Table_1_Enhanced Triacylglycerol Production With Genetically Modified Trichosporon oleaginosus.PDF

<p>Mitochondrial pyruvate dehydrogenase (PDH) is important in the production of lipids in oleaginous yeast, but other yeast may bypass the mitochondria (PDH bypass), converting pyruvate in the cytosol to acetaldehyde, then acetate and acetyl CoA which is further converted to lipids. Using a metabolic model based on the oleaginous yeast Yarrowia lipolytica, we found that introduction of this bypass to an oleaginous yeast should result in enhanced yield of triacylglycerol (TAG) on substrate. Trichosporon oleaginosus (formerly Cryptococcus curvatus) is an oleaginous yeast which can produce TAGs from both glucose and xylose. Based on the sequenced genome, it lacks at least one of the enzymes needed to complete the PDH bypass, acetaldehyde dehydrogenase (ALD), and may also be deficient in pyruvate decarboxylase and acetyl-CoA synthetase under production conditions. We introduced these genes to T. oleaginosus in various combinations and demonstrated that the yield of TAG on both glucose and xylose was improved, particularly at high C/N ratio. Expression of a phospholipid:diacyltransferase encoding gene in conjunction with the PDH bypass further enhanced lipid production. The yield of TAG on xylose (0.27 g/g) in the engineered strain approached the theoretical maximum yield of 0.289 g/g. Interestingly, TAG production was also enhanced compared to the control in some strains which were given only part of the bypass pathway, suggesting that these genes may contribute to alternative routes to cytoplasmic acetyl CoA. The metabolic model indicated that the improved yield of TAG on substrate in the PDH bypass was dependent on the production of NADPH by ALD. NADPH for lipid synthesis is otherwise primarily supplied by the pentose phosphate pathway (PPP). This would contribute to the greater improvement of TAG production from xylose compared to that observed from glucose when the PDH bypass was introduced, since xylose enters metabolism through the non-oxidative part of the PPP. Yield of TAG from xylose in the engineered strains (0.21–0.27 g/g) was comparable to that obtained from glucose and the highest so far reported for lipid or TAG production from xylose.</p

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原著和名: [記載なし]科名: ナス科 = Solanaceae採集地:採集日:採集者: 鴻海左九三整理番号: JH046360国立科学博物館整理番号: []備考: DB作成協力会による補足あ

Predicted <i>T. reesei</i> secretion network.

<p>A) The proteins annotated as secretory (242) and unknown (14) are included. Proteins are nodes and they are labelled with best matching <i>S. cerevisiae</i> protein name or if no match was found with <i>T. reesei</i> gene ID number. Thick edges signify either negative (red) or positive (green) absolute Pearson correlation of > 0.3 in transcriptomic data. Pink nodes do not have any interactions in STRING. B) Pie chart of functional classes of the 320 proteins included in the <i>T. reesei</i> secretion network.</p

Frequency distribution of percentage of amino acid sequence identity between natural <i>S. cerevisiae</i> sequences and (1) sets of artificial sequences created from from <i>S. cerevisiae</i> with different Blosum matrices, (2) natural <i>T. reesei</i> sequences.

<p>Frequency distribution of percentage of amino acid sequence identity between natural <i>S. cerevisiae</i> sequences and (1) sets of artificial sequences created from from <i>S. cerevisiae</i> with different Blosum matrices, (2) natural <i>T. reesei</i> sequences.</p

Schematic representation of the duality between (A) the PPI network and (B) the adjacency matrix for the proteins in the training set (blue) and testing set (yellow) and their interactions: training interactions (black), training-testing interactions (gray) and testing interactions (white).

<p>Schematic representation of the duality between (A) the PPI network and (B) the adjacency matrix for the proteins in the training set (blue) and testing set (yellow) and their interactions: training interactions (black), training-testing interactions (gray) and testing interactions (white).</p

ROC curves and Precision-Recall (PR) curves for predicting secretory PPIs from the full <i>S. cerevisiae</i> genome with Output Kernel trees (OK3), Tensor kernels on protein pairs (Tensor Kernel on PP), and supervised and semi-supervised Input-Output Kernel Regression (IOKR).

<p>AUCROC and AUPR statistics are shown in the legend for each curve.</p

Precision-Recall (PR) curves for predicting PPIs in different artificial data sets with Output Kernel trees (OK3), Tensor kernels on protein pairs (Tensor Kernel on PP), and supervised and semisupervised Input-Output Kernel Regression (IOKR).

<p>AUPR statistic is shown in the legend for each curve (standard devation in parenthesis.</p

ROC curves and Precision-Recall (PR) curves for predicting secretory PPIs from the full <i>S. cerevisiae</i> genome with semi-supervised Input-Output Kernel Regression (IOKR) and different Multiple Kernel Learning (MKL) methods compared to no MKL (UNIMKL).

<p>AUCROC and AUPR statistics are shown in the legend for each curve.</p

Unknown genes and genes without any interactions in STRING in predicted <i>T. reesei</i> secretion network.

<p>Column ‘Gene’ contains the <i>T. reesei</i> gene ID. ‘In STRING’ tells if the gene has interactions in STRING. Columns ‘Btw’ and ‘Deg’ denote the betweenness and degree network statistics of the corresponding gene. Columns ‘Class’ and ‘Putative secretion pathway component’ are author assigned classifications. ‘Taxon specificity’ gives the largest taxonomic group the gene was found in.</p

Chagas disease patient diagnosis and treatment results of four Médecins Sans Frontières programs in Central and South America, 1999–2008.

<p>Chagas disease patient diagnosis and treatment results of four Médecins Sans Frontières programs in Central and South America, 1999–2008.</p