Comparing genome versus proteome-based identification of clinical bacterial isolates.

Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.

A rapid, highly sensitive and culture-free detection of pathogens from blood by positive enrichment.

Molecular diagnostics is a promising alternative to culture based methods for the detection of bloodstream infections, notably due to its overall lower turnaround time when starting directly from patient samples. Whole blood is usually the starting diagnostic sample in suspected bloodstream infections. The detection of low concentrations of pathogens in blood using a molecular assay necessitates a fairly high starting volume of blood sample in the range of 5-10mL. This large volume of blood sample has a substantial accompanying human genomic content that interferes with pathogen detection. In this study, we have established a workflow using magnetic beads coated with Apolipoprotein H that makes it possible to concentrate pathogens from a 5.0mL whole blood sample, thereby enriching pathogens from whole blood background and also reducing the sample volume to ~200µL or less. We have also demonstrated that this method of enrichment allows detection of 1CFU/mL of Escherichia coli, Enterococcus gallinarum and Candida tropicalis from 5mL blood using quantitative PCR; a detection limit that is not possible in unenriched samples. The enrichment method demonstrated here took 30min to complete and can be easily integrated with various downstream molecular and microbiological techniques.