ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

MOTIVATION: High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. RESULTS: ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type. AVAILABILITY AND IMPLEMENTATION: ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

Bacterial retrons encode phage-defending tripartite toxin-antitoxin systems.

Retrons are prokaryotic genetic retroelements encoding a reverse transcriptase that produces multi-copy single-stranded DNA1 (msDNA). Despite decades of research on the biosynthesis of msDNA2, the function and physiological roles of retrons have remained unknown. Here we show that Retron-Sen2 of Salmonella enterica serovar Typhimurium encodes an accessory toxin protein, STM14_4640, which we renamed as RcaT. RcaT is neutralized by the reverse transcriptase-msDNA antitoxin complex, and becomes active upon perturbation of msDNA biosynthesis. The reverse transcriptase is required for binding to RcaT, and the msDNA is required for the antitoxin activity. The highly prevalent RcaT-containing retron family constitutes a new type of tripartite DNA-containing toxin-antitoxin system. To understand the physiological roles of such toxin-antitoxin systems, we developed toxin activation-inhibition conjugation (TAC-TIC), a high-throughput reverse genetics approach that identifies the molecular triggers and blockers of toxin-antitoxin systems. By applying TAC-TIC to Retron-Sen2, we identified multiple trigger and blocker proteins of phage origin. We demonstrate that phage-related triggers directly modify the msDNA, thereby activating RcaT and inhibiting bacterial growth. By contrast, prophage proteins circumvent retrons by directly blocking RcaT. Consistently, retron toxin-antitoxin systems act as abortive infection anti-phage defence systems, in line with recent reports3,4. Thus, RcaT retrons are tripartite DNA-regulated toxin-antitoxin systems, which use the reverse transcriptase-msDNA complex both as an antitoxin and as a sensor of phage protein activities.

Structure-Function Characterization of the Conserved Regulatory Mechanism of the Escherichia coli M48 Metalloprotease BepA.

The asymmetric Gram-negative outer membrane (OM) is the first line of defense for bacteria against environmental insults and attack by antimicrobials. The key component of the OM is lipopolysaccharide, which is transported to the surface by the essential lipopolysaccharide transport (Lpt) system. Correct folding of the Lpt system component LptD is regulated by a periplasmic metalloprotease, BepA. Here, we present the crystal structure of BepA from Escherichia coli, solved to a resolution of 2.18 Å, in which the M48 protease active site is occluded by an active-site plug. Informed by our structure, we demonstrate that free movement of the active-site plug is essential for BepA function, suggesting that the protein is autoregulated by the active-site plug, which is conserved throughout the M48 metalloprotease family. Targeted mutagenesis of conserved residues reveals that the negative pocket and the tetratricopeptide repeat (TPR) cavity are required for function and degradation of the BAM complex component BamA under conditions of stress. Last, we show that loss of BepA causes disruption of OM lipid asymmetry, leading to surface exposed phospholipid.IMPORTANCE M48 metalloproteases are widely distributed in all domains of life. E. coli possesses four members of this family located in multiple cellular compartments. The functions of these proteases are not well understood. Recent investigations revealed that one family member, BepA, has an important role in the maturation of a central component of the lipopolysaccharide (LPS) biogenesis machinery. Here, we present the structure of BepA and the results of a structure-guided mutagenesis strategy, which reveal the key residues required for activity that inform how all M48 metalloproteases function.

Outer membrane lipoprotein NlpI scaffolds peptidoglycan hydrolases within multi-enzyme complexes in Escherichia coli.

The peptidoglycan (PG) sacculus provides bacteria with the mechanical strength to maintain cell shape and resist osmotic stress. Enlargement of the mesh-like sacculus requires the combined activity of peptidoglycan synthases and hydrolases. In Escherichia coli, the activity of two PG synthases is driven by lipoproteins anchored in the outer membrane (OM). However, the regulation of PG hydrolases is less well understood, with only regulators for PG amidases having been described. Here, we identify the OM lipoprotein NlpI as a general adaptor protein for PG hydrolases. NlpI binds to different classes of hydrolases and can specifically form complexes with various PG endopeptidases. In addition, NlpI seems to contribute both to PG elongation and division biosynthetic complexes based on its localization and genetic interactions. Consistent with such a role, we reconstitute PG multi-enzyme complexes containing NlpI, the PG synthesis regulator LpoA, its cognate bifunctional synthase, PBP1A, and different endopeptidases. Our results indicate that peptidoglycan regulators and adaptors are part of PG biosynthetic multi-enzyme complexes, regulating and potentially coordinating the spatiotemporal action of PG synthases and hydrolases.

Biofilm Inhibitor Taurolithocholic Acid Alters Colony Morphology, Specialized Metabolism, and Virulence of Pseudomonas aeruginosa.

Biofilm inhibition by exogenous molecules has been an attractive strategy for the development of novel therapeutics. We investigated the biofilm inhibitor taurolithocholic acid (TLCA) and its effects on the specialized metabolism, virulence, and biofilm formation of the clinically relevant bacterium Pseudomonas aeruginosa strain PA14. Our study shows that TLCA alters the specialized metabolism, thereby affecting P. aeruginosa colony biofilm physiology. We observed an upregulation of metabolites correlated to virulence such as the siderophore pyochelin. A wax moth virulence assay confirmed that treatment with TLCA increases the virulence of P. aeruginosa. On the basis of our results, we believe that future endeavors to identify biofilm inhibitors must consider how a putative lead alters the specialized metabolism of a bacterial community to prevent pathogens from entering a highly virulent state.

Structure, Function, and Evolution of the Pseudomonas aeruginosa Lysine Decarboxylase LdcA.

The only enzyme responsible for cadaverine production in the major multidrug-resistant human pathogen Pseudomonas aeruginosa is the lysine decarboxylase LdcA. This enzyme modulates the general polyamine homeostasis, promotes growth, and reduces bacterial persistence during carbenicillin treatment. Here we present a 3.7-Å resolution cryoelectron microscopy structure of LdcA. We introduce an original approach correlating phylogenetic signal with structural information and reveal possible recombination among LdcA and arginine decarboxylase subfamilies within structural domain boundaries. We show that LdcA is involved in full virulence in an insect pathogenesis model. Furthermore, unlike its enterobacterial counterparts, LdcA is regulated neither by the stringent response alarmone ppGpp nor by the AAA+ ATPase RavA. Instead, the P. aeruginosa ravA gene seems to play a defensive role. Altogether, our study identifies LdcA as an important player in P. aeruginosa physiology and virulence and as a potential drug target.

Learning directed acyclic graphs from large-scale genomics data.

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Correction: A Chemical-Genomic Screen of Neglected Antibiotics Reveals Illicit Transport of Kasugamycin and Blasticidin S.

[This corrects the article DOI: 10.1371/journal.pgen.1006124.].

A tool named Iris for versatile high-throughput phenotyping in microorganisms.

Advances in our ability to systematically introduce and track controlled genetic variance in microorganisms have, in the past decade, fuelled high-throughput reverse genetics approaches. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now, all efforts to quantify microbial macroscopic phenotypes have been restricted to monitoring growth in a small number of model microorganisms. We have developed an image analysis software named Iris, which allows for systematic exploration of a number of orthogonal-to-growth processes, including biofilm formation, colony morphogenesis, envelope biogenesis, sporulation and reporter activity. In addition, Iris provides more sensitive growth measurements than currently available software and is compatible with a variety of different microorganisms, as well as with endpoint or kinetic data. We used Iris to reanalyse existing chemical genomics data in Escherichia coli and to perform proof-of-principle screens on colony biofilm formation and morphogenesis of different bacterial species and the pathogenic fungus Candida albicans. We thereby recapitulated existing knowledge but also identified a plethora of additional genes and pathways involved in both processes.

Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis.

A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria.

Chemical genetics in drug discovery.

Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

A Chemical-Genomic Screen of Neglected Antibiotics Reveals Illicit Transport of Kasugamycin and Blasticidin S.

Fighting antibiotic resistance requires a deeper understanding of the genetic factors that determine the antibiotic susceptibility of bacteria. Here we describe a chemical-genomic screen in Escherichia coli K-12 that was designed to discover new aspects of antibiotic resistance by focusing on a set of 26 antibiotics and other stresses with poorly characterized mode-of-action and determinants of resistance. We show that the screen identifies new resistance determinants for these antibiotics including a common signature from two antimicrobials, kasugamycin and blasticidin S, used to treat crop diseases like rice blast and fire blight. Following this signature, we further investigated the mechanistic basis for susceptibility to kasugamycin and blasticidin S in E. coli using both genetic and biochemical approaches. We provide evidence that these compounds hijack an overlapping set of peptide ABC-importers to enter the bacterial cell. Loss of uptake may be an underappreciated mechanism for the development of kasugamycin resistance in bacterial plant pathogens.

A genome-wide screen for bacterial envelope biogenesis mutants identifies a novel factor involved in cell wall precursor metabolism.

The cell envelope of Gram-negative bacteria is a formidable barrier that is difficult for antimicrobial drugs to penetrate. Thus, the list of treatments effective against these organisms is small and with the rise of new resistance mechanisms is shrinking rapidly. New therapies to treat Gram-negative bacterial infections are therefore sorely needed. This goal will be greatly aided by a detailed mechanistic understanding of envelope assembly. Although excellent progress in the identification of essential envelope biogenesis systems has been made in recent years, many aspects of the process remain to be elucidated. We therefore developed a simple, quantitative, and high-throughput assay for mutants with envelope biogenesis defects and used it to screen an ordered single-gene deletion library of Escherichia coli. The screen was robust and correctly identified numerous mutants known to be involved in envelope assembly. Importantly, the screen also implicated 102 genes of unknown function as encoding factors that likely impact envelope biogenesis. As a proof of principle, one of these factors, ElyC (YcbC), was characterized further and shown to play a critical role in the metabolism of the essential lipid carrier used for the biogenesis of cell wall and other bacterial surface polysaccharides. Further analysis of the function of ElyC and other hits identified in our screen is likely to uncover a wealth of new information about the biogenesis of the Gram-negative envelope and the vulnerabilities in the system suitable for drug targeting. Moreover, the screening assay described here should be readily adaptable to other organisms to study the biogenesis of different envelope architectures.

Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme.

BACKGROUND: Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. RESULTS: We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. CONCLUSIONS: The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.