Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection

Banaganapalli, Babajan; Alrayes, Nuha; Awan, Zuhier; Alsulaimany, Faten A. S.; Alamri, Abdulhakeem S.; Malik, Md. Zubbair; Shaik, Noor Ahmad

doi:10.1016/j.compbiomed.2021.104570

Cited by 37 publications

(37 citation statements)

References 71 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Activation of MYC has been observed in IAV infection [112], as well as proposed to target deregulated genes in SARS-CoV-2 infected cells [81]. Analysis of SARS-CoV-2 infected lung transcriptomics data identifies miRNAs that could target MYC indicating a possible role of MYC in the pathogenesis of COVID-19 [141]. Hyperactivation of the NFκB pathway involves the survival, activation, and differentiation of innate immune cells inflammatory T cells and has been implicated in the pathogenesis of the severe/critical COVID-19 patients [77].…”

Section: Mechanism Of Deregulation Of Lncrna and Pcgmentioning

confidence: 96%

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Saha

Laha

Chatterjee

et al. 2021

ncRNA

View full text Add to dashboard Cite

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.

show abstract

Section: Mechanism Of Deregulation Of Lncrna and Pcgmentioning

confidence: 96%

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Saha

Laha

Chatterjee

et al. 2021

ncRNA

View full text Add to dashboard Cite

show abstract

“…Four key miRNAs (hsa-miR-342-5p, hsa-miR-432-5p, hsa-miR-98-5p, and hsa-miR-17-5p) are believed to be involved in antiviral SARS-CoV-2 gene silencing (ORF1ab) [ 72 ]. For three miRNA (hsa-miR-17-5p, hsa-miR-20b-5p, and hsa-miR-323a-5p), there is experimental evidence of having antiviral roles during infections against SARS-CoV-1 and SARS-CoV-2 ( Table 3 ) [ 73 ].…”

Section: Changes In Mrna Expression—mirna ‘’Silencing’’ Interferencementioning

confidence: 99%

“…Published data show that virus RNA highly interacts with human miRNAs to change the expression of important defense molecules [ 29 , 72 , 73 ]. Our analysis showed that numerous human miRNA may form duplexes with different coding and non-coding regions of viral RNA.…”

Section: Changes In Mrna Expression—mirna ‘’Silencing’’ Interferencementioning

confidence: 99%

Viral and Host Genetic and Epigenetic Biomarkers Related to SARS-CoV-2 Cell Entry, Infection Rate, and Disease Severity

Gašperšič

Dolžan

2022

Biology

View full text Add to dashboard Cite

The rapid spread of COVID-19 outbreak lead to a global pandemic declared in March 2020. The common features of corona virus family helped to resolve structural characteristics and entry mechanism of SARS-CoV-2. However, rapid mutagenesis leads to the emergence of new strains that may have different reproduction rates or infectivity and may impact the course and severity of the disease. Host related factors may also play a role in the susceptibility for infection as well as the severity and outcomes of the COVID-19. We have performed a literature and database search to summarize potential viral and host-related genomic and epigenomic biomarkers, such as genetic variability, miRNA, and DNA methylation in the molecular pathway of SARS-CoV-2 entry into the host cell, that may be related to COVID-19 susceptibility and severity. Bioinformatics tools may help to predict the effect of mutations in the spike protein on the binding to the ACE2 receptor and the infectivity of the strain. SARS-CoV-2 may also target several transcription factors and tumour suppressor genes, thus influencing the expression of different host genes and affecting cell signalling. In addition, the virus may interfere with RNA expression in host cells by exploiting endogenous miRNA and its viral RNA. Our analysis showed that numerous human miRNA may form duplexes with different coding and non-coding regions of viral RNA. Polymorphisms in human genes responsible for viral entry and replication, as well as in molecular damage response and inflammatory pathways may also contribute to disease prognosis and outcome. Gene ontology analysis shows that proteins encoded by such polymorphic genes are highly interconnected in regulation of defense response. Thus, virus and host related genetic and epigenetic biomarkers may help to predict the course of the disease and the response to treatment.

show abstract

“…However, differences in the type of clinical samples, array platforms, and statistical approaches used, created a discordance in interpreting massive transcriptomics data. Advances in statistical modeling and bioinformatics approaches have accelerated the identification of disease-centric genes by employing gene networking methods based on graph topological parameters for many infectious diseases (7,8). Moreover, the new bioinformatic methods like estimating relative subsets of RNA transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER), and Estimating the Proportions of Immune and Cancer cells (EPIC) are developed to characterize immune cell composition using large-scale gene expression data (9,10).…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

et al. 2022

Self Cite

View full text Add to dashboard Cite

BackgroundTuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers.MethodsThe blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels.ResultsPatients with PTB had higher levels of four immune subpopulations like CD8+ T cells (P = 1.9 × 10−8), natural killer (NK) cells resting (P = 6.3 × 10−5), monocytes (P = 6.4 × 10−6), and neutrophils (P = 1.6 × 10−7). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8+ T cell-associated genes (ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E, and IL7R) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r2 ≥ |0.7|) approaches.ConclusionThe changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

show abstract

Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection

Cited by 37 publications

References 71 publications

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Viral and Host Genetic and Epigenetic Biomarkers Related to SARS-CoV-2 Cell Entry, Infection Rate, and Disease Severity

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

Contact Info

Product

Resources

About