Metabolic network analysis of lysine producingCorynebacterium glutamicum at a miniaturized scale

2004 ◽  
Vol 87 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Christoph Wittmann ◽  
Hyung Min Kim ◽  
Elmar Heinzle
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Metabolic Network Analysis

scholarly journals Transcriptomic and Metabolic Network Analysis of Metabolic Reprogramming and IGF-1 Modulation in SCA3 Transgenic Mice

2021 ◽  
Vol 22 (15) ◽  
pp. 7974
Author(s):  
Yu-Te Lin ◽  
Yong-Shiou Lin ◽  
Wen-Ling Cheng ◽  
Jui-Chih Chang ◽  
Yi-Chun Chao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Transgenic Mice ◽  
Signaling Pathways ◽  
Metabolic Network ◽  
Metabolic Reprogramming ◽  
Spinocerebellar Ataxia Type ◽  
Positive Effects ◽  
Metabolic Network Analysis ◽  
Potential Biomarker ◽  
Metabolic Remodeling

Spinocerebellar ataxia type 3 (SCA3) is a genetic neurodegenerative disease for which a cure is still needed. Growth hormone (GH) therapy has shown positive effects on the exercise behavior of mice with cerebellar atrophy, retains more Purkinje cells, and exhibits less DNA damage after GH intervention. Insulin-like growth factor 1 (IGF-1) is the downstream mediator of GH that participates in signaling and metabolic regulation for cell growth and modulation pathways, including SCA3-affected pathways. However, the underlying therapeutic mechanisms of GH or IGF-1 in SCA3 are not fully understood. In the present study, tissue-specific genome-scale metabolic network models for SCA3 transgenic mice were proposed based on RNA-seq. An integrative transcriptomic and metabolic network analysis of a SCA3 transgenic mouse model revealed that metabolic signaling pathways were activated to compensate for the metabolic remodeling caused by SCA3 genetic modifications. The effect of IGF-1 intervention on the pathology and balance of SCA3 disease was also explored. IGF-1 has been shown to invoke signaling pathways and improve mitochondrial function and glycolysis pathways to restore cellular functions. As one of the downregulated factors in SCA3 transgenic mice, IGF-1 could be a potential biomarker and therapeutic target.

scholarly journals Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

mSystems ◽  
2021 ◽  
Author(s):  
Matthew L. Jenior ◽  
Jhansi L. Leslie ◽  
Deborah A. Powers ◽  
Elizabeth M. Garrett ◽  
Kimberly A. Walker ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Virulence Factors ◽  
Metabolic Pathways ◽  
Content Type ◽  
Hospital Acquired Infections ◽  
Metabolic Network Analysis ◽  
Clostridioides Difficile ◽  
Context Specific ◽  
Hospital Acquired

Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors.

scholarly journals Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease

2020 ◽  
Vol 1 (8) ◽  
pp. 100138
Author(s):  
Priyanka Baloni ◽  
Cory C. Funk ◽  
Jingwen Yan ◽  
James T. Yurkovich ◽  
Alexandra Kueider-Paisley ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bile Acid ◽  
Network Analysis ◽  
Metabolic Network ◽  
Acid Synthesis ◽  
Bile Acid Synthesis ◽  
Metabolic Network Analysis

Multiobjective flux balancing using the NISE method for metabolic network analysis

2009 ◽  
Vol 25 (4) ◽  
pp. 999-1008 ◽  
Author(s):  
Young-Gyun Oh ◽  
Dong-Yup Lee ◽  
Sang Yup Lee ◽  
Sunwon Park
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Metabolic Network Analysis

scholarly journals Genealogy Profiling through Strain Improvement by Using Metabolic Network Analysis: Metabolic Flux Genealogy of Several Generations of Lysine-Producing Corynebacteria

2002 ◽  
Vol 68 (12) ◽  
pp. 5843-5859 ◽  
Author(s):  
Christoph Wittmann ◽  
Elmar Heinzle
Keyword(s):  
Mass Spectrometry ◽  
Network Analysis ◽  
Metabolic Network ◽  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Strain Improvement ◽  
Gas Chromatography Mass Spectrometry ◽  
Limiting Factor ◽  
Strain Optimization ◽  
Metabolic Network Analysis

ABSTRACT A comprehensive approach of metabolite balancing, 13C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C3 carboxylation and C4 decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C3 carboxylation and C4 decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the fact that the observed differences were clearly strain specific.

Database and tools for metabolic network analysis

2014 ◽  
Vol 19 (4) ◽  
pp. 568-585 ◽  
Author(s):  
Lu Shi Jing ◽  
Farah Fathiah Muzaffar Shah ◽  
Mohd Saberi Mohamad ◽  
Nur Laily Hamran ◽  
Abdul Hakim Mohamed Salleh ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Metabolic Network Analysis
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