Cell envelope structural and functional contributions to antibiotic resistance in Burkholderia cenocepacia.

Antibiotic activity is limited by the physical construction of the Gram-negative cell envelope. Species of the Burkholderia cepacia complex (Bcc) are known as intrinsically multidrug-resistant opportunistic pathogens with low permeability cell envelopes. Here, we re-examined a previously performed chemical-genetic screen of barcoded transposon mutants in B. cenocepacia K56-2, focusing on cell envelope structural and functional processes. We identified structures mechanistically important for resistance to singular and multiple antibiotic classes. For example, susceptibility to novobiocin, avibactam, and the LpxC inhibitor, PF-04753299, was linked to the BpeAB-OprB efflux pump, suggesting these drugs are substrates for this pump in B. cenocepacia. Defects in peptidoglycan precursor synthesis specifically increased susceptibility to cycloserine and revealed a new putative amino acid racemase, while defects in divisome accessory proteins increased susceptibility to multiple ß-lactams. Additionally, disruption of the periplasmic disulfide bond formation system caused pleiotropic defects on outer membrane integrity and ß-lactamase activity. Our findings highlight the layering of resistance mechanisms in the structure and function of the cell envelope. Consequently, we point out processes that can be targeted for developing antibiotic potentiators.IMPORTANCEThe Gram-negative cell envelope is a double-layered physical barrier that protects cells from extracellular stressors, such as antibiotics. The Burkholderia cell envelope is known to contain additional modifications that reduce permeability. We investigated Burkholderia cell envelope factors contributing to antibiotic resistance from a genome-wide view by re-examining data from a transposon mutant library exposed to an antibiotic panel. We identified susceptible phenotypes for defects in structures and functions in the outer membrane, periplasm, and cytoplasm. Overall, we show that resistance linked to the cell envelope is multifaceted and provides new targets for the development of antibiotic potentiators.

A CRISPR-Cas-associated transposon system for genome editing in Burkholderia cepacia complex species.

Genome editing in non-model bacteria is important to understand gene-to-function links that may differ from those of model microorganisms. Although species of the Burkholderia cepacia complex (Bcc) have great biotechnological capacities, the limited genetic tools available to understand and mitigate their pathogenic potential hamper their utilization in industrial applications. To broaden the genetic tools available for Bcc species, we developed RhaCAST, a targeted DNA insertion platform based on a CRISPR-associated transposase driven by a rhamnose-inducible promoter. We demonstrated the utility of the system for targeted insertional mutagenesis in the Bcc strains B. cenocepacia K56-2 and Burkholderia multivorans ATCC17616. We showed that the RhaCAST system can be used for loss- and gain-of-function applications. Importantly, the selection marker could be excised and reused to allow iterative genetic manipulation. The RhaCAST system is faster, easier, and more adaptable than previous insertional mutagenesis tools available for Bcc species and may be used to disrupt pathogenicity elements and insert relevant genetic modules, enabling Bcc biotechnological applications. IMPORTANCE: Species of the Burkholderia cepacia complex (Bcc) have great biotechnological potential but are also opportunistic pathogens. Genetic manipulation of Bcc species is necessary to understand gene-to-function links. However, limited genetic tools are available to manipulate Bcc, hindering our understanding of their pathogenic traits and their potential in biotechnological applications. We developed a genetic tool based on CRISPR-associated transposase to increase the genetic tools available for Bcc species. The genetic tool we developed in this study can be used for loss and gain of function in Bcc species. The significance of our work is in expanding currently available tools to manipulate Bcc.

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery.

Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.

Rationally Designed Pooled CRISPRi-Seq Uncovers an Inhibitor of Bacterial Peptidyl-tRNA Hydrolase.

Pooled knockdown libraries of essential genes are useful tools for elucidating the mechanisms of action of antibacterial compounds, a pivotal step in antibiotic discovery. However, achieving genomic coverage of antibacterial targets poses a challenge due to the uneven proliferation of knockdown mutants during pooled growth, leading to the unintended loss of important targets. To overcome this issue, we describe the construction of CIMPLE ( C RISPR i - m ediated p ooled library of e ssential genes), a rationally designed pooled knockdown library built in a model antibiotic-resistant bacteria, Burkholderia cenocepacia. By analyzing growth parameters of clonal knockdown populations of an arrayed CRISPRi library, we predicted strain depletion levels during pooled growth and adjusted mutant relative abundance, approaching genomic coverage of antibacterial targets during antibiotic exposure. We first benchmarked CIMPLE by chemical-genetic profiling of known antibacterials, then applied it to an uncharacterized bacterial growth inhibitor from a new class. CRISPRi-Seq with CIMPLE, followed by biochemical validation, revealed that the novel compound targets the peptidyl-tRNA hydrolase (Pth). Overall, CIMPLE leverages the advantages of arrayed and pooled CRISPRi libraries to uncover unexplored targets for antibiotic action. Summary: Bacterial mutant libraries in which antibiotic targets are downregulated are useful tools to functionally characterize novel antimicrobials. These libraries are used for chemical-genetic profiling as target-compound interactions can be inferred by differential fitness of mutants during pooled growth. Mutants that are functionally related to the antimicrobial mode of action are usually depleted from the pool upon exposure to the drug. Although powerful, this method can fail when the unequal proliferation of mutant strains before exposure causes mutants to fall below the detection level in the library pool. To address this issue, we constructed an arrayed essential gene mutant library (EGML) in the antibiotic-resistant bacterium Burkholderia cenocepacia using CRISPR interference (CRISPRi) and analyzed the growth parameters of individual mutant strains. We then modelled depletion levels during pooled growth and used the model to rationally design an optimized CRISPR interference-mediated pooled library of essential genes (CIMPLE). By adjusting the initial inoculum of the knockdown mutants, we achieved coverage of the bacterial essential genome with mutant sensitization. We exposed CIMPLE to a recently discovered antimicrobial of a novel class and discovered it inhibits the peptidyl-tRNA hydrolase, an essential bacterial enzyme. In summary, we demonstrate the utility of CIMPLE and CRISPRi-Seq to uncover the mechanism of action of novel antimicrobial compounds.

Profiling cell envelope-antibiotic interactions reveals vulnerabilities to ß-lactams in a multidrug-resistant bacterium.

The cell envelope of Gram-negative bacteria belonging to the Burkholderia cepacia complex (Bcc) presents unique restrictions to antibiotic penetration. As a consequence, Bcc species are notorious for causing recalcitrant multidrug-resistant infections in immunocompromised individuals. Here, we present the results of a genome-wide screen for cell envelope-associated resistance and susceptibility determinants in a Burkholderia cenocepacia clinical isolate. For this purpose, we construct a high-density, randomly-barcoded transposon mutant library and expose it to 19 cell envelope-targeting antibiotics. By quantifying relative mutant fitness with BarSeq, followed by validation with CRISPR-interference, we profile over a hundred functional associations and identify mediators of antibiotic susceptibility in the Bcc cell envelope. We reveal connections between ß-lactam susceptibility, peptidoglycan synthesis, and blockages in undecaprenyl phosphate metabolism. The synergy of the ß-lactam/ß-lactamase inhibitor combination ceftazidime/avibactam is primarily mediated by inhibition of the PenB carbapenemase. In comparison with ceftazidime, avibactam more strongly potentiates the activity of aztreonam and meropenem in a panel of Bcc clinical isolates. Finally, we characterize in Bcc the iron and receptor-dependent activity of the siderophore-cephalosporin antibiotic, cefiderocol. Our work has implications for antibiotic target prioritization, and for using additional combinations of ß-lactam/ß-lactamase inhibitors that can extend the utility of current antibacterial therapies.

Identification of putative essential protein domains from high-density transposon insertion sequencing.

A first clue to gene function can be obtained by examining whether a gene is required for life in certain standard conditions, that is, whether a gene is essential. In bacteria, essential genes are usually identified by high-density transposon mutagenesis followed by sequencing of insertion sites (Tn-seq). These studies assign the term "essential" to whole genes rather than the protein domain sequences that encode the essential functions. However, genes can code for multiple protein domains that evolve their functions independently. Therefore, when essential genes code for more than one protein domain, only one of them could be essential. In this study, we defined this subset of genes as "essential domain-containing" (EDC) genes. Using a Tn-seq data set built-in Burkholderia cenocepacia K56-2, we developed an in silico pipeline to identify EDC genes and the essential protein domains they encode. We found forty candidate EDC genes and demonstrated growth defect phenotypes using CRISPR interference (CRISPRi). This analysis included two knockdowns of genes encoding the protein domains of unknown function DUF2213 and DUF4148. These putative essential domains are conserved in more than two hundred bacterial species, including human and plant pathogens. Together, our study suggests that essentiality should be assigned to individual protein domains rather than genes, contributing to a first functional characterization of protein domains of unknown function.

Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles.

MOTIVATION: Chemical-genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical-genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data. METHODS AND RESULTS: In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy. CONCLUSION: This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.

A Broad-Host-Range CRISPRi Toolkit for Silencing Gene Expression in Burkholderia.

Genetic tools are critical to dissecting the mechanisms governing cellular processes, from fundamental physiology to pathogenesis. Members of the genus Burkholderia have potential for biotechnological applications but can also cause disease in humans with a debilitated immune system. The lack of suitable genetic tools to edit Burkholderia GC-rich genomes has hampered the exploration of useful capacities and the understanding of pathogenic features. To address this, we have developed CRISPR interference (CRISPRi) technology for gene silencing in Burkholderia, testing it in B. cenocepacia, B. multivorans, and B. thailandensis. Tunable expression was provided by placing a codon-optimized dcas9 from Streptococcus pyogenes under control of a rhamnose-inducible promoter. As a proof of concept, the paaABCDE operon controlling genes necessary for phenylacetic acid degradation was targeted by plasmid-borne gRNAs, resulting in near complete inhibition of growth on phenylacetic acid as the sole carbon source. This was supported by reductions in paaA mRNA expression. The utility of CRISPRi to probe other functions at the single cell level was demonstrated by knocking down phbC and fliF, which dramatically reduces polyhydroxybutyrate granule accumulation and motility, respectively. As a hallmark of the mini-CTX system is the broad host-range of integration, we putatively identified 67 genera of Proteobacteria that might be amenable to modification with our CRISPRi toolkit. Our CRISPRi toolkit provides a simple and rapid way to silence gene expression to produce an observable phenotype. Linking genes to functions with CRISPRi will facilitate genome editing with the goal of enhancing biotechnological capabilities while reducing Burkholderia's pathogenic arsenal.

Molecular analysis of hemagglutinin, neuraminidase, matrix genes provide insight into the genetic diversity of seasonal H3N2 human influenza a viruses in Bangladesh during July-August, 2012.

Influenza A virus subtype H3 is a threat to public health and it is important to understand the evolution of the viruses for the surveillance and the selection of vaccine strains. Comparative analysis of four Bangladeshi isolates with isolates circulating other parts of the world based on three candidate genes hemagglutinin (HA), neuraminidase (NA), matrix protein (MA) showed no evidence of significant distinct subclade of viruses circulating in the country over the period of study. Despite these findings, we found N161S substitution in all four H3N2 influenza stains resulting in the gain of NSS160-162 glycosylation site. All H3N2 Influenza subtypes in the study had amino acid substitution at position 31 on the M2 protein (Aspartic acid to Asparagine) which is known to be responsible for amantadine drug resistance.