Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote

Multiple SigB homologues govern the transcription of the ssgBp promoter in the sporulation-specific ssgB gene in Streptomyces coelicolor A3(2)

Unlike Bacillus subtilis, Streptomyces coelicolor contains nine SigB homologues of the stress-response sigma factor SigB. By using a two-plasmid system, we previously identified promoters recognized by these sigma factors. Almost all promoters were recognized by several SigB homologues. However, no specific sequences of these promoters were found. One of these promoters, ssgBp, was selected to examine this cross-recognition in the native host. It controls the expression of the sporulation-specific gene ssgB. Using a luciferase reporter, the activity of this promoter in S. coelicolor and nine mutant strains lacking individual sigB homologous genes showed that sgBp is dependent on three sigma factors, SigH, SigN, and SigI. To determine which nucleotides in the-10 region are responsible for the selection of a specific SigB homologue, promoters mutated at the last three nucleotide positions were tested in the two-plasmid system. Some mutant promoters were specifically recognized by a distinct set of SigB homologues. Analysis of these mutant promoters in the native host showed the role of these nucleotides. A conserved nucleotide A at position 5 was essential for promoter activity, and two variable nucleotides at positions 4 and 6 were responsible for the partial selectivity of promoter recognition by SigB homologues.

Optimization of Constitutive Promoters Using a Promoter-Trapping Vector in Burkholderia pyrrocinia JK-SH007

Selecting suitable promoters to drive gene overexpression can provide significant insight into the development of engineered bacteria. In this study, we analyzed the transcriptome data of Burkholderia pyrrocinia JK-SH007 and identified 54 highly expressed genes. The promoter sequences were located using genome-wide data and scored using the prokaryotic promoter prediction software BPROM to further screen out 18 promoter sequences. We also developed a promoter trap system based on two reporter proteins adapted for promoter optimization in B. pyrrocinia JK-SH007: firefly luciferase encoded by the luciferase gene set (Luc) and trimethoprim (TP)-resistant dihydrofolate reductase (TP^r). Ultimately, eight constitutive promoters were successfully inserted into the probe vector and transformed into B. pyrrocinia JK-SH007. The transformants were successfully grown on Tp antibiotic plates, and firefly luciferase expression was determined by measuring the relative light unit (RLU). Five of the promoters (P4, P9, P10, P14, and P19) showed 1.01-2.51-fold higher activity than the control promoter λ phage transcriptional promoter (PRPL). The promoter activity was further validated via qPCR analysis, indicating that promoters P14 and P19 showed stable high transcription levels at all time points. Then, GFP and RFP proteins were overexpressed in JK-SH007. In addition, promoters P14 and P19 were successfully used to drive gene expression in Burkholderia multivorans WS-FJ9 and Escherichia coli S17-1. The two constitutive promoters can be used not only in B. pyrrocinia JK-SH007 itself to gene overexpression but also to expand the scope of application.

Cross-Recognition of Promoters by the Nine SigB Homologues Present in Streptomyces coelicolor A3(2)

In contrast to Bacillus subtilis, Streptomyces coelicolor A3(2) contains nine homologues of stress response sigma factor SigB with a major role in differentiation and osmotic stress response. The aim of this study was to further characterize these SigB homologues. We previously established a two-plasmid system to identify promoters recognized by sigma factors and used it to identify promoters recognized by the three SigB homologues, SigF, SigG, and SigH from S. coelicolor A3(2). Here, we used this system to identify 14 promoters recognized by SigB. The promoters were verified in vivo in S. coelicolor A3(2) under osmotic stress conditions in sigB and sigH operon mutants, indicating some cross-recognition of these promoters by these two SigB homologues. This two-plasmid system was used to examine the recognition of all identified SigB-, SigF-, SigG-, and SigH-dependent promoters with all nine SigB homologues. The results confirmed this cross-recognition. Almost all 24 investigated promoters were recognized by two or more SigB homologues and data suggested some distinguishing groups of promoters recognized by these sigma factors. However, analysis of the promoters did not reveal any specific sequence characteristics for these recognition groups. All promoters showed high similarity in the -35 and -10 regions. Immunoblot analysis revealed the presence of SigB under osmotic stress conditions and SigH during morphological differentiation. Together with the phenotypic analysis of sigB and sigH operon mutants in S. coelicolor A3(2), the results suggest a dominant role for SigB in the osmotic stress response and a dual role for SigH in the osmotic stress response and morphological differentiation. These data suggest a complex regulation of the osmotic stress response in relation to morphological differentiation in S. coelicolor A3(2).

Sigma Factor Modulation for Cyanobacterial Metabolic Engineering

Sigma (σ) factors are key regulatory proteins that control the transcription initiation in prokaryotes. In response to environmental or developmental cues, σ factors initiate the transcription of necessary genes responsible for maintaining a life-sustaining metabolic balance. Due to the significant role of σ factors in bacterial metabolism, their rational engineering for commercial metabolite production in photoautotrophic, cyanobacterial cells is a desirable venture. As cyanobacterial genomes typically encode multiple σ factors, effective execution of metabolic engineering efforts largely relies on uncovering the complicated gene regulatory network and further characterization of the members of σ factor regulatory circuits. This review outlines the prospects of σ factor in metabolic engineering of cyanobacteria, summarizes the challenges in the path towards an efficient strain construction and highlights the genomic context of putative regulators of cyanobacterial σ factors.

Pleiotropic anti-anti-sigma factor BldG is phosphorylated by several anti-sigma factor kinases in the process of activating multiple sigma factors in Streptomyces coelicolor A3(2)

The anti-anti-sigma factor BldG has a pleiotropic function in Streptomyces coelicolor A3(2), regulating both morphological and physiological differentiation. Together with the anti-sigma factor UshX, it participates in a partner-switching activation of the sigma factor σ^H, which has a dual role in the osmotic stress response and morphological differentiation in S. coelicolor A3(2). In addition to UshX, BldG also interacts with the anti-sigma factor ApgA, although no target sigma factor has yet been identified. However, neither UshX nor ApgA phosphorylates BldG. This phosphorylation is provided by the anti-sigma factor RsfA, which is specific for the late developmental sigma factor σ^F. However, BldG is phosphorylated in the rsfA mutant, suggesting that some other anti-sigma factors containing HATPase_c kinase domain are capable to phosphorylate BldG in vivo. Bacterial two-hybrid system (BACTH) was therefore used to investigate the interactions of all suitable anti-sigma factors of S. coelicolor A3(2) with BldG. At least 15 anti-sigma factors were found to interact with BldG. These interactions were confirmed by native PAGE. In addition to RsfA, BldG is specifically phosphorylated on the conserved phosphorylation Ser57 residue by at least seven additional anti-sigma factors. However, only one of them, SCO7328, has been shown to interact with three sigma factors, σ^G, σ^K and σ^M. A mutant with deleted SCO7328 gene was prepared in S. coelicolor A3(2), however, no specific function of SCO7328 in growth, differentiation or stress response could be attributed to this anti-sigma factor. These results suggest that BldG is specifically phosphorylated by several anti-sigma factors and it plays a role in the regulation of several sigma factors in S. coelicolor A3(2). This suggests a complex regulation of the stress response and differentiation in S. coelicolor A3(2) through this pleiotropic anti-sigma factor.

Defining the regulon of genes controlled by σE , a key regulator of the cell envelope stress response in Streptomyces coelicolor

The extracytoplasmic function (ECF) σ factor, σ^E , is a key regulator of the cell envelope stress response in Streptomyces coelicolor. Although its role in maintaining cell wall integrity has been known for over a decade, a comprehensive analysis of the genes under its control has not been undertaken. Here, using a combination of chromatin immunoprecipitation-sequencing (ChIP-seq), microarray transcriptional profiling and bioinformatic analysis, we attempt to define the σ^E regulon. Approximately half of the genes identified encode proteins implicated in cell envelope function. Seventeen novel targets were validated by S1 nuclease mapping or in vitro transcription, establishing a σ^E -binding consensus. Subsequently, we used bioinformatic analysis to look for conservation of the σ^E target promoters identified in S. coelicolor across 19 Streptomyces species. Key proteins under σ^E control across the genus include the actin homolog MreB, three penicillin-binding proteins, two L,D-transpeptidases, a LytR-CpsA-Psr-family protein predicted to be involved in cell wall teichoic acid deposition and a predicted MprF protein, which adds lysyl groups to phosphatidylglycerol to neutralize membrane surface charge. Taken together, these analyses provide biological insight into the σ^E -mediated cell envelope stress response in the genus Streptomyces.

Unraveling Nutritional Regulation of Tacrolimus Biosynthesis in Streptomyces tsukubaensis through omic Approaches

Streptomyces tsukubaensis stands out among actinomycetes by its ability to produce the immunosuppressant tacrolimus. Discovered about 30 years ago, this macrolide is widely used as immunosuppressant in current clinics. Other potential applications for the treatment of cancer and as neuroprotective agent have been proposed in the last years. In this review we introduce the discovery of S. tsukubaensis and tacrolimus, its biosynthetic pathway and gene cluster (fkb) regulation. We have focused this work on the omic studies performed in this species in order to understand tacrolimus production. Transcriptomics, proteomics and metabolomics have improved our knowledge about the fkb transcriptional regulation and have given important clues about nutritional regulation of tacrolimus production that can be applied to improve production yields. Finally, we address some points of S. tsukubaensis biology that deserve more attention.

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data

BACKGROUND

Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks.

RESULTS

We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package.

CONCLUSIONS

Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

Streptomyces coelicolor strains lacking polyprenol phosphate mannose synthase and protein O-mannosyl transferase are hyper-susceptible to multiple antibiotics

Polyprenol phosphate mannose (PPM) is a lipid-linked sugar donor used by extra-cytoplasmic glycosyl tranferases in bacteria. PPM is synthesized by polyprenol phosphate mannose synthase, Ppm1, and in most Actinobacteria is used as the sugar donor for protein O-mannosyl transferase, Pmt, in protein glycosylation. Ppm1 and Pmt have homologues in yeasts and humans, where they are required for protein O-mannosylation. Actinobacteria also use PPM for lipoglycan biosynthesis. Here we show that ppm1 mutants of Streptomyces coelicolor have increased susceptibility to a number of antibiotics that target cell wall biosynthesis. The pmt mutants also have mildly increased antibiotic susceptibilities, in particular to β-lactams and vancomycin. Despite normal induction of the vancomycin gene cluster, vanSRJKHAX, the pmt and ppm1 mutants remained highly vancomycin sensitive indicating that the mechanism of resistance is blocked post-transcriptionally. Differential RNA expression analysis indicated that catabolic pathways were downregulated and anabolic ones upregulated in the ppm1 mutant compared to the parent or complemented strains. Of note was the increase in expression of fatty acid biosynthetic genes in the ppm1^- mutant. A change in lipid composition was confirmed using Raman spectroscopy, which showed that the ppm1^- mutant had a greater relative proportion of unsaturated fatty acids compared to the parent or the complemented mutant. Taken together, these data suggest that an inability to synthesize PPM (ppm1) and loss of the glycoproteome (pmt^- mutant) can detrimentally affect membrane or cell envelope functions leading to loss of intrinsic and, in the case of vancomycin, acquired antibiotic resistance.

Secondary Metabolites Produced during the Germination of Streptomyces coelicolor

Spore awakening is a series of actions that starts with purely physical processes and continues via the launching of gene expression and metabolic activities, eventually achieving a vegetative phase of growth. In spore-forming microorganisms, the germination process is controlled by intra- and inter-species communication. However, in the Streptomyces clade, which is capable of developing a plethora of valuable compounds, the chemical signals produced during germination have not been systematically studied before. Our previously published data revealed that several secondary metabolite biosynthetic genes are expressed during germination. Therefore, we focus here on the secondary metabolite production during this developmental stage. Using high-performance liquid chromatography-mass spectrometry, we found that the sesquiterpenoid antibiotic albaflavenone, the polyketide germicidin A, and chalcone are produced during germination of the model streptomycete, S. coelicolor. Interestingly, the last two compounds revealed an inhibitory effect on the germination process. The secondary metabolites originating from the early stage of microbial growth may coordinate the development of the producer (quorum sensing) and/or play a role in competitive microflora repression (quorum quenching) in their nature environments.

A Waking Review: Old and Novel Insights into the Spore Germination in Streptomyces

The complex development undergone by Streptomyces encompasses transitions from vegetative mycelial forms to reproductive aerial hyphae that differentiate into chains of single-celled spores. Whereas their mycelial life – connected with spore formation and antibiotic production – is deeply investigated, spore germination as the counterpoint in their life cycle has received much less attention. Still, germination represents a system of transformation from metabolic zero point to a new living lap. There are several aspects of germination that may attract our attention: (1) Dormant spores are strikingly well-prepared for the future metabolic restart; they possess stable transcriptome, hydrolytic enzymes, chaperones, and other required macromolecules stabilized in a trehalose milieu; (2) Germination itself is a specific sequence of events leading to a complete morphological remodeling that include spore swelling, cell wall reconstruction, and eventually germ tube emergences; (3) Still not fully unveiled are the strategies that enable the process, including a single cell’s signal transduction and gene expression control, as well as intercellular communication and the probability of germination across the whole population. This review summarizes our current knowledge about the germination process in Streptomyces, while focusing on the aforementioned points.

Streptomyces tsukubaensis as a new model for carbon repression: transcriptomic response to tacrolimus repressing carbon sources

In this work, we identified glucose and glycerol as tacrolimus repressing carbon sources in the important species Streptomyces tsukubaensis. A genome-wide analysis of the transcriptomic response to glucose and glycerol additions was performed using microarray technology. The transcriptional time series obtained allowed us to compare the transcriptomic profiling of S. tsukubaensis growing under tacrolimus producing and non-producing conditions. The analysis revealed important and different metabolic changes after the additions and a lack of transcriptional activation of the fkb cluster. In addition, we detected important differences in the transcriptional response to glucose between S. tsukubaensis and the model species Streptomyces coelicolor. A number of genes encoding key players of morphological and biochemical differentiation were strongly and permanently downregulated by the carbon sources. Finally, we identified several genes showing transcriptional profiles highly correlated to that of the tacrolimus biosynthetic pathway regulator FkbN that might be potential candidates for the improvement of tacrolimus production.

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.

Genome-wide identification and characterization of reference genes with different transcript abundances for Streptomyces coelicolor

The lack of reliable reference genes (RGs) in the genus Streptomyces hampers effort to obtain the precise data of transcript levels. To address this issue, we aimed to identify reliable RGs in the model organism Streptomyces coelicolor. A pool of potential RGs containing 1,471 genes was first identified by determining the intersection of genes with stable transcript levels from four time-series transcriptome microarray datasets of S. coelicolor M145 cultivated in different conditions. Then, following a strict rational selection scheme including homology analysis, disturbance analysis, function analysis and transcript abundance analysis, 13 candidates were selected from the 1,471 genes. Based on real-time quantitative reverse transcription PCR assays, SCO0710, SCO6185, SCO1544, SCO3183 and SCO4758 were identified as the top five genes with the most stable transcript levels among the 13 candidates. Further analyses showed these five genes also maintained stable transcript levels in different S. coelicolor strains, as well as in Streptomyces avermitilis MA-4680 and Streptomyces clavuligerus NRRL 3585, suggesting they could fulfill the requirements of accurate data normalization in streptomycetes. Moreover, the systematic strategy employed in this work could be used for reference in other microorganism to select reliable RGs.

Genome-wide identification and evaluation of constitutive promoters in streptomycetes

Background

Streptomycetes attract a lot of attention in metabolic engineering and synthetic biology because of their well-known ability to produce secondary metabolites. However, the available constitutive promoters are rather limited in this genus.

Results

In this work, constitutive promoters were selected from a pool of promoters whose downstream genes maintained constant expression profiles in various conditions. A total of 941 qualified genes were selected based on systematic analysis of five sets of time-series transcriptome microarray data of Streptomyces coelicolor M145 cultivated under different conditions. Then, 166 putative constitutive promoters were selected by following a rational selection workflow containing disturbance analysis, function analysis, genetic loci analysis, and transcript abundance analysis. Further, eight promoters with different strengths were chosen and subjected to experimental validation by green fluorescent protein reporter and real-time reverse-transcription quantitative polymerase chain reaction in S. coelicolor, Streptomyces venezuelae and Streptomyces albus. The eight promoters drove the stable expression of downstream genes in different conditions, implying that the 166 promoters that we identified might be constitutive under the genus Streptomyces. Four promoters were used in a plug-and-play platform to control the expression of the cryptic cluster of jadomycin B in S. venezuelae ISP5230 and resulted in different levels of the production of jadomycin B that corresponded to promoter strength.

Conclusions

This work identified and evaluated a set of constitutive promoters with different strengths in streptomycetes, and it enriched the presently available promoter toolkit in this genus. These promoters should be valuable in current platforms of metabolic engineering and synthetic biology for the activation of cryptic biosynthetic clusters and the optimization of pathways for the biosynthesis of important natural products in Streptomyces species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-015-0351-0) contains supplementary material, which is available to authorized users.

High-Resolution Mass Spectrometry Based Proteomic Analysis of the Response to Vancomycin-Induced Cell Wall Stress in Streptomyces coelicolor A3(2)

Understanding how bacteria survive periods of cell wall stress is of fundamental interest and can help generate ideas for improved antibacterial treatments. In this study we use tandem mass tagging to characterize the proteomic response of vancomycin resistant Streptomyces coelicolor to the exposure to sublethal levels of the antibiotic. A common set of 804 proteins were identified in triplicate experiments. Contrasting changes in the abundance of proteins closely associated with the cytoplasmic membrane with those taking place in the cytosol identified aspects of protein spatial localization that are associated with the response to vancomycin. Enzymes for peptidoglycan precursor, mycothiol, ectoine and menaquinone biosynthesis together with a multisubunit nitrate reductase were recruited to the membrane following vancomycin treatment. Many proteins with regulatory functions (including sensor protein kinases) also exhibited significant changes in abundance exclusively in the membrane-associated protein fraction. Several enzymes predicted to be involved in extracellular peptidoglycan crossbridge formation became significantly depleted from the membrane. A comparison with data previously acquired on the changes in gene transcription following vancomycin treatment identified a common high-confidence set of changes in gene expression. Generalized changes in protein abundance indicate roles for proteolysis, the pentose phosphate pathway and a reorganization of amino acid biosynthesis in the stress response.

Changes in activity of metabolic and regulatory pathways during germination of S. coelicolor

BACKGROUND

Bacterial spore germination is a developmental process during which all required metabolic pathways are restored to transfer cells from their dormant state into vegetative growth. Streptomyces are soil dwelling filamentous bacteria with complex life cycle, studied mostly for they ability to synthesize secondary metabolites including antibiotics.

RESULTS

Here, we present a systematic approach that analyzes gene expression data obtained from 13 time points taken over 5.5 h of Streptomyces germination. Genes whose expression was significantly enhanced/diminished during the time-course were identified, and classified to metabolic and regulatory pathways. The classification into metabolic pathways revealed timing of the activation of specific pathways during the course of germination. The analysis also identified remarkable changes in the expression of specific sigma factors over the course of germination. Based on our knowledge of the targets of these factors, we speculate on their possible roles during germination. Among the factors whose expression was enhanced during the initial part of germination, SigE is though to manage cell wall reconstruction, SigR controls protein re-aggregation, and others (SigH, SigB, SigI, SigJ) control osmotic and oxidative stress responses.

CONCLUSIONS

From the results, we conclude that most of the metabolic pathway mRNAs required for the initial phases of germination were synthesized during the sporulation process and stably conserved in the spore. After rehydration in growth medium, the stored mRNAs are being degraded and resynthesized during first hour. From the analysis of sigma factors we conclude that conditions favoring germination evoke stress-like cell responses.

Small non-coding RNAs in streptomycetes

Streptomycetes are Gram-positive, GC-rich, soil dwelling bacteria, occurring ubiquitary throughout nature. They undergo extensive morphological changes from spores to filamentous mycelia and produce a plethora of secondary metabolites. Owing to their complex life cycle, streptomycetes require efficient regulatory machinery for the control of gene expression. Therefore, they possess a large diversity of regulators. Within this review we summarize the current knowledge about the importance of small non-coding RNA for the control of gene expression in these organisms.