IL10RB as a key regulator of COVID-19 host susceptibility and severity

Voloudakis, Georgios; Hoffman, Gabriel E.; Venkatesh, Sanan; Lee, Kyung Min; Dobrindt, Kristina; Vicari, James M.; Zhang, Wen; Beckmann, Noam D.; Jiang, Shan; Hoagland, Daisy A.; Bian, Jiantao; Gao, Lina; Corvelo, André; Cho, Kelly; Lee, Jennifer S.; Iyengar, Sudha K.; Luoh, Shiuh‐Wen; Akbarian, Schahram; Striker, Rob; Assimes, Themistocles L.; Schadt, Eric E.; Mérad, Miriam; tenOever, Benjamin R.; Charney, Alexander; Brennand, Kristen J.; Lynch, Julie; Fullard, John F.; Roussos, Panos

doi:10.1101/2021.05.31.21254851

Cited by 9 publications

(7 citation statements)

References 86 publications

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“…Reduced expression of IFNAR2 in immune cells has been shown to be associated with a higher risk of COVID-19, likely due to impaired interferon signaling in the blood ( 34 ). IL10RB codes for IL-10 receptor beta, and its expression correlates with the severity of COVID-19 disease ( 35 ).…”

Section: Resultsmentioning

confidence: 99%

ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs

Boytsov

Abramov

Aiusheeva

et al. 2022

Nucleic Acids Research

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We present ANANASTRA, https://ananastra.autosome.org, a web server for the identification and annotation of regulatory single-nucleotide polymorphisms (SNPs) with allele-specific binding events. ANANASTRA accepts a list of dbSNP IDs or a VCF file and reports allele-specific binding (ASB) sites of particular transcription factors or in specific cell types, highlighting those with ASBs significantly enriched at SNPs in the query list. ANANASTRA is built on top of a systematic analysis of allelic imbalance in ChIP-Seq experiments and performs the ASB enrichment test against background sets of SNPs found in the same source experiments as ASB sites but not displaying significant allelic imbalance. We illustrate ANANASTRA usage with selected case studies and expect that ANANASTRA will help to conduct the follow-up of GWAS in terms of establishing functional hypotheses and designing experimental verification.

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Section: Resultsmentioning

confidence: 99%

ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs

Boytsov

Abramov

Aiusheeva

et al. 2022

Nucleic Acids Research

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“…Hence, patients with hyperinflammation are often placed on immunosuppression, immunomodulation, and selective cytokine blocking drugs to improve their outcome [ 32 , 38 , 39 ]. Additionally, interleukin 10 receptor, IL10RB, was identified to be significant to patients’ severity and has been previously noted as a regulator of host susceptibility to COVID-19 severity and potential drug target [ 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

Bello

Bundey

Bhave

et al. 2023

IJMS

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The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough of an indicator of COVID-19 severity and survival, but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.

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“…Hence, patients with hyperinflammation are often placed on immunosuppression, immunomodulation and selective cytokine blocking drugs to improve their [38,42,43]. Also, interleukin 10 receptor, IL10RB, identified to be significant to patients' severity, has been previously noted as regulator of host susceptibility to COVID-19 severity and potential drug target [44,45].…”

Section: Discussionmentioning

confidence: 99%

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

Bello¹,

Bundey²,

Bhave³

et al. 2023

Preprint

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The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough an indicator of COVID-19 severity and survival but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization of best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.

show abstract

IL10RB as a key regulator of COVID-19 host susceptibility and severity

Cited by 9 publications

References 86 publications

ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs

ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

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