GeneXplain (Germany)

Aberrant activation of fibroblasts is a pivotal component of cardiac fibrosis predisposing to heart failure. However, the molecular regulation of the functional state of cardiac fibroblasts in fibrosis resolution remains largely unexplored, and therefore, effective antifibrosis therapies are still lacking. By translating mouse transcriptomics to humans, we unlocked common molecular denominators connecting the fibroblast phenotypic state and fibrogenic signaling pathways in cardiac fibrosis. Through the construction of a fibroblast-specific transcriptional gene regulatory network, we found ITGAL and DUSP9 as key druggable targets for human myocardial fibrosis. A computational drug repurposing approach predicted 367 antifibrotic candidate compounds for heart disease. In primary cardiac fibroblasts derived from patients with heart failure, we provided experimental validation of the top 2-ranked repositioned drug candidates and their combination. These innovative approaches facilitate the identification of potential targets and drug candidates for cardiac fibrosis, providing actionable opportunities for clinical translation.

Abstract Background Primary sclerosing cholangitis (PSC) is a progressive choleostatic disease and up to 80% of patients also have ulcerative colitis (PSC-UC). This presents a clinical challenge owing to difficulty in diagnosis and increased risk for developing cancer. While several multifactorial processes including inflammation and microbial dysbiosis have been associated with PSC-UC pathogenesis, the precise molecular factors that regulate the phenotype of this disease subtype remain unknown. Methods We applied methyl-capture sequencing and mRNA sequencing to colonic mucosal biopsies derived from the DOCHAS study (GEN-193/11), to identify transcriptomic and epigenetic differences between treatment naïve paediatric UC (n=10), PSC-UC (n=10) and healthy controls (n=10) samples. Results Differential gene expression between UC and PSC-UC identified 9 up-regulated genes - ADMTS14, PNCK, NLRP3, SLC6A19, DLL1, FCGR2C, KLHL17, APOB, EHBP1L1 and 5 downregulated - SLC37A2, SLC14A2, RPL27, RPS25, SLC38A4 in PSC-UC relative to UC. Importantly, we show that expression of these genes was intricately regulated by master transcriptional regulators (pro-caspases, IL7RA) and transcription factors (TFs) :AR, p53, JUND, CEBPA. Similarly, differential methylation analysis between PSC-UC and UC identified 22 differentially methylated regions (DMRs) relative to controls, where 5 were hypermethylated and 8 hypomethylated. Intriguingly, in general we show that these DMRs are largely localised in gene promoter regions as opposed to enhancers. Importantly, we show that these DMR’s identified between PSC-UC vs UC is enriched for binding sites for the TF: ASCL1, suggesting its activity likely is effected due to the altered methylation of its binding site in PSC-UC patients. Collectively, these results highlight the importance of TF’s in driving molecular differences between PSC-UC and UC paediatric patients in a treatment naïve setting. Conclusion In summary, for the first time this study provides a critical insight into the transcriptional differences between treatment naïve children with PSC-UC vs UC as well as highlights the intricate regulatory processes involving master transcriptional regulators, transcription factors and DNA methylation. These processes thus warrant further examination in larger cohorts to rationalise their role as diagnostic/therapeutic targets.

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSDTM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

BackgroundIn culturing normal diploid cells, senescence may either happen naturally, in the form of replicative senescence, or it may be a consequence of external challenges such as oxidative stress. Here we present a comparative analysis aimed at reconstruction of molecular cascades specific for replicative (RS) and stressinduced senescence (SIPS) in human fibroblasts.ResultsAn involvement of caspase-3/keratin-18 pathway and serine/threonine kinase Aurora A/ MDM2 pathway was shared between RS and SIPS. Moreover, stromelysin/MMP3 and N-acetylglucosaminyltransferase enzyme MGAT1, which initiates the synthesis of hybrid and complex Nglycans, were identified as key orchestrating components in RS and SIPS, respectively. In RS only, Aurora-B driven cell cycle signaling was deregulated in concert with the suppression of anabolic branches of the fatty acids and estrogen metabolism. In SIPS, Aurora-B signaling is deprioritized, and the synthetic branches of cholesterol metabolism are upregulated, rather than downregulated. Moreover, in SIPS, proteasome/ubiquitin ligase pathways of protein degradation dominate the regulatory landscape. This picture indicates that SIPS proceeds in cells that are actively fighting stress which facilitates premature senescence while failing to completely activate the orderly program of RS. The promoters of genes differentially expressed in either RS or SIPS are unusually enriched by the binding sites for homeobox family proteins, with particular emphasis on HMX1, IRX2, HDX and HOXC13. Additionally, we identified Iroquois Homeobox 2 (IRX2) as a master regulator for the secretion of SPP1-encoded osteopontin, a stromal driver for tumor growth that is overexpressed by both RS and SIPS fibroblasts. The latter supports the hypothesis that senescence-specific de-repression of SPP1 aids in SIPS-dependent stromal activation.ConclusionsReanalysis of previously published experimental data is cost-effective approach for extraction of additional insignts into the functioning of biological systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3352-4) contains supplementary material, which is available to authorized users.

There are two physical processes that influence the spatial distribution of transcription factor molecules entering the nucleus of a eukaryotic cell, the binding to genomic DNA and the diffusion throughout the nuclear volume. Comparison of the DNA-protein association rate constant and the protein diffusion constant may determine which one is the limiting factor. If the process is diffusion-limited, transcription factor molecules are captured by DNA before their even distribution in the nuclear volume. Otherwise, if the reaction rate is limiting, these molecules diffuse evenly and then find their binding sites. Using well-studied human NF-κB dimer as an example, we calculated its diffusion constant using the Debye-Smoluchowski equation. The value of diffusion constant was about 10(-15) cm(3)/s, and it was comparable to the NF-κB association rate constant for DNA binding known from previous studies. Thus, both diffusion and DNA binding play an equally important role in NF-κB spatial distribution. The importance of genome 3D-structure in gene expression regulation and possible dependence of gene expression on the local concentration of open chromatin can be hypothesized from our theoretical estimate.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

In adipocytes, mitochondrial uncoupling is known to trigger a triglyceride loss comparable with the one induced by TNFα, a proinflammatory cytokine. However, the impact of a mitochondrial uncoupling on the abundance/composition of mitochondria and its connection with triglyceride content in adipocytes is largely unknown. In this work, the effects of a mild mitochondrial uncoupling triggered by FCCP were investigated on the mitochondrial population of 3T3-L1 adipocytes by both quantitative and qualitative approaches. We found that mild mitochondrial uncoupling does not stimulate mitochondrial biogenesis in adipocytes but induces an adaptive cell response characterized by quantitative modifications of mitochondrial protein content. Superoxide anion radical level was increased in mitochondria of both TNFα- and FCCP-treated adipocytes, whereas mitochondrial DNA copy number was significantly higher only in TNFα-treated cells. Subproteomic analysis revealed that the abundance of pyruvate carboxylase was reduced significantly in mitochondria of TNFα- and FCCP-treated adipocytes. Functional study showed that overexpression of this major enzyme of lipid metabolism is able to prevent the triglyceride content reduction in adipocytes exposed to mitochondrial uncoupling or TNFα. These results suggest a new mechanism by which the effects of mitochondrial uncoupling might limit triglyceride accumulation in adipocytes.