Predicting functional variants in enhancer and promoter elements using RegulomeDB

Here we present a computational model, Score of Unified Regulatory Features (SURF), that predicts functional variants in enhancer and promoter elements. SURF is trained on data from massively parallel reporter assays and predicts the effect of variants on reporter expression levels. It achieved the top performance in the Fifth Critical Assessment of Genome Interpretation “Regulation Saturation” challenge. We also show that features queried through RegulomeDB, which are direct annotations from functional genomics data, help improve prediction accuracy beyond transfer learning features from DNA sequence‐based deep learning models. Some of the most important features include DNase footprints, especially when coupled with complementary ChIP‐seq data. Furthermore, we found our model achieved good performance in predicting allele‐specific transcription factor binding events. As an extension to the current scoring system in RegulomeDB, we expect our computational model to prioritize variants in regulatory regions, thus help the understanding of functional variants in noncoding regions that lead to disease.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151875/1/humu23791-sup-0001-Supp_Mat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151875/2/humu23791.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151875/3/humu23791_am.pd

Computational Methods to Identify Regulatory Variants in the Non-Coding Regions of the Human Genome

Evidence from Genome Wide Association Studies (GWAS) has provided us with insights into human phenotypes by identifying genetic variation statistically associated with diseases and complex traits. However, the functional consequences of these genetic variants remain unknown in many cases, especially for those in the non-coding regions of the human genome. My dissertation focuses on single nucleotide polymorphisms (SNPs) as the most common genetic variation type. I define some SNPs as regulatory SNPs that can alter the transcription factor binding affinities within the DNA sequences of regulatory elements. This change affects downstream gene expression and plays a role in disease progression and trait development. Characterizing genome-wide regulatory variants is particularly challenging because the gene regulatory network is dynamic across various cell types and environmental conditions. In addition to the DNA sequence context, the gene regulatory network relies on epigenetic factors, such as chromatin accessibility, histone modification, and chromatin looping. In this dissertation, I applied computational approaches to predict regulatory variants by incorporating sequence information and functional genomics annotations from various high-throughput assays. In chapter 2, I developed a computation tool, SURF, to prioritize the regulatory variants within promoters and enhancers with clinical relevance. These variants were validated by massively parallel reporter assays and used as an unbiased test set in CAGI5 “Regulation Saturation” challenge. My algorithm achieved the best performance in this challenge compared to other participant groups. In chapter 3, I extended SURF to TURF, a computational tool to predict tissue-specific functions of regulatory variants and provide a more robust prediction on genome-wide non-coding regions. By leveraging tissue-specific genomic annotations of tissues from the same organ, I also calculated TURF organ-specific scores covering most ENCODE project organs. Many of the GWAS traits showed enrichment of regulatory variants prioritized by TURF scores in their relevant organs, which indicates that these regulatory variants are likely to be involved in the trait developments and can be a valuable source for future studies. In chapter 4, to enable the quick annotation on non-coding variants for the scientific community, I designed some major updates to an online tool, RegulomeDB. With the user's input of query variant, RegulomeDB returns the evidence from diverse functional genomics assays that overlaps the variant’s position, displayed with interactive charts and a genome browser view. The new probabilistic score derived from SURF was also integrated into the query system. To further provide functional hypotheses to putative regulatory variants, I finally explored the pipeline to assign their target genes with evidence from eQTL studies and Hi-C experiments. Together, my dissertation developed computational tools for broad community use on prioritizing and assigning target genes to regulatory variants in non-coding regions of the human genome.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167962/1/shengchd_1.pd

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

The integrative analysis of highâ throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five diseaseâ associated human enhancers and nine diseaseâ associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cellâ types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of diseaseâ associated genetic variation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/1/humu23797_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/2/humu23797.pd

Influence Analysis of Geometric Error and Compensation Method for Four-Axis Machining Tools with Two Rotary Axes

Four-axis machine tools with two rotary axes are widely used in the machining of complex parts. However, due to an irregular kinematic relationship and non-linear kinematic function with geometric error, it is difficult to analyze the influence the geometry error of each axis has and to compensate for such a geometry error. In this study, an influence analysis method of geometric error based on the homogeneous coordinate transformation matrix and a compensation method was developed, using the Newton iterative method. Geometric errors are characterized by a homogeneous coordinate transformation matrix in the proposed method, and an error matrix is integrated into the kinematic model of the four-axis machine tool as a means of studying the influence the geometric error of each axis has on the tool path. Based on the kinematic model of the four-axis machine tool considering the geometric error, a comprehensive geometric error compensation calculation model based on the Newton iteration was then constructed for calculating the tool path as a means of compensating for the geometric error. Ultimately, the four-axis machine tool with a curve tool path for an off-axis optical lens was chosen for verification of the proposed method. The results showed that the proposed method can significantly improve the machining accuracy

Pigment Epithelium-Derived Factor (PEDF) Protects Osteoblastic Cell Line from Glucocorticoid-Induced Apoptosis via PEDF-R

Pigment epithelial-derived factor (PEDF) is known as a widely expressed multifunctional secreted glycoprotein whose biological actions are cell-type dependent. Recent studies demonstrated that PEDF displays cytoprotective activity in several cell types. However, it remains unknown whether PEDF is involved in glucocorticoid-induced osteoblast death. The aim of this study was to examine the role of PEDF in osteoblast survival in response to dexamethasone, an active glucocorticoid analogue, and explore the underlying mechanism. In the present study, dexamethasone (DEX) was used to induce MC3T3-E1 pre-osteoblast apoptosis. PEDF mRNA and protein levels and cell apoptosis were determined respectively. Then PEDF receptor (PEDF-R)- and lysophosphatidic acid (LPA)-related signal transductions were assessed. Here we show that DEX down-regulates PEDF expression, which contributes to osteoblast apoptosis. As a result, exogenous recombinant PEDF (rPEDF) inhibited DEX-induced cell apoptosis. We confirmed that PEDF-R was expressed on MC3T3-E1 pre-osteoblast membrane and could bind to PEDF which increased the level of LPA and activated the phosphorylation of Akt. Our results suggest that PEDF attenuated DEX-induced apoptosis in MC3T3-E1 pre-osteoblasts through LPA-dependent Akt activation via PEDF-R

Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome

Summary: Dynamic dysregulation of the promoter DNA methylome is a signature of cancer. However, comprehensive understandings about how the DNA methylome is incorporated in the transcriptional regulation circuitry and involved in regulating the gene expression abnormality in cancers are still missing. We introduce an integrative analysis pipeline based on mutual information theory and tailored for the multi-omics profiling data in The Cancer Genome Atlas (TCGA) to systematically find dependencies of transcriptional regulation circuits on promoter CpG methylation profiles for each of 21 cancer types. By coupling transcription factors with CpG sites, this cancer type-specific transcriptional regulation circuitry recovers a significant layer of expression regulation for many cancer-related genes. The coupled CpG sites and transcription factors also serve as markers for classifications of cancer subtypes with different prognoses, suggesting physiological relevance of such regulation machinery recapitulated here. Our results therefore generate a resource for further studies of the epigenetic scheme in gene expression dysregulations in cancers. : Using an analysis pipeline based on information theory and tailored for cancer multi-omics data in TCGA, Yu et al. conducted genome-wide surveys of DNA promoter methylome in modulating transcriptional regulation circuits in cancers. Results serve as a resource for dissecting gene expression dysregulation in cancer. Keywords: DNA methylation, CpG dinucleotides, cancer type-specificity, transcriptional regulation, gene expression regulation, genome-wide analysis, transcriptional regulation network, conditional mutual information, pan-cancer analysis, MeTR

MicroRNA-326 attenuates immune escape and prevents metastasis in lung adenocarcinoma by targeting PD-L1 and B7-H3

Abstract Tumor-infiltrating T cells are highly expressive of inhibitory receptor/immune checkpoint molecules that bind to ligand expressed by tumor cells and antigen-presenting cells, and eventually lead to T cell dysfunction. It is a hot topic to restore T cell function by targeting immune checkpoint. In recent years, immunotherapy of blocking immune checkpoint and its receptor, such as PD-L1/PD-1 targeted therapy, has made effective progress, which brings hope for patients with advanced malignant tumor. However, only a few patients benefit from directly targeting these checkpoints or their receptors by small compounds or antibodies. Since the complexity of the regulation of immune checkpoints in tumor cells, further research is needed to identify the novel endogenous regulators of immune checkpoints which can help for developing effective drug target to improve the effect of immunotherapy. Here, we verified that microRNA-326 (miR-326) repressed the gene expression of immune checkpoint molecules PD-L1 and B7-H3 in lung adenocarcinoma (LUAD). We detected that the expression of miR-326 in LUAD tissue was negatively correlated with PD-L1/B7-H3. The repression of PD-L1 and B7-H3 expression through miR-326 overexpression leads to the modification the cytokine profile of CD8+ T cells and decreased migration capability of tumor cells. Meanwhile, the downregulation of miR-326 promoted tumor cell migration. Moreover, blocking PD-L1 and B7-H3 attenuated the tumor-promoting effect induced by miR-326 inhibitor. In tumor-bearing mice, the infiltration of CD8+ T cells was significantly increased and the expression of TNF-α, and IFN-γ was significantly enhanced which contributed to tumor progression after miR-326 overexpression. Collectively, miR-326 restrained tumor progression by downregulating PD-L1 and B7-H3 expression and increasing T cell cytotoxic function in LUAD. Our findings revealed a novel perspective on the complex regulation of immune checkpoint molecules. A new strategy of using miR-326 in tumor immunotherapy is proposed

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

The integrative analysis of high-throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five disease-associated human enhancers and nine disease-associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cell-types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of disease-associated genetic variation