A fundamental question in structural biology centres around understanding protein evolution. Key to this process is mutational robustness, defined as the protein fold's ability to absorb sequence changes without collapsing its structure. Here, we show that robustness is systematically shaped by simple features such as protein size, geometry, and oligomeric state. We used Foldseek-identified (structural) homologs to quantify family size across monomers and higher homo-oligomers. We found that proteins in larger families are consistently larger in size, more compact in atomic density, and less exposed to solvent. Strikingly, homo-oligomers occupy systematically larger families than monomers, revealing quaternary structure itself as a driver of mutational tolerance, not merely a functional supplement. This signature of robustness can be further linked to increasing functional complexity in proteins; those with adaptive, multifaceted biological roles belong to larger structural families than those with specific roles, thereby linking structural flexibility directly to evolutionary versatility. In short, simple yet overlooked features of protein geometry can explain mutational robustness and evolvability, offering a structural rationale for why certain protein families have diversified extensively while others remain in evolutionary stasis.

The human gut microbiome is increasingly viewed as an active regulator of host physiology, extending beyond earlier taxonomy-centered descriptions of a complex microbial community. Accumulating evidence supports an organ-like conceptual framework in which the gut microbiome exhibits spatially structured organization, extensive metabolic capacity, and continuous bidirectional communication with host systems. Through the production of bioactive metabolites with endocrine-like, immunomodulatory, and neuromodulatory properties, the microbiome contributes to metabolic, immune, and neuroendocrine regulation, thereby influencing systemic homeostasis and disease susceptibility. Recent advances in multi-omics, spatial biology, and computational modeling are moving the field from taxonomic association toward functional interpretation, mechanistic insight, and causal inference. These approaches are beginning to reveal microbiome-derived functional modules and host–microbe signaling networks that are shaped by host genetics, diet, medications, feeding patterns, circadian rhythms, and environmental exposures. In this review, we synthesize current mechanistic and translational evidence to conceptualize the gut microbiome as an organ-like functional system, delineate its structural and functional organization, and propose a framework for mapping, modeling, and therapeutically targeting microbiome-derived circuits to support precision medicine in metabolic, inflammatory, and selected gut–brain axis-related disorders.

The affordability and high-throughput capabilities of Oxford Nanopore Technologies (ONT) have democratized genomic sequencing, yet the complexity of bioinformatics environment setup and analysis remains a barrier for many biologists.  microbiONT addresses this gap by providing a user-friendly, privacy-focused platform that features a streamlined, one-command installation process. Integrating a local Large Language Model (Llama 3.1) with an intuitive graphical interface, microbiONT serves not only as an analysis tool but also as an AI copilot to assist users in learning bioinformatics concepts. By translating natural language requests into executable commands and simplifying deployment, microbiONT empowers non-experts to perform rigorous or custom 16S/18S amplicon analysis locally. This ensures rapid data insights without the need for command line use. Beyond its AI-integrated GUI, the novelty of microbiONT lies in its fully localized, highly accurate, and privacy-preserving architecture for flexible data analysis.

Circulating microbial DNA (cmDNA) has been proposed as a non-invasive cancer biomarker, but most evidence comes from cancer-sequencing datasets not designed for microbial analysis and lacking contamination controls. Whether reported signatures reflect biology or artifact is unclear in low-biomass specimens, where standard taxonomic pipelines are prone to systematic error. In a tightly controlled pilot study of metastatic castration-resistant prostate cancer, we profiled plasma cell-free DNA (cfDNA) and buffy-coat genomic DNA (gDNA) from two patients and two healthy volunteers alongside mock blood-draw and reagent controls, each with and without host-DNA depletion. Much of the apparent cmDNA structure, including its agreement with a published cancer-microbiome catalog, is explained by base composition and reference-database architecture rather than authentic biology, and short-read k-mer pipelines cannot separate the two on their own. We find little positive evidence of an authentic circulating microbial signal, though our small sample cannot prove its absence. To limit false discovery in low-biomass metagenomics, we recommend specimen-matched negative controls, corroboration with a conservative second classifier, per-base shuffling (with GC-matched synthetic reads as a stricter floor), and GC-aware analysis.

Transcription underpins the expression of genetic information and is viewed as a series of independent initiation and termination events. In bacteria, promoters and terminators of transcription are therefore considered distinct regulatory elements. Here, we challenge this view. Genome-scale analyses reveal that promoters and terminators are extensively coupled. Thus, initiation and termination sites, for adjacent transcription units, frequently overlap. This conserved organization arises because the DNA sequences, which direct termination, also contribute to promoter function. This couples neighboring transcription units and generates regulatory interference between RNA polymerases. These findings define a universal mechanism for coordinating transcription across bacterial genomes.

Microorganisms interact through the exchange of metabolites and competition for shared substrates, and this metabolic coupling shapes the composition and function of microbial communities. Community flux balance analysis (cFBA) can predict such behavior - the maximum community growth rate, the metabolic fluxes and the relative abundances of the species - from stoichiometric models of their metabolism, but existing formulations are either complex and hard to scale as communities grow or cannot predict optimal growth rates. Here we present a physiology-based formulation of cFBA in which each species' metabolism is reduced to a few macrochemical equations, one for each 'metabolic mode' the species can use, and the whole community is then solved as a single linear program. From this, the method predicts the optimal composition of the community, its maximum growth rate, the metabolites exchanged between the species, and the net conversion the community carries out as a whole; its ecological service. This reduction makes it far simpler to build and solve models of larger communities. We illustrate the approach on a two-species synergistic community that can be verified by hand, apply it to a five-member anaerobic digestion community, and use it to predict the metabolic interactions of a genome-scale syngas-fermenting coculture. Characterizing these communities at their optimal steady states, we show that each species is driven to a distinct metabolic strategy. We discuss the method both as a practical tool for larger microbial communities and as a means of uncovering the ecological principles that govern them.

Denitrification is a modular process that is mediated by an assemblage of microbes with varying denitrification gene combinations. The controls on these gene combinations, known as modularity, are poorly understood and marine observations are mostly limited to permanently anoxic systems. In this global metagenomic analysis representing 69 water column metagenome samples we report different modularity patterns associated with environmental parameters based on the permanence of anoxia. Thermodynamic favorability alone is not enough to explain the distribution of modularity patterns. Instead, variables such as the permanence (or transience) of anoxia, oxygen availability, biogeography and ratios of organic matter to nitrogen supply all help shape the denitrifier community gene assemblage. Environmental correlates in transiently anoxic compared to permanently anoxic systems suggest that the pressures of a more complex environment may favor shorter pathways due to resource allocation trade-off regardless of organic matter availability. Nitrate reduction is the dominant step compared to the rest of the denitrification pathway irrespective of anoxia type. As increases in global temperature result in more seasonally anoxic and hypoxic waters, these results highlight the importance of understanding the controls on denitrification modularity under varying states of anoxia.