Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death

Longitudinal analysis of the lung proteome reveals persistent repair months after mild to moderate COVID-19

In order to assess homeostatic mechanisms in the lung after COVID-19, changes in the protein signature of bronchoalveolar lavage from 45 patients with mild to moderate disease at three phases (acute, recovery, and convalescent) are evaluated over a year. During the acute phase, inflamed and uninflamed phenotypes are characterized by the expression of tissue repair and host defense response molecules. With recovery, inflammatory and fibrogenic mediators decline and clinical symptoms abate. However, at 9 months, quantified radiographic abnormalities resolve in the majority of patients, and yet compared to healthy persons, all showed ongoing activation of cellular repair processes and depression of the renin-kallikrein-kinin, coagulation, and complement systems. This dissociation of prolonged reparative processes from symptom and radiographic resolution suggests that occult ongoing disruption of the lung proteome is underrecognized and may be relevant to recovery from other serious viral pneumonias.

ADAM-17 Activity and Its Relation to ACE2: Implications for Severe COVID-19

There is a lack of studies aiming to assess cellular a disintegrin and metalloproteinase-17 (ADAM-17) activity in COVID-19 patients and the eventual associations with the shedding of membrane-bound angiotensin-converting enzyme 2 (mACE2). In addition, studies that investigate the relationship between ACE2 and ADAM-17 gene expressions in organs infected by SARS-CoV-2 are lacking. We used data from the Massachusetts general hospital COVID-19 study (306 COVID-19 patients and 78 symptomatic controls) to investigate the association between plasma levels of 33 different ADAM-17 substrates and COVID-19 severity and mortality. As a surrogate of cellular ADAM-17 activity, an ADAM-17 substrate score was calculated. The associations between soluble ACE2 (sACE2) and the ADAM-17 substrate score, renin, key inflammatory markers, and lung injury markers were investigated. Furthermore, we used data from the Genotype-Tissue Expression (GTEx) database to evaluate ADAM-17 and ACE2 gene expressions by age and sex in ages between 20-80 years. We found that increased ADAM-17 activity, as estimated by the ADAM-17 substrates score, was associated with COVID-19 severity (p = 0.001). ADAM-17 activity was also associated with increased mortality but did not reach statistical significance (p = 0.06). Soluble ACE2 showed the strongest positive correlation with the ADAM-17 substrate score, follow by renin, interleukin-6, and lung injury biomarkers. The ratio of ADAM-17 to ACE2 gene expression was highest in the lung. This study indicates that increased ADAM-17 activity is associated with severe COVID-19. Our findings also indicate that there may a bidirectional relationship between membrane-bound ACE2 shedding via increased ADAM-17 activity, dysregulated renin-angiotensin system (RAS) and immune signaling. Additionally, differences in ACE2 and ADAM-17 gene expressions between different tissues may be of importance in explaining why the lung is the organ most severely affected by COVID-19, but this requires further evaluation in prospective studies.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality

BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).

Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections

BACKGROUND

Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored.

METHODS

Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information.

RESULTS

Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors.

CONCLUSIONS

Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives.

Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing Ab pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on human serum samples from the National Institutes of Health IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. First, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Second, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Third, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

GDF-15 Levels and Other Laboratory Findings as Predictors of COVID-19 Severity and Mortality: A Pilot Study

Growth differentiation factor 15 (GDF-15) is a stress-induced cytokine associated with acute and chronic inflammatory states. This prospective observational study aimed to investigate the prognostic roles of GDF-15 and routine clinical laboratory parameters in COVID-19 patients. Upon the admission of 95 adult hospitalized COVID-19 patients in Croatia, blood analysis was performed, and medical data were collected. The patients were categorized based on survival, ICU admission, and hospitalization duration. Logistic regression and ROC curve methods were employed for the statistical analysis. Logistic regression revealed two independent predictors of negative outcomes: CURB-65 score (OR = 2.55) and LDH (OR = 1.005); one predictor of ICU admission: LDH (OR = 1.004); and one predictor of prolonged hospitalization: the need for a high-flow nasal cannula (HFNC) upon admission (OR = 4.75). The ROC curve showed diagnostic indicators of negative outcomes: age, CURB-65 score, LDH, and GDF-15. The largest area under the curve (AUC = 0.767, specificity = 65.6, sensitivity = 83.9) was represented by GDF-15, with a cutoff value of 3528 pg/mL. For ICU admission, significant diagnostic indicators were LDH, CRP, and IL-6. Significant diagnostic indicators of prolonged hospitalization were CK, GGT, and oxygenation with an HFNC upon admission. This study reaffirms the significance of the commonly used laboratory parameters and clinical scores in evaluating COVID-19. Additionally, it introduces the potential for a new diagnostic approach and research concerning GDF-15 levels in this widespread disease.

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BACKGROUND

The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making.

METHODS

Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections.

RESULTS

A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient.

CONCLUSION

This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Proteomic profiling identifies biomarkers of COVID-19 severity

SARS-CoV-2 infection remains a major public health concern, particularly for the aged and those individuals with co-morbidities at risk for developing severe COVID-19. Understanding the pathogenesis and biomarkers associated with responses to SARS-CoV-2 infection remain critical components in developing effective therapeutic approaches, especially in cases of severe and long-COVID-19. In this study blood plasma protein expression was compared in subjects with mild, moderate, and severe COVID-19 disease. Evaluation of an inflammatory protein panel confirms upregulation of proteins including TNFβ, IL-6, IL-8, IL-12, already associated with severe cytokine storm and progression to severe COVID-19. Importantly, we identify several proteins not yet associated with COVID-19 disease, including mesothelin (MSLN), that are expressed at significantly higher levels in severe COVID-19 subjects. In addition, we find a subset of markers associated with T-cell and dendritic cell responses to viral infection that are significantly higher in mild cases and decrease in expression as severity of COVID-19 increases, suggesting that an immediate and effective activation of T-cells is critical in modulating disease progression. Together, our findings identify new targets for further investigation as therapeutic approaches for the treatment of SARS-CoV-2 infection and prevention of complications of severe COVID-19.

Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants

Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.

Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort 1

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

Disrupted epithelial permeability as a predictor of severe COVID-19 development

BACKGROUND

An impaired epithelial barrier integrity in the gastrointestinal tract is important to the pathogenesis of many inflammatory diseases. Accordingly, we assessed the potential of biomarkers of epithelial barrier dysfunction as predictive of severe COVID-19.

METHODS

Levels of bacterial DNA and zonulin family peptides (ZFP) as markers of bacterial translocation and intestinal permeability and a total of 180 immune and inflammatory proteins were analyzed from the sera of 328 COVID-19 patients and 49 healthy controls.

RESULTS

Significantly high levels of circulating bacterial DNA were detected in severe COVID-19 cases. In mild COVID-19 cases, serum bacterial DNA levels were significantly lower than in healthy controls suggesting epithelial barrier tightness as a predictor of a mild disease course. COVID-19 patients were characterized by significantly elevated levels of circulating ZFP. We identified 36 proteins as potential early biomarkers of COVID-19, and six of them (AREG, AXIN1, CLEC4C, CXCL10, CXCL11, and TRANCE) correlated strongly with bacterial translocation and can be used to predict and discriminate severe cases from healthy controls and mild cases (area under the curve (AUC): 1 and 0.88, respectively). Proteomic analysis of the serum of 21 patients with moderate disease at admission which progressed to severe disease revealed 10 proteins associated with disease progression and mortality (AUC: 0.88), including CLEC7A, EIF4EBP1, TRANCE, CXCL10, HGF, KRT19, LAMP3, CKAP4, CXADR, and ITGB6.

CONCLUSION

Our results demonstrate that biomarkers of intact or defective epithelial barriers are associated with disease severity and can provide early information on the prediction at the time of hospital admission.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Characterising biological mechanisms underlying ethnicity-associated outcomes in COVID-19 through biomarker trajectories: a multicentre registry analysis

BACKGROUND

Differences in routinely collected biomarkers between ethnic groups could reflect dysregulated host responses to disease and to treatments, and be associated with excess morbidity and mortality in COVID-19.

METHODS

A multicentre registry analysis from patients aged ≥16 yr with SARS-CoV-2 infection and emergency admission to Barts Health NHS Trust hospitals during January 1, 2020 to May 13, 2020 (wave 1) and September 1, 2020 to February 17, 2021 (wave 2) was subjected to unsupervised longitudinal clustering techniques to identify distinct phenotypic patient clusters based on trajectories of routine blood results over the first 15 days of hospital admission. Distribution of trajectory clusters across ethnic categories was determined, and associations between ethnicity, trajectory clusters, and 30-day survival were assessed using multivariable Cox proportional hazards modelling. Secondary outcomes were ICU admission, survival to hospital discharge, and long-term survival to 640 days.

RESULTS

We included 3237 patients with hospital length of stay ≥7 days. In patients who died, there was greater representation of Black and Asian ethnicity in trajectory clusters for C-reactive protein and urea-to-creatinine ratio associated with increased risk of death. Inclusion of trajectory clusters in survival analyses attenuated or abrogated the higher risk of death in Asian and Black patients. Inclusion of C-reactive protein went from hazard ratio (HR) 1.36 [0.95-1.94] to HR 0.97 [0.59-1.59] (wave 1), and from HR 1.42 [1.15-1.75]) to HR 1.04 [0.78-1.39] (wave 2) in Asian patients. Trajectory clusters associated with reduced 30-day survival were similarly associated with worse secondary outcomes.

CONCLUSIONS

Clinical biochemical monitoring of COVID-19 and progression and treatment response in SARS-CoV-2 infection should be interpreted in the context of ethnic background.

Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system

Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.

Humans with inherited MyD88 and IRAK-4 deficiencies are predisposed to hypoxemic COVID-19 pneumonia

X-linked recessive deficiency of TLR7, a MyD88- and IRAK-4-dependent endosomal ssRNA sensor, impairs SARS-CoV-2 recognition and type I IFN production in plasmacytoid dendritic cells (pDCs), thereby underlying hypoxemic COVID-19 pneumonia with high penetrance. We report 22 unvaccinated patients with autosomal recessive MyD88 or IRAK-4 deficiency infected with SARS-CoV-2 (mean age: 10.9 yr; 2 mo to 24 yr), originating from 17 kindreds from eight countries on three continents. 16 patients were hospitalized: six with moderate, four with severe, and six with critical pneumonia, one of whom died. The risk of hypoxemic pneumonia increased with age. The risk of invasive mechanical ventilation was also much greater than in age-matched controls from the general population (OR: 74.7, 95% CI: 26.8-207.8, P < 0.001). The patients' susceptibility to SARS-CoV-2 can be attributed to impaired TLR7-dependent type I IFN production by pDCs, which do not sense SARS-CoV-2 correctly. Patients with inherited MyD88 or IRAK-4 deficiency were long thought to be selectively vulnerable to pyogenic bacteria, but also have a high risk of hypoxemic COVID-19 pneumonia.

Circulating proteins to predict COVID-19 severity

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.

The role of immune activation and antigen persistence in acute and long COVID

In late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) triggered the global coronavirus disease 2019 (COVID-19) pandemic. Although most infections cause a self-limited syndrome comparable to other upper respiratory viral pathogens, a portion of individuals develop severe illness leading to substantial morbidity and mortality. Furthermore, an estimated 10%-20% of SARS-CoV-2 infections are followed by post-acute sequelae of COVID-19 (PASC), or long COVID. Long COVID is associated with a wide variety of clinical manifestations including cardiopulmonary complications, persistent fatigue, and neurocognitive dysfunction. Severe acute COVID-19 is associated with hyperactivation and increased inflammation, which may be an underlying cause of long COVID in a subset of individuals. However, the immunologic mechanisms driving long COVID development are still under investigation. Early in the pandemic, our group and others observed immune dysregulation persisted into convalescence after acute COVID-19. We subsequently observed persistent immune dysregulation in a cohort of individuals experiencing long COVID. We demonstrated increased SARS-CoV-2-specific CD4⁺ and CD8⁺ T-cell responses and antibody affinity in patients experiencing long COVID symptoms. These data suggest a portion of long COVID symptoms may be due to chronic immune activation and the presence of persistent SARS-CoV-2 antigen. This review summarizes the COVID-19 literature to date detailing acute COVID-19 and convalescence and how these observations relate to the development of long COVID. In addition, we discuss recent findings in support of persistent antigen and the evidence that this phenomenon contributes to local and systemic inflammation and the heterogeneous nature of clinical manifestations seen in long COVID.

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Combination of WFDC2, CHI3L1, and KRT19 in Plasma Defines a Clinically Useful Molecular Phenotype Associated with Prognosis in Critically Ill COVID-19 Patients

BACKGROUND

COVID-19 is now a common disease, but its pathogenesis remains unknown. Blood circulating proteins reflect host defenses against COVID-19. We investigated whether evaluation of longitudinal blood proteomics for COVID-19 and merging with clinical information would allow elucidation of its pathogenesis and develop a useful clinical phenotype.

METHODS

To achieve the first goal (determining key proteins), we derived plasma proteins related to disease severity by using a first discovery cohort. We then assessed the association of the derived proteins with clinical outcome in a second discovery cohort. Finally, the candidates were validated by enzyme-linked immunosorbent assay in a validation cohort to determine key proteins. For the second goal (understanding the associations of the clinical phenotypes with 28-day mortality and clinical outcome), we assessed the associations between clinical phenotypes derived by latent cluster analysis with the key proteins and 28-day mortality and clinical outcome.

RESULTS

We identified four key proteins (WFDC2, GDF15, CHI3L1, and KRT19) involved in critical pathogenesis from the three different cohorts. These key proteins were related to the function of cell adhesion and not immune response. Considering the multicollinearity, three clinical phenotypes based on WFDC2, CHI3L1, and KRT19 were identified that were associated with mortality and clinical outcome.

CONCLUSION

The use of these easily measured key proteins offered new insight into the pathogenesis of COVID-19 and could be useful in a potential clinical application.

Proteomics Investigation of Diverse Serological Patterns in COVID-19

Serum antibodies IgM and IgG are elevated during Coronavirus Disease 2019 (COVID-19) to defend against viral attacks. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative, whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their 2-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense and that high titers of IgM might not be favorable to COVID-19 recovery.

Hemodialysis-Associated Immune Dysregulation in SARS-CoV-2-Infected End-Stage Renal Disease Patients

Patients on hemodialysis show dysregulated immunity, basal hyperinflammation and a marked vulnerability to COVID-19. We evaluated the immune profile in COVID-19 hemodialysis patients and the changes associated with clinical deterioration after the hemodialysis session. Recruited patients included eight hemodialysis subjects with active, PCR-confirmed SARS-CoV-2 infection, five uninfected hemodialysis patients and five healthy controls. In SARS-CoV-2-infected hemodialysis patients TNF-α, IL-6 and IL-8 were particularly increased. Lymphopenia was mostly due to reduction in CD4+ T, B and central memory CD8+ T cells. There was a predominance of classical and intermediate monocytes with reduced HLA-DR expression and enhanced production of pro-inflammatory molecules. Immune parameters were analysed pre- and post-hemodialysis in three patients with COVID-19 symptoms worsening after the hemodialysis session. There was a higher than 2.5-fold increase in GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-17A and IL-21 in serum, and augmentation of monocytes-derived TNF-α, IL-1β and IL-8 and CXCL10 (p < 0.05). In conclusion, COVID-19 in hemodialysis patients associates with alteration of lymphocyte subsets, increasing of pro-inflammatory cytokines and monocyte activation. The observed worsening during the hemodialysis session in some patients was accompanied by augmentation of particular inflammatory cytokines, which might suggest biomarkers and therapeutic targets to prevent or mitigate the hemodialysis-related deterioration during SARS-CoV-2 infection.

Plasma protein profiling analysis in patients with atrial fibrillation before and after three different ablation techniques

Background

There are controversies on the pathophysiological alteration in patients with atrial fibrillation (AF) undergoing pulmonary vein isolation using different energy sources.

Objectives

We evaluated the changes in plasma proteins in acute phase post-ablation in patients receiving cryoballoon ablation, radiofrequency balloon ablation, or radiofrequency ablation.

Methods

Blood samples from eight healthy controls and 24 patients with AF were taken on the day of admission, day 1, and day 2 post-ablation and analyzed by the Olink proximity extension assay. Proteins were identified and performed with enrichment analysis. Protein-protein interaction network and module analysis were conducted using Cytoscape software.

Results

Of 181 proteins, 42 proteins in the cryoballoon group, 46 proteins in the radiofrequency balloon group, and 43 proteins in the radiofrequency group significantly changed after ablation. Most of the proteins altered significantly on the first day after ablation. Altered proteins were mainly involved in cytokine-cytokine receptor interaction. Both balloon-based ablations showed a similar shift toward enhancing cell communication and regulation of signaling while inhibiting neutrophil chemotaxis. However, radiofrequency ablation presented a different trend. Seed proteins, including osteopontin, interleukin-6, interleukin-10, C-C motif ligand 8, and matrix metalloproteinase-1, were identified. More significant proteins associated with hemorrhage and coagulation were selected in balloon-based ablations by machine learning.

Conclusion

Plasma protein response after three different ablations in patients with AF mainly occurred on the first day. Radiofrequency balloon ablation shared similar alteration in protein profile as cryoballoon ablation compared with radiofrequency ablation, suggesting that lesion size rather than energy source is the determinant in pathophysiological responses to the ablation.

Mapping CircRNA-miRNA-mRNA regulatory axis identifies hsa_circ_0080942 and hsa_circ_0080135 as a potential theranostic agents for SARS-CoV-2 infection

Non-coding RNAs (ncRNAs) can control the flux of genetic information; affect RNA stability and play crucial roles in mediating epigenetic modifications. A number of studies have highlighted the potential roles of both virus-encoded and host-encoded ncRNAs in viral infections, transmission and therapeutics. However, the role of an emerging type of non-coding transcript, circular RNA (circRNA) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been fully elucidated so far. Moreover, the potential pathogenic role of circRNA-miRNA-mRNA regulatory axis has not been fully explored as yet. The current study aimed to holistically map the regulatory networks driven by SARS-CoV-2 related circRNAs, miRNAs and mRNAs to uncover plausible interactions and interplay amongst them in order to explore possible therapeutic options in SARS-CoV-2 infection. Patient datasets were analyzed systematically in a unified approach to explore circRNA, miRNA, and mRNA expression profiles. CircRNA-miRNA-mRNA network was constructed based on cytokine storm related circRNAs forming a total of 165 circRNA-miRNA-mRNA pairs. This study implies the potential regulatory role of the obtained circRNA-miRNA-mRNA network and proposes that two differentially expressed circRNAs hsa_circ_0080942 and hsa_circ_0080135 might serve as a potential theranostic agents for SARS-CoV-2 infection. Collectively, the results shed light on the functional role of circRNAs as ceRNAs to sponge miRNA and regulate mRNA expression during SARS-CoV-2 infection.

A virological view of tenascin-C in infection

Tenascin-C is a large extracellular matrix glycoprotein with complex, not yet fully unveiled roles. Its context- and structure-dependent modus operandi renders tenascin-C a puzzling protein. Since its discovery ∼40 years ago, research into tenascin-C biology continues to reveal novel functions, the most recent of all being its immunomodulatory activity, especially its role in infection, which is just now beginning to emerge. Here, we explore the role of tenascin-C in the immune response to viruses, including SARS-CoV-2 and HIV-1. Recently, tenascin-C has emerged as a biomarker of disease severity during COVID-19 and other viral infections, and we highlight relevant RNA sequencing and proteomic analyses that suggest a correlation between tenascin-C levels and disease severity. Finally, we ask what the function of this protein during viral replication is and propose tenascin-C as an intercellular signal of inflammation shuttled to distal sites via exosomes, a player in the repair and remodeling of infected and damaged tissues during severe infectious disease, as well as a ligand for specific pathogens with distinct implications for the host.

Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

Growth Differentiation Factor 15 (GDF-15) Levels Associate with Lower Survival in Chronic Kidney Disease Patients with COVID-19

A cytokine storm drives the pathogenesis of severe COVID-19 infection and several biomarkers have been linked to mortality. Chronic kidney disease (CKD) emerged as a risk factor for severe COVID-19. We investigated the association between selected biomarkers and mortality in 77 patients hospitalized for COVID-19, and whether they differ in patients with eGFR higher and lower than 45 mL/min. The association between patients’ characteristics, plasma biomarkers and mortality was conducted by univariate logistic regression models and independent predictors of mortality were then used to create a multivariate prediction model through Cox regression. Patients with lower eGFR had a significant increase of GDF-15, CD-25 and RAGE, with higher plasma levels in non-survivors and in patients who needed ventilation. At univariate analysis, low and mid-low GDF-15 quartiles (<4.45 ng/mL) were associated with lower mortality risk, while mid-high and high quartiles (>4.45 ng/mL) were associated with higher mortality risk. Independent association between GDF-15 quartiles and mortality risk was confirmed in the Cox model and adjusted for eGFR, age, fever and dyspnea (HR 2.28, CI 1.53−3.39, p < 0.0001). The strength of the association between GDF-15 quartiles and mortality risk increased in patients with lower compared to higher eGFR (HR 2.53, CI 1.34−4.79 versus HR 1.99, CI 1.17−3.39). Our findings may suggest a further investigation of the effect of GDF-15 signaling pathway inhibition in CKD.

Validating a Proteomic Signature of Severe COVID-19

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19.

DESIGN

Prospective observational cohort study.

SETTING

Two hospitals in the United States.

PATIENTS

One hundred sixty-seven hospitalized adults with COVID-19.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006).

CONCLUSIONS

Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection

BACKGROUND

The majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.

METHODS

We performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.

FINDINGS

Within this cohort (median age 39 years, interquartile range 30-47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.

INTERPRETATION

Our findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.

FUNDING INFORMATION

The COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

COVID-19 Salivary Protein Profile: Unravelling Molecular Aspects of SARS-CoV-2 Infection

COVID-19 is the most impacting global pandemic of all time, with over 600 million infected and 6.5 million deaths worldwide, in addition to an unprecedented economic impact. Despite the many advances in scientific knowledge about the disease, much remains to be clarified about the molecular alterations induced by SARS-CoV-2 infection. In this work, we present a hybrid proteomics and in silico interactomics strategy to establish a COVID-19 salivary protein profile. Data are available via ProteomeXchange with identifier PXD036571. The differential proteome was narrowed down by the Partial Least-Squares Discriminant Analysis and enrichment analysis was performed with FunRich. In parallel, OralInt was used to determine interspecies Protein-Protein Interactions between humans and SARS-CoV-2. Five dysregulated biological processes were identified in the COVID-19 proteome profile: Apoptosis, Energy Pathways, Immune Response, Protein Metabolism and Transport. We identified 10 proteins (KLK 11, IMPA2, ANXA7, PLP2, IGLV2-11, IGHV3-43D, IGKV2-24, TMEM165, VSIG10 and PHB2) that had never been associated with SARS-CoV-2 infection, representing new evidence of the impact of COVID-19. Interactomics analysis showed viral influence on the host immune response, mainly through interaction with the degranulation of neutrophils. The virus alters the host’s energy metabolism and interferes with apoptosis mechanisms.

Growth differentiation factor 15 as an emerging novel biomarker in SARS-CoV-2 infection

BACKGROUND

Growth differentiation factor (GDF)-15 is a member of a transforming growth factor-β cytokine superfamily that regulates metabolism and is released in response to inflammation, hypoxia and tissue injury. It has evolved as one of the most potent cytokines for predicting the severity of infections and inflammatory conditions, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

AIM

To investigate the utility of GDF-15 in predicting the severity of SARS-CoV-2 infection.

METHODS

PubMed, Reference Citation Analysis, CNKI, and Goggle Scholar were explored by using related MeSH keywords and data such as the first author’s name, study duration, type and place of study, sample size and subgroups of participants if any, serum/plasma GDF- 15 level in pg/mL, area under the curve and cut-off value in receiver operating characteristic analysis, method of measurement of GDF-15, and the main conclusion were extracted.

RESULTS

In all studies, the baseline GDF-15 level was elevated in SARS-CoV-2-infected patients, and it was significantly associated with severity, hypoxemia, viral load, and worse clinical consequences. In addition, GDF-15 levels were correlated with C-reactive protein, D-dimer, ferritin and procalcitonin, and it had superior discriminatory ability to detect severity and in-hospital mortality of SARS-CoV-2 infection. Hence, GDF-15 might be used to predict the severity and prognosis of hospitalized patients with SARS-CoV-2.

CONCLUSION

Serial estimation of GDF-15 levels in hospitalized patients with SARS-CoV-2 infection appeared to have useful prognostic value and GDF-15 can be considered a clinically prominent sepsis biomarker for SARS-CoV-2 infection.

The effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19: A systematic review and meta-analysis

Background

Vaccination, one of the most important and effective ways of preventing infectious diseases, has recently been used to control the COVID-19 pandemic. The present meta-analysis study aimed to evaluate the effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19.

Methods

A systematic search was performed independently in Scopus, PubMed via Medline, ProQuest, and Google Scholar electronic databases as well as preprint servers using the keywords under study. We used random-effect models and the heterogeneity of the studies was assessed using I ² and χ² statistics. In addition, the Pooled Vaccine Effectiveness (PVE) obtained from the studies was calculated by converting based on the type of outcome.

Results

A total of 54 studies were included in this meta-analysis. The PVE against SARS-COV 2 infection were 71% [odds ratio (OR) = 0.29, 95% confidence intervals (CI): 0.23-0.36] in the first dose and 87% (OR = 0.13, 95% CI: 0.08-0.21) in the second dose. The PVE for preventing hospitalization due to COVID-19 infection was 73% (OR = 0.27, 95% CI: 0.18-0.41) in the first dose and 89% (OR = 0.11, 95% CI: 0.07-0.17) in the second dose. With regard to the type of vaccine, mRNA-1273 and combined studies in the first dose and ChAdOx1 and mRNA-1273 in the second dose had the highest effectiveness in preventing infection. Regarding the COVID-19-related mortality, PVE was 68% (HR = 0.32, 95% CI: 0.23-0.45) in the first dose and 92% (HR = 0.08, 95% CI: 0.02-0.29) in the second dose.

Conclusion

The results of this meta-analysis indicated that vaccination against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, and also their combination, was associated with a favorable effectiveness against SARS-CoV2 incidence rate, hospitalization, and mortality rate in the first and second doses in different populations. We suggest that to prevent the severe form of the disease in the future, and, in particular, in the coming epidemic picks, vaccination could be the best strategy to prevent the severe form of the disease.

Systematic review registration

PROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42021289937].

The Potential Role of Growth Differentiation Factor 15 in COVID-19: A Corollary Subjective Effect or Not?

Coronavirus disease 2019 (COVID-19) is primarily caused by various forms of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants. COVID-19 is characterized by hyperinflammation, oxidative stress, multi-organ injury (MOI)-like acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Different biomarkers are used in the assessment of COVID-19 severity including D-dimer, ferritin, lactate dehydrogenase (LDH), and hypoxia-inducible factor (HIF). Interestingly, growth differentiation factor 15 (GDF15) has recently become a potential biomarker correlated with the COVID-19 severity. Thus, this critical review aimed to determine the critical association between GDF15 and COVID-19. The perfect function of GDF15 remains not well-recognized; nevertheless, it plays a vital role in controlling cell growth, apoptosis and inflammatory activation. Furthermore, GDF15 may act as anti-inflammatory and pro-inflammatory signaling in diverse cardiovascular complications. Furthermore, the release of GDF15 is activated by various growth factors and cytokines including macrophage colony-stimulating factor (M-CSF), angiotensin II (AngII) and p53. Therefore, higher expression of GDF15 in COVID-19 might a compensatory mechanism to stabilize and counteract dysregulated inflammatory reactions. In conclusion, GDF15 is an anti-inflammatory cytokine that could be associated with the COVID-19 severity. Increased GDF15 could be a compensatory mechanism against hyperinflammation and exaggerated immune response in the COVID-19. Experimental, preclinical and large-scale clinical studies are warranted in this regard.

Prognostic Value of Catestatin in Severe COVID-19: An ICU-Based Study

Catestatin is a pleiotropic peptide with a wide range of immunomodulatory effects. Considering that patients with a severe COVID-19 infection have a major immunological dysregulation, the aim of this study was to evaluate catestatin levels in patients with COVID-19 treated in the intensive care unit (ICU) and to compare them between the fatal and non-fatal outcomes. The study included 152 patients with severe COVID-19, out of which 105 had a non-fatal outcome and 47 had a fatal outcome. Serum catestatin levels were estimated by an enzyme-linked immunosorbent assay in a commercially available diagnostic kit. The results show that catestatin levels were significantly lower in the fatal group compared to the non-fatal group (16.6 ± 7.8 vs. 23.2 ± 9.2 ng/mL; p < 0.001). Furthermore, there was a significant positive correlation between serum catestatin levels and vitamin D levels (r = 0.338; p < 0.001) while there was also a significant positive correlation between serum catestatin levels and growth differentiation factor-15 (GDF-15) levels (r = −0.345; p < 0.001). Furthermore, multivariate logistic regression showed that catestatin, GDF-15 and leukocyte count were significant predictors for COVID-19 survival. These findings imply that catestatin could be playing a major immunomodulatory role in the complex pathophysiology of the COVID-19 infection and that serum catestatin could also be a predictor of a poor COVID-19 outcome.

Immune responses to SARS-CoV-2 in dialysis and kidney transplantation

Despite progressive improvements in the management of patients with coronavirus disease 2019 (COVID-19), individuals with end-stage kidney disease (ESKD) are still at high risk of infection-related complications. Although the risk of infection in these patients is comparable to that of the general population, their lower rate of response to vaccination is a matter of concern. When prevention strategies fail, infection is often severe. Comorbidities affecting patients on maintenance dialysis and kidney transplant recipients clearly account for the increased risk of severe COVID-19, while the role of uremia and chronic immunosuppression is less clear. Immune monitoring studies have identified differences in the innate and adaptive immune response against the virus that could contribute to the increased disease severity. In particular, individuals on dialysis show signs of T cell exhaustion that may impair antiviral response. Similar to kidney transplant recipients, antibody production in these patients occurs, but with delayed kinetics compared with the general population, leaving them more exposed to viral expansion during the early phases of infection. Overall, unique features of the immune response during COVID-19 in individuals with ESKD may occur with severe comorbidities affecting these individuals in explaining their poor outcomes.

Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19

The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.

Structural basis of human IL-18 sequestration by the decoy receptor IL-18 binding protein (IL-18BP) in inflammation and tumor immunity

Human Interleukin-18 (IL-18) is an omnipresent proinflammatory cytokine of the IL-1 family with central roles in autoimmune and inflammatory diseases and serves as a staple biomarker in the evaluation of inflammation in physiology and disease, including the inflammatory phase of COVID-19. The sequestration of IL-18 by its soluble decoy receptor IL-18-Binding Protein (IL-18BP) is critical to the regulation of IL-18 activity. Since an imbalance in expression and circulating levels of IL-18 is associated with disease, structural insights into how IL-18BP outcompetes binding of IL-18 by its cognate cell-surface receptors are highly desirable; however, the structure of human IL-18BP in complex with IL-18 has been elusive. Here, we elucidate the sequestration mechanism of human IL-18 mediated by IL-18BP based on the crystal structure of the IL-18:IL-18BP complex. These detailed structural snapshots reveal the interaction landscape leading to the ultra-high affinity of IL-18BP toward IL-18 and identify substantial differences with respect to previously characterized complexes of IL-18 with IL-18BP of viral origin. Furthermore, our structure captured a fortuitous higher-order assembly between IL-18 and IL-18BP coordinated by a disulfide-bond distal to the binding surface connecting IL-18 and IL-18BP molecules from different complexes, resulting in a novel tetramer with 2:2 stoichiometry. This tetrapartite assembly was found to restrain IL-18 activity more effectively than the canonical 1:1 complex. Collectively, our findings provide a framework for innovative, structure-driven therapeutic strategies and further functional interrogation of IL-18 in physiology and disease.

Coping With Stress: The Mitokine GDF-15 as a Biomarker of COVID-19 Severity

Growth differentiation factor 15 (GDF-15) is a transforming growth factor (TGF)-β superfamily cytokine that plays a central role in metabolism regulation. Produced in response to mitochondrial stress, tissue damage or hypoxia, this cytokine has emerged as one of the strongest predictors of disease severity during inflammatory conditions, cancers and infections. Reports suggest that GDF-15 plays a tissue protective role via sympathetic and metabolic adaptation in the context of mitochondrial damage, although the exact mechanisms involved remain uncertain. In this review, we discuss the emergence of GDF-15 as a distinctive marker of viral infection severity, especially in the context of COVID-19. We will critically review the role of GDF-15 as an inflammation-induced mediator of disease tolerance, through metabolic and immune reprogramming. Finally, we discuss potential mechanisms of GDF-15 elevation during COVID-19 cytokine storm and its limitations. Altogether, this cytokine seems to be involved in disease tolerance to viral infections including SARS-CoV-2, paving the way for novel therapeutic interventions.

SARS-CoV-2 infection in dialysis and kidney transplant patients: immunological and serological response

Background

Dialysis and kidney transplant patients with moderate-severe COVID-19 have a high mortality rate, around 30%, that is similar in the two populations, despite differences in their baseline characteristics. In these groups, the immunology of the disease has been poorly explored.

Methods

Thirty-two patients on dialysis or with kidney transplant and SARS-CoV-2 infection requiring hospitalization (COV group) were included in our study. Lymphocyte subsets, dendritic cell (DC) counts and monocyte activation were studied. SARS-CoV-2 anti-spike/anti-nucleocapsid were monitored, and baseline cytokines and chemokines were measured in 10 patients.

Results

The COV group, compared to healthy subjects and uninfected dialysis/kidney transplant controls, showed lower numbers of CD4 + and CD8 + T cells, Natural-Killer (NK), B cells, plasmacytoid and myeloid DCs, while the proportion of terminally differentiated B-cells was increased. IL6, IL10, IFN-α and chemokines involved in monocyte and neutrophil recruitment were higher in the COV group, compared to uninfected dialysis/kidney transplant controls.

Patients with severe disease had lower CD4 + , CD8 + and B-cell counts and lower monocyte HLA-DR expression. Of note, when comparing dialysis and kidney transplant patients with COVID-19, the latter group presented lower NK and pDC counts and monocyte HLA-DR expression. Up to 60 days after symptom onset, kidney transplant recipients showed lower levels of anti-spike antibodies compared to dialysis patients.

Conclusions

During SARS-CoV-2 infection, dialysis and kidney transplant patients manifest immunophenotype abnormalities; these are similar in the two groups, however kidney transplant recipients show more profound alterations of the innate immune system and lower anti-spike antibody response.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-021-01214-8.

The unfavorable clinical outcome of COVID-19 in smokers is mediated by H3K4me3, H3K9me3 and H3K27me3 histone marks

Smoking could predispose individuals to a more severe COVID-19 by upregulating a particular gene known as mdig, which is mediated through a number of well-known histone modifications. Smoking might regulate the transcription-activating H3K4me3 mark, along with the transcription-repressing H3K9me3 and H3K27me3 marks, in a way to favor SARS-CoV-2 entry by enhancing the expression of ACE2, NRP1 and NRP2, AT1R, CTSD and CTSL, PGE2 receptors 2-4, SLC6A20 and IL-6, all of which interact either directly or indirectly with important receptors, facilitating viral entry in COVID-19.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVD-19 respiratory disease

Some patients hospitalized with acute COVID-19 suffer respiratory symptoms that persist for many months. We delineated the immune-proteomic landscape in the airways and peripheral blood of healthy controls and post-COVID-19 patients 3 to 6 months after hospital discharge. Post-COVID-19 patients showed abnormal airway (but not plasma) proteomes, with an elevated concentration of proteins associated with apoptosis, tissue repair, and epithelial injury versus healthy individuals. Increased numbers of cytotoxic lymphocytes were observed in individuals with greater airway dysfunction, while increased B cell numbers and altered monocyte subsets were associated with more widespread lung abnormalities. A one-year follow-up of some post-COVID-19 patients indicated that these abnormalities resolved over time. In summary, COVID-19 causes a prolonged change to the airway immune landscape in those with persistent lung disease, with evidence of cell death and tissue repair linked to the ongoing activation of cytotoxic T cells.

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Post-COVID-19 airways, but not blood, show immune and proteomic changes

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Different post-COVID-19 lung abnormalities relate to distinct immunological features

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Increased BAL cytotoxic T cells are linked to epithelial damage and airway disease

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BAL myeloid and B cell numbers correlate with the degree of lung CT abnormality

Interleukin-18 binding protein in infants and children hospitalized with pneumonia in low-resource settings

Pneumonia is the leading infectious cause of death in children, with especially high mortality in low- and middle-income countries. Interleukin-18 binding protein (IL-18BP) is a natural antagonist of the pro-inflammatory cytokine interleukin-18 and is elevated in numerous autoimmune conditions and infectious diseases. We conducted a prospective cohort study to determine the association between admission IL-18BP levels and clinical severity among children admitted to two hospitals in Uganda for hypoxemic pneumonia. A total of 42 children (median age of 1.2 years) were included. IL-18BP levels were higher in patients with respiratory distress, including chest indrawing (median 15 ng/mL (IQR 9.8-18) versus 4.5 ng/mL (IQR 3.8-11) without chest indrawing, P = 0.0064) and nasal flaring (median 15 ng/mL (IQR 9.7-19) versus 11 ng/mL (IQR 5.4-14) without nasal flaring, P = 0.034). IL-18BP levels were positively correlated with the composite clinical severity score, Pediatric Early Death Index for Africa (PEDIA-e, ρ = 0.46, P = 0.0020). Patients with IL-18BP > 14 ng/mL also had slower recovery times, including time to sit (median 0.69 days (IQR 0.25-1) versus 0.15 days (IQR 0.076-0.36) with IL-18BP < 14 ng/mL, P = 0.036) and time to fever resolution (median 0.63 days (IQR 0.16-2) versus 0.13 days (IQR 0-0.42), P = 0.016). In summary, higher IL-18BP levels were associated with increased disease severity and prolonged recovery times in Ugandan children with pneumonia.

Can the SARS-CoV-2 Spike Protein Bind Integrins Independent of the RGD Sequence?

The RGD motif in the Severe Acute Syndrome Coronavirus 2 (SARS-CoV-2) spike protein has been predicted to bind RGD-recognizing integrins. Recent studies have shown that the spike protein does, indeed, interact with αVβ3 and α5β1 integrins, both of which bind to RGD-containing ligands. However, computational studies have suggested that binding between the spike RGD motif and integrins is not favourable, even when unfolding occurs after conformational changes induced by binding to the canonical host entry receptor, angiotensin-converting enzyme 2 (ACE2). Furthermore, non-RGD-binding integrins, such as αx, have been suggested to interact with the SARS-CoV-2 spike protein. Other viral pathogens, such as rotaviruses, have been recorded to bind integrins in an RGD-independent manner to initiate host cell entry. Thus, in order to consider the potential for the SARS-CoV-2 spike protein to bind integrins independent of the RGD sequence, we investigate several factors related to the involvement of integrins in SARS-CoV-2 infection. First, we review changes in integrin expression during SARS-CoV-2 infection to identify which integrins might be of interest. Then, all known non-RGD integrin-binding motifs are collected and mapped to the spike protein receptor-binding domain and analyzed for their 3D availability. Several integrin-binding motifs are shown to exhibit high sequence similarity with solvent accessible regions of the spike receptor-binding domain. Comparisons of these motifs with other betacoronavirus spike proteins, such as SARS-CoV and RaTG13, reveal that some have recently evolved while others are more conserved throughout phylogenetically similar betacoronaviruses. Interestingly, all of the potential integrin-binding motifs, including the RGD sequence, are conserved in one of the known pangolin coronavirus strains. Of note, the most recently recorded mutations in the spike protein receptor-binding domain were found outside of the putative integrin-binding sequences, although several mutations formed inside and close to one motif, in particular, may potentially enhance binding. These data suggest that the SARS-CoV-2 spike protein may interact with integrins independent of the RGD sequence and may help further explain how SARS-CoV-2 and other viruses can evolve to bind to integrins.

SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care

Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22-2.77] adjusted for age and sex). RNAemia is comparable in performance to the best protein predictors. Mannose binding lectin 2 and pentraxin-3 (PTX3), two activators of the complement pathway of the innate immune system, are positively associated with mortality. Machine learning identified 'Age, RNAemia' and 'Age, PTX3' as the best binary signatures associated with 28-day ICU mortality. In longitudinal comparisons, COVID-19 ICU patients have a distinct proteomic trajectory associated with mortality, with recovery of many liver-derived proteins indicating survival. Finally, proteins of the complement system and galectin-3-binding protein (LGALS3BP) are identified as interaction partners of SARS-CoV-2 spike glycoprotein. LGALS3BP overexpression inhibits spike-pseudoparticle uptake and spike-induced cell-cell fusion in vitro.

Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions

Mechanisms underlying severe coronavirus disease 2019 (COVID-19) disease remain poorly understood. We analyze several thousand plasma proteins longitudinally in 306 COVID-19 patients and 78 symptomatic controls, uncovering immune and non-immune proteins linked to COVID-19. Deconvolution of our plasma proteome data using published scRNA-seq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who died to severely ill survivors identifies dynamic immune-cell-derived and tissue-associated proteins associated with survival, including exocrine pancreatic proteases. Using derived tissue-specific and cell-type-specific intracellular death signatures, cellular angiotensin-converting enzyme 2 (ACE2) expression, and our data, we infer whether organ damage resulted from direct or indirect effects of infection. We propose a model in which interactions among myeloid, epithelial, and T cells drive tissue damage. These datasets provide important insights and a rich resource for analysis of mechanisms of severe COVID-19 disease.

Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19

Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at the FAS locus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.