Bacterial persistence in the rhizosphere and colonization of root niches are critical for the establishment of many beneficial plant-bacteria interactions including those between Rhizobium leguminosarum and its host legumes. Despite this, most studies on R. leguminosarum have focused on its symbiotic lifestyle as an endosymbiont in root nodules. Here, we use random barcode transposon sequencing to assay gene contributions of R. leguminosarum during competitive growth in the rhizosphere and colonization of various plant species. This facilitated the identification of 189 genes commonly required for growth in diverse plant rhizospheres, mutation of 111 of which also affected subsequent root colonization (rhizosphere progressive), and a further 119 genes necessary for colonization. Common determinants reveal a need to synthesize essential compounds (amino acids, ribonucleotides, and cofactors), adapt metabolic function, respond to external stimuli, and withstand various stresses (such as changes in osmolarity). Additionally, chemotaxis and flagella-mediated motility are prerequisites for root colonization. Many genes showed plant-specific dependencies highlighting significant adaptation to different plant species. This work provides a greater understanding of factors promoting rhizosphere fitness and root colonization in plant-beneficial bacteria, facilitating their exploitation for agricultural benefit.

There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (\"functional modules\"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.
OBJECTIVE: This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

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.

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.

Sulfate-reducing bacteria (SRB) are obligate anaerobes that can couple their growth to the reduction of sulfate. Despite the importance of SRB to global nutrient cycles and their damage to the petroleum industry, our molecular understanding of their physiology remains limited. To systematically provide new insights into SRB biology, we generated a randomly barcoded transposon mutant library in the model SRB Desulfovibrio vulgaris Hildenborough (DvH) and used this genome-wide resource to assay the importance of its genes under a range of metabolic and stress conditions. In addition to defining the essential gene set of DvH, we identified a conditional phenotype for 1,137 non-essential genes. Through examination of these conditional phenotypes, we were able to make a number of novel insights into our molecular understanding of DvH, including how this bacterium synthesizes vitamins. For example, we identified DVU0867 as an atypical L-aspartate decarboxylase required for the synthesis of pantothenic acid, provided the first experimental evidence that biotin synthesis in DvH occurs via a specialized acyl carrier protein and without methyl esters, and demonstrated that the uncharacterized dehydrogenase DVU0826:DVU0827 is necessary for the synthesis of pyridoxal phosphate. In addition, we used the mutant fitness data to identify genes involved in the assimilation of diverse nitrogen sources and gained insights into the mechanism of inhibition of chlorate and molybdate. Our large-scale fitness dataset and RB-TnSeq mutant library are community-wide resources that can be used to generate further testable hypotheses into the gene functions of this environmentally and industrially important group of bacteria.

Ordered transposon-insertion collections, in which specific transposon-insertion mutants are stored as monocultures in a genome-scale collection, represent a promising tool for genetic dissection of human gut microbiota members. However, publicly available collections are scarce and the construction methodology remains in early stages of development.
Here, we describe the assembly of a genome-scale ordered collection of transposon-insertion mutants in the model gut anaerobe Bacteroides thetaiotaomicron VPI-5482 that we created as a resource for the research community. We used flow cytometry to sort single cells from a pooled library, located mutants within this initial progenitor collection by applying a pooling strategy with barcode sequencing, and re-arrayed specific mutants to create a condensed collection with single-insertion strains covering >2500 genes. To demonstrate the potential of the condensed collection for phenotypic screening, we analyzed growth dynamics and cell morphology. We identified both growth defects and altered cell shape in mutants disrupting sphingolipid synthesis and thiamine scavenging. Finally, we analyzed the process of assembling the B. theta condensed collection to identify inefficiencies that limited coverage. We demonstrate as part of this analysis that the process of assembling an ordered collection can be accurately modeled using barcode sequencing data.
We expect that utilization of this ordered collection will accelerate research into B. theta physiology and that lessons learned while assembling the collection will inform future efforts to assemble ordered mutant collections for an increasing number of gut microbiota members.

UV radiation (UVR) has significant physiological effects on organisms living at or near the Earth\'s surface, yet the full suite of genes required for fitness of a photosynthetic organism in a UVR-rich environment remains unknown. This study reports a genome-wide fitness assessment of the genes that affect UVR tolerance under environmentally relevant UVR dosages in the model cyanobacterium Synechococcus elongatus PCC 7942. Our results highlight the importance of specific genes that encode proteins involved in DNA repair, glutathione synthesis, and the assembly and maintenance of photosystem II, as well as genes that encode hypothetical proteins and others without an obvious connection to canonical methods of UVR tolerance. Disruption of a gene that encodes a leucyl aminopeptidase (LAP) conferred the greatest UVR-specific decrease in fitness. Enzymatic assays demonstrated a strong pH-dependent affinity of the LAP for the dipeptide cysteinyl-glycine, suggesting an involvement in glutathione catabolism as a function of night-time cytosolic pH level. A low differential expression of the LAP gene under acute UVR exposure suggests that its relative importance would be overlooked in transcript-dependent screens. Subsequent experiments revealed a similar UVR-sensitivity phenotype in LAP knockouts of other organisms, indicating conservation of the functional role of LAPs in UVR tolerance.

Biofilm formation by photosynthetic organisms is a complex behavior that serves multiple functions in the environment. Biofilm formation in the unicellular cyanobacterium Synechococcus elongatus PCC 7942 is regulated in part by a set of small secreted proteins that promotes biofilm formation and a self-suppression mechanism that prevents their expression. Little is known about the regulatory and structural components of the biofilms in PCC 7942, or response to the suppressor signal(s). We performed transcriptomics (RNA-Seq) and phenomics (RB-TnSeq) screens that identified four genes involved in biofilm formation and regulation, more than 25 additional candidates that may impact biofilm formation, and revealed the transcriptomic adaptation to the biofilm state. In so doing, we compared the effectiveness of these two approaches for gene discovery.

Plant diseases are an important threat to food production. While major pathogenicity determinants required for disease have been extensively studied, less is known on how pathogens thrive during host colonization, especially at early infection stages. Here, we used randomly barcoded-transposon insertion site sequencing (RB-TnSeq) to perform a genome-wide screen and identify key bacterial fitness determinants of the vascular pathogen Xanthomonas campestris pv campestris (Xcc) during infection of the cauliflower host plant (Brassica oleracea). This high-throughput analysis was conducted in hydathodes, the natural entry site of Xcc, in xylem sap and in synthetic media. Xcc did not face a strong bottleneck during hydathode infection. In total, 181 genes important for fitness were identified in plant-associated environments with functional enrichment in genes involved in metabolism but only few genes previously known to be involved in virulence. The biological relevance of 12 genes was independently confirmed by phenotyping single mutants. Notably, we show that XC_3388, a protein with no known function (DUF1631), plays a key role in the adaptation and virulence of Xcc possibly through c-di-GMP-mediated regulation. This study revealed yet unsuspected social behaviors adopted by Xcc individuals when confined inside hydathodes at early infection stages.

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop-out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. Furthermore, we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers. IMPORTANCE Understanding the genetic basis of P. putida\'s diverse metabolism is imperative for us to reach its full potential as a host for metabolic engineering. Many target molecules of the bioeconomy and their precursors contain nitrogen. This study provides functional evidence linking hundreds of genes to their roles in the metabolism of nitrogenous compounds, and provides an interactive tool for visualizing these data. We further characterize several aminotransferases, lactamases, and regulators, which are of particular interest for metabolic engineering.