Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data

Transforming growth factor-beta gene family in Labeo rohita: Genome-wide molecular characterization and phylogenetic relationship with other vertebrates

Recent advancements in animal genetics, biotechnology, and bioinformatics can potentially contribute to the required improvement in fish production and quality. Current study explored for the first-time, various genes from Transforming growth factor-beta (TGF-β) gene family which could play vital role in growth and development of Labeo rohita, an economically important freshwater fish. A number of tools and analysis like characterizing the selected genes, multiple alignment and clade-wise phylogeny, dual Synteny analysis and chromosomal distribution, characterization of proteins, secondary structure and 3D models of proteins, PROSITE scan analysis, and transcription factor binding sites (TFBSs) of selected genes were performed to explore the relationship between selected genes from Labeo rohita and referenced species (Homo sapiens, Danio rerio, Oreochromis niloticus, Chelonia mydas, and Parus major). The study evaluated 27 genes from TGF-β gene family. The results suggested that physicochemical characteristics of studied genes exhibit a basic nature with the few exceptions. Two main clades (BPM like and GDF like) were obtained by the phylogenetic analysis across six vertebrate species. Homogeneity was observed in the gene structure for all selected genes of TGF-β gene superfamily. TGF-β family and TGF-β propeptide family were dominant in domain regions of studied genes. Gene structure comparisons suggested that the TGF-β gene superfamily has arisen by gene duplications events. The study identified five pairs of duplicated gene (segmental duplications) with the Ka/Ks < 1, indicating the negative selection pressure of these genes. The length of selected TFBSs (GATA, HOXD, STAT, HNF-1A, and YY1) ranged from 5 to 9 bp, with the HOXD having maximum bp. This study explored the molecular characterization for TGF-β gene family and related proteins in L. rohita and will potentially serve as basis of development of novel strategies in improvement of fish culture.

When HSFs bring the heat-mapping the transcriptional circuitries of HSF-type regulators in Candida albicans

Heat shock factor (HSF)-type regulators are stress-responsive transcription factors widely distributed among eukaryotes, including fungi. They carry a four-stranded winged helix-turn-helix DNA-binding domain considered as the signature domain for HSFs. The genome of the opportunistic yeast Candida albicans encodes four HSF members, namely, Sfl1, Sfl2, Skn7, and the essential regulator, Hsf1. C. albicans HSFs do not only respond to heat shock and/or temperature variation but also to CO2 levels, oxidative stress, and quorum sensing, acting this way as central decision makers. In this minireview, I follow on the heels of my mSphere of Influence commentary (2020) to provide an overview of the repertoire of HSF regulators in Saccharomyces cerevisiae and C. albicans and describe how their genetic perturbation in C. albicans, coupled with genome-wide expression and location analyses, allow to map their transcriptional circuitry. I highlight how they can regulate, in common, a crucial developmental program: filamentous growth.

In silico molecular characterization of TGF-β gene family in Bufo bufo: genome-wide analysis

Bufo bufo is a living example of evolutionary processes due to its numerous physiological and ecological adaptations. This is the first study to genetically characterize the TGF-β gene family in B. bufo at the genome-wide level, and a total of 28 TGF-β gene family homologs are identified. Physicochemical characteristics of TGF-β homologs exhibit a basic nature except for BMP1, BMP4, BMP10, BMP15, AMH, INHA, NODAL Modulator and TGFB1. Phylogenetic analysis divided the TGF-β gene family homologs into 2 major clades along with other vertebrate species. In domain and motif composition analysis, the gene structure for all TGF-β homologs exhibited homogeneity except BMP1. We have identified the TGF-β propeptide domain together with the TGF-β in all family homologs of TGF-β superfamily. Gene structure comparisons indicated that the TGF-β gene family have arisen by gene duplications. We also identified 10 duplicated gene pairs, all of which were detected to be segmental duplications. The Ka/Ks test ratio findings for every pair of genes revealed that none of the ratios surpassed 1 except for one gene pair (INHA/BMP1), indicating that these proteins are under positive selection. Circos analysis showed that TGF-β gene family homologs are arranged in 11 dispersed clusters and all were segmentally arrayed in the genome. This study provides a molecular basis for TGF-β ligand protein functional analysis and may serve as a reference for in-depth phylogenomics and may promote the development of novel strategies.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions’ strength distribution, the challenges to studying these interactions’ strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

Uncovering the cellular capacity for intensive and specific feedback self-control of the argonautes and MicroRNA targeting activity

The miRNA pathway has three segments—biogenesis, targeting and downstream regulatory effectors. We aimed to better understand their cellular control by exploring the miRNA-mRNA-targeting relationships. We first used human evolutionarily conserved sites. Strikingly, AGOs 1–3 are all among the top 14 mRNAs with the highest miRNA site counts, along with ANKRD52, the phosphatase regulatory subunit of the recently identified AGO phosphorylation cycle; and the AGO phosphorylation cycle mRNAs share much more than expected miRNA sites. The mRNAs for TNRC6, which acts with AGOs to channel miRNA-mediated regulatory actions onto specific mRNAs, are also heavily miRNA-targeted. In contrast, upstream miRNA biogenesis mRNAs are not, and neither are downstream regulatory effectors. In short, binding site enrichment in miRNA targeting machinery mRNAs, but neither upstream biogenesis nor downstream effector mRNAs, was observed, endowing a cellular capacity for intensive and specific feedback control of the targeting activity. The pattern was confirmed with experimentally determined miRNA-mRNA target relationships. Moreover, genetic experiments demonstrated cellular utilization of this capacity. Thus, we uncovered a capacity for intensive, and specific, feedback-regulation of miRNA targeting activity directly by miRNAs themselves, i.e. segment-specific feedback auto-regulation of miRNA pathway, complementing miRNAs pairing with transcription factors to form hybrid feedback-loop.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.

IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Hierarchical and Dynamic Regulation of Defense-Responsive Specialized Metabolism by WRKY and MYB Transcription Factors

The plant kingdom produces hundreds of thousands of specialized bioactive metabolites, some with pharmaceutical and biotechnological importance. Their biosynthesis and function have been studied for decades, but comparatively less is known about how transcription factors with overlapping functions and contrasting regulatory activities coordinately control the dynamics and output of plant specialized metabolism. Here, we performed temporal studies on pathogen-infected intact host plants with perturbed transcription factors. We identified WRKY33 as the condition-dependent master regulator and MYB51 as the dual functional regulator in a hierarchical gene network likely responsible for the gene expression dynamics and metabolic fluxes in the camalexin and 4-hydroxy-indole-3-carbonylnitrile (4OH-ICN) pathways. This network may have also facilitated the regulatory capture of the newly evolved 4OH-ICN pathway in Arabidopsis thaliana by the more-conserved transcription factor MYB51. It has long been held that the plasticity of plant specialized metabolism and the canalization of development should be differently regulated; our findings imply a common hierarchical regulatory architecture orchestrated by transcription factors for specialized metabolism and development, making it an attractive target for metabolic engineering.

From Endogenous to Synthetic microRNA-Mediated Regulatory Circuits: An Overview

MicroRNAs are short non-coding RNAs that are evolutionarily conserved and are pivotal post-transcriptional mediators of gene regulation. Together with transcription factors and epigenetic regulators, they form a highly interconnected network whose building blocks can be classified depending on the number of molecular species involved and the type of interactions amongst them. Depending on their topology, these molecular circuits may carry out specific functions that years of studies have related to the processing of gene expression noise. In this review, we first present the different over-represented network motifs involving microRNAs and their specific role in implementing relevant biological functions, reviewing both theoretical and experimental studies. We then illustrate the recent advances in synthetic biology, such as the construction of artificially synthesised circuits, which provide a controlled tool to test experimentally the possible microRNA regulatory tasks and constitute a starting point for clinical applications.

MicroRNA-27a/b-3p and PPARG regulate SCAMP3 through a feed-forward loop during adipogenesis

MicroRNAs (miRNA) modulate gene expression through feed-back and forward loops. Previous studies identified miRNAs that regulate transcription factors, including Peroxisome Proliferator Activated Receptor Gamma (PPARG), in adipocytes, but whether they influence adipogenesis via such regulatory loops remain elusive. Here we predicted and validated a novel feed-forward loop regulating adipogenesis and involved miR-27a/b-3p, PPARG and Secretory Carrier Membrane Protein 3 (SCAMP3). In this loop, expression of both PPARG and SCAMP3 was independently suppressed by miR-27a/b-3p overexpression. Knockdown of PPARG downregulated SCAMP3 expression at the late phase of adipogenesis, whereas reduction of SCAMP3 mRNA levels increased PPARG expression at early phase in differentiation. The latter was accompanied with upregulation of adipocyte-enriched genes, including ADIPOQ and FABP4, suggesting an anti-adipogenic role for SCAMP3. PPARG and SCAMP3 exhibited opposite behaviors regarding correlations with clinical phenotypes, including body mass index, body fat mass, adipocyte size, lipolytic and lipogenic capacity, and secretion of pro-inflammatory cytokines. While adipose PPARG expression was associated with more favorable metabolic phenotypes, SCAMP3 expression was linked to increased fat mass and insulin resistance. Together, we identified a feed-forward loop through which miR-27a/b-3p, PPARG and SCAMP3 cooperatively fine tune the regulation of adipogenesis, which potentially may impact whole body metabolism.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein–protein, genetic and homologous interactions, and directed protein–DNA, regulatory and miRNA–mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

An in-silico approach for discovery of microRNA-TF regulation of DISC1 interactome mediating neuronal migration

Neuronal migration constitutes an important step in corticogenesis; dysregulation of the molecular mechanisms mediating this crucial step in neurodevelopment may result in various neuropsychiatric disorders. By curating experimental data from published literature, we identified eight functional modules involving Disrupted-in-schizophrenia 1 (DISC1) and its interacting proteins that regulate neuronal migration. We then identified miRNAs and transcription factors (TFs) that form functional feedback loops and regulate gene expression of the DISC1 interactome. Using this curated data, we conducted in-silico modeling of the DISC1 interactome involved in neuronal migration and identified the proteins that either facilitate or inhibit neuronal migrational processes. We also studied the effect of perturbation of miRNAs and TFs in feedback loops on the DISC1 interactome. From these analyses, we discovered that STAT3, TCF3, and TAL1 (through feedback loop with miRNAs) play a critical role in the transcriptional control of DISC1 interactome thereby regulating neuronal migration. To the best of our knowledge, regulation of the DISC1 interactome mediating neuronal migration by these TFs has not been previously reported. These potentially important TFs can serve as targets for undertaking validation studies, which in turn can reveal the molecular processes that cause neuronal migration defects underlying neurodevelopmental disorders. This underscores the importance of the use of in-silico techniques in aiding the discovery of mechanistic evidence governing important molecular and cellular processes. The present work is one such step towards the discovery of regulatory factors of the DISC1 interactome that mediates neuronal migration.

An Integrated Regulatory Network Based on Comprehensive Analysis of mRNA Expression, Gene Methylation and Expression of Long Non-coding RNAs (lncRNAs) in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are a heterogeneous group of disorders characterized by ineffective hematopoiesis, defective differentiation of hematopoietic precursors, and expansion of the abnormal clones. The prevalence of MDS has raised great concerns worldwide, but its pathogenetic mechanisms remain elusive. To provide insights on novel biomarkers for the diagnosis and therapy of MDS, we performed high-throughput genome-wide mRNA expression profiling, DNA methylation analysis, and long non-coding RNAs (lncRNA) analysis on bone marrows from four MDS patients and four age-matched healthy controls. We identified 1,937 differentially expressed genes (DEGs), 515 methylated genes, and 214 lncRNA that showed statistically significant differences. As the most significant module-related DEGs, TCL1A, PTGS2, and MME were revealed to be enriched in regulation of cell differentiation and cell death pathways. In addition, the GeneGo pathway maps identified by top DEGs were shown to converge on cancer, immunoregulation, apoptosis and regulation of actin cytoskeleton, most of which are known contributors in MDS etiology and pathogenesis. Notably, as potential biomarkers for diagnosis of MDS, four specific genes (ABAT, FADD, DAPP1, and SMPD3) were further subjected to detailed pathway analysis. Our integrative analysis on mRNA expression, gene methylation and lncRNAs profiling facilitates further understanding of the pathogenesis of MDS, and may promote the diagnosis and novel therapeutics for this disease.

Hierarchical Transcription Factor and Chromatin Binding Network for Wood Formation in Black Cottonwood (Populus trichocarpa)

Wood remains the world's most abundant and renewable resource for timber and pulp and is an alternative to fossil fuels. Understanding the molecular regulation of wood formation can advance the engineering of wood for more efficient material and energy productions. We integrated a black cottonwood (Populus trichocarpa) wood-forming cell system with quantitative transcriptomics and chromatin binding assays to construct a transcriptional regulatory network (TRN) directed by a key transcription factor (TF), PtrSND1-B1 (secondary wall-associated NAC-domain protein). The network consists of four layers of TF-target gene interactions with quantitative regulatory effects, describing the specificity of how the regulation is transduced through these interactions to activate cell wall genes (effector genes) for wood formation. PtrSND1-B1 directs 57 TF-DNA interactions through 17 TFs transregulating 27 effector genes. Of the 57 interactions, 55 are novel. We tested 42 of these 57 interactions in 30 genotypes of transgenic P. trichocarpa and verified that ∼90% of the tested interactions function in vivo. The TRN reveals common transregulatory targets for distinct TFs, leading to the discovery of nine TF protein complexes (dimers and trimers) implicated in regulating the biosynthesis of specific types of lignin. Our work suggests that wood formation may involve regulatory homeostasis determined by combinations of TF-DNA and TF-TF (protein-protein) regulations.

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

A microRNA feedback loop regulates global microRNA abundance during aging

Expression levels of many microRNAs (miRNAs) change during aging, notably declining globally in a number of organisms and tissues across taxa. However, little is known about the mechanisms or the biological relevance for this change. We investigated the network of genes that controls miRNA transcription and processing during C. elegans aging. We found that miRNA biogenesis genes are highly networked with transcription factors and aging-associated miRNAs. In particular, miR-71, known to influence life span and itself up-regulated during aging, represses alg-1/Argonaute expression post-transcriptionally during aging. Increased ALG-1 abundance in mir-71 loss-of-function mutants led to globally increased miRNA expression. Interestingly, these mutants demonstrated widespread mRNA expression dysregulation and diminished levels of variability both in gene expression and in overall life span. Thus, the progressive molecular decline often thought to be the result of accumulated damage over an organism's life may be partially explained by a miRNA-directed mechanism of age-associated decline.

Systematic target function annotation of human transcription factors

Background

Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs.

Result

We compiled a human gene regulatory network for 384 TFs covering the 146,096 TF–target gene (TF–TG) relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF–TF and TF–TG relationships with 3715 functional concepts from six sources of gene function annotations, we obtained over 9000 confident functional annotations for 279 TFs. We observe extensive connectivity between TFs and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that TFs link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that the increased number of upstream regulators contributes to the functional pleiotropy of TFs.

Conclusion

Our computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for the discovery of unknown roles of TFs in human disease and drug response.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-017-0469-0) contains supplementary material, which is available to authorized users.

Comparative assessment of differential network analysis methods

Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.

An intestinal microRNA modulates the homeostatic adaptation to chronic oxidative stress in C. elegans

Adaptation to an environmental or metabolic perturbation is a feature of the evolutionary process. Recent insights into microRNA function suggest that microRNAs serve as key players in a robust adaptive response against stress in animals through their capacity to fine-tune gene expression. However, it remains largely unclear how a microRNA-modulated downstream mechanism contributes to the process of homeostatic adaptation. Here we show that loss of an intestinally expressed microRNA gene, mir-60, in the nematode C. elegans promotes an adaptive response to chronic - a mild and long-term - oxidative stress exposure. The pathway involved appears to be unique since the canonical stress-responsive factors, such as DAF-16/FOXO, are dispensable for mir-60 loss to enhance oxidative stress resistance. Gene expression profiles revealed that genes encoding lysosomal proteases and those involved in xenobiotic metabolism and pathogen defense responses are up-regulated by the loss of mir-60. Detailed genetic studies and computational microRNA target prediction suggest that endocytosis components and a bZip transcription factor gene zip-10, which functions in innate immune response, are directly modulated by miR-60 in the intestine. Our findings suggest that the mir-60 loss facilitates adaptive response against chronic oxidative stress by ensuring the maintenance of cellular homeostasis.

MicroRNA-mediated regulatory circuits: outlook and perspectives

MicroRNAs have been found to be necessary for regulating genes implicated in almost all signaling pathways, and consequently their dysfunction influences many diseases, including cancer. Understanding of the complexity of the microRNA-mediated regulatory network has grown in terms of size, connectivity and dynamics with the development of computational and, more recently, experimental high-throughput approaches for microRNA target identification. Newly developed studies on recurrent microRNA-mediated circuits in regulatory networks, also known as network motifs, have substantially contributed to addressing this complexity, and therefore to helping understand the ways by which microRNAs achieve their regulatory role. This review provides a summarizing view of the state-of-the-art, and perspectives of research efforts on microRNA-mediated regulatory motifs. In this review, we discuss the topological properties characterizing different types of circuits, and the regulatory features theoretically enabled by such properties, with a special emphasis on examples of circuits typifying their biological significance in experimentally validated contexts. Finally, we will consider possible future developments, in particular regarding microRNA-mediated circuits involving long non-coding RNAs and epigenetic regulators.

A Hierarchical Framework for State-Space Matrix Inference and Clustering

In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden state of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred states. Although there are a number of methods tailored for specific datasets, there is currently no state-of-the-art modeling framework for this general class of problems. In this paper, we develop the MBASIC (Matrix Based Analysis for State-space Inference and Clustering) framework. MBASIC consists of two parts: state-space mapping and state-space clustering. In state-space mapping, it maps observations onto a finite state-space, representing the activation states of units across conditions. In state-space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred state-space profiles across all conditions. Both the state-space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. It allows for imposing structural assumptions on each cluster, and enables model selection using information criterion. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying state-space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. The first application grouped genes based on transcription factor occupancy profiles of their promoter regions in two different cell types. The second application focused on identifying groups of loci that are similar to a GATA2 binding site that is functional at its endogenous locus by utilizing transcription factor occupancy data and illustrated applicability of MBASIC in a wide variety of problems. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

BACKGROUND

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.

RESULTS

We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.

CONCLUSIONS

In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Integrative analysis of cancer genes in a functional interactome

The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs

Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable 'hairballs'. We argue that one way of untangling these hairballs is through cross-disciplinary network comparison-leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases where the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant day-to-day experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems.

Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma

The combinatorial cross-regulation of transcription factors (TFs) plays an important role in cellular identity and function; however, the dynamic regulation of TFs during glioma progression remains largely unknown. Here, we used the genome-wide expression of TFs to construct an extensive human TF network comprising interactions among 513 TFs and to analyse the dynamics of the TF-TF network during glioma progression. We found that the TF regulatory networks share a common architecture and that the topological structures are conserved. Strikingly, despite the conservation of the network architecture, TF regulatory networks are highly grade specific, and TF circuitry motifs are dynamically rewired during glioma progression. In addition, the most frequently observed structure in the grade-specific TF networks was the feedforward loop (FFL). We described applications that show how investigating the behaviour of FFLs in glioblastoma can reveal FFLs (such as RARG-NR1I2-CDX2) that are associated with prognosis. We constructed comprehensive TF-TF networks and systematically analysed the circuitry, dynamics, and topological principles of the networks during glioma progression, which will further enhance our understanding of the functions of TFs in glioma.

A predictive modeling approach for cell line-specific long-range regulatory interactions

Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

A gene regulatory network (GRN) represents how some genes encoding regulatory molecules such as transcription factors or microRNAs regulate the expression of other genes. Researchers commonly study GRNs involved in a specific biological process with the aim of identifying a few important regulatory genes. In higher organisms such as humans, a regulatory gene regulates multiple target genes and correspondingly any gene is regulated by multiple regulatory genes. Due to such multiplicity of interactions, a GRN usually resembles a tangled hairball wherein it is difficult to identify few most influential regulatory genes. In this study, we show that network analysis algorithms such as K-core, pagerank and betweenness centrality are useful for identifying a few important or core regulatory genes in a GRN, and the K-core algorithm is also useful for organizing regulatory genes in a hierarchical layered structure where the most influential genes in a GRN are found within the innermost layer or core. These few core regulatory genes determine to a large extent the expression status of the remaining genes in the network. We illustrate a pragmatic application of this technique to GRNs reconstructed from genome-wide gene expression measurements in the MCF-7 human breast cancer cell line.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Background

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T.

Results

In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods.

Conclusions

In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. Our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Loregic: a method to characterize the cooperative logic of regulatory factors

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

Gene expression is controlled by various gene regulatory factors. Those factors work cooperatively forming a complex regulatory circuit genome wide. Corruptions of regulatory cooperativity may lead to abnormal gene expression activity such as cancer. Traditional experimental methods, however, can only identify small-scale regulatory activity. Thus, to systematically understand the cooperativity between and among different types of regulatory factors, we need the efficient and systematic computational methods. Regulatory circuits have been suggested to behave analogously to the electronic circuits in which a wide variety of electronic elements work coordinately to function correctly. Recently, an increasing amount of next generation sequencing data provides a great resource to study regulatory activity. Thus, we developed a general-purpose computational method using logic-circuit models from electronics and applied it to a human leukemia dataset, identifying the genome-wide cooperativity of transcription factors and microRNAs.

An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome

Many biological networks naturally form a hierarchy with a preponderance of downward information flow. In this study, we define a score to quantify the degree of hierarchy in a network and develop a simulated-annealing algorithm to maximize the hierarchical score globally over a network. We apply our algorithm to determine the hierarchical structure of the phosphorylome in detail and investigate the correlation between its hierarchy and kinase properties. We also compare it to the regulatory network, finding that the phosphorylome is more hierarchical than the regulome.

ncRDeathDB: A comprehensive bioinformatics resource for deciphering network organization of the ncRNA-mediated cell death system

Programmed cell death (PCD) is a critical biological process involved in many important processes, and defects in PCD have been linked with numerous human diseases. In recent years, the protein architecture in different PCD subroutines has been explored, but our understanding of the global network organization of the noncoding RNA (ncRNA)-mediated cell death system is limited and ambiguous. Hence, we developed the comprehensive bioinformatics resource (ncRDeathDB, www.rna-society.org/ncrdeathdb ) to archive ncRNA-associated cell death interactions. The current version of ncRDeathDB documents a total of more than 4600 ncRNA-mediated PCD entries in 12 species. ncRDeathDB provides a user-friendly interface to query, browse and manipulate these ncRNA-associated cell death interactions. Furthermore, this resource will help to visualize and navigate current knowledge of the noncoding RNA component of cell death and autophagy, to uncover the generic organizing principles of ncRNA-associated cell death systems, and to generate valuable biological hypotheses.

Regression analysis of combined gene expression regulation in acute myeloid leukemia

Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML prognostic marker. Together, we provided a novel framework that successfully integrated the TCGA and ENCODE data in revealing AML-specific regulatory program at global level.

Recent studies from The Cancer Genome Atlas (TCGA) showed that most Acute Myeloid Leukemia (AML) patients lack DNA mutations, which can potentially explain the tumorigenesis, and motivated a systematic approach to elucidate aberrant molecular signatures at the transcriptional and epigenetic levels. Using recently available data from two large consortia namely Encyclopedia of DNA Elements and TCGA, we developed a novel computational model to infer the regulatory activities of the expression regulators and their target genes in AML samples. Our analysis revealed 18 regulators whose dysregulation contributed significantly to explaining the global mRNA expression changes. Encouragingly, the inferred activities of these regulatory features followed a consistent pattern with cytogenetic phenotypes of the AML patients. Among these regulators, we identified microRNA hsa-miR-548p, whose regulatory relationships with leukemia-related genes including YY1 suggest its novel role in AML pathogenesis. Additionally, we discovered that the inferred activities of transcription factor C-Fos can be used as a prognostic marker to characterize survival rate of the AML patients. Together, we demonstrated an effective model that can integrate useful information from a large amount of heterogeneous data to dissect regulatory effects. Furthermore, the novel biological findings from this study may be constructive to future experimental research in AML.

Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection

Background

Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.

Results

Here we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs.

Conclusion

Compared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.

A functional and evolutionary perspective on transcription factor binding in Arabidopsis thaliana

Understanding the mechanisms underlying gene regulation is paramount to comprehend the translation from genotype to phenotype. The two are connected by gene expression, and it is generally thought that variation in transcription factor (TF) function is an important determinant of phenotypic evolution. We analyzed publicly available genome-wide chromatin immunoprecipitation experiments for 27 TFs in Arabidopsis thaliana and constructed an experimental network containing 46,619 regulatory interactions and 15,188 target genes. We identified hub targets and highly occupied target (HOT) regions, which are enriched for genes involved in development, stimulus responses, signaling, and gene regulatory processes in the currently profiled network. We provide several lines of evidence that TF binding at plant HOT regions is functional, in contrast to that in animals, and not merely the result of accessible chromatin. HOT regions harbor specific DNA motifs, are enriched for differentially expressed genes, and are often conserved across crucifers and dicots, even though they are not under higher levels of purifying selection than non-HOT regions. Distal bound regions are under purifying selection as well and are enriched for a chromatin state showing regulation by the Polycomb repressive complex. Gene expression complexity is positively correlated with the total number of bound TFs, revealing insights in the regulatory code for genes with different expression breadths. The integration of noncanonical and canonical DNA motif information yields new hypotheses on cobinding and tethering between specific TFs involved in flowering and light regulation.

Integrative bayesian network analysis of genomic data

Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.

MicroRNAs mediate dietary-restriction-induced longevity through PHA-4/FOXA and SKN-1/Nrf transcription factors

BACKGROUND

Dietary restriction (DR) has been shown to prolong longevity across diverse taxa, yet the mechanistic relationship between DR and longevity remains unclear. MicroRNAs (miRNAs) control aging-related functions such as metabolism and lifespan through regulation of genes in insulin signaling, mitochondrial respiration, and protein homeostasis.

RESULTS

We have conducted a network analysis of aging-associated miRNAs connected to transcription factors PHA-4/FOXA and SKN-1/Nrf, which are both necessary for DR-induced lifespan extension in Caenorhabditis elegans. Our network analysis has revealed extensive regulatory interactions between PHA-4, SKN-1, and miRNAs and points to two aging-associated miRNAs, miR-71 and miR-228, as key nodes of this network. We show that miR-71 and miR-228 are critical for the response to DR in C. elegans. DR induces the expression of miR-71 and miR-228, and the regulation of these miRNAs depends on PHA-4 and SKN-1. In turn, we show that PHA-4 and SKN-1 are negatively regulated by miR-228, whereas miR-71 represses PHA-4.

CONCLUSIONS

Based on our findings, we have discovered new links in an important pathway connecting DR to aging. By interacting with PHA-4 and SKN-1, miRNAs transduce the effect of dietary-restriction-mediated lifespan extension in C. elegans. Given the conservation of miRNAs, PHA-4, and SKN-1 across phylogeny, these interactions are likely to be conserved in more-complex species.

Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems

The availability of omic data produced from international consortia, as well as from worldwide laboratories, is offering the possibility both to answer long-standing questions in biomedicine/molecular biology and to formulate novel hypotheses to test. However, the impact of such data is not fully exploited due to a limited availability of multi-omic data integration tools and methods. In this paper, we discuss the interplay between gene expression and epigenetic markers/transcription factors. We show how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms. In particular, we discuss the two following questions: (i) Can transcription factor occupancies or histone modification data predict gene expression? (ii) Can ChIP-seq and RNA-seq data be used to infer gene regulatory networks? We propose potential directions for statistical data integration. We discuss the importance of incorporating underestimated aspects (such as alternative splicing and long-range chromatin interactions). We also highlight the lack of data benchmarks and the need to develop tools for data integration from a statistical viewpoint, designed in the spirit of reproducible research.

Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks

The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

The comprehensive transcriptional analysis in Caenorhabditis elegans by integrating ChIP-seq and gene expression data

The fundamental step of learning transcriptional regulation mechanism is to identify the target genes regulated by transcription factors (TFs). Despite numerous target genes identified by chromatin immunoprecipitation followed by high-throughput sequencing technology (ChIP-seq) assays, it is not possible to infer function from binding alone in vivo. This is equally true in one of the best model systems, the nematode Caenorhabditis elegans (C. elegans), where regulation often occurs through diverse TF binding features of transcriptional networks identified in modENCODE. Here, we integrated ten ChIP-seq datasets with genome-wide expression data derived from tiling arrays, involved in six TFs (HLH-1, ELT-3, PQM-1, SKN-1, CEH-14 and LIN-11) with tissue-specific and four TFs (CEH-30, LIN-13, LIN-15B and MEP-1) with broad expression patterns. In common, TF bindings within 3 kb upstream of or within its target gene for these ten studies showed significantly elevated level of expression as opposed to that of non-target controls, indicated that these sites may be more likely to be functional through up-regulating its target genes. Intriguingly, expression of the target genes out of 5 kb upstream of their transcription start site also showed high levels, which was consistent with the results of following network component analysis. Our study has identified similar transcriptional regulation mechanisms of tissue-specific or broad expression TFs in C. elegans using ChIP-seq and gene expression data. It may also provide a novel insight into the mechanism of transcriptional regulation not only for simple organisms but also for more complex species.

Integrative identification of deregulated miRNA/TF-mediated gene regulatory loops and networks in prostate cancer

MicroRNAs (miRNAs) have attracted a great deal of attention in biology and medicine. It has been hypothesized that miRNAs interact with transcription factors (TFs) in a coordinated fashion to play key roles in regulating signaling and transcriptional pathways and in achieving robust gene regulation. Here, we propose a novel integrative computational method to infer certain types of deregulated miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional and signaling levels. To reliably predict miRNA-target interactions from mRNA/miRNA expression data, our method collectively utilizes sequence-based miRNA-target predictions obtained from several algorithms, known information about mRNA and miRNA targets of TFs available in existing databases, certain molecular structures identified to be statistically over-represented in gene regulatory networks, available molecular subtyping information, and state-of-the-art statistical techniques to appropriately constrain the underlying analysis. In this way, the method exploits almost every aspect of extractable information in the expression data. We apply our procedure on mRNA/miRNA expression data from prostate tumor and normal samples and detect numerous known and novel miRNA-mediated deregulated loops and networks in prostate cancer. We also demonstrate instances of the results in a number of distinct biological settings, which are known to play crucial roles in prostate and other types of cancer. Our findings show that the proposed computational method can be used to effectively achieve notable insights into the poorly understood molecular mechanisms of miRNA-mediated interactions and dissect their functional roles in cancer in an effort to pave the way for miRNA-based therapeutics in clinical settings.