Part:BBa_K857000

acetaldehyde dehydrogenase

Coding protein for acetaldehyde dehydrogenase convert acetaldehyde into acetyl-CoA.

CH3CHO + NAD+ + CoA → acetyl-CoA + NADH + H+

This part was designed by the 2012 iGEM UC Merced team and it is a gene that is involved in the E. Coli glucose metabolism pathway. Our team characterized this part by performing an in silico knockout of this gene. Using quantitative analysis, we discovered that its deletion does not affect the growth of E. Coli when under optimal conditions to produce a 4:1 hydrogen production to glucose uptake ratio. More importantly, we identified possible non-lethal gene deletions that may help maximize hydrogen production in the dark fermentation process.

First, we measured the growth of E. Coli bacteria under the conditions in which the insertion of mhpF allows the production of hydrogen to glucose uptake ratio to be 4:1. This was done using the Constraint Model Based Reconstruction and Analysis (COBRA) toolbox on Matlab platform which allowed us to constrain the uptake of glucose and hydrogen release to a ratio of 4:1 on our E. Coli glucose metabolism model. Our results were performed using Flux Balance Analysis (FBA) on the Core E. Coli Model by the Systems Biology Research Group at the University of California generated from the current gene expression data on E. coli. (graph) Our results showed that when the MhpF gene is introduced, E. Coli growth is higher under anaerobic conditions than aerobic conditions when under stress requirement to produce a 4:1 hydrogen to glucose ratio. This was achieved by setting oxygen flux values to both unlimited and zero for aerobic and anaerobic conditions respectively, whilst measuring E. coli’s growth in rate per hour. Growth measurements were done as a quantitative measure of how E.coli performs against the gene modification added to its metabolism.

Furthermore, our analysis from Flux Balance Analysis (FBA) validated team UC Merced hypothesis that removing IdhA, pfIB and adhE from E. coli fermentation pathway and replacing it with mhpF, pyruvate decarboxylase and ferredoxin oxidoreductase. This was achieved by performing in silico knockouts of IdhA, pfIB and adhE on the E. coli model and multiple gene insertions of mhpF, pyruvate decarboxylate and ferredoxin and looking at how the shift in metabolism network improve hydrogen yield under anaerobic condition. (image) Figure 1.Shift in metabolism flux network when E. coli model is subjected to hydrogen release to glucose uptake 4:1 ratio under aerobic conditions (left) and anaerobic conditions(right).

Next, we analyzed the effect of deleting the mhpF gene on the growth rate of E. Coli (in biomass/hour). The quantitative analysis shows that the growth remained the same, which indicates that the mhpF gene does not affect the growth rate of E. Coli. This served as motivation to perform more single gene deletions on the E.coli model. We analyzed the effect of creating knockouts of all the genes involved in glucose metabolism on the bacteria by examining the lethality of deleting each gene. Team UC Merced attempted to create knockouts of three genes, and below we present suggestions regarding the genes in the dark fermentation process that can be deleted without being lethal. Deletions of genes in the dark fermentation process, a form of anaerobic conversion would facilitate the conversion of organic substrate to biohydrogen. For instance our analysis from the gene deletion suggested that the deletion zwf gene( glucose-6-phosphate 1-dehydrogenase) which is principal in the pentose pathway would be lethal to the E. coli. It turns out that zwf and mphF gene is overexpressed in the dark fermentation process. Even though both are very necessary for hydrogen production, mphF deletion would not result in much difference in the flux. Further our metabolic network flux predicts that the overexpression of zwf leads to the diversion of carbon flux through the PP pathway and hence result in an increase in the efficiency of the production of H2. Finally, a robustness analysis was performed to verify the validity of the results by setting fixed amounts of glucose and hydrogen and measuring the growth as a function of the oxygen concentration. This analysis was performed to demonstrate the strength of our model's prediction. The linear graph predicts a linear rise in growth with an intermittent increase in glucose intake. This agrees and supports our previous data which suggested that mphF is implied in the increase in growth of E.coli when subject to the condition stipulated above. In sum , using metabolic modeling allow for in silico gene knockouts and metabolic analysis that unveiled non lethal gene deletions and insertion that would prove useful for the generation of biohydrogen from E.coli. Using metabolic modeling, it is easier to generate genetic changes that would optimize hydrogen production from E.coli. graph robustness

Finally, a robustness analysis was performed to verify the validity of the results by setting fixed amounts of glucose and hydrogen and measuring the growth as a function of the oxygen concentration.

Sequence and Features

Assembly Compatibility:

• 10
  COMPATIBLE WITH RFC[10]
• 12
  COMPATIBLE WITH RFC[12]
• 21
  INCOMPATIBLE WITH RFC[21]

  Illegal XhoI site found at 349
  
• 23
  COMPATIBLE WITH RFC[23]
• 25
  INCOMPATIBLE WITH RFC[25]

  Illegal AgeI site found at 765
  
• 1000
  COMPATIBLE WITH RFC[1000]