Automatic Discrimination of Species within the Enterobacter cloacae Complex Using Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry and Supervised Algorithms

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Abstract

ABSTRACT

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may have different acquired antimicrobial resistance and virulence mechanisms, and their identification is challenging. This study aims to develop predictive models based on matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) profiles and machine learning for species-level identification. A total of 219 ECC and 118 Klebsiella aerogenes clinical isolates from three hospitals were included. The capability of the proposed method to differentiate the most common ECC species ( Enterobacter asburiae , Enterobacter kobei , Enterobacter hormaechei , Enterobacter roggenkampii , Enterobacter ludwigii , and Enterobacter bugandensis ) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with principal-component analysis (PCA) preprocessing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development data set. Thus, we developed supervised, nonlinear predictive models (support vector machine with radial basis function and random forest). The external validation of these models with protein spectra from two participating hospitals yielded 100% correct species-level assignment for E. asburiae , E. kobei , and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species; with data analyzed in the three participating centers, the accuracy was close to 100%. Similar results were obtained with the Mass Spectrometric Identification (MSI) database developed recently ( https://msi.happy-dev.fr ) except in the case of E. hormaechei , which was more accurately identified with the random forest algorithm. In short, MALDI-TOF MS combined with machine learning was demonstrated to be a rapid and accurate method for the differentiation of ECC species.

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Rapid and precise alignment of raw reads against redundant databases with KMA

Philip T L C Clausen, Frank Aarestrup, Ole Lund (2018)

Background As the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another. In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates. Results The functionality of KMA was compared with SRST2, MGmapper, BWA-MEM, Bowtie2, Minimap2 and Salmon, using both simulated data and a dataset of Escherichia coli mapped against resistance genes and core genome MLST alleles. KMA outperforms current methods with respect to both accuracy and speed, while using a comparable amount of memory. Conclusion With KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency. Electronic supplementary material The online version of this article (10.1186/s12859-018-2336-6) contains supplementary material, which is available to authorized users.

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Rapid whole-genome sequencing for detection and characterization of microorganisms directly from clinical samples.

Dhany Saputra, Christina A. Svendsen, Frank Aarestrup … (2013)

Whole-genome sequencing (WGS) is becoming available as a routine tool for clinical microbiology. If applied directly on clinical samples, this could further reduce diagnostic times and thereby improve control and treatment. A major bottleneck is the availability of fast and reliable bioinformatic tools. This study was conducted to evaluate the applicability of WGS directly on clinical samples and to develop easy-to-use bioinformatic tools for the analysis of sequencing data. Thirty-five random urine samples from patients with suspected urinary tract infections were examined using conventional microbiology, WGS of isolated bacteria, and direct sequencing on pellets from the urine samples. A rapid method for analyzing the sequence data was developed. Bacteria were cultivated from 19 samples but in pure cultures from only 17 samples. WGS improved the identification of the cultivated bacteria, and almost complete agreement was observed between phenotypic and predicted antimicrobial susceptibilities. Complete agreement was observed between species identification, multilocus sequence typing, and phylogenetic relationships for Escherichia coli and Enterococcus faecalis isolates when the results of WGS of cultured isolates and urine samples were directly compared. Sequencing directly from the urine enabled bacterial identification in polymicrobial samples. Additional putative pathogenic strains were observed in some culture-negative samples. WGS directly on clinical samples can provide clinically relevant information and drastically reduce diagnostic times. This may prove very useful, but the need for data analysis is still a hurdle to clinical implementation. To overcome this problem, a publicly available bioinformatic tool was developed in this study.

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Benchmarking of methods for genomic taxonomy.

Oksana Lukjancenko, Salvatore Cosentino, Thomas Sicheritz-Pontén … (2014)

One of the first issues that emerges when a prokaryotic organism of interest is encountered is the question of what it is--that is, which species it is. The 16S rRNA gene formed the basis of the first method for sequence-based taxonomy and has had a tremendous impact on the field of microbiology. Nevertheless, the method has been found to have a number of shortcomings. In the current study, we trained and benchmarked five methods for whole-genome sequence-based prokaryotic species identification on a common data set of complete genomes: (i) SpeciesFinder, which is based on the complete 16S rRNA gene; (ii) Reads2Type that searches for species-specific 50-mers in either the 16S rRNA gene or the gyrB gene (for the Enterobacteraceae family); (iii) the ribosomal multilocus sequence typing (rMLST) method that samples up to 53 ribosomal genes; (iv) TaxonomyFinder, which is based on species-specific functional protein domain profiles; and finally (v) KmerFinder, which examines the number of cooccurring k-mers (substrings of k nucleotides in DNA sequence data). The performances of the methods were subsequently evaluated on three data sets of short sequence reads or draft genomes from public databases. In total, the evaluation sets constituted sequence data from more than 11,000 isolates covering 159 genera and 243 species. Our results indicate that methods that sample only chromosomal, core genes have difficulties in distinguishing closely related species which only recently diverged. The KmerFinder method had the overall highest accuracy and correctly identified from 93% to 97% of the isolates in the evaluations sets.