Biology subjects

Evelo, C.

Publications and source records attributed to Evelo, C..

6 recordsLinked to original sources

bioRxiv2024

GSEA and co-expression network approach to identify molecular processes affected in Porto-sinusoidal Vascular Disease

11.1 BackgroundPorto-sinusoidal vascular disease (PSVD) is a complex, rare liver disease characterized by the absence of cirrhosis, with or without the presence of portal hypertension or histological lesions. Given the knowledge gaps in the mechanisms involved in this disease with unknown etiology, we used omics-based approaches to further elucidate the pathways affected by PSVD, facilitating improvements in the prognosis, diagnosis, and treatment options for these patients. 1.2 MethodsWe applied gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) to identify pathways dysregulated in PSVD. Network construction and visualization were performed in Cytoscape to explore interconnectivity among enriched processes. Within key modules, candidate genes were prioritized by ranking approaches and cross-referenced with findings from previous studies. 1.3 Results and ConclusionThis study revealed that PSVD is characterized by coordinated dysregulation, with immune and signaling pathways activated alongside the suppression of metabolic, ribosomal, and mitochondrial programs. Alterations in ribosomal proteins, ATP synthase subunits, and serpin family members highlight translational, bioenergetic, and anticoagulant dysfunction as core mechanisms. Together, these findings define PSVD as a disorder of integrated immune, vascular, and metabolic imbalance.

bioinformatics↗

bioRxiv2022

Different B cell activation patterns in asymptomatic and symptomatic COVID-19 patients

Early and persistent defects in B cell subsets such as memory B cells were shown to be correlated with poor outcomes in COVID-19 patients. This research aimed to develop a molecular pathway model to understand the B cell development in COVID-19. A B cell transcriptomics dataset, obtained from COVID-19 patients, was analyzed on the resulting pathway model to study B cell activation. The pathway showed two distinct gene expression profiles between asymptomatic and symptomatic patients. In asymptomatic patients, there is an increase in transcript levels of antiviral interferon-stimulated genes such as ISG15, IFITM1, and NEAT1 and a driving gene for the extrafollicular pathway CXCR4 indicating a formation of plasmablast. In symptomatic patients, the results suggest an inhibition occurring at the germinal center hinting at a reduction in memory B cell production. Transcripts of driver gene CXCR5 involved in germinal center development is one of the most downregulated genes. This could contribute to the shortage in the formation of memory B cells in COVID-19. Concluding, in SARS-CoV-2 infection, B cells follow different activation routes in asymptomatic and symptomatic patients. In this study, we constructed a pathway that allowed us to analyze and interpret activation patterns of B cells in COVID-19 patients and their link to disease severity. Importantly, the pathway and approach can be reused for further research in COVID-19 or other diseases.

immunology↗

bioRxiv2022

Converging pathways found in copy number variation syndromes with high schizophrenia risk

Schizophrenia genetics is complex, and the contribution of common and rare variants are not fully understood. Several specific copy number variations (CNVs) confer increased risk for schizophrenia, and the study of their effects is central to molecular models of mental illness. However, these CNVs - microdeletions or -duplications - are spread across the genome and differ in the number of genes affected and classes of coded proteins. This suggests that, in order to fully understand the contribution of these genetic variants to mental illness, we need to look beyond the deleted or duplicated genes, to their interaction partners and involved molecular pathways. In this study, we developed machine-readable interactive pathways to enable analysis of downstream effects of genes within CNV loci and identify common pathways between CNVs with high schizophrenia risk using the WikiPathways database, and schizophrenia risk gene collections from GWAS studies and a gene-disease association database. For CNVs that are pathogenic for schizophrenia, we found overlapping pathways, including BDNF signaling, cytoskeleton, cell-cell connections, inflammation and MAPK3 signaling. Common schizophrenia risk genes identified by different studies are found in all CNV pathways but not enriched. Our findings suggest that specific pathways - such as BDNF signaling - may be critical contributors to schizophrenia risk conferred by rare CNVs, and common risk variants may operate through distinct mechanisms. Our approach also highlights the importance of not only investigating deleted or duplicated genes within pathogenic CNV loci, but also study their direct interaction partners, which may explain pleiotropic effects of CNVs on schizophrenia risk.

systems biology↗

bioRxiv2020

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

BackgroundTo grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of biological pathways, such as signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format that is easier for humans to interpret. It has already been established that these diagrams can be represented in machine readable formats, as done in KEGG, Reactome, and WikiPathways. However, while humans are good at interpreting the message of the creator of such a diagram, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles, and therefore needs to harmonize this to offer semantically enriched access via the Resource Description Framework format. Particularly challenging in the normalization of diagrams are the interactions between the biological entities, so that we can glean information about the connectivity of the entities represented. These interactions include information about the type of interaction (metabolic conversion, inhibition, etc.), the direction, and the participants. Availability of the interactions in a semantic and harmonized format enables searching the full network of biological interactions and integration with the linked data cloud. ResultsWe here study how the graphically modelled biological knowledge in diagrams can be semantified and harmonized efficiently, and exemplify how the resulting data can be used to programmatically answer biological questions. We find that we can translate graphically modelled biological knowledge to a sufficient degree into a semantic model of biological knowledge and discuss some of the current limitations. Furthermore, we show how this interaction knowledge base can be used to answer specific biological questions. ConclusionThis paper demonstrates that most of the graphical biological knowledge from WikiPathways is modelled in the semantic layer of WikiPathways with the semantic information intact and connectivity information preserved. The usability of the WikiPathways pathway and connectivity information has shown to be useful and has been integrated into other platforms. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

bioinformatics↗

bioRxiv2019

History of rare diseases and their genetic causes - a data driven approach

This dataset provides information about monogenic, rare diseases with a known genetic cause supplemented with manually extracted provenance of both the disease and the discovery of the underlying genetic cause of the disease.\n\nWe collected 4166 rare monogenic diseases according to their OMIM identifier, linked them to 3163 causative genes which are annotated with Ensembl identifiers and HGNC symbols. The PubMed identifier of the scientific publication, which for the first time describes the rare disease, and the publication which found the gene causing this disease were added using information from OMIM, Wikipedia, Google Scholar, Whonamedit, and PubMed. The data is available as a spreadsheet and as RDF in a semantic model modified from DisGeNET.\n\nThis dataset relies on publicly available data and publications with a PubMed IDs but this is to our knowledge the first time this data has been linked and made available for further study under a liberal license. Analysis of this data reveals the timeline of rare disease and causative genes discovery and links them to developments in methods and databases.

systems biology↗

bioRxiv2018

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a data-driven insight in downstream effects. Information about changed gene expression was extracted from five previously published studies. We identified a set of genes which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. Using overrepresentation analysis of molecular pathways and gene ontology we found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organization via MEF2C and CAPG. Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available.\n\nAbbreviationsRett syndrome (RTT), embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), fold change (FC), Gene Ontology (GO), EIF (eukaryotic initiation of transcription factor)\n\nFor genes the symbols according to the HGNC nomenclature were used.

systems biology↗