PATRIC as a unique resource for studying antimicrobial resistance

Antonopoulos, Dionysios A.; Assaf, Rida; Aziz, Ramy K.; Brettin, Thomas; Bun, Christopher; Conrad, Neal; Davis, James J.; Dietrich, Emily; Disz, Terry; Gerdes, Svetlana; Kenyon, Ronald W.; Machi, Dustin; Mao, Chunhong; Murphy-Olson, Daniel E.; Nordberg, Eric K.; Olsen, Gary J.; Olson, Robert; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Santerre, John; Shukla, Maulik; Stevens, Rick; VanOeffelen, Margo; Vonstein, Veronika; Warren, Andrew S.; Wattam, Alice R.; Xia, Fangfang; Yoo, Hyun Seung

doi:10.1093/bib/bbx083

Cited by 105 publications

(88 citation statements)

References 46 publications

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“…Reference bacterial and mouse genomes for C. difficile ATCC43255 (NZ_CM000604.1) and C. bifermentans ATCC638 (NZ_AVNC01000001.1) were obtained from the PATRIC Genome Annotation service (71) and Mus musculus C57BL6/J (GCF_000001635.26) from the NCBI host genome reference consortium to map reads. A genome for CSAR was generated using the methods as described in Nudel, et al (72) by Illumina MiSeq and was annotated in PATRIC (73).…”

Section: Transcriptome Data Processingmentioning

confidence: 99%

In vivocommensal control ofClostridioides difficilevirulence

Girinathan

DiBenedetto

Worley

et al. 2020

Preprint

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We define multiple mechanisms by which commensals protect against or worsen Clostridioides difficile infection.Using a systems-level approach we show how two species of Clostridia with distinct metabolic capabilities modulate the pathogen's virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Clostridium bifermentans survived infection, while colonization with the butyrate-producer, Clostridium sardiniense, more rapidly succumbed. Systematic in vivo analyses revealed how each commensal altered the pathogen's carbon source metabolism, cellular machinery, stress responses, and toxin production. Protective effects were replicated in infected conventional mice receiving C. bifermentans as an oral bacteriotherapeutic that prevented lethal infection. Leveraging a systematic and organism-level approach to host-commensalpathogen interactions in vivo, we lay the groundwork for mechanistically-informed therapies to treat and prevent this disease.Clostridioides difficile, the etiology of pseudomembranous colitis, causes substantantial morbidity, mortality and >$5 billion/year in US healthcare costs. Infections commonly arise after antibiotic disruption of the microbiota, allowing the pathogen to proliferate and release toxins that ADP ribosylate host rho GTPases (1, 2). In patients with recurrent C. difficile infections, fecal microbiota transplant (FMT) has become standard of care to reconstitute the microbiota and prevent recurrence. While intensive efforts to develop defined microbial replacements for FMT have been undertaken, relatively little is known about the molecular, metabolic, and microbiologic mechanisms by which specific members of the microbiota modulate the pathogen's virulence in vivo, information critical for therapeutics development (3,4). Given deaths in immunocompromised patients from drug-resistant pathogens in FMT preparations (5), therapies informed by molecular mechanisms of action among will enable options with improved safety and efficacy (6, 7).C. difficile's pathogenicity locus (PaLoc) contains the tcdA, tcdB and tcdE genes that encode the A and B toxins, and holin involved in toxin export, respectively. tcdR encodes a sigma factor specific for the toxin gene promoters, and the tcdC gene a TcdR anti-sigma factor (8-10). Multiple metabolic regulators influence PaLoc expression (11,12). In particular, C. difficile elaborates toxin under starvation conditions to extract nutrients from the host and promote the shedding of spores.C. difficile, like other cluster XI Clostridia, possesses diverse genetic machinery to utilize different carbon sources for energy and growth. In addition to carbohydrate fermentation, the pathogen uses Stickland fermentations, and Stickland-independent fermentations of other amino acids including threonine and cysteine (13), to extract energy from amino acids. The pathogen can ferment ethanolamine, extract electrons from primary bile salts, and undergo carbon fixation through the Wood-Ljungdhal pathway to generate acetate for metabolism...

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Section: Transcriptome Data Processingmentioning

confidence: 99%

In vivocommensal control ofClostridioides difficilevirulence

Girinathan

DiBenedetto

Worley

et al. 2020

Preprint

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“…Whole genome sequence data and paired laboratory-derived antimicrobial susceptibility test (AST) data were downloaded from the PATRIC FTP server (ftp://ftp.patricbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt) on or after December 1, 2018 29,30 . We chose to analyze Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, which had the largest collections of genomes paired with AST data in this collection at the time (Table 1, Tables S1-6).…”

Section: Data Acquisitionmentioning

confidence: 99%

“…All genomes were reannotated so that they had the same set of protein family calls based on the May 31, 2018 protein family build, using the PATRIC annotation server 37 . All families with a PATRIC annotation associated with AMR were removed from consideration in this study 30 .…”

Section: Core Conserved Gene Setsmentioning

confidence: 99%

Predicting Antimicrobial Resistance Using Conserved Genes

Nguyen

Olson

Shukla

et al. 2020

Preprint

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Abbreviations AIartificial intelligence CI confidence interval ME major error, susceptible genomes predicted to be resistant MIC minimum inhibitory concentration PATRIC Pathosystems resource integration center PLF PATRIC local protein family RAST Rapid annotation using subsystem technology RF Random Forest SR susceptible and resistant VME very major error, resistant genomes predicted to be susceptible XGB XGBoost Abstract A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases where the primary AMR mechanism results from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes for use in these models, we show that F1 scores and error rates are stable and have little variance between replicates. Potential biases from strainspecific SNPs, phylogenetic sampling, and imbalances in the phylogenetic distribution of susceptible and resistant strains do not appear to have an impact on this result. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes. Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.

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“…The data used in this study were extracted from the Pathosystems Resource Integration Center (PATRIC) database, one of the most comprehensive public databases of bacterial genomes and antimicrobial resistance metadata 10,24 . The protocol used to acquire the data is detailed in Methods and the amount of data extracted is shown, per species, in Fig.…”

Section: Overview Of the Datamentioning

confidence: 99%

“…The data were extracted from the Pathosystems Resource Integration Center (PATRIC) database 10,24 FTP backend using the PATRIC tools Python package 54 (February 4, 2018). First, AMR phenotypes taking the form of SIR (susceptible, intermediate, resistant) labels were extracted for several bacterial isolates and antibiotics.…”

Section: Data Acquisitionmentioning

confidence: 99%

Interpretable genotype-to-phenotype classifiers with performance guarantees

Drouin

Letarte

Raymond

et al. 2018

Preprint

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Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potentially new ones. An open-source disk-based implementation that is both memory and computationally efficient is provided with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials.

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PATRIC as a unique resource for studying antimicrobial resistance

Cited by 105 publications

References 46 publications

In vivocommensal control ofClostridioides difficilevirulence

In vivocommensal control ofClostridioides difficilevirulence

Predicting Antimicrobial Resistance Using Conserved Genes

Interpretable genotype-to-phenotype classifiers with performance guarantees

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