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COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management.

ABSTRACT: Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annotation and deep-analysis for functional enrichment. COVID-19 caused extensive remodelling of the plasma microenvironment associated with a relative immunosuppressive milieu between ICU Days 3-7, and characterized by extensive organ damage. COVID-19 resulted in (1) reduced antigen presentation and B/T-cell function, (2) increased repurposed neutrophils and M1-type macrophages, (3) relatively immature or disrupted endothelia and fibroblasts with a defined secretome, and (4) reactive myeloid lines. Extracellular matrix changes identified in COVID-19 plasma could represent impaired immune cell homing and programmed cell death. The major functional modules disrupted in COVID-19 were exaggerated in patients with fatal outcome. Taken together, these findings provide systems-level insight into the mechanisms of COVID-19 inflammation and identify potential prognostic biomarkers. Therapeutic strategies could be tailored to the immune response of severely ill patients.

SUBMITTER: Iosef C

PROVIDER: S-EPMC9806290 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management.

Iosef Cristiana C Martin Claudio M CM Slessarev Marat M Gillio-Meina Carolina C Cepinskas Gediminas G Han Victor K M VKM Fraser Douglas D DD

Journal of cellular and molecular medicine 20221219 1

Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annot ...[more]

PMID: 36537107

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Project description:BackgroundThe SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.MethodsIn order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.ResultsHere, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.ConclusionsOur study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Project description:BackgroundRecent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.Methods and findingsWe focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((?,?)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.ConclusionsApplying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages.

Dataset Information

COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management.

Publications

COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management.

Similar Datasets

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