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While extensively studied in several model organisms, the role of small, non-coding RNAs in the stress response remains largely unexplored in Clostridium organisms. About 100 years after the first industrial Acetone-Butanol-Ethanol fermentation process, based on the Weizmann Clostridium acetobutylicum strain, strain tolerance to butanol remains a crucial factor limiting the economics of the process. Several studies have examined the response of this organism to metabolite stress, and several genes have been engaged to impart enhanced tolerance, but no sRNAs have yet been directly engaged in this task. We show that the two stress-responsive sRNAs, 6S and tmRNA, upon overexpression impart tolerance to butanol as assessed by viability assays under process-relevant conditions. 6S overexpression enhances cell densities as well as butanol titres. We discuss the likely mechanisms that these two sRNAs might engage in this tolerance phenotype. Our data support the continued exploration of sRNAs as a basis for engineering enhanced tolerance and enhanced solvent production, especially because sRNA-based strategies impose a minimal metabolic burden on the cells., (© FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum., (Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Background: Small non-coding RNAs (sRNA) are emerging as major components of the cell's regulatory network, several possessing their own regulons. A few sRNAs have been reported as being involved in general or toxic-metabolite stress, mostly in Gram- prokaryotes, but hardly any in Gram+ prokaryotes. Significantly, the role of sRNAs in the stress response remains poorly understood at the genome-scale level. It was previously shown that toxic-metabolite stress is one of the most comprehensive and encompassing stress responses in the cell, engaging both the general stress (or heat-shock protein, HSP) response as well as specialized metabolic programs., Results: Using RNA deep sequencing (RNA-seq) we examined the sRNome of C. acetobutylicum in response to the native but toxic metabolites, butanol and butyrate. 7.5% of the RNA-seq reads mapped to genome outside annotated ORFs, thus demonstrating the richness and importance of the small RNome. We used comparative expression analysis of 113 sRNAs we had previously computationally predicted, and of annotated mRNAs to set metrics for reliably identifying sRNAs from RNA-seq data, thus discovering 46 additional sRNAs. Under metabolite stress, these 159 sRNAs displayed distinct expression patterns, a select number of which was verified by Northern analysis. We identified stress-related expression of sRNAs affecting transcriptional (6S, S-box &solB) and translational (tmRNA & SRP-RNA) processes, and 65 likely targets of the RNA chaperone Hfq., Conclusions: Our results support an important role for sRNAs for understanding the complexity of the regulatory network that underlies the stress response in Clostridium organisms, whether related to normophysiology, pathogenesis or biotechnological applications.

BACKGROUND: Small non-coding RNAs (sRNA) are emerging as major components of the cells regulatory network, several possessing their own regulons. A few sRNAs have been reported as being involved in general or toxic-metabolite stress, mostly in Gram- prokaryotes, but hardly any in Gram+ prokaryotes. Significantly, the role of sRNAs in the stress response remains poorly understood at the genome-scale level. It was previously shown that toxic-metabolite stress is one of the most comprehensive and encompassing stress responses in the cell, engaging both the general stress (or heat-shock protein, HSP) response as well as specialized metabolic programs. RESULTS: Using RNA deep sequencing (RNA-seq) we examined the sRNome of C. acetobutylicum in response to the native but toxic metabolites, butanol and butyrate. 7.5% of the RNA-seq reads mapped to genome outside annotated ORFs, thus demonstrating the richness and importance of the small RNome. We used comparative expression analysis of 113 sRNAs we had previously computationally predicted, and of annotated mRNAs to set metrics for reliably identifying sRNAs from RNA-seq data, thus discovering 46 additional sRNAs. Under metabolite stress, these 159 sRNAs displayed distinct expression patterns, a select number of which was verified by Northern analysis. We identified stress-related expression of sRNAs affecting transcriptional (6S, S-box &solB) and translational (tmRNA & SRP-RNA) processes, and 65 likely targets of the RNA chaperone Hfq. CONCLUSIONS: Our results support an important role for sRNAs for understanding the complexity of the regulatory network that underlies the stress response in Clostridium organisms, whether related to normophysiology, pathogenesis or biotechnological applications.

Background: Clostridium acetobutylicum is a model organism for both clostridial biology and solvent production. The organism is exposed to its own toxic metabolites butyrate and butanol, which trigger an adaptive stress response. Integrative analysis of proteomic and RNAseq data may provide novel insights into post-transcriptional regulation. Results: The identified iTRAQ-based quantitative stress proteome is made up of 616 proteins with a 15 % genome coverage. The differentially expressed proteome correlated poorly with the corresponding differential RNAseq transcriptome. Up to 31 % of the differentially expressed proteins under stress displayed patterns opposite to those of the transcriptome, thus suggesting significant post-transcriptional regulation. The differential proteome of the translation machinery suggests that cells employ a different subset of ribosomal proteins under stress. Several highly upregulated proteins but with low mRNA levels possessed mRNAs with long 5'UTRs and strong RBS scores, thus supporting the argument that regulatory elements on the long 5'UTRs control their translation. For example, the oxidative stress response rubrerythrin was upregulated only at the protein level up to 40-fold without significant mRNA changes. We also identified many leaderless transcripts, several displaying different transcriptional start sites, thus suggesting mRNA-trimming mechanisms under stress. Downregulation of Rho and partner proteins pointed to changes in transcriptional elongation and termination under stress. Conclusions: The integrative proteomic-transcriptomic analysis demonstrated complex expression patterns of a large fraction of the proteome. Such patterns could not have been detected with one or the other omic analyses. Our analysis proposes the involvement of specific molecular mechanisms of post-transcriptional regulation to explain the observed complex stress response. [ABSTRACT FROM AUTHOR]

Background Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. In particular, Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress-related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation. Results We describe here the construction and validation of a GSM model for C. acetobutylicum ATCC 824, iCac802. iCac802 spans 802 genes and includes 1,137 metabolites and 1,462 reactions, along with gene-protein-reaction associations. Both 13C-MFA and gene deletion data in the ABE fermentation pathway were used to test the predicted flux ranges allowed by the model. We also describe the CoreReg method, introduced in this paper, to integrate transcriptomic data and identify core sets of reactions that, when their flux was selectively restricted, reproduced flux and biomass-formation ranges seen under all regulatory constraints. CoreReg was used in response to butanol and butyrate stress to tighten bounds for 50 reactions within the iCac802 model. These bounds affected the flux of tens of reactions in core metabolism. The model, incorporating the regulatory restrictions from CoreReg under chemical stress, exhibited an approximate 70% reduction in biomass yield for most stress conditions. Conclusions The regulation placed on the model for the two stresses using CoreReg identified differences in the respective responses, including distinct core sets and the restriction of biomass production similar to experimental observations. Given the core sets predicted by the CoreReg method, remedial actions can be taken to counteract the effect of stress on metabolism. For less well-known systems, plausible regulatory loops can be suggested around the affected metabolic reactions, and the hypotheses can be tested experimentally. [ABSTRACT FROM AUTHOR]