Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Network-based drug repurposing for schizophrenia

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BACKGROUND

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS

This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS

Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS

Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS

Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

miRNAs and genes as molecular regulators of rice grain morphology and yield

Rice is one of the most consumed crops worldwide and the genetic and molecular basis of its grain yield attributes are well understood. Various studies have identified different yield-related parameters in rice that are regulated by the microRNAs (miRNAs). MiRNAs are endogenous small non-coding RNAs that silence gene expression during or after transcription. They control a variety of biological or genetic activities in plants including growth, development and response to stress. In this review, we have summarized the available information on the genetic control of panicle architecture and grain yield (number and morphology) in rice. The miRNA nodes that are associated with their regulation are also described while focussing on the central role of miR156-SPL node to highlight the co-regulation of two master regulators that determine the fate of panicle development. Since abiotic stresses are known to negatively affect yield, the impact of abiotic stress induced alterations on the levels of these miRNAs are also discussed to highlight the potential of miRNAs for regulating crop yields.

Should we really use graph neural networks for transcriptomic prediction?

The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.

Master regulators of biological systems in higher dimensions

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.

Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealized due to technical and computational challenges. In this work we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: data pre-processing, feature extraction, and inference. We observed that the regulatory signature is captured in the statistical moments of scRNA-seq data and requires computationally intensive minimization solvers to extract it. Furthermore, current data pre-processing might not conserve these statistical moments. Although our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking—finding computationally feasible multi-dimensional statistics of data—is imperative for designing GRN inference methods.

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Single-cell RNA-seq temporal snapshot data for detecting regulation

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Challenges in data pre-processing, feature extraction, and network inference for GRNs

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Encoding of regulatory information in higher-order raw moments

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Non-linear least-squares inference for temporal scRNA-seq snapshot data

Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. A recent benchmark of 12 GRN methods demonstrated that the algorithms struggled to predict the ground-truth GRNs and speculated that the low performance was due to the insufficient resolution in the scRNA-seq data. Rather than proposing another method, this paper focuses on how to decompose a GRN problem into three subproblems (pre-processing, feature extraction, and inference), so that the gene regulatory information is preserved in each step. Subsequently, we discuss how to best approach each of the three subproblems.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

Internetwork connectivity of molecular networks across species of life

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Family-specific analysis of variant pathogenicity prediction tools

Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants. We find that despite the high overall accuracy of all tested methods, each tool has its Achilles heel, i.e. protein families in which its predictions prove unreliable (expected accuracy does not exceed 51% in any method). As a proof of principle, we show that choosing the optimal tool and pathogenicity threshold at a protein family-individual level allows obtaining reliable predictions in all Pfam domains (accuracy no less than 68%). A functional analysis of the sets of protein domains annotated exclusively by neutral or pathogenic mutations indicates that specific protein functions can be associated with a high or low sensitivity to mutations, respectively. The highly sensitive sets of protein domains are involved in the regulation of transcription and DNA sequence-specific transcription factor binding, while the domains that do not result in disease when mutated are responsible for mediating immune and stress responses. These results suggest that future predictors of pathogenicity and especially variant prioritization tools may benefit from considering functional annotation.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks

Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element–target gene pairs (E–G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning

Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning.

Detecting phenotype-driven transitions in regulatory network structure

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense “communities” of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Cells are controlled by complex regulatory networks, and disruptions in the structure of these networks can lead to disease. Understanding disease requires that we accurately identify changes in gene regulatory network structure. However, cellular networks have tens of thousands of components with complex connections between them. Megha Padi from the University of Arizona and John Quackenbush from Dana-Farber Cancer Institute developed a new algorithm that is far more effective than previous methods at finding disease-associated modules in regulatory networks. Applying this to ovarian cancer, they found new regulatory processes that may lead to more targeted treatments. In human breast tissue, they found that sex-specific differences were driven by hormone signaling and differentiation pathways. Decoding how network modules promote new functions may help to better model the relationship between genotype and phenotype.

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.

Network science in clinical trials: A patient-centered approach

There has been a paradigm shift in translational oncology with the advent of novel molecular diagnostic tools in the clinic. However, several challenges are associated with the integration of these sophisticated tools into clinical oncology and daily practice. High-throughput profiling at the DNA, RNA and protein levels (omics) generate a massive amount of data. The analysis and interpretation of these is non-trivial but will allow a more thorough understanding of cancer. Linear modelling of the data as it is often used today is likely to limit our understanding of cancer as a complex disease, and at times under-performs to capture a phenotype of interest. Network science and systems biology-based approaches, using machine learning and network science principles, that integrate multiple data sources, can uncover complex changes in a biological system. This approach will integrate a large number of potential biomarkers in preclinical studies to better inform therapeutic decisions and ultimately make substantial progress towards precision medicine. It will however require development of a new generation of clinical trials. Beyond discussing the challenges of high-throughput technologies, this review will develop a framework on how to implement a network science approach in new clinical trial designs in order to advance cancer care.

Race Disparities in the Contribution of miRNA Isoforms and tRNA-Derived Fragments to Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a breast cancer subtype characterized by marked differences between White and Black/African-American women. We performed a systems-level analysis on datasets from The Cancer Genome Atlas to elucidate how the expression patterns of mRNAs are shaped by regulatory noncoding RNAs (ncRNA). Specifically, we studied isomiRs, that is, isoforms of miRNAs, and tRNA-derived fragments (tRF). In normal breast tissue, we observed a marked cohesiveness in both the ncRNA and mRNA layers and the associations between them. This cohesiveness was widely disrupted in TNBC. Many mRNAs become either differentially expressed or differentially wired between normal breast and TNBC in tandem with isomiR or tRF dysregulation. The affected pathways included energy metabolism, cell signaling, and immune responses. Within TNBC, the wiring of the affected pathways with isomiRs and tRFs differed in each race. Multiple isomiRs and tRFs arising from specific miRNA loci (e.g., miR-200c, miR-21, the miR-17/92 cluster, the miR-183/96/182 cluster) and from specific tRNA loci (e.g., the nuclear tRNA^Gly and tRNA^Leu, the mitochondrial tRNA^Val and tRNA^Pro) were strongly associated with the observed race disparities in TNBC. We highlight the race-specific aspects of transcriptome wiring by discussing in detail the metastasis-related MAPK and the Wnt/β-catenin signaling pathways, two of the many key pathways that were found differentially wired. In conclusion, by employing a data- and knowledge-driven approach, we comprehensively analyzed the normal and cancer transcriptomes to uncover novel key contributors to the race-based disparities of TNBC.Significance: This big data-driven study comparing normal and cancer transcriptomes uncovers RNA expression differences between Caucasian and African-American patients with triple-negative breast cancer that might help explain disparities in incidence and aggressive character. Cancer Res; 78(5); 1140-54. ©2017 AACR.

Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components

BACKGROUND

A single bout of exercise induces changes in gene expression in skeletal muscle. Regular exercise results in an adaptive response involving changes in muscle architecture and biochemistry, and is an effective way to manage and prevent common human diseases such as obesity, cardiovascular disorders and type II diabetes. However, the biomolecular mechanisms underlying such responses still need to be fully elucidated. Here we performed a transcriptome-wide analysis of skeletal muscle tissue in a large cohort of untrained Thoroughbred horses (n = 51) before and after a bout of high-intensity exercise and again after an extended period of training. We hypothesized that regular high-intensity exercise training primes the transcriptome for the demands of high-intensity exercise.

RESULTS

An extensive set of genes was observed to be significantly differentially regulated in response to a single bout of high-intensity exercise in the untrained cohort (3241 genes) and following multiple bouts of high-intensity exercise training over a six-month period (3405 genes). Approximately one-third of these genes (1025) and several biological processes related to energy metabolism were common to both the exercise and training responses. We then developed a novel network-based computational analysis pipeline to test the hypothesis that these transcriptional changes also influence the contextual molecular interactome and its dynamics in response to exercise and training. The contextual network analysis identified several important hub genes, including the autophagosomal-related gene GABARAPL1, and dynamic functional modules, including those enriched for mitochondrial respiratory chain complexes I and V, that were differentially regulated and had their putative interactions 're-wired' in the exercise and/or training responses.

CONCLUSION

Here we have generated for the first time, a comprehensive set of genes that are differentially expressed in Thoroughbred skeletal muscle in response to both exercise and training. These data indicate that consecutive bouts of high-intensity exercise result in a priming of the skeletal muscle transcriptome for the demands of the next exercise bout. Furthermore, this may also lead to an extensive 're-wiring' of the molecular interactome in both exercise and training and include key genes and functional modules related to autophagy and the mitochondrion.

A novel statistical approach for identification of the master regulator transcription factor

BACKGROUND

Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.

METHODS

We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.

RESULTS

In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software .

CONCLUSION

We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.

MicroRNA and Transcription Factor: Key Players in Plant Regulatory Network

Recent achievements in plant microRNA (miRNA), a large class of small and non-coding RNAs, are very exciting. A wide array of techniques involving forward genetic, molecular cloning, bioinformatic analysis, and the latest technology, deep sequencing have greatly advanced miRNA discovery. A tiny miRNA sequence has the ability to target single/multiple mRNA targets. Most of the miRNA targets are transcription factors (TFs) which have paramount importance in regulating the plant growth and development. Various families of TFs, which have regulated a range of regulatory networks, may assist plants to grow under normal and stress environmental conditions. This present review focuses on the regulatory relationships between miRNAs and different families of TFs like; NF-Y, MYB, AP2, TCP, WRKY, NAC, GRF, and SPL. For instance NF-Y play important role during drought tolerance and flower development, MYB are involved in signal transduction and biosynthesis of secondary metabolites, AP2 regulate the floral development and nodule formation, TCP direct leaf development and growth hormones signaling. WRKY have known roles in multiple stress tolerances, NAC regulate lateral root formation, GRF are involved in root growth, flower, and seed development, and SPL regulate plant transition from juvenile to adult. We also studied the relation between miRNAs and TFs by consolidating the research findings from different plant species which will help plant scientists in understanding the mechanism of action and interaction between these regulators in the plant growth and development under normal and stress environmental conditions.

INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery

Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.