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Abstract Carbonic anhydrase (CA) enzymes catalyze the interconversion of carbon dioxide and bicarbonate with an efficiency exceeded only by superoxide dismutase. CA enzymes have evolved convergently in phylogenetically distant organisms, forming eight structurally unrelated classes that share physiological functions involved in photosynthesis, respiration, pH homeostasis, CO2 transport, and carbonyl sulfide hydrolysis that play central roles in medicine and the environment. Here, we leverage the recent surge in publicly available genomes and metagenomes to re-examine our understanding of the abundance, diversity, and phylogenetic relationships of the three major CA classes in Bacteria/Archaea and microbial Eukaryotes (Fungi, algae). We recovered a total of 57 218 α-, β-, and γ-CA sequences from 24 184 metagenomes and genomes, including the first putative α-CA from an archaeal species. CA sequences formed 3859 protein clusters (1188 with three or more sequences). Sequences within a cluster were typically taxonomically conserved only at higher levels (i.e. Superkingdom, Phylum). When viewed within a phylogenetic framework, the majority of subclades for each CA class contained CAs representing multiple Superkingdoms, although numerous novel β-CA clades appear unique to Fungi. Queries of CA Hidden Markov models against all public metagenome and metatranscriptome datasets revealed that CA is a ubiquitous enzyme present in virtually all sampled environments. However, CA clusters that were taxonomically conserved also appeared more environment-specific, which may explain high CA diversity. This work represents an important contribution to our understanding of the evolution, diversity, and environmental distribution of an enzyme that is key to life and has broad environmental and industrial applications. 

Abstract: Supporting information for the manuscript entitled: "Enabling the prediction of phage receptor specificity from genome data". This records contains:Supplementary information (descriptive listing of all supplements)Supplementary Tables S1-S10Supplementary Datasets S1-S7Supplementary Figures S1-S13 (combined)All Supplementary Figures in high-resolution .png filesAll Figures in high-resolution .png files 

Abstract Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats. 

Abstract Many microbes in nature reside in dense, metabolically interdependent communities. We investigated the nature and extent of microbe-virus interactions in relation to microbial density and syntrophy by examining microbe-virus interactions in a biomass dense, deep-sea hydrothermal mat. Using metagenomic sequencing, we find numerous instances where phylogenetically distant (up to domain level) microbes encode CRISPR-based immunity against the same viruses in the mat. Evidence of viral interactions with hosts cross-cutting microbial domains is particularly striking between known syntrophic partners, for example those engaged in anaerobic methanotrophy. These patterns are corroborated by proximity-ligation-based (Hi-C) inference. Surveys of public datasets reveal additional viruses interacting with hosts across domains in diverse ecosystems known to harbour syntrophic biofilms. We propose that the entry of viral particles and/or DNA to non-primary host cells may be a common phenomenon in densely populated ecosystems, with eco-evolutionary implications for syntrophic microbes and CRISPR-mediated inter-population augmentation of resilience against viruses. 

Abstract Background Rock-dwelling microorganisms are key players in ecosystem functioning of Antarctic ice free-areas. Yet, little is known about their diversity and ecology, and further still, viruses in these communities have been largely unexplored despite important roles related to host metabolism and nutrient cycling. To begin to address this, we present a large-scale viral catalog from Antarctic rock microbial communities. Results We performed metagenomic analyses on rocks from across Antarctica representing a broad range of environmental and spatial conditions, and which resulted in a predicted viral catalog comprising > 75,000 viral operational taxonomic units (vOTUS). We found largely undescribed, highly diverse and spatially structured virus communities which had predicted auxiliary metabolic genes (AMGs) with functions indicating that they may be potentially influencing bacterial adaptation and biogeochemistry. Conclusion This catalog lays the foundation for expanding knowledge of virosphere diversity, function, spatial ecology, and dynamics in extreme environments. This work serves as a step towards exploring adaptability of microbial communities in the face of a changing climate. 

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of aRhizosoleniasp. bloom, the initiation and complete bloom cycle ofChaetoceros socialis(8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms. 

Abstract Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phenotypic data required to train and validate predictive models have not been available at sufficient scale. Here we address this by conducting 1,050 genome-wide genetic screens across 255 taxonomically diverseEscherichia colidsDNA phages, assigning host receptors to 193 phages across 19 receptor classes. Comparative genomics and AlphaFold3 structural modelling resolved the sequence determinants of specificity to defined receptor-binding protein domains and individual residues. Machine learning models trained on this dataset predicted host receptor identity from phage genome sequence alone without prior annotation of receptor-binding genes, achieving perfect precision and greater than 80% recall on 49 independently validated phages, and yielding predictions for 1,060 of 1,875E. coliphage genomes in NCBI. Domain swaps redirected receptor specificity as predicted, and a single amino acid substitution proved both necessary and sufficient to switch recognition between two distinct porins. These results demonstrate that systematic phenotyping at scale makes sequence-based prediction of molecular interaction specificity tractable, with direct implications for phage-based medicine, microbiome engineering and the broader challenge of inferring host-pathogen interaction outcomes from sequence. 

The biology and ecology of marine microbial eukaryotes is known to be constrained by oceanic conditions. In contrast, how viruses that infect this important group of organisms respond to environmental change is less well known, despite viruses being recognized as key microbial community members. 

Abstract The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics.