Summary of Study ST000622

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000454. The data can be accessed directly via it's Project DOI: 10.21228/M8CC9H This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study ID	ST000622
Study Title	Identification and metabolite profiling of chemical activators of lipid accumulation in green algae
Study Type	GC-MS metabolite profiling of algal lipid activators
Study Summary	Microalgae are proposed as feedstock organisms useful for producing biofuels and co-products. However, several limitations must be overcome before algae-based production is economically feasible. Among these is the ability to induce lipid accumulation and storage without affecting biomass yield. To overcome this barrier, a chemical genetics approach was employed in which 43,783 compounds were screened against Chlamydomonas reinhardtii and 243 compounds were identified that increase triacylglyceride (TAG) accumulation without terminating growth. Identified compounds were classified by structural similarity and 15 selected for secondary analyses addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein and starch concentrations. TAG accumulation was verified using GC-MS quantification of total fatty acids and targeted TAG and galactolipid (GL) measurements using LC-MRM/MS. These results demonstrated TAG accumulation does not necessarily proceed at the expense of GL. Untargeted metabolite profiling provided important insights into pathway shifts due to 5 different compound treatments and verified the anabolic state of the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle and amino acid biosynthetic pathways. Metabolite patterns were distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil accumulation in algae. The efficacy of these compounds was also demonstrated in 3 other algal species. These lipid inducing compounds offer a valuable set of tools for delving into the biochemical mechanisms of lipid accumulation in algae and a direct means to improve algal oil content independent of the severe growth limitations associated with nutrient deprivation.
Institute	University of Nebraska-Lincoln
Department	Biochemistry
Laboratory	FATTTLab
Last Name	Wase
First Name	Nishikant
Address	1901 Beadle Center, Vine Street, 1901 VINE STREET, Lincoln, NE, 68588-0664, USA
Email	nishikant.wase@gmail.com
Phone	4023109931
Submit Date	2017-06-16
Num Groups	6
Publications	1. Nishikant Wase, Boqiang Tu, James W Allen, Paul N Black, Concetta C DiRusso. Identification and metabolite profiling of chemical activators of lipid accumulation in green algae. Plant Physiology Jun 2017. DOI: 10.1104/pp.17.00433. http://www.plantphysiol.org/content/early/2017/06/26/pp.17.00433; 2. DiRusso, C., & Wase, N. (2016). Compounds for Increasing Lipid Synthesis and Storage. United States. NUtech Ventures (Lincoln, NE, US) http://www.freepatentsonline.com/y2016/0312253.html
Raw Data Available	Yes
Raw Data File Type(s)	cdf
Analysis Type Detail	GC-MS
Release Date	2017-10-03
Release Version	1

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Sample Preparation:

Sampleprep ID:	SP000652
Sampleprep Summary:	Harvested cells were flash frozen in liquid N and freeze dried. Accurately measured 50 ± 0.5 mg of freeze dried powder was used for metabolite extraction. Sample powder was pulverized with a single steel ball using TissueLyser LT (Qiagen) at 50 Hz speed for 5 min. One milliliter of extraction buffer containing MeOH:CHCl3:H2O (5:2:2; v/v/v; pre-cooled at -20 °C) was added and vortexed for 5 min. Ribitol (0.2 mg/mL in water; 10 µL) was spiked in the extraction buffer as internal standard in order to identify potential chromatographic errors. The homogenized material was centrifuged at 14000 rpm for 5 min and the supernatant was transferred to new tubes. 400 µL of pure water was added to the supernatant, samples were vortexed and centrifuged at 14,000 rpm for 5 min. The upper polar phase was transferred to new tubes for GC-MS analysis. An aliquot of 300 µL was dried out in vacuum concentrator without heating. To the dried material, 10 µL methoxyamine HCL in 100% pyridine (40 mg/mL) was added and shaken at 30 °C for 90 minutes and subsequently 90 µL of MSTFA 1% TMCS was added for trimethylsilylation of acidic protons and shaken at 37 °C for 30 minutes. The reaction mixture was transferred to GCvials with glass microinserts and closed by crimp caps. GC-MS data acquisition was performed as per previously published report (Wase et al., 2014)

Sampleprep ID:

SP000652

Sampleprep Summary:

Harvested cells were flash frozen in liquid N and freeze dried. Accurately measured 50 ± 0.5 mg of freeze dried powder was used for metabolite extraction. Sample powder was pulverized with a single steel ball using TissueLyser LT (Qiagen) at 50 Hz speed for 5 min. One milliliter of extraction buffer containing MeOH:CHCl3:H2O (5:2:2; v/v/v; pre-cooled at -20 °C) was added and vortexed for 5 min. Ribitol (0.2 mg/mL in water; 10 µL) was spiked in the extraction buffer as internal standard in order to identify potential chromatographic errors. The homogenized material was centrifuged at 14000 rpm for 5 min and the supernatant was transferred to new tubes. 400 µL of pure water was added to the supernatant, samples were vortexed and centrifuged at 14,000 rpm for 5 min. The upper polar phase was transferred to new tubes for GC-MS analysis. An aliquot of 300 µL was dried out in vacuum concentrator without heating. To the dried material, 10 µL methoxyamine HCL in 100% pyridine (40 mg/mL) was added and shaken at 30 °C for 90 minutes and subsequently 90 µL of MSTFA 1% TMCS was added for trimethylsilylation of acidic protons and shaken at 37 °C for 30 minutes. The reaction mixture was transferred to GCvials with glass microinserts and closed by crimp caps. GC-MS data acquisition was performed as per previously published report (Wase et al., 2014)