The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bastian, Frederic; Roux, Julien; Niknejad, Anne; Comte, Aurélie; Costa, Sara Simões; Farias, Tarcisio Mendes de; Moretti, Sébastien; Parmentier, Gilles; Laval, Valentine Rech de; Rosikiewicz, Marta; Wollbrett, Julien; Echchiki, Amina; Gharib, Walid H.; Gonzales-Porta, Mar; Jarosz, Yohan; Laurenczy, Balazs; Moret, Philippe; Person, Emilie; Roelli, Patrick; Sanjeev, Komal; Seppey, Mathieu; Robinson‐Rechavi, Marc

doi:10.1101/2020.05.28.119560

Cited by 29 publications

(48 citation statements)

References 86 publications

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“…At the next step, we performed the comparisons of expression patterns of GV-related genes in different tissues with the use of Bgee [ 49 ]. The greatest expression of GV-related genes was observed in the cardiovascular system, followed by pancreas, adipose and muscle tissues, gastrointestinal tract, and kidney; retina and nerves demonstrated a less maximal expression score ( Table S4 ).…”

Section: Resultsmentioning

confidence: 99%

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Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

Saik

Климонтов

2020

IJMS

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Glucose variability (GV) has been recognized recently as a promoter of complications and therapeutic targets in diabetes. The aim of this study was to reconstruct and analyze gene networks related to GV in diabetes and its complications. For network analysis, we used the ANDSystem that provides automatic network reconstruction and analysis based on text mining. The network of GV consisted of 37 genes/proteins associated with both hyperglycemia and hypoglycemia. Cardiovascular system, pancreas, adipose and muscle tissues, gastrointestinal tract, and kidney were recognized as the loci with the highest expression of GV-related genes. According to Gene Ontology enrichment analysis, these genes are associated with insulin secretion, glucose metabolism, glycogen biosynthesis, gluconeogenesis, MAPK and JAK-STAT cascades, protein kinase B signaling, cell proliferation, nitric oxide biosynthesis, etc. GV-related genes were found to occupy central positions in the networks of diabetes complications (cardiovascular disease, diabetic nephropathy, retinopathy, and neuropathy) and were associated with response to hypoxia. Gene prioritization analysis identified new gene candidates (THBS1, FN1, HSP90AA1, EGFR, MAPK1, STAT3, TP53, EGF, GSK3B, and PTEN) potentially involved in GV. The results expand the understanding of the molecular mechanisms of the GV phenomenon in diabetes and provide molecular markers and therapeutic targets for future research.

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Section: Resultsmentioning

confidence: 99%

“…Comparative analysis of the expression of GV-related genes in the tissues was carried out by the “Expression comparison” function ( ) provided on the website of Bgee resource [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

Saik

Климонтов

2020

IJMS

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“…It has been used as a monitoring biomarker in early stages of the disease to determine progression from mild to severe 52 , 53 . IL-6 expression is high in lung and arteries for healthy individual 54 . IL-6 is a known cancer biomarker 35 .…”

Section: Resultsmentioning

confidence: 97%

COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases

Gogate

Lyman

Crandall

et al. 2020

Preprint

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Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA,TNF-∝, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4 and IL-2). These trends align with proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19.

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“…Each node of the network consists of genes identified in/around THSSs and is linked by an edge to associated enriched GO terms. The mouse cerebral cortex RNA expression data in TPMs was used to color each node using Mouse ENCODE project dataset and color genes by expression function on GOnet [59, 61, 62].…”

Section: Methodsmentioning

confidence: 99%

Exposure to sevoflurane results in changes of transcription factor occupancy in sperm and inheritance of autism

Wang

Forestier

Corcés

2021

Preprint

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One in 54 children in the U.S. is diagnosed with Autism Spectrum Disorder (ASD). De novo germline and somatic mutations cannot account for all cases of ASD, suggesting that epigenetic alterations triggered by environmental exposures may be responsible for a subset of ASD cases. Human and animal studies have shown that exposure of the developing brain to general anesthetic (GA) agents can trigger neurodegeneration and neurobehavioral abnormalities but the effects of general anesthetics on the germ line have not been explored in detail. We exposed pregnant mice to sevoflurane during the time of embryonic development when the germ cells undergo epigenetic reprogramming and found that more than 38% of the directly exposed F1 animals exhibit impairments in anxiety and social interactions. Strikingly, 44-47% of the F2 and F3 animals, which were not directly exposed to sevoflurane, show the same behavioral problems. We performed ATAC-seq and identified more than 1,200 differentially accessible sites in the sperm of F1 animals, 69 of which are also present in the sperm of F2 animals. These sites are located in regulatory regions of genes strongly associated with ASD, including Arid1b, Ntrk2, and Stmn2. These findings suggest that epimutations caused by exposing germ cells to sevoflurane can lead to ASD in the offspring, and this effect can be transmitted through the male germline inter and trans-generationally.

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The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Cited by 29 publications

References 86 publications

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases

Exposure to sevoflurane results in changes of transcription factor occupancy in sperm and inheritance of autism

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