Epigenomic Data Research Articles

Identification of the Molecular Components of Enhancer-Mediated Gene Expression Variation in Multiple Tissues Regulating Blood Pressure.

Inter-individual variation in blood pressure (BP) arises in part from sequence variants within enhancers modulating the expression of causal genes. We propose that these genes, active in tissues relevant to BP physiology, can be identified from tissue-level epigenomic data and genotypes of BP-phenotyped individuals. We used chromatin accessibility data from the heart, adrenal, kidney, and artery to identify cis-regulatory elements (CREs) in these tissues and estimate the impact of common human single-nucleotide variants within these CREs on gene expression, using machine learning methods. To identify causal genes, we performed a gene-wise association test. We conducted analyses in 2 separate large-scale cohorts: 77 822 individuals from the Genetic Epidemiology Research on Adult Health and Aging and 315 270 individuals from the UK Biobank. We identified 309, 259, 331, and 367 genes (false discovery rate <0.05) for diastolic BP and 191, 184, 204, and 204 genes for systolic BP in the artery, kidney, heart, and adrenal, respectively, in Genetic Epidemiology Research on Adult Health and Aging; 50% to 70% of these genes were replicated in the UK Biobank, significantly higher than the 12% to 15% expected by chance (P<0.0001). These results enabled tissue expression prediction of these 988 to 2875 putative BP genes in individuals of both cohorts to construct an expression polygenic score. This score explained ≈27% of the reported single-nucleotide variant heritability, substantially higher than expected from prior studies. Our work demonstrates the power of tissue-restricted comprehensive CRE analysis, followed by CRE-based expression prediction, for understanding BP regulation in relevant tissues and provides dual-modality supporting evidence, CRE and expression, for the causality genes.

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity.

Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.

Functional variants in a TTTG microsatellite on 15q26.1 cause familial nonautoimmune thyroid abnormalities

Insufficient thyroid hormone production in newborns is referred to as congenital hypothyroidism. Multinodular goiter (MNG), characterized by an enlarged thyroid gland with multiple nodules, is usually seen in adults and is recognized as a separate disorder from congenital hypothyroidism. Here we performed a linkage analysis of a family with both nongoitrous congenital hypothyroidism and MNG and identified a signal at 15q26.1. Follow-up analyses with whole-genome sequencing and genetic screening in congenital hypothyroidism and MNG cohorts showed that changes in a noncoding TTTG microsatellite on 15q26.1 were frequently observed in congenital hypothyroidism (137 in 989) and MNG (3 in 33) compared with controls (3 in 38,722). Characterization of the noncoding variants with epigenomic data and in vitro experiments suggested that the microsatellite is located in a thyroid-specific transcriptional repressor, and its activity is disrupted by the variants. Collectively, we presented genetic evidence linking nongoitrous congenital hypothyroidism and MNG, providing unique insights into thyroid abnormalities.

Genome-wide profiles of H3K9me3, H3K27me3 modifications, and DNA methylation during diapause of Asian corn borer (Ostrinia furnacalis).

Diapause represents a crucial adaptive strategy used by insects to cope with changing environmental conditions. In North China, the Asian corn borer (Ostrinia furnacalis) enters a winter larval diapause stage. Although there is growing evidence implicating epigenetic mechanisms in diapause regulation, it remains unclear whether dynamic genome-wide profiles of epigenetic modifications exist during this process. By investigating multiple histone modifications, we have discovered the essential roles of H3K9me3 and H3K27me3 during diapause of the Asian corn borer. Building upon previous findings in vertebrates highlighting the connection between DNA methylation and repressive histone methylations, we have examined changes in the genome-wide profile of H3K9me3, H3K27me3, and DNA methylation at the nondiapause, prediapause, and diapause stages. Data analysis reveals significant alterations in these three modifications during diapause. Moreover, we observe a correlation between the H3K9me3 and H3K27me3 modification sites during diapause, whereas DNA modifications show little association with either H3K9me3 or H3K27me3. Integrative analysis of epigenome and expression data unveils the relationship between these epigenetic modifications and gene expression levels at corresponding diapause stages. Furthermore, by studying the function of histone modifications on genes known to be important in diapause, especially those involved in the juvenile pathway, we discover that the juvenile hormone pathway lies downstream from H3K9me3 and H3K27me3 histone modifications. Finally, the analysis of gene loci with modified modifications unreported in diapause uncovers novel pathways potentially crucial in diapause regulation. This study provides a valuable resource for future investigations aiming to elucidate the underlying mechanisms of diapause.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank with balanced sex representation, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Furthermore, both DNAmEMRAge and OMICmAge exhibit greater accuracy (average ROC = 0.88) in predicting 5-year and 10-year mortality than current biological age predictors. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process. This work was funded in part by TruDiagnostic for data generation and manuscript writing. Efforts for RK, KM, YC, SB, and JALS are supported by R01HL123915 from the NIH/NHLBI; PK is supported by K99HL159234 from the NIH/NHLBI; RSK is supported by K01HL146980 from the NIH/NHLBI; SHC is supported by K01HL153941 from the NIH/NHLBI; YC and JAILS are supported by R01HL141826 from the NIH/NHLBI; AD is supported by K01HL130629 from the NIH/NHLBI; AD and JALS are supported by 1R01HL152244 from the NIH/NHLBI; MM and JALS are supported by R01HL155742 from the NIH/NHLBI; MM is supported by R01HL139634 from the NIH/NHLBI; JALS, STW, EWK are supported by the NIH U01HG008685; VNG is supported by NIH AG065403 and AG064223; CEW is supported by the JSPS KAKENHI Grant (JP19K21239), the Japanese Environment Research and Technology Development Fund (No. 5- 1752), JPJ SBP-1201854), HLF 20180290, HLF 20200693), and the Swedish Research Council (2016-02798); NJWR is supported by MR/Y010736/1 and IF\R1\231034; EM is supported by the NIH (ZICTR000410-03). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract P15: Aberrant Non-canonical NF-κB Signalling Reprograms the Epigenome Landscape to Drive Oncogenic Transcriptomes in Multiple Myeloma

Abstract Activation of the NF-κB pathway is recognized to be necessary for the survival and progression of haematological cancers including Multiple Myeloma (MM). In particular, recurrent genetic lesions resulting in the chronic induction of non-canonical NF-κB (ncNF-κB) signalling have been exclusively linked to MM. Such mutations among patients are associated with significant proliferative and metastatic advantages and have been causally linked to treatment resistance. How the constitutive activity of ncNF-κB transcription factors can support such adaptations in this common haematological malignancy has been a fundamental question in cancer. In this study, we captured the epigenomic landscape of patient-derived MM cell lines harbouring recurrent NF-κB activating mutations through perturbation of nfkb2/p52 and integrated these cis-regulatory changes with MM patient epigenome data. Here, we find that sustained NF-κB/p52 levels resulting from such mutations favours the recruitment of enhancers beyond the normal B-cell repertoire. Furthermore, through targeted disruption of p52, we characterise how such enhancers are complicit in the formation of super-enhancers and the establishment of cis-regulatory interactions with myeloma dependencies during constitutive activation of p52. Finally, we functionally validate the pathological impact of these cis-regulatory modules on cell and tumour phenotypes using in vitro and in vivo models, confirming RGS1 as a novel p52-dependent myeloma driver. We conclude that the divergent epigenomic reprogramming enforced by aberrant non-canonical NF-κB signalling potentiates transcriptional programs beneficial for multiple myeloma progression. Citation Format: Daniel A. Ang, Jean-Michel Carter, Kamalakshi Deka, Joel H.L. Tan, Jianbiao Zhou, Qingfeng Chen, Wee Joo Chng, Nathan Harmston, Yinghui Li. Aberrant Non-canonical NF-κB Signalling Reprograms the Epigenome Landscape to Drive Oncogenic Transcriptomes in Multiple Myeloma [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P15.

Nuclear lamina component KAKU4 regulates chromatin states and transcriptional regulation in the Arabidopsis genome

BackgroundThe nuclear lamina links the nuclear membrane to chromosomes and plays a crucial role in regulating chromatin states and gene expression. However, current knowledge of nuclear lamina in plants is limited compared to animals and humans.ResultsThis study mainly focused on elucidating the mechanism through which the putative nuclear lamina component protein KAKU4 regulates chromatin states and gene expression in Arabidopsis leaves. Thus, we constructed a network using the association proteins of lamin-like proteins, revealing that KAKU4 is strongly associated with chromatin or epigenetic modifiers. Then, we conducted ChIP-seq technology to generate global epigenomic profiles of H3K4me3, H3K27me3, and H3K9me2 in Arabidopsis leaves for mutant (kaku4-2) and wild-type (WT) plants alongside RNA-seq method to generate gene expression profiles. The comprehensive chromatin state-based analyses indicate that the knockdown of KAKU4 has the strongest effect on H3K27me3, followed by H3K9me2, and the least impact on H3K4me3, leading to significant changes in chromatin states in the Arabidopsis genome. We discovered that the knockdown of the KAKU4 gene caused a transition between two types of repressive epigenetics marks, H3K9me2 and H3K27me3, in some specific PLAD regions. The combination analyses of epigenomic and transcriptomic data between the kaku4-2 mutant and WT suggested that KAKU4 may regulate key biological processes, such as programmed cell death and hormone signaling pathways, by affecting H3K27me3 modification in Arabidopsis leaves.ConclusionsIn summary, our results indicated that KAKU4 is directly and/or indirectly associated with chromatin/epigenetic modifiers and demonstrated the essential roles of KAKU4 in regulating chromatin states, transcriptional regulation, and diverse biological processes in Arabidopsis.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BackgroundRegulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.ResultsHere we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes.We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.ConclusionBased on these associations of (linearly) distal genes’ transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Abstract LB240: Cell type annotation using singleR with custom reference for single-nucleus multiome data derived from frozen human breast tumors

Abstract Single-nucleus joint ATAC- and RNA-sequencing (snMultiome) can be used to identify functionally divergent cell subpopulations based on their transcriptomic and epigenetic profiles within complex samples. Accurate cell type annotation is critical to successful snMultiome data analysis. Several computational methods have been developed for automatic annotation. Traditional cell type annotation methods initially cluster cells using unsupervised learning methods based on the gene expression profiles, then label the clusters using aggregated cluster-level expression profiles and marker genes. These methods rely heavily on the clustering results. As the purity of clusters cannot be guaranteed, false detection of cluster features may lead to incorrect annotations. Further, canonical cell surface markers may not always be suitable to be applied in single-nucleus RNA-seq studies because single-nucleus RNA-seq generally yields lower detected transcript numbers compared to typical single-cell RNA-seq. Moreover, cell type marker genes in the snRNA-seq data may differ from the ones obtained with scRNA-seq data, reflecting biological differences in the cytoplasmic and nuclear RNA pools. Lastly, the data obtained from malignant cells are best left out in establishing cell type reference data because they are too heterogeneous and patient-specific. Reference-based automated algorithms such as SingleR enable quick and unbiased classifications by leveraging a collection of built-in reference data sets for human (e.g. Human Primary Cell Atlas (microarray-based) and the combined Blueprint Epigenomics and Encode data set (RNA-seq-based)). Still, SingleR may return erroneous cell type classifications. Our dataset was generated using the 10x Genomics snMultiome platform to yield 296,557 nuclei from 82 frozen breast tumors, representing patients from diverse genetic ancestral background. Using these data, we sought to improve the accuracy of cell type annotation by SingleR. To achieve this, we first separated malignant and non-malignant cells based on DNA copy number aberrations (aneuploidy) through CopyKAT. For cells determined to be non-malignant, we built the custom reference from snRNA-seq data set, recently made available by The Human Breast Cell Atlas, and then applied singleR with a custom reference where each cell type is represented by single-cells of that type, allowing a well-founded estimate of the confidence with which a cell type call can be made. Using this approach, we successfully identified 11 distinct cell types for non-malignant cells, including fibroblast, adipocyte, pericyte, basal, luminal-secretory, luminal-HR, myeloid, mast, vascular, lymphatic, and T-cells, which can then be further subclassified. Furthermore, we interrogated each cluster using known canonical markers and transferred the cell type labels to snATAC-seq. This approach enabled us to link peaks to genes in each cell type. We believe this new approach that refines SingleR can greatly improve accuracy and minimize misclassification when annotating cell types in breast tumors using snMultiome data. Citation Format: Huaitian Liu, Alexandra Harris, Brittany Jenkins-Lord, Tiffany H. Dorsey, Francis Makokha, Shahin Sayed, Gretchen Gierach, Stefan Ambs. Cell type annotation using singleR with custom reference for single-nucleus multiome data derived from frozen human breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB240.

The interplay between X-chromosome functional dosage and circadian regulation in females.

Biological factors and mechanisms that drive higher prevalence of insomnia in females are poorlyunderstood. This study focused on the neurological consequences of X-chromosome functional imbalancesbetween sexes. Benefited from publicly available large-scale genetic, transcriptional and epigenomic data, we curated andcontrasted different gene lists: (1) X-liked genes, including assignments for X-chromosome inactivation patterns anddisease associations; (2) sleep-associated genes; (3) gene expression markers for the suprachiasmatic nucleus. We show that X-linked markers for the suprachiasmatic nucleus are significantly enriched for clinicallyrelevant genes in the context of rare genetic syndromes and brain waves modulation. Considering female-specific patterns on brain transcriptional programs becomes essential whendesigning health care strategies for mental and sleep illnesses with sex bias in prevalence.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach.

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

Abstract 6614: Integrating single-cell RNA-seq and large genome-wide association study data to identify colorectal cancer cell-of-origin populations

Abstract Background: Single-cell RNA sequencing (scRNA-seq) enables the precise identification of distinct cell populations and the measurement of gene expression within each population. Recently, integrating scRNA-seq and genome-wide association study (GWAS) data has shown promise in identifying cell-of-origin populations in human diseases. Colorectal cancer (CRC) progresses through a phenotypic continuum from normal colon to adenoma, and ultimately CRC. However, the specific disease cell-of-origin populations underlying each stage of CRC development at a single-cell resolution have not been well studied. Additionally, while transcriptome-wide association studies (TWAS) have identified more than 200 putative risk genes in CRC, the precise disease cell-of-origin populations associated with these genes remain elusive. Materials and methods: We analyzed scRNA-seq datasets from 31 normal colon, 8 colon serrated polyps, and 15 conventional adenoma tissues of individuals participated the Colorectal Molecular Atlas Project (COLON MAP). We next conducted an integrative analysis of the aforementioned scRNA-seq data, with extensive epigenomics data and summary statistics from a large CRC GWAS conducted among European ancestry populations (78,473 CRC cases and 107,143 controls) using single-cell disease relevance score (scDRS), to investigate cell-of-origin populations underlying the CRC development. We conducted correlation analysis between gene expression and individual cell risk scores derived from scDRS. Additionally, we performed differential expression analysis comparing disease cell-of-origin populations with other cell populations at each stage. Results: We identified multiple cell-of-origin populations associated with CRC, including absorptive cells (ABS) (P &amp;lt; 1 × 10-4) and goblet cells (GOB) (P &amp;lt; 7 × 10-3) in the normal colon, ABS (P &amp;lt; 2 × 10-3), enteroendocrine cells (EE) (P &amp;lt; 0.04), and serrated-specific cells (SSC) (P &amp;lt; 1 × 10-4) in serrated polyps, and ABS (P &amp;lt; 9 × 10-3) and SSC (P &amp;lt; 6 × 10-4) in adenomas. Among the previously reported 205 risk genes from TWAS, a majority show high correlations of their expressions with the risk scores of the single cells (median value R2 = 0.07, 0.14, 0.137 at normal colon, serrated polyps, adenoma, respectively). Moreover, 57 of these TWAS-reported genes (27.8% of 205 genes) exhibited evidence of differential expression in disease cell-of-origin populations compared to other cell populations at a nominal p-value &amp;lt; 0.05 in at least one stage (Binomial test, P &amp;lt; 0.01 for all). Conclusion: This study reveals the cell-of-origin populations that influence the development of CRC, and provides evidence of the role of risk genes in these populations. These findings are crucial for identifying causal genes and understanding the cellular mechanisms that drive the biology and etiology of CRC. Citation Format: Chao Li, Zhishan Chen, Qing Li, Jungyoon Choi, Quanhu Sheng, Wanqing Wen, Xiang Shu, Xiao-ou Shu, Jirong Long, Qiuyin Cai, Bhuminder Singh, Martha J. Shrubsole, Ken Lau, Wei Zheng, Xingyi Guo. Integrating single-cell RNA-seq and large genome-wide association study data to identify colorectal cancer cell-of-origin populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6614.

Abstract 3499: Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies

Abstract Background: Non-small cell lung cancer (NSCLC) makes up ~85% of lung cancers, and its major subtypes are adenocarcinoma (AD) and squamous cell carcinoma (SC). The histology classification of NSCLC has significant therapeutic and prognostic implications. As liquid biopsies are rapid and non-invasive, computational methods to classify NSCLC subtypes using cell-free DNA (cfDNA) are desired. Methods: Using The Cancer Genome Atlas (TCGA) NSCLC methylation data, we developed a method to classify AD and SC subtypes from cfDNA samples. We mapped TCGA methylation data to Guardant Infinity’s promoter regions and transformed them to allow for the transfer of the TCGA trained model to Infinity cfDNA epigenomic data. An advantage of this approach is that TCGA training data originates from the primary tumor, while cfDNA samples have low tumor fraction (TF) and may also contain signals from metastatic tumors. We trained a penalized Logistic Regression model with LASSO. To better fit the model to Infinity cfDNA data, we performed region filtering. Results: Our model effectively classified AD and SC subtypes from TCGA samples (AUC 0.97) (10-fold cross validation; Table 1). When evaluating on cfDNA samples with epigenomic TF ≥ 1%, the final model shows reasonable performance (AUC 0.94) (Table 1); the performance decreases when reduced to TF &amp;gt; 0.05% (AUC 0.89) as expected (Table 1). When evaluating cfDNA from uncategorized lung samples, the predicted AD probabilities have significant association with KRAS mutations (p-value = 0.027). Conclusion: Our results show that the penalized Logistic Regression model trained with tissue TCGA methylation data through mapping, transformation, and region selection can classify NSCLC subtypes from cfDNA samples. This method may offer a fast, non-invasive tool to inform subtype-based clinical decisions, especially in the setting of poorly-differentiated carcinomas, and to identify subtype switches upon resistance to targeted therapies. Table 1: Model evaluation on tissue TCGA samples and cfDNA Infinity samples (different TF cutoffs) Sample type N samples AUC Accuracy Sensitivity Specificity Precision F1 score tissue TCGA 907 0.97 0.93 0.94 +/- 0.02 0.93 +/- 0.02 0.94 +/- 0.02 0.94 +/- 0.01 cfDNA Infinity 17 (TF ≥ 1%) 0.94 0.88 0.83 +/- 0.3 0.91 +/- 0.17 0.83 +/- 0.3 0.83 +/- 0.21 cfDNA Infinity 29 (TF &amp;gt; 0.05%) 0.89 0.83 0.88 +/- 0.23 0.81 +/- 0.17 0.64 +/- 0.28 0.74 +/- 0.2 Citation Format: Laura Tung, Denis Tolkunov, Catalin Barbacioru, Deepika Kale, Scott Shell, Jill Tsai, Leylah Drusbosky, Kaushal Parikh, Han-Yu Chuang. Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3499.

Robust chromatin state annotation.

With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.

Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis

BackgroundMastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes.ResultsFunctional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as ‘Staphylococcus aureus infection pathway’ and ‘natural killer cell mediated cytotoxicity pathway’, etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows.ConclusionThe integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.

Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes

ObjectivesOsteoarthritis is a complex disease with a huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain...

Triterpenoid ursolic acid regulates the environmental carcinogen benzo[a]pyrene-driven epigenetic and metabolic alterations in SKH-1 hairless mice for skin cancer interception.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental carcinogens accountable to developing skin cancers. Recently, we reported that exposure to benzo[a]pyrene (B[a]P), a common PAH, causes epigenetic and metabolic alterations in the initiation, promotion and progression of non-melanoma skin cancer (NMSC). As a follow-up investigation, this study examines how dietary triterpenoid ursolic acid (UA) regulates B[a]P-driven epigenetic and metabolic pathways in SKH-1 hairless mice. Our results show UA intercepts against B[a]P-induced tumorigenesis at different stages of NMSC. Epigenomic cytosines followed by guanine residues (CpG) methyl-seq data showed UA diminished B[a]P-mediated differentially methylated regions (DMRs) profiles. Transcriptomic RNA-seq revealed UA revoked B[a]P-induced differentially expressed genes (DEGs) of skin cancer-related genes, such as leucine-rich repeat LGI family member 2 (Lgi2) and kallikrein-related peptidase 13 (Klk13), indicating UA plays a vital role in B[a]P-mediated gene regulation and its potential consequences in NMSC interception. Association analysis of DEGs and DMRs found that the mRNA expression of KLK13 gene was correlated with the promoter CpG methylation status in the early-stage comparison group, indicating UA could regulate the KLK13 by modulating its promoter methylation at an early stage of NMSC. The metabolomic study showed UA alters B[a]P-regulated cancer-associated metabolisms like thiamin metabolism, ascorbate and aldarate metabolism during the initiation phase; pyruvate, citrate and thiamin metabolism during the promotion phase; and beta-alanine and pathothenate coenzyme A (CoA) biosynthesis during the late progression phase. Taken together, UA reverses B[a]P-driven epigenetic, transcriptomic and metabolic reprogramming, potentially contributing to the overall cancer interception against B[a]P-mediated NMSC.

Exploration and determination of lung cancer targets using multi-omics approaches

Multi-omics analysis is a relatively new approach which sets of data are combined for analysis. It integrates the genomic, epigenomic, transcriptomic, proteomic, and metabolomic data from diseased individuals and is capable of comparing the different changes in levels of each identified molecules with the assist of charts and graphs by information technologies, ultimately proposing potential biomarkers and correlations that are possible targets for cancer drugs. Lung cancer, nowadays is the most common cancer being diagnosed worldwide. Thus, lung cancer, amongst the remaining cancer, should not be overlooked. The pathogenesis and treatment of lung cancer is an urgent problem in the medical field that needs to be researched thoroughly. By integrating this new approach to the most common cancer, we try to find more possibilities for targeted drug development. We analyzed the data from the existing database to find out whether the control group and the disease group were up-regulated or down-regulated to analyze some targets, contributing to the development of targeted drugs.

Kdm1a safeguards the topological boundaries of PRC2-repressed genes and prevents aging-related euchromatinization in neurons

Kdm1a is a histone demethylase linked to intellectual disability with essential roles during gastrulation and the terminal differentiation of specialized cell types, including neurons, that remains highly expressed in the adult brain. To explore Kdm1a’s function in adult neurons, we develop inducible and forebrain-restricted Kdm1a knockouts. By applying multi-omic transcriptome, epigenome and chromatin conformation data, combined with super-resolution microscopy, we find that Kdm1a elimination causes the neuronal activation of nonneuronal genes that are silenced by the polycomb repressor complex and interspersed with active genes. Functional assays demonstrate that the N-terminus of Kdm1a contains an intrinsically disordered region that is essential to segregate Kdm1a-repressed genes from the neighboring active chromatin environment. Finally, we show that the segregation of Kdm1a-target genes is weakened in neurons during natural aging, underscoring the role of Kdm1a safeguarding neuronal genome organization and gene silencing throughout life.

KAKU4 regulates leaf senescence through modulation of H3K27me3 deposition in the Arabidopsis genome

Lamins are the major components of the nuclear lamina, which regulate chromatin structure and gene expression. KAKU4 is a unique nuclear lamina component in the nuclear periphery, modulates nuclear shape and size in Arabidopsis. The knowledge about the regulatory role of KAKU4 in leaf development remains limited. Here we found that knockdown of KAKU4 resulted in an accelerated leaf senescence phenotype, with elevated levels of H2O2 and hormones, particularly SA, JA, and ABA. Our results demonstrated the importance of KAKU4 as a potential negative regulator in age-triggered leaf senescence in Arabidopsis. Furthermore, we conducted combination analyses of transcriptomic and epigenomic data for the kaku4 mutant and WT leaves. The knockdown of KAKU4 lowered H3K27me3 deposition in the up-regulated genes associated with hormone pathways, programmed cell death, and leaf senescence, including SARD1, SAG113/HAI1, PR2, and so forth. In addition, we found the functional crosstalks between KAKU4 and its associated proteins (CRWN1/4, PNET2, GBPL3, etc.) through comparing multiple transcriptome datasets. Overall, our results indicated that KAKU4 may inhibit the expression of a series of genes related to hormone signals and H2O2 metabolism by affecting the deposition of H3K27me3, thereby suppressing leaf senescence.