A validated LC-MS/MS method for clinical pharmacokinetics and presumptive phase II metabolic pathways following oral administration of Andrographis paniculata extract.

Andrographis paniculata, a medicinal plant in Thailand national list of essential medicines, has been proposed for treatment of patients with mild to moderate coronavirus disease 2019. This study aims to develop a highly selective and sensitive liquid chromatography triple quadrupole tandem mass spectrometry method for quantitative determination of major diterpenoids in plasma and urine with application in pharmacokinetics. Chromatographic separation was performed on C18 column using a gradient mobile phase of water and acetonitrile. Mass spectrometry was analyzed using multiple reaction monitoring with negative ionization mode. This validated analytical method was very sensitive, less time consuming in analysis, and allowed the reliability and reproducibility on its application. The clinical pharmacokinetics was evaluated after single oral administration of A. paniculata extract (calculated as 60 mg of andrographolide). The disposition kinetics demonstrated that major diterpenoids could enter into systemic circulation, but they are mostly biotransformed (phase II) into conjugated glucuronide and sulfate metabolites. These metabolites are predominantly found in plasma and then extremely eliminated, in part through urinary excretion. The successful application of this analytical method supports its suitable uses in further clinical benefits after oral administration of A. paniculata.

Development and Validation of an Innovative Analytical Approach for the Quantitation of Tris(Hydroxymethyl)Aminomethane (TRIS) in Pharmaceutical Formulations by Liquid Chromatography Tandem Mass Spectrometry.

A novel COVID-19 vaccine (BriLife®) has been developed by the Israel Institute for Biological Research (IIBR) to prevent the spread of the SARS-CoV-2 virus throughout the population in Israel. One of the components in the vaccine formulation is tris(hydroxymethyl)aminomethane (tromethamine, TRIS), a buffering agent. TRIS is a commonly used excipient in various approved parenteral medicinal products, including the mRNA COVID-19 vaccines produced by Pfizer/BioNtech and Moderna. TRIS is a hydrophilic basic compound that does not contain any chromophores/fluorophores and hence cannot be retained and detected by reverse-phase liquid chromatography (RPLC)-ultraviolet (UV)/fluorescence methods. Among the few extant methods for TRIS determination, all exhibit a lack of selectivity and/or sensitivity and require laborious sample treatment. In this study, LC−mass spectrometry (MS) with its inherent selectivity and sensitivity in the multiple reaction monitoring (MRM) mode was utilized, for the first time, as an alternative method for TRIS quantitation. Extensive validation of the developed method demonstrated suitable specificity, linearity, precision, accuracy and robustness over the investigated concentration range (1.2−4.8 mg/mL). Specifically, the R2 of the standard curve was >0.999, the recovery was >92%, and the coefficient of variance (%CV) was <12% and <6% for repeatability and intermediate precision, respectively. Moreover, the method was validated in accordance with strict Good Manufacturing Practice (GMP) guidelines. The developed method provides valuable tools that pharmaceutical companies can use for TRIS quantitation in vaccines and other pharmaceutical products.

Circulating Peptidome Is Strongly Altered in COVID-19 Patients.

Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Understanding metabolic alterations after SARS-CoV-2 infection: insights from the patients' oral microenvironmental metabolites.

BACKGROUND: Coronavirus disease 2019 is a type of acute infectious pneumonia and frequently confused with influenza since the initial symptoms. When the virus colonized the patient's mouth, it will cause changes of the oral microenvironment. However, few studies on the alterations of metabolism of the oral microenvironment affected by SARS-CoV-2 infection have been reported. In this study, we explored metabolic alterations of oral microenvironment after SARS-CoV-2 infection. METHODS: Untargeted metabolomics (UPLC-MS) was used to investigate the metabolic changes between oral secretion samples of 25 COVID-19 and 30 control participants. To obtain the specific metabolic changes of COVID-19, we selected 25 influenza patients to exclude the metabolic changes caused by the stress response of the immune system to the virus. Multivariate analysis (PCA and PLS-DA plots) and univariate analysis (students' t-test) were used to compare the differences between COVID-19 patients and the controls. Online hiplot tool was used to perform heatmap analysis. Metabolic pathway analysis was conducted by using the MetaboAnalyst 5.0 web application. RESULTS: PLS-DA plots showed significant separation of COVID-19 patients and the controls. A total of 45 differential metabolites between COVID-19 and control group were identified. Among them, 35 metabolites were defined as SARS-CoV-2 specific differential metabolites. Especially, the levels of cis-5,8,11,14,17-eicosapentaenoic acid and hexanoic acid changed dramatically based on the FC values. Pathway enrichment found the most significant pathways were tyrosine-related metabolism. Further, we found 10 differential metabolites caused by the virus indicating the body's metabolism changes after viral stimulation. Moreover, adenine and adenosine were defined as influenza virus-specific differential metabolites. CONCLUSIONS: This study revealed that 35 metabolites and tyrosine-related metabolism pathways were significantly changed after SARS-CoV-2 infection. The metabolic alterations of oral microenvironment in COVID-19 provided new insights into its molecular mechanisms for research and prognostic treatment.

In-depth characterization of protein N-glycosylation for a COVID-19 variant-design vaccine spike protein.

COVID-19 is caused by SARS-CoV-2 infection and remains one of the biggest pandemics around the world since 2019. Vaccination has proved to be an effective way of preventing SARS-CoV-2 infection and alleviating the hospitalization burden. Among different forms of COVID-19 vaccine design, the spike protein of SARS-CoV-2 virus is widely used as a candidate vaccine antigen. As a surface protein on the virus envelop, the spike was reported to be heavily N-glycosylated and glycosylation had a great impact on its immunogenicity and efficacy. Besides, N-glycosylation might vary greatly on different expression systems and sequence variant designs. Therefore, comprehensive analysis of spike N-glycosylation is of great significance for better vaccine understanding and quality control. In this study, full characterization of N-glycosylation was performed for a Chinese Hamster Ovary (CHO) cell expressed variant-designed spike protein. The spike protein featured the latest six-proline substitution design together with the incorporation of a combination of mutation sites. Trypsin and Glu-C digestion coupled with PNGase F strategies were adopted, and effective LC-MS/MS methods were applied to analyze samples. As a result, a total of 19 N-glycosites were identified in the recombinant pike protein at intact N-glycopeptide level. Quantitative analysis of released glycan by LC-MS/MS was also performed, and 31 high-abundance N-glycans were identified. Sequencing analysis of glycan was further provided to assist glycan structure confirmation. Moreover, all of the analyses were performed on three consecutive manufactured batches and the glycosylation results on both glycosite and glycans showed good batch-to-batch consistency. Thus, the reported analytical strategy and N-glycosylation information may well facilitate studies on SARS-CoV-2 spike protein analysis and quality studies.

HPLC-DAD quantification of favipiravir in whole blood after extraction from volumetric absorptive microsampling devices.

Favipiravir is a prodrug of T-1105 made by modifying the pyrazine group as a COVID-19 therapy. During the pandemic, a safe and comfortable biosampling technique is needed for the subject or patient. Volumetric Absorptive Microsampling (VAMS) is a biosampling technique with a small blood volume and minimum hematocrit effect. The aims of this study were to develop and validate an analytical method for quantifying favipiravir extracted from VAMS using High Performance Liquid Chromatography - Photodiode Array with remdesivir as an internal standard. Analysis of favipiravir was performed using a C18 column (Waters, Sunfire™ 5 µm; 250 × 4.6 mm), with injection volume of 50 µL, flow rate of 0.8 mL/min, column temperature 30 ℃, and wavelength 300 nm. The separation was conducted under gradient elution with mobile phase consists of acetonitrile-0.2 % formic acid-20 mM sodium dihydrogen phosphate pH 3.5 and run time 12 min. Sample preparation was carried out using a protein precipitation method with 500 µL of methanol as precipitating agent. Samples were mixed on vortex for 30 s, sonicated for 15 min, and centrifuged at 10,000 rpm for 10 min. Lower Limit of Quantification (LLOQ) obtained was 0.5 µg/mL and the calibration curve ranged from 0.5 to 160 µg/mL. Sensitivity, linearity, selectivity, carry-over, accuracy, precision, recovery, and stability were validated by the guideline from Food and Drug Administration 2018. The method developed has successfully met the full validation requirements by FDA 2018 with the LLOQ obtained was 0.5 µg /mL.

Sugar-sweetened beverages consumption during Covid-19 pandemic among office workers in semi-urban area in southern Thailand: a cross-sectional study.

Background: During COVID-19 pandemic, office worker has spent more than 6-8 hours per day sitting for online working following social distancing policy. Considering the popularity of online ordering and home delivery services, sugar-sweetened beverages (SSB) consumption have increased. However, the link between the types SSB consumption and their BMI was less well documented. Objective: To determine the association of the habitual intake (type, frequency, and volume) of sugar-sweetened beverages (SSB) with body mass index (BMI). Material and methods: A cross-sectional study, 337 office workers were selected according to probability proportionto-size and systematic random sampling. Data were collected using face-to-face interviews on the type, frequency, and volume of sugar-sweetened beverage intake. Samples of sugar-containing beverages were analyzed using high-throughput liquid chromatography/mass spectrometry (LC-MS/MS). The chi-square test was used to determine the relationship of SSB consumption with BMI. Unadjusted binary logistic regression analysis was used to assess the associations between BMI and metabolic diseases. Results: Most respondents (56.1%) were overweight (BMI >23 kg/m2). The most consumed SSB was milk tea (e.g., Thai tea and green tea), which was significantly related with BMI (p=0.03). LC-MS/MS analysis showed that sucrose and lactose were the major sugars in milk tea (34.7 g/100mL, on average). 70.6% of the respondents consumed >24 g/day of sugar, which is more than the World Health Organization's recommendation. Conclusions: Health control policies and health education, for example warning labels for the reduction of SSB consumption, may urgently be required to promote health in workplaces and prevent SSB-related metabolic diseases.

Follow-Up: Effects of a Pandemic and Isolation on Alcohol and Psychoactive Medication Use in a Population of Rehabilitation and Pain Patients.

OBJECTIVE: The conjunction of the coronavirus disease lockdown and the use of illicit drugs suggests the potential increase in drug usage and opioid deaths. Because of other studies, we felt the need to examine if the lockdown has caused a change in the drug intake of our population of substance abuse and pain management patients. Our initial study indicated no increase in the use of illicit and antianxiety drugs. This study is a continuation of that work. MATERIALS: Urine drug testing is a strategy to reduce harm to patients in pain management and substance abuse treatment programs. We analyzed trends in the clinical drug testing patterns of urine specimens sent by substance abuse and pain clinics to monitor their patients. These specimens were tested by a national clinical laboratory using LC-MS/MS definitive methods. The time frame of these comparative observations was the past six years, including the two years of the pandemic. RESULTS: We observed a 30% reduction in test requests during the second quarter of 2020, the number of test requests and specimens submitted was similar during other times of the six-year period. The observed drug use pattern was similar to the earlier study. Among the patients tested, positivity decreased greatly for the illicit drugs heroin and cocaine but increased for methamphetamine and fentanyl. Use of the antidepressant and anxiolytic drugs remained consistent or declined for some drugs, relative to pre-pandemic patterns. The percent of patients prescribed the opiates morphine and oxycodone decreased, while the use of hydrocodone increased. Positivity for the drug gabapentin increased greatly. The use of alcohol did not increase significantly during the lockdown period. CONCLUSION: In summary, these findings demonstrate relatively consistent drug use, with decreased positivity for high-risk drugs and dangerous drug combinations. We speculate that monitoring of these patients mitigates the possibility of drug misuse and potential overdose and is in concordance with the goals of these monitoring programs.

Liquid Chromatography-Mass Spectrometry-Based Qualitative Profiling of mRNA Therapeutic Reagents Using Stable Isotope-Labeled Standards Followed by the Automatic Quantitation Software Ariadne.

mRNA-based medicines are a promising modality for preventing virus-caused illnesses, including COVID-19, and treating various types of cancer and genetic diseases. To develop such medicines, methods to characterize long mRNA molecules are needed for quality control and metabolic analysis. Here, we developed an analytical platform based on isotope-dilution liquid chromatography-mass spectrometry (LC-MS) that quantitatively characterizes long, modified mRNAs by comparing them to a stable isotope-labeled reference with an identical sequence to that of the target medicine. This platform also includes database searching using the mass spectra as a query, which allowed us to confirm the primary structures of 200 to 4300 nt mRNAs including chemical modifications, with sequence coverage at 100%, to detect/identify defects in the sequences, and to define the efficiencies of the 5'-capping and integrity of the polyadenylated tail. Our findings indicated that this platform should be valuable for quantitatively characterizing mRNA vaccines and other mRNA medicines.

Nanoparticle Formulation Composition Analysis by Liquid Chromatography on Reversed-Phase Monolithic Silica.

Multifunctional nanoparticle (NP) formulations for medical purposes have already found their way toward envisaged translation. A persistent challenge of those systems is, next to NP size analysis, the compositional analysis of the NPs with the polymer as the matrix component and the encapsulated drug, particularly in a quantitative manner. Herein, we report the formulation of poly(lactic-co-glycolic acid) (PLGA) NPs by nanoprecipitation and the analysis of their integrity and size by dynamic light scattering (DLS) and scanning electron microscopy (SEM). Those NPs feature a variety of encapsulated drugs including the well-known ibuprofen (Ibu) as well as dexamethasone (Dex) and dexamethasone acetate (DexAce), with the latter being of potential interest for clinical treatment of SARS-CoV-2 patients. All those dissolved formulation compositions have been subjected to liquid chromatography on reversed-phase silica monolithic columns, allowing to quantitatively assess amounts of small molecule drug and NP constituting PLGA polymer in a single run. The chromatographically resolved hydrophobicity differences of the drugs correlated with their formulation loading and were clearly separated from the PLGA matrix polymer with high resolution. Our study identifies the viability of reversed-phase monolithic silica in the chromatography of both small drug molecules and particularly pharmapolymers in a repeatable and simultaneous fashion, and can provide a valuable strategy for analysis of diverse precursor polymer systems and drug components in multifunctional drug formulations.

LC-MS/MS methods for the determination of 30 quaternary ammonium compounds including benzalkonium and paraquat in human serum and urine.

Benzalkyldimethylammonium (or benzalkonium; BACs), alkyltrimethylammonium (ATMACs), and dialkyldimethylammonium compounds (DDACs) have been widely used for over six decades as disinfectants, especially during the COVID-19 pandemic. Here we describe methods for the determination of 7 BACs, 6 ATMACs, 6 DDACs, 8 BAC metabolites, and the structurally similar quaternary ammonium compound (QAC) herbicides diquat, paraquat, and difenzoquat in human serum and urine using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The methods were optimized using isotopically labelled internal standards and solid-phase extraction with weak cation-exchange cartridges. We separated diquat and paraquat chromatographically using a mixed-mode LC column, and BACs, ATMACs, DDACs, difenzoquat, and BAC metabolites using reversed-phase (C8 and C18) LC columns. Method limits of detection (MLODs) and quantification (MLOQs) were 0.002-0.42 and 0.006-1.40 ng/mL, respectively. Recoveries of all analytes fortified at 1, 5, and 20 ng/mL concentrations in serum and urine matrices were 61-129%, with standard deviations of 0-20%. Repeated analysis of similarly fortified serum and urine samples yielded intra-day and inter-day variations of 0.22-17.4% and 0.35-17.3%, respectively. Matrix effects for analytes spiked into serum and urine matrices ranged from -27% to 15.4%. Analysis of real urine and serum samples revealed the presence of several QACs in human serum. Although no parent BACs were found in urine, we detected, for the first time, several ω-hydroxy and ω-carboxylic acid metabolites of BACs at average concentrations in the range of 0.05-0.35 ng/mL. The developed method is suitable for application in large-scale biomonitoring of human exposure to QACs and their metabolites in human serum and urine.

Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients.

Metabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC-MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.

Screening and evaluation of cytotoxicity and antiviral effects of secondary metabolites from water extracts of Bersama abyssinica against SARS-CoV-2 Delta.

BACKGROUND: Bersama abyssinica is a common herb in Africa, with diverse medical uses in different areas. The plant is well-known in Tanzania for treating respiratory disorders such as TB, tonsillitis, bronchitis, and asthma, and it has lately been utilized to treat COVID-19 symptoms. Water extract of leaf and stem bark has been registered as an herbal medication known as 'Coviba Dawa' in Tanzania for the relief of bacterial respiratory infections. The extracts, however, have not been scientifically tested for their anti-viral activities. The aim of this work was to test for the cytotoxicity and antiviral effects of bioactive ingredients from B. abyssinica extracts against the Delta variant of the SARS-CoV-2 coronavirus. METHODS: B. abyssinica leaves and stem bark were dried under shade in room temperature and then pulverized to obtain small pieces before soaking into different solvents. One hundred grams of each, leaves and stem bark, were extracted in petroleum ether, dichloromethane, ethyl acetate and methanol. Water extract was obtained by decoction of stem bark and leaves into water. Phenols, flavonoids, tannins, and antioxidants were confirmed as components of the extracts. Analysis of polar extracts of bark stem bark and leaves was done. Antiviral screening and cytotoxicity experiments were conducted in a Biosafety Level 3 (BSL-3) Laboratory facility according to International Standard Operating Procedures (SOPs). RESULTS: By the use of LC-MS/MS analysis, this study confirmed the existence of four phenolic compounds in B. abyssinica water extract; 2,4-di-tert-butylphenol, 4-formyl-2-methoxyphenyl propionate, 7,8-Dihydroxy-4-methylcoumarin, and 2,3, 6-trimethoxyflavone with antioxidant activity. This study showed that, while the water extracts of B. abyssinica had significant antiviral activity against SARS Cov2 virus, it showed no cytotoxicity effect on Vero E6 cells. In particular, the water extract (Coviba dawa) showed 75% while ethylacetate fraction of B. abyssinica leaves showed a 50% in vitro viral inhibition, indicating that these substances may be useful for the development of future anti-viral agents. CONCLUSION: We therefore recommend isolation of compounds for further profiling and development with a broader concentration range. We further recommend studies that determine the antiviral activity of extracts of B.abyssinica on other viral pathogens of clinical concern.

Multiplex Hybrid Antigen-Capture LC-MRM Quantification in Sera and Nasal Lining Fluid of AZD7442, a SARS-CoV-2-Targeting Antibody Combination.

AZD7442 (tixagevimab [AZD8895]/cilgavimab [AZD1061]) is a monoclonal antibody (mAb) combination in development for the prevention and treatment of coronavirus disease 2019. Traditionally, bioanalysis of mAbs is performed using ligand binding assays (LBAs), which offer sensitivity, robustness, and ease of implementation. However, LBAs frequently require generation of critical reagents that typically take several months. Instead, we developed a highly sensitive (5 ng/mL limit of quantification) method using a hybrid LBA-liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) approach for quantification of the two codosed antibodies in serum and nasal lining fluid (NLF), a rare matrix. The method was optimized by careful selection of multiple reaction monitoring, capture reagents, magnetic beads, chromatographic conditions, evaluations of selectivity, and matrix effect. The final assay used viral spike protein receptor-binding domain as capture reagent and signature proteotypic peptides from the complementarity-determining region of each mAb for detection. In contrast to other methods of similar/superior sensitivity, our approach did not require multidimensional separations and can be operated in an analytical flow regime, ensuring high throughput and robustness required for clinical analysis at scale. The sensitivity of this method significantly exceeds typical sensitivity of ∼100 ng/mL for analytical flow 1D LBA-LC-MS/MS methods for large macromolecules, such as antibodies. Furthermore, infection and vaccination status did not impact method performance, ensuring method robustness and applicability to a broad patient population. This report demonstrated the general applicability of the hybrid LBA-LC-MS/MS approach to platform quantification of antibodies with high sensitivity and reproducibility, with specialized extension to matrices of increasing interest, such as NLF.

Saliva metabolomic profile of COVID-19 patients associates with disease severity.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is strongly linked to dysregulation of various molecular, cellular, and physiological processes that change abundance of different biomolecules including metabolites that may be ultimately used as biomarkers for disease progression and severity. It is important at early stage to readily distinguish those patients that are likely to progress to moderate and severe stages. OBJECTIVES: This study aimed to investigate the utility of saliva and plasma metabolomic profiles as a potential parameter for risk stratifying COVID-19 patients. METHOD: LC-MS/MS-based untargeted metabolomics were used to profile the changes in saliva and plasma metabolomic profiles of COVID-19 patients with different severities. RESULTS: Saliva and plasma metabolites were screened in 62 COVID-19 patients and 18 non-infected controls. The COVID-19 group included 16 severe, 15 moderate, 16 mild, and 15 asymptomatic cases. Thirty-six differential metabolites were detected in COVID-19 versus control comparisons. SARS-CoV-2 induced metabolic derangement differed with infection severity. The metabolic changes were identified in saliva and plasma, however, saliva showed higher intensity of metabolic changes. Levels of saliva metabolites such as sphingosine and kynurenine were significantly different between COVID-19 infected and non-infected individuals; while linoleic acid and Alpha-ketoisovaleric acid were specifically increased in severe compared to non-severe patients. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid, and positively correlated with L-Tryptophan and L-Kynurenine. CONCLUSION: Saliva disease-specific and severity-specific metabolite could be employed as potential COVID-19 diagnostic and prognostic biomarkers.

Low expression of EXOSC2 protects against clinical COVID-19 and impedes SARS-CoV-2 replication.

New therapeutic targets are a valuable resource for treatment of SARS-CoV-2 viral infection. Genome-wide association studies have identified risk loci associated with COVID-19, but many loci are associated with comorbidities and are not specific to host-virus interactions. Here, we identify and experimentally validate a link between reduced expression of EXOSC2 and reduced SARS-CoV-2 replication. EXOSC2 was one of the 332 host proteins examined, all of which interact directly with SARS-CoV-2 proteins. Aggregating COVID-19 genome-wide association studies statistics for gene-specific eQTLs revealed an association between increased expression of EXOSC2 and higher risk of clinical COVID-19. EXOSC2 interacts with Nsp8 which forms part of the viral RNA polymerase. EXOSC2 is a component of the RNA exosome, and here, LC-MS/MS analysis of protein pulldowns demonstrated interaction between the SARS-CoV-2 RNA polymerase and most of the human RNA exosome components. CRISPR/Cas9 introduction of nonsense mutations within EXOSC2 in Calu-3 cells reduced EXOSC2 protein expression and impeded SARS-CoV-2 replication without impacting cellular viability. Targeted depletion of EXOSC2 may be a safe and effective strategy to protect against clinical COVID-19.

A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment.

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

Evaluation of Variant-Specific Peptides for Detection of SARS-CoV-2 Variants of Concern.

The SARS-CoV-2 omicron variant presented significant challenges to the global effort to counter the pandemic. SARS-CoV-2 is predicted to remain prevalent for the foreseeable future, making the ability to identify SARS-CoV-2 variants imperative in understanding and controlling the pandemic. The predominant variant discovery method, genome sequencing, is time-consuming, insensitive, and expensive. Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) offers an exciting alternative detection modality provided that variant-containing peptide markers are sufficiently detectable from their tandem mass spectra (MS/MS). We have synthesized model tryptic peptides of SARS-CoV-2 variants alpha, beta, gamma, delta, and omicron and evaluated their signal intensity, HCD spectra, and reverse phase retention time. Detection limits of 781, 781, 65, and 65 amol are obtained for the molecular ions of the proteotypic peptides, beta (QIAPGQTGNIADYNYK), gamma (TQLPSAYTNSFTR), delta (VGGNYNYR), and omicron (TLVKQLSSK), from neat solutions. These detection limits are on par with the detection limits of a previously reported proteotypic peptide from the SARS-CoV-2 spike protein, HTPINLVR. This study demonstrates the potential to differentiate SARS-CoV-2 variants through their proteotypic peptides with an approach that is broadly applicable across a wide range of pathogens.