Consolidating and Exploring Antibiotic Resistance Gene Data Resources

Xavier, Basil Britto; Das, Anupam; Cochrane, Guy; Ganck, Sandra De; Kumar‐Singh, Samir; Aarestrup, Frank Møller; Goossens, Herman; Malhotra-Kumar, Surbhi

doi:10.1128/jcm.02717-15

Cited by 83 publications

(54 citation statements)

References 47 publications

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“…This database was originally focused heavily on TEM, SHV, and OXA type β‐lactamases, only a subset of β‐lactam antimicrobial resistance genes. Other β‐lactamases that are now included in the database are CTX‐M, CMY, AmpC, CARB, IMP, VIM, KPC, GES, PER, and VEB …”

Section: Sequence‐based Methods For Resistance Discoverymentioning

confidence: 99%

Genomic and functional techniques to mine the microbiome for novel antimicrobials and antimicrobial resistance genes

Adu-Oppong

Gasparrini

Dantas

2016

Annals of the New York Academy of Sciences

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Microbial communities contain diverse bacteria that play important functional roles in every environment. Advances in sequencing and computational methodologies over the past decades have illuminated the phylogenetic and functional diversity of microbial communities from diverse habitats. Among the functions encoded in microbiomes are the abilities to synthesize and resist small molecules, yielding antimicrobial activity. These functions are of particular interest when viewed in light of the public health emergency posed by the increase in clinical antimicrobial resistance and the dwindling antimicrobial discovery and approval pipeline, and given the intimate ecological and evolutionary relationship between antimicrobial biosynthesis and resistance. Here, we review genomic and functional methods that have been developed for accessing the antimicrobial biosynthesis and resistance capacity of microbiomes and highlight outstanding examples of their applications.

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Section: Sequence‐based Methods For Resistance Discoverymentioning

confidence: 99%

Genomic and functional techniques to mine the microbiome for novel antimicrobials and antimicrobial resistance genes

Adu-Oppong

Gasparrini

Dantas

2016

Annals of the New York Academy of Sciences

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“…The establishment and maintenance of databases and bioinformatics tools is essential for this effort, particularly as the accumulation of whole‐genome and next‐generation sequencing data rapidly expands our knowledge of resistance determinants . There are already a number of databases dedicated to antibiotic resistance determinants (Table ), each of which has its own advantages and disadvantages …”

Section: Antibiotic Resistance and Databasesmentioning

confidence: 99%

“…Note: This tabulation is representative rather than exhaustive. Detailed considerations of the relative advantages of some of these resources have recently been published …”

Section: Antibiotic Resistance and Databasesmentioning

confidence: 99%

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Genomics and the evolution of antibiotic resistance

Gillings

Paulsen

Tetu

2016

Annals of the New York Academy of Sciences

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Antibiotic resistance arises as a consequence of complex interactions among genes, mobile elements, and their bacterial hosts, coupled with the intense selection pressures imposed by humans in an attempt to control bacterial growth. Understanding the evolution of resistance requires an understanding of interacting cellular and genetic components. Here, we review how DNA analysis has helped reconstruct the origins of the mosaic, multiresistant mobile elements that have spread through pathogens in the last 60 years. This history helps inform the future, such that resistance might be better managed. Whole-genome sequencing has great potential for epidemiological tracking and for understanding the development of resistance via experimental evolution. DNA analysis also offers the opportunity for constructing databases that record genes of interest, the mobile elements that move these genes, and the cells or species that acquire such genes. Linking these DNA elements to their human and animal hosts and to the environments where they occur should help us establish a more robust ecological and evolutionary framework for controlling and managing resistance. Such efforts need to be well coordinated because, like many other issues that face humanity, antibiotic resistance is a global problem that requires global solutions.

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“…These methods work well for finding known and highly conserved AR sequences and have low probability of producing false positives, that is, predicting non‐AR genes as AR genes (Forsberg et al ). However, they may produce high false‐negative rates when they fail to identify AR genes that have lower sequence identity with known AR genes (Xavier et al ; Yang et al ; McArthur and Tsang ). Machine learning can be a useful alternative computational framework for identifying AR phenotypes accurately using features, that is, characteristics of known AR protein sequences, found in the genomic data regardless of sequence similarity (Niehaus et al ; Santerre et al ).…”

Section: Introductionmentioning

confidence: 99%

Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method

2019

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Aims Predicting bacterial resistance provides valuable information that can assist in clinical decisions. With recent advances in whole genome sequencing technology, the detection of antibiotic resistance (AR) proteins directly from genomic data is becoming feasible. AR genes/proteins can be identified using best‐hit methods that work by comparing candidate sequences with known AR genes in public databases. However, these approaches may fail to detect resistance genes with sequences that differ significantly from known sequences. Our goal is to develop a machine learning technique to accurately predict capreomycin resistance in Mycobacteria with low false discovery rates. Methods and Results We present a stacked ensemble learning model as an alternative to traditional DNA sequence alignment‐based methods using optimal features generated from the physicochemical, evolutionary and secondary structure properties of protein sequences. We train logistic regression, C5.0 and support vector machine (SVM) algorithms as our base classifiers, and our stacked ensemble predictors combine the results from the base classifiers to achieve higher accuracy. Compared with our most accurate base classifier (SVM), our most accurate stacked ensemble predictor increases training accuracy by 2·43%. Our stacked ensemble predictors achieve test accuracy up to 81·25%. Conclusions We developed a stacked ensemble model to predict capreomycin resistance for Mycobacteria with an accuracy >80% using protein sequences with sequence similarity ranging between 10% and 70%. This performance cannot be achieved with best‐hit methods due to differences in sequence similarity. Significance and Impact of the Study Today an estimated one‐half million cases of multidrug‐resistant (MDR) and extensively drug‐resistant (XDR) tuberculosis (TB) occur annually worldwide at a great cost. Because capreomycin is a second‐line drug used to treat drug‐resistant TB, the ability to use a machine learning approach to classify capreomycin‐resistant TB in a timely manner is crucial for the successful treatment of MDR or XDR TB.

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Consolidating and Exploring Antibiotic Resistance Gene Data Resources

Cited by 83 publications

References 47 publications

Genomic and functional techniques to mine the microbiome for novel antimicrobials and antimicrobial resistance genes

Genomic and functional techniques to mine the microbiome for novel antimicrobials and antimicrobial resistance genes

Genomics and the evolution of antibiotic resistance

Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method

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