Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available. RESULTS: We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions. AVAILABILITY: The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.

Syotti: scalable bait design for DNA enrichment.

MOTIVATION: Bait enrichment is a protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ('baits') are designed, manufactured and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. Metsky et al. demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. RESULTS: We formalize the problem of designing baits by defining the Minimum Bait Cover problem, show that the problem is NP-hard even under very restrictive assumptions, and design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 min to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% of the data in 72 h. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnalanko/syotti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metagenome SNP calling via read-colored de Bruijn graphs.

MOTIVATION: Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms and, thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single-nucleotide polymorphisms (SNPs) can be effectively used to 'fingerprint' specific organisms and genes within the microbiome and resistome and trace their movement across various samples. However, to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such an SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this article, we address this need. RESULTS: We present LueVari, a reference-free SNP caller based on the read-colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% and 99%) and precision (between 71% and 99%) as the performance of competing methods varies widely. Furthermore, we show that LueVari constructs sequences containing the variation, which span up to 97.8% of genes in datasets, which can be helpful in detecting distinct AMR genes in large metagenomic datasets. AVAILABILITY AND IMPLEMENTATION: Code and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of the Impact of Antimicrobial Use Protocols in Porcine Reproductive and Respiratory Syndrome Virus-Infected Swine on Phenotypic Antimicrobial Resistance Patterns.

A longitudinal study was conducted to assess the impact of different antimicrobial exposures of nursery-phase pigs on patterns of phenotypic antimicrobial resistance (AMR) in fecal indicator organisms throughout the growing phase. Based on practical approaches used to treat moderate to severe porcine reproductive and respiratory syndrome virus (PRRSV)-associated secondary bacterial infections, two antimicrobial protocols of differing intensities of exposure [44.1 and 181.5 animal-treatment days per 1000 animal days at risk (ATD)] were compared with a control group with minimal antimicrobial exposure (2.1 ATD). Litter-matched pigs (n = 108) with no prior antimicrobial exposure were assigned randomly to the treatment groups. Pen fecal samples were collected nine times during the wean-to-finish period and cultured for Escherichia coli and Enterococcus spp. Antimicrobial-susceptibility testing was conducted using NARMS Gram-negative and Gram-positive antibiotic panels. Despite up to 65-fold difference in ATD, few and modest differences were observed between groups and over time. Resistance patterns at marketing overall remained similar to those observed at weaning, prior to any antimicrobial exposures. Those differences observed could not readily be reconciled with the patterns of antimicrobial exposure. Resistance of E. coli to streptomycin was higher in the group exposed to 44.1 ATD, but no aminoglycosides were used. In all instances where resistances differed between time points, the higher resistance occurred early in the trial prior to any antimicrobial exposures. These minimal impacts on AMR despite substantially different antimicrobial exposures point to the lack of understanding of the drivers of AMR at the population level and the likely importance of factors other than antimicrobial exposure. IMPORTANCE Despite a recognized need for more longitudinal studies to assess the effects of antimicrobial use on resistance in food animals, they remain sparse in the literature, and most longitudinal studies of pigs have been observational. The current experimental study had the advantages of greater control of potential confounding, precise measurement of antimicrobial exposures which differed markedly between groups and tracking of pigs until market age. Overall, resistance patterns were remarkably stable between the treatment groups over time, and the differences observed could not be readily reconciled with the antimicrobial exposures, indicating the likely importance of other determinants of AMR at the population level.

MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data.

Antimicrobial resistance (AMR) is a threat to global public health and the identification of genetic determinants of AMR is a critical component to epidemiological investigations. High-throughput sequencing (HTS) provides opportunities for investigation of AMR across all microbial genomes in a sample (i.e. the metagenome). Previously, we presented MEGARes, a hand-curated AMR database and annotation structure developed to facilitate the analysis of AMR within metagenomic samples (i.e. the resistome). Along with MEGARes, we released AmrPlusPlus, a bioinformatics pipeline that interfaces with MEGARes to identify and quantify AMR gene accessions contained within a metagenomic sequence dataset. Here, we present MEGARes 2.0 (https://megares.meglab.org), which incorporates previously published resistance sequences for antimicrobial drugs, while also expanding to include published sequences for metal and biocide resistance determinants. In MEGARes 2.0, the nodes of the acyclic hierarchical ontology include four antimicrobial compound types, 57 classes, 220 mechanisms of resistance, and 1,345 gene groups that classify the 7,868 accessions. In addition, we present an updated version of AmrPlusPlus (AMR ++ version 2.0), which improves accuracy of classifications, as well as expanding scalability and usability.

Factors influencing usage of antimicrobial drugs among pastoralists in Kenya.

Agricultural use of antimicrobials in food animal production may contribute to the global emergence of antimicrobial resistance (AMR). However, considerable gaps exist in research on the use of antimicrobial drugs (AMDs) in food animals in small-scale production systems in low- and middle-income countries, despite the minimal regulation of antimicrobials in such regions. The aim of this study was to identify factors that may influence AMD use in livestock among pastoral communities in Kenya. We collected data related to household and herd demographics, herd health, and herd management from 55 households in the Maasai Mara ecosystem, Kenya, between 2018 and 2019. We used multi-model logistic regression inference (supervised machine learning) to ascertain trends in AMD use within these households. AMD use in cattle was significantly associated with AMD use in sheep and goats (p = 0.05), implying that decisions regarding AMD use in cattle or sheep and goats were interdependent. AMD use in sheep and goats was negatively associated with vaccination against the foot and mouth disease (FMD) virus in cattle (OR = 0.06, 95% CI 0.01-0.67, p = 0.02). Less AMD use was observed for vaccine-preventable diseases like contagious ecthyma when households had access to state veterinarians (OR = 0.06, p = 0.05, 95% CI 0.004-0.96). Overall, decisions to use AMDs were associated with vaccine usage, occurrence of respiratory diseases, and access to animal health advice. This hypothesis-generating study suggests that applying community-centric methods may be necessary to understand the use of AMDs in pastoral communities.

MEGARes: an antimicrobial resistance database for high throughput sequencing.

Antimicrobial resistance has become an imminent concern for public health. As methods for detection and characterization of antimicrobial resistance move from targeted culture and polymerase chain reaction to high throughput metagenomics, appropriate resources for the analysis of large-scale data are required. Currently, antimicrobial resistance databases are tailored to smaller-scale, functional profiling of genes using highly descriptive annotations. Such characteristics do not facilitate the analysis of large-scale, ecological sequence datasets such as those produced with the use of metagenomics for surveillance. In order to overcome these limitations, we present MEGARes (https://megares.meglab.org), a hand-curated antimicrobial resistance database and annotation structure that provides a foundation for the development of high throughput acyclical classifiers and hierarchical statistical analysis of big data. MEGARes can be browsed as a stand-alone resource through the website or can be easily integrated into sequence analysis pipelines through download. Also via the website, we provide documentation for AmrPlusPlus, a user-friendly Galaxy pipeline for the analysis of high throughput sequencing data that is pre-packaged for use with the MEGARes database.

Succinct colored de Bruijn graphs.

MOTIVATION: In 2012, Iqbal et al. introduced the colored de Bruijn graph, a variant of the classic de Bruijn graph, which is aimed at 'detecting and genotyping simple and complex genetic variants in an individual or population'. Because they are intended to be applied to massive population level data, it is essential that the graphs be represented efficiently. Unfortunately, current succinct de Bruijn graph representations are not directly applicable to the colored de Bruijn graph, which requires additional information to be succinctly encoded as well as support for non-standard traversal operations. RESULTS: Our data structure dramatically reduces the amount of memory required to store and use the colored de Bruijn graph, with some penalty to runtime, allowing it to be applied in much larger and more ambitious sequence projects than was previously possible. AVAILABILITY AND IMPLEMENTATION: https://github.com/cosmo-team/cosmo/tree/VARI. CONTACT: martin.muggli@colostate.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of Ceftiofur and Chlortetracycline on the Resistomes of Feedlot Cattle.

Treatment of food-producing animals with antimicrobial drugs (AMD) is controversial because of concerns regarding promotion of antimicrobial resistance (AMR). To investigate this concern, resistance genes in metagenomic bovine fecal samples during a clinical trial were analyzed to assess the impacts of treatment on beef feedlot cattle resistomes. Four groups of cattle were exposed, using a 2-by-2 factorial design, to different regimens of antimicrobial treatment. Injections of ceftiofur crystalline-free acid (a third-generation cephalosporin) were used to treat all cattle in treatment pens or only a single animal, and either chlortetracycline was included in the feed of all cattle in a pen or the feed was untreated. On days 0 and 26, respectively, pre- and posttrial fecal samples were collected, and resistance genes were characterized using shotgun metagenomics. Treatment with ceftiofur was not associated with changes to ß-lactam resistance genes. However, cattle fed chlortetracycline had a significant increase in relative abundance of tetracycline resistance genes. There was also an increase of an AMR class not administered during the study, which is a possible indicator of coselection of resistance genes. Samples analyzed in this study had previously been evaluated by culture characterization (Escherichia coli and Salmonella) and quantitative PCR (qPCR) of metagenomic fecal DNA, which allowed comparison of results with this study. In the majority of samples, genes that were selectively enriched through culture and qPCR were not identified through shotgun metagenomic sequencing in this study, suggesting that changes previously documented did not reflect changes affecting the majority of bacterial genetic elements found in the predominant fecal resistome.IMPORTANCE Despite significant concerns about public health implications of AMR in relation to use of AMD in food animals, there are many unknowns about the long- and short-term impact of common uses of AMD for treatment, control, and prevention of disease. Additionally, questions commonly arise regarding how to best measure and quantify AMR genes in relation to public health risks and how to determine which genes are most important. These data provide an introductory view of the utility of using shotgun metagenomic sequencing data as an outcome for clinical trials evaluating the impact of using AMD in food animals.

Use of Metagenomic Shotgun Sequencing Technology To Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain.

Foodborne illnesses associated with pathogenic bacteria are a global public health and economic challenge. The diversity of microorganisms (pathogenic and nonpathogenic) that exists within the food and meat industries complicates efforts to understand pathogen ecology. Further, little is known about the interaction of pathogens within the microbiome throughout the meat production chain. Here, a metagenomic approach and shotgun sequencing technology were used as tools to detect pathogenic bacteria in environmental samples collected from the same groups of cattle at different longitudinal processing steps of the beef production chain: cattle entry to feedlot, exit from feedlot, cattle transport trucks, abattoir holding pens, and the end of the fabrication system. The log read counts classified as pathogens per million reads for Salmonella enterica,Listeria monocytogenes,Escherichia coli,Staphylococcus aureus, Clostridium spp. (C. botulinum and C. perfringens), and Campylobacter spp. (C. jejuni,C. coli, and C. fetus) decreased over subsequential processing steps. Furthermore, the normalized read counts for S. enterica,E. coli, and C. botulinumwere greater in the final product than at the feedlots, indicating that the proportion of these bacteria increased (the effect on absolute numbers was unknown) within the remaining microbiome. From an ecological perspective, data indicated that shotgun metagenomics can be used to evaluate not only the microbiome but also shifts in pathogen populations during beef production. Nonetheless, there were several challenges in this analysis approach, one of the main ones being the identification of the specific pathogen from which the sequence reads originated, which makes this approach impractical for use in pathogen identification for regulatory and confirmation purposes.

Temporal dynamics of the fecal microbiome in female pigs from early life through estrus, parturition, and weaning of the first litter of piglets.

BACKGROUND: Age-associated changes in the gastrointestinal microbiome of young pigs have been robustly described; however, the temporal dynamics of the fecal microbiome of the female pig from early life to first parity are not well understood. Our objective was to describe microbiome and antimicrobial resistance dynamics of the fecal microbiome of breeding sows from early life through estrus, parturition and weaning of the first litter of piglets (i.e., from 3 to 53 weeks of age). RESULTS: Our analysis revealed that fecal bacterial populations in developing gilts undergo changes consistent with major maturation milestones. As the pigs progressed towards first estrus, the fecal bacteriome shifted from Rikenellaceae RC9 gut group- and UCG-002-dominated enterotypes to Treponema- and Clostridium sensu stricto 1-dominated enterotypes. After first estrus, the fecal bacteriome stabilized, with minimal changes in enterotype transition and associated microbial diversity from estrus to parturition and subsequent weaning of first litter piglets. Unlike bacterial communities, fecal fungal communities exhibited low diversity with high inter- and intra-pig variability and an increased relative abundance of certain taxa at parturition, including Candida spp. Counts of resistant fecal bacteria also fluctuated over time, and were highest in early life and subsequently abated as the pigs progressed to adulthood. CONCLUSIONS: This study provides insights into how the fecal microbial community and antimicrobial resistance in female pigs change from three weeks of age throughout their first breeding lifetime. The fecal bacteriome enterotypes and diversity are found to be age-driven and established by the time of first estrus, with minimal changes observed during subsequent physiological stages, such as parturition and lactation, when compared to the earlier age-related shifts. The use of pigs as a model for humans is well-established, however, further studies are needed to understand how our results compare to the human microbiome dynamics. Our findings suggest that the fecal microbiome exhibited consistent changes across individual pigs and became more diverse with age, which is a beneficial characteristic for an animal model system.

Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves.

BACKGROUND: Beef cattle experience several management challenges across their lifecycle. Castration and weaning, two major interventions in the early life of beef cattle, can have a substantial impact on animal performance. Despite the key role of the rumen microbiome on productive traits of beef cattle, the effect of castration timing and weaning strategy on this microbial community has not been formally described. We assessed the effect of four castration time windows (at birth, turnout, pre-weaning and weaning) and two weaning strategies (fence-line and truck transportation) on the rumen microbiome in a randomized controlled study with 32 male calves across 3 collection days (i.e., time points). Ruminal fluid samples were submitted to shotgun metagenomic sequencing and changes in the taxonomic (microbiota) and functional profile (metagenome) of the rumen microbiome were described. RESULTS: Using a comprehensive yet stringent taxonomic classification approach, we identified 10,238 unique taxa classified under 40 bacterial and 7 archaeal phyla across all samples. Castration timing had a limited long-term impact on the rumen microbiota and was not associated with changes in alpha and beta diversity. The interaction of collection day and weaning strategy was associated with changes in the rumen microbiota, which experienced a significant decrease in alpha diversity and shifts in beta diversity within 48 h post-weaning, especially in calves abruptly weaned by truck transportation. Calves weaned using a fence-line weaning strategy had lower relative abundance of Bacteroides, Lachnospira, Fibrobacter and Ruminococcus genera compared to calves weaned by truck transportation. Some genes involved in the hydrogenotrophic methanogenesis pathway (fwdB and fwdF) had higher relative abundance in fence-line-weaned calves post-weaning. The antimicrobial resistance gene tetW consistently represented more than 50% of the resistome across time, weaning and castration groups, without significant changes in relative abundance. CONCLUSIONS: Within the context of this study, castration timing had limited long-term effects on the rumen microbiota, while weaning strategy had short-term effects on the rumen microbiota and methane-associated metagenome, but not on the rumen resistome.

Age influences the temporal dynamics of microbiome and antimicrobial resistance genes among fecal bacteria in a cohort of production pigs.

BACKGROUND: The pig gastrointestinal tract hosts a diverse microbiome, which can serve to select and maintain a reservoir of antimicrobial resistance genes (ARG). Studies suggest that the types and quantities of antimicrobial resistance (AMR) in fecal bacteria change as the animal host ages, yet the temporal dynamics of AMR within communities of bacteria in pigs during a full production cycle remains largely unstudied. RESULTS: A longitudinal study was performed to evaluate the dynamics of fecal microbiome and AMR in a cohort of pigs during a production cycle; from birth to market age. Our data showed that piglet fecal microbial communities assemble rapidly after birth and become more diverse with age. Individual piglet fecal microbiomes progressed along similar trajectories with age-specific community types/enterotypes and showed a clear shift from E. coli/Shigella-, Fusobacteria-, Bacteroides-dominant enterotypes to Prevotella-, Megaspheara-, and Lactobacillus-dominated enterotypes with aging. Even when the fecal microbiome was the least diverse, the richness of ARGs, quantities of AMR gene copies, and counts of AMR fecal bacteria were highest in piglets at 2 days of age; subsequently, these declined over time, likely due to age-related competitive changes in the underlying microbiome. ARGs conferring resistance to metals and multi-compound/biocides were detected predominately at the earliest sampled ages. CONCLUSIONS: The fecal microbiome and resistome-along with evaluated descriptors of phenotypic antimicrobial susceptibility of fecal bacteria-among a cohort of pigs, demonstrated opposing trajectories in diversity primarily driven by the aging of pigs.

The microbiome of common bedding materials before and after use on commercial dairy farms.

BACKGROUND: Bovine mastitis is one of the most economically important diseases affecting dairy cows. The choice of bedding material has been identified as an important risk factor contributing to the development of mastitis. However, few reports examine both the culturable and nonculturable microbial composition of commonly used bedding materials, i.e., the microbiome. Given the prevalence of nonculturable microbes in most environments, this information could be an important step to understanding whether and how the bedding microbiome acts as a risk factor for mastitis. Therefore, our objective was to characterize the microbiome composition and diversity of bedding material microbiomes, before and after use. METHODS: We collected 88 bedding samples from 44 dairy farms in the U.S. Unused (from storage pile) and used (out of stalls) bedding materials were collected from four bedding types: new sand (NSA), recycled manure solids (RMS), organic non-manure (ON) and recycled sand (RSA). Samples were analyzed using 16S rRNA sequencing of the V3-V4 region. RESULTS: The overall composition as well as the counts of several microbial taxa differed between bedding types, with Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes dominating across all types. Used bedding contained a significantly different microbial composition than unused bedding, but the magnitude of this difference varied by bedding type, with RMS bedding exhibiting the smallest difference. In addition, positive correlations were observed between 16S rRNA sequence counts of potential mastitis pathogens (bacterial genera) and corresponding bedding bacterial culture data. CONCLUSION: Our results strengthen the role of bedding as a potential source of mastitis pathogens. The consistent shift in the microbiome of all bedding types that occurred during use by dairy cows deserves further investigation to understand whether this shift promotes pathogen colonization and/or persistence, or whether it can differentially impact udder health outcomes. Future studies of bedding and udder health may be strengthened by including a microbiome component to the study design.

Evaluation of Contamination in Milk Samples Pooled From Independently Collected Quarters Within a Laboratory Setting.

The primary objective of this observational study was to evaluate the prevalence of contamination from independently collected quarter-level milk samples pooled in a laboratory and subjected to bacterial culture. To address this objective, weekly quarter-level milk samples were collected longitudinally from a cohort of 503 primiparous cows from five organic dairy farms during the first 5 weeks after calving. Individual quarter milk samples were pooled in a laboratory using aseptic technique ("lab-pooled") and subjected to bacterial culture. In the sample set of 2,006 lab-pooled milk samples, 207 (10.3%) were classified as contaminated using a standard definition (i.e., growth of three or more distinct microorganisms). Subsequent culturing of corresponding quarter-level milk samples revealed that many of the contaminated lab-pooled sample results (i.e., 46.7%) were the result of intramammary infections with different pathogens across the quarters, rather than actual contamination within any single quarter (i.e., "true contamination"). The odds of true contamination were lower when the lab-pooled sample exhibited growth of three microorganisms compared to more than 3 microorganisms. Our findings suggest that pooling of quarter samples within a laboratory setting may yield lower rates of contamination compared to those previously reported from samples composited on-farm, but that current cut-offs to define contamination may need to be evaluated for use with lab-pooled samples. Further investigation of use of lab-pooled samples may be warranted to reduce costs while still providing useful scientific insight.

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

BACKGROUND: Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq). RESULTS: Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT. CONCLUSIONS: TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.

Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/.

A longitudinal investigation of the effects of age, dietary fiber type and level, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance of finisher pigs.

Age and diet are among the factors that influence the community composition of the fecal microbiome. Additionally, antimicrobial use can alter the composition of bacterial communities. An 86-d study with finisher pigs aimed to evaluate age-related dynamics (day 98 to 177 of age), effects of types and levels of dietary fiber, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance (AMR) was conducted. A total of 287 pigs, housed in 36 pens, with 7 to 8 pigs per pen, fed a corn grain and soybean meal-based basal diet, formulated to contain 8.7% neutral detergent fiber (NDF), were randomly assigned to one of three treatments: 1) basal diet with no supplement, 2) basal diet supplemented with 20% distillers dried grains with solubles (DDGS) formulated to contain 13.6% NDF, or 3) basal diet supplemented with 14.5% sugar beet pulp (SBP) formulated to contain 13.6% NDF. Five finisher pigs from each treatment group were selected randomly, and fecal samples were collected on days 98, 110, 144, and 177 of age. In addition, fecal samples were collected from pigs that were injected intramuscularly ceftiofur hydrochloride or penicillin G on days 1 and 3 along with pen-mate-untreated controls on day 1. Fecal samples were subjected to 16S rRNA amplicon-based microbiome analysis and culture methods to quantify the abundance of total AMR coliforms and enterococci populations. The alpha-diversity, such as species richness, increased with age, and the overall bacterial composition changed with age (P =0.001) and diet (P = 0.001). Diet-associated shifts in the specific bacterial taxa were observed. The richness, diversity, and evenness of bacterial taxa did not differ between pigs that were injected with ceftiofur vs. their untreated pen mates or by dietary treatments but differed in pigs that received penicillin G injection. Both antimicrobial treatments contributed to changes in the overall fecal bacterial composition at the genus level. Collectively, the data demonstrate that both age and the diet (control vs. DDGS-, control vs. SBP-, or DDGS- vs. SBP-based diets) were associated with the overall bacterial community composition, and the impact of age on variations in fecal microbiome composition was greater than the diet. Antibiotic treatment had minimal effect on bacterial diversity and relative abundance of taxa. Furthermore, diets and antimicrobial treatment had minimal impact on the overall counts of AMR coliforms and enterococci populations in feces.

Bacterial communities in the gut and the feces are strongly influenced by a number of factors, particularly the age of the animal and the diet. In addition, antibiotic administration routinely used to treat bacterial diseases can also affect the community composition. A study with finisher pigs was conducted to evaluate age-related changes, effects of types­distiller's dried grains with solubles (DGGS) or sugar beet pulp (SBP)­and levels of dietary fiber, and injectable antibiotics on the fecal bacterial composition and antibiotic resistance in fecal bacteria. Fecal samples were collected from five pigs in each of the three dietary treatment groups, control diet with no supplement or supplemented with DDGS or SBP, on days 98, 110, 144, and 177 of age and on days 1 and 3 after the first injection of antibiotics, ceftiofur or penicillin G. Samples were analyzed to identify the bacterial community composition and prevalence of antibiotic resistance in fecal bacteria. Data generated suggested that the overall bacterial composition changed with age and diet, and age appeared to have a greater impact than diet. Antibiotics had only a modest impact on the bacterial community and had minimum impact on antibiotic resistance of fecal bacteria.

Evaluating the effects of antimicrobial drug use on the ecology of antimicrobial resistance and microbial community structure in beef feedlot cattle.

Introduction: Use of antimicrobial drugs (AMDs) in food producing animals has received increasing scrutiny because of concerns about antimicrobial resistance (AMR) that might affect consumers. Previously, investigations regarding AMR have focused largely on phenotypes of selected pathogens and indicator bacteria, such as Salmonella enterica or Escherichia coli. However, genes conferring AMR are known to be distributed and shared throughout microbial communities. The objectives of this study were to employ target-enriched metagenomic sequencing and 16S rRNA gene amplicon sequencing to investigate the effects of AMD use, in the context of other management and environmental factors, on the resistome and microbiome in beef feedlot cattle. Methods: This study leveraged samples collected during a previous longitudinal study of cattle at beef feedlots in Canada. This included fecal samples collected from randomly selected individual cattle, as well as composite-fecal samples from randomly selected pens of cattle. All AMD use was recorded and characterized across different drug classes using animal defined daily dose (ADD) metrics. Results: Overall, fecal resistome composition was dominated by genes conferring resistance to tetracycline and macrolide-lincosamide-streptogramin (MLS) drug classes. The diversity of bacterial phyla was greater early in the feeding period and decreased over time in the feedlot. This decrease in diversity occurred concurrently as the microbiome represented in different individuals and different pens shifted toward a similar composition dominated by Proteobacteria and Firmicutes. Some antimicrobial drug exposures in individuals and groups were associated with explaining a statistically significant proportion of the variance in the resistome, but the amount of variance explained by these important factors was very small (<0.6% variance each), and smaller than associations with other factors measured in this study such as time and feedlot ID. Time in the feedlot was associated with greater changes in the resistome for both individual animals and composite pen-floor samples, although the proportion of the variance associated with this factor was small (2.4% and 1.2%, respectively). Discussion: Results of this study are consistent with other investigations showing that, compared to other factors, AMD exposures did not have strong effects on antimicrobial resistance or the fecal microbial ecology of beef cattle.

Beyond Antimicrobial Use: A Framework for Prioritizing Antimicrobial Resistance Interventions.

Antimicrobial resistance (AMR) is a threat to animal and human health. Antimicrobial use has been identified as a major driver of AMR, and reductions in use are a focal point of interventions to reduce resistance. Accordingly, stakeholders in human health and livestock production have implemented antimicrobial stewardship programs aimed at reducing use. Thus far, these efforts have yielded variable impacts on AMR. Furthermore, scientific advances are prompting an expansion and more nuanced appreciation of the many nonantibiotic factors that drive AMR, as well as how these factors vary across systems, geographies, and contexts. Given these trends, we propose a framework to prioritize AMR interventions. We use this framework to evaluate the impact of interventions that focus on antimicrobial use. We conclude by suggesting that priorities be expanded to include greater consideration of host-microbial interactions that dictate AMR, as well as anthropogenic and environmental systems that promote dissemination of AMR.