Improving genome-scale metabolic models of incomplete genomes with deep learning.

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes.

Seasonal dynamics and diversity of Antarctic marine viruses reveal a novel viral seascape.

The Southern Ocean microbial ecosystem, with its pronounced seasonal shifts, is vulnerable to the impacts of climate change. Since viruses are key modulators of microbial abundance, diversity, and evolution, we need a better understanding of the effects of seasonality on the viruses in this region. Our comprehensive exploration of DNA viral diversity in the Southern Ocean reveals a unique and largely uncharted viral landscape, of which 75% was previously unidentified in other oceanic areas.

Graphite: painting genomes using a colored de Bruijn graph.

The recent growth of microbial sequence data allows comparisons at unprecedented scales, enabling the tracking of strains, mobile genetic elements, or genes. Querying a genome against a large reference database can easily yield thousands of matches that are tedious to interpret and pose computational challenges. We developed Graphite that uses a colored de Bruijn graph (cDBG) to paint query genomes, selecting the local best matches along the full query length.

Phylogeny and disease associations of a widespread and ancient intestinal bacteriophage lineage.

Viruses are core components of the human microbiome, impacting health through interactions with gut bacteria and the immune system. Most human microbiome viruses are bacteriophages, which exclusively infect bacteria. Until recently, most gut virome studies focused on low taxonomic resolution (e.

Structural color in the bacterial domain: The ecogenomics of a 2-dimensional optical phenotype.

Structural color is an optical phenomenon resulting from light interacting with nanostructured materials. Although structural color (SC) is widespread in the tree of life, the underlying genetics and genomics are not well understood. Here, we collected and sequenced a set of 87 structurally colored bacterial isolates and 30 related strains lacking SC.

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes.

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

Background: As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs.

A global survey of host, aquatic, and soil microbiomes reveals shared abundance and genomic features between bacterial and fungal generalists.

Environmental change, coupled with alteration in human lifestyles, is profoundly impacting the microbial communities critical to the health of the Earth and its inhabitants. To identify bacteria and fungi that are resistant and susceptible to habitat change, we analyze thousands of genera detected in 1,580 host, soil, and aquatic samples. This large-scale analysis identifies 48 bacterial and 4 fungal genera that are abundant across the three biomes, demonstrating fitness in diverse environmental conditions.

Taxonomic resolution of different 16S rRNA variable regions varies strongly across plant-associated bacteria.

Plant-microbiome research plays a pivotal role in understanding the relationships between plants and their associated microbial communities, with implications for agriculture and ecosystem dynamics. Metabarcoding analysis on variable regions of the 16S ribosomal RNA (rRNA) gene remains the dominant technology to study microbiome diversity in this field. However, the choice of the targeted variable region might affect the outcome of the microbiome studies.

Ultraconserved bacteriophage genome sequence identified in 1300-year-old human palaeofaeces.

Bacteriophages are widely recognised as rapidly evolving biological entities. However, knowledge about ancient bacteriophages is limited. Here, we analyse DNA sequence datasets previously generated from ancient palaeofaeces and human gut-content samples, and identify an ancient phage genome nearly identical to present-day Mushuvirus mushu, a virus that infects gut commensal bacteria.

Identifying and tracking mobile elements in evolving compost communities yields insights into the nanobiome.

Microbial evolution is driven by rapid changes in gene content mediated by horizontal gene transfer (HGT). While mobile genetic elements (MGEs) are important drivers of gene flux, the nanobiome-the zoo of Darwinian replicators that depend on microbial hosts-remains poorly characterised. New approaches are necessary to increase our understanding beyond MGEs shaping individual populations, towards their impacts on complex microbial communities.

Growth rate is a dominant factor predicting the rhizosphere effect.

The root microbiome is shaped by plant root activity, which selects specific microbial taxa from the surrounding soil. This influence on the microorganisms and soil chemistry in the immediate vicinity of the roots has been referred to as the rhizosphere effect. Understanding the traits that make bacteria successful in the rhizosphere is critical for developing sustainable agriculture solutions.

Changes to virus taxonomy and the ICTV Statutes ratified by the International Committee on Taxonomy of Viruses (2023).

This article reports changes to virus taxonomy and taxon nomenclature that were approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in April 2023. The entire ICTV membership was invited to vote on 174 taxonomic proposals that had been approved by the ICTV Executive Committee in July 2022, as well as a proposed revision of the ICTV Statutes. All proposals and the revised ICTV Statutes were approved by a majority of the voting membership.

Bacterial Oncotraits Rather than Spatial Organization Are Associated with Dysplasia in Ulcerative Colitis.

Background And Aims: Colonic bacterial biofilms are frequently present in ulcerative colitis [UC] and may increase dysplasia risk through pathogens expressing oncotraits. This prospective cohort study aimed to determine [1] the association of oncotraits and longitudinal biofilm presence with dysplasia risk in UC, and [2] the relation of bacterial composition with biofilms and dysplasia risk.

Methods: Faeces and left- and right-sided colonic biopsies were collected from 80 UC patients and 35 controls.

Virus taxonomy and the role of the International Committee on Taxonomy of Viruses (ICTV).

The taxonomy of viruses is developed and overseen by the International Committee on Taxonomy of Viruses (ICTV), which scrutinizes, approves and ratifies taxonomic proposals, and maintains a list of virus taxa with approved names (https://ictv.global). The ICTV has approximately 180 members who vote by simple majority.

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria.

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.

A social niche breadth score reveals niche range strategies of generalists and specialists.

Generalists can survive in many environments, whereas specialists are restricted to a single environment. Although a classical concept in ecology, niche breadth has remained challenging to quantify for microorganisms because it depends on an objective definition of the environment. Here, by defining the environment of a microorganism as the community it resides in, we integrated information from over 22,000 environmental sequencing samples to derive a quantitative measure of the niche, which we call social niche breadth.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons.

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences.