Predicting Antimicrobial Resistance Using Partial Genome Alignments.

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

SinEx DB 2.0 update 2020: database for eukaryotic single-exon coding sequences.

Single-exon coding sequences (CDSs), also known as 'single-exon genes' (SEGs), are defined as nuclear, protein-coding genes that lack introns in their CDSs. They have been studied not only to determine their origin and evolution but also because their expression has been linked to several types of human cancers and neurological/developmental disorders, and many exhibit tissue-specific transcription. We developed SinEx DB that houses DNA and protein sequence information of SEGs from 10 mammalian genomes including human. SinEx DB includes their functional predictions (KOG (euKaryotic Orthologous Groups)) and the relative distribution of these functions within species. Here, we report SinEx 2.0, a major update of SinEx DB that includes information of the occurrence, distribution and functional prediction of SEGs from 60 completely sequenced eukaryotic genomes, representing animals, fungi, protists and plants. The information is stored in a relational database built with MySQL Server 5.7, and the complete dataset of SEG sequences and their GO (Gene Ontology) functional assignations are available for downloading. SinEx DB 2.0 was built with a novel pipeline that helps disambiguate single-exon isoforms from SEGs. SinEx DB 2.0 is the largest available database for SEGs and provides a rich source of information for advancing our understanding of the evolution, function of SEGs and their associations with disorders including cancers and neurological and developmental diseases. Database URL: http://v2.sinex.cl/.

Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks.

Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness.IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.