SARS-CoV-2 triggers DNA damage response in Vero E6 cells

Victor, Joshua; Deutsch, Jamie; Whitaker, Annalis; Lamkin, Erica N; March, Anthony; Zhou, Pei; Botten, Jason; Chatterjee, Nimrat

doi:10.1101/2021.09.08.459535

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…In a study investigating the ability of SARS-CoV-2 infection to affect DNA damage response and telomere stability, it was reported that TRF2 expression was decreased in infected Vero E6 kidney cells. In the study, it was shown that decreased TRF2 expression destabilizes telomeres, initiates telomere fusions, and causes genomic instability 36 . The contributions of the Shelterin proteins TRF1 and TRF2 to the maintenance of genomic integrity and telomeric stability are invaluable.…”

Section: Discussionmentioning

confidence: 99%

Üri̇ner Si̇stem Enfeksi̇yonlarinda Shelteri̇n Protei̇nleri̇ Ve Agmati̇n İli̇şki̇si̇

BEDİR,

BAKIR,

ENGİN

2023

CMJ

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The presence of microorganisms in the kidney, collecting system, or bladder with pyuria and clinical symptoms is called Urinary Tract Infection (UTI). UTI is one of the most common diseases in both nosocomial and community-acquired infections and affects millions of people every year. The clinical types of UTI range from cystitis to sepsis. In this study, we aimed to contribute to the literature by comparing the levels of shelterin proteins and agmatine in the serum of patients diagnosed with cystitis, one of the types of UTI, and healthy volunteers without any known medical disorder, and to bring new approaches to issues such as clinical severity, response to treatment, and pathogenesis of the disease. Methods: In this study, 30 individuals diagnosed with cystitis from UTIs and 30 individuals without any systemic disease participated. Serum agmatine levels were measured by a fluorescence detector using the ultra-high-performance liquid chromatography method (UHPLC). Serum shelterin proteins levels were measured using the ELISA method. Results: When the patients and healthy controls were compared, serum Telomeric Repeat Binding Factor2 (TRF2) levels of the patients were found to be lower than the controls; serum agmatine levels were found to be higher than the controls and statistically significant (p0.05). Conclusion: In conclusion, the statistically significant difference in serum TRF2 and agmatine levels between the groups may be associated with oxidative stress. It is thought that the shortening of telomere lengths may be associated with decreased TRF2 levels.

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Section: Discussionmentioning

confidence: 99%

Üri̇ner Si̇stem Enfeksi̇yonlarinda Shelteri̇n Protei̇nleri̇ Ve Agmati̇n İli̇şki̇si̇

BEDİR,

BAKIR,

ENGİN

2023

CMJ

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“…On the other hand, some of the processes that we found signi cantly enriched among asymptomatic patients have been previously put in connection to SARS viral infection. For example, members of the machinery for DNA damage response have been shown to interact and affect the response to several DNA and RNA viruses (Lilley et al, 2007) and it has been recently demonstrated that these pathways are also triggered by SARS-CoV2 in vitro cellular models (Victor et al, 2021). The Fanconi anemia pathway is tightly linked to DNA repair processes involving homologous recombination and genome integrity (Michl et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

An explainable model of host genetic interactions linked to COVID-19 severity

Onoja¹,

Missale²,

Fallerini³

et al. 2022

Preprint

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We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from tree-based models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.

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“…On the other hand, some of the processes that we found significantly enriched among asymptomatic patients have been previously put in connection to SARS viral infection. For example, members of the machinery for DNA damage response have been shown to interact and affect the response to several DNA and RNA viruses 30 and it has been recently demonstrated that these pathways are also triggered by SARS-CoV-2 in vitro cellular models 31 . The Fanconi anemia pathway is tightly linked to DNA repair processes involving homologous recombination and genome integrity 32 .…”

Section: Respiratory or Thoracic Diseasementioning

confidence: 99%

An explainable model of host genetic interactions linked to COVID-19 severity

Onoja

Picchiotti²,

Fallerini³

et al. 2022

Commun Biol

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We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome.

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SARS-CoV-2 triggers DNA damage response in Vero E6 cells

Cited by 3 publications

References 28 publications

Üri̇ner Si̇stem Enfeksi̇yonlarinda Shelteri̇n Protei̇nleri̇ Ve Agmati̇n İli̇şki̇si̇

Üri̇ner Si̇stem Enfeksi̇yonlarinda Shelteri̇n Protei̇nleri̇ Ve Agmati̇n İli̇şki̇si̇

An explainable model of host genetic interactions linked to COVID-19 severity

An explainable model of host genetic interactions linked to COVID-19 severity

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