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Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients.

ABSTRACT: Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

SUBMITTER: Marwick JA

PROVIDER: S-EPMC8458684 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.Methods and findingsWe perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions.ConclusionsOur study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Project description:OBJECTIVE:Emerging evidence supports a role for epigenetic regulation in the pathogenesis of scleroderma (SSc). We aimed to assess the role of methyl-CpG-binding protein 2 (MeCP2), a key epigenetic regulator, in fibroblast activation and fibrosis in SSc. METHODS:Dermal fibroblasts were isolated from patients with diffuse cutaneous SSc (dcSSc) and from healthy controls. MeCP2 expression was measured by qPCR and western blot. Myofibroblast differentiation was evaluated by gel contraction assay in vitro. Fibroblast proliferation was analysed by ki67 immunofluorescence staining. A wound healing assay in vitro was used to determine fibroblast migration rates. RNA-seq was performed with and without MeCP2 knockdown in dcSSc to identify MeCP2-regulated genes. The expression of MeCP2 and its targets were modulated by siRNA or plasmid. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) using anti-MeCP2 antibody was performed to assess MeCP2 binding sites within MeCP2-regulated genes. RESULTS:Elevated expression of MeCP2 was detected in dcSSc fibroblasts compared with normal fibroblasts. Overexpressing MeCP2 in normal fibroblasts suppressed myofibroblast differentiation, fibroblast proliferation and fibroblast migration. RNA-seq in MeCP2-deficient dcSSc fibroblasts identified MeCP2-regulated genes involved in fibrosis, including PLAU, NID2 and ADA. Plasminogen activator urokinase (PLAU) overexpression in dcSSc fibroblasts reduced myofibroblast differentiation and fibroblast migration, while nidogen-2 (NID2) knockdown promoted myofibroblast differentiation and fibroblast migration. Adenosine deaminase (ADA) depletion in dcSSc fibroblasts inhibited cell migration rates. Taken together, antifibrotic effects of MeCP2 were mediated, at least partly, through modulating PLAU, NID2 and ADA. ChIP-seq further showed that MeCP2 directly binds regulatory sequences in NID2 and PLAU gene loci. CONCLUSIONS:This study demonstrates a novel role for MeCP2 in skin fibrosis and identifies MeCP2-regulated genes associated with fibroblast migration, myofibroblast differentiation and extracellular matrix degradation, which can be potentially targeted for therapy in SSc.

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Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients.

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