Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Caloric restriction (CR) stimulates development of functional beige fat and extends healthy lifespan. Here we show that compositional and functional changes in the gut microbiota contribute to a number of CR-induced metabolic improvements and promote fat browning. Mechanistically, these effects are linked to a lower expression of the key bacterial enzymes necessary for the lipid A biosynthesis, a critical lipopolysaccharide (LPS) building component. The decreased LPS dictates the tone of the innate immune response during CR, leading to increased eosinophil infiltration and anti-inflammatory macrophage polarization in fat of the CR animals. Genetic and pharmacological suppression of the LPS-TLR4 pathway or transplantation with Tlr4 bone-marrow-derived hematopoietic cells increases beige fat development and ameliorates diet-induced fatty liver, while Tlr4 or microbiota-depleted mice are resistant to further CR-stimulated metabolic alterations. These data reveal signals critical for our understanding of the microbiota-fat signaling axis during CR and provide potential new anti-obesity therapeutics.

Background: Metagenomics studies provide valuable insight into the composition and function of microbial populations from diverse environments; however, the data processing pipelines that rely on mapping reads to gene catalogs or genome databases for cultured strains yield results that underrepresent the genes and functional potential of uncultured microbes. Recent improvements in sequence assembly methods have eased the reliance on genome databases, thereby allowing the recovery of genomes from uncultured microbes. However, configuring these tools, linking them with advanced binning and annotation tools, and maintaining provenance of the processing continues to be challenging for researchers. Results: Here we present ATLAS, a software package for customizable data processing from raw sequence reads to functional and taxonomic annotations using state-of-theart tools to assemble, annotate, quantify, and bin metagenome data. Abundance estimates at genome resolution are provided for each sample in a dataset. ATLAS is written in Python and the workflow implemented in Snakemake; it operates in a Linux environment, and is compatible with Python 3.5+ and Anaconda 3+ versions. The source code for ATLAS is freely available, distributed under a BSD-3 license. Conclusions: ATLAS provides a user-friendly, modular and customizable Snakemake workflow for metagenome data processing; it is easily installable with conda and maintained as open-source on GitHub at https://github.com/metagenome-atlas/atlas.

Crosstalk between immune cells and the microbiota in mucosal tissues can set an individual on a trajectory toward health or disease. Little is known about these early-life events in the human respiratory tract. We examined bacterial colonization and immune system maturation in the lower airways over the first year of life. The lower respiratory tract microbiota forms within the first 2 postnatal months. Within the first weeks, three microbial profiles are evident, broadly distinguished as dysbiotic or diverse, and representing different microbial virulence potentials, including proteolysis of immunoglobulin A (IgA) that is critical for mucosal defense. Delivery mode determines microbiota constituents in preterm, but not term, births. Gestational age is a key determinant of immune maturation, with airway cells progressively increasing expression of proallergic cytokine interleukin-33 and genes linked with IgA. These data reveal microbial and immunological development in human airways, and may inform early-life interventions to prevent respiratory diseases.