Slizovskiy et al. Colocalization Analysis of Resistomes-Mobilomes 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.
There is a need for standardized, efficient, and practical sampling methods to support large population-based studies of the internal and external epithelial microbiomes of the bovine udder. The primary objective of this study was to evaluate different sampling devices for the isolation of microbial DNA originating from the internal and external teat epithelium. Secondary objectives were to survey and compare the microbial diversity of external and teat canal epithelial microbiomes using amplicon and shotgun metagenomic sequencing approaches. To address these objectives, we enrolled a convenience sample of 24 Holstein dairy cows and collected samples from the external epithelium at the base of udder, the external teat barrel epithelium, the external teat apex epithelium, and the teat canal epithelium. Extracted DNA was quantified and subjected to PCR amplification of the V4 hypervariable region of the 16S rRNA gene and sequenced on the Illumina MiSeq platform (Illumina Inc., San Diego, CA). A subset of samples was subjected to a shallow shotgun metagenomic assay on the Illumina HiSeq platform. For samples collected from the external teat epithelium, we found that gauze squares consistently yielded more DNA than swabs, and Simpson's reciprocal index of diversity was higher for gauze than for swabs. The teat canal epithelial samples exhibited significantly lower diversity than the external sampling locations, but there were no significant differences in diversity between teat apex, teat barrel, and base of the udder samples. There were, however, differences in the microbial distribution and abundances of specific bacteria across external epithelial surfaces. The proportion of shotgun sequence reads classified as Bos taurus was highly variable between sampling locations, ranging from 0.33% in teat apex samples to 99.91% in teat canal samples. These results indicate that gauze squares should be considered for studying the microbiome of the external epithelium of the bovine udder, particularly if DNA yield must be maximized. Further, the relative proportion of host to non-host DNA present in samples collected from the internal and external teat epithelium should be considered when designing studies that utilize shotgun metagenomic sequencing.
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.
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.
Background Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. Results We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2–0.9). On semi-synthetic metagenomic data—external test—on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. Conclusions AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.
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