Anti-CRISPR Anopheles mosquitoes inhibit gene drive spread under challenging behavioural conditions in large cages.

CRISPR-based gene drives have the potential to spread within populations and are considered as promising vector control tools. A doublesex-targeting gene drive was able to suppress laboratory Anopheles mosquito populations in small and large cages, and it is considered for field application. Challenges related to the field-use of gene drives and the evolving regulatory framework suggest that systems able to modulate or revert the action of gene drives, could be part of post-release risk-mitigation plans. In this study, we challenge an AcrIIA4-based anti-drive to inhibit gene drive spread in age-structured Anopheles gambiae population under complex feeding and behavioural conditions. A stochastic model predicts the experimentally-observed genotype dynamics in age-structured populations in medium-sized cages and highlights the necessity of large-sized cage trials. These experiments and experimental-modelling framework demonstrate the effectiveness of the anti-drive in different scenarios, providing further corroboration for its use in controlling the spread of gene drive in Anopheles.

Reduced ambiguity and improved interpretability of bacterial genome-wide associations using gene-cluster-centric k-mers.

The wide adoption of bacterial genome sequencing and encoding both core and accessory genome variation using k-mers has allowed bacterial genome-wide association studies (GWAS) to identify genetic variants associated with relevant phenotypes such as those linked to infection. Significant limitations still remain because of k-mers being duplicated across gene clusters and as far as the interpretation of association results is concerned, which affects the wider adoption of GWAS methods on microbial data sets. We have developed a simple computational method (panfeed) that explicitly links each k-mer to their gene cluster at base-resolution level, which allows us to avoid biases introduced by a global de Bruijn graph as well as more easily map and annotate associated variants. We tested panfeed on two independent data sets, correctly identifying previously characterized causal variants, which demonstrates the precision of the method, as well as its scalable performance. panfeed is a command line tool written in the python programming language and is available at https://github.com/microbial-pangenomes-lab/panfeed.

The bacterial genetic determinants of Escherichia coli capacity to cause bloodstream infections in humans.

Escherichia coli is both a highly prevalent commensal and a major opportunistic pathogen causing bloodstream infections (BSI). A systematic analysis characterizing the genomic determinants of extra-intestinal pathogenic vs. commensal isolates in human populations, which could inform mechanisms of pathogenesis, diagnostic, prevention and treatment is still lacking. We used a collection of 912 BSI and 370 commensal E. coli isolates collected in France over a 17-year period (2000-2017). We compared their pangenomes, genetic backgrounds (phylogroups, STs, O groups), presence of virulence-associated genes (VAGs) and antimicrobial resistance genes, finding significant differences in all comparisons between commensal and BSI isolates. A machine learning linear model trained on all the genetic variants derived from the pangenome and controlling for population structure reveals similar differences in VAGs, discovers new variants associated with pathogenicity (capacity to cause BSI), and accurately classifies BSI vs. commensal strains. Pathogenicity is a highly heritable trait, with up to 69% of the variance explained by bacterial genetic variants. Lastly, complementing our commensal collection with an older collection from 1980, we predict that pathogenicity continuously increased through 1980, 2000, to 2010. Together our findings imply that E. coli exhibit substantial genetic variation contributing to the transition between commensalism and pathogenicity and that this species evolved towards higher pathogenicity.

Epistatic interactions between the high pathogenicity island and other iron uptake systems shape Escherichia coli extra-intestinal virulence.

The intrinsic virulence of extra-intestinal pathogenic Escherichia coli is associated with numerous chromosomal and/or plasmid-borne genes, encoding diverse functions such as adhesins, toxins, and iron capture systems. However, the respective contribution to virulence of those genes seems to depend on the genetic background and is poorly understood. Here, we analyze genomes of 232 strains of sequence type complex STc58 and show that virulence (quantified in a mouse model of sepsis) emerged in a sub-group of STc58 due to the presence of the siderophore-encoding high-pathogenicity island (HPI). When extending our genome-wide association study to 370 Escherichia strains, we show that full virulence is associated with the presence of the aer or sit operons, in addition to the HPI. The prevalence of these operons, their co-occurrence and their genomic location depend on strain phylogeny. Thus, selection of lineage-dependent specific associations of virulence-associated genes argues for strong epistatic interactions shaping the emergence of virulence in E. coli.

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

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

The DNA cytosine methylome revealed two methylation motifs in the upstream regions of genes related to morphological and physiological differentiation in Streptomyces coelicolor A(3)2 M145.

DNA methylation is an epigenetic modification detected in both prokaryotic and eukaryotic genomic DNAs. In bacteria, the importance of 5-methylcytosine (m5C) in gene expression has been less investigated than in eukaryotic systems. Through dot-blot analysis employing m5C antibodies against chromosomal DNA, we have previously demonstrated that m5C influences the differentiation of Streptomyces coelicolor A(3)2 M145 in solid sporulating and liquid non-sporulating complex media. Here, we mapped the methylated cytosines of the M145 strain growing in the defined Maltose Glutamate (MG) liquid medium. Sequencing of the M145 genome after bisulfite treatment (BS-sequencing) evidenced 3360 methylated cytosines and the two methylation motifs, GGCmCGG and GCCmCG, in the upstream regions of 321 genes. Besides, the role of cytosine methylation was investigated using the hypo-methylating agent 5'-aza-2'-deoxycytidine (5-aza-dC) in S. coelicolor cultures, demonstrating that m5C affects both growth and antibiotic biosynthesis. Finally, quantitative reverse-transcription polymerase-chain-reaction (RT-qPCR) analysis of genes containing the methylation motifs in the upstream regions showed that 5-aza-dC treatment influenced their transcriptional levels and those of the regulatory genes for two antibiotics. To the best of our knowledge, this is the first study that reports the cytosine methylome of S. coelicolor M145, supporting the crucial role ascribed to cytosine methylation in controlling bacterial gene expression.

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.

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome.

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

High-throughput functional characterization of protein phosphorylation sites in yeast.

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, fewer than 5% of thousands of recently discovered phosphosites have been functionally annotated. In this study, we devised a chemical genetic approach to study the functional relevance of phosphosites in Saccharomyces cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion, suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale.

A Code of Ethics for Gene Drive Research.

Gene drives hold promise for use in controlling insect vectors of diseases, agricultural pests, and for conservation of ecosystems against invasive species. At the same time, this technology comes with potential risks that include unknown downstream effects on entire ecosystems as well as the accidental or nefarious spread of organisms that carry the gene drive machinery. A code of ethics can be a useful tool for all parties involved in the development and regulation of gene drives and can be used to help ensure that a balanced analysis of risks, benefits, and values is taken into consideration in the interest of society and humanity. We have developed a code of ethics for gene drive research with the hope that this code will encourage the development of an international framework that includes ethical guidance of gene drive research and is incorporated into scientific practice by gaining broad agreement and adherence.

Tn-Core: Functionally Interpreting Transposon-Sequencing Data with Metabolic Network Analysis.

Transposon-sequencing (Tn-seq) is a powerful tool facilitating the genome-scale identification of genes required for bacterial growth or survival in an environment of interest. However, Tn-seq suffers from two primary drawbacks: (1) genetic interactions masking phenotypes thereby resulting in important cellular functions remaining undiscovered and (2) a difficulty in easily going from a list of essential genes to a functional understanding of cell physiology. Tn-Core is a computational toolbox to help overcome these limitations through combining the output of Tn-seq studies with in silico genome-scale metabolic networks. In this chapter, we outline how to use Tn-Core to contextualize Tn-seq data (and optionally RNA-seq data) with metabolic models to: (1) generate a complete view of essential metabolism, (2) prepare context-specific metabolic models for further computational analyses, and (3) refine genome-scale metabolic models. All functions of Tn-Core are provided for download from a freely available repository ( github.com/diCenzo-GC/Tn-Core ), and a web-app requiring limited computational experience is also available ( combo.dbe.unifi.it /tncore).

Major role of iron uptake systems in the intrinsic extra-intestinal virulence of the genus Escherichia revealed by a genome-wide association study.

The genus Escherichia is composed of several species and cryptic clades, including E. coli, which behaves as a vertebrate gut commensal, but also as an opportunistic pathogen involved in both diarrheic and extra-intestinal diseases. To characterize the genetic determinants of extra-intestinal virulence within the genus, we carried out an unbiased genome-wide association study (GWAS) on 370 commensal, pathogenic and environmental strains representative of the Escherichia genus phylogenetic diversity and including E. albertii (n = 7), E. fergusonii (n = 5), Escherichia clades (n = 32) and E. coli (n = 326), tested in a mouse model of sepsis. We found that the presence of the high-pathogenicity island (HPI), a ~35 kbp gene island encoding the yersiniabactin siderophore, is highly associated with death in mice, surpassing other associated genetic factors also related to iron uptake, such as the aerobactin and the sitABCD operons. We confirmed the association in vivo by deleting key genes of the HPI in E. coli strains in two phylogenetic backgrounds. We then searched for correlations between virulence, iron capture systems and in vitro growth in a subset of E. coli strains (N = 186) previously phenotyped across growth conditions, including antibiotics and other chemical and physical stressors. We found that virulence and iron capture systems are positively correlated with growth in the presence of numerous antibiotics, probably due to co-selection of virulence and resistance. We also found negative correlations between virulence, iron uptake systems and growth in the presence of specific antibiotics (i.e. cefsulodin and tobramycin), which hints at potential "collateral sensitivities" associated with intrinsic virulence. This study points to the major role of iron capture systems in the extra-intestinal virulence of the genus Escherichia.

Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions.

Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially.IMPORTANCE Being able to identify the genetic variants responsible for specific bacterial phenotypes has been the goal of bacterial genetics since its inception and is fundamental to our current level of understanding of bacteria. This identification has been based primarily on painstaking experimentation, but the availability of large data sets of whole genomes with associated phenotype metadata promises to revolutionize this approach, not least for important clinical phenotypes that are not amenable to laboratory analysis. These models of phenotype-genotype association can in the future be used for rapid prediction of clinically important phenotypes such as antibiotic resistance and virulence by rapid-turnaround or point-of-care tests. However, despite much effort being put into adapting genome-wide association study (GWAS) approaches to cope with bacterium-specific problems, such as strong population structure and horizontal gene exchange, current approaches are not yet optimal. We describe a method that advances methodology for both association and generation of portable prediction models.

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae.

Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.

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.

pyseer: a comprehensive tool for microbial pangenome-wide association studies.

Summary: Genome-wide association studies (GWAS) in microbes have different challenges to GWAS in eukaryotes. These have been addressed by a number of different methods. pyseer brings these techniques together in one package tailored to microbial GWAS, allows greater flexibility of the input data used, and adds new methods to interpret the association results. Availability and implementation: pyseer is written in python and is freely available at https://github.com/mgalardini/pyseer, or can be installed through pip. Documentation and a tutorial are available at http://pyseer.readthedocs.io. Supplementary information: Supplementary data are available at Bioinformatics online.

Comparison of Highly and Weakly Virulent Dickeya solani Strains, With a View on the Pangenome and Panregulon of This Species.

Bacteria belonging to the genera Dickeya and Pectobacterium are responsible for significant economic losses in a wide variety of crops and ornamentals. During last years, increasing losses in potato production have been attributed to the appearance of Dickeya solani. The D. solani strains investigated so far share genetic homogeneity, although different virulence levels were observed among strains of various origins. The purpose of this study was to investigate the genetic traits possibly related to the diverse virulence levels by means of comparative genomics. First, we developed a new genome assembly pipeline which allowed us to complete the D. solani genomes. Four de novo sequenced and ten publicly available genomes were used to identify the structure of the D. solani pangenome, in which 74.8 and 25.2% of genes were grouped into the core and dispensable genome, respectively. For D. solani panregulon analysis, we performed a binding site prediction for four transcription factors, namely CRP, KdgR, PecS and Fur, to detect the regulons of these virulence regulators. Most of the D. solani potential virulence factors were predicted to belong to the accessory regulons of CRP, KdgR, and PecS. Thus, some differences in gene expression could exist between D. solani strains. The comparison between a highly and a low virulent strain, IFB0099 and IFB0223, respectively, disclosed only small differences between their genomes but significant differences in the production of virulence factors like pectinases, cellulases and proteases, and in their mobility. The D. solani strains also diverge in the number and size of prophages present in their genomes. Another relevant difference is the disruption of the adhesin gene fhaB2 in the highly virulent strain. Strain IFB0223, which has a complete adhesin gene, is less mobile and less aggressive than IFB0099. This suggests that in this case, mobility rather than adherence is needed in order to trigger disease symptoms. This study highlights the utility of comparative genomics in predicting D. solani traits involved in the aggressiveness of this emerging plant pathogen.

Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies.

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

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.

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.