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. 2017 Aug;44(8):1137-1144.
doi: 10.1007/s10295-017-1941-0. Epub 2017 Apr 20.

Developing Elite Neurospora Crassa Strains for Cellulosic Ethanol Production Using Fungal Breeding

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Free PMC article

Developing Elite Neurospora Crassa Strains for Cellulosic Ethanol Production Using Fungal Breeding

Joshua C Waters et al. J Ind Microbiol Biotechnol. .
Free PMC article

Abstract

The demand for renewable and sustainable energy has generated considerable interest in the conversion of cellulosic biomass into liquid fuels such as ethanol using a filamentous fungus. While attempts have been made to study cellulose metabolism through the use of knock-out mutants, there have been no systematic effort to characterize natural variation for cellulose metabolism in ecotypes adapted to different habitats. Here, we characterized natural variation in saccharification of cellulose and fermentation in 73 ecotypes and 89 laboratory strains of the model fungus Neurospora crassa. We observed significant variation in both traits among natural and laboratory generated populations, with some elite strains performing better than the reference strain. In the F1 population N345, 15% of the population outperformed both parents with the top performing strain having 10% improvement in ethanol production. These results suggest that natural alleles can be exploited through fungal breeding for developing elite industrial strains for bioethanol production.

Keywords: Cellulase; Cellulosic ethanol; Fungal breeding; Natural variation; Strain improvement.

Figures

Fig. 1
Fig. 1
Natural variation in cellulolytic activity among natural isolates. a, b Significant variation was observed among 73 natural strains in the indices analyzed in plate clearing experiments. The area of cellulase activity (ACA), cell mass increase (CMI), and cellulase production index (CPI) demonstrated normal distributions, while substrate utilization index (SUI) was positively skewed. The skew of SUI is expected to arise from variation in sorbose resistance opposed to cellulolytic activity, as sorbose was used to restrict lateral growth of hyphae. c Significant variation was observed in FPA assay among natural strains, presented as percentage of glucose equivalents released by top performer since values fall outside the range of the standard curve used. Red bars represent mean and 95% CI
Fig. 2
Fig. 2
Natural variation in cellulolytic activity among top performing natural ecotypes. Variation was observed among the top performing strains activity in the FPA assay and CMCase assay (a). No correlation was observed between the amount of protein secreted and the level of cellulase activity measured by FPA assay (b) or CMCase assay (b) (R 2 = 0.02285, p = 0.6768 and R 2 = 0.03114, p = 0.6258, respectively). QM9414 (a) is T. reesei, used as a standard for cellulase activity. Error bar represents one standard deviation
Fig. 3
Fig. 3
Natural variation in fermentation among top performing ecotypes. a Ethanol produced from fermentation of glucose. b Ethanol produced from fermentation of 2% Xylose or 2% CMC. c, d Correlations were observed between ethanol produced from fermentation of glucose and xylose (c), and between fermentation of glucose and CMC (d) (R 2 = 0.7511, p = 0.0025 and R 2 = 0.6711, p = 0.0069, respectively). Error bars represent one standard deviation
Fig. 4
Fig. 4
Natural variation in cellulolytic activity and fermentation among a laboratory population (N345 population). A 2-dimensional scatterhist plot illustrating distributions for saccharification of cellulose (x axis) and fermentation of glucose (y axis). Red dots represent strains with the highest potential for both traits
Fig. 5
Fig. 5
Fermentation of a High Energy crop by elite ascensions. Elite ascensions (JW220, JW228, and N345-2) were able to produce more ethanol from a 2% Miscanthus culture than the sequence strain (FGSC2489) which serves as a wild-type reference. Significant difference was observed between each strain and reference strain using 2-tailed t test (JW220 p = 0.000005, JW228 p = 0.000005, N345-2 p = .00007). Error bars represent one standard deviation

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