SARS-CoV-2 potential drugs, drug targets, and biomarkers: a viral-host interaction network-based analysis

Samy, Asmaa; Maher, Mohamed A; Abdelsalam, Nehal Adel; Badr, Eman

doi:10.1038/s41598-022-15898-w

Cited by 20 publications

(15 citation statements)

References 78 publications

(99 reference statements)

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“…It was observed that hsa-miR-33a-3p targets A46L and A41L genes respectively thereby modulating the host defense mechanisms ( Hasan et al., 2016 ). Recently, other groups have also utilized a diverse set of algorithms to identify the miRNAs that could target SARS-CoV-2 genomes ( Mosharaf et al., 2022 ; Samy et al., 2022 ; Shirvani et al., 2022 ; Zeng et al., 2022 ). In order to further advance our understanding of host miRNAs targeting SARS-CoV-2 genomes, we compared our data with other studies.…”

Section: Resultsmentioning

confidence: 99%

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Periwal

Bhardwaj

Sarma

et al. 2022

Front. Cell. Infect. Microbiol.

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The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 has resulted in enormous deaths around the world. Clues from genomic sequences of parent and their mutants can be obtained to understand the evolving pathogenesis of this virus. Apart from the viral proteins, virus-encoded microRNAs (miRNAs) have been shown to play a vital role in regulating viral pathogenesis. Thus we sought to investigate the miRNAs encoded by SARS-CoV-2, its mutants, and the host. Here, we present the results obtained using a dual approach i.e (i) identifying host-encoded miRNAs that might regulate viral pathogenesis and (ii) identifying viral-encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing the first approach resulted in the identification of ten host-encoded miRNAs that could target the SARS, SARS-CoV-2, and its mutants. Interestingly our analysis revealed that there is a significantly higher number of host miRNAs that could target the SARS-CoV-2 genome as compared to the SARS reference genome. Results from the second approach resulted in the identification of a set of virus-encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar “GA” rich motif in the SARS-CoV-2 and its mutant genomes that was shown to play a vital role in lung pathogenesis during severe SARS infections. In summary, we have identified human and virus-encoded miRNAs that might regulate the pathogenesis of SARS coronaviruses and describe similar non-coding RNA sequences in SARS-CoV-2 that were shown to regulate SARS-induced lung pathology in mice.

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Section: Resultsmentioning

confidence: 99%

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Periwal

Bhardwaj

Sarma

et al. 2022

Front. Cell. Infect. Microbiol.

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“…45 This suggests that the search for potential antiviral drugs should also consider targets with anti-inflammatory pathways. 46 We also meta-analysed the SNP-h 2 (proportion of phenotypic variance explained by genetic factors) of COVID-19 susceptibility and severity. The SNP-h 2 estimates for susceptibility and severity phenotypes on observed scale were found to be 6% and 7%, respectively, as depicted in Figure 7.…”

Section: Discussionmentioning

confidence: 99%

“…These genes induce overproduction of pro‐inflammatory cytokines, such as type I interferons that cause immunological dysregulation 45 . This suggests that the search for potential antiviral drugs should also consider targets with anti‐inflammatory pathways 46 …”

Section: Discussionmentioning

confidence: 99%

Host genetic determinants of COVID‐19 susceptibility and severity: A systematic review and meta‐analysis

Eshetie

Jullian

Benyamin

et al. 2023

Reviews in Medical Virology

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Genome‐wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) associated with susceptibility and severity of coronavirus disease 2019 (COVID‐19). However, identified SNPs are inconsistent across studies, and there is no compelling consensus that COVID‐19 status is determined by genetic factors. Here, we conducted a systematic review and meta‐analysis to determine the effect of genetic factors on COVID‐19. A random‐effect meta‐analysis was performed to estimate pooled odds ratios (ORs) of SNP effects, and SNP‐based heritability (SNP‐h2) of COVID‐19. The analyses were performed using meta‐R package, and Stata version 17. The meta‐analysis included a total of 96,817 COVID‐19 cases and 6,414,916 negative controls. The meta‐analysis showed that a cluster of highly correlated 9 SNPs (R2 > 0.9) at 3p21.31 gene locus covering LZTFL1 and SLC6A20 genes was significantly associated with COVID‐19 severity, with a pooled OR of 1.8 [1.5–2.0]. Meanwhile, another 3 SNPs (rs2531743‐G, rs2271616‐T, and rs73062389‐A) within the locus was associated with COVID‐19 susceptibility, with pooled estimates of 0.95 [0.93–0.96], 1.23 [1.19–1.27] and 1.15 [1.13–1.17], respectively. Interestingly, SNPs associated with susceptibility and SNPs associated with severity in this locus are in linkage equilibrium (R2 < 0.026). The SNP‐h2 on the liability scale for severity and susceptibility was estimated at 7.6% (Se = 3.2%) and 4.6% (Se = 1.5%), respectively. Genetic factors contribute to COVID‐19 susceptibility and severity. In the 3p21.31 locus, SNPs that are associated with susceptibility are not in linkage disequilibrium (LD) with SNPs that are associated with severity, indicating within‐locus heterogeneity.

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“…We found that higher expression levels in ERG increased the risk of severe COVID-19 which is described for the first time with this role. It has been described as a potential drug target for treatment of COVID-19, but nothing is reported as a severity biomarker [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity

Martinez-Diz,

Morales-Álvarez,

Garcia-Iglesias

et al. 2023

Hum Genomics

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Background The use of molecular biomarkers for COVID-19 remains unconclusive. The application of a molecular biomarker in combination with clinical ones that could help classifying aggressive patients in first steps of the disease could help clinician and sanitary system a better management of the disease. Here we characterize the role of ACE2, AR, MX1, ERG, ETV5 and TMPRSS2 for trying a better classification of COVID-19 through knowledge of the disease mechanisms. Methods A total of 329 blood samples were genotyped in ACE2, MX1 and TMPRSS2. RNA analyses were also performed from 258 available samples using quantitative polymerase chain reaction for genes: ERG, ETV5, AR, MX1, ACE2, and TMPRSS2. Moreover, in silico analysis variant effect predictor, ClinVar, IPA, DAVID, GTEx, STRING and miRDB database was also performed. Clinical and demographic data were recruited from all participants following WHO classification criteria. Results We confirm the use of ferritin (p < 0.001), D-dimer (p < 0.010), CRP (p < 0.001) and LDH (p < 0.001) as markers for distinguishing mild and severe cohorts. Expression studies showed that MX1 and AR are significantly higher expressed in mild vs severe patients (p < 0.05). ACE2 and TMPRSS2 are involved in the same molecular process of membrane fusion (p = 4.4 × 10–3), acting as proteases (p = 0.047). Conclusions In addition to the key role of TMPSRSS2, we reported for the first time that higher expression levels of AR are related with a decreased risk of severe COVID-19 disease in females. Moreover, functional analysis demonstrates that ACE2, MX1 and TMPRSS2 are relevant markers in this disease. Graphical abstract

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SARS-CoV-2 potential drugs, drug targets, and biomarkers: a viral-host interaction network-based analysis

Cited by 20 publications

References 78 publications

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Host genetic determinants of COVID‐19 susceptibility and severity: A systematic review and meta‐analysis

Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity

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