New Microfluidic Technologies for Studying Histone ...

New Microfluidic Technologies for Studying

Histone Modifications and Long Non-Coding RNA Bindings

Yuan-Pang Hsieh

Dissertation submitted to the faculty of the Virginia Polytechnic Institute and

State University in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

In

Chemical Engineering

Chang Lu, Chair

Liwu Li

Aaron S. Goldstein

Rong Tong

May 1, 2020

Blacksburg, VA

Keywords: Microfluidics, Epigenomics, Precision Medicine, Chromatin

Immunoprecipitation, Histone Modification, Long Non-coding RNA Interaction,

Next Generation Sequencing, NGS

ii

New Microfluidic Technologies for Studying Histone Modifications and Long Non-Coding RNA Bindings

Yuan-Pang Hsieh

Abstract

Previous studies have shown that genes can be switched on or off by age,

environmental factors, diseases, and lifestyles. The open or compact structures of

chromatin is a crucial factor that affects gene expression. Epigenetics refers to

hereditary mechanisms that change gene expression and regulations without

changing DNA sequences. Epigenetic modifications, such as DNA methylation,

histone modification, and non-coding RNA interaction, play critical roles in cell

differentiation and disease processes. The conventional approach requires the use

of a few million or more cells as starting material. However, such quantity is not

available when samples from patients and small lab animals are examined.

Microfluidic technology offers advantages to utilize low-input starting material and for

high-throughput.

In this thesis, I developed novel microfluidic technologies to study epigenomic

regulations, including 1) profiling epigenomic changes associated with LPS-induced

murine monocytes for immunotherapy, 2) examining cell-type-specific epigenomic

changes associated with BRCA1 mutation in breast tissues for breast cancer

treatment, and 3) developing a novel microfluidic oscillatory hybridized ChIRP-seq

assay to profile genome-wide lncRNA binding for numerous human diseases.

We used 20,000 and 50,000 primary cells to study histone modifications in

inflammation and breast cancer of BRCA1 mutation, respectively. In the project of

whole-genome lncRNA bindings, our microfluidic ChIRP-seq assay, for the first time,

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allowed us to probe native lncRNA bindings in mouse tissue samples successfully.

The technology is a promising approach for scientists to study lncRNA bindings in

primary patients. Our works pave the way for low-input and high-throughput

epigenomic profiling for precision medicine development.

謝元榜

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New Microfluidic Technologies for Studying Histone Modifications and Long Non-Coding RNA Bindings

Yuan-Pang Hsieh

General Audience Abstract

Traditionally, physicians treat patients with a one-size-fits-all approach, in

which disease prevention and treatment are designed for the average person. The

one-size-fits-all approach fits many patients, but does not work on some. Precision

medicine is launched to improve the low efficiency and diminish side effects, and all

of these drawbacks are happening in the traditional approaches. The genomic,

transcriptomic, and epigenomic data from patients is a valuable resource for

developing precision medicine.

Conventional approaches in profiling functional epigenomic regulation use

tens to hundreds of millions cells per assay, that is why applications in clinical

samples are restricted for several decades. Due to the small volume manipulated in

microfluidic devices, microfluidic technology exhibits high efficiency in easy

operation, reducing the required number of cells, and improving the sensitivity of

assays. In order to examine functional epigenomic regulations, we developed novel

microfluidic technologies for applications with the small number of cells.

We used 20,000 cells from mice to study the epigenomic changes in

monocytes. We also used 50,000 cells from patients and mice to study epigenomic

changes associated with BRCA1 mutation in different cell types. We developed a

novel microfluidic technology for studying lncRNA bindings. We used 100,000-

500,000 cells from cell lines and primary tissues to test several lncRNAs.

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Traditional approaches require 20-100 million cells per assay, and these cells

are infected by virus for over-producing specific lncRNA. However, our technology

just needs 100,000 cells (non-over-producing state) to study lncRNA bindings. To

the best of our knowledge, this is the first allowed us to study native lncRNA bindings

in mouse samples successfully. Our efforts in developing microfluidic technologies

and studying epigenomic regulations pave the way for precision medicine

development.

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Acknowledgements

I would not have been able to accomplish my research and this dissertation

without the support from my family, friends, and professors. They play pivotal roles in

my life in making me the person who I am today. They provide love, help, support,

assistance, and advice to me not only in my research, but in my life. I would like to

express my sincere gratitude to all of them.

Most of all, I would like to thank my advisor, Dr. Chang Lu, for his kind help

and support. He educates knowledge and shares his personal life experience as not

only a teacher, but a friend and mentor to me. His enthusiastic, motivated, and

vigorous personality inspires me to explore the scientific field. He provided brilliant

advices when I faced difficulty in my research. He always generously offered his

praises and encouragements to me. He provided me the room to grow and gave me

the chance to examine three mechanisms of epigenomics. The experimental

experience gave me the comprehensive understanding in epigenomic regulations.

He paid tremendous attention and help to me for getting a job. It is my pleasure to

work with him during my doctoral research.

I would like to thank my committee members, Dr. Liwu Li, Dr. Aaron

Goldstein, and Dr. Rong Tong for their insightful advice. I had some collaborations

with Dr. Liwu Li in inflammatory research. His knowledge in inflammation and

immunoscience led me to accomplish my project of profiling epigenomic changes

associated with LPS-induced murine monocytes. Dr. Aaron Goldstein gave me

brilliant advice in my preliminary examination based on his professional background

in biomedical and tissue engineering. Dr. Rong Tong proffered precious guidance in

culturing cells, virus infection, and mouse tissue dissection.

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I am really grateful to gain assistance from my collaborators, Prof. Liwu Li, Dr.

Ruoxi Yuan, Dr. Shuo Geng, Dr. Yao Zhang (from Virginia Tech), Prof. Rong Li, Dr,

Xiaowen Zhang (from George Washington University), and Prof. Zhen Chen (from

City of Hope). Your valuable help is very important in my works.

I cannot express my appreciation to the Department of Chemical Engineering

at Virginia Tech. The Department Head, Prof. David Cox, and all office staffs provide

beneficial help during my Ph.D. program.

I really enjoy my Ph.D. journey due to the fun and support from members of

Lu lab: Dr. Zhenning Cao, Dr. Chen Sun, Dr. Sai Ma, Dr. Yan Zhu, Dr. Travis W.

Murphy, Dr. Mimosa Sarma, Chengyu Deng, Qiang Zhang, Bohan Zhu, Lynette B.

Naler, Zhengzhi Liu, Zirui Zhou, Yining Hao, and Zeren Sheng. I would like to

especially thank Dr. Yan Zhu, Dr. Travis W. Murphy, and Lynette B. Naler for their

kind help. Dr. Yan Zhu guided me the knowledge in epigenomics and instructed me

in conducting MOWChIP-seq process. Dr. Travis W. Murphy and Lynette B. Naler

gave me numerous help in data analysis and English writing.

Last but not least, I am deeply grateful to my parents and best friends, Tzu-

Yin Hsieh, Hsiu-Chun Chen, Yu-Min Chang, and Machi, for all their continuous love

and support throughout my life. They have always been there for me and provided

their endless love and support for me in my whole life. Thank you, my parents, for

giving me strength to reach for the stars and chase my dream.

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Table of Contents

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

General Audience Abstract ..................................................................................... iv

Acknowledgements .................................................................................................. vi

Table of Contents ................................................................................................... viii

List of Figures ........................................................................................................... xi

List of Tables .......................................................................................................... xiii

List of Abbreviations .............................................................................................. xiv

Chapter 1 Overview ................................................................................................... 1

Chapter 2 Introduction .............................................................................................. 4

2.1 Epigenetics ................................................................................................................... 4 2.1.1 Histone modification ............................................................................................................. 4 2.1.2 Long non-coding RNA (lncRNA) .......................................................................................... 7

2.2 Chromatin immunoprecipitation (ChIP) ..................................................................... 8 2.2.1 Conventional approaches ..................................................................................................... 9 2.2.2 Microfluidic oscillatory washing-based ChIP (MOWChIP) .................................................. 10 2.2.3 SurfaceChIP ....................................................................................................................... 11 2.2.4 Low-Input Fluidized-Bed Enabled ChIP (LIFEChIP) .......................................................... 12

2.3 Chromatin isolation by RNA purification (ChIRP) .................................................. 13 2.3.1 Capture hybridization analysis of RNA targets (CHART) ................................................... 16 2.3.2 RNA antisense purification (RAP) ...................................................................................... 16

2.4 Next Generation Sequencing (NGS) ......................................................................... 18

2.5 ChIP-seq ..................................................................................................................... 18

2.6 Microfluidic device .................................................................................................... 20

2.7 Precision medicine .................................................................................................... 21

Chapter 3 Study of epigenomic changes associated with LPS-induced murine monocytes ............................................................................................................... 24

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3.1 Project Summary ....................................................................................................... 24

3.2 Introduction ................................................................................................................ 24

3.3 Results and discussion ............................................................................................. 26

3.4 Materials and methods .............................................................................................. 38

3.5 Conclusions ............................................................................................................... 45

Chapter 4 Profiled cell-type-specific epigenomic changes associated with BRCA1 mutation in breast tissues using MOWChIP-seq .................................... 47

4.1 Project Summary ....................................................................................................... 47

4.2 Introduction ................................................................................................................ 48

4.3 Results and discussion ............................................................................................. 50 4.3.1 BRCA1 mutation in human samples .................................................................................. 50

4.3.1-1 Pearson’s correlation of human sequencing data ....................................................... 51 4.3.1-2 Differential H3K27ac peak regions ............................................................................. 53 4.3.1-3 Examination of enhancers of patient samples ........................................................... 59 4.3.1-4 Motif analysis at enhancers of patient samples ......................................................... 60 4.3.1-5 Cell-type-specific transcription factors ........................................................................ 61

4.3.2 Profiling mouse samples ................................................................................................... 64


4.5 Conclusions ............................................................................................................... 76

Chapter 5 Microfluidic oscillatory hybridized ChIRP-seq assay to profile genome-wide lncRNA binding ............................................................................... 77

5.1 Project Summary ....................................................................................................... 77

5.2 Introduction ................................................................................................................ 78

5.3 Results and discussion ............................................................................................. 80 5.3.1 Development of microfluidic oscillatory hybridized ChIRP technology ............................... 80 5.3.2 Adenovirus transfection and infection ................................................................................ 82 5.3.3 Profiling LEENE using pooling and splitting probes with microfluidic ChIRP technology ... 86 5.3.4 Profiling BY707159.1 with microfluidic ChIRP technology ................................................. 92 5.3.5 Profiling native HOTAIR with microfluidic ChIRP technology ............................................. 96


5.5 Conclusions and Future work ................................................................................ 104

Chapter 6 Summary and Outlook ........................................................................ 105

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References ............................................................................................................. 108

Publications ........................................................................................................... 127

Journal Articles .............................................................................................................. 127

Conference Presentation .............................................................................................. 128

Appendix A ............................................................................................................ 129

Microfluidic oscillatory hybridized ChIRP-seq protocol ............................................ 129

Summary of sequencing data information .................................................................. 144

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List of Figures

Fig. 2.1-1 Epigenetic mechanisms: DNA methylation, histone modification and non-

coding RNA (ncRNA) bindings .................................................................................... 5

Fig. 2.1-2 Chromosomes composed of condensed histones wrapped in DNA ........... 6

Fig. 2.1-3 Cell differentiation and self-renewal are regulated by lncRNAs .................. 8

Fig. 2.2-1 Chromatin immunoprecipitation .................................................................. 9

Fig. 2.2-2 Overview of the MOWChIP process ......................................................... 11

Fig. 2.2-3 Overview of SurfaceChIP .......................................................................... 12

Fig. 2.2-4 Overview of LIFEChIP .............................................................................. 13

Fig. 2.3-1 Overview of the ChIRP process ................................................................ 14

Fig. 2.3-2 Even and odd probe groups are used in ChIRP-seq ................................ 15

Fig. 2.3-3 Schematic of three approaches for profiling the genomic localization of

lncRNAs .................................................................................................................... 17

Fig. 2.5-1 Overview of ChIP-seq ............................................................................... 20

Fig. 2.7-1 The strategy of precision medicine compares with conventional one-size-

fits-all approach ......................................................................................................... 22

Fig. 3.2-1 The inflammatory responses with high-dose and low-dose LPS .............. 25

Fig. 3.3-1 Fragment size for ChIP-seq ...................................................................... 27

Fig. 3.3-2 Fragment size among three conditions ..................................................... 27

Fig. 3.3-3 Optimization of MOWChIP with different concentrations of antibody

(H3K27ac) ................................................................................................................. 28

Fig. 3.3-4 Optimization of MOWChIP with different oscillatory washing pressure .... 29

Fig. 3.3-5 Optimization of MOWChIP with different oscillatory washing time ........... 29

Fig. 3.3-6 MOWChIP sequencing data of mouse samples visualized in IGV ........... 30

Fig. 3.3-7 Pearson correlation matrix of MOWChIP sequencing data ...................... 31

Fig. 3.3-8 Epigenetic regulation of Apoe (left) and Bcl3 (right) ................................. 34

Fig. 3.3-9 Epigenetic regulations of C1qa, C1qb, C1qc ............................................ 34

Fig. 3.3-10 Epigenetic regulation of Fpr1 and Fpr2 ................................................... 34

Fig. 3.3-11 Gene ontology (GO) ................................................................................ 35

Fig. 3.3-12 Venn diagrams of enhancers and super enhancers ............................... 36

Fig. 3.3-13 Motif analysis at enhancers .................................................................... 38

Fig. 4.2-1 Four different cell types in breast tissues .................................................. 49

Fig. 4.3-1 ChIP-seq data from H3K27ac of human breast samples .......................... 52

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Fig. 4.3-2 ChIP-seq data from H3K4me3 of human breast samples ........................ 53

Fig. 4.3-3 Differential H3K27ac peak regions ........................................................... 54

Fig. 4.3-4 MOWChIP sequencing data of human samples, visualized in IGV .......... 55

Fig. 4.3-5 Different histone modification pattern in human basal cells ...................... 57

Fig. 4.3-6 Different histone modification pattern in human luminal progenitor cells .. 57

Fig. 4.3-7 Different histone modification pattern in human mature luminal and stromal

cells ........................................................................................................................... 58

Fig. 4.3-8 Different histone modification pattern in human stromal cells ................... 58

Fig. 4.3-9 Enhancers ................................................................................................. 59

Fig. 4.3-10 Motif analysis at enhancers .................................................................... 61

Fig. 4.3-11 Cell-type-specific transcription factors in NC and their enrichment in MUT

.................................................................................................................................. 63

Fig. 4.3-12 ChIP-seq data from mouse samples ....................................................... 65

Fig. 4.3-13 Mouse H3K27ac data in basal cells (8-month old mice), luminal

progenitors (8-month old mice) and young luminal progenitor (8-week old mice) .... 66

Fig. 5.3-1 The schematic of microfluidic oscillatory hybridized ChIRP ...................... 81

Fig. 5.3-2 Overview of adenovirus transfection system ............................................ 83

Fig. 5.3-3 Adenovirus transfection in HEK293 cells .................................................. 85

Fig. 5.3-4 Adenovirus infection in HUVECs .............................................................. 86

Fig. 5.3-5 Visualization of normalized sequencing data with pooling probes in IGV

(LEENE) .................................................................................................................... 89

Fig. 5.3-6 Visualization of normalized sequencing data with splitting probes in IGV

(LEENE) .................................................................................................................... 90

Fig. 5.3-7 The comparison of LEENE binding among Ad-GFP-LEENE, Ad-GFP, and

Control ....................................................................................................................... 92


(BY707159.1) ............................................................................................................ 94

Fig. 5.3-9 The comparison of BY707159.1 binding between wild type and

BY707159.1 knockout mouse ................................................................................... 95


(HOTAIR) .................................................................................................................. 97

Fig. 5.3-11 The comparison of HOTAIR binding between Chu’s and our data ......... 99

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List of Tables

Table 3.3-2 Epigenomic regulations among different conditions .............................. 33

Table 3.3-1 Summary of MOWChIP sequencing data information ......................... 144

Table 4.3-1 Summary of MOWChIP sequencing data (H3K27ac, non-carrier)

information in human samples ................................................................................ 144

Table 4.3-2 Summary of MOWChIP sequencing data (H3K27ac, carrier) information

in human samples ................................................................................................... 146

Table 4.3-3 Summary of MOWChIP sequencing data (H3K4me3, non-carrier)

information in human samples ................................................................................ 147

Table 4.3-4 Summary of MOWChIP sequencing data (H3K4me3, carrier) information

in human samples ................................................................................................... 147

Table 4.3-5 Summary of MOWChIP sequencing data (H3K27ac, wild type)

information in mouse samples ................................................................................ 148

Table 4.3-6 Summary of MOWChIP sequencing data (H3K27ac, knockout)


Table 4.3-7 Summary of MOWChIP sequencing data (H3K4me3, wild type)


Table 4.3-8 Summary of MOWChIP sequencing data (H3K4me3, knockout)


Table 5.3-1 Summary of ChIRP-seq in HUVECs with pooling all probes ............... 152

Table 5.3-2 Summary of ChIRP-seq in HUVECs with splitting probes ................... 153

Table 5.3-3 Summary of ChIRP-seq from wild type and BY707159.1 knockout mice

................................................................................................................................ 154

Table 5.3-4 Summary of HOTAIR ChIRP-seq from Chu and our data ................... 154

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List of Abbreviations

3’ UTR 3’ untranslated region

5’ UTR 5’ untranslated region

ATAC-seq Assay for Transposase-Accessible Chromatin-seq

bp base pair

CHART Capture Hybridization Analysis of RNA Targets

ChIP Chromatin ImmunoPrecipitation

ChIP-seq Chromatin ImmunoPrecipitation followed by sequencing

ChIRP Chromatin Isolation by RNA Purification

DI water Deionized water

DMSO Dimethyl sulfoxide

DNA Deoxyribonucleic acid

dNTP Nucleoside triphosphate

DRIP-seq R-loop-specific DNA-RNA ImmunoPrecipitation-seq

EDTA Ethylenediaminetetraacetic acid

ENCODE Encyclopedia of DNA Elements

FACS Fluorescence-activated cell sorting

FBS Fetal bovine serum

GFP Green fluorescent protein

GO Gene Ontology

H3K4me3 tri-methylation at the 4th lysine residue of the histone H3 protein

H3K27ac acetylation at the 27th lysine residue of the histone H3 protein

H3K27me3 tri-methylation at the 27th lysine residue of the histone H3 protein

LIFEChIP Low-Input Fluidized-bed Enabled ChIP

lncRNA long non-coding RNA

xv

LPS Lipopolysaccharide

MNase Micrococcal Nuclease

MOWChIP Microfluidic Oscillatory Washing-based ChIP

NChIP Native ChIP

ncRNA non-coding RNA

NGS Next Generation Sequencing

nt nucleotide

PBS Phosphate-buffered saline

PCR Polymerase Chain Reaction

PDMS Polydimethylsiloxane

PIC Protease inhibitor cocktail

PMSF Phenylmethylsulfonyl fluoride

PS Penicillin streptomycin

PTM Post-Translational Modification

qPCR quantitative PCR

qRT-PCR quantitative Reverse Transcription-PCR

RAP RNA Antisense Purification

RFU Relative fluorescence units

RNA Ribonucleic acid

mRNA messenger RNA

rRNA ribosomal RNA

tRNA transfer RNA

SDS Sodium dodecyl sulfate

ssDNA Single stranded DNA

Tris Tris(hydroxymethyl)aminomethane

xvi

TSS Transcription Start Site

XChIP Crosslinked ChIP

1

Chapter 1 Overview

Previous studies have shown that genes can be switched on or off by age,

environmental factors, diseases, and lifestyle. The open or compact structure of

chromatin is a crucial factor that impacts on gene regulation. Epigenetics refers to

hereditary mechanisms that change gene expression and regulation without altering

DNA sequences. Epigenetics plays critical roles in cell differentiation1 and disease

processes2. Common epigenetics mechanisms include histone modification, DNA

methylation and non-coding RNA interactions.

The gold-standard method for studying histone modification and transcription

factor binding is chromatin immunoprecipitation coupled with next generation

sequencing (ChIP-seq)3. The limitations of conventional ChIP include the

requirement of a large number of cells as starting materials (e.g. 106 cells for ChIP-

qPCR and 107 cells for ChIP-seq)4,5. The application of ChIP-seq assays in clinical

studies is significantly restricted because primary patient samples rarely provide

such a large number of cells. Employing microfluidic devices allows this requirement

to be lowered to a few hundred cells, thus more primary patient samples can be

tested. Recent advances in DNA sequencing tools in combination with microfluidic

devices, provide a more comprehensive view of epigenetic regulations.

Non-coding RNAs (ncRNAs) are functional and informational RNAs

transcribed from DNA, but do not further encode proteins6. Recent studies have

suggested that histone modification and DNA methylation are regulated by ncRNAs7-

11. Long non-coding RNAs (lncRNAs) are ncRNAs more than 200 nucleotides in

length. They play crucial roles in cancers and human diseases, such as HORTAIR

participating in breast cancer formation12. They also have important functions in

regulating chromatin, such as dosage compensation and gene expression13-17.

2

Chromatin isolation by RNA purification (ChIRP) was developed by Chu et al. in

201118 and this technology provides scientists an approach to profile genome-wide

mapping of long non-coding RNA interaction. The requirement of 20-100 million cells

leaves this technology incapable of examining primary patient samples.

In this thesis, I focus on profiling epigenomic regulation of histone

modifications in inflammation and breast cancer and developing a new, low input

microfluidic technology to examine whole-genome lncRNA bindings. The work is

divided into three projects.

For Project 1, I collected primary monocytes from mice as target cells.

Lipopolysaccharide (LPS) was used to activate cellular responses and the resulting

histone modification profile was examined with our previously developed microfluidic

device - microfluidic oscillatory washing-based ChIP (MOWChIP)19. Twenty

thousand cells were used per assay and histone mark H3K27ac was used to map

the genome-wide histone modification. We compared H3K27ac binding level among

three conditions (no treatment, low-dose LPS, and high-dose LPS treatment), and

different epigenetic regulations (e.g. upregulation and downregulation) were

detected. Our results showed the relationship between histone modification of

H3K27ac and different treating conditions in monocytes.

For Project 2, we investigated epigenetic regulations in mutant and normal

breast tissues. Three different cell types were isolated from two murine strains (wild

type (Brca1f/f, BFF) and BRCA1 knockout (BKO)), respectively; four cell types were

isolated from each human sample (BRCA1 mutation carrier and non-carrier, all

human samples were cancer-free). Fifty thousand cells were used per assay for

ChIP-seq with two histone marks (H3K4me3 and H3K27ac). Different epigenetic

regulations between mutant and normal human samples were identified. Similar

3

result was observed from a published paper20 that used western blot and quantitative

reverse transcription-PCR (qRT–PCR) analysis. To the best of our knowledge, this is

the first time ChIP-seq was performed on four different breast cell types from the

same patient to confirm the existence and importance of epigenomic regulations in

histone modifications. Sequencing data from mouse model showed 40% overlap with

transcription factors enriched in human samples. The homozygous exon 11 knockout

mouse is not a good epigenomic model for BRCA1 mutation in human research.

For Project 3, we designed a novel microfluidic technology to map genome-

wide profile of long non-coding RNA interactions. We used adenovirus to

overexpress specific lncRNA (LEENE) in cells due to the low concentration of

lncRNAs in cells. Overexpressing specific lncRNAs is prevalent in examining lncRNA

interactions. We found distinct genome-wide profile of lncRNA interactions when

native and overexpressing states were compared. We suspected overexpression

may change lncRNA interactions. We used the new microfluidic system with

oscillatory hybridization to examine native primary cells from mouse lung tissue with

500,000 cells and this sequencing data provided original in vivo lncRNA binding

profile. This technology permitted profiling genome-wide lncRNA bindings using as

few as 100,000 cells, compared to 20-100 million cells required by conventional

assays. This, for the first time, allowed us to probe native lncRNA bindings in mouse

tissue samples successfully. This technology coupled with sequencing provides us

valuable knowledge about the role of lncRNAs in epigenetic regulations.

4

Chapter 2 Introduction

2.1 Epigenetics

Epigenetics is a phenomenon of hereditary changes in gene expression and

regulations without changing in DNA sequences. It is also defined as change in

phenotype with no change in genotype. There are three main systems of epigenetic

mechanisms, including DNA methylation, histone modifications, and non-coding

RNAs interactions, as shown in Fig. 2.1-1. Genes can be switched on or off by

epigenetic regulations. Age, environmental factors, diseases, and lifestyle can

contribute to epigenetic changes. The most common example of epigenetic change

is cell differentiation21,22. Even though the DNA sequences of cells in an individual’s

whole body are the same, cells differentiate into different types with varied

characteristics. Epigenetic changes also cause the development of cancer and other

diseases such as Prader-Willi syndrome, Angelman syndrome, and Beckwith-

Wiedemann syndrome23. We conducted experiments on histone modifications using

primary mouse and human samples and investigated whole-genome lncRNA

bindings to explore epigenetic regulations.

2.1.1 Histone modification

Chromosome is made up of condensed chromatin, which consists of proteins,

RNA, and DNA, the hereditary information materials. The subunit of chromatin is

nucleosome, 8 histones wrapped by a ~146bp long DNA chain, as shown in Fig.2.1-

2. There are five types of histone subunits: H1, H2A, H2B, H3 and H4. The

chromatin structure is stabilized by H1 linker binding. DNA wraps around the core

histones (two copies of H2A, H2B, H3 and H4 each) to form nucleosome.

5

Fig. 2.1-1 Epigenetic mechanisms: DNA methylation, histone modification and non-coding RNA (ncRNA) bindings. Adapted with permission from “Prenatal influences on temperament

development: The role of environmental epigenetics,” by Maria A. Gartstein, Development and Psychopathology (2018)24

6

Fig. 2.1-2 Chromosomes composed of condensed histones wrapped in DNA. Adapted

with permission from “Life: The Science of Biology,” by David E. Sadava, Sinauer associates (2008)25

7

Histone is modified by covalent post-translational modification (PTM) to its

tails. The main modifications include, but not limited to, methylation, acetylation,

phosphorylation, ubiquitylation and SUMOylating. These modifications alter the

structure of chromatin (open/compact), which in turn modulating gene expression

without changes to DNA sequences. More than one hundred unique histone

modification variants have been found in humans26, such as H3K4me3, H3K27ac,

H3K27me3, etc.

H3K27ac is the acetylation of the 27th lysine on histone H3, and is used as an

active promoter and enhancer mark in analyzing ChIP-seq data. H3K4me3 is the tri-

methylation of the 4th lysine on histone H3, and is used as an active promoter mark.

When ChIP-seq data of H3K4me3 and H3K27ac from the same sample are

compared to each other, the promoters are recognized as containing both H3K4me3

and H3K27ac peaks, and the enhancers are clarified as containing H3K27ac but no

H3K4me3 peaks27. A promoter is usually located ahead of the transcription start

sites (TSS) of that gene, and is key for RNA polymerase binding. Enhancer is

positioned randomly (a few to several thousand of bp away in the upstream or

downstream direction of TSS), and is only promotional for transcription. Knowing the

positions of promoter and enhancer can help to comprehensively understand the

whole genome regulation mechanism and explore the functions of super-enhancer,

which is an important factor in cell specification, gene regulations, and diseases28.

2.1.2 Long non-coding RNA (lncRNA)

A non-coding RNA is a functional and informational RNA transcribed from

DNA, but does not further encode proteins6. If non-coding RNA is longer than 200 nt,

it is called long non-coding RNA (lncRNA). Recent studies have shown that lncRNAs

play crucial roles in transcriptional and post-transcriptional regulations, epigenetic

8

regulations, organ and tissue development, and metabolic processes29-35. Cell

differentiation and self-renewal are regulated by lncRNAs36-39, as shown in Fig. 2.1-

3. By means of mutations and dysregulations35,40, lncRNAs contribute to certain

human diseases, such as breast cancer41, prostate cancer42, hepatocellular

cancer43,44, lung cancer44, and diabetes45. With increasing knowledge in regulation

mechanisms of lncRNAs, novel treatment can be devised to combat these diseases.

Fig. 2.1-3 Cell differentiation and self-renewal are regulated by lncRNAs. Adapted with permission from “Long noncoding RNAs in cell fat programming and reprogramming,” by Ryan A. Flynn, Cell Stem Cell (2014)39 2.2 Chromatin immunoprecipitation (ChIP)

Chromatin immunoprecipitation is a powerful tool to study the interaction

between proteins and DNA. It has been used to explore the relationship between

specific proteins (e.g. transcription factors and modified histones) and certain

genomic regions (e.g. promoters and enhancers) to verify putative epigenetic

9

mechanisms. Sample chromatin is sheared via physical methods (e.g. sonication) or

enzyme digestion (e.g. MNase) to gain optimal fragment size for ChIP process.

The specific histone modifications are recognized by antibodies coated on the

surface of magnetic beads, and DNA with wrapped histones are pulled down,

simultaneously. DNA is then isolated and purified by chemical extraction and ethanol

precipitation (Fig. 2.2-1).

Fig. 2.2-1 Chromatin immunoprecipitation. Adapted under CC BY from “Choosing a suitable method for

the identification of replication origins in microbial genomes,” by Chengcheng Song, Front Microbiol (2015)46

ChIP is the gold-standard method for studying histone modifications and

transcription factors3. Recent advances in DNA sequencing tools in combination with

microfluidic devices contribute to a more comprehensive view in epigenetics.

2.2.1 Conventional approaches

There are two types of conventional ChIP: native ChIP and crosslinked ChIP.

Native ChIP (NChIP) relies on the strong binding between proteins and DNA, and

does not involve crosslinking. Crosslinked ChIP (XChIP) uses a reversible

10

crosslinker (e.g. formaldehyde) to promote the binding interaction between proteins

and DNA, and is used for investigating histone modifications and transcription factor

bindings. One drawback of conventional ChIP is the need for a large number of cells

as starting materials (e.g. 106 cells for ChIP-qPCR and 107 cells for ChIP-seq). The

application of ChIP-seq in clinical studies have been limited since typical primary

patient samples are rare to provide the necessary cells. Using microfluidic devices,

this requirement can be lowered to a few hundred cells, thus accommodating

primary patient samples with wider specifications.

2.2.2 Microfluidic oscillatory washing-based ChIP (MOWChIP)

Cao et al. developed a microfluidic device for ChIP research (MOWChIP), as

shown in Fig. 2.2-2. Packed beads are used for highly efficient adsorption and

oscillatory washing is applied to remove non-specific bindings and physical trapping

for high efficiency. Magnetic beads coated with specific antibody are loaded into the

microfluidic chamber and packed in front of the sieve valve, which is partially closed

to trap beads but allow solution flow. Sample is loaded into the chamber by

electronic syringe pump. All chromatin molecules will contact the packed beads

before they leave the chamber. This drastically increases the likelihood of chromatin-

bead contact, and lowers the requirement on cell number. However, using packed

beads also increases the possibility of non-specific binding and physical trapping.

Oscillatory washing is designed to remove these non-target materials. After loading

sample, low/high salt washing buffer is loaded through inlet and outlet, and

oscillatory washing is performed by alternate pressure pulse. Beads with target

chromatin adsorbed are collected, reversed crosslinked and digested by proteinase

K. DNA is extracted by phenol-chloroform-isoamyl alcohol, purified by ethanol

precipitation, amplified with library preparation and sequenced through NGS. The

11

required starting material for MOWChIP is as few as 100 cells, hence it is a huge

improvement compared to conventional ChIP.

Fig. 2.2-2 Overview of the MOWChIP process. Adapted with permission from “A microfluidic device for

epigenomic profiling using 100 cells,” by Zhenning Cao, Nature Methods (2015)19 2.2.3 SurfaceChIP

Ma et al. developed a simpler microfluidic device for ChIP research known as

SurfaceChIP. Specific antibody is coated onto the surface of glass. Fragmented

chromatin is loaded and flowed through the channel containing immobilized

antibody. Target chromatin is captured by the antibody, and oscillatory washing is

used to remove non-specific bound fragments. Purified DNA is obtained after

proteinase K digestion, and ethanol precipitation, and used in library preparation for

sequencing (Fig. 2.2-3). SurfaceChIP is an improvement since as few as 30 cells

can be used per assay and the ChIP process is simplified without using beads.

12

Fig. 2.2-3 Overview of SurfaceChIP. Adapted with permission from “Low-input and multiplexed microfluidic

assay reveals epigenomic variation across cerebellum and prefrontal cortex,” by Sai Ma, Science Advances (2018)47 2.2.4 Low-Input Fluidized-Bed Enabled ChIP (LIFEChIP)

Murphy et al. developed a convenient, multiplexed microfluidic device for

ChIP research. Beads are loaded into four chambers simultaneously in parallel by

software-controlled syringe pumps and held in place using a magnet. Chromatin

fragments are then loaded into the chambers, followed by washing buffer. Purified

chromatin-beads are collected and DNA is isolated through reverse-crosslinking,

proteinase K digestion, phenol-chloroform-isoamyl alcohol extraction, and ethanol

precipitation. The isolated DNA is ready for the sequencing process, as shown in

Fig. 2.2-4. As few as 50 cells can be used per assay and four parallel reactions can

be performed simultaneously for high throughput.

13

Fig. 2.2-4 Overview of LIFEChIP. Adapted with permission from “Microfluidic Low-Input Fluidized-Bed Enabled

ChIP-seq Device for Automated and Parallel Analysis of Histone Modifications,” by Travis W. Murphy, Anal. Chem (2018)48

2.3 Chromatin isolation by RNA purification (ChIRP)

ChIRP, developed by Chu et al. in 2011, is a technology of choice for

examining lncRNA bindings18. As shown in Fig. 2.3-1, ChIRP includes 3 main steps:

fixation of chromatin with lncRNAs, hybridization of specific biotinylated tiling probes

to target lncRNAs, and isolation of chromatin complexes by streptavidin magnetic

beads.

14

Fig. 2.3-1 Overview of the ChIRP process. Adapted with permission from “Genomic Maps of Long Noncoding RNA Occupancy Reveal Principles of RNA-Chromatin Interactions,” by Ci Chu, Molecular Cell (2011)18

One limitation in profiling lncRNA bindings by ChIRP is knowing its sequence.

The biotinylated probes are 20 nt long oligonucleotides, whose sequences are

complementary to target lncRNA, and one biotin is designed to attach at the end of

each probe. The probes are separated into two sets according to the number, even

or odd, shown in Fig. 2.3-2.

15

Fig. 2.3-2 Even and odd probe groups are used in ChIRP-seq.

Two independent ChIRP-seq are performed by using even or odd probes.

Cells are fixed with glutaraldehyde and sheared with a sonicator to optimal fragment

size (100-500 bp). Crosslinked and sheared chromatin is mixed with biotinylated

tiling probes and hybridization buffer in a microtube and incubated on a rotator at

37˚C for 4 hr to hybridize. Washed streptavidin magnetic beads are added to the

microtube and incubated on a rotator at 37˚C for 30 min to capture lncRNA. DNA,

RNA, and proteins are isolated with TRIzol from the surface of streptavidin beads for

further biological analyses. Sequencing reads reacting with only one probe may be

false reads from non-target materials, and they may lower the sequencing quality of

lncRNA bindings. As such, peaks are kept if they appear in both even and odd

sequences simultaneously, and discarded if they exist in either even or odd probe

group. The probes of the third set are designed according to the sequence of lacZ

mRNA to act as negative control, due to lacZ is absent from human cells.

Conventional ChIRP requires 20-100 million target-lncRNA-over-expressed cells to

obtain enough lncRNAs, which makes it unsuitable for clinical application since

primary samples cannot provide such a large amount of cells. In order to overcome

16

this limitation, we developed a novel microfluidic technology for profiling whole-

genome lncRNA bindings.

2.3.1 Capture hybridization analysis of RNA targets (CHART)

CHART, developed by Simon et al.49 in 2011, is another method for profiling

genomic binding sites of non-coding RNAs. The whole process is similar to ChIRP

(including fixation of chromatin with lncRNAs, hybridization of specific biotinylated

tiling probes to target lncRNAs, and isolation of chromatin complexes by streptavidin

magnetic beads), except the design of tiling probes. Twenty-five nt desthiobiotin-

conjugated probes are designed to hybridize target lncRNA, and all probes are

pooled together.

2.3.2 RNA antisense purification (RAP)

RAP, designed by Engreitz et al.50, is another approach for genomic

localization of lncRNAs profiling. One hundred and twenty nt probes, which are tiled

every 15 nt across the target lncRNA, are designed to cover entire lncRNA. The

steps of purification and elution are similar to ChIRP.

The schematic of these three approaches for profiling the genomic localization

of lncRNAs is shown in Fig. 2.3-3.

17

Fig. 2.3-3 Schematic of three approaches for profiling the genomic localization of lncRNAs. Adapted under CC BY from “Unveiling the hidden function of long non-coding RNA by identifying its major partner-protein,” by Yang et al. Cell Biosci, (2015)51

18

2.4 Next Generation Sequencing (NGS)

Nucleic acid sequencing examines the exact order of nucleotides in DNA or

RNA. Since the completion of Human Genome Project in 2003, the sequencing

research has expanded exponentially52. The sequencing tool used for Human

Genome Project was Sanger sequencing, also known as first generation

sequencing. Due to its complicated process and high cost, a new sequencing

technology (second generation sequencing or next generation sequencing) was

developed.

Massively parallel sequencing enables NGS to sequence 200 billion

nucleotides in 3 days, while lowering the cost to below $2000 per lane. The most

common NGS platform is Illumina Hi-seq, based on cluster generation and

sequencing by synthesis. In order to be sequenced by Illumina system, all target

DNA should be end-repaired and have adaptors ligated on both ends. These single

strands of DNA are attached to flow cell surface with oligoenucleotides having

complementary sequences to the adaptors. Bridge amplification is then performed by

polymerase to create complementary strands and amplify the amount of DNA. Four

fluorescently tagged nucleotides (dNTPs) are used to sequence DNA fragments and

light of different wavelengths are emitted when different dNTPs are attached to DNA

clusters. DNA sequences are determined by recording the emitted wavelength.

2.5 ChIP-seq

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has

become the technology of choice for studying the binding of proteins to specific

genome regions. Recent progress in NGS enables ChIP-seq to offer higher

resolution, less bias, more comprehensive coverage of whole-genome, and lower

19

cost. It has become the most commonly employed tool for researching gene

regulations and epigenetic manipulation53.

As shown in Fig. 2.5-1, ChIP-seq follows the following steps: chromatin

immunoprecipitation by histone mark (e.g. H3K4me3, H3K27ac and H3K27me3) or

non-histone mark (transcription factors), DNA fragments isolation, library preparation

(attaching adaptors and amplification), NGS sequencing, and data analysis (mapping

reads to reference genome).

20

Fig. 2.5-1 Overview of ChIP-seq. Adapted with permission from “ChIP–seq: advantages and challenges of a

maturing technology,” by Peter J. Park, Nature Reviews Genetics (2009)53

2.6 Microfluidic device

Microfluidic devices are effective tools to circumvent the restriction of large

amount of cells for conventional ChIP-seq. They possess several advantages, such

21

as easy operation, low cost, short reaction time, accurate temperature control,

reduced contamination, small operating volume, and improved sensitivity of assay.

Microfluidic devices come in different designs and features for their specific

application in research. Most of these devices are made of polydimethylsiloxane

(PDMS), silicon, or glass. PMDS is usually adhered to glass by plasma bonding to

increase the bonding strength. In the patterns of microfluidic devices, channel shape

is the most common one. The possibility of contact between sample and target

antibody is increased when sample flows through a small volume. A device

integrating sonication and ChIP for reduced contamination and sample loss was

developed in 201654. More elaborate devices have been developed to explore the

epigenomic profiles within very low-input samples. It is beneficial to investigate rare

samples, such as circulating tumor cells (CTCs) and embryonic stem cells.

Holistic epigenomic regulations can be examined by assembling all

epigenomic information from different cell types with distinct target antibodies. Novel

therapies for human diseases can be developed from an expanded knowledge base

of epigenomics.

2.7 Precision medicine

Precision medicine, or personalized medicine, refers to a treatment targeting

individuals based on their genes, environment and lifestyle55. Different individuals

may need different treatments even though they present similar symptoms.

Traditionally, physicians treat patients with an one-size-fits-all approach, in which

disease prevention and treatment are designed for the average person56, as shown

in Fig. 2.7-1 (a). In 2015, the total prescription spending in the U.S. was $419.4

billion57, most of which based on one-size-fits-all strategy. This approach fits many

patients, but does not work on some. Precision medicine was launched to improve

22

the low efficiency and diminish side effects of the traditional approach58. President

Obama announced the Precision Medicine Initiative in his 2015 State of the Union

address, ‘Delivering the right treatments, at the right time, every time to the right

person.’

Fig. 2.7-1 The strategy of precision medicine compares with conventional one-size-fits-all approach.

Due to the accomplishment of Human Genome Project, genetic and

epigenetic examinations are thriving. Epigenomic data play crucial roles in diagnosis

and detection of cancer and other human diseases at early development stage59.

The advance in whole genome sequencing technology and evolution of healthcare

promote the development of precision medicine. Several published papers report

a) one-size-fits-all approach

All patients are treated with the same approach

(effective to 25% patients)

b) precision medicine

Different patient groups are treated with different approachs

(effective to 100% patients)

23

that precision medicine provides promising treatment for cancers60-62, cardiovascular

disease63, and Alzheimer’s disease64,65. Obtaining more genetic and epigenetic

profiles is more eager in developing precision medicine. Hundreds of transcription

factors and histone modifications have been identified26,66, but few have been used

to probe clinical samples as a result of limitations in technology and restrictions in

sample size67,68. To solve these problems, we have developed a semi-automated

high-throughput MOWChIP system to run 8 assays in parallel with low-input

samples69. This technology profiles clinical samples faster and more easily. In

addition, our microfluidic oscillatory hybridized ChIRP technology is the first one to

permit whole-genome profiling of lncRNA binding in primary murine cells. With these

important data, we can devise better precision medicine solutions for individual

patients.

24

Chapter 3 Study of epigenomic changes associated with LPS-induced murine monocytes 3.1 Project Summary

Lipopolysaccharide (LPS) is used to induce cells for LPS-stimulated

inflammation. Different concentrations of LPS play distinct roles in inflammatory

processes. High-dose LPS leads to acute inflammation followed by tolerance

resolution, while low-dose LPS causes persistent and non-resolving inflammation.

H3K27ac histone mark is used to detect active promoter and enhancer regions.

Several groups used transcriptome to calculate the gene expression to confirm the

relationship between gene expression and LPS, and most of them focused on high-

dose LPS stimulation. However, few laboratories explored the roles of LPS under

epigenomic regulations.

Here, we used different concentrations of LPS to induce murine monocytes

for 5 days and conducted ChIP with H3K27ac to profile active promoter and

enhancer regions. Different regulations by histone modifications among 3 conditions

(control, super low-dose LPS, and high-dose LPS) were located by DiffBind70. We

profiled condition-specific transcription factors. The gene list of epigenomic

regulations may provide valuable information to summarize the mechanism of

inflammation and design precision medicine.

3.2 Introduction

Monocytes and macrophages play crucial roles in immune responses,

inflammatory processes and homeostasis71. A holistic understanding of the

modulation of monocytes and macrophages can help develop more effective

immunotherapy. Lipopolysaccharide (LPS), also known as endotoxin, is composed

25

of a lipid and a polysaccharide. It is the main material on the surface of Gram-

negative bacteria and can induce intense inflammatory responses by stimulating

different cell types to release inflammatory cytokines72.

LPS challenge is defined as stimulating animals or cells by LPS to modulate

the respective pro-inflammatory cytokines, such as TNFα, IL-1β, and IL-673, and anti-

inflammatory cytokines, such as TRIF, IL4, and IL-1074, which are produced by

activated macrophages and monocytes, to induce innate inflammatory immune

responses. Endotoxin tolerance refers to the condition of low sensitivity to endotoxin

(LPS) after the first stimulation exhibited in animal tissues or cells 75-77. Cells and

animals treat with different amounts of LPS displayed dissimilar results. Recent

studies suggest that high-dose LPS leads to acute inflammation followed by

tolerance resolution, while low-dose LPS causes persistent and non-resolving

inflammation71,78-80, as shown in Fig. 3.2-1.

Fig. 3.2-1 The inflammatory responses with high-dose and low-dose LPS. Adapted under CC BY from “Innate immune programing by endotoxin and its pathological consequences,” by Morris, M.C. et al. , Front Immunol. (2015)71.

Research shows that low-grade inflammation causes cellular stress, inhibits

wound healing, and can lead to chronic diseases, such as diabetes, obesity-

associated complications, and heart diseases81-86. Li’s group confirm the correlation

between super-low-dose endotoxin and impaired wound healing, which exacerbated

26

steatohepatitis in high-fat diet fed mice87-89. However, the context of epigenomic

regulations of low-grade inflammation in monocytes remains to be defined. Knowing

the correct place of transcription start sites (TSS) is important for examining gene

regulations. The histone modification of H3K27ac is an indicator of active enhancer

regions. Whole-genome histone modification is profiled by chromatin

immunoprecipitation coupled with next generation sequencing (ChIP-seq).

Due to the relative low number of monocytes, conventional ChIP-seq cannot

be used. Instead, we employed MOWChIP technology, which is designed for low-

input samples. We induced cultured murine monocytes with 3 conditions (control:

PBS, super low-dose LPS: low LPS, high-dose LPS: high LPS) and conducted

MOWChIP with histone mark H3K27ac to profile epigenomic regulations of

inflammation.

3.3 Results and discussion

The fragment size of sheared DNA is important to ChIP, and the optimal size

range is 200-600 bp90. Based on our previous experiments, we decided on the

optimal range of 200-250 bp. One million cells were resuspended in 130 μl

sonication buffer and sonicated for 16 min by Covaris M220 (Covaris). The fragment

size we obtained was shown in Fig. 3.3-1 and Fig. 3.3-2.

27

Fig. 3.3-1 Fragment size for ChIP-seq. The green line region was our sheared DNA fragments, and the average size was 225bp. The blue lines were lower (25 bp) and upper (1500 bp) marker.

Fig. 3.3-2 Fragment size among three conditions. Green: control set; red: low-dose LPS set; blue: high-dose LPS set.

Chromatin equivalent to 20,000 cells were used to conduct MOWChIP. First

we optimized all performing conditions. Based on the published paper from our lab19,

we tested 3 different antibody concentrations (5 μg/ml, 6.7 μg/ml, and 8.3 μg/ml).

The original concentration of antibody (H3K27ac) was 1 μg/μl. When 1μl H3K27ac

was mixed with 149 μl IP buffer and beads, the concentration was 6.7 μg/ml. Two

positive primers (LAPTM5 and CHMP4B) and two negative primers (MYOD1 and

28

CCZ1) were used to check the quality of our ChIP samples. As shown in Fig. 3.3-3,

6.7 μg/ml of H3K27ac was better than 5 μg/ml and 8.3 μg/ml. The reason for this

phenomenon was that beads coated with more antibody had more binding sites for

the specific. However, excess amounts of antibody increased the chance of non-

specific binding and physical trapping.

Fig. 3.3-3 Optimization of MOWChIP with different concentrations of antibody (H3K27ac). The concentration of H3K27ac in 6.7 ug/ml showed higher relative fold enrichment.

Ideally, ChIP samples should have high positive percent input and low

negative percent input. Packed beads used in our microfluidic device increased the

efficiency of target chromatin capture, but also increased non-specific binding and

physical trapping. Oscillatory washing was used to diminish these problems.

Washing pressure was optimized to remove non-target DNA fragments but retain

target materials, so as to get the best enrichment possible. Three washing pressure

conditions (2.5 psi, 3 psi, and 3.5 psi) were tested. The enrichment of all three LPS-

induced conditions was improved when 3 psi of washing pressure was exerted (Fig.

3.3-4).

0

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9

LAPTM5 CHMP4B MYOD1 CCZ1

Rela

tive

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Differnt concentrations of antibody (H3K27ac)

PBS - 5 μg/ml H3K27ac PBS - 6.7 μg/mll H3K27ac PBS - 8.3 μg/ml H3K27acLow LPS - 5 μg/ml H3K27ac Low LPS - 6.7 μg/mlH3K27ac Low LPS - 8.3 μg/ml H3K27acHigh LPS - 5 μg/ml H3K27ac High LPS - 6.7 μg/ml H3K27ac High LPS - 8.3 μg/ml H3K27ac

29

Fig. 3.3-4 Optimization of MOWChIP with different oscillatory washing pressure. The washing pressure of 5 psi exhibited the better relative fold enrichment.

In addition to washing pressure, washing time was another possible factor for

improving enrichment. We experimented with 5, 7.5 and 10 min of washing and

results showed that 7.5 min of washing time gave the best enrichment (Fig. 3.3-5).

Fig. 3.3-5 Optimization of MOWChIP with different oscillatory washing time. 7.5 min washing time displayed the best relative fold enrichment.

0

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PBS - 2.5 psi PBS - 3 psi PBS - 3.5 psi

Low LPS - 2.5 psi Low LPS - 3 psi Low LPS - 3.5 psi

High LPS - 2.5 psi High LPS - 3 psi High LPS - 3.5 psi

0

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PBS - 5 min PBS - 7.5 min PBS - 10 min

Low LPS - 5 min Low LPS - 7.5 min Low LPS - 10 min

High LPS - 5 min High LPS - 7.5 min High LPS - 10 min

30

The optimized conditions (6.7 μg/ml of H3K27ac, 3 psi of washing pressure,

and 7.5 min of washing time) provided the best enrichment in PBS, low LPS, or high

LPS.

Accel-NGS 2S plus DNA library kit was used to prepare library. Different

barcodes were attached to different samples, which were pooled together for

sequencing. Sequencing data from different samples were separated and saved to

different files according to barcode sequences. Data were processed by Bowtie and

MACS and results were shown in Fig. 3.3-6, visualized in Integrative Genomics

Viewer (IGV). Peaks of all six different files appeared clear. The sequencing data

information was summarized in Table 3.3-1. Pearson correlation matrix of six

MOWChIP-seq samples was shown in Fig. 3.3-7. The values of correlation between

replications of different conditions were above 0.88 (PBS: 0.90, low LPS: 0.94, and

high LPS: 0.88), meaning that replications of each samples were highly reproducible

and consistent.

Fig. 3.3-6 MOWChIP sequencing data of mouse samples visualized in IGV. Red: control set; green: low-dose LPS; purple: high-dose LPS. Peak patterns were similar among three conditions.

[0-8.00]

[0-8.00]

[0-8.00]

[0-8.00]

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Refseq genes

PBS_Rep1

PBS_Rep2

Low LPS_Rep1

Low LPS_Rep2

High LPS_Rep1

High LPS_Rep2

Mouse(mm10)Chr10

Supv3l1 Vps26a Srgn Kif1bp Ddx21

31

Fig. 3.3-7 Pearson correlation matrix of MOWChIP sequencing data. Software DiffBind was used to analyze ChIP-seq data to locate differentially

modified peak regions among different conditions. Fifty-two gene regions analyzed

by DiffBind were listed in Table 3.3-2.

As shown in Fig. 3.3-8, the epigenetic regulation of Apoe was downregulated

in high LPS. However, there was no obvious difference at the region of gene Bcl3.

Apoe plays a crucial role in atherosclerosis and inflammation91. The gene expression

of Apoe is repressed in the condition of high-dose LPS has been reported by several

labs in examining mRNA expression92,93. This effect is consistent with our work

based on the property of H3K27ac, defining as an active enhancer mark. The same

tendency was observed in Fig. 3.3-9, and there was no clear peak at the region of

gene C1q in high LPS. C1q is a pivotal component in immune response to induce

0.65

0.68

0.72

0.76

0.79

0.82

0.86

0.9

0.93

0.96

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Rep

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PBS_Rep2

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High LPS_Rep2

32

inflammation. Literature shows that gene expression of C1q is decreased when cells

are treated with high-dose LPS94,95.

At the other end of the spectrum, epigenetic regulation of genes Fpr1 and

Fpr2 was upregulated in high LPS (Fig. 3.3-10). Fpr1 and Fpr2 are crucial regulators

in inflammation96 and wound healing97. Since low LPS causes impaired wound

healing, it is reasonable that Fpr1 and Fpr2 are upregulated in the condition of high

LPS. The gene expression of Fpr1 and Fpr2 is promoted with high dose LPS has

been reported by different research laboratories98,99. All of above results were

consistent with the gene list from DiffBind in Table 3.3-2.

33

Table 3.3-2 Epigenomic regulations among different conditions. Blue: upregulation; red: downregulation.

Chr Gene High1 High2 Low1 Low2 PBS1 PBS2chr4 C1qbchr5 Pf4chr2 Ier5lchr12 4921507G05Rikchr7 Apoechr7 Cracr2bchr6 Apobec1chr11 Clec10achr19 Arhgap19chr19 Ms4a7chr6 Emp1chr2 Mafbchr15 Syngr1chr15 Gpd1chr15 Sepp1chr2 Abhd12chr14 Mir5131chr5 LOC108169116chr11 Slfn1chr7 Ifitm3chr8 Mir7238chr16 Socs1chr7 Ifitm1chr9 Ccr2chr5 Oasl2chr11 Socs3chr12 Irf2bplchr16 Rtp4chr15 Ly6echr7 Fam181bchr3 4931440P22Rikchr2 Zbp1chr13 Uqcrfs1chr7 Lipt2chr5 Upk3bchr6 LOC108168694chr11 Cd300echr7 Saa3chr10 1700124M09Rikchr7 3110009M11Rikchr19 Mir3084-1chr5 Mir7024chr2 6530402F18Rikchr11 Upp1chr17 Fpr1chr10 Gja1chr10 Adora2achr1 Ccdc185chr13 1700019C18RikchrX Mir680-2chr3 Il12achr1 Marco

34

Fig. 3.3-8 Epigenetic regulation of Apoe (left) and Bcl3 (right).

Fig. 3.3-9 Epigenetic regulations of C1qa, C1qb, C1qc.

Fig. 3.3-10 Epigenetic regulation of Fpr1 and Fpr2. The differentially modified regions were then mapped to the nearest genes

and examined significant enrichment of gene ontologies (Fig. 3.3-11). Gene

Ontology (GO) was a valuable tool to profile biological processes and molecular

functions for developing gene regulation mechanism. The immune response and

Clptm1 Apoc4-apoc2 Apoc1 Tomm40 Nectin2 Bcam Cblc Bcl3

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PBS_Rep1

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Mouse(mm10)Chr10

Apoe

Epha8C1qaC1qcC1qb

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Mouse(mm10)Chr10

Mir99b Has1 Fpr1 Fpr2

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Mouse(mm10)Chr10

35

metabolic process were enriched in both low-LPS and high-LPS, and both of them

were associated with inflammaion.

Fig. 3.3-11 Gene ontology (GO) analysis using GREAT of significant differentially modified peak regions between two of LPS, low-LPS, and high-LPS. Created by Lynette Naler.

36

We then examined enhancer regions among three stimulated conditions (Fig.

3.3-12 (a)). Enhancer regions were defined by signals in H3K27achigh that did not

correspond to areas nearby transcription start sites (+/- 2kb from TSS). Using PBS

condition as the reference, low-LPS covered 43.57% and high-LPS covered 49.30%.

Low-LPS and high-LPS exhibited 0.72% and 4.08% additional enhancers,

respectively. The super enhancers, clusters of active enhancers in close proximity,

were profiled in Fig. 3.3-12 (b). Low-LPS and high-LPS occupied 39.37% and

44.44% of super enhancers of PBS state, respectively. Low-LPS and high-LPS

showed 2.91% and 15.43% additional super enhancers. Significant numbers of

enhancers and super enhancers were diminished when monocytes were stimulated

with either low-LPS or high-LPS. Especially, more numbers of enhancers and super

enhancers were deteriorated and less additional aberrant enhancers and super

enhancers were profiled in low-LPS stimulated monocytes than in high-LPS

stimulated monocytes.

Fig. 3.3-12 Venn diagrams of enhancers and super enhancers. (a) Overlap among three stimulated conditions of enhancers and (b) super enhancers. Created by Lynette Naler.

37

Significant enriched transcription factor binding motifs were then profiled with

enhancer regions. Heatmap of enriched motifs were shown in Fig. 3.3-13. Nkx2.2

and Hif-1b were significant enriched only in high-LPS stimulated condition. Nkx2.2

regulates β cells100 and plays a critical role in inflammation101. Hif-1, a heterodimeric

transcription factor, is composed by Hif-1a and Hif-1b, and regulates tumor

progression and inflammatory process102. Six-2 and Bcl6 were considerably enriched

only in low-LPS stimulated condition. Six-2 dampens the promoter activity of

inflammatory genes to prevent damage associated with cytokine storm and plays a

pivotal role in chronic inflammation103. Bcl6 inhibits NF-κB signaling and attenuates

chronic and low-grade inflammation104. In the control group, Zfp3 and Olig2 exhibited

unique enrichment. Based on literature, they have few association with inflammation.

Zfp3 is a zinc finger protein, and its function is still unknown105. Olig2 regulates motor

neurons and oligodendrocytes106.

38

Fig. 3.3-13 Motif analysis at enhancers. Created by Lynette Naler.

3.4 Materials and methods

Fabrication of microfluidic chip

The microfluidic device was fabricated using polydimethylsiloxane (PDMS) and was

a two layers (fluidic and control layer)107 structure as described in previous

39

publication19. Briefly, two photomasks (for fabricating fluidic and control layers) with

microscale patterns were manufactured by software FreeHand MX (Macromedia,

San Francisco, CA, USA) and printed at high-resolution (5080 dpi). We poured

negative photoresist SU-8 2025 (Microchem, Newton, MA, USA) on 3-inch silicon

wafers (University Wafer, South Boston, MA, USA) and spun 500 rpm for 10 s and

2500 rpm for 30 s for fluidic master to make ~30 μm in photoresist thickness and 500

rpm for 10 s and 1500 rpm for 30 s for control layer to make ~50 μm in photoresist

thickness to make masters of the fluidic and control layer. The negative photoresist

SU-8 2025 produced partial close valves that allow solution to leave but keep beads

within the chamber. We baked both fluidic and control masters at 95 ˚C for 8 min

(soft bake), exposed UV (580 mW) for 17 s, then baked at 95 ˚C for another 8 min

(post bake). The masters were washed with SU-8 developer, shook for 2-3 min, and

rinsed with IPA, acetone, and DI water. PDMS (RTV615) with mass ratio of A:B = 5:1

was poured onto the fluidic layer master placed in the Petri dish to create ~5 mm in

thickness of fluidic layer PDMS, and degased for 1 h. PDMS with mass ratio of A:B =

20:1 was degased for 1 hour and poured onto the control layer master which was set

in the spinner and spun at 500 rpm for 10 s and 1100 rpm for 30 s to fabricate ~108

um in thickness of control layer PDMS. Both fluidic and control layer PDMS were

cured at 80 ˚C for 12 min. The fluidic layer was peeled off from the master by a

scalpel. The fluidic and control layer were aligned and combined by the pattern (the

neck of chamber of fluidic pattern was crossed with the bar shape of control pattern)

to form tow-layer PDMS. The two-layer PDMS was baked at 80 ˚C for 2 h and

peeled off from control layer master. The two-layer PDMS was punched by Harris

Uni-Cores 2.0 mm puncher (Electron Microscopy Sciences) at specific sites (inlet

part of control layer, inlet and outlet parts of fluidic layer). The two-layer PDMS and a

40

pre-cleaned glass slide (25mm x 75mm, VWR) were treated by plasma (Harrick

Plasma) and then bonded together. The chip device was cured at 80 ˚C for 1 h to

increase bonding strength and remove bubbles.

Mice

C57BL/6 mice were purchased from Jackson Laboratory. 8-12 week-old male mice

were used. All processes performed on mice were approved by the Institutional

Animal Care and Use Committee (IACUC) of Virginia Tech.

Primary cells from mice

We followed cell culture condition as previously described108. The RPMI 1640

medium supplemented with 10% fetal bovine serum (FBS), 2mM L-glutamine, 1%

penicillin-streptomycin and M-CSF (10ng/ml) in the presence of super low-dose LPS

(100pg/ml), high-dose LPS (1µg/ml) or vehicle control PBS was used to culture the

crude bone marrow cells isolated from wild type C57BL/6 mice at 37 ˚C in a humid

incubator with 5% CO2. We added fresh high/low-dose LPS or PBS to the cell

cultures every 2 days. The cells were harvested after 5 days.

Chromatin Shearing

Each sample containing 106 cells was centrifuged at 1600 g for 5 min at 4˚C and

washed twice with chilled 1 ml PBS. Cells were resuspened in 9.375 ml PBS in a

15ml tube. 0.625 ml 16% formaldehyde was added and incubated at room

temperature on a shaker for 5 min. 0.667ml 2M glycine was added to quench

crosslinking for 5 min on a rotator at room temperature. The crosslinked cells were

centrifuged at 1600 g for 5 min and washed twice with 1 ml PBS (4˚C). The pellet

was resuspended with 130ul of the Covaris buffer (10 mM Tris-HCl, pH 8.0, 1 mM

EDTA, 0.1% SDS and 1x protease inhibitor cocktail (PIC)) and sonicated at 4 ˚C with

75 W peak incident power, 5% duty factor, and 200 cycles per burst for 16 min by

41

Covaris M220 (Covaris). We centrifuged the sonicated sample at 16100 g for 10 min

at 4˚C. The supernatant (sheared chromatin) was transferred to a pre-autoclaved 1.5

ml microtube (VWR). 2.4 µl sheared chromatin was mixed with 46.6 µl IP buffer (20

Mm Tris-HCl, pH 8.0, 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1 % (w/v)

sodium deoxycholate, 0.1 % SDS, 1 % (v/v) Triton X-100, with 1% freshly added

PMSF and PIC) so the amount of chromatin in each sample was equal to 20 k cells.

The amount of sheared chromation of 2.5 k cells was used to check fragment size by

HS D1000 tapes on Tapestation 2200 (Agilent).

Bead preparation

5 μl of protein A Dyanbeads for immunoprecipitation (Invitrogen) were washed twice

with 150 μl IP buffer. The beads were mixed with 149 μl IP buffer and 1ul of

H3K27ac antibody (abcam) and incubated at 4 ˚C on the rotator overnight.

Afterwards, the beads were washed twice with 150 μl IP buffer and resuspended in 5

μl IP buffer for following on-chip use.

Device operation

The overview of MOWChIP shown in Fig. 2.2-2 was composed of the fluidic layer

(oval chamber, inlet (point 1), and outlet (point 2)) and control layer (bar-shape and

inlet (point 3)). The chip device was placed on the microscope (Olympus microscope

IX71) and a tube was filled with DI water and connected with the inlet of control layer

and 30 psi gas pressure which was controlled by LabVIEW. After 10 min, the bar-

shaped control valve was filled with DI water. A 1 ml syringe filled with IP buffer was

connected with a 30 cm tube and placed on a syringe pump (Chenyx). The other end

of the aforementioned 30 cm tube was connected to the inlet of fluidic layer and filled

with IP buffer at the speed of 200 μl/min. To eliminate bubbles within the chamber,

the valve of the control layer was closed for a few seconds to allow pressure buildup

42

within the chamber. Then it was opened to release the pressure inside the chamber

and to eject the unwanted bubbles simultaneously. 5 μl beads coated with H3K27ac

antibody were loaded into the chamber and a magnet was used to confine the beads

within the chamber. 0.5 μl PMSF and 0.5 μl PIC were added to 49 μl sample solution

containing sheared chromatin of 20 k cells. The syringe connected with a 30 cm tube

was used to withdraw 50 μl sample solution (leaving a small space in the tube before

withdrawing sample solution to prevent contact between sample and the pre-loaded

IP buffer). The syringe pump was used to load sample solution into chamber at 1.5

μl/min. The coated beads were packed in front of the valve (the valve was partially

closed, so the solution could leave but beads were retained) when the sample

solution was loaded to the chamber. All sample solution was injected into the

chamber filled with beads in 50 min, the 30 cm tube was removed from the inlet of

the fluidic layer, and the valve was opened. Two 10 cm tubes were loaded with low

salt washing buffer (20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 2 mM EDTA, 0.1% SDS,

1% (v/v) Triton X-100) and connected to inlet and outlet of fluidic layer respectively.

Oscillatory washing (pressure pulse at 3 psi for 0.5 second at inlet and outlet

alternatively) was performed for 7.5 minutes to remove nonspecifically adsorbed and

physically trapped materials from coated beads surface. The low salt washing buffer

was replaced by high salt washing buffer (20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 2

mM EDTA, 0.1% SDS, 1% (v/v) Triton X-100) and the same process was repeated.

After that, the beads were held in place by a magnet within the chamber and IP

buffer was flushed through at 3 μl/min to remove all debris/ waste materials. The

beads were then flushed out of the chamber at 200 μl/min, and collected to a new

1.5 ml microtube.

DNA purification

43

The beads and input chromatin samples were incubated at 65 ˚C overnight with 198

μl reverse crosslinking buffer (200 mM NaCl, 50mM Tris-HCl, 10 mM EDTA, 1%

SDS, 0.1M NaHCO3) and 2 μl of 20 μg/μl proteinase K (Sigma). DNA was isolated

by adding 200 μl of Phenol:Chloroform:Isoamyl Alcohol 25:24:1 to the above solution

after incubation and vortexing. The solution was centrifuged at 16100 g for 5 min at

room temperature, and the supernatant was transferred to a new 1.5 ml microtube to

mix with 750 μl 100% ethanol, 50 μl 10M ammonium acetate and 5 μl of 5 μg/μl

glycogen. The mixture was vortexed and incubated at -80˚C for at least 2 h. After

that, the sample was centrifuged at 16100 g for 10 min at 4˚C and the supernatant

was discarded. 500 ml 70% ethanol was added to the microtube without disturbing

the pellet. The microtube was centrifuged at 16100 g for 5 min at 4 ˚C and

supernatant was discarded. The pellet was air dried for 5 min in BSC and then was

resuspended with 12 μl TE buffer.

Quantitative PCR (qPCR) Assay

qPCR assays were performed by a Real-Time PCR Detection System (CFX

Connect, BIO-RAD, Hercules, CA). 12.5 μl SYBR Green supermix (BIO-RAD), 1 μl

forward primer, 1 μl reverse primer, and 10.5 μl diluted DNA sample were mixed

together to perform PCR processes, including 95 ˚C for 15 s for denaturing, 58 ˚C for

40 s for annealing, and 72 ˚C for 30 s for extending. The following primers were used

to detect the ChIP-sample enrichment.

(positive primer) LAPTM5-forward 5’- TAC TCT GGG CCT TAG TAG TTC C -3’

(positive primer) LAPTM5-reverse 5’- AAC AAT CCC AAC CTG CAC TC -3’

(positive primer) CHMP4B-forward 5’- GCA TCA TCA GAC TCC CTT CAA -3’

(positive primer) CHMP4B-reverse 5’- GAA CAG AGT GGA ACA GAC TGA G -3’

(negative primer)MYOD1-forward 5’- CTC CAA ACC TCC TGC AAT CT -3’

44

(negative primer)MYOD1-reverse 5’- AAG CCT CTC CAG TGT CTA CT -3’

(negative primer)CCZ1-forward 5’- GGG ACA CGG AGT GTT TAC AA -3’

(negative primer)CCZ1-reverse 5’- CCA CCA CCT CCC AGT TTA AG -3’

Library preparation

We used Accel-NGS 2S Plus DNA Library Kit (Swift-Bio) to prepare library for

sequencing. The process was based on the protocol from Swift-Bio with minor

modification. Briefly, the purified DNA underwent 5 steps, including: repair I for

dephosphorylation, repair II for end repair and polishing, ligation I for 3’ ligation of

P7, ligation II for 5’ ligation of P5, and PCR for amplification of DNA. At the last step

of PCR, 2.5 μl of 20x EvaGreen was added to the reaction mixture to check the

amount of DNA during amplification. Amplification was terminated when the

fluorescent signal increased by 3000 RFU (CFX Connect, BIO-RAD, Hercules, CA).

SPRI beads were used to purify the amplified DNA and remove dimers which

resulted during amplifying due to low amount of input DNA. 10 μl TE buffer was used

to elute pure DNA after amplification. Different samples attached with different

barcodes were pooled together for sequencing. We used Illumina HiSeq 4000 with

single-end 50 nt read in sequencing.

Read Mapping and Normalization

Sequencing reads were trimmed by Trim Galore and then mapped to the mouse

genome (mm10) using bowtie v2.4109 with default parameter settings. Uniquely

mapped reads were computed to normalize signal for 25 bp bin across the genome

according to the following equation:

Normalized signals =

IP( !"#$%'("#)*+'(,-.#/0('10"/23#44"$5"#$%

× 1,000,000) – Input( 6789:;<78=>?;<@AB8CD<;ED7CFG8HH79I789:

× 1,000,000)

45

The mapped reads were extended to 250 bp for accurate description and

visualization before normalization, and the extended reads of IP were normalized

according to the extended reads of input. The normalized data were converted to

bigwig format by UCSC bedGraphtoBigWig.

Peak Calling

Uniquely mapped reads were used for peak calling. Peak calling was performed by

MACS110 using the default setting (P value < 10-5).

Determination of Pearson correlation coefficients

Correlation analysis was performed for checking the consistency among replications

and samples from different conditions. Promoter regions were defined as 2kb

upstream and 2kb downstream from the transcription start sites (TSS) which were

obtained from RefSeq data. The average signals across the promoter region were

used and the correlation was calculated by a custom Perl script.

Performance of DiffBind

DiffBind70 was performed with the default setting (width of peak = 500 bp) in order to

examine the different peaks among samples from different conditions. The replicates

of the same condition were needed for eliminating inaccurate peak information.

3.5 Conclusions

The MOWChIP technology conducted robust and high quality data from low-

input samples. After step-by-step optimization, the fold enrichment was increased 8

times. We would be able to summarize the relationship between epigenomic

regulation and stimulation of different amount of LPS from sequencing data. We

found Apoe and C1q (C1qa, C1qb, and C1qc) were upregulated in low-dose LPS

treatment, but Fpr1 and Fpr2 were upregulated in high-dose LPS. These results

were consistent with literature. The number of enhancer or super enhancer in either

46

low-LPS or high-LPS was lower than control (non-LPS-stimulated) group. Especially,

low-LPS lost more enhancers and super enhancers than high-LPS. In gene ontology,

a number of biological processes, such as immune response and metabolic

process, were enriched in both low-LPS and high-LPS. The condition-specific

transcription factors existed in high-LPS were associated with inflammation and

existed in low-LPS were associated with chronic immune responses. The

sequencing data of histone modification provides potential candidates to profile the

mechanism of inflammation and would healing. Taken together, our technology and

data are able to makes contributions to developing precision medicine.

47

Chapter 4 Profiled cell-type-specific epigenomic changes associated with BRCA1 mutation in breast tissues using MOWChIP-seq 4.1 Project Summary

Breast cancer is one of the most common cancers to women not only in

American, but in whole world, no matter developed, developing, or undeveloped

countries. Currently, the risk of American women being diagnosed with breast cancer

over the course of her lifetime is about 13%111. It means that one in eight American

women will develop breast cancer. Women having germline mutations in a gene of

BRCA1 or BRCA2 have 71.4% or 87.6% average possibility, respectively, to develop

breast cancer by age 70112.

Here, we implemented MOWChIP-seq technology with cells from human

breast and mouse mammary tissues to examine existence and importance of

epigenomic regulations. We used BRCA1 mutation carrier and non-carrier patient

samples and BRCA1 knockout and wild type mouse samples. We found significant

epigenomic differences in human basal and stromal cells between BRCA1 mutation

carriers and non-carriers. As H3K27ac is used as an active enhancer mark, the

enriched regions in H3K27ac associate with incresed ontologies. Some biological

processes, such as immune response and unforded protein response (UPR), were

enriched in both basal and stromal cells. Then, enhancer regions were studied to

examine the significantly enriched transcription factor binding motifs. Some

transcription factors including, but not limited to C-MYC, CTCF, VDR, and

P53,enriched in our analyses were also reported in literature associating with

BRCA1. However, the sequencing data from mouse model showed only 40%

48

overlap with transcription factors enriched in human samples, indicating that the

homozygous exon 11 knockout mouse was not a good epigenomic model for BRCA1

mutation in human research. This, for the first time, allowed us to profile epigenomic

regulations of histone modification by BRCA1 mutation in individual patients of

BRCA1 mutation carrier or non-carrier.

4.2 Introduction

Breast cancer is the second mortal cancer among American women, following

lung cancer. It is predicted that 42 thousand women will die due to breast cancer in

2020. BRCA1 and BRCA2 are extremely influential in repairing DNA double-strand

break through homologous recombination to suppress breast cancer113-116. Gene

mutations in BRCA1 and BRCA2 increase the risk of developing breast cancer117,118.

Human female mammary epithelium is a two-layer tissue, including inner

(luminal) layer and outer (basal) layer. The luminal progenitors (LP) and mature

luminal cells (ML) are from the inner layer and basal cells (B) are from the outer

layer. The fourth cell type is stromal cells (S) from mesodermal tissue. The four cell

types can be distinguished by expression of surface markers CD49f and EpCAM

following staining with anti-CD49f and anti-EpCAM antibody: CD49f-EpCAM- stromal

cells, CD49fhighEpCAMlow basal cells, CD49f+EpCAMhigh luminal progenitor cells, and

CD49f-EpCAMhigh mature luminal epithelial cells, as shown in Fig. 4.2-1.

49

Fig. 4.2-1 Four different cell types in breast tissues. Adapted under CC BY from “Attenuation of RNA polymerase II pausing mitigates BRCA1 associated R-loop accumulation and tumorigenesis,” by Xiaowen Zhang, Nat Commun (2017)119.

Recent advances in epigenetic research have enabled us to use sequencing

data to analyze the gene mutation sites among different cell types to explore gene

regulation mechanisms. Opinions vary as to which type of cells is the main cause for

breast cancer120-123. Pellacani et al.124 first proposed the enhancer and transcription

factor mechanisms of human mammary epigenomes by analyzing four cell types.

However, they only used normal human mammary cells (non-carriers) and combined

the same cell type from six different patient samples. Pooling of samples are likely to

cause individual sequencing profiles from different patients to be less defined. Zhang

et al.119 used four different cell types from individual normal (non-carrier) or mutant

(BRCA1 mutation carrier) patients for research. However, they only focused on R-

loop-specific DNA-RNA immunoprecipitation-seq (DRIP-seq), but did not reach to

profile promoters and enhancers across the entire genome. The mechanisms of

50

gene regulations will not be clearly understood unless histone modifications are also

investigated.

Conventional ChIP-seq necessitate the use of up to 1 to 10 million cells per

assay. Fortunately, the number of cells can be reduced to a few hundred when

microfluidic tools are employed. Patient samples contain various numbers of cells,

ranging from a few thousands to hundreds of thousands. It is difficult for conventional

methods to produce reliable ChIP-seq data from primary patient samples.

Microfluidic tools have the potential to overcome this limitation.

Based on the success in chapter 3, MOWChIP devices were used to explore

the cell-type specific epigenomic changes associated with BRCA1 mutation in breast

tissues.


Based on our success in using MOWChIP with pooled primary murine

monocytes (chapter 3), we used the same conditions with minor modifications to

profile epigenomic changes associated with BRCA1 mutation using primary human

and mouse samples. Due to the different number of cells from individual

patient/mouse samples, sonication time was changed to produce the optimal

fragnment size (200-250 bp). Fifty thousand cells were used for H3K27ac and ten

thousdand cells for H3K4me3 in both human and mouse samples.

4.3.1 BRCA1 mutation in human samples

Breast tissue samples, from women undergoing cosmetic reduction

mammoplasty or diagnostic biopsy, were obtained from our collaborator, Dr. Rong Li.

We focused on cancer-free patients, including normal (non-carrier, NC) and BRCA1

mutant (carrier, MUT) samples. For privacy purposes, patients were assigned with

specific numbers (e.g. BSCXXX). In this set, four different cell types (stromal (SC),

51

basal (BC), luminal progenitor (LP), and mature luminal (ML) cells) from eight

patients (five normal (BSC 157, BSC162, BSC167, BSC173, and BSC186) and three

mutant (BSC164, BSC189, and BSC191)) were examined.

4.3.1-1 Pearson’s correlation of human sequencing data

The replicates of each condition from H3K27ac were pooled together and

then profiled for Pearson’s correlation, as shown in Fig. 4.3-1 (a). It showed high

correlation between technical replicates in the same group (0.950). The average

correlation among biological replicates in a group was 0.918. BCs and SCs exhibited

considerable difference between NC and MUT (0.739 in BCs and 0.877 in SCs). In

contrast, LPs and MLs exhibited higher correlation between NC and MUT (0.914 in

LPs and 0.888 in MLs). LPs and MLs were concordant with each other well (0.832 in

MUT and 0.872 in NC) than other cell types (0.668-0.794). The visual representation

of H3K27ac consisted with above results (Fig. 4.3-1 (b)).

No cell-type-specific cancer-free BRCA1 mutation data exist in literature, but

unsorted cancer-free BRCA1 mutation data were published125. We compared our

data with published data (Mix, breast tissue homogenate), as shown in Fig.4.3-1 (a).

There was no obvious difference between NC and MUT. This also underscored the

importance for cell-type-specific profiling to pinpoint specific roles for each cell type.

52

Fig. 4.3-1 ChIP-seq data from H3K27ac of human breast samples. (a) Pearson’s correlation in four cell types of BRCA1 carrier (MUT) and non-carrier (NC) and breast tissue homogenate from literature (Mix) along protomer regions (TSS +/- 2kb). (b) Visualization of normalized sequencing data in IGV. Created by Lynette Naler. (NC: BSC157, BSC162, BSC167, and BSC173; MUT: BSC164, BSC189, and BSC191)

We also profiled H3K4me3 with MOWChIP-seq, as shown in Fig. 4.3-2. It

exhibited higher correlation between technical replicates in the same group (average

r = 0.962). Besides, the average correlation among biological replicates in a group

was 0.960. Taken together, H3K4me3 was not a differentiating mark to profiling

BRCA1 mutation carriers and non-carriers.

53

Fig. 4.3-2 ChIP-seq data from H3K4me3 of human breast samples. (a) Pearson’s correlation in four cell types of BRCA1 carrier (MUT) and non-carrier (WT) along protomer regions (TSS +/- 2kb). (b) Visualization of normalized sequencing data in IGV. Created by Lynette Naler. (WT: BSC186; MUT: BSC189 and BSC191)

4.3.1-2 Differential H3K27ac peak regions

The H3K27ac sequencing data was then be analyzed by DiffBind (fold-

change ≥ 2, FDR < 0.05) to locate differentially modified peak regions, shown in Fig.

4.3-3. There were very little differences in MLs and LPs (2 in MLs and 518 in LPs).

However, the differential regions were moderate in BCs (3,545), and substantial in

SCs (19,946), shown in Fig. 4.3-3 (a). BCs had higher H3K27ac signal regions (dark

blue blocks) in either NC or MUT samples and the numbers of higher signal regions

in NC and MUT were 2,048 and 1,497. All higher signal regions in stromal cells were

54

in NC. Then, we analyzed the normalized H3K27ac signal based on the result from

DiffBind analysis (Fig 4.3-3 (b)). BCs and LPs showed similar overall values between

normal and mutant patients, but MLs and SCs exhibited slightly lower overall values

in MUT.

Fig. 4.3-3 Differential H3K27ac peak regions. (a) Heatmaps of differentially modified H3K27ac peak regions found to be significant (fold-change ≥ 2, FDR < 0.05) between normal and mutant patients. (b) Boxplots of normalized H3K27ac signal at all peak regions, and at those found to be significantly. (c) Gene Ontology (GO) analysis using GREAT of significant differentially modified peak regions between NC and MUT. Created by Lynette Naler.

BCs and SCs had significant differences (enriched H3K27ac signal) between

NC and MUT than other cell types, so we next examined gene ontology (GO) in

55

these two cell types (Fig. 4.3-3). Some biological processes, such as immune

response126-128 and unfolded protein repsonse (UPR)129, were enriched in both BCs

and SCs. Reduction in BRCA1 expression promotes the expression of GRP78,

which plays a critical role to regulate UPR expression129. In addition, some

processes were solely enriched in BCs or SCs. For instance, apoptosis130, RNA

polymerase II transcription131, and DNA damage response132 were enriched in SCs

and cell motility133 and adhesion were significant in BCs.

Human data were visualized in IGV, as shown in Fig. 4.3-4. Here, each cell

type displayed its specific pattern, regardless of being normal or mutant.

Fig. 4.3-4 MOWChIP sequencing data of human samples, visualized in IGV. BSC173: NC, BSC189L: MUT.

However, each cell type also exhibited the different peak patterns between

normal and mutant states implicating the existence of epigenomic regulations when

Human (hg19)Chr12

BSC173-B1BSC173-B2BSC189L-B1

BSC189L-LP1BSC189L-LP2BSC173-ML1BSC173-ML1BSC189L-ML1BSC189L-ML2BSC173-S1BSC173-S2BSC189L-S1BSC189L-S2

BSC173-LP2BSC173-LP1

BSC189L-B2

Refseq genes

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

VWF CD9 PLEKHG6 SCNN1A CD27-AS1 MRPL51 GAPDH CHD4 LPAR5 ZNF384 COPS7A

56

we focused on specific regions of gene identified by DiffBind analysis (Fig. 4.3-5 –

4.3-8). The detailed sequencing data were summarized in Table 4.3-1 to Table 4.3-4.

Gong et al. used western blot and quantitative reverse transcription-PCR

(qRT–PCR) to identify that repression of FOCA1 was correlated to the loss of

BRCA1 in luminal subtype20 and this result was accordant with our data in Fig. 4.3-6.

As H3K27ac is used as an active enhancer mark, its function is highly associated

with activation of transcription. Downregulation in FOCA1 due to BRCA1 mutation

correlated with repressing gene expression of FOCA1. This finding was the first time

confirmed by sequencing data.

qPCR can only quantify the genes with available primers and data are limited

because individual gene needs specific primers to detect. However, ChIP-seq can

profile the whole genome and all interesting genes can be localized by software and

listed. Owing to the advance in computer programs, interactions among all genes

can be identified. ChIP-seq generates far more information while saving cost and

labor.

57

Fig. 4.3-5 Different histone modification pattern in human basal cells. Light color (upper): non-carrier, dark color (lower): carrier.

Fig. 4.3-6 Different histone modification pattern in human luminal progenitor cells. Light color (upper): non-carrier, dark color (lower): carrier.

Human (hg19)Chr14




BSC189L-B2

Refseq genesVSX2 VRTN SYNDIG1L NPC2 LTBP2 AREL1 YLPM1 PROX2 PGF EIF2B2 ACYP1 TMED10 FOS

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

Human (hg19)Chr14




BSC189L-B2

Refseq genes

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

MIPOL1 FOXA1 TTC6

58

Fig. 4.3-7 Different histone modification pattern in human mature luminal and stromal cells. Light color (upper): non-carrier, dark color (lower): carrier.

Fig. 4.3-8 Different histone modification pattern in human stromal cells. Light color (upper): non-carrier, dark color (lower): carrier.

PRDM16 ARHGEF16 MEGF6 TPRG1L TP73

Human (hg19)Chr1




BSC189L-B2

Refseq genes

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

Human (hg19)Chr15




BSC189L-B2

Refseq genes

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

[0-3.00]

SPTBN5 NR_135681 EHD4 PLA2G4E-AS1

59

4.3.1-3 Examination of enhancers of patient samples

We examined enhancer regions of each cell type in NC and MUT (Fig. 4.3-9

(a)). Enhancers were defined by H3K27achigh signal regions, exclusive of regions

nearby transcription start sites (TSS). NC was used as the reference, mutant BCs,

LPs, MLs, and SCs covered 67%, 90%, 79%, and 29%, respectively. Mutant cells

had higher number of additional enhancers in BCs, LPs, and MLs. NC was used as

the reference again, mutant BCs, LPs, MLs, and SCs had 384.48%, 57.57%,

66.42%, and 7.94% of additional enhancers, respectively.

Fig. 4.3-9 Enhancers (a) Venn diagrams of overlap between NC and MUT in different cell types. (b) Distribution of genomic location in enhancers. Created by Lynette Naler.

The enhancers were then mapped to genomic regions, as showen in Fig 4.3-9

(b). There were some slight differences in each cell type between NC and MUT,

except for LPs. BCs exhibited exaggerated differences between NC and MUT due to

BRCA1 mutation. It suggested that BRCA1 mutation played different roles in

enhancers in different cell types.

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4.3.1-4 Motif analysis at enhancers of patient samples

Enhancer regions were studied to profile the significantly enriched

transcription factor binding motifs in Fig. 4.3-10. BCs had substantially more

differentially enriched transcription factor binding motifs than other cell types, likely

as the result of smaller overlap between NC and MUT in BCs. BCs had numerous

additional transcription factors in MUT, however, all transcription factors that SCs

had were in NC. Even though over 50% of transcription factors had few connection

with BRCA1 when exploring in Pubmed (search term = “(Transcription Factor) AND

(BRCA1)”), some transcription factors were reported in literature to associate with

BRCA1 including, but not limited to, C-MYC134,135, CTCF136,137, VDR138, and

P53139,140.

We next examined the pathways of differential transcription factors by

PANTHER, and found that Gonadotropin-releasing hormone (GnRH) receptor

pathway and Wnt signaling pathway were present in all four cell types. BRCA1 has

been shown to be crucial in the Wnt signaling pathway144 and GnRH agonists have

effective treatment in breast cancer145.

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Fig. 4.3-10 Motif analysis at enhancers. (a) Heatmap of motifs significantly enriched in either NC or MUT. (b) Venn diagrams of overlap enriched motifs between NC and MUT. Created by Lynette Naler.

4.3.1-5 Cell-type-specific transcription factors

We found that BCs and SCs had significant epigenomic regulations in cancer-

free human samples due to BRCA1 mutation, while LPs had few differences. This

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result is unanticipated because LPs have been thought to possess functions in

driving BRCA1-mutation breast cancer122. Here, we examined the likelihood of BCs

differentiating into LPs, as hypothesized by Holliday et al146.

The cell-type-specific gene were identified from previous literature124. After

comparison, 6% (44/712) of BC-specific genes had changes due to BRCA1

mutation. LPs and MLs had fewer changes (1% (4/305) in LPs and 2% (23/444) in

MLs). We found 95% of BC-specific genes in MUT had lower H3K27ac signal,

implying that there was a loss of basal gene expression in MUT.

We also examined the cell-type-specific transcription factors, as shown in Fig

4.3-11. We extracted 8, 55, and 6 cell-type-specific TFs (in BC, LP, and ML,

respectively) in NC based on motif analysis. Mutant BCs enriched 27 of the LP-

specific TFs and 6 of the ML-specific TFs. In contrast, mutant LPs enriched 2 of the

BC-specific TFs and 6 of the ML-specific TFs; and mutant MLs enriched 1 of the BC-

specific TFs and 8 of the LP-specific TFs. The results suggested that BCs

experienced more substantial changes due to BRCA1 mutation. Taken together,

BCs might differentiate into LPs due to BRCA1 mutation.

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Fig. 4.3-11 Cell-type-specific transcription factors in NC and their enrichment in MUT. Created by Lynette Naler.

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4.3.2 Profiling mouse samples

Next, we examined mouse samples and checked whether the results from

Brca1 mutant mouse samples were accordant with human samples. We wanted to

check whether the homozygous exon 11 knockout mice147 model was good for

human research or not. We also studied the effect of age in Brca1 mutation. This

Brca1 mutation model was used to compare with wild type mice in this study. We

used BCs (from 8-month old mice) and LPs (from 8-week old and 8-month old mice)

(Fig. 4.3-12). The data showed good average correlation of H3K27ac signals at

promoter regions between biological replicates in the same group (0.948) and

between wild type and mutant cells (0.946), as shown in Fig. 4.3-12 (a). Young (8-

week old) and old (8-month old) luminal progenitors showed better correlation with

each other (0.916) than with basal cells (0.813). Visualization indicated slightly

different patterns for young and old luminal progenitor cells (Fig. 4-3-12 (b)). Similar

results were also exhibited in correlation of H3K4me3 signal at promoter regions

from 8-month old mice (Fig. 4.3-12 (c) & (d)).

Even though there were no significant differences in modified H3K27ac peak

regions, the differences in enhancers were still evaluated (Fig. 4.3-13 (a)). Mutant

BCs possessed fewer enhancers, and this result contrasted with our human data.

The LPs from young mice had more enhancers than old mice. Mutant LPs exhibited

almost all WT enhancers, and they also showed more aberrant enhancers. The

enhancers were then mapped to genomic regions, as showen in Fig 4.3-13 (b).

There were slight differences in each cell types between wild type and knockout

mice. BCs exhibited extremely different between WT and MUT than young and old

LPs. BCs had many additional TFs in WT, in sharp contrast to human BCs (Fig. 4.3-

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13 (c)). The number of TFs in mouse basal cells was only half of that in human basal

cell. The detailed sequencing data were summarized in Table 4.3-5 to Table 4.3-8.

Fig. 4.3-12 ChIP-seq data from mouse samples. (a) Pearson’s correlation of H3K27ac signals at promoter regions. (b) Visualization of normalized sequencing data of H3K27ac in IGV. (c) Pearson’s correlation of H3K4me3 signals at promoter regions. (d) Visualization of normalized sequencing data of H3K4me3 in IGV. (basal cells and luminal progenitors from 8-month old mice, young luminal progenitors from 8-week old mice) Created by Lynette Naler.

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Fig. 4.3-13 Mouse H3K27ac data in basal cells (8-month old mice), luminal progenitors (8-month old mice) and young luminal progenitor (8-week old mice). (a) Venn diagrams of enhancers (left) and super enhancers (right) between normal and mutant mice. (b) Genomic locations of enhancers (left) and super enhancers (right). (c) Motif analysis at enhancers. Heatmap of motifs significantly enriched in either normal or mutant mice. Created by Lynette Naler.



The microfluidic device was fabricated using polydimethylsiloxane (PDMS) and

consisted of two layers (fluidic and control layer)107 as described in previous

publication19. Briefly, two photomasks (for fabricating fluidic and control layers) with

microscale patterns were manufactured by computer-aided design software

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FreeHand MX (Macromedia, San Francisco, CA, USA) and printed at high-resolution

(5080 dpi). The masters of the fluidic and control layer were created by pouring

negative photoresist SU-8 2025 (Microchem, Newton, MA, USA) on 3-inch silicon

wafers (University Wafer, South Boston, MA, USA) and spun 500 rpm for 10 s and

2500 rpm for 30 s for fluidic master to make ~30 μm in photoresist thickness and 500

rpm for 10 s and 1500 rpm for 30 s for control layer to make ~50 μm in photoresist

thickness. The negative photoresist SU-8 2025 produced partial close valves that

allow solution to leave but keep beads within the chamber. Both fluidic and control

masters were baked at 95 ˚C for 8 min (soft bake), exposed UV (580 mW) for 17 s,

then baked at 95 ˚C for another 8 min (post bake). The masters were washed with

SU-8 developer, shook for 2-3 min, and rinsed with IPA, acetone, and DI water.

PDMS (RTV615) with mass ratio of A:B = 5:1 was poured onto the fluidic layer

master placed in the Petri dish to create ~5 mm in thickness of fluidic layer PDMS,

and degased for 1 h. PDMS with mass ratio of A:B = 20:1 was degased for 1 h and

poured onto the control layer master which was set in the spinner and spun at 500

rpm for 10 s and 1100 rpm for 30 s to fabricate ~108 um in thickness of control layer

PDMS. Both fluidic and control layer PDMS were cured at 80 ˚C for 12 min. The

fluidic layer was peeled off from the master by a scalpel. The fluidic and control layer

were aligned and combined by the pattern (the neck of chamber of fluidic pattern

was crossed with the bar shape of control pattern) to form tow-layer PDMS. The two-

layer PDMS was baked at 80 ˚C for 2 h and peeled off from control layer master. The

two-layer PDMS was punched by Harris Uni-Cores 2.0 mm puncher (Electron

Microscopy Sciences) at specific sites (inlet part of control layer, inlet and outlet

parts of fluidic layer). The two-layer PDMS and a pre-cleaned glass slide (25mm x

75mm, VWR) were treated by plasma (Harrick Plasma) and then bonded together.

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The chip device was cured at 80 ˚C for 1 h to increase bonding strength and remove

bubbles.

Mice

C57BL6 BRCA1f/f mice were obtained from Mouse Model of Human Cancer

Consortium, National Cancer Institute. Wild type BRCA1 mice were purchased from

Jackson Laboratory. All processes performed on animals were approved by the

Institutional Animal Care and Use Committee (IACUC) at the University of Texas

Health Science Center at San Antonio.

Breast tissue

Cancer-free BRCA1 mutant (carrier) or normal (non-carrier) breast tissue was

obtained from adult female patients who underwent cosmetic reduction of

mammoplasty, diagnostic biopsies, or mastectomy and all procedures were

approved by the University of Texas Health Science Center at San Antonio. The

consent forms were signed by donors to approve the use of the tissue for breast

cancer research.

Single-cell isolation of human and mouse breast tissue

Unfixed human or mouse breast tissue was processed following previous published

protocols with minor modification148. Briefly, breast tissue was fragmented by

scalpels and then digested in DMEM/F-12 complemented (StemCell) with 5% FBS

(StemCell), 300 U/ml collagenase (StemCell), 100 U/ml hyaluronidase (StemCell),

0.1% BSA (Fisher Scientific), 10 ng/ml epidermal growth factor (Invitrogen), 10 ng/ml

cholera toxin (Sigma), 5 μg/ml insulin (Sigma) and 0.5mg/ml hydrocortisone (Sigma)

with agitation at 37 ˚C for 15-18 h. The epithelial-rich tissue was obtained by

centrifuging at 100 g for 3 min. Red blood cells in epithelial-rich tissue were lysed by

0.8 % NH4Cl and then epithelial-rich tissue was digested with 0.05 % trypsin-EDTA

69

(Life Technologies), washed with Hank’s balanced salt solution complemented with

2 % FBS and resuspended in 5 mg/ml Dispase with 0.1mg/ml DNase I (Roche).

Single cells were obtained by filtration with a 40 μm filter (Fisher) and aggregates

were removed.

Flow cytometry and cell sorting

Single cells were incubated with 10 % rat serum on ice for 10 min for preblocking,

and then stained with anti-CD45 (eBiosciences), anti-CD235a (eBiosciences), and

anti-CD31 (eBiosciences) biotin-conjugated antibodies followed by Pacific Blue-

conjugated Streptavidin (Thermo Fisher Scientific) to remove haematopoietic and

endothelial cells and stained with anti-CD49f allophycocyanin-conjugated antibody

(R&D Systems) and anti-EpCAM FITC-conjugated antibody (StemCell) to separate

different cell types following the previously published protocols148. Cells were

incubated with 7-aminoactinomycin D (BD Biosciences) to separate live and dead

cells. Cells were sorted by a BD FACSAria Flow Cytometer (BD Biosciences). Cells

from human breast tissue were separated into 4 different cell types: EpCAM-CD49f-:

stromal cells (S), EpCAMlowCD49fhigh: basal cells (B), EpCAMhighCD49f+: luminal

progenitor cells (LP), and EpCAMhighCD49f-: mature luminal epithelial cells (ML).

Cells from mouse breast tissue were separated into 3 different cell types: EpCAM-

CD49f-: stromal cells (S), EpCAMhighCD49fmed: luminal epithelial cells (L), and

EpCAMmedCD49fhigh: myoepithelial cells. The purification of each cell type was

verified by performing real-time PCR to analyze VIM (stromal), KRT14 (basal),

KRT18 (luminal) and ESR1 (mature luminal) mRNA using ACTB to normalize.

Chromatin Shearing

Each sample containing 106 cells was centrifuged at 1600 g for 5 min at room

temperature and washed twice with 1 ml PBS (4˚C). Cells were resuspened in 9.375

70

ml PBS. 0.625 ml 16% formaldehyde was added and incubated at room temperature

on a shaker for 5 min. Crosslinking was quenched by adding 0.667ml 2M glycine and

shaking for 5 min at room temperature. The crosslinked cells were centrifuged at

1600 g for 5 min and washed twice with 1 ml PBS (4˚C). The pelleted cells were

resuspended in 130ul of the Covaris buffer (10 mM Tris-HCl, pH 8.0, 1 mM EDTA,

0.1% SDS and 1x protease inhibitor cocktail (PIC)) and sonicated at 4 ˚C with 105 W

peak incident power, 5% duty factor, and 200 cycles per burst for 16 min by Covaris

S220 (Covaris). The sonicated sample was centrifuged at 16100 g for 10 min at 4 ˚C.

The sheared chromatin in the supernatant was transferred to a pre-autoclaved 1.5 ml

microcentrifuge tube (VWR). 2.4 µl sheared chromatin was mixed with 46.6 µl IP

buffer (20 Mm Tris-HCl, pH 8.0, 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1 %

(w/v) sodium deoxycholate, 0.1 % SDS, 1 % (v/v) Triton X-100, with 1% freshly

added PMSF and PIC) so the amount of chromatin in each sample was equal to 20 k

cells. The amount of sheared chromation of 2.5 k cells was used to check fragment

size by HS D1000 tapes on Tapestation 2200 (Agilent).

Bead preparation

5 μl of protein A Dyanbeads for immunoprecipitation (Invitrogen) were washed twice

with 150 μl IP buffer. The beads were mixed with 149 μl IP buffer and 1ul of

H3K27ac/H3K4me3 antibody (abcam) and incubated at 4 ˚C on the rotator overnight.

Afterwards, the beads were washed twice with 150 μl IP buffer and resuspended in 5

μl IP buffer for on-chip use.

Device operation

The overview of MOWChIP shown in Fig. 2.2-2 was composed of the fluidic layer

(oval chamber, inlet (point 1), and outlet (point 2)) and control layer (bar-shape and

inlet (point 3)). The chip device was placed on the microscope (Olympus microscope

71

IX71) and a tube was filled with DI water and connected with the inlet of control layer

and 30 psi gas pressure which was controlled by LabVIEW. After 10 min, the bar-

shaped control valve was filled with DI water. A 1 ml syringe filled with IP buffer was

connected with a 30 cm tube and placed on a syringe pump (Chenyx). The other end

of the aforementioned 30 cm tube was connected to the inlet of fluidic layer and filled

with IP buffer at the speed of 200 μl/min. To eliminate bubbles within the chamber,

the valve of the control layer was closed for a few seconds to allow pressure buildup

within the chamber. Then it was opened to release the pressure inside the chamber

and to eject the unwanted bubbles simultaneously. 5 μl beads coated with H3K27ac

antibody were loaded into the chamber and a magnet was used to confine the beads

within the chamber. 0.5 μl PMSF and 0.5 μl PIC were added to 49 μl sample solution

containing sheared chromatin of 20 k cells. The syringe connected with a 30 cm tube

was used to withdraw 50 μl sample solution (leaving a small space in the tube before

withdrawing sample solution to prevent contact between sample and the pre-loaded

IP buffer). The syringe pump was used to load sample solution into chamber at 1.5

μl/min. The coated beads were packed in front of the valve (the valve was partially

closed, so the solution could leave but beads were retained) when the sample

solution was loaded to the chamber. All sample solution was injected into the

chamber filled with beads in 50 min, the 30 cm tube was removed from the inlet of

the fluidic layer, and the valve was opened. Two 10 cm tubes were loaded with low

salt washing buffer (20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 2 mM EDTA, 0.1% SDS,

1% (v/v) Triton X-100) and connected to inlet and outlet of fluidic layer respectively.

Oscillatory washing (pressure pulse at 3 psi for 0.5 second at inlet and outlet

alternatively) was performed for 5 minutes to remove nonspecifically adsorbed and

physically trapped materials from coated beads surface. The low salt washing buffer

72

was replaced by high salt washing buffer (20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 2

mM EDTA, 0.1% SDS, 1% (v/v) Triton X-100) and the same process was repeated.

After that, the beads were held in place by a magnet within the chamber and IP

buffer was flushed through at 3 μl/min to remove all debris/ waste materials. The

beads were then flushed out of the chamber at 200 μl/min, and collected to a new

1.5 ml microtube.

DNA purification

The beads and input chromatin samples were incubated at 65 ˚C overnight with 198

μl reverse crosslinking buffer (200 mM NaCl, 50mM Tris-HCl, 10 mM EDTA, 1%

SDS, 0.1M NaHCO3) and 2 μl of 20 μg/μl proteinase K (Sigma). DNA was separated

from protein and beads by adding 200 μl of Phenol:Chloroform:Isoamyl Alcohol

25:24:1 to the above solution after incubation and vortexing. The solution was

centrifuged at 16100 g for 5 min, and the supernatant was transferred to a new 1.5

ml microtube to mix with 750 μl 100% ethanol, 50 μl 10M ammonium acetate and 5

μl of 5 μg/μl glycogen. The mixture was vortexed and incubated at -80 ˚C for 2 h.

After that, the sample was centrifuged at 16100 g for 10 min at 4 ˚C and the

supernatant was discarded. 500 ml 70% ethanol was added to the microtube without

disturbing the pellet. The microtube was centrifuged at 16100 g for 5 min at 4 ˚C and

supernatant was discarded. The pellet was air dried for 5 min and then was

resuspended with 12 μl TE buffer.

Quantitative PCR (qPCR) Assay

qPCR assays were performed by a Real-Time PCR Detection System (CFX

Connect, BIO-RAD, Hercules, CA). 12.5 μl SYBR Green supermix (BIO-RAD), 1 μl

forward primer, 1 μl reverse primer, and 10.5 μl diluted DNA sample were mixed

together to perform PCR processes, including 95 ˚C for 15 s for denaturing, 58 ˚C for

73

40 s for annealing, and 72 ˚C for 30 s for extending. The following primers were used

to detect the ChIP-sample enrichment.

H3K27ac human mammary cells:

(positive primer) KLF6-forward 5’- GCG TTT ACC TGT TGC CAG TA -3’

(positive primer) KLF6-reverse 5’- CCA TGT GCA GCA TCT TCC A -3’

(positive primer) BAZ1A-forward 5’- TCT CAA CTC CGC TCC TCT CT -3’

(positive primer) BAZ1A-reverse 5’- TGG GCT GGG CTT CGT TT -3’

(negative primer) N-SLC-forward 5’- TTC CCA ACG TCA CAG AGT TAG -3’

(negative primer) N-SLC-reverse 5’- GAC AGT ACA GCA CAG AGG TTA G -3’

(negative primer) N-PER-forward 5’- GGT GCT CCC TGA TTG TTA GT -3’

(negative primer) N-PER-reverse 5’- CTT GTG CTT TGG GTC CAT TAA G -3’

H3K4me3 human mammary cells:

(positive primer) UNKL-forward 5’- CAG CCA CCC ACC TAG GAA -3’

(positive primer) UNKL-reverse 5’- TCC TAT GGC TCC CCA GGT -3’

(positive primer) C9ORF3-forward 5’- CCT CCT CAG TTC TCC CAG ACT -3’

(positive primer) C9ORF3-reverse 5’- AGC TGA GGT GGT AAG ATG TGA C -3’

(negative primer) N1-forward 5’- TCA TCT GCA AAT GGG GAC AA -3’

(negative primer) N1-reverse 5’- AGG ACA CCC CCT CTC AAC AC -3’

(negative primer) N2-forward 5’- ATG GTT GCC ACT GGG GAT CT -3’

(negative primer) N2-reverse 5’- TGC CAA AGC CTA GGG GAA GA -3’

H3K27ac mouse mammary cells:

(positive primer) CDIPT-forward 5’- CTC GGG ACG GAG TAA TCT CTA -3’

(positive primer) CDIPT-reverse 5’- GCA AAG GAA GAA ACT GGA AAG G -3’

(positive primer) BRD2-forward 5’- CCC TCG CGC GAA AGT AAA -3’

(positive primer) BRD2-reverse 5’- GAC GTG CAC ACC TGT CTT -3’

74

(negative primer) H2-OB-forward 5’- ACA CAG GTG CAC TGT ATT CC -3’

(negative primer) H2-OB-reverse 5’- TCA CTC CCT ACA TTA ATG CTT CC -3’

(negative primer) ZG16-forward 5’- GAG ACA GGG TTT CTC TGT GTA G -3’

(negative primer) ZG16-reverse 5’- GAC AGA GGC AGG TGG ATT T -3’

H3K4me3 mouse mammary cells:

(positive primer) FOXC1-forward 5’- CCC TTC TAT CGG GAC AAT AAG C -3’

(positive primer) FOXC1-reverse 5’- CTT GAC GAA GCA CTC GTT GA -3’

(positive primer) BAHCC1-forward 5’- GGC AGG ACA GAA CAC AAA GA -3’

(positive primer) BAHCC1-reverse 5’- CTC TCA AGC CTG CTA GAC TTT C -3’

(negative primer) N-ZG16-forward 5’- GAT TTC TGA CCT GGA GAG ATG G -3’

(negative primer) N-ZG16-reverse 5’- CAT GTG GTT GCT GGG ATT TG -3’

(negative primer) N-GRIK5-forward 5’- GAC TGA CAT ACG CAA GGA CTA C -3’

(negative primer) N-GRIK5-reverse 5’- GAC TGC TAC TTG GAT GGT GAT AC -3’

Library preparation

The Accel-NGS 2S Plus DNA Library Kit (Swift-Bio) was used to perform library

preparation. The process was based on the protocol from Swift-Bio with minor









to elute pure DNA after amplification and different samples attached with different

75

barcodes were pooled together for sequencing by Illumina HiSeq 4000 with single-

end 50 nt read.

Read Mapping and Normalization

Sequencing reads were trimmed by Trim Galore and then mapped to the mouse

genome (mm10) using bowtie v2.4109 with default parameter settings. Uniquely

mapped reads were computed to normalize signal for 25 bp bin across the genome

according to the following equation:

Normalized signals =

IP( !"#$%'("#)*+'(,-.#/0('10"/23#44"$5"#$%

× 1,000,000) – Input( 6789:;<78=>?;<@AB8CD<;ED7CFG8HH79I789:

× 1,000,000)

The mapped reads were extended to 250 bp for accurate description and

visualization before normalization, and the extended reads of IP were normalized

according to the extended reads of input. The normalized data were converted to

bigwig format by UCSC bedGraphtoBigWig.

Peak Calling

Uniquely mapped reads were used for peak calling. Peak calling was performed by

MACS110 using the default setting (P value < 10-5).

Determination of Pearson correlation coefficients

Correlation analysis was performed for checking the consistency among replications

and samples from different conditions. Promoter regions were defined as 2kb

upstream and 2kb downstream from the transcription start sites (TSS) which were

obtained from RefSeq data. The average signals across the promoter region were

used and the correlation was calculated by a custom Perl script.

Performance of DiffBind

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DiffBind70 was performed with the default setting (width of peak = 500 bp) in order to

examine the different peaks among samples from different conditions. The replicates

of the same condition were needed for eliminating inaccurate peak information.

4.5 Conclusions

We found significant epigenomic changes in human BCs and SCs when

comparing NC and MUT states. The top 5 cell-type-specific TFs in BCs, LPs, and

MLs from literature were also enriched in our NC cell populations. Our NC data

matched well with previously published results. Based on the data of transcription

factor binding motifs, we analyzed cell-type-specific TFs in BCs, LPs, and MLs, and

the results suggested that BCs might differentiate into LPs due to BRCA1mutation.

Human basal cells had far more TF motifs than other cell types, but the

number of motifs in mouse basal cells was only half of that in human basal cells.

Sequencing data from mouse model showed only 40% overlap with transcription

factors enriched in human samples. The Brca1 knockout mouse model of deleting

homozygous exon 11 was not a good epigenomic model for BRCA1 mutation in

human research. This, for the first time, allowed us to profile epigenomic regulations

of histone modification by BRCA1 mutation in different cell types (BCs, LPs, MLs,

and SCs) from individual patient.

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Chapter 5 Microfluidic oscillatory hybridized ChIRP-seq assay to profile genome-wide lncRNA binding 5.1 Project Summary

Only 2% of human genome is translated to functional proteins (25,000).

However, most of the human genome (>70%) is transcribed to RNAs with no further

encoding reactions149,150. Since non-coding RNAs (ncRNAs) are the overwhelming

majority, they may possess some specific functions instead of being effete

products151. Recent studies have shown that lncRNAs play crucial roles in

transcriptional and post-transcriptional regulations, epigenetic regulations, organ and

tissue development, and metabolic processes29-35. Chromatin isolation by RNA

purification coupled with next generation sequencing (ChIRP-seq) is a gold-standard

approach to profile genome-wide lncRNA binding. However, ChIRP-seq requires 20-

100 million cells per assay, and these cells are infected by

retrovirus/lentivirus/adenovirus to overexpress specific lncRNAs. This requirement

impedes the application of examining whole-genome lncRNA bindings of clinical

samples.

We developed a novel microfluidic technology, microfluidic oscillatory

hybridized ChIRP, to profile whole-genome lncRNA bindings. This technology

permits profiling genome-wide lncRNA bindings using as few as 100,000 cells

(conventional ChIRP-seq assays requiring 20-100 million infected cells per assay).

We used this technology to examine different lncRNAs (LEENE (lncRNA that

enhances eNOS expression), HOTAIR and BY707159) in a cell line (MDA-MB-231),

a primary cell line (HUVECs) and primary endothelial cells from murine lung tissues.

We conducted microfluidic ChIRP-seq with not only adenovirus-infected cells but

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native state cells. This, for the first time, allowed us to probe native lncRNA bindings

in mouse tissue samples successfully. This technology is promising for developing

precision medicine in examining epigenomic regulations in binding activities of

lncRNAs.

5.2 Introduction

Recent advances in next generation sequencing technologies allow whole-

genome profile with higher resolution to be acquired, thus the information in gene

regulation would be more comprehensive. The new knowledge – epigenetics,

defined as the modification in gene regulation and expression without changes to

DNA sequence, has become increasingly important in many different fields, such as

gene therapy152, cancer treatment153-158, and immunotherapy158-161.

Only 2% of human genome is translated to functional proteins (25,000). Most

of the human genome (>70%) is transcribed to RNAs with no further encoding

reactions149,150. Since non-coding RNAs are the overwhelming majority, they may

possess some specific functions instead of being effete products151.

The field of epigenetics consists of three different mechanisms for regulation

of gene expression: histone modification, DNA methylation, and non-coding RNA

(ncRNA) interactions. Non-coding RNA is a functional and informational RNA

transcribed from DNA that does not further encode proteins6. Recent studies suggest

that histone modification and DNA methylation are regulated by ncRNAs7-10,162, and

they are crucial in developing cancers, such as breast cancer163-167, lung cancer168-

176, liver cancer177-185, gastric cancer186-188,and endometrial cancer189-191 , and non-

tumoural diseases, such as cardiovascular disease166,192-199, oesophageal

adenocarcinoma200,201, melanoma185,202-204, Crohn’s disease201, Silver-Russell

79

syndrome205, McCune-Albright syndrome205, Alzheimer’s disease206,

Pseudohypoparathyroidism207 and Beckwith-Wiedeman syndrome205.

ncRNAs include transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small

RNAs, long non-coding RNAs (lncRNAs)208,209 etc. lncRNAs have more than 200

nucleotides but do not translate to proteins. They play pivotal roles in the above

human diseases and have important functions in regulating chromatin assembly and

disassembly (e.g. dosage compensation) and gene expression14-17,210. Above

evidence suggests that lncRNAs have key roles in epigenetics, and scientists are

concentrating on them in order to better understand the holistic networks among

gene expression, epigenetics, human diseases and lncRNAs.

Exploring the roles of lncRNAs in such scenarios is important to uncover

underlying epigenetic regulations, but research of lncRNAs has been limited for

several years due to their extremely low concentration in cells. Chu et al. developed

the Chromatin Isolation by RNA Purification (ChIRP) assay with sequencing (ChIRP-

seq)18,211 method to explore the new field of lncRNA bindings, which offer an

expanded field of view on the roles of lncRNAs in epigenetics. The main three steps

in ChIRP-seq include fixation of chromatin with lncRNAs, hybridization of specific

biotinylated tiling probes to target lncRNAs, and isolation of chromatin complexes by

streptavidin magnetic beads. However, the original ChIRP-seq assay requires 20-

100 million cells, infected by retrovirus to overexpress target lncRNA, per assay, and

this requirement constrains its applications212.

We developed a novel microfluidic technology for reducing the required

number of cells and this technology was used to profile lncRNA bindings with not

only cell line and primary cell line, but primary murine cells from lung tissues. It can

80

be used to investigate the epigenomic regulation of lncRNA bindings in clinical

patient samples for developing precision medicine.


5.3.1 Development of microfluidic oscillatory hybridized ChIRP

technology

We developed a novel microfluidic oscillatory hybridized ChIRP-seq

technology to profile genome-wide lncRNA binding sites. The two steps

(hybridization of specific biotinylated tiling probes to target lncRNAs, and isolation of

chromatin complexes by streptavidin magnetic beads) in conventional ChIRP were

combined into one step in a microfluidic device. The schematic of microfluidic

oscillatory hybridized ChIRP is shown in Fig. 5.3-1.

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Fig. 5.3-1 The schematic of microfluidic oscillatory hybridized ChIRP. Left side: experimental processes; right side: molecular vision referring to the step at left side.

The streptavidin magnetic beads were coated with biotinylated tiling 20-nt

probes (the sequence of these probes was complementary to the target lncRNA).

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Then the hybridized pulldown of target lncRNA was conducted on the surface of

streptavidin magnetic beads in a microfluidic device. The RNA binding proteins

(RBPs) and chromatin fragments were isolated simultaneously in this process,

including the following two steps. First, the oscillatory motion of sample solution

(fragmented fixed cells and hybridization buffer) was conducted with long pressure

pulses alternatingly on two ends of the chamber. Beads were retained in the

chamber area with a magnet to increase the possibility of hybridization. Second, the

sample solution was collected after oscillatory motion. Then, the sample was

reloaded to the chamber and ran through a packed bed of magnetic beads formed

by closing the sieving valve and a magnet. The closing sieving valve allowed the

solution to flow but retained beads in the chamber. This step made high absorbing

efficiency for lncRNA binding to the surface of beads. The oscillatory washing step

was conducted with washing buffer to remove nonspecific bound and physically

trapped RNA. Finally, the beads were collected for subsequent biochemical analyses

including, but not limited to, efficiency of RNA retrieval, enrichment of target loci, and

next generation sequencing.

5.3.2 Adenovirus transfection and infection

Overexpressed lncRNAs were used to test the efficiency of this technology in

catching target lncRNA. Methods to amplify the amount of lncRNAs in vivo by virus

infection had recently been developed. Multiple virus systems, such as

retrovirus18,211,213, lentivirus214,215, adenovirus216-218, and adeno-associated

virus(AAV)219,220, have been used to overexpress lncRNAs. We followed protocol216

to amplify LEENE (lncRNA) by adenovirus infection. The gene of interest (LEENE)

was cloned into a shuttle vector (pAdTrack-CMV), as shown in Fig. 5.3-2.

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Fig. 5.3-2 Overview of adenovirus transfection system. Adapted with permission from “A simplified system for generating recombinant adenoviruses,” by Tong-Chuan He, PNAS (1998), Copyright (1998) National Academy of Science, U.S.A.221

Then the cloned shuttle vector and adenoviral vector (pAdEasy-1), deleted E1

for eliminating self-replication capacity, were co-transformed into E. Coli (BJ5183)

and kanamycin was used to select recombinants. The recombinants were digested

with PacI to liberate adenoviral genomes, and then transfected into a packaging cell

line (HEK 293 cells) when the culture confluence reached to 50-70%. Due to the lack

of E1 proteins, the adenovirus cannot replicate in most cell types. HEK293 could

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create E1 to produce adenovirus when HEK293 was transfected with adenovirus.

Two types of adenovirus, Ad-GFP-LEENE for LEENE overexpression and Ad-GFP

as a control set-up, were used. They transfected HEK293 for adenovirus replication.

The HEK 293 cells transfected by adenovirus were harvested when cell dissociation

was around 50% or by checking GFP signals. Shown in Fig. 5.3-3, cells were

harvested after 2 days transfection.

We used snap-freezing physical lysis method to break HEK293 cells and kept

the pellet after centrifugation for isolating pure adenovirus. HUVECs were infected

with adenovirus (Ad-GFP-LEENE) to overexpress LEENE (lncRNA) and earlier

passages (less than 10) of HUVECs were used. When the dissociation of infected

HUVEC cells reached to 50%, cells were harvested, as shown in Fig. 5.3-4.

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Fig. 5.3-3 Adenovirus transfection in HEK293 cells. Ad-GFP-LEENE for LEENE (lncRNA) overexpression, Ad-GFP as control set-up.

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Fig. 5.3-4 Adenovirus infection in HUVECs. 5.3.3 Profiling LEENE using pooling and splitting probes with microfluidic

ChIRP technology

Endothelial nitric oxide synthase (eNOS) plays important roles in endothelial

homeostasis and vascular functions. eNOS expression is regulated by transcription

factors (KLF2 and KLF4), histone modifications, and shear stress (pulsatile and

oscillatory)216. However, the role of lncRNAs in epigenomic modulation of eNOS

expression is still not well characterized. Our collaborator, Dr. Chen, found the

amount of LEENE expression was overwhelming majority when endothelial cells

(ECs) were stimulated with pulsatile shear stress (PS) and LEENE was highly co-

regulated with eNOS by analyzing RNA-seq. She proposed LEENE played crucial

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roles in regulating eNOS and endothelial function. Due to the large number of cells

required, the sequencing data was difficult to create with conventional approaches.

Based on our experience in profiling histone modification by using microfluidic

system, we developed and applied a novel microfluidic technology to examine

whole-genome lncRNA bindings with low-input samples.

We designed Ad-GFP-LEENE to infect HUVECs for LEENE overexpression,

and Ad-GFP as a control set. We also used native HUVECs as the real control set.

We designed ten tiling probes for profiling LEENE binding. For the first test, we

pooled all probes together to test three conditions (LEENE: Ad-GFP-LEENE; GFP:

Ad-GFP; control: no Adenovirus infection). Five hundred thousand and one hundred

thousand cells were used in three conditions to conduct microfluidic oscillatory

hybridized ChIRP, shown in Fig. 5.3-5. Ad-GFP-LEENE as shown in red patterns,

Ad-GFP in green, and native (control) in purple. Fig. 5.3-5 (a) shows the locus of

LINC00520 (LEENE), and it was reasonable that the magnitude of peak in the

condition of LEENE was higher than the other two due to overexpression of LEENE.

There was no LEENE overexpression in conditions of GFP and control, so the

patterns in these two conditions were similar with each other and magnitude of peak

in these two conditions was lower than Ad-GFP-LEENE. Fig. 5.3-5 (b) and (c) show

peak patterns in other loci. We could see clear LEENE binding sites in control set of

100,000 cells. The detail sequencing data information was also listed in Table 5.3-1.

Next, we used splitting-probe groups (even and odd) to examine LEENE

bindings (Fig. 5.3-6). As LEENE overexpressed in Ad-GFP-LEENE, we saw more

significant magnitude of peak in both even and odd probes, than others (Fig. 5.3-6

(a)). Some peak patterns were different between even and odd tiling probes in

infected cells (Ad-GFP-LEENE and Ad-GFP), however, this phenomenon was not

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recognized in native cells (Fig. 5.3-6 (b) & (c)). It seemed that virus infection might

impact on lncRNA bindings in whole genome. The genome-wide magnitude of peaks

was similar among three conditions, except the locus of LINC00520. Since there was

no significant improvement in lncRNA genome-wide bindings when using virus

infection and flase sequencing peaks might be created, we next used primary cells

(no virus infection) to conduct microfluidic ChIRP-seq. The sequencing data

information is listed in Table 5.3-2.

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Fig. 5.3-5 Visualization of normalized sequencing data with pooling probes in IGV (LEENE). Red patterns: Ad-GFP-LEENE; green patterns: Ad-GFP; purple patterns: no adenovirus infection. (a) focusing on LEENE (LINC00520), (b) and (c) focusing on other loci.

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Fig. 5.3-6 Visualization of normalized sequencing data with splitting probes in IGV (LEENE). Red patterns: Ad-GFP-LEENE; green patterns: Ad-GFP; purple patterns: no adenovirus infection. (a) focusing on LEENE (LINC00520), (b) and (c) focusing on other loci.

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The binding sites of LEENE were examined and three conditions (Ad-GFP-

LEENE, Ad-GFP, and Control) were compared, as shown in Fig. 5.3-7. The binding

sites were mapped to genomic regions (Fig. 5.3-7 (a)). We studied data of pooling

probes and splitting probe, and we also examined data of overlapping even and odd

peaks. All Ad-GFP-LEENE data were consistent with each other. No significant

differences were observed. Some slight differences were showed in Ad-GFP and

control samples. Peaks of overlapping even and odd probes from three conditions

were investigated and compared with pooling probes (Fig. 5.3-7 (b)). Using Venn

diagram of splitting probes as a reference, the Ad-GFP-LEENE of pooling probes

showed substantially additional number of peaks. The overexpression might be the

reason for causing these aberrant additional peaks in Ad-GFP-LEENE. Either Ad-

GFP-LEENE or Ad-GFP exhibited lower overlap with control, and it also confirmed

that epigenomic regulation might be changed in the process of overexpression.

Then, we profiled transcription factor binding motifs based on the ChIRP-seq data,

shown in Fig. 5.3-7 (c). LEENE regulates eNOS (endothelial nitric oxide synthase) to

modulate endothelial and vascular function216. Functions of some TFs, enriched in

Fig. 5.3-7 (c), were associated with cardiovascular diseases and atherosclerosis.

FOXH1 is crucial in lymphatic vessel development, angiogenesis, and cardiac

outflow tract formation222. The protein-protein interaction of FOXH1 and NKX2.5 is

essential in anterior heart field (AHF) development223. NKX2.5, a cardiac homeobox

protein, is pivotal in cardiac development224. Mutation in the gene of NKX2.5 causing

cardiac malformations implicates NKX2.5 playing an important role in regulation of

cardiac morphogenesis225. Some of these valuable TFs were not enriched in Ad-

GFP and Ad-GFP-LEENE, but all of them were significantly enriched in native

HUVECs.

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Fig. 5.3-7 The comparison of LEENE binding among Ad-GFP-LEENE, Ad-GFP, and Control. (a) Genomic locations of peaks. Pooling: pooling probes; Splitting: splitting probes; Overlap: overlapping even and odd data. (b) Venn diagram of peak overlap. Pooling probes (upper) and splitting probes (lower). (c) Enriched transcription factors. Black: not significant. 5.3.4 Profiling BY707159.1 with microfluidic ChIRP technology

Chen’s group examined the genomic structure and chromosomal region

between KTN1 and PELI2 to locate BY707159.1 (lncRNA) in mouse chromosome 14

working as LEENE in human216. Her group create wild type and BY707159.1

knockout mice, and shipped murine lung tissues to us. We followed the protocol, as

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shown in method section of this chapter, to isolate single endothelial cells and

sonicated cells following fixation for ChIRP. In here, we used unsorted cells from

murine lung tissues. As shown in Fig. 5.3-8, we could see significant differences

between even and odd tiling probes, however, just few differences between wild type

and BY707159.1 knockout mouse samples. Nevertheless, we still could find

remarkable differences between wild type and knockout mouse samples in the

number of peaks after we filtered the peaks, keeping shared peaks and comparing

even and odd data (Table 5.3-3). The number of peaks in wild type mouse was twice

than in the knockout mouse. It was reasonable that some lncRNA bindings might be

diminished in knockout condition. The fragments of lncRNA binding cannot be pulled

down when target lncRNA is knocked out.

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Fig. 5.3-8 Visualization of normalized sequencing data with splitting probes in IGV (BY707159.1). Red patterns: wild type mouse; green patterns: BY707159.1 knockout mouse. The binding sites of BY7077159.1 were examined and compared between the

wild type and knockout mouse (Fig. 5.3-9). As shown in Fig. 5.3-9 (a), we used even,

odd, and overlapping data from the wild type and knockout mouse to profile genomic

locations of their bindings. Some insignificant changes were shown. The peaks from

knockout data covered 38% of the peaks from wild type sample (Fig. 5.3-9 (b)).

Some epigenomic regulations might persist through process of knockout. Next, we

profiled transcription factor binding motifs based on the ChIRP-seq data, shown in

Fig. 5.3-9 (c). BY7077159.1 is a lncRNA in mice and its function is similar to LEENE

in human in modulating endothelial homeostasis and vascular functions, directly or

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indirectly. Numerous ETS (endothelial specific) factors, such as ETV1 and ETV4,

play important roles in vascular system development226,227. ZNF263, a zinc finger

transcription factor, showed a significant enrichment in our wild type sample. ROR-

alpha mutant mice are vulnerable in atherosclerosis228. ZNF263 may bind to ROR-

alpha to suppress the risk in cardiovascular diseases and atherosclerosis229. All of

them are crucial in modulating endothelial homeostasis and vascular functions, and

these properties are consistent with BY7077159.1.

Fig. 5.3-9 The comparison of BY707159.1 binding between wild type and BY707159.1 knockout mouse. (a) Genomic locations of peaks. (b) Venn diagram of peak overlap. (c) Enriched transcription factors. Black: insignificance.

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5.3.5 Profiling native HOTAIR with microfluidic ChIRP technology

Chu et al. developed ChIRP technology and conducted ChIRP to profile

HOTAIR with MDA-MB-231 cells18. We planned to evaluate the accuracy of our

technology by comparing Chu’s data with ours. We conducted microfluidic ChIRP

with native MDA-MB-231 cells to examine the HOTAIR bindings, and data are shown

in Fig. 5.3-10. The upper two tracks are from Chu’s data, and the rest are from ours.

All four racks show high intensive peaks at the region of HOTAIR (Fig. 5.3-10 (a)).

Chu’s data exhibited taller peaks than ours due to HOTAIR overexpression (y-scale:

180:20). Some peak patterns are similar (Fig. 5.3-10 (b)), but some are different (Fig.

5.3-10 (c) & (d)). We found lncRNA bindings are changed by virus infection at 5.3.3.

Chu used retrovirus to overexpress HOTAIR in MDA-MB-231 cells, and this might be

the reason that there are abundant differences between Chu’s and our data.

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Fig. 5.3-10 Visualization of normalized sequencing data with splitting probes in IGV (HOTAIR). Red patterns: HOTAIR overexpression from Chang’s group; green patterns: native HOTAIR from our group. (a) Focusing on HOTAIR, (b) similar patterns between two groups, (c) and (d) distinct patterns.

We then compared the HOTAIR bindings between Chu’s and our data. We

examined even, odd, and overlapping sequencing data to investigate genomic

locations (Fig. 5.3-11 (a)). There was no significant difference among all data, but

some slight differences existed after overlapping. The peaks of overlapping were

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compared, shown in Fig. 5.3-11 (b). Most of Chu’s and our peaks did not overlap

with each other well. We suspect that the difference in epigenomic regulation is a

result of the overexpression process. Next, we profiled transcription factor binding

motifs (Fig. 5.3-11 (c)). Even though Chu’s data exhibited more additional TFs than

ours, some TFs were enriched in both data. ETS (epithelium specific) genes, such

as EHF, ELF3, ELF4, ERG, ETS1, and ETV2, associate with Ewing’s sarcoma230,

play critical roles in cell proliferation and apoptosis. In particular, the expression of

EHF and ELF3 is significantly upregulated in breast cancer cells231. EHF promotes

the migration and invasion of gastric cancer cells by modulating HRP family of

receptor tyrosine kinases232. More expression of ELF3 has been observed in breast

carcinomas than normal tissues233. Both GABPA and PU.1 are members of the ETS-

domain transcription factor family234,235. Taken together, our results suggests that

HOTAIR plays a crucial role in breast cancer cells or other carcinoma by modulating

the mechanisms of ETS family.

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Fig. 5.3-11 The comparison of HOTAIR binding between Chu’s and our data. (a) Genomic locations of peaks. (b) Venn diagram of peak overlap. (c) Enriched transcription factors.


Detailed experimental processes and information of ChIRP-seq are shown in

Appendix


As shown in the Chapter 3

Mice

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C57BL/6 mice were purchased from Jackson Laboratory. 6-8 month-old male and

female mice were used. All processes performed on mice were approved by the

Institutional Animal Care and Use Committee (IACUC) of City of Hope National

Medical Center.

Cell culture The stored cells were centrifuged at 300 rcf for 5 min at 4˚C and supernatant was

discarded for diminishing DMSO. The cell pellet was resuspend with pre-warmed

medium and added to a 25T flask with 5ml pre-warmed medium. Then, the flask was

incubated at 37˚C in a humid incubator with 5% CO2. When the confluence was

around 80%, we used PBS to wash cells and used trypsin to harvest cells.

Adenovirus transfection

The adenovirus was added to 150T flask when the confluence of HEK293 reached to

50-70%, then the flask was incubated at 37˚C in a humid incubator with 5% CO2 for

2-4 days. The transfected HEK293 cells were harvested by trypsin and isolated at

1000 rpm for 5 min at 4˚C. The pellet was resuspended with PBS. We used snap-

freeze approach to purify amplified adenovirus.

Adenovirus infection and cells harvest (LEENE)

Adenovirus was used to infect HUVECs for overexpressing LEENE when the

confluence reached to 80%. The medium was changed after 6 hr. The HUVECs

were harvested after 2-4 days by checking the signal of fluorescence. We collected

and isolated HUVECs at 1000 rpm for 5 min at 4˚C.

Lentivirus infection and cell harvest (HOTAIR)

We added lentivirus to infect MDA-MB-231 for overexpressing HOTAIR when the

confluence reached to 80%. The medium was changed after overnight culture. The

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MDA-MB-231 were harvested after 2-4 days by checking fluorescence. We collected

and isolated MDA-MB-231 at 1000 rpm for 5 min at 4˚C.

Isolation of mouse lung endothelial cells from lung tissue (BY707159.1)

The murine lung tissue was soaked in Collagenase solution and cut into pipettable

pieces. The tissue digestion was performed on the rotator at 37˚C for 45 min. 70 um

sterile syringe filter was used to discard undigested tissue. The cell pellet, after

centrifuging at 300 rcf 4˚C for 10 min, was resuspended with binding buffer, and

incubated on a rotator at 4˚C for 15 min with CD31 microbeads. Endothelial cells

were isolated with a magnet rack.

Cell fixations

The cell pellet was resuspended with 1% glutaraldehyde on a rotator at room

temperature for 10 min. The fixation was quenched by adding 125 mM glycine for 5

min. Fixed cells were isolated by centrifuging at 2000 rcf 4˚C for 5 min.

Chromatin shearing

The fixed cells were resuspended with lysis buffer and 1/100th volume of PIC, PMSF,

and Superase were added. We used 30 s ON and 45 s OFF pulse intervals to

sonicate fixed cells. The target fragment size was in the range of 100-500 bp.

Probes designing

We used the software offered by Biosearch Technologies to design probes. More

detail information was shown in Appendix.

Beads and sample preparation

MyOne streptavidin C1 Dynabeads were washed with lysis buffer thrice and

incubated with lysis buffer, probes, PIC, PMSF, and Superase on a rotator at 37˚C

for 2 hr. After that, Dynabeads were washed with lysis buffer, isolated with a magnet

rack, and resuspended with lysis buffer. The sample solution (after sonication) was

102

mixed with at least 2X volume of hybridization buffer, and PIC, PMSF, and Superase

were added.

Device operation for ChIRP

The chamber was filled with lysis buffer and sieve valve was filled with DI water. A

magnet was placed under the glass of the chamber and a heat control plate (37˚C)

was located under the magnet. A long PFA tube was connected with outlet part of a

device and one solenoid valve in a two-set solenoid manifold. The sieve valve was

closed after sample was loaded through syringe pump to a long PFA tube (prior

step). The beads were loaded into the inlet part by a pipette. The sieve valve was

opened after another long PFA tube was connected with inlet part and another

solenoid valve in the two-set solenoid manifold. We used LabVIEW to manipulate the

oscillatory hybridization for 4 hr. The sample solution was withdraw to a long tube

connected with a syringe. The sieve valve was closed again and beaded was leaded

to stay before the sieve valve by a magnet. The sample solution was loaded to

chamber and flowed through packed beads for increasing the efficiency of

hybridization (around 40-60 min). Washing buffer was loaded to two small tubes

connected with inlet and outlet parts respectively, and oscillatory washing was

performed twice. The beads were collected after washing and separated 2 parts

(10% and 90%).

RNA isolation and assay

The 10% beads were resuspended with 95 ul RNA PK buffer and 5 ul PK and

incubated at 50˚C for 45 min with shaking. The activity of PK was quenched by

incubated at 95˚C for 10 min. TRIzol and chloroform were used to isolate RNA

sample. The aqueous phase was kept after centrifugation at 12000 rcf at 4˚C for 15

min. 2ul RNase-free glycogen and 250 ul IPA were mixed well with aqueous phase.

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The pellet after centrifugation was resuspended with nuclease-free H2O. Following

the protocol of High-capacity cDNA reverse transcription kit (ThermoFisher), we got

the cDNA for examining the efficiency of RNA retrieval by qPCR.

DNA isolation and assay

The 90% beads were mixed well with RNA digestion solution, and incubated at 37˚C

for 45 min with shaking. The supernatant was kept and beads were incubated with

RNA digestion solution again. The total supernatant was mixed with PK and

incubated at 50˚C for 2 hr with shaking. Phenol-chloroform-isoamyl alcohol was

added and aqueous phase was kept after centrifugation. Glycogen, NaOAc, and

ethanol were added for ethanol precipitation. The pellet, after centrifugation, was

used for analysis by qPCR and library preparation.

Library preparation













end 50 nt read.

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5.5 Conclusions and Future work

We took advantages of the ease of operation, reduced number of cells, and

improved sensitivity of microfluidic technology to develop a novel microfluidic

oscillatory hybridized ChIRP assay. Using this device, we found significant

differences between overexpressed and native cells in profiling genome-wide

lncRNA bindings. We used this technology to examine different lncRNAs (LEENE

(lncRNA that enhances eNOS expression), HOTAIR and BY707159) in a cell line

(MDA-MB-231), a primary cell line (HUVECs) and primary murine cells from lung

tissues. We conducted microfluidic ChIRP-seq with not only adenovirus-infected

cells but native state cells. We found some different peak patterns between even and

odd tiling probe conditions in profiling LEENE binding sites with infected cells. The

binding sites were mapped to genomic regions, and there was no significant

difference among even-, odd-probe data and overlapping data. However, substantial

peaks from virus-overexpression did not overlap well with native state. Next, we

profiled transcription factor binding motifs and found some TFs were downregulated

in examining LEENE of overexpressed state but some additional TFs were

upregulated in studying HOTAIR of overexpressed state comparing to native state.

Overexpression might change the epigenomic regulations and epigenomic results of

specific lncRNA in overexpression could not represent the native/true state. This, for

the first time, allowed us to probe native lncRNA bindings in mouse tissue samples

successfully. This technology is a promising approach for scientists to study lncRNA

binding in primary patients and it may make contributions to developing precision

medicine.

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Chapter 6 Summary and Outlook

Owing to the rapid advances in sequencing technology, immunotherapy, and

genome editing, the success of developing more effective precision medicine is a

stone’s throw away. FDA approved 25 precision medicine treatments in 2018.

However, the number is not enough for human disease treatments. More and more

laboratories focus on the epigenomics for outlining the feasible mechanism of gene

regulation. This is a very important step to provide valuable information for designing

novel treatments and medicines.

Microfluidic technology provides an alternative approach to explore the field of

genomic and epigenomic regulations using less cells. In this thesis, I described my

projects in 1) profiling epigenomic changes associated with LPS-induced murine

monocytes for immunotherapy, 2) examining cell-type-specific epigenomic changes

associated with BRCA1 mutation in breast tissues for breast cancer treatment, and

3) developing novel microfluidic oscillatory hybridized ChIRP-seq assay to profile

genome-wide lncRNA binding for numerous human diseases.

Scientists have used bulk cells to construct and develop the knowledge of

epigenomic regulations. Since intercellular differences exist at the single-cell level

and signal of an individual single cell may be diminished or even erased in bulk cell

assays, studying epigenomics in single-cell may transform our understanding of

gene regulation mechanism. Based on the advance in technologies, profiling

epigenomics in single cell is not an unreachable dream any more. Guo et al.

developed single-cell DNA methylation technology in 2013236, and random priming

technology was used to improve the quality of sequencing data in 2014237. Now,

single-cell DNA methylation is prevalent in profiling cell identity238-240. Single-cell

ChIP-seq was developed in 2015241 and single cell ATAC-seq (Assay for

106

Transposase-Accessible Chromatin) was also launched in 2015242. Both methods

are conducted with microfluidic technologies. We developed microfluidic oscillatory

hybridized ChIRP-seq assay to profile whole-genome lncRNA bindings, and our

approach reduced the required number of cells to few thousands. However,

technologies for exploring single-cell lncRNA binding remain to be developed.

Brouzes et al. developed a droplet microfluidic technology for single-cell high-

throughput screening243. Droplet is an ideal microreactor or nanoreactor that

contains single cell in it for reaction. Single cell droplet technology has been applied

in RNA-seq244,245 and ChIP-seq241. Drop-seq is an accessible method to profile

single-cell lncRNA bindings. However, due to the extremely few amount of lncRNAs

possessing in one cell (copy number of MLLT4-AS1 and PRINS in Bladder Urothelial

Carcinoma (BLCA) is 3 and 1, respectively246), numerous substantial challenges

need to be considered and overcome for studying lncRNA bindings in single cell.

Based on success in profiling lncRNA bindings with primary native cells, we

next turn to examine proteins associated with lncRNA in modulating epigenomic

mechanism and gene expression. RNA-binding Protein Immunoprecipitation (RIP)

with quantitative real-time PCR (RIP-PCR) was used to study the mechanism of

lncRNA and associated proteins binding247. The possible associated protein was

pulled down with specific antibody and then the expression of lncRNA was evaluated

by RT-PCR following lncRNA isolation. The mechanism of HOTAIR mediating gene

silencing is reported using RIP-PCR data. HOTAIR interacts with PRC2 (containing

EZH2, SUZ12, and EED) to bind to target gene loci and silence their expression

through EZH2 introducing H3K27-trimethylation248. Knowing possible associated

proteins and using one specific antibody to capture associated protein are significant

obstacles in conducting RIP-PCR for building mechanism of lncRNA regulation.

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Liquid chromatography (LC) is a technique to separate molecules by different

retention time (RT) and mass spectrometry (MS) is a high-throughput technique for

protein identification by analyzing peptides. Liquid chromatography-tandem mass

spectrometry (LC-MS/MS) is a prevalent technology for protein identification249,250.

Chu et al. profiled Xist (lncRNA) binding proteins through LC-MS/MS with cell

lines251. Native cells and cells from cell lines show different genetic and epigenetic

regulations, and these changes also affect the associated protein binding.

The growing evidence implies that HOTAIR plays a critical role in promoting

tumor growth and cancer metastasis41,252. However, the regulatory mechanism of

HOTAIR in genome-wide association study remains to be defined. Our microfluidic

ChIRP can be used to examine HOTAIR binding sites and isolate HOTAIR

associated proteins with primary animal and patient samples. These associated

proteins can be recognized by LC-MS/MS and western blot can be used for further

identification. In addition, the histone modification by HOTAIR regulation is pivotal in

epigenomic alternation. CRISPR-Cas13d is a robust technology in modulating

splicing of endogenous transcripts and specific RNA knockdown without interrupting

other gene expression and regulation253,254. We use this technique to knockdown

HOTAIR in primary cells and conduct MOWChIP-seq with anti-H3K27ac and anti-

H3K27me3 to profile epigenomic regulation between native and HOTAIR knockdown

primary cells. H3K27ac is defined as an active enhancer mark and H3K27me3 as a

repressive mark. Combination of whole-genome HOTAIR binding sites, HOTAIR

associated proteins, RNA-seq, and histone modification by HOTAIR regulation

provide a more comprehensive insight into epigenetic regulations of HOTAIR. This,

will be the first time to profile HOTAIR mechanism with primary mammalian cells.

108

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127

Publications

Journal Articles

1. Hsieh, Y.-P., Naler, L. B., Lu, C., “Microfluidic oscillatory hybridized ChIRP-

seq assay to profile genome-wide lncRNA bindings,” In Preparation.

2. Hsieh, Y.-P., Naler, L. B., Zhang, X., Murphy, T. W., Li, R., Lu, C., “Profiling

cell-type-specific epigenomic changes associated with BRCA1 mutation in

breast tissues using a low-input microfluidic technology,” In Preparation.

3. Hsieh, Y.-P., Sarma, M., Geng, S., Lu, C., Li, L., “Profiling epigenomic

changes and regulation associated with LPS-infected macrophages,” In

Preparation.

4. Murphy, T. W., Hsieh, Y.-P., Zhu, B., Naler, L. B., Lu, C., “Microfluidic

platform for next-generation sequencing library preparation with low-input

samples,” Analytical Chemistry 92 (2020) 2519-2526. (impact factor =6.350)

5. Zhu, B.#, Hsieh, Y.-P.#, Murphy, T. W., Zhang, Q., Naler, L. B., Lu, C.,

“MOWChIP-seq for low-input and multiplexe profiling of genome-wide histone

modifications,” Nature Protocols 14 (2019) 3366-3394. (co-first authors)

(impact factor =11.334)

6. Zhang, X., Wang, Y., Chiang, H.-C., Hsieh, Y.-P., Lu, C., Park, B. H., Jatoi, I.,

Jin, V. X., Hu, Y., Li, R., “BRCA1 mutations attenuated super-enhancer

function and chromatin looping in haploinsufficient human breast epithelial

cells,” Breast Cancer Research 21 (2019) 51. (impact factor =5.676)

7. Zhang, R., Qi, C.-F., Hu, Y., Shan, Y., Hsieh, Y.-P., Xu, F., Lu, G., Dai, J.,

Gupta, M., Gui, M., Peng, L., Yang, J., Xue, Q., Chen-Liang, R., Chen, K.,

Zhang, Y., Fung-Leung, W.-P., Mora, J. R., Li, L., Morse, H. C., Ozato, K.,

Heeger, P. S., Xiong, H., “T follicular helper cells restricted by IRF8 contribute

128

to T cell-mediated inflammation,” Journal of autoimmunity 96 (2019) 113-121.

(impact factor =7.543)

8. Murphy, T. W., Hsieh, Y.-P., Ma, S., Zhu, Y., Lu, C., “Microfluidic low-Input

fluidized-bed enabled ChIP-seq device for automated and parallel analysis of

histone modifications,” Analytical Chemistry 90 (2018) 7666–7674. (impact

factor =6.350)

9. Ma, S., Hsieh, Y.-P., Ma, J., Lu, C., “Low-input and multiplexed microfluidic

assay reveals epigenomic variation across cerebellum and prefrontal cortex,”

Science Advances 4 (2018) eaar8187. (impact factor =12.804)

10. Sun, C., Hsieh, Y.-P., Ma, S., Geng, S., Cao, Z., Li, L., Lu, C.,

“Immunomagnetic separation of tumor initiating cells by screening two surface

markers,” Scientific Reports 7 (2017) 40632. (impact factor =4.122)

11. Hsieh, Y.-P., Lin, S.-C., “Effect of PEGylation on the activity and stability of

horseradish peroxidase and L-Ncarbamoylase in aqueous phases,” Process

Biochemistry 50 (2015) 1372-1378. (impact factor =2.529)

Conference Presentation

Hsieh, Y.-P., Naler, L. B., Zhang, X., Murphy, T. W., Li, R., Lu, C., Profiling cell-type-

specific epigenomic changes associated with BRCA1 mutation in breast tissues

using a low-input microfluidic technology. AIChE 2019, Orlando, FL (Oral

presentation)

129

Appendix A

Microfluidic oscillatory hybridized ChIRP-seq protocol


As shown in the Chapter 3

Mice

C57BL/6 mice were purchased from Jackson Laboratory. 6-8 month-old male and

female mice were used. All processes performed on mice were approved by the

Institutional Animal Care and Use Committee (IACUC) of City of Hope National

Medical Center.

Cell culture

1. Thaw stored vial (liquid nitrogen) at 37˚C water bath for 1-2 min

2. Move cells to a microtube and centrifuge suspension at 300 rcf for 5 min at

4˚C

3. Discard the supernatant, and resuspend cell pellet with 1ml pre-warmed

medium

4. Add 4ml prewarmed medium to a 25T flack, then add step 3 cells

5. Mix well the 25 flask, and incubate at 37˚C in a humid incubator with 5% CO2

6. Change medium 2 days later, and subculture cells when the confluence is

around 80%

7. Remove medium, add 5ml prewarmed PBS for washing, and then discard

PBS

8. Add 1ml 0.25% trypsin to a 25T flask (2ml for 75T, 4ml for 150T) and mix well

9. Incubate for 5-15 min at 37˚C in a humid incubator with 5% CO2 (check cell

dissociation every 5 min)

130

10. Add medium (over 2x volume of trypsin for quenching), and collect cells by a

scraper to a tube

11. Centrifuge a tube at 300 rcf for 5 min at 4˚C

12. Discard supernatant, add medium, and repeat step 11

13. Discard supernatant, resuspend cell pellet with prewarmed medium, and add

to a bigger task

Adenovirus transfection

Due to the lack of E1 protein, adenovirus cannot be produced with most cell types.

HEK293 can create E1 to produce adenovirus when HEK293 is transfected with

adenovirus

1. Culture HEK293 in a 150T flask until confluence reaches to 50-70%

2. Change medium and add 100ul adenovirus (-80˚C, thaw at ice)

3. Incubate at 37˚C in a humid incubator with 5% CO2 for 2 days (check the

efficiency of transfection by detecting fluorescence)

4. Discard used medium, add 4ml 0.25% trypsin and incubate at 37˚C in a humid

incubator with 5% CO2 for 5 min


scraper to a tube

6. Centrifuge a tube at 1000 rpm for 5 min at 4˚C

7. Discard supernatant, wash the pellet with 5ml DPBS, and discard supernatant

after centrifuge (1000 rpm for 5 min at 4˚C)

8. Repeat step 7

9. Add 450ul PBS, resuspend, and transfer to 1.5ml cryogenic tube.

10. Snap-freeze the 1.5ml cryogenic tune into liquid nitrogen for 10s and stay in

37˚C water bath for 1 min and do more 5 cycles for lysis

131

11. Transfer to 1.5ml microtube, centrifuge at 1000 rpm for 5 mins at 4˚C and

keep the supernatant with released virus particles (~400ul purified

adenovirus)

12. Store at -80˚C

Adenovirus infection and cells harvest (LEENE)

1. Culture HUVECs in a 150T flask until confluence reaches to 80%

2. Change medium and add 200ul adenovirus to incubate for more 4-6 hr

3. Change medium and incubate for more 2-3 days (check the efficiency of

transfection by detecting fluorescence)




scraper to a tube


7. Discard supernatant, wash the pellet with 5ml DPBS 2, and discard

supernatant after centrifuge (1000 rpm for 5 min at 4˚C)

8. Repeat step 7

9. Store cell pellet at -80˚C

Lentivirus infection and cell harvest (HOTAIR)

1. Culture MDA-MB-231 in a 150T flask until confluence reaches to 80%

2. Change medium and add 100ul lentivirus to incubate overnight

3. Change medium and incubate for more 2-3 days (check the efficiency of

transfection by detecting fluorescence)



132


scraper to a tube


7. Discard supernatant, wash the pellet with 5ml DPBS 2, and discard

supernatant after centrifuge (1000 rpm for 5 min at 4˚C)

8. Repeat step 7

9. Store cell pellet at -80˚C

Isolation of mouse lung endothelial cells from lung tissue (BY707159.1)

1. Take frozen murine lung tissue from -80˚C and thaw on ice

2. Resuspend 50 mg type I Collagenase with 25 ml DPBS+Ca2+/Mg2+ (2

mg/ml) and warm at 37˚C water bath for 10 min

3. Filter step 2 with 0.22 um sterile syringe filter to a new 50ml tube

4. Prepare binding/washing buffer- 0.5% BSA, 2mM EDTA in PBS, keep on ice

5. Wash lung tissue with cold PBS and then move lung tissue to a new petri dish

6. Add 5ml Collagenase (step 3) to step 5, cut lung tissue into pipettable pieces

and transfer to the tube of step 3

7. Incubate on a rotator at 37˚C for 45 min

8. Pour to a petri dish, use a 30 ml syringe to gently perform push-pull action 15

times for getting single cells (avoid bubble)

9. Filter step 8 with 70 um sterile syringe filter to a new 50 ml tube

10. Centrifuge at 300 rcf 4˚C for 10 min

11. Discard suspension (I leave 0.5 ml solution in the bottom, cannot see the

pellet), resuspend with 3ml binding/washing buffer

12. Add 20 ul CD31 microbeads (mix well before use it)

13. Mix well and incubate on a rotator at 4˚C for 15 min

133

14. Add 2 ml binding/washing buffer, and centrifuge at 300 rcf 4˚C for 10 min for

washing

15. Discard supernatant and resuspend with 500 ul binding/washing buffer and

move to a 1.5 ml microtube

16. Place on a magnetic rack, discard supernatant, and add 500 ul

binding/washing buffer (do not disturb beads)

17. Repeat step 16 twice

18. Discard supernatant, and store cell pellet with magnetic beads at -80˚C

Cells fixation

1. Prepare 1% glutaraldehyde with PBS at room temperature (10 ml for 10

million cells)

2. Resuspend cell pellet with 10 ml 1% glutaraldehyde, and put on a rotator at

room temperature for 10 min

3. Add 1/10th volume of 1.25 M glycine to step 2 on a rotator at room

temperature for 5 min to quench the fixation

4. Centrifuge at 2000 rcf 4˚C for 5 min, and discard supernatant

5. Add 10 ml chilled PBS, centrifuge at 2000 rcf 4˚C for 5 min, and discard

supernatant

6. Add 3 ml chilled PBS, centrifuge at 2000 rcf 4˚C for 3 min, and discard

supernatant

7. Perform chromatin shearing immediately or store at -80˚C

Chromatin shearing

1. Prepare Lysis buffer (50mM Tris-Cl pH 7.0, 10mM EDTA, 1% SDS), and set

4˚C for Bioruptor (check the amount of D.I. water in the cooler)

134

2. Thaw the cell pellet on ice, add proper amount of Lysis buffer and transfer to

Bioruptor microtubes

3. Add 1/100th volume of PIC, PMSF and Superase, and mix well

4. Place on the tube holder for Bioruptor (each site of holder needs to load a

microtube, put microtube with water if number of samples is less than 6)

5. Sonicate at 4˚C with 30 s ON and 45 s OFF pulse intervals until most

chromatin is solubilized and the size of DNA fragments is in the range of 100-

500 bp

6. Move cells to a new microtube and centrifuge at 16100 rcf and 4˚C for 5 min

7. Keep supernatant (do sonicate more time if the pellet is big) and store at

-80˚C

Fragment size checking

1. Take the proper volume (~2500 cells) of sonicated chromatin to a 1.5 ml

microtube, add 198 ul PBS and 2 ul proteinase K, and mix well

2. Place on a thermal cycle plate at 65˚C for 8 hr

3. Add 200 ul phenol-chloroform-isoamyl alcohol and mix well by vortex

4. Centrifuge at 16100 rcf and room temperature for 5 min

5. Transfer supernatant to a new 1.5 ml microtube and add 750 ul 100% ethanol,

50 ul 10M ammonium acetate and 5 ul of 5 ug/ul glycogen. Place on -80˚C for

2 hr for precipitation.

6. Centrifuge at 16100 rcf and 4˚C for 10 min

7. Discard supernatant, add 500 ul 70% enthanol (do not disturb the pellet) and

centrifuge at 16100 rcf and 4˚C for 5 min

8. Discard supernatant and air dry in the biosafety cabinet for 5 min

9. Resuspend with 3 ul D.I. water

135

10. Take 2 ul from step 9 mix well with 2 ul High sensitivity D1000 sample buffer

and load to TapeStation machine to analyze DNA fragment size

Probes designing

Based on the sequence of target long noncoding RNA, we can design several 20 nt

biotinylated probes whose sequences are complementary to the sequence of target

lncRNA. All probes we used for profiling lncRNAs binding sites were designed and

produced by LGC Biosearch Technologies with ChIRP Probe Designer version 4.2.

The probes are stored in a deep 96-well plate.

1. Add proper amount of Low EDTA to each sample well and resuspend. The

final concentration of each probe is 100 uM.

2. Prepard even and odd pooling probe sets. The final concentration of even and

odd pooling probe sets 10 uM.

3. Store at -20˚C.

LEENE – https://www.ncbi.nlm.nih.gov/nuccore/NR_026796 2092 bp

Oligo length:20nt, min. spacing length:80nt, masking level:5

LEENE-1 atagaatcttgcttgggcag

LEENE-2 ctgagtggattgtaggtgtt

LEENE-3 tcctgtcttcttacttgtac

LEENE-4 ccccaaaatcctttaaggta

LEENE-5 tgtctctgggaagaggagag

LEENE-6 tggatgtaaagactggtgcc

LEENE-7 ctgagctgtagaatccacag

LEENE-8 ctgacatctcatcaagggag

LEENE-9 gctcatcaagaagcagctag

LEENE-10 agtcacaagaagtccaaggc

136

HOTAIR – https://www.ncbi.nlm.nih.gov/nuccore/NR_003716 2364 bp


HOTAIR-1 gatcaagctccagagcacag

HOTAIR-2 caggacctttctgattgaga

HOTAIR-3 tggcatttctggtcttgtaa

HOTAIR-4 ttcgtggttgctttttctac

HOTAIR-5 cacgtttgttccgggaactg

HOTAIR-6 gattattctctctgtactcc

HOTAIR-7 gtcttgttaacaagcctcat

HOTAIR-8 acgcccaacagatatattgt

HOTAIR-9 gtttgggcctcctaaaattg

HOTAIR-10 caagtagcagggaaaggctt

HOTAIR-11 ctatgttcctctcaaattcc

HOTAIR-12 tcaagagcttccaaaggcta

HOTAIR-13 gatgcattcttctagaccta

HOTAIR-14 ggggtctatatttagagtgc

HOTAIR-15 aggaggaagttcaggcattg

HOTAIR-16 tacctacccaatgtatggaa

HOTAIR-17 cctgagtctatttagctaca

BY707159 – https://www.ncbi.nlm.nih.gov/nuccore/BY707159 680 bp


BY707159-1 aaggggtgagacagcatctt

BY707159-2 cacaataacacacccctttg

BY707159-3 ctgagaggtagttcttctca

BY707159-4 acatcagttggcaactcgtc

137

BY707159-5 atgagcacagtgagtcatga

BY707159-6 gcaagggagcaaggttgaca

BY707159-7 gcaggcaggaaatctgtgaa

BY707159-8 ccaaagggatgcctaatgtg

Bead and sample preparation

10 ul Dynabeads MyOne streptavidin and 10 ul 10 uM probes for one assay.

1. Prepare hybridization buffer (750 mM NaCl, 50 mM Tris-Cl pH 7.0, 1 mM

EDTA and 15% Formamide). Cover with aluminum foil. Formamide is light

sensitive. Place on the 37˚C water bath for 5 min if it is stored at 4˚C.

2. Wash 20 ul Dynabeads MyOne streptavidin C1 with 100 ul lysis buffer thrice.

Place on the magnetic rack for 1-2 min, discard supernatant (do not disturb

beads), and add lysis buffer.

3. Add 130 ul lysis buffer, 20 ul (even or odd) probes, 1.5 ul PIC, 1.5 ul PMSF,

and 1.5 ul Superase into a 1.5 ml microtube.

4. Place on a rotator at 37˚C for 2 hr.

5. Place microtube on a magnetic rack until pellet is formed. (~2 min)

6. Wash pellet with 100 ul lysis buffer twice. (Do not disturb beads)

7. Add 20 ul lysis buffer (the original volume of beads), and resuspend

8. Add proper amount of sample and at least 2X volume of hybridization buffer

(compared to the volume of sample) to a new 1.5 microtube. The total volume

is 100ul.

9. Add 1 ul PIC, 1 ul PMSF, and 1 ul Superase.

Device operation for ChIRP

1. Prepare washing buffer (2x SSC, 0.5% SDS)

2. Place a microfluidic device on a microscopy

138

3. Set the pressure to 30 psi for the solenoid valve, and fill the sieve valve in the

microfluidic device when turn on the solenoid valve (~5 min)

4. Fill the a syringe with lysis buffer, and load lysis buffer to a microfluidic device

by injecting a PFA tube, connecting with a syringe, to the inlet part of the

device to remove air bubbles (load buffer at 50 ul/min for 5 s while turning on

the solenoid valve following by turning off the solenoid valve thrice)

5. Place a magnet, contacting with a heat control plate (37˚C), under a

microfluidic device

6. Connect the outlet part of a microfluidic device and one solenoid valve in a

two-set solenoid manifold with a long PFA tube whiling turning off the solenoid

valve

7. Load sample to the long PFA tube, connecting with a syringe, by withdrawing

the syringe (keep air space before withdrawing)

8. Load sample to a long PFA tube at step 5 through chamber at 200 ul/min by

connecting at inlet part of the device. Turn on the solenoid valve and turn off

the pump simultaneously when sample is almost loaded.

9. Load beads into the chamber by using a pipette from the inlet part of the

device

10. Connect the inlet part of a microfluidic device and another solenoid valve in a

two-set solenoid manifold with another long PFA tube whiling turning on the

solenoid valve

11. Turn off the solenoid valve, and adjust pulses to make amount of sample in

two long PFA tubes same

139

12. Use LabVIEW program to set interval to 60 s, adjust to proper pressure (~0.5

psi) for pulses to make as much as possible volume of sample in each tube

can move through the chamber but no bubble created in the chamber

13. Perform the oscillatory hybridization at step 11 at 37˚C, 60 s interval, 0.5 psi

for 4 hr. Make beads stay in the chamber by using magnet

14. Use LabVIEW to make pulse through the inlet part of the solenoid valve in a

two-set solenoid manifold, and turn off the solenoid valve. Make almost all

sample stay in the long PFA tube connecting with outlet part of the chamber

15. Connect the inlet part of the chamber with long PFA tube, connecting with the

syringe, turn off the solenoid valve, and withdraw the syringe to collect

sample. Keep space before withdrawing, and use magnet to make beads stay

in the chamber

16. Remove the long PFA tube connecting with outlet part, turn on the solenoid

valve, make packed-bed beads before sieve valve with magnet, load sample

to chamber by operating the syringe with pump at 2 ul/min for around 40-60

min

17. Load more washing buffer to the chamber to wash out non-target material at

10 ul/min for 2 min

18. Remove syringe tube, turn on the solenoid valve, and load washing buffer to

two washing tubes (each tube connecting with inlet/outlet part and one

solenoid valve in a two-set solenoid manifold)

19. Turn off the solenoid valve, perform oscillatory washing at 1 psi, 0.3 s interval

for 7.5 min

20. Do step 17-19 again

140

21. Collect beads to a new 1.5 ml microtube and place on the magnetic rack until

a pellet is formed (~1-2 min)

22. Discard supernatant and load 100 ul washing buffer to mix the pellet

23. Place on the magnetic rack until a pellet is formed

24. Discard supernatant and load 100ul lysis buffer to mix

25. Separate 2 parts (10ul for RNA extraction and 90ul for DNA extraction)

RNA isolation and assay

Follow the instructions of TRIzol (invitrogen) and High-capacity cDNA reverse

transcription kit (ThermoFisher) with minor modification

1. Prepare RNA PK buffer (100mM NaCl, 10 mM Tris-Cl pH 8.0, 1 mM EDTA,

0.5 % SDS)

2. Place 10ul sample (after ChIRP) to magnetic rack and discard supernatant

3. Add 95 ul RNA PK buffer and 5 ul PK, mix well with vortex, and spin down

briefly

4. Incubate at 50˚C for 45 min with shaking (1000 rpm)

5. Spin down briefly and incubate sample at 95˚C for 10 min

6. Chill sample on ice for 5 min immediately following step 5

7. Add 500 ul TRIzol and mix well by pipetting

8. Incubate for 5 min at room temperature

9. Add 100 ul chloroform and mix well by pipetting

10. Incubate for 5 min at room temperature

11. Centrifuge at 12000 rcf at 4˚C for 15 min

12. Transfer the aqueous phase to a new 1.5 ml microtube (containing RNA)

13. Add 2 ul RNase-free glycogen and 250 ul IPA, and incubate for 10 min at

room temperature

141


15. Discard the supernatant and resuspend the pellet with 500 ul 75 % ethanol

16. Vortex briefly, and centrifuge at 7500 rcf at 4˚C for 5 min

17. Discard the supernatant and air dry in BSC for 5-10 min

18. Resuspend the pellet with 15 ul nuclease-free H2O

19. Add 2 ul 10X RT buffer, 0.8 ul 25X dNTP Mix, 2 ul 10X RT Random Primers,

and 1 ul MultiScribe Reverse Transcriptase

20. Mix well by pipetting and load to 96-well plate

21. Centrifuge briefly and treat with the following program using a thermocycle:

25˚C for 10 min, 37˚C for 120 min, 85˚C for 5min, and 4˚C forever)

22. Check the efficiency of RNA retrieval by qPCR

DNA isolation and assay

1. Prepare DNA elution buffer (100 mM NaCl, 10 mM Tris-Cl pH 8.0, 1 mM

EDTA, 0.5 % SDS) and 3M NaOAc

2. Prepare fresh RNA digestion solution (2 ul RNase A (100 mg/m) and 3.2 ul

RNase H (60 U/ul) in 1 ml DNA elution buffer), and mix well with a pipette

3. Place 90ul sample (after ChIRP) to magnetic rack and discard supernatant

4. Add 150 ul RNA digestion solution and incubate at 37˚C for 45 min with

shaking (1000 rmp)

5. Place on a magnetic rack until a bead-pellet is form

6. Transfer supernatant to a new labeled microtube

7. Resuspend bead-pellet with 150 ul RNA digestion buffer and incubate at 37˚C

for 45 min with shaking (1000 rmp)

8. Place on a magnetic rack and transfer supernatant to the labeled microtube at

step 6

142

9. Add 15 ul PK to step 8 and incubate at 50˚C for 2 hr with shaking (1000 rpm)

10. Spin down briefly

11. Add 300 ul phenol-chloroform-isoamyl alcohol and mix well by vortex for 5 min


12. Transfer the aqueous phase (~300 ul) to a new microtube

13. Add 5 ul glycogen, 30 ul NaOAc and 900 ul 100 % ethanol. Mix well by vortex

and store at -20˚C overnight


15. Discard supernatant and add 500 ul 70 % ethanol


17. Discard supernatant and air dry in BSC for 5 min

18. DNA is ready for analysis by qPCR and library preparation

Library preparation












143


end 50 nt read.

144

Summary of sequencing data information

Table 3.3-1 Summary of MOWChIP sequencing data information. Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks (k)

Mapping

efficiency (%)

PBS_Rep1 15.91 15.88 15.36 31.84 96.73

PBS_Rep2 13.81 13.78 13.45 26.35 97.59

Low LPS_Rep1 21.37 21.33 20.40 26.03 95.65

Low LPS_Rep2 19.38 19.37 18.89 21.72 97.51

High LPS_Rep1 19.37 19.35 18.56 16.65 95.91

High LPS_Rep2 38.29 38.27 37.24 15.80 97.32

Table 4.3-1 Summary of MOWChIP sequencing data (H3K27ac, non-carrier) information in human samples.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks (k)

Mapping

efficiency (%)

BSC157-L-B1 19.64 19.49 18.77 13.58 96.28

BSC162-L-B1 8.15 7.53 7.03 3.56 93.27

BSC162-L-B2 8.28 7.95 7.47 1.71 94.01

BSC162-R-B1 16.44 15.37 14.17 19.32 92.19

BSC173-M-B1 10.14 10.08 9.63 10.55 95.48

BSC173-M-B2 17.66 17.43 16.68 12.26 95.66

BSC157-L-LP1 14.14 14.09 13.28 17.06 94.23

BSC157-L-LP2 16.92 16.81 15.91 28.43 94.61

BSC162-L-LP1 8.93 8.74 8.14 27.64 93.04

BSC162-L-LP2 10.40 9.72 9.16 52.97 94.21

BSC162-R-LP1 22.79 22.30 21.21 53.27 95.15

145

BSC162-R-LP2 13.64 13.49 12.93 71.43 95.80

BSC167-L-LP1 20.51 20.28 19.33 41.14 95.28

BSC167-L-LP2 18.62 18.37 17.31 39.59 94.26

BSC167-R-LP1 13.85 13.82 13.22 83.49 95.63

BSC167-R-LP2 22.13 21.86 20.98 54.36 95.98

BSC173-M-LP1 5.20 5.15 4.97 43.90 96.38

BSC173-M-LP2 23.32 23.04 22.23 61.19 96.51

BSC173-M-LP3 31.17 30.25 28.72 44.12 94.93

BSC157-L-ML1 12.06 11.74 11.01 40.97 93.75

BSC157-L-ML2 13.95 13.79 13.04 19.38 94.60

BSC162-L-ML1 10.00 9.42 9.00 30.74 95.62

BSC162-L-ML2 9.95 9.38 8.84 13.20 94.22

BSC162-R-ML1 12.25 12.06 11.43 12.38 94.75

BSC162-R-ML2 16.21 15.98 15.20 22.72 95.11

BSC167-L-ML1 17.55 17.44 16.75 31.00 96.05

BSC167-L-ML2 17.99 17.84 17.12 33.36 95.94

BSC167-R-ML1 19.60 19.55 18.91 33.70 96.75

BSC167-R-ML2 16.70 16.67 16.10 31.92 96.61

BSC173-M-ML1 41.90 41.72 40.56 37.43 97.22

BSC173-M-ML2 44.35 43.71 42.40 32.53 97.02

BSC157-L-S1 13.89 13.84 13.06 42.52 94.35

BSC157-L-S2 13.56 13.49 12.90 54.93 95.62

BSC162-L-S1 9.49 9.30 9.00 48.14 96.79

BSC162-L-S2 11.10 10.89 10.46 54.27 96.06

146

BSC162-R-S1 17.28 17.20 16.63 46.81 96.70

BSC162-R-S2 11.70 11.66 11.25 49.75 96.44

BSC173-M-S1 38.52 38.49 37.03 53.99 96.22

BSC173-M-S2 28.43 28.39 27.47 57.58 86.77

Table 4.3-2 Summary of MOWChIP sequencing data (H3K27ac, carrier) information in human samples.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks (k)

Mapping

efficiency (%)

BSC164-R-B1 19.78 19.72 18.93 20.88 96.00

BSC189-L-B1 18.58 18.57 18.03 58.28 97.10

BSC189-L-B2 16.15 16.15 15.68 54.57 97.09

BSC191-M-B1 18.58 18.57 18.15 38.11 97.72

BSC191-M-B2 13.62 13.61 13.23 15.42 97.22

BSC164-R-LP1 14.60 14.55 14.01 69.81 96.27

BSC164-R-LP2 15.17 15.09 14.44 69.79 95.65

BSC164-R-LP3 20.58 20.52 19.71 40.69 96.03

BSC189-L-LP1 13.03 13.03 12.67 64.18 97.24

BSC189-L-LP2 21.42 21.41 20.78 85.55 97.05

BSC191-M-LP1 14.76 14.74 14.30 43.96 97.02

BSC191-M-LP2 13.34 13.33 12.96 41.81 97.17

BSC164-R-ML1 15.56 15.46 14.77 14.83 95.50

BSC189-L-ML1 18.32 18.31 17.74 57.39 96.88

BSC189-L-ML2 17.92 17.91 17.39 50.36 97.10

BSC191-M-ML1 13.50 13.49 13.08 17.93 96.90

BSC191-M-ML2 13.99 13.98 13.57 21.14 97.09

147

BSC164-R-S1 13.97 13.96 13.46 27.40 96.37

BSC164-R-S2 13.05 12.98 12.44 18.29 95.82

BSC164-R-S3 12.98 12.97 12.15 17.19 93.73

BSC189-L-S1 22.48 22.47 21.92 60.14 97.55

BSC189-L-S2 16.70 16.70 16.26 69.00 97.38

BSC191-M-S1 12.37 12.36 12.04 19.43 97.36

BSC191-M-S2 13.35 13.34 13.00 17.94 97.49

Table 4.3-3 Summary of MOWChIP sequencing data (H3K4me3, non-carrier) information in human samples.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks (k)

Mapping

efficiency (%)

BSC186-B1 17.32 16.98 16.09 27.21 94.79

BSC186-B2 13.82 13.68 13.04 27.26 95.32

BSC186-LP1 18.39 18.38 17.38 31.83 94.57

BSC186-LP2 2.15 2.00 1.02 4.96 51.12

BSC186-ML1 13.06 12.92 12.29 23.96 95.12

BSC186-ML2 13.58 13.49 12.82 21.04 94.98

BSC186-S1 11.32 11.30 10.71 29.86 94.80

BSC186-S2 15.07 14.92 14.15 26.18 94.88

Table 4.3-4 Summary of MOWChIP sequencing data (H3K4me3, carrier) information in human samples.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks (k)

Mapping

efficiency (%)

BSC189-L-B1 16.29 16.27 15.35 35.76 94.37

BSC189-L-B2 15.80 15.76 14.89 28.29 94.50

148

BSC191-B1 13.62 13.59 13.03 29.25 95.84

BSC191-B2 14.70 14.67 14.02 31.53 95.53

BSC189-L-LP1 12.73 12.61 11.74 31.62 93.12

BSC189-L-LP2 13.27 13.27 12.40 21.75 93.43

BSC189-L-LP3 12.88 12.86 12.00 23.05 93.27

BSC191-LP1 13.92 13.73 12.82 23.87 93.41

BSC191-LP2 12.62 12.52 11.88 19.09 94.88

BSC189-L-ML1 13.36 12.97 11.87 30.89 91.48

BSC189-L-ML2 15.48 15.41 14.16 31.54 91.86

BSC189-L-ML3 13.79 13.78 12.78 30.37 92.76

BSC191-ML1 15.19 15.13 14.36 18.37 94.89

BSC191-ML2 12.63 12.59 11.96 20.29 95.04

BSC189-L-S1 16.02 15.99 15.07 30.31 94.25

BSC189-L-S2 13.83 13.82 13.01 26.41 94.15

BSC191-S1 14.41 14.36 13.65 24.87 95.02

BSC191-S2 12.34 12.33 11.74 23.81 95.26

Table 4.3-5 Summary of MOWChIP sequencing data (H3K27ac, wild type) information in mouse samples.

Total

reads (M)

Aligned reads

(M)

Number of

peaks (k)

Mapping

efficiency (%)

8W-BFF1971-LP1 13.99 13.93 8.34 99.6

8W-BFF1971-LP2 15.66 15.60 19.89 99.6

8W-BFF1971-LP3 17.58 17.50 24.37 99.6

8W-BFF2009-LP1 21.70 21.27 40.81 98.0

8W-BFF2009-LP2 24.36 22.82 25.84 93.7

149

8W-BFF2009-LP3 17.83 17.64 41.47 98.9

8W-BFF2015-LP1 17.40 17.14 56.34 98.5

8W-BFF2015-LP2 17.17 17.11 46.76 99.7

8W-BFF2018-LP1 16.04 15.95 60.41 99.4

8W-BFF2018-LP2 15.09 14.97 42.93 99.2

8W-BFF2019-LP1 22.23 22.22 37.27 100.0

8W-BFF2019-LP2 18.04 18.03 38.86 99.9

8M-BFF2006-B1 22.19 21.95 32.69 98.9

8M-BFF2006-B2 19.29 19.28 34.44 99.9

8M-BFF2023-B1 16.11 16.05 41.04 99.7

8M-BFF2023-B2 24.26 24.10 40.04 99.4

8M-BFF2028-B1 12.29 12.26 32.65 99.8

8M-BFF2028-B2 20.35 20.34 29.87 100.0

8M-BFF1988-LP1 17.64 17.59 16.09 99.7

8M-BFF1988-LP2 18.05 17.96 11.78 99.5

8M-BFF1993-LP1 24.10 24.07 30.87 99.9

8M-BFF1993-LP2 17.06 16.99 24.41 99.6

8M-BFF1994-LP1 13.87 13.83 31.42 99.7

8M-BFF1994-LP2 20.33 20.24 34.29 99.5

8M-BFF2003-LP1 17.19 17.08 36.36 99.4

8M-BFF2003-LP2 19.48 19.45 20.43 99.8

8M-BFF2006-S1 10.93 10.85 48.27 99.3

8M-BFF2006-S2 11.10 10.90 62.16 98.2

Table 4.3-6 Summary of MOWChIP sequencing data (H3K27ac, knockout) information in mouse samples.

150

Total

reads (M)

Aligned reads

(M)

Number of

peaks (k)

Mapping

efficiency (%)

8W-BFF1970-LP1 15.93 15.81 43.85 99.3

8W-BFF1970-LP2 12.10 11.94 39.76 98.7

8W-BFF1970-LP3 16.39 15.78 39.84 96.3

8W-BFF2013-LP1 31.08 30.56 42.90 98.3

8W-BFF2013-LP2 15.77 15.57 49.15 98.7

8W-BFF2014-LP1 25.33 24.85 59.37 98.1

8W-BFF2014-LP2 23.93 23.52 60.11 98.3

8W-BFF2016-LP1 20.40 20.34 65.05 99.7

8W-BFF2016-LP2 15.85 15.79 73.79 99.7

8W-BFF2017-LP1 19.94 19.90 58.20 99.8

8W-BFF2017-LP2 16.84 16.81 54.02 99.8

8M-BFF2005-B1 14.37 14.35 8.60 99.8

8M-BFF2022-B1 12.99 12.98 18.56 99.9

8M-BFF2022-B2 19.09 19.08 16.09 100.0

8M-BFF2026-B1 11.44 11.40 21.63 99.6

8M-BFF2026-B2 15.14 14.91 29.19 98.5

8M-BFF2027-B1 18.69 18.68 42.51 99.9

8M-BFF1987-LP1 15.79 15.78 29.85 99.9

8M-BFF1987-LP2 20.07 20.00 31.42 99.6

8M-BFF1991-LP1 16.53 16.49 27.37 99.8

8M-BFF1991-LP2 18.82 18.77 26.29 99.7

8M-BFF1992-LP1 15.52 15.44 34.38 99.5

151

8M-BFF1992-LP2 23.73 23.64 27.69 99.6

8M-BFF2001-LP1 15.64 15.62 30.66 99.9

8M-BFF2001-LP2 17.79 17.71 26.80 99.6

8M-BFF2027-S1 11.55 11.46 54.39 99.3

Table 4.3-7 Summary of MOWChIP sequencing data (H3K4me3, wild type) information in mouse samples.

Total

reads (M)

Aligned reads

(M)

Number of

peaks (k)

Mapping

efficiency (%)

8M-BFF2006-B1 13.70 13.69 11.26 99.9

8M-BFF2006-B2 13.97 13.96 9.24 99.9

8M-BFF2023-B1 13.71 13.66 8.00 99.7

8M-BFF2023-B2 10.74 10.73 10.51 99.9

8M-BFF2028-B1 20.48 20.47 3.57 99.9

8M-BFF2028-B2 20.26 20.26 2.33 100.0

8M-BFF2029-B1 18.03 17.93 4.77 99.5

8M-BFF2029-B2 13.08 13.06 3.04 99.9

8M-BFF2006-LP1 12.41 12.40 13.31 99.9

8M-BFF2006-LP2 22.75 22.74 12.22 99.9

8M-BFF2006-LP3 18.14 18.14 12.34 100.0

8M-BFF2023-LP1 16.96 16.95 14.06 100.0

8M-BFF2023-LP2 19.24 19.23 12.11 100.0

8M-BFF2028-LP1 20.41 20.40 11.31 100.0

8M-BFF2028-LP2 16.17 16.17 12.22 100.0

8M-BFF2029-LP1 20.56 20.56 3.30 100.0

8M-BFF2029-LP2 19.20 19.19 9.79 100.0

152

8M-BFF2006-S1 18.40 18.38 17.41 99.9

8M-BFF2006-S2 22.22 22.21 12.31 99.9

Table 4.3-8 Summary of MOWChIP sequencing data (H3K4me3, knockout) information in mouse samples.

Total

reads (M)

Aligned reads

(M)

Number of

peaks (k)

Mapping

efficiency (%)

8M-BFF2005-B1 14.19 14.13 7.71 99.6

8M-BFF2005-B2 15.16 15.07 4.71 99.4

8M-BFF2022-B1 13.46 13.46 6.31 100.0

8M-BFF2026-B1 14.40 14.38 3.21 99.9

8M-BFF2026-B2 14.87 14.87 3.49 100.0

8M-BFF2027-B1 12.95 12.79 9.06 98.8

8M-BFF2005-LP1 17.49 17.43 11.40 99.6

8M-BFF2005-LP2 13.38 13.34 6.54 99.7

8M-BFF2022-LP1 20.61 20.60 8.51 100.0

8M-BFF2026-LP1 16.40 16.39 14.07 100.0

8M-BFF2026-LP2 20.82 20.80 9.15 99.9

8M-BFF2027-LP1 23.69 23.66 13.62 99.9

8M-BFF2027-LP2 24.09 24.08 12.79 100.0

8M-BFF2027-S1 12.63 12.61 12.55 99.9

8M-BFF2027-S2 16.10 16.06 14.29 99.8

Table 5.3-1 Summary of ChIRP-seq in HUVECs with pooling all probes.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks

Mapping

efficiency (%)

LEENE-500K-1 22.27 22.07 9.48 5770 42.9

153

LEENE-500K-2 26.37 26.18 20.77 12327 79.3

LEENE-100K 24.09 23.64 14.98 6950 63.4

GFP-500K-1 28.07 28.01 8.23 4745 29.4

GFP-500K-2 26.56 25.20 14.62 4618 58.0

GFP-100K 21.63 21.19 12.06 3055 56.9

Control-500K-1 26.69 26.54 18.75 3357 70.6

Control-500K-2 22.79 22.33 13.26 2893 59.4

Control-100K 20.51 20.17 10.54 3619 52.2

Table 5.3-2 Summary of ChIRP-seq in HUVECs with splitting probes.

Total

reads (M)

Reads of after

trimming (M)

Aligned

reads

(M)

Number

of peaks

Mapping

efficiency

(%)

Overlap

peaks

LEENE-

500K-even 34.55 34.30 27.23 25,305 79.4

899 LEENE-

500K-odd 36.26 31.88 17.72 17,527 55.6

GFP-500K-

even 28.24 27.62 22.74 49,168 82.3

2,468 GFP-500K-

odd 31.02 30.42 20.75 40,451 68.2

Control-

500K-even 25.56 24.63 20.57 52,743 83.5

6,474 Control-

500K-odd 1.58 1.52 1.27 6,972 83.4

154

Table 5.3-3 Summary of ChIRP-seq from wild type and BY707159.1 knockout mice. Total

reads

(M)

Reads of after

trimming (M)

Aligned

reads (M)

Number of

peaks

Mapping

efficiency

(%)

Overlap

peaks

Wild type-

even 24.92 24.87 24.00 111,067 96.5

13,991 Wild type-

odd 25.90 25.85 24.64 171,137 95.3

Knockout-

even 26.55 26.49 25.16 77,730 95.0

7,818 Knockout-

odd 19.05 18.91 17.62 167,224 93.2

Table 5.3-4 Summary of HOTAIR ChIRP-seq from Chu and our data. Total

reads

(M)

Reads of after

trimming (M)

Aligned

reads

(M)

Number of

peaks

Mapping

efficiency

(%)

Overlap

peaks

Chu-even 13.55 12.36 10.28 72,259 83.2 4,488

Chu-odd 12.01 10.73 10.03 92,469 93.4

Hsieh-even 28.09 27.30 25.88 61,361 94.8 3,308

Hsieh-odd 21.89 19.54 18.56 66,184 95.0

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