Longitudinal proteomic investigation of COVID-19 vaccination (preprint)

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac) at a 28-day interval. Using TMT-based proteomics, we identified 1715 serum and 7342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

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

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.

Proteomics Investigation of Diverse Serological Patterns in COVID-19 (preprint)

Serum antibodies IgM and IgG are elevated during COVID-19 to defend against viral attack. 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 two-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.

Pulmonary and Renal Long COVID at Two-year Revisit (preprint)

More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.

Integration of protein context improves protein- based COVID-19 patient stratification (preprint)

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating the four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods: A SWATH-based proteomic data set of 54 sera samples from 40 COVID-19 patients was employed as the training cohort. Results: Machine learning prioritized two complexes, one stoichiometric ratio, five pathways, twelve proteins and five network degrees. A model based on these 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP complex, the stoichiometric ratio of SAA2/ YLPM1, and the network extent of SIRT7 and A2M were highlighted in this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort and an independent SWATH-based proteomic data set from Germany, reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Feasibility to Reduce Swab RT-PCR Tests by Serum Proteomics for COVID-19 Disease Course Monitoring (preprint)

The diagnosis and disease course monitoring of COVID-19 are mainly based on RT-PCR analysis of RNAs extracted from pharyngeal or nasopharyngeal swabs with potential live virus, posing a high risk to medical practitioners. Here, we investigated the feasibility of applying serum proteomics to classify COVID-19 patients in the nucleic acid positive (NCP) and negative (NCN) stages. We analyzed the proteome of 320 inactivated serum samples from 144 COVID-19 patients, and 45 controls and shortlisted 42 regulated proteins in the severe group and 12 regulated proteins in the non-severe group. Together with several key clinical indexes including days after symptom onset, platelet counts and magnesium, we developed machine learning models to classify NCP and NCN with an AUC of 0.94 for the severe cases and 0.89 for the non-severe cases. This study suggests the feasibility of utilizing quantitative serum proteomics for NCP-NCN classification.Funding: This work was supported by grants from the National Key R&D Program of China(No. 2020YFE0202200), National Natural Science Foundation of China (81672086), Zhejiang Province Analysis Test Project (2018C37032), the National Natural Science Foundation of China (81972492, 21904107), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Zhejiang Medical and Health Science and Technology Plan (2021KY394), Hangzhou Agriculture andSociety Advancement Program (20190101A04), and Westlake Education Foundation, Tencent Foundation.Conflict of Interest: Tiannan Guo is shareholder of Westlake Omics Inc. W.G. and N.X. are employees of Westlake Omics Inc. The remaining authors declare no competing interests.Ethical Approval: This study has been approved by both the Ethical/Institutional Review Boards of Taizhou Hospital and Westlake University. Informed contents from patients were waived by the boards.

The landscape of human brain immune response in patients with severe COVID-19 (preprint)

In coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the relationship between brain tropism, neuroinflammation and host immune response has not been well characterized. We analyzed 68,557 single-nucleus transcriptomes from three brain regions (dorsolateral prefrontal cortex, medulla oblongata and choroid plexus) and identified an increased proportion of stromal cells and monocytes in the choroid plexus of COVID-19 patients. Differential gene expression, pseudo-temporal trajectory and gene regulatory network analyses revealed microglial transcriptome perturbations, mediating a range of biological processes, including cellular activation, mobility and phagocytosis. Quantification of viral spike S1 protein and SARS-CoV-2 transcripts did not support the notion of brain tropism. Overall, our findings suggest extensive neuroinflammation in patients with acute COVID-19.

Immunological and cardio-vascular pathologies associated with SARS-CoV-2 infection in golden syrian hamster (preprint)

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection in golden Syrian hamster (GSH) causes lungs pathology and resembles to human corona virus disease (Covid-19). Extra-pulmonary pathologies and immunological parameters of SARS-CoV-2 infection remained undefined in GSH. Using in silico modelling, we identified the similarities between human and hamster angiotensin-converting enzyme-2 (ACE-2), neuropilin-1 (NRP-1) that bind to receptor-binding domain (RBD) and S1 fragment of spike protein of SARS-CoV-2. SARS-CoV-2 infection led to lung pathologies, and cardiovascular complications (CVC) marked by interstitial coronary fibrosis and acute inflammatory response. Serum lipidomic and metabolomic profile of SARS-CoV-2-infected GSH revealed changes in serum triglycerides (TG) and low-density lipoprotein (LDL), and alterations in metabolites that correlated with Covid19. Together, we propose GSH as an animal model to study SARS-CoV-2 infection and its therapy associated with pulmonary and extra-pulmonary pathologies.

Hypoxia reduces cell attachment of SARS-CoV-2 spike protein by modulating the expression of ACE2 and heparan sulfate (preprint)

A main clinical parameter of Covid-19 pathophysiology is hypoxia. Here we show that hypoxia decreases the attachment of the receptor binding domain (RBD) and the S1 subunit (S1) of the spike protein to epithelial cells. In Vero E6 cells, hypoxia reduces the protein levels of ACE2, which might in part explain the observed reduction of the infection rate. However, hypoxia also inhibits the binding of the spike to human lung epithelial cells lacking ACE2 expression, indicating that hypoxia modulates the expression of additional binding partners of SARS-CoV-2. We show that hypoxia also decreases the total cell surface levels of heparan sulfate, a known attachment receptor of SARS-CoV-2, by reducing the expression of syndecan-1 and syndecan3, the main proteoglycans containing heparan sulfate. Our study indicates that hypoxia acts to prevent SARS-CoV-2 infection, suggesting that the hypoxia signaling pathway might offer therapeutic opportunities for the treatment of Covid-19.

Proteomic and Metabolomic Investigation of COVID-19 Patients with Elevated Serum Lactate Dehydrogenase (preprint)

Serum lactate dehydrogenase (LDH) has been established as a prognostic indicator given its differential expression in COVID-19 patients. However, the molecular mechanisms underneath remain poorly understood. In this study, 144 COVID-19 patients were enrolled to monitor the clinical and laboratory parameters over three weeks. Serum lactate dehydrogenase (LDH) was shown elevated in the COVID-19 patients on admission and declined during the convalescence period, and its ability to classify patient severity outperformed other clinical indicators. A threshold of 247 U/L serum LDH on admission was determined for severity prognosis. Next, we classified a subset of 14 patients into high- and low-risk groups based on serum LDH expression and compared their quantitative serum proteomic and metabolomic differences. The results found COVID-19 patients with high serum LDH exhibited differentially expressed blood coagulation and immune responses including acute inflammatory responses, platelet degranulation, complement cascade, as well as multiple different metabolic responses including lipid metabolism, protein ubiquitination and pyruvate fermentation. Specifically, activation of hypoxia responses was highlighted in patients with high LDH expressions. Taken together, our data showed that serum LDH levels is associated COVID-19 severity, and that elevated serum LDH might be consequences of hypoxia and tissue injuries induced by inflammation.

Immunogenicity and Protective Efficacy of an Intranasal Live-attenuated Vaccine Against SARS-CoV-2 in Preclinical Animal Models (preprint)

The global deployment of an effective and safe vaccine is currently a public health priority to curtail the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we evaluated a Newcastle disease virus (NDV)-based intranasal vectored-vaccine in mice and hamsters for its immunogenicity, safety and protective efficacy in challenge studies with SARS-CoV-2. The recombinant (r)NDV-S vaccine expressing spike (S) protein of SARS-CoV-2 administrated via intranasal route in mice induced high levels of SARS-CoV-2-specific neutralizing immunoglobulin A (IgA) and IgG2a antibodies and T cell-mediated immunity. Hamsters vaccinated with two doses of vaccine showed complete protection from clinical disease including lung infection, inflammation, and pathological lesions after SARS-CoV-2 challenge. Importantly, a single or double dose of intranasal rNDV-S vaccine completely blocked SARS-CoV-2 shedding in nasal turbinate and lungs within 4 days of vaccine administration in hamsters. Taken together, intranasal administration of rNDV-S has the potential to control infection at the site of inoculation, which should prevent both the clinical disease and transmission to halt the spread of the COVID-19 pandemic.

Molecular Dynamics Analysis of a Flexible Loop at the Binding Interface of the SARS-CoV-2 Spike Protein Receptor-Binding Domain (preprint)

Since the identification of the SARS-CoV-2 virus as the causative agent of the current COVID-19 pandemic, considerable effort has been spent characterizing the interaction between the Spike protein receptor-binding domain (RBD) and the human angiotensin converting enzyme 2 (ACE2) receptor. This has provided a detailed picture of the end point structure of the RBD-ACE2 binding event, but what remains to be elucidated is the conformation and dynamics of the RBD prior to its interaction with ACE2. In this work we utilize molecular dynamics simulations to probe the flexibility and conformational ensemble of the unbound state of the receptor-binding domain from SARS-CoV-2 and SARS-CoV. We have found that the unbound RBD has a localized region of dynamic flexibility in Loop 3 and that mutations identified during the COVID-19 pandemic in Loop 3 do not affect this flexibility. We use a loop-modeling protocol to generate and simulate novel conformations of the CoV2-RBD Loop 3 region that sample conformational space beyond the ACE2 bound crystal structure. This has allowed for the identification of interesting substates of the unbound RBD that are lower energy than the ACE2-bound conformation, and that block key residues along the ACE2 binding interface. These novel unbound substates may represent new targets for therapeutic design.

Modular basis for potent SARS-CoV-2 neutralization by a prevalent VH1-2-derived antibody class (preprint)

Antibodies with heavy chains that derive from the VH1-2 gene constitute some of the most potent SARS-CoV-2-neutralizing antibodies yet identified. To provide insight into whether these genetic similarities inform common modes of recognition, we determined structures of the SARS-CoV-2 spike in complex with three VH1-2-derived antibodies: 2-15, 2-43, and H4. All three utilized VH1-2-encoded motifs to recognize the receptor-binding domain (RBD), with heavy chain N53I enhancing binding and light chain tyrosines recognizing F486RBD. Despite these similarities, class members bound both RBD up and down conformations of the spike, with a subset of antibodies utilizing elongated CDRH3s to recognize glycan N343 on a neighboring RBD - a quaternary interaction accommodated by an increase in RBD separation of up to 12 angstrom. The VH1-2-antibody class thus utilizes modular recognition encoded by modular genetic elements to effect potent neutralization, with VH-gene component specifying recognition of RBD and CDRH3 component specifying quaternary interactions.

Eleven Routine Clinical Features Predict COVID-19 Severity Uncovered by Machine Learning of Longitudinal Measurements (preprint)

Background: Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. In this study, we aim to establish a model for COVID-19 severity prediction and depict dynamic changes of key clinical features over 7 weeks.Methods: In our retrospective study, a total of 841 patients have been screened with the SARS-CoV-2 nucleic acid test, of which 144 patients were virus RNA (COVID-19) positive, resulting in a data matrix containing of 3,065 readings for 124 types of measurements from 17 categories. We built a support vector machine model assisted with genetic algorithm for feature selection based on the longitudinal measurement. 25 patients as a test cohort were included from an independent hospital.Findings: A panel of 11 routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving an accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved an accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. This study presents a practical model for timely severity prediction for COVID-19, which is freely available at a webserver https://guomics.shinyapps.io/covidAI/.Interpretation: The model provided a classifier composed of 11 routine clinical features which are widely available during COVID-19 management which could predict the severity and may guide the medical care of COVID-19 patients.Funding: This work is supported by grants from Tencent Foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).Declaration of Interests: NAEthics Approval Statement: This study was approved by the Medical Ethics Committee of Taizhou Hospital, Shaoxing People’s Hospital and Westlake University, Zhejiang province of China, and informed consent was obtained from each enrolled subject.

SARS-CoV-2 ORF9c Is a Membrane-Associated Protein that Suppresses Antiviral Responses in Cells (preprint)

Disrupted antiviral immune responses are associated with severe COVID-19, the disease caused by SAR-CoV-2. Here, we show that the 73-amino-acid protein encoded by ORF9c of the viral genome contains a putative transmembrane domain, interacts with membrane proteins in multiple cellular compartments, and impairs antiviral processes in a lung epithelial cell line. Proteomic, interactome, and transcriptomic analyses, combined with bioinformatic analysis, revealed that expression of only this highly unstable small viral protein impaired interferon signaling, antigen presentation, and complement signaling, while inducing IL-6 signaling. Furthermore, we showed that interfering with ORF9c degradation by either proteasome inhibition or inhibition of the ATPase VCP blunted the effects of ORF9c. Our study indicated that ORF9c enables immune evasion and coordinates cellular changes essential for the SARS-CoV-2 life cycle. One-sentence summarySARS-CoV-2 ORF9c is the first human coronavirus protein localized to membrane, suppressing antiviral response, resembling full viral infection.

Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis (preprint)

There is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

Multi-organ Proteomic Landscape of COVID-19 Autopsies (preprint)

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report an in-depth multi-organ proteomic landscape of COVID-19 patient autopsy samples. By integrative analysis of proteomes of seven organs, namely lung, spleen, liver, heart, kidney, thyroid and testis, we characterized 11,394 proteins, in which 5336 were perturbed in COVID-19 patients compared to controls. Our data showed that CTSL, rather than ACE2, was significantly upregulated in the lung from COVID-19 patients. Dysregulation of protein translation, glucose metabolism, fatty acid metabolism was detected in multiple organs. Our data suggested upon SARS-CoV-2 infection, hyperinflammation might be triggered which in turn induces damage of gas exchange barrier in the lung, leading to hypoxia, angiogenesis, coagulation and fibrosis in the lung, kidney, spleen, liver, heart and thyroid. Evidence for testicular injuries included reduced Leydig cells, suppressed cholesterol biosynthesis and sperm mobility. In summary, this study depicts the multi-organ proteomic landscape of COVID-19 autopsies, and uncovered dysregulated proteins and biological processes, offering novel therapeutic clues. HIGHLIGHTSO_LICharacterization of 5336 regulated proteins out of 11,394 quantified proteins in the lung, spleen, liver, kidney, heart, thyroid and testis autopsies from 19 patients died from COVID-19. C_LIO_LICTSL, rather than ACE2, was significantly upregulated in the lung from COVID-19 patients. C_LIO_LIEvidence for suppression of glucose metabolism in the spleen, liver and kidney; suppression of fatty acid metabolism in the kidney; enhanced fatty acid metabolism in the lung, spleen, liver, heart and thyroid from COVID-19 patients; enhanced protein translation initiation in the lung, liver, renal medulla and thyroid. C_LIO_LITentative model for multi-organ injuries in patients died from COVID-19: SARS-CoV-2 infection triggers hyperinflammatory which in turn induces damage of gas exchange barrier in the lung, leading to hypoxia, angiogenesis, coagulation and fibrosis in the lung, kidney, spleen, liver, heart, kidney and thyroid. C_LIO_LITesticular injuries in COVID-19 patients included reduced Leydig cells, suppressed cholesterol biosynthesis and sperm mobility. C_LI

NVX-CoV2373 vaccine protects cynomolgus macaque upper and lower airways against SARS-CoV-2 challenge (preprint)

There is an urgent need for a safe and protective vaccine to control the global spread of SARS-CoV-2 and prevent COVID-19. Here, we report the immunogenicity and protective efficacy of a SARS-CoV-2 subunit vaccine (NVX-CoV2373) produced from the full-length SARS-CoV-2 spike (S) glycoprotein stabilized in the prefusion conformation. Cynomolgus macaques (Macaca fascicularis) immunized with NVX-CoV2373 and the saponin-based Matrix-M adjuvant induced anti-S antibody that was neutralizing and blocked binding to the human angiotensin-converting enzyme 2 (hACE2) receptor. Following intranasal and intratracheal challenge with SARS-CoV-2, immunized macaques were protected against upper and lower infection and pulmonary disease. These results support ongoing phase 1/2 clinical studies of the safety and immunogenicity of NVX-CoV2327 vaccine (NCT04368988). HighlightsO_LIFull-length SARS-CoV-2 prefusion spike with Matrix-M1 (NVX-CoV2373) vaccine. C_LIO_LIInduced hACE2 receptor blocking and neutralizing antibodies in macaques. C_LIO_LIVaccine protected against SARS-CoV-2 replication in the nose and lungs. C_LIO_LIAbsence of pulmonary pathology in NVX-CoV2373 vaccinated macaques. C_LI

Eleven Routine Clinical Features Predict COVID-19 Severity (preprint)

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

Gut Microbiota May Underlie the Predisposition of Healthy Individuals to COVID-19-Sensitive Proteomic Biomarkers (preprint)

Background: The COVID-19 pandemic is spreading globally with high disparity in the susceptibility of the disease severity. Identification of the key underlying factors for this disparity is highly warranted. Results: Here we describe constructing a proteomic risk score (PRS) based on 20 blood proteomic biomarkers which related to the progression to severe COVID-19. Among COVID-19 patients, per 10% increment in the PRS was associated with a 57% higher risk of progressing to clinically severe phase (RR=1.57; 95% CI, 1.35-1.82). We demonstrate that in our own cohort of 990 individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discovered that a core set of gut microbiota could accurately predict the blood proteomic biomarkers of COVID-19 using a machine learning model. The core OTU-predicted PRS had a significant correlation with actual PRS both cross-sectionally (n=132, p<0.001) and prospectively (n=169, p<0.05). Most of the core OTUs were highly correlated with proinflammatory cytokines. Fecal metabolomics analysis suggested potential amino acid-related pathways linking the above core gut microbiota to inflammation.Conclusions: Our study suggests that gut microbiota may underlie the predisposition of healthy individuals to COVID-19-sensitive proteomic biomarkers.