A novel method for effectively breeding bacteria with high yield of γ-pga Background Our client was facing the challenge of generating a Bacillus licheniformis strain with high yields of γ-pga (γ-poly Glutamic Acid) within timeline. The traditional experience-based breeding method is very complex, time consuming and inefficient. Moreover, instead of using the desired traits, other characters such as morphological mutation were used as the screening criteria to select and purify the strains before optimizing fermentation conditions. Therefore, it is very possible that the desired strain would be missed in the screening steps. Based on Directional Control Technology (DCT), we helped the client to develop a novel mutation breeding strategy, which is named as mix-ferment breeding. The method began with exposing the starter bacteria culture to various mutagenesis conditions. Then, all the bacteria with the induced mutations were mixed and fermented. Fermentation conditions for the bacteria mix were optimized according to the yield of target variable, γ-pga. As a result, the optimal conditions would enrich the positive strains that can produce high level of γ-pga In the next step, a group of bacteria strains from the bacterial mix that produced the highest level γ-pga was selected. Then the optimal fermentation conditions discovered by DCT from the same experiment were used to breed the select group of bacteria in order to identify the superior strain that ultimately produced the highest level of γ-pga. Method and process Identify the client s needs and expectations. The client provided the following information: 1. Objective: Generating a genetically stable strain with high yields of γ-pga Target variable (Y): yield of γ-pga (g/l) Current yield from the starter strain: 4.825 g/l 2. Parameters: As listed in Table 1, there are 21 parameters (X 1 to X 21 ) that are possibly involved in the breeding process. The client also provided attributes, and acceptable value ranges for each parameter.
Design and perform system diagnostic experiments We designed 3 diagnostic experiments that are illustrated in Table 2. In each experiment, all 21 factors were included and the level of each parameter was initialized according to DCT s unique principles. The client treated the bacteria mix under these experimental conditions, measured the yield of γ-pga (Y) and provided the results to us for analysis. Analyze the results and further design system behavior experiments The analysis revealed that several parameters, such as X 4 and X 6, did not contribute to the production of γ-pga. Therefore, they were eliminated in the subsequent experiments. To gain higher yield of γ-pga from the mix, we further adjusted the remaining 16 factors shown in Table 3 and Table 4. Experimental results from the client suggested that the yield of γ-pga from the bacteria mix could reach as high as 3.847 g/l. The fermentation conditions used in that experiment were set as the optimal bacteria breeding condition. Complete the project To obtain the superior strain producing the highest yield of γ-pga production from the bacteria mix, the client then performed strain selection procedures with the bacteria mix. The fermentation conditions employed in the selection process were the same as the optimal fermentation condition we established previously. From the mix, the client was able to successfully identify the superior strain, which produced γ-pga as much as 19.529 g/l. In addition, the strain did not lose the desired characteristic in the repeated passage. Conclusions In this project, Directional Control Technology (DCT) helped to develop a new mix-ferment breeding method with significant advantages over the traditional method. Simplified process. The entire breeding process was greatly simplified, cutting at least by half the time required by traditional method. For example, the complex process of selecting and purifying negative and positive mutations from large amounts of strains after mutation is eliminated with DCT. High efficiency. With only 8 experiments, the client was able to identify the optimal fermentation conditions while screening the bacteria mix at the same time. The isolated superior strain had 4.3-fold higher yield rate of γ-pga than the unrefined starter strain. High accuracy. Before optimizing fermentation conditions, there was no need to screen the bacteria mix using characteristics other than γ-pga production. Instead, all the mutated bacteria were included in the optimization process. Therefore, any bacteria with positive mutations couldn t have been missed in the breeding process.
Overall, this case provides a good example of how to apply DCT to very complex processing systems. This application also demonstrates that DCT is very efficient and could produce consistent output with high levels of reliability.