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Label-free quantification of proteome changes in Chlamydomonas reinhardtii treated with lipid inducing small molecules

ABSTRACT: A study to investigate the effect of small molecule lipid inducing compounds that leads to hyper-accumulation of lipids in N replete cells of Chlamydomonas reinhardtii. These compounds were identified through a high throughput screening designed for that purpose. The highthrouhput screen (HTS) evaluated 43,783 compounds and identified 367 primary hits. These 367 hits were further retested using a 8-point dilution series (from 0.25 to 30 uM) and verified the activity of 243 compounds that induce the hyper lipid accumulating phenotype in algae. Once the hit compounds were identified and confirmed, we then performed extensive chemoinformatics analysis to look for common scaffolds and identified several common substructures. We then selected 15 top performing compounds from 5 diverse structural groups and tested for biochemical parameters such as growth, lipid accumulating capacity, effect on photosynthetic rates, respiration rates, oxygen consumption rates, analysis of different lipid species to identify and quantify fatty acid species using GC-MS, transcriptome analysis using next generation sequencing (RNASeq), untargeted metabolomics using LC-MS, lipidome analysis using FT-ICR MS. To understand the global changes in the proteome, 2 structurally different compounds were selected and compared to the control without compound treatment.

INSTRUMENT(S): Synapt MS

ORGANISM(S): Chlamydomonas Reinhardtii

TISSUE(S): Cell Suspension Culture, Photosynthetic Cell

SUBMITTER: Nishikant Wase

LAB HEAD: Concetta DiRusso

PROVIDER: PXD006736 | Pride | 2017-08-11

REPOSITORIES: Pride

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Project description:Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures. Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures.

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Label-free quantification of proteome changes in Chlamydomonas reinhardtii treated with lipid inducing small molecules

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