RegulatorTrail: a web service for the identification of key transcriptional regulators

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

The growth hormone receptor interacts with transcriptional regulator HMGN1 upon GH-induced nuclear translocation

Growth hormone (GH) actions are mediated through binding to its cell-surface receptor, the GH receptor (GHR), with consequent activation of downstream signalling. However, nuclear GHR localisation has also been observed and is associated with increased cancer cell proliferation. Here we investigated the functional implications of nuclear translocation of the GHR in the human endometrial cancer cell-line, RL95-2, and human mammary epithelial cell-line, MCF-10A. We found that following GH treatment, the GHR rapidly translocates to the nucleus, with maximal localisation at 5-10 min. Combined immunoprecipitation-mass spectrometry analysis of RL95-2 whole cell lysates identified 40 novel GHR binding partners, including the transcriptional regulator, HMGN1. Moreover, microarray analysis demonstrated that the gene targets of HMGN1 were differentially expressed following GH treatment, and co-immunoprecipitation showed that HMGN1 associates with the GHR in the nucleus. Therefore, our results suggest that GHR nuclear translocation might mediate GH actions via interaction with chromatin factors that then drive changes in specific downstream transcriptional programs.

ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data

ChIP-Atlas (https://chip-atlas.org) is a web service providing both GUI- and API-based data-mining tools to reveal the architecture of the transcription regulatory landscape. ChIP-Atlas is powered by comprehensively integrating all data sets from high-throughput ChIP-seq and DNase-seq, a method for profiling chromatin regions accessible to DNase. In this update, we further collected all the ATAC-seq and whole-genome bisulfite-seq data for six model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast) with the latest genome assemblies. These together with ChIP-seq data can be visualized with the Peak Browser tool and a genome browser to explore the epigenomic landscape of a query genomic locus, such as its chromatin accessibility, DNA methylation status, and protein–genome interactions. This epigenomic landscape can also be characterized for multiple genes and genomic loci by querying with the Enrichment Analysis tool, which, for example, revealed that inflammatory bowel disease-associated SNPs are the most significantly hypo-methylated in neutrophils. Therefore, ChIP-Atlas provides a panoramic view of the whole epigenomic landscape. All datasets are free to download via either a simple button on the web page or an API.

GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

Oocytes suppress FOXL2 expression in cumulus cells in mice†

Cumulus cells and mural granulosa cells (MGCs) play distinct roles during follicular development, and normal development of these cell lineages is critical for the female fertility. Transcriptomic diversification between the two cell lineages is obviously a critical mechanism for their functional diversification; however, the transcriptional regulators responsible for this event have not been fully defined. In this study, we sought to identify key transcriptional regulators responsible for the differential gene expression between the two cell lineages. In silico analysis of transcriptomic comparison between cumulus cells and MGCs identified several candidate regulators responsible for the diversification of the two cell lineages. Among them, we herein focused on forkhead box L2 (FOXL2) and showed that expressions of FOXL2 as well as its target transcripts were differentially regulated between cumulus cells and MGCs. The lower expression of FOXL2 in cumulus cells seemed to be due to the suppression by oocyte-derived paracrine signals. These results suggest that FOXL2 is one of the critical transcription factors that determine cumulus cell and MGC lineages under the control of oocytes.

GeneTrail 3: advanced high-throughput enrichment analysis

We present GeneTrail 3, a major extension of our web service GeneTrail that offers rich functionality for the identification, analysis, and visualization of deregulated biological processes. Our web service provides a comprehensive collection of biological processes and signaling pathways for 12 model organisms that can be analyzed with a powerful framework for enrichment and network analysis of transcriptomic, miRNomic, proteomic, and genomic data sets. Moreover, GeneTrail offers novel workflows for the analysis of epigenetic marks, time series experiments, and single cell data. We demonstrate the capabilities of our web service in two case-studies, which highlight that GeneTrail is well equipped for uncovering complex molecular mechanisms. GeneTrail is freely accessible at: http://genetrail.bioinf.uni-sb.de.

Quantitative and time-resolved miRNA pattern of early human T cell activation

T cells are central to the immune response against various pathogens and cancer cells. Complex networks of transcriptional and post-transcriptional regulators, including microRNAs (miRNAs), coordinate the T cell activation process. Available miRNA datasets, however, do not sufficiently dissolve the dynamic changes of miRNA controlled networks upon T cell activation. Here, we established a quantitative and time-resolved expression pattern for the entire miRNome over a period of 24 h upon human T-cell activation. Based on our time-resolved datasets, we identified central miRNAs and specified common miRNA expression profiles. We found the most prominent quantitative expression changes for miR-155-5p with a range from initially 40 molecules/cell to 1600 molecules/cell upon T-cell activation. We established a comprehensive dynamic regulatory network of both the up- and downstream regulation of miR-155. Upstream, we highlight IRF4 and its complexes with SPI1 and BATF as central for the transcriptional regulation of miR-155. Downstream of miR-155-5p, we verified 17 of its target genes by the time-resolved data recorded after T cell activation. Our data provide comprehensive insights into the range of stimulus induced miRNA abundance changes and lay the ground to identify efficient points of intervention for modifying the T cell response.

Characterization of a Novel Compound That Stimulates STING-Mediated Innate Immune Activity in an Allele-Specific Manner

The innate immune response to cytosolic DNA involves transcriptional activation of type I interferons (IFN-I) and proinflammatory cytokines. This represents the culmination of intracellular signaling pathways that are initiated by pattern recognition receptors that engage DNA and require the adaptor protein Stimulator of Interferon Genes (STING). These responses lead to the generation of cellular and tissue states that impair microbial replication and facilitate the establishment of long-lived, antigen-specific adaptive immunity. Ultimately this can lead to immune-mediated protection from infection but also to the cytotoxic T cell-mediated clearance of tumor cells. Intriguingly, pharmacologic activation of STING-dependent phenotypes is known to enhance both vaccine-associated immunogenicity and immune-based anti-tumor therapies. Unfortunately, the STING protein exists as multiple variant forms in the human population that exhibit differences in their reactivity to chemical stimuli and in the intensity of molecular signaling they induce. In light of this, STING-targeting drug discovery efforts require an accounting of protein variant-specific activity. Herein we describe a small molecule termed M04 that behaves as a novel agonist of human STING. Importantly, we find that the molecule exhibits a differential ability to activate STING based on the allelic variant examined. Furthermore, while M04 is inactive in mice, expression of human STING in mouse cells rescues reactivity to the compound. Using primary human cells in ex vivo assays we were also able to show that M04 is capable of simulating innate responses important for adaptive immune activation such as cytokine secretion, dendritic cell maturation, and T cell cross-priming. Collectively, this work demonstrates the conceivable utility of a novel agonist of human STING both as a research tool for exploring STING biology and as an immune potentiating molecule.

TEPIC 2-an extended framework for transcription factor binding prediction and integrative epigenomic analysis

Summary

Prediction of transcription factor (TF) binding from epigenetics data and integrative analysis thereof are challenging. Here, we present TEPIC 2 a framework allowing for fast, accurate and versatile prediction, and analysis of TF binding from epigenetics data: it supports 30 species with binding motifs, computes TF gene and scores up to two orders of magnitude faster than before due to improved implementation, and offers easy-to-use machine learning pipelines for integrated analysis of TF binding predictions with gene expression data allowing the identification of important TFs.

Availability and implementation

TEPIC is implemented in C++, R, and Python. It is freely available at https://github.com/SchulzLab/TEPIC and can be used on Linux based systems.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

REGGAE: a novel approach for the identification of key transcriptional regulators

Motivation

Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes.

Results

Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov-Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms.

Availability and implementation

REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data

We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

The role of TCF3 as potential master regulator in blastemal Wilms tumors

Wilms tumors are the most common type of pediatric kidney tumors. While the overall prognosis for patients is favorable, especially tumors that exhibit a blastemal subtype after preoperative chemotherapy have a poor prognosis. For an improved risk assessment and therapy stratification, it is essential to identify the driving factors that are distinctive for this aggressive subtype. In our study, we compared gene expression profiles of 33 tumor biopsies (17 blastemal and 16 other tumors) after neoadjuvant chemotherapy. The analysis of this dataset using the Regulator Gene Association Enrichment algorithm successfully identified several biomarkers and associated molecular mechanisms that distinguish between blastemal and nonblastemal Wilms tumors. Specifically, regulators involved in embryonic development and epigenetic processes like chromatin remodeling and histone modification play an essential role in blastemal tumors. In this context, we especially identified TCF3 as the central regulatory element. Furthermore, the comparison of ChIP-Seq data of Wilms tumor cell cultures from a blastemal mouse xenograft and a stromal tumor provided further evidence that the chromatin states of blastemal cells share characteristics with embryonic stem cells that are not present in the stromal tumor cell line. These stem-cell like characteristics could potentially add to the increased malignancy and chemoresistance of the blastemal subtype. Along with TCF3, we detected several additional biomarkers that are distinctive for blastemal Wilms tumors after neoadjuvant chemotherapy and that may provide leads for new therapeutic regimens.

Multilevel omic data clustering reveals variable contribution of methylator phenotype to integrative cancer subtypes

AIM

We aimed to assess to what extent CpG island methylator phenotype (CIMP) contributes to cancer subtypes obtained by multilevel omic data analysis.

MATERIALS & METHODS

16 The Cancer Genome Atlas datasets encompassing three data layers in 4688 tumor samples were analyzed. We identified cancer integrative subtypes (ISs) by the use of similarity network fusion and consensus clustering. CIMP high (CIMP-H) associated ISs were profiled by gene sets and transcriptional regulators enrichment analysis.

RESULTS & CONCLUSION

In nine out of 16 cancer datasets CIMP-H clusters significantly overlaped with unique ISs. The contribution of CIMP-H on integrative molecular profiling is variable; therefore, only in a subset of cancer types does CIMP-H contribute to homogenous integrative subtype. CIMP-H associated ISs are heterogenous groups with regard to deregulated pathways and transcriptional regulators.