Flux-sum analysis identifies metabolite targets for strain improvement

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

BACKGROUND

Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.

RESULTS

Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.

CONCLUSIONS

Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Development and applications of genome-scale metabolic network models

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

Systems-level analysis of the plasticity of the maize metabolic network reveals novel hypotheses in the nitrogen-use efficiency of maize roots

This article comments on: Chowdhury NB, Schroeder WL, Sarkar D, Amiour N, Quilleré I, Hirel B, Maranas CD, Saha R. 2022. Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. Journal of Experimental Botany 73, 275–291.

In silico model-guided identification of transcriptional regulator targets for efficient strain design

BACKGROUND

Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.

RESULTS

We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.

CONCLUSIONS

In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

Overflow metabolism and growth cessation in Clostridium thermocellum DSM1313 during high cellulose loading fermentations

As a model thermophilic bacterium for the production of second-generation biofuels, the metabolism of Clostridium thermocellum has been widely studied. However, most studies have characterized C. thermocellum metabolism for growth at relatively low substrate concentrations. This outlook is not industrially relevant, however, as commercial viability requires substrate loadings of at least 100 g/L cellulosic materials. Recently, a wild-type C. thermocellum DSM1313 was cultured on high cellulose loading batch fermentations and reported to produce a wide range of fermentative products not seen at lower substrate concentrations, opening the door for a more in-depth analysis of how this organism will behave in industrially relevant conditions. In this work, we elucidated the interconnectedness of overflow metabolism and growth cessation in C. thermocellum during high cellulose loading batch fermentations (100 g/L). Metabolic flux and thermodynamic analyses suggested that hydrogen and formate accumulation perturbed the complex redox metabolism and limited conversion of pyruvate to acetyl-CoA conversion, likely leading to overflow metabolism and growth cessation in C. thermocellum. Pyruvate formate lyase (PFL) acts as an important redox valve and its flux is inhibited by formate accumulation. Finally, we demonstrated that manipulation of fermentation conditions to alleviate hydrogen accumulation could dramatically alter the fate of pyruvate, providing valuable insight into process design for enhanced C. thermocellum production of chemicals and biofuels. Biotechnol. Bioeng. 2017;114: 2592-2604. © 2017 Wiley Periodicals, Inc.

Metabolic systems modeling for cell factories improvement

Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.

Metabolic engineering of Klebsiella pneumoniae based on in silico analysis and its pilot-scale application for 1,3-propanediol and 2,3-butanediol co-production

Klebsiella pneumoniae naturally produces relatively large amounts of 1,3-propanediol (1,3-PD) and 2,3-butanediol (2,3-BD) along with various byproducts using glycerol as a carbon source. The ldhA and mdh genes in K. pneumoniae were deleted based on its in silico gene knockout simulation with the criteria of maximizing 1,3-PD and 2,3-BD production and minimizing byproducts formation and cell growth retardation. In addition, the agitation speed, which is known to strongly affect 1,3-PD and 2,3-BD production in Klebsiella strains, was optimized. The K. pneumoniae ΔldhA Δmdh strain produced 125 g/L of diols (1,3-PD and 2,3-BD) with a productivity of 2.0 g/L/h in the lab-scale (5-L bioreactor) fed-batch fermentation using high-quality guaranteed reagent grade glycerol. To evaluate the industrial capacity of the constructed K. pneumoniae ΔldhA Δmdh strain, a pilot-scale (5000-L bioreactor) fed-batch fermentation was carried out using crude glycerol obtained from the industrial biodiesel plant. The pilot-scale fed-batch fermentation of the K. pneumoniae ΔldhA Δmdh strain produced 114 g/L of diols (70 g/L of 1,3-PD and 44 g/L of 2,3-BD), with a yield of 0.60 g diols per gram glycerol and a productivity of 2.2 g/L/h of diols, which should be suitable for the industrial co-production of 1,3-PD and 2,3-BD.

Application of theoretical methods to increase succinate production in engineered strains

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Background

Succinic acid is a valuable product due to its wide-ranging utilities. To improve succinate production and reduce by-products formation, Escherichia coli NZN111 was constructed by insertional inactivation of lactate dehydrogenase (LDH) and pyruvate formate lyase (PFL) encoded by the genes ldhA and pflB, respectively. However, this double-deletion mutant is incapable of anaerobically growing on glucose in rich or minimal medium even with acetate supplementation. A widespread hold view is that the inactivation of NADH-dependent LDH limits the regeneration of NAD⁺ and consequently disables proper growth under anaerobic conditions.

Results

In this study, genome-scale metabolic core model of E. coli was reconstructed and employed to perform all simulations in silico according to the reconstruction of engineered strain E. coli NZN111. Non-optimized artificial centering hit-and-run (ACHR) method and metabolite flux-sum analysis were utilized to evaluate metabolic characteristics of strains. Thus, metabolic characteristics of the strains wild-type E. coli, ldhA mutant, pflB mutant, and NZN111 under anaerobic conditions were successfully unraveled.

Conclusions

We found a viewpoint contrary to the widespread realization that an NADH/NAD⁺ in NZN111 mainly resulted from the inactivation of PFL rather than the inactivation of LDH. In addition, the two alternative anaerobic fermentation pathways, lactate and ethanol production pathways, were blocked owing to the disruption of ldhA and pflB, resulting in insufficient NAD⁺ regeneration to oxidize or metabolize glucose for cell growth. Furthermore, we speculated reaction NADH16, the conversion of ubiquinone-8 (q8) to ubiquinol-8 (q8h2), as a potential amplification target for anaerobically improving cell growth and succinate production in NZN111.

Electronic supplementary material

The online version of this article (doi:10.1186/s40643-016-0125-5) contains supplementary material, which is available to authorized users.