The composition and function of the human gut microbiome has been linked to multiple health outcomes across all world regions, often with region-specific associations. Unfortunately, the extent to whi...

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Here, the authors profile the gut microbiota and metabolites of Indian agrarians to understand the impact of fermented foods consumption, and identify seasonal variation in the gut microbiota structur...

A 471-metagenome time series from Lake Mendota in Wisconsin, USA, reveals seasonal and decadal shifts in bacterial functional and ecological dynamics, especially in response to environmental extremes.

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures. ### Competing Interest Statement C.H. declares the following associations: Seres Therapeutics (scientific advisory board, microbiome therapies), Microbiome Insights (scientific advisory board, microbiome data generation), Zoe (scientific advisory board), Empress (scientific advisory board, microbiome therapies).

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction ...

This study models early life stress (ELS) to observe sex-specific effects on fear memory formation using innovative unsupervised classification together with cellular metabolism alterations in key str...

Nature - Some non-antibiotic drugs have been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Athanasios Typas and colleagues screened more than...

An observational longitudinal clinical trial, incorporating a SmartPill and metabolomics, reveals the role of host factors in shaping the gut microbiome in healthy human adults.

Understanding the interrelationship between the gut microbiota and host physiology, although still in its relative infancy, has taken important steps forward over the past decade. In the context of...

Background The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbi...