Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster

Co-regulation and synteny of GFM2 and NSA2 links ribosomal function in mitochondria and the cytosol with chronic kidney disease

BACKGROUND

We previously reported aberrant expression of the cytosolic ribosomal biogenesis factor Nop-7-associated 2 (NSA2) in diabetic nephropathy, the latter also known to involve mitochondrial dysfunction, however the connections between NSA2, mitochondria and renal disease were unclear. In the current paper, we show that NSA2 expression is co-regulated with the GTP-dependent ribosome recycling factor mitochondrial 2 (GFM2) and provide a molecular link between cytosolic and mitochondrial ribosomal biogenesis with mitochondrial dysfunction in chronic kidney disease (CKD).

METHODS

Human renal tubular cells (HK-2) were cultured (+/- zinc, or 5mM/20mM glucose). mRNA levels were quantified using real-time qPCR. Transcriptomics data were retrieved and analysed from Nakagawa chronic kidney disease (CKD) Dataset (GSE66494) and Kidney Precision Medicine Project (KPMP) ( https://atlas.kpmp.org/ ). Protein levels were determined by immunofluorescence and Western blotting. Cellular respiration was measured using Agilent Seahorse XF Analyzer. Data were analysed using one-way ANOVA, Students' t-test and Pearson correlation.

RESULTS

The NSA2 gene, on human chromosome 5q13 was next to GFM2. The two genes were syntenic on opposite strands and orientation in multiple species. Their common 381 bp 5' region contained multiple transcription factor binding sites (TFBS) including the zinc-responsive transcription factor MTF1. NSA2 and GFM2 mRNAs showed a dose-dependent increase to zinc in-vitro and were highly expressed in proximal tubular cells in renal biopsies. CKD patients showed higher renal NSA2/GFM2 expression. In HK-2 cells, hyperglycaemia led to increased expression of both genes. The total cellular protein content remained unchanged, but GFM2 upregulation resulted in increased levels of several mitochondrial oxidative phosphorylation (OXPHOS) subunits. Furthermore, increased GFM2 expression, via transient transfection or hyperglycemia, correlated with decrease cellular respiration.

CONCLUSION

The highly conserved synteny of NSA2 and GFM2, their shared 5' region, and co-expression in-vitro and in CKD, shows they are co-regulated. Increased GFM2 affects mitochondrial function with a disconnect between an increase in certain mitochondrial respiratory proteins but a decrease in cellular respiration. These data link the regulation of 2 highly conserved genes, NSA2 and GFM2, connected to ribosomes in two different cellular compartments, cytosol and mitochondria, to kidney disease and shows that their dysregulation may be involved in mitochondrial dysfunction.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Single-cell transcriptomics illuminates regulatory steps driving anterior-posterior patterning of Drosophila embryonic mesoderm

Single-cell technologies promise to uncover how transcriptional programs orchestrate complex processes during embryogenesis. Here, we apply a combination of single-cell technology and genetic analysis to investigate the dynamic transcriptional changes associated with Drosophila embryo morphogenesis at gastrulation. Our dataset encompassing the blastoderm-to-gastrula transition provides a comprehensive single-cell map of gene expression across cell lineages validated by genetic analysis. Subclustering and trajectory analyses revealed a surprising stepwise progression in patterning to transition zygotic gene expression and specify germ layers as well as uncovered an early role for ecdysone signaling in epithelial-to-mesenchymal transition in the mesoderm. We also show multipotent progenitors arise prior to gastrulation by analyzing the transcription trajectory of caudal mesoderm cells, including a derivative that ultimately incorporates into visceral muscles of the midgut and hindgut. This study provides a rich resource of gastrulation and elucidates spatially regulated temporal transitions of transcription states during the process.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Homeostatic regulation of neuronal function: importance of degeneracy and pleiotropy

Neurons maintain their average firing rate and other properties within narrow bounds despite changing conditions. This homeostatic regulation is achieved using negative feedback to adjust ion channel expression levels. To understand how homeostatic regulation of excitability normally works and how it goes awry, one must consider the various ion channels involved as well as the other regulated properties impacted by adjusting those channels when regulating excitability. This raises issues of degeneracy and pleiotropy. Degeneracy refers to disparate solutions conveying equivalent function (e.g., different channel combinations yielding equivalent excitability). This many-to-one mapping contrasts the one-to-many mapping described by pleiotropy (e.g., one channel affecting multiple properties). Degeneracy facilitates homeostatic regulation by enabling a disturbance to be offset by compensatory changes in any one of several different channels or combinations thereof. Pleiotropy complicates homeostatic regulation because compensatory changes intended to regulate one property may inadvertently disrupt other properties. Co-regulating multiple properties by adjusting pleiotropic channels requires greater degeneracy than regulating one property in isolation and, by extension, can fail for additional reasons such as solutions for each property being incompatible with one another. Problems also arise if a perturbation is too strong and/or negative feedback is too weak, or because the set point is disturbed. Delineating feedback loops and their interactions provides valuable insight into how homeostatic regulation might fail. Insofar as different failure modes require distinct interventions to restore homeostasis, deeper understanding of homeostatic regulation and its pathological disruption may reveal more effective treatments for chronic neurological disorders like neuropathic pain and epilepsy.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

The Genetic Mechanisms Underlying the Concerted Expression of the yellow and tan Genes in Complex Patterns on the Abdomen and Wings of Drosophila guttifera

How complex morphological patterns form is an intriguing question in developmental biology. However, the mechanisms that generate complex patterns remain largely unknown. Here, we sought to identify the genetic mechanisms that regulate the tan (t) gene in a multi-spotted pigmentation pattern on the abdomen and wings of Drosophila guttifera. Previously, we showed that yellow (y) gene expression completely prefigures the abdominal and wing pigment patterns of this species. In the current study, we demonstrate that the t gene is co-expressed with the y gene in nearly identical patterns, both transcripts foreshadowing the adult abdominal and wing melanin spot patterns. We identified cis-regulatory modules (CRMs) of t, one of which drives reporter expression in six longitudinal rows of spots on the developing pupal abdomen, while the second CRM activates the reporter gene in a spotted wing pattern. Comparing the abdominal spot CRMs of y and t, we found a similar composition of putative transcription factor binding sites that are thought to regulate the complex expression patterns of both terminal pigmentation genes y and t. In contrast, the y and t wing spots appear to be regulated by distinct upstream factors. Our results suggest that the D. guttifera abdominal and wing melanin spot patterns have been established through the co-regulation of y and t, shedding light on how complex morphological traits may be regulated through the parallel coordination of downstream target genes.

Transformation to ischaemia tolerance of frog brain function corresponds to dynamic changes in mRNA co-expression across metabolic pathways

Neural activity is costly and requires continuous ATP from aerobic metabolism. Brainstem motor function of American bullfrogs normally collapses after minutes of ischaemia, but following hibernation, it becomes ischaemia-tolerant, generating output for up to 2 h without oxygen or glucose delivery. Transforming the brainstem to function during ischaemia involves a switch to anaerobic glycolysis and brain glycogen. We hypothesized that improving neural performance during ischaemia involves a transcriptional program for glycogen and glucose metabolism. Here we measured mRNA copy number of genes along the path from glycogen metabolism to lactate production using real-time quantitative PCR. The expression of individual genes did not reflect enhanced glucose metabolism. However, the number of co-expressed gene pairs increased early into hibernation, and by the end, most genes involved in glycogen metabolism, glucose transport and glycolysis exhibited striking linear co-expression. By contrast, co-expression of genes in the Krebs cycle and electron transport chain decreased throughout hibernation. Our results uncover reorganization of the metabolic transcriptional network associated with a shift to ischaemia tolerance in brain function. We conclude that modifying gene co-expression may be a critical step in synchronizing storage and use of glucose to achieve ischaemia tolerance in active neural circuits.

Transcription Factors as Novel Therapeutic Targets and Drivers of Prostate Cancer Progression

Prostate cancer is the second most diagnosed cancer among men worldwide. Androgen deprivation therapy, the most common targeted therapeutic option, is circumvented as prostate cancer progresses from androgen dependent to castrate-resistant disease. Whilst the nuclear receptor transcription factor, androgen receptor, drives the growth of prostate tumor during initial stage of the disease, androgen resistance is associated with poorly differentiated prostate cancer. In the recent years, increased research has highlighted the aberrant transcriptional activities of a small number of transcription factors. Along with androgen receptors, dysregulation of these transcription factors contributes to both the poorly differentiated phenotypes of prostate cancer cells and the initiation and progression of prostate carcinoma. As master regulators of cell fate decisions, these transcription factors may provide opportunity for the development of novel therapeutic targets for the management of prostate cancer. Whilst some transcriptional regulators have previously been notoriously difficult to directly target, technological advances offer potential for the indirect therapeutic targeting of these transcription factors and the capacity to reprogram cancer cell phenotype. This mini review will discuss how recent advances in our understanding of transcriptional regulators and material science pave the way to utilize these regulatory molecules as therapeutic targets in prostate cancer.

Emergence of co-expression in gene regulatory networks

Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.

Enhanced identification of significant regulators of gene expression

Background

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

Results

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

Conclusions

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

Human-Specific Transcriptome of Ventral and Dorsal Midbrain Dopamine Neurons

Objective

Neuronal loss in the substantia nigra pars compacta (SNpc) in Parkinson disease (PD) is not uniform, as dopamine neurons from the ventral tier are lost more rapidly than those of the dorsal tier. Identifying the intrinsic differences that account for this differential vulnerability may provide a key for developing new treatments for PD.

Methods

Here, we compared the RNA‐sequenced transcriptomes of ~100 laser captured microdissected SNpc neurons from each tier from 7 healthy controls.

Results

Expression levels of dopaminergic markers were similar across the tiers, whereas markers specific to the neighboring ventral tegmental area were virtually undetected. After accounting for unwanted sources of variation, we identified 106 differentially expressed genes (DEGs) between the SNpc tiers. The genes higher in the dorsal/resistant SNpc tier neurons displayed coordinated patterns of expression across the human brain, their protein products had more interactions than expected by chance, and they demonstrated evidence of functional convergence. No significant shared functionality was found for genes higher in the ventral/vulnerable SNpc tier. Surprisingly but importantly, none of the identified DEGs was among the familial PD genes or genome‐wide associated loci. Finally, we found some DEGs in opposite tier orientation between human and analogous mouse populations.

Interpretation

Our results highlight functional enrichments of vesicular trafficking, ion transport/homeostasis and oxidative stress genes showing higher expression in the resistant neurons of the SNpc dorsal tier. Furthermore, the comparison of gene expression variation in human and mouse SNpc populations strongly argues for the need of human‐focused omics studies. ANN NEUROL 2020;87:853–868

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.

Identifying functions and prognostic biomarkers of network motifs marked by diverse chromatin states in human cell lines

Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression patterns of targets. Notably, diverse chromatin state compositions could help C1- or I1-FFL to control a large number of distinct biological functions in human cell lines, such as four different types of chromatin state compositions cooperating with K562-associated C1-FFLs controlling “regulation of cytokinesis,” “G1/S transition of mitotic cell cycle,” “DNA recombination,” and “telomere maintenance,” respectively. Remarkably, we identified six chromatin state-marked C1-FFL instances (HCFC1-NFYA-ABL1, THAP1-USF1-BRCA2, ZNF263-USF1-UBA52, MYC-ATF1-UBA52, ELK1-EGR1-CCT4, and YY1-EGR1-INO80C) could act as prognostic biomarkers of acute myelogenous leukemia though influencing cancer-related biological functions, such as cell proliferation, telomere maintenance, and DNA recombination. Our results will provide novel insight for better understanding of chromatin state-mediated gene regulation and facilitate the identification of novel diagnostic and therapeutic biomarkers of human cancers.

Global landscape of mouse and human cytokine transcriptional regulation

Cytokines are cell-to-cell signaling proteins that play a central role in immune development, pathogen responses, and diseases. Cytokines are highly regulated at the transcriptional level by combinations of transcription factors (TFs) that recruit cofactors and the transcriptional machinery. Here, we mined through three decades of studies to generate a comprehensive database, CytReg, reporting 843 and 647 interactions between TFs and cytokine genes, in human and mouse respectively. By integrating CytReg with other functional datasets, we determined general principles governing the transcriptional regulation of cytokine genes. In particular, we show a correlation between TF connectivity and immune phenotype and disease, we discuss the balance between tissue-specific and pathogen-activated TFs regulating each cytokine gene, and cooperativity and plasticity in cytokine regulation. We also illustrate the use of our database as a blueprint to predict TF-disease associations and identify potential TF-cytokine regulatory axes in autoimmune diseases. Finally, we discuss research biases in cytokine regulation studies, and use CytReg to predict novel interactions based on co-expression and motif analyses which we further validated experimentally. Overall, this resource provides a framework for the rational design of future cytokine gene regulation studies.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Environmental and Evolutionary Drivers of the Modular Gene Regulatory Network Underlying Phenotypic Plasticity for Stress Resistance in the Nematode Caenorhabditis remanei

Organisms can cope with stressful environments via a combination of phenotypic plasticity at the individual level and adaptation at the population level. Changes in gene expression can play an important role in both. Significant advances in our understanding of gene regulatory plasticity and evolution have come from comparative studies in the field and laboratory. Experimental evolution provides another powerful path by which to learn about how differential regulation of genes and pathways contributes to both acclimation and adaptation. Here we present results from one such study using the nematode Caenorhabditis remanei We selected one set of lines to withstand heat stress and another oxidative stress. We then compared transcriptional responses to acute heat stress of both and an unselected control to the ancestral population using a weighted gene coexpression network analysis, finding that the transcriptional response is primarily dominated by a plastic response that is shared in the ancestor and all evolved populations. In addition, we identified several modules that respond to artificial selection by (1) changing the baseline level of expression, (2) altering the magnitude of the plastic response, or (3) a combination of the two. Our findings therefore reveal that while patterns of transcriptional response can be perturbed with short bouts of intense selection, the overall ancestral structure of transcriptional plasticity is largely maintained over time.

Comparative genome based cis-elements analysis in the 5' upstream and 3' downstream region of cell wall invertase and Phenylalanine ammonia lyase in Nicotiana benthamiana

Plant secondary metabolites are widely used in human disease treatment; though primary metabolism provides precursors for secondary metabolism, not much has been studied to unravel the link connecting both the processes. Most common form of gene regulation interconnecting diverse metabolism occurs at the transcriptional and/or posttranscriptional level mediated by regulatory cis-elements. The present study aims at understanding the common cis-elements network connecting the major primary metabolic enzyme, cell wall invertase (CWIN) and secondary metabolism genes in Nicotiana benthamiana (N. benthamiana). The CWIN and thirty one other gene sequences were extracted from N. benthamiana genome, followed by cis-element analysis of their 5' upstream and 3' downstream region using different programs (Genomatix software suite; PLACE and PlantCARe). Comparative cis-element analysis of CWIN (N. benthamiana and other plant species) and other primary, secondary metabolism and transcription factor genes (N. benthamiana) revealed the occurrence of common stress associated cis-elements. Predominantly, AHBP, L1BX, MYBL, MADS, MYBS, GTBX, DOFF and CCAF were found in the 5' upstream region of all genes, whereas AHBP, MYBL, L1BX, HEAT, CCAF and KAN1 were largely occurring in the 3' downstream region of all genes; indicating common function of these elements in transcriptional and posttranscriptional gene regulation. Further, genomic analysis using FGENESH, GenScan and homology based methods (BlastX and BlastN) was performed on the N. benthamiana contigs harboring CWIN and PAL, in an attempt to identify genomic neighborhood genes. The 5' upstream and 3' downstream region of genes in the genomic neighborhood of CWIN and PAL were also subjected to similar cis-element analysis, and the results indicated cis-elements profile similar to CWIN, PAL and other primary, secondary metabolism and transcription factor genes. The results of evolutionary studies confirmed that the 5' upstream region of NbCWINs significantly showed more proximity to secondary metabolism genes 4CL and the redox gene SOD, followed by the phenylpropanoid pathway gene CHI. The 3' downstream regions of NbCWINs were more closely related to other plant CWINs, followed by the redox gene, SOD and primary metabolism gene FBA. Thus, the commonly found stress responsive cis-elements in our study can play a vital role in modulating key pathways of both primary and secondary metabolism; thereby postulating their role in regulating plant growth and metabolisms under unfavourable growth conditions.

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

Exploring Transcription Factors-microRNAs Co-regulation Networks in Schizophrenia

BACKGROUND

Transcriptional factors (TFs) and microRNAs (miRNAs) have been recognized as 2 classes of principal gene regulators that may be responsible for genome coexpression changes observed in schizophrenia (SZ).

METHODS

This study aims to (1) identify differentially coexpressed genes (DCGs) in 3 mRNA expression microarray datasets; (2) explore potential interactions among the DCGs, and differentially expressed miRNAs identified in our dataset composed of early-onset SZ patients and healthy controls; (3) validate expression levels of some key transcripts; and (4) explore the druggability of DCGs using the curated database.

RESULTS

We detected a differential coexpression network associated with SZ and found that 9 out of the 12 regulators were replicated in either of the 2 other datasets. Leveraging the differentially expressed miRNAs identified in our previous dataset, we constructed a miRNA-TF-gene network relevant to SZ, including an EGR1-miR-124-3p-SKIL feed-forward loop. Our real-time quantitative PCR analysis indicated the overexpression of miR-124-3p, the under expression of SKIL and EGR1 in the blood of SZ patients compared with controls, and the direction of change of miR-124-3p and SKIL mRNA levels in SZ cases were reversed after a 12-week treatment cycle. Our druggability analysis revealed that many of these genes have the potential to be drug targets.

CONCLUSIONS

Together, our results suggest that coexpression network abnormalities driven by combinatorial and interactive action from TFs and miRNAs may contribute to the development of SZ and be relevant to the clinical treatment of the disease.

Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development

Background

During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known.

Results

Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of pronounced changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each targeted by a distinct set of transcriptional regulators and associated to specific biological functions.

Conclusions

Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.

Electronic supplementary material

The online version of this article (doi:10.1186/s12861-016-0111-3) contains supplementary material, which is available to authorized users.

CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms

The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA–miRNA and gene–transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial. We developed CrossHub software, which enables two-way identification of most possible TF–gene interactions: on the basis of ENCODE ChIP-Seq binding evidence or Jaspar prediction and co-expression according to the data of The Cancer Genome Atlas (TCGA) project, the largest cancer omics resource. Similarly, CrossHub identifies mRNA–miRNA pairs with predicted or validated binding sites (TargetScan, mirSVR, PicTar, DIANA microT, miRTarBase) and strong negative expression correlations. We observed partial consistency between ChIP-Seq or miRNA target predictions and gene–TF/miRNA co-expression, demonstrating a link between these indicators. Additionally, CrossHub expression-methylation correlation analysis can be used to identify hypermethylated CpG sites or regions with the greatest potential impact on gene expression. Thus, CrossHub is capable of outlining molecular portraits of a specific gene and determining the three most common sources of expression regulation: promoter/enhancer methylation, miRNA interference and TF-mediated activation or repression. CrossHub generates formatted Excel workbooks with the detailed results. CrossHub is freely available at https://sourceforge.net/projects/crosshub/.

Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia

Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best coherence with CML. Using this strategy, we found a transcriptome-wide reduction of co-expression connectivity in CML, reflecting potentially loosened molecular regulation. Conversely, when we focused on nucleophosmin 1 (NPM1) associated networks, NPM1 established more co-expression linkages with BCR-ABL pathways and ribosomal protein networks in CML than normal. This finding implicates a new role of NPM1 in conveying tumorigenic signals from the BCR-ABL oncoprotein to ribosome biogenesis, affecting cellular growth. Transcription factors may be regulators of the differential co-expression patterns between CML and normal.

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be extracted from high-dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming 'hub genes' direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi-omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease-related prioritization of genes and molecular systems and network meta-analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality-lethality relationship, integration with machine learning approaches and network pharmacology.

Transcriptomics of the rice blast fungus Magnaporthe oryzae in response to the bacterial antagonist Lysobacter enzymogenes reveals candidate fungal defense response genes

Plants and animals have evolved a first line of defense response to pathogens called innate or basal immunity. While basal defenses in these organisms are well studied, there is almost a complete lack of understanding of such systems in fungal species, and more specifically, how they are able to detect and mount a defense response upon pathogen attack. Hence, the goal of the present study was to understand how fungi respond to biotic stress by assessing the transcriptional profile of the rice blast pathogen, Magnaporthe oryzae, when challenged with the bacterial antagonist Lysobacter enzymogenes. Based on microscopic observations of interactions between M. oryzae and wild-type L. enzymogenes strain C3, we selected early and intermediate stages represented by time-points of 3 and 9 hours post-inoculation, respectively, to evaluate the fungal transcriptome using RNA-seq. For comparative purposes, we also challenged the fungus with L. enzymogenes mutant strain DCA, previously demonstrated to be devoid of antifungal activity. A comparison of transcriptional data from fungal interactions with the wild-type bacterial strain C3 and the mutant strain DCA revealed 463 fungal genes that were down-regulated during attack by C3; of these genes, 100 were also found to be up-regulated during the interaction with DCA. Functional categorization of genes in this suite included those with roles in carbohydrate metabolism, cellular transport and stress response. One gene in this suite belongs to the CFEM-domain class of fungal proteins. Another CFEM class protein called PTH11 has been previously characterized, and we found that a deletion in this gene caused advanced lesion development by C3 compared to its growth on the wild-type fungus. We discuss the characterization of this suite of 100 genes with respect to their role in the fungal defense response.

A network-based method to assess the statistical significance of mild co-regulation effects

Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.

miRNA-target prediction based on transcriptional regulation

BACKGROUND

microRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules.

RESULTS

Here, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method.

CONCLUSIONS

This is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.

Computing with competition in biochemical networks

Cells rely on limited resources such as enzymes or transcription factors to process signals and make decisions. However, independent cellular pathways often compete for a common molecular resource. Competition is difficult to analyze because of its nonlinear global nature, and its role remains unclear. Here we show how decision pathways such as transcription networks may exploit competition to process information. Competition for one resource leads to the recognition of convex sets of patterns, whereas competition for several resources (overlapping or cascaded regulons) allows even more general pattern recognition. Competition also generates surprising couplings, such as correlating species that share no resource but a common competitor. The mechanism we propose relies on three primitives that are ubiquitous in cells: multiinput motifs, competition for a resource, and positive feedback loops.

MicroRNAs from the same precursor have different targeting properties

BACKGROUND

The processing of a microRNA results in an intermediate duplex of two potential mature products that derive from the two arms (5' and 3') of the precursor hairpin. It is often suggested that one of the sequences is degraded and the other is incorporated into the RNA-induced silencing complex. However, both precursor arms may give rise to functional levels of mature microRNA and the dominant product may change from species to species, from tissue to tissue, or between developmental stages. Therefore, both arms of the precursor have the potential to produce functional mature microRNAs.

RESULTS

We have investigated the relationship between predicted mRNA targets of mature sequences derived from the 5' and 3' arms of the same pre-microRNAs. Using six state-of-the-art target prediction algorithms, we find that 5'/3' microRNA pairs target different sites in 3' untranslated regions of mRNAs. We also find that these pairs do not generally target overlapping sets of genes, or functionally related genes.

CONCLUSIONS

We show that alternative mature products produced from the same precursor microRNAs have different targeting properties and therefore different biological functions. These data strongly suggest that developmental or evolutionary changes in arm choice will have significant functional consequences.

Combinatorial control of temporal gene expression in the Drosophila wing by enhancers and core promoters

Background

The transformation of a developing epithelium into an adult structure is a complex process, which often involves coordinated changes in cell proliferation, metabolism, adhesion, and shape. To identify genetic mechanisms that control epithelial differentiation, we analyzed the temporal patterns of gene expression during metamorphosis of the Drosophila wing.

Results

We found that a striking number of genes, approximately 50% of the Drosophila transcriptome, exhibited changes in expression during a time course of wing development. While cis-acting enhancer sequences clearly correlated with these changes, a stronger correlation was discovered between core-promoter types and the dynamic patterns of gene expression within this differentiating tissue. In support of the hypothesis that core-promoter type influences the dynamics of expression, expression levels of several TATA-box binding protein associated factors (TAFs) and other core promoter-associated components changed during this developmental time course, and a testes-specific TAF (tTAF) played a critical role in timing cellular differentiation within the wing.

Conclusions

Our results suggest that the combinatorial control of gene expression via cis-acting enhancer sequences and core-promoter types, determine the complex changes in gene expression that drive morphogenesis and terminal differentiation of the Drosophila wing epithelium.

Evolution and function of the extended miR-2 microRNA family

MicroRNAs are essential post-transcriptional regulators. Many animal microRNAs are clustered in the genome, and it has been shown that clustered microRNAs may be transcribed as a single transcript. Polycistronic microRNAs are often members of the same family, suggesting a role of tandem duplication in the emergence of clusters. The mir-2 microRNA family is the largest in Drosophila melanogaster, with 8 members that are mostly clustered in the genome. Previous studies suggest that the copy number and genomic distribution of mir-2 family members has been subject to significant change during evolution. The effects of such changes on their function are still unknown. Here we study the evolution of function in the mir-2 family. Our analyses show that, in spite of the change in number and organization among invertebrates, most mir-2 loci produce very similar mature microRNA products. Multiple mature miR-2 sequences are predicted to target genes involved in neural development in Drosophila. These targeting properties are conserved in the distant species Caenorhabditis elegans. Duplication followed by functional diversification is frequent during protein-coding gene evolution. However, our results suggest that the production of microRNA clusters by gene duplication rarely involves functional changes. This pattern of functional redundancy among clustered paralogous microRNAs reflects birth-and-death evolutionary dynamics. However, we identified a small number of mir-2 sequences in Drosophila that may have undergone functional shifts associated with genomic rearrangements. Therefore, redundancy in microRNA families may facilitate the acquisition of novel functional features.

Towards resolving the transcription factor network controlling myelin gene expression

In the central nervous system (CNS), myelin is produced from spirally-wrapped oligodendrocyte plasma membrane and, as exemplified by the debilitating effects of inherited or acquired myelin abnormalities in diseases such as multiple sclerosis, it plays a critical role in nervous system function. Myelin sheath production coincides with rapid up-regulation of numerous genes. The complexity of their subsequent expression patterns, along with recently recognized heterogeneity within the oligodendrocyte lineage, suggest that the regulatory networks controlling such genes drive multiple context-specific transcriptional programs. Conferring this nuanced level of control likely involves a large repertoire of interacting transcription factors (TFs). Here, we combined novel strategies of computational sequence analyses with in vivo functional analysis to establish a TF network model of coordinate myelin-associated gene transcription. Notably, the network model captures regulatory DNA elements and TFs known to regulate oligodendrocyte myelin gene transcription and/or oligodendrocyte development, thereby validating our approach. Further, it links to numerous TFs with previously unsuspected roles in CNS myelination and suggests collaborative relationships amongst both known and novel TFs, thus providing deeper insight into the myelin gene transcriptional network.

Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks

The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality–centrality relationship in disease-related networks, we hypothesized that network changes between control and major depression-related networks would be centered around coexpression hubs, and secondly, that differentially expressed (DE) genes would have a characteristic position and connectivity level in those networks. Mathematically, the first hypothesis tests the relationship of differential coexpression to network connectivity, while the second “hybrid” expression-and-network hypothesis tests the relationship of differential expression to network connectivity. To answer these questions about the potential interaction of coexpression network structure with differential expression, we utilized all available human post-mortem depression-related datasets appropriate for coexpression analysis, which spanned different microarray platforms, cohorts, and brain regions. Similar studies were also performed in an animal model of depression and in schizophrenia and bipolar disorder microarray datasets. We now provide results which consistently support (1) that genes assemble into small-world and scale-free networks in control subjects, (2) that this efficient network topology is largely resilient to changes in depressed subjects, and (3) that DE genes are positioned on the periphery of coexpression networks. Similar results were observed in a mouse model of depression, and in selected bipolar- and schizophrenia-related networks. Finally, we show that baseline expression variability contributes to the propensity of genes to be network hubs and/or to be DE in disease. In summary, our results suggest that the small-world and scale-free properties of gene networks are resilient to pathological changes in major depression, and that the network structure may constrain the extent to which a gene may be DE in the illness, hence informing further gene-network-based mechanistic studies of neuropsychiatric disorders.

A biologically inspired measure for coexpression analysis

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus. As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure, called BioSim, which assumes values between -1 and +1 corresponding to negative and positive dependency and 0 for independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it accounts for the features of expression similarity, expression deviation and also the relative dependence. It is demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim measure.

Gene coexpression clusters and putative regulatory elements underlying seed storage reserve accumulation in Arabidopsis

Background

In Arabidopsis, a large number of genes involved in the accumulation of seed storage reserves during seed development have been characterized, but the relationship of gene expression and regulation underlying this physiological process remains poorly understood. A more holistic view of this molecular interplay will help in the further study of the regulatory mechanisms controlling seed storage compound accumulation.

Results

We identified gene coexpression networks in the transcriptome of developing Arabidopsis (Arabidopsis thaliana) seeds from the globular to mature embryo stages by analyzing publicly accessible microarray datasets. Genes encoding the known enzymes in the fatty acid biosynthesis pathway were found in one coexpression subnetwork (or cluster), while genes encoding oleosins and seed storage proteins were identified in another subnetwork with a distinct expression profile. In the triacylglycerol assembly pathway, only the genes encoding diacylglycerol acyltransferase 1 (DGAT1) and a putative cytosolic "type 3" DGAT exhibited a similar expression pattern with genes encoding oleosins. We also detected a large number of putative cis-acting regulatory elements in the promoter regions of these genes, and promoter motifs for LEC1 (LEAFY COTYLEDON 1), DOF (DNA-binding-with-One-Finger), GATA, and MYB transcription factors (TF), as well as SORLIP5 (Sequences Over-Represented in Light-Induced Promoters 5), are overrepresented in the promoter regions of fatty acid biosynthetic genes. The conserved CCAAT motifs for B3-domain TFs and binding sites for bZIP (basic-leucine zipper) TFs are enriched in the promoters of genes encoding oleosins and seed storage proteins.

Conclusions

Genes involved in the accumulation of seed storage reserves are expressed in distinct patterns and regulated by different TFs. The gene coexpression clusters and putative regulatory elements presented here provide a useful resource for further experimental characterization of protein interactions and regulatory networks in this process.

Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle

BACKGROUND

Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.

RESULTS

We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively.

CONCLUSION

The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

Background

The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.

Results

Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.

Conclusions

Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.