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

Reduced peptidoglycan synthesis capacity impairs growth of E. coli at high salt concentration

Gram-negative bacteria have a thin peptidoglycan layer between the cytoplasmic and outer membranes protecting the cell from osmotic challenges. Hydrolases of this structure are needed to cleave bonds to allow the newly synthesized peptidoglycan strands to be inserted by synthases. These enzymes need to be tightly regulated and their activities coordinated to prevent cell lysis. To better understand this process in Escherichia coli, we probed the genetic interactions of mrcA (encodes PBP1A) and mrcB (encodes PBP1B) with genes encoding peptidoglycan amidases and endopeptidases in envelope stress conditions. Our extensive genetic interaction network analysis revealed relatively few combinations of hydrolase gene deletions with reduced fitness in the absence of PBP1A or PBP1B, showing that none of the amidases or endopeptidases is strictly required for the functioning of one of the class A PBPs. This illustrates the robustness of the peptidoglycan growth mechanism. However, we discovered that the fitness of ∆mrcB cells is significantly reduced under high salt stress and in vitro activity assays suggest that this phenotype is caused by a reduced peptidoglycan synthesis activity of PBP1A at high salt concentration.IMPORTANCEEscherichia coli and many other bacteria have a surprisingly high number of peptidoglycan hydrolases. These enzymes function in concert with synthases to facilitate the expansion of the peptidoglycan sacculus under a range of growth and stress conditions. The synthases PBP1A and PBP1B both contribute to peptidoglycan expansion during cell division and growth. Our genetic interaction analysis revealed that these two penicillin-binding proteins (PBPs) do not need specific amidases, endopeptidases, or lytic transglycosylases for function. We show that PBP1A and PBP1B do not work equally well when cells encounter high salt stress and demonstrate that PBP1A alone cannot provide sufficient PG synthesis activity under this condition. These results show how the two class A PBPs and peptidoglycan hydrolases govern cell envelope integrity in E. coli in response to environmental challenges and particularly highlight the importance of PBP1B in maintaining cell fitness under high salt conditions.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

Randomly barcoded transposon mutant libraries for gut commensals II: Applying libraries for functional genetics

The critical role of the intestinal microbiota in human health and disease is well recognized. Nevertheless, there are still large gaps in our understanding of the functions and mechanisms encoded in the genomes of most members of the gut microbiota. Genome-scale libraries of transposon mutants are a powerful tool to help us address this gap. Recent advances in barcoded transposon mutagenesis have dramatically lowered the cost of mutant fitness determination in hundreds of in vitro and in vivo experimental conditions. In an accompanying review, we discuss recent advances and caveats for the construction of pooled and arrayed barcoded transposon mutant libraries in human gut commensals. In this review, we discuss how these libraries can be used across a wide range of applications, the technical aspects involved, and expectations for such screens.

A high-throughput and low-waste viability assay for microbes

Counting viable cells is a universal practice in microbiology. The colony-forming unit (CFU) assay has remained the gold standard to measure viability across disciplines, but it is time-intensive and resource-consuming. Here we describe the geometric viability assay (GVA) that replicates CFU measurements over 6 orders of magnitude while reducing over 10-fold the time and consumables required. GVA computes a sample's viable cell count on the basis of the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with Gram-positive and Gram-negative planktonic bacteria (Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis), biofilms and fungi (Saccharomyces cerevisiae). Laborious CFU experiments such as checkerboard assays, treatment time-courses and drug screens against slow-growing cells are simplified by GVA. The ease and low cost of GVA evinces that it can replace existing viability assays and enable viability measurements at previously impractical scales.

High-throughput functional genomics: A (myco)bacterial perspective

Advances in sequencing technologies have enabled unprecedented insights into bacterial genome composition and dynamics. However, the disconnect between the rapid acquisition of genomic data and the (much slower) confirmation of inferred genetic function threatens to widen unless techniques for fast, high-throughput functional validation can be applied at scale. This applies equally to Mycobacterium tuberculosis, the leading infectious cause of death globally and a pathogen whose genome, despite being among the first to be sequenced two decades ago, still contains many genes of unknown function. Here, we summarize the evolution of bacterial high-throughput functional genomics, focusing primarily on transposon (Tn)-based mutagenesis and the construction of arrayed mutant libraries in diverse bacterial systems. We also consider the contributions of CRISPR interference as a transformative technique for probing bacterial gene function at scale. Throughout, we situate our analysis within the context of functional genomics of mycobacteria, focusing specifically on the potential to yield insights into M. tuberculosis pathogenicity and vulnerabilities for new drug and regimen development. Finally, we offer suggestions for future approaches that might be usefully applied in elucidating the complex cellular biology of this major human pathogen.

Tools of the Trade: Image Analysis Programs for Confocal Laser-Scanning Microscopy Studies of Biofilms and Considerations for Their Use by Experimental Researchers

Confocal laser-scanning microscopy (CLSM) is the bedrock of the microscopic visualization of biofilms. Previous applications of CLSM in biofilm studies have largely focused on observations of bacterial or fungal elements of biofilms, often seen as aggregates or mats of cells. However, the field of biofilm research is moving beyond qualitative observations alone, toward the quantitative analysis of the structural and functional features of biofilms, across clinical, environmental, and laboratory conditions. In recent times, several image analysis programs have been developed to extract and quantify biofilm properties from confocal micrographs. These tools not only vary in their scope and relevance to the specific biofilm features under study but also with respect to the user interface, compatibility with operating systems, and raw image requirements. Understanding these considerations is important when selecting tools for quantitative biofilm analysis, including at the initial experimental stages of image acquisition. In this review, we provide an overview of image analysis programs for confocal micrographs of biofilms, with a focus on tool selection and image acquisition parameters that are relevant for experimental researchers to ensure reliability and compatibility with downstream image processing.

Quantitative approaches to study phenotypic effects of large-scale genetic perturbations

How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

High-throughput functional profiling of the human fungal pathogen Candida albicans genome

Candida albicans is a major fungal pathogen of humans. Although its genome has been sequenced more than two decades ago, there are still over 4300 uncharacterized C. albicans genes. We previously generated an ORFeome as well as a collection of destination vectors to facilitate overexpression of C. albicans ORFs. Here, we report the construction of ∼2500 overexpression mutants and their evaluation by in vitro spotting on rich medium and in a liquid pool experiment in rich medium, allowing the identification of genes whose overexpression has a fitness cost. The candidates were further validated at the individual strain level. This new resource allows large-scale screens in different growth conditions to be performed routinely. Altogether, based on the concept of identifying functionally related genes by cluster analysis, the availability of this overexpression mutant collection will facilitate the characterization of gene functions in C. albicans.

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.

Mirubactin C rescues the lethal effect of cell wall biosynthesis mutations in Bacillus subtilis

Growth of most rod-shaped bacteria is accompanied by the insertion of new peptidoglycan into the cylindrical cell wall. This insertion, which helps maintain and determine the shape of the cell, is guided by a protein machine called the rod complex or elongasome. Although most of the proteins in this complex are essential under normal growth conditions, cell viability can be rescued, for reasons that are not understood, by the presence of a high (mM) Mg2+ concentration. We screened for natural product compounds that could rescue the growth of mutants affected in rod-complex function. By screening > 2,000 extracts from a diverse collection of actinobacteria, we identified a compound, mirubactin C, related to the known iron siderophore mirubactin A, which rescued growth in the low micromolar range, and this activity was confirmed using synthetic mirubactin C. The compound also displayed toxicity at higher concentrations, and this effect appears related to iron homeostasis. However, several lines of evidence suggest that the mirubactin C rescuing activity is not due simply to iron sequestration. The results support an emerging view that the functions of bacterial siderophores extend well beyond simply iron binding and uptake.

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.

Continuous Production of Human Epidermal Growth Factor Using Escherichia coli Biofilm

Increasing demand for recombinant proteins necessitates efficient protein production processes. In this study, a continuous process for human epidermal growth factor (hEGF) secretion by Escherichia coli was developed by taking advantage of biofilm formation. Genes bcsB, fimH, and csgAcsgB that have proved to facilitate biofilm formation and some genes moaE, yceA, ychJ, and gshB potentially involved in biofilm formation were examined for their effects on hEGF secretion as well as biofilm formation. Finally, biofilm-based fermentation processes were established, which demonstrated the feasibility of continuous production of hEGF with improved efficiency. The best result was obtained from ychJ-disruption that showed a 28% increase in hEGF secretion over the BL21(DE3) wild strain, from 24 to 32 mg/L. Overexpression of bcsB also showed great potential in continuous immobilized fermentation. Overall, the biofilm engineering here represents an effective strategy to improve hEGF production and can be adapted to produce more recombinant proteins in future.

High-Throughput, High-Precision Colony Phenotyping with Pyphe

Colony fitness screens are powerful approaches for functional genomics and genetics. This protocol describes experimental and computational procedures for assaying the fitness of thousands of microbial strains in numerous conditions in parallel. Data analysis is based on pyphe, an all-in-one bioinformatics toolbox for scanning, image analysis, data normalization, and interpretation. We describe a standard protocol where endpoint colony areas are used as fitness proxy and two variations on this, one using colony growth curves and one using colony viability staining with phloxine B. Different strategies for experimental design, normalization and quality control are discussed. Using these approaches, it is possible to collect hundreds of thousands of data points, with low technical noise levels around 5%, in an experiment typically lasting 2 weeks or less.

Advances and opportunities in image analysis of bacterial cells and communities

The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.

Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated RNAP mechanism, and RNAP mutations drive Escherichia coli adaptation to many health- and industry-relevant environments, yet a paucity of systematic analyses hampers our understanding of the fitness trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and prevent killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships in vivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.

The World of Stable Ribonucleoproteins and Its Mapping With Grad-Seq and Related Approaches

Macromolecular complexes of proteins and RNAs are essential building blocks of cells. These stable supramolecular particles can be viewed as minimal biochemical units whose structural organization, i.e., the way the RNA and the protein interact with each other, is directly linked to their biological function. Whether those are dynamic regulatory ribonucleoproteins (RNPs) or integrated molecular machines involved in gene expression, the comprehensive knowledge of these units is critical to our understanding of key molecular mechanisms and cell physiology phenomena. Such is the goal of diverse complexomic approaches and in particular of the recently developed gradient profiling by sequencing (Grad-seq). By separating cellular protein and RNA complexes on a density gradient and quantifying their distributions genome-wide by mass spectrometry and deep sequencing, Grad-seq charts global landscapes of native macromolecular assemblies. In this review, we propose a function-based ontology of stable RNPs and discuss how Grad-seq and related approaches transformed our perspective of bacterial and eukaryotic ribonucleoproteins by guiding the discovery of new RNA-binding proteins and unusual classes of noncoding RNAs. We highlight some methodological aspects and developments that permit to further boost the power of this technique and to look for exciting new biology in understudied and challenging biological models.

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

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.

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.

Structure of dual BON-domain protein DolP identifies phospholipid binding as a new mechanism for protein localisation

The Gram-negative outer-membrane envelops the bacterium and functions as a permeability barrier against antibiotics, detergents, and environmental stresses. Some virulence factors serve to maintain the integrity of the outer membrane, including DolP (formerly YraP) a protein of unresolved structure and function. Here, we reveal DolP is a lipoprotein functionally conserved amongst Gram-negative bacteria and that loss of DolP increases membrane fluidity. We present the NMR solution structure for Escherichia coli DolP, which is composed of two BON domains that form an interconnected opposing pair. The C-terminal BON domain binds anionic phospholipids through an extensive membrane:protein interface. This interaction is essential for DolP function and is required for sub-cellular localisation of the protein to the cell division site, providing evidence of subcellular localisation of these phospholipids within the outer membrane. The structure of DolP provides a new target for developing therapies that disrupt the integrity of the bacterial cell envelope.

The functional proteome landscape of Escherichia coli

Recent developments in high-throughput reverse genetics^1,2 have revolutionized our ability to map gene function and interactions^3–6. The power of these approaches lies on their ability to discover functionally-associated genes, which elicit similar phenotypic changes across multiple perturbations (chemical, environmental, or genetic) when knocked out^7–9. However, due to the large number of perturbations, these approaches have been limited to growth or morphological readouts¹⁰. Here, we have used a high-content biochemical readout, thermal proteome profiling¹¹, to measure proteome-wide abundance and thermal stability of 121 genetic perturbations in Escherichia coli. We observed that thermal stability, and therefore the state and interactions of essential proteins is commonly modulated, opening up the possibility to study a protein group that is particularly inaccessible to genetics. We show that functionally-associated proteins have coordinated abundance and thermal stability changes across perturbations, due to their co-regulation and physical interactions (with proteins, metabolites, or co-factors). Finally, we provide mechanistic insights into previously determined growth phenotypes¹² that go beyond the deleted gene. These data, available at http://ecoliTPP.shiny.embl.de, represent a rich resource for inferring protein functions and interactions.

Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes

Mycobacterium tuberculosis possesses a large number of genes of unknown or predicted function, undermining fundamental understanding of pathogenicity and drug susceptibility. To address this challenge, we developed a high-throughput functional genomics approach combining inducible CRISPR-interference and image-based analyses of morphological features and sub-cellular chromosomal localizations in the related non-pathogen, M. smegmatis. Applying automated imaging and analysis to 263 essential gene knockdown mutants in an arrayed library, we derive robust, quantitative descriptions of bacillary morphologies consequent on gene silencing. Leveraging statistical-learning, we demonstrate that functionally related genes cluster by morphotypic similarity and that this information can be used to inform investigations of gene function. Exploiting this observation, we infer the existence of a mycobacterial restriction-modification system, and identify filamentation as a defining mycobacterial response to histidine starvation. Our results support the application of large-scale image-based analyses for mycobacterial functional genomics, simultaneously establishing the utility of this approach for drug mechanism-of-action studies.

Domestication of the Emblematic White Cheese-Making Fungus Penicillium camemberti and Its Diversification into Two Varieties

Domestication involves recent adaptation under strong human selection and rapid diversification and therefore constitutes a good model for studies of these processes. We studied the domestication of the emblematic white mold Penicillium camemberti, used for the maturation of soft cheeses, such as Camembert and Brie, about which surprisingly little was known, despite its economic and cultural importance. Whole-genome-based analyses of genetic relationships and diversity revealed that an ancient domestication event led to the emergence of the gray-green P. biforme mold used in cheese making, by divergence from the blue-green wild P. fuscoglaucum fungus. Another much more recent domestication event led to the generation of the P. camemberti clonal lineage as a sister group to P. biforme. Penicillium biforme displayed signs of phenotypic adaptation to cheese making relative to P. fuscoglaucum, in terms of whiter color, faster growth on cheese medium under cave conditions, lower amounts of toxin production, and greater ability to prevent the growth of other fungi. The P. camemberti lineage displayed even stronger signs of domestication for all these phenotypic features. We also identified two differentiated P. camemberti varieties, apparently associated with different kinds of cheeses and with contrasted phenotypic features in terms of color, growth, toxin production, and competitive ability. We have thus identified footprints of domestication in these fungi, with genetic differentiation between cheese and wild populations, bottlenecks, and specific phenotypic traits beneficial for cheese making. This study has not only fundamental implications for our understanding of domestication but can also have important effects on cheese making.

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

CRISPR interference-based gene repression in the plant growth promoter Paenibacillus sonchi genomovar Riograndensis SBR5

Gene repression using the endonucleolytically deactivated dCas9 protein and sgRNAs (CRISPR interference or CRISPRi) is a useful approach to study gene functions. Here, we established CRISPRi in Paenibacillus sonchi genomovar Riograndensis SBR5, a plant growth promoting bacterium. CRISPRi system with sgRNAs targeting SBR5 endogenous genes spo0A, yaaT and ydjJ and plasmid-borne gfpUV was constructed and analyzed. Flow cytometry analysis revealed a significant decrease of reporter protein GFPUV signal in P. sonchi strains expressing gfpUV sgRNA in comparison with non-targeting controls. CRISPRi-based repression of chromosomal genes for regulation of sporulation spo0A and yaaT decreased sporulation and increased biofilm formation in SBR5. Repression of the sorbitol catabolic gene ydjJ revealed decreased specific activity of YdjJ in crude cell extracts and reduced biomass formation from sorbitol in growth experiments. Our work on CRISPRi-based gene repression serves as basis for gene function studies of the plant growth promoter P. sonchi SBR5. To our knowledge, the present study presents the first tool for gene repression established in Paenibacillus species.Key points• CRISPRi toward gene repression was applied for the first time in Paenibacillus.• CRISPRi of spo0A and yaaT depleted spores and increased biofilms in SBR5.• CRISPRi-based ydjJ repression decreased specific activity of sorbitol dehydrogenase.

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.

An African Salmonella Typhimurium ST313 sublineage with extensive drug-resistance and signatures of host adaptation

Bloodstream infections by Salmonella enterica serovar Typhimurium constitute a major health burden in sub-Saharan Africa (SSA). These invasive non-typhoidal (iNTS) infections are dominated by isolates of the antibiotic resistance-associated sequence type (ST) 313. Here, we report emergence of ST313 sublineage II.1 in the Democratic Republic of the Congo. Sublineage II.1 exhibits extensive drug resistance, involving a combination of multidrug resistance, extended spectrum β-lactamase production and azithromycin resistance. ST313 lineage II.1 isolates harbour an IncHI2 plasmid we name pSTm-ST313-II.1, with one isolate also exhibiting decreased ciprofloxacin susceptibility. Whole genome sequencing reveals that ST313 II.1 isolates have accumulated genetic signatures potentially associated with altered pathogenicity and host adaptation, related to changes observed in biofilm formation and metabolic capacity. Sublineage II.1 emerged at the beginning of the 21st century and is involved in on-going outbreaks. Our data provide evidence of further evolution within the ST313 clade associated with iNTS in SSA.

Matrix Linear Models for High-Throughput Chemical Genetic Screens

We develop a flexible and computationally efficient approach for analyzing high-throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. Typically, interactions between genes and stresses are detected by grouping the mutants and stresses into categories, and performing modified t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or nonoverlapping annotations (e.g., if conditions have doses or a mutant falls into more than one category simultaneously). We develop a matrix linear model (MLM) framework that allows us to model relationships between mutants and conditions in a simple, yet flexible, multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. We develop a fast estimation algorithm that takes advantage of the structure of MLMs. We evaluate our method's performance in simulations and in an Escherichia coli chemical genetic screen, comparing it with an existing univariate approach based on modified t-tests. We show that MLMs perform slightly better than the univariate approach when mutants and conditions are classified in nonoverlapping categories, and substantially better when conditions can be ordered in dosage categories. Therefore, it is an attractive alternative to current methods, and provides a computationally scalable framework for larger and complex chemical genetic screens. A Julia language implementation of MLMs and the code used for this paper are available at https://github.com/janewliang/GeneticScreen.jl and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

Transcriptional noise and exaptation as sources for bacterial sRNAs

Understanding how new genes originate and integrate into cellular networks is key to understanding evolution. Bacteria present unique opportunities for both the natural history and experimental study of gene origins, due to their large effective population sizes, rapid generation times, and ease of genetic manipulation. Bacterial small non-coding RNAs (sRNAs), in particular, many of which operate through a simple antisense regulatory logic, may serve as tractable models for exploring processes of gene origin and adaptation. Understanding how and on what timescales these regulatory molecules arise has important implications for understanding the evolution of bacterial regulatory networks, in particular, for the design of comparative studies of sRNA function. Here, we introduce relevant concepts from evolutionary biology and review recent work that has begun to shed light on the timescales and processes through which non-functional transcriptional noise is co-opted to provide regulatory functions. We explore possible scenarios for sRNA origin, focusing on the co-option, or exaptation, of existing genomic structures which may provide protected spaces for sRNA evolution.

Enabling genetic analysis of diverse bacteria with Mobile-CRISPRi

The vast majority of bacteria, including human pathogens and microbiome species, lack genetic tools needed to systematically associate genes with phenotypes. This is the major impediment to understanding the fundamental contributions of genes and gene networks to bacterial physiology and human health. Clustered regularly interspaced short palindromic repeats interference (CRISPRi), a versatile method of blocking gene expression using a catalytically inactive Cas9 protein (dCas9) and programmable single guide RNAs, has emerged as a powerful genetic tool to dissect the functions of essential and non-essential genes in species ranging from bacteria to humans1-6. However, the difficulty of establishing effective CRISPRi systems across bacteria is a major barrier to its widespread use to dissect bacterial gene function. Here, we establish 'Mobile-CRISPRi', a suite of CRISPRi systems that combines modularity, stable genomic integration and ease of transfer to diverse bacteria by conjugation. Focusing predominantly on human pathogens associated with antibiotic resistance, we demonstrate the efficacy of Mobile-CRISPRi in gammaproteobacteria and Bacillales Firmicutes at the individual gene scale, by examining drug-gene synergies, and at the library scale, by systematically phenotyping conditionally essential genes involved in amino acid biosynthesis. Mobile-CRISPRi enables genetic dissection of non-model bacteria, facilitating analyses of microbiome function, antibiotic resistances and sensitivities, and comprehensive screens for host-microorganism interactions.

A resource of variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure-based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

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.

Induced conformational changes activate the peptidoglycan synthase PBP1B

Bacteria surround their cytoplasmic membrane with an essential, stress‐bearing peptidoglycan (PG) layer consisting of glycan chains linked by short peptides into a mesh‐like structure. Growing and dividing cells expand their PG layer using inner‐membrane anchored PG synthases, including Penicillin‐binding proteins (PBPs), which participate in dynamic protein complexes to facilitate cell wall growth. In Escherichia coli, and presumably other Gram‐negative bacteria, growth of the mainly single layered PG is regulated by outer membrane‐anchored lipoproteins. The lipoprotein LpoB is required to activate PBP1B, which is a major, bi‐functional PG synthase with glycan chain polymerising (glycosyltransferase) and peptide cross‐linking (transpeptidase) activities. In this work we show how the binding of LpoB to the regulatory UB2H domain of PBP1B activates both activities. Binding induces structural changes in the UB2H domain, which transduce to the two catalytic domains by distinct allosteric pathways. We also show how an additional regulator protein, CpoB, is able to selectively modulate the TPase activation by LpoB without interfering with GTase activation.

Species-specific activity of antibacterial drug combinations

The spread of antimicrobial resistance has become a serious public health concern, making once-treatable diseases deadly again and undermining the achievements of modern medicine^1,2. Drug combinations can help to fight multi-drug-resistant bacterial infections, yet they are largely unexplored and rarely used in clinics. Here we profile almost 3,000 dose-resolved combinations of antibiotics, human-targeted drugs and food additives in six strains from three Gram-negative pathogens-Escherichia coli, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa-to identify general principles for antibacterial drug combinations and understand their potential. Despite the phylogenetic relatedness of the three species, more than 70% of the drug-drug interactions that we detected are species-specific and 20% display strain specificity, revealing a large potential for narrow-spectrum therapies. Overall, antagonisms are more common than synergies and occur almost exclusively between drugs that target different cellular processes, whereas synergies are more conserved and are enriched in drugs that target the same process. We provide mechanistic insights into this dichotomy and further dissect the interactions of the food additive vanillin. Finally, we demonstrate that several synergies are effective against multi-drug-resistant clinical isolates in vitro and during infections of the larvae of the greater wax moth Galleria mellonella, with one reverting resistance to the last-resort antibiotic colistin.

Gene flow contributes to diversification of the major fungal pathogen Candida albicans

Elucidating population structure and levels of genetic diversity and recombination is necessary to understand the evolution and adaptation of species. Candida albicans is the second most frequent agent of human fungal infections worldwide, causing high-mortality rates. Here we present the genomic sequences of 182 C. albicans isolates collected worldwide, including commensal isolates, as well as ones responsible for superficial and invasive infections, constituting the largest dataset to date for this major fungal pathogen. Although, C. albicans shows a predominantly clonal population structure, we find evidence of gene flow between previously known and newly identified genetic clusters, supporting the occurrence of (para)sexuality in nature. A highly clonal lineage, which experimentally shows reduced fitness, has undergone pseudogenization in genes required for virulence and morphogenesis, which may explain its niche restriction. Candida albicans thus takes advantage of both clonality and gene flow to diversify.

The fungal pathogen Candida albicans can undergo a parasexual process that may contribute to genetic diversity, but its actual relevance is unclear. Here, Ropars et al. analyse the genomic sequences of 182 C. albicans isolates collected worldwide and find evidence of gene flow and thus parasexuality in nature.

Extensive impact of non-antibiotic drugs on human gut bacteria

A few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.

Phenotype inference in an Escherichia coli strain panel

Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.

Chemical genomics reveals mechanistic hypotheses for uncharacterized bioactive molecules in bacteria

In an effort to combat the perpetual emergence of new antibiotic-resistant human pathogens, research in industry and academe aims to find new means of controlling infection. The discovery of new antimicrobial chemicals is not the bottleneck in an era where high-throughput screening rapidly uncovers new bioactive compounds. Rather, the rate-limiting step in antimicrobial discovery pipelines is identifying mechanisms of action (MOA) of bioactive molecules produced by these increasingly large-scale efforts. Chemical genomics has proven to be of high value in providing mechanistic hypotheses for novel bioactive chemical matter. Several techniques fall under this blanket term, including interactions with deletion or transposon libraries, fluorescent or luminescent reporter library profiles, or deep sequencing approaches. Each of these provide unique and complementary outputs, and have high value in generating target lists for chemical screens, or assisting in downstream MOA discovery. We review here the broad usefulness of this technique to aid in MOA determination, to identify targets for new lead molecules, and to expand our mechanistic understanding of existing drugs.

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.

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.