Links between gut microbiota and autism spectrum disorder (ASD) have been explored in many studies using 16S rRNA gene amplicon and shotgun sequencing. Based on these links, microbiome therapies have been proposed to improve gastrointestinal (GI) and ASD symptoms in ASD individuals. Previously, our open-label microbiota transfer therapy (MTT) study provided insight into the changes in the gut microbial community of children with ASD after MTT and showed significant and long-term improvement in ASD and GI symptoms. Using samples from the same study, the objective of this work was to perform a deeper taxonomic and functional analysis applying shotgun metagenomic sequencing. Taxonomic analyses revealed that ASD Baseline had many bacteria at lower relative abundances, and their abundance increased after MTT. The relative abundance of fiber consuming and beneficial microbes including Prevotella (P. dentalis, P. enoeca, P. oris, P. meloninogenica), Bifidobacterium bifidum, and a sulfur reducer Desulfovibrio piger increased after MTT-10wks in children with ASD compared to Baseline (consistent at genus level with the previous 16S rRNA gene study). Metabolic pathway analysis at Baseline compared to typically developing (TD) children found an altered abundance of many functional genes but, after MTT, they became similar to TD or donors. Important functional genes that changed included: genes encoding enzymes involved in folate biosynthesis, sulfur metabolism and oxidative stress. These results show that MTT treatment not only changed the relative abundance of important genes involved in metabolic pathways, but also seemed to bring them to a similar level to the TD controls. However, at a two-year follow-up, the microbiota and microbial genes shifted into a new state, distinct from their levels at Baseline and distinct from the TD group. Our current findings suggest that microbes from MTT lead to initial improvement in the metabolic profile of children with ASD, and major additional changes at two years post-treatment. In the future, larger cohort studies, mechanistic in vitro experiments and metatranscriptomics studies are recommended to better understand the role of these specific microbes, functional gene expression, and metabolites relevant to ASD.

Fecal microbiota transplant (FMT) holds significant promise for patients with Autism Spectrum Disorder (ASD) and gastrointestinal (GI) symptoms. Prior work has demonstrated that plasma metabolite profiles of children with ASD become more similar to those of their typically developing (TD) peers following this treatment. This work measures the concentration of 669 biochemical compounds in feces of a cohort of 18 ASD and 20 TD children using ultrahigh performance liquid chromatography-tandem mass spectroscopy. Subsequent measurements were taken from the ASD cohort over the course of 10-week Microbiota Transfer Therapy (MTT) and 8 weeks after completion of this treatment. Univariate and multivariate statistical analysis techniques were used to characterize differences in metabolites before, during, and after treatment. Using Fisher Discriminant Analysis (FDA), it was possible to attain multivariate metabolite models capable of achieving a sensitivity of 94% and a specificity of 95% after cross-validation. Observations made following MTT indicate that the fecal metabolite profiles become more like those of the TD cohort. There was an 82–88% decrease in the median difference of the ASD and TD group for the panel metabolites, and among the top fifty most discriminating individual metabolites, 96% report more comparable values following treatment. Thus, these findings are similar, although less pronounced, as those determined using plasma metabolites.

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

The aim of this study was to identify genes with higher expression in solid tumor cells by comparing human tumor biopsies with healthy blood samples using both in silico statistical analysis and experimental validations. This approach resulted in a novel panel of 80 RNA biomarkers with high discrimination power to detect circulating tumor cells in blood samples. To identify the 80 RNA biomarkers, Affymetrix HG-U133 plus 2.0 microarrays datasets were used to compare breast tumor tissue biopsies and breast cancer cell lines with blood samples from patients with conditions other than cancer. A total of 859 samples were analyzed at the discovery stage, consisting of 417 mammary tumors, 41 breast lines, and 401 control samples. To confirm this discovery, external datasets of eight types of tumors were used, and experimental validation studies (NanoString n-counter gene expression assay) were performed, totaling 5028 samples analyzed. In these analyses, the 80 biomarkers showed higher expression in all solid tumors analyzed relative to healthy blood samples. Experimental validation studies using NanoString assay confirmed the results were not dependent of the gene expression platform. A panel of 80 RNA biomarkers was described here, with the potential to detect solid tumor cells present in the blood of multiple tumor types.