Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study

Abstract

Background:The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.Methods:Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.Results:We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer–BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2–7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.Conclusions:Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.Funding:Support for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford’s Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

SARS-CoV-2 viral load and shedding kinetics

“…Indeed, differences have been observed in the duration of infectious virus detection and in nasal and oral viral loads for both ancestral SARS-CoV-2 and Alpha 33 . Inter-individual variability was suggested to have a role in the observed heterogeneity of viral load dynamics, as some early immune signatures were significantly associated with higher oropharyngeal RNA viral loads in patients 134 . Therefore, observed heterogeneity between individuals has an important role in ongoing viral transmission 33 .…”

Longitudinal home self-collection of capillary blood usinghomeRNA correlates interferon and innate viral defense pathways with SARS-CoV-2 viral clearance

“…To date, transcriptional profiling of the immune response, especially in longitudinal studies, is heavily focused on hospital-acquired samples and controlled human challenge studies ( 23-25 ), oftentimes from patients with moderate-to-severe symptoms or requiring oxygen support ( 26 ). When mild outpatient cases are evaluated, single time-point samples are collected through initial clinic visit or resource-intensive mobile phlebotomy ( 27 ) and longitudinal studies are oftentimes limited to infrequent sampling timepoints ( 28, 29 ). One study in outpatients with asymptomatic to moderate SARS-CoV-2 collected blood for RNA-seq at days 0 and 5 post-enrollment, which was at a median of 5 days post symptom onset ( 28 ).…”

“…When mild outpatient cases are evaluated, single time-point samples are collected through initial clinic visit or resource-intensive mobile phlebotomy ( 27 ) and longitudinal studies are oftentimes limited to infrequent sampling timepoints ( 28, 29 ). One study in outpatients with asymptomatic to moderate SARS-CoV-2 collected blood for RNA-seq at days 0 and 5 post-enrollment, which was at a median of 5 days post symptom onset ( 28 ). Although extremely informative, transcriptional profiles of severe infection cases may not be representative of the host immune response in mild outpatient cases and thus to date, the precise characterization of the kinetics underlying the immune response to mild infections is scarce and broad transcriptional response in mild outpatient cases is not well understood.…”

Proteomic Research of Extracellular Vesicles in Clinical Biofluid

“…-Omics data from above facilitated the determination of candidate biomarkers on different molecular levels for EVs, which diagnose disease-specific subtypes, monitor the progress of diseases, or respond to therapeutic intervention. The goal of -omics is to obtain a large amount of comprehensive information in a short time to be processed by advanced computational algorithms that preserve real biological variation by eliminating systematic experimental bias and technical variation [ 65 , 113 , 114 , 115 ]. Achieving this goal depends on ongoing method development for EVs to deal with general and specific challenges ( Table 4 ).…”