Escherichia coli has been widely used for the production of recombinant proteins. However, the unbalances between host metabolism and recombinant biosynthesis continue to hamper the efficiency of these recombinant bioprocesses. The additional drainage of biosynthetic precursors toward recombinant processes burdens severely the metabolism of cells that, ultimately, elicits a series of stress responses, reducing biomass growth and recombinant protein production. Several strategies to overcome these metabolic limitations have been implemented; however, in most cases, improvements in recombinant protein expression were achieved at the expense of biomass growth arrest, which significantly hampers the efficiency of recombinant bioprocesses. With the advent of high throughput techniques and modelling approaches that provide a system-level understanding of the cellular systems, it is now expected that new advances in recombinant bioprocesses are achieved. By providing means to deal with these systems, our understanding on the metabolic behaviour of recombinant cells will advance and can be further explored to the design of suitable hosts and more efficient and cost-effective bioprocesses. Here, we review the major metabolic responses associated with recombinant processes and the engineering strategies relevant to overcome these stresses. Moreover, the advantages of applying systems levels engineering strategies to enhance recombinant protein production in E. coli cells are discussed and future perspectives on the advances of mathematical modelling approaches to study these systems are exposed.

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Introduction Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies. Materials and MethodsThe uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow. Conclusions Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

Metabolic footprinting has become a valuable analytical approach for the characterization of phenotypes and the distinction of specific metabolic states resulting from environmental and/or genetic alterations. The metabolic impact of heterologous protein production in Escherichia coli cells is of particular interest, since there are numerous cellular stresses triggered during this process that limit the overall productivity, e.g. the stringent response. Because the knowledge on the metabolic responses in recombinant bioprocesses is still scarce, metabolic footprinting can provide relevant information on the intrinsic metabolic adjustments. Thus, the metabolic footprints generated by E. coli W3110 and the ΔrelA mutant strain during recombinant fed-batch fermentations at different experimental conditions were measured and interpreted. The IPTG-induction of the heterologous protein expression resulted in the rapid accumulation of inhibitors of the glyoxylate shunt in the culture broth, suggesting the clearance of this anaplerotic route to replenish the TCA intermediaries withdrawn for the additional formation of the heterologous protein. Nutritional shifts were also critical in the recombinant cellular metabolism, indicating that cells employ diverse strategies to counteract imbalances in the cellular metabolism, including the secretion of certain metabolites that are, most likely, used as a metabolic relief to survival processes.

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and open-source third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used.

BackgroundActinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions.ResultsThe model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide.ConclusionsThe established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0585-7) contains supplementary material, which is available to authorized users.

The specific growth rate is one of the most important process variables characterizing the state of microorganisms during fermentations mainly because the biosynthesis of many products of interest is often related with the values assumed by this parameter. In the particular case of the fed-batch operation of Escherichia coli for the production of recombinant proteins, it is important to maintain the specific growth rate below a certain threshold in order to avoid the accumulation of acetic acid throughout the fermentation and, additionally, it is often argued that both pre-and the post-induction specific growth rates should be closely controlled in order to achieve maximum productivities of the desired recombinant protein. In a previous work the authors have developed and validated by simulations a strategy for the automatic control of the specific growth rate in E. coli fed-batch fermentations based on an asymptotic observer for biomass and on developed estimators for the specific growth rates. The main purpose of the present work was to implement experimentally the developed observer, estimator and controller in a real fed-batch fermentation process. For that purpose a data acquisition and control program was developed in LabVIEW that allows the acquisition of the necessary on line data (off gas and dissolved oxygen concentration and culture weight) and the calculation of the feeding rates using the developed equations. The feedforward-feedback controller developed was able to keep the culture growing in an exponential phase throughout the fermentation without accumulation of glucose and acetate.

Summary The present study addresses the regulatory network of Escherichia coli and offers a global view of the short- and long-term regulation of its metabolic pathways. The regulatory mechanisms responsible for key metabolic activities and the structure behind such mechanisms are detailed. Most metabolic functions are dependent on the activity of transcriptional regulators over gene expression - the so-called long-term regulation. However, enzymatic regulation - the so-called short-term regulation - often overlays transcriptional regulation and even, in particular metabolic pathways, enzymatic regulation may prevail. As such, understanding the balance between these two types of regulation is necessary to be able to predict and control cell responses, specifically cell responses to the various environmental stresses.