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Graphical abstract, Highlights • Distinct groups of HIV elite controllers’ transcription profiles from CD8+ cells are exist. • Three groups associated with increased metabolism, survival, and proliferation of CD8+ cells. • One group may be associated with residual activation and dysfunction of CD8+ cells. • Elite controllers’ transcription is dissimilar to treated and untreated progressors. • Cytokine and androgen receptors on CD8+ cells may be essential for HIV control., 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.

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.

SummaryHub 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. 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 to the DREAM5 challenge data and an independent data set of human gene expression, proved a robust performance of ComHub over all data sets. Lastly, we implemented ComHub to work with both predefined networks and to do standard network inference, which we believe will make it generally applicable.AvailabilityCode is available at https://gitlab.com/Gustafsson-lab/comhubContactmika.gustafsson@liu.se, rasmus.magnusson@liu.se

Background: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed ‘SURFACER’. Methods: Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity. Results: Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms. Conclusions: BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

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.

Supplementary material to the manuscript.

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&rsquo, 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&rsquo, 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&rsquo, 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.

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.

This article belongs to the Special Issue Bioinformatics and Precision Computational Biology: Selected Papers from the X International Conference on Bioinformatics #SoIBio+10., 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., This research was funded by Fondo de Investigación Sanitaria—Instituto de Salud Carlos III (FIS—ISCIII, Spanish Ministry of Health, project reference PI18/00591) where J.D.L.R. was the PI. The research was also partially funded by Fondo de Investigación Sanitaria (FIS—ISCIII, project reference PI16/01407). The work within these projects was also cofunded by the FEDER program of the European Union. J.D.L.R. lab acknowledges also the support given by the European Project H2020 ArrestAD (Project ID: 737390, H2020-FETOPEN-1-2016-2017).

In today&rsquo, 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&ndash, 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- &beta, 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.

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.

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.

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. Open-Access-Publikationsfonds 2019 peerReviewed

Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. Some parts of the networks, such as hubs serving as master regulators, are more important for maintaining a function. To find the cause of diseases, where hubs are possible disease regulators, it is critical to know the structure of these biological systems. Such structures can be reverse engineered from high-throughput data with measured levels of biological entities. However, the complexity of biological systems makes inferring their structure a complicated task, demanding the use of computational methods, called network inference methods. Today, many network inference methods have been developed, that predicts the interactions of biological networks, with varying degree of success. In the DREAM5 challenge 35 network inference methods were evaluated on how well interactions in gene regulatory networks (GRNs) were predicted. Herein, in contrast to the DREAM5 challenge, we have evaluated network inference methods’ ability to predict hubs in GRNs. In accordance with the DREAM5 challenge, different methods performed the best on different data sets. Moreover, we discovered that network inference methods were not able to identify hubs from groups of similarly expressed genes. Also, we noticed that hubs in GRNs had a distinct expression in the data, leading to the development of a new method (the PCA method) for the prediction of hubs. Furthermore, the DREAM5 challenge showed that community predictions, combining the predictions from many network inference methods, resulted in more robust predictions of interactions. Herein, the community approach was applied on predicting hubs, with the conclusion that community predictions is the more robust approach. However, we also concluded that it was enough to combine 6-7 network inference methods to achieve robust predictions of hubs.

Biomedical sciences have seen radical growth in recent decades, inspired by a plethora of technological breakthroughs, of which sequencing and imaging are two particular technologies whose advancements have enabled scientists to explore areas that were previously impossible. High-throughput sequencing, for instance, is perhaps one of the most groundbreaking advancements in biology; it allows genetic material (e.g DNA, RNA, proteins) to be identified cheaply and accurately, granting investigators unprecedented insight into the inner workings of the genome—the blueprint of all living organisms. Therefore, high-throughput technology, and in recent years single cell sequencing in particular, has become the cornerstone of genetics research. Sequencing can reveal the genomic location of a gene, but often times the physical locations where a gene is expressed in a cell are also biologically meaningful, and with imaging technologies like florescent tagging and powerful electronic microscopes, this information is now possible to ascertain. Of course, the field of imaging technology is vast, and other areas have also seen tremendous leaps forward. For instance, with the development of CT scans and better PET tracers, researchers now have an in vivo view of the metabolic activities in organs, allowing researchers to monitor and study diseases as they progress, thus generating an unprecedented level of understanding of devastating conditions such as Alzheimer’s.In response to the profusion of quality data, statistical techniques that attempts to analyzethese data have also flourished into the field of computational biology and statisticalgenomics, which has since emerged as an indispensable part of scientific discovery pipeline as well as an important interface between statistics/machine learning and biomedical sciences. In this thesis we examine applications of statistical techniques to three vastly different data sets. In the first work we analyze data from PET brain scans of Alzheimer’s Disease patients and explore how linear mixed effect model offers a powerful and flexible alternative for gauging β-Amyloid accumulation. The data we study in the second work consists of singlecell RNAseq data from mouse embryonic, human embryonic, and human cancer cells, from which we introduce a biclustering method to simultaneously extract biologically relevant cell clusters and genes that are active in those clusters. In the third work, multiple sources of biological databases consisting of both imaging and sequencing data were leveraged into a machine learning problem, on which random forest is applied to mine organogenesis master regulators.

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.

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.

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.

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). Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2540-6) contains supplementary material, which is available to authorized users.

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.

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.

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.

1MotivationBiological 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 localised re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to discovery of novel relevant signatures.2ResultsSeveral procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. 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. 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.3AvailabilityThe scripts implementing the proposed algorithms are available in R at https://sites.google.com/site/raghvendramallmlresearcher/codes.4Contactrmall@qf.org.qa