Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

SEgene identifies links between super enhancers and gene expression across cell types

Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis. To address these limitations, we developed the super-enhancer to gene links (SE-to-gene Links) analysis, a platform named "SEgene" which incorporates the peak-to-gene links approach-a statistical method designed to reveal correlations between genes and peak regions ( https://github.com/hamamoto-lab/SEgene ). This platform enables a targeted evaluation of super-enhancer regions in relation to gene expression, facilitating the identification of super-enhancers that are functionally linked to transcriptional activity. Here, we demonstrate the application of SE-to-gene Links analysis to public datasets, confirming its efficacy in accurately detecting super-enhancers and identifying functionally associated genes. Additionally, SE-to-gene Links analysis identified ERBB2 as a significant gene of interest in the lung adenocarcinoma dataset from the National Cancer Center Japan cohort, suggesting a potential impact across multiple patient samples. Thus, the SE-to-gene Links analysis provides an analytical tool for evaluating super-enhancers as potential therapeutic targets, supporting the identification of clinically significant super-enhancer regions and their functionally associated genes.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

CSSQ: a ChIP-seq signal quantifier pipeline

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has revolutionized the studies of epigenomes and the massive increase in ChIP-seq datasets calls for robust and user-friendly computational tools for quantitative ChIP-seq. Quantitative ChIP-seq comparisons have been challenging due to noisiness and variations inherent to ChIP-seq and epigenomes. By employing innovative statistical approaches specially catered to ChIP-seq data distribution and sophisticated simulations along with extensive benchmarking studies, we developed and validated CSSQ as a nimble statistical analysis pipeline capable of differential binding analysis across ChIP-seq datasets with high confidence and sensitivity and low false discovery rate with any defined regions. CSSQ models ChIP-seq data as a finite mixture of Gaussians faithfully that reflects ChIP-seq data distribution. By a combination of Anscombe transformation, k-means clustering, estimated maximum normalization, CSSQ minimizes noise and bias from experimental variations. Further, CSSQ utilizes a non-parametric approach and incorporates comparisons under the null hypothesis by unaudited column permutation to perform robust statistical tests to account for fewer replicates of ChIP-seq datasets. In sum, we present CSSQ as a powerful statistical computational pipeline tailored for ChIP-seq data quantitation and a timely addition to the tool kits of differential binding analysis to decipher epigenomes.

The EGR3 regulome of infant KMT2A-r acute lymphoblastic leukemia identifies differential expression of B-lineage genes predictive for outcome

KMT2A-rearranged acute lymphoblastic infant leukemia (KMT2A-r iALL) is associated with outsize risk of relapse and relapse mortality. We previously reported strong upregulation of the immediate early gene EGR3 in KMT2A::AFF1 iALL at relapse; now we provide analyses of the EGR3 regulome, which we assessed through binding and expression target analysis of an EGR3-overexpressing t(4;11) cell culture model. Our data identify EGR3 as a regulator of early B-lineage commitment. Principal component analysis of 50 KMT2A-r iALL patients at diagnosis and 18 at relapse provided strictly dichotomous separation of patients based on the expression of four B-lineage genes. Absence of B-lineage gene expression translates to more than two-fold poorer long-term event-free survival. In conclusion, our study presents four B-lineage genes with prognostic significance, suitable for gene expression-based risk stratification of KMT2A-r iALL patients.

Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state

Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.

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.

Transcriptional regulation by a RecQ helicase

RecQ helicases participate in a variety of DNA metabolic processes through their multiple biochemical activities. In vitro characterization and cellular studies have suggested that RECQ1 (also known as RECQL or RECQL1) performs its diverse functions through specific interactions with DNA and protein partners. We have taken an unbiased approach to determine the contribution of RECQ1 in genome maintenance and as a putative susceptibility factor in breast cancer. Here, we provide methodology to map the genome-wide binding sites of RECQ1 together with the profiling of RECQ1-dependent transcriptome to investigate its role in gene regulation. The described approach will be helpful to develop a mechanistic framework for elucidating critical functions of RECQ1 and other RecQ homologs in distinct chromatin and biological contexts.

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

New biomarkers and therapeutic targets of human liver cancer: Transcriptomic findings

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths worldwide, causing 782,000 deaths in 2018. Poor prognosis and lack of treatment are the reasons for the high mortality rate of HCC. In the current study, we conducted a comparative transcriptomic analysis, followed by a series of bioinformatics analyses, including Gene Ontology (GO) enrichment analysis and Ingenuity Pathway Analysis (IPA), aiming to unfold the detailed molecular mechanisms underlying the development of HCC. In the comparative transcriptomic analysis of 10 pairs of HCC tumoral tissues and adjunct nontumoral tissues, we identified 115 common differentially expressed genes in HCC. The GO enrichment analysis of these genes highlighted alterations in the immune response, cell proliferation and DNA damage, energetic metabolism, cell-matrix adhesion, and filament assembly in HCC. In addition, the canonical pathway analysis of IPA further showed the importance of many cell-signaling pathways involved in the carcinogenesis of HCC. The findings of this study provide a cluster of novel biomarkers and molecular therapeutic targets for HCC diagnosis and treatment.

A guide to changing paradigms of glucocorticoid receptor function-a model system for genome regulation and physiology

The glucocorticoid receptor (GR) is a bona fide ligand-regulated transcription factor. Cloned in the 80s, the GR has become one of the best-studied and clinically most relevant members of the nuclear receptor superfamily. Cooperative activity of GR with other transcription factors and a plethora of coregulators contribute to the tissue- and context-specific response toward the endogenous and pharmacological glucocorticoids (GCs). Furthermore, nontranscriptional activities in the cytoplasm are emerging as an additional function of GR. Over the past 40 years, the concepts of GR mechanisms of action had been constantly changing. Different methodologies in the pregenomic and genomic era of molecular biological research and recent cutting-edge technology in single-cell and single-molecule analysis are steadily evolving the views, how the GR in particular and transcriptional regulation in general act in physiological and pathological processes. In addition to the development of technologies for GR analysis, the use of model organisms provides insights how the GR in vivo executes GC action in tissue homeostasis, inflammation, and energy metabolism. The model organisms, namely the mouse, but also rats, zebrafish, and recently fruit flies carrying mutations of the GR became a major driving force to analyze the molecular function of GR in disease models. This guide provides an overview of the exciting research and paradigm shifts in the GR field from past to present with a focus on GR transcription factor networks, GR DNA-binding and single-cell analysis, and model systems.

Analysis of H3K4me3-ChIP-Seq and RNA-Seq data to understand the putative role of miRNAs and their target genes in breast cancer cell lines

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)‒specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3′-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

The role of epigenetic modifications for the pathogenesis of Crohn's disease

Epigenetics has become a promising field for finding new biomarkers and improving diagnosis, prognosis, and drug response in inflammatory bowel disease. The number of people suffering from inflammatory bowel diseases, especially Crohn's disease, has increased remarkably. Crohn's disease is assumed to be the result of a complex interplay between genetic susceptibility, environmental factors, and altered intestinal microbiota, leading to dysregulation of the innate and adaptive immune response. While many genetic variants have been identified to be associated with Crohn's disease, less is known about the influence of epigenetics in the pathogenesis of this disease. In this review, we provide an overview of current epigenetic studies in Crohn's disease. In particular, we enable a deeper insight into applied bioanalytical and computational tools, as well as a comprehensive update toward the cell-specific evaluation of DNA methylation and histone modifications.

Differential chromatin binding of the lung lineage transcription factor NKX2-1 resolves opposing murine alveolar cell fates in vivo

Differential transcription of identical DNA sequences leads to distinct tissue lineages and then multiple cell types within a lineage, an epigenetic process central to progenitor and stem cell biology. The associated genome-wide changes, especially in native tissues, remain insufficiently understood, and are hereby addressed in the mouse lung, where the same lineage transcription factor NKX2-1 promotes the diametrically opposed alveolar type 1 (AT1) and AT2 cell fates. Here, we report that the cell-type-specific function of NKX2-1 is attributed to its differential chromatin binding that is acquired or retained during development in coordination with partner transcriptional factors. Loss of YAP/TAZ redirects NKX2-1 from its AT1-specific to AT2-specific binding sites, leading to transcriptionally exaggerated AT2 cells when deleted in progenitors or AT1-to-AT2 conversion when deleted after fate commitment. Nkx2-1 mutant AT1 and AT2 cells gain distinct chromatin accessible sites, including those specific to the opposite fate while adopting a gastrointestinal fate, suggesting an epigenetic plasticity unexpected from transcriptional changes. Our genomic analysis of single or purified cells, coupled with precision genetics, provides an epigenetic basis for alveolar cell fate and potential, and introduces an experimental benchmark for deciphering the in vivo function of lineage transcription factors.

Genome-in-a-Box: Building a Chromosome from the Bottom Up

Chromosome structure and dynamics are essential for life, as the way that our genomes are spatially organized within cells is crucial for gene expression, differentiation, and genome transfer to daughter cells. There is a wide variety of methods available to study chromosomes, ranging from live-cell studies to single-molecule biophysics, which we briefly review. While these technologies have yielded a wealth of data, such studies still leave a significant gap between top-down experiments on live cells and bottom-up in vitro single-molecule studies of DNA-protein interactions. Here, we introduce "genome-in-a-box" (GenBox) as an alternative in vitro approach to build and study chromosomes, which bridges this gap. The concept is to assemble a chromosome from the bottom up by taking deproteinated genome-sized DNA isolated from live cells and subsequently add purified DNA-organizing elements, followed by encapsulation in cell-sized containers using microfluidics. Grounded in the rationale of synthetic cell research, the approach would enable to experimentally study emergent effects at the global genome level that arise from the collective action of local DNA-structuring elements. We review the various DNA-structuring elements present in nature, from nucleoid-associated proteins and SMC complexes to phase separation and macromolecular crowders. Finally, we discuss how GenBox can contribute to several open questions on chromosome structure and dynamics.