Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.
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ABSTRACT:
SUBMITTER: Pavel A
PROVIDER: S-EPMC7929418 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.
| S-EPMC9863794 | biostudies-literature
Project description:The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these include patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble machine learning, deep learning and quantum computing approach. Within this gene network, Structural Causal Modeling identified several ARDS driver genes, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. Hence we apply a machine learning approach to identify driver genes for severe forms of COVID-19 in a small, uncluttered cohort of patients.
2021-11-25 | PXD025265 | Pride
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-07-24 | GSE180743 | GEO
Project description:'Tripartite network' (TN) and 'combined gene network' (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as 'target genes' (TG) to identify 21 'candidate genes' (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise 'semantic similarity scores' (SSS). A new integrated 'weighted harmonic mean score' was formulated assimilating values of SSS and STRING-based 'combined score' of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and 'indispensable nodes' in CGN. Finally, six pairs sharing seven 'prevalent CGs' (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of 'prevalent CGs' has been discussed to interpret neurological phenotypes of COVID-19.
| S-EPMC9558001 | biostudies-literature
Project description:BackgroundSeveral ecologic studies have suggested that the bacillus Calmette-Guérin (BCG) vaccine may be protective against SARS-CoV-2 infection including a highly-cited published pre-print by Miller et al., finding that middle/high- and high-income countries that never had a universal BCG policy experienced higher COVID-19 burden compared to countries that currently have universal BCG vaccination policies. We provide a case study of the limitations of ecologic analyses by evaluating whether these early ecologic findings persisted as the pandemic progressed.MethodsSimilar to Miller et al., we employed Wilcoxon Rank Sum Tests to compare population medians in COVID-19 mortality, incidence, and mortality-to-incidence ratio between countries with universal BCG policies compared to those that never had such policies. We then computed Pearson's r correlations to evaluate the association between year of BCG vaccination policy implementation and COVID-19 outcomes. We repeated these analyses for every month in 2020 subsequent to Miller et al.'s March 2020 analysis.ResultsWe found that the differences in COVID-19 burden associated with BCG vaccination policies in March 2020 generally diminished in magnitude and usually lost statistical significance as the pandemic progressed. While six of nine analyses were statistically significant in March, only two were significant by the end of 2020.DiscussionThese results underscore the need for caution in interpreting ecologic studies, given their inherent methodological limitations, which can be magnified in the context of a rapidly evolving pandemic in which there is measurement error of both exposure and outcome status.
| S-EPMC9488757 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.
| S-EPMC8577732 | biostudies-literature
Project description:The novel coronavirus disease 2019 (COVID-19) emerged in late December 2019 in china and has rapidly spread to many countries around the world. The effective pharmacotherapy can reduce the mortality of COVID-19. Antiviral medications are the candidate therapies for the management of COVID-19. Molnupiravir is an antiviral drug with anti-RNA polymerase activity and currently is under investigation for the treatment of patients with COVID-19. This review focuses on summarizing published literature for the mechanism of action, safety, efficacy, and clinical trials of molnupiravir in the treatment of COVID-19 patients.
| S-EPMC8929331 | biostudies-literature
Project description:BackgroundPatients with some types of immunodeficiency can experience chronic or relapsing infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This leads to morbidity and mortality, infection control challenges, and the risk of evolution of novel viral variants. The optimal treatment for chronic coronavirus disease 2019 (COVID-19) is unknown.ObjectiveOur aim was to characterize a cohort of patients with chronic or relapsing COVID-19 disease and record treatment response.MethodsWe conducted a UK physician survey to collect data on underlying diagnosis and demographics, clinical features, and treatment response of immunodeficient patients with chronic (lasting ≥21 days) or relapsing (≥2 episodes) of COVID-19.ResultsWe identified 31 patients (median age 49 years). Their underlying immunodeficiency was most commonly characterized by antibody deficiency with absent or profoundly reduced peripheral B-cell levels; prior anti-CD20 therapy, and X-linked agammaglobulinemia. Their clinical features of COVID-19 were similar to those of the general population, but their median duration of symptomatic disease was 64 days (maximum 300 days) and individual patients experienced up to 5 episodes of illness. Remdesivir monotherapy (including when given for prolonged courses of ≤20 days) was associated with sustained viral clearance in 7 of 23 clinical episodes (30.4%), whereas the combination of remdesivir with convalescent plasma or anti-SARS-CoV-2 mAbs resulted in viral clearance in 13 of 14 episodes (92.8%). Patients receiving no therapy did not clear SARS-CoV-2.ConclusionsCOVID-19 can present as a chronic or relapsing disease in patients with antibody deficiency. Remdesivir monotherapy is frequently associated with treatment failure, but the combination of remdesivir with antibody-based therapeutics holds promise.
| S-EPMC8585958 | biostudies-literature
Project description:BackgroundThe clinical course of chronic lymphocytic leukemia (CLL) is highly variable; some patients follow an indolent course, but others progress to a more advanced stage. The mutational status of rearranged immunoglobulin heavy chain variable (IGVH) genes in CLL is a feature that is widely recognized for dividing patients into groups that are related to their prognoses. However, the regulatory programs associated with the IGVH statuses are poorly understood, and markers that can precisely predict survival outcomes have yet to be identified.MethodsIn this study, (i) we reconstructed gene regulatory networks in CLL by applying an information-theoretic approach to the expression profiles of 5 cohorts. (ii) We applied master regulator analysis (MRA) to these networks to identify transcription factors (TFs) that regulate an IGVH mutational status signature. The IGVH mutational status signature was developed by searching for differentially expressed genes between the IGVH mutational statuses in numerous CLL cohorts. (iii) To evaluate the biological implication of the inferred regulators, prognostic values were determined using time to treatment (TTT) and overall survival (OS) in two different cohorts.ResultsA robust IGVH expression signature was obtained, and various TFs emerged as regulators of the signature in most of the reconstructed networks. The TF targets expression profiles exhibited significant differences with respect to survival, which allowed the definition of a reduced profile with a high value for OS. TCF7 and its targets stood out for their roles in progression.ConclusionTFs and their targets, which were obtained merely from inferred regulatory associations, have prognostic implications and reflect a regulatory context for prognosis.
| S-EPMC4659237 | biostudies-literature