Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

Multi-omics Analysis Reveals Key Gut Microbiota and Metabolites Closely Associated with Huntington's Disease

Dysbiosis of the gut microbiota is closely associated with neurodegenerative diseases, including Huntington's disease (HD). Gut microbiome-derived metabolites are key factors in host-microbiome interactions. This study aimed to investigate the crucial gut microbiome and metabolites in HD and their correlations. Fecal and serum samples from 11 to 26 patients with HD, respectively, and 16 and 23 healthy controls, respectively, were collected. The fecal samples were used for shotgun metagenomics while the serum samples for metabolomics analysis. Integrated analysis of the metagenomics and metabolomics data was also conducted. Firmicutes, Bacteroidota, Proteobacteria, Uroviricota, Actinobacteria, and Verrucomicrobia were the dominant phyla. At the genus level, the presence of Bacteroides, Faecalibacterium, Parabacteroides, Alistipes, Dialister, and Christensenella was higher in HD patients, while the abundance of Lachnospira, Roseburia, Clostridium, Ruminococcus, Blautia, Butyricicoccus, Agathobaculum, Phocaeicola, Coprococcus, and Fusicatenibacter decreased. A total of 244 differential metabolites were identified and found to be enriched in the glycerophospholipid, nucleotide, biotin, galactose, and alpha-linolenic acid metabolic pathways. The AUC value from the integrated analysis (1) was higher than that from the analysis of the gut microbiota (0.8632). No significant differences were found in the ACE, Simpson, Shannon, Sobs, and Chao indexes between HD patients and controls. Our study determined crucial functional gut microbiota and potential biomarkers associated with HD pathogenesis, providing new insights into the role of the gut microbiota-brain axis in HD occurrence and development.

[Diversity and functional prediction of gut microbiota in children with autism spectrum disorder]

OBJECTIVES

To study the structure and diversity of gut microbiota in children with autism spectrum disorder (ASD), and to predict the metabolic function of gut microbiota.

METHODS

Fecal samples were collected from 30 ASD children (ASD group) and 20 typically developing (TD) children (TD group). Genomic DNA was extracted, the 16S rDNA V4 region was amplified by PCR, and Illumina NovaSeq6000 platform was used for high-throughput sequencing. The composition and distribution characteristics of gut microbiota were analyzed for the two groups, and the metabolic function of gut microbiota was predicted.

RESULTS

There were no significant differences in alpha diversity indices (Chao1, Shannon, and Simpson) of gut microbiota between the ASD and TD groups (P>0.05). At the phylum and class levels, there was no significant difference in the structure of gut microbiota between the two groups (P>0.05). Compared with the TD group, the ASD group had significantly higher abundance of Megamonas, Barnesiella, Dialister, Megasphaera, Ruminococcus_torques_group, and Fusobacterium at the genus level (P<0.05). Functional prediction analysis showed that compared with the TD group, the ASD group had a significantly lower abundance of the gut microbiota with the metabolic functions such as tryptophan degradation, glutamate degradation, and butyrate production (P<0.05) and a significantly higher abundance of the gut microbiota with the metabolic function of GABA degradation (P<0.05).

CONCLUSIONS

There is no significant difference in the alpha diversity of gut microbiota between ASD children and TD children, while there are differences in the composition of species at the genus level and the metabolic functions of gut microbiota.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case-control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.