ZapG (YhcB/DUF1043), a novel cell division protein in gamma-proteobacteria linking the Z-ring to septal peptidoglycan synthesis.

YhcB, a poorly understood protein conserved across gamma-proteobacteria, contains a domain of unknown function (DUF1043) and an N-terminal transmembrane domain. Here, we used an integrated approach including X-ray crystallography, genetics, and molecular biology to investigate the function and structure of YhcB. The Escherichia coli yhcB KO strain does not grow at 45 °C and is hypersensitive to cell wall-acting antibiotics, even in the stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septum development. The Z-ring is essential for the positioning of the septa and the initiation of cell division. We found that YhcB interacts with proteins of the divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). Seven of these interactions are also conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we mapped the amino acid residues likely involved in the interactions of YhcB with FtsI and RodZ. The 2.8 Å crystal structure of the cytosolic domain of Haemophilus ducreyi YhcB shows a unique tetrameric α-helical coiled-coil structure likely to be involved in linking the Z-ring to the septal peptidoglycan-synthesizing complexes. In summary, YhcB is a conserved and conditionally essential protein that plays a role in cell division and consequently affects envelope biogenesis. Based on these findings, we propose to rename YhcB to ZapG (Z-ring-associated protein G). This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.

A Gaussian process-based definition reveals new and bona fide genetic interactions compared to a multiplicative model in the Gram-negative Escherichia coli.

MOTIVATION: A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E.coli. RESULTS: Using our published E.coli GI datasets, we show computationally that a machine learning Gaussian process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E.coli, and, potentially, other microbes. AVAILABILITY AND IMPLEMENTATION: The source code and parameters used to generate the machine learning models in WEKA software were provided in the Supplementary information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Legionella pneumophila effector Lem4 is a membrane-associated protein tyrosine phosphatase.

Legionella pneumophila is a Gram-negative pathogenic bacterium that causes severe pneumonia in humans. It establishes a replicative niche called Legionella-containing vacuole (LCV) that allows bacteria to survive and replicate inside pulmonary macrophages. To hijack host cell defense systems, L. pneumophila injects over 300 effector proteins into the host cell cytosol. The Lem4 effector (lpg1101) consists of two domains: an N-terminal haloacid dehalogenase (HAD) domain with unknown function and a C-terminal phosphatidylinositol 4-phosphate-binding domain that anchors Lem4 to the membrane of early LCVs. Herein, we demonstrate that the HAD domain (Lem4-N) is structurally similar to mouse MDP-1 phosphatase and displays phosphotyrosine phosphatase activity. Substrate specificity of Lem4 was probed using a tyrosine phosphatase substrate set, which contained a selection of 360 phosphopeptides derived from human phosphorylation sites. This assay allowed us to identify a consensus pTyr-containing motif. Based on the localization of Lem4 to lysosomes and to some extent to plasma membrane when expressed in human cells, we hypothesize that this protein is involved in protein-protein interactions with an LCV or plasma membrane-associated tyrosine-phosphorylated host target.

Insights from protein-protein interaction studies on bacterial pathogenesis.

INTRODUCTION: The threat bacterial pathogens pose to human health is increasing with the number and distribution of antibiotic-resistant bacteria, while the rate of discovery of new antimicrobials dwindles. Proteomics is playing key roles in understanding the molecular mechanisms of bacterial pathogenesis, and in identifying disease outcome determinants. The physical associations identified by proteomics can provide the means to develop pathogen-specific treatment methods that reduce the spread of antibiotic resistance and alleviate the negative effects of broad-spectrum antibiotics on beneficial bacteria. Areas covered: This review discusses recent trends in proteomics and introduces new and developing approaches that can be applied to the study of protein-protein interactions (PPIs) underlying bacterial pathogenesis. The approaches examined encompass options for mapping proteomes as well as stable and transient interactions in vivo and in vitro. We also explored the coverage of bacterial and human-bacterial PPIs, knowledge gaps in this area, and how they can be filled. Expert commentary: Identifying potential antimicrobial candidates is confounded by the complex molecular biology of bacterial pathogenesis and the lack of knowledge about PPIs underlying this process. Proteomics approaches can offer new perspectives for mechanistic insights and identify essential targets for guiding the discovery of next generation antimicrobials.

Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli.

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.

Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli.

MOTIVATION: The model bacterium Escherichia coli is among the best studied prokaryotes, yet nearly half of its proteins are still of unknown biological function. This is despite a wealth of available large-scale physical and genetic interaction data. To address this, we extended the GeneMANIA function prediction web application developed for model eukaryotes to support E.coli. RESULTS: We integrated 48 distinct E.coli functional interaction datasets and used the GeneMANIA algorithm to produce thousands of novel functional predictions and prioritize genes for further functional assays. Our analysis achieved cross-validation performance comparable to that reported for eukaryotic model organisms, and revealed new functions for previously uncharacterized genes in specific bioprocesses, including components required for cell adhesion, iron-sulphur complex assembly and ribosome biogenesis. The GeneMANIA approach for network-based function prediction provides an innovative new tool for probing mechanisms underlying bacterial bioprocesses. CONTACT: gary.bader@utoronto.ca; mohan.babu@uregina.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Investigating Bacterial Protein Synthesis Using Systems Biology Approaches.

Protein synthesis is essential for bacterial growth and survival. Its study in Escherichia coli helped uncover features conserved among bacteria as well as universally. The pattern of discovery and the identification of some of the longest-known components of the protein synthesis machinery, including the ribosome itself, tRNAs, and translation factors proceeded through many stages of successively more refined biochemical purifications, finally culminating in the isolation to homogeneity, identification, and mapping of the smallest unit required for performing the given function. These early studies produced a wealth of information. However, many unknowns remained. Systems biology approaches provide an opportunity to investigate protein synthesis from a global perspective, overcoming the limitations of earlier ad hoc methods to gain unprecedented insights. This chapter reviews innovative systems biology approaches, with an emphasis on those designed specifically for investigating the protein synthesis machinery in E. coli.

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways.

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among > 235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.

Investigating the Ustilago maydis/Zea mays pathosystem: transcriptional responses and novel functional aspects of a fungal calcineurin regulatory B subunit.

The sustainable control of basidiomycete biotrophic plant pathogenesis requires an understanding of host responses to infection, as well as the identification and functional analysis of fungal genes involved in disease development. The creation and analysis of a suppressive subtractive hybridization (SSH) cDNA library from Ustilago maydis-infected Zea mays seedlings enabled the identification of fungal and plant genes expressed during disease development, and uncovered new insights into the interactions of this model system. Candidate U. maydis pathogenesis genes were identified by using the current SSH cDNA library analysis, and by knowledge generated from previous cDNA microarray and comparative genomic analyses. These identifications were supported by the independent determination of transcript level changes in different cell-types and during pathogenic development. The basidiomycete specific um01632, the highly in planta expressed um03046 (zig1), and the calcineurin regulatory B subunit (um10226, cnb1), were chosen for deletion experiments. um01632 and zig1 mutants showed no difference in morphology and did not have a statistically significant impact on pathogenesis. cnb1 mutants had a distinct cell division phenotype and reduced virulence in seedling assays. Infections with reciprocal wild-type×Δcnb1 haploid strain crosses revealed that the wild-type allele was unable to fully compensate for the lack of a second cnb1 allele. This haploinsufficiency was undetected in other fungal cnb1 mutational analyses. The reported data improves U. maydis genome annotation and expands on the current understanding of pathogenesis genes in this model basidiomycete.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain.

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.