Selection analyses of insertional mutants using subgenic-resolution arrays

Construction of high-density transposon mutant library of Staphylococcus aureus using bacteriophage ϕ11

Transposon mutant libraries are an important resource to study bacterial metabolism and pathogenesis. The fitness analysis of mutants in the libraries under various growth conditions provides important clues to study the physiology and biogenesis of structural components of a bacterial cell. A transposon library in conjunction with next-generation sequencing techniques, collectively named transposon sequencing (Tn-seq), enables high-throughput genome profiling and synthetic lethality analysis. Tn-seq has also been used to identify essential genes and to study the mode of action of antibacterials. To construct a high-density transposon mutant library, an efficient delivery system for transposition in a model bacterium is essential. Here, I describe a detailed protocol for generating a high-density phage-based transposon mutant library in a Staphylococcus aureus strain, and this protocol is readily applicable to other S. aureus strains including USA300 and MW2.

Metabolic engineering of E. coli for β-alanine production using a multi-biosensor enabled approach

β-alanine is an important biomolecule used in nutraceuticals, pharmaceuticals, and chemical synthesis. The relatively eco-friendly bioproduction of β-alanine has recently attracted more interest than petroleum-based chemical synthesis. In this work, we developed two types of in vivo high-throughput screening platforms, wherein one was utilized to identify a novel target ribonuclease E (encoded by rne) as well as a redox-cofactor balancing module that can enhance de novo β-alanine biosynthesis from glucose, and the other was employed for screening fermentation conditions. When combining these approaches with rational upstream and downstream module engineering, an engineered E. coli producer was developed that exhibited 3.4- and 6.6-fold improvement in β-alanine yield (0.85 mol β-alanine/mole glucose) and specific β-alanine production (0.74 g/L/OD₆₀₀), respectively, compared to the parental strain in a minimal medium. Across all of the strains constructed, the best yielding strain exhibited 1.08 mol β-alanine/mole glucose (equivalent to 81.2% of theoretic yield). The final engineered strain produced 6.98 g/L β-alanine in a batch-mode bioreactor and 34.8 g/L through a whole-cell catalysis. This approach demonstrates the utility of biosensor-enabled high-throughput screening for the production of β-alanine.

Transposon insertion site sequencing (TIS) of Pseudomonas aeruginosa

Transposon insertion site sequencing (TIS) is a technique that determines the insertion profile of a transposon mutant library by massive parallel sequencing of transposon-genomic DNA junctions. Because the transposon insertion profile reflects the abundance of each mutant in the library, it provides information to assess the fitness contribution of each genetic locus of a bacterial genome in a specific growth condition or strain background. Although introduced only about a dozen years ago, TIS has become an important tool in bacterial genetics that provides clues to study biological functions and regulatory mechanisms. Here, I describe a protocol for generating high density transposon insertion mutant libraries and preparing Illumina sequencing samples for mapping the transposon junctions of the transposon mutant libraries using Pseudomonas aeruginosa as an example.

Sorting for secreted molecule production using a biosensor-in-microdroplet approach

Sorting large libraries of cells for improved small molecule secretion is throughput limited. Here, we combine producer/secretor cell libraries with whole-cell biosensors using a microfluidic-based screening workflow. This approach enables a mix-and-match capability using off-the-shelf biosensors through either coencapsulation or pico-injection. We demonstrate the cell type and library agnostic nature of this workflow by utilizing single-guide RNA, transposon, and ethyl-methyl sulfonate mutagenesis libraries across three distinct microbes (Escherichia coli, Saccharomyces cerevisiae, and Yarrowia lipolytica), biosensors from two organisms (E. coli and S. cerevisiae), and three products (triacetic acid lactone, naringenin, and L-DOPA) to identify targets improving production/secretion.

A magneto-fluorescence bacteria assay strategy based on dual colour sulfide fluorescent nanoparticles with high near-IR conversion efficiency

Anti-Stokes fluorescence induced by near-IR (NIR) radiation is particularly advantageous for the bioassay of complex samples, but most of the commonly used NIR-induced fluorescence nanomaterials such as up-conversion nanoparticles (UCNPs) do not exhibit satisfactory fluorescence intensity and work against achieving a highly sensitive bioassay. In this study, we a construct sensitive and specific bacteria biosensor based on the NIR-stimulated CaS: Eu, Sm, Mn and SrS: Ce, Sm, Mn nanoparticles. The fluorescent nanoparticles are conjugated with bacteria recognition fragments. In addition, the independent emission bands of these two types of fluorescent nanoparticles make it possible to detect and quantify Gram-positive strain and Gram-negative strain, simultaneously. Intense fluorescence and magnetic enrichment of magneto-fluorescence systems enable bacteria discrimination with the naked eye and improve sensitivity in trace bacteria detection (<20 CFU mL^-1). The linear relationship between the fluorescence intensity and bacterial concentration is established with a detection range of 25-10⁶ CFU mL^-1. Furthermore, this NIR-excited assay strategy demonstrates better anti-interference capability than UV/visible-excited assay methods, showing high potential and practical value for medical diagnostics and bacteria monitoring.

Transposable element persistence via potential genome-level ecosystem engineering

BACKGROUND

The nuclear genomes of eukaryotes vary enormously in size, with much of this variability attributable to differential accumulation of transposable elements (TEs). To date, the precise evolutionary and ecological conditions influencing TE accumulation remain poorly understood. Most previous attempts to identify these conditions have focused on evolutionary processes occurring at the host organism level, whereas we explore a TE ecology explanation.

RESULTS

As an alternative (or additional) hypothesis, we propose that ecological mechanisms occurring within the host cell may contribute to patterns of TE accumulation. To test this idea, we conducted a series of experiments using a simulated asexual TE/host system. Each experiment tracked the accumulation rate for a given type of TE within a particular host genome. TEs in this system had a net deleterious effect on host fitness, which did not change over the course of experiments. As one might expect, in the majority of experiments TEs were either purged from the genome or drove the host population to extinction. However, in an intriguing handful of cases, TEs co-existed with hosts and accumulated to very large numbers. This tended to occur when TEs achieved a stable density relative to non-TE sequences in the genome (as opposed to reaching any particular absolute number). In our model, the only way to maintain a stable density was for TEs to generate new, inactive copies at a rate that balanced with the production of active (replicating) copies.

CONCLUSIONS

From a TE ecology perspective, we suggest this could be interpreted as a case of ecosystem engineering within the genome, where TEs persist by creating their own "habitat".

Pseudomonas orientalis F9 Pyoverdine, Safracin, and Phenazine Mutants Remain Effective Antagonists against Erwinia amylovora in Apple Flowers

Pseudomonas orientalis F9 is an antagonist of the economically important phytopathogen Erwinia amylovora, the causal agent of fire blight in pomme fruit. On King’s B medium, P. orientalis F9 produces a pyoverdine siderophore and the antibiotic safracin. P. orientalis F9 transposon mutants lacking these factors fail to antagonize E. amylovora, depending on the in vitro assay. On isolated flowers and in soil microcosms, however, pyoverdine, safracin, and phenazine mutants control phytopathogens as clearly as their parental strains.

The recently characterized strain Pseudomonas orientalis F9, an isolate from apple flowers in a Swiss orchard, exhibits antagonistic traits against phytopathogens. At high colonization densities, it exhibits phytotoxicity against apple flowers. P. orientalis F9 harbors biosynthesis genes for the siderophore pyoverdine as well as for the antibiotics safracin and phenazine. To elucidate the role of the three compounds in biocontrol, we screened a large random knockout library of P. orientalis F9 strains for lack of pyoverdine production or in vitro antagonism. Transposon mutants that lacked the ability for fluorescence carried transposons in pyoverdine production genes. Mutants unable to antagonize Erwinia amylovora in an in vitro double-layer assay carried transposon insertions in the safracin gene cluster. As no phenazine transposon mutant could be identified using the chosen selection criteria, we constructed a site-directed deletion mutant. Pyoverdine-, safracin-, and phenazine mutants were tested for their abilities to counteract the fire blight pathogen Erwinia amylovoraex vivo on apple flowers or the soilborne pathogen Pythium ultimumin vivo in a soil microcosm. In contrast to some in vitro assays, ex vivo and in vivo assays did not reveal significant differences between parental and mutant strains in their antagonistic activities. This suggests that, ex vivo and in vivo, other factors, such as competition for resources or space, are more important than the tested antibiotics or pyoverdine for successful antagonism of P. orientalis F9 against phytopathogens in the performed assays.

IMPORTANCEPseudomonas orientalis F9 is an antagonist of the economically important phytopathogen Erwinia amylovora, the causal agent of fire blight in pomme fruit. On King’s B medium, P. orientalis F9 produces a pyoverdine siderophore and the antibiotic safracin. P. orientalis F9 transposon mutants lacking these factors fail to antagonize E. amylovora, depending on the in vitro assay. On isolated flowers and in soil microcosms, however, pyoverdine, safracin, and phenazine mutants control phytopathogens as clearly as their parental strains.

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures - temperature and osmolarity - across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

Biosensor-guided improvements in salicylate production by recombinant Escherichia coli

Background

Salicylate can be biosynthesized from the common metabolic intermediate shikimate and has found applications in pharmaceuticals and in the bioplastics industry. While much metabolic engineering work focused on the shikimate pathway has led to the biosynthesis of a variety of aromatic compounds, little is known about how the relative expression levels of pathway components influence salicylate biosynthesis. Furthermore, some host strain gene deletions that improve salicylate production may be impossible to predict. Here, a salicylate-responsive transcription factor was used to optimize the expression levels of shikimate/salicylate pathway genes in recombinant E. coli, and to screen a chromosomal transposon insertion library for improved salicylate production.

Results

A high-throughput colony screen was first developed based on a previously designed salicylate-responsive variant of the E. coli AraC regulatory protein (“AraC-SA”). Next, a combinatorial library was constructed comprising a series of ribosome binding site sequences corresponding to a range of predicted protein translation initiation rates, for each of six pathway genes (> 38,000 strain candidates). Screening for improved salicylate production allowed for the rapid identification of optimal gene expression patterns, conferring up to 123% improved production of salicylate in shake-flask culture. Finally, transposon mutagenesis and screening revealed that deletion of rnd (encoding RNase D) from the host chromosome further improved salicylate production by 27%.

Conclusions

These results demonstrate the effectiveness of the salicylate sensor-based screening platform to rapidly identify beneficial gene expression patterns and gene knockout targets for improving production. Such customized high-throughput tools complement other cell factory engineering strategies. This approach can be generalized for the production of other shikimate-derived compounds.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1069-1) contains supplementary material, which is available to authorized users.

Minimal Cells-Real and Imagined

A minimal cell is one whose genome only encodes the minimal set of genes necessary for the cell to survive. Scientific reductionism postulates the best way to learn the first principles of cellular biology would be to use a minimal cell in which the functions of all genes and components are understood. The genes in a minimal cell are, by definition, essential. In 2016, synthesis of a genome comprised of only the set of essential and quasi-essential genes encoded by the bacterium Mycoplasma mycoides created a near-minimal bacterial cell. This organism performs the cellular functions common to all organisms. It replicates DNA, transcribes RNA, translates proteins, undergoes cell division, and little else. In this review, we examine this organism and contrast it with other bacteria that have been used as surrogates for a minimal cell.

Creation of a Dense Transposon Insertion Library Using Bacterial Conjugation in Enterobacterial Strains Such As Escherichia Coli or Shigella flexneri

Transposon mutagenesis is a method that allows gene disruption via the random genomic insertion of a piece of DNA called a transposon. The protocol below outlines a method for high efficiency transfer between bacterial strains of a plasmid harboring a transposon containing a kanamycin resistance marker. The plasmid-borne transposase is encoded by a variant tnp gene that inserts the transposon into the genome of the recipient strain with very low insertional bias. This method thus allows the creation of large mutant libraries in which transposons have been inserted into unique genomic positions in a recipient strain of either Escherichia coli or Shigella flexneri bacteria. By using bacterial conjugation, as opposed to other methods such as electroporation or chemical transformation, large libraries with hundreds of thousands of unique clones can be created. This yields high-density insertion libraries, with insertions occurring as frequently as every 4-6 base pairs in non-essential genes. This method is superior to other methods as it allows for an inexpensive, easy to use, and high efficiency method for the creation of a dense transposon insertion library. The transposon library can be used in downstream applications such as transposon sequencing (Tn-Seq), to infer genetic interaction networks, or more simply, in mutational (forward genetic) screens.

Salmonella Enteritidis flagellar mutants have a colonization benefit in the chicken oviduct

Egg borne Salmonella Enteritidis is still a major cause of human food poisoning. Eggs can become internally contaminated following colonization of the hen's oviduct. In this paper we aimed to analyze the role of flagella of Salmonella Enteritidis in colonization of the hen's oviduct. Using a transposon library screen we showed that mutants lacking functional flagella are significantly more efficient in colonizing the hen's oviduct in vivo. A micro-array analysis proved that transcription of a number of flagellar genes is down-regulated inside chicken oviduct cells. Flagella contain flagellin, a pathogen associated molecular pattern known to bind to Toll-like receptor 5, activating a pro-inflammatory cascade. In vitro tests using primary oviduct cells showed that flagellin is not involved in invasion. Using a ligated loop model, a diminished inflammatory reaction was seen in the oviduct resulting from injection of an aflagellated mutant compared to the wild-type. It is hypothesized that Salmonella Enteritidis downregulates flagellar gene expression in the oviduct and consequently prevents a flagellin-induced inflammatory response, thereby increasing its oviduct colonization efficiency.

Combining Shigella Tn-seq data with gold-standard E. coli gene deletion data suggests rare transitions between essential and non-essential gene functionality

BACKGROUND

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms.

RESULTS

Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 481 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 revealed that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, implying that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth.

CONCLUSIONS

The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.

Fe2+ chelator proferrorosamine A: a gene cluster of Erwinia rhapontici P45 involved in its synthesis and its impact on growth of Erwinia amylovora CFBP1430

Proferrorosamine A (proFRA) is an iron (Fe2+) chelator produced by the opportunistic plant pathogen Erwinia rhapontici P45. To identify genes involved in proFRA synthesis, transposon mutagenesis was performed. The identified 9.3 kb gene cluster, comprising seven genes, designated rosA-rosG, encodes proteins that are involved in proFRA synthesis. Based on gene homologies, a biosynthetic pathway model for proFRA is proposed. To obtain a better understanding of the effect of proFRA on non-proFRA producing bacteria, E. rhapontici P45 was co-cultured with Erwinia amylovora CFBP1430, a fire-blight-causing plant pathogen. E. rhapontici P45, but not corresponding proFRA-negative mutants, led to a pink coloration of E. amylovora CFBP1430 colonies on King's B agar, indicating accumulation of the proFRA-iron complex ferrorosamine, and growth inhibition in vitro. By saturating proFRA-containing extracts with Fe2+, the inhibitory effect was neutralized, suggesting that the iron-chelating capability of proFRA is responsible for the growth inhibition of E. amylovora CFBP1430.

New insights into TB physiology suggest untapped therapeutic opportunities

The current regimens used to treat tuberculosis are largely comprised of serendipitously discovered drugs that are combined based on clinical experience. Despite curing millions, these drug regimens are limited by the long course of therapy, the emergence of resistance, and the persistent tissue damage that remains after treatment. The last two decades have produced only a single new drug but have represented a renaissance in our understanding of the physiology of tuberculosis infection. The advent of mycobacterial genetics, sophisticated immunological methods, and imaging technologies have transformed our understanding of bacterial physiology as well as the contribution of the host response to disease outcome. Specific alterations in bacterial metabolism, heterogeneity in bacterial state, and drug penetration all limit the effectiveness of antimicrobial therapy. This review summarizes these new biological insights and discusses strategies to exploit them for the rational development of more effective therapeutics. Three general strategies are discussed. First, our emerging insight into bacterial physiology suggests new pathways that might be targeted to accelerate therapy. Second, we explore whether the concept of genetic synergy can be used to design effective combination therapies. Finally, we outline possible approaches to modulate the host response to accentuate antibiotic efficacy. These biology-driven strategies promise to produce more effective therapies.

Transcriptomic Analysis of 3-Hydroxypropanoic Acid Stress in Escherichia coli

The stress response of Escherichia coli to 3-hydroxypropanoic acid (3-HP) was elucidated through global transcriptomic analysis. Around 375 genes showed difference of more than 2-fold in 3-HP-treated samples. Further analysis revealed that the toxicity effect of 3-HP was due to the cation and anion components of this acid and some effects-specific to 3-HP. Genes related to the oxidative stress, DNA protection, and repair were upregulated in treated cells due to the lowered cytoplasmic pH caused by accumulated cations. 3-HP-treated E. coli used the arginine acid tolerance mechanism to increase the cytoplasmic pH. Additionally, the anion effects were manifested as imbalance in the osmotic pressure. Analysis of top ten highly upregulated genes suggests the formation of 3-hydroxypropionaldehyde under 3-HP stress. The transcriptomic analysis shed light on the global genetic reprogramming due to 3-HP stress and suggests strategies for increasing the tolerance of E. coli toward 3-HP.

High-throughput bacterial functional genomics in the sequencing era

High-throughput functional genomic technologies are accelerating progress in understanding the diversity of bacterial life and in developing a systems-level understanding of model bacterial organisms. Here we highlight progress in deep-sequencing-based functional genomics, show how whole genome sequencing is enabling phenotyping in organisms recalcitrant to genetic approaches, recount the rapid proliferation of functional genomic approaches to non-growth phenotypes, and discuss how advances are enabling genome-scale resource libraries for many different bacteria.

Rapid prototyping of microbial cell factories via genome-scale engineering

Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories.

Harnessing the power of transposon mutagenesis for antibacterial target identification and evaluation

Determining the mechanism of action of bacterial growth inhibitors can be a formidable challenge in the progression of small molecules into antibacterial therapies. To help address this bottleneck, we have developed a robust transposon mutagenesis system using a suite of outward facing promoters in order to generate a comprehensive range of expression genotypes in Staphylococcus aureus from which to select defined compound-resistant transposon insertion mutants. Resistance stemming from either gene or operon over/under-expression, in addition to deletion, provides insight into multiple factors that contribute to a compound's observed activity, including means of cell envelope penetration and susceptibility to efflux. By profiling the entire resistome, the suitability of an antibacterial target itself is also evaluated, sometimes with unanticipated results. We herein show that for the staphylococcal signal peptidase (SpsB) inhibitors, modulating expression of lipoteichoic acid synthase (LtaS) confers up to a 100-fold increase in the minimal inhibitory concentration. As similarly efficient transposition systems are or will become established in other bacteria and cell types, we discuss the utility, limitations and future promise of Tnp mutagenesis for determining both a compound's mechanism of action and in the evaluation of novel targets.

Microarray-based detection of Salmonella enterica serovar Enteritidis genes involved in chicken reproductive tract colonization

Salmonella enterica serovar Enteritidis has developed the potential to contaminate table eggs internally, by colonization of the chicken reproductive tract and internalization in the forming egg. The serotype Enteritidis has developed mechanisms to colonize the chicken oviduct more successfully than other serotypes. Until now, the strategies exploited by Salmonella Enteritidis to do so have remained largely unknown. For that reason, a microarray-based transposon library screen was used to identify genes that are essential for the persistence of Salmonella Enteritidis inside primary chicken oviduct gland cells in vitro and inside the reproductive tract in vivo. A total of 81 genes with a potential role in persistence in both the oviduct cells and the oviduct tissue were identified. Major groups of importance include the Salmonella pathogenicity islands 1 and 2, genes involved in stress responses, cell wall, and lipopolysaccharide structure, and the region-of-difference genomic islands 9, 21, and 40.

Microbiota-derived hydrogen fuels Salmonella typhimurium invasion of the gut ecosystem

The intestinal microbiota features intricate metabolic interactions involving the breakdown and reuse of host- and diet-derived nutrients. The competition for these resources can limit pathogen growth. Nevertheless, some enteropathogenic bacteria can invade this niche through mechanisms that remain largely unclear. Using a mouse model for Salmonella diarrhea and a transposon mutant screen, we discovered that initial growth of Salmonella Typhimurium (S. Tm) in the unperturbed gut is powered by S. Tm hyb hydrogenase, which facilitates consumption of hydrogen (H2), a central intermediate of microbiota metabolism. In competitive infection experiments, a hyb mutant exhibited reduced growth early in infection compared to wild-type S. Tm, but these differences were lost upon antibiotic-mediated disruption of the host microbiota. Additionally, introducing H2-consuming bacteria into the microbiota interfered with hyb-dependent S. Tm growth. Thus, H2 is an Achilles' heel of microbiota metabolism that can be subverted by pathogens and might offer opportunities to prevent infection.

Fructose-asparagine is a primary nutrient during growth of Salmonella in the inflamed intestine

Salmonella enterica serovar Typhimurium (Salmonella) is one of the most significant food-borne pathogens affecting both humans and agriculture. We have determined that Salmonella encodes an uptake and utilization pathway specific for a novel nutrient, fructose-asparagine (F-Asn), which is essential for Salmonella fitness in the inflamed intestine (modeled using germ-free, streptomycin-treated, ex-germ-free with human microbiota, and IL10-/- mice). The locus encoding F-Asn utilization, fra, provides an advantage only if Salmonella can initiate inflammation and use tetrathionate as a terminal electron acceptor for anaerobic respiration (the fra phenotype is lost in Salmonella SPI1- SPI2- or ttrA mutants, respectively). The severe fitness defect of a Salmonella fra mutant suggests that F-Asn is the primary nutrient utilized by Salmonella in the inflamed intestine and that this system provides a valuable target for novel therapies.

A genome-wide screen identifies Salmonella Enteritidis lipopolysaccharide biosynthesis and the HtrA heat shock protein as crucial factors involved in egg white persistence at chicken body temperature

Eggs contaminated with Salmonella Enteritidis are an important source of human foodborne Salmonella infections. Salmonella Enteritidis is able to contaminate egg white during formation of the egg within the chicken oviduct, and it has developed strategies to withstand the antimicrobial properties of egg white to survive in this hostile environment. The mechanisms involved in the persistence of Salmonella Enteritidis in egg white are likely to be complex. To address this issue, a microarray-based transposon library screen was performed to identify genes necessary for survival of Salmonella Enteritidis in egg white at chicken body temperature. The majority of identified genes belonged to the lipopolysaccharide biosynthesis pathway. Additionally, we provide evidence that the serine protease/heat shock protein (HtrA) appears essential for the survival of Salmonella Enteritidis in egg white at chicken body temperature.

Genomic tools to profile antibiotic mode of action

The increasing emergence of antimicrobial multiresistant bacteria is of great concern to public health. While these bacteria are becoming an ever more prominent cause of nosocomial and community-acquired infections worldwide, the antibiotic discovery pipeline has been stalled in the last few years with very few efforts in the research and development of novel antibacterial therapies. Some of the root causes that have hampered current antibiotic drug development are the lack of understanding of the mode of action (MOA) of novel antibiotic molecules and the poor characterization of the bacterial physiological response to antibiotics that ultimately causes resistance. Here, we review how bacterial genetic tools can be applied at the genomic level with the goal of profiling resistance to antibiotics and elucidating antibiotic MOAs. Specifically, we highlight how chemical genomic detection of the MOA of novel antibiotic molecules and antibiotic profiling by next-generation sequencing are leveraging basic antibiotic research to unprecedented levels with great opportunities for knowledge translation.

Strategies for the multiplex mapping of genes to traits

Rewiring and optimization of metabolic networks to enable the production of commercially valuable chemicals is a central goal of metabolic engineering. This prospect is challenged by the complexity of metabolic networks, lack of complete knowledge of gene function(s), and the vast combinatorial genotype space that is available for exploration and optimization. Various approaches have thus been developed to aid in the efficient identification of genes that contribute to a variety of different phenotypes, allowing more rapid design and engineering of traits desired for industrial applications. This review will highlight recent technologies that have enhanced capabilities to map genotype-phenotype relationships on a genome wide scale and emphasize how such approaches enable more efficient design and engineering of complex phenotypes.

Flux balance impact degree: a new definition of impact degree to properly treat reversible reactions in metabolic networks

MOTIVATION

Metabolic pathways are complex systems of chemical reactions taking place in every living cell to degrade substrates and synthesize molecules needed for life. Modeling the robustness of these networks with respect to the dysfunction of one or several reactions is important to understand the basic principles of biological network organization, and to identify new drug targets. While several approaches have been proposed for that purpose, they are computationally too intensive to analyze large networks, and do not properly handle reversible reactions.

RESULTS

We propose a new model-the flux balance impact degree-to model the robustness of large metabolic networks with respect to gene knock-out. We formulate the computation of the impact of one or several reaction blocking as linear programs, and propose efficient strategies to solve them. We show that the proposed method better predicts the phenotypic impact of single gene deletions on Escherichia coli than existing methods.

AVAILABILITY

https://sunflower.kuicr.kyoto-u.ac.jp/∼tyoyo/fbid/index.html

Combinatorial approaches for inverse metabolic engineering applications

Traditional metabolic engineering analyzes biosynthetic and physiological pathways, identifies bottlenecks, and makes targeted genetic modifications with the ultimate goal of increasing the production of high-value products in living cells. Such efforts have led to the development of a variety of organisms with industrially relevant properties. However, there are a number of cellular phenotypes important for research and the industry for which the rational selection of cellular targets for modification is not easy or possible. In these cases, strain engineering can be alternatively carried out using “inverse metabolic engineering”, an approach that first generates genetic diversity by subjecting a population of cells to a particular mutagenic process, and then utilizes genetic screens or selections to identify the clones exhibiting the desired phenotype. Given the availability of an appropriate screen for a particular property, the success of inverse metabolic engineering efforts usually depends on the level and quality of genetic diversity which can be generated. Here, we review classic and recently developed combinatorial approaches for creating such genetic diversity and discuss the use of these methodologies in inverse metabolic engineering applications.

Genome-scale identification and characterization of ethanol tolerance genes in Escherichia coli

The identification of relevant gene targets for engineering a desired trait is a key step in combinatorial strain engineering. Here, we applied the multi-Scalar Analysis of Library Enrichments (SCALEs) approach to map ethanol tolerance onto 1,000,000 genomic-library clones in Escherichia coli. We assigned fitness scores to each of the ∼4,300 genes in E. coli, and through follow-up confirmatory studies identified 9 novel genetic targets (12 genes total) that increase E. coli ethanol tolerance (up to 6-fold improved growth). These genetic targets are involved in the processes related to cell membrane composition, translation, serine biosynthesis, and transcription regulation. Transcriptional profiling of the ethanol stress response in 5 of these ethanol-tolerant clones revealed a total of 700 genes with significantly altered expression (mapped to 615 significantly enriched gene ontology terms) across all five clones, with similar overall changes in global gene expression between two clone clusters. All ethanol-tolerant clones analyzed shared 6% of the overexpressed genes and showed enrichment for transcription regulation-related GO terms. iTRAQ-based proteomic analysis of ethanol-tolerant strains identified upregulation of proteins related to ROS mitigation, fatty acid biosynthesis, and vitamin biosynthesis as compared to the parent strain's ethanol response. The approach we outline here will be useful for engineering a variety of other traits and further improvements in alcohol tolerance.

High-throughput comparison of gene fitness among related bacteria

Background

The contribution of a gene to the fitness of a bacterium can be assayed by whether and to what degree the bacterium tolerates transposon insertions in that gene. We use this fact to compare the fitness of syntenic homologous genes among related Salmonella strains and thereby reveal differences not apparent at the gene sequence level.

Results

A transposon Tn5 derivative was used to construct mutants in Salmonella Typhimurium ATCC14028 (STM1) and Salmonella Typhi Ty2 (STY1), which were then grown in rich media. The locations of 234,152 and 53,556 integration sites, respectively, were mapped by sequencing. These data were compared to similar data available for a different Ty2 isolate (STY2) and essential genes identified in E. coli K-12 (ECO). Of 277 genes considered essential in ECO, all had syntenic homologs in STM1, STY1, and STY2, and all but nine genes were either devoid of transposon insertions or had very few. For three of these nine genes, part of the annotated gene lacked transposon integrations (yejM, ftsN and murB). At least one of the other six genes, trpS, had a potentially functionally redundant gene encoded elsewhere in Salmonella but not in ECO. An additional 165 genes were almost entirely devoid of transposon integrations in all three Salmonella strains examined, including many genes associated with protein and DNA synthesis. Four of these genes (STM14_1498, STM14_2872, STM14_3360, and STM14_5442) are not found in E. coli. Notable differences in the extent of gene selection were also observed among the three different Salmonella isolates. Mutations in hns, for example, were selected against in STM1 but not in the two STY strains, which have a defect in rpoS rendering hns nonessential.

Conclusions

Comparisons among transposon integration profiles from different members of a species and among related species, all grown in similar conditions, identify differences in gene contributions to fitness among syntenic homologs. Further differences in fitness profiles among shared genes can be expected in other selective environments, with potential relevance for comparative systems biology.

Signature tagged mutagenesis in the functional genetic analysis of gastrointestinal pathogens

Signature tagged mutagenesis is a genetic approach that was developed to identify novel bacterial virulence factors. It is a negative selection method in which unique identification tags allow analysis of pools of mutants in mixed populations. The approach is particularly well suited to functional genetic analysis of the gastrointestinal phase of infection in foodborne pathogens and has the capacity to guide the development of novel vaccines and therapeutics. In this review we outline the technical principles underpinning signature-tagged mutagenesis as well as novel sequencing-based approaches for transposon mutant identification such as TraDIS (transposon directed insertion-site sequencing). We also provide an analysis of screens that have been performed in gastrointestinal pathogens which are a global health concern (Escherichia coli, Listeria monocytogenes, Helicobacter pylori, Vibrio cholerae and Salmonella enterica). The identification of key virulence loci through the use of signature tagged mutagenesis in mice and relevant larger animal models is discussed.

Microarray-based genetic footprinting strategy to identify strain improvement genes after competitive selection of transposon libraries

Successful strain engineering involves perturbing key nodes within the cellular network. How the -network's connectivity affects the phenotype of interest and the ideal nodes to modulate, however, are frequently not readily apparent. To guide the generation of a list of candidate nodes for detailed investigation, designers often examine the behavior of a representative set of strains, such as a library of transposon insertion mutants, in the environment of interest. Here, we first present design principles for creating a maximally informative competitive selection. Then, we describe how to globally quantify the change in distribution of strains within a transposon library in response to a competitive selection by amplifying the DNA adjacent to the transposons and hybridizing it to a microarray. Finally, we detail strategies for analyzing the resulting hybridization data to identify genes and pathways that contribute both negatively and positively to fitness in the desired environment.

Using transcription machinery engineering to elicit complex cellular phenotypes

Cellular hosts are widely used for the production of chemical compounds, including pharmaceutics, fuels, and specialty chemicals. However, common metabolic engineering techniques are limited in their capacity to elicit multigenic, complex phenotypes. These phenotypes can include non-pathway-based traits, such as tolerance and productivity. Global transcription machinery engineering (gTME) is a generic methodology for eliciting these complex cellular phenotypes. In gTME, dominant mutant alleles of a transcription-related protein are screened for their ability to reprogram cellular metabolism and regulation, resulting in a unique and desired phenotype. gTME has been successfully applied to both prokaryotic and eukaryotic systems, resulting in improved environmental tolerances, metabolite production, and substrate utilization. The underlying principle involves creating mutant libraries of transcription factors, screening for a desired phenotype, and iterating the process in a directed evolution fashion. The successes of this approach and details for its implementation and application are described here.

Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera

Background

Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited.

Results

To investigate the metabolism of N. meningitidis we generated and then selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium, such as a requirement for phosphoenolpyruvate carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids.

Conclusions

This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight into the pathogen's metabolism both in vitro and during infection.

Synthetic biology: new strategies for directing design

The advancement of synthetic biology is thanks, in large part, to continuing improvements in DNA synthesis. The expansion of synthetic biology into the realm of metabolic engineering has shifted the focus from simply making novel synthetic biological parts to answering the question of how we employ these biological parts to construct genomes that ultimately give rise to useful phenotypes. Much like protein engineering, the answer to this will be arrived at following the combination of rational design and evolutionary approaches. This review will highlight some of the new DNA synthesis-enabled search methods and discuss the application of such methods to the creation of synthetic gene networks and genomes.

High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism

The pathways that comprise cellular metabolism are highly interconnected, and alterations in individual enzymes can have far-reaching effects. As a result, global profiling methods that measure gene expression are of limited value in predicting how the loss of an individual function will affect the cell. In this work, we employed a new method of global phenotypic profiling to directly define the genes required for the growth of Mycobacterium tuberculosis. A combination of high-density mutagenesis and deep-sequencing was used to characterize the composition of complex mutant libraries exposed to different conditions. This allowed the unambiguous identification of the genes that are essential for Mtb to grow in vitro, and proved to be a significant improvement over previous approaches. To further explore functions that are required for persistence in the host, we defined the pathways necessary for the utilization of cholesterol, a critical carbon source during infection. Few of the genes we identified had previously been implicated in this adaptation by transcriptional profiling, and only a fraction were encoded in the chromosomal region known to encode sterol catabolic functions. These genes comprise an unexpectedly large percentage of those previously shown to be required for bacterial growth in mouse tissue. Thus, this single nutritional change accounts for a significant fraction of the adaption to the host. This work provides the most comprehensive genetic characterization of a sterol catabolic pathway to date, suggests putative roles for uncharacterized virulence genes, and precisely maps genes encoding potential drug targets.

High-throughput insertion tracking by deep sequencing for the analysis of bacterial pathogens

Whole-genome techniques toward identification of microbial genes required for their survival and growth during infection have been useful for studies of bacterial pathogenesis. The advent of massively parallel sequencing platforms has created the opportunity to markedly accelerate such genome-scale analyses and achieve unprecedented sensitivity, resolution, and quantification. This chapter provides an overview of a genome-scale methodology that combines high-density transposon mutagenesis with a mariner transposon and deep sequencing to identify genes that are needed for survival in experimental models of pathogenesis. Application of this approach to a model pathogen, Haemophilus influenzae, has provided a comprehensive analysis of the relative role of each gene of this human respiratory pathogen in a murine pulmonary model. The method is readily adaptable to nearly any organism amenable to transposon mutagenesis.

Interaction of Salmonella spp. with the Intestinal Microbiota

Salmonella spp. are major cause of human morbidity and mortality worldwide. Upon entry into the human host, Salmonella spp. must overcome the resistance to colonization mediated by the gut microbiota and the innate immune system. They successfully accomplish this by inducing inflammation and mechanisms of innate immune defense. Many models have been developed to study Salmonella spp. interaction with the microbiota that have helped to identify factors necessary to overcome colonization resistance and to mediate disease. Here we review the current state of studies into this important pathogen/microbiota/host interaction in the mammalian gastrointestinal tract.

Probing bacterial pathogenesis with genetics, genomics, and chemical biology: past, present, and future approaches

Classical genetic approaches for studying bacterial pathogenesis have provided a solid foundation for our current understanding of microbial physiology and the interactions between pathogen and host. During the past decade however, advances in several arenas have expanded the ways in which the biology of pathogens can be studied. This review discussed the impact of these advances on bacterial genetics, including the application of genomics and chemical biology to the study of pathogenesis.

Global strain engineering by mutant transcription factors

Cellular hosts are widely used for the production of chemical compounds including pharmaceutics, fuels, and specialty chemicals. Strain engineering focuses on manipulating and improving these hosts for new and enhanced functionalities including increased titers and better bioreactor performance. These tasks have traditionally been accomplished using a combination of random mutation, screening and selection, and metabolic engineering. However, common metabolic engineering techniques are limited in their capacity to elicit multigenic, complex phenotypes. These phenotypes can also include nonpathway-based traits such as tolerance and productivity. Global transcription machinery engineering (gTME) is a generic methodology for engineering strains with these complex cellular phenotypes. In gTME, dominant mutant alleles of a transcription-related protein are screened for their ability to reprogram cellular metabolism and regulation, resulting in a unique and desired phenotype. gTME has been successfully applied to both prokaryotic and eukaryotic systems, resulting in improved environmental tolerances, metabolite production, and substrate utilization. The underlying principle involves creating mutant libraries of transcription factors, screening for a desired phenotype, and iterating the process in a directed evolution fashion. The successes of this approach and details for its implementation and application are described here.

Signature-tagged mutagenesis to characterize genes through competitive selection of bar-coded genome libraries

The availability of collections of genome-wide deletion mutants greatly accelerates systematic analyses of gene function. However, each of the thousands of genes that comprise a genome must be phenotyped individually unless they can be assayed in parallel and subsequently deconvolved. To this end, unique molecular identifiers have been developed for a variety of microbes. Specifically, the addition of DNA "tags," or "bar codes," to each mutant allows all mutants in a collection to be pooled and phenotyped in parallel, greatly increasing experimental throughput. In this chapter, we provide an overview of current methodologies used to create such tagged mutant collections and outline how they can be applied to understand gene function, gene-gene interactions, and drug-gene interactions. Finally, we present a methodology that uses universal TagModules, capable of bar coding a wide range of microorganisms, and demonstrate its reduction to practice by creating tagged mutant collections in the pathogenic yeast Candida albicans.

Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli

We have designed an experimental/computational framework for studying complex phenotypes in bacteria.

Our framework relies on whole-genome fitness profiling coupled with a module-level analysis to discover pathways that directly affect fitness.

As a proof-of-principle, we studied ethanol tolerance in Escherichia coli and we identified key pathways that contribute to this phenotype.

We then validated our findings through genetic manipulations, gene-expression profiling, metabolite-level measurements, and stable-isotope labeling.

Elucidating the genetic basis of complex phenotypes remains a fundamental challenge in biology. We have developed a systematic framework for comprehensive genetic analysis of microbial phenotypes. Our approach combines the power of fitness profiling (Girgis et al, 2007; Amini et al, 2009) with the sensitivity of module-level analysis (Goodarzi et al, 2009a) to identify key genetic modules that directly affect a phenotype under study. We applied our technology to ethanol tolerance, a complex phenotype with broad industrial relevance. Ethanol affects a variety of cellular components and pathways, including but not limited to membrane integrity (Dombek and Ingram, 1984), enzyme activities (Millar et al, 1982), and proton flux (D'Amore et al, 1990). Given the diversity of targets, the emergence of ethanol tolerance requires modifications to multiple pathway (D'Amore and Stewart, 1987).

To reveal the genetic basis of ethanol tolerance in Escherichia coli, we used two high-coverage mutant libraries (a transposon library and an overexpression library) to assess the fitness consequences of single-locus perturbations. Each cell in our transposon library contains a random transposon insertion in its genome (Girgis et al, 2007); whereas the cells in the overexpression library carry 1–3 kb genomic fragments cloned into a cloning vector (Amini et al, 2009). We grew these libraries under mild (4% v/v) and harsh (5.5% v/v) ethanol concentrations. On growth, the abundance of each transposon insertion or overexpression mutant changes as a function of its fitness, a process that can be monitored through parallel genetic footprinting and microarray hybridization (Figure 1A). This results in a global fitness profile, where the contribution of each genetic locus to ethanol tolerance can be quantified in parallel. However, in the context of ethanol tolerance and other complex phenotypes, single-locus perturbations typically result in modest changes in fitness. Although these small differences can be amplified through multiple rounds of selection, the number of generations is limited as spontaneous beneficial mutations emerge in the population and cause strong biases in the resulting fitness profiles. To boost our analytical power without introducing these biases in the data, we used a module-level computational method to discover the pathways and components that are strongly associated with the data as opposed to focusing on the genes individually (Goodarzi et al, 2009a). Genes function in the context of pathways and modules and module-level analyses increase statistical power through combining information from multiple genes functioning as part of a given pathway (Subramanian et al, 2005).

The module-level analysis of the fitness scores from both libraries revealed a diverse set of pathways that have a direct function in ethanol tolerance. Some of these pathways, including heat-shock stress response and osmoregulation, are known modifiers of ethanol tolerance; whereas others such as acid-stress response and fimbrial structures are novel pathways. Among our findings was the important function of three regulatory proteins: FNR, ArcA, and CafA. Knocking out FNR/ArcA that upregulates aerobic respiration proteins and TCA cycle components results in a marked increase in ethanol tolerance. Similarly, knocking out CafA, a post-transcriptional regulator of alcohol dehydrogenase, is beneficial for tolerance. Given these observations, we hypothesized that selection for ethanol tolerance can result in higher ethanol degradation.

As a large fraction of discovered pathways belonged to central metabolism, we used metabolomics to evaluate our findings. To directly assess the metabolic consequences of adaptation to ethanol, we evolved ethanol-tolerant strains in minimal media plus glucose for ∼30 and 160 generations. We then compared the steady-state level of metabolites in these strains to that of the wild type (Figure 1B). In agreement with our fitness profiling results, we observed a significant increase in TCA cycle metabolites in one of our ethanol-tolerant strains. Higher concentrations of TCA cycle components along with a high free coenzyme A (CoA) to acetyl-coenzyme A (acetyl-CoA) ratio hinted at the capacity of this strain to metabolize ethanol. To test this hypothesis, we performed stable-isotope labeling on our ethanol-tolerant strain versus wild type. After growth on labeled ethanol, we measured the fraction of metabolites that were labeled at each timepoint (Figure 1B). Our results confirmed that the ethanol-tolerant strain has the capacity to consume ethanol through its conversion into acetyl-CoA and further assimilation in the TCA cycle.

By using a variety of systems-level approaches, we have been able to genetically dissect ethanol tolerance in E. coli. We have shown that fitness profiling, in combination with module-level analysis tools, can serve as a powerful approach for revealing the genetic basis of complex phenotypes. The fact that laboratory evolution ended up using the very modules that we discovered, highlights the biological and adaptive relevance of the proposed framework.

Understanding the genetic basis of adaptation is a central problem in biology. However, revealing the underlying molecular mechanisms has been challenging as changes in fitness may result from perturbations to many pathways, any of which may contribute relatively little. We have developed a combined experimental/computational framework to address this problem and used it to understand the genetic basis of ethanol tolerance in Escherichia coli. We used fitness profiling to measure the consequences of single-locus perturbations in the context of ethanol exposure. A module-level computational analysis was then used to reveal the organization of the contributing loci into cellular processes and regulatory pathways (e.g. osmoregulation and cell-wall biogenesis) whose modifications significantly affect ethanol tolerance. Strikingly, we discovered that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation. Through phenotypic and metabolomic analysis of laboratory-evolved ethanol-tolerant strains, we investigated naturally accessible pathways of ethanol tolerance. Remarkably, these laboratory-evolved strains, by and large, follow the same adaptive paths as inferred from our coarse-grained search of the fitness landscape.

Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides

A fundamental goal in biotechnology and biology is the development of approaches to better understand the genetic basis of traits. Here we report a versatile method, trackable multiplex recombineering (TRMR), whereby thousands of specific genetic modifications are created and evaluated simultaneously. To demonstrate TRMR, in a single day we modified the expression of >95% of the genes in Escherichia coli by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene. Barcode sequences and microarrays were then used to quantify population dynamics. Within a week we mapped thousands of genes that affect E. coli growth in various media (rich, minimal and cellulosic hydrolysate) and in the presence of several growth inhibitors (beta-glucoside, D-fucose, valine and methylglyoxal). This approach can be applied to a broad range of traits to identify targets for future genome-engineering endeavors.

Tracking insertion mutants within libraries by deep sequencing and a genome-wide screen for Haemophilus genes required in the lung

Rapid genome-wide identification of genes required for infection would expedite studies of bacterial pathogens. We developed genome-scale "negative selection" technology that combines high-density transposon mutagenesis and massively parallel sequencing of transposon/chromosome junctions in a mutant library to identify mutants lost from the library after exposure to a selective condition of interest. This approach was applied to comprehensively identify Haemophilus influenzae genes required to delay bacterial clearance in a murine pulmonary model. Mutations in 136 genes resulted in defects in vivo, and quantitative estimates of fitness generated by this technique were in agreement with independent validation experiments using individual mutant strains. Genes required in the lung included those with characterized functions in other models of H. influenzae pathogenesis and genes not previously implicated in infection. Genes implicated in vivo have reported or potential roles in survival during nutrient limitation, oxidative stress, and exposure to antimicrobial membrane perturbations, suggesting that these conditions are encountered by H. influenzae during pulmonary infection. The results demonstrate an efficient means to identify genes required for bacterial survival in experimental models of pathogenesis, and this approach should function similarly well in selections conducted in vitro and in vivo with any organism amenable to insertional mutagenesis.

Comprehensive identification of Salmonella enterica serovar typhimurium genes required for infection of BALB/c mice

Genes required for infection of mice by Salmonella Typhimurium can be identified by the interrogation of random transposon mutant libraries for mutants that cannot survive in vivo. Inactivation of such genes produces attenuated S. Typhimurium strains that have potential for use as live attenuated vaccines. A quantitative screen, Transposon Mediated Differential Hybridisation (TMDH), has been developed that identifies those members of a large library of transposon mutants that are attenuated. TMDH employs custom transposons with outward-facing T7 and SP6 promoters. Fluorescently-labelled transcripts from the promoters are hybridised to whole-genome tiling microarrays, to allow the position of the transposon insertions to be determined. Comparison of microarray data from the mutant library grown in vitro (input) with equivalent data produced after passage of the library through mice (output) enables an attenuation score to be determined for each transposon mutant. These scores are significantly correlated with bacterial counts obtained during infection of mice using mutants with individual defined deletions of the same genes. Defined deletion mutants of several novel targets identified in the TMDH screen are effective live vaccines.

Global discovery of adaptive mutations

Although modern DNA sequencing enables rapid identification of genetic variation, characterizing the phenotypic consequences of individual mutations remains a labor-intensive task. Here we describe array-based discovery of adaptive mutations (ADAM), a technology that searches an entire bacterial genome for mutations that contribute to selectable phenotypic variation between an evolved strain and its parent. We found that ADAM identified adaptive mutations in laboratory-evolved Escherichia coli strains with high sensitivity and specificity.

Analysis of pools of targeted Salmonella deletion mutants identifies novel genes affecting fitness during competitive infection in mice

Pools of mutants of minimal complexity but maximal coverage of genes of interest facilitate screening for genes under selection in a particular environment. We constructed individual deletion mutants in 1,023 Salmonella enterica serovar Typhimurium genes, including almost all genes found in Salmonella but not in related genera. All mutations were confirmed simultaneously using a novel amplification strategy to produce labeled RNA from a T7 RNA polymerase promoter, introduced during the construction of each mutant, followed by hybridization of this labeled RNA to a Typhimurium genome tiling array. To demonstrate the ability to identify fitness phenotypes using our pool of mutants, the pool was subjected to selection by intraperitoneal injection into BALB/c mice and subsequent recovery from spleens. Changes in the representation of each mutant were monitored using T7 transcripts hybridized to a novel inexpensive minimal microarray. Among the top 120 statistically significant spleen colonization phenotypes, more than 40 were mutations in genes with no previously known role in this model. Fifteen phenotypes were tested using individual mutants in competitive assays of intraperitoneal infection in mice and eleven were confirmed, including the first two examples of attenuation for sRNA mutants in Salmonella. We refer to the method as Array-based analysis of cistrons under selection (ABACUS).

Concurrent growth rate and transcript analyses reveal essential gene stringency in Escherichia coli

Background

Genes essential for bacterial growth are of particular scientific interest. Many putative essential genes have been identified or predicted in several species, however, little is known about gene expression requirement stringency, which may be an important aspect of bacterial physiology and likely a determining factor in drug target development.

Methodology/Principal Findings

Working from the premise that essential genes differ in absolute requirement for growth, we describe silencing of putative essential genes in E. coli to obtain a titration of declining growth rates and transcript levels by using antisense peptide nucleic acids (PNA) and expressed antisense RNA. The relationship between mRNA decline and growth rate decline reflects the degree of essentiality, or stringency, of an essential gene, which is here defined by the minimum transcript level for a 50% reduction in growth rate (MTL₅₀). When applied to four growth essential genes, both RNA silencing methods resulted in MTL₅₀ values that reveal acpP as the most stringently required of the four genes examined, with ftsZ the next most stringently required. The established antibacterial targets murA and fabI were less stringently required.

Conclusions

RNA silencing can reveal stringent requirements for gene expression with respect to growth. This method may be used to validate existing essential genes and to quantify drug target requirement.