Whether human-associated environments drive adaptive genome evolution in gut commensal bacteria remains largely unknown. Here, we investigate how genomic adaptation in gut bacteria differs between industrialized and non-industrialized human populations. We generated a collection of isolate genomes spanning diverse host geographies, lifestyles, species, and strains. We first found that, compared with metagenome-assembled genomes (MAGs), paired isolate genomes recover more functional elements and signals of horizontal gene transfer (HGT). Leveraging isolate genomes from multiple species, we find that strains from industrialized hosts experience an expansion of proteome size and harbor greater pangenome fluidity, driven by recent HGT events. Gene- and variant-level analyses reveal convergent patterns of lifestyle-specific adaptation in functions that are critical for ecological adaptation, such as the stress response, cell-envelope remodeling, and central metabolism. Our results demonstrate that industrialization drives convergent genome evolution in gut bacteria, providing a framework for understanding the mechanisms of microbial adaptation to rapidly changing human environments.
Understanding how industrialization reshapes the human metaorganism requires integrating microbiome ecology with host physiology across globally diverse populations. Here, we generate multivariate data from the Global Microbiome Conservancy cohort, including gut microbiome, immunoglobulin A (IgA) sequencing, host genotyping, diet, lifestyle, and fecal biomarker profiles, to investigate host-microbiome interactions across gradients of industrialization and geography. We show that industrialization is associated with homogenized microbial compositions, reduced microbial diversity, and lower community stability, independent of host confounders. We further show that industrialization is linked to elevated markers of gut stress, increased IgA secretion, and altered patterns of IgA-bacteria interactions. Finally, we show that microbiome-based disease predictors trained on industrialized populations are uninformative in less industrialized cohorts, highlighting limited cross-population transferability. Together, our results reveal coordinated restructuring of the microbial ecosystem and host physiology and emphasize the need for inclusive, globally representative data to improve translational microbiome applications across diverse human populations.
The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The datasets used correspond to an integrated luminosity of up to 5 and 20 fb^{-1} of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exploit both the semileptonic and hadronic decays of the top quark, and a measurement using events enriched in single top quark production via the electroweak t channel. The combination accounts for the correlations between measurements and achieves an improvement in the total uncertainty of 31% relative to the most precise input measurement. The result is m_{t}=172.52±0.14(stat)±0.30(syst) GeV, with a total uncertainty of 0.33 GeV.