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The Prokaryotic Microalga Limnothrix redekei KNUA012 to Improve Aldehyde Decarbonylase Expression for Use as a Biological Resource.

ABSTRACT: The prokaryotic microalga Limnothrix redekei KNUA012 isolated from a freshwater bloom sample from Lake Hapcheon, Hapcheon-gun, South Korea, was investigated for its potential as a biofuel feedstock. Microalgae produce straight-chain alkanes/alkenes from acyl carrier protein-linked fatty acyls via aldehyde decarbonylase (AD; EC 1.2.1.3), which can convert aldehyde intermediates into various biofuel precursors, such as alkanes and free fatty acids. In L. redekei KNUA012, long-chain ADs can convert fatty aldehyde intermediates into alkanes. After heterologous AD expression in Escherichia coli (pET28-AD), we identified an AD in L. redekei KNUA012 that can synthesize various alkanes, such as pentadecane (C₁₅H₃₂), 8-heptadecene (C₁₇H₃₄), and heptadecane (C₁₇H₃₆). These alkanes can be directly used as fuels without transesterification. Biodiesel constituents including dodecanoic acid (C₁₃H₂₆O₂), tetradecanoic acid (C₁₅H₃₀O₂), 9-hexa decenoic acid (C₁₇H₃₂O₂), palmitoleic acid (C₁₇H₃₂O₂), hexadecanoic acid (C₁₇H₃₄O₂), 9-octadecenoic acid (C₁₉H₃₆O₂), and octadecanoic acid (C₁₉H₃₈O₂) are produced by L. redekei KNUA012 as the major fatty acids. Our findings suggest that Korean domestic L. redekei KNUA012 is a promising resource for microalgae-based biofuels and biofuel feedstock.

SUBMITTER: Kim YS

PROVIDER: S-EPMC10561079 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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The Prokaryotic Microalga Limnothrix redekei KNUA012 to Improve Aldehyde Decarbonylase Expression for Use as a Biological Resource.

Kim Young-Saeng YS Baek Haeri H Yun Hyun-Sik HS Lee Jae-Hak JH Lee Kyoung-In KI Kim Han-Soon HS Yoon Ho-Sung HS

Polish journal of microbiology 20230920 3

The prokaryotic microalga Limnothrix redekei KNUA012 isolated from a freshwater bloom sample from Lake Hapcheon, Hapcheon-gun, South Korea, was investigated for its potential as a biofuel feedstock. Microalgae produce straight-chain alkanes/alkenes from acyl carrier protein-linked fatty acyls via aldehyde decarbonylase (AD; EC 1.2.1.3), which can convert aldehyde intermediates into various biofuel precursors, such as alkanes and free fatty acids. In L. redekei KNUA012, long-chain A ...[more]

PMID: 37725893

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Project description:BackgroundSet-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.MethodsWe define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach.ResultsThe novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers.ConclusionNovel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Dataset Information

The Prokaryotic Microalga Limnothrix redekei KNUA012 to Improve Aldehyde Decarbonylase Expression for Use as a Biological Resource.

Publications

The Prokaryotic Microalga <i>Limnothrix redekei</i> KNUA012 to Improve Aldehyde Decarbonylase Expression for Use as a Biological Resource.

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