Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation.publishe

Staphylococcus aureus Survives with a Minimal Peptidoglycan Synthesis Machine but Sacrifices Virulence and Antibiotic Resistance

Many important cellular processes are performed by molecular machines, composed of multiple proteins that physically interact to execute biological functions. An example is the bacterial peptidoglycan (PG) synthesis machine, responsible for the synthesis of the main component of the cell wall and the target of many contemporary antibiotics. One approach for the identification of essential components of a cellular machine involves the determination of its minimal protein composition. Staphylococcus aureus is a Gram-positive pathogen, renowned for its resistance to many commonly used antibiotics and prevalence in hospitals. Its genome encodes a low number of proteins with PG synthesis activity (9 proteins), when compared to other model organisms, and is therefore a good model for the study of a minimal PG synthesis machine. We deleted seven of the nine genes encoding PG synthesis enzymes from the S. aureus genome without affecting normal growth or cell morphology, generating a strain capable of PG biosynthesis catalyzed only by two penicillin-binding proteins, PBP1 and the bi-functional PBP2. However, multiple PBPs are important in clinically relevant environments, as bacteria with a minimal PG synthesis machinery became highly susceptible to cell wall-targeting antibiotics, host lytic enzymes and displayed impaired virulence in a Drosophila infection model which is dependent on the presence of specific peptidoglycan receptor proteins, namely PGRP-SA. The fact that S. aureus can grow and divide with only two active PG synthesizing enzymes shows that most of these enzymes are redundant in vitro and identifies the minimal PG synthesis machinery of S. aureus. However a complex molecular machine is important in environments other than in vitro growth as the expendable PG synthesis enzymes play an important role in the pathogenicity and antibiotic resistance of S. aureus

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics

Differential daptomycin resistance development in Staphylococcus aureus strains with active and mutated gra regulatory systems

The first-in-class lipopeptide antibiotic daptomycin (DAP) is highly active against Gram-positive pathogens including ß-lactam and glycopeptide resistant strains. Its molecular mode of action remains enigmatic, since a defined target has not been identified so far and multiple effects, primarily on the cell envelope have been observed. Reduced DAP susceptibility has been described in S. aureus and enterococci after prolonged treatment courses. In line with its pleiotropic antibiotic activities, a unique, defined molecular mechanism of resistance has not emerged, instead non-susceptibility appears often accompanied by alterations in membrane composition and changes in cell wall homeostasis. We compared S. aureus strains HG001 and SG511, which differ primarily in the functionality of the histidine kinase GraS, to evaluate the impact of the GraRS regulatory system on the development of DAP non-susceptibility. After extensive serial passing, both DAPR variants reached a minimal inhibitory concentration of 31 μg/ml and shared some phenotypic characteristics (e.g. thicker cell wall, reduced autolysis). However, based on comprehensive analysis of the underlying genetic, transcriptomic and proteomic changes, we found that both strains took different routes to achieve DAP resistance. Our study highlights the impressive genetic and physiological capacity of S. aureus to counteract pleiotropic activities of cell wall- and membrane-active compounds even when a major cell wall regulatory system is dysfunctional

Correlation of immune phenotypes derived from H&E-stained whole slide images with prognosis and response to checkpoint inhibitors in NSCLC.

International audience8539 Background: The classification of tumors as inflamed, excluded or desert based on spatial patterns of tumor infiltrating lymphocytes (TILs) is a potential biomarker of patients likely to respond to checkpoint inhibitors (CPI). However, the subjectivity of manual methods to assess these immune phenotypes (IPs) and poor standardization in the methods and thresholds to define IPs have hampered their clinical adoption. Here, we describe a data-driven approach to inform IP threshold selection on hematoxylin and eosin (H&E)-stained whole slide images (WSI) by maximizing differences in overall survival (OS) between IPs. Methods: A model to classify the IPs of NSCLC samples from H&E images was developed using PathExplore models applied to a TCGA non-small cell lung cancer (NSCLC) cohort of LUAD (N=459) and LUSC (N=424). TIL densities were extracted within cancer and stroma for 0.01 mm2 patches tiled across each WSI. Cut-offs to define cancer and stroma patches as hot or cold were defined based on the 75th and 50th percentiles, respectively, of cancer TIL (cTIL) densities (386 cTIL/mm2) and stroma TIL (sTIL) densities (423 sTIL/mm2) in a TCGA cohort of 4,082 H&E-stained WSI from 10 epithelial tumor types. Hierarchical fitting yielded optimal thresholds minimizing the p-values of OS differences between IPs, leading to classifications of inflamed (iIP, >40% cancer hot patches), excluded (eIP, ≤40% cancer hot patches; >45% stromal hot patches) and desert (dIP, ≤40% cancer hot patches; ≤45% stromal hot patches). The model was then deployed in a clinical cohort of PD-(L)1 inhibitor-treated NSCLC patients (N=95) enrolled in the BIP precision medicine study (NCT02534649; Institut Bergonié, Bordeaux, France). Model-predicted IPs were compared to progression-free survival (PFS) and OS. FDR correction was done with Benjamini-Hochberg. Results: In the TCGA NSCLC cohort, model-predicted iIP (N=196) and eIP (N=607) patients had significantly better OS compared to dIP (N=80; HR=0.53, p=0.003 and HR=0.59, p=0.003, respectively). In the clinical cohort, cTIL density and fraction of hot epithelial patches were significantly associated with PFS (HR=0.64, q=0.04 and HR=0.69, q=0.04, respectively). PFS was significantly shorter in model-predicted eIP patients (N=46) compared to iIP (N=39; HR=0.54, p=0.045). Notably, in PD-L1(-) patients (N=43, tumor proportion score ≤1%), iIP patients had longer PFS than eIP and dIP patients (HR=0.35, p=0.02). No difference in PFS was observed for PD-L1(+) patients. Conclusions: We developed a data-driven approach for predicting IPs using patch-level TIL features. Model-predicted IPs were prognostic in a TCGA NSCLC dataset and predictive of PFS in a CPI- treated clinical NSCLC cohort. Association of IP and PFS was independent of PD-L1 status, potentially allowing the identification of PD-L1(-) patients who may derive greater benefit from CPI

Chemical Genomics-Based Antifungal Drug Discovery: Targeting Glycosylphosphatidylinositol (GPI) Precursor Biosynthesis

Steadily increasing antifungal drug resistance and persistent high rates of fungal-associated mortality highlight the dire need for the development of novel antifungals. Characterization of inhibitors of one enzyme in the GPI anchor pathway, Gwt1, has generated interest in the exploration of targets in this pathway for further study. Utilizing a chemical genomics-based screening platform referred to as the Candida albicans fitness test (CaFT), we have identified novel inhibitors of Gwt1 and a second enzyme in the glycosylphosphatidylinositol (GPI) cell wall anchor pathway, Mcd4. We further validate these targets using the model fungal organism Saccharomyces cerevisiae and demonstrate the utility of using the facile toolbox that has been compiled in this species to further explore target specific biology. Using these compounds as probes, we demonstrate that inhibition of Mcd4 as well as Gwt1 blocks the growth of a broad spectrum of fungal pathogens and exposes key elicitors of pathogen recognition. Interestingly, a strong chemical synergy is also observed by combining Gwt1 and Mcd4 inhibitors, mirroring the demonstrated synthetic lethality of combining conditional mutants of GWT1 and MCD4. We further demonstrate that the Mcd4 inhibitor M720 is efficacious in a murine infection model of systemic candidiasis. Our results establish Mcd4 as a promising antifungal target and confirm the GPI cell wall anchor synthesis pathway as a promising antifungal target area by demonstrating that effects of inhibiting it are more general than previously recognized.Genome Canada (Firm)Genome Quebe

<i>S</i>. <i>aureus</i> minimal mutant strain COL MIN displays normal growth and requires PBP1 and PBP2 for survival.

<p><b>(A)</b> Growth of the parental strain COL and the minimal mutant strain COL MIN was followed in rich liquid medium by monitoring the absorbance at OD_600nm. The mutant strain COL MIN (doubling time 40 min) showed similar growth to the parental strain COL (doubling time 36 min). <b>(B)</b> Growth of COL and COL MIN was followed in minimal medium by monitoring the absorbance at OD_600nm. The mutant strain COL MIN (doubling time 67 min) showed similar growth to the parental strain COL (doubling time 61 min). <b>(C)</b> Depletion of PBP1 from COL PBP1i and COL MIN PBP1i, in which PBP1 expression is under the control of the IPTG inducible P_<i>spac</i> promoter, by growing cells in the absence of IPTG, led to a halt in cell growth and subsequent drop in optical density indicating PBP1 is essential for survival of both the parental and mutant strains. <b>(D)</b> In the absence of PBP2, strain COL PBP2i (parental strain COL with PBP2 expression under the control of the IPTG inducible P_<i>spac</i> promoter) continues to grow. However, depletion of PBP2 from COL MIN PBP2i causes arrest in growth indicating PBP2 is essential for growth of COL MIN. Averages of three independent replicates are shown and error bars show standard deviations.</p

<i>S</i>. <i>aureus</i> COL MIN showed attenuated virulence in a <i>Drosophila</i> infection model and increased susceptibility to lysozyme.

<p><b>(A, B)</b> Estimated survival curves for wild type (WT) and PGRP-SA mutant (<i>seml</i>) flies infected with COL and COL MIN <i>S</i>. <i>aureus</i> strains or PBS (to monitor the physical effects of the injection <i>per se</i>). WT flies strongly succumbed to infection with COL by 96 hours whereas 88% of WT flies infected with COL MIN survived. Curves were statistically separable, log-rank test P<0.05. At least 90% of the PGRP-SA-deficient flies were killed by WT bacteria (within 60 hours) and by COL MIN mutant strain (within 96 hours). Curves were statistically separable, log-rank test P<0.05. (<b>C)</b> Bacterial cell lysis monitored through the decrease of OD₆₀₀ was determined for COL and COL MIN strains in the presence (+) or absence (-) of lysozyme (300 μg/ml). The minimal strain showed increased cell lysis in the presence of lysozyme. Data shows mean with 95% confidence intervals of three independent biological repeats.</p

Phylogenetic distribution of peptidoglycan synthesis enzymes across selected bacterial species.

<p><b>(A)</b> The blue, stacked bars represent the total number of proteins present in each species that have a transpeptidase (dark blue) or transglycosylase (light blue) domain. The total number of proteins that have at least one of the two domains is displayed numerically. Strains highlighted in pink are bacteria reported not to possess cell wall, although they may produce small but functional amounts of peptidoglycan. The maximum likelihood species tree was calculated using PhyML [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004891#ppat.1004891.ref053" target="_blank">53</a>] and concatenated bacterial marker genes identified with the AMPHORA2 [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004891#ppat.1004891.ref052" target="_blank">52</a>] software. <b>(B)</b> Table showing the peptidoglycan synthesis proteins from <i>S</i>. <i>aureus</i> and their established or hypothetical activities.</p