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.Peer reviewe

Whole-Genome Sequencing and Concordance Between Antimicrobial Susceptibility Genotypes and Phenotypes of Bacterial Isolates Associated with Bovine Respiratory Disease.

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.Comment: Accepted by IEEE PES 2018 General Meetin

Epidemiology and ecology of antimicrobial use and resistance in North American beef production systems, The

2016 Spring.Includes bibliographical references.Antimicrobial resistance (AMR) is a critical public health issue (1), and analysis of historical Escherichia coli isolates reveals that AMR has been increasing steadily since the introduction of antimicrobial drugs (AMDs) (2). Meat production systems are thought to contribute to the problem by harboring a reservoir of AMR that interfaces with humans either through persistence in the food chain or dissemination of wastes into the environment (3–6). Antimicrobial use (AMU) in food producing animals is often cited as a driver of AMR in humans, but it is extremely challenging to design and execute studies that can be used to infer causality between the two. As a result, producers and policy makers alike have relatively little high-quality evidence on which to base informed and rational decisions with regard to AMU and other production management practices. The four studies presented in this doctoral thesis attempt to overcome some of the obstacles that currently impede inferential analysis regarding AMU practices and AMR. The first two studies stem from a project in which detailed AMU and AMR data were collected throughout the feeding period for over 5,000 individual cattle across 300 pens. The unprecedented collection of prospective data from such a large number of uniquely identified commercial cattle enabled us to achieve a much more robust level of causal inference compared to many previous studies. The last two studies employed shotgun metagenomics to interrogate the entire AMR potential (the “resistome”) of a given sample, enabling novel insight into the longitudinal, microbe-level genetic ecology of AMR within beef production systems. Because AMR develops and is maintained within the genetic context a microbial population, the resistome-microbiome approach contributes a critical and long-lacking piece to the overall puzzle of AMR within beef production. Thus, while each study in this dissertation approaches the research question of AMR from a slightly different angle, all of them provide crucial and novel information to our scientific understanding of AMU and AMR in beef production. The 4 studies also complement one another through investigation of different aspects of AMU and AMR across nearly the entire beef production system. The first study not only investigates AMU-AMR associations within Mannheimia haemolytica, but also examines how these associations affect respiratory-related morbidity and mortality outcomes in commercial cattle. As such, this study is focused on the animal health and economic dimensions of AMU and AMR in a critically important respiratory pathogen. The second study investigates within-feedlot AMU-AMR associations in non-type-specific Escherichia coli, a widely used “indicator” species for AMR, and compares different analytical methods for analyzing the types of data collected as part of ongoing surveillance of AMR in livestock production. Therefore, this study focuses on the public health and regulatory dimension of AMU-AMR in feedlot beef production. The third study tracks AMR in cattle production effluents such as feces, soil and water, thus encompassing the environmental dissemination routes that may play a role in the transmission of AMR from livestock to humans. And finally the fourth study tracks AMR in cattle and their environments from feedlot entry through slaughter and fabrication, thereby delving into the food supply dimension of beef production. Importantly, all 4 studies were conducted in commercial beef feedlot operations, and samples are collected from commercial cattle and their environments. All 4 studies are strictly observational; the participating operations did not alter their production practices and cattle were not managed differently for any of the studies. While this approach may add complexity to the interpretation of study findings, it has the distinct advantage of enabling insight into AMU-AMR dynamics on operations that constitute an integral part of the fabric of our society. The AMU and other production practices utilized on these operations were not contrived, and therefore the external validity of the study findings are more widely applicable than those gleaned from research animals and herds. The findings of the 4 studies in this dissertation are therefore novel, complementary and highly relevant to the societal, political and scientific debate surrounding AMU and AMR in beef production

Database used for creation of antimicrobial resistance and virulence bait-capture panel

These 2 databases were used for design and validation of a bait-capture system for enrichment of antimicrobial resistance and virulence genes within metagenomic samples.USDA NIFA grant 2016-67012-24679.USDA NIFA grant 2015-68003-23048

Supporting data for "Mobilization of antibiotic resistance: Are current approaches co-localizing microbial resistomes and mobilomes useful?"

BLAST parsing scripts for colocalization analysis using metagenomic short-read assembliesAntimicrobial resistance (AMR) poses a global threat to human, public, and animal health and predicting the evolution, persistence and transmission of AMR has been a mainstay challenge. Shotgun metagenomic sequencing helps overcome this by enabling characterization of resistance genes within all bacterial taxa, including those uncultivatable using standard laboratory methods. In this way, shotgun sequencing provides a more comprehensive assessment of the AMR ‘potential’ within samples, i.e., the “resistome”. The data-dense metagenomic assessment of the resistome, however, encumbers a clear understanding of how and where resistance likely develops and spreads. Recently, it has been proposed that risk stratification of all AMR determinants of a sample are most informative if their profile is considered in light of the mobilizable genomic milieu flanking or encompassing microbial resistomes. This includes mobility factors such as plasmids, integrative conjugative elements (ICE), insertional sequences, transposons, and other mobile genetic elements (MGEs). As these components are responsible for horizontal gene transfer (HGT) and potentially the uptake of resistance by pathogenic species, investigators are turning their attention to studying that portion of the resistome closely associated with HGT elements, i.e., the “mobilome”. As metagenomics is poised to be at the forefront of characterizing resistance mobility potential, there is currently an inexorable need to perform resistome-mobilome colocalization analyses. In this study, we explored currently available colocalization approaches with a focus on alignment-based methods as well as co-occurrence analysis using assembly-based techniques, using default or common literature-supported parameters. We analyzed a clinical (human) and an agricultural (cattle) publicly available fecal metagenomic dataset, obtained from trials employing antimicrobials in individuals sampled over time. For human and cattle datasets, AMR and MGE input data for colocalization analysis starkly differed in gene class richness, depending on alignment and assembly techniques used. Ordination revealed that tulathromycin use in cattle was associated with a shift in ICE and plasmid composition relative to untreated animals, the resistome was not significantly impacted (ANOSIM P >0.05) during the 11-day monitoring period. Contrarily, in the human dataset, while ordination of resistome and ICE, plasmid, and prophage components of the mobilome showed a shift shortly after the administration of antimicrobials (ANOSIM P 70% bootstraps accounted for 19% of edges of the human network and 2% of edges of the cattle network. Conversely, using the Mobility Index (MI) at the level of the metagenomic sample (defined as proportion of all present AMR-containing contigs with flanking MGE sequences) colocalizations identified from de novo assembly indicates that AMR-MGE co-occurrence increases shortly after exposure to antibiotics within the human metagenome (up to 75%), however >40 days after peak antimicrobial exposure, such contigs were rare (~2 %). For the cattle metagenome, MI was not altered by antimicrobial exposure, ranging 0.5–4.0%. Our results highlight that alignment-based and assembly based techniques used in colocalization will yield often contradictory and incomplete conclusions about resistance mobility, and that current bioinformatic approaches are limited by technical and computational challenges that prevent reliable colocalization analysis. We conclude by discussing development of laboratory, sequencing, and computational methods that may be useful in contextualizing that portion of the resistome most likely to be mobilizable, and therefore, enhance relevance of metagenomic resistome analysis in clinical, regulatory, and commercial applications.National Institute of Allergy and Infectious Diseases (NIAID) of the U.S. National Institutes of Health (NIH), project no. 1R01AI141810-0

Genome Sequence of Listeria innocua Strain MEZLIS26, Isolated from a Goat in South Africa

Here, we report the draft genome sequence of Listeria innocua strain MEZLIS26, isolated from a healthy goat in Flagstaff, Eastern Cape Province, South Africa. The genome was sequenced using the Illumina MiSeq platform and had a length of 2,800,777 bp, with a G+C content of 37.4%, 2,755 coding DNA sequences (CDSs), 49 transfer RNAs (tRNAs), and 4 noncoding RNAs (ncRNAs)