Production of succinic acid via separate enzymatic hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) are alternatives and are environmentally friendly processes. These processes have attained considerable positions in the industry with their own share of challenges and problems. The high-value succinic acid is extensively used in chemical, food, pharmaceutical, leather and textile industries and can be efficiently produced via several methods. Previously, succinic acid production via chemical synthesis from petrochemical or refined sugar has been the focus of interest of most reviewers. However, these expensive substrates have been recently replaced by alternative sustainable raw materials such as lignocellulosic biomass, which is cheap and abundantly available. Thus, this review focuses on succinic acid production utilizing lignocellulosic material as a potential substrate for SSF and SHF. SSF is an economical single-step process which can be a substitute for SHF - a two-step process where biomass is hydrolyzed in the first step and fermented in the second step. SSF of lignocellulosic biomass under optimum temperature and pH conditions results in the controlled release of sugar and simultaneous conversion into succinic acid by specific microorganisms, reducing reaction time and costs and increasing productivity. In addition, main process parameters which influence SHF and SSF processes such as batch and fed-batch fermentation conditions using different microbial strains are discussed in detail.
This paper presents a study on gene knockout strategies to identify candidate genes to be knocked out for improving the production of succinic acid in Escherichia coli. Succinic acid is widely used as a precursor for many chemicals, for example production of antibiotics, therapeutic proteins and food. However, the chemical syntheses of succinic acid using the traditional methods usually result in the production that is far below their theoretical maximums. In silico gene knockout strategies are commonly implemented to delete the gene in E. coli to overcome this problem. In this paper, a hybrid of Ant Colony Optimization (ACO) and Minimization of Metabolic Adjustment (MoMA) is proposed to identify gene knockout strategies to improve the production of succinic acid in E. coli. As a result, the hybrid algorithm generated a list of knockout genes, succinic acid production rate and growth rate for E. coli after gene knockout. The results of the hybrid algorithm were compared with the previous methods, OptKnock and MOMAKnock. It was found that the hybrid algorithm performed better than OptKnock and MOMAKnock in terms of the production rate. The information from the results produced from the hybrid algorithm can be used in wet laboratory experiments to increase the production of succinic acid in E. coli.
Succinic acid is an important platform chemical that has broad applications and is been listed as one of the top twelve bio-based chemicals produced from biomass by the US Department of Energy. The metabolic role of Escherichia coli formate dehydrogenase-O (fdoH) under anaerobic conditions in relation to succinic acid production remained largely unspecified. Herein we report, what are to our knowledge, the first metabolic fdoH gene knockout that have enhanced succinate production using glucose and glycerol substrates in E. coli. Using the most recent E. coli reconstruction iJO1366, we engineered its host metabolism to enhance the anaerobic succinate production by deleting the fdoH gene, which blocked H(+) conduction across the mutant cell membrane for the enhanced succinate production. The engineered mutant strain BMS4 showed succinate production of 2.05 g l(-1) (41.2-fold in 7 days) from glycerol and .39 g l(-1) (6.2-fold in 1 day) from glucose. This work revealed that a single deletion of the fdoH gene is sufficient to increase succinate production in E. coli from both glucose and glycerol substrates.
Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems.
Succinic acid is an important platform chemical with a variety of applications. Model-guided metabolic engineering strategies in Escherichia coli for strain improvement to increase succinic acid production using glucose and glycerol remain largely unexplored. Herein, we report what are, to our knowledge, the first metabolic knockout of the atpE gene to have increased succinic acid production using both glucose and alternative glycerol carbon sources in E. coli. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic production of succinic acid by deleting the atpE gene, thereby generating additional reducing equivalents by blocking H(+) conduction across the mutant cell membrane. This strategy produced 1.58 and .49 g l(-1) of succinic acid from glycerol and glucose substrate, respectively. This work further elucidates a model-guided and/or system-based metabolic engineering, involving only a single-gene deletion strategy for enhanced succinic acid production in E. coli.
Cancer is the second leading cause of death-1 in 6 deaths globally is due to cancer. Cancer metabolism is a complex and one of the most actively researched area in cancer biology. Metabolic reprogramming in cancer cells entails activities that involve several enzymes and metabolites to convert nutrient into building blocks that alter energy metabolism to fuel rapid cell division. Metabolic dependencies in cancer generate signature metabolites that have key regulatory roles in tumorigenesis. In this minireview, we highlight recent advances in the popular methods ingrained in biochemistry research such as stable and flux isotope analysis, as well as radioisotope labeling, which are valuable in elucidating cancer metabolites. These methods together with analytical tools such as chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry have helped to bring about exploratory work in understanding the role of important as well as obscure metabolites in cancer cells. Information obtained from these analyses significantly contribute in the diagnosis and prognosis of tumors leading to potential therapeutic targets for cancer therapy.