Correlation between phenotypic antibiotic susceptibility and the resistome in Pseudomonas aeruginosa

Genetic determinants of antibiotic resistance (AR) have been extensively investigated. High-throughput sequencing allows for the assessment of the relationship between genotype and phenotype. A panel of 672 Pseudomonas aeruginosa strains was analysed, including representatives of globally disseminated multidrug-resistant and extensively drug-resistant clones; genomes and multiple antibiograms were available. This panel was annotated for AR gene presence and polymorphism, defining a resistome in which integrons were included. Integrons were present in >70 distinct cassettes, with In5 being the most prevalent. Some cassettes closely associated with clonal complexes, whereas others spread across the phylogenetic diversity, highlighting the importance of horizontal transfer. A resistome-wide association study (RWAS) was performed for clinically relevant antibiotics by correlating the variability in minimum inhibitory concentration (MIC) values with resistome data. Resistome annotation identified 147 loci associated with AR. These loci consisted mainly of acquired genomic elements and intrinsic genes. The RWAS allowed for correct identification of resistance mechanisms for meropenem, amikacin, levofloxacin and cefepime, and added 46 novel mutations. Among these, 29 were variants of the oprD gene associated with variation in meropenem MIC. Using genomic and MIC data, phenotypic AR was successfully correlated with molecular determinants at the whole-genome sequence level

Phylogenetic Distribution of CRISPR-Cas Systems in Antibiotic-Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa is an antibiotic-refractory pathogen with a large genome and extensive genotypic diversity. Historically, P. aeruginosa has been a major model system for understanding the molecular mechanisms underlying type I clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated protein (CRISPR-Cas)-based bacterial immune system function. However, little information on the phylogenetic distribution and potential role of these CRISPR-Cas systems in molding the P. aeruginosa accessory genome and antibiotic resistance elements is known. Computational approaches were used to identify and characterize CRISPR-Cas systems within 672 genomes, and in the process, we identified a previously unreported and putatively mobile type I-C P. aeruginosa CRISPR-Cas system. Furthermore, genomes harboring noninhibited type I-F and I-E CRISPR-Cas systems were on average ~300 kb smaller than those without a CRISPR-Cas system. In silico analysis demonstrated that the accessory genome (n = 22,036 genes) harbored the majority of identified CRISPR-Cas targets. We also assembled a global spacer library that aided the identification of difficult-to-characterize mobile genetic elements within next-generation sequencing (NGS) data and allowed CRISPR typing of a majority of P. aeruginosa strains. In summary, our analysis demonstrated that CRISPR-Cas systems play an important role in shaping the accessory genomes of globally distributed P. aeruginosa isolates

IRGM Is a Common Target of RNA Viruses that Subvert the Autophagy Network

Autophagy is a conserved degradative pathway used as a host defense mechanism against intracellular pathogens. However, several viruses can evade or subvert autophagy to insure their own replication. Nevertheless, the molecular details of viral interaction with autophagy remain largely unknown. We have determined the ability of 83 proteins of several families of RNA viruses (Paramyxoviridae, Flaviviridae, Orthomyxoviridae, Retroviridae and Togaviridae), to interact with 44 human autophagy-associated proteins using yeast two-hybrid and bioinformatic analysis. We found that the autophagy network is highly targeted by RNA viruses. Although central to autophagy, targeted proteins have also a high number of connections with proteins of other cellular functions. Interestingly, immunity-associated GTPase family M (IRGM), the most targeted protein, was found to interact with the autophagy-associated proteins ATG5, ATG10, MAP1CL3C and SH3GLB1. Strikingly, reduction of IRGM expression using small interfering RNA impairs both Measles virus (MeV), Hepatitis C virus (HCV) and human immunodeficiency virus-1 (HIV-1)-induced autophagy and viral particle production. Moreover we found that the expression of IRGM-interacting MeV-C, HCV-NS3 or HIV-NEF proteins per se is sufficient to induce autophagy, through an IRGM dependent pathway. Our work reveals an unexpected role of IRGM in virus-induced autophagy and suggests that several different families of RNA viruses may use common strategies to manipulate autophagy to improve viral infectivity

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From genotype to antibiotic susceptibility phenotype in the order Enterobacterales: a clinical perspective

Objectives: Predicting the antibiotic susceptibility phenotype from genomic data is challenging, especially for some specific antibiotics in the order Enterobacterales. Here we aimed to assess the performance of whole genomic sequencing (WGS) for predicting the antibiotic susceptibility in various Enterobacterales species using the detection of antibiotic resistance genes (ARGs), specific mutations and a knowledge-based decision algorithm

A metagenomics method for the quantitative detection of bacterial pathogens causing hospital-associated and ventilator-associated pneumonia

The management of ventilator-associated and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen(s). To achieve this, we propose a metagenomics next-generation sequencing (mNGS) assay that includes the use of an internal sample processing control (SPC) for the quantitative detection of 20 relevant bacterial species of interest (SOI) from bronchoalveolar lavage (BAL) samples. To avoid very major errors in the identification of respiratory pathogens due to "false-negative" cases, each sample was spiked withBacillus subtilis, at a precisely defined concentration, using rehydrated BioBall. This SPC ensured the detection and quantification of the pathogen(s) at defined minimum concentrations. In the presented mNGS workflow, absolute quantification ofStaphylococcus aureuswas as accurate as quantitative PCR. We defined a metagenomics threshold at 5.3 × 103genome equivalent unit per milliliter of the sample for each SOI, to distinguish colonization from higher amounts of pathogens that may be associated with infection. Complete mNGS process and metrics were assessed on 40 clinical samples, showing >99.9% sensitivity compared to microbial culture. However, 19 out of the 29 (66%) SOI detections above the metagenomics threshold were not associated with bacterial growth above classical culture-based clinical thresholds. Taxonomic classification of 7 (37%) of the "false-positive" detections was confirmed by finding specific 16S/MetaPhlAn2 markers, the 12 other "false-positive" detections did not yield enough reads to check their taxonomic classification. Our SPC design and analytical workflow allowed efficient detection and absolute quantification of pathogens from BAL samples, even when the bacterial DNA quantity was largely below the manufacturer's recommendations for NGS. The frequent "false-positive" detections suggested the presence of nonculturable cells within the tested BAL samples. Furthermore, mNGS detected mixed infections, including bacterial species not reported by routine cultures. IMPORTANCE The management of ventilator-associated pneumonia and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen. To this end, we propose a metagenomic sequencing assay that includes the use of an internal sample processing control for the quantitative detection of 20 relevant bacterial species from bronchoalveolar lavage samples

Phosphorylation-Dependent Feedback Inhibition of RIG-I by DAPK1 Identified by Kinome-wide siRNA Screening

Cell-autonomous induction of type I interferon must be stringently regulated. Rapid induction is key to control virus infection, whereas proper limitation of signaling is essential to prevent immunopathology and autoimmune disease. Using unbiased kinome-wide RNAi screening followed by thorough validation, we identified 22 factors that regulate RIG-I/IRF3 signaling activity. We describe a negative-feedback mechanism targeting RIG-I activity, which is mediated by death associated protein kinase 1 (DAPK1). RIG-I signaling triggers DAPK1 kinase activation, and active DAPK1 potently inhibits RIG-I stimulated IRF3 activity and interferon-beta production. DAPK1 phosphorylates RIG-I in vitro at previously reported as well as other sites that limit 5'ppp-dsRNA sensing and virtually abrogate RIG-I activation