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.

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.

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.

The mechanism of action of auranofin analogs in B. cenocepacia revealed by chemogenomic profiling.

Drug repurposing efforts led to the discovery of bactericidal activity in auranofin, a gold-containing drug used to treat rheumatoid arthritis. Auranofin kills Gram-positive bacteria by inhibiting thioredoxin reductase, an enzyme that scavenges reactive oxygen species (ROS). Despite the presence of thioredoxin reductase in Gram-negative bacteria, auranofin is not always active against them. It is not clear whether the lack of activity in several Gram-negative bacteria is due to the cell envelope barrier or the presence of other ROS protective enzymes such as glutathione reductase (GOR). We previously demonstrated that chemical analogs of auranofin (MS-40 and MS-40S), but not auranofin, are bactericidal against the Gram-negative Burkholderia cepacia complex. Here, we explore the targets of auranofin, MS-40, and MS-40S in Burkholderia cenocepacia and elucidate the mechanism of action of the auranofin analogs by a genome-wide, randomly barcoded transposon screen (BarSeq). Auranofin and its analogs inhibited the B. cenocepacia thioredoxin reductase and induced ROS but did not inhibit the bacterial GOR. Genome-wide, BarSeq analysis of cells exposed to MS-40 and MS-40S compared to the ROS inducers arsenic trioxide, diamide, hydrogen peroxide, and paraquat revealed common and unique mediators of drug susceptibility. Furthermore, deletions of gshA and gshB that encode enzymes in the glutathione biosynthetic pathway led to increased susceptibility to MS-40 and MS-40S. Overall, our data suggest that the auranofin analogs kill B. cenocepacia by inducing ROS through inhibition of thioredoxin reductase and that the glutathione system has a role in protecting B. cenocepacia against these ROS-inducing compounds.IMPORTANCEThe Burkholderia cepacia complex is a group of multidrug-resistant bacteria that can cause infections in the lungs of people with the autosomal recessive disease, cystic fibrosis. Specifically, the bacterium Burkholderia cenocepacia can cause severe infections, reducing lung function and leading to a devastating type of sepsis, cepacia syndrome. This bacterium currently does not have an accepted antibiotic treatment plan because of the wide range of antibiotic resistance. Here, we further the research on auranofin analogs as antimicrobials by finding the mechanism of action of these potent bactericidal compounds, using a powerful technique called BarSeq, to find the global response of the cell when exposed to an antimicrobial.

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.

The Burkholderia contaminans prevalent phenotypes as possible markers of poor clinical outcomes in chronic lung infection of children with cystic fibrosis.

Burkholderia contaminans, a species of the Burkholderia cepacia complex-prevalent in certain Latin-American and European countries-can cause chronic pulmonary infection in persons with cystic fibrosis. Our aim was to gain insights into long-term lung infections with a focus on correlating how bacterial phenotypic traits in the chronic infection impact on patients' clinical outcome. Genotypic characteristics of 85 B. contaminans isolates recovered from 70 patients were investigated. For 16 of those patients, the clinical status and bacterial phenotypic characteristics, e.g. several virulence factors, phenotypic variants, and the antimicrobial susceptibility pattern, were evaluated. Two clones were found in the whole bacterial population: (i) the multiresistant ST 872 PCR-recA-RFLP-HaeIII-K-pattern clone, which carries a pathogenic island homologous to BcenGI11 of B. cenocepacia J2315, and (ii) the ST 102 PCR-recA-RFLP-HaeIII-AT-pattern clone. The emergence of certain bacterial phenotypes in the chronic infection such as the nonmucoid phenotype, small colony variants, brownish pigmented colonies, and hypermutators, proved to be, together with coinfection with Pseudomonas aeruginosa, the possible markers of more challenging infections and poor prognosis. The presence of cocolonizers and the bacterial phenotypes that are especially adapted to persist in long-term respiratory tract infections have a crucial role in patients' clinical outcomes.

ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction.

Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.

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.

The Phenylacetic Acid Catabolic Pathway Regulates Antibiotic and Oxidative Stress Responses in Acinetobacter.

The opportunistic pathogen Acinetobacter baumannii is responsible for a wide range of infections that are becoming increasingly difficult to treat due to extremely high rates of multidrug resistance. Acinetobacter's pathogenic potential is thought to rely on a "persist and resist" strategy that facilitates its remarkable ability to survive under a variety of harsh conditions. The paa operon is involved in the catabolism of phenylacetic acid (PAA), an intermediate in phenylalanine degradation, and is the most differentially regulated pathway under many environmental conditions. We found that, under subinhibitory concentrations of antibiotics, A. baumannii upregulates expression of the paa operon while simultaneously repressing chaperone-usher Csu pilus expression and biofilm formation. These phenotypes are reverted either by exogenous addition of PAA and its nonmetabolizable derivative 4-fluoro-PAA or by a mutation that blocks PAA degradation. Interference with PAA degradation increases susceptibility to antibiotics and hydrogen peroxide treatment. Transcriptomic and proteomic analyses identified a subset of genes and proteins whose expression is affected by addition of PAA or disruption of the paa pathway. Finally, we demonstrated that blocking PAA catabolism results in attenuated virulence in a murine catheter-associated urinary tract infection (CAUTI) model. We conclude that the paa operon is part of a regulatory network that responds to antibiotic and oxidative stress and is important for virulence. PAA has known regulatory functions in plants, and our experiments suggest that PAA is a cross-kingdom signaling molecule. Interference with this pathway may lead, in the future, to novel therapeutic strategies against A. baumannii infections. IMPORTANCE Acinetobacter baumannii causes a wide range of infections that are difficult to treat due to increasing rates of multidrug resistance; however, the mechanisms that this pathogen uses to respond to stress are poorly understood. Here, we describe a new mechanism of stress signaling in Acinetobacter that is mediated by the metabolite phenylacetic acid (PAA). We found that disrupting PAA catabolism interfered with A. baumannii's ability to adapt to stress, leading to decreased antibiotic tolerance and hydrogen peroxide resistance. We propose that investigating this stress response could lead to the development of novel therapeutics. In fact, PAA derivatives constitute a group of FDA-approved nonsteroidal anti-inflammatory drugs that could potentially be repurposed as antivirulence therapies to target multidrug-resistant Acinetobacter infections.

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means.

BACKGROUND: Converting molecules into computer-interpretable features with rich molecular information is a core problem of data-driven machine learning applications in chemical and drug-related tasks. Generally speaking, there are global and local features to represent a given molecule. As most algorithms have been developed based on one type of feature, a remaining bottleneck is to combine both feature sets for advanced molecule-based machine learning analysis. Here, we explored a novel analytical framework to make embeddings of the molecular features and apply them in the clustering of a large number of small molecules. RESULTS: In this novel framework, we first introduced a principal component analysis method encoding the molecule-specific atom and bond information. We then used a variational autoencoder (AE)-based method to make embeddings of the global chemical properties and the local atom and bond features. Next, using the embeddings from the encoded local and global features, we implemented and compared several unsupervised clustering algorithms to group the molecule-specific embeddings. The number of clusters was treated as a hyper-parameter and determined by the Silhouette method. Finally, we evaluated the corresponding results using three internal indices. Applying the analysis framework to a large chemical library of more than 47,000 molecules, we successfully identified 50 molecular clusters using the K-means method with 32 embeddings based on the AE method. We visualized the clustering result via t-SNE for the overall distribution of molecules and the similarity maps for the structural analysis of randomly selected cluster-specific molecules. CONCLUSIONS: This study developed a novel analytical framework that comprises a feature engineering scheme for molecule-specific atomic and bonding features and a deep learning-based embedding strategy for different molecular features. By applying the identified embeddings, we show their usefulness for clustering a large molecule dataset. Our novel analytic algorithms can be applied to any virtual library of chemical compounds with diverse molecular structures. Hence, these tools have the potential of optimizing drug discovery, as they can decrease the number of compounds to be screened in any drug screening campaign.

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.

Gradients in gene essentiality reshape antibacterial research.

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

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.

New Auranofin Analogs with Antibacterial Properties against Burkholderia Clinical Isolates.

Bacteria of the genus Burkholderia include pathogenic Burkholderia mallei, Burkholderia pseudomallei and the Burkholderia cepacia complex (Bcc). These Gram-negative pathogens have intrinsic drug resistance, which makes treatment of infections difficult. Bcc affects individuals with cystic fibrosis (CF) and the species B. cenocepacia is associated with one of the worst clinical outcomes. Following the repurposing of auranofin as an antibacterial against Gram-positive bacteria, we previously synthetized auranofin analogs with activity against Gram-negatives. In this work, we show that two auranofin analogs, MS-40S and MS-40, have antibiotic activity against Burkholderia clinical isolates. The compounds are bactericidal against B. cenocepacia and kill stationary-phase cells and persisters without selecting for multistep resistance. Caenorhabditis elegans and Galleria mellonella tolerated high concentrations of MS-40S and MS-40, demonstrating that these compounds have low toxicity in these model organisms. In summary, we show that MS-40 and MS-40S have antimicrobial properties that warrant further investigations to determine their therapeutic potential against Burkholderia infections.

Improved Dynamic Range of a Rhamnose-Inducible Promoter for Gene Expression in Burkholderia spp.

A diverse genetic toolkit is critical for understanding bacterial physiology and genotype-phenotype relationships. Inducible promoter systems are an integral part of this toolkit. In Burkholderia and related species, the l-rhamnose-inducible promoter is among the first choices due to its tight control and the lack of viable alternatives. To improve upon its maximum activity and dynamic range, we explored the effect of promoter system modifications in Burkholderia cenocepacia with a LacZ-based reporter. By combining the bacteriophage T7 gene 10 stem-loop and engineered rhaI transcription factor-binding sites, we obtained a rhamnose-inducible system with a 6.5-fold and 3.0-fold increases in maximum activity and dynamic range, respectively, compared to the native promoter. We then added the modified promoter system to pSCrhaB2 and pSC201, common genetic tools used for plasmid-based and chromosome-based gene expression, respectively, in Burkholderia, creating pSCrhaB2plus and pSC201plus. We demonstrated the utility of pSCrhaB2plus for gene expression in B. thailandensis, B. multivorans, and B. vietnamiensis and used pSC201plus to control highly expressed essential genes from the chromosome of B. cenocepacia. The utility of the modified system was demonstrated as we recovered viable mutants to control ftsZ, rpoBC, and rpsF, whereas the unmodified promoter was unable to control rpsF. The modified expression system allowed control of an essential gene depletion phenotype at lower levels of l-rhamnose, the inducer. pSCRhaB2plus and pSC201plus are expected to be valuable additions to the genetic toolkit for Burkholderia and related species. IMPORTANCE Species of Burkholderia are dually recognized as being of attractive biotechnological potential but also opportunistic pathogens for immunocompromised individuals. Understanding the genotype-phenotype relationship is critical for synthetic biology approaches in Burkholderia to disentangle pathogenic from beneficial traits. A diverse genetic toolkit, including inducible promoters, is the foundation for these investigations. Thus, we sought to improve on the commonly used rhamnose-inducible promoter system. Our modifications resulted in both higher levels of heterologous protein expression and broader control over highly expressed essential genes in B. cenocepacia. The significance of our work is in expanding the genetic toolkit to enable more comprehensive studies into Burkholderia and related bacteria.

Comparative genomics of ST5 and ST30 methicillin-resistant Staphylococcus aureus sequential isolates recovered from paediatric patients with cystic fibrosis.

Staphylococcus aureus chronic airway infection in patients with cystic fibrosis (CF) allows this pathogen to adapt over time in response to different selection pressures. We have previously shown that the main sequence types related to community-acquired methicillin-resistant S. aureus (MRSA) infections in Argentina - ST5 and ST30 - are also frequently isolated from the sputum of patients with CF, but in these patients they usually display multi-drug antimicrobial resistance. In this study, we sequenced the genomes of MRSA from four paediatric CF patients with the goal of identifying mutations among sequential isolates, especially those possibly related to antimicrobial resistance and virulence, which might contribute to the adaptation of the pathogen in the airways of patients with CF. Our results revealed genetic differences in sequential MRSA strains isolated from patients with CF in both their core and accessory genomes. Although the genetic adaptation of S. aureus was distinct in different hosts, we detected independent mutations in thyA, htrA, rpsJ and gyrA - which are known to have crucial roles in S. aureus virulence and antimicrobial resistance - in isolates recovered from multiple patients. Moreover, we identified allelic variants that were detected in all of the isolates recovered after a certain time point; these non-synonymous mutations were in genes associated with antimicrobial resistance, virulence, iron scavenging and oxidative stress resistance. In conclusion, our results provide evidence of genetic variability among sequential MRSA isolates that could be implicated in the adaptation of these strains during chronic CF airway infection.

Phenylacetyl Coenzyme A, Not Phenylacetic Acid, Attenuates CepIR-Regulated Virulence in Burkholderia cenocepacia.

During phenylalanine catabolism, phenylacetic acid (PAA) is converted to phenylacetyl coenzyme A (PAA-CoA) by a ligase, PaaK, and then PAA-CoA is epoxidized by a multicomponent monooxygenase, PaaABCDE, before further degradation through the tricarboxylic acid (TCA) cycle. In the opportunistic pathogen Burkholderia cenocepacia, loss of paaABCDE attenuates virulence factor expression, which is under the control of the LuxIR-like quorum sensing (QS) system, CepIR. To further investigate the link between CepIR-regulated virulence and PAA catabolism, we created knockout mutants of the first step of the pathway (PAA-CoA synthesis by PaaK) and characterized them in comparison to a paaABCDE mutant using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and virulence assays. We found that while loss of PaaABCDE decreased virulence, deletion of the paaK genes resulted in a more virulent phenotype than that of the wild-type strain. Deletion of either paaK or paaABCDE led to higher levels of released PAA but no differences in levels of internal accumulation compared to the wild-type level. While we found no evidence of direct cepIR downregulation by PAA-CoA or PAA, a low-virulence cepR mutant reverted to a virulent phenotype upon removal of the paaK genes. On the other hand, removal of paaABCDE in the cepR mutant did not impact its attenuated phenotype. Together, our results suggest an indirect role for PAA-CoA in suppressing B. cenocepacia CepIR-activated virulence.IMPORTANCE The opportunistic pathogen Burkholderia cenocepacia uses a chemical signal process called quorum sensing (QS) to produce virulence factors. In B. cenocepacia, QS relies on the presence of the transcriptional regulator CepR which, upon binding QS signal molecules, activates virulence. In this work, we found that even in the absence of CepR, B. cenocepacia can elicit a pathogenic response if phenylacetyl-CoA, an intermediate of the phenylacetic acid degradation pathway, is not produced. Instead, accumulation of phenylacetyl-CoA appears to attenuate pathogenicity. Therefore, we have discovered that it is possible to trigger virulence in the absence of CepR, challenging the classical view of activation of virulence by this QS mechanism. Our work provides new insight into the relationship between metabolism and virulence in opportunistic bacteria. We propose that in the event that QS signaling molecules cannot accumulate to trigger a pathogenic response, a metabolic signal can still activate virulence in B. cenocepacia.

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.

The Effect of 2-Thiocyanatopyridine Derivative 11026103 on Burkholderia Cenocepacia: Resistance Mechanisms and Systemic Impact.

Bacteria of the Burkholderia cepacia complex (Bcc) are associated with significant decline of lung functions in cystic fibrosis patients. Bcc infections are virtually impossible to eradicate due to their irresponsiveness to antibiotics. The 2-thiocyanatopyridine derivative 11026103 is a novel, synthetic compound active against Burkholderia cenocepacia. To characterize mechanisms of resistance to 11026103, B. cenocepacia was subjected to chemical mutagenesis, followed by whole genome sequencing. Parallel mutations in resistant isolates were localized in a regulatory protein of the efflux system Resistance-Nodulation-Division (RND)-9 (BCAM1948), RNA polymerase sigma factor (BCAL2462) and its cognate putative anti-sigma factor (BCAL2461). Transcriptomic analysis identified positive regulation of a major facilitator superfamily (MFS) efflux system BCAL1510-1512 by BCAL2462. Artificial overexpression of both efflux systems increased resistance to the compound. The effect of 11026103 on B. cenocepacia was analyzed by RNA-Seq and a competitive fitness assay utilizing an essential gene knockdown mutant library. 11026103 exerted a pleiotropic effect on transcription including profound downregulation of cluster of orthologous groups (COG) category "Translation, ribosomal structure, and biogenesis". The competitive fitness assay identified many genes which modulated susceptibility to 11026103. In summary, 11026103 exerts a pleiotropic cellular response in B. cenocepacia which can be prevented by efflux system-mediated export.

Synthesis and antibiotic activity of novel acylated phloroglucinol compounds against methicillin-resistant Staphylococcus aureus.

The rise in antibiotic resistance among pathogenic microorganisms has created an imbalance in the drugs available for treatment, in part due to the slow development of new antibiotics. Cystic fibrosis (CF) patients are highly susceptible to antibiotic-resistant pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). Phloroglucinols and related polyketide natural products have demonstrated antimicrobial activity against a number of Gram-positive bacteria including S. aureus. In this study, we investigated a series of acylated phloroglucinol derivatives to determine their potential as lead compounds for the design of novel therapeutics. To assess the activity of these compounds, we determined the minimum inhibitory and bactericidal concentration (MIC and MBC, respectively), the minimum biofilm inhibitory and biofilm eradication concentration (MBIC and MBEC, respectively), and evaluated hemolytic activity, as well as their interaction with clinically relevant antibiotics. Of the 12 compounds tested against MRSA and methicillin-susceptible strains, four showed MIC values ranging from 0.125 to 8 µg ml-1 and all of them were bactericidal. However, none of the compounds were able to eradicate biofilms at the concentrations tested. Three of the four did not display hemolytic activity under the conditions tested. Further studies on the interactions of these compounds with clinically relevant antibiotics showed that phlorodipropanophenone displayed synergistic activity when paired with doxycycline. Our results suggest that these acylated phloroglucinols have potential for being further investigated as antibacterial leads.