Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
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Abstract
The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum.
Methods
Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques.
Results
Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified.
Conclusions
In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.
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SciScore for 10.1101/2021.07.06.21260080: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Participants were identified by clinical staff to ensure that they had the capacity to consent to the study, and were asked to sign an Informed Consent Form; those that did not have this capacity or who did not sign the form were not sampled. Sex as a biological variable not detected. Randomization Each day consisted of a run incorporating blank injections (n=2), field blank injections (n=3), pooled QC injections (n=6, 3 at the start and finish), as well as QCs to measure instrumental and extraction variation (n=7 and 3 respectively), and 10 participant samples, randomised for positive/negative, with 3 repeat analyses for each. 2.3 Materials and chemicals: The materials and solvents … SciScore for 10.1101/2021.07.06.21260080: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Participants were identified by clinical staff to ensure that they had the capacity to consent to the study, and were asked to sign an Informed Consent Form; those that did not have this capacity or who did not sign the form were not sampled. Sex as a biological variable not detected. Randomization Each day consisted of a run incorporating blank injections (n=2), field blank injections (n=3), pooled QC injections (n=6, 3 at the start and finish), as well as QCs to measure instrumental and extraction variation (n=7 and 3 respectively), and 10 participant samples, randomised for positive/negative, with 3 repeat analyses for each. 2.3 Materials and chemicals: The materials and solvents utilised in this study were as follows: 2 mL microcentrifuge tubes (Eppendorf, UK), 0.22 µm cellulose acetate sterile Spin-X centrifuge tube filters (Corning incorporated, USA), 200 µL micropipette tips (Starlab, UK) and Qsert™ clear glass insert LC vials (Supelco, UK). Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources 23 Features identified by mass spectrometry were initially annotated using accurate mass match with reference to external databases (KEGG, Human Metabolome Database, DrugBank, LipidMaps and BioCyc), and then validation was performed using data dependent MS/MS analysis. DrugBanksuggested: (DrugBank, RRID:SCR_002700)BioCycsuggested: (BioCyc, RRID:SCR_002298)2.6 Statistical Analysis: PCA analyses were conducted in SIMCA (Sartorius Stedim Biotech, France). SIMCAsuggested: (SIMCA, RRID:SCR_014688)KEGG pathway analysis was performed using MetaboAnalyst. KEGGsuggested: (KEGG, RRID:SCR_012773)MetaboAnalystsuggested: (MetaboAnalyst, RRID:SCR_015539)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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