Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs. IMPORTANCE: Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

The European livestock resistome.

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

ARGprofiler-a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets.

MOTIVATION: Analyzing metagenomic data can be highly valuable for understanding the function and distribution of antimicrobial resistance genes (ARGs). However, there is a need for standardized and reproducible workflows to ensure the comparability of studies, as the current options involve various tools and reference databases, each designed with a specific purpose in mind. RESULTS: In this work, we have created the workflow ARGprofiler to process large amounts of raw sequencing reads for studying the composition, distribution, and function of ARGs. ARGprofiler tackles the challenge of deciding which reference database to use by providing the PanRes database of 14 078 unique ARGs that combines several existing collections into one. Our pipeline is designed to not only produce abundance tables of genes and microbes but also to reconstruct the flanking regions of ARGs with ARGextender. ARGextender is a bioinformatic approach combining KMA and SPAdes to recruit reads for a targeted de novo assembly. While our aim is on ARGs, the pipeline also creates Mash sketches for fast searching and comparisons of sequencing runs. AVAILABILITY AND IMPLEMENTATION: The ARGprofiler pipeline is a Snakemake workflow that supports the reuse of metagenomic sequencing data and is easily installable and maintained at https://github.com/genomicepidemiology/ARGprofiler.

MAGICIAN: MAG simulation for investigating criteria for bioinformatic analysis.

BACKGROUND: The possibility of recovering metagenome-assembled genomes (MAGs) from sequence reads allows for further insights into microbial communities and their members, possibly even analyzing such sequences with tools designed for single-isolate genomes. As result quality depends on sequence quality, performance of tools for single-isolate genomes on MAGs should be tested beforehand. Bioinformatics can be leveraged to quickly create varied synthetic test sets with known composition for this purpose. RESULTS: We present MAGICIAN, a flexible, user-friendly pipeline for the simulation of MAGs. MAGICIAN combines a synthetic metagenome simulator with a metagenomic assembly and binning pipeline to simulate MAGs based on user-supplied input genomes, allowing users to test performance of tools on MAGs while having a ground truth to compare results to. Using MAGICIAN, we found that even very slight (1%) changes in depth of coverage can drastically affect whether a genome can be recovered. We also demonstrate the use of simulated MAGs by evaluating the suitability of such genomes obtained with MAGICIAN's current default pipeline for analysis with the antimicrobial resistance gene identification tool ResFinder. CONCLUSIONS: Using MAGICIAN, it is possible to simulate MAGs which, while generally high in quality, reflect issues encountered with real-world data, thus providing realistic best-case data. Evaluating the results of ResFinder analysis of these genomes revealed a risk for plausible-looking false positives, which underlines the need for pipeline validation so that researchers are aware of the potential issues when interpreting real-world data. Furthermore, the effects of fluctuations in depth of coverage on genome recovery in our simulated "random sequencing" warrant further investigation and indicate random subsampling of reads may affect discovery of more genomes.

Flankophile: a bioinformatic pipeline for prokaryotic genomic synteny analysis.

IMPORTANCE: The Flankophile pipeline enables the analysis and visualization of flanking regions of prokaryotic sequences of interest on large data sets in one step and in a consistent manner. A specific tool for flanking region analysis with automated visualization has not been developed before, and Flankophile will make flanking region analysis easier and accessible to more people. Flankophile will be especially useful in the field of genomic epidemiology of acquired antimicrobial resistance genes. Here, information from flanking region sequences can be instrumental in rejecting or supporting the possibility of a recent common source of the same resistance gene found in different samples.

Association of health, nutrition, and socioeconomic variables with global antimicrobial resistance: a modelling study.

BACKGROUND: Although antimicrobial use is a key selector for antimicrobial resistance, recent studies have suggested that the ecological context in which antimicrobials are used might provide important factors for the prediction of the emergence and spread of antimicrobial resistance. METHODS: We used 1547 variables from the World Bank dataset consisting of socioeconomic, developmental, health, and nutritional indicators; data from a global sewage-based study on antimicrobial resistance (abundance of antimicrobial resistance genes [ARGs]); and data on antimicrobial usage computed from the ECDC database and the IQVIA database. We characterised and built models predicting the global resistome at an antimicrobial class level. We used a generalised linear mixed-effects model to estimate the association between antimicrobial usage and ARG abundance in the sewage samples; a multivariate random forest model to build predictive models for each antimicrobial resistance class and to select the most important variables for ARG abundance; logistic regression models to test the association between the predicted country-level antimicrobial resistance abundance and the country-level proportion of clinical resistant bacterial isolates; finite mixture models to investigate geographical heterogeneities in the abundance of ARGs; and multivariate finite mixture models with covariates to investigate the effect of heterogeneity in the association between the most important variables and the observed ARG abundance across the different country subgroups. We compared our predictions with available clinical phenotypic data from the SENTRY Antimicrobial Surveillance Program from eight antimicrobial classes and 12 genera from 56 countries. FINDINGS: Using antimicrobial use data from between Jan 1, 2016, and Dec 31, 2019, we found that antimicrobial usage was not significantly associated with the global ARG abundance in sewage (p=0·72; incidence rate ratio 1·02 [95% CI 0·92-1·13]), whereas country-specific World Bank's variables explained a large amount of variation. The importance of the World Bank variables differed between antimicrobial classes and countries. Generally, the estimated global ARG abundance was positively associated with the prevalence of clinical phenotypic resistance, with a strong association for bacterial groups in the human gut. The associations between bacterial groups and ARG abundance were positive and significantly different from zero for the aminoglycosides (three of the four of the taxa tested), ß-lactam (all the six microbial groups), fluoroquinolones (seven of nine of the microbial groups), glycopeptide (one microbial group tested), folate pathway antagonists (four of five microbial groups), and tetracycline (two of nine microbial groups). INTERPRETATION: Metagenomic analysis of sewage is a robust approach for the surveillance of antimicrobial resistance in pathogens, especially for bacterial groups associated with the human gut. Additional studies on the associations between important socioeconomic, nutritional, and health factors and antimicrobial resistance should consider the variation in these associations between countries and antimicrobial classes. FUNDING: EU Horizon 2020 and Novo Nordisk Foundation.

Global within-species phylogenetics of sewage microbes suggest that local adaptation shapes geographical bacterial clustering.

Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

Detection of specific uncultured bacteriophages by fluorescence in situ hybridisation in pig microbiome.

Microbial communities have huge impacts on their ecosystems and local environments spanning from marine and soil communities to the mammalian gut. Bacteriophages (phages) are important drivers of population control and diversity in the community, but our understanding of complex microbial communities is halted by biased detection techniques. Metagenomics have provided a method of novel phage discovery independent of in vitro culturing techniques and have revealed a large proportion of understudied phages. Here, five jumbophage genomes, that were previously assembled in silico from pig faecal metagenomes, are detected and observed directly in their natural environment using a modified phageFISH approach, and combined with methods to decrease bias against large-sized phages (e.g., jumbophages). These phages are uncultured with unknown hosts. The specific phages were detected by PCR and fluorescent in situ hybridisation in their original faecal samples as well as across other faecal samples. Co-localisation of bacterial signals and phage signals allowed detection of the different stages of phage life cycle. All phages displayed examples of early infection, advanced infection, burst, and free phages. To our knowledge, this is the first detection of jumbophages in faeces, which were investigated independently of culture, host identification, and size, and based solely on the genome sequence. This approach opens up opportunities for characterisation of novel in silico phages in vivo from a broad range of gut microbiomes.

Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance.

Antimicrobial resistance (AMR) is a major threat to global health. Understanding the emergence, evolution, and transmission of individual antibiotic resistance genes (ARGs) is essential to develop sustainable strategies combatting this threat. Here, we use metagenomic sequencing to analyse ARGs in 757 sewage samples from 243 cities in 101 countries, collected from 2016 to 2019. We find regional patterns in resistomes, and these differ between subsets corresponding to drug classes and are partly driven by taxonomic variation. The genetic environments of 49 common ARGs are highly diverse, with most common ARGs carried by multiple distinct genomic contexts globally and sometimes on plasmids. Analysis of flanking sequence revealed ARG-specific patterns of dispersal limitation and global transmission. Our data furthermore suggest certain geographies are more prone to transmission events and should receive additional attention.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome.

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have been transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources to analyze such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76∙108 read fragments were assigned to ARGs and 3.21∙109 to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.

Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine: a prospective, laboratory-based, proof-of-concept study.

BACKGROUND: Semi-quantitative bacterial culture is the reference standard to diagnose urinary tract infection, but culture is time-consuming and can be unreliable if patients are receiving antibiotics. Metagenomics could increase diagnostic accuracy and speed by sequencing the microbiota and resistome directly from urine. We aimed to compare metagenomics to culture for semi-quantitative pathogen and resistome detection from urine. METHODS: In this proof-of-concept study, we prospectively included consecutive urine samples from a clinical diagnostic laboratory in Amsterdam. Urine samples were screened by DNA concentration, followed by PCR-free metagenomic sequencing of randomly selected samples with a high concentration of DNA (culture positive and negative). A diagnostic index was calculated as the product of DNA concentration and fraction of pathogen reads. We compared results with semi-quantitative culture using area under the receiver operating characteristic curve (AUROC) analyses. We used ResFinder and PointFinder for resistance gene detection and compared results to phenotypic antimicrobial susceptibility testing for six antibiotics commonly used for urinary tract infection treatment: nitrofurantoin, ciprofloxacin, fosfomycin, cotrimoxazole, ceftazidime, and ceftriaxone. FINDINGS: We screened 529 urine samples of which 86 were sequenced (43 culture positive and 43 culture negative). The AUROC of the DNA concentration-based screening was 0·85 (95% CI 0·81-0·89). At a cutoff value of 6·0 ng/mL, culture positivity was ruled out with a negative predictive value of 91% (95% CI 87-93; 26 of 297 samples), reducing the number of samples requiring sequencing by 56% (297 of 529 samples). The AUROC of the diagnostic index was 0·87 (95% CI 0·79-0·95). A diagnostic index cutoff value of 17·2 yielded a positive predictive value of 93% (95% CI 85-97) and a negative predictive value of 69% (55-80), correcting for a culture-positive prevalence of 66%. Gram-positive pathogens explained eight (89%) of the nine false-negative metagenomic test results. Agreement of phenotypic and genotypic antimicrobial susceptibility testing varied between 71% (22 of 31 samples) and 100% (six of six samples), depending on the antibiotic tested. INTERPRETATION: This study provides proof-of-concept of metagenomic semi-quantitative pathogen and resistome detection for the diagnosis of urinary tract infection. The findings warrant prospective clinical validation of the value of this approach in informing patient management and care. FUNDING: EU Horizon 2020 Research and Innovation Programme.

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest.

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.

Global Distribution of mcr Gene Variants in 214K Metagenomic Samples.

Since the initial discovery of a mobilized colistin resistance gene (mcr-1), several other variants have been reported, some of which might have circulated a while beforehand. Publicly available metagenomic data provide an opportunity to reanalyze samples to understand the evolutionary history of recently discovered antimicrobial resistance genes (ARGs). Here, we present a large-scale metagenomic study of 442 Tbp of sequencing reads from 214,095 samples to describe the dissemination and emergence of nine mcr gene variants (mcr-1 to mcr-9). Our results show that the dissemination of each variant is not uniform. Instead, the source and location play a role in the spread. However, the genomic context and the genes themselves remain primarily unchanged. We report evidence of new subvariants occurring in specific environments, such as a highly prevalent and new variant of mcr-9. This work emphasizes the importance of sharing genomic data for the surveillance of ARGs in our understanding of antimicrobial resistance. IMPORTANCE The ever-growing collection of metagenomic samples available in public data repositories has the potential to reveal new details on the emergence and dissemination of mobilized colistin resistance genes. Our analysis of metagenomes deposited online in the last 10 years shows that the environmental distribution of mcr gene variants depends on sampling source and location, possibly leading to the emergence of new variants, although the contig on which the mcr genes were found remained consistent.

Secrets of the Hospital Underbelly: Patterns of Abundance of Antimicrobial Resistance Genes in Hospital Wastewater Vary by Specific Antimicrobial and Bacterial Family.

Background: Hospital wastewater is a major source of antimicrobial resistance (AMR) outflow into the environment. This study uses metagenomics to study how hospital clinical activity impacts antimicrobial resistance genes (ARGs) abundances in hospital wastewater. Methods: Sewage was collected over a 24-h period from multiple wastewater collection points (CPs) representing different specialties within a tertiary hospital site and simultaneously from community sewage works. High throughput shotgun sequencing was performed using Illumina HiSeq4000. ARG abundances were correlated to hospital antimicrobial usage (AMU), data on clinical activity and resistance prevalence in clinical isolates. Results: Microbiota and ARG composition varied between CPs and overall ARG abundance was higher in hospital wastewater than in community influent. ARG and microbiota compositions were correlated (Procrustes analysis, p=0.014). Total antimicrobial usage was not associated with higher ARG abundance in wastewater. However, there was a small positive association between resistance genes and antimicrobial usage matched to ARG phenotype (IRR 1.11, CI 1.06-1.16, p<0.001). Furthermore, analyzing carbapenem and vancomycin resistance separately indicated that counts of ARGs to these antimicrobials were positively associated with their increased usage [carbapenem rate ratio (RR) 1.91, 95% CI 1.01-3.72, p=0.07, and vancomycin RR 10.25, CI 2.32-49.10, p<0.01]. Overall, ARG abundance within hospital wastewater did not reflect resistance patterns in clinical isolates from concurrent hospital inpatients. However, for clinical isolates of the family Enterococcaceae and Staphylococcaceae, there was a positive relationship with wastewater ARG abundance [odds ratio (OR) 1.62, CI 1.33-2.00, p<0.001, and OR 1.65, CI 1.21-2.30, p=0.006 respectively]. Conclusion: We found that the relationship between hospital wastewater ARGs and antimicrobial usage or clinical isolate resistance varies by specific antimicrobial and bacterial family studied. One explanation, we consider is that relationships observed from multiple departments within a single hospital site will be detectable only for ARGs against parenteral antimicrobials uniquely used in the hospital setting. Our work highlights that using metagenomics to identify the full range of ARGs in hospital wastewater is a useful surveillance tool to monitor hospital ARG carriage and outflow and guide environmental policy on AMR.

Risk Factors for Antimicrobial Resistance in Turkey Farms: A Cross-Sectional Study in Three European Countries.

Food-producing animals are an important reservoir and potential source of transmission of antimicrobial resistance (AMR) to humans. However, research on AMR in turkey farms is limited. This study aimed to identify risk factors for AMR in turkey farms in three European countries (Germany, France, and Spain). Between 2014 and 2016, faecal samples, antimicrobial usage (AMU), and biosecurity information were collected from 60 farms. The level of AMR in faecal samples was quantified in three ways: By measuring the abundance of AMR genes through (i) shotgun metagenomics sequencing (n = 60), (ii) quantitative real-time polymerase chain reaction (qPCR) targeting ermB, tetW, sul2, and aph3'-III; (n = 304), and (iii) by identifying the phenotypic prevalence of AMR in Escherichia coli isolates by minimum inhibitory concentrations (MIC) (n = 600). The association between AMU or biosecurity and AMR was explored. Significant positive associations were detected between AMU and both genotypic and phenotypic AMR for specific antimicrobial classes. Beta-lactam and colistin resistance (metagenomics sequencing); ampicillin and ciprofloxacin resistance (MIC) were associated with AMU. However, no robust AMU-AMR association was detected by analyzing qPCR targets. In addition, no evidence was found that lower biosecurity increases AMR abundance. Using multiple complementary AMR detection methods added insights into AMU-AMR associations at turkey farms.

Genomic evolution of antimicrobial resistance in Escherichia coli.

The emergence of antimicrobial resistance (AMR) is one of the biggest health threats globally. In addition, the use of antimicrobial drugs in humans and livestock is considered an important driver of antimicrobial resistance. The commensal microbiota, and especially the intestinal microbiota, has been shown to have an important role in the emergence of AMR. Mobile genetic elements (MGEs) also play a central role in facilitating the acquisition and spread of AMR genes. We isolated Escherichia coli (n = 627) from fecal samples in respectively 25 poultry, 28 swine, and 15 veal calf herds from 6 European countries to investigate the phylogeny of E. coli at country, animal host and farm levels. Furthermore, we examine the evolution of AMR in E. coli genomes including an association with virulence genes, plasmids and MGEs. We compared the abundance metrics retrieved from metagenomic sequencing and whole genome sequenced of E. coli isolates from the same fecal samples and farms. The E. coli isolates in this study indicated no clonality or clustering based on country of origin and genetic markers; AMR, and MGEs. Nonetheless, mobile genetic elements play a role in the acquisition of AMR and virulence genes. Additionally, an abundance of AMR was agreeable between metagenomic and whole genome sequencing analysis for several AMR classes in poultry fecal samples suggesting that metagenomics could be used as an indicator for surveillance of AMR in E. coli isolates and vice versa.

Patterns of Gene Content and Co-occurrence Constrain the Evolutionary Path toward Animal Association in Candidate Phyla Radiation Bacteria.

Candidate Phyla Radiation (CPR) bacteria are small, likely episymbiotic organisms found across Earth's ecosystems. Despite their prevalence, the distribution of CPR lineages across habitats and the genomic signatures of transitions among these habitats remain unclear. Here, we expand the genome inventory for Absconditabacteria (SR1), Gracilibacteria, and Saccharibacteria (TM7), CPR bacteria known to occur in both animal-associated and environmental microbiomes, and investigate variation in gene content with habitat of origin. By overlaying phylogeny with habitat information, we show that bacteria from these three lineages have undergone multiple transitions from environmental habitats into animal microbiomes. Based on co-occurrence analyses of hundreds of metagenomes, we extend the prior suggestion that certain Saccharibacteria have broad bacterial host ranges and constrain possible host relationships for Absconditabacteria and Gracilibacteria. Full-proteome analyses show that animal-associated Saccharibacteria have smaller gene repertoires than their environmental counterparts and are enriched in numerous protein families, including those likely functioning in amino acid metabolism, phage defense, and detoxification of peroxide. In contrast, some freshwater Saccharibacteria encode a putative rhodopsin. For protein families exhibiting the clearest patterns of differential habitat distribution, we compared protein and species phylogenies to estimate the incidence of lateral gene transfer and genomic loss occurring over the species tree. These analyses suggest that habitat transitions were likely not accompanied by large transfer or loss events but rather were associated with continuous proteome remodeling. Thus, we speculate that CPR habitat transitions were driven largely by availability of suitable host taxa and were reinforced by acquisition and loss of some capacities. IMPORTANCE Studying the genetic differences between related microorganisms from different environment types can indicate factors associated with their movement among habitats. This is particularly interesting for bacteria from the Candidate Phyla Radiation because their minimal metabolic capabilities require associations with microbial hosts. We found that shifts of Absconditabacteria, Gracilibacteria, and Saccharibacteria between environmental ecosystems and mammalian mouths/guts probably did not involve major episodes of gene gain and loss; rather, gradual genomic change likely followed habitat migration. The results inform our understanding of how little-known microorganisms establish in the human microbiota where they may ultimately impact health.