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

The Crystal Structure of the Domain of Unknown Function 1480 (DUF1480) From Klebsiella pneumoniae

Domains of unknown function (DUFs) continue to comprise a significant portion of bacterial proteomes, with more than 20% of bacterial proteins remaining annotated as DUFs. The characterization of their molecular structure can provide valuable insight that is not captured by the primary sequence analysis, thus providing a segue into the identification of the molecular function of DUF representatives. Here, we present the crystal structure of KPN_02352 from Klebsiella pneumoniae subsp. pneumoniae, a DUF1480 domain-containing protein, which was determined to be 1.75 Å resolution. Representatives of the DUF1480 family are found broadly across Enterobacterales and have been previously shown to contribute to the antibiotic response. Our structural analysis suggests that DUF1480 is comprised of a six-stranded split barrel fold featuring a small alpha helix that is positioned to cap one end of the split barrel. DUF1480 was found to be monomeric in solution, and harbors structural similarity to response regulators. The crystal structure of DUF1480 is the first experimental insight into the molecular structure of this conserved protein family, revealing several conserved features that may be functionally relevant.

Investigating the Activities of CAF20 and ECM32 in the Regulation of PGM2 mRNA Translation

Translation is a fundamental process in biology, and understanding its mechanisms is crucial to comprehending cellular functions and diseases. The regulation of this process is closely linked to the structure of mRNA, as these regions prove vital to modulating translation efficiency and control. Thus, identifying and investigating these fundamental factors that influence the processing and unwinding of structured mRNAs would be of interest due to the widespread impact in various fields of biology. To this end, we employed a computational approach and identified genes that may be involved in the translation of structured mRNAs. The approach is based on the enrichment of interactions and co-expression of genes with those that are known to influence translation and helicase activity. The in silico prediction found CAF20 and ECM32 to be highly ranked candidates that may play a role in unwinding mRNA. The activities of neither CAF20 nor ECM32 have previously been linked to the translation of PGM2 mRNA or other structured mRNAs. Our follow-up investigations with these two genes provided evidence of their participation in the translation of PGM2 mRNA and several other synthetic structured mRNAs.

The rise and future of CRISPR-based approaches for high-throughput genomics

Clustered regularly interspaced short palindromic repeats (CRISPR) has revolutionized the field of genome editing. To circumvent the permanent modifications made by traditional CRISPR techniques and facilitate the study of both essential and nonessential genes, CRISPR interference (CRISPRi) was developed. This gene-silencing technique employs a deactivated Cas effector protein and a guide RNA to block transcription initiation or elongation. Continuous improvements and a better understanding of the mechanism of CRISPRi have expanded its scope, facilitating genome-wide high-throughput screens to investigate the genetic basis of phenotypes. Additionally, emerging CRISPR-based alternatives have further expanded the possibilities for genetic screening. This review delves into the mechanism of CRISPRi, compares it with other high-throughput gene-perturbation techniques, and highlights its superior capacities for studying complex microbial traits. We also explore the evolution of CRISPRi, emphasizing enhancements that have increased its capabilities, including multiplexing, inducibility, titratability, predictable knockdown efficacy, and adaptability to nonmodel microorganisms. Beyond CRISPRi, we discuss CRISPR activation, RNA-targeting CRISPR systems, and single-nucleotide resolution perturbation techniques for their potential in genome-wide high-throughput screens in microorganisms. Collectively, this review gives a comprehensive overview of the general workflow of a genome-wide CRISPRi screen, with an extensive discussion of strengths and weaknesses, future directions, and potential alternatives.

CRISPRi functional genomics in bacteria and its application to medical and industrial research

SUMMARYFunctional genomics is the use of systematic gene perturbation approaches to determine the contributions of genes under conditions of interest. Although functional genomic strategies have been used in bacteria for decades, recent studies have taken advantage of CRISPR (clustered regularly interspaced short palindromic repeats) technologies, such as CRISPRi (CRISPR interference), that are capable of precisely modulating expression of all genes in the genome. Here, we discuss and review the use of CRISPRi and related technologies for bacterial functional genomics. We discuss the strengths and weaknesses of CRISPRi as well as design considerations for CRISPRi genetic screens. We also review examples of how CRISPRi screens have defined relevant genetic targets for medical and industrial applications. Finally, we outline a few of the many possible directions that could be pursued using CRISPR-based functional genomics in bacteria. Our view is that the most exciting screens and discoveries are yet to come.

Structure-Function Insights into the Dual Role in Nucleobase and Nicotinamide Metabolism and a Possible Use in Cancer Gene Therapy of the URH1p Riboside Hydrolase

The URH1p enzyme from the yeast Saccharomyces cerevisiae has gained significant interest due to its role in nitrogenous base metabolism, particularly involving uracil and nicotinamide salvage. Indeed, URH1p was initially classified as a nucleoside hydrolase (NH) with a pronounced preference for uridine substrate but was later shown to also participate in a Preiss-Handler-dependent pathway for recycling of both endogenous and exogenous nicotinamide riboside (NR) towards NAD+ synthesis. Here, we present the detailed enzymatic and structural characterisation of the yeast URH1p enzyme, a member of the group I NH family of enzymes. We show that the URH1p has similar catalytic efficiencies for hydrolysis of NR and uridine, advocating a dual role of the enzyme in both NAD+ synthesis and nucleobase salvage. We demonstrate that URH1p has a monomeric structure that is unprecedented for members of the NH homology group I, showing that oligomerisation is not strictly required for the N-ribosidic activity in this family of enzymes. The size, thermal stability and activity of URH1p towards the synthetic substrate 5-fluoruridine, a riboside precursor of the antitumoral drug 5-fluorouracil, make the enzyme an attractive tool to be employed in gene-directed enzyme-prodrug activation therapy against solid tumours.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional responses to gene perturbations (single-gene knockdowns and overexpressions) to minimize the transcriptional difference between a given pair of initial and target states. We demonstrate the flexibility of our approach by applying it to a microarray dataset comprising over 9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of over 10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an average AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases as the developmental relatedness decreases. We also show that fewer genes are needed to progress along developmental paths than to regress. Together, these findings establish a proof-of-concept for our approach to computationally design control strategies and demonstrate their ability to provide insights into the dynamics of gene regulatory networks.

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli

Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152. This mobile CRISPRi library can be conjugated into other ordered genetic libraries to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two complementary crosses: (1) an Lpp deletion into every CRISPRi knockdown strain and (2) the lolA knockdown plasmid into the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed suppressing interactions for the loci in question.

RavA-ViaA antibiotic response is linked to Cpx and Zra2 envelope stress systems in Vibrio cholerae

IMPORTANCE

The RavA-ViaA complex was previously found to sensitize Escherichia coli to aminoglycosides (AGs) in anaerobic conditions, but the mechanism is unknown. AGs are antibiotics known for their high efficiency against Gram-negative bacteria. In order to elucidate how the expression of the ravA-viaA genes increases bacterial susceptibility to aminoglycosides, we aimed at identifying partner functions necessary for increased tolerance in the absence of RavA-ViaA, in Vibrio cholerae. We show that membrane stress response systems Cpx and Zra2 are required in the absence of RavA-ViaA, for the tolerance to AGs and for outer membrane integrity. In the absence of these systems, the ∆ravvia strain's membrane becomes permeable to external agents such as the antibiotic vancomycin.

Genetic interactions under the microscope

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

groEL gene-based molecular detection and antibiogram profile of Riemerella anatipestifer from duck in Bangladesh

Objectives

This study was designed to detect Riemerella anatipestifer through polymerase chain reaction (PCR) from duck farming areas of the Mymensingh and Sylhet divisions and to determine the antibiogram profile of the PCR-positive isolates using the disc diffusion method.

Materials and Methods

Fifty two samples were collected, comprising clinically sick (32 ducks) and dead ducks (20). PCR confirmation was accomplished, and consistent findings were observed, employing R. anatipestifer groEL (271-bp) gene as appropriate molecular markers. For further clarification, see R. anatipestifer specific PCR assay (546-bp) and gyrB-based PCR (162-bp) were also done. The disc diffusion method was followed for the antibiotic susceptibility test of the isolates against commonly used antibiotics.

Results

A total of 21 samples, 8 from clinically sick birds and 13 from dead birds, showed positive results in both conventional and molecular assays out of 52 samples. High occurrences were found in oropharyngeal swabs from sick ducks and the liver and heart from dead ducks. Antibiotic susceptibility testing revealed that the isolates were 100% resistant to penicillin G, cefradine, streptomycin, neomycin, gentamycin, meropenem, and erythromycin, but 100% sensitive to -cotrimoxazole, florfenicol, and levofloxacin.

Conclusion

For diverse duck-populated areas in Bangladesh, this study shows the severity of R. anatipestifer infection among ducks.

Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

Synthetic Genetic Interactions Reveal a Dense and Cryptic Regulatory Network of Small Noncoding RNAs in Escherichia coli

Over the past 20 years, we have learned that bacterial small noncoding RNAs (sRNAs) can rapidly effect changes in gene expression in response to stress. However, the broader role and impact of sRNA-mediated regulation in promoting bacterial survival has remained elusive. Indeed, there are few examples where disruption of sRNA-mediated gene regulation results in a discernible change in bacterial growth or survival. The lack of phenotypes attributable to loss of sRNA function suggests that either sRNAs are wholly dispensable or functional redundancies mask the impact of deleting a single sRNA. We investigated synthetic genetic interactions among sRNA genes in Escherichia coli by constructing pairwise deletions in 54 genes, including 52 sRNAs. Some 1,373 double deletion strains were studied for growth defects under 32 different nutrient stress conditions and revealed 1,131 genetic interactions. In one example, we identified a profound synthetic lethal interaction between ArcZ and CsrC when E. coli was grown on pyruvate, lactate, oxaloacetate, or d-/l-alanine, and we provide evidence that the expression of ppsA is dysregulated in the double deletion background, causing the conditionally lethal phenotype. This work employs a unique platform for studying sRNA-mediated gene regulation and sheds new light on the genetic network of sRNAs that underpins bacterial growth.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.

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 and characterisation of sPEPs in Cryptococcus neoformans

Short open reading frame (sORF)-encoded peptides (sPEPs) have been found across a wide range of genomic locations in a variety of species. To date, their identification, validation, and characterisation in the human fungal pathogen Cryptococcus neoformans has been limited due to a lack of standardised protocols. We have developed an enrichment process that enables sPEP detection within a protein sample from this polysaccharide-encapsulated yeast, and implemented proteogenomics to provide insights into the validity of predicted and hypothetical sORFs annotated in the C. neoformans genome. Novel sORFs were discovered within the 5' and 3' UTRs of known transcripts as well as in "non-coding" RNAs. One novel candidate, dubbed NPB1, that resided in an RNA annotated as "non-coding", was chosen for characterisation. Through the creation of both specific point mutations and a full deletion allele, the function of the new sPEP, Npb1, was shown to resemble that of the bacterial trans-translation protein SmpB.

Structure, Oligomerization and Activity Modulation in N-Ribohydrolases

Enzymes catalyzing the hydrolysis of the N-glycosidic bond in nucleosides and other ribosides (N-ribohydrolases, NHs) with diverse substrate specificities are found in all kingdoms of life. While the overall NH fold is highly conserved, limited substitutions and insertions can account for differences in substrate selection, catalytic efficiency, and distinct structural features. The NH structural module is also employed in monomeric proteins devoid of enzymatic activity with different physiological roles. The homo-oligomeric quaternary structure of active NHs parallels the different catalytic strategies used by each isozyme, while providing a buttressing effect to maintain the active site geometry and allow the conformational changes required for catalysis. The unique features of the NH catalytic strategy and structure make these proteins attractive targets for diverse therapeutic goals in different diseases.

Adaptation of the periplasm to maintain spatial constraints essential for cell envelope processes and cell viability

The cell envelope of Gram-negative bacteria consists of two membranes surrounding a periplasm and peptidoglycan layer. Molecular machines spanning the cell envelope depend on spatial constraints and load-bearing forces across the cell envelope and surface. The mechanisms dictating spatial constraints across the cell envelope remain incompletely defined. In Escherichia coli, the coiled-coil lipoprotein Lpp contributes the only covalent linkage between the outer membrane and the underlying peptidoglycan layer. Using proteomics, molecular dynamics, and a synthetic lethal screen, we show that lengthening Lpp to the upper limit does not change the spatial constraint but is accommodated by other factors which thereby become essential for viability. Our findings demonstrate E. coli expressing elongated Lpp does not simply enlarge the periplasm in response, but the bacteria accommodate by a combination of tilting Lpp and reducing the amount of the covalent bridge. By genetic screening, we identified all of the genes in E. coli that become essential in order to enact this adaptation, and by quantitative proteomics discovered that very few proteins need to be up- or down-regulated in steady-state levels in order to accommodate the longer Lpp. We observed increased levels of factors determining cell stiffness, a decrease in membrane integrity, an increased membrane vesiculation and a dependance on otherwise non-essential tethers to maintain lipid transport and peptidoglycan biosynthesis. Further this has implications for understanding how spatial constraint across the envelope controls processes such as flagellum-driven motility, cellular signaling, and protein translocation

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments

Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.

A Mycobacterial Systems Resource for the Research Community

Functional characterization of bacterial proteins lags far behind the identification of new protein families. This is especially true for bacterial species that are more difficult to grow and genetically manipulate than model systems such as Escherichia coli and Bacillus subtilis To facilitate functional characterization of mycobacterial proteins, we have established a Mycobacterial Systems Resource (MSR) using the model organism Mycobacterium smegmatis This resource focuses specifically on 1,153 highly conserved core genes that are common to many mycobacterial species, including Mycobacterium tuberculosis, in order to provide the most relevant information and resources for the mycobacterial research community. The MSR includes both biological and bioinformatic resources. The biological resource includes (i) an expression plasmid library of 1,116 genes fused to a fluorescent protein for determining protein localization; (ii) a library of 569 precise deletions of nonessential genes; and (iii) a set of 843 CRISPR-interference (CRISPRi) plasmids specifically targeted to silence expression of essential core genes and genes for which a precise deletion was not obtained. The bioinformatic resource includes information about individual genes and a detailed assessment of protein localization. We anticipate that integration of these initial functional analyses and the availability of the biological resource will facilitate studies of these core proteins in many Mycobacterium species, including the less experimentally tractable pathogens M. abscessus, M. avium, M. kansasii, M. leprae, M. marinum, M. tuberculosis, and M. ulceransIMPORTANCE Diseases caused by mycobacterial species result in millions of deaths per year globally, and present a substantial health and economic burden, especially in immunocompromised patients. Difficulties inherent in working with mycobacterial pathogens have hampered the development and application of high-throughput genetics that can inform genome annotations and subsequent functional assays. To facilitate mycobacterial research, we have created a biological and bioinformatic resource (https://msrdb.org/) using Mycobacterium smegmatis as a model organism. The resource focuses specifically on 1,153 proteins that are highly conserved across the mycobacterial genus and, therefore, likely perform conserved mycobacterial core functions. Thus, functional insights from the MSR will apply to all mycobacterial species. We believe that the availability of this mycobacterial systems resource will accelerate research throughout the mycobacterial research community.

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

G2G: A web-server for the prediction of human synthetic lethal interactions

Genetic interactions (GIs) are fundamental to our understanding of biological processes in the cell. While GIs have been systematically mapped in yeast, there is scarce information about them in humans. Recently, we have suggested a state-of-the-art hierarchical method that leverages gene ontology information for predicting GIs in yeast. Here, we adapt this method and apply it for the first time to predict GIs in human. We introduce a web service called G2G for this task that is available at http://bnet.cs.tau.ac.il/g2g/.

A framework for exhaustive modelling of genetic interaction patterns using Petri nets

MOTIVATION

Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs.

RESULTS

In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets.

AVAILABILITY AND IMPLEMENTATION

The framework is available at http://www.ibi.vu.nl/programs/ExhMod.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic and Chemical-Genetic Interactions Map Biogenesis and Permeability Determinants of the Outer Membrane of Escherichia coli

Gram-negative bacteria are intrinsically resistant to many antibiotics due to their outer membrane barrier. Although the outer membrane has been studied for decades, there is much to uncover about the biology and permeability of this complex structure. Investigating synthetic genetic interactions can reveal a great deal of information about genetic function and pathway interconnectivity. Here, we performed synthetic genetic arrays (SGAs) in Escherichia coli by crossing a subset of gene deletion strains implicated in outer membrane permeability with nonessential gene and small RNA (sRNA) deletion collections. Some 155,400 double-deletion strains were grown on rich microbiological medium with and without subinhibitory concentrations of two antibiotics excluded by the outer membrane, vancomycin and rifampin, to probe both genetic interactions and permeability. The genetic interactions of interest were synthetic sick or lethal (SSL) gene deletions that were detrimental to the cell in combination but had a negligible impact on viability individually. On average, there were ∼30, ∼36, and ∼40 SSL interactions per gene under no-drug, rifampin, and vancomycin conditions, respectively; however, many of these involved frequent interactors. Our data sets have been compiled into an interactive database called the Outer Membrane Interaction (OMI) Explorer, where genetic interactions can be searched, visualized across the genome, compared between conditions, and enriched for gene ontology (GO) terms. A set of SSL interactions revealed connectivity and permeability links between enterobacterial common antigen (ECA) and lipopolysaccharide (LPS) of the outer membrane. This data set provides a novel platform to generate hypotheses about outer membrane biology and permeability.IMPORTANCE Gram-negative bacteria are a major concern for public health, particularly due to the rise of antibiotic resistance. It is important to understand the biology and permeability of the outer membrane of these bacteria in order to increase the efficacy of antibiotics that have difficulty penetrating this structure. Here, we studied the genetic interactions of a subset of outer membrane-related gene deletions in the model Gram-negative bacterium E. coli We systematically combined these mutants with 3,985 nonessential gene and small RNA deletion mutations in the genome. We examined the viability of these double-deletion strains and probed their permeability characteristics using two antibiotics that have difficulty crossing the outer membrane barrier. An understanding of the genetic basis for outer membrane integrity can assist in the development of new antibiotics with favorable permeability properties and the discovery of compounds capable of increasing outer membrane permeability to enhance the activity of existing antibiotics.

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.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

CRISPR-based genomic tools for the manipulation of genetically intractable microorganisms

Genetic manipulation of microorganisms has been crucial in understanding their biology, yet for many microbial species, robust tools for comprehensive genetic analysis were lacking until the advent of CRISPR-Cas-based gene editing techniques. In this Progress article, we discuss advances in CRISPR-based techniques for the genetic analysis of genetically intractable microorganisms, with an emphasis on mycobacteria, fungi and parasites. We discuss how CRISPR-based analyses in these organisms have enabled the discovery of novel gene functions, the investigation of genetic interaction networks and the identification of virulence factors.

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.

FunCoup 4: new species, data, and visualization

This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.

Detecting genetic interactions using parallel evolution in experimental populations

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Use of genetic and chemical synthetic lethality as probes of complexity in bacterial cell systems

Different conditions and genomic contexts are known to have an impact on gene essentiality and interactions. Synthetic lethal interactions occur when a combination of perturbations, either genetic or chemical, result in a more profound fitness defect than expected based on the effect of each perturbation alone. Synthetic lethality in bacterial systems has long been studied; however, during the past decade, the emerging fields of genomics and chemical genomics have led to an increase in the scale and throughput of these studies. Here, we review the concepts of genomics and chemical genomics in the context of synthetic lethality and their revolutionary roles in uncovering novel biology such as the characterization of genes of unknown function and in antibacterial drug discovery. We provide an overview of the methodologies, examples and challenges of both genetic and chemical synthetic lethal screening platforms. Finally, we discuss how to apply genetic and chemical synthetic lethal approaches to rationalize the synergies of drugs, screen for new and improved antibacterial therapies and predict drug mechanism of action.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

Multiplexed deactivated CRISPR-Cas9 gene expression perturbations deter bacterial adaptation by inducing negative epistasis

The ever-increasing threat of multi-drug resistant bacteria, a shrinking antibiotic pipeline, and the innate ability of microorganisms to adapt necessitates long-term strategies to slow the evolution of antibiotic resistance. Here we develop an approach, dubbed Controlled Hindrance of Adaptation of OrganismS or CHAOS, involving induction of epistasis between gene perturbations to deter adaption. We construct a combinatorial library of multiplexed, deactivated CRISPR-Cas9 devices to systematically perturb gene expression in Escherichia coli. While individual perturbations improved fitness during antibiotic exposure, multiplexed perturbations caused large fitness loss in a significant epistatic fashion. Strains exhibiting epistasis adapted significantly more slowly over three to fourteen days, and loss in adaptive potential was shown to be sustainable. Finally, we show that multiplexed peptide nucleic acids increase the antibiotic susceptibility of clinically isolated Carbapenem-resistant E. coli in an epistatic fashion. Together, these results suggest a new therapeutic strategy for restricting the evolution of antibiotic resistance.

Peter Otoupal et al. present CHAOS, an approach for preventing the development of antibiotic resistance in E. coli through CRISPR-Cas9-based perturbation of gene expression. They show that multiplexed perturbations decrease fitness of clinically-isolated Carbapenem-resistant E. coli upon antibiotic exposure.

Evidence for interaction between Hsp90 and the ER membrane complex

Numerous putative heat shock protein 90 (Hsp90)-interacting proteins, which could represent novel folding clients or co-chaperones, have been identified in recent years. Two separate high-throughput screens in yeast uncovered genetic effects between Hsp90 and components of the ER membrane complex (EMC), which is required for tolerance to unfolded protein response stress in yeast. Herein, we provide the first experimental evidence supporting that there is a genuine interaction of Hsp90 with the EMC. We demonstrate genetic interactions between EMC2 and the known Hsp90 co-chaperone encoded by STI1, as well as Hsp90 point mutant allele-specific differences in inherent growth and Hsp90 inhibitor tolerance in the absence and presence of EMC2. In co-precipitation experiments, Hsp90 interacts with Emc2p, whether or not Emc2p contains amino acid sequences designated as a tetratricopeptide repeat motif. Yeast with multiple EMC gene deletions exhibit increased sensitivity to Hsp90 inhibitor as well as defective folding of the well-established Hsp90 folding client, the glucocorticoid receptor. Altogether, our evidence of physical, genetic, and functional interaction of Hsp90 with the EMC, as well as bioinformatic analysis of shared interactors, supports that there is a legitimate interaction between them in vivo.

Mapping the Genetic Landscape of Human Cells

Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

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.

Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence

Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP) tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function.

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TAP tag and purification of endogenous Arc protein complexes from the mouse brain

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PSD95 is the major Arc binding protein, and both assemble into 1.5-MDa supercomplexes

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PSD95 is essential for recruitment of Arc to synapses

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Mutations and genetic variants in Arc-PSD95 are linked to cognition

Testing the Domino Theory of Gene Loss in Buchnera aphidicola: The Relevance of Epistatic Interactions

The domino theory of gene loss states that when some particular gene loses its function and cripples a cellular function, selection will relax in all functionally related genes, which may allow for the non-functionalization and loss of these genes. Here we study the role of epistasis in determining the pattern of gene losses in a set of genes participating in cell envelope biogenesis in the endosymbiotic bacteria Buchnera aphidicola. We provide statistical evidence indicating pairs of genes in B. aphidicola showing correlated gene loss tend to have orthologs in Escherichia coli known to have alleviating epistasis. In contrast, pairs of genes in B. aphidicola not showing correlated gene loss tend to have orthologs in E. coli known to have aggravating epistasis. These results suggest that during the process of genome reduction in B. aphidicola by gene loss, positive or alleviating epistasis facilitates correlated gene losses while negative or aggravating epistasis impairs correlated gene losses. We interpret this as evidence that the reduced proteome of B. aphidicola contains less pathway redundancy and more compensatory interactions, mimicking the situation of E. coli when grown under environmental constrains.

Assembly pathway of a bacterial complex iron sulfur molybdoenzyme

Protein folding and assembly into macromolecule complexes within the living cell are complex processes requiring intimate coordination. The biogenesis of complex iron sulfur molybdoenzymes (CISM) requires use of a system specific chaperone - a redox enzyme maturation protein (REMP) - to help mediate final folding and assembly. The CISM dimethyl sulfoxide (DMSO) reductase is a bacterial oxidoreductase that utilizes DMSO as a final electron acceptor for anaerobic respiration. The REMP DmsD strongly interacts with DMSO reductase to facilitate folding, cofactor-insertion, subunit assembly and targeting of the multi-subunit enzyme prior to membrane translocation and final assembly and maturation into a bioenergetic catalytic unit. In this article, we discuss the biogenesis of DMSO reductase as an example of the participant network for bacterial CISM maturation pathways.

Computational Analysis of the Chaperone Interaction Networks

We provide computational protocols to identify chaperone interacting proteins using a combination of both physical (protein-protein) and genetic (gene-gene or epistatic) interaction data derived from the published large-scale proteomic and genomic studies for the budding yeast Saccharomyces cerevisiae. Using these datasets, we discuss bioinformatic analyses that can be employed to build comprehensive high-fidelity chaperone interaction networks. Given that many proteins typically function as complexes in the cell, we highlight various step-wise approaches for combining both the genetic and physical interaction datasets to decipher intra- and inter-connections for distinct chaperone- and non-chaperone-containing complexes in the network. Together, these informatics procedures will aid in identifying protein complexes with distinctive functional specializations in the cell that yield a very broad and diverse set of interactions. The described procedures can also be leveraged to datasets from other eukaryotes, including humans.

Global landscape of cell envelope protein complexes in Escherichia coli

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the β-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

Systematic Genetic Screens Reveal the Dynamic Global Functional Organization of the Bacterial Translation Machinery

Bacterial protein synthesis is an essential, conserved, and environmentally responsive process. Yet, many of its components and dependencies remain unidentified. To address this gap, we used quantitative synthetic genetic arrays to map functional relationships among >48,000 gene pairs in Escherichia coli under four culture conditions differing in temperature and nutrient availability. The resulting data provide global functional insights into the roles and associations of genes, pathways, and processes important for efficient translation, growth, and environmental adaptation. We predict and independently verify the requirement of unannotated genes for normal translation, including a previously unappreciated role of YhbY in 30S biogenesis. Dynamic changes in the patterns of genetic dependencies across the four growth conditions and data projections onto other species reveal overarching functional and evolutionary pressures impacting the translation system and bacterial fitness, underscoring the utility of systematic screens for investigating protein synthesis, adaptation, and evolution.

Top-Down LESA Mass Spectrometry Protein Analysis of Gram-Positive and Gram-Negative Bacteria

We have previously shown that liquid extraction surface analysis (LESA) mass spectrometry (MS) is a technique suitable for the top-down analysis of proteins directly from intact colonies of the Gram-negative bacterium Escherichia coli K-12. Here we extend the application of LESA MS to Gram-negative Pseudomonas aeruginosa PS1054 and Gram-positive Staphylococcus aureus MSSA476, as well as two strains of E. coli (K-12 and BL21 mCherry) and an unknown species of Staphylococcus. Moreover, we demonstrate the discrimination between three species of Gram-positive Streptococcus (Streptococcus pneumoniae D39, and the viridans group Streptococcus oralis ATCC 35037 and Streptococcus gordonii ATCC35105), a recognized challenge for matrix-assisted laser desorption ionization time-of-flight MS. A range of the proteins detected were selected for top-down LESA MS/MS. Thirty-nine proteins were identified by top-down LESA MS/MS, including 16 proteins that have not previously been observed by any other technique. The potential of LESA MS for classification and characterization of novel species is illustrated by the de novo sequencing of a new protein from the unknown species of Staphylococcus. Graphical Abstract ᅟ.

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.

Application of Distributive Conjugal DNA Transfer in Mycobacterium smegmatis To Establish a Genome-Wide Synthetic Genetic Array

Genetic redundancy can obscure phenotypic effects of single-gene mutations. Two individual mutations may be viable separately but are lethal when combined, thus synthetically linking the two gene products in an essential process. Synthetic genetic arrays (SGAs), in which defined mutations are combined, provide a powerful approach to identify novel genetic interactions and redundant pathways. A genome-scale SGA can offer an initial assignment of function to hypothetical genes by uncovering interactions with known genes or pathways. Here, we take advantage of the chromosomal conjugation system of Mycobacterium smegmatis to combine individual donor and recipient mutations on a genome-wide scale. We demonstrated the feasibility of a high-throughput mycobacterial SGA (mSGA) screen by using mutants of esx3, fxbA, and recA as query genes, which were combined with an arrayed library of transposon mutants by conjugation. The mSGA identified interacting genes that we had predicted and, most importantly, identified novel interacting genes-encoding both proteins and a noncoding RNA (ncRNA). In combination with other molecular genetic approaches, the mSGA has great potential to both reduce the high number of conserved hypothetical protein annotations in mycobacterial genomes and further define mycobacterial pathways and gene interactions.IMPORTANCE Mycobacterium smegmatis is the model organism of choice for the study of mycobacterial pathogens, because it is a fast-growing nonpathogenic species harboring many genes that are conserved throughout mycobacteria. In this work, we describe a synthetic genetic array (mSGA) approach for M. smegmatis, which combines mutations on a genome-wide scale with high efficiency. Analysis of the double mutant strains enables the identification of interacting genes and pathways that are normally hidden by redundant biological pathways. The mSGA is a powerful genetic tool that enables functions to be assigned to the many conserved hypothetical genes found in all mycobacterial species.