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

Cardiac Alpha-Myosin (MYH6) Is the Predominant Sarcomeric Disease Gene for Familial Atrial Septal Defects

Secundum-type atrial septal defects (ASDII) account for approximately 10% of all congenital heart defects (CHD) and are associated with a familial risk. Mutations in transcription factors represent a genetic source for ASDII. Yet, little is known about the role of mutations in sarcomeric genes in ASDII etiology. To assess the role of sarcomeric genes in patients with inherited ASDII, we analyzed 13 sarcomeric genes (MYH7, MYBPC3, TNNT2, TCAP, TNNI3, MYH6, TPM1, MYL2, CSRP3, ACTC1, MYL3, TNNC1, and TTN kinase region) in 31 patients with familial ASDII using array-based resequencing. Genotyping of family relatives and control subjects as well as structural and homology analyses were used to evaluate the pathogenic impact of novel non-synonymous gene variants. Three novel missense mutations were found in the MYH6 gene encoding alpha-myosin heavy chain (R17H, C539R, and K543R). These mutations co-segregated with CHD in the families and were absent in 370 control alleles. Interestingly, all three MYH6 mutations are located in a highly conserved region of the alpha-myosin motor domain, which is involved in myosin-actin interaction. In addition, the cardiomyopathy related MYH6-A1004S and the MYBPC3-A833T mutations were also found in one and two unrelated subjects with ASDII, respectively. No mutations were found in the 11 other sarcomeric genes analyzed. The study indicates that sarcomeric gene mutations may represent a so far underestimated genetic source for familial recurrence of ASDII. In particular, perturbations in the MYH6 head domain seem to play a major role in the genetic origin of familial ASDII

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

Switching industrial production processes from complex to defined media: method development and case study using the example of <it>Penicillium chrysogenum</it>

<p>Abstract</p> <p>Background</p> <p>Filamentous fungi are versatile cell factories and widely used for the production of antibiotics, organic acids, enzymes and other industrially relevant compounds at large scale. As a fact, industrial production processes employing filamentous fungi are commonly based on complex raw materials. However, considerable lot-to-lot variability of complex media ingredients not only demands for exhaustive incoming components inspection and quality control, but unavoidably affects process stability and performance. Thus, switching bioprocesses from complex to defined media is highly desirable.</p> <p>Results</p> <p>This study presents a strategy for strain characterization of filamentous fungi on partly complex media using redundant mass balancing techniques. Applying the suggested method, interdependencies between specific biomass and side-product formation rates, production of fructooligosaccharides, specific complex media component uptake rates and fungal strains were revealed. A 2-fold increase of the overall penicillin space time yield and a 3-fold increase in the maximum specific penicillin formation rate were reached in defined media compared to complex media.</p> <p>Conclusions</p> <p>The newly developed methodology enabled fast characterization of two different industrial <it>Penicillium chrysogenum</it> candidate strains on complex media based on specific complex media component uptake kinetics and identification of the most promising strain for switching the process from complex to defined conditions. Characterization at different complex/defined media ratios using only a limited number of analytical methods allowed maximizing the overall industrial objectives of increasing both, method throughput and the generation of scientific process understanding.</p

Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L−1 for Penicillin V and 0.32 g L−1 for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L−1 for Penicillin V and 0.15 g L−1 for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.Fil: Koch, Cosima. Vienna University of Technology. Institute of Chemical Technologies and Analytics; AustriaFil: Posch, Andreas E.. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; AustriaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; ArgentinaFil: Herwig, Christoph. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; AustriaFil: Lendl, Bernhard. Vienna University of Technology. Institute of Chemical Technologies and Analytics; Austri

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

Choosing wisely: assessment of current US top five list recommendations' trustworthiness using a pragmatic approach

OBJECTIVES: Identification of sufficiently trustworthy top 5 list recommendations from the US Choosing Wisely campaign. SETTING: Not applicable. PARTICIPANTS: All top 5 list recommendations available from the American Board of Internal Medicine Foundation website. MAIN OUTCOME MEASURES/INTERVENTIONS: Compilation of US top 5 lists and search for current German highly trustworthy (S3) guidelines. Extraction of guideline recommendations, including grade of recommendation (GoR), for suggestions comparable to top 5 list recommendations. For recommendations without guideline equivalents, the methodological quality of the top 5 list development process was assessed using criteria similar to that used to judge guidelines, and relevant meta-literature was identified in cited references. Judgement of sufficient trustworthiness of top 5 list recommendations was based either on an 'A' GoR of guideline equivalents or on high methodological quality and citation of relevant meta-literature. RESULTS: 412 top 5 list recommendations were identified. For 75 (18%), equivalents were found in current German S3 guidelines. 44 of these recommendations were associated with an 'A' GoR, or a strong recommendation based on strong evidence, and 26 had a 'B' or a 'C' GoR. No GoR was provided for 5 recommendations. 337 recommendations had no equivalent in the German S3 guidelines. The methodological quality of the development process was high and relevant meta-literature was cited for 87 top 5 list recommendations. For a further 36, either the methodological quality was high without any meta-literature citations or meta-literature citations existed but the methodological quality was lacking. For the remaining 214 recommendations, either the methodological quality was lacking and no literature was cited or the methodological quality was generally unsatisfactory. CONCLUSIONS: 131 of current US top 5 list recommendations were found to be sufficiently trustworthy. For a substantial number of current US top 5 list recommendations, their trustworthiness remains unclear. Methodological requirements for developing top 5 lists are recommended

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

Emergingantibiotic resistanceis a major global health threat. The analysis of nucleic acidsequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistancedeterminants 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 the11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including18 main species spanning a time period of 30 years. Species and drug specific resistance patternswere observed including increased resistance rates forAcinetobacter baumanniito carbapenemsand forEscherichia colito fluoroquinolones. Species-levelpan-genomeswere constructed to reflectthe genetic repertoire of the respective species, including conserved essential genes and known resis-tance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed toinfer 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 athttps://gear-base.co