Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition with hallmark behavioral manifestations including impaired social communication and restricted repetitive behavior. In addition, many affected individuals display metabolic imbalances, immune dysregulation, gastrointestinal (GI) dysfunction, and altered gut microbiome compositions. We sought to better understand non-behavioral features of ASD by determining molecular signatures in peripheral tissues. Herein, we present the untargeted metabolome of 231 plasma and 97 fecal samples from a large cohort of children with ASD and typically developing (TD) controls. Differences in lipid, amino acid, and xenobiotic metabolism discriminate ASD and TD samples. We reveal correlations between specific metabolite profiles and clinical behavior scores, and identify metabolites particularly associated with GI dysfunction in ASD. These findings support a connection between GI physiology, metabolism, and complex behavioral traits, and may advance discovery and development of molecular biomarkers for ASD. Sterol 1 1 1 Gly, Ser, Thr 0 1 1

Nanomaterials‐assisted metabolic analysis toward in vitro diagnostics

“…Particularly, blood samples including serum and plasma, have been well explored for the diagnosis of different diseases in recent years. [89,[109][110][111][112][113][114] Especially, Qian's group was dedicated to developing novel nanomaterials and their conjugation with LDI MS systems for the untargeted metabolic fingerprinting of blood samples. [15,89,91,109,115] For example, Vedarethinam et al extracted the plasma metabolic fingerprints using LDI MS, which was assisted by V 2 O 5 nanorods F I G U R E  (A) Metabolic changes inspected by vanadium core-shell nanorods fordiagnosing diabetic retinopathy.…”

Microbiome-metabolome interactions predict host phenotype

“…While metabolome studies have become increasingly used in characterizing emerging properties of the metabolome and in relating metabolomic change to host pathological states [32,33,34,35,36,37,38,39,40,41,42], metabolome based ML also has its limitations: (1) high cost; (2) extremely high dimension of input (i.e., number of different metabolites vs the number of samples), especially in untargeted studies [22]; (3) a large number of unknown metabolites that have a molecular composition, but no known function [43,44]; (4) large variability of nomenclature and experimental protocols among different studies [45,46]. Three approaches can be proposed to combine microbiome and metabolome data to cope with the limitations of each information source by itself: (1) Qualitative microbiome-metabolites relations.…”