Mutant phenotypes for thousands of bacterial genes of unknown function

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function, while accessory genes are dispensable, particularly in nutrient-rich environments. However, this assumption is seldom tested genetically within the pangenome context. In this study, we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome. To evaluate the robustness of fitness category assignment, Tn-seq data for three independent mutant libraries per strain were analyzed by three methods, which indicates that the Hidden Markov Model (HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size, outperforming the Bayesian and Monte Carlo simulation-based methods. Consequently, the HMM method was used to classify the fitness category. Fitness genes, categorized as essential (ES), advantage (GA), and disadvantage (GD) genes for growth, are enriched in core genes, while nonessential genes (NE) are over-represented in accessory genes. Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes. Connectivity degrees in the cofitness network decrease in the order of ES, GD, and GA/NE. In addition to accessory genes, 1599 out of 3284 core genes display differential essentiality across test strains. Within the pangenome core, both shared quasi-essential (ES and GA) and strain-dependent fitness genes are enriched in similar functional categories. Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

High-throughput fitness experiments reveal specific vulnerabilities of human-adapted Salmonella during stress and infection

Salmonella enterica is comprised of genetically distinct 'serovars' that together provide an intriguing model for exploring the genetic basis of pathogen evolution. Although the genomes of numerous Salmonella isolates with broad variations in host range and human disease manifestations have been sequenced, the functional links between genetic and phenotypic differences among these serovars remain poorly understood. Here, we conduct high-throughput functional genomics on both generalist (Typhimurium) and human-restricted (Typhi and Paratyphi A) Salmonella at unprecedented scale in the study of this enteric pathogen. Using a comprehensive systems biology approach, we identify gene networks with serovar-specific fitness effects across 25 host-associated stresses encountered at key stages of human infection. By experimentally perturbing these networks, we characterize previously undescribed pseudogenes in human-adapted Salmonella. Overall, this work highlights specific vulnerabilities encoded within human-restricted Salmonella that are linked to the degradation of their genomes, shedding light into the evolution of this enteric pathogen.

Acarbose Impairs Gut Bacteroides Growth by Targeting Intracellular GH97 Enzymes

Acarbose is a type-2 diabetes medicine that inhibits dietary starch breakdown into glucose by inhibiting host amylase and glucosidase enzymes. Numerous gut species in the Bacteroides genus enzymatically break down starch and change in relative abundance within the gut microbiome in acarbose-treated individuals. To mechanistically explain this observation, we used two model starch-degrading Bacteroides, Bacteroides ovatus (Bo) and Bacteroides thetaiotaomicron (Bt). Bt growth is severely impaired by acarbose whereas Bo growth is not. The Bacteroides use a starch utilization system (Sus) to grow on starch. We hypothesized that Bo and Bt Sus enzymes are differentially inhibited by acarbose. Instead, we discovered that although acarbose primarily targets the Sus periplasmic GH97 enzymes in both organisms, the drug affects starch processing at multiple other points. Acarbose competes for transport through the Sus beta-barrel proteins and binds to the Sus transcriptional regulators. Further, Bo expresses a non-Sus GH97 (BoGH97D) when grown in starch with acarbose. The Bt homolog, BtGH97H, is not expressed in the same conditions, nor can overexpression of BoGH97D complement the Bt growth inhibition in the presence of acarbose. This work informs us about unexpected complexities of Sus function and regulation in Bacteroides, including variation between related species. Further, this indicates that the gut microbiome may be a source of variable response to acarbose treatment for diabetes.

Surviving the Potato Stems: Differences in Genes Required for Fitness by Dickeya dadantii and Dickeya dianthicola

Bacteria belonging to the genus Dickeya cause blackleg and soft rot symptoms on many plant hosts, including potato. Although there is considerable knowledge about the genetic determinants that allow Dickeya to colonize host plants, as well as the genes that contribute to virulence, much is still unknown. To identify the genes important for fitness in potato stems, we constructed and evaluated randomly barcoded transposon mutant (RB-TnSeq) libraries of Dickeya dadantii and Dickeya dianthicola. We identified 169 and 157 genes important for growth in D. dadantii and D. dianthicola in stems, respectively. This included genes related to metabolic pathways, chemotaxis and motility, transcriptional regulation, transport across membranes, membrane biogenesis, detoxification mechanisms, and virulence-related genes, including a potential virulence cluster srfABC, c-di-GMP modulating genes, and pectin degradation genes. When we compared the results of the stem assay with other datasets, we identified genes important for growth in stems versus tubers and in vitro conditions. Additionally, our data showed differences in fitness determinants for D. dadantii and D. dianthicola. These data provide important insights into the mechanisms used by Dickeya when interacting with and colonizing plants and thus might provide targets for management.

Sinorhizobium meliloti BR-bodies promote fitness during host colonization

Biomolecular condensates, such as the nucleoli or P-bodies, are non-membrane-bound assemblies of proteins and nucleic acids that facilitate specific cellular processes. Like eukaryotic P-bodies, the recently discovered bacterial ribonucleoprotein bodies (BR-bodies) organize the mRNA decay machinery, yet the similarities in molecular and cellular functions across species have been poorly explored. Here, we examine the functions of BR-bodies in the nitrogen-fixing endosymbiont Sinorhizobium meliloti, which colonizes the roots of compatible legume plants. Assembly of BR-bodies into visible foci in S. meliloti cells requires the C-terminal intrinsically disordered region (IDR) of RNase E, and foci fusion is readily observed in vivo, suggesting they are liquid-like condensates that form via mRNA sequestration. Using Rif-seq to measure mRNA lifetimes, we found a global slowdown in mRNA decay in a mutant deficient in BR-bodies, indicating that compartmentalization of the degradation machinery promotes efficient mRNA turnover. While BR-bodies are constitutively present during exponential growth, the abundance of BR-bodies increases upon cell stress, whereby they promote stress resistance. Finally, using Medicago truncatula as host, we show that BR-bodies enhance competitiveness during colonization and appear to be required for effective symbiosis, as mutants without BR-bodies failed to stimulate plant growth. These results suggest that BR-bodies provide a fitness advantage for bacteria during infection, perhaps by enabling better resistance against the host immune response.

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.

RpoS and the bacterial general stress response

SUMMARYThe general stress response (GSR) is a widespread strategy developed by bacteria to adapt and respond to their changing environments. The GSR is induced by one or multiple simultaneous stresses, as well as during entry into stationary phase and leads to a global response that protects cells against multiple stresses. The alternative sigma factor RpoS is the central GSR regulator in E. coli and conserved in most γ-proteobacteria. In E. coli, RpoS is induced under conditions of nutrient deprivation and other stresses, primarily via the activation of RpoS translation and inhibition of RpoS proteolysis. This review includes recent advances in our understanding of how stresses lead to RpoS induction and a summary of the recent studies attempting to define RpoS-dependent genes and pathways.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Co-evolution of gene transfer agents and their alphaproteobacterial hosts

Gene transfer agents (GTAs) are enigmatic elements that resemble small viruses and are known to be produced during nutritional stress by some bacteria and archaea. The production of GTAs is regulated by quorum sensing, under which a small fraction of the population acts as GTA producers, while the rest becomes GTA recipients. In contrast to canonical viruses, GTAs cannot propagate themselves because they package pieces of the producing cell's genome. In alphaproteobacteria, GTAs are mostly vertically inherited and reside in their hosts' genomes for hundreds of millions of years. While GTAs' ability to transfer genetic material within a population and their long-term preservation suggest an increased fitness of GTA-producing microbes, the associated benefits and type of selection that maintains GTAs are poorly understood. By comparing rates of evolutionary change in GTA genes to the rates in gene families abundantly present across 293 alphaproteobacterial genomes, we detected 59 gene families that likely co-evolve with GTA genes. These gene families are predominantly involved in stress response, DNA repair, and biofilm formation. We hypothesize that biofilm formation enables the physical proximity of GTA-producing cells, limiting GTA-derived benefits only to a group of closely related cells. We further conjecture that the population structure of biofilm-forming sub-populations ensures that the trait of GTA production is maintained despite the inevitable rise of "cheating" genotypes. Because release of GTA particles kills the producing cell, maintenance of GTAs is an exciting example of social evolution in a microbial population.IMPORTANCEGene transfer agents (GTAs) are viruses domesticated by some archaea and bacteria as vehicles for carrying pieces of the host genome. Produced under certain environmental conditions, GTA particles can deliver DNA to neighboring, closely related cells. The function of GTAs remains uncertain. While making GTAs is suicidal for a cell, GTA-encoding genes are widespread in genomes of alphaproteobacteria. Such GTA persistence implies functional benefits but raises questions about how selection maintains this lethal trait. By showing that GTA genes co-evolve with genes involved in stress response, DNA repair, and biofilm formation, we provide support for the hypothesis that GTAs facilitate DNA exchange during the stress conditions and present a model for how GTAs persist in biofilm-forming bacterial populations despite being lethal.

Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking

Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complex in vivo environment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of these in vivo evolutionary forces. By analyzing >400,000 lineages across four human Bacteroides strains in gnotobiotic mice, we observed positive selection on thousands of cryptic variants - most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending variants, which can strongly influence their emergent evolutionary tradeoffs.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Computational analysis of genes with lethal knockout phenotype and prediction of essential genes in archaea

The identification of microbial genes essential for survival as those with lethal knockout phenotype (LKP) is a common strategy for functional interrogation of genomes. However, interpretation of the LKP is complicated because a substantial fraction of the genes with this phenotype remains poorly functionally characterized. Furthermore, many genes can exhibit LKP not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes (conditionally essential genes). We analyzed the sets of LKP genes for two archaea, Methanococcus maripaludis and Sulfolobus islandicus, using a variety of computational approaches aiming to differentiate between essential and conditionally essential genes and to predict at least a general function for as many of the proteins encoded by these genes as possible. This analysis allowed us to predict the functions of several LKP genes including previously uncharacterized subunit of the GINS protein complex with an essential function in genome replication and of the KEOPS complex that is responsible for an essential tRNA modification as well as GRP protease implicated in protein quality control. Additionally, several novel antitoxins (conditionally essential genes) were predicted, and this prediction was experimentally validated by showing that the deletion of these genes together with the adjacent genes apparently encoding the cognate toxins caused no growth defect. We applied principal component analysis based on sequence and comparative genomic features showing that this approach can separate essential genes from conditionally essential ones and used it to predict essential genes in other archaeal genomes.IMPORTANCEOnly a relatively small fraction of the genes in any bacterium or archaeon is essential for survival as demonstrated by the lethal effect of their disruption. The identification of essential genes and their functions is crucial for understanding fundamental cell biology. However, many of the genes with a lethal knockout phenotype remain poorly functionally characterized, and furthermore, many genes can exhibit this phenotype not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes. We applied state-of-the-art computational methods to predict the functions of a number of uncharacterized genes with the lethal knockout phenotype in two archaeal species and developed a computational approach to predict genes involved in essential functions. These findings advance the current understanding of key functionalities of archaeal cells.

mBARq: a versatile and user-friendly framework for the analysis of DNA barcodes from transposon insertion libraries, knockout mutants, and isogenic strain populations

MOTIVATION

DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization, and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills.

RESULTS

Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups. The tool is seamlessly integrated with an intuitive web app for interactive data exploration via the STRING and KEGG databases to accelerate scientific discovery.

AVAILABILITY AND IMPLEMENTATION

The tool is implemented in Python. The source code is freely available (https://github.com/MicrobiologyETHZ/mbarq) and the web app can be accessed at: https://microbiomics.io/tools/mbarq-app.

Functional and evolutionary significance of unknown genes from uncultivated taxa

Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

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.

Randomly barcoded transposon mutant libraries for gut commensals I: Strategies for efficient library construction

Randomly barcoded transposon mutant libraries are powerful tools for studying gene function and organization, assessing gene essentiality and pathways, discovering potential therapeutic targets, and understanding the physiology of gut bacteria and their interactions with the host. However, construction of high-quality libraries with uniform representation can be challenging. In this review, we survey various strategies for barcoded library construction, including transposition systems, methods of transposon delivery, optimal library size, and transconjugant selection schemes. We discuss the advantages and limitations of each approach, as well as factors to consider when selecting a strategy. In addition, we highlight experimental and computational advances in arraying condensed libraries from mutant pools. We focus on examples of successful library construction in gut bacteria and their application to gene function studies and drug discovery. Given the need for understanding gene function and organization in gut bacteria, we provide a comprehensive guide for researchers to construct randomly barcoded transposon mutant libraries.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

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.

Application of functional genomics for domestication of novel non-model microbes

With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications.

ONE-SENTENCE SUMMARY

The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

Molecular mechanisms and environmental adaptations of flagellar loss and biofilm growth of Rhodanobacter under environmental stress

Biofilms aid bacterial adhesion to surfaces via direct and indirect mechanisms, and formation of biofilms is considered as an important strategy for adaptation and survival in suboptimal environmental conditions. However, the molecular underpinnings of biofilm formation in subsurface sediment/groundwater ecosystems where microorganisms often experience fluctuations in nutrient input, pH, and nitrate or metal concentrations are underexplored. We examined biofilm formation under different nutrient, pH, metal, and nitrate regimens of 16 Rhodanobacter strains isolated from subsurface groundwater wells spanning diverse levels of pH (3.5 to 5) and nitrates (13.7 to 146 mM). Eight Rhodanobacter strains demonstrated significant biofilm growth under low pH, suggesting adaptations for survival and growth at low pH. Biofilms were intensified under aluminum stress, particularly in strains possessing fewer genetic traits associated with biofilm formation, findings warranting further investigation. Through random barcode transposon-site sequencing (RB-TnSeq), proteomics, use of specific mutants, and transmission electron microscopy analysis, we discovered flagellar loss under aluminum stress, indicating a potential relationship between motility, metal tolerance, and biofilm growth. Comparative genomic analyses revealed the absence of flagella and chemotaxis genes and the presence of a putative type VI secretion system in the highly biofilm-forming strain FW021-MT20. In this study we identified genetic determinants associated with biofilm growth under metal stress in a predominant environmental genus, Rhodanobacter, and identified traits aiding survival and adaptation to contaminated subsurface environments.

Fitness factors impacting survival of a subsurface bacterium in contaminated groundwater

Many factors contribute to the ability of a microbial species to persist when encountering complexly contaminated environments, including time of exposure, the nature and concentration of contaminants, availability of nutritional resources, and possession of a combination of appropriate molecular mechanisms needed for survival. Herein we sought to identify genes that are most important for survival of Gram-negative Enterobacteriaceae in contaminated groundwater environments containing high concentrations of nitrate and metals using the metal-tolerant Oak Ridge Reservation isolate, Pantoea sp. MT58 (MT58). Survival fitness experiments in which a randomly barcoded transposon insertion (RB-TnSeq) library of MT58 was exposed directly to contaminated Oak Ridge Reservation groundwater samples from across a nitrate and mixed metal contamination plume were used to identify genes important for survival with increasing exposure times and concentrations of contaminants, and availability of a carbon source. Genes involved in controlling and using carbon, encoding transcriptional regulators, and related to Gram-negative outer membrane processes were among those found to be important for survival in contaminated Oak Ridge Reservation groundwater. A comparative genomics analysis of 75 Pantoea genus strains allowed us to further separate the survival determinants into core and non-core genes in the Pantoea pangenome, revealing insights into the survival of subsurface microorganisms during contaminant plume intrusion.

Transposon sequencing reveals the essential gene set and genes enabling gut symbiosis in the insect symbiont Caballeronia insecticola

Caballeronia insecticola is a bacterium belonging to the Burkholderia genus sensu lato, which is able to colonize multiple environments like soils and the gut of the bean bug Riptortus pedestris. We constructed a saturated Himar1 mariner transposon library and revealed by transposon-sequencing that 498 protein-coding genes constitute the essential genome of Caballeronia insecticola for growth in free-living conditions. By comparing essential gene sets of Caballeronia insecticola and seven related Burkholderia s.l. strains, only 120 common genes were identified, indicating that a large part of the essential genome is strain-specific. In order to reproduce specific nutritional conditions that are present in the gut of Riptortus pedestris, we grew the mutant library in minimal media supplemented with candidate gut nutrients and identified several condition-dependent fitness-defect genes by transposon-sequencing. To validate the robustness of the approach, insertion mutants in six fitness genes were constructed and their growth deficiency in media supplemented with the corresponding nutrient was confirmed. The mutants were further tested for their efficiency in Riptortus pedestris gut colonization, confirming that gluconeogenic carbon sources, taurine and inositol, are nutrients consumed by the symbiont in the gut. Thus, our study provides insights about specific contributions provided by the insect host to the bacterial symbiont.

CRISPR Interference-Based Functional Small RNA Genomics

Small RNAs (sRNAs) are versatile regulators universally present in species across the prokaryotic kingdom, yet their functional characterization remains a major bottleneck. Gene inactivation through random transposon insertion has proven extremely valuable in discovering hidden gene functions. However, this approach is biased toward long genes and usually results in the underrepresentation of sRNA mutants. In contrast, CRISPR interference (CRISPRi) harnesses guide RNAs to recruit cleavage-deficient Cas nucleases to specific DNA loci. The ensuing steric hindrance inhibits RNA polymerase assembly at-or migration along-predefined genes, allowing for targeted knockdown screens without major length bias. In this chapter, we provide a detailed protocol for CRISPRi-based functional screening of bacterial sRNAs. Using the abundant microbiota species Bacteroides thetaiotaomicron as a model, we describe the design and generation of a guide library targeting the full intergenic sRNA repertoire of this organism and its application to identify sRNA knockdown-associated fitness effects. Our protocol is generic and thus suitable for the systematic assessment of sRNA-associated phenotypes in a wide range of bacterial species and experimental conditions. We expect CRISPRi-based functional genomics to boost sRNA research in understudied bacterial taxa, for instance, members of the gut microbiota.

Eliminating genes for a two-component system increases PHB productivity in Cupriavidus basilensis 4G11 under PHB suppressing, nonstress conditions

Species of bacteria from the genus Cupriavidus are known, in part, for their ability to produce high amounts of poly-hydroxybutyrate (PHB) making them attractive candidates for bioplastic production. The native synthesis of PHB occurs during periods of metabolic stress, and the process regulating the initiation of PHB accumulation in these organisms is not fully understood. Screening an RB-TnSeq transposon library of Cupriavidus basilensis 4G11 allowed us to identify two genes of an apparent, uncharacterized two-component system, which when omitted from the genome enable increased PHB productivity in balanced, nonstress growth conditions. We observe average increases in PHB productivity of 56% and 41% relative to the wildtype parent strain upon deleting each gene individually from the genome. The increased PHB phenotype disappears, however, in nitrogen-free unbalanced growth conditions suggesting the phenotype is specific to fast-growing, replete, nonstress growth. Bioproduction modeling suggests this phenotype could be due to a decreased reliance on metabolic stress induced by nitrogen limitation to initiate PHB production in the mutant strains. Due to uncertainty in the two-component system's input signal and regulon, the mechanism by which these genes impart this phenotype remains unclear. Such strains may allow for the use of single-stage, continuous bioreactor systems, which are far simpler than many PHB bioproduction schemes used previously, given a similar product yield to batch systems in such a configuration. Bioproductivity modeling suggests that omitting this regulation in the cells may increase PHB productivity up to 24% relative to the wildtype organism when using single-stage continuous systems. This work expands our understanding of the regulation of PHB accumulation in Cupriavidus, in particular the initiation of this process upon transition into unbalanced growth regimes.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

A novel highly thermostable and stress resistant ROS scavenging metalloprotein from Paenibacillus

Reactive oxygen species (ROS) are unstable metabolites produced during cellular respiration that can cause extensive damage to the body. Here we report a unique structural metalloprotein called RSAPp for the first time, which exhibits robust ROS-scavenging activity, high thermostability, and stress resistance. RSAPp is a previously uncharacterized DUF2935 (domain of unknown function, accession number: cl12705) family protein from Paenibacillus, containing a highly conserved four-helix bundle with binding sites for variable-valence metal ions (Mn2+/Fe2+/Zn2+). Enzymatic characterization results indicated that RSAPp displays the functionality of three different antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD). In particular, RSAPp exhibits a significant SOD-like activity that is remarkably effective in eliminating superoxide radicals (up to kcat/KM = 2.27 × 1011 mol-1 s-1), and maintains the catalytical active in a wide range of temperatures (25-100 °C) and pH (pH 2.0-9.0), as well as resistant to high temperature, alkali and acidic pH, and 55 different concentrations of detergent agents, chemical solvents, and inhibitors. These properties make RSAPp an attractive candidate for various industrial applications, including cosmetics, food, and pharmaceuticals.

Analysis of key genes for the survival of Pantoea agglomerans under nutritional stress

The absolute amount of nutrients on plant leaves is usually low, and the growth of epiphytic bacteria is typically limited by nutrient content. Thus, is of great significance to study the survival mechanism of epiphytes under nutritional stress for plant disease control. In this paper, Pantoea agglomerans CHTF15 isolated from walnut leaves was used to detect the key genes for the survival of the bacterium under simulated nutrient stress in artificial medium. Genome sequencing was combined with transposon insertion sequencing (Tn-seq) for the detection technique. A total of 105 essential genes were screened from the whole genome. The genes were mainly related to the nucleotide metabolism, protein metabolism, biological oxidation and the gene repair of bacteria analyzed by gene ontology (GO) enrichment analysis. Volcano map analysis demonstrated that the functions of the 15 genes with the most significant differences were generally related to the synthesis of amino acids or proteins, the nucleotide metabolism and homologous recombination and repair. Competitive index analysis revealed that the deletion of the genes dksA and epmA regulating protein synthesis, the gene ribB involved in the nucleotide metabolism and the gene xerD involved in recombination repair induced a significant reduction in the survival ability of the corresponding mutants in the 0.10 % YEP medium and the walnut leaf surface. The results act as a foundation for further in-depth research on the infection process and the mechanisms of pathogenic bacteria.

The genetics of aerotolerant growth in an alphaproteobacterium with a naturally reduced genome

IMPORTANCE

The inherent complexity of biological systems is a major barrier to our understanding of cellular physiology. Bacteria with markedly fewer genes than their close relatives, or reduced genome bacteria, are promising biological models with less complexity. Reduced genome bacteria can also have superior properties for industrial use, provided the reduction does not overly restrict strain robustness. Naturally reduced genome bacteria, such as the alphaproteobacterium Zymomonas mobilis, have fewer genes but remain environmentally robust. In this study, we show that Z. mobilis is a simplified genetic model for Alphaproteobacteria, a class with important impacts on the environment, human health, and industry. We also identify genes that are only required in the absence of atmospheric oxygen, uncovering players that maintain and utilize the cellular energy state. Our findings have broad implications for the genetics of Alphaproteobacteria and industrial use of Z. mobilis to create biofuels and bioproducts.

Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data

The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, does the type of ecological interaction, such as mutualism or competition, change the average effect of a mutation (i.e., the mean of the distribution of fitness effects)? Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, we discovered that in the mutualism S. enterica obtained vitamins and more amino acids than previously known. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Learning sequence, structure, and function representations of proteins with language models

The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.

Microbial Primer: Transposon directed insertion site sequencing (TraDIS): A high throughput method for linking genotype to phenotype

Genetic screens are a key tool for linking phenotype and genotype. Transposon mutagenesis was one of the first genetic methodologies to associate genetic loci with phenotypes. The advent of next-generation sequencing transformed the use of this technique allowing rapid interrogation of whole genomes for genes that correlate with phenotype. One method is transposon directed insertion-site sequencing (TraDIS). Here we describe the method, recent developments in technology, and the advantages and disadvantages of this method compared to other genetic screening tools.

Decoding the microbiome: advances in genetic manipulation for gut bacteria

Studies of the gut microbiota have revealed associations between specific bacterial species or community compositions with health and disease, yet the causal mechanisms underlying microbiota gene-host interactions remain poorly understood. This is partly due to limited genetic manipulation (GM) tools for gut bacteria. Here, we review current advances and challenges in the development of GM approaches, including clustered regularly interspaced short palindromic repeats (CRISPR)-Cas and transposase-based systems in either model or non-model gut bacteria. By overcoming barriers to 'taming' the gut microbiome, GM tools allow molecular understanding of host-microbiome associations and accelerate microbiome engineering for clinical treatment of cancer and metabolic disorders. Finally, we provide perspectives on the future development of GM for gut microbiome species, where more effort should be placed on assembling a generalized GM pipeline to accelerate the application of groundbreaking GM tools in non-model gut bacteria towards both basic understanding and clinical translation.

Synechococcus elongatus Argonaute reduces natural transformation efficiency and provides immunity against exogenous plasmids

IMPORTANCE

S. elongatus is an important cyanobacterial model organism for the study of its prokaryotic circadian clock, photosynthesis, and other biological processes. It is also widely used for genetic engineering to produce renewable biochemicals. Our findings reveal an SeAgo-based defense mechanism in S. elongatus against the horizontal transfer of genetic material. We demonstrate that deletion of the ago gene facilitates genetic studies and genetic engineering of S. elongatus.

CRISPR interference screens reveal growth-robustness tradeoffs in Synechocystis sp. PCC 6803 across growth conditions

Barcoded mutant libraries are a powerful tool for elucidating gene function in microbes, particularly when screened in multiple growth conditions. Here, we screened a pooled CRISPR interference library of the model cyanobacterium Synechocystis sp. PCC 6803 in 11 bioreactor-controlled conditions, spanning multiple light regimes and carbon sources. This gene repression library contained 21,705 individual mutants with high redundancy over all open reading frames and noncoding RNAs. Comparison of the derived gene fitness scores revealed multiple instances of gene repression being beneficial in 1 condition while generally detrimental in others, particularly for genes within light harvesting and conversion, such as antennae components at high light and PSII subunits during photoheterotrophy. Suboptimal regulation of such genes likely represents a tradeoff of reduced growth speed for enhanced robustness to perturbation. The extensive data set assigns condition-specific importance to many previously unannotated genes and suggests additional functions for central metabolic enzymes. Phosphoribulokinase, glyceraldehyde-3-phosphate dehydrogenase, and the small protein CP12 were critical for mixotrophy and photoheterotrophy, which implicates the ternary complex as important for redirecting metabolic flux in these conditions in addition to inactivation of the Calvin cycle in the dark. To predict the potency of sgRNA sequences, we applied machine learning on sgRNA sequences and gene repression data, which showed the importance of C enrichment and T depletion proximal to the PAM site. Fitness data for all genes in all conditions are compiled in an interactive web application.

Proteomic survey of the DNA damage response in Caulobacter crescentus

The bacterial DNA damage response is a critical, coordinated response to endogenous and exogenous sources of DNA damage. Response dynamics are dependent on coordinated synthesis and loss of relevant proteins. While much is known about its global transcriptional control, changes in protein abundance that occur upon DNA damage are less well characterized at the system level. Here, we perform a proteome-wide survey of the DNA damage response in Caulobacter crescentus. We find that while most protein abundance changes upon DNA damage are readily explained by changes in transcription, there are exceptions. The survey also allowed us to identify the novel DNA damage response factor, YaaA, which has been overlooked by previously published, transcription-focused studies. A similar survey in a ∆lon strain was performed to explore lon's role in DNA damage survival. The ∆lon strain had a smaller dynamic range of protein abundance changes in general upon DNA damage compared to the wild-type strain. This system-wide change to the dynamics of the response may explain this strain's sensitivity to DNA damage. Our proteome survey of the DNA damage response provides additional insight into the complex regulation of stress response and nominates a novel response factor that was overlooked in prior studies. IMPORTANCE The DNA damage response helps bacteria to react to and potentially survive DNA damage. The mutagenesis induced during this stress response contributes to the development of antibiotic resistance. Understanding how bacteria coordinate their response to DNA damage could help us to combat this growing threat to human health. While the transcriptional regulation of the bacterial DNA damage response has been characterized, this study is the first to our knowledge to assess the proteomic response to DNA damage in Caulobacter.

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn's disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

A protease and a lipoprotein jointly modulate the conserved ExoR-ExoS-ChvI signaling pathway critical in Sinorhizobium meliloti for symbiosis with legume hosts

Sinorhizobium meliloti is a model alpha-proteobacterium for investigating microbe-host interactions, in particular nitrogen-fixing rhizobium-legume symbioses. Successful infection requires complex coordination between compatible host and endosymbiont, including bacterial production of succinoglycan, also known as exopolysaccharide-I (EPS-I). In S. meliloti EPS-I production is controlled by the conserved ExoS-ChvI two-component system. Periplasmic ExoR associates with the ExoS histidine kinase and negatively regulates ChvI-dependent expression of exo genes, necessary for EPS-I synthesis. We show that two extracytoplasmic proteins, LppA (a lipoprotein) and JspA (a lipoprotein and a metalloprotease), jointly influence EPS-I synthesis by modulating the ExoR-ExoS-ChvI pathway and expression of genes in the ChvI regulon. Deletions of jspA and lppA led to lower EPS-I production and competitive disadvantage during host colonization, for both S. meliloti with Medicago sativa and S. medicae with M. truncatula. Overexpression of jspA reduced steady-state levels of ExoR, suggesting that the JspA protease participates in ExoR degradation. This reduction in ExoR levels is dependent on LppA and can be replicated with ExoR, JspA, and LppA expressed exogenously in Caulobacter crescentus and Escherichia coli. Akin to signaling pathways that sense extracytoplasmic stress in other bacteria, JspA and LppA may monitor periplasmic conditions during interaction with the plant host to adjust accordingly expression of genes that contribute to efficient symbiosis. The molecular mechanisms underlying host colonization in our model system may have parallels in related alpha-proteobacteria.

Transcriptome architecture of the three main lineages of agrobacteria

Agrobacteria are a diverse, polyphyletic group of prokaryotes with multipartite genomes capable of transferring DNA into the genomes of host plants, making them an essential tool in plant biotechnology. Despite their utility in plant transformation, genome-wide transcriptional regulation is not well understood across the three main lineages of agrobacteria. Transcription start sites (TSSs) are a necessary component of gene expression and regulation. In this study, we used differential RNA-seq and a TSS identification algorithm optimized on manually annotated TSS, then validated with existing TSS to identify thousands of TSS with nucleotide resolution for representatives of each lineage. We extend upon the 356 TSSs previously reported in Agrobacterium fabrum C58 by identifying 1,916 TSSs. In addition, we completed genomes and phenotyping of Rhizobium rhizogenes C16/80 and Allorhizobium vitis T60/94, identifying 2,650 and 2,432 TSSs, respectively. Parameter optimization was crucial for an accurate, high-resolution view of genome and transcriptional dynamics, highlighting the importance of algorithm optimization in genome-wide TSS identification and genomics at large. The optimized algorithm reduced the number of TSSs identified internal and antisense to the coding sequence on average by 90.5% and 91.9%, respectively. Comparison of TSS conservation between orthologs of the three lineages revealed differences in cell cycle regulation of ctrA as well as divergence of transcriptional regulation of chemotaxis-related genes when grown in conditions that simulate the plant environment. These results provide a framework to elucidate the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. IMPORTANCE Transcription start sites (TSSs) are fundamental for understanding gene expression and regulation. Agrobacteria, a group of prokaryotes with the ability to transfer DNA into the genomes of host plants, are widely used in plant biotechnology. However, the genome-wide transcriptional regulation of agrobacteria is not well understood, especially in less-studied lineages. Differential RNA-seq and an optimized algorithm enabled identification of thousands of TSSs with nucleotide resolution for representatives of each lineage. The results of this study provide a framework for elucidating the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. The optimized algorithm also highlights the importance of parameter optimization in genome-wide TSS identification and genomics at large.

A mutant fitness compendium in Bifidobacteria reveals molecular determinants of colonization and host-microbe interactions

Bifidobacteria commonly represent a dominant constituent of human gut microbiomes during infancy, influencing nutrition, immune development, and resistance to infection. Despite interest as a probiotic therapy, predicting the nutritional requirements and health-promoting effects of Bifidobacteria is challenging due to major knowledge gaps. To overcome these deficiencies, we used large-scale genetics to create a compendium of mutant fitness in Bifidobacterium breve (Bb). We generated a high density, randomly barcoded transposon insertion pool in Bb, and used this pool to determine Bb fitness requirements during colonization of germ-free mice and chickens with multiple diets and in response to hundreds of in vitro perturbations. To enable mechanistic investigation, we constructed an ordered collection of insertion strains covering 1462 genes. We leveraged these tools to improve models of metabolic pathways, reveal unexpected host- and diet-specific requirements for colonization, and connect the production of immunomodulatory molecules to growth benefits. These resources will greatly reduce the barrier to future investigations of this important beneficial microbe.

Multiplexed fitness profiling by RB-TnSeq elucidates pathways for lignin-related aromatic catabolism in Sphingobium sp. SYK-6

Bioconversion of lignin-related aromatic compounds relies on robust catabolic pathways in microbes. Sphingobium sp. SYK-6 (SYK-6) is a well-characterized aromatic catabolic organism that has served as a model for microbial lignin conversion, and its utility as a biocatalyst could potentially be further improved by genome-wide metabolic analyses. To this end, we generate a randomly barcoded transposon insertion mutant (RB-TnSeq) library to study gene function in SYK-6. The library is enriched under dozens of enrichment conditions to quantify gene fitness. Several known aromatic catabolic pathways are confirmed, and RB-TnSeq affords additional detail on the genome-wide effects of each enrichment condition. Selected genes are further examined in SYK-6 or Pseudomonas putida KT2440, leading to the identification of new gene functions. The findings from this study further elucidate the metabolism of SYK-6, while also providing targets for future metabolic engineering in this organism or other hosts for the biological valorization of lignin.

Geochemical constraints on bacteriophage infectivity in terrestrial environments

Lytic phages can be potent and selective inhibitors of microbial growth and can have profound impacts on microbiome composition and function. However, there is uncertainty about the biogeochemical conditions under which phage predation modulates microbial ecosystem function, particularly in terrestrial systems. Ionic strength is critical for infection of bacteria by many phages, but quantitative data is limited on the ion thresholds for phage infection that can be compared with environmental ion concentrations. Similarly, while carbon composition varies in the environment, we do not know how this variability influences the impact of phage predation on microbiome function. Here, we measured the half-maximal effective concentrations (EC50) of 80 different inorganic ions for the infection of E. coli with two canonical dsDNA and ssRNA phages, T4 and MS2, respectively. Many alkaline earth metals and alkali metals enabled lytic infection but the ionic strength thresholds varied for different ions between phages. Additionally, using a freshwater nitrate-reducing microbiome, we found that the ability of lytic phages to influence nitrate reduction end-products depended upon the carbon source as well as ionic strength. For all phage:host pairs, the ion EC50s for phage infection exceeded the ion concentrations found in many terrestrial freshwater systems. Thus, our findings support a model where phages most influence terrestrial microbial functional ecology in hot spots and hot moments such as metazoan guts, drought influenced soils, or biofilms where ion concentration is locally or transiently elevated and nutrients are available to support the growth of specific phage hosts.

The conserved iol gene cluster in Pseudomonas is involved in rhizosphere competence

The Pseudomonas genus has shown great potential as a sustainable solution to support agriculture through its plant-growth-promoting and biocontrol activities. However, their efficacy as bioinoculants is limited by unpredictable colonization in natural conditions. Our study identifies the iol locus, a gene cluster in Pseudomonas involved in inositol catabolism, as a feature enriched among superior root colonizers in natural soil. Further characterization revealed that the iol locus increases competitiveness, potentially caused by an observed induction of swimming motility and the production of fluorescent siderophore in response to inositol, a plant-derived compound. Public data analyses indicate that the iol locus is broadly conserved in the Pseudomonas genus and linked to diverse host-microbe interactions. Together, our findings suggest the iol locus as a potential target for developing more effective bioinoculants for sustainable agriculture.

Genome-wide fitness profiling reveals molecular mechanisms that bacteria use to interact with Trichoderma atroviride exometabolites

Trichoderma spp. are ubiquitous rhizosphere fungi capable of producing several classes of secondary metabolites that can modify the dynamics of the plant-associated microbiome. However, the bacterial-fungal mechanisms that mediate these interactions have not been fully characterized. Here, a random barcode transposon-site sequencing (RB-TnSeq) approach was employed to identify bacterial genes important for fitness in the presence of Trichoderma atroviride exudates. We selected three rhizosphere bacteria with RB-TnSeq mutant libraries that can promote plant growth: the nitrogen fixers Klebsiella michiganensis M5aI and Herbaspirillum seropedicae SmR1, and Pseudomonas simiae WCS417. As a non-rhizosphere species, Pseudomonas putida KT2440 was also included. From the RB-TnSeq data, nitrogen-fixing bacteria competed mainly for iron and required the siderophore transport system TonB/ExbB for optimal fitness in the presence of T. atroviride exudates. In contrast, P. simiae and P. putida were highly dependent on mechanisms associated with membrane lipid modification that are required for resistance to cationic antimicrobial peptides (CAMPs). A mutant in the Hog1-MAP kinase (Δtmk3) gene of T. atroviride showed altered expression patterns of many nonribosomal peptide synthetase (NRPS) biosynthetic gene clusters with potential antibiotic activity. In contrast to exudates from wild-type T. atroviride, bacterial mutants containing lesions in genes associated with resistance to antibiotics did not show fitness defects when RB-TnSeq libraries were exposed to exudates from the Δtmk3 mutant. Unexpectedly, exudates from wild-type T. atroviride and the Δtmk3 mutant rescued purine auxotrophic mutants of H. seropedicae, K. michiganensis and P. simiae. Metabolomic analysis on exudates from wild-type T. atroviride and the Δtmk3 mutant showed that both strains excrete purines and complex metabolites; functional Tmk3 is required to produce some of these metabolites. This study highlights the complex interplay between Trichoderma-metabolites and soil bacteria, revealing both beneficial and antagonistic effects, and underscoring the intricate and multifaceted nature of this relationship.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays

The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.

Novel method to recover Salmonella enterica cells for Tn-Seq approaches from lettuce leaves and agricultural environments using combination of sonication, filtration, and dialysis membrane

Salmonella enterica in agricultural environments has become an important concern, due to its potential transmission to humans and the associated public health risks. To identify genes contributing to Salmonella adaptation to such environments, transposon sequencing has been used in recent years. However, isolating Salmonella from atypical hosts, such as plant leaves, can pose technical challenges due to low bacterial content and the difficulty to separate an adequate number of bacteria from host tissues. In this study, we describe a modified methodology using a combination of sonication and filtration to recover S. enterica cells from lettuce leaves. We successfully recovered over a total of 3.5 × 106Salmonella cells in each biological replicate from two six-week old lettuce leaves, 7 days after infiltration with a Salmonella suspension of 5 × 107 colony forming units (CFU)/mL. Moreover, we have developed a dialysis membrane system as an alternative method for recovering bacteria from culture medium, mimicking a natural environment. Inoculating 107 CFU/mL of Salmonella into the media based on plant (lettuce and tomato) leaf and diluvial sand soil, a final concentration of 109.5 and 108.5 CFU/mL was obtained, respectively. One millilitre of the bacterial suspension after 24 h incubation at 28 °C using 60 rpm agitation was pelleted, corresponding to 109.5 and 108.5 cells from leaf- or soil-based media. The recovered bacterial population, from both lettuce leaves and environment-mimicking media, can adequately cover a presumptive library density of 106 mutants. In conclusion, this protocol provides an effective method to recover a Salmonella transposon sequencing library from in planta and in vitro systems. We expect this novel technique to foster the study of Salmonella in atypical hosts and environments, as well as other comparable scenarios.