epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis

A Multilayered Post-Genome-Wide Association Study Analysis Pipeline Defines Functional Variants and Target Genes for Systemic Lupus Erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome-wide association studies (GWASs) have revealed multiple SLE susceptibility loci and associated single-nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown.

METHODS

Here, we report a comprehensive post-GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine-mapping. We compile and cross-reference immune cell-specific expression quantitative trait loci (cis- and trans-expression quantitative trait loci) with promoter capture high-throughput capture chromatin conformation (PCHi-C), allele-specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, quantitative polymerase chain reaction, and Western blot.

RESULTS

Anchoring on 452 index SNPs, we selected 9,931 high linkage disequilibrium (r2 > 0.8) SNPs and defined 182 independent non-human leukocyte antigen (HLA) SLE loci. The 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus-related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele-specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated reference SNP identifier 57668933 (rs57668933) as a functional variant regulating multiple targets, including SLE-risk gene ELF1 in B cells.

CONCLUSION

We demonstrate and validate post-GWAS strategies for using multidimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE-associated SNPs and loci, target genes, and likely biochemical mechanisms to guide experimental characterization.

NAT10/ac4C/FOXP1 Promotes Malignant Progression and Facilitates Immunosuppression by Reprogramming Glycolytic Metabolism in Cervical Cancer

Immunotherapy has recently emerged as the predominant therapeutic approach for cervical cancer (CCa), driven by the groundbreaking clinical achievements of immune checkpoint inhibitors (ICIs), such as anti-PD-1/PD-L1 antibodies. N4-acetylcytidine (ac4C) modification, catalyzed by NAT10, is an important posttranscriptional modification of mRNA in cancers. However, its impact on immunological dysregulation and the tumor immunotherapy response in CCa remains enigmatic. Here, a significant increase in NAT10 expression in CCa tissues is initially observed that is clinically associated with poor prognosis. Subsequently, it is found that HOXC8 activated NAT10 by binding to its promoter, thereby stimulating ac4C modification of FOXP1 mRNA and enhancing its translation efficiency, eventually leading to induction of GLUT4 and KHK expression. Moreover, NAT10/ac4C/FOXP1 axis activity resulted in increased glycolysis and a continuous increase in lactic acid secretion by CCa cells. The lactic acid-enriched tumor microenvironment (TME) further contributed to amplifying the immunosuppressive properties of tumor-infiltrating regulatory T cells (Tregs). Impressively, NAT10 knockdown enhanced the efficacy of PD-L1 blockade-mediated tumor regression in vivo. Taken together, the findings revealed the oncogenic role of NAT10 in initiating crosstalk between cancer cell glycolysis and immunosuppression, which can be a target for synergistic PD-1/PD-L1 blockade immunotherapy in CCa.

Landscape of enhancer disruption and functional screen in melanoma cells

BACKGROUND

The high mutation rate throughout the entire melanoma genome presents a major challenge in stratifying true driver events from the background mutations. Numerous recurrent non-coding alterations, such as those in enhancers, can shape tumor evolution, thereby emphasizing the importance in systematically deciphering enhancer disruptions in melanoma.

RESULTS

Here, we leveraged 297 melanoma whole-genome sequencing samples to prioritize highly recurrent regions. By performing a genome-scale CRISPR interference (CRISPRi) screen on highly recurrent region-associated enhancers in melanoma cells, we identified 66 significant hits which could have tumor-suppressive roles. These functional enhancers show unique mutational patterns independent of classical significantly mutated genes in melanoma. Target gene analysis for the essential enhancers reveal many known and hidden mechanisms underlying melanoma growth. Utilizing extensive functional validation experiments, we demonstrate that a super enhancer element could modulate melanoma cell proliferation by targeting MEF2A, and another distal enhancer is able to sustain PTEN tumor-suppressive potential via long-range interactions.

CONCLUSIONS

Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

A multilayered post-GWAS analysis pipeline defines functional variants and target genes for systemic lupus erythematosus (SLE)

Objectives

Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome-wide association studies (GWAS) have revealed multiple SLE susceptibility loci and associated single nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown.

Methods

Here, we report a comprehensive post-GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine-mapping. We compile and cross-reference immune cell-specific expression quantitative trait loci ( cis - and trans -eQTLs) with promoter-capture Hi-C, allele-specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, qPCR, and Western blot.

Results

Anchoring on 452 index SNPs, we selected 9,931 high-linkage disequilibrium (r 2 >0.8) SNPs and defined 182 independent non-HLA SLE loci. 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus-related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele-specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated rs57668933 as a functional variant regulating multiple targets, including SLE risk gene ELF1 , in B-cells.

Conclusion

We demonstrate and validate post-GWAS strategies for utilizing multi-dimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE-associated SNPs and loci, target genes, and likely biochemical mechanisms, to guide experimental characterization.

An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping

Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Cogito: automated and generic comparison of annotated genomic intervals

Background

Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently.

Results

Cogito “COmpare annotated Genomic Intervals TOol” provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation.

Conclusions

Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html, a comprehensive documentation with detailed descriptions and reproducible examples is included.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04853-1.

GenomicDistributions: fast analysis of genomic intervals with Bioconductor

Background

Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data.

Results

GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs.

Conclusions

GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08467-y.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

Background

Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software.

Results

To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata.

Conclusions

The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.

Bedshift: perturbation of genomic interval sets

Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. There is no way to objectively evaluate the effectiveness of region set similarity metrics. We present Bedshift, a tool for perturbing BED files by randomly shifting, adding, and dropping regions from a reference file. The perturbed files can be used to benchmark similarity metrics, as well as for other applications. We highlight differences in behavior between metrics, such as that the Jaccard score is most sensitive to added or dropped regions, while coverage score is most sensitive to shifted regions.

The Vital Roles of LINC00662 in Human Cancers

Long non-coding RNAs (lncRNAs) play crucial roles in many human diseases, particularly in tumorigenicity and progression. Although lncRNA research studies are increasing rapidly, our understanding of lncRNA mechanisms is still incomplete. The long intergenic non-protein coding RNA 662 (LINC00662) is a novel lncRNA, and accumulating evidence suggests that it is related to a variety of tumors in multiple systems, including the respiratory, reproductive, nervous, and digestive systems. LINC00662 has been shown to be upregulated in malignant tumors and has been confirmed to promote the development of malignant tumors. LINC00662 has also been reported to facilitate a variety of cellular events, such as tumor-cell proliferation, invasion, and migration, and its expression has been correlated to clinicopathological characteristics in patients with tumors. In terms of mechanisms, LINC00662 regulates gene expression by interacting with both proteins and with RNAs, so it may be a potential biomarker for cancer diagnosis, prognosis, and treatment. This article reviews the expression patterns, biological functions, and underlying molecular mechanisms of LINC00662 in tumors.

Recommendations for the FAIRification of genomic track metadata

Background: Many types of data from genomic analyses can be represented as genomic tracks, i.e. features linked to the genomic coordinates of a reference genome. Examples of such data are epigenetic DNA methylation data, ChIP-seq peaks, germline or somatic DNA variants, as well as RNA-seq expression levels. Researchers often face difficulties in locating, accessing and combining relevant tracks from external sources, as well as locating the raw data, reducing the value of the generated information.

Description of work: We propose to advance the application of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to produce searchable metadata for genomic tracks. Findability and Accessibility of metadata can then be ensured by a track search service that integrates globally identifiable metadata from various track hubs in the Track Hub Registry and other relevant repositories. Interoperability and Reusability need to be ensured by the specification and implementation of a basic set of recommendations for metadata. We have tested this concept by developing such a specification in a JSON Schema, called FAIRtracks, and have integrated it into a novel track search service, called TrackFind. We demonstrate practical usage by importing datasets through TrackFind into existing examples of relevant analytical tools for genomic tracks: EPICO and the GSuite HyperBrowser.

Conclusion: We here provide a first iteration of a draft standard for genomic track metadata, as well as the accompanying software ecosystem. It can easily be adapted or extended to future needs of the research community regarding data, methods and tools, balancing the requirements of both data submitters and analytical end-users.