EzBioCloud: a genome-driven database and platform for microbiome identification and discovery.

With the continued evolution of DNA sequencing technologies, the role of genome sequence data has become more integral in the classification and identification of Bacteria and Archaea. Six years after introducing EzBioCloud, an integrated platform representing the taxonomic hierarchy of Bacteria and Archaea through quality-controlled 16S rRNA gene and genome sequences, we present an updated version, that further refines and expands its capabilities. The current update recognizes the growing need for accurate taxonomic information as defining a species increasingly relies on genome sequence comparisons. We also incorporated an advanced strategy for addressing underrepresented or less studied lineages, bolstering the comprehensiveness and accuracy of our database. Our rigorous quality control protocols remain, where whole-genome assemblies from the NCBI Assembly Database undergo stringent screening to remove low-quality sequence data. These are then passed through our enhanced identification bioinformatics pipeline which initiates a 16S rRNA gene similarity search and then calculates the average nucleotide identity (ANI). For genome sequences lacking a 16S rRNA sequence and without a closely related genomic representative for ANI calculation, we apply a different ANI approach using bacterial core genes for improved taxonomic placement (core gene ANI, cgANI). Because of the increase in genome sequences available in NCBI and our newly introduced cgANI method, EzBioCloud now encompasses a total of 109 835 species, of which 21 964 have validly published names. 47 896 are candidate species identified either through 16S rRNA sequence similarity (phylotypes) or through whole genome ANI (genomospecies), and the remaining 39 975 were positioned in the taxonomic tree by cgANI (species clusters). Our EzBioCloud database is accessible at www.ezbiocloud.net/db.

Gut microbial and clinical characteristics of individuals with autism spectrum disorder differ depending on the ecological structure of the gut microbiome.

Understanding the relationship between the gut microbiome and autism spectrum disorder (ASD) is challenging due to the heterogeneous nature of ASD. Here, we analyzed the microbial and clinical characteristics of individuals with ASD using enterotypes. A total of 456 individuals participated in the study, including 249 participants with ASD, 106 typically developing siblings, and 101 controls. The alpha and beta diversities of the ASD, sibling, and control groups did not show significant differences. Analysis revealed a negative association between the Bifidobacterium longum group and the Childhood Autism Rating Scale, as well as a negative association between the Streptococcus salivarus group and the Social Responsiveness Scale (SRS) within the ASD group. When clustered based on microbial composition, participants with ASD exhibited two distinct enterotypes, E1 and E2. In the E2 group, the SRS score was significantly higher, and the Vineland Adaptive Behavior Scale score was significantly lower compared to the E1 group. Machine learning results indicated that the microbial species predicting SRS scores were distinct between the two enterotypes. Our study suggests that the microbial composition in individuals with ASD exhibits considerable variability, and the patterns of associations between the gut microbiome and clinical symptoms may vary depending on the enterotype.

Longitudinal associations between gut microbiome diversity and emotional well-being.

OBJECTIVE: While a significant link between emotional well-being (EWB) and the gut microbiome has been reported recently, their temporal relationships remain elusive. This study aims to fill this gap by examining the longitudinal associations between EWB and the Shannon Index (SI), an indicator of gut microbiome diversity. METHOD: The analysis focused on a dataset that collected participants' current EWB and fecal samples in both 2019 and 2022 (N = 57, 56.1% female, Mage = 52.47 years, SD = 12.65). Gut microbiome profiles were generated by sequencing the 16S rRNA gene, from which SI was subsequently calculated. RESULTS: The cross-lagged panel analysis revealed significant positive cross-sectional associations between EWB and SI in both 2019 (ß = .296, SE = 0.121, p = .014) and 2022 (ß = .324, SE = 0.119, p = .006). However, no significant longitudinal associations were found between 2019 EWB and 2022 SI (ß = .068, SE = 0.138, p = .623), nor between 2019 SI and 2022 EWB (ß = -.016, SE = 0.13, p = .899). CONCLUSIONS: Our findings indicate that emotional happiness may be associated with gut microbiome profiles at a particular time point, but they may not serve as predictive factors for each other over time. Future research is needed to establish causal relationships between them. (PsycInfo Database Record (c) 2024 APA, all rights reserved).