Longitudinal proteomic investigation of COVID-19 vaccination.

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac®) at a 28-day interval. Using TMT-based proteomics, we identified 1,715 serum and 7,342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) at baseline using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers before vaccination for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding.

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

Chinese herbal Huo-Gu formula for the treatment of steroid-associated osteonecrosis of femoral head: A 14-year follow-up of convalescent SARS patients.

PURPOSE: The coronavirus disease 2019 (COVID-19) reminds us of the severe acute respiratory syndrome (SARS) outbreak in 2003, and up to date, corticosteroid is commonly administrated to severe patients with COVID-19. Osteonecrosis of the femoral head (ONFH) is a common disabling complication among convalescent SARS patients who received corticosteroid therapy. In China, a considerable number of convalescent SARS patients with steroid-associated ONFH had undergone conservative treatment by traditional Chinese medicine, and this study aims to evaluate the long-term results of a spleen-invigorating Huo-Gu formula (HGF) therapy in these patients. PARTICIPANTS AND METHODS: A total of 33 convalescent SARS patients (9 males and 24 females) with bilateral steroid-associated ONFH (66 hips) were enrolled in this study. All patients received oral HGF therapy for 6 months when they were confirmed the diagnosis of steroid-associated ONFH. They had been regularly followed up at an interval of 1 year. Harris hip score and medical imaging modalities, including plain radiography, computed tomography and magnetic resonance imaging, were performed to evaluate the outcomes. RESULTS: Based on average 14 years of follow-up of HGF therapy (ranging from 6 to 16 years), 38 hips (57%) among the 66 hips developed definite osteoarthritis, and 14 hips (26%) in 53 precollapse hips (Association Research Circulation Osseous [ARCO] Stage I or II) progressed to femoral head collapse (ARCO Stage III or IV). Only five patients (also 5 hips) underwent total hip arthroplasty, and the mean hip survival time was over 15 years by the Kaplan-Meier analysis. We observed a mean Harris hip score of 63 points, which represented the reserve of 55% in pain score and 70% in physical function score. The severity of groin pain was not correlated to the severity of osteoarthritis. CONCLUSION: Chinese herbal HGF therapy demonstrates beneficial effects on preventing femoral head collapse, delaying total hip arthroplasty, and maintaining physical function in the treatment of steroid-associated ONFH. HGF therapy might be therefore a good alternative for the treatment of steroid-associated ONFH secondary to rheumatologic and infection diseases. TRANSLATIONAL POTENTIAL OF THE ARTICLE: HGF therapy might be a good alternative for the treatment of steroid-associated ONFH secondary to rheumatologic and infectious diseases.

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.