Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.

BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

Cross-immunity against SARS-COV-2 variants of concern in naturally infected critically ill COVID-19 patients.

Critically ill patients infected with SARS-CoV-2 display adaptive immunity, but it is unknown if they develop cross-reactivity to variants of concern (VOCs). We profiled cross-immunity against SARS-CoV-2 VOCs in naturally infected, non-vaccinated, critically ill COVID-19 patients. Wave-1 patients (wild-type infection) were similar in demographics to Wave-3 patients (wild-type/alpha infection), but Wave-3 patients had higher illness severity. Wave-1 patients developed increasing neutralizing antibodies to all variants, as did patients during Wave-3. Wave-3 patients, when compared to Wave-1, developed more robust antibody responses, particularly for wild-type, alpha, beta and delta variants. Within Wave-3, neutralizing antibodies were significantly less to beta and gamma VOCs, as compared to wild-type, alpha and delta. Patients previously diagnosed with cancer or chronic obstructive pulmonary disease had significantly fewer neutralizing antibodies. Naturally infected ICU patients developed adaptive responses to all VOCs, with greater responses in those patients more likely to be infected with the alpha variant, versus wild-type.

Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism.

BACKGROUND: Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. METHODS: A case-control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. RESULTS: Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P < 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P < 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P < 0.05). CONCLUSIONS: Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy.

The COMPASS-COVID-19-ICU Study: Identification of Factors to Predict the Risk of Intubation and Mortality in Patients with Severe COVID-19

In some patients, SARS-CoV-2 infection induces cytokine storm, hypercoagulability and endothelial cell activation leading to worsening of COVID-19, intubation and death. Prompt identification of patients at risk of intubation is an urgent need. Objectives. To derive a prognostic score for the risk of intubation or death in patients with COVID-19 admitted in intensive care unit (ICU), by assessing biomarkers of hypercoagulability, endothelial cell activation and inflammation and a large panel of clinical analytes. Design, Setting and Participants. A prospective, observational study enrolled 118 patients with COVID-19 admitted in the ICU. On the first day of ICU admission, all patients were assessed for biomarkers (protein C, protein S, antithrombin, D-Dimer, fibrin monomers, FVIIa, FV, FXII, FXII, FVIII, FvW antigen, fibrinogen, procoagulant phospholipid dependent clotting time, TFPI, thrombomodulin, P-selectin, heparinase, microparticles exposing TF, IL-6, complement C3a, C5a, thrombin generation, PT, aPTT, hemogram, platelet count) and clinical predictors. Main Outcomes and Measures. The clinical outcomes were intubation and mortality during hospitalization in ICU. Results: The intubation and mortality rates were 70% and 18%, respectively. The COMPASS-COVID-19-ICU score composed of P-Selectin, D-Dimer, free TFPI, TF activity, IL-6 and FXII, age and duration of hospitalization predicted the risk of intubation or death with high sensitivity and specificity (0.90 and 0.92, respectively). Conclusions and Relevance. COVID-19 is related to severe endothelial cell activation and hypercoagulability orchestrated in the context of inflammation. The COMPASS-COVID-19-ICU risk assessment model is accurate for the evaluation of the risk of mechanical ventilation and death in patients with critical COVID-19. The COMPASS-COVID-19-ICU score is feasible in tertiary hospitals and could be placed in the diagnostic procedure of personalized medical management and prompt therapeutic intervention.

Endothelial Injury and Glycocalyx Degradation in Critically Ill Coronavirus Disease 2019 Patients: Implications for Microvascular Platelet Aggregation.

OBJECTIVES: Coronavirus disease 2019 is caused by the novel severe acute respiratory syndrome coronavirus 2 virus. Patients admitted to the ICU suffer from microvascular thrombosis, which may contribute to mortality. Our aim was to profile plasma thrombotic factors and endothelial injury markers in critically ill coronavirus disease 2019 ICU patients to help understand their thrombotic mechanisms. DESIGN: Daily blood coagulation and thrombotic factor profiling with immunoassays and in vitro experiments on human pulmonary microvascular endothelial cells. SETTING: Tertiary care ICU and academic laboratory. SUBJECTS: All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had daily blood samples collected until testing was confirmed coronavirus disease 2019 negative on either ICU day 3 or ICU day 7 if the patient was coronavirus disease 2019 positive. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Age- and sex-matched healthy control subjects and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients were more likely than coronavirus disease 2019 negative patients to suffer bilateral pneumonia. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Compared with healthy control subjects, coronavirus disease 2019 positive patients had higher plasma von Willebrand factor (p < 0.001) and glycocalyx-degradation products (chondroitin sulfate and syndecan-1; p < 0.01). When compared with coronavirus disease 2019 negative patients, coronavirus disease 2019 positive patients had persistently higher soluble P-selectin, hyaluronic acid, and syndecan-1 (p < 0.05), particularly on ICU day 3 and thereafter. Thrombosis profiling on ICU days 1-3 predicted coronavirus disease 2019 status with 85% accuracy and patient mortality with 86% accuracy. Surface hyaluronic acid removal from human pulmonary microvascular endothelial cells with hyaluronidase treatment resulted in depressed nitric oxide, an instigating mechanism for platelet adhesion to the microvascular endothelium. CONCLUSIONS: Thrombosis profiling identified endothelial activation and glycocalyx degradation in coronavirus disease 2019 positive patients. Our data suggest that medications to protect and/or restore the endothelial glycocalyx, as well as platelet inhibitors, should be considered for further study.

Novel Outcome Biomarkers Identified With Targeted Proteomic Analyses of Plasma From Critically Ill Coronavirus Disease 2019 Patients.

OBJECTIVES: Coronavirus disease 2019 patients admitted to the ICU have high mortality. The host response to coronavirus disease 2019 has only been partially elucidated, and prognostic biomarkers have not been identified. We performed targeted proteomics on critically ill coronavirus disease 2019 patients to better understand their pathophysiologic mediators and to identify potential outcome markers. DESIGN: Blood was collected at predetermined ICU days for proximity extension assays to determine the plasma concentrations of 1,161 proteins. SETTING: Tertiary care ICU and academic laboratory. SUBJECTS: All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient positive (coronavirus disease 2019 positive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age- and sex-matched healthy control subjects and ICU patients who were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well-balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top performing proteins for identifying coronavirus disease 2019 positive ICU patients from both healthy control subjects and coronavirus disease 2019 negative ICU patients (classification accuracies 100%). The coronavirus disease 2019 proteome was dominated by interleukins and chemokines, as well as several membrane receptors linked to lymphocyte-associated microparticles and/or cell debris. Mortality was predicted for coronavirus disease 2019 positive patients based on plasma proteome profiling on both ICU day 1 (accuracy 92%) and ICU day 3 (accuracy 83%). Promising prognostic proteins were then narrowed down to six, each of which provided excellent classification performance for mortality when measured on ICU day 1 CMRF-35-like molecule, interleukin receptor-12 subunit B1, cluster of differentiation 83 [CD83], family with sequence similarity 3, insulin-like growth factor 1 receptor and opticin; area-under-the-curve =1.0; p = 0.007). CONCLUSIONS: Targeted proteomics with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 tested negative ICU patients from coronavirus disease 2019 tested positive ICU patients. Multiple proteins were identified that accurately predicted coronavirus disease 2019 tested positive patient mortality.

Critically Ill COVID-19 Patients Exhibit Anti-SARS-CoV-2 Serological Responses.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global health care emergency. Anti-SARS-CoV-2 serological profiling of critically ill COVID-19 patients was performed to determine their humoral response. Blood was collected from critically ill ICU patients, either COVID-19 positive (+) or COVID-19 negative (-), to measure anti-SARS-CoV-2 immunoglobulins: IgM; IgA; IgG; and Total Ig (combined IgM/IgA/IgG). Cohorts were similar, with the exception that COVID-19+ patients had a greater body mass indexes, developed bilateral pneumonias more frequently and suffered increased hypoxia when compared to COVID-19- patients (p < 0.05). The mortality rate for COVID-19+ patients was 50%. COVID-19 status could be determined by anti-SARS-CoV-2 serological responses with excellent classification accuracies on ICU day 1 (89%); ICU day 3 (96%); and ICU days 7 and 10 (100%). The importance of each Ig isotype for determining COVID-19 status on combined ICU days 1 and 3 was: Total Ig, 43%; IgM, 27%; IgA, 24% and IgG, 6%. Peak serological responses for each Ig isotype occurred on different ICU days (IgM day 13 > IgA day 17 > IgG persistently increased), with the Total Ig peaking at approximately ICU day 18. Those COVID-19+ patients who died had earlier or similar peaks in IgA and Total Ig in their ICU stay when compared to patients who survived (p < 0.005). Critically ill COVID-19 patients exhibit anti-SARS-CoV-2 serological responses, including those COVID-19 patients who ultimately died, suggesting that blunted serological responses did not contribute to mortality. Serological profiling of critically ill COVID-19 patients may aid disease surveillance, patient cohorting and help guide antibody therapies such as convalescent plasma.

Detection and Profiling of Human Coronavirus Immunoglobulins in Critically Ill Coronavirus Disease 2019 Patients.

OBJECTIVES: Coronavirus disease 2019 continues to spread worldwide with high levels of morbidity and mortality. We performed anticoronavirus immunoglobulin G profiling of critically ill coronavirus disease 2019 patients to better define their underlying humoral response. DESIGN: Blood was collected at predetermined ICU days to measure immunoglobulin G with a research multiplex assay against four severe acute respiratory syndrome coronavirus 2 proteins/subunits and against all six additionally known human coronaviruses. SETTING: Tertiary care ICU and academic laboratory. SUBJECTS: ICU patients suspected of being infected with severe acute respiratory syndrome coronavirus 2 had blood collected until either polymerase chain reaction testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until death or discharge if the patient tested polymerase chain reaction positive (coronavirus disease 2019 positive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age- and sex-matched healthy controls and ICU patients who were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well-balanced with the exception that coronavirus disease 2019 positive patients had greater body mass indexes, presented with bilateral pneumonias more frequently, and suffered lower Pao2:Fio2 ratios, when compared with coronavirus disease 2019 negative patients (p < 0.05). Mortality rate for coronavirus disease 2019 positive patients was 50%. On ICU days 1-3, anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G was significantly elevated in coronavirus disease 2019 positive patients, as compared to both healthy control subjects and coronavirus disease 2019 negative patients (p < 0.001). Weak severe acute respiratory syndrome coronavirus immunoglobulin G serologic responses were also detected, but not other coronavirus subtypes. The four anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G were maximal by ICU day 3, with all four anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G providing excellent diagnostic potential (severe acute respiratory syndrome coronavirus 2 Spike 1 protein immunoglobulin G, area under the curve 1.0, p < 0.0005; severe acute respiratory syndrome coronavirus receptor binding domain immunoglobulin G, area under the curve, 0.93-1.0; p ≤ 0.0001; severe acute respiratory syndrome coronavirus 2 Spike proteins immunoglobulin G, area under the curve, 1.0; p < 0.0001; severe acute respiratory syndrome coronavirus 2 Nucleocapsid protein immunoglobulin G area under the curve, 0.90-0.95; p ≤ 0.0003). Anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G increased and/or plateaued over 10 ICU days. CONCLUSIONS: Critically ill coronavirus disease 2019 patients exhibited anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G, whereas serologic responses to non-severe acute respiratory syndrome coronavirus 2 antigens were weak or absent. Detection of human coronavirus immunoglobulin G against the different immunogenic structural proteins/subunits with multiplex assays may be useful for pathogen identification, patient cohorting, and guiding convalescent plasma therapy.

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers.

OBJECTIVES: Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers. DESIGN: Blood was collected at predetermined ICU days to measure the plasma concentrations of 162 metabolites using both direct injection-liquid chromatography-tandem mass spectrometry and proton nuclear magnetic resonance. SETTING: Tertiary-care ICU and academic laboratory. SUBJECTS: Patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient tested positive (coronavirus disease 2019 positive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top-performing metabolites for identifying coronavirus disease 2019 positive patients from healthy control subjects and was dominated by increased kynurenine and decreased arginine, sarcosine, and lysophosphatidylcholines. Arginine/kynurenine ratio alone provided 100% classification accuracy between coronavirus disease 2019 positive patients and healthy control subjects (p = 0.0002). When comparing the metabolomes between coronavirus disease 2019 positive and coronavirus disease 2019 negative patients, kynurenine was the dominant metabolite and the arginine/kynurenine ratio provided 98% classification accuracy (p = 0.005). Feature selection identified creatinine as the top metabolite for predicting coronavirus disease 2019-associated mortality on both ICU days 1 and 3, and both creatinine and creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death with 100% accuracy (p = 0.01). CONCLUSIONS: Metabolomics profiling with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 negative patients from coronavirus disease 2019 positive patients. Arginine/kynurenine ratio accurately identified coronavirus disease 2019 status, whereas creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death. Administration of tryptophan (kynurenine precursor), arginine, sarcosine, and/or lysophosphatidylcholines may be considered as potential adjunctive therapies.