Saliva metabolome alterations after acute stress.

Major stress has systemic effects on the body that can have adverse consequences for physical and mental health. However, the molecular basis of these damaging effects remains incompletely understood. Here we use a longitudinal approach to characterise the acute systemic impact of major psychological stress in a pig model. We perform untargeted metabolomics on non-invasively obtained saliva samples from pigs before and 24 h after transfer to the novel physical and social environment of a slaughterhouse. The main molecular changes occurring include decreases in amino acids, B-vitamins, and amino acid-derived metabolites synthesized in B-vitamin-dependent reactions, as well as yet-unidentified metabolite features. Decreased levels of several of the identified metabolites are implicated in the pathology of human psychological disorders and neurodegenerative disease, suggesting a possible neuroprotective function. Our results provide a fingerprint of the acute effect of psychological stress on the metabolome and suggest candidate biomarkers with potential roles in stress-related disorders.

RawHummus: an R Shiny app for automated raw data quality control in metabolomics.

MOTIVATION: Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data need to be inspected before data processing to detect measurement bias and verify system consistency. RESULTS: Here, we present RawHummus, an R Shiny app for an automated raw data quality control (QC) in metabolomics studies. It produces a comprehensive QC report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with 13 metabolomics/lipidomics datasets and 1 proteomics dataset obtained from 5 different liquid chromatography mass spectrometry platforms. AVAILABILITY AND IMPLEMENTATION: RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command 'runGui()'. Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CCWeights: an R package and web application for automated evaluation and selection of weighting factors for accurate quantification using linear calibration curve.

Summary: The accuracy of any analytical method is highly dependent on the selection of an appropriate calibration model. Here, we present CCWeights, an R package for automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. Additionally, CCWeights includes a web application that allows users to analyze their data using an interactive graphical user interface, without any programming requirements. The workflow and features of CCWeights are illustrated by the analyses of two datasets acquired by liquid chromatography-mass spectrometry (LC-MS). The resulting quantification table can be directly utilized for further model assessment and subsequent data analysis. Availability and implementation: CCWeights is publicly available on CRAN repository (https://cran.r-project.org/web/packages/CCWeights), with source code available on GitHub (https://github.com/YonghuiDong/CCWeights) under a GPL-3 license. The web application can be run locally from R console using a simple command "runGui()". Alternatively, the web application can be freely accessed for direct online use at https://bcdd.shinyapps.io/CCWeights/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.