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

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.

Yttrium and aluminium complexes bearing dithiodiolate ligands: synthesis and application in cyclic ester polymerization

Yttrium and aluminium complexes of two dithiodiolate ligands that feature different bridges (CF3)2C(OH)CH2SRSCH2C(OH)(CF3)2 (L(1)-H2, R = CH2CH2 and L(2)-H2, R = C6H4) were synthesized in good yields by reacting tris(silylamide)yttrium or trimethylaluminium with one equivalent of the proligand. All complexes were characterized by NMR and elemental analysis, and single-crystal X-ray structural analysis was also performed for one of the yttrium complexes. The catalytic activities of the four complexes in the ring-opening polymerization of ε-caprolactone and rac-lactide have been investigated. Furthermore, DOSY experiment and DFT calculations have been carried out to determine the structure of the isopropoxo derivative of the complex L(2)Y amide.