Building the repertoire of dispensable chromosome regions in Bacillus subtilis entails major refinement of cognate large-scale metabolic model

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration

Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.

Standards, dissemination, and best practices in systems biology

The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.

New Insights on Metabolic Features of Bacillus subtilis Based on Multistrain Genome-Scale Metabolic Modeling

Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level.

The media composition as a crucial element in high-throughput metabolic network reconstruction

In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.

etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

In silico-guided metabolic engineering of Bacillus subtilis for efficient biosynthesis of purine nucleosides by blocking the key backflow nodes

Background

Purine nucleosides play essential roles in cellular physiological processes and have a wide range of applications in the fields of antitumor/antiviral drugs and food. However, microbial overproduction of purine nucleosides by de novo metabolic engineering remains a great challenge due to their strict and complex regulatory machinery involved in biosynthetic pathways.

Results

In this study, we designed an in silico-guided strategy for overproducing purine nucleosides based on a genome-scale metabolic network model in Bacillus subtilis. The metabolic flux was analyzed to predict two key backflow nodes, Drm (purine nucleotides toward PPP) and YwjH (PPP–EMP), to resolve the competitive relationship between biomass and purine nucleotide synthesis. In terms of the purine synthesis pathway, the first backflow node Drm was inactivated to block the degradation of purine nucleotides, which greatly increased the inosine production to 13.98–14.47 g/L without affecting cell growth. Furthermore, releasing feedback inhibition of the purine operon by promoter replacement enhanced the accumulation of purine nucleotides. In terms of the central carbon metabolic pathways, the deletion of the second backflow node YwjH and overexpression of Zwf were combined to increase inosine production to 22.01 ± 1.18 g/L by enhancing the metabolic flow of PPP. By switching on the flux node of the glucose-6-phosphate to PPP or EMP, the final inosine engineered strain produced up to 25.81 ± 1.23 g/L inosine by a pgi-based metabolic switch with a yield of 0.126 mol/mol glucose, a productivity of 0.358 g/L/h and a synthesis rate of 0.088 mmol/gDW/h, representing the highest yield in de novo engineered inosine bacteria. Under the guidance of this in silico-designed strategy, a general chassis bacterium was generated, for the first time, to efficiently synthesize inosine, adenosine, guanosine, IMP and GMP, which provides sufficient precursors for the synthesis of various purine intermediates.

Conclusions

Our study reveals that in silico-guided metabolic engineering successfully optimized the purine synthesis pathway by exploring efficient targets, which could be applied as a superior strategy for efficient biosynthesis of biotechnological products.

Efficient Large-Scale and Scarless Genome Engineering Enables the Construction and Screening of Bacillus subtilis Biofuel Overproducers

Bacillus subtilis is a versatile microbial cell factory that can produce valuable proteins and value-added chemicals. Long fragment editing techniques are of great importance for accelerating bacterial genome engineering to obtain desirable and genetically stable host strains. Herein, we develop an efficient CRISPR-Cas9 method for large-scale and scarless genome engineering in the Bacillus subtilis genome, which can delete up to 134.3 kb DNA fragments, 3.5 times as long as the previous report, with a positivity rate of 100%. The effects of using a heterologous NHEJ system, linear donor DNA, and various donor DNA length on the engineering efficiencies were also investigated. The CRISPR-Cas9 method was then utilized for Bacillus subtilis genome simplification and construction of a series of individual and cumulative deletion mutants, which are further screened for overproducer of isobutanol, a new generation biofuel. These results suggest that the method is a powerful genome engineering tool for constructing and screening engineered host strains with enhanced capabilities, highlighting the potential for synthetic biology and metabolic engineering.

Effectiveness of the Bacillus subtilis genome-reduced strain as an ethanol production host

We investigated the performance of a genome-reduced strain of Bacillus subtilis MGB874, whose 0.87 Mbp of genomic DNA was cumulatively deleted, as an ethanol production host. A recombinant strain A267_EtOH was constructed by introducing the pdc and adhB genes from Zymomonas mobilis, both of which were expressed from an isopropyl-β-d-1-thiogalactopyranoside-inducible spac promoter, into the A267 strain, a tryptophan prototrophic derivative of the MGB874 with disruption of metabolic pathways for producing lactic acid, acetic acid, and acetoin. Focusing on the stationary phase in fed-batch fermentation, 1.6 g L-1 ethanol was produced by the A267_EtOH strain after 144 h. Moreover, its ethanol production further increased by approximately 3.7-fold (5.9 g L-1) at 80 h through replacing the spac promoter for expressing pdc and adhB genes with the lytR promoter and the yield was about 112%. These results indicate that the MGB874 is an effective host for ethanol production during the stationary phase.

The Bacillus subtilis Minimal Genome Compendium

To better understand cellular life, it is essential to decipher the contribution of individual components and their interactions. Minimal genomes are an important tool to investigate these interactions. Here, we provide a database of 105 fully annotated genomes of a series of strains with sequential deletion steps of the industrially relevant model bacterium Bacillus subtilis starting with the laboratory wild type strain B. subtilis 168 and ending with B. subtilis PG38, which lacks approximately 40% of the original genome. The annotation is supported by sequencing of key intermediate strains as well as integration of literature knowledge for the annotation of the deletion scars and their potential effects. The strain compendium presented here represents a comprehensive genome library of the entire MiniBacillus project. This resource will facilitate the more effective application of the different strains in basic science as well as in biotechnology.

Identification of an RNA sponge that controls the RoxS riboregulator of central metabolism in Bacillus subtilis

sRNAs are a taxonomically-restricted but transcriptomically-abundant class of post-transcriptional regulators. While of major importance for adaption to the environment, we currently lack global-scale methodology enabling target identification, especially in species without known RNA hub proteins (e.g. Hfq). Using psoralen RNA cross-linking and Illumina-sequencing we identify RNA-RNA interacting pairs in vivo in Bacillus subtilis, resolving previously well-described interactants. Although sRNA-sRNA pairings are rare (compared with sRNA-mRNA), we identify a robust example involving the conserved sRNA RoxS and an unstudied sRNA RosA (Regulator of sRNA A). We show RosA to be the first confirmed RNA sponge described in a Gram-positive bacterium. RosA interacts with at least two sRNAs, RoxS and FsrA. The RosA/RoxS interaction not only affects the levels of RoxS but also its processing and regulatory activity. We also found that the transcription of RosA is repressed by CcpA, the key regulator of carbon-metabolism in B. subtilis. Since RoxS is already known to be transcriptionally controlled by malate via the transcriptional repressor Rex, its post-transcriptional regulation by CcpA via RosA places RoxS in a key position to control central metabolism in response to varying carbon sources.

Production of proteins and commodity chemicals using engineered Bacillus subtilis platform strain

Currently, increasing demand of biochemicals produced from renewable resources has motivated researchers to seek microbial production strategies instead of traditional chemical methods. As a microbial platform, Bacillus subtilis possesses many advantages including the generally recognized safe status, clear metabolic networks, short growth cycle, mature genetic editing methods and efficient protein secretion systems. Engineered B. subtilis strains are being increasingly used in laboratory research and in industry for the production of valuable proteins and other chemicals. In this review, we first describe the recent advances of bioinformatics strategies during the research and applications of B. subtilis. Secondly, the applications of B. subtilis in enzymes and recombinant proteins production are summarized. Further, the recent progress in employing metabolic engineering and synthetic biology strategies in B. subtilis platform strain to produce commodity chemicals is systematically introduced and compared. Finally, the major limitations for the further development of B. subtilis platform strain and possible future directions for its research are also discussed.

The Surfactin-Like Lipopeptides From Bacillus spp.: Natural Biodiversity and Synthetic Biology for a Broader Application Range

Surfactin is a lipoheptapeptide produced by several Bacillus species and identified for the first time in 1969. At first, the biosynthesis of this remarkable biosurfactant was described in this review. The peptide moiety of the surfactin is synthesized using huge multienzymatic proteins called NonRibosomal Peptide Synthetases. This mechanism is responsible for the peptide biodiversity of the members of the surfactin family. In addition, on the fatty acid side, fifteen different isoforms (from C12 to C17) can be incorporated so increasing the number of the surfactin-like biomolecules. The review also highlights the last development in metabolic modeling and engineering and in synthetic biology to direct surfactin biosynthesis but also to generate novel derivatives. This large set of different biomolecules leads to a broad spectrum of physico-chemical properties and biological activities. The last parts of the review summarized the numerous studies related to the production processes optimization as well as the approaches developed to increase the surfactin productivity of Bacillus cells taking into account the different steps of its biosynthesis from gene transcription to surfactin degradation in the culture medium.

Screening of a genome-reduced Corynebacterium glutamicum strain library for improved heterologous cutinase secretion

The construction of microbial platform organisms by means of genome reduction is an ongoing topic in biotechnology. In this study, we investigated whether the deletion of single or multiple gene clusters has a positive effect on the secretion of cutinase from Fusarium solani pisi in the industrial workhorse Corynebacterium glutamicum. A total of 22 genome-reduced strain variants were compared applying two Sec signal peptides from Bacillus subtilis. High-throughput phenotyping using robotics-integrated microbioreactor technology with automated harvesting revealed distinct cutinase secretion performance for a specific combination of signal peptide and genomic deletions. The biomass-specific cutinase yield for strain GRS41_51_NprE was increased by ~ 200%, although the growth rate was reduced by ~ 60%. Importantly, the causative deletions of genomic clusters cg2801-cg2828 and rrnC-cg3298 could not have been inferred a priori. Strikingly, bioreactor fed-batch cultivations at controlled growth rates resulted in a complete reversal of the screening results, with the cutinase yield for strain GRS41_51_NprE dropping by ~ 25% compared to the reference strain. Thus, the choice of bioprocess conditions may turn a 'high-performance' strain from batch screening into a 'low-performance' strain in fed-batch cultivation. In conclusion, future studies are needed in order to understand metabolic adaptations of C. glutamicum to both genomic deletions and different bioprocess conditions.

Genome scale metabolic models and analysis for evaluating probiotic potentials

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

Advances on systems metabolic engineering of Bacillus subtilis as a chassis cell

The Gram-positive model bacterium Bacillus subtilis, has been broadly applied in various fields because of its low pathogenicity and strong protein secretion ability, as well as its well-developed fermentation technology. B. subtilis is considered as an attractive host in the field of metabolic engineering, in particular for protein expression and secretion, so it has been well studied and applied in genetic engineering. In this review, we discussed why B. subtilis is a good chassis cell for metabolic engineering. We also summarized the latest research progress in systematic biology, synthetic biology and evolution-based engineering of B. subtilis, and showed systemic metabolic engineering expedite the harnessing B. subtilis for bioproduction.

Efficient secretory production of large-size heterologous enzymes in Bacillus subtilis: A secretory partner and directed evolution

Secretory production of recombinant proteins provides a simple approach to the production and purification of target proteins in the enzyme industry. We developed a combined strategy for the secretory production of three large-size heterologous enzymes with a special focus on 83-kDa isoamylase (IA) from an archaeon Sulfolobus tokodaii in a bacterium Bacillus subtilis. First, a secretory protein of the B. subtilis family 5 glycoside hydrolase endoglucanase (Cel5) was used as a fusion partner, along with the NprB signal peptide, to facilitate secretory production of IA. This secretory partner strategy was effective for the secretion of two other large enzymes: family 9 glycoside hydrolase from Clostridium phytofermentas and cellodextrin phosphorylase from Clostridium thermocellum. Second, the secretion of Cel5-IA was improved by directed evolution with two novel double-layer Petri-dish-based high-throughput screening (HTS) methods. The high-sensitivity HTS relied on the detection of high-activity Cel5 on the carboxymethylcellulose/Congo-red assay. The second modest-sensitivity HTS focused on the detection of low-activity IA on the amylodextrin-I₂ assay. After six rounds of HTS, a secretory Cel5-IA level was increased to 234 mg/L, 155 times the wild-type IA with the NprB signal peptide only. This combinatory strategy could be useful to enhance the secretory production of large-size heterologous proteins in B. subtilis.

A bacterial cell factory converting glucose into scyllo-inositol, a therapeutic agent for Alzheimer's disease

A rare stereoisomer of inositol, scyllo-inositol, is a therapeutic agent that has shown potential efficacy in preventing Alzheimer’s disease. Mycobacterium tuberculosis ino1 encoding myo-inositol-1-phosphate (MI1P) synthase (MI1PS) was introduced into Bacillus subtilis to convert glucose-6-phosphate (G6P) into MI1P. We found that inactivation of pbuE elevated intracellular concentrations of NAD⁺·NADH as an essential cofactor of MI1PS and was required to activate MI1PS. MI1P thus produced was dephosphorylated into myo-inositol by an intrinsic inositol monophosphatase, YktC, which was subsequently isomerized into scyllo-inositol via a previously established artificial pathway involving two inositol dehydrogenases, IolG and IolW. In addition, both glcP and glcK were overexpressed to feed more G6P and accelerate scyllo-inositol production. Consequently, a B. subtilis cell factory was demonstrated to produce 2 g L⁻¹scyllo-inositol from 20 g L⁻¹ glucose. This cell factory provides an inexpensive way to produce scyllo-inositol, which will help us to challenge the growing problem of Alzheimer’s disease in our aging society.

Michon et al. describe the use of a recombinant Bacillus subtilis as a cell factory capable of producing scyllo-inositol, a therapeutic compound for Alzheimer’s disease, from inexpensive glucose. They demonstrate that it could produce 2 g L⁻¹ of scyllo-inositol from 20 g L⁻¹ glucose.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Multiple integration of the gene ganA into the Bacillus subtilis chromosome for enhanced β-galactosidase production using the CRISPR/Cas9 system

The ganA gene from Bacillus subtilis encoding a β-galactosidase for degradation of the galactomannan was integrated in different loci of the B. subtilis chromosome employing the CRISPR/Cas9 system. Hereby a total of five copies of ganA cassettes in which the ganA gene was fused with the glucitol-promoter were inserted in the recipient chromosome wherein hypothetical, sporulation and protease genes were deleted. The strain with five copies of ganA expression cassette showed a β-galactosidase activity similar to the one with the same gene on a pUB110 derived multi-copy plasmid and under the same regulatory control of the glucitol promoter and GutR activator. The production of β-galactosidase in the strain with the multi-copy plasmid decreased rapidly when growth was performed under induced conditions and without antibiotic selection. In contrast, the strain with the five copies of ganA in the chromosome produced β-galactosidase for at least 40 generations. This demonstrates that the CRISPR/Cas9 system is a valuable and easy tool for constructing stable producer strains. The bigger efforts that are needed for the multiple target gene integration into the chromosome compared to cloning in expression vectors were justified by the higher stability of the target genes and the lack of antibiotic resistance genes.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Differential expression of a prophage-encoded glycocin and its immunity protein suggests a mutualistic strategy of a phage and its host

Sublancin 168 is a highly potent and stable antimicrobial peptide secreted by the Gram-positive bacterium Bacillus subtilis. Production of sublancin gives B. subtilis a major competitive growth advantage over a range of other bacteria thriving in the same ecological niches, the soil and plant rhizosphere. B. subtilis protects itself against sublancin by producing the cognate immunity protein SunI. Previous studies have shown that both the sunA gene for sublancin and the sunI immunity gene are encoded by the prophage SPβ. The sunA gene is under control of several transcriptional regulators. Here we describe the mechanisms by which sunA is heterogeneously expressed within a population, while the sunI gene encoding the immunity protein is homogeneously expressed. The key determinants in heterogeneous sunA expression are the transcriptional regulators Spo0A, AbrB and Rok. Interestingly, these regulators have only a minor influence on sunI expression and they have no effect on the homogeneous expression of sunI within a population of growing cells. Altogether, our findings imply that the homogeneous expression of sunI allows even cells that are not producing sublancin to protect themselves at all times from the active sublancin produced at high levels by their isogenic neighbors. This suggests a mutualistic evolutionary strategy entertained by the SPβ prophage and its Bacillus host, ensuring both stable prophage maintenance and a maximal competitive advantage for the host at minimal costs.

Less Is More: Toward a Genome-Reduced Bacillus Cell Factory for "Difficult Proteins"

The availability of complete genome sequences and the definition of essential gene sets were fundamental in the start of the genome engineering era. In a recent study, redundant and unnecessary genes were systematically deleted from the Gram-positive bacterium Bacillus subtilis, an industrial production host of high-value secreted proteins. This culminated in strain PG10, which lacks about 36% of the genome, thus representing the most minimal Bacillus chassis currently available. Here, we show that this “miniBacillus” strain has synthetic traits that are favorable for producing “difficult-to-produce proteins”. As exemplified with different staphylococcal antigens, PG10 overcomes several bottlenecks in protein production related to the secretion process and instability of the secreted product. These findings show for the first time that massive genome reduction can substantially improve secretory protein production by a bacterial expression host, and underpin the high potential of genome-engineered strains as future cell factories.

Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Background

Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design.

Results

Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain.

Conclusions

This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.

A novel method for transforming the thermophilic bacterium Geobacillus kaustophilus

BACKGROUND

Bacterial strains of the genus Geobacillus grow at high temperatures of 50-75 °C and could thus be useful for biotechnological applications. However, genetic manipulation of these species is difficult because the current techniques for transforming Geobacillus species are not efficient. In this study, we developed an easy and efficient method for transforming Geobacillus kaustophilus using the conjugative plasmid pLS20cat.

RESULTS

We constructed a transformation system comprising (i) a mobilizable Bacillus subtilis-G. kaustophilus shuttle plasmid named pGK1 that carries the elements for selection and replication in Geobacillus, and (ii) a pLS20cat-harboring B. subtilis donor strain expressing the dam methylase gene of Escherichia coli and the conjugation-stimulating rapLS20 gene of pLS20cat. This system can be used to efficiently introduce pGK1 into G. kaustophilus by mobilization in a pLS20cat-dependent way. Whereas the thermostable kanamycin marker and Geobacillus replication origin of pGK1 as well as expression of dam methylase in the donor were indispensable for mobilization, ectopic expression of rapLS20 increased its efficiency. In addition, the conditions of the recipient influenced mobilization efficiency: the highest mobilization efficiencies were obtained using recipient cells that were in the exponential growth phase. Furthermore, elimination of the origin of transfer from pLS20cat enhanced the mobilization.

CONCLUSIONS

We describe a novel method of plasmid mobilization into G. kaustophilus recipient from B. subtilis donor depending on the helper function of pLS20cat, which enables simple, rapid, and easy transformation of the thermophilic Gram-positive bacterium.

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.

Molecular and Physiological Logics of the Pyruvate-Induced Response of a Novel Transporter in Bacillus subtilis

At the heart of central carbon metabolism, pyruvate is a pivotal metabolite in all living cells. Bacillus subtilis is able to excrete pyruvate as well as to use it as the sole carbon source. We herein reveal that ysbAB (renamed pftAB), the only operon specifically induced in pyruvate-grown B. subtilis cells, encodes a hetero-oligomeric membrane complex which operates as a facilitated transport system specific for pyruvate, thereby defining a novel class of transporter. We demonstrate that the LytST two-component system is responsible for the induction of pftAB in the presence of pyruvate by binding of the LytT response regulator to a palindromic region upstream of pftAB. We show that both glucose and malate, the preferred carbon sources for B. subtilis, trigger the binding of CcpA upstream of pftAB, which results in its catabolite repression. However, an additional CcpA-independent mechanism represses pftAB in the presence of malate. Screening a genome-wide transposon mutant library, we find that an active malic enzyme replenishing the pyruvate pool is required for this repression. We next reveal that the higher the influx of pyruvate, the stronger the CcpA-independent repression of pftAB, which suggests that intracellular pyruvate retroinhibits pftAB induction via LytST. Such a retroinhibition challenges the rational design of novel nature-inspired sensors and synthetic switches but undoubtedly offers new possibilities for the development of integrated sensor/controller circuitry. Overall, we provide evidence for a complete system of sensors, feed-forward and feedback controllers that play a major role in environmental growth of B. subtilis.

Pyruvate is a small-molecule metabolite ubiquitous in living cells. Several species also use it as a carbon source as well as excrete it into the environment. The bacterial systems for pyruvate import/export have yet to be discovered. Here, we identified in the model bacterium Bacillus subtilis the first import/export system specific for pyruvate, PftAB, which defines a novel class of transporter. In this bacterium, extracellular pyruvate acts as the signal molecule for the LytST two-component system (TCS), which in turn induces expression of PftAB. However, when the pyruvate influx is high, LytST activity is drastically retroinhibited. Such a retroinhibition challenges the rational design of novel nature-inspired sensors and synthetic switches but undoubtedly offers new possibilities for the development of integrated sensor/controller circuitry.

Genome modularity and synthetic biology: Engineering systems

Whole genome sequencing projects running in various laboratories around the world has generated immense data. A systematic phylogenetic analysis of this data shows that genome complexity goes on decreasing as it evolves, due to its modular nature. This modularity can be harnessed to minimize the genome further to reduce it with the bare minimum essential genes. A reduced modular genome, can fuel progress in the area of synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health.

Large-scale reduction of the Bacillus subtilis genome: consequences for the transcriptional network, resource allocation, and metabolism

Understanding cellular life requires a comprehensive knowledge of the essential cellular functions, the components involved, and their interactions. Minimized genomes are an important tool to gain this knowledge. We have constructed strains of the model bacterium, Bacillus subtilis, whose genomes have been reduced by ∼36%. These strains are fully viable, and their growth rates in complex medium are comparable to those of wild type strains. An in-depth multi-omics analysis of the genome reduced strains revealed how the deletions affect the transcription regulatory network of the cell, translation resource allocation, and metabolism. A comparison of gene counts and resource allocation demonstrates drastic differences in the two parameters, with 50% of the genes using as little as 10% of translation capacity, whereas the 6% essential genes require 57% of the translation resources. Taken together, the results are a valuable resource on gene dispensability in B. subtilis, and they suggest the roads to further genome reduction to approach the final aim of a minimal cell in which all functions are understood.

The Blueprint of a Minimal Cell: MiniBacillus

Bacillus subtilis is one of the best-studied organisms. Due to the broad knowledge and annotation and the well-developed genetic system, this bacterium is an excellent starting point for genome minimization with the aim of constructing a minimal cell. We have analyzed the genome of B. subtilis and selected all genes that are required to allow life in complex medium at 37°C. This selection is based on the known information on essential genes and functions as well as on gene and protein expression data and gene conservation. The list presented here includes 523 and 119 genes coding for proteins and RNAs, respectively. These proteins and RNAs are required for the basic functions of life in information processing (replication and chromosome maintenance, transcription, translation, protein folding, and secretion), metabolism, cell division, and the integrity of the minimal cell. The completeness of the selected metabolic pathways, reactions, and enzymes was verified by the development of a model of metabolism of the minimal cell. A comparison of the MiniBacillus genome to the recently reported designed minimal genome of Mycoplasma mycoides JCVI-syn3.0 indicates excellent agreement in the information-processing pathways, whereas each species has a metabolism that reflects specific evolution and adaptation. The blueprint of MiniBacillus presented here serves as the starting point for a successive reduction of the B. subtilis genome.

F-Type Bacteriocins of Listeria monocytogenes: a New Class of Phage Tail-Like Structures Reveals Broad Parallel Coevolution between Tailed Bacteriophages and High-Molecular-Weight Bacteriocins

Listeria monocytogenes is a significant foodborne human pathogen that can cause severe disease in certain high-risk individuals. L. monocytogenes is known to produce high-molecular-weight, phage tail-like bacteriocins, or "monocins," upon induction of the SOS system. In this work, we purified and characterized monocins and found them to be a new class of F-type bacteriocins. The L. monocytogenes monocin genetic locus was cloned and expressed in Bacillus subtilis, producing specifically targeted bactericidal particles. The receptor binding protein, which determines target cell specificity, was identified and engineered to change the bactericidal spectrum. Unlike the F-type pyocins of Pseudomonas aeruginosa, which are related to lambda-like phage tails, monocins are more closely related to TP901-1-like phage tails, structures not previously known to function as bacteriocins. Monocins therefore represent a new class of phage tail-like bacteriocins. It appears that multiple classes of phage tails and their related bacteriocins have coevolved separately in parallel.

IMPORTANCE

Phage tail-like bacteriocins (PTLBs) are structures widespread among the members of the bacterial kingdom that are evolutionarily related to the DNA delivery organelles of phages (tails). We identified and characterized "monocins" of Listeria monocytogenes and showed that they are related to the tail structures of TP901-1-like phages, structures not previously known to function as bacteriocins. Our results show that multiple types of envelope-penetrating machines have coevolved in parallel to function either for DNA delivery (phages) or as membrane-disrupting bacteriocins. While it has commonly been assumed that these structures were coopted from phages, we cannot rule out the opposite possibility, that ancient phages coopted complex bacteriocins from the cell, which then underwent adaptations to become efficient at translocating DNA.

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Background

Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur.

Results

Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent.

Conclusions

Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users.

Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

Constitutive Stringent Response Restores Viability of Bacillus subtilis Lacking Structural Maintenance of Chromosome Protein

Bacillus subtilis mutants lacking the SMC-ScpAB complex are severely impaired for chromosome condensation and partitioning, DNA repair, and cells are not viable under standard laboratory conditions. We isolated suppressor mutations that restored the capacity of a smc deletion mutant (Δsmc) to grow under standard conditions. These suppressor mutations reduced chromosome segregation defects and abrogated hypersensitivity to gyrase inhibitors of Δsmc. Three suppressor mutations were mapped in genes involved in tRNA aminoacylation and maturation pathways. A transcriptomic survey of isolated suppressor mutations pointed to a potential link between suppression of Δsmc and induction of the stringent response. This link was confirmed by (p)ppGpp quantification which indicated a constitutive induction of the stringent response in multiple suppressor strains. Furthermore, sublethal concentrations of arginine hydroxamate (RHX), a potent inducer of stringent response, restored growth of Δsmc under non permissive conditions. We showed that production of (p)ppGpp alone was sufficient to suppress the thermosensitivity exhibited by the Δsmc mutant. Our findings shed new light on the coordination between chromosome dynamics mediated by SMC-ScpAB and other cellular processes during rapid bacterial growth.

Quantitative prediction of genome-wide resource allocation in bacteria

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

SubtiWiki 2.0--an integrated database for the model organism Bacillus subtilis

To understand living cells, we need knowledge of each of their parts as well as about the interactions of these parts. To gain rapid and comprehensive access to this information, annotation databases are required. Here, we present SubtiWiki 2.0, the integrated database for the model bacterium Bacillus subtilis (http://subtiwiki.uni-goettingen.de/). SubtiWiki provides text-based access to published information about the genes and proteins of B. subtilis as well as presentations of metabolic and regulatory pathways. Moreover, manually curated protein-protein interactions diagrams are linked to the protein pages. Finally, expression data are shown with respect to gene expression under 104 different conditions as well as absolute protein quantification for cytoplasmic proteins. To facilitate the mobile use of SubtiWiki, we have now expanded it by Apps that are available for iOS and Android devices. Importantly, the App allows to link private notes and pictures to the gene/protein pages. Today, SubtiWiki has become one of the most complete collections of knowledge on a living organism in one single resource.

Heading in the right direction: thermodynamics-based network analysis and pathway engineering

Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods.

Pseudomonas aeruginosa essentials: an update on investigation of essential genes

Pseudomonas aeruginosa is the leading cause of nosocomial infections, particularly in immunocompromised, cancer, burn and cystic fibrosis patients. Development of novel antimicrobials against P. aeruginosa is therefore of the highest importance. Although the first reports on P. aeruginosa essential genes date back to the early 2000s, a number of more sensitive genomic approaches have been used recently to better define essential genes in this organism. These analyses highlight the evolution of the definition of an 'essential' gene from the traditional to the context-dependent. Essential genes, particularly those indispensable under the clinically relevant conditions, are considered to be promising targets of novel antibiotics against P. aeruginosa. This review provides an update on the investigation of P. aeruginosa essential genes. Special focus is on recently identified P. aeruginosa essential genes and their exploitation for the development of antimicrobials.

The phosphoenolpyruvate:sugar phosphotransferase system is involved in sensitivity to the glucosylated bacteriocin sublancin

The mode of action of a group of glycosylated antimicrobial peptides known as glycocins remains to be elucidated. In the current study of one glycocin, sublancin, we identified the phosphoenolpyruvate:sugar phosphotransferase system (PTS) of Bacillus species as a key player in bacterial sensitivity. Sublancin kills several Gram-positive bacteria, such as Bacillus species and Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA). Unlike other classes of bacteriocins for which the PTS is involved in their mechanism of action, we show that the addition of PTS-requiring sugars leads to increased resistance rather than increased sensitivity, suggesting that sublancin has a distinct mechanism of action. Collectively, our present mutagenesis and genomic studies demonstrate that the histidine-containing phosphocarrier protein (HPr) and domain A of enzyme II (PtsG) in particular are critical determinants for bacterial sensitivity to sublancin.

Development of a markerless gene deletion system for Bacillus subtilis based on the mannose phosphoenolpyruvate-dependent phosphotransferase system

To optimize Bacillus subtilis as a production strain for proteins and low molecular substances by genome engineering, we developed a markerless gene deletion system. We took advantage of a general property of the phosphoenolpyruvate-dependent phosphotransferase system (PTS), in particular the mannose PTS. Mannose is phosphorylated during uptake by its specific transporter (ManP) to mannose 6-phosphate, which is further converted to fructose 6-phosphate by the mannose-6-phosphate isomerase (ManA). When ManA is missing, accumulation of the phosphorylated mannose inhibits cell growth. This system was constructed by deletion of manP and manA in B. subtilis Δ6, a 168 derivative strain with six large deletions of prophages and antibiotic biosynthesis genes. The manP gene was inserted into an Escherichia coli plasmid together with a spectinomycin resistance gene for selection in B. subtilis. To delete a specific region, its up- and downstream flanking sites (each of approximately 700 bp) were inserted into the vector. After transformation, integration of the plasmid into the chromosome of B. subtilis by single cross-over was selected by spectinomycin. In the second step, excision of the plasmid was selected by growth on mannose. Finally, excision and concomitant deletion of the target region were verified by colony PCR. In this way, all nine prophages, seven antibiotic biosynthesis gene clusters and two sigma factors for sporulation were deleted and the B. subtilis genome was reduced from 4215 to 3640 kb. Despite these extensive deletions, growth rate and cell morphology remained similar to the B. subtilis 168 parental strain.

The reduction in small ribosomal subunit abundance in ethanol-stressed cells of Bacillus subtilis is mediated by a SigB-dependent antisense RNA

One of the best-characterized general stress responses in bacteria is the σB-mediated stress response of the Gram-positive soil bacterium Bacillus subtilis. The σB regulon contains approximately 200 protein-encoding genes and 136 putative regulatory RNAs. One of these σB-dependent RNAs, named S1136-S1134, was recently mapped as being transcribed from the S1136 promoter on the opposite strand of the essential rpsD gene, which encodes the ribosomal primary-binding protein S4. Accordingly, S1136-S1134 transcription results in an rpsD-overlapping antisense RNA (asRNA). Upon exposure of B. subtilis to ethanol, the S1136 promoter was found to be induced, while rpsD transcription was downregulated. By quantitative PCR, we show that the activation of transcription from the S1136 promoter is directly responsible for the downregulation of rpsD upon ethanol exposure. We also show that this downregulation of rpsD leads to a reduced level of the small (30S) ribosomal subunit upon ethanol stress. The activation of the S1136 promoter thus represents the first example of antisense transcription-mediated regulation in the general stress response of B. subtilis and implicates the reduction of ribosomal protein abundance as a new aspect in the σB-dependent stress response. We propose that the observed reduction in the level of the small ribosomal subunit, which contains the ribosome-decoding center, may protect B. subtilis cells against misreading and spurious translation of possibly toxic aberrant peptides under conditions of ethanol stress.

Tracking the Elusive Function of Bacillus subtilis Hfq

RNA-binding protein Hfq is a key component of the adaptive responses of many proteobacterial species including Escherichia coli, Salmonella enterica and Vibrio cholera. In these organisms, the importance of Hfq largely stems from its participation to regulatory mechanisms involving small non-coding RNAs. In contrast, the function of Hfq in Gram-positive bacteria has remained elusive and somewhat controversial. In the present study, we have further addressed this point by comparing growth phenotypes and transcription profiles between wild-type and an hfq deletion mutant of the model Gram-positive bacterium, Bacillus subtilis. The absence of Hfq had no significant consequences on growth rates under nearly two thousand metabolic conditions and chemical treatments. The only phenotypic difference was a survival defect of B. subtilis hfq mutant in rich medium in stationary phase. Transcriptomic analysis correlated this phenotype with a change in the levels of nearly one hundred transcripts. Albeit a significant fraction of these RNAs (36%) encoded sporulation-related functions, analyses in a strain unable to sporulate ruled out sporulation per se as the basis of the hfq mutant’s stationary phase fitness defect. When expressed in Salmonella, B. subtilis hfq complemented the sharp loss of viability of a degP hfq double mutant, attenuating the chronic σ^E-activated phenotype of this strain. However, B. subtilis hfq did not complement other regulatory deficiencies resulting from loss of Hfq-dependent small RNA activity in Salmonella indicating a limited functional overlap between Salmonella and B. subtilis Hfqs. Overall, this study confirmed that, despite structural similarities with other Hfq proteins, B. subtilis Hfq does not play a central role in post-transcriptional regulation but might have a more specialized function connected with stationary phase physiology. This would account for the high degree of conservation of Hfq proteins in all 17 B. subtilis strains whose genomes have been sequenced.