Mapping functional transcription factor networks from gene expression data

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration

MOTIVATION

Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks.

RESULTS

We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.

AVAILABILITY AND IMPLEMENTATION

All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prothymosin Alpha: A Novel Contributor to Estradiol Receptor Alpha-Mediated CD8+ T-Cell Pathogenic Responses and Recognition of Type 1 Collagen in Rheumatic Heart Valve Disease

BACKGROUND

Rheumatic heart valve disease (RHVD) is a leading cause of cardiovascular death in low- and middle-income countries and affects predominantly women. The underlying mechanisms of chronic valvular damage remain unexplored and regulators of sex predisposition are unknown.

METHODS

Proteomics analysis of human heart valves (nondiseased aortic valves, nondiseased mitral valves [NDMVs], valves from patients with rheumatic aortic valve disease, and valves from patients with rheumatic mitral valve disease; n=30) followed by system biology analysis identified ProTα (prothymosin alpha) as a protein associated with RHVD. Histology, multiparameter flow cytometry, and enzyme-linked immunosorbent assay confirmed the expression of ProTα. In vitro experiments using peripheral mononuclear cells and valvular interstitial cells were performed using multiparameter flow cytometry and quantitative polymerase chain reaction. In silico analysis of the RHVD and Streptococcuspyogenes proteomes were used to identify mimic epitopes.

RESULTS

A comparison of NDMV and nondiseased aortic valve proteomes established the baseline differences between nondiseased aortic and mitral valves. Thirteen unique proteins were enriched in NDMVs. Comparison of NDMVs versus valves from patients with rheumatic mitral valve disease and nondiseased aortic valves versus valves from patients with rheumatic aortic valve disease identified 213 proteins enriched in rheumatic valves. The expression of the 13 NDMV-enriched proteins was evaluated across the 213 proteins enriched in diseased valves, resulting in the discovery of ProTα common to valves from patients with rheumatic mitral valve disease and valves from patients with rheumatic aortic valve disease. ProTα plasma levels were significantly higher in patients with RHVD than in healthy individuals. Immunoreactive ProTα colocalized with CD8⁺ T cells in RHVD. Expression of ProTα and estrogen receptor alpha correlated strongly in circulating CD8⁺ T cells from patients with RHVD. Recombinant ProTα induced expression of the lytic proteins perforin and granzyme B by CD8⁺ T cells as well as higher estrogen receptor alpha expression. In addition, recombinant ProTα increased human leukocyte antigen class I levels in valvular interstitial cells. Treatment of CD8⁺ T cells with specific estrogen receptor alpha antagonist reduced the cytotoxic potential promoted by ProTα. In silico analysis of RHVD and Spyogenes proteomes revealed molecular mimicry between human type 1 collagen epitope and bacterial collagen-like protein, which induced CD8⁺ T-cell activation in vitro.

CONCLUSIONS

ProTα-dependent CD8⁺ T-cell cytotoxicity was associated with estrogen receptor alpha activity, implicating ProTα as a potential regulator of sex predisposition in RHVD. ProTα facilitated recognition of type 1 collagen mimic epitopes by CD8⁺ T cells, suggesting mechanisms provoking autoimmunity.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

Challenges of Translating Gene Regulatory Information into Agronomic Improvements

Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.

NIN Acts as a Network Hub Controlling a Growth Module Required for Rhizobial Infection

The symbiotic infection of root cells by nitrogen-fixing rhizobia during nodulation requires the transcription factor Nodule Inception (NIN). Our root hair transcriptomic study extends NIN's regulon to include Rhizobium Polar Growth and genes involved in cell wall modification, gibberellin biosynthesis, and a comprehensive group of nutrient (N, P, and S) uptake and assimilation genes, suggesting that NIN's recruitment to nodulation was based on its role as a growth module, a role shared with other NIN-Like Proteins. The expression of jasmonic acid genes in nin suggests the involvement of NIN in the resolution of growth versus defense outcomes. We find that the regulation of the growth module component Nodulation Pectate Lyase by NIN, and its function in rhizobial infection, are conserved in hologalegina legumes, highlighting its recruitment as a major event in the evolution of nodulation. We find that Nodulation Pectate Lyase is secreted to the infection chamber and the lumen of the infection thread. Gene network analysis using the transcription factor mutants for ERF Required for Nodulation1 and Nuclear Factor-Y Subunit A1 confirms hierarchical control of NIN over Nuclear Factor-Y Subunit A1 and shows that ERF Required for Nodulation1 acts independently to control infection. We conclude that while NIN shares functions with other NIN-Like Proteins, the conscription of key infection genes to NIN's control has made it a central regulatory hub for rhizobial infection.

Unraveling synthesis of the cryptococcal cell wall and capsule

Fungal pathogens cause devastating infections in millions of individuals each year, representing a huge but underappreciated burden on human health. One of these, the opportunistic fungus Cryptococcus neoformans, kills hundreds of thousands of patients annually, disproportionately affecting people in resource-limited areas. This yeast is distinguished from other pathogenic fungi by a polysaccharide capsule that is displayed on the cell surface. The capsule consists of two complex polysaccharide polymers: a mannan substituted with xylose and glucuronic acid, and a galactan with galactomannan side chains that bear variable amounts of glucuronic acid and xylose. The cell wall, with which the capsule is associated, is a matrix of alpha and beta glucans, chitin, chitosan, and mannoproteins. In this review, we focus on synthesis of the wall and capsule, both of which are critical for the ability of this microbe to cause disease and are distinct from structures found in either model yeasts or the mammals afflicted by this infection. Significant research effort over the last few decades has been applied to defining the synthetic machinery of these two structures, including nucleotide sugar metabolism and transport, glycosyltransferase activities, polysaccharide export, and assembly and association of structural elements. Discoveries in this area have elucidated fundamental biology and may lead to novel targets for antifungal therapy. In this review, we summarize the progress made in this challenging and fascinating area, and outline future research questions.

A Wor1-Like Transcription Factor Is Essential for Virulence of Cryptococcus neoformans

Gti1/Pac2 transcription factors occur exclusively in fungi and their roles vary according to species, including regulating morphological transition and virulence, mating and secondary metabolism. Many of these functions are important for fungal pathogenesis. We therefore hypothesized that one of the two proteins of this family in Cryptococcus neoformans, a major pathogen of humans, would also control virulence-associated cellular processes. Elimination of this protein in C. neoformans results in reduced polysaccharide capsule expression and defective cytokinesis and growth at 37°C. The mutant loses virulence in a mouse model of cryptococcal infection and retains only partial virulence in the Galleria mellonella alternative model at 30°C. We performed RNA-Seq experiments on the mutant and found abolished transcription of genes that, in combination, are known to account for all the observed phenotypes. The protein has been named Required for cytokinesis and virulence 1 (Rcv1).

Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in high-throughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA-binding proteins. Overall, this review will help to clarify a system-level view of gene expression programs in cells. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Turnover and Surveillance > Regulation of RNA Stability RNA Methods > RNA Analyses in vitro and In Silico.

ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Background

Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.

Results

We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads.

Conclusions

We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users.

Seven-Up Is a Novel Regulator of Insulin Signaling

Insulin resistance is associated with obesity, cardiovascular disease, non-alcoholic fatty liver disease, and type 2 diabetes. These complications are exacerbated by a high-calorie diet, which we used to model type 2 diabetes in Drosophila melanogaster Our studies focused on the fat body, an adipose- and liver-like tissue that stores fat and maintains circulating glucose. A gene regulatory network was constructed to predict potential regulators of insulin signaling in this tissue. Genomic characterization of fat bodies suggested a central role for the transcription factor Seven-up (Svp). Here, we describe a new role for Svp as a positive regulator of insulin signaling. Tissue-specific loss-of-function showed that Svp is required in the fat body to promote glucose clearance, lipid turnover, and insulin signaling. Svp appears to promote insulin signaling, at least in part, by inhibiting ecdysone signaling. Svp also impairs the immune response possibly via inhibition of antimicrobial peptide expression in the fat body. Taken together, these studies show that gene regulatory networks can help identify positive regulators of insulin signaling and metabolic homeostasis using the Drosophila fat body.

Genome-wide analyses of direct target genes of four rice NAC-domain transcription factors involved in drought tolerance

BACKGROUND

Plant stress responses and mechanisms determining tolerance are controlled by diverse sets of genes. Transcription factors (TFs) have been implicated in conferring drought tolerance under drought stress conditions, and the identification of their target genes can elucidate molecular regulatory networks that orchestrate tolerance mechanisms.

RESULTS

We generated transgenic rice plants overexpressing the 4 rice TFs, OsNAC5, 6, 9, and 10, under the control of the root-specific RCc3 promoter. We showed that they were tolerant to drought stress with reduced loss of grain yield under drought conditions compared with wild type plants. To understand the molecular mechanisms underlying this tolerance, we here performed chromatin immunoprecipitation (ChIP)-Seq and RNA-Seq analyses to identify the direct target genes of the OsNAC proteins using the RCc3:6MYC-OsNAC expressing roots. A total of 475 binding loci for the 4 OsNAC proteins were identified by cross-referencing their binding to promoter regions and the expression levels of the corresponding genes. The binding loci were distributed among the promoter regions of 391 target genes that were directly up-regulated by one of the OsNAC proteins in four RCc3:6MYC-OsNAC transgenic lines. Based on gene ontology (GO) analysis, the direct target genes were related to transmembrane/transporter activity, vesicle, plant hormones, carbohydrate metabolism, and TFs. The direct targets of each OsNAC range from 4.0-8.7% of the total number of up-regulated genes found in the RNA-Seq data sets. Thus, each OsNAC up-regulates a set of direct target genes that alter root system architecture in the RCc3:OsNAC plants to confer drought tolerance. Our results provide a valuable resource for functional dissection of the molecular mechanisms of drought tolerance.

CONCLUSIONS

Many of the target genes, including transmembrane/transporter, vesicle related, auxin/hormone related, carbohydrate metabolic processes, and transcription factor genes, that are up-regulated by OsNACs act as the cellular components which would alter the root architectures of RCc3:OsNACs for drought tolerance.

NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources

Motivation

Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.

Results

We present NetProphet 2.0, a ‘data light’ algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map.

Availability and implementation

Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0.

Supplementary information

Supplementary data are available at Bioinformatics online.

Associating transcription factors and conserved RNA structures with gene regulation in the human brain

Anatomical subdivisions of the human brain can be associated with different neuronal functions. This functional diversification is reflected by differences in gene expression. By analyzing post-mortem gene expression data from the Allen Brain Atlas, we investigated the impact of transcription factors (TF) and RNA secondary structures on the regulation of gene expression in the human brain. First, we modeled the expression of a gene as a linear combination of the expression of TFs. We devised an approach to select robust TF-gene interactions and to determine localized contributions to gene expression of TFs. Among the TFs with the most localized contributions, we identified EZH2 in the cerebellum, NR3C1 in the cerebral cortex and SRF in the basal forebrain. Our results suggest that EZH2 is involved in regulating ZIC2 and SHANK1 which have been linked to neurological diseases such as autism spectrum disorder. Second, we associated enriched regulatory elements inside differentially expressed mRNAs with RNA secondary structure motifs. We found a group of purine-uracil repeat RNA secondary structure motifs plus other motifs in neuron related genes such as ACSL4 and ERLIN2.

SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets

Transcription factors and their associated DNA binding sites are key regulatory elements of cellular differentiation, development, and environmental response. New tools that predict transcriptional regulation of biological processes are valuable to researchers studying both model and emerging-model plant systems. SeqEnrich predicts transcription factor networks from co-expressed Arabidopsis or Brassica napus gene sets. The networks produced by SeqEnrich are supported by existing literature and predicted transcription factor–DNA interactions that can be functionally validated at the laboratory bench. The program functions with gene sets of varying sizes and derived from diverse tissues and environmental treatments. SeqEnrich presents as a powerful predictive framework for the analysis of Arabidopsis and Brassica napus co-expression data, and is designed so that researchers at all levels can easily access and interpret predicted transcriptional circuits. The program outperformed its ancestral program ChipEnrich, and produced detailed transcription factor networks from Arabidopsis and Brassica napus gene expression data. The SeqEnrich program is ideal for generating new hypotheses and distilling biological information from large-scale expression data.

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

Networks of fibers and factors: regulation of capsule formation in Cryptococcus neoformans

The ability of the pathogenic fungus Cryptococcus neoformans to cause life-threatening meningoencephalitis in immunocompromised individuals is due in large part to elaboration of a capsule consisting of polysaccharide fibers. The size of the cell-associated capsule is remarkably responsive to a variety of environmental and host conditions, but the mechanistic details of the regulation, synthesis, trafficking, and attachment of the polysaccharides are poorly understood. Recent studies reveal a complex network of transcription factors that influence capsule elaboration in response to several different signals of relevance to disease (e.g., iron deprivation). The emerging complexity of the network is consistent with the diversity of conditions that influence the capsule and illustrates the responsiveness of the fungus to both the environment and mammalian hosts.

Computational Analysis Reveals a Key Regulator of Cryptococcal Virulence and Determinant of Host Response

Cryptococcus neoformans is a ubiquitous, opportunistic fungal pathogen that kills over 600,000 people annually. Here, we report integrated computational and experimental investigations of the role and mechanisms of transcriptional regulation in cryptococcal infection. Major cryptococcal virulence traits include melanin production and the development of a large polysaccharide capsule upon host entry; shed capsule polysaccharides also impair host defenses. We found that both transcription and translation are required for capsule growth and that Usv101 is a master regulator of pathogenesis, regulating melanin production, capsule growth, and capsule shedding. It does this by directly regulating genes encoding glycoactive enzymes and genes encoding three other transcription factors that are essential for capsule growth: GAT201, RIM101, and SP1. Murine infection with cryptococci lacking Usv101 significantly alters the kinetics and pathogenesis of disease, with extended survival and, unexpectedly, death by pneumonia rather than meningitis. Our approaches and findings will inform studies of other pathogenic microbes.

Cryptococcus neoformans causes fatal meningitis in immunocompromised individuals, mainly HIV positive, killing over 600,000 each year. A unique feature of this yeast, which makes it particularly virulent, is its polysaccharide capsule; this structure impedes host efforts to combat infection. Capsule size and structure respond to environmental conditions, such as those encountered in an infected host. We have combined computational and experimental tools to elucidate capsule regulation, which we show primarily occurs at the transcriptional level. We also demonstrate that loss of a novel transcription factor alters virulence factor expression and host cell interactions, changing the lethal condition from meningitis to pneumonia with an exacerbated host response. We further demonstrate the relevant targets of regulation and kinetically map key regulatory and host interactions. Our work elucidates mechanisms of capsule regulation, provides methods and resources to the research community, and demonstrates an altered pathogenic outcome that resembles some human conditions.

Understanding transcriptional regulatory networks using computational models

Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship.

Determination of specificity influencing residues for key transcription factor families

Transcription factors (TFs) are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information (MI) to delineate specificity influencing residues (SIRs) from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods (Direct Information, PSICOV and adjusted mutual information) that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs forhomeodomain (HD), helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at stormo.wustl.edu/SpecPred.

RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasets

Gene targeting is a protocol for introducing a mutation to a specific gene in an organism. Because of the importance of in vivo assessment of gene function and modeling of human diseases, this technique has been widely adopted to generate a large number of mutant mouse models. Due to the recent breakthroughs in high-throughput sequencing technologies, RNA-Seq experiments have been performed on many of these mouse models, leading to hundreds of publicly available datasets. To facilitate the reuse of these datasets, we collected the associated metadata and organized them in a database called RNASeqMetaDB. The metadata were manually curated to ensure annotation consistency. We developed a web server to allow easy database navigation and data querying. Users can search the database using multiple parameters like genes, diseases, tissue types, keywords and associated publications in order to find datasets that match their interests. Summary statistics of the metadata are also presented on the web server showing interesting global patterns of RNA-Seq studies.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://rnaseqmetadb.ece.tamu.edu.

By the company they keep: interaction networks define the binding ability of transcription factors

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein–protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda.

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus—its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and “foreman” TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation.

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

MOTIVATION

Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses.

RESULTS

We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry).

AVAILABILITY AND IMPLEMENTATION

An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Glucocorticoid receptor coordinates transcription factor-dominated regulatory network in macrophages

Background

Inflammation triggered by infection or injury is tightly controlled by glucocorticoid hormones which signal via a dedicated transcription factor, the Glucocorticoid Receptor (GR), to regulate hundreds of genes. However, the hierarchy of transcriptional responses to GR activation and the molecular basis of their oftentimes non-linear dynamics are not understood.

Results

We investigated early glucocorticoid-driven transcriptional events in macrophages, a cell type highly responsive to both pro- and anti-inflammatory stimuli. Using whole transcriptome analyses in resting and acutely lipopolysaccharide (LPS)-stimulated macrophages, we show that early GR target genes form dense networks with the majority of control nodes represented by transcription factors. The expression dynamics of several glucocorticoid-responsive genes are consistent with feed forward loops (FFL) and coincide with rapid GR recruitment. Notably, GR binding sites in genes encoding members of the KLF transcription factor family colocalize with KLF binding sites. Moreover, our gene expression, transcription factor binding and computational data are consistent with the existence of the GR-KLF9-KLF2 incoherent FFL. Analysis of LPS-downregulated genes revealed striking enrichment in multimerized Zn-fingers- and KRAB domain-containing proteins known to bind nucleic acids and repress transcription by propagating heterochromatin. This raises an intriguing possibility that an increase in chromatin accessibility in inflammatory macrophages results from broad downregulation of negative chromatin remodelers.

Conclusions

Pro- and anti-inflammatory stimuli alter the expression of a vast array of transcription factors and chromatin remodelers. By regulating multiple transcription factors, which propagate the initial hormonal signal, GR acts as a coordinating hub in anti-inflammatory responses. As several KLFs promote the anti-inflammatory program in macrophages, we propose that GR and KLFs functionally cooperate to curb inflammation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-656) contains supplementary material, which is available to authorized users.

A comprehensive protein-protein interactome for yeast PAS kinase 1 reveals direct inhibition of respiration through the phosphorylation of Cbf1

PAS kinase is a conserved sensory protein kinase required for glucose homeostasis. The interactome for yeast PAS kinase 1 (Psk1) is identified, revealing 93 binding partners. Evidence is provided for in vivo phosphorylation of Cbf1 and subsequent inhibition of respiration, supporting a role for Psk1 in partitioning glucose for cell growth.

Per-Arnt-Sim (PAS) kinase is a sensory protein kinase required for glucose homeostasis in yeast, mice, and humans, yet little is known about the molecular mechanisms of its function. Using both yeast two-hybrid and copurification approaches, we identified the protein–protein interactome for yeast PAS kinase 1 (Psk1), revealing 93 novel putative protein binding partners. Several of the Psk1 binding partners expand the role of PAS kinase in glucose homeostasis, including new pathways involved in mitochondrial metabolism. In addition, the interactome suggests novel roles for PAS kinase in cell growth (gene/protein expression, replication/cell division, and protein modification and degradation), vacuole function, and stress tolerance. In vitro kinase studies using a subset of 25 of these binding partners identified Mot3, Zds1, Utr1, and Cbf1 as substrates. Further evidence is provided for the in vivo phosphorylation of Cbf1 at T211/T212 and for the subsequent inhibition of respiration. This respiratory role of PAS kinase is consistent with the reported hypermetabolism of PAS kinase–deficient mice, identifying a possible molecular mechanism and solidifying the evolutionary importance of PAS kinase in the regulation of glucose homeostasis.

Synergetic regulatory networks mediated by oncogene-driven microRNAs and transcription factors in serous ovarian cancer

Although high-grade serous ovarian cancer (OVC) is the most lethal gynecologic malignancy in women, little is known about the regulatory mechanisms in the cellular processes that lead to this cancer. Recently, accumulated lines of evidence have shown that the interplay between transcription factors (TFs) and microRNAs (miRNAs) is critical in cellular regulation during tumorigenesis. A comprehensive investigation of TFs and miRNAs, and their target genes, may provide a deeper understanding of the regulatory mechanisms in the pathology of OVC. In this study, we have integrated three complementary algorithms into a framework, aiming to infer the regulation by miRNAs and TFs in conjunction with gene expression profiles. We demonstrated the utility of our framework by inferring 67 OVC-specific regulatory feed-forward loops (FFL) initiated by miRNAs or TFs in high-grade serous OVC. By analyzing these regulatory behaviors, we found that all the 67 FFLs are consistent in their regulatory effects on genes that are jointly targeted by miRNAs and TFs. Remarkably, we unveiled an unbalanced distribution of FFLs with different oncogenic effects. In total, 31 of the 67 coherent FFLs were mainly initiated by oncogenes. On the contrary, only 4 of the FFLs were initiated by tumor suppressor genes. These overwhelmingly observed oncogenic genes were further detected in a sub-network with 32 FFLs centered by miRNA let-7b and TF TCF7L1 to regulate cell differentiation. Closer inspection of 32 FFLs revealed that 75% of the miRNAs reportedly play functional roles in cell differentiation, especially when enriched in epithelial-mesenchymal transitions. This study provides a comprehensive pathophysiological overview of recurring coherent circuits in OVC that are co-regulated by miRNAs and TFs. The prevalence of oncogenic coherent FFLs in serous OVC suggests that oncogene-driven regulatory motifs could cooperatively act upon critical cellular processes such as cell differentiation in a highly efficient and consistent manner.