Explore the vast range of titles available.

The gut microbiome plays an important role in early life, protecting newborns from enteric pathogens, promoting immune system development and providing key functions to the infant host. Currently, there are limited data to broadly assess the status of the US healthy infant gut microbiome. To address this gap, we performed a multi-state metagenomic survey and found high levels of bacteria associated with enteric inflammation (e.g. Escherichia , Klebsiella), antibiotic resistance genes, and signatures of dysbiosis, independent of location, age, and diet. Bifidobacterium were less abundant than generally expected and the species identified, including B. breve, B. longum and B. bifidum, had limited genetic capacity to metabolize human milk oligosaccharides (HMOs), while B. infantis strains with a complete capacity for HMOs utilization were found to be exceptionally rare. Considering microbiome composition and functional capacity, this survey revealed a previously unappreciated dysbiosis that is widespread in the contemporary US infant gut microbiome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the ( ) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.