Objective Development of obesity and type 2 diabetes (T2D) are associated with gut microbiota (GM) changes. The gut viral community is predominated by bacteriophages (phages), which are viruses that attack bacteria in a host-specific manner. The antagonistic behaviour of phages has the potential to alter the GM. As a proof-of-concept, we demonstrate the efficacy of faecal virome transplantation (FVT) from lean donors for shifting the phenotype of obese mice into closer resemblance of lean mice. Design The FVT consisted of viromes with distinct profiles extracted from the caecal content of mice from different vendors that were fed a low-fat (LF) diet for 14 weeks. Male C57BL/6NTac mice were divided into five groups: LF (as diet control), high-fat (HF) diet, HF+ampicillin (Amp), HF+Amp+FVT and HF+FVT. At weeks 6 and 7 of the study, the HF+FVT and HF+Amp+FVT mice were treated with FVT by oral gavage. The Amp groups were treated with Amp 24 hours prior to first FVT treatment. Results Six weeks after first FVT, the HF+FVT mice showed a significant decrease in weight gain compared with the HF group. Further, glucose tolerance was comparable between the LF and HF+FVT mice, while the other HF groups all had impaired glucose tolerance. These observations were supported by significant shifts in GM composition, blood plasma metabolome and expression levels of genes associated with obesity and T2D development. Conclusions Transfer of caecal viral communities from mice with a lean phenotype into mice with an obese phenotype led to reduced weight gain and normalised blood glucose parameters relative to lean mice. We hypothesise that this effect is mediated via FVT-induced GM changes.

MicrobeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms’ role in ecology and human health.

A bacterial strain designated L2-7^T, phylogenetically related to Eubacterium hallii DSM 3353^T, was previously isolated from infant faeces. The complete genome of strain L2-7^T contains eight copies of the 16S rRNA gene with only 98.0–98.5 % similarity to the 16S rRNA gene of the previously described type strain E. hallii . The next closest validly described species is Anaerostipes hadrus DSM 3319^T (90.7 % 16S rRNA gene similarity). A polyphasic taxonomic approach showed strain L2-7^T to be a novel species, related to type strain E. hallii DSM 3353^T. The experimentally observed DNA–DNA hybridization value between strain L2-7^T and E. hallii DSM 3353^T was 26.25 %, close to that calculated from the genomes (34.3 %). The G+C content of the chromosomal DNA of strain L2-7^T was 38.6 mol%. The major fatty acids were C_16 : 0, C_16 : 1 cis9 and a component with summed feature 10 (C_18 : 1c11/t9/t6c). Strain L2-7^T had higher amounts of C_16 : 0 (30.6 %) compared to E. hallii DSM 3353^T (19.5 %) and its membrane contained phosphatidylglycerol and phosphatidylethanolamine, which were not detected in E. hallii DSM 3353^T. Furthermore, 16S rRNA gene phylogenetic analysis advocates that E. hallii DSM 3353^T is misclassified, and its reclassification as a member of the family Lachnospiraceae is necessary. Using a polyphasic approach, we propose that E. hallii (=DSM 3353^T=ATCC 27751^T) be reclassified as the type strain of a novel genus Anaerobutyricum sp. nov., comb. nov. and we propose that strain L2-7^T should be classified as a novel species, Anaerobutyricum soehngenii sp. nov. The type strain is L2-7^T (=DSM 17630^T=KCTC 15707^T).

Evidence from cross-sectional human studies, and preliminary microbial-based intervention studies, have implicated the microbiota-gut-brain axis in the neurobiology of autism spectrum disorder (ASD). Using a prospective longitudinal study design, we investigated the developmental profile of the fecal microbiota and metabolome in infants with (n = 16) and without (n = 19) a family history of ASD across the first 36 months of life. In addition, the general developmental levels of infants were evaluated using the Mullen Scales of Early Learning (MSEL) test at 5 and 36 months of age, and with ADOS-2 at 36 months of age. At 5 months of age, infants at elevated-likelihood of ASD (EL) harbored less Bifidobacterium and more Clostridium and Klebsiella species compared to the low-likelihood infants (LL). Untargeted metabolic profiling highlighted that LL infants excreted a greater amount of fecal γ-aminobutyric acid (GABA) at 5 months, which progressively declined with age. Similar age-dependent patterns were not observed in the EL group, with GABA being consistently low across all timepoints. Integrated microbiome-metabolome analysis showed a positive correlation between GABA and Bifidobacterium species and negative associations with Clostridium species. In vitro experiments supported these observations demonstrating that bifidobacteria can produce GABA while clostridia can consume it. At the behavioral level, there were no significant differences between the EL and LL groups at 5 months. However, at 36 months of age, the EL group had significantly lower MSEL and ADOS-2 scores compared to the LL group. Taken together, the present results reveal early life alterations in gut microbiota composition and functionality in infants at elevated-likelihood of ASD. These changes occur before any behavioral impairments can be detected, supporting a possible role for the gut microbiota in emerging behavioral variability later in life.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.