Core Genome Multilocus Sequence Typing and Antibiotic Susceptibility Prediction from Whole-Genome Sequence Data of Multidrug-Resistant Pseudomonas aeruginosa Isolates.

Over the past decade, whole-genome sequencing (WGS) has overtaken traditional bacterial typing methods for studies of genetic relatedness. Further, WGS data generated during epidemiologic studies can be used in other clinically relevant bioinformatic applications, such as antibiotic resistance prediction. Using commercially available software tools, the relatedness of 38 clinical isolates of multidrug-resistant Pseudomonas aeruginosa was defined by two core genome multilocus sequence typing (cgMLST) methods, and the WGS data of each isolate was analyzed to predict antibiotic susceptibility to nine antibacterial agents. The WGS typing and resistance prediction data were compared with pulsed-field gel electrophoresis (PFGE) and phenotypic antibiotic susceptibility results, respectively. Simpson's Diversity Index and adjusted Wallace pairwise assessments of the three typing methods showed nearly identical discriminatory power. Antibiotic resistance prediction using a trained analytical pipeline examined 342 bacterial-drug combinations with an overall categorical agreement of 92.4% and very major, major, and minor error rates of 3.6, 4.1, and 4.1%, respectively. IMPORTANCE Multidrug-resistant Pseudomonas aeruginosa isolates are a serious public health concern due to their resistance to nearly all or all of the available antibiotics, including carbapenems. Utilizing molecular approaches in conjunction with antibiotic susceptibility prediction software warrants investigation for use in the clinical laboratory workflow. These molecular tools coupled with antibiotic resistance prediction tools offer the opportunity to overcome the extended turnaround time and technical challenges of phenotypic susceptibility testing.

Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and Antimicrobial Susceptibility Prediction in ARESdb.

Whole-genome sequencing (WGS) is rapidly replacing traditional typing methods for the investigation of infectious disease outbreaks. Additionally, WGS data are being used to predict phenotypic antimicrobial susceptibility. Acinetobacter baumannii, which is often multidrug-resistant, is a significant culprit in outbreaks in health care settings. A well-characterized collection of A. baumannii was studied using core genome multilocus sequence typing (cgMLST). Seventy-two isolates previously typed by PCR-electrospray ionization mass spectrometry (PCR/ESI-MS) provided by the Antimicrobial Resistance Leadership Group (ARLG) were analyzed using a clinical microbiology laboratory developed workflow for cgMLST with genomic susceptibility prediction performed using the ARESdb platform. Previously performed PCR/ESI-MS correlated with cgMLST using relatedness thresholds of allelic differences of ≤9 and ≤200 allelic differences in 78 and 94% of isolates, respectively. Categorical agreement between genotypic and phenotypic antimicrobial susceptibility across a panel of 11 commonly used drugs was 89%, with minor, major, and very major error rates of 8%, 11%, and 1%, respectively.

Defining Baseline Mechanisms of Cefiderocol Resistance in the Enterobacterales.

The objective of this study was to identify putative mechanisms contributing to baseline cefiderocol resistance among carbapenem-resistant Enterobacterales (CRE). We evaluated 56 clinical CRE isolates with no previous exposure to cefiderocol. Cefiderocol and comparator agent minimum inhibitory concentrations (MICs) were determined by broth microdilution. Short-read and/or long-read whole genome sequencing was pursued. Cefiderocol nonwild type (NWT; i.e., MICs ≥4 mg/L) CRE were compared with species-specific reference genomes and with cefiderocol wild type (WT) CRE isolates to identify genes or missense mutations, potentially contributing to elevated cefiderocol MICs. A total of 14 (25%) CRE isolates met cefiderocol NWT criteria. Of the 14 NWT isolates, various ß-lactamases (e.g., carbapenemases in Klebsiella pneumoniae and AmpC ß-lactamases in Enterobacter cloacae complex) in combination with permeability defects were associated with a ≥ 80% positive predictive value in identifying NWT isolates. Unique mutations in the sensor kinase gene baeS were identified among NWT isolates. Cefiderocol NWT isolates were more likely to be resistant to colistin than WT isolates (29% vs. 0%). Our findings suggest that no consistent antimicrobial resistance markers contribute to baseline cefiderocol resistance in CRE isolates and, rather, cefiderocol resistance results from a combination of heterogeneous mechanisms.

Predictive Antibiotic Susceptibility Testing by Next-Generation Sequencing for Periprosthetic Joint Infections: Potential and Limitations.

Joint replacement surgeries are one of the most frequent medical interventions globally. Infections of prosthetic joints are a major health challenge and typically require prolonged or even indefinite antibiotic treatment. As multidrug-resistant pathogens continue to rise globally, novel diagnostics are critical to ensure appropriate treatment and help with prosthetic joint infections (PJI) management. To this end, recent studies have shown the potential of molecular methods such as next-generation sequencing to complement established phenotypic, culture-based methods. Together with advanced bioinformatics approaches, next-generation sequencing can provide comprehensive information on pathogen identity as well as antimicrobial susceptibility, potentially enabling rapid diagnosis and targeted therapy of PJIs. In this review, we summarize current developments in next generation sequencing based predictive antibiotic susceptibility testing and discuss potential and limitations for common PJI pathogens.

Core Genome Multilocus Sequence Typing and Prediction of Antimicrobial Susceptibility Using Whole-Genome Sequences of Escherichia coli Bloodstream Infection Isolates.

In total, 50 Escherichia coli bloodstream isolates from the clinical laboratory and 12 E. coli isolates referred for pulsed-field gel electrophoresis (PFGE) were sequenced, assessed for clonality using core genome multilocus sequence typing (cgMLST), and evaluated for genomic susceptibility predictions using ARESdb. Results of sequence typing using whole-genome sequencing (WGS)-based MLST and sequence type (ST)-specific PCR were identical. Overall categorical agreement between genotypic (ARESdb) and phenotypic susceptibility testing for 62 isolates and 11 antimicrobial agents was 91%. Among the referred isolates, high major error rates were found for ceftazidime, cefepime, and piperacillin-tazobactam.

Culture-Free Detection of Antibiotic Resistance Markers from Native Patient Samples by Hybridization Capture Sequencing.

The increasing incidence of antimicrobial resistance (AMR) is a major global challenge. Routine techniques for molecular AMR marker detection are largely based on low-plex PCR and detect dozens to hundreds of AMR markers. To allow for comprehensive and sensitive profiling of AMR markers, we developed a capture-based next generation sequencing (NGS) workflow featuring a novel AMR marker panel based on the curated AMR database ARESdb. Our primary objective was to compare the sensitivity of target enrichment-based AMR marker detection to metagenomics sequencing. Therefore, we determined the limit of detection (LOD) in synovial fluid and urine samples across four key pathogens. We further demonstrated proof-of-concept for AMR marker profiling from septic samples using a selection of urine samples with confirmed monoinfection. The results showed that the capture-based workflow is more sensitive and requires lower sequencing depth compared with metagenomics sequencing, allowing for comprehensive AMR marker detection with an LOD of 1000 CFU/mL. Combining the ARESdb AMR panel with 16S rRNA gene sequencing allowed for the culture-free detection of bacterial taxa and AMR markers directly from septic patient samples at an average sensitivity of 99%. Summarizing, the newly developed ARESdb AMR panel may serve as a valuable tool for comprehensive and sensitive AMR marker detection.

Cefiderocol Activity Against Clinical Pseudomonas aeruginosa Isolates Exhibiting Ceftolozane-Tazobactam Resistance.

BACKGROUND: Mutations in the AmpC-AmpR region are associated with treatment-emergent ceftolozane-tazobactam (TOL-TAZ) and ceftazidime-avibactam (CAZ-AVI) resistance. We sought to determine if these mutations impact susceptibility to the novel cephalosporin-siderophore compound cefiderocol. METHODS: Thirty-two paired isolates from 16 patients with index P. aeruginosa isolates susceptible to TOL-TAZ and subsequent P. aeruginosa isolates available after TOL-TAZ exposure from January 2019 to December 2020 were included. TOL-TAZ, CAZ-AVI, imipenem-relebactam (IMI-REL), and cefiderocol minimum inhibitory concentrations (MICs) were determined using broth microdilution. Whole-genome sequencing of paired isolates was used to identify mechanisms of resistance to cefiderocol that emerged, focusing on putative mechanisms of resistance to cefiderocol or earlier siderophore-antibiotic conjugates based on the previously published literature. RESULTS: Analyzing the 16 pairs of P. aeruginosa isolates, ≥4-fold increases in cefiderocol MICs occurred in 4 of 16 isolates. Cefiderocol nonsusceptibility criteria were met for only 1 of the 4 isolates, using Clinical and Laboratory Standards Institute criteria. Specific mechanisms identified included the following: AmpC E247K (2 isolates), MexR A66V and L57D (1 isolate each), and AmpD G116D (1 isolate) substitutions. For both isolates with AmpC E247K mutations, ≥4-fold MIC increases occurred for both TOL-TAZ and CAZ-AVI, while a ≥4-fold reduction in IMI-REL MICs was observed. CONCLUSIONS: Our findings suggest that alterations in the target binding sites of P. aeruginosa-derived AmpC ß-lactamases have the potential to reduce the activity of 3 of 4 novel ß-lactams (ie, ceftolozane-tazobactam, ceftazidime-avibactam, and cefiderocol) and potentially increase susceptibility to imipenem-relebactam. These findings are in need of validation in a larger cohort.

Genomic and Phenotypic Analysis of Linezolid-Resistant Staphylococcus epidermidis in a Tertiary Hospital in Innsbruck, Austria.

Whole genome sequencing is a useful tool to monitor the spread of resistance mechanisms in bacteria. In this retrospective study, we investigated genetic resistance mechanisms, sequence types (ST) and respective phenotypes of linezolid-resistant Staphylococcus epidermidis (LRSE, n = 129) recovered from a cohort of patients receiving or not receiving linezolid within a tertiary hospital in Innsbruck, Austria. Hereby, the point mutation G2603U in the 23S rRNA (n = 91) was the major resistance mechanism followed by the presence of plasmid-derived cfr (n = 30). The majority of LRSE isolates were ST2 strains, followed by ST5. LRSE isolates expressed a high resistance level to linezolid with a minimal inhibitory concentration of ≥256 mg/L (n = 83) in most isolates, particularly in strains carrying the cfr gene (p < 0.001). Linezolid usage was the most prominent (but not the only) trigger for the development of linezolid resistance. However, administration of linezolid was not associated with a specific resistance mechanism. Restriction of linezolid usage and the monitoring of plasmid-derived cfr in LRSE are potential key steps to reduce linezolid resistance and its transmission to more pathogenic Gram-positive bacteria.

Learning From Limited Data: Towards Best Practice Techniques for Antimicrobial Resistance Prediction From Whole Genome Sequencing Data.

Antimicrobial resistance prediction from whole genome sequencing data (WGS) is an emerging application of machine learning, promising to improve antimicrobial resistance surveillance and outbreak monitoring. Despite significant reductions in sequencing cost, the availability and sampling diversity of WGS data with matched antimicrobial susceptibility testing (AST) profiles required for training of WGS-AST prediction models remains limited. Best practice machine learning techniques are required to ensure trained models generalize to independent data for optimal predictive performance. Limited data restricts the choice of machine learning training and evaluation methods and can result in overestimation of model performance. We demonstrate that the widely used random k-fold cross-validation method is ill-suited for application to small bacterial genomics datasets and offer an alternative cross-validation method based on genomic distance. We benchmarked three machine learning architectures previously applied to the WGS-AST problem on a set of 8,704 genome assemblies from five clinically relevant pathogens across 77 species-compound combinations collated from public databases. We show that individual models can be effectively ensembled to improve model performance. By combining models via stacked generalization with cross-validation, a model ensembling technique suitable for small datasets, we improved average sensitivity and specificity of individual models by 1.77% and 3.20%, respectively. Furthermore, stacked models exhibited improved robustness and were thus less prone to outlier performance drops than individual component models. In this study, we highlight best practice techniques for antimicrobial resistance prediction from WGS data and introduce the combination of genome distance aware cross-validation and stacked generalization for robust and accurate WGS-AST.

A high leukocyte count and administration of hydrocortisone hamper PCR-based diagnostics for bloodstream infections.

Bloodstream infections (BSIs) require an accurate and fast identification of causative pathogens. Molecular diagnostics, in particular polymerase chain reaction (PCR)-based approaches for BSI diagnostics directly from whole blood, suffer from limitations such as inhibition leading to invalid results. In this retrospective study, we analyzed 23 parameters for their potential interference with LightCycler SeptiFast PCR tests (n = 2167) routinely performed at our institution. The overall inhibition rate was 9.1%. Test date, type of ward, procalcitonin levels, high leukocyte counts, and absolute neutrophil count were significantly associated with inhibition. For a subset (n = 448), cut-off values for leukocyte counts of < 5700 cells/µL and ≥ 26,900 cells/µL were significantly associated with a low (5%) and high (67%) inhibition risk. For patients with a moderate to high leukocyte count (5700-26,900 cells/µL), the additional administration of hydrocortisone significantly increased the inhibition risk. Furthermore, freezing of blood samples prior to DNA extraction and SF testing appeared to neutralize inhibitory factors. It remains to be investigated whether other molecular diagnostic tests are susceptible to similar inhibiting parameters.

Modifiable Risk Factors for the Emergence of Ceftolozane-tazobactam Resistance.

BACKGROUND: Ceftolozane-tazobactam (TOL-TAZ) affords broad coverage against Pseudomonas aeruginosa. Regrettably, TOL-TAZ resistance has been reported. We sought to identify modifiable risk factors that may reduce the emergence of TOL-TAZ resistance. METHODS: Twenty-eight consecutive patients infected with carbapenem-resistant P. aeruginosa isolates susceptible to TOL-TAZ, treated with ≥72 hours of TOL-TAZ , and with P. aeruginosa isolates available both before and after TOL-TAZ exposure between January 2018 and December 2019 in Baltimore, Maryland, were included. Cases were defined as patients with at least a 4-fold increase in P. aeruginosa TOL-TAZ MICs after exposure to TOL-TAZ. Independent risk factors for the emergence of TOL-TAZ resistance comparing cases and controls were investigated using logistic regression. Whole genome sequencing of paired isolates was used to identify mechanisms of resistance that emerged during TOL-TAZ therapy. RESULTS: Fourteen patients (50%) had P. aeruginosa isolates which developed at least a 4-fold increase in TOL-TAZ MICs(ie, cases). Cases were more likely to have inadequate source control (29% vs 0%, P = .04) and were less likely to receive TOL-TAZ as an extended 3-hour infusion (0% vs 29%; P = .04). Eighty-six percent of index isolates susceptible to ceftazidime-avibactam (CAZ-AVI) had subsequent P. aeruginosa isolates with high-level resistance to CAZ-AVI, after TOL-TAZ exposure and without any CAZ-AVI exposure. Common mutations identified in TOL-TAZ resistant isolates involved AmpC, a known binding site for both ceftolozane and ceftazidime, and DNA polymerase. CONCLUSIONS: Due to our small sample size, our results remain exploratory but forewarn of the potential emergence of TOL-TAZ resistance during therapy and suggest extending TOL-TAZ infusions may be protective. Larger studies are needed to investigate this association.

Multicenter Evaluation of the Unyvero Platform for Testing Bronchoalveolar Lavage Fluid.

Bronchoalveolar lavage (BAL) culture is a standard, though time-consuming, approach for identifying microorganisms in patients with severe lower respiratory tract (LRT) infections. The sensitivity of BAL culture is relatively low, and prior antimicrobial therapy decreases the sensitivity further, leading to overuse of empirical antibiotics. The Unyvero LRT BAL Application (Curetis GmbH, Germany) is a multiplex molecular panel that detects 19 bacteria, 10 antibiotic resistance markers, and a fungus, Pneumocystis jirovecii, in BAL fluid in ∼4.5 h. Its performance was evaluated using 1,016 prospectively collected and 392 archived specimens from 11 clinical trial sites in the United States. Overall positive and negative percent agreements with culture results for identification of bacteria that grow in routine cultures were 93.4% and 98.3%, respectively, with additional potential pathogens identified by Unyvero in 21.7% of prospectively collected specimens. For detection of P. jirovecii, the positive percent agreement with standard testing was 87.5%. Antibiotic resistance marker results were compared to standard antibiotic susceptibility test results to determine positive predictive values (PPVs). PPVs ranged from 80 to 100%, based on the microorganism and specific resistance marker(s). The Unyvero LRT BAL Application provides accurate detection of common agents of bacterial pneumonia and of P. jirovecii The sensitivity and rapidity of this panel suggest significant clinical value for choosing appropriate antibiotics and for antibiotic stewardship.

Analytical Performance Validation of Next-Generation Sequencing Based Clinical Microbiology Assays Using a K-mer Analysis Workflow.

Next-generation sequencing (NGS) enables clinical microbiology assays such as molecular typing of bacterial isolates which is now routinely applied for infection control and epidemiology. Additionally, feasibility for NGS-based identification of antimicrobial resistance (AMR) markers as well as genetic prediction of antibiotic susceptibility testing results has been demonstrated. Various bioinformatics approaches enabling NGS-based clinical microbiology assays exist, but standardized, computationally efficient and scalable sample-to-results workflows including validated quality control parameters are still lacking. Bioinformatics analysis workflows based on k-mers have been shown to allow for fast and efficient analysis of large genomics data sets as obtained from microbial sequencing applications. We here demonstrate applicability of k-mer based clinical microbiology assays for whole-genome sequencing (WGS) including variant calling, taxonomic identification, bacterial typing as well as AMR marker detection. The wet-lab and dry-lab workflows were developed and validated in line with Clinical Laboratory Improvement Act (CLIA) guidelines for laboratory-developed tests (LDTs) on multi-drug resistant ESKAPE pathogens. The developed k-mer based workflow demonstrated ≥99.39% repeatability, ≥99.09% reproducibility and ≥99.76% accuracy for variant calling and applied assays as determined by intra-day and inter-day triplicate measurements. The limit of detection (LOD) across assays was found to be at 20× sequencing depth and 15× for AMR marker detection. Thorough benchmarking of the k-mer based workflow revealed analytical performance criteria are comparable to state-of-the-art alignment based workflows across clinical microbiology assays. Diagnostic sensitivity and specificity for multilocus sequence typing (MLST) and phylogenetic analysis were 100% for both approaches. For AMR marker detection, sensitivity and specificity were 95.29 and 99.78% for the k-mer based workflow as compared to 95.17 and 99.77% for the alignment-based approach. Summarizing, results illustrate that k-mer based analysis workflows enable a broad range of clinical microbiology assays, potentially not only for WGS-based typing and AMR gene detection but also genetic prediction of antibiotic susceptibility testing results.

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.

BACKGROUND: Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance. METHODS: We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates. RESULTS: CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%). CONCLUSIONS: We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Species Identification and Antibiotic Resistance Prediction by Analysis of Whole-Genome Sequence Data by Use of ARESdb: an Analysis of Isolates from the Unyvero Lower Respiratory Tract Infection Trial.

Whole-genome sequencing (WGS) is now routinely performed in clinical microbiology laboratories to assess isolate relatedness. With appropriately developed analytics, the same data can be used for prediction of antimicrobial susceptibility. We assessed WGS data for identification using open-source tools and antibiotic susceptibility testing (AST) prediction using ARESdb compared to matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) identification and broth microdilution phenotypic susceptibility testing on clinical isolates from a multicenter clinical trial of the FDA-cleared Unyvero lower respiratory tract infection (LRTI) application (Curetis). For the trial, more than 2,000 patient samples were collected from intensive care units across nine hospitals and tested for LRTI. The isolate subset used in this study included 620 clinical isolates originating from 455 LRTI culture-positive patient samples. Isolates were sequenced using the Illumina Nextera XT protocol and FASTQ files with raw reads uploaded to the ARESdb cloud platform (ares-genetics.cloud; released for research use in 2020). The platform combines Ares Genetics' proprietary database ARESdb with state-of-the-art bioinformatics tools and curated public data. For identification, WGS showed 99 and 93% concordance with MALDI-TOF MS at the genus and species levels, respectively. WGS-predicted susceptibility showed 89% categorical agreement with phenotypic susceptibility across a total of 129 species-compound pairs analyzed, with categorical agreement exceeding 90% in 78 species-compound pairs and reaching 100% in 32. Results of this study add to the growing body of literature showing that, with improvement of analytics, WGS data could be used to predict antimicrobial susceptibility.

Clinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms.

Multidrug-resistant pathogens represent one of the biggest global healthcare challenges. Molecular diagnostics can guide effective antibiotics therapy but relies on validated, predictive biomarkers. Here we present a novel, universally applicable workflow for rapid identification of antimicrobial resistance (AMR) biomarkers from clinical Escherichia coli isolates and quantitatively evaluate the potential to recover causal biomarkers for observed resistance phenotypes. For this, a metagenomic plasmid library from 1,110 clinical E. coli isolates was created and used for high-throughput screening to identify biomarker candidates against Tobramycin (TOB), Ciprofloxacin (CIP), and Trimethoprim-Sulfamethoxazole (TMP-SMX). Identified candidates were further validated in vitro and also evaluated in silico for their diagnostic performance based on matched genotype-phenotype data. AMR biomarkers recovered by the metagenomics screening approach mechanistically explained 77% of observed resistance phenotypes for Tobramycin, 76% for Trimethoprim-Sulfamethoxazole, and 20% Ciprofloxacin. Sensitivity for Ciprofloxacin resistance detection could be improved to 97% by complementing results with AMR biomarkers that are undiscoverable due to intrinsic limitations of the workflow. Additionally, when combined in a multiplex diagnostic in silico panel, the identified AMR biomarkers reached promising positive and negative predictive values of up to 97 and 99%, respectively. Finally, we demonstrate that the developed workflow can be used to identify potential novel resistance mechanisms.

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.

Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates.

High-throughput next-generation shotgun sequencing of pathogenic bacteria is growing in clinical relevance, especially for chromosomal DNA-based taxonomic identification and for antibiotic resistance prediction. Genetic exchange is facilitated for extrachromosomal DNA, e.g. plasmid-borne antibiotic resistance genes. Consequently, accurate identification of plasmids from whole-genome sequencing (WGS) data remains one of the major challenges for sequencing-based precision medicine in infectious diseases. Here, we assess the heterogeneity of four state-of-the-art tools (cBar, PlasmidFinder, plasmidSPAdes and Recycler) for the in silico prediction of plasmid-derived sequences from WGS data. Heterogeneity, sensitivity and precision were evaluated by reference-independent and reference-dependent benchmarking using 846 Gram-negative clinical isolates. Interestingly, the majority of predicted sequences were tool-specific, resulting in a pronounced heterogeneity across tools for the reference-independent assessment. In the reference-dependent assessment, sensitivity and precision values were found to substantially vary between tools and across taxa, with cBar exhibiting the highest median sensitivity (87.45%) but a low median precision (27.05%). Furthermore, integrating the individual tools into an ensemble approach showed increased sensitivity (95.55%) while reducing the precision (25.62%). CBar and plasmidSPAdes exhibited the strongest concordance with respect to identified antibiotic resistance factors. Moreover, false-positive plasmid predictions typically contained only few antibiotic resistance factors. In conclusion, while high degrees of heterogeneity and variation in sensitivity and precision were observed across the different tools and taxa, existing tools are valuable for investigating the plasmid-borne resistome. Nevertheless, additional studies on representative clinical data sets will be necessary to translate in silico plasmid prediction approaches from research to clinical application.

Genomic structural variations lead to dysregulation of important coding and non-coding RNA species in dilated cardiomyopathy.

The transcriptome needs to be tightly regulated by mechanisms that include transcription factors, enhancers, and repressors as well as non-coding RNAs. Besides this dynamic regulation, a large part of phenotypic variability of eukaryotes is expressed through changes in gene transcription caused by genetic variation. In this study, we evaluate genome-wide structural genomic variants (SVs) and their association with gene expression in the human heart. We detected 3,898 individual SVs affecting all classes of gene transcripts (e.g., mRNA, miRNA, lncRNA) and regulatory genomic regions (e.g., enhancer or TFBS). In a cohort of patients (n = 50) with dilated cardiomyopathy (DCM), 80,635 non-protein-coding elements of the genome are deleted or duplicated by SVs, containing 3,758 long non-coding RNAs and 1,756 protein-coding transcripts. 65.3% of the SV-eQTLs do not harbor a significant SNV-eQTL, and for the regions with both classes of association, we find similar effect sizes. In case of deleted protein-coding exons, we find downregulation of the associated transcripts, duplication events, however, do not show significant changes over all events. In summary, we are first to describe the genomic variability associated with SVs in heart failure due to DCM and dissect their impact on the transcriptome. Overall, SVs explain up to 7.5% of the variation of cardiac gene expression, underlining the importance to study human myocardial gene expression in the context of the individual genome. This has immediate implications for studies on basic mechanisms of cardiac maladaptation, biomarkers, and (gene) therapeutic studies alike.

Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs.

The analysis of small RNA NGS data together with the discovery of new small RNAs is among the foremost challenges in life science. For the analysis of raw high-throughput sequencing data we implemented the fast, accurate and comprehensive web-based tool miRMaster. Our toolbox provides a wide range of modules for quantification of miRNAs and other non-coding RNAs, discovering new miRNAs, isomiRs, mutations, exogenous RNAs and motifs. Use-cases comprising hundreds of samples are processed in less than 5 h with an accuracy of 99.4%. An integrative analysis of small RNAs from 1836 data sets (20 billion reads) indicated that context-specific miRNAs (e.g. miRNAs present only in one or few different tissues / cell types) still remain to be discovered while broadly expressed miRNAs appear to be largely known. In total, our analysis of known and novel miRNAs indicated nearly 22 000 candidates of precursors with one or two mature forms. Based on these, we designed a custom microarray comprising 11 872 potential mature miRNAs to assess the quality of our prediction. MiRMaster is a convenient-to-use tool for the comprehensive and fast analysis of miRNA NGS data. In addition, our predicted miRNA candidates provided as custom array will allow researchers to perform in depth validation of candidates interesting to them.