Is Carbapenem Therapy Necessary for the Treatment of Non-CTX-M Extended-Spectrum β-Lactamase-Producing Enterobacterales Bloodstream Infections?

BACKGROUND

Investigations into antibiotics for extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) bloodstream infections (BSIs) have focused on blaCTX-M genes. Patient outcomes from non-CTX-M-producing ESBL-E BSIs and optimal treatment are unknown.

METHODS

A multicenter observational study investigating 500 consecutive patients with ceftriaxone-resistant Enterobacterales BSIs during 2018-2022 was conducted. Broth microdilution and whole-genome sequencing confirmed antibiotic susceptibilities and ESBL gene presence, respectively. Inverse probability weighting (IPW) using propensity scores ensured patients with non-CTX-M and CTX-M ESBL-E BSIs were similar before outcome evaluation.

RESULTS

396 patients (79.2%) were confirmed to have an ESBL-E BSI. ESBL gene family prevalence was as follows: blaCTX-M (n = 370), blaSHV (n = 16), blaOXY (n = 12), and blaVEB (n = 5). ESBL gene identification was not limited to Escherichia coli and Klebsiella species. In the IPW cohort, there was no difference in 30-day mortality or ESBL-E infection recurrence between the non-CTX-M and CTX-M groups (odds ratio [OR], 0.99; 95% confidence interval [CI], .87-1.11; P = .83 and OR, 1.10; 95% CI, .85-1.42; P = .47, respectively). In an exploratory analysis limited to the non-CTX-M group, 86% of the 21 patients who received meropenem were alive on day 30; none of the 5 patients who received piperacillin-tazobactam were alive on day 30.

CONCLUSIONS

Our findings suggest that non-CTX-M and CTX-M ESBL-E BSIs are equally concerning and associated with similar clinical outcomes. Meropenem may be associated with improved survival in patients with non-CTX-M ESBL-E BSIs, underscoring the potential benefit of comprehensive molecular diagnostics to enable early antibiotic optimization for ESBL-E BSIs beyond just blaCTX-M genes.

Rapid nanopore sequencing and predictive susceptibility testing of positive blood cultures from intensive care patients with sepsis

We aimed to evaluate the performance of Oxford Nanopore Technologies (ONT) sequencing from positive blood culture (BC) broths for bacterial identification and antimicrobial susceptibility prediction. Patients with suspected sepsis in four intensive care units were prospectively enrolled. Human-depleted DNA was extracted from positive BC broths and sequenced using ONT (MinION). Species abundance was estimated using Kraken2, and a cloud-based system (AREScloud) provided in silico predictive antimicrobial susceptibility testing (AST) from assembled contigs. Results were compared to conventional identification and phenotypic AST. Species-level agreement between conventional methods and AST predicted from sequencing was 94.2% (49/52), increasing to 100% in monomicrobial infections. In 262 high-quality AREScloud AST predictions across 24 samples, categorical agreement (CA) was 89.3%, with major error (ME) and very major error (VME) rates of 10.5% and 12.1%, respectively. Over 90% CA was achieved for some taxa (e.g., Staphylococcus aureus) but was suboptimal for Pseudomonas aeruginosa. In 470 AST predictions across 42 samples, with both high quality and exploratory-only predictions, overall CA, ME, and VME rates were 87.7%, 8.3%, and 28.4%. VME rates were inflated by false susceptibility calls in a small number of species/antibiotic combinations with few representative resistant isolates. Time to reporting from sequencing could be achieved within 8-16 h from BC positivity. Direct sequencing from positive BC broths is feasible and can provide accurate predictive AST for some species. ONT-based approaches may be faster but significant improvements in accuracy are required before it can be considered for clinical use.IMPORTANCESepsis and bloodstream infections carry a high risk of morbidity and mortality. Rapid identification and susceptibility prediction of causative pathogens, using Nanopore sequencing direct from blood cultures, may offer clinical benefit. We assessed this approach in comparison to conventional phenotypic methods and determined the accuracy of species identification and susceptibility prediction from genomic data. While this workflow holds promise, and performed well for some common bacterial species, improvements in sequencing accuracy and more robust predictive algorithms across a diverse range of organisms are required before this can be considered for clinical use. However, results could be achieved in timeframes that are faster than conventional phenotypic methods.

An NDM-Producing Escherichia coli Clinical Isolate Exhibiting Resistance to Cefiderocol and the Combination of Ceftazidime-Avibactam and Aztreonam: Another Step Toward Pan-β-Lactam Resistance

Background

Cefiderocol and ceftazidime-avibactam plus aztreonam (CZA-ATM) are preferred treatment regimens for New Delhi metallo-β-lactamase (NDM)-producing infections.

Methods

We report the case of a US patient who traveled to India to receive a renal transplant. He subsequently experienced pyelonephritis by an NDM-producing Escherichia coli. Broth microdilution and the broth disk elution method indicated resistance to all β-lactams, including cefiderocol and CZA-ATM. Whole-genome sequencing investigations were undertaken to identify resistance mechanisms.

Results

An E. coli isolate belonging to sequence type (ST) 167 containing a bla_NDM-5 gene was identified on a plasmid of the IncFIA/IncFIB/IncFIC replicon groups. When compared with the genome of another ST167 E. coli clinical isolate containing bla_NDM-5 and exhibiting susceptibility to cefiderocol and CZA-ATM, a 12-base pair insertion in ftsI, translating to a 4-amino acid duplication in PBP3, was identified. Moreover, a bla_CMY-59 gene was harbored on an IncI-γ replicon type, and frameshift mutations were identified in the cirA iron transport gene.

Conclusions

This is the first clinical case of a US patient harboring an NDM-producing isolate exhibiting resistance to all available β-lactam agents. The isolate's unexpected resistance to cefiderocol and CZA-ATM was likely due to a combination of (1) a modified PBP3 (increased MICs to both regimens), (2) truncated iron-binding protein (increased cefiderocol MIC), and (3) a bla_CMY gene (reduced CZA-ATM activity). E. coli ST167 clinical isolates harboring bla_NDM-5 genes are a recognized international high-risk clone. When coupled with the additional mechanisms identified in our patient's isolate, which is not uncommon for this high-risk clone, pan-β-lactam resistance may occur.

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.

Progressive Development of Cefiderocol Resistance in Escherichia coli During Therapy is Associated With an Increase in blaNDM-5 Copy Number and Gene Expression

BACKGROUND

As cefiderocol is increasingly being prescribed in clinical practice, it is critical that we understand key mechanisms contributing to acquired resistance to this agent.

METHODS

We describe a patient with acute lymphoblastic leukemia and a New Delhi metallo-ß-lactamase (NDM)-5-producing Escherichia coli intra-abdominal infection in whom resistance to cefiderocol evolved approximately 2 weeks after the start of treatment. Through whole-genome sequencing (WGS), messenger RNA expression studies, and ethylenediaminetetraacetic acid inhibition analysis, we investigated the role of increased NDM-5 production and genetic mutations contributing to the development of cefiderocol resistance, using 5 sequential clinical E. coli isolates obtained from the patient.

RESULTS

In all 5 isolates, blaNDM-5 genes were identified. The minimum inhibitory concentrations for cefiderocol were 2, 4, and >32 μg/mL for isolates 1-2, 3, and 4-5, respectively. WGS showed that isolates 1-3 contained a single copy of the blaNDM-5 gene, whereas isolates 4 and 5 had 5 and 10 copies of the blaNDM-5 gene, respectively, on an IncFIA/FIB/IncFII plasmid. These findings were correlated with those of blaNDM-5 messenger RNA expression analysis, in which isolates 4 and 5 expressed blaNDM-5 1.7- and 2.8-fold, respectively, compared to, isolate 1. Synergy testing with the combination of ceftazidime-avibactam and aztreonam demonstrated expansion of the zone of inhibition between the disks for all isolates. The patient was successfully treated with this combination and remained infection free 1 year later.

CONCLUSIONS

The findings in our patient suggest that increased copy numbers of blaNDM genes through translocation events are used by Enterobacterales to evade cefiderocol-mediated cell death. The frequency of increased blaNDM-5 expression in contributing to cefiderocol resistance needs investigation.

Automated antimicrobial susceptibility testing and antimicrobial resistance genotyping using Illumina and Oxford Nanopore Technologies sequencing data among Enterobacteriaceae

Whole-genome sequencing (WGS) enables the molecular characterization of bacterial pathogens. We compared the accuracy of the Illumina and Oxford Nanopore Technologies (ONT) sequencing platforms for the determination of AMR classes and antimicrobial susceptibility testing (AST) among 181 clinical Enterobacteriaceae isolates. Sequencing reads for each isolate were uploaded to AREScloud (Ares Genetics) to determine the presence of AMR markers and the predicted WGS-AST profile. The profiles of both sequencing platforms were compared to broth microdilution (BMD) AST. Isolates were delineated by resistance to third-generation cephalosporins and carbapenems as well as the presence of AMR markers to determine clinically relevant AMR classes. The overall categorical agreement (CA) was 90% (Illumina) and 88% (ONT) across all antimicrobials, 96% for the prediction of resistance to third-generation cephalosporins for both platforms, and 94% (Illumina) and 91% (ONT) for the prediction of resistance to carbapenems. Carbapenem resistance was overestimated on ONT with a major error of 16%. Sensitivity for the detection of carbapenemases, extended-spectrum β-lactamases, and plasmid-mediated ampC genes was 98, 95, and 70% by ONT compared to the Illumina dataset as the reference. Our results highlight the potential of the ONT platform’s use in clinical microbiology laboratories. When combined with robust bioinformatics methods, WGS-AST predictions may be a future approach to guide effective antimicrobial decision-making.

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 β-lactamases (e.g., carbapenemases in Klebsiella pneumoniae and AmpC β-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.

OUP accepted manuscript

Background

Pseudomonas aeruginosa has the ability to exhibit resistance to a broad range of antibiotics, highlighting the importance of identifying alternative or adjunctive treatment options, such as phages.

Patients and methods

We report the case of a 25-year-old male who experienced an accidental electrocution resulting in exposed calvarium in the left parieto-temporal region, complicated by a difficult-to-treat P. aeruginosa (DTR-P. aeruginosa) infection. Cefiderocol was the sole antibiotic with consistent activity against six bacterial isolates obtained from the infected region over a 38 day period.

Results

WGS analysis identified a bla_GES-1 gene as well as the MDR efflux pumps MexD and MexX in all six of the patient’s ST235 DTR-P. aeruginosa isolates, when compared with the reference genome P. aeruginosa PA01 and a P. aeruginosa ST235 isolate from an unrelated patient. After debridement of infected scalp and bone, the patient received approximately 6 weeks of cefiderocol in conjunction with IV phage Pa14NPøPASA16. Some improvement was observed after the initiation of cefiderocol; however, sustained local site improvement and haemodynamic stability were not achieved until phage was administered. No medication-related toxicities were observed. The patient remains infection free more than 12 months after completion of therapy.

Conclusions

This report adds to the growing literature that phage therapy may be a safe and effective approach to augment antibiotic therapy for patients infected with drug-resistant pathogens. Furthermore, it highlights the importance of the GES β-lactamase family in contributing to inactivation of a broad range of β-lactam antibiotics in P. aeruginosa, including ceftolozane/tazobactam, ceftazidime/avibactam and imipenem/relebactam.

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 β-lactamases have the potential to reduce the activity of 3 of 4 novel β-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.

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.

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.

High-density transposon libraries utilising outward-oriented promoters identify mechanisms of action and resistance to antimicrobials

The use of bacterial transposon mutant libraries in phenotypic screens is a well-established technique for determining which genes are essential or advantageous for growth in conditions of interest. Standard, inactivating, transposon libraries cannot give direct information about genes whose over-expression gives a selective advantage. We report the development of a system wherein outward-oriented promoters are included in mini-transposons, generation of transposon mutant libraries in Escherichia coli and Pseudomonas aeruginosa and their use to probe genes important for growth under selection with the antimicrobial fosfomycin, and a recently-developed leucyl-tRNA synthase inhibitor. In addition to the identification of known mechanisms of action and resistance, we identify the carbon-phosphorous lyase complex as a potential resistance liability for fosfomycin in E. coli and P. aeruginosa. The use of this technology can facilitate the development of novel mechanism-of-action antimicrobials that are urgently required to combat the increasing threat worldwide from antimicrobial-resistant pathogenic bacteria.

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 patients infected with carbapenem-resistant P. aeruginosa isolates susceptible to TOL-TAZ and treated with ≥72 hours of TOL-TAZ between January 2018 and December 2019 in Baltimore, Maryland were included. The 28 patients had P. aeruginosa isolates available both before and after TOL-TAZ exposure. Cases were defined as patients with at least a four-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 exposure.

RESULTS

Fourteen patients (50%) had P. aeruginosa isolates which developed high-level TOL-TAZ resistance (i.e., cases). Cases were more likely to have inadequate source control (29% vs. 0%, p=0.04) and were less likely to receive TOL-TAZ as an extended 3-hour infusion (0% vs. 29%; p=0.04). 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. Common mutations identified in TOL-TAZ resistant isolates involved AmpC, a known binding site for both ceftolozane and ceftazidime, and DNA polymerase.

CONCLUSION

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.

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.

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.

SpeckTackle: JavaScript charts for spectroscopy

BACKGROUND

Spectra visualisation from methods such as mass spectroscopy, infrared spectroscopy or nuclear magnetic resonance is an essential part of every web-facing spectral resource. The development of an intuitive and versatile visualisation tool is a time- and resource-intensive task, however, most databases use their own embedded viewers and new databases continue to develop their own viewers.

RESULTS

We present SpeckTackle, a custom-tailored JavaScript charting library for spectroscopy in life sciences. SpeckTackle is cross-browser compatible and easy to integrate into existing resources, as we demonstrate for the MetaboLights database. Its default chart types cover common visualisation tasks following the de facto 'look and feel' standards for spectra visualisation.

CONCLUSIONS

SpeckTackle is released under GNU LGPL to encourage uptake and reuse within the community. The latest version of the library including examples and documentation on how to use and extend the library with additional chart types is available online in its public repository.

BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology

Background

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.

Results

We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.

Conclusions

BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0486-3) contains supplementary material, which is available to authorized users.

Metabolic differences in ripening of Solanum lycopersicum 'Ailsa Craig' and three monogenic mutants

Application of mass spectrometry enables the detection of metabolic differences between groups of related organisms. Differences in the metabolic fingerprints of wild-type Solanum lycopersicum and three monogenic mutants, ripening inhibitor (rin), non-ripening (nor) and Colourless non-ripening (Cnr), of tomato are captured with regard to ripening behaviour. A high-resolution tandem mass spectrometry system coupled to liquid chromatography produced a time series of the ripening behaviour at discrete intervals with a focus on changes post-anthesis. Internal standards and quality controls were used to ensure system stability. The raw data of the samples and reference compounds including study protocols have been deposited in the open metabolomics database MetaboLights via the metadata annotation tool Isatab to enable efficient re-use of the datasets, such as in metabolomics cross-study comparisons or data fusion exercises.

MassCascade: Visual Programming for LC-MS Data Processing in Metabolomics

Liquid chromatography coupled to mass spectrometry (LC-MS) is commonly applied to investigate the small molecule complement of organisms. Several software tools are typically joined in custom pipelines to semi-automatically process and analyse the resulting data. General workflow environments like the Konstanz Information Miner (KNIME) offer the potential of an all-in-one solution to process LC-MS data by allowing easy integration of different tools and scripts. We describe MassCascade and its workflow plug-in for processing LC-MS data. The Java library integrates frequently used algorithms in a modular fashion, thus enabling it to serve as back-end for graphical front-ends. The functions available in MassCascade have been encapsulated in a plug-in for the workflow environment KNIME, allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming. The design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated. MassCascade is an open-source library for LC-MS data processing in metabolomics. It embraces the concept of visual programming through its KNIME plug-in, simplifying the process of building complex workflows. The library was validated using open data.

KNIME-CDK: Workflow-driven cheminformatics

Background

Cheminformaticians have to routinely process and analyse libraries of small molecules. Among other things, that includes the standardization of molecules, calculation of various descriptors, visualisation of molecular structures, and downstream analysis. For this purpose, scientific workflow platforms such as the Konstanz Information Miner can be used if provided with the right plug-in. A workflow-based cheminformatics tool provides the advantage of ease-of-use and interoperability between complementary cheminformatics packages within the same framework, hence facilitating the analysis process.

Results

KNIME-CDK comprises functions for molecule conversion to/from common formats, generation of signatures, fingerprints, and molecular properties. It is based on the Chemistry Development Toolkit and uses the Chemical Markup Language for persistence. A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK supports a similar range of chemical classes and adds new functionality to the framework. We describe the design and integration of the plug-in, and demonstrate the usage of the nodes on ChEBI, a library of small molecules of biological interest.

Conclusions

KNIME-CDK is an open-source plug-in for the Konstanz Information Miner, a free workflow platform. KNIME-CDK is build on top of the open-source Chemistry Development Toolkit and allows for efficient cross-vendor structural cheminformatics. Its ease-of-use and modularity enables researchers to automate routine tasks and data analysis, bringing complimentary cheminformatics functionality to the workflow environment.