Dataset Information

Metabolite profiling of Schizochytrium sp. by GC-MS, an oleaginous microbial source of biodiesel.

ABSTRACT: The chemical screening carried out on Schizochytrium sp. biomass led the identification of 24 types of organic compounds belonging to n-alkanes, 1-alkenes, 1-alkanols, free fatty acids, methyl and ethyl esters of saturated and unsaturated fatty acids, saturated tri- and diglycerides, unsaturated monoglycerides, wax esters, sterols, triterpenes, and mono- and sesquiterpenes. Moreover, a sample containing fully saturated ethyl biodiesel was obtained experimentally with a yield of 28.72% w/w of the crude extract, and an average chain length of 15.52 carbons. This strain produced no toxins, but showed important nutrients, making it potentially applicable to the field of functional food, and biodiesel production.

SUBMITTER: Mioso R

PROVIDER: S-EPMC4166263 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Metabolite profiling of Schizochytrium sp. by GC-MS, an oleaginous microbial source of biodiesel.

Mioso Roberto R Toledo Marante Francisco J FJ González Juan E G JE Rodríguez Juan J S JJ de Laguna Irma Herrera Bravo IH

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] 20140829 2

The chemical screening carried out on Schizochytrium sp. biomass led the identification of 24 types of organic compounds belonging to n-alkanes, 1-alkenes, 1-alkanols, free fatty acids, methyl and ethyl esters of saturated and unsaturated fatty acids, saturated tri- and diglycerides, unsaturated monoglycerides, wax esters, sterols, triterpenes, and mono- and sesquiterpenes. Moreover, a sample containing fully saturated ethyl biodiesel was obtained experimentally with a yield of 28.72% w/w of the ...[more]

PMID: 25242922

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Project description:Soybean is one of the most economically important crops worldwide. However, soybean yield can be substantially decreased by many diseases. Soybean genotypes could have different reactions to pathogen infection. As a first step toward investigating the biochemical basis of soybean resistance and susceptibility to disease, phytochemicals in the seeds of 52 soybean genotypes previously reported to have different reactions to diseases of soybean rust (SBR), Phomopsis seed decay (PSD), and purple seed stain (PSS) were analyzed. Using GC-MS, a total of 46 compounds were tentatively identified which included 11 chemical groups. Among those, the major group was esters, followed by carboxylic acid, ketone, and sugar moieties. Compounds having reported antioxidant, anti-microbial, and anti-inflammatory activities were also identified. UHPLC-DAD/MS analysis indicated that there were five major isoflavone components presented in the samples, including daidzin, glycitin, genistin, malonyldaidzin, and malonylglycitin. Isoflavones have been reported to play an important role in defense from plant pathogens. Although there was variance in the isoflavone content among soybean genotypes, those with the SBR resistance Rpp6 gene (PI 567102B, PI 567104B, PI 567129) consistently exhibited the highest concentrations of daidzin, glycitin, genistin, and malonyldaidzin. The SBR resistant genotype, PI 230970 (Rpp2) had the greatest amount of genistin. The SBR resistant genotype, PI 200456 (Rpp5) resistant genotype uniquely contained glycitein, a compound that was absent in the other 51 genotypes examined. A PSD-resistant genotype PI 424324B had nearly four times the amount of stigmasterol as PI 556625, which was susceptible to SBR, PSD, and PSS in our previous tests. Results of this study provide useful information for further investigation of the biochemical basis of soybean resistance to diseases. The results may also aid in selection of soybean lines for breeding for resistance to soybean rust and other diseases.

Dataset Information

Metabolite profiling of Schizochytrium sp. by GC-MS, an oleaginous microbial source of biodiesel.

Publications

Metabolite profiling of Schizochytrium sp. by GC-MS, an oleaginous microbial source of biodiesel.

Similar Datasets

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