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.

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

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.

Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course (preprint)

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we present a longitudinal sera proteomic resource for 37 COVID-19 patients over nine weeks, in which 2700 proteins were quantified with high quality. Remarkably, we found that during the first three weeks since disease onset, while clinical symptoms and outcome were indistinguishable, patients with prolonged disease course displayed characteristic immunological responses including enhanced Natural Killer (NK) cell-mediated innate immunity and regulatory T cell-mediated immunosuppression. We further showed that it is possible to predict the length of disease course using machine learning based on blood protein levels during the first three weeks. Validation in an independent cohort achieved an accuracy of 82%. In summary, this study presents a rich serum proteomic resource to understand host responses in COVID-19 patients and identifies characteristic Treg-mediated immunosuppression in LC patients, nominating new therapeutic target and diagnosis strategy.

Gut microbiota may underlie the predisposition of healthy individuals to COVID-19 (preprint)

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. Here we describe constructing a proteomic risk score based on 20 blood proteomic biomarkers which predict the progression to severe COVID-19. 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 above proteomic biomarkers among 301 individuals using a machine learning model, and that these gut microbiota features are highly correlated with proinflammatory cytokines in another set of 366 individuals. Fecal metabolomic analysis suggested potential amino acid-related pathways linking gut microbiota to inflammation. This study suggests that gut microbiota may underlie the predisposition of normal individuals to severe COVID-19.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera (preprint)

Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.Funding: This work is supported by grants from Westlake Special Program for COVID19 (2020), and 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). Conflict of Interest: The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera (preprint)

Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.