Genomic, Proteomic and Phenotypic Biomarkers of COVID-19 Severity::Protocol for a Retrospective Observational Study

Background:Background - Health organisations and countries around the world have found it difficult to control the spread of the coronavirus disease 2019. To minimise the impact on the NHS and improve patient care, there is a drive for rapid tests capable of detecting individuals who are at high risk of contracting severe COVID-19. Early work focused on single omic approaches, highlighting a limited amount of information.Objective:Objective - The Covid Response Study (COVRES, NCT05548829) aims to carry out an integrated multi-omic analysis of factors contributing to host susceptibility to SARS-CoV-2 among a patient cohort of 1000 people from the geographically isolated island of Ireland.Methods:Methods - The protocol below describes the study to be carried out in Northern Ireland (NI-COVRES) by Ulster University, the Republic of Ireland component will be described separately. All participants (n=519) were recruited from the Western Health and Social Care Trust, Northern Ireland, forty patients are also being followed up at 1, 3, 6 and 12 months to assess the longitudinal impact of infection on symptoms, general health, and immune response, this is ongoing. Data will be sourced from whole blood, saliva samples, and clinical data from the Northern Ireland Electronic Care Record, general health questionnaire, and the GHQ12 mental health survey. Saliva and blood samples were processed for DNA and RNA prior to whole genomic sequencing, RNA sequencing, DNA methylation, microbiome, 16S, and proteomic analysis. Multi-omics data will be combined with clinical data to produce sensitive and specific prognostic models of severity risk.Results:Results - An initial profile of the cohort has been completed: n=249 hospitalised and n=270 non-hospitalised patients were recruited, 64% were female, the mean age was 45 years. High levels of comorbidity were evident in the hospitalised cohort, with cardiovascular disease and metabolic and respiratory disorders (P<0.001) being the most significant.Conclusions:Conclusion – This study will provide a comprehensive opportunity to study multi-omic mechanisms of COVID-19 severity in re-contactable participants. Clinical Trial: Trial Registration - The trial has been registered as an observational study on clinicaltrials.gov as NCT05548829. An outline of the trial protocol is included; SPIRIT checklist (Supplementary Figure 1)

Senescence Signatures Predict Hospitalization Risk and Severity in COVID-19 Patients

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with a particular risk for severe disease and mortality in the elderly population. The more aged you are the higher the risk for mortality and severity due to COVID-19. Why age is the single largest risk factor for severity in COVID-19 is not known. Together virus-induced cell senesence and aging are believed to play a central role in COVID-19 severity and pathogenesis. A deeper understanding of COVID-19 pathophysiology and the involvement of senescence/aging proteins is therefore required. This can help identify patients, at an earlier stage, who are more susceptible to acquiring a severe COVID-19 infection and those who are most likely to go on to develop post-COVID-19 syndrome. This early detection remains a major challenge however largely due to limited understanding of SARS-CoV-2 pathogenesis.In this study, we investigate whether the levels of senescence-specific plasma proteins from COVID-19 patients can be utilized to predict severity and post-COVID-19 syndrome. We performed proteomic profiling of plasma from COVID-19 patients (n = 400) using the Olink Explore 384 Inflammation Panel. Data analysis identified differences in plasma concentrations of proteins, which are linked to senescence while considering patient hospitalization status, age, and their World Health Organization (WHO) clinical progression score.The statistically significant changes were found in the senescence-associated plasma proteome of COVID-19 patients who were hospitalized, more aged, and those with severe WHO classification (TPPI, CXCL10, HGF, VEGFA, SIRPB1, IL-6, TNFRSF11B, and B4GALT1; p < 0.05) and which may be linked to post-COVID-19 syndrome. Epigenetic analysis of the methylome, using the GrimAge Clock, found that biological and chronological age did not correlate in hospitalized patients. We also identified that PTX3, CXCL10, KYNU, and SIRPB1 genes had increased promoter methylation in hospitalized patients.Machine learning analysis showed that characteristic protein changes perform with a similar accuracy to that of a whole panel biomarker signature in terms of hospitalization, age, and WHO clinical progression score.This study revealed senescence specific protein changes (sendotypes) in the plasma of COVID-19 patients, which can be used as determinants for predicting COVID-19 severity, viral signature persistence, and ultimately which may lead to post-COVID-19 syndrome. We propose that the identification of such sendotypes could be exploited for therapeutic intervention via senolytics in COVID-19

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity:Protocol for a Retrospective Observational Study

BACKGROUND: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression.OBJECTIVE: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19.METHODS: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk.RESULTS: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P&lt;.001), grouped according to the International Classification of Diseases 10 codes.CONCLUSIONS: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50733.</p

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity:Protocol for a Retrospective Observational Study

BACKGROUND: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression.OBJECTIVE: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19.METHODS: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk.RESULTS: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P&lt;.001), grouped according to the International Classification of Diseases 10 codes.CONCLUSIONS: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50733.</p

Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value &lt; 0.001), LAMP3 (p-value &lt; 0.001), PRSS8 (p-value &lt; 0.001) and AGRN (p-value &lt; 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.<br/