Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Link between serum lipid signature and prognostic factors in COVID-19 patients.

Although the serum lipidome is markedly affected by COVID-19, two unresolved issues remain: how the severity of the disease affects the level and the composition of serum lipids and whether serum lipidome analysis may identify specific lipids impairment linked to the patients' outcome. Sera from 49 COVID-19 patients were analyzed by untargeted lipidomics. Patients were clustered according to: inflammation (C-reactive protein), hypoxia (Horowitz Index), coagulation state (D-dimer), kidney function (creatinine) and age. COVID-19 patients exhibited remarkable and distinctive dyslipidemia for each prognostic factor associated with reduced defense against oxidative stress. When patients were clustered by outcome (7 days), a peculiar lipidome signature was detected with an overall increase of 29 lipid species, including-among others-four ceramide and three sulfatide species, univocally related to this analysis. Considering the lipids that were affected by all the prognostic factors, we found one sphingomyelin related to inflammation and viral infection of the respiratory tract and two sphingomyelins, that are independently related to patients' age, and they appear as candidate biomarkers to monitor disease progression and severity. Although preliminary and needing validation, this report pioneers the translation of lipidome signatures to link the effects of five critical clinical prognostic factors with the patients' outcomes.

The trans-omics landscape of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19.

Exosomes represent a subtype of extracellular vesicle that is released through retrograde transport and fusion of multivesicular bodies with the plasma membrane1. Although no perfect methodologies currently exist for the high-throughput, unbiased isolation of pure plasma exosomes2,3, investigation of exosome-enriched plasma fractions of extracellular vesicles can confer a glimpse into the endocytic pathway on a systems level. Here we conduct high-coverage lipidomics with an emphasis on sterols and oxysterols, and proteomic analyses of exosome-enriched extracellular vesicles (EVs hereafter) from patients at different temporal stages of COVID-19, including the presymptomatic, hyperinflammatory, resolution and convalescent phases. Our study highlights dysregulated raft lipid metabolism that underlies changes in EV lipid membrane anisotropy that alter the exosomal localization of presenilin-1 (PS-1) in the hyperinflammatory phase. We also show in vitro that EVs from different temporal phases trigger distinct metabolic and transcriptional responses in recipient cells, including in alveolar epithelial cells, which denote the primary site of infection, and liver hepatocytes, which represent a distal secondary site. In comparison to the hyperinflammatory phase, EVs from the resolution phase induce opposing effects on eukaryotic translation and Notch signalling. Our results provide insights into cellular lipid metabolism and inter-tissue crosstalk at different stages of COVID-19 and are a resource to increase our understanding of metabolic dysregulation in COVID-19.

Mass Spectrometry Techniques in Emerging Pathogens Studies: COVID-19 Perspectives.

As corona virus disease 2019 (COVID-19) is a rapidly growing public health crisis across the world, our knowledge of meaningful diagnostic tests and treatment for severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is still evolving. This novel coronavirus disease COVID-19 can be diagnosed using RT-PCR, but inadequate access to reagents, equipment, and a nonspecific target has slowed disease detection and management. Precision medicine, individualized patient care, requires suitable diagnostics approaches to tackle the challenging aspects of viral outbreaks where many tests are needed in a rapid and deployable approach. Mass spectrometry (MS)-based technologies such as proteomics, glycomics, lipidomics, and metabolomics have been applied in disease outbreaks for identification of infectious disease agents such as virus and bacteria and the molecular phenomena associated with pathogenesis. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is widely used in clinical diagnostics in the United States and Europe for bacterial pathogen identification. Paper spray ionization mass spectrometry (PSI-MS), a rapid ambient MS technique, has recently open a new opportunity for future clinical investigation to diagnose pathogens. Ultra-high-pressure liquid chromatography coupled high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics have been employed in large-scale biomedical research to discriminate infectious pathogens and uncover biomarkers associated with pathogenesis. PCR-MS has emerged as a new technology with the capability to directly identify known pathogens from the clinical specimens and the potential to identify genetic evidence of undiscovered pathogens. Moreover, miniaturized MS offers possible applications with relatively fast, highly sensitive, and potentially portable ways to analyze for viral compounds. However, beneficial aspects of these rapidly growing MS technologies in pandemics like COVID-19 outbreaks has been limited. Hence, this perspective gives a brief of the existing knowledge, current challenges, and opportunities for MS-based techniques as a promising avenue in studying emerging pathogen outbreaks such as COVID-19.