Life cycle assessment of Chlorella species producing biodiesel and remediating wastewater.

Constantly rising energy demands, finite fossil fuel reserves and deteriorating environmental conditions have invoked worldwide interest to explore the sustainable sources of renewable biofuels. Locally adapted photosynthetic oleaginous microalgae with rapid growth on variable temperatures could be an ideal way for bioremediating the wastewater (WW) while producing the feedstock for biodiesel. To test this notion, an unknown strain was isolated from a sewage fed lake (Neela-Hauz). It was discerned as Chlorella sorokiniana-I using the 16S rDNA and 18S rDNA barcodes. The culture conditions such as pH, illumination, different temperature ranges and growth medium were cohesively optimized prior to the assessment of C. sorokiniana-I's efficacy to remediate the WWand biodiesel production. The strain has thrived well up to 40°C when continuously grown for 15 days. The highest lipid accumulation and biomass productivity were recorded in 100% WW. Fatty acid methyl ester (FAME) content was observed to be more than twice in WW (47%), compared to control synthetic media, TAP (20%) and BG11 (10%), which indicate the importance of this new isolate for producing economically viable biodiesel. Moreover, it is highly efficient in removing the total nitrogen (77%), total phosphorous (81%), iron (67%) and calcium (42%) from the WW. The quality of WW was considerably improved by reducing the overall chemical oxygen demand (48%), biological oxygen demand (47%) and alkalinity (15%). Thus, C. sorokiniana-I could be an ideal alga for the tropical countries in the remediation of WW while producing feedstock for biodiesel in a cost-effective manner.

Functional assessment of plant and microalgal lipid pathway genes in yeast to enhance microbial industrial oil production.

As promising alternatives to fossil-derived oils, microbial lipids are important as industrial feedstocks for biofuels and oleochemicals. Our broad aim is to increase lipid content in oleaginous yeast through expression of lipid accumulation genes and use Saccharomyces cerevisiae to functionally assess genes obtained from oil-producing plants and microalgae. Lipid accumulation genes DGAT (diacylglycerol acyltransferase), PDAT (phospholipid: diacylglycerol acyltransferase), and ROD1 (phosphatidylcholine: diacylglycerol choline-phosphotransferase) were separately expressed in yeast and lipid production measured by fluorescence, solvent extraction, thin layer chromatography, and gas chromatography (GC) of fatty acid methyl esters. Expression of DGAT1 from Arabidopsis thaliana effectively increased total fatty acids by 1.81-fold above control, and ROD1 led to increased unsaturated fatty acid content of yeast lipid. The functional assessment approach enabled the fast selection of candidate genes for metabolic engineering of yeast for production of lipid feedstocks.

In silico assessment of the metabolic capabilities of an engineered functional reversal of the ß-oxidation cycle for the synthesis of longer-chain (C≥4) products.

The modularity and versatility of an engineered functional reversal of the ß-oxidation cycle make it a promising platform for the synthesis of longer-chain (C≥4) products. While the pathway has recently been exploited for the production of n-alcohols and carboxylic acids, fully capitalizing on its potential for the synthesis of a diverse set of product families requires a system-level assessment of its biosynthetic capabilities. To this end, we utilized a genome scale model of Escherichia coli, in combination with Flux Balance Analysis and Flux Variability Analysis, to determine the key characteristics and constraints of this pathway for the production of a variety of product families under fermentative conditions. This analysis revealed that the production of n-alcohols, alkanes, and fatty acids of lengths C3-C18 could be coupled to cell growth in a strain lacking native fermentative pathways, a characteristic enabling product synthesis at maximum rates, titers, and yields. While energetic and redox constraints limit the production of target compounds from alternative platforms such as the fatty acid biosynthesis and α-ketoacid pathways, the metabolic efficiency of a ß-oxidation reversal allows the production of a wide range of products of varying length and functionality. The versatility of this platform was investigated through the simulation of various termination pathways for product synthesis along with the use of different priming molecules, demonstrating its potential for the efficient synthesis of a wide variety of functionalized compounds. Overall, specific metabolic manipulations suggested by this systems-level analysis include deletion of native fermentation pathways, the choice of priming molecules and specific routes for their synthesis, proper choice of termination enzymes, control of flux partitioning at the pyruvate node and the pentose phosphate pathway, and the use of an NADH-dependent trans-enoyl-CoA reductase instead of a ferredoxin-dependent enzyme.

Derivation of a Bayes factor to distinguish between linked or pleiotropic quantitative trait loci.

A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The procedure was compared with a multivariate least-squares method. The proposed procedure showed better results in terms of power of detection of linkage when low information is available. As information increases, the performance of both procedures becomes similar. An example using data provided by an Iberian by Landrace pig intercross is presented. The results showed that three different QTL segregate in SSC6: a pleiotropic QTL affects myristic, palmitic, and eicosadienoic fatty acids; another pleiotropic QTL affects palmitoleic, stearic, and vaccenic fatty acids; and a third QTL affects the percentage of linoleic acid. In the example, the Bayes factor approach was more powerful than the multivariate least-squares approach.