Inhibition of the mitochondrial pyruvate carrier simultaneously mitigates hyperinflammation and hyperglycemia in COVID-19.

The relationship between diabetes and coronavirus disease 2019 (COVID-19) is bidirectional: Although individuals with diabetes and high blood glucose (hyperglycemia) are predisposed to severe COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can also cause hyperglycemia and exacerbate underlying metabolic syndrome. Therefore, interventions capable of breaking the network of SARS-CoV-2 infection, hyperglycemia, and hyperinflammation, all factors that drive COVID-19 pathophysiology, are urgently needed. Here, we show that genetic ablation or pharmacological inhibition of mitochondrial pyruvate carrier (MPC) attenuates severe disease after influenza or SARS-CoV-2 pneumonia. MPC inhibition using a second-generation insulin sensitizer, MSDC-0602K (MSDC), dampened pulmonary inflammation and promoted lung recovery while concurrently reducing blood glucose levels and hyperlipidemia after viral pneumonia in obese mice. Mechanistically, MPC inhibition enhanced mitochondrial fitness and destabilized hypoxia-inducible factor-1α, leading to dampened virus-induced inflammatory responses in both murine and human lung macrophages. We further showed that MSDC enhanced responses to nirmatrelvir (the antiviral component of Paxlovid) to provide high levels of protection against severe host disease development after SARS-CoV-2 infection and suppressed cellular inflammation in human COVID-19 lung autopsies, demonstrating its translational potential for treating severe COVID-19. Collectively, we uncover a metabolic pathway that simultaneously modulates pulmonary inflammation, tissue recovery, and host metabolic health, presenting a synergistic therapeutic strategy to treat severe COVID-19, particularly in patients with underlying metabolic disease.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Atypical presentations of fungal osteomyelitis during post COVID-19 outbreak - Case series.

Introduction: Mucormycosis and Aspergillosis are opportunistic fungal infections causing significant morbidity and mortality. Post the outbreak of COVID-19, these fungal osteomyelitis have seen a global rise with few atypical presentations noted. Case report: Current case series reports three such atypical presentations of fungal osteomyelitis including mandibular fungal osteomyelitis in two patients, fungal osteomyelitis mimicking space infection in a middle aged male, and suspected mixed fungal osteomyelitis involving maxillary sinus. Aggressive surgical debridement was indicated along with institution of antifungal therapy (Liposomal Amphotericin B, and Posaconazole). The fungal osteomyelitis was successfully treated with surgical and medical management with no recurrence. Discussion: The injudicious use of corticosteroids in COVID-19 patients along with their immunocompromised status increases their susceptibility to opportunistic fungal osteomyelitis. Prompt and aggressive surgical intervention along with antifungal therapy is important after diagnosing fungal osteomyelitis, as a delay could increase the mortality rate considerably.

Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.

Changing paradigms in anatomy teaching-learning during a pandemic: Modification of curricular delivery based on student perspectives.

Objectives: This study is designed to explore students' perception of major educational changes and challenges encountered during the pandemic, as well as the effectiveness of Anatomy online teaching. Feedback from the students will be utilized to reform the online sessions in Anatomy, and make them more engaging. Methods: This observational study includes 250 first-year undergraduate medical students attending online Anatomy classes during the pandemic. A semi-structured questionnaire was designed to seek student responses, including mode/hours of interaction, facilitating/hindering factors, and assessment in online teaching. The quantitative findings were expressed in percentages. The open-ended questions were subjected to qualitative analysis, and themes were identified. Results: The number of hours spent on online sessions per day showed an increase during the pandemic. Students most often interacted with peers (n=124) through social media (n = 97) to clarify queries. Students opted for both asynchronous (55%) and synchronous modes (45%) of learning. The qualitative analysis identified the following thematic categories: facilitating factors, hindering factors, and measures taken to overcome hindering factors in online learning. Conclusion: There should be a balance between synchronous and asynchronous teaching methods to provide a better learning pace. Incorporation of more self-directed learning strategies would motivate students to learn better. The study concludes that online teaching should be designed to keep student feedback in mind, and tailored to suit student learning needs.

Analytical Sensitivity and Specificity of Four Point of Care Rapid Antigen Diagnostic Tests for SARS-CoV-2 Using Real-Time Quantitative PCR, Quantitative Droplet Digital PCR, and a Mass Spectrometric Antigen Assay as Comparator Methods.

BACKGROUND: We evaluated the analytical sensitivity and specificity of 4 rapid antigen diagnostic tests (Ag RDTs) for severe acute respiratory syndrome coronavirus 2, using reverse transcription quantitative PCR (RT-qPCR) as the reference method and further characterizing samples using droplet digital quantitative PCR (ddPCR) and a mass spectrometric antigen test. METHODS: Three hundred fifty (150 negative and 200 RT-qPCR positive) residual PBS samples were tested for antigen using the BD Veritor lateral flow (LF), ACON LF, ACON fluorescence immunoassay (FIA), and LumiraDx FIA. ddPCR was performed on RT-qPCR-positive samples to quantitate the viral load in copies/mL applied to each Ag RDT. Mass spectrometric antigen testing was performed on PBS samples to obtain a set of RT-qPCR-positive, antigen-positive samples for further analysis. RESULTS: All Ag RDTs had nearly 100% specificity compared to RT-qPCR. Overall analytical sensitivity varied from 66.5% to 88.3%. All methods detected antigen in samples with viral load >1 500 000 copies/mL RNA, and detected ≥75% of samples with viral load of 500 000 to 1 500 000 copies/mL. The BD Veritor LF detected only 25% of samples with viral load between 50 000 to 500 000 copies/mL, compared to 75% for the ACON LF device and >80% for LumiraDx and ACON FIA. The ACON FIA detected significantly more samples with viral load <50 000 copies/mL compared to the BD Veritor. Among samples with detectable antigen and viral load <50 000 copies/mL, sensitivity of the Ag RDT varied between 13.0% (BD Veritor) and 78.3% (ACON FIA). CONCLUSIONS: Ag RDTs differ significantly in analytical sensitivity, particularly at viral load <500 000 copies/mL.

Acute Kidney Injury in Severe COVID-19 Has Similarities to Sepsis-Associated Kidney Injury: A Multi-Omics Study.

OBJECTIVE: To compare coronavirus disease 2019 (COVID-19) acute kidney injury (AKI) to sepsis-AKI (S-AKI). The morphology and transcriptomic and proteomic characteristics of autopsy kidneys were analyzed. PATIENTS AND METHODS: Individuals 18 years of age and older who died from COVID-19 and had an autopsy performed at Mayo Clinic between April 2020 to October 2020 were included. Morphological evaluation of the kidneys of 17 individuals with COVID-19 was performed. In a subset of seven COVID-19 cases with postmortem interval of less than or equal to 20 hours, ultrastructural and molecular characteristics (targeted transcriptome and proteomics analyses of tubulointerstitium) were evaluated. Molecular characteristics were compared with archived cases of S-AKI and nonsepsis causes of AKI. RESULTS: The spectrum of COVID-19 renal pathology included macrophage-dominant microvascular inflammation (glomerulitis and peritubular capillaritis), vascular dysfunction (peritubular capillary congestion and endothelial injury), and tubular injury with ultrastructural evidence of mitochondrial damage. Investigation of the spatial architecture using a novel imaging mass cytometry revealed enrichment of CD3+CD4+ T cells in close proximity to antigen-presenting cells, and macrophage-enriched glomerular and interstitial infiltrates, suggesting an innate and adaptive immune tissue response. Coronavirus disease 2019 AKI and S-AKI, as compared to nonseptic AKI, had an enrichment of transcriptional pathways involved in inflammation (apoptosis, autophagy, major histocompatibility complex class I and II, and type 1 T helper cell differentiation). Proteomic pathway analysis showed that COVID-19 AKI and to a lesser extent S-AKI were enriched in necroptosis and sirtuin-signaling pathways, both involved in regulatory response to inflammation. Upregulation of the ceramide-signaling pathway and downregulation of oxidative phosphorylation in COVID-19 AKI were noted. CONCLUSION: This data highlights the similarities between S-AKI and COVID-19 AKI and suggests that mitochondrial dysfunction may play a pivotal role in COVID-19 AKI. This data may allow the development of novel diagnostic and therapeutic targets.

Targeted Detection of SARS-CoV-2 Nucleocapsid Sequence Variants by Mass Spectrometric Analysis of Tryptic Peptides.

COVID-19 vaccines are becoming more widely available, but accurate and rapid testing remains a crucial tool for slowing the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Although the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) remains the most prevalent testing methodology, numerous tests have been developed that are predicated on detection of the SARS-CoV-2 nucleocapsid protein, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and immunoassay-based approaches. The continuing emergence of SARS-CoV-2 variants has complicated these approaches, as both qRT-PCR and antigen detection methods can be prone to missing viral variants. In this study, we describe several COVID-19 cases where we were unable to detect the expected peptide targets from clinical nasopharyngeal swabs. Whole genome sequencing revealed that single nucleotide polymorphisms in the gene encoding the viral nucleocapsid protein led to sequence variants that were not monitored in the targeted assay. Minor modifications to the LC-MS/MS method ensured detection of the variants of the target peptide. Additional nucleocapsid variants could be detected by performing the bottom-up proteomic analysis of whole viral genome-sequenced samples. This study demonstrates the importance of considering variants of SARS-CoV-2 in the assay design and highlights the flexibility of mass spectrometry-based approaches to detect variants as they evolve.

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.

Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients.

BACKGROUND: Multiple parameters may be used to prognosticate coronavirus disease-2019 (COVID-19) patients, which are often expensive laboratory or radiological investigations. We evaluated the utility of age-adjusted Charlson comorbidity index (CCI) as a predictor of outcome in COVID-19 patients treated with remdesivir. MATERIALS AND METHODS: This was a single-center, retrospective study on 126 COVID-19 patients treated with remdesivir. The age-adjusted CCI, length of hospital stay (LOS), need for invasive mechanical ventilation (IMV), and survival were recorded. RESULTS: The mean and standard deviation (SD) of age-adjusted CCI were 3.37 and 2.186, respectively. Eighty-six patients (70.5%) had age-adjusted CCI ≤4, and 36 (29.5%) had age-adjusted CCI >4. Among patients with age-adjusted CCI ≤4, 20 (23.3%) required IMV, whereas in those with age-adjusted CCI >4, 19 (52.8%) required IMV (p <0.05, Pearson's chi-square test). In those with age-adjusted CCI ≤4, the mortality was 18.6%, whereas it was 41.7% in patients with age-adjusted CCI >4 (p <0.05, Pearson's chi-square test). The receiver operating curve (ROC) of age-adjusted CCI for predicting the mortality had an area under the curve (AUC) of 0.709, p = 0.001, and sensitivity 68%, specificity 62%, and 95% confidence interval (CI) [0.608, 0.810], for a cutoff score >4. The ROC for age-adjusted CCI for predicting the need for IMV had an AUC of 0.696, p = 0.001, and sensitivity 67%, specificity 63%, and 95% CI [0.594, 0.797], for a cutoff score >4. ROC for age-adjusted CCI as a predictor of prolonged LOS (≥14 days) was insignificant. CONCLUSION: In COVID-19 patients, the age-adjusted CCI is an independent predictor of the need for IMV (score >4) and mortality (score >4) but is not useful to predict LOS (CTRI/2020/11/029266). HOW TO CITE THIS ARTICLE: Shanbhag V, Arjun NR, Chaudhuri S, Pandey AK. Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients. Indian J Crit Care Med 2021;25(9):987-991.

A pathway map of signaling events triggered upon SARS-CoV infection.

Severe acute respiratory syndrome coronaviruses (SARS-CoVs) caused worldwide epidemics over the past few decades. Extensive studies on various strains of coronaviruses provided a basic understanding of the pathogenesis of the disease. Presently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is leading a global pandemic with unprecedented challenges. This is the third coronavirus outbreak of this century. A signaling pathway map of signaling events induced by SARS-CoV infection is not yet available. In this study, we present a literature-annotated signaling pathway map of reactions induced by SARS-CoV infected cells. Multiple signaling modules were found to be orchestrated including PI3K-AKT, Ras-MAPK, JAK-STAT, Type 1 IFN and NFκB. The signaling pathway map of SARS-CoV consists of 110 molecules and 101 reactions mediated by SARS-CoV proteins. The pathway reaction data are available in various community standard data exchange formats including Systems Biology Graphical Notation (SBGN). The pathway map is publicly available through the GitHub repository and data in various formats can be freely downloadable.

Proteomic Signature of Host Response to SARS-CoV-2 Infection in the Nasopharynx.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has become a global health pandemic. COVID-19 severity ranges from an asymptomatic infection to a severe multiorgan disease. Although the inflammatory response has been implicated in the pathogenesis of COVID-19, the exact nature of dysregulation in signaling pathways has not yet been elucidated, underscoring the need for further molecular characterization of SARS-CoV-2 infection in humans. Here, we characterize the host response directly at the point of viral entry through analysis of nasopharyngeal swabs. Multiplexed high-resolution MS-based proteomic analysis of confirmed COVID-19 cases and negative controls identified 7582 proteins and revealed significant upregulation of interferon-mediated antiviral signaling in addition to multiple other proteins that are not encoded by interferon-stimulated genes or well characterized during viral infections. Downregulation of several proteasomal subunits, E3 ubiquitin ligases, and components of protein synthesis machinery was significant upon SARS-CoV-2 infection. Targeted proteomics to measure abundance levels of MX1, ISG15, STAT1, RIG-I, and CXCL10 detected proteomic signatures of interferon-mediated antiviral signaling that differentiated COVID-19-positive from COVID-19-negative cases. Phosphoproteomic analysis revealed increased phosphorylation of several proteins with known antiviral properties as well as several proteins involved in ciliary function (CEP131 and CFAP57) that have not previously been implicated in the context of coronavirus infections. In addition, decreased phosphorylation levels of AKT and PKC, which have been shown to play varying roles in different viral infections, were observed in infected individuals relative to controls. These data provide novel insights that add depth to our understanding of SARS-CoV-2 infection in the upper airway and establish a proteomic signature for this viral infection.

DIA-Based Proteome Profiling of Nasopharyngeal Swabs from COVID-19 Patients.

Since the recent outbreak of COVID-19, there have been intense efforts to understand viral pathogenesis and host immune response to combat SARS-CoV-2. It has become evident that different host alterations can be identified in SARS-CoV-2 infection based on whether infected cells, animal models or clinical samples are studied. Although nasopharyngeal swabs are routinely collected for SARS-CoV-2 detection by RT-PCR testing, host alterations in the nasopharynx at the proteomic level have not been systematically investigated. Thus, we sought to characterize the host response through global proteome profiling of nasopharyngeal swab specimens. A mass spectrometer combining trapped ion mobility spectrometry (TIMS) and high-resolution QTOF mass spectrometer with parallel accumulation-serial fragmentation (PASEF) was deployed for unbiased proteome profiling. First, deep proteome profiling of pooled nasopharyngeal swab samples was performed in the PASEF enabled DDA mode, which identified 7723 proteins that were then used to generate a spectral library. This approach provided peptide level evidence of five missing proteins for which MS/MS spectrum and mobilograms were validated with synthetic peptides. Subsequently, quantitative proteomic profiling was carried out for 90 individual nasopharyngeal swab samples (45 positive and 45 negative) in DIA combined with PASEF, termed as diaPASEF mode, which resulted in a total of 5023 protein identifications. Of these, 577 proteins were found to be upregulated in SARS-CoV-2 positive samples. Functional analysis of these upregulated proteins revealed alterations in several biological processes including innate immune response, viral protein assembly, and exocytosis. To the best of our knowledge, this study is the first to deploy diaPASEF for quantitative proteomic profiling of clinical samples and shows the feasibility of adopting such an approach to understand mechanisms and pathways altered in diseases.

The aftermath of COVID-19 pandemic on the diagnosis of TB at a tertiary care hospital in India.

BACKGROUND: The recent COVID-19 pandemic became a looming catastrophe over global public health and severely disrupted essential healthcare services like tuberculosis (TB). This study estimated the impact of the COVID-19 in the diagnosis of TB, a microbiology laboratory-based overview. METHOD: This ambispective observational study was conducted at the Department of Microbiology in a tertiary care hospital in South Karnataka from January 2019 to December 2020. A standardized data collection sheet was prepared to collect the month-wise total number of suspected TB and confirmed TB samples. Data were analyzed using EZR 3.4.3 (R, open-source). Categorical variables were expressed in frequency and percentage. The Chi-square test was performed to test the difference in proportions and p < 0.05 indicated statistical significance. RESULTS: In this study, a significant drop was observed in suspected TB specimens in 2020 compared to 2019, i.e. 54.8% for microscopy, along with 34.2% and 49.7% for Xpert MTB/RIF and MGIT culture respectively. Also, a sharp decline in confirmed TB samples was noted in 2020 with 49%, 43.8%, and 59.7% reduction with microscopy, Xpert MTB/RIF, and MGIT culture respectively, compared to 2019. Another major finding from this study reveals the PTB: EPTB proportion changed from 2.7:1 in 2019 to 2.1:1 in 2020, divulging an overall increase in EPTB sample proportion in 2020 (p = 0.0385). CONCLUSION: The COVID-19 pandemic adversely impacted the TB diagnostic services, resulting in a significant reduction of active TB case detection. It highlights an urgent need to revise the strategies to control and eliminate TB in this hour of the pandemic crisis.

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

BACKGROUND: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time. METHODS: Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data. FINDINGS: The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method. INTERPRETATION: Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures. FUNDING: This study was supported by DBT/Wellcome Trust India Alliance Margdarshi Fellowship grant IA/M/15/1/502023 awarded to AP and the generosity of Eric and Wendy Schmidt.

Mass Spectrometric Analysis of Urine from COVID-19 Patients for Detection of SARS-CoV-2 Viral Antigen and to Study Host Response.

SARS-CoV-2 infection has become a major public health burden and affects many organs including lungs, kidneys, the liver, and the brain. Although the virus is readily detected and diagnosed using nasopharyngeal swabs by reverse transcriptase polymerase chain reaction (RT-PCR), detection of its presence in body fluids is fraught with difficulties. A number of published studies have failed to detect viral RNA by RT-PCR methods in urine. Although microbial identification in clinical microbiology using mass spectrometry is undertaken after culture, here we undertook a mass spectrometry-based approach that employed an enrichment step to capture and detect SARS-CoV-2 nucleocapsid protein directly from urine of COVID-19 patients without any culture. We detected SARS-CoV-2 nucleocapsid protein-derived peptides from 13 out of 39 urine samples. Further, a subset of COVID-19 positive and COVID-19 negative urine samples validated by mass spectrometry were used for the quantitative proteomics analysis. Proteins with increased abundance in urine of SARS-CoV-2 positive individuals were enriched in the acute phase response, regulation of complement system, and immune response. Notably, a number of renal proteins such as podocin (NPHS2), an amino acid transporter (SLC36A2), and sodium/glucose cotransporter 5 (SLC5A10), which are intimately involved in normal kidney function, were decreased in the urine of COVID-19 patients. Overall, the detection of viral antigens in urine using mass spectrometry and alterations of the urinary proteome could provide insights into understanding the pathogenesis of COVID-19.