bioRxiv Science⌕ Search

Biology subjects

Mannella, V.

Publications and source records attributed to Mannella, V..

3 recordsLinked to original sources

Integrated metabolomic and metagenomic profiling reveals distinct signatures in treatment naive multiple sclerosis patients.

Imbalances in gut microbiota composition and microbiota-associated metabolites have been linked with several neurological diseases, including multiple sclerosis (MS). However, a comprehensive multi-biofluid approach remains lacking. Most studies on MS include patients already receiving treatment and often neglect to account for sex-related differences, which could serve as potential confounding factors. Moreover, they mostly investigate only stools or plasma metabolomics. In this study, we recruited 18 treatment-naive neuroinflammatory patients at diagnosis and compared them with 20 healthy controls, matched for sex and age. We conducted multi-biofluids metabolomic analysis of urine, stool, serum, and cerebrospinal fluid, complemented by taxonomical and functional profiling of the gut microbiome using shotgun metagenomic sequencing. Our results show that MS patients exhibit distinct microbiome composition and urinary metabolomic profiles compared to healthy controls. Furthermore, neuroinflammation is associated with dysregulation of microbially-produced short-chain fatty acids, their intestinal absorption and systemic bioavailability, as illustrated by the altered plasma levels.

neuroscience↗

MargheRita: an R package for LC-MS/MS SWATH metabolomics data analysis and confident metabolite identification based on a spectral library of reference standards

In the field of untargeted metabolomics, the deployment of high-resolution mass spectrometry technologies generates an immense volume of complex metabolite signals. This data density necessitates sophisticated computational frameworks for post-acquisition processing and the integration of specialized databases for accurate metabolite identification. Currently, many web-based data processing solutions offer fragmented workflows, covering only specific stages of the analysis and frequently requiring researchers to migrate data across multiple, often incompatible, platforms. To address these challenges, we introduced margheRita, an R package designed to streamline the workflow for untargeted metabolomic profiling. Developed to work seamlessly with MS-DIAL output, margheRita provides a comprehensive pipeline for liquid chromatography-tandem mass spectrometry (LC-MS/MS) data. This tool is particularly effective for Data-Independent Acquisition (DIA) experiments, where the high-resolution acquisition of all MS/MS spectra demands rigorous and integrated processing capabilities. A key innovation of margheRita is its ability to significantly enhance fragment matching accuracy. It achieves this by utilizing an original, curated high-quality spectral library from authentic reference standards. This library includes data acquired in both positive and negative ionization polarities using various chromatographic columns, ensuring high versatility. By bridging the gap between initial MS-DIAL processing and final biological insights, margheRita offers a holistic solution from metabolite identification to the functional interpretation of complex biological datasets.

bioinformatics↗

Diflunisal targets the HMGB1/CXCL12 heterocomplex and blocks immune cell recruitment

Extracellular HMGB1 triggers inflammation following infection or injury, and supports tumorigenesis in inflammation-related malignancies. HMGB1 has several redox states: reduced HMGB1 recruits inflammatory cells to injured tissues forming a heterocomplex with CXCL12 and signaling via its receptor CXCR4; disulfide-containing HMGB1 binds to TLR4 and promotes inflammatory responses. Here we show that Diflunisal, an aspirin-like nonsteroidal anti-inflammatory drug (NSAID) that has been in clinical use for decades, specifically inhibits in vitro and in vivo the chemotactic activity of HMGB1 at nanomolar concentrations, at least in part by binding directly to both HMGB1 and CXCL12 and disrupting their heterocomplex. Importantly, Diflunisal does not inhibit TLR4-dependent responses. Our findings clarify the mode of action of Diflunisal, and open the way to the rational design of functionally specific anti-inflammatory drugs.

biochemistry↗