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Global multi-omics and systems pharmacological strategy unravel the multi-targeted therapeutic potential of natural bioactive molecules against COVID-19: An in silico approach.

ABSTRACT: Understanding the immunological behavior of COVID-19 cases at molecular level is essential for therapeutic development. In this study, multi-omics and systems pharmacology analyses were performed to unravel the multi-targeted mechanisms of novel bioactives to combat COVID-19. Immuno-transcriptomic dataset of healthy controls and COVID-19 cases was retrieved from ArrayExpress. Phytocompounds from ethnobotanical plants were collected from PubChem. Differentially expressed 98 immune genes associated with COVID-19 were derived through NetworkAnalyst 3.0. Among 259 plant derived compounds, 154 compounds were targeting 13 COVID-19 immune genes involved in diverse signaling pathways. In addition, pharmacological properties of these phytocompounds were compared with COVID-19 drugs prescribed by WHO, and 25 novel phytocompounds were found to be more efficient with higher bioactive scores. The current study unravels the virogenomic signatures which can serve as therapeutic targets and identified phytocompounds with anti-COVID-19 efficacy. However, further experimental validation is essential to bring out these molecules as commercial drug candidates.

SUBMITTER: Muthuramalingam P

PROVIDER: S-EPMC7410791 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Global multi-omics and systems pharmacological strategy unravel the multi-targeted therapeutic potential of natural bioactive molecules against COVID-19: An in silico approach.

Muthuramalingam Pandiyan P Jeyasri Rajendran R Valliammai Alaguvel A Selvaraj Anthonymuthu A Karthika Chandrasekar C Gowrishankar Shanmugaraj S Pandian Shunmugiah Karutha SK Ramesh Manikandan M Chen Jen-Tsung JT

Genomics 20200807 6

Understanding the immunological behavior of COVID-19 cases at molecular level is essential for therapeutic development. In this study, multi-omics and systems pharmacology analyses were performed to unravel the multi-targeted mechanisms of novel bioactives to combat COVID-19. Immuno-transcriptomic dataset of healthy controls and COVID-19 cases was retrieved from ArrayExpress. Phytocompounds from ethnobotanical plants were collected from PubChem. Differentially expressed 98 immune genes associate ...[more]

PMID: 32771622

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Project description:BackgroundCXCL12 is a vital factor in physiological and pathological processes, by inducing migration of multiple cells. We aimed to comprehensively detect the role of CXCL12 in breast cancer, and explore novel CXCL12-related biomarkers through integrative multi-omics analyses to build a powerful prognostic model for breast cancer patients.MethodsImmunohistochemistry analysis of the tissue microarray was performed to evaluate the correlation between CXCL12 expression levels and breast cancer patient outcomes. Combined single-nucleus and spatial transcriptomics data was used to uncover the expression distribution of CXCL12 in breast cancer microenvironment. CXCL12-related genes were identified by WGCNA analysis. Univariate Cox and LASSO regression analyses were then conducted to screen prognostic genes from above CXCL12-related genes, followed by the construction of the CXCL12-related prognostic signature, identification of risk groups, and external validation of the prognostic signature. Analyses of biological function, mutation landscape, immune checkpoint genes and immune cells, were performed to further reveal the differences between high/low-risk groups. Paired single-cell RNA-seq and bulk RNA-seq were analyzed to further disclose the association between the risk score and the complex tumor immune microenvironment. To screen potential therapeutic agents for breast cancer patients, analyses of gene-drug correlation and sensitivity to immunotherapy were conducted.ResultsHigh expression of CXCL12 was linked with a prolonged survival in breast cancer. A total of 402 genes were identified by WGCNA analysis and 11 genes, covering VAT1L, TMEM92, SDC1, RORB, PCSK9, NRN1, NACAD, JPH3, GJA1, BMP8B and ADAMTS2, were screened as the candidate prognostic genes. Next, the prognostic signature was built and validated using these genes to predict the outcomes of breast cancers. The high-risk group patients exhibited significantly inferior prognoses. The combination of the risk score and tumor mutational burden (TMB) had remarkably improved performance in predicting patient outcomes. Besides, high-risk group patients showed higher infiltration of M2-like macrophages. Finally, several potential anticancer drugs were identified. The high-risk group patients were more sensitive to immunotherapy but resistant to docetaxel.ConclusionsCXCL12 has important immunological implication and prognostic significance in breast cancer. The CXCL12-related prognostic model could well predict the prognosis and treatment response of breast cancers.

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Global multi-omics and systems pharmacological strategy unravel the multi-targeted therapeutic potential of natural bioactive molecules against COVID-19: An in silico approach.

Publications

Global multi-omics and systems pharmacological strategy unravel the multi-targeted therapeutic potential of natural bioactive molecules against COVID-19: An in silico approach.

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