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.

Integration of protein context improves protein-based COVID-19 patient stratification.

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 four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 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 (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), 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.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19.

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection.

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Clinical evaluation of a rapid colloidal gold immunochromatography assay for SARS-Cov-2 IgM/IgG.

BACKGROUND: Since December 2019, there had been an outbreak of COVID-19 in Wuhan, China. At present, diagnosis COVID-19 were based on real-time RT-PCR, which have to be performed in biosafe laboratory and is unsatisfactory for suspect case screening. Therefore, there is an urgent need for rapid diagnostic test for COVID-19. OBJECTIVE: To evaluate the diagnostic performance and clinical utility of the colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body detection in suspected COVID-19 cases. METHODS: In the prospective cohort, 150 patients with fever or respiratory symptoms were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang province, China, between January 20 to February 2, 2020. All patients were tested by the colloidal gold immunochromatography assay for COVID-19. At least two samples of each patient were collected for RT-PCR assay analysis, and the PCR results were performed as the reference standard of diagnosis. Meanwhile 26 heathy blood donor were recruited. The sensitivity and specificity of the immunochromatography assay test were evaluated. Subgroup analysis were performed with respect to age, sex, period from symptom onset and clinical severity. RESULTS: The immunochromatography assay test had 69 positive result in the 97 PCR-positive cases, achieving sensitivity 71.1% [95% CI 0.609-0.797], and had 2 positive result in the 53 PCR-negative cases, achieving specificity 96.2% [95% CI 0.859-0.993]. In 26 healthy donor blood samples, the immunochromatography assay had 0 positive result. In subgroup analysis, the sensitivity was significantly higher in patients with symptoms more than 14 days 95.2% [95% CI 0.741-0.998] and patients with severe clinical condition 86.0% [95% CI 0.640-0.970]. CONCLUSIONS: The colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body had 71.1% sensitivity and 96.2% specificity in this population, showing the potential for a useful rapid diagnosis test for COVID-19. Further investigations should be done to evaluate this assay in variety of clinical settings and populations.