Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors - Vernocchi, Pamela; Marangelo, Chiara; Guerrera, Silvia; Del Chierico, Federica; Guarrasi, Valerio; Gardini, Simone; Conte, Federica; Paci, Paola; Ianiro, Gianluca; Gasbarrini, Antonio; Vicari, Stefano; Putignani, Lorenza (Frontiers in Microbiology, 2023)
Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways and anamnestic, clinical and nutritional patient metadata. Fecal samples collected from ASDs and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC-MS/SPME) to determine volatile organic compounds (VOCs) and associated with metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns. The GM core volatilome for all ASD patients was characterised by high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; o-cymene. Patients were stratified based on age, GI symptoms and ASD severity symptoms, and disease risk prediction allowed us to associate butanoic acid to subjects older than 5 years, indole to absence of GI symptoms and to low disease severity, propanoic acid to ASD risk group and p-cymene to ASDs with symptoms, both based on predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole and tetradecanal features. Logistic Regression models corroborated methyl isobutil ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole and acetic acid as ASD predictors. Our results bring us towards advanced clinical decision support systems (CDSSs), assisted by ML models for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.