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

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

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

Genome sequencing and transcriptome analysis of Trichoderma reesei QM9978 strain reveals a distal chromosome translocation to be responsible for loss of vib1 expression and loss of cellulase induction

Abstract Background The hydrolysis of biomass to simple sugars used for the production of biofuels in biorefineries requires the action of cellulolytic enzyme mixtures. During the last 50 years, the ascomycete Trichoderma reesei, the main source of industrial cellulase and hemicellulase cocktails, has been subjected to several rounds of classical mutagenesis with the aim to obtain higher production levels. During these random genetic events, strains unable to produce cellulases were generated. Here, whole genome sequencing and transcriptomic analyses of the cellulase-negative strain QM9978 were used for the identification of mutations underlying this cellulase-negative phenotype. Results Sequence comparison of the cellulase-negative strain QM9978 to the reference strain QM6a identified a total of 43 mutations, of which 33 were located either close to or in coding regions. From those, we identified 23 single-nucleotide variants, nine InDels, and one translocation. The translocation occurred between chromosomes V and VII, is located upstream of the putative transcription factor vib1, and abolishes its expression in QM9978 as detected during the transcriptomic analyses. Ectopic expression of vib1 under the control of its native promoter as well as overexpression of vib1 under the control of a strong constitutive promoter restored cellulase expression in QM9978, thus confirming that the translocation event is the reason for the cellulase-negative phenotype. Gene deletion of vib1 in the moderate producer strain QM9414 and in the high producer strain Rut-C30 reduced cellulase expression in both cases. Overexpression of vib1 in QM9414 and Rut-C30 had no effect on cellulase production, most likely because vib1 is already expressed at an optimal level under normal conditions. Conclusion We were able to establish a link between a chromosomal translocation in QM9978 and the cellulase-negative phenotype of the strain. We identified the transcription factor vib1 as a key regulator of cellulases in T. reesei whose expression is absent in QM9978. We propose that in T. reesei, as in Neurospora crassa, vib1 is involved in cellulase induction, although the exact mechanism remains to be elucidated. The data presented here show an example of a combined genome sequencing and transcriptomic approach to explain a specific trait, in this case the QM9978 cellulase-negative phenotype, and how it helps to better understand the mechanisms during cellulase gene regulation. When focusing on mutations on the single base-pair level, changes on the chromosome level can be easily overlooked and through this work we provide an example that stresses the importance of the big picture of the genomic landscape during analysis of sequencing data