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Abstract Background Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. Materials and methods We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. Results Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. Conclusion Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers. Graphical Abstract

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance. [ABSTRACT FROM AUTHOR]

Introduction: There are difficulties in creating direct antiviral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenesis-directed therapy is often necessary to treat severe viral infections and comorbidities associated with them. Despite significant differences in the etiopathogenesis of viral diseases, in general, they are associated with significant dysfunction of the immune system. Study of common mechanisms of immune dysfunction caused by different viral infections can help develop novel therapeutic strategies to combat infections and associated comorbidities. Methods: To identify common mechanisms of immune functions disruption during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we analyzed the corresponding transcription profiles from peripheral blood mononuclear cells (PBMC) using the originally developed pipeline that include transcriptome data collection, processing, normalization, analysis and search for master regulators of several viral infections. The ten datasets containing transcription data from patients infected by nine viruses and healthy people were obtained from Gene Expression Omnibus. The analysis of the data was performed by Genome Enhancer pipeline. Results: We revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. Using network analysis, we identified PBMC receptors, representing proteins at the top of signaling pathways that may be responsible for the observed transcriptional changes and maintain the current functional state of cells. Discussion: The identified relationships between some of them and virus-induced alteration of immune functions are new and have not been found earlier, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon. Modulation of the identified receptors can be investigated as one of therapeutic strategies for the treatment of severe viral infections. [ABSTRACT FROM AUTHOR]

Expression of quantitative disease resistance in many host–pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant–pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant–pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

IntroductionThere are difficulties in creating direct antiviral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenesis-directed therapy is often necessary to treat severe viral infections and comorbidities associated with them. Despite significant differences in the etiopathogenesis of viral diseases, in general, they are associated with significant dysfunction of the immune system. Study of common mechanisms of immune dysfunction caused by different viral infections can help develop novel therapeutic strategies to combat infections and associated comorbidities.MethodsTo identify common mechanisms of immune functions disruption during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we analyzed the corresponding transcription profiles from peripheral blood mononuclear cells (PBMC) using the originally developed pipeline that include transcriptome data collection, processing, normalization, analysis and search for master regulators of several viral infections. The ten datasets containing transcription data from patients infected by nine viruses and healthy people were obtained from Gene Expression Omnibus. The analysis of the data was performed by Genome Enhancer pipeline.ResultsWe revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. Using network analysis, we identified PBMC receptors, representing proteins at the top of signaling pathways that may be responsible for the observed transcriptional changes and maintain the current functional state of cells.DiscussionThe identified relationships between some of them and virus-induced alteration of immune functions are new and have not been found earlier, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon. Modulation of the identified receptors can be investigated as one of therapeutic strategies for the treatment of severe viral infections.

Background: Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. Materials and methods: We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. Results: Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. Conclusion: Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers. [ABSTRACT FROM AUTHOR]

Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from non-varicose vein segments left after surgery of 3 patients by growing the cells in selective media after magnetic immunosorting. Endothelial cells were either exposed to oscillatory shear stress or left at the static condition. Then, other cell types were treated with preconditioned media from the adjacent layer's cells. DNA isolated from the harvested cells was subjected to epigenome-wide study using Illumina microarrays followed by data analysis with GenomeStudio (Illumina), Excel (Microsoft), and Genome Enhancer (geneXplain) software packages. Differential (hypo-/hyper-) methylation was revealed for each cell layer's DNA. The most targetable master regulators controlling the activity of certain transcription factors regulating the genes near the differentially methylated sites appeared to be the following: (1) HGS, PDGFB, and AR for endothelial cells; (2) HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 for smooth muscle cells; and (3) WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN for fibroblasts. Some of the identified master regulators may serve as promising druggable targets for treating varicose veins in the future. [ABSTRACT FROM AUTHOR]

The recent literature on causation has seen the introduction of several distinctions within causation, which are thought to be important for understanding the widespread scientific practice of focusing causal explanations on a subset of the factors that are causally relevant for a phenomenon. Concepts used to draw such distinctions include, among others, stability, specificity, proportionality, or actual-difference making. In this contribution, I propose a new distinction that picks out an explanatorily salient class of causes in biological systems. Some select causes in complex biological systems, I argue, have the property of enabling coherent causal control of these systems. Examples of such control variables include hormones and other signaling molecules, e.g., TOR (target of rapamycin), morphogens or the products of homeotic selector genes in embryonic pattern formation. I propose an analysis of this notion based on concepts borrowed from causal graph theory. [ABSTRACT FROM AUTHOR]

Introduction: In sarcoidosis, peripheral lymphopenia and anergy have been associated with increased inflammation and maladaptive immune activity, likely promoting development of chronic and progressive disease. However, the molecular mechanisms that lead to reduced lymphocyte proportions, particularly CD4+ T-cells, have not been fully elucidated. We posit that paradoxical peripheral lymphopenia is characterized by a dysregulated transcriptomic network associated with cell function and fate that results from altered transcription factor targeting activity. Methods: Messenger RNA-sequencing (mRNA-seq) was performed on peripheral blood mononuclear cells (PBMCs) from ACCESS study subjects with sarcoidosis and matched controls and findings validated on a sarcoidosis case-control cohort and a sarcoidosis case series. Preserved PBMC transcriptomic networks between case-control cohorts were assessed to establish cellular associations with gene modules and define regulatory targeting involved in sarcoidosis immune dysregulation utilizing weighted gene co-expression network analysis and differential transcription factor involvement analysis. Network centrality measures identified master transcriptional regulators of subnetworks related to cell proliferation and death. Predictive models of differential PBMC proportions constructed from ACCESS target gene expression corroborated the relationship between aberrant transcription factor regulatory activity and imputed and clinical PBMC populations in the validation cohorts. Results: We identified two unique and preserved gene modules significantly associated with sarcoidosis immune dysregulation. Strikingly, increased expression of a monocyte-driven, and not a lymphocyte-driven, gene module related to innate immunity and cell death was the best predictor of peripheral CD4+ T-cell proportions. Within the gene network of this monocyte-driven module, TLE3 and CBX8 were determined to be master regulators of the cell death subnetwork. A core gene signature of differentially over-expressed target genes of TLE3 and CBX8 involved in cellular communication and immune response regulation accurately predicted imputed and clinical monocyte expansion and CD4+ T-cell depletion. Conclusions: Altered transcriptional regulation associated with aberrant gene expression of a monocyte-driven transcriptional network likely influences lymphocyte function and survival. Although further investigation is warranted, this indicates that crosstalk between hyperactive monocytes and lymphocytes may instigate peripheral lymphopenia and underlie sarcoidosis immune dysregulation and pathogenesis. Future therapies selectively targeting master regulators, or their targets, may mitigate dysregulated immune processes in sarcoidosis and disease progression. [ABSTRACT FROM AUTHOR]

IntroductionIn sarcoidosis, peripheral lymphopenia and anergy have been associated with increased inflammation and maladaptive immune activity, likely promoting development of chronic and progressive disease. However, the molecular mechanisms that lead to reduced lymphocyte proportions, particularly CD4+ T-cells, have not been fully elucidated. We posit that paradoxical peripheral lymphopenia is characterized by a dysregulated transcriptomic network associated with cell function and fate that results from altered transcription factor targeting activity.MethodsMessenger RNA-sequencing (mRNA-seq) was performed on peripheral blood mononuclear cells (PBMCs) from ACCESS study subjects with sarcoidosis and matched controls and findings validated on a sarcoidosis case-control cohort and a sarcoidosis case series. Preserved PBMC transcriptomic networks between case-control cohorts were assessed to establish cellular associations with gene modules and define regulatory targeting involved in sarcoidosis immune dysregulation utilizing weighted gene co-expression network analysis and differential transcription factor involvement analysis. Network centrality measures identified master transcriptional regulators of subnetworks related to cell proliferation and death. Predictive models of differential PBMC proportions constructed from ACCESS target gene expression corroborated the relationship between aberrant transcription factor regulatory activity and imputed and clinical PBMC populations in the validation cohorts.ResultsWe identified two unique and preserved gene modules significantly associated with sarcoidosis immune dysregulation. Strikingly, increased expression of a monocyte-driven, and not a lymphocyte-driven, gene module related to innate immunity and cell death was the best predictor of peripheral CD4+ T-cell proportions. Within the gene network of this monocyte-driven module, TLE3 and CBX8 were determined to be master regulators of the cell death subnetwork. A core gene signature of differentially over-expressed target genes of TLE3 and CBX8 involved in cellular communication and immune response regulation accurately predicted imputed and clinical monocyte expansion and CD4+ T-cell depletion.ConclusionsAltered transcriptional regulation associated with aberrant gene expression of a monocyte-driven transcriptional network likely influences lymphocyte function and survival. Although further investigation is warranted, this indicates that crosstalk between hyperactive monocytes and lymphocytes may instigate peripheral lymphopenia and underlie sarcoidosis immune dysregulation and pathogenesis. Future therapies selectively targeting master regulators, or their targets, may mitigate dysregulated immune processes in sarcoidosis and disease progression.

Abstract Background Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs. Results We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets. Conclusions In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

Cytotoxic and noncytotoxic CD8+ T lymphocyte responses are essential for the control of HIV infection. Understanding the mechanisms underlying HIV control in elite controllers (ECs), which maintain undetectable viral load in the absence of antiretroviral therapy, may facilitate the development of new effective therapeutic strategies.We developed an original pipeline for an analysis of the transcriptional profiles of CD8+ cells from ECs, treated and untreated progressors. Hierarchical cluster analysis of CD8+ cells’ transcription profiles allowed us to identify five distinct groups (EC groups 1–5) of ECs. The transcriptional profiles of EC group 1 were opposite to those of groups 2–4 and similar to those of the treated progressors, which can be associated with residual activation and dysfunction of CD8+ T-lymphocytes. The profiles of groups 2–4 were associated with different numbers of differentially expressed genes compared to healthy controls, but the corresponding genes shared the same cellular processes. These three groups were associated with increased metabolism, survival, proliferation, and the absence of an “exhausted” phenotype, compared to both untreated progressors and healthy controls. The CD8+ lymphocytes from these groups of ECs may contribute to the control under HIV replication and slower disease progression. The EC group 5 was indistinguishable from normal.Application of master regulator analysis allowed us to identify 22 receptors, including interferon-gamma, interleukin-2, and androgen receptors, which may be responsible for the observed expression changes and the functional states of CD8+ cells from ECs. These receptors can be considered potential targets of therapeutic intervention, which may decelerate disease progression.

The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred., (© 2024 Wiley-VCH GmbH.)

Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.

Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories "epithelial-to-mesenchymal transition" and "response to hypoxia." In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs. Results: We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets. Conclusions: In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable. [ABSTRACT FROM AUTHOR]

Abstract Background Alzheimer’s disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets. Methods In this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing. Results We identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat). Conclusions Using a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.

Key message: BIG regulates the shoot stem cell population. The shoot apical meristem (SAM) contains a population of self-renewing cells, and provides daughter cells for initiation and development of aerial parts of plants. However, the underlying mechanisms of SAM size regulation remain largely unclear. Here, we identified a mutant that displayed a large SAM, designated big-shoot meristem (big-m), in Arabidopsis thaliana. The phenotype of big-m is caused by a new T-DNA insertion allele of BIG, causing a loss of function. The big-m mutant had more stem cells in the SAM than in the wild type. Expression of WUSCHEL (WUS) and SHOOTMERISTEMLESS (STM) was promoted in big-m compared with the wild type, showing that BIG functions upstream of WUS and STM. Therefore, BIG is an important regulator of the stem cell population in the SAM. Furthermore, genetic analysis indicated that BIG acts synergistically with PIN-FORMED1 (PIN1) in controlling SAM size. Our results suggest that BIG plays an important role in controlling Arabidopsis thaliana SAM growth via PIN1-mediated auxin homeostasis. [ABSTRACT FROM AUTHOR]

In today’s chicken egg industry, maintaining the strength of eggshells in longer laying cycles is pivotal for improving the persistency of egg laying. Eggshell development and mineralization underlie a complex regulatory interplay of various proteins and signaling cascades involving multiple organ systems. Understanding the regulatory mechanisms influencing this dynamic trait over time is imperative, yet scarce. To investigate the temporal changes in the signaling cascades, we considered eggshell strength at two different time points during the egg production cycle and studied the genotype–phenotype associations by employing the Random Forests algorithm on chicken genotypic data. For the analysis of corresponding genes, we adopted a well established systems biology approach to delineate gene regulatory pathways and master regulators underlying this important trait. Our results indicate that, while some of the master regulators (Slc22a1 and Sox11) and pathways are common at different laying stages of chicken, others (e.g., Scn11a, St8sia2, or the TGF-β pathway) represent age-specific functions. Overall, our results provide: (i) significant insights into age-specific and common molecular mechanisms underlying the regulation of eggshell strength; and (ii) new breeding targets to improve the eggshell quality during the later stages of the chicken production cycle. [ABSTRACT FROM AUTHOR]

Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.

African animal trypanosomiasis (AAT) is caused by a protozoan parasite that affects the health of livestock. Livestock production in Ethiopia is severely hampered by AAT and various controlling measures were not successful to eradicate the disease. AAT affects the indigenous breeds in varying degrees. However, the Sheko breed shows better trypanotolerance than other breeds. The tolerance attributes of Sheko are believed to be associated with its taurine genetic background but the genetic controls of these tolerance attributes of Sheko are not well understood. In order to investigate the level of taurine background in the genome, we compare the genome of Sheko with that of 11 other African breeds. We find that Sheko has an admixed genome composed of taurine and indicine ancestries. We apply three methods: (i) The integrated haplotype score (iHS), (ii) the standardized log ratio of integrated site specific extended haplotype homozygosity between populations (Rsb), and (iii) the composite likelihood ratio (CLR) method to discover selective sweeps in the Sheko genome. We identify 99 genomic regions harboring 364 signature genes in Sheko. Out of the signature genes, 15 genes are selected based on their biological importance described in the literature. We also identify 13 overrepresented pathways and 10 master regulators in Sheko using the TRANSPATH database in the geneXplain platform. Most of the pathways are related with oxidative stress responses indicating a possible selection response against the induction of oxidative stress following trypanosomiasis infection in Sheko. Furthermore, we present for the first time the importance of master regulators involved in trypanotolerance not only for the Sheko breed but also in the context of cattle genomics. Our finding shows that the master regulator Caspase is a key protease which plays a major role for the emergence of adaptive immunity in harmony with the other master regulators. These results suggest that designing and implementing genetic intervention strategies is necessary to improve the performance of susceptible animals. Moreover, the master regulatory analysis suggests potential candidate therapeutic targets for the development of new drugs for trypanosomiasis treatment.

African animal trypanosomiasis (AAT) is caused by a protozoan parasite that affects the health of livestock. Livestock production in Ethiopia is severely hampered by AAT and various controlling measures were not successful to eradicate the disease. AAT affects the indigenous breeds in varying degrees. However, the Sheko breed shows better trypanotolerance than other breeds. The tolerance attributes of Sheko are believed to be associated with its taurine genetic background but the genetic controls of these tolerance attributes of Sheko are not well understood. In order to investigate the level of taurine background in the genome, we compare the genome of Sheko with that of 11 other African breeds. We find that Sheko has an admixed genome composed of taurine and indicine ancestries. We apply three methods: (i) The integrated haplotype score (iHS), (ii) the standardized log ratio of integrated site specific extended haplotype homozygosity between populations (Rsb), and (iii) the composite likelihood ratio (CLR) method to discover selective sweeps in the Sheko genome. We identify 99 genomic regions harboring 364 signature genes in Sheko. Out of the signature genes, 15 genes are selected based on their biological importance described in the literature. We also identify 13 overrepresented pathways and 10 master regulators in Sheko using the TRANSPATH database in the geneXplain platform. Most of the pathways are related with oxidative stress responses indicating a possible selection response against the induction of oxidative stress following trypanosomiasis infection in Sheko. Furthermore, we present for the first time the importance of master regulators involved in trypanotolerance not only for the Sheko breed but also in the context of cattle genomics. Our finding shows that the master regulator Caspase is a key protease which plays a major role for the emergence of adaptive immunity in harmony with the other master regulators. These results suggest that designing and implementing genetic intervention strategies is necessary to improve the performance of susceptible animals. Moreover, the master regulatory analysis suggests potential candidate therapeutic targets for the development of new drugs for trypanosomiasis treatment. [ABSTRACT FROM AUTHOR]

Caulobacter crescentus is a free-living Alphaproteobacterium that thrives in oligotrophic environments. This review focuses on the regulatory network used by this bacterium to control the levels of cell division proteins, their organization inside the cell and their activity as a function of the cell cycle. Strikingly, C. crescentus makes frequent use of master transcriptional regulators and epigenetic signals, most likely to synchronize cell division with other events of the cell cycle. In addition, cellular metabolism and DNA damage sensors emerge as central players regulating cell division in response to changing environmental conditions. • Master cell cycle transcriptional regulators control cell division genes. • Epigenetics is essential for efficient cell division. • Cell division is under metabolic control. • DNA damage blocks cytokinesis in SOS-dependent and SOS-independent manners. [ABSTRACT FROM AUTHOR]

Rice blast disease caused by the fungus Magnaporthe grisea (M. grisea) is one of the most serious diseases for the cultivated rice Oryza sativa (O. sativa). A key factor causing rice blast disease and defense might be protein–protein interactions (PPIs) between rice and fungus. In this research, we have developed a computational pipeline to predict PPIs between blast fungus and rice. After cross-prediction by interolog-based and domain-based method, we achieved 532 potential PPIs between 27 fungus proteins and 236 rice proteins. Accuracy of jackknife test, 10-fold cross-validation test and independent test for these PPIs were 90.43, 93.85 and 84.67%, respectively, by using support vector machine classification method. Meanwhile, the pathogenic genes of blast fungus were enriched in the predicted PPIs network when compared with 1000 random interaction networks. The rice regulatory network was downloaded and divided into 228 subnetworks with over six nodes, and the top seven subnetworks affected by blast fungus through PPIs were investigated. The results indicated that 34 upregulated and 12 downregulated master regulators in rice interacting with the fungus proteins in response to the infection of blast fungus. The common master regulators in rice in response to the infection of M. grisea , Xanthomonas oryzae pv.oryzae and rice stripe virus were analyzed. The ubiquitin proteasome pathway was the common pathway in rice regulated by these three pathogens, while apoptosis signaling pathway was induced by fungus and bacteria. In summary, the results in this article provide insight into the process of blast fungus infection. [ABSTRACT FROM AUTHOR]

African Animal Trypanosomiasis (AAT) is transmitted by the tsetse fly which carries pathogenic trypanosomes in its saliva, thus causing debilitating infection to livestock health. As the disease advances, a multistage progression process is observed based on the progressive clinical signs displayed in the host’s body. Investigation of genes expressed with regular monotonic patterns (known as Monotonically Expressed Genes (MEGs)) and of their master regulators can provide important clue for the understanding of the molecular mechanisms underlying the AAT disease. For this purpose, we analysed MEGs for three tissues (liver, spleen and lymph node) of two cattle breeds, namely trypanosusceptible Boran and trypanotolerant N’Dama. Our analysis revealed cattle breed-specific master regulators which are highly related to distinguish the genetic programs in both cattle breeds. Especially the master regulators MYC and DBP found in this study, seem to influence the immune responses strongly, thereby susceptibility and trypanotolerance of Boran and N’Dama respectively. Furthermore, our pathway analysis also bolsters the crucial roles of these master regulators. Taken together, our findings provide novel insights into breed-specific master regulators which orchestrate the regulatory cascades influencing the level of trypanotolerance in cattle breeds and thus could be promising drug targets for future therapeutic interventions.

Mesenchymal Stromal Cells (MSC) are multipotent cells characterized by self-renewal, multilineage differentiation, and immunomodulatory properties. To obtain a gene regulatory profile of human MSCs, we generated a compendium of more than two hundred cell samples with genome-wide expression data, including a homogeneous set of 93 samples of five related primary cell types: bone marrow mesenchymal stem cells (BM-MSC), hematopoietic stem cells (HSC), lymphocytes (LYM), fibroblasts (FIB), and osteoblasts (OSTB). All these samples were integrated to generate a regulatory gene network using the algorithm ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks; based on mutual information), that finds regulons (groups of target genes regulated by transcription factors) and regulators (i.e., transcription factors, TFs). Furtherly, the algorithm VIPER (Algorithm for Virtual Inference of Protein-activity by Enriched Regulon analysis) was used to inference protein activity and to identify the most significant TF regulators, which control the expression profile of the studied cells. Applying these algorithms, a footprint of candidate master regulators of BM-MSCs was defined, including the genes EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3, that presented consistent upregulation and hypomethylation in BM-MSCs. These TFs regulate the activation of the genes in the bone marrow MSC lineage and are involved in development, morphogenesis, cell differentiation, regulation of cell adhesion, and cell structure.

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.

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. [ABSTRACT FROM AUTHOR]

• Cells derived from trabecular bone (HB), healing fracture callus (CA) or fracture nonunion (NU) show homogeneous surface markers expression, but different molecular profiles. • In vitro osteogenic differentiation of cells from NU samples resulted in the highest mineralization compared to those from HB or CA. • Cells from NU samples showed an overactivation of FOS, STAT1, STAT3, JUN and MITF compared to cells from CA. • Increased FOS activity in fracture tissues is associated with the molecular profile of nonunion. Despite the advances in bone fracture treatment, a significant fraction of fracture patients will develop non-union. Most non-unions are treated with surgery since identifying the molecular causes of these defects is exceptionally challenging. In this study, compared with marrow bone, we generated a transcriptional atlas of human osteoprogenitor cells derived from healing callus and non-union fractures. Detailed comparison among the three conditions revealed a substantial similarity of callus and nonunion at the gene expression level. Nevertheless, when assayed functionally, they showed different osteogenic potential. Utilizing longitudinal transcriptional profiling of the osteoprogenitor cells, we identified FOS as a putative master regulator of non-union fractures. We validated FOS activity by profiling a validation cohort of 31 tissue samples. Our work identified new molecular targets for non-union classification and treatment while providing a valuable resource to better understand human bone healing biology. [ABSTRACT FROM AUTHOR]

Human papillomaviruses (HPVs) play a major role in development of cervical cancer, and HPV oncoproteins are being targeted by immunotherapies. Although these treatments show promising results in the clinic, many patients do not benefit or the durability is limited. In addition to HPV antigens, neoantigens derived from somatic mutations may also generate an effective immune response and represent an additional and distinct immunotherapy strategy against this and other HPV-associated cancers. To explore the landscape of neoantigens in cervix cancer, we predicted all possible mutated neopeptides in two large sequencing data sets and analyzed whether mutation and neoantigen load correlate with antigen presentation, infiltrating immune cell types, and a HPV-induced master regulator gene expression signature. We found that targetable neoantigens are detected in most tumors, and there are recurrent mutated peptides from known oncogenic driver genes (KRAS, MAPK1, PIK3CA, ERBB2, and ERBB3) that are predicted to be potentially immunogenic. Our studies show that HPV-induced master regulators are not only associated with HPV load but may also play crucial roles in relation to mutation and neoantigen load, and also the immune microenvironment of the tumor. A subset of these HPV-induced master regulators positively correlated with expression of immune-suppressor molecules such as PD-L1, TGFB1, and IL-10 suggesting that they may be involved in abrogating antitumor response induced by the presence of mutations and neoantigens. Based on these results, we predict that HPV master regulators identified in our study might be potentially effective targets in cervical cancer.

Background: Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. Methods: In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. Results: In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. Conclusions: We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches. [ABSTRACT FROM AUTHOR]

• The molecular signature of severe COVID-19 in lung autopsies was stablished. • Six transcription factors were found as Master Regulators for COVID-19 severe form. • 52 drugs were proposed as reversers of the severe COVID-19 molecular signature. • A limited overlap between clinical and pre-clinical COVID-19 data was identified. Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG , and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage. [ABSTRACT FROM AUTHOR]

Objectives/hypothesis: To generate novel insights and hypotheses in keloid development from potential master regulators.Study Design: Prospective cohort.Methods: Six fresh keloid and six normal skin samples from 12 anonymous donors were used in a prospective cohort study. Genome-wide profiling was done previously on the cohort using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA). The 190 statistically significant CpG islands between keloid and normal tissue mapped to 152 genes (P < .05). The top 10 statistically significant genes (VAMP5, ACTR3C, GALNT3, KCNAB2, LRRC61, SCML4, SYNGR1, TNS1, PLEKHG5, PPP1R13-α, false discovery rate <.015) were uploaded into the Ingenuity Pathway Analysis software's Causal Network Analysis (QIAGEN, Redwood City, CA). To reflect expected gene expression direction in the context of methylation changes, the inverse of the methylation ratio from keloid versus normal tissue was used for the analysis. Causal Network Analysis identified disease-specific master regulator molecules based on downstream differentially expressed keloid-specific genes and expected directionality of expression (hypermethylated vs. hypomethylated).Results: Causal Network Analysis software identified four hierarchical networks that included four master regulators (pyroxamide, tributyrin, PRKG2, and PENK) and 19 intermediate regulators.Conclusions: Causal Network Analysis of differentiated methylated gene data of keloid versus normal skin demonstrated four causal networks with four master regulators. These hierarchical networks suggest potential driver roles for their downstream keloid gene targets in the pathogenesis of the keloid phenotype, likely triggered due to perturbation/injury to normal tissue.Level Of Evidence: NA Laryngoscope, 126:E319-E324, 2016. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is poorly characterized at genetic and non-genetic levels. The current study evaluates in a large cohort of patients the prognostic relevance of molecular subtypes and key transcription factors in pancreatic ductal adenocarcinoma (PDAC).Methods: We performed gene expression analysis of whole-tumor tissue obtained from 118 surgically resected PDAC and 13 histologically normal pancreatic tissue samples. Cox regression models were used to study the effect on survival of molecular subtypes and 16 clinicopathological prognostic factors. In order to better understand the biology of PDAC we used iRegulon to identify transcription factors (TFs) as master regulators of PDAC and its subtypes.Results: We confirmed the PDAssign gene signature as classifier of PDAC in molecular subtypes with prognostic relevance. We found molecular subtypes, but not clinicopathological factors, as independent predictors of survival. Regulatory network analysis predicted that HNF1A/B are among thousand TFs the top enriched master regulators of the genes expressed in the normal pancreatic tissue compared to the PDAC regulatory network. On immunohistochemistry staining of PDAC samples, we observed low expression of HNF1B in well differentiated towards no expression in poorly differentiated PDAC samples. We predicted IRF/STAT, AP-1, and ETS-family members as key transcription factors in gene signatures downstream of mutated KRAS.Conclusions: PDAC can be classified in molecular subtypes that independently predict survival. HNF1A/B seem to be good candidates as master regulators of pancreatic differentiation, which at the protein level loses its expression in malignant ductal cells of the pancreas, suggesting its putative role as tumor suppressor in pancreatic cancer.Trial Registration: The study was registered at ClinicalTrials.gov under the number NCT01116791 (May 3, 2010). [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes. [ABSTRACT FROM AUTHOR]

Background The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution. Results We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the cascades that define relevant cellular processes in this organism. We discussed that some particular functions were distributed in more than one module and that some modules contained more than one related function. We confirm that the presence of paralogous proteins confers advantages to B. subtilis to adapt and select strategies to successfully face the extreme and changing environmental conditions in which it lives. Conclusions The intricate organization is the product of a non-random network evolution that primarily follows a hierarchical organization based on the presence of transcription and σ factor, which is reflected in the connections that exist within and between modules. [ABSTRACT FROM AUTHOR]

Naive T cells differentiate and become distinct subsets in response to changes in the cytokine milieu. Such specialization arises through a complex and dynamic utilization of cis-regulatory enhancer elements. In this brief essay, we review recent findings on the relative contributions of sensors of the cytokine milieu, especially the signal transducer and activator of transcription family transcription factors, 'master regulators', and other transcription factors in the enhancer architecture of T cells. These findings provide new insights into how signal transduction impinges upon the genome. [ABSTRACT FROM AUTHOR]

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Avian influenza poses a great risk to gallinaceous poultry, while mallard ducks can withstand most virus strains. To date, the mechanisms underlying the susceptibility of chicken and the effective immune response of duck have not been completely understood. In this study, our aim is to investigate the transcriptional gene regulation governing the expression of important avian-influenza-induced genes and to reveal the master regulators stimulating an effective immune response after virus infection in ducks while dysfunctioning in chicken. The avian influenza virus (AIV) mainly affects birds and not only causes animals' deaths, but also poses a great risk of zoonotically infecting humans. While ducks and wild waterfowl are seen as a natural reservoir for AIVs and can withstand most virus strains, chicken mostly succumb to infection with high pathogenic avian influenza (HPAI). To date, the mechanisms underlying the susceptibility of chicken and the effective immune response of duck have not been completely unraveled. In this study, we investigate the transcriptional gene regulation underlying disease progression in chicken and duck after AIV infection. For this purpose, we use a publicly available RNA-sequencing dataset from chicken and ducks infected with low-pathogenic avian influenza (LPAI) H5N2 and HPAI H5N1 (lung and ileum tissues, 1 and 3 days post-infection). Unlike previous studies, we performed a promoter analysis based on orthologous genes to detect important transcription factors (TFs) and their cooperation, based on which we apply a systems biology approach to identify common and species-specific master regulators. We found master regulators such as EGR1, FOS, and SP1, specifically for chicken and ETS1 and SMAD3/4, specifically for duck, which could be responsible for the duck's effective and the chicken's ineffective immune response. [ABSTRACT FROM AUTHOR]