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ABSTRACT Despite the importance of intra-species variants of viruses for causing disease and/or disrupting ecosystem functioning, there is no universally applicable standard to define these. A (natural) gap in whole-genome average nucleotide identity (ANI) values around 95% is commonly used to define species, especially for bacteriophages, but whether a similar gap exists within species that can be used to define intra-species units has not been evaluated yet. Whole-genome comparisons among members of 1,016 bacteriophage (Caudoviricetes) species revealed a region of low frequency of ANI values around 99.2%–99.8%, showing threefold or fewer pairs than expected for an even distribution. This second gap is prevalent in viruses infecting various cultured or uncultured hosts from a variety of environments, although a few exceptions to this pattern were also observed (3.7% of total species) and are likely attributed to cultivation biases or other factors. Similar results were observed for a limited set of eukaryotic viruses that are adequately sampled, including SARS-CoV-2, whose ANI-based clusters matched well with the WHO-defined variants of concern, indicating that our findings from bacteriophages might be more broadly applicable and the ANI-based clusters may represent functionally and/or ecologically distinct units. These units appear to be predominantly driven by (high) ecological cohesiveness coupled to either frequent recombination for bacteriophages or selection and clonal evolution for other viruses such as SARS-CoV-2, indicating that fundamentally different underlying mechanisms could lead to similar diversity patterns. Accordingly, we propose the ANI gap approach outlined above for defining viral intra-species units, for which we propose the term genomovars. IMPORTANCEViral species are composed of an ensemble of intra-species variants whose individual dynamics may have major implications for human and animal health and/or ecosystem functioning. However, the lack of universally accepted standards to define these intra-species variants has led researchers to use different approaches for this task, creating inconsistent intra-species units across different viral families and confusion in communication. By comparing hundreds of mostly bacteriophage genomes, we show that there is a widely distributed natural gap in whole-genome average nucleotide identity values in most, but not all, of these species that can be used to define intra-species units. Therefore, these results advance the molecular toolbox for tracking viral intra-species units and should facilitate future epidemiological and environmental studies. 

ABSTRACT Large-scale surveys of prokaryotic communities (metagenomes), as well as isolate genomes, have revealed that their diversity is predominantly organized in sequence-discrete units that may be equated to species. Specifically, genomes of the same species commonly show genome-aggregate average nucleotide identity (ANI) >95% among themselves and ANI <90% to members of other species, while genomes showing ANI 90%–95% are comparatively rare. However, it remains unclear if such “discontinuities” or gaps in ANI values can be observed within species and thus used to advance and standardize intra-species units. By analyzing 18,123 complete isolate genomes from 330 bacterial species with at least 10 genome representatives each and available long-read metagenomes, we show that another discontinuity exists between 99.2% and 99.8% (midpoint 99.5%) ANI in most of these species. The 99.5% ANI threshold is largely consistent with how sequence types have been defined in previous epidemiological studies but provides clusters with ~20% higher accuracy in terms of evolutionary and gene-content relatedness of the grouped genomes, while strains should be consequently defined at higher ANI values (>99.99% proposed). Collectively, our results should facilitate future micro-diversity studies across clinical or environmental settings because they provide a more natural definition of intra-species units of diversity. IMPORTANCEBacterial strains and clonal complexes are two cornerstone concepts for microbiology that remain loosely defined, which confuses communication and research. Here we identify a natural gap in genome sequence comparisons among isolate genomes of all well-sequenced species that has gone unnoticed so far and could be used to more accurately and precisely define these and related concepts compared to current methods. These findings advance the molecular toolbox for accurately delineating and following the important units of diversity within prokaryotic species and thus should greatly facilitate future epidemiological and micro-diversity studies across clinical and environmental settings. 

Abstract Genome search and/or classification typically involves finding the best-match database (reference) genomes and has become increasingly challenging due to the growing number of available database genomes and the fact that traditional methods do not scale well with large databases. By combining k-mer hashing-based probabilistic data structures (i.e. ProbMinHash, SuperMinHash, Densified MinHash and SetSketch) to estimate genomic distance, with a graph based nearest neighbor search algorithm (Hierarchical Navigable Small World Graphs, or HNSW), we created a new data structure and developed an associated computer program, GSearch, that is orders of magnitude faster than alternative tools while maintaining high accuracy and low memory usage. For example, GSearch can search 8000 query genomes against all available microbial or viral genomes for their best matches (n = ∼318 000 or ∼3 000 000, respectively) within a few minutes on a personal laptop, using ∼6 GB of memory (2.5 GB via SetSketch). Notably, GSearch has an O(log(N)) time complexity and will scale well with billions of genomes based on a database splitting strategy. Further, GSearch implements a three-step search strategy depending on the degree of novelty of the query genomes to maximize specificity and sensitivity. Therefore, GSearch solves a major bottleneck of microbiome studies that require genome search and/or classification. 

Abstract Whether prokaryotes, and other microorganisms, form distinct clusters that can be recognized as species remains an issue of paramount theoretical as well as practical consequence in identifying, regulating, and communicating about these organisms. In the past decade, comparisons of thousands of genomes of isolates and hundreds of metagenomes have shown that prokaryotic diversity may be predominantly organized in such sequence‐discrete clusters, albeit organisms of intermediate relatedness between the identified clusters are also frequently found. Accumulating evidence suggests, however, that the latter “intermediate” organisms show enough ecological and/or functional distinctiveness to be considered different species. Notably, the area of discontinuity between clusters often—but not always—appears to be around 85%–95% genome‐average nucleotide identity, consistently among different taxa. More recent studies have revealed remarkably similar diversity patterns for viruses and microbial eukaryotes as well. This high consistency across taxa implies a specific mechanistic process that underlies the maintenance of the clusters. The underlying mechanism may be a substantial reduction in the efficiency of homologous recombination, which mediates (successful) horizontal gene transfer, around 95% nucleotide identity. Deviations from the 95% threshold (e.g., species showing lower intraspecies diversity) may be caused by ecological differentiation that imposes barriers to otherwise frequent gene transfer. While this hypothesis that clusters are driven by ecological differentiation coupled to recombination frequency (i.e., higher recombination within vs. between groups) is appealing, the supporting evidence remains anecdotal. The data needed to rigorously test the hypothesis toward advancing the species concept are also outlined. 

Abstract Background Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro - and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity ( π and θ ), and selective pressures (pN/pS and Tajima’s D ) within and fixation indices ( F ST ) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity ( π ) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences. Conclusions These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro - and microdiversity across microbial communities. 

ABSTRACT Identification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla . Application of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice. IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings.