNA targets of a single miRNA.

In BMOL cells, the pulldown method identified 254 significant putative targets for let7g-

5p, 32 for miR-142-3p, 243 for miR-34a-5p and 1130 for miR-365a-3p (Table 5.3).

Whereas in LX2 cells, 1016 putative targets were identified for let7g-5p, 624 for miR-

142-3p, 683 for miR-34a-5p and 2361 for miR-365a-3p (Table 5.4). Hierarchical

clustering analysis showed some technical variation between replicates in BMOL cells

with the four selected miRNAs (Figure 5.1 - Figure 5.4), while LX2 showed a slight

variation between replicates with miR-34a-5p and miR-365a-3p (Figure 5.8). The

most likely explanation for this is due to differences during the experimental process

that affected the assay performance including transfection efficiency, day-to-day

variation, cell density or the effect of pooling cells together. Despite these observed

variations, the pulldown assay was demonstrated to be a robust method enriching

computationally predicted targets (Figure 5.9). When comparing the pulldown

enriched targets with TargetScan predicted targets (226), an overlap of 125 genes for

let-7g-5p (Figure 5.9.A), 67 genes for miR-142-3p (Figure 5.9.B), 56 genes for miR-

34a-5p (Figure 5.9.C) and 96 genes for miR-365a-3p (Figure 5.9.D) was found. This

overlap was significantly higher than the number of genes that would be expected by

chance (P=0.0013 for let7g-5p, P=0.0121 for miR-142-3p, P= 0.0121 for miR-34a-59

and P<0.0001 for miR-365a-3p) confirming that the pulldown method enriched true

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miRNA targets. A previous study exploring miRNAs and their targets in human

quiescent HSCs identified MTHFD1L, PFKP, DOCK7 and WNKY as mRNA targets of

miR-142, whereas PPAP2A was identified as a target of let-7g (354). These previously

identified targets were also enriched during the pulldown assay performed in this study

confirming the validity of the methodological approach used for the miRNA target

identification. No large scale experimental miRNA target validation has been

performed for miR-34a and miR-365a in LX2 cells or for any miRNA in BMOL cells.

Gene set enrichment analysis (GSEA) of pulldown-enriched mRNA targets in BMOL

cells revealed core functions involved in cell homeostasis (Table 5.5) including cellular

movement and survival for let7g-5p; cellular development, maintenance and

proliferation for miR34a; and cell cycle and survival for miR365a. Several potential

putative targets were selected for further validation in all miRNAs except miR-142

(Table 5.6) due the low number of significantly enriched targets found in BMOL cells

suggesting that miR-142 does not appear to play a significant role in BMOL cells.

These putative targets were selected based on their participation in differentiation,

proliferation or cell-interaction pathways important in the role of LPCs during hepatic

fibrosis and will be discussed in more depth in chapter 6. In LX2 cells, GSEA analysis

also revealed the enrichment of putative targets in key cellular functions (Table 5.5)

including cellular movement and metabolism for let-7g; cellular movement,

organization, survival and maintenance for miR-142; cellular development,

proliferation and survival in miR-34a; and cell cycle and organization, gene expression

and DNA replication for miR-365a. As for BMOL, several potential putative targets

were selected for further validation based on their involvement in pathways that drive

the activation of HSCs (Table 5.7), important in the establishment, progression and

maintenance of hepatic fibrosis. The implication of these pathways with the selected

miRNAs will be discussed in more detail in chapter 6.

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In this chapter, I have used an experimental method to identify putative targets for let-

7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p in LPC and HSCs, known to play

an important role during chronic liver injuries including CFLD. An advantage of the

biotin pulldown method used in this chapter is the capacity to capture all the mRNAs

that interact with a specific miRNA in a specific cell. This approach allows a better

understanding of the complex gene networks that are regulated by miRNAs that would

not be possible by using gene-specific target identification. Furthermore, these

findings reflect interactions in cells directly involved in the hepatic fibrosis process

mirrored in the BMOL and LX2 selected as in vitro models of two cell populations from

the LPC niche. Although some variation in the replicates was observed between the

control and pulldown enriched samples, the assay was robust as demonstrated by the

significant enrichment of predicted targets when compared to TargetScan and

previously validated targets of miR-142 and let7g in quiescent HSCs. The use of gene

set enrichment analysis identified key cellular and molecular functions in BMOL and

LX2 cells which reveal part of the gene regulation pathways mediated by miRNAs and

how they are implicated in CFLD development. For this reason, several putative

targets were selected for validation. The next chapter will show the validation of these

putative targets and will further investigate their function in regulating hepatic fibrosis

and the LPC niche and provide some context to their potential role in the mechanisms

associated with the development of CFLD.

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CHAPTER 6

Validation of miRNA targets and the role of microRNAs

in HSC and LPC biology

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6.1 INTRODUCTION

Bile duct plugging and toxic bile acid accumulation during CFLD is proposed to lead

to the development of hepatic fibrosis which in some cases progresses to focal biliary

cirrhosis and ultimately multilobular biliary cirrhosis. As with other chronic liver

diseases where the persistent insult to the liver impairs hepatocyte-mediated liver

regeneration, liver progenitor cells (LPCs) contribute to the restoration of liver integrity.

Despite the diverse aetiologies of chronic liver disease, LPC-mediated liver

regeneration follows a common pathway of liver wound healing and repair involving

inflammation, activation and expansion of LPCs and fibrogenesis which incorporates

activation of hepatic stellate cells (355, 356).

LPCs, also called oval cells in rodents due to their characteristically ovoid shape, are

facultative bipotential stem-like cells with the capacity to differentiate in hepatocytes

or cholangiocytes (357, 358). Following activation, LPCs expand and form reactive

ductular structures and intermediate hepatocytes in a process known as the ductular

reaction (DR). The DR is a regenerative response in which LPCs predominantly from

terminal ducts of the biliary tree or the canals of Hering proliferate and form bile ducts

resulting in biliary hyperplasia (359). The DR has a heterogeneous profile which

seems to depend on the regenerative conditions influenced by the disease aetiology.

For instance in chronic biliary damage, the DR mainly consists of cells with biliary traits

and stem cell markers with strong activation of the Notch signalling pathway (52, 360),

while in chronic hepatocyte damage, the DR mainly consists of cells with intermediate

hepatocyte characteristics with strong activation of the Wnt signalling pathway (360).

Several factors can promote LPC activation including the cellular environment, injury

severity and specific inflammatory cytokines involved in the liver injury (361). The LPC

response during liver regeneration can be characterised into four distinctive phases:

activation, proliferation, migration and differentiation (362) with several factors

influencing the extent of the response of these phases. Activation and expansion of

LPCs occurs during the first 7 days after liver injury with differentiation happening over

the following 7 days (363), a much slower response compared to hepatocyte-mediated

regeneration. Expansion of LPCs starts from Sox9 expressing ductal cells via the

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action of IFN-γ (364) and TNF-α from inflammatory cells (365). Other mediators such

as hedgehog ligand released by injured hepatocytes (366), hormones such as

somatostatin (367) and adipokines (368) also regulate LPC proliferation and

differentiation. In addition to these factors, several signalling pathways have been

identified as important regulators of LPC-mediated regeneration such as TNF-related

weak inducer of apoptosis (TWEAK), Notch, Wnt – β catenin, HGF or EGF among

others (reviewed elsewhere (369)). The LPC niche formed as part of the wound

healing response of hepatic regeneration incorporates LPCs, macrophages, hepatic

stellate cells (HSCs) and extracellular matrix (ECM) (48).

In the absence of injury, HSCs are non-proliferative quiescent vitamin A-storing cells

that reside in the Space of Dissé regulating vascular tone. During liver injury, HSCs

are activated into a myofibroblast-like phenotype that is proliferative, contractile and

chemotactic playing an important role in fibrogenesis by producing fibrotic tissue

through the accumulation of ECM (370). This accumulation depends on a fine balance

between matrix synthesis and degradation in which tissue inhibitor of

metalloproteinase (TIMP) proteins and matrix metalloproteases (MMPs) have a

predominant role (371). HSC activation occurs in two distinctive steps: initiation and

perpetuation. During initiation, induction of growth factor receptors, modulation of

growth factor signalling and the change into a contractile and fibrogenic phenotype

sensitise HSC to activation signals. During the perpetuation stage, characteristics of

active HSC are amplified in response to cytokines and growth factors enhancing HSC

proliferation, contractility and pro-inflammatory signalling. (372). As with LPCs, HSC

activation is controlled by different mediators and signalling pathways. TGF-β, a

fibrogenic cytokine (373), promotes the synthesis of collagen 1 and 3 (abundant in

fibrotic livers) in active HSCs through the activation of SMAD3 (374, 375). TGF-β also

promotes HSC activation by other mechanisms involving the regulation of ERK, c-JUN

and JNK (376, 377). Furthermore, studies have increasingly revealed (reviewed

elsewhere (378)) the implication of additional cytokines (e.g. PDGF, VEGF or CTGF)

and pathways (e.g. Hedgehog, Notch, JNK, PTEN, WNT or Hippo) in promoting HSC

proliferation and migration.

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The LPC niche is closely related to ECM (379) as observed by the reduced activation

and expansion of LPCs due to inhibition of ECM remodelling (380). Moreover,

important cell crosstalk occurs between cells of the LPC niche due to their close spatial

association. Communication between LPCs and HSCs has been observed through

lymphotoxin-β (LT-β) expressed on LPCs and its receptor on activated HSCs (381,

382). Expression of MCP-1 by cholangiocytes and mature bile ducts enhances

chemotaxis of HSCs (31). Similarly, activated HSCs express ICAM-1 and RANTES

which attracts LPCs to the injury site (381) whereas TWEAK produced by

macrophages induces proliferation of LPCs (383, 384).

The hypothesis for this chapter is that the putative miRNA target mRNAs identified in

Chapter 5 mediate LPC differentiation and HSC activation which drives fibrogenesis

during CFLD. Hence, the proposed aims of this Chapter are to assess the effects of

let-7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p on the expression of their

putative targets (and validate them as definitive targets) in LPCs and HSCs; and to

determine the potential role of let-7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p

on the function and biology of LPCs and HSCs using the respective cell lines, BMOL

and LX2.

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6.2 RESULTS

6.2.1 LPCs

6.2.1.1 Effect of miRNAs on putative target mRNA expression

Following synthetic miRNA duplex transient transfection of BMOL cells, biotin-

pulldown and NGS analysis to identify putative miRNA targets in BMOLs, I identified

seven potential targets of interest for let7g-5p, seven for miR-34a-5p and nine for miR-

365a-3p (see Chapter 5, Table 5.6) and subsequently assessed their gene

expression by qRT-PCR. Differential expression between miRNA negative control and

miRNA mimic of interest was assessed at 24, 48 and 72 hours post-transfection in six

replicates. Pair-wise comparison between miRNA negative control and selected

miRNA mimic at each time point was performed using multiple T-test corrected with

Holm-Sidak post-hoc test.

BMOL cells transfected with let7g-5p had significantly decrease expression of Rock1

(P<0.001), Hnf4α (P=0.0288) and Ep300 (P=0.0244) at 24 hours; Rock1 (P=0.0041)

and Ep300 (P=0.0328) at 48 hours; and Hnf4α (P=0.0288) and Ep300 (P=0.0354) at

72 hours compared to BMOL cells transfected with miRNA negative control (Figure

6.1.A-C). Ptch1 showed decreased expression in BMOL cells transfected with let7g-

5p compared to miRNA negative control at 24 (P=0.1860) and 48 hours (P=0.0995)

however this did not reach statistical significance (Figure 6.1.D).

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Figure 6.1. qRT-PCR assessment of putative let7g-5p targets in BMOL cells.

Relative expression of putative let7g-5p targets assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and let7g-5p mimic (n= 6) for (A) Rock1, (B) Hnf4α, (C) Ep300 and (D) Ptch1. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

No significant decreased expression was found on any of the selected potential

putative targets in BMOL cells transfected with miR-34a-5p when compared to miRNA

negative control, however, Itga2 showed decreased relative expression at 48 hours

(P=0.2031) (Figure 6.2).

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Figure 6.2. qRT-PCR assessment of putative miR-34a-5p targets in BMOL cells.

Relative expression of Itga2, a putative miR-34a-5p target assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and miR-34a-5p mimic (n=6). Lines represent mean ± SEM. Analysis made by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

BMOL cells transfected with miR-365a-3p showed significantly decrease expression

of Cx3xr1 (P=0.047), Shp2 (P=0.0118) and Akt3 (P=0.0060) at 48 hours, whereas

significant decreased expression of Fzd2 was observed at 48 (P=0.0040) and 72 hours

(P=0.0040) (Figure 6.3.A-D). Adam17 (P=0.4521) and Nfkβ1 (P=0.2055) had

decreased expression at 72 hours, although this did not reach statistical significance

(Figure 6.3.E-F). Similarly, Acvr2a also showed decreased expression in miR-365a-

3p transfected cells at 24 (P=0.2796), 48 (P=0.0619) and 72 hours (P=0.2796) (Figure

6.3.G).

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Figure 6.3. qRT-PCR assessment of putative miR-365a-3p targets in BMOL cells.

Relative expression of putative miR-365a-3p targets assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and miR-365a-3p mimic (n=6) for (A) Cx3cr1, (B) Shp2, (C) Akt3, (D) Fzd2, (E) Adam17, (F) Nfkβ1 and (G) Acvr2a. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

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6.2.1.2 Impact of miRNAs on LPC differentiation

In order to evaluate the effect of the selected miRNAs on the differentiation of LPCs,

gene expression of the cholangiocyte markers Ggt1 and Cx43, and of the hepatocyte

markers Tat and Hnf4α were assessed in six replicates by qRT-PCR. BMOL cells

transfected with let7g-5p showed a significantly decreased expression of Ggt1

(P=0.0060) at 24 hours (Figure 6.4.A) compared to the cells transfected with negative

control. No significant difference was observed in the expression of the other

cholangiocyte marker Cx43 (Figure 6.4.B). When hepatocyte markers were assessed

cells transfected with let7g-5p showed a significant decreased expression in Tat at 48

hours (P=0.0153) (Figure 6.4.C) and in Hnf4α at 24 (P=0.0288) and 72 hours

(P=0.0287) (Figure 6.4.D).

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Figure 6.4. qRT-PCR assessment of differentiation markers on BMOL cells transfected with let7g-5p.

Relative expression of LPC differentiation markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and let7g-5p mimic for cholangiocyte markers (n=6): (A) Ggt1 and (B) Cx43; and for hepatocyte markers (n=6): (C) Tat and (D) Hnf4α. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

No statistical significance was observed between BMOL cells transfected with miR-

34a-5p and negative control for any of the differentiation markers (Figure 6.5). Of note,

miR-34a-5p transfected cells exhibited a trend toward increased expression of Ggt1

at 48 (P=0.6352) and 72 hours (P=0.8459) (Figure 6.5.A) as well as increased

expression of Hnf4α at 24 (P=0.5348) and 48 hours (P=0.7443) (Figure 6.5.D).

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Figure 6.5. qRT-PCR assessment of differentiation markers on BMOL cells transfected with miR-34a-5p.

Relative expression of LPC differentiation markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and miR-34a-5p mimic for cholangiocyte markers (n=6): (A) Ggt1 and (B) Cx43; and for hepatocyte markers (n=6): (C) Tat and (D) Hnf4α. Lines represent mean ± SEM. Analysis made by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

BMOL cells transfected with miR-365a-3p showed a significant decrease in both

hepatocyte markers. Expression of Tat was decreased at 48 (P=0.0025) and 72 hours

(P=0.0220) (Figure 6.6.C), while Hnf4α exhibited decreased expression at 24

(P=0.0494), 48 (P=0.0403) and 72 hours (P=0.0232) (Figure 6.6.D). Interestingly,

miR-365a-3p transfected cells also showed a trend towards decrease expression of

Ggt1 at 24 (P=0.1098), 48 (P=0.1626) and 72 hours (P=0.1626) (Figure 6.6.A); and

of Cx43 at 48 (P=0.1507) and 72 hours (P=0.1507) (Figure 6.6.B) although this did

not reach statistical significance.

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Figure 6.6. qRT-PCR assessment of differentiation markers on BMOL cells transfected with miR-365a-3p.

Relative expression of LPC differentiation markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between BMOL cells transfected with miRNA negative control and miR-365a-3p mimic for cholangiocyte markers (n=6): (A) Ggt1 and (B) Cx43; and for hepatocyte markers (n=6): (C) Tat and (D) Hnf4α. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. Hprt was used as endogenous control for normalization.

Next, the effect of let7g-5p on the expression of cholangiocyte proteins Ck19 and

acetylated α-tubulin, as well as the hepatocyte specification protein Hnf4α were

assessed in six replicates by western blot (Figure 6.7). Overall, no statistical

significance was observed between BMOL cells transfected with let7g-5p and negative

control for any of the assessed proteins. However, in let7g-5p transfected BMOL cells,

there was a time-dependent increase in the expression of Ck19 (P<0.0001 by two-

way ANOVA) (Figure 6.7.B). Conversely, the expression of the hepatocyte marker

Hnf4α (P=0.7311) was decreased at 72 hours in let7g-5p transfected BMOL cells, but

this did not reach statistical significance (Figure 6.7.D).

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Figure 6.7. Western blot assessment of cholangiocyte and hepatocyte proteins in BMOL cells transiently transfected with let7g-5p.

Relative protein expression of cholangiocyte and hepatocyte proteins assessed at 24 hour intervals up to 72 hours. Protein expression was compared between BMOL cells transfected with miRNA negative control and let7g-5p mimic (n=6). (A) Size distribution (in kDa) of the assessed proteins. Band signal quantification for the cholangiocyte proteins (B) Ck19 and (C) acetylated α-tubulin, and for the hepatocyte protein (D) Hnf4α. Protein extracted from the hepatocyte cell line Huh7 was used as positive control in (A). Histograms represent mean ± SEM. Analysis made by multiple T-test with Holm-Sidak post-hoc test. Β-actin was used as endogenous control for normalization. Ctrl = Control; Let7g = let7g-5p; kDa = kilo Dalton; Trunc= truncated. h= hours.

6.2.1.3 Effect of miRNAs on BMOL proliferation and migration

Functional assays, performed in triplicates, were used to assess the effect of let7g-5p,

miR-34a-5p and miR-365a-3p on the proliferation and migration potential of BMOL

cells. Cells transfected with let7g-5p exhibited a significantly decreased proliferation

rate compared to negative control (P=0.0005) (Figure 6.8.A-B). Similarly, let7g-5p

significantly decreased the migration of BMOL cells compared to the negative control

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(P=0.0025) (Figure 6.8.C-D). Assessment of transfection efficiency was performed by

qRT-PCR at T=0 (beginning of the experiments) showing an average of 300-fold and

50-fold higher expression of let7g-5p compared to controls in the proliferation and

migration assays, respectively.

Figure 6.8. BMOL functional assays for let7g-5p.

Proliferation assays on BMOL cells transfected with let7g-5p mimic (n=3). (A) Surface area occupied by BMOL cells (confluency) over time, (B) Growth rate constant (K) of BMOL transfected cells with let7g-5p compared to negative control; Migration assays on BMOL cells transfected with let7g-5p mimic. (C) Surface area of the scratch wound occupied by BMOL cells (wound density) over time, (D) Migration rate constant (K) of BMOL transfected cells with let7g-5p compared to negative control. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test.

BMOL cells transfected with miR-34a-5p showed decreased proliferation (P=0.1447)

(Figure 6.9.A-B) although this did not reach statistical significance. In contrast, a

significant decrease in migration was observed in BMOL cells transfected with miR-

34a-5p (P=0.032) (Figure 6.9.C-D). An average of 120-fold and 10-fold increased

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expression of miR-34a-5p was observed in the proliferation and migration assays,

respectively, when transfection efficiency was assessed compared to controls at T=0.

Figure 6.9. BMOL functional assays for miR-34a-5p.

Proliferation assays on BMOL cells transfected with miR-34a-5p mimic (n=3). (A) Surface area occupied by BMOL cells (confluency) over time, (B) Growth rate constant (K) of BMOL transfected cells with miR-34a-5p compared to negative control; Migration assays on BMOL cells transfected with miR-34a-5p mimic. (C) Surface area of the scratch wound occupied by BMOL cells (wound density) over time, (D) Migration rate constant (K) of BMOL transfected cells with miR-34a-5p compared to negative control. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test.

BMOL cells transfected with miR-365a-3p showed markedly decreased proliferation

(P=0.1544) (Figure 6.10.A-B) although this did not reach statistical significance,

potentially due to technical variation between the (n=3) replicates. miR-365a-3p did

not have any effect on the migration of BMOL cells compared to negative control

(P=0.8577) (Figure 6.10.C-D). miRNA transfected BMOL cells had an average of 170-

fold and 155-fold increased expression of miR-365a-3p in the proliferation and

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migration assays, respectively, when transfection efficiency was assessed compared

to controls at T=0.

Figure 6.10. BMOL functional assays for miR-365a-3p.

Proliferation assays on BMOL cells transfected with miR-365a-3p mimic (n=3). (A) Surface area occupied by BMOL cells (confluency) over time, (B) Growth rate constant (K) of BMOL transfected cells with miR-365a-3p compared to negative control; Migration assays on BMOL cells transfected with miR-365a-3p mimic. (C) Surface area of the scratch wound occupied by BMOL cells (wound density) over time, (D) Migration rate constant (K) of BMOL transfected cells with miR-365a-3p compared to negative control. Lines represent mean ± SEM. Analysis made by T-test.

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6.2.2 HSCs

6.2.2.1 Effect of miRNAs on putative target mRNA expression

Following synthetic miRNA transfection and biotin pulldown assay to identify putative

miRNA targets in LX2 cells, seven potential targets for let7g-5p, 13 for miR-142-3p,

three for miR-34a-5p and nine for miR-365a-3p were selected (see Chapter 5, Table

5.7) to assess their gene expression by qRT-PCR. Differential expression between

miRNA negative control and miRNA mimic of interest was assessed at 24, 48 and 72

hours post-transfection in five replicates. Pair-wise comparison between miRNA

negative control and selected miRNA mimic at each time point was performed using

multiple T-test corrected with Holm-Sidak post-hoc test.

No significantly decreased expression was found on any of the selected potential

putative targets in LX2 cells transfected with let7g-5p when compared to miRNA

negative control (not shown), however, CD248 showed decreased relative expression

at 72 hours (P=0.1014) (Figure 6.11).

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Figure 6.11. qRT-PCR assessment of putative let7g-5p targets in LX2 cells.

Relative expression of CD248, a putative let7g-5p target assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and let7g-5p mimic (n=5). Lines represent mean ± SEM. Analysis made by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for

normalization.

LX2 cells transfected with miR-142-3p showed a significant decrease in expression of

AMOT at 72 hours post-transfection (P=0.0279) (Figure 6.12.A) and of MAP3K9 at

24 hours post-transfection (P=0.0048) (Figure 6.12.B). NOTCH3 also showed a

decrease expression at 24 hours post-transfection, however, this did not reach

statistical significance (P=0.1972) (Figure 6.12.C).

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Figure 6.12. qRT-PCR assessment of putative miR-142-3p targets in LX2 .

Relative expression of putative miR-142-3p targets assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-142-3p mimic (n=5) for (A) AMOT, (B) MAP3K9 and (C) NOTCH3. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

As with LX2 transfected with let7g-5p, none of the selected potential putative targets

for miR-34a-5p showed decreased expression in any time point post-transfection (not

shown). Nevertheless, WWC1 showed decrease expression in LX2 transfected cells

at 48 (P=0.5924) and 72 hours (P=0.7440) (Figure 6.13).

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Figure 6.13. qRT-PCR assessment of putative miR-34a-5p targets in LX2 cells.

Relative expression of WWC1, a putative miR-34a-5p target assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-34a-5p mimic (n=5). Lines represent mean ± SEM. Analysis made by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

LX2 cells transfected with miR-365a-3p showed a significant decreased expression of

AMOT (P=0.0271) (Figure 6.14.A) and LATS1 (P=0.0487) (Figure 6.14.B) at 72

hours post-transfection. Decreased expression of COL4A5 was also observed at 24

hours post-transfection of miR-365a-3p, however, this decrease was not statistically

significant (P= 0.3928) (Figure 6.14.C).

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Figure 6.14. qRT-PCR assessment of putative miR-365a-3p targets in LX2 cells.

Relative expression of putative miR-365a-3p targets assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-365a-3p mimic (n=5) for (A) AMOT, (B) LATS1 and (C) COL4A5. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

6.2.2.2 Impact of miRNA transfection on HSC activation

In order to evaluate the effect of the selected miRNAs on the processes of HSC

activation and fibrogenesis, gene expression of known activation/quiescence markers

were assessed in five replicates of transiently transfected LX2 cells by qRT-PCR. LX2

cells transfected with let7g-5p showed significant increased expression of HSC

activation markers ACTA2 (P=0.0167), TIMP3 (P=0.0164), TGFβ1 (P=0.0034) and

COL1A1 (P=0.0416) at 24 hours post-transfection. At 48 hours post–transfection

ACTA2 (P=0.0041), TIMP3 (P=0.0118), TGFβ1 (P<0.001) and COL1A2 (P=0.0023)

also showed significant increased expression compared to cells transfected with the

negative control (Figure 6.15.A-F). No significant difference was observed in the

expression of HSC quiescence markers, however, CDH1 showed a tendency towards

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increased expression at 24 (P=0.1485), 48 (P=0.1485) and 72 hours (P=0.4606) in

cells transfected with let7g-5p compared to negative control (Figure 6.15.H).

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Figure 6.15. qRT-PCR assessment of HSC activation and quiescence markers on LX2 cells transfected with let7g-5p.

Relative expression of HSC activation and quiescence markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and let7g-5p mimic for HSC activation markers (n=5): (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2; and for HSC quiescence markers (n=5): (G) PPARγ and (H) CDH1. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous

control for normalization.

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LX2 cells transfected with miR-142-3p showed significant increased expression of

HSC activation markers TGFβ1 at 24 hours (P=0.0498) and 48 hours (P=0.0498) post-

transfection (Figure 6.16.D), whereas, TGFβR1 was significantly increased at 48

hours post-transfection (P=0.0282) (Figure 6.16.C). Other HSC activation markers

showed a non-significant increase; such as ACTA2 at 24 hours (P=0.4936) and 48

hours (P=0.1879) post-transfection (Figure 6.16.A); TIMP3 at 24 hours (P=0.5634)

and 48 hours (P=0.3539) post-transfection (Figure 6.16.B) and COL1A1 at 24 hours

(P=0.6963) and 72 hours (P=0.7777) post-transfection (Figure 6.16.E). A significant

reduced expression was observed in the HSC quiescent marker PPARγ at 72 hours

(P=0.0127) post-transfection (Figure 6.16.G) in contrast to the non-significant

increased expression of CDH1 at 24 hours (P=0.2512) and 48 hours (P=0.2551) post-

transfection (Figure 6.16.H).

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Figure 6.16. qRT-PCR assessment of HSC activation and quiescence markers on LX2 cells transfected with miR-142-3p.

Relative expression of HSC activation and quiescence markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-142-3p mimic for HSC activation markers (n=5): (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2; and for HSC quiescence markers (n=5): (G) PPARγ and (H) CDH1. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

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LX2 cells transfected with miR-34a-5p showed significant decreased expression of the

HSC activation marker COL1A1 at 24 hours (P=0.0337) post-transfection (Figure

6.17.E). Although non-significant, decreased expression of TIMP3 at 48 hours

(P=0.7836) post-transfection and 72 hours post-transfection (P=0.7836) (Figure

6.17.B); TGFβR1 at 48 hours (P=0.9520) post-transfection and 72 hours post-

transfection (P=0.9520) (Figure 6.17.C) and TGFβ1 at 48 hours (P=0.7716) post-

transfection and 72 hours post-transfection (P=0.7716) (Figure 6.17.D) was observed

in LX2 transfected cells compared to negative control. When HSC quiescent markers

were assessed, both PPARγ at 24 hours (P=0.3832) and 48 hours (P=0.3673) (Figure

6.17.G) and CDH1 at 24 hours (P=0.4316) and 48 hours (P=0.4316) (Figure 6.17.H)

were decreased in LX2 miR-34a-5p transfected cells although this did not react

statistical significance.

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Figure 6.17. qRT-PCR assessment of HSC activation and quiescence markers on LX2 cells transfected with miR-34a-5p.

Relative expression of HSC activation and quiescence markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-34a-5p mimic for HSC activation markers (n=5): (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2; and for HSC quiescence markers (n=5): (G) PPARγ and (H) CDH1. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

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Transfected LX2 cells with miR-365a-3p showed significant increased expression of

ACTA2 at 72 hours (P=0.0142) post-transfection (Figure 6.18.A), TIMP3 at 48 hours

(P=0.0326) post-transfection (Figure 6.18.B) and TGFβ1 at 24 hours (P=0.0014), 48

hours (P=0.0149) and 72 hours (P=0.0139) post-transfection (Figure 6.18.D). A non-

significant increase of COL1A1 at 48 hours (P=0.6780) and 72 hours (P=0.6780) post-

transfection and COL1A2 at 24 hours (P=0.3667) post-transfection was observed in

miRNA transfected LX2 compared to negative control (Figure 6.18.E-F). No significant

difference was observed in HSC quiescent markers, however, PPARγ exhibited a

decreased expression at 72 hours (P=0.2747) post-transfection, whereas, CDH1

increased its expression at 48 hours (P=0.6021) and 72 hours (P=0.6021) post-

transfection (Figure 6.18.G-H).

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Figure 6.18. qRT-PCR assessment of HSC activation and quiescence markers on LX2 cells transfected with miR-365a-3p.

Relative expression of HSC activation and quiescence markers assessed at 24 hours intervals up to 72 hours. Gene expression was compared between LX2 cells transfected with miRNA negative control and miR-365a-3p mimic for HSC activation markers (n=5): (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2; and for HSC quiescence markers (n=5): (G) PPARγ and (H) CDH1. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by multiple T-test with Holm-Sidak post-hoc test. GAPDH was used as endogenous control for normalization.

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6.2.2.3 Stable miRNA expression in LX2 cells

During the assessment of putative miRNA targets and the impact of transiently

transfected miRNAs on HSC activation markers, variation between replicates and

different post-transfection time points was observed. The most likely explanation for

this variation is transfection efficiency and the use of transient transfection. Therefore,

a lentiviral miRNA-construct system was used to transduce LX2 cells thus producing

a constant and stable overexpression of miRNA over time. Quantification of

transduced cells performed in three replicates by qRT-PCR showed an increased

expression of miRNA in all cases. Let7g-5p was significantly increased in transduced

LX2 cells compared to both, non-transduced (P=0.0471) and LX2 cells transduced

with miRNA negative control (P=0.0394) (Figure 6.19.A). miR-365a-3p was

significantly increased in transduced cells compared to both, non-transduced LX2 cells

(P=0.0051) and LX2 transduced with miRNA negative control (P=0.0051) (Figure

6.19.B). miR-34a-5p was significantly increased in transduced cells compared to both,

non-transduced LX2 cells (P=0.0015) and LX2 transduced with miRNA negative

control (P=0.0015) (Figure 6.19.C). Similarly, a clear overexpression was observed in

LX2 cells transduced with miR-142-3p compared to miRNA negative control

transduced cells, although this did not reach statistical significance (P=0.0521)

(Figure 6.19.D). Thus, stable transfection of LX2 cells corresponded to a 20-fold, 42-

fold and 16-fold increased expression of let7g-5p, miR-365a-3p and miR-34a-5p,

respectively.

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Figure 6.19. Transduction of LX2 cells using lentivirus miRNA constructs.

Quantification of miRNA in LX2 transduced cells using lentiviral miRNA-constructs (n=3) for (A) let7g-5p, (B) miR-365a-3p, (C) miR-34a-5p and (D) miR-142-3p. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by by ANOVA with Tukey post-hoc test. RNU6 was used as endogenous control for normalization. LX2 = non-transduced LX2 cells. Lenti NegCtrl = LX2 cells transduced with miRNA negative control. Lenti miR = LX2 cells transduced with miRNA of interest.

6.2.2.4 Effect of stably transfected miRNAs on LX2 proliferation and migration

Functional assays, performed in triplicate on LX2 transduced cells, were used to

assess the effect of let7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p on

proliferation and migration of LX2 cells. No significant difference was observed in the

proliferation of LX2 cells transduced with let7g-5p (P=0.4672) (Figure 6.20.A-B). In

contrast, let7g-5p significantly decreased the migration of LX2 cells compared to

negative control (P=0.0152) (Figure 6.20.C-D).

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Figure 6.20. LX2 functional assays for let7g-5p.

Proliferation assays on LX2 cells transduced with let7g-5p (n=3). (A) Surface area occupied by LX2 cells (confluency) over time, (B) Growth rate constant (K) of LX2 transduced with let7g-5p compared to negative control; Migration assays on LX2 cells transduced with let7g-5p. (C) Surface area of the scratch wound occupied by LX2 cells (wound density) over time, (D) Migration rate constant (K) of LX2 transduced cells with let7g-5p compared to negative control. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test.

LX2 cells transduced with miR-142-3p showed significantly decreased proliferation

compared to the negative control (P=0.0413) (Figure 6.21.A-B). Similarly, decreased

migration was observed in LX2 cells overexpressing miR-142-3p although this did not

reach statistical significance (P=0.2058) (Figure 6.21.C-D).

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Figure 6.21. LX2 functional assays for miR-142-3p.

Proliferation assays on LX2 cells transduced with miR-142-3p (n=3). (A) Surface area occupied by LX2 cells (confluency) over time, (B) Growth rate constant (K) of LX2 transduced with miR-142-3p compared to negative control; Migration assays on LX2 cells transduced with miR-142-3p. (C) Surface area of the scratch wound occupied by LX2 cells (wound density) over time, (D) Migration rate constant (K) of LX2 transduced cells with miR-142-3p compared to negative control. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test.

No significant difference was observed in LX2 cells transduced with miR-34a-5p for

either proliferation (P=0.1463) or migration (P=0.8293) when compared to the negative

control (Figure 6.22).

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Figure 6.22. LX2 functional assays for miR-34a-5p.

Proliferation assays on LX2 cells transduced with miR-34a-5p (n=3). (A) Surface area occupied by LX2 cells (confluency) over time, (B) Growth rate constant (K) of LX2 transduced with miR-34a-5p compared to negative control; Migration assays on LX2 cells transduced with miR-34a-5p. (C) Surface area of the scratch wound occupied by LX2 cells (wound density) over time, (D) Migration rate constant (K) of LX2 transduced cells with miR-34a-5p compared to negative control. Lines represent mean ± SEM. Analysis made by T-test.

In LX2 cells transduced with miR-365a-3p, no difference was observed in proliferation

(P=0.8702) (Figure 6.23.A-B), however, LX2 cells showed decrease migration

compared to controls, although this difference was not statistically significant

(P=0.1444) (Figure 6.23.C-D).

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Figure 6.23. LX2 functional assays for miR-365a-3p.

Proliferation assays on LX2 cells transduced with miR-365a-3p (n=3). (A) Surface area occupied by LX2 cells (confluency) over time, (B) Growth rate constant (K) of LX2 transduced with miR-365a-3p compared to negative control; Migration assays on LX2 cells transduced with miR-365a-3p. (C) Surface area of the scratch wound occupied by LX2 cells (wound density) over time, (D) Migration rate constant (K) of LX2 transduced cells with miR-365a-3p compared to negative control. Lines represent mean ± SEM. Analysis made by T-test.

6.2.2.5 Effect of stable transfection with either let7g-5p or miR-365a-3p on HSC

activation.

In order to confirm the observed effects of transiently transfected miRNAs on HSC

activation, LX2 stably transduced cells overexpressing let7g-5p or miR-365a-3p were

selected to assess the expression of HSC activation markers (in triplicate) by qRT-

PCR. Transduced LX2 cells overexpressing let7g-5p showed significantly increased

expression of TIMP3 (P=0.0033), TGFβR1 (P=0.0391), COL1A1 (P=0.0164) and

COL1A2 (P=0.0337) (Figure 6.24.B-C,E-F). ACTA2 (P=0.1317) and TGFβ1

(P=0.8341) also showed increased expression although this did not reach statistical

significance (Figure 6.24.A-D).

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Figure 6.24. qRT-PCR assessment of HSC activation markers on LX2 cells transduced with let7g-5p.

Relative expression of HSC activation markers. Gene expression was compared between LX2 cells transduced with miRNA negative control vs. let7g-5p (n=3). (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test. GAPDH was used as endogenous control for normalization. Lenti NegCtrl = LX2 cells transduced with miRNA negative control. Lenti let7g-5p = LX2 cells transduced with let7g-5p.

Transduced LX2 cells overexpressing miR-365a-3p showed significantly increased

expression of TGFβR1 (P=0.0100), COL1A1 (P=0.0156) and COL1A2 (P=0.0359)

(Figure 6.25.C, E-F). ACTA2 (P=0.2205), TIMP3 (P=0.4015) and TGFβ1 (P=0.7485)

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also showed increased expression although this did not reach statistical significance

(Figure 6.25.A-B, D).

Figure 6.25. qRT-PCR assessment of HSC activation markers on LX2 cells transduced with miR-365a-3p.

Relative expression of HSC activation markers. Gene expression was compared between LX2 cells transduced with miRNA negative control vs. miR-365a-3p (n=3). (A) ACTA2, (B) TIMP3, (C) TGFβR1, (D) TGFβ1, (E) COL1A1 and (F) COL1A2. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test. GAPDH was used as endogenous control for normalization. Lenti NegCtrl = LX2 cells transduced with miRNA negative control. Lenti miR-365a-3p = LX2 cells transduced with miR-365a-3p.

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6.2.2.6 Validation of putative pulldown targets of let7g-5p and miR-365a-3p

involved in HSC activation.

The overexpression of let7g-5p and miR-365a-3p increased the expression of several

HSC activation markers, especially those involved in the collagen synthetic pathway

associated with fibrosis, as described above. These activation markers have not been

described as targets for let7g-5p or miR-365a-3p in any previous experimental study

or predictive algorithms. Therefore, based on the identified putative targets obtained

for these miRNAs (see Chapter 5), IPA was used to construct a network model of

putative targets that are reported transcription factors for the HSC activation markers

assessed in this study. Subsequently, a number of putative targets were then selected

for further validation by luciferase reporter assay based on fold change, P-value and

relevant previously published information. For let7g-5p, EP300, GATA4 and SP1 were

selected for further validation (Table 6.1 and Figure 6.26). Similarly, for miR-365a-3p,

NUPR1, RFXAP, RFX5, CD109, DDX5 and SP1 were selected for further validation

(Table 6.2 and Figure 6.27).

Table 6.1. Let7g-5p putative targets mediating HSC activation markers.

Putative target FC P-value Potential role

EP300 1.67 4.94x10-13 Inhibits collagen

GATA4 1.83 2.62x10-8 Inhibits collagen

SP1 1.61 1.06x10-9 Transcription factor mediating

genes involved in fibrogenesis

Let7g-5p putative targets with direct interactions to HSC activation markers selected for validation by luciferase activity assay. FC = fold change; EP300 = E1A Binding protein P300; GATA4 = GATA binding protein 4; SP1 = Specificity protein 1.

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Table 6.2. miR-365a-3p putative targets mediating HSC activation markers.

Putative target FC P-value Potential role

NUPR1 2.87 2.53x10-6 Inhibits collagen

RFXAP 2.81 1.49x10-5 Inhibits collagen

RFX5 2.07 3.92x10-4 Inhibits collagen

CD109 2.43 0.0019 Regulates TGFβ signalling

DDX5 2.85 1.92x10-6 Regulates TGFβ signalling

SP1 2.27 9.84x10-5 Transcription factor mediating

genes involved in fibrogenesis

miR-365a-3p putative targets with direct interactions to HSC activation markers selected for validation by luciferase activity assay. FC = fold change; NUPR1 = Nuclear protein 1; RFXAP = Regulatory factor X associated protein; RFX5 = Regulatory factor X5; CD109 = CD109 molecule; DDX5 = DEAD-Box helicase 5; SP1 = Specificity protein 1.

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Figure 6.26. Let7g-5p pulldown putative targets interacting with genes associated with HSC activation.

Network model showing putative targets of let7g-5p identified during the biotin pulldown assay (see Chapter 5). HSC activation markers used in this chapter are highlighted in blue. Selected putative targets for validation by luciferase reporter assay are highlighted in purple.

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Figure 6.27. miR-365a-3p pulldown putative targets interacting with genes associated with HSC activation.

Network model showing putative targets of miR-365a-3p identified during the biotin pulldown assay (see Chapter 5). HSC activation markers used in this chapter are highlighted in blue. Selected putative targets for validation by luciferase reporter assay are highlighted in purple.

Dual luciferase reporter assay showed a 34-fold significant decrease for GATA4

(P=0.0389), 12-fold significant decrease for SP1(a) (P=0.0013) and 58-fold significant

decrease for SP1(b) (P=0.1964) in relative luciferase activity for LX2 cells co-

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transfected with let7g-5p (Figure 6.28.B,D-E), demonstrating that these genes are

definitive targets of let7g-5p. In contrast, no significant difference was observed in the

relative luciferase activity of EP300 in LX2 cells co-transfected with miRNA negative

control or let7g-5p mimic (Figure 6.28.C). LX2 cells co-transfected with miR-365a-3p

showed a 42-fold, 66-fold, 26-fold, 4-fold, 18-fold, 31-fold and 27-fold significant

decrease in relative luciferase activity for NUPR1 (P=0.0147), RFXAP (P=0.0103),

RFX5 (P=0.0130), Sp1 (a) (P=0.0003), SP1 (b) (P=0.0018), CD109 (a) (P=0.0461)

and DDX5 (P=0.0032), respectively (Figure 6.29.B-G,I), identifying these genes as

definitive targets of miR-365a. No significant difference was observed in the luciferase

activity of CD109 (b) (P=0.8310) transfected with miR-365a-3p compared to miRNA

negative control mimic (Figure 6.29.H). Of note, due to the large size of their 3’UTR

region, SP1 and CD109 were cloned into two separate pEZX-MT06 luciferase reporter

vectors.

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Figure 6.28. Dual luciferase assay of validated LX2 let7g-5p targets.

LX2 cells were co-transfected with miRNA negative control or let7g-5p mimic and pEZ-MT06 luciferase construct (n=6). (A) empty; or containing the 3’UTR region of (B) GATA4, (C) EP300, (D-E) SP1. Due to the size of SP1 3’UTR region, it was cloned in two different constructs. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test. Luciferase activity was normalized to Renilla activity.

215

Figure 6.29. Dual luciferase assay of validated LX2 miR-365a-3p targets.

LX2 cells were co-transfected with miRNA negative control or miR-365a-3p mimic and pEZ-MT06 luciferase construct (n=6). (A) empty; or containing the 3’UTR region of (B) NUPR1, (C) RFXAP, (D) RFX5, (E-F) SP1, (G-H) CD109 and (I) DDX5. Due to the size of SP1 and CD109 3’UTR region, they were cloned in two different constructs. Lines represent mean ± SEM. * P<.05, **P<.01 and ***P<.001 by T-test. Luciferase activity was normalized to Renilla activity.

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6.3 DISCUSSION

Several studies have explored the role of miRNAs in the context of chronic liver

disease and their potential implication in either fibrogenesis or its progression

(reviewed elsewhere (248, 385). miRNAs finely tune rather than completely repress

the expression of their target genes (163); this characteristic along with their

ubiquitous expression in every cell and involvement in the regulation of multiple

signalling pathways show their importance as regulators of cellular and tissue

homeostasis. However, studying how miRNAs exert their regulatory effects is

challenging as several miRNAs can target the same gene, one miRNA can target

several members of the same pathway and some effects can be the product of indirect

regulation. Moreover, due to the varied mechanisms by which miRNAs repress their

respective targets decreased gene expression measured by qRT-PCR is not enough

to identify miRNA targets. miRNAs can either induce mRNA degradation or inhibit its

translation, thus additional confirmation through luciferase reporter assays or western

blot are necessary in targets repressed by translation inhibition. Therefore, this chapter

expands on the previous findings of Chapter 5 where putative LPC and HSC targets

for let7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p were identified using biotin

pulldown assay.

6.3.1 LPCs

mRNA expression levels of putative targets for let7g-5p, miR-34a-5p and miR-365a-

3p were assessed by qRT-PCR in the murine LPC line, BMOL, that were transiently

transfected for each specific miRNA. In the case of BMOL cells, it was decided not to

use miR-142-3p due to the low numbers of putative targets identified in the biotin

pulldown assay compared to putative targets identified in LX2 cells, which suggests

that miR-142-3p does not play a specific role in LPCs (See Chapter 5).

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BMOL cells transfected with let7g-5p showed significant decreased expression of

Rock1, Hnf4α and Ep300 (Figure 6.1), whereas miR-365a-3p showed significant

decreased expression in Cx3cr1, Ptpn11, Akt3 and Fzd2 (Figure 6.3). Rock1 is a

serine/threonine kinase that constitutes one of the two members of the Rock family.

The Rock family is a major downstream effector of the small GTPase RhoA that play

an important role in cytoskeleton organization including matrix deposition and

degradation (386). Increased expression of Rock1 has been described in

hepatocellular carcinoma or breast cancer (386, 387). Rock1 is also part of the VEGF

signalling pathway by which LPCs influence HSCs (388), suggesting a potential role

of let7g-5p in the crosstalk between LPCs and HSCs during fibrogenesis. Hnf4α is a

transcription factor that affects liver specific target genes and other liver-enriched

transcription factors (389). The main role of Hnf4α is to maintain the hepatocyte

phenotype, supported by the high number of genes expressed in the liver with binding

sites for Hnf4α which makes it a global regulator in the liver (390). The decreased

expression of Hnf4α in BMOL cells transfected with let7g-5p, suggests a potential role

of let7g-5p in regulating LPC differentiation, favouring the cholangiocyte lineage.

Ep300 is a histone acetyltransferase and acts as a transcriptional regulator and co-

activator in different signalling pathways (391). Ep300 has been described as both a

tumour suppressor and an oncogene (392, 393). Ep300 is a member of the

Lymphotoxin β (LT-β) pathway, thus potentially important in the context of LPC biology

and the ductular reaction (388). LT-β expressed on LPCs interacts with the LT-β

receptor on HSCs which triggers a proinflamatory cascade that contributes to

fibrogenesis (381). Decreased expression of Ep300 in BMOLs mediated by let7g-5p

suggests a potential negative regulatory effect on fibrogenesis. Cx3cr1, the chemokine

receptor of Cx3cl1, is expressed in several liver cells including hepatocytes, HSCs and

Kupffer cells (394-396). Cx3cl1-Cx3cr1 interaction has a functional effect on migration

and homeostasis (397, 398). Furthermore, a deficiency of Cx3cr1 has been reported

to increase liver inflammation and fibrosis by regulating the TNFα pathway (394, 399).

It has been hypothesised that the expression of Cx3cl1 by LPCs and ductular reaction

cells promotes chemotaxis of macrophages that contributes to the activation of HSCs

and the establishment of fibrosis (359). This suggests a potential role for miR-365a in

cell-cell communication within the LPC niche by targeting Cx3cr1. Ptpn11 is a tyrosine

phosphatase which has been shown to play an important role in the early phase of

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liver regeneration, migration, proliferation or apoptosis (400-402). Ptpn11 is a

downstream effector of the LT-β pathway and as such, miR-365a could have a

regulatory effect on fibrogenesis via this pathway. Akt3 is a serine/threonine kinase

involved in several cellular processes including apoptosis, cell proliferation and

migration (403). Akt3 is downstream of EGF which has been shown to maintain LPC

niche activation (388) suggesting a potential role for miR-365a in LPC-mediated

fibrogenesis. Fd2 is a seven-transmembrane receptor of the Wnt5a/b ligand which

forms part of the non-canonical Wnt signalling pathway (404). Wnt5a has been shown

to be upregulated in liver fibrosis (405, 406), mediated by the induction of TGFβ (407)

and it has been proposed to play an important role in non-canonical Wnt signalling

through Fzd2 and in the development of liver fibrosis (406). Thus, this suggests a

potential regulatory role of miR-365a in the LPC niche in the development of fibrosis

during CFLD.

During chronic liver injury when hepatocyte-mediated regeneration is impaired, LPCs

are induced to proliferate and can differentiate in either hepatocytes or cholangiocytes

where the subsequent formation of reactive bile ducts contribute to the appearance of

the ductular reaction and fibrosis (408). Therefore, the effect of let7g-5p, miR-34a-5p

and miR-365a-3p on LPC differentiation was assessed by measuring the mRNA

expression levels of cholangiocyte markers Ggt1 and Cx43; and hepatocyte markers

Hnf4α and Tat. BMOL cells transfected with let7g-5p showed a significant decrease in

the expression of hepatocyte specific markers Tat and Hnf4α (Figure 6.4.C-D).

Interestingly, let7g-5p also decreased the expression levels of Ggt1, an enzyme

expressed and secreted by functional cholangiocytes (Figure 6.4.A), suggesting that

let7g-5p may play a role in LPCs differentiation. In order to further clarify the role of

let7g-5p in LPCs differentiation; hepatocyte and cholangiocyte markers were also

assessed at a protein level. Transient transfection of let7g-5p induced a significant

increase in the time-dependent expression of the cholangiocyte specific structural

protein Ck19 (Figure 6.7.B), but had no significant effect on acetylated α-tubulin. In

contrast, the expression of the hepatocyte specification protein HNF4α was decreased

after 72hrs, although this did not reach statistical significance (Figure 6.7.C-D). An

interesting observation remains that BMOL cells transfected with let7g-5p showed a

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decreased expression of both hepatocyte and cholangiocyte specific genes/proteins

(apart from CK19 which was increased). A possible explanation is that let7g-5p might

have an effect in maintaining the stem phenotype or stemnness of LPCs. Stem cells

have two important properties: self-renewal while maintaining an undifferentiated

status and the potential to differentiate into specific cell types (409). Many pathways

including Wnt, TGFβ or FGF have been implicated as regulators that control

differentiation of stem cells by modulating lineage commitment (410). This suggests

that let7g-5p could be regulating one or more of these pathways maintaining LPCs in

an undifferentiated state, although further experimental work is necessary to confirm

the effect of let7g-5p on LPCs differentiation. A decrease in the mRNA levels of the

hepatocyte differentiation markers Hnf4α and Tat was also observed in BMOL cells

transfected with miR-365a-3p (Figure 6.6.C-D), once again suggesting an effect

favouring the differentiation of LPCs towards a cholangiocyte type phenotype.

After chronic injury, the LPC response can be characterized in four distinctive phases:

activation, proliferation, migration and differentiation (411). BMOLs transfected with

let7g-5p showed a significant decrease in both proliferation (Figure 6.8.A-B) and

migration (Figure 6.8.C-D) suggesting, perhaps, an effect towards the end of the LPC

response to chronic liver disease. As the work described in this thesis is among the

first to assess the role of these miRNAs in LPC biology, further extensive investigation

into the mechanisms involved in regulating LPC differentiation are required.

6.3.2 HSCs

mRNA expression levels of putative targets of let7g-5p, miR-142-3p, miR-34a-5p and

miR-365a-3p were assessed by qRT-PCR in LX2 cells transiently transfected for each

specific miRNA. A significant decreased expression in the levels of MAP3K9 and

AMOT for miR-142 (Figure 6.12.A-B) and AMOT and LATS1 for miR-365a (Figure

6.14.A-B) transfected LX2 cells was observed. MAP3K9 is a serine/threonine kinase

member of the mixed lineage kinases family that regulate the activity of MAPKs (412).

MAP3K9 is an important component of the JNK transduction pathway (413) acting as

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an upstream activator of the MKK/JNK cascade that activates JNK (414). It has been

shown that JNK promotes HSC activation (376, 377), moreover, JNK in HSCs but not

in hepatocytes enhance fibrosis in murine animal models (415, 416). This suggests a

potential role of miR-142 in fibrosis by regulating the activation of JNK through

MAP3K9. AMOT is a member of the angiomotin protein family (417, 418) that regulate

YAP and LATS in the Hippo signalling pathway (419, 420). The regulatory

mechanisms of AMOT are not fully understood (421) as it has been described as both

a stimulator (422) and suppressor of YAP (423). LATS1 is a kinase with a key

regulatory role by phosphorylating YAPS which increase its cytoplasmic retention and

proteasome-mediated degradation (424, 425) and as such, together with AMOT, is a

key component of the Hippo signalling pathway. The Hippo pathway intervenes in

several cellular functions such as organ growth and stem cell development of cancer

progression (426, 427). The cascade of kinase activity of this pathway results in the

phosphorylation and inactivation of YAP1. YAP1 has a critical role in the activation of

HSCs with animal model experiments showing improvement in liver fibrosis when

YAP1 is inhibited (428, 429). This suggests a potential regulatory role for miR-142 and

miR-365a in the Hippo pathway by targeting AMOT and LATS1 which contributes to

the activation of HSCs. It is also possible, that the role of AMOT as a promoter or

inhibitor of YAP could be partially controlled by the activity of a specific miRNA, with

miR-142 promoting the inactivation of YAP1 in CFnoLD children, while miR-365a

activates it in children with CFLD, although this speculatory and would require further

extensive investigation to fully elucidate the mechanisms involved.

Transient transfection of HSCs with miRNAs resulted in significantly increased mRNA

expression of HSC activation markers ACTA2, TIMP3, TGFβ1, COL1A1 and COL1A2

when transfected with let7g-5p (Figure 6.15); TGFβ1 and TGFβR1 with miR-142-3p

(Figure 6.16); COL1A1 with miR-34a-5p (Figure 6.17); and ACTA2, TIMP3 and

TGFβ1 with miR-365a-3p (Figure 6.18). In contrast, only miR-142-3p transfected LX2

cells showed significant decrease of the HSC quiescence marker PPARγ (Figure

6.16). qRT-PCR expression data used to assess putative targets and effect of

overexpressed miRNAs on HSC activation showed a high variation between replicates

and inconsistent effects over time, probably due to differing transfection efficiencies

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and the use of a transient transfection system. Therefore, it was decided to induce

stable overexpression of the miRNAs in LX2 cells using a miRNA lentiviral construct.

This transduction of LX2 cells resulted in a clear overexpression of miRNA in the cells

transduced with the lenti-miRNA vector compared to cells transduced with lenti-

negative miRNA vector (Figure 6.19). Stable over-expressing LX2 cells were used for

the remaining functional assays.

Chronic liver injury causes HSCs to transactivate from a quiescent phenotype to

activated myofibroblasts that are highly proliferative, contractile (hence motile),

fibrogenic and chemotactic (378). As a result of stable transfection (transduction) of

LX2 cells with miR-142-3p, there was a significant decrease in the proliferation rate

(Figure 6.21.A-B). Decreased migration was observed in LX2 cells transduced with

let7g-5p (Figure 6.20.C-D) and while miR-365-5p also inhibited migration, this was

not statistically significant but with further replicates may prove to be a real effect

(Figure 6.23.C-D). If considered together with their effect on the increased expression

of several collagen synthetic pathway markers, it could be postulated that let7g-5p and

miR-365a-3p may play a role in immobilised HSCs at the site of injury where they

synthesise increased levels of ECM characteristic of hepatic fibrosis, although again

this speculation would require further functional validation in future studies

Further assessment of HSC activation markers in let7g-5p and miR-365a-3p was

performed on transduced LX2 cells to validate their effect on cells stably over-

expressing these miRNAs. As expected, the results obtained using transient

transfection (Figure 6.24; Figure 6.25) were replicated using stable transfection of

miRNAs, confirming a role of let7g-5p and miR-365a-3p in promoting HSC activation.

Pathway analysis was performed to identify putative targets for let7g-5p and miR-

365a-3p with a direct interaction in regulating these HSC activation markers (Figure

6.26; Figure 6.27). From all the possible targets three transcription factors were

selected for validation in let7g-5p transduced LX2 cells: GATA4, EP300 and SP1,

whereas six transcription factors were selected in miR-365a-3p transduced cells:

NUPR1, RFXAP, RFX5, SP1, CD109 and DDX5. GATA4 is a zinc finger transcription

factor member of the GATA family comprising two subfamilies: GATA 1, 2 and 3

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expressed in hematopoietic stem cells and neurons; and GATA 4, 5 and 6 expressed

in mesodermal and endodermal tissues (430). Several studies have reported a

regulatory role of the GATA family in pathways such as TGFβ, Rho or VEGF (431,

432). GATA4 shares a consensus sequence with the interferon regulatory transcription

factor of the unique open chromatin site of the COL1A2 gene which inhibits its

transcription (433, 434). As stated above, EP300 is a histone acetyltransferase

transcriptional regulator which has been reported to act as a mediator in the inhibition

of collagen through several pathways in fibroblast-type cells including HSCs (435-

437). SP1 is a zinc finger transcription factor member of the SP/KLF family that binds

to GC-motifs of several promoters (438, 439). It has been described in the regulation

of several cellular processes including proliferation or cell growth and cancer (440,

441). SP1 has been shown to be greatly increased in activated HSCs mediating the

expression of several fibrotic genes. When binding of SP1 to the promoter region of

genes is effectively blocked, proteins such as cyclin D1, TGFβ1, α-SMA or COL1A1

are decreased in HSC, providing evidence of its important role in HSC activation (442).

NUPR1 or P8 is a nuclear helix-loop-helix protein which is a stress response molecule

associated to several functions such as transcriptional regulation, cell cycle or

apoptosis (443-445). NUPR1 acts as a co-regulator for several transcription factors

including P300 or FoxO3 (444, 446). Although few studies have explored its role in the

liver, ablation of NUPR1 in mouse cardiac cells increases Col1A2 and it has been

speculated that this occurs through the inhibition of MMP9 (447). RFXAP and RFX5

are part of the three member RFX5 transcriptional complex which also includes RFXB.

Interaction with DNA is primarily mediated by RFX5 (448), while RFXB promotes

protein-protein interactions (449) and the C-terminal domain of RFXAP serves as a

bridge between RFX5 and RFXB (448). The RFX5 complex can bind to the

transcription start site of the Col1A2 gene were it represses its expression (450-452).

CD109 is a glycosylphosphatidylinositol anchored protein member of the α2-

macroglobulin/complement family (453). Decreased expression of CD109 has been

reported in breast cancer, melanoma or colorectal cancer (454, 455). TGFβ signal

transduction requires receptor endocytosis in order to be effective. Internalization of

TGFβR in clathrin-coated pits leads to phosphorylation of SMAD 2/3 and TGFβR

recycling (456-458). In contrast, lipid raft-dependent internalization in caveolae (459)

promotes the interaction of TGFβR with caveolin-1 (460) which downregulates

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SMAD2/3 signalling and degrades the receptor via ubiquitination by the E3-ubiquitin

ligase SMURF2 (460, 461). CD109 has been shown to inhibit TGFβ signalling in a

caveolin-dependent manner by promoting TGFβR caveolae endocytosis and

promoting TGFβR degradation (462). DDX5 is a transcriptional repressor member of

the DEAD box helicase family (463). Expression of DDX5 has been described in the

nucleus of both primary HSCs and LX2 cells (464). Furthermore, inhibition of DDX5 in

LX2 cells increased the expression of COL1A2, TIMP1 and TGFβ1 indicating the role

DDX5 plays in regulating HSC activation (464).

From all these potential targets, all except EP300 were confirmed as direct targets of

either let7g-5p or miR-365a-3p via luciferase reporter activity assays in LX2 cells co-

transfected with the 3’UTR luciferase reporter vector and the miRNA mimic. This

suggests that miR-365a-3p and let7g-5p may act as pro-fibrotic mediators of HSC

activation by promoting the synthesis of collagen as modelled in the following

schematic representation (Figure 6.30). miR-365a-3p targets the TGFβ1 co-receptor

CD109 decreasing TGFβR lipid raft endocytosis and thus preventing its degradation.

LX2 miR-365a-3p biotin pulldown assay (performed in Chapter 5) also identified

SMURF2 as a potential target (fold change= 1.80; P=0.0273). SMURF2 is an E3-

ligase that ubiquitinates TGFβR in a process that is necessary for its degradation. By

targeting CD109 and SMURF2, miR-365a-3p represses TGFβR degradation mediated

by lipid-raft endocytosis. This in turn, promotes TGFβ signalling through the

internalization in clathrin-coated pits leading to the synthesis of COL1A1. By targeting

RFXAP and RFX5, miR-365a-3p counteracts the inhibitory effect of the RFX5 complex

allowing RNA polymerase II to bind to the TATA box of the COL1A2 gene to start its

transcription, thus increasing collagen synthesis. Similarly, by targeting DDX5, miR-

365a-3p prevents the inhibitory effect of DDX5 which increases the expression of

COL1A2 and presumably TIMP1 and TGFβ1. Finally, it is possible that the repression

of NUPR1 by miR-365a-3p decreases collagen degradation by inhibiting MMP activity,

which results in a net increase of collagen in the liver. Regarding the effect of let7g-

5p, by targeting GATA4, let7g-5p reduces the competition on the open chromatin site

of the COL1A2 gene increasing the transcription of COL1A2. These proposed actions

of miR-365a-3p and let7g-5p on the collagen synthetic pathway in HSCs require

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further validation. However, the mechanisms described would appear to be plausible

and if proven in future confirmatory studies have the potential to provide important

novel new targets for therapeutic intervention in the treatment of hepatic fibrosis in all

chronic liver disease.

As shown in this thesis, miRNAs have an impact on the biological function of liver

progenitor cells and hepatic stellate cells, specifically in processes related to

fibrogenesis. There is potential for therapeutic manipulation of endogenous miRNA

expression in the treatment of hepatic fibrogenesis in chronic liver disease. Several

challenges need to be overcome in order to use miRNAs as successful drug therapies

including the potential for degradation by serum nucleases (465), organ/tissue/cell-

specific targeting and delivery strategies for uptake of miRNAs into the cell cytoplasm

(466). Several strategies have been successfully implemented to overcome some of

these challenges including the use of locked nucleic acid (LNA) modifications which

protect against degradation (467). Miravirsen, a 15 nucleotide LNA oligonucleotide

miRNA inhibitor, has been successfully tested in phase II clinical trials for the treatment

of HCV infection (468) by targeting human liver miR-122 to inhibit HCV RNA viral

replication (469). Another successful example of miRNA therapeutic intervention is

MRX34 which is in phase II clinical trials. MRX34 consists of a liposomal nanoparticle

containing miR-34a mimics used as a tumour suppressor in solid tumours including

liver (470-472). These examples show the promising adaptation of miRNAs as

therapeutics. It has been suggested that the liver is a promising target for miRNA

therapeutics due to the use of lipid based delivery methods (468, 471). The use of

nanoparticles as a delivery strategy has also been reported by others (473) as an

important mechanism to overcome Sorafenib resistance in HCC by using miR-7 (474).

In this thesis, I have shown a potential pro-fibrotic role of miR-365a-3p and let7g-5p in

HSCs by increasing collagen synthesis. In the future the use of miRNAs inhibitors

against miR-365a-3p and let7g-5p could potentially be used as an anti-fibrotic liver

therapy.

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Figure 6.30. Potential mechanistic role of let7g-5p and miR-365a-3p in the synthesis of collagen.

miR-365a-3p targets CD109, a co-receptor of TGFβ1, which permits clathrin-coated pit endocytosis promoting signalling towards synthesis of COL1A1, instead of CD109-driven lipid raft endocytosis which degrades TGFβR1. miR-365a-3p also induces the synthesis of COL1A2 by inhibiting the RFX5 complex by targeting RFXAP and RFX5 and by targeting the transcriptional repressor DDX5. Increased hepatic collagen levels can also be mediated by miR-365a-3p by targeting NUPR1 which inhibits metalloproteases (thus decreasing potential MMP-driven collagen degradation). Let7g-5p promotes the expression of COL1A2 by targeting GATA4 which releases the competition for the transcription site of the COL1A2 gene. Faint red highlights luciferase assay validated miR-365a-3p targets. Faint green highlights yet to be validated miR-365a-targets. Faint purple highlights luciferase assay validated let7g-5p targets.

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It is interesting to note that miR-365a-3p targets molecules of different pathways but also

targets core molecules within the same pathway. Considering the effect of miRNAs on gene

expression, where translation is decreased but not totally repressed, targeting several key

molecules within the same signalling pathway represents an effective regulatory

mechanism. It is also interesting to note the large number of potential targets for each

miRNA and the apparent contradictory effects each miRNA may have on different genes.

For instance, miR-365a-3p and let7g-5p which this study has shown to have pro-fibrotic

roles in HSCs increasing collagen synthesis by inhibiting collagen repressors, also directly

target SP1, a known transcription factor for several pro-fibrogenic genes including collagen.

This demonstrates the complexity of miRNAs and highlights the need to consider not only

direct miRNA targets, but also indirect effects driven by miRNA regulation.

Limitations of this study include the use of transient transfection of BMOLs and LX2 cells

which resulted in high variation between replicates and inconsistent effects of miRNAs on

target genes over time. Given that the experimental conditions were the same, it is likely

that the transfection efficiency for each replicate was responsible for this variation. This had

a clear impact on the statistical significance of several assessed mRNA targets and cell

function assays and was mostly corrected when stable overexpression by lentiviral

transduction was used, as shown for the additional miR-365a-3p and let7g-5p experiments

conducted in LX2 cells. In addition, mRNA assessment by qRT-PCR is not the best method

to validate miRNA targets as miRNAs control gene expression by either degrading mRNA

(when there is perfect complementarity with the seed sequence, not common in mammals)

or by translational repression which is not reflected in the measured mRNA levels.

Therefore, although the decreased expression of several of the selected putative targets is

encouraging, validation by luciferase reporter assay or alternatively, assessment of protein

expression by western blot would be required for those putative targets not further assessed

in this thesis. Due to the very large number of genes identified as potential targets for the

four separate miRNAs in two different cell types it was not possible to investigate all of the

identified putative targets.

Another limitation of this study is the use of a single immortalized progenitor cell line and a

single immortalised hepatic stellate cell line. Thus, the data generated in this thesis may not

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be a full representation of miRNA function in complex physiological processes and therefore

should be further validated using additional cell lines and primary cultures of both liver

progenitor cells and hepatic stellate cells.

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In this chapter, I have used several cellular and molecular techniques to determine the

potential role of let-7g-5p, miR-142-3p, miR-34a-5p and miR-365a-3p in the biology of LPCs

and HSCs and in their potential to drive fibrogenesis. To do so, a detailed exploration of

miRNA targets principally at mRNA level but also at the protein level was conducted. The

performance of these functional assays was optimised by stably overexpressing miRNAs

instead of using transient transfection which reduced the high variation initially observed

between experimental replicates. Moreover, functional assays including proliferation,

migration, differentiation and activation assays were performed to determine the biological

effects of these miRNAs in both LPCs and HSCs.

The work presented in this chapter is far from complete. It would be of interest to replicate

the work done in BMOL cells by using miRNA stably-transduced cells as was performed

with LX2 cells. This would provide a more accurate description of the mechanistic role of the

investigated miRNAs in LPCs. I have also shown a potential role, especially for let7g-5p, in

regulating the differentiation of LPCs (as shown at both the mRNA and protein level) which

deserves to be explored in greater depth. Using a similar approach to than used for HSCs,

identifying putative targets that directly interact with LPC differentiation markers would be of

great interest.

The most remarkable result of this chapter was the demonstration of the potential pro-fibrotic

role of miR-365a-3p in HSCs. I have proposed a regulatory mechanism by which miR-365a-

3p may increase collagen synthesis and could contribute to understand the development of

hepatic fibrosis, not just in CFLD but potentially in all chronic liver disease.

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CHAPTER 7

General discussion

230


The paragraph summarising challenges when working with circulatory miRNAs (Chapter 4)

was published in the following literature review:


MicroRNAs and Their Potential as Biomarkers in Paediatric Liver Disease. International

Journal of Molecular Sciences 2016; 17:1795.

Diego A. Calvopina researched the content, wrote the manuscript and designed tables and

figures.

Miranda A. Coleman, Peter J. Lewindon and Grant A. Ramm edited, revised and approved

the content of the literature review.

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CFLD is a significant cause of morbidity and mortality in children with CF. CFLD

manifestations range from benign non-specific elevation of liver enzymes to portal

hypertension, end-stage liver cirrhosis and premature death. Thus, CFLD is a high impact

disease in paediatric practice as evidenced by the documented increased childhood

mortality by all causes in children with CFLD compared to those who do not have liver

disease (64, 230). In fact, others have shown that liver disease is predictive of increased

risk of death and lung transplantation (62, 475). Moreover, CFLD impacts the quality of life

and overall survival of affected children due to complications such as steatosis, nutrition,

altered lipid metabolism, constipation or varices among others (59, 476, 477). The only

treatment for liver disease in patients with CF is ursodeoxycholic acid (UDCA), an

endogenous hydrophilic bile acid, which is thought to delay CFLD progression by promoting

bile flow in the bile ducts and thus improving liver histology (16, 478). However, there is no

evidence that UDCA alters the natural progression of the disease or improves survival (479,

480). Additional nutritional management is also recommended to control the essential fatty

acid deficiencies and low body-mass index common in CFLD (475). When multilobular

cirrhosis and portal hypertension are established, liver transplantation is the only remaining

therapeutic option that reflects a survival benefit (481, 482). The most appropriate time to

perform liver transplantation is a topic of controversy with some authors recommending

transplantation at an early disease stage before lung function is compromised (483, 484). In

contrast, others suggest liver transplantation to be reserved for cases with hepatic

decompensation (485).

Overall, CFLD is a complex disease with multiple and subtle manifestations which lacks a

clear definition. This lack of definition and the use of non-specific methods for detection

make the diagnosis of CFLD challenging. Furthermore, monitoring and assessment of

disease severity by liver biopsy is not only invasive and associated with severe

complications, but due to the focal distribution of CFLD lesions, can be unreliable. Thus,

finding non-invasive alternative modalities to diagnose and monitor CFLD is a clinical

imperative. The implementation of these new methods have the potential to identify CF

children at risk of developing CFLD and more severe complications such as portal

hypertension. It could also assist planning strategies for disease management including the

frequency of clinical assessment or choosing the best time for liver transplantation.

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In this thesis, I explored the potential of circulatory miRNAs and Supersonic shearwave

elastography (SSWE) as novel non-invasive methods for the diagnosis of CFLD in children.

These modalities are not only non-invasive but also could be used as bedside diagnostic

tools in the clinical setting. Moreover, miRNAs have the potential to be used as a new

therapeutic avenue due to their function in regulating gene expression in disease. Although

several studies have reported the use of circulatory miRNAs and SSWE in various different

chronic liver diseases, studies investigating their potential utility in paediatric CFLD are

scarce. Chapter 3 demonstrated that SSWE is capable of differentiating liver disease in CF

children with a better diagnostic accuracy than other methods such as the validated serum

biomarker APRI (124). Interestingly, the diagnostic performance of SSWE was increased

when combined with APRI which suggests a synergistic effect when these two modalities

are combined capturing patients that would not be detected if these tests were used

separately. There are no published studies of SSWE in CFLD, however, when compared to

studies performed in CFLD using Fibroscan, similar outcomes were observed including

AUROCs, sensitivity, specificity and cut-off values. Although this study did not include liver

biopsy validated fibrosis staging within the CFLD group, SSWE was also capable to

distinguish between no/mild/moderate fibrosis and severe fibrosis/cirrhosis children. The

fact that SSWE was able to discriminate disease severity when two different classification

methods such as clinical assessment and APRI-based classification were used shows the

robust nature of this technique. Future studies performed in larger cohorts including liver

biopsy validated CFLD children would strength the findings reported in this thesis. It would

also be interesting to explore the diagnostic accuracy of SSWE when combined with

biomarkers other than APRI such as miRNAs. One of the most clinically useful aspects of

SSWE lays in its use as a bedside diagnostic tool. In Chapter 3, I have also shown that

SSWE can be used to monitor liver stiffness progression over time. This is particularly

important as it gives a frame of reference for clinicians to assess if the liver is deteriorating

and how rapidly, providing vital information to manage the disease in a better way. Despite

being able to be used as a diagnostic method for CFLD in children, SSWE does not improve

the diagnostic accuracy of other currently used methods and thus when used alone would

not be an ideal test to replace liver biopsy.

Therefore, Chapter 4 explored the use of serum miRNAs as biomarkers to diagnose and

assess the progression of CFLD. I have shown that serum of CFLD children have a

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distinctive miRNA signature compared to those CF children without liver disease.

Furthermore, a panel combining serum levels of miR-365a-3p, miR-142-3p, let-7g-5p and

APRI outperforms biomarkers that have been proposed until now for the detection of CFLD.

In addition, in Chapter 4, I showed that miR-18a-5p was able to stratify fibrosis severity in

CFLD children that had fibrosis validated by liver biopsy. miR-18a-5p showed the potential

to discriminate between fibrosis grouped in different combinations and between intermediate

degrees of fibrosis when assessed using several statistical methods. This answers the

clinical need of continuous assessment of disease progression, providing key information

that can be objectively used to decide patients that could benefit from liver transplantation.

As with SSWE, miRNAs benefit from the use in combination with APRI and therefore, future

studies combining miRNAs, SSWE and other biomarkers could provide even greater levels

of diagnostic accuracy.

It is worth noting that the study of circulatory miRNAs has some inherent challenges. Pre-

analytical variation can affect the quantification of miRNAs. During sample collection it is

essential to remove cellular components such as erythrocytes, leukocytes and platelets from

blood. In particular, erythrocytes express high levels of miR-451 and miR-16, which

correlates with haemolysis in plasma samples (208, 486). Heparin, a traditional

anticoagulant used in plasma, shows a dose dependent inhibitory effect by binding to

calcium and magnesium used in PCR reactions (487, 488). Although miRNAs are resilient

in serum or plasma, repeated freeze-thaw cycles decreases detectable miRNA levels and

should be avoided (489). One major challenge is the relatively low abundance of miRNA in

plasma and serum along with high levels of proteins and lipids which can interfere with the

isolation process. Originally, TRIzol was used as an effective method for small RNA

isolation, however, phenol contamination and difficulty with pellet resuspension regularly

interfered with the process. These problems have been partially solved by method

modifications including small RNA binding columns common to multiple commercial kits.

Traditional RNA quantification methods, such as spectrophotometer or capillary

electrophoresis, are unable to assess size or quality of the isolated miRNA accurately, due

to the low yield and limits of detection. As such, this study demanded extensive optimization

tailored specifically to the disease and application which resulted in the methodology

detailed at length in Chapter 2. Future studies will need to consider these methodological

differences (e.g. NGS design or choice of endogenous reference miRNAs to normalise

234

circulatory expression) when assessing and comparing the clinical utility of these findings.

As with SSWE, circulatory miRNAs are not yet developed or accepted enough to replace

liver biopsy, which is still the gold standard to assess CFLD (although less used every time).

However, the potential combination of these new modalities with physical examination and

imaging techniques could provide promising tools that may be useful in the clinical setting.

Validation studies in larger cohorts would be required before the three miRNA + APRI panel

described in this thesis were found to be acceptable for routine clinical practise. The findings

of this chapter would be greatly strengthened by studies exploring questions that have not

been answered here. For instance, identifying the origin of the described miRNAs would

confirm their specificity as liver disease specific markers and thus of potential therapeutic

use. It is not possible to do these studies in liver tissue as it is unethical to perform liver

biopsy in CF children with no liver disease where there is no clinical benefit. Alternatively,

animal models could be used; however, there are no well-defined animal models for CFLD.

Thus, the use of cultured liver cells is the best option to explore the generic role of miRNAs

in chronic liver disease and how they regulate fibrogenesis and its progression.

miRNAs described in Chapter 4 were explored in more depth in Chapters 5 and 6 using

cell lines of LPCs (BMOL) and HSCs (LX2), which are two crucial liver cell populations

associated with wound repair and fibrogenesis in all chronic liver disease. In Chapter 5 I

used an experimental method of miRNA cell transfection to pulldown mRNA targets,

successfully identifying a long list of potential miRNA targets in both cell lines. miRNAs are

known to regulate cell functions; thus, it was not surprising to find via pathway analysis that

key cellular and molecular processes involved in the LPCs and HSCs response to fibrosis

were affected by the transfected miRNAs. Due to the large number of potential targets

identified, only a few were selected for further validation. Several of the remaining potential

targets were not selected and provide a comprehensive list of candidates to be studied by

others in the future. This opens a myriad of possibilities, especially in LPCs where no miRNA

work has previously been conducted.

The work on miRNA targets was extended in Chapter 6. Selected targets were validated

and functional assays performed in BMOL and LX2 cells to determine the role of these

miRNAs in the biology of LPCs and HSCs respectively. qRT-PCR showed the decrease

235

mRNA expression of some of the selected genes confirming them as putative miRNA

targets. miRNAs are known to regulate gene expression by either inducing mRNA

degradation or by inhibiting its translation. In mammals, about 20% of miRNA-induced

repression is due to translation inhibition (490). Thus, some of the genes that did not show

decreased expression might be still miRNA targets regulated by the translation inhibitory

effect of miRNAs. Several mRNAs were confirmed as definitive targets for let7g-5p and miR-

365a-3p in HSCs by using luciferase reporter assays, which demonstrated a direct link

between the specific miRNA and its binding site in the 3’UTR region of the target mRNA.

Future experiments could be performed on those additional potential mRNA targets that did

not show decreased expression in response to miRNA transfection using luciferase reporter

assays, which may assist in identifying new regulatory mechanistic roles of these miRNAs

in both LPCs and HSCs. I have also shown the effect that these miRNAs appear to have on

LPC differentiation and HSC activation, both crucial events in fibrogenesis following chronic

liver injury. Further investigation is required to elucidate the complex mechanisms by which

miRNAs exert their effects on these processes. An important contribution of this chapter is

the demonstration of the potential pro-fibrotic role of miR-365a-3p in regulating the collagen

synthetic pathway in HSCs. This effect needs to be confirmed and expanded; however, it

offers a potential mechanistic process via which molecules that can be targeted for a long

awaited therapeutic intervention not only in CFLD but also in other fibrosing liver diseases.

Interestingly, it was observed that the miRNAs targeted several members of the same

pathway, as demonstrated with the impact of miR-365a-3p on the collagen synthetic

pathway in HSCs. This is encouraging as others have shown that miRNAs regulate specific

cellular processes by concomitant repression of gene members of the same pathway (491,

492) confirming the findings of this chapter. It was also observed that miRNAs can bind to

targets promoting opposite effects. For example, let7g-5p and miR-365a-3p showed a pro-

fibrotic effect in HSCs which translates to the increased synthesis of collagen, however, SP1

(a pro-fibrogenic transcription factor) is also a direct target of both miRNAs. This potential

dual function of miRNA has been described previously (493-495) and has been proposed

that the activation or inhibitory effect of the miRNA depends on the relative expression levels

of the targets (496). An alternative and not exclusive explanation is that SP1 is a strong

transcription factor that could easily compensate the subtle regulation by miRNA to the point

that it could even mask the contribution of other pathway members. Future studies exploring

236

mechanisms and conditions in which this regulation occurs could lead to advances in anti-

fibrotic therapies.

A clear limitation of this chapter is the use of transient transfection. miRNA cell uptake was

not constant across the different experiments which translated as high variability between

replicates and inconsistent miRNA effects over time. These problems were mostly overcome

with the use of cells stably overexpressing miRNA as observed in LX2 cells transduced with

lentiviral miRNA vectors. The results presented in this chapter would benefit from repeating

the same experimental settings (i.e. qRT-PCR validation, functional assays, western blot)

using this stable transfection system to produce miRNA-transduced BMOL and LX2 cells.

An additional benefit of using miRNA transduced cells is that it closely replicates

physiological conditions. Studies have shown that the range by which most endogenous

miRNAs fluctuate to modulate gene expression is 20-30% (497). Overexpression of miRNA,

generally significantly greater than physiological levels, have the potential to generate false

positive results (498). In Chapter 6, I have shown miRNA overexpression that ranges from

16-40 fold using lentiviral transduction compared to the 100-300 fold increase when using

transient transfection.

In conclusion, this thesis has successfully demonstrated the utility of SSWE and serum

miRNAs as non-invasive methods for the diagnosis of CFLD and stratification of liver

disease severity. Specifically, I have shown that SSWE is a feasible and useful bedside tool

to discriminate CFLD especially when combined with other modalities. I have also shown

that CFLD children have a specific serum miRNA signature compared to CF children with

no liver disease that is more accurate than other currently used methods to identify liver

disease and monitor liver fibrosis progression. Finally, I have identified targets of these

miRNAs which play an important role in the liver’s fibrogenic response mediated by LPCs

and HSCs, which has a potential role not just in CFLD but in all fibrosing liver disease. It has

been hypothesized that cross-talk between LPCs and HSCs may drive the ductular reaction

and fibrogenesis in chronic liver disease (325, 388), therefore I selected these two liver cells

to investigate the potential mechanistic role of miRNAs, identified as being modified in

CFLD, in the development of fibrosis. The identification and functional work on these targets

237

may reveal the potential to discover novel therapeutic interventions for the future treatment

of CFLD as well as other chronic liver diseases.

238

CHAPTER 8

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CHAPTER 9

Appendices

20 April 2018

Phone: 07 3362 0117

Fax: 07 3362 0109 E-mail: [email protected]

Investigators: A

Surname

Ramm

Title

Prof

Initials

G

Dear Prof Ramm,

HREC Reference number: P1083 Project title: Mechanisms of Hepatic Fibrogenesis Associated with Paediatric

Cholestatic Liver Diseases The annual report for this project submitted on 5 March 2018 was reviewed by the QIMR Berghofer-HREC on 20 April 2018.

The study meets the requirements of the NHMRC National Statement and the documents listed below are approved:

5 Mar 2018 – P1083: 2017 Human Annual Report

Renewed approval of this project is valid from 20 April 2018 to 19 April 2019 subject to the following conditions being met:

This QIMR Berghofer-HREC Approval is subject to governance approval/s from all collaborating institutions.

The Principal Investigator will submit an annual report to the QIMR Berghofer-HREC by no later than 5 weeks prior to approval expiry.

The Principal Investigator will immediately report anything that might warrant review of ethical

approval of the project.

The Principal Investigator will notify the QIMR Berghofer-HREC of any event that requires a modification to the protocol, including site changes, or other project documents and submit any required amendments in accordance with the ToR provided by the HREC. These instructions can be found at http://www.qimrberghofer.edu.au/about-us/ethics-committees/qimr-berghofer-human-research-ethics-committee/

The Principal Investigator will submit any necessary reports related to the safety of research participants in accordance with QIMR Berghofer-HREC policy and procedures. These instructions can be found at http://www.qimrberghofer.edu.au/about-us/ethics-committees/qimr-berghofer-human-research-ethics-committee/

The Coordinating Principal Investigator will notify the QIMR Berghofer-HREC of any plan to extend the duration of the project past the approval period listed above and will submit any associated required documentation.

The Coordinating Principal Investigator will notify the QIMR Berghofer-HREC of his or her inability to continue as Coordinating Principal Investigator including the name of and contact information for a replacement.

Should you wish to discuss this matter, please contact the HREC Secretariat at [email protected].

The QIMR-Berghofer HREC wishes you every continued success in your research.

Yours sincerely,

Ian Wilkey QIMR-Berghofer HREC Chair (NHMRC HREC #EC00278)

This HREC is constituted and operates in accordance with the National Health and Medical Research Council’s (NHMRC) National Statement on Ethical Conduct in Human Research (2007). The processes used by this HREC to review multi-centre research proposals have been certified by the National Health and Medical Research Council.

http://www.qimrberghofer.edu.au/about-us/ethics-committees/qimr-berghofer-human-research-ethics-committee/




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