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Public Library of Science, PLoS ONE, 11(9), p. e111253, 2014

DOI: 10.1371/journal.pone.0111253

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Enhancement of Lipid Productivity in Oleaginous Colletotrichum Fungus through Genetic Transformation Using the Yeast CtDGAT2b Gene under Model-Optimized Growth Condition

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Oleaginous fungi are of special interest among microorganisms for the production of lipid feedstocks as they can be cultured on a variety of substrates, particularly waste lingocellulosic materials, and few fungal strains are reported to accumulate inherently higher neutral lipid than bacteria or microalgae. Previously, we have characterized an endophytic filamentous fungus Colletotrichum sp. DM06 that can produce total lipid ranging from 34% to 49% of its dry cell weight (DCW) upon growing with various carbon sources and nutrient-stress conditions. In the present study, we report on the genetic transformation of this fungal strain with the CtDGAT2b gene, which encodes for a catalytically efficient isozyme of type-2 diacylglycerol acyltransferase (DGAT) from oleaginous yeast Candida troplicalis SY005. Besides the increase in size of lipid bodies, total lipid titer by the transformed Colletotrichum (lipid content ∼73% DCW) was found to be ∼1.7-fold more than the wild type (lipid content ∼38% DCW) due to functional activity of the CtDGAT2b transgene when grown under standard condition of growth without imposition of any nutrient-stress. Analysis of lipid fractionation revealed that the neutral lipid titer in transformants increased up to 1.8-, 1.6- and 1.5-fold compared to the wild type when grown under standard, nitrogen stress and phosphorus stress conditions, respectively. Lipid titer of transformed cells was further increased to 1.7-fold following model-based optimization of culture conditions. Taken together, ∼2.9-fold higher lipid titer was achieved in Colletotrichum fungus due to overexpression of a rate-limiting crucial enzyme of lipid biosynthesis coupled with prediction-based bioprocess optimization.