The aim of this thesis is to bridge the gap between the state-of-the-art bioinformatic tools and resources, currently at the forefront of epigenetic analysis, and their emerging applications to non-model species in the context of plant ecology. New, high-resolution research tools are presented; first in a specific sense, by providing new genomic resources for a selected non-model plant species, and also in a broader sense, by developing new software pipelines to streamline the analysis of bisulfite sequencing data, in a manner which is applicable to a wide range of non-model plant species. The selected species is the annual field pennycress, Thlaspi arvense, which belongs in the same lineage of the Brassicaceae as the closely-related model species, Arabidopsis thaliana, and yet does not benefit from such extensive genomic resources. It is one of three key species in a Europe-wide initiative to understand how epigenetic mechanisms contribute to natural variation, stress responses and long-term adaptation of plants. To this end, this thesis provides a high-quality, chromosome-level assembly for T. arvense, alongside a rich complement of feature annotations of particular relevance to the study of epigenetics. The genome assembly encompasses a hybrid approach, involving both PacBio continuous long reads and circular consensus sequences, alongside Hi-C sequencing, PCR-free Illumina sequencing and genetic maps. The result is a significant improvement in contiguity over the existing draft state from earlier studies. Much of the basis for building an understanding of epigenetic mechanisms in non-model species centres around the study of DNA methylation, and in particular the analysis of bisulfite sequencing data to bring methylation patterns into nucleotide-level resolution. In order to maintain a broad level of comparison between T. arvense and the other selected species under the same initiative, a suite of software pipelines which include mapping, the quantification of methylation values, differential methylation between groups, and epigenome-wide association studies, have also been developed. Furthermore, presented herein is a novel algorithm which can facilitate accurate variant calling from bisulfite sequencing data using conventional approaches, such as FreeBayes or Genome Analysis ToolKit (GATK), which until now was feasible only with specifically-adapted software. This enables researchers to obtain high-quality genetic variants, often essential for contextualising the results of epigenetic experiments, without the need for additional sequencing libraries alongside. Each of these aspects are thoroughly benchmarked, integrated to a robust workflow management system, and adhere to the principles of FAIR (Findability, Accessibility, Interoperability and Reusability). Finally, further consideration is given to the unique difficulties presented by population-scale data, and a number of concepts and ideas are explored in order to improve the feasibility of such analyses. In summary, this thesis introduces new high-resolution tools to facilitate the analysis of epigenetic mechanisms, specifically relating to DNA methylation, in non-model plant data. In addition, thorough benchmarking standards are applied, showcasing the range of technical considerations which are of principal importance when developing new pipelines and tools for the analysis of bisulfite sequencing data. The complete “Epidiverse Toolkit” is available at https://github.com/EpiDiverse and will continue to be updated and improved in the future.:ABSTRACT ACKNOWLEDGEMENTS 1 INTRODUCTION 1.1 ABOUT THIS WORK 1.2 BIOLOGICAL BACKGROUND 1.2.1 Epigenetics in plant ecology 1.2.2 DNA methylation 1.2.3 Maintenance of 5mC patterns in plants 1.2.4 Distribution of 5mC patterns in plants 1.3 TECHNICAL BACKGROUND 1.3.1 DNA sequencing 1.3.2 The case for a high-quality genome assembly 1.3.3 Sequence alignment for NGS 1.3.4 Variant calling approaches 2 BUILDING A SUITABLE REFERENCE GENOME 2.1 INTRODUCTION 2.2 MATERIALS AND METHODS 2.2.1 Seeds for the reference genome development 2.2.2 Sample collection, library preparation, and DNA sequencing 2.2.3 Contig assembly and initial scaffolding 2.2.4 Re-scaffolding 2.2.5 Comparative genomics 2.3 RESULTS 2.3.1 An improved reference genome sequence 2.3.2 Comparative genomics 2.4 DISCUSSION 3 FEATURE ANNOTATION FOR EPIGENOMICS 3.1 INTRODUCTION 3.2 MATERIALS AND METHODS 3.2.1 Tissue preparation for RNA sequencing 3.2.2 RNA extraction and sequencing 3.2.3 Transcriptome assembly 3.2.4 Genome annotation 3.2.5 Transposable element annotations 3.2.6 Small RNA annotations 3.2.7 Expression atlas 3.2.8 DNA methylation 3.3 RESULTS 3.3.1 Transcriptome assembly 3.3.2 Protein-coding genes 3.3.3 Non-coding loci 3.3.4 Transposable elements 3.3.5 Small RNA 3.3.6 Pseudogenes 3.3.7 Gene expression atlas 3.3.8 DNA Methylation 3.4 DISCUSSION 4 BISULFITE SEQUENCING METHODS 4.1 INTRODUCTION 4.2 PRINCIPLES OF BISULFITE SEQUENCING 4.3 EXPERIMENTAL DESIGN 4.4 LIBRARY PREPARATION 4.4.1 Whole Genome Bisulfite Sequencing (WGBS) 4.4.2 Reduced Representation Bisulfite Sequencing (RRBS) 4.4.3 Target capture bisulfite sequencing 4.5 BIOINFORMATIC ANALYSIS OF BISULFITE DATA 4.5.1 Quality Control 4.5.2 Read Alignment 4.5.3 Methylation Calling 4.6 ALTERNATIVE METHODS 5 FROM READ ALIGNMENT TO DNA METHYLATION ANALYSIS 5.1 INTRODUCTION 5.2 MATERIALS AND METHODS 5.2.1 Reference species 5.2.2 Natural accessions 5.2.3 Read simulation 5.2.4 Read alignment 5.2.5 Mapping rates 5.2.6 Precision-recall 5.2.7 Coverage deviation 5.2.8 DNA methylation analysis 5.3 RESULTS 5.4 DISCUSSION 5.5 A PIPELINE FOR WGBS ANALYSIS 6 THERE AND BACK AGAIN: INFERRING GENOMIC INFORMATION 6.1 INTRODUCTION 6.1.1 Implementing a new approach 6.2 MATERIALS AND METHODS 6.2.1 Validation datasets 6.2.2 Read processing and alignment 6.2.3 Variant calling 6.2.4 Benchmarking 6.3 RESULTS 6.4 DISCUSSION 6.5 A PIPELINE FOR SNP VARIANT ANALYSIS 7 POPULATION-LEVEL EPIGENOMICS 7.1 INTRODUCTION 7.2 CHALLENGES IN POPULATION-LEVEL EPIGENOMICS 7.3 DIFFERENTIAL METHYLATION 7.3.1 A pipeline for case/control DMRs 7.3.2 A pipeline for population-level DMRs 7.4 EPIGENOME-WIDE ASSOCIATION STUDIES (EWAS) 7.4.1 A pipeline for EWAS analysis 7.5 GENOTYPING-BY-SEQUENCING (EPIGBS) 7.5.1 Extending the epiGBS pipeline 7.6 POPULATION-LEVEL HAPLOTYPES 7.6.1 Extending the EpiDiverse/SNP pipeline 8 CONCLUSION APPENDICES A. SUPPLEMENT: BUILDING A SUITABLE REFERENCE GENOME B. SUPPLEMENT: FEATURE ANNOTATION FOR EPIGENOMICS C. SUPPLEMENT: FROM READ ALIGNMENT TO DNA METHYLATION ANALYSIS D. SUPPLEMENT: INFERRING GENOMIC INFORMATION BIBLIOGRAPHY

Infertility is an issue for approximately 12% of couples attempting to have a child. Of this group, 50% of the cases are due to male factor infertility. There are many reasons for decreased fecundity in men, but there remains 10% to 15% of infertile men that are diagnosed with the most severe form of infertility, non-obstructive azoospermia (NOA). A diagnosis of NOA implies the lack of sperm cells in the ejaculate with no physiological reason. The current diagnostic test and treatment consist of microscopic examination of seminal fluid and a biopsy to extract any viable sperm from the testis. This treatment is known to be problematic because of the destructive nature of surgery as well as expense. A non-invasive diagnostic test that could identify the presence of sperm in the testis at the beginning of fertility treatment would inform the patient and the physician about the functionality of the testis and thus lead to more informed decisions about treatment and potentially a decrease in cost. The ability to identify the tissue source of DNA present in the reproductive tract could facilitate a fertility diagnostic tool. Tissue specific epigenetic mechanisms are known to play a role in an organism's development. The identification of an epigenetic signature unique to sperm DNA would allow for the identification of sperm DNA in a heterologous mixture. Our lab has been able to identify a methylation signature that can consistently differentiate between sperm DNA and somatic DNA. We compared the sperm DNA signature with that of blood and testicular tissue and found that there was no overlap in epigenetic markers. To create an assay that could evaluate the presence of sperm DNA we used an Oxford Nanopore next-generation sequencing platform. Sequencing bisulfite converted DNA; we were able to retrieve the methylation status at locations of interest. A bioinformatic tool was created to analyze the thousands of reads obtained and analyze the individual methylation points within single molecules of DNA. To create a more accessible fertility test, we used the sperm DNA analysis tool to evaluate seminal cell-free DNA (cfDNA). The presence of sperm cfDNA in a patient's seminal fluid may indicate that there is sperm somewhere in the male reproductive tract even if the cells are not intact. A clinician could use this information to better advise the patient about treatment and potentially decrease cost of care.

Ovarian cancer (OVCA) is the most lethal gynecologic cancer. Chemoresistance remains a major hurdle to successful therapy and patient survival. The secreted isoform of the actin-associated protein, gelsolin (plasma gelsolin; pGSN), is highly expressed in chemoresistant than chemosensitive OVCA cells, although the mechanism underlying the differential expression is not known. Also, its overexpression significantly correlates with shortened survival of OVCA patients. DNA methylation plays a key role in the regulation of genes expression and contributing to cancer development and chemoresistance with the help of DNA methyltransferases (DNMTs) or Ten eleven translocation (TETs) enzymes. TET1 is the most studied isoform of TETs family and primarily responsible for 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) oxidation to initiate demethylation and increase in the expression of methylated genes. Whether pGSN expression in OVCA cells is regulated by DNA methylation and TET1 regulates the differential pGSN expression between chemosensitive and resistant OVCA cells is not known. In this study, we hypothesized pGSN overexpression in chemoresistant OVCA cells is due to the hypomethylation at its promoter region by TET1. Our objective was to investigate whether DNA methylation and specifically TET1 plays a role in the regulation of differential pGSN expression and chemosensitivity in OVCA. Chemosensitive and resistant OVCA cell lines of different histological subtypes were used in this study to measure pGSN and TET1 mRNA abundance and protein contents by qPCR and Western blotting respectively. Cisplatin-induced chemoresponsiveness was morphologically assessed by Hoechst staining (apoptosis). Infinium HumanMethylation450 BeadChip assay was used for global methylation analysis of twelve (12) different OVCA cells and to investigate the role of DNA methylation specifically in pGSN regulation and pGSN-induced chemoresistance. DNMTs and TETs were pharmacologically inhibited in sensitive and resistant OVCA cell using specific inhibitors. Gain-and-loss-of-function assays were carried to identify the relationship between TET1 and pGSN in OVCA chemoresponsiveness. Differential protein and mRNA expressions of pGSN and TET1 were observed between sensitive and resistant OVCA cells and cisplatin reduced their expression in sensitive but not in resistant cells. Global methylation analysis revealed hypomethylation in resistant cells compared to sensitive cells. Pharmacological inhibition of DNMTs increased pGSN protein levels in sensitive OVCA cells and decreases their responsiveness to cisplatin, however we did not observe any difference in methylation level at pGSN promoter region. TETs inhibition resulted in hypermethylation at multiple CpG sites and decreased pGSN protein level in resistant OVCA cells which was also associated with enhanced response to cisplatin, findings that suggested the methylation role of TETs in the regulation of pGSN expression in OVCA cells. Further, we found that TET1 is inversely related to pGSN and positively related to chemoresponsiveness of OVCA cells. This project does not only broaden our knowledge about the mechanistic insights into the epigenetic regulation of pGSN in OVCA chemoresistance, but it also reveals a new potential target to re-sensitize chemotherapy resistant OVCA cells. This may provide a future strategy to improve overall OVCA patient survival.

DNA methylation is an epigenetic mechanism that usually occurs at CpG sites in the genome. Both sequencing and array-based techniques are available to detect methylation patterns. Whole-genome bisulfite sequencing is the most comprehensive but cost-prohibitive approach, and microarrays represent an affordable alternative approach. Array-based methods are generally cheaper but assess a specific number of genomic loci, such as Illumina methylation arrays. Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple CpG sites that associate with a phenotype. Methylation at nearby sites tends to be correlated, therefore it may be more powerful to study sets of sites to detect methylation differences as well as reduce the multiple testing burden, compared to utilizing individual sites. Several statistical approaches exist for identifying DMRs, and a few prior publications compared the performance of several commonly used DMR methods. However, as far as we know, no comprehensive comparisons have been made based on genome-wide simulation studies. This dissertation provides some comprehensive suggestions for DMR analysis based on genome-wide evaluations of existing DMR tools and presents the development of a novel approach to increase the power to identify DMRs with clinical value in genomic research. The second chapter presents genome-wide null simulations to compare five commonly used array-based DMR methods (Bumphunter, comb-p, DMRcate, mCSEA and coMethDMR) and identifies coMethDMR as the only approach that consistently yields appropriate Type I error control. We suggest that a genome-wide evaluation of false positive (FP) rates is critical for DMR methods. The third chapter develops a novel Principal Component Analysis based DMR method (denoted as DMRPC), which demonstrates its ability to identify DMRs using genome-wide methylation arrays with well-controlled FP rates at the level of 0.05. Compared to coMethDMR, DMRPC is a robust and powerful novel DMR tool that can examine more genomic regions and extract signals from low-correlation regions. The fourth chapter applies the new DMR approach DMRPC in two “real-world” datasets and identifies novel DMRs that are associated with several inflammatory markers.

The CpG Island Methylator Phenotype (CIMP) is a distinct form of aberrant DNA methylation in cancer, and it is seen in 20-40% of colorectal cancers (CRC) where its causes remain elusive. Intestinal microbiota represents an important environmental component implicated in CRC development. Interestingly, microbiota have been shown to modulate DNA methylation in preclinical models but the relationship between tumor-infiltrating bacteria and CIMP status in currently unknown. Our hypothesis is that the gut microbiota affects colonic neoplasia through modulating aberrant DNA methylation in host epigenome. To test this hypothesis, we analyzed CIMP status in CRC patient tumor samples. We used a genome-wide approach to determine the CIMP status by filtering cancer-related sites. A total of 1317 CpG sites were filtered and used to determine distinct CIMP classifications that aligned with well-known characteristics of CIMP cases, including localization in the proximal colon, a higher prevalence in female patients, and a higher frequency of MLH1 hypermethylation. To study the association between CIMP and the gut microbiota, we analyzed the enrichment of four bacterial species associated with CRC, including Bacteroides fragilis, Escherichia coli, Fusobacterium nucleatum, and Klebsiella pneumoniae. Notably, they exhibited higher enrichment in CIMP-Positive tumor samples, except for E. coli. This analysis also identified a group of samples referred to as bacterial "Superhigh," characterized by remarkably high abundances of these three bacterial species. The bacterial Superhigh cases displayed a significant association with CIMP status and MLH1 methylation. We validated the association between the CRC-associated bacteria and CIMP by analyzing the Cancer Genome Atlas (TCGA) 450K methylation array data and whole exome sequencing data. The analysis demonstrated that bacterial Superhigh cases in the TCGA datasets also had significantly higher odds of being CIMP-Positive and having MLH1 methylation. To expand our investigation, we conducted 16S rRNA gene sequencing to identify additional bacterial taxa linked to CIMP. Numerous bacterial genera and species were found to be enriched in CRC tumor tissues, with specific enrichments in CIMP-Positive and CIMP-High groups. Notably, Bergeyella, Campylobacter concisus, and Fusobacterium canifelinum were significantly enriched in CIMP-Positive tumors. Additionally, I studied the causal relationship between gut microbiota and CpG island methylation by colonizing germ-free mice ApcMinΔ850/+;Il10–/– with E. coli NC101 & K. pneumoniae, specific pathogen free bacteria, and the mouse bacterial Consortium. Differential methylation analyses of adjacent normal colon tissue revealed a pronounced tissue side-specific difference, particularly in non-CpG island regions. The tissue specificity diminished with the increasing tumorigenic potential of the microbiota group. Comparisons between microbiota groups and germ-free mice indicated a more significant increase in methylation within CpG islands when gut microbiota with higher tumorigenic potential was present. In conclusion, our study underscores the association between CIMP in CRC and the gut microbiota and the causal relationship between the cut microbiota and CpG island methylation. It highlights specific bacterial taxa that may impact DNA methylation especially in CpG islands and contribute to the development ang progression of CIMP in colorectal cancer. / Biomedical Sciences

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