Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing

Principles of digital sequencing using unique molecular identifiers

Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.

Hybrid-Capture Target Enrichment in Human Pathogens: Identification, Evolution, Biosurveillance, and Genomic Epidemiology

High-throughput sequencing (HTS) has revolutionised the field of pathogen genomics, enabling the direct recovery of pathogen genomes from clinical and environmental samples. However, pathogen nucleic acids are often overwhelmed by those of the host, requiring deep metagenomic sequencing to recover sufficient sequences for downstream analyses (e.g., identification and genome characterisation). To circumvent this, hybrid-capture target enrichment (HC) is able to enrich pathogen nucleic acids across multiple scales of divergences and taxa, depending on the panel used. In this review, we outline the applications of HC in human pathogens-bacteria, fungi, parasites and viruses-including identification, genomic epidemiology, antimicrobial resistance genotyping, and evolution. Importantly, we explored the applicability of HC to clinical metagenomics, which ultimately requires more work before it is a reliable and accurate tool for clinical diagnosis. Relatedly, the utility of HC was exemplified by COVID-19, which was used as a case study to illustrate the maturity of HC for recovering pathogen sequences. As we unravel the origins of COVID-19, zoonoses remain more relevant than ever. Therefore, the role of HC in biosurveillance studies is also highlighted in this review, which is critical in preparing us for the next pandemic. We also found that while HC is a popular tool to study viruses, it remains underutilised in parasites and fungi and, to a lesser extent, bacteria. Finally, weevaluated the future of HC with respect to bait design in the eukaryotic groups and the prospect of combining HC with long-read HTS.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Plasmids, a molecular cornerstone of antimicrobial resistance in the One Health era

Antimicrobial resistance (AMR) poses a substantial threat to human health. The widespread prevalence of AMR is, in part, due to the horizontal transfer of antibiotic resistance genes (ARGs), typically mediated by plasmids. Many of the plasmid-mediated resistance genes in pathogens originate from environmental, animal or human habitats. Despite evidence that plasmids mobilize ARGs between these habitats, we have a limited understanding of the ecological and evolutionary trajectories that facilitate the emergence of multidrug resistance (MDR) plasmids in clinical pathogens. One Health, a holistic framework, enables exploration of these knowledge gaps. In this Review, we provide an overview of how plasmids drive local and global AMR spread and link different habitats. We explore some of the emerging studies integrating an eco-evolutionary perspective, opening up a discussion about the factors that affect the ecology and evolution of plasmids in complex microbial communities. Specifically, we discuss how the emergence and persistence of MDR plasmids can be affected by varying selective conditions, spatial structure, environmental heterogeneity, temporal variation and coexistence with other members of the microbiome. These factors, along with others yet to be investigated, collectively determine the emergence and transfer of plasmid-mediated AMR within and between habitats at the local and global scale.

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.

Comparison of Oral Microbial Composition and Determinants Encoding Antimicrobial Resistance in Dogs and Their Owners

Consolidated studies on animal, human, and environmental health have become very important for understanding emerging zoonotic diseases and the spread of antimicrobial resistance (AMR). The aim of this study was to analyse the oral microbiomes of healthy dogs and their owners, including determinants of AMR. Shotgun metagenomic sequencing detected 299 bacterial species in pets and their owners, from which 70 species were carried by dogs and 229 species by humans. Results demonstrated a unique microbial composition of dogs and their owners. At an order level, Bacteroidales were the most prevalent oral microbiota of dogs with significantly lower prevalence in their owners where Actinomycetales and Lactobacillales predominated. Porphyromonas and Corynebacterium were the most prevalent genera in dogs, whereas Streptococcus and Actinomyces were in animal owners. The resistances to macrolides, tetracyclines, lincosamides and Cfx family A class broad-spectrum β-lactamase were detected in both animal and human microbiomes. Resistance determinants to amphenicols, aminoglycosides, sulphonamides, and quaternary ammonium compounds were detected exceptionally in dogs. In conclusion, the study demonstrated different bacterial composition in oral microbiomes of healthy dogs without clinical signs of periodontal disease and their owners. Due to the low numbers of the samples tested, further investigations with an increased number of samples should be performed.

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian

Changing climatic conditions influence parameters associated with the growth of pathogenic Vibrio spp. in the environment and, hence, are linked to increased incidence of vibriosis. Between 1992 and 2022, a long-term increase in Vibrio spp. infections was reported in Florida, USA. Furthermore, a spike in Vibrio spp. infections was reported post Hurricane Ian, a category five storm that made landfall in Florida on 28 September 2022. During October 2022, water and oyster samples were collected from three stations in Lee County in an area significantly impacted by Ian. Vibrio spp. were isolated, and whole-genome sequencing and phylogenetic analysis were done, with a focus on Vibrio parahaemolyticus and Vibrio vulnificus to provide genetic insight into pathogenic strains circulating in the environment. Metagenomic analysis of water samples provided insight with respect to human health-related factors, notably the detection of approximately 12 pathogenic Vibrio spp., virulence and antibiotic resistance genes, and mobile genetic elements, including the SXT/R391 family of integrative conjugative elements. Environmental parameters were monitored as part of a long-term time series analysis done using satellite remote sensing. In addition to anomalous rainfall and storm surge, changes in sea surface temperature and chlorophyll concentration during and after Ian favored the growth of Vibrio spp. In conclusion, genetic analysis coupled with environmental data and remote sensing provides useful public health information and, hence, constitute a valuable tool to proactively detect and characterize environmental pathogens, notably vibrios. These data can aid the development of early warning systems by yielding a larger source of information for public health during climate change. Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Targeted metagenomics using bait-capture to detect antibiotic resistance genes in retail meat and seafood

Metagenomics analysis of foods has the potential to provide comprehensive data on the presence and prevalence of antimicrobial resistance (AMR) genes in the microbiome of foods. However, AMR genes are generally present in low abundance compared to other bacterial genes in the food microbiome and consequently require multiple rounds of in-depth sequencing for detection. Here, a metagenomics approach, using bait-capture probes targeting antimicrobial resistance and plasmid genes, is used to characterize the resistome and plasmidome of retail beef, chicken, oyster, shrimp, and veal enrichment cultures (n = 15). Compared to total shotgun metagenomics, bait-capture required approximately 40-fold fewer sequence reads to detect twice the number of AMR gene classes, AMR gene families, and plasmid genes across all sample types. For the detection of critically important extended spectrum beta-lactamase (ESBL) genes the bait capture method had a higher overall positivity rate (44%) compared to shotgun metagenomics (26%), and a culture-based method (29%). Overall, the results support the use of bait-capture for the identification of low abundance genes such as AMR genes from food samples.

Combined Quantification and Deep Serotyping for Salmonella Risk Profiling in Broiler Flocks

Despite a reduction of Salmonella contamination on final poultry products, the level of human salmonellosis cases attributed to poultry has remained unchanged over the last few years. There needs to be improved effort to target serovars which may survive antimicrobial interventions and cause illness, as well as to focus on lessening the amount of contamination entering the processing plant. Advances in molecular enumeration approaches allow for the rapid detection and quantification of Salmonella in pre- and postharvest samples, which can be combined with deep serotyping to properly assess the risk affiliated with a poultry flock. In this study, we collected a total of 160 boot sock samples from 20 broiler farms across four different integrators with different antibiotic management programs. Overall, Salmonella was found in 85% (68/80) of the houses, with each farm having at least one Salmonella-positive house. The average Salmonella quantity across all four complexes was 3.6 log10 CFU/sample. Eleven different serovars were identified through deep serotyping, including all three key performance indicators (KPIs; serovars Enteritidis, Infantis, and Typhimurium) defined by the U.S. Department of Agriculture-Food Safety and Inspection Service (USDA-FSIS). There were eight multidrug resistant isolates identified in this study, and seven which were serovar Infantis. We generated risk scores for each flock based on the presence or absence of KPIs, the relative abundance of each serovar as calculated with CRISPR-SeroSeq (serotyping by sequencing the clustered regularly interspaced palindromic repeats), and the quantity of Salmonella organisms detected. The work presented here provides a framework to develop directed processing approaches and highlights the limitations of conventional Salmonella sampling and culturing methods. IMPORTANCE Nearly one in five foodborne Salmonella illnesses are derived from chicken, making it the largest single food category to cause salmonellosis and indicating a need for effective pathogen mitigation. Although industry has successfully reduced Salmonella incidence in poultry products, there has not been a concurrent reduction in human salmonellosis linked to chicken consumption. New efforts are focused on improved control at preharvest, which requires improved Salmonella surveillance. Here, we present a high-resolution surveillance approach that combines quantity and identity of Salmonella in broiler flocks prior to processing which will further support improved Salmonella controls in poultry. We developed a framework for this approach, indicating that it is possible and important to harness deep serotyping and molecular enumeration to inform on-farm management practices and to minimize risk of cross-contamination between flocks at processing. Additionally, this framework could be adapted to Salmonella surveillance in other food animal production systems.

Exploiting a targeted resistome sequencing approach in assessing antimicrobial resistance in retail foods

BACKGROUND

With the escalating risk of antimicrobial resistance (AMR), there are limited analytical options available that can comprehensively assess the burden of AMR carried by clinical/environmental samples. Food can be a potential source of AMR bacteria for humans, but its significance in driving the clinical spread of AMR remains unclear, largely due to the lack of holistic-yet-sensitive tools for surveillance and evaluation. Metagenomics is a culture-independent approach well suited for uncovering genetic determinants of defined microbial traits, such as AMR, present within unknown bacterial communities. Despite its popularity, the conventional approach of non-selectively sequencing a sample's metagenome (namely, shotgun-metagenomics) has several technical drawbacks that lead to uncertainty about its effectiveness for AMR assessment; for instance, the low discovery rate of resistance-associated genes due to their naturally small genomic footprint within the vast metagenome. Here, we describe the development of a targeted resistome sequencing method and demonstrate its application in the characterization of the AMR gene profile of bacteria associated with several retail foods.

RESULT

A targeted-metagenomic sequencing workflow using a customized bait-capture system targeting over 4,000 referenced AMR genes and 263 plasmid replicon sequences was validated against both mock and sample-derived bacterial community preparations. Compared to shotgun-metagenomics, the targeted method consistently provided for improved recovery of resistance gene targets with a much-improved target detection efficiency (> 300-fold). Targeted resistome analyses conducted on 36 retail-acquired food samples (fresh sprouts, n = 10; ground meat, n = 26) and their corresponding bacterial enrichment cultures (n = 36) reveals in-depth features regarding the identity and diversity of AMR genes, most of which were otherwise undetected by the whole-metagenome shotgun sequencing method. Furthermore, our findings suggest that foodborne Gammaproteobacteria could be the major reservoir of food-associated AMR genetic determinants, and that the resistome structure of the selected high-risk food commodities are, to a large extent, dictated by microbiome composition.

CONCLUSIONS

For metagenomic sequencing-based surveillance of AMR, the target-capture method presented herein represents a more sensitive and efficient approach to evaluate the resistome profile of complex food or environmental samples. This study also further implicates retail foods as carriers of diverse resistance-conferring genes indicating a potential impact on the dissemination of AMR.

More than an anthropogenic phenomenon: Antimicrobial resistance in ungulates from natural and agricultural environments

Widely considered an anthropogenic phenomenon, antimicrobial resistance (AMR) is a naturally occurring mechanism that microorganisms use to gain competitive advantage. AMR represents a significant threat to public health and has generated criticism towards the overuse of antimicrobial drugs. Livestock have been proposed as important reservoirs for AMR accumulation. Here, we show that assemblages of AMR genes in cattle and ungulates from natural environments (Yellowstone and Rocky Mountain National Parks) are all dominated by genes conferring resistance to tetracyclines. However, cattle feces contained higher proportions of erm(A-X) genes conferring resistance to macrolide antibiotics. Medically important AMR genes differed between cattle and natural ungulates, but cumulatively were more predominant in natural soils. Our findings suggest that the commonly described predominance of tetracycline resistance in cattle feces is a natural phenomenon among multiple ungulate species and not solely a result of antimicrobial drug exposure. Yet, the virtual absence of macrolide resistance genes in natural ungulates suggests that macrolide usage in agriculture may enrich these genes in cattle. Our results show that antimicrobial use in agriculture may be promoting a potential reservoir for specific types of AMR (i.e., macrolide resistance) but that a significant proportion of the ungulate resistome appears to have natural origins.

Critical Assessment of Whole Genome and Viral Enrichment Shotgun Metagenome on the Characterization of Stool Total Virome in Hepatocellular Carcinoma Patients

Viruses are the most abundant form of life on earth and play important roles in a broad range of ecosystems. Currently, two methods, whole genome shotgun metagenome (WGSM) and viral-like particle enriched metagenome (VLPM) sequencing, are widely applied to compare viruses in various environments. However, there is no critical assessment of their performance in recovering viruses and biological interpretation in comparative viral metagenomic studies. To fill this gap, we applied the two methods to investigate the stool virome in hepatocellular carcinoma (HCC) patients and healthy controls. Both WGSM and VLPM methods can capture the major diversity patterns of alpha and beta diversities and identify the altered viral profiles in the HCC stool samples compared with healthy controls. Viral signatures identified by both methods showed reductions of Faecalibacterium virus Taranis in HCC patients' stool. Ultra-deep sequencing recovered more viruses in both methods, however, generally, 3 or 5 Gb were sufficient to capture the non-fragmented long viral contigs. More lytic viruses were detected than lysogenetic viruses in both methods, and the VLPM can detect the RNA viruses. Using both methods would identify shared and specific viral signatures and would capture different parts of the total virome.

Multiplexed Target Enrichment Enables Efficient and In-Depth Analysis of Antimicrobial Resistome in Metagenomes

Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny fraction of metagenomic content, which makes it difficult to investigate low-abundance ARGs in various environmental settings. Thus, a highly sensitive, accurate, and comprehensive method is needed to describe ARG profiles in complex metagenomic samples. In this study, we established a high-throughput sequencing method based on targeted amplification, which could simultaneously detect ARGs (n = 251), mobile genetic element genes (n = 8), and metal resistance genes (n = 19) in metagenomes. The performance of amplicon sequencing was compared with traditional metagenomic shotgun sequencing (MetaSeq). A total of 1421 primer pairs were designed, achieving extremely high coverage of target genes. The amplicon sequencing significantly improved the recovery of target ARGs (~9 × 104-fold), with higher sensitivity and diversity, less cost, and computation burden. Furthermore, targeted enrichment allows deep scanning of single nucleotide polymorphisms (SNPs), and elevated SNPs detection was shown in this study. We further performed this approach for 48 environmental samples (37 feces, 20 soils, and 7 sewage) and 16 clinical samples. All samples tested in this study showed high diversity and recovery of targeted genes. Our results demonstrated that the approach could be applied to various metagenomic samples and served as an efficient tool in the surveillance and evolution assessment of ARGs. Access to the resistome using the enrichment method validated in this study enabled the capture of low-abundance resistomes while being less costly and time-consuming, which can greatly advance our understanding of local and global resistome dynamics. IMPORTANCE ARGs, an increasing global threat to human health, can be transferred into health-related microorganisms in the environment by horizontal gene transfer, posing a serious threat to public health. Advancing profiling methods are needed for monitoring and predicting the potential risks of ARGs in metagenomes. Our study described a customized amplicon sequencing assay that could enable a high-throughput, targeted, in-depth analysis of ARGs and detect a low-abundance portion of resistomes. This method could serve as an efficient tool to assess the variation and evolution of specific ARGs in the clinical and natural environment.

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.

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.

The impacts of viral infection and subsequent antimicrobials on the microbiome-resistome of growing pigs

BACKGROUND

Antimicrobials are used in food-producing animals for purposes of preventing, controlling, and/or treating infections. In swine, a major driver of antimicrobial use is porcine reproductive and respiratory syndrome (PRRS), which is caused by a virus that predisposes infected animals to secondary bacterial infections. Numerous antimicrobial protocols are used to treat PRRS, but we have little insight into how these treatment schemes impact antimicrobial resistance (AMR) dynamics within the fecal microbiome of commercial swine. The aim of this study was to determine whether different PRRS-relevant antimicrobial treatment protocols were associated with differences in the fecal microbiome and resistome of growing pigs. To accomplish this, we used a metagenomics approach to characterize and compare the longitudinal wean-to-market resistome and microbiome of pigs challenged with PRRS virus and then exposed to different antimicrobial treatments, and a group of control pigs not challenged with PRRS virus and having minimal antimicrobial exposure. Genomic DNA was extracted from pen-level composite fecal samples from each treatment group and subjected to metagenomic sequencing and microbiome-resistome bioinformatic and statistical analysis. Microbiome-resistome profiles were compared over time and between treatment groups.

RESULTS

Fecal microbiome and resistome compositions both changed significantly over time, with a dramatic and stereotypic shift between weaning and 9 days post-weaning (dpw). Antimicrobial resistance gene (ARG) richness and diversity were significantly higher at earlier time points, while microbiome richness and diversity were significantly lower. The post-weaning shift was characterized by transition from a Bacteroides-dominated enterotype to Lactobacillus- and Streptococcus-dominated enterotypes. Both the microbiome and resistome stabilized by 44 dpw, at which point the trajectory of microbiome-resistome maturation began to diverge slightly between the treatment groups, potentially due to physical clustering of the pigs. Challenge with PRRS virus seemed to correspond to the re-appearance of many very rare and low-abundance ARGs within the feces of challenged pigs. Despite very different antimicrobial exposures after challenge with PRRS virus, resistome composition remained largely similar between the treatment groups. Differences in ARG abundance between the groups were mostly driven by temporal changes in abundance that occurred prior to antimicrobial exposures, with the exception of ermG, which increased in the feces of treated pigs, and was significantly more abundant in the feces of these pigs compared to the pigs that did not receive post-PRRS antimicrobials.

CONCLUSIONS

The fecal microbiome-resistome of growing pigs exhibited a stereotypic trajectory driven largely by weaning and physiologic aging of the pigs. Events such as viral illness, antimicrobial exposures, and physical grouping of the pigs exerted significant yet relatively minor influence over this trajectory. Therefore, the AMR profile of market-age pigs is the culmination of the life history of the individual pigs and the populations to which they belong. Disease status alone may be a significant driver of AMR in market-age pigs, and understanding the interaction between disease processes and antimicrobial exposures on the swine microbiome-resistome is crucial to developing effective, robust, and reproducible interventions to control AMR. Video Abstract.

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.

Critical review of antibiotic resistance genes in the atmosphere

We conducted a critical review to establish what is known about the sources, characteristics, and dissemination of ARGs in the atmosphere. We identified 52 papers that reported direct measurements of bacterial ARGs in air samples and met other inclusion criteria. The settings of the studies fell into the following categories: urban, rural, hospital, industrial, wastewater treatment plants (WWTPs), composting and landfill sites, and indoor environments. Certain genes were commonly studied and generally abundant: sul1, intI1, β-lactam ARGs, and tetracycline ARGs. Abundances of total ARGs varied by season and setting, with air in urban areas having higher ARG abundance than rural areas during the summer and vice versa during the winter. There was greater consistency in the types and abundances of ARGs throughout the seasons in urban areas. Human activity within indoor environments was also linked to increased ARG content (abundance, diversity, and concentration) in the air. Several studies found that human exposure to ARGs through inhalation was comparable to exposure through drinking water or ingesting soil. Detection of ARGs in air is a developing field, and differences in sampling and analysis methods reflect the many possible approaches to studying ARGs in air and make direct comparisons between studies difficult. Methodologies need to be standardized to facilitate identification of the dominant ARGs in the air, determine their major sources, and quantify the role of atmospheric transport in dissemination of ARGs in the environment. With such knowledge we can develop better policies and guidelines to limit the spread of antimicrobial resistance.

Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM): a method for detection of antimicrobial resistance in environmental samples

Effective monitoring of antibiotic resistance genes and their dissemination in environmental ecosystems has been hindered by the cost and efficiency of methods available for the task. We developed the Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM), a method implementing TruSeq high-throughput sequencing to simultaneously sequence thousands of antibiotic resistant gene targets representing a full-spectrum of antibiotic resistance classes common to environmental systems. In this study, we demonstrated DARTE-QM by screening 662 antibiotic resistance genes within complex environmental samples originated from manure, soil, and livestock feces, in addition to a mock-community reference to assess sensitivity and specificity. DARTE-QM offers a new approach to studying antibiotic resistance in environmental microbiomes, showing advantages in efficiency and the ability to scale for many samples. This method provides a means of data acquisition that will alleviate some of the obstacles that many researchers in this area currently face.

Smith et al. present DARTE-QM, a highthroughput sequencing method for screening environmental DNA samples for antibiotic resistance genes on a broad scale. This method is demonstrated as effective on soil, manure and livestock fecal samples, as well as a synthetic mock-community reference.

Resistomes and microbiome of meat trimmings and colon content from culled cows raised in conventional and organic production systems

Background

The potential to distribute bacteria resistant to antimicrobial drugs in the meat supply is a public health concern. Market cows make up a fifth of the U.S. beef produced but little is known about the entire population of bacteria (the microbiome) and entirety of all resistance genes (the resistome) that are found in this population. The objective of this study was to characterize and compare the resistomes and microbiome of beef, dairy, and organic dairy market cows at slaughter.

Methods

Fifty-four (N = 54) composite samples of both colon content and meat trimmings rinsate samples were collected over six visits to two harvest facilities from cows raised in three different production systems: conventional beef, conventional dairy, and organic dairy (n = 3 samples per visit per production system). Metagenomic DNA obtained from samples were analyzed using target-enriched sequencing (resistome) and 16S rRNA gene sequencing (microbiome).

Results

All colon content samples had at least one identifiable antimicrobial resistance gene (ARG), while 21 of the 54 meat trimmings samples harbored at least one identifiable ARGs. Tetracycline ARGs were the most abundant class in both colon content and carcass meat trimmings. The resistome found on carcass meat trimmings was not significantly different by production system (P = 0.84, R² = 0.00) or harvest facility (P = 0.10, R² = 0.09). However, the resistome of colon content differed (P = 0.01; R² = 0.05) among production systems, but not among the harvest facilities (P = 0.41; R² = 0.00). Amplicon sequencing revealed differences (P < 0.05) in microbial populations in both meat trimmings and colon content between harvest facilities but not production systems (P > 0.05).

Conclusions

These data provide a baseline characterization of an important segment of the beef industry and highlight the effect that the production system where cattle are raised and the harvest facilities where an animal is processed can impact associated microbiome and resistomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-022-00166-z.

Characterization and comparison of the microbiomes and resistomes of colostrum from selectively treated dry cows

Professionals in animal agriculture promote prudent use of antimicrobials to address public and animal health concerns, such as reduction of antimicrobial residues and antimicrobial resistance (AMR) in products. Few studies evaluate the effect of selective dry-cow therapy on preservation of the milk microbiome or the profile of AMR genes (the resistome) present at freshening. Our objectives were to characterize and compare the microbiomes and resistomes in the colostrum of cows with low somatic cell count that were treated or not treated with intramammary cephapirin benzathine at dry-off. From a larger parent study, cows on a New York dairy farm eligible for dry-off and with histories of somatic cell counts ≤200,000 cells/mL were enrolled to this study (n = 307). Cows were randomly assigned to receive an intramammary antimicrobial and external teat sealant (ABXTS) or sealant only (TS) at dry-off. Composite colostrum samples taken within 4 h of freshening, and quarter milk samples taken at 1 to 7 d in milk were subjected to aerobic culture. The DNA extraction was performed on colostrum from cows with culture-negative samples (ABXTS = 43; TS = 33). The DNA from cows of the same treatment group and parity were pooled (26 pools; ABXTS = 12; TS = 14) for 16S rRNA metagenomic sequencing. Separately, the resistome was captured using a custom RNA bait library for target-enriched sequencing. Sequencing reads were aligned to taxonomic and AMR databases to characterize the microbiome and resistome, respectively. The R statistical program was used to tabulate abundances and to analyze differences in diversity measures and in composition between treatment groups. In the microbiome, the most abundant phyla were Firmicutes (68%), Proteobacteria (23%), Actinobacteria (4%), and Bacteroidetes (3%). Shannon and richness diversity means were 0.93 and 14.7 for ABXTS and 0.94 and 13.1 for TS, respectively. Using analysis of similarities (ANOSIM), overall microbiome composition was found to be similar between treatment groups at the phylum (ANOSIM R = 0.005), class (ANOSIM R = 0.04), and order (ANOSIM R = -0.04) levels. In the resistome, we identified AMR gene accessions associated with 14 unique mechanisms of resistance across 9 different drug classes in 14 samples (TS = 9, ABXTS = 5). The majority of reads aligned to gene accessions that confer resistance to aminoglycoside (TS = ABXTS each 35% abundance), tetracycline (TS = 22%, ABXTS = 54%), and β-lactam classes (TS = 15%, ABXTS = 12%). Shannon diversity means for AMR class and mechanism, respectively, were 0.66 and 0.69 for TS and 0.19 and 0.19 for ABXTS. Resistome richness diversity means for class and mechanism were 3.1 and 3.4 for TS and 1.4 and 1.4 for ABXTS. Finally, resistome composition was similar between groups at the class (ANOSIM R = -0.20) and mechanism levels (ANOSIM R = 0.01). Although no critical differences were found between treatment groups regarding their microbiome or resistome composition in this study, a larger sample size, deeper sequencing, and additional methodology is needed to identify more subtle differences, such as between lower-abundance features.

High-Throughput Identification of Allergens in a Food System via Hybridization Probe Cluster-Targeted Next-Generation Sequencing

Food allergies (FAs) are a crucial public health problem and a severe food safety issue, resulting in an urgent need for an accurate method to detect all of the hidden allergens that exist in food systems. Current methods for detecting allergens typically utilize ELISA, PCR, or LC-MS, which are suitable for the confirmatory analysis of allergens from ingredients rather than unintended contaminants. In this study, we demonstrate a hybridization probe cluster-targeted next-generation sequencing (HPC-NGS) platform for high-throughput screening of potential allergens in food systems. The HPC-NGS successfully captured target DNA fragments and identified 19 allergenic ingredients in a complex food system. Additionally, the HPC-NGS provided expected allergenic species matching rates of 94.24-100% in single food materials and 99.87-99.98% in processed food products. Thus, HPC-NGS enables the accurate characterization of allergenic ingredients and unintended allergenic contaminants in foods. Our results provide new perspectives on the use of HPC-NGS in the accuracy of high-throughput detection technologies for allergens imposed by the complex matrix effect.

Escaping the fate of Sisyphus: assessing resistome hybridization baits for antimicrobial resistance gene capture

Finding, characterizing and monitoring reservoirs for antimicrobial resistance (AMR) is vital to protecting public health. Hybridization capture baits are an accurate, sensitive and cost-effective technique used to enrich and characterize DNA sequences of interest, including antimicrobial resistance genes (ARGs), in complex environmental samples. We demonstrate the continued utility of a set of 19 933 hybridization capture baits designed from the Comprehensive Antibiotic Resistance Database (CARD)v1.1.2 and Pathogenicity Island Database (PAIDB)v2.0, targeting 3565 unique nucleotide sequences that confer resistance. We demonstrate the efficiency of our bait set on a custom-made resistance mock community and complex environmental samples to increase the proportion of on-target reads as much as >200-fold. However, keeping pace with newly discovered ARGs poses a challenge when studying AMR, because novel ARGs are continually being identified and would not be included in bait sets designed prior to discovery. We provide imperative information on how our bait set performs against CARDv3.3.1, as well as a generalizable approach for deciding when and how to update hybridization capture bait sets. This research encapsulates the full life cycle of baits for hybridization capture of the resistome from design and validation (both in silico and in vitro) to utilization and forecasting updates and retirement.

Characterization of the microbiota and resistome of bulk tank milk samples from Prince Edward Island dairy farms

Bulk tank milk (BTM) is regularly used for surveillance on dairy farms for disease conditions such as mastitis and Johne's disease. In this study, we used 16S rRNA sequencing and bait-capture enrichment to characterize the microbiota and resistome of BTM, and investigate potential differences between the cream or pellet fractions. A total of 12 BTM samples were taken from 12 Prince Edward Island dairy farms, in Atlantic Canada, in duplicates. The DNA was analyzed by high-throughput sequencing of the 16S rRNA gene and a suite of antimicrobial resistance (AMR) genes. Target-capture enrichment of AMR genes was conducted before shotgun sequencing. The bioinformatics pipelines QIIME 2 and AMR++ were used for microbiota and resistome analysis, respectively. Differences between microbiotae were evaluated qualitatively with nonmetric multidimensional scaling and quantitatively with permutational ANOVA of UniFrac distances. A total of 47 phyla were present across the BTM samples. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were the most abundant phyla. At the genus level, Corynebacterium, Acinetobacter, Lactobacillus, and Turicibacter were the most abundant. There was no significant difference in the Faith's phylogenetic diversity between the cream and pellet fraction. Faith's phylogenetic diversity differed marginally by stall type. There were 10,217 hits across 80 unique AMR genes, with tetracycline resistance being the most common class.

BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays makes possible the identification of duplicated molecules, thereby facilitating the counting of distinct molecules from sequenced reads. However, we show that the naïve removal of duplicates can lead to a bias due to a "pooled amplification paradox," and we propose an improved quantification method based on unseen species modeling. Our correction called BUTTERFLY uses a zero truncated negative binomial estimator implemented in the kallisto bustools workflow. We demonstrate its efficacy across cell types and genes and show that in some cases it can invert the relative abundance of genes.

The Cantaloupe Farm Environment Has a Diverse Genetic Pool of Antibiotic-Resistance and Virulence Genes

Cantaloupes contaminated with pathogens have led to many high-profile outbreaks and illnesses. Since bacterial virulence genes (VGs) can act in tandem with antibiotic-resistance and mobile genetic elements, there is a need to evaluate these gene reservoirs in fresh produce, such as cantaloupes. The goal of this study was to assess the distribution of antibiotic-resistance, virulence, and mobile genetic elements genes (MGEGs) in cantaloupe farm environments. A total of 200 samples from cantaloupe melons (n = 99), farm workers' hands (n = 66), and production water (n = 35) were collected in México. Each sample was assayed for the presence of 14 antibiotic-resistance genes, 15 VGs, and 5 MGEGs by polymerase chain reaction. Our results indicated that tetracycline (tetA and tetB) (18% of cantaloupe, 45% of hand samples) and sulfonamide (sul1) (30% of cantaloupe, 71% of hand samples) resistance genes were frequently detected. The colistin resistance gene (mcr1) was detected in 10% of cantaloupe and 23% of farm workers' hands. Among VGs, Salmonella genes invA and spiA were the most abundant. There was a significantly higher likelihood of detecting antibiotic-resistance, virulence, and MGEGs on hands compared with water samples. These results demonstrate a diverse pool of antibiotic-resistance and VGs in cantaloupe production.

Metagenomic Characterization of the Microbiome and Resistome of Retail Ground Beef Products

Ground beef can be a reservoir for a variety of bacteria, including spoilage organisms, and pathogenic foodborne bacteria. These bacteria can exhibit antimicrobial resistance (AMR) which is a public health concern if resistance in pathogens leads to treatment failure in humans. Culture-dependent techniques are commonly used to study individual bacterial species, but these techniques are unable to describe the whole community of microbial species (microbiome) and the profile of AMR genes they carry (resistome), which is critical for getting a holistic perspective of AMR. The objective of this study was to characterize the microbiome and resistome of retail ground beef products labeled as coming from conventional or raised without antibiotics (RWA) production systems. Sixteen ground beef products were purchased from 6 retail grocery outlets in Fort Collins, CO, half of which were labeled as produced from cattle raised conventionally and half of products were from RWA production. Total DNA was extracted and isolated from each sample and subjected to 16S rRNA amplicon sequencing for microbiome characterization and target-enriched shotgun sequencing to characterize the resistome. Differences in the microbiome and resistome of RWA and conventional ground beef were analyzed using the R programming software. Our results suggest that the resistome and microbiome of retail ground beef products with RWA packaging labels do not differ from products that do not carry claims regarding antimicrobial drug exposures during cattle production. The resistome predominantly consisted of tetracycline resistance making up more than 90% of reads mapped to resistance gene accessions in our samples. Firmicutes and Proteobacteria predominated in the microbiome of all samples (69.6% and 29.0%, respectively), but Proteobacteria composed a higher proportion in ground beef from conventionally raised cattle. In addition, our results suggest that product management, such as packaging type, could exert a stronger influence on the microbiome than the resistome in consumer-ready products. Metagenomic analyses of ground beef is a promising tool to investigate community-wide shifts in retail ground beef. Importantly, however, results from metagenomic sequencing must be carefully considered in parallel with traditional methods to better characterize the risk of AMR in retail products.

A unified catalog of 204,938 reference genomes from the human gut microbiome

Comprehensive, high-quality reference genomes are required for functional characterization and taxonomic assignment of the human gut microbiota. We present the Unified Human Gastrointestinal Genome (UHGG) collection, comprising 204,938 nonredundant genomes from 4,644 gut prokaryotes. These genomes encode >170 million protein sequences, which we collated in the Unified Human Gastrointestinal Protein (UHGP) catalog. The UHGP more than doubles the number of gut proteins in comparison to those present in the Integrated Gene Catalog. More than 70% of the UHGG species lack cultured representatives, and 40% of the UHGP lack functional annotations. Intraspecies genomic variation analyses revealed a large reservoir of accessory genes and single-nucleotide variants, many of which are specific to individual human populations. The UHGG and UHGP collections will enable studies linking genotypes to phenotypes in the human gut microbiome.

More than 200,000 gut prokaryotic reference genomes and the proteins they encode are collated, providing comprehensive resources for microbiome researchers.

Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful?

Antimicrobial resistance (AMR) poses a global human and animal health threat, and predicting AMR persistence and transmission remains an intractable challenge. Shotgun metagenomic sequencing can help overcome this by enabling characterization of AMR genes within all bacterial taxa, most of which are uncultivatable in laboratory settings. Shotgun sequencing, therefore, provides a more comprehensive glance at AMR "potential" within samples, i.e., the "resistome." However, the risk inherent within a given resistome is predicated on the genomic context of various AMR genes, including their presence within mobile genetic elements (MGEs). Therefore, resistome risk stratification can be advanced if AMR profiles are considered in light of the flanking mobilizable genomic milieu (e.g., plasmids, integrative conjugative elements (ICEs), phages, and other MGEs). Because such mediators of horizontal gene transfer (HGT) are involved in uptake by pathogens, investigators are increasingly interested in characterizing that resistome fraction in genomic proximity to HGT mediators, i.e., the "mobilome"; we term this "colocalization." We explored the utility of common colocalization approaches using alignment- and assembly-based techniques, on clinical (human) and agricultural (cattle) fecal metagenomes, obtained from antimicrobial use trials. Ordination revealed that tulathromycin-treated cattle experienced a shift in ICE and plasmid composition versus untreated animals, though the resistome was unaffected during the monitoring period. Contrarily, the human resistome and mobilome composition both shifted shortly after antimicrobial administration, though this rebounded to pre-treatment status. Bayesian networks revealed statistical AMR-MGE co-occurrence in 19 and 2% of edges from the cattle and human networks, respectively, suggesting a putatively greater mobility potential of AMR in cattle feces. Conversely, using Mobility Index (MI) and overlap analysis, abundance of de novo-assembled contigs supporting resistomes flanked by MGE increased shortly post-exposure within human metagenomes, though > 40 days after peak dose such contigs were rare (∼2%). MI was not substantially altered by antimicrobial exposure across all cattle metagenomes, ranging 0.5-4.0%. We highlight that current alignment- and assembly-based methods estimating resistome mobility yield contradictory and incomplete results, likely constrained by approach-specific data inputs, and bioinformatic limitations. We discuss recent laboratory and computational advancements that may enhance resistome risk analysis in clinical, regulatory, and commercial applications.

In silico mapping of microbial communities and stress responses in a porcine slaughterhouse and pork products through its production chain, and the efficacy of HLE disinfectant

The use of shotgun metagenomic sequencing to understand ecological-level spread of microbes and their genes has provided new insights for the prevention, surveillance and control of microbial contaminants in the slaughterhouse environment. Here, microbial samples were collected from products and surrounding areas though a porcine slaughter process; shotgun metagenomic DNA-sequencing of these samples revealed a high community diversity within the porcine slaughterhouse and pork products, in zones originating from animal arrival through to the sale zones. Bacteria were more prevalent in the first zones, such as arrival- and anesthesia-zones, and DNA viruses were prevalent in the scorching-and-whip zone, animal products and sale zone. Data revealed the dominance of Firmicutes and Proteobacteria phyla followed by Actinobacteria, with a clear shift in the relative abundance of lactic acid bacteria (mainly Lactobacillus sp.) from early slaughtering steps to Proteobacteria and then to viruses suggesting site-specific community compositions occur in the slaughterhouse. Porcine-type-C oncovirus was the main virus found in slaughterhouse, which causes malignant diseases in animals and humans. As such, to guarantee food safety in a slaughterhouse, a better decipher of ecology and adaptation strategies of microbes becomes crucial. Analysis of functional genes further revealed high abundance of diverse genes associated with stress, especially in early zones (animal and environmental surfaces of arrival zone with 57,710 and 40,806 genes, respectively); SOS responsive genes represented the most prevalent, possibly associated with genomic changes responsible of biofilm formation, stringent response, heat shock, antimicrobial production and antibiotic response. The presence of several antibiotic resistance genes suggests horizontal gene transfer, thus increasing the likelihood for resistance selection in human pathogens. These findings are of great concern, with the suggestion to focus control measures and establish good disinfection strategies to avoid gene spread and microbial contaminants (bacteria and viruses) from the animal surface into the food chain and environment, which was achieved by applying HLE disinfectant after washing with detergent.

Differences in the Microbial Community and Resistome Structures of Feces from Preweaned Calves and Lactating Dairy Cows in Commercial Dairy Herds

Preweaned dairy calves and lactating dairy cows are known reservoirs of antibiotic-resistant bacteria. To further understand the differences in the resistomes and microbial communities between the two, we sequenced the metagenomes of fecal composite samples from preweaned dairy calves and lactating dairy cows on 17 commercial dairy farms (n = 34 samples). Results indicated significant differences in the structures of the microbial communities (analysis of similarities [ANOSIM] R = 0.81, p = 0.001) and resistomes (ANOSIM R = 0.93 to 0.96, p = 0.001) between the two age groups. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were the predominant members of the communities, but when the groups were compared, Bacteroidetes and Verrumicrobia were significantly more abundant in calf fecal composite samples, whereas Firmicutes, Spirochaetes, Deinococcus-Thermus, Lentisphaerae, Planctomycetes, Chlorofexi, and Saccharibacteria-(TM7) were more abundant in lactating cow samples. Diverse suites of antibiotic resistance genes (ARGs) were identified in all samples, with the most frequently detected being assigned to tetracycline and aminoglycoside resistance. When the two groups were compared, ARGs were significantly more abundant in composite fecal samples from calves than those from lactating cows (calf median ARG abundance = 1.8 × 10⁰ ARG/16S ribosomal RNA [rRNA], cow median ARG abundance = 1.7 × 10^-1 ARG/16S rRNA) and at the antibiotic resistance class level, the relative abundance of tetracycline, trimethoprim, aminoglycoside, macrolide-lincosamide-streptogramin B, β-lactam, and phenicol resistance genes was significantly higher in calf samples than in cow samples. Results of this study indicate that composite feces from preweaned calves harbor different bacterial communities and resistomes than composite feces from lactating cows, with a greater abundance of resistance genes detected in preweaned calf feces.

A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project

High-throughput DNA sequencing techniques enable time- and cost-effective sequencing of large portions of the genome. Instead of sequencing and annotating whole genomes, many phylogenetic studies focus sequencing effort on large sets of pre-selected loci, which further reduces costs and bioinformatic challenges while increasing coverage. One common approach that enriches loci before sequencing is often referred to as target sequence capture. This technique has been shown to be applicable to phylogenetic studies of greatly varying evolutionary depth. Moreover, it has proven to produce powerful, large multi-locus DNA sequence datasets suitable for phylogenetic analyses. However, target capture requires careful considerations, which may greatly affect the success of experiments. Here we provide a simple flowchart for designing phylogenomic target capture experiments. We discuss necessary decisions from the identification of target loci to the final bioinformatic processing of sequence data. We outline challenges and solutions related to the taxonomic scope, sample quality, and available genomic resources of target capture projects. We hope this review will serve as a useful roadmap for designing and carrying out successful phylogenetic target capture studies.

Discovery of Bat Coronaviruses through Surveillance and Probe Capture-Based Next-Generation Sequencing

Coronaviruses (CoVs) of bat origin have caused two pandemics in this century. Severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV both originated from bats, and it is highly likely that bat coronaviruses will cause future outbreaks. Active surveillance is both urgent and essential to predict and mitigate the emergence of these viruses in humans. Next-generation sequencing (NGS) is currently the preferred methodology for virus discovery to ensure unbiased sequencing of bat CoVs, considering their high genetic diversity. However, unbiased NGS is an expensive methodology and is prone to missing low-abundance CoV sequences due to the high background level of nonviral sequences present in surveillance field samples. Here, we employ a capture-based NGS approach using baits targeting most of the CoV species. Using this technology, we effectively reduced sequencing costs by increasing the sensitivity of detection. We discovered nine full genomes of bat CoVs in this study and revealed great genetic diversity for eight of them.IMPORTANCE Active surveillance is both urgent and essential to predict and mitigate the emergence of bat-origin CoV in humans and livestock. However, great genetic diversity increases the chance of homologous recombination among CoVs. Performing targeted PCR, a common practice for many surveillance studies, would not reflect this diversity. NGS, on the other hand, is an expensive methodology and is prone to missing low-abundance CoV sequences. Here, we employ a capture-based NGS approach using baits targeting all CoVs. Our work demonstrates that targeted, cost-effective, large-scale, genome-level surveillance of bat CoVs is now highly feasible.

Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes

Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome.

Selective culture enrichment and sequencing of feces to enhance detection of antimicrobial resistance genes in third-generation cephalosporin resistant Enterobacteriaceae

Metagenomic sequencing of fecal DNA can usefully characterise an individual's intestinal resistome but is limited by its inability to detect important pathogens that may be present at low abundance, such as carbapenemase or extended-spectrum beta-lactamase producing Enterobacteriaceae. Here we aimed to develop a hybrid protocol to improve detection of resistance genes in Enterobacteriaceae by using a short period of culture enrichment prior to sequencing of DNA extracted directly from the enriched sample. Volunteer feces were spiked with carbapenemase-producing Enterobacteriaceae and incubated in selective broth culture for 6 hours before sequencing. Different DNA extraction methods were compared, including a plasmid extraction protocol to increase the detection of plasmid-associated resistance genes. Although enrichment prior to sequencing increased the detection of carbapenemase genes, the differing growth characteristics of the spike organisms precluded accurate quantification of their concentration prior to culture. Plasmid extraction increased detection of resistance genes present on plasmids, but the effects were heterogeneous and dependent on plasmid size. Our results demonstrate methods of improving the limit of detection of selected resistance mechanisms in a fecal resistome assay, but they also highlight the difficulties in using these techniques for accurate quantification and should inform future efforts to achieve this goal.

A Cautionary Report for Pathogen Identification Using Shotgun Metagenomics; A Comparison to Aerobic Culture and Polymerase Chain Reaction for Salmonella enterica Identification

This study was conducted to compare aerobic culture, polymerase chain reaction (PCR), lateral flow immunoassay (LFI), and shotgun metagenomics for identification of Salmonella enterica in feces collected from feedlot cattle. Samples were analyzed in parallel using all four tests. Results from aerobic culture and PCR were 100% concordant and indicated low S. enterica prevalence (3/60 samples positive). Although low S. enterica prevalence restricted formal statistical comparisons, LFI and deep metagenomic sequencing results were discordant with these results. Specifically, metagenomic analysis using k-mer-based classification against the RefSeq database indicated that 11/60 of samples contained sequence reads that matched to the S. enterica genome and uniquely identified this species of bacteria within the sample. However, further examination revealed that plasmid sequences were often included with bacterial genomic sequence data submitted to NCBI, which can lead to incorrect taxonomic classification. To circumvent this classification problem, we separated all plasmid sequences included in bacterial RefSeq genomes and reassigned them to a unique taxon so that they would not be uniquely associated with specific bacterial species such as S. enterica. Using this revised database and taxonomic structure, we found that only 6/60 samples contained sequences specific for S. enterica, suggesting increased relative specificity. Reads identified as S. enterica in these six samples were further evaluated using BLAST and NCBI's nr/nt database, which identified that only 2/60 samples contained reads exclusive to S. enterica chromosomal genomes. These two samples were culture- and PCR-negative, suggesting that even deep metagenomic sequencing suffers from lower sensitivity and specificity in comparison to more traditional pathogen detection methods. Additionally, no sample reads were taxonomically classified as S. enterica with two other metagenomic tools, Metagenomic Intra-species Diversity Analysis System (MIDAS) and Metagenomic Phylogenetic Analysis 2 (MetaPhlAn2). This study re-affirmed that the traditional techniques of aerobic culture and PCR provide similar results for S. enterica identification in cattle feces. On the other hand, metagenomic results are highly influenced by the classification method and reference database employed. These results highlight the nuances of computational detection of species-level sequences within short-read metagenomic sequence data, and emphasize the need for cautious interpretation of such results.

Horizontal Gene Transfer and Acquired Antibiotic Resistance in Salmonella enterica Serovar Heidelberg following In Vitro Incubation in Broiler Ceca

The chicken gastrointestinal tract harbors microorganisms that play a role in the health and disease status of the host. The cecum is the part of the gut that carries the highest microbial densities, has the longest residence time of digesta, and is a vital site for urea recycling and water regulation. Therefore, the cecum provides a rich environment for bacteria to horizontally transfer genes between one another via mobile genetic elements such as plasmids and bacteriophages. In this study, we used broiler chicken cecum as a model to investigate antibiotic resistance genes that can be transferred in vitro from cecal flora to Salmonella enterica serovar Heidelberg. We used whole-genome sequencing and resistome enrichment to decipher the interactions between S Heidelberg, the gut microbiome, and acquired antibiotic resistance. After 48 h of incubation of ceca under microaerophilic conditions, we recovered one S Heidelberg isolate with an acquired IncK2 plasmid (88 kb) carrying an extended-spectrum-β-lactamase gene (bla _CMY-2). In vitro, this plasmid was transferable between Escherichia coli and S Heidelberg strains but transfer was unsuccessful between S Heidelberg strains. An in-depth genetic characterization of transferred plasmids suggests that they share significant homology with P1-like phages. This study contributes to our understanding of horizontal gene transfer between an important foodborne pathogen and the chicken gut microbiome.IMPORTANCE S. Heidelberg is a clinically important serovar, linked to foodborne illness and among the top 5 serovars isolated from poultry in the United States and Canada. Acquisition of new genetic material from the microbial flora in the gastrointestinal tract of food animals, including broilers, may contribute to increased fitness of pathogens like S. Heidelberg and may increase their level of antibiotic tolerance. Therefore, it is critical to gain a better understanding of the interactions that occur between important pathogens and the commensals present in the animal gut and other agroecosystems. In this report, we show that the native flora in broiler ceca were capable of transferring mobile genetic elements carrying the AmpC β-lactamase (bla _CMY-2) gene to an important foodborne pathogen, S Heidelberg. The potential role for bacteriophage transduction is also discussed.

The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

BACKGROUND

Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, 'ResPipe'.

RESULTS

Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment.

CONCLUSIONS

Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available ( https://gitlab.com/hsgweon/ResPipe ).

Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences

The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases.

Identification of a novel chitinase from Aeromonas hydrophila AH-1N for the degradation of chitin within fungal mycelium

Defined organic waste products are ideal and sustainable secondary feedstocks for production organisms in microbial biotechnology. Chitin from mycelia of fungal fermentation processes represents a homogeneous and constantly available waste product that can, however, not be utilised by typical bacterial production strains. Therefore, enzymes that degrade chitin within fungal mycelia have to be identified and expressed in production organisms. In this study, chitin-degrading bacteria were enriched and isolated from lake water with mycelia of Aspergillus tubingensis as sole organic growth substrate. This approach yielded solely strains of Aeromonas hydrophila. Comparison of the isolated strains with other A. hydrophila strains regarding their chitinolytic activities on fungal mycelia identified strain AH-1N as the best enzyme producer. From this strain, a chitinase (EC:3.2.1.14) was identified by peptide mass fingerprinting. Heterologous expression of the respective gene combined with mass spectrometry showed that the purified enzyme was capable of releasing chitobiose from fungal mycelia with a higher yield than a well-described chitinase from Serratia marcescens. Expression of the newly identified chitinase in biotechnological production strains could be the first step for making fungal mycelium accessible as a secondary feedstock. Additionally, the enrichment strategy proved to be feasible for identifying strains able to degrade fungal chitin.

Capturing sequence diversity in metagenomes with comprehensive and scalable probe design

Metagenomic sequencing has the potential to transform microbial detection and characterization, but new tools are needed to improve its sensitivity. Here we present CATCH, a computational method to enhance nucleic acid capture for enrichment of diverse microbial taxa. CATCH designs optimal probe sets, with a specified number of oligonucleotides, that achieve full coverage of, and scale well with, known sequence diversity. We focus on applying CATCH to capture viral genomes in complex metagenomic samples. We design, synthesize, and validate multiple probe sets, including one that targets the whole genomes of the 356 viral species known to infect humans. Capture with these probe sets enriches unique viral content on average 18-fold, allowing us to assemble genomes that could not be recovered without enrichment, and accurately preserves within-sample diversity. We also use these probe sets to recover genomes from the 2018 Lassa fever outbreak in Nigeria and to improve detection of uncharacterized viral infections in human and mosquito samples. The results demonstrate that CATCH enables more sensitive and cost-effective metagenomic sequencing.

Exploring Foodborne Pathogen Ecology and Antimicrobial Resistance in the Light of Shotgun Metagenomics

In this chapter, applications of shotgun metagenomics for taxonomic profiling and functional investigation of food microbial communities with a focus on antimicrobial resistance (AMR) were overviewed in the light of last data in the field. Potentialities of metagenomic approach, along with the challenges encountered for a wider and routinely use in food safety was discussed.

Deciphering Resistome and Virulome Diversity in a Porcine Slaughterhouse and Pork Products Through Its Production Chain

We aimed to better understand resistome and virulome patterns on animal and process-area surfaces through a pig slaughterhouse to track possible contamination within the food production chain. Culture-dependent methods revealed high levels of microbial contamination, corresponding to mesophilic and pathogenic bacteria on both the animal and process-area surfaces mainly in the anesthesia (AA and AS) zone followed by “scorching and whip” (FA and FS) zone and also in the end products. To evaluate the potential risk of antibiotic resistance and virulence determinants, shotgun metagenomic DNA-sequencing of isolates from selected areas/products uncovered a high diversity and richness of antibiotic resistance genes (ARGs): 55–62 genes in the anesthesia area (AA and AS) and 35–40 in “animal-arrival zone” (MA and MS). The “scorching and whip” (FA and FS) area, however, exhibited lowered abundance of ARGs (1–6), indicating that the scalding and depilating process (an intermediate zone between “anesthesia” and “scorching and whip”) significantly decreased bacterial load by 1–3 log₁₀ but also diminished the resistome. The high prevalence of antibiotic-inactivating enzyme genes in the “animal-arrival zone” (60–65%) and “anesthesia” area (56%) were mainly represented by those for aminoglycoside (46–51%) and lincosamide (14–19%) resistance, which did not reflect selective pressures by antibiotics most commonly used in pig therapy—tetracyclines and beta-lactams. Contrary to ARGs, greater number of virulence resistance genes were detected after evisceration in some products such as kidney, which reflected the poor hygienic practices. More than 19 general virulence features—mainly adherence, secretion system, chemotaxis and motility, invasion and motility were detected in some products. However, immune evasion determinants were detected in almost all samples analyzed from the beginning of the process, with highest amounts found from the anesthesia area. We conclude that there are two main sources of contamination in a pig slaughterhouse: the microorganisms carried on the animals’ hide, and those from the evisceration step. As such, focussing control measures, e.g., enhanced disinfection procedures, on these contamination-source areas may reduce risks to food safety and consumer health, since the antibiotic and virulence determinants may spread to end products and the environment; further, ARG and virulence traits can exacerbate pathogen treatments.

16S metagenomics for diagnosis of bloodstream infections: opportunities and pitfalls

INTRODUCTION

Bacterial bloodstream infections (BSI) form a large public health threat worldwide. Current routine diagnosis is based on blood culture (BC) but this technique suffers from limited sensitivity. Molecular diagnostic tools have been developed for identification of bacteria in the blood of BSI patients. 16S metagenomics is an open-ended technique that can detect simultaneously all bacteria in a given sample based on PCR amplification of the 16S ribosomal RNA gene (rDNA) followed by sequencing of the PCR amplicons and taxonomic labeling of the sequence reads at genus or species level. Areas covered: Here we review the studies that have used 16S metagenomics for the identification of bacteria in human blood samples. We also discuss the potential added value of 16S metagenomics in the diagnosis of BSI, challenges as well as future directions for implementation in clinical settings. Expert commentary: 16S metagenomics has the potential to complement conventional BC; however, the technique currently suffers from several technical limitations jeopardizing implementation in routine clinical microbiology laboratories. Further studies are required to assess the cost-efficiency and clinical impact of 16S metagenomics in comparison to BC which remains the gold standard diagnostic method for BSI.