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Metabolomic analysis reveals mechanism of antioxidant butylated hydroxyanisole on lipid accumulation in Crypthecodinium cohnii Xiao Sui, Xiangfeng Niu, Mengliang Shi, Guangsheng Pei, Jinghan Li, Lei Chen, Jiangxin Wang, and Weiwen Zhang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf503671m • Publication Date (Web): 01 Dec 2014 Downloaded from http://pubs.acs.org on December 3, 2014

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Journal of Agricultural and Food Chemistry

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Metabolomic analysis reveals mechanism of antioxidant

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butylated hydroxyanisole on lipid accumulation in

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Crypthecodinium cohnii

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Xiao Sui 1,2,3, Xiangfeng Niu 1,2,3, Mengliang Shi 1,2,3, Guangsheng Pei 1,2,3, Jinghan Li 1,2,3, Lei

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Chen 1,2,3, Jiangxin Wang 1,2,3, Weiwen Zhang 1,2,3,*

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University, Tianjin 300072, P.R. China; 2 Key Laboratory of Systems Bioengineering, Ministry of

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Education of China, Tianjin 300072, P.R. China; 3 Collaborative Innovation Center of Chemical

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Science & Engineering, Tianjin, P.R. China.

Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin

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* To whom all correspondence should be addressed:

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Prof. Dr. Weiwen Zhang

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Laboratory of Synthetic Microbiology

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School of Chemical Engineering & Technology

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Tianjin University, Tianjin 300072, P.R. China

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Tel : 0086-22-2740-6394; Fax : 0086-22-27406364

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Email: [email protected]

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Running title: Butylated hydroxyanisole on Crypthecodinium lipid accumulation

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ACS Paragon Plus Environment


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Abstract

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The heterotrophic dinoflagellate alga Crypthecodinium cohnii is known to accumulate lipids with

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a high fraction of docosahexaenoic acid (DHA). In this study, we first evaluated two antioxidant

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compounds, butylated hydroxyanisole (BHA) and propyl gallate (PG) for their effects on lipid

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accumulation in C. cohnii. The results showed that antioxidant BHA could increase lipid

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accumulation in C. cohnii by 8.80% at a final concentration of 30 µM, while PG had no obvious

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effect on lipid accumulation at the tested concentrations. To decipher molecular mechanism

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responsible for the increased lipid accumulation by BHA, we employed an integrated GC-MS

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and LC-MS metabolomic approach to determine the time-series metabolic profiles with or

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without BHA, and then subjected the metabolomic data to a principal component analysis (PCA)

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and a weighted gene co-expression network analysis (WGCNA) network analyses to identify the

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key metabolic modules and metabolites possibly relevant to the increased lipid accumulation.

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LC-MS analysis showed that several metabolites, including NADPH, could be important for the

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stimulation role of BHA on lipid accumulation. Meanwhile GC-MS and network analyses

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allowed identification of eight metabolic modules and nine hub metabolites possibly relevant to

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the stimulation role of BHA in C. cohnii. The study provided a metabolomics view of the BHA

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mode of action on lipid accumulation in C. cohnii, and the information could be valuable for a

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better understanding of antioxidant effects on lipid accumulation in other microalgae as well.

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Keywords: lipid, accumulation, butylated hydroxyanisole; Crypthecodinium cohnii

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Introduction

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Many members of the ω-3 polyunsaturated fatty acids family (PUFAs) have been demonstrated

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with valuable medical and nutritional applications.1, 2 Among them, the beneficial effects of

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docosahexaenoic acid (DHA) that is a polyunsaturated fatty acid composed of 22 carbon atoms

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and six double bonds have been extensively studied,3,

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accumulate in membranes of human nervous, visual and reproductive tissues, and is the most

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abundant fatty acid in the grey matter of the brain, and also related to many other functions, such

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as anti-cholesterolaemic and anti-inflammatory activities.5-7 Nowadays, DHA has been widely

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used in various infant foods and supplements to improve brain health.

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and the results showed that it can

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Crypthecodinium cohnii, a heterotrophic dinoflagellate alga known to accumulate lipids with

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a high fraction of DHA, has been used in industrial fermentation processes for algal oil and DHA

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production.5, 6, 8, 9 It has been reported that under optimized conditions, C. cohnii can accumulate

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lipid up to more than 40% of its total dry weight and DHA content more than 30–50% of the

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total lipids, respectively.5 Meanwhile, other types of PUFAs account for only less than 1% of the

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total lipid content in C. cohnii,10, 11 which presents remarkable advantages for downstream DHA

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purification process. To make the C. cohnii fermentation process economically more competitive

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to other major DHA sources, such as oil from deep-sea fishes,3 significant efforts have been

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made in recent years to optimize the various fermentation parameters in both batch and fed batch

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experiments,4, 6, 10, 11 and a high production of 109 g/L dry biomass, 61 g/L lipid and 19 g/L DHA

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has been reported for C. cohnii.11

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In addition to optimizing cultivation conditions, an alternate approach to modulate lipid

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pathways was proposed, which was to identify chemical triggers that can directly elicit the lipid

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accumulation.12,

13

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established to screen a collection of diverse bioactive organic molecules (e.g., kinase inhibitors,

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fatty acid synthase inhibitors, plant hormones, and oxidative molecules) with four strains of

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oleaginous microalgae (Nannochloropsis salina, N.

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Phaeodactylum tricornutum), and the results showed that nanomolar or micromolar

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concentrations of common antioxidants such as epigallocatechin gallate (EGCG) and butylated

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hydroxyanisole (BHA) could increase lipid productivity by > 60% in N. salina.14 The study

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demonstrated that antioxidants could enhance lipid accumulation, which may represent a

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practical and inexpensive approach to achieve high lipid accumulation in alga.14 However, so far

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no similar investigation has been conducted to identify chemical triggers for the enhanced lipid

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accumulation in C. cohnii.

In a recent study, a Nile Red fluorescence-based microplate assay was

oculata, Nannochloris sp., and

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In this study, we first evaluated effects of two antioxidant chemical triggers on lipid

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accumulation in C. cohnii. The results showed a differential effect of different antioxidant on

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lipid accumulation in C. cohnii, with BHA enhancing lipid content by 8.80%, while another

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antioxidant propyl gallate (PG) inhibiting lipid accumulation in C. cohnii. Currently little is

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known about mode of action (MOA) of antioxidants on lipid production in microalgae, although

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various possible biological effects, such as modulation of photooxidative stress pathways and

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therefore enhance photosynthetic efficiency, have been proposed.14 To determine the MOA, we

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first applied a metabolomics approach integrating both GC-MS and LC-MS to reveal time-series

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responses of C. cohnii to the antioxidant BHA. As a measurement and study of the

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small-molecule metabolites that constitute biochemical networks, metabolomics has recently

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been applied to studies of various microalgae,15-17 and was demonstrated as a valuable tool in

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analyzing cell metabolism.18 Second, we constructed a metabolic network using a weighted gene

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co-expression network analysis (WGCNA)

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significantly associated with BHA supplementation. The study provided an overview of the

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metabolomics basis of the BHA stimulation effect of lipid accumulation in C. cohnii.

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to reveal metabolic modules and hub metabolites

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Materials and Methods

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Chemicals

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Antioxidant BHA and PG were purchased from Sigma Aldrich (Taufkirchen, Germany), and

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stored at 4ºC until use. A stock solution of 10 mM BHA or PG in dimethyl sulfoxide (DMSO)

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was made in amber glass vials and stored in the dark at -20ºC.

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Strains and cultivation

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Crypthecodinium cohnii ATCC 30772 was obtained from American Type Culture Collection

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(ATCC), and grown in 50 mL basal medium (pH 6.5) consisted of 9 g/L glucose, 2 g/L yeast

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extract (OXOID, Basingstoke, UK), and 25 g/L sea salt (Sigma-Aldrich, USA) in 250 mL

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Erlenmeyer flasks at 25ºC and 180 rpm. The seed cultures were cultivated for 2 days after

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reaching exponential growth phase, and then used to inoculate the fermentation flasks at

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inoculum size of 10% (v/v). Two control cultures, one with 0.4% DMSO (v/v) and another

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without DMSO, were established in parallel with the chemical-spiked cultures. Varying

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concentrations of chemicals in 0.4% DMSO (v/v) were added at beginning of the cultivation.

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Three biological replicates were established for each concentration. Cell density was measured

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on a UV-1750 spectrophotometer (Shimadzu, Japan) at OD490. Cell number was determined by

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direct counting under microscope.

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Lipid analysis and extraction

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Two methods were used to determine the lipid accumulation under various chemicals in C.

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cohnii cells. The first protocol involves fluorescence intensity measurements at 510/585 nm after

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Nile Red staining.20-22 A fluorescence spectrophotometer (F-2700FL, HITACHI) was used for

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the assay. The second protocol involves direct lipid extraction using a modified method

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described previously.23 Briefly, C. cohnii cells were collected at 72 h by centrifugation (3500 x

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g), and then freeze-dried to generate a lyophilized algal powder. For lipid extraction, 15-25 mg

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of lyophilized algal powder was used for extraction using a chloroform:methanol solution (2:1,

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v/v). The extraction process was repeated three-four times, and then all organic solvent were

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combined and dried using a vacuum concentrator system (ZLS-1, Hunan, China).

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GC-MS and LC-MS based metabolomic analysis

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All chemicals used for metabolome isolation, GC-MS and LC-MS analyses were obtained from

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Sigma-Aldrich (Taufkirchen, Germany). For metabolomic analysis, cells were collected by

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centrifugation at 8,000 × g for 10 min at 4°C (Eppendorf 5430R, Hamburg, Germany) at three

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time points (i.e., 24, 48 and 72 h corresponding to exponential, transition and stationary phases).

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The cell pellets were immediately frozen in liquid nitrogen and then stored at -80ºC before use.

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GC-MS metabolomic analysis was preformed according to methods described previously.24, 25

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Briefly, the protocol included: i) Metabolome extraction with cold 10:3:1 (v/v/v)

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methanol:chloroform:H2O solution (MCW); ii) Sample derivatization according to the two-stage

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technique;26 iii) GC-MS analysis on a GC-MS system-GC 7890 coupled to an MSD 5975

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(Agilent Technologies, Inc., Santa Clara, CA) equipped with a HP-5MS capillary column (30 m

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× 250 mm id); iv) Data processing and statistical analysis using the Automated MassSpectral

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Deconvolution and Identification System (AMDIS) to identify the compounds by matching the

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data with Fein Library and the mass spectral library of the National Institute of Standards and

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Technology (NIST). All metabolomic profile data was first normalized by the internal control

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and the cell numbers of the samples, and then subjected to partial least square-discriminant

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analyses (PLS-DA) using software SIMCA-P 11.5.

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LC-MS metabolomic analysis was performed according to a previous publication with

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minor modification.27 Briefly, the protocol included: i) Metabolome extraction by quenching

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quickly and methanol extraction;28 ii) LC-MS analysis using an Agilent 1260 series binary HPLC

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system (Agilent Technologies, Waldbronn, Germany) coupled to an Agilent 6410 triple

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quadrupole mass analyser equipped with an electronic spray ion (ESI) source. SYnergi

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Hydro-RP（C18, 150 mm × 2.0 mm I.D., 4 μm 80 Å particles , Phenomenex, Torrance, CA）

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column was used for separation of metabolites. Mobile phase A (MPA) was an aqueous 10 mM

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tributylamine solution adjusted to pH 4.95 with acetic acid and Mobile phase B (MPB) was 100%

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HPLC grade methanol. Constant flow rate was maintained at 0.2 mL/min; iii) Data processing

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and statistical analysis using Agilent Mass Hunter workstation LC/QQQ acquisition software

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(version B.04.01) and analyzed using Agilent Mass Hunter workstation qualitative analysis

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software version (B.04.00). The MS was operated in negative mode for multiple reaction

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monitoring (MRM) development, method optimization, and sample analysis. Injected sample

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volume for all cases was 10 µL; capillary voltage was 4000 V; and nebulizer gas flow rate and

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pressure were 10 L/min and 50 psi, respectively. Nitrogen nebulizer gas temperature was 300ºC.

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Metabolomic data was normalized by interior control and cell number, and then submitted to

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principal component analysis (PCA) using SIMCA-P 11.5.29 Heatmap were created using

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MultiExperiment Viewer software available publically at http://www.tm4.org/.

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WGCNA Network Construction

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Metabolic correlation network was constructed from the metabolomic data, first by calculating

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weighted Pearson correlation matrices corresponding to metabolite abundance, and then by

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following the standard procedure of WGCNA to create the networks.19, 30 Hierarchical clustering

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based on topological overlap (TO) was used to group metabolites with highly similar correlation

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relationships into modules. The associated modules with correlation coefficient r > 0.6, and a

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statistical significance p-value less than 0.05 were extracted for further analysis. Hub metabolites

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were screened by high connectivity with other metabolites (≥5) in the modules strongly

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associated with phenotype.

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Results and Discussion

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Screening chemical enhancers for lipid accumulation

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Based on previous results of antioxidant effects on lipid accumulation,13 two antioxidant

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chemicals, BHA and PG were selected for evaluation of their effects on lipid accumulation in C.

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cohnii. In a previous study, BHA has been found to be an efficient lipophilic antioxidant in

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plant.31 The roles of PG in lipid accumulation is not clearly defined yet, with one early study

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showing that PG can inhibit lipid accumulation in C. cohnii,32 while another study showing that

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it can enhance lipid accumulation in N. salina.14 Since both BHA and PG were dissolved in

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DMSO, two control cultures were established: one with 0.4% DMSO and another without. In the

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screening process, we determined the changes of growth and lipid content in cells, first in

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96-well cultivation plates with Nile Red staining, and then confirmed in 250-mL flask cultivation

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with lipids extracted for more accurate quantitation. Multiple concentrations were evaluated for

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each chemical. The results showed that neither antioxidant of BHA and PG was found to affect

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cell growth under the tested conditions (Suppl. Fig. S1), meanwhile both antioxidants were

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found affecting lipid accumulation in C. cohnii (Table 1, Suppl. Fig. S2). Interestingly, BHA

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had positive effects on lipid accumulation at concentration range of 40 nM to 30 µM, while PG

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issued no visible effect at low concentration but negative effects at high concentration (> 30 µM)

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on lipid accumulation (Suppl. Fig. S2), consistent with the early study.32 In addition, cell

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morphology of C. cohnii under various chemicals was also compared using microscopic analyses,

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and no significant difference was observed between treatments and controls (data not shown).

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Moreover, the opposite roles of antioxidant BHA and PG on lipid accumulation in C. cohnii

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found in this study suggested that the mechanisms antioxidant involved in microalgae may be

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more complicated than previously proposed.14 Finally, quantitative determination of DHA in the

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lipid was also conducted and the results showed that although BHA can enhance total lipid

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content, it didn’t change the proportion of DHA in the total lipid (data now shown).

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LC-MS metabolomics analysis

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Considering its low cost,14 BHA could be a very useful chemical enhancer for lipid accumulation

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in large-scale fermentation of C. cohnii, so it worth further investigation of its enhancement

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mechanism on lipid accumulation. In the experiments, C. cohnii was cultivated with or without

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BHA for three days, and samples were collected at 24, 48 and 72 h, which are corresponding to

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exponential, transition and stationary phases of the batch cultures, respectively. To exclude

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possible effects of DMSO since the BHA was dissolved and introduced as a DMSO solution,

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additional controls were also established for C. cohnii supplemented with 0.4% DMSO only. We

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then applied a LC-MS based metabolomics to quantify the time series changes of selected

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metabolites related to central carbohydrate and energy metabolism in the C. cohnii cells with and

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without 30 µM BHA. Using the optimized protocol modified from a previous method for

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photosynthetic cyanobacteria,26 reproducible analyses of 24 selected intracellular metabolites was

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achieved for C. cohnii, including acetyl coenzyme A (Ac-CoA), NADPH, NADP, NADH, NAD,

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ADP, UDP, ATP, ADP, Coenzyme A hydrate (CoA), AMP, D-fructose 1,6-bisphosphate (FBP),

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D-Ribulose 1,5-bisphosphate (RuBP), D-fructose 6-phosphate (F6P), D-glucose 6-phosphate

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(G6P), D-ribose 5-phosphate (R5P), D-3-Phosphoglyceric acid (3PG), dihydroxyacetone

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phosphate (DHAP), DL-glyceraldehyde 3-phosphate (GAP), phosphor-(enol)-pyruvic acid (PEP),

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L-glutamic acid (Glu), α-ketoglutaric acid (AKG), oxaloacetic acid (OXA) and fumarate dibasic

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(FUM), mostly of which are unstable metabolites (Suppl. Table S1). Quantitative analyses of

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these targeted intracellular metabolites in samples collected at three time points (i.e., 24, 48 and 72

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h) were conducted, and each with three biological replicates and two analytical replicates, which

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led to time-series metabolomic profiles of C. cohnii with or without BHA. The quality of the

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LC-MS analysis was demonstrated from the following aspects (Fig. 1): i) all 24 metabolites were

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successfully detected in replicate samples of all time points and treatments; ii) a PCA analysis of

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all samples showed an obvious clustering pattern for cell samples of same time point and condition;

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iii) samples of time points (i.e., 24, 48 and 72 h) were well separated; in addition, a clear moving

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trend of metabolomic profiles along the time courses was observed, suggesting that the gradual

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change of the metabolic status; moreover, the analysis suggested that growth time may be a key

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distinguishing component which contributed significantly to the difference between samples; iv),

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the samples with or without BHA can also be separated at any time points, suggesting differential

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metabolic changes in cells with or without BHA. Overall the results demonstrated that the LC-MS

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methodology used in the study provided a good analytical resolution to distinguish various

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samples (Fig. 1). In addition, variation of detected metabolites among biological replicates was

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also calculated, and, a reasonably small variation was also observed, suggesting in general good

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quality of the LC-MS metabolomic analysis (Suppl. Table 2).

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Heat maps of 24 metabolites in the cells with or without BHA treatment at 24, 48 and 72 h

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were generated (Fig. 2). In the analysis, ratio of a given metabolites was calculated between the

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concentration of the metabolite in a given condition and the average concentration of the

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metabolite in all samples, the similar approach has been successfully applied in transcriptomic

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analysis.33 Although the fold changes were relatively small, the results showed that a clear

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up-regulation of ADP, NADH, NADPH and PEP at 24 h and ATP, UDP, and GAP at 48 h,

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respectively. Among the BHA-induced metabolites, NADPH is an essential reducing agent for

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fatty acid biosynthesis, for example, it is estimated that the formation of one C18 fatty acid

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requires 54 NADPH from oxygenic photosynthesis in a micro-algal Phaeodactylum

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tricornutum.34 In addition, the NADPH supply has been found crucial for the DHA production in

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Schizochytrium sp. HX-308, where a reinforced acetyl-CoA and NADPH supply increased DHA

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content of total fatty acids from 35 to 60%.35 Moreover, biochemical studies with the N. salina

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indicated that lipid accumulation is controlled by the availability of NADPH.36 However, no

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significant increase of NADPH was observed at late stages after BHA treatment, suggesting that

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the effects may be related to fast growth of the cells. Interestingly, our LC-MS analysis showed

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that acetyl-CoA, which was considered as the major precursor for fatty acid biosynthesis, was

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not significantly regulated in BHA-treated samples, suggesting that the enhanced lipid synthesis

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by BHA may not be directly due to improved supply of the precursor acetyl-CoA.

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Heat maps showed that AKG was down-regulated at 24 h but up-regulated at 72 h,

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respectively; and R5P was down-regulated at all three time points. Meanwhile, at 72 h of the

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stationary phase, most of the metabolites except AKG, PEP and ADP, were significantly

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down-regulated, consistent with the slowed central carbohydrate and energy metabolisms in the

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aging cells. AKG is an important microbial metabolic intermediate in citric acid cycle, a key

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node connecting the intracellular carbon - nitrogen metabolism.37 Previous studies showed that in

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the oleaginous yeast Yarrowia lipolytica, AKG can behave as potent inhibitors of the malic

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enzyme that has been suggested as a key path to generate NADPH.38 The down-regulation of

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AKG at the early phases (i.e., 24 h) and the up-regulation of AKG at the later phase (i.e., 72 h)

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may thus be consistent with the increased NADPH at the early phase, and the decreased NADPH

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at the later phase, respectively. R5P is an intermediate of the pentose phosphate pathway. In a

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recent study examining induction of lipid accumulation by nitrogen starvation in the green

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microalga Chlamydomonas reinhardtii, pentose phosphate pathway was found down-regulated

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after lipid accumulation started.38

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GC-MS metabolomic analysis

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GC-MS based metabolomics has become a popular approach in recent years since it can achieve

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a good coverage of polar metabolites, and allow analysis of a wide range of chemical metabolite

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classes in a single run.39,41 In the previous study, we developed an optimized protocol

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characterizing the time-series metabolic responses for metabolite isolation and MS analysis, and

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achieved identification of more than 65 and 60 chemically classified metabolites from

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Escherichia coli23 and Synechocystis sp. PCC 6803,42 respectively. In this study, we first

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evaluated the protocol for C. cohnii and found that more than a hundred metabolites can be

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identified with good reproducibility (Suppl. Table S3), demonstrating the GC-MS metabolomics

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approach can be applied to the study of C. cohnii. Following the same experimental design as

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LC-MS analysis described above, cells with or without 30 µM BHA treatments were collected 24

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h, 48 h and 72 h, respectively; and an extra control of only DMSO was also established as

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described above. Each condition carried three biological replicates, generating a total of 24

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samples for GC-MS analysis. The results showed that a good separation of intracellular

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metabolites was achieved on the GC column, and the further MS analysis allowed the chemical

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classification of 96 intracellular metabolites in all C. cohnii samples, including large number of

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amino acids, sugars and organic acids (Suppl. Table S3). The metabolites were excluded from

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the list if it was detected in only one out of three replicates. The number of metabolites identified

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in C. cohnii is almost double that detected in E. coli and Synechocystis,23, 42 indicating possibly

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more abundant metabolites in C. cohnii cells. Supervised PLS-DA plots were applied to evaluate

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the similarities and differences between a total of 24 metabolomic profiles (Fig. 3). Within each

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time point, BHA treated samples were well separated from the controls at all time points. In

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addition, most biological replicate samples tended to be cluster together, demonstrating an

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overall good reproducibility.

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WGCNA correlation network construction

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To further decipher mode of action of BHA in C. cohnii, we applied a WGCNA network analysis

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to the GC-MS metabolomic datasets. WGCNA is a correlation-based and unsupervised

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computational method to describe and visualizes correlation patterns of data points,18 and was

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successfully applied to metabolomic data to define metabolic modules.23,

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standard protocol, we constructed unsigned networks using the GC-MS metabolomic datasets of

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three time points (i.e., 24, 48 and 72 h), and then localized the correlated metabolites into various

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metabolic modules; in addition, the association of each distinguished metabolic module with

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BHA treatment was also determined, as highly associated modules indicated on the plots (Fig. 4).

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Setting a minimal number of metabolites in any module greater than 3, a cutoff of correlation

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coefficients (r value) between module and treatment condition greater than 0.6 and their

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statistical confidence (p value) less than 0.05, the WGCNA analysis identified 4, 2 and 2 distinct

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metabolic modules highly associated with BHA at 24, 48 and 72 h, respectively (Fig. 4).

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Member metabolites clustered into each of the BHA-responsive modules were provided in Table

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2. Among all eight BHA-responsive modules, it worth noting that only three modules were

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positively associated with BHA treatment (i.e., module M-24 h-4 at 24 h, module M-48 h-1 and

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M-48 h-2 at 48 h, respectively), and no module positively associated with BHA was found at 72

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h. Analysis of the metabolites within the three modules showed a similar trend of high content of

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amino acids and sugar, such as alanine, glycine, glyceric acid, isoleucine, proline, isomaltose,

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lactose, maltose L-sorbose and D-lyxose (Table 2). It may worth further investigation whether

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these metabolites directly contributes to the enhanced lipid accumulation in C. cohnii.

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Following the

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Hub metabolites are with high degree of connectivity in biological interaction networks and

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are thus supposed with high biological importance.44 Assuming hub metabolites with

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connectivity greater than 5, from the WGCNA network we were able to identify 3, 5 and 1 hub

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metabolites significantly associated with BHA at 24, 48 and 72 h, respectively (Fig. 5). At 24 h,

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the first hub metabolite, isomaltose, was located in module M-24 h-1 negatively associated with

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BHA treatment (r = -0.67, p = 0.05); and adenosine and hydroquinone were located in module

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M-24 h-2 negatively associated with BHA treatment (r = -0.89, p = 0.002). At 48 h, three hub

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metabolites, 3-hydroxypropanoic-acid, L-isoleucine and L-proline, were located in module M-48

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h-1 positively associated with BHA treatment (r = 0.89, p = 0.001). Although never reported for

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any role in microalga, a chemical analog of 3-hydroxypropanoic-acid, 2-hydroxypropanoic-acid

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extracted from grass Lolium multiflorum was found increasing the lipid accumulation in 3T3-L1

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cells.45 At 48 h, two hub metabolites, methylmyristate and methyloleate were located in module

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M-48 h-2 positively associated with BHA treatment (r = 0.67, p = 0.05), which may be

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consistent with their roles as free fatty acids that can be incorporated into lipids.47 At 72 h, one

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hub metabolite, malonic acid, was located in module M-72 h-2 negatively associated with BHA

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treatment (r = -0.87, p = 0.002). The coenzyme A derivative of malonic acid, malonyl-CoA, is an

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important precursor in lipid biosynthesis, and a previous study has found that malonic acid can

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enhance astaxanthin accumulation in Haematococcus pluvialis.47 It is thus unexpected that

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malonic acid was negatively associated with BHA treatment, although another plausible

327

explanation was that malonic acid is a well-known competitive inhibitor of succinate

328

dehydrogenase and TCA cycle,48 this negative association at 72 h may be due to cell aging and

329

death occurred in the same phase.

330

An recent study demonstrated that the lipid accumulation in several microalga could be

331

increased by antioxidant molecules in nanomolar concentration.13 In this study, we study the

332

effects of selected antioxidants on lipid accumulation in C. cohnii. The efforts led to the

15



333

discovery of an antioxidant BHA that could increase lipid content by 8.80% at a final

334

concentration of 30 µM. To further explore the enhancing mechanism, we employed an

335

integrated LC-MS and GC-MS metabolomic approach to determine the metabolic changes in

336

cells treated with BHA, which allowed identification of 24 and 96 intracellular metabolites from

337

the C. cohnii cells, respectively. In addition, analysis of metabolic networks constructed using a

338

WGCNA approach uncovered eight distinguished metabolic modules and nine hub metabolites

339

highly associated with BHA treatment, respectively. The metabolomic overview of cellular

340

status under BHA treatment could be a useful knowledge base to further understand the

341

molecular mechanisms of BHA and to discover relevant chemical enhancers for lipid

342

accumulation in C. cohnii. Finally, as the first metabolomic study of DHA-producing C. cohnii,

343

the study demonstrated that the technology is a powerful tool in deciphering mode of action of

344

chemical enhancers and could be useful in guiding optimization of lipid and DHA production in

345

microalga.

346 347

Abbreviations

348

3PG: D-3-Phosphoglyceric acid; Ac-CoA: acetyl coenzyme A; AKG: α-ketoglutaric acid; ATCC:

349

American Type Culture Collection; AMDIS: Automated MassSpectral Deconvolution and

350

Identification System; BHA: butylated hydroxyanisole; CoA: Coenzyme A hydrate; DHA:

351

docosahexaenoic acid; DHAP: dihydroxyacetone phosphate; DMSO: dimethyl sulfoxide; EGCG:

352

epigallocatechin gallate; ESI: electronic spray ion; F6P: D-fructose 6-phosphate; FBP: D-fructose

353

1,6-bisphosphat; FUM: fumarate dibasic; G6P: D-glucose 6-phosphate; GAP: DL-glyceraldehyde

354

3-phosphate; GC-MS: Gas Chromatography-Mass Spectrometry; Glu: L-glutamic acid; LC-MS:

16


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Page 17 of 30


355

Liquid Chromatography–Mass Spectrometry; MCW: methanol:chloroform:H2O solution; MPA:

356

Mobile phase A; MPB: Mobile phase B; NIST: National Institute of Standards and Technology;

357

OXA: oxaloacetic acid; PCA: Principal Component Analysis; PEP: phosphor-(enol)-pyruvic acid;

358

PG: propyl gallate; PLS-DA ： partial least square-discriminant analysis; R5P: D-ribose

359

5-phosphate; RuBP: D-Ribulose 1,5-bisphosphate; WGCNA: Weighted Gene Co-expression

360

Network Analysis.

361

362

Acknowledgements

363

The research was supported by grants from the National High-tech R&D Program (“863”

364

program, project No. 2012AA02A707), the National Basic Research Program of China (“973”

365

program, project No. 2011CBA00803, No. 2014CB745101 and No. 2012CB721101), and the

366

Doctoral Program of Higher Education of China (No. 20120032110020 and 20130032120022).

367 368

The supplementary information is available free of charge via the Internet at http: //pubs.acs.org.

369 370

Authors’ contributions

371

LC, JW and WZ conceived of the study. XS carried out the induction experiment. XS, JL and

372

MS performed GC and LC sample preparation and analysis. XS and GP performed the GC-MS

373

data analysis. XS, GP and XN performed the LC-MS analysis. GP and JW performed the

374

WGCNA analysis. XS, GP, MS, XN, LC, JW and WZ drafted the manuscript. All authors read

375

and approved the final manuscript.

376 377

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378

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Figure Legends:

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537 538

Fig. 1: PCA analysis of LC-MS metabolomic profiles. Score plot of cell samples collected at

539

24 h, 48 h and 72 h. BHA represents microalgal cell sample grown in media supplemented with

540

30 µM BHA in 0.4% (v/v) DMSO. CDMSO represents microalgal cell sample grown in media

541

supplemented only with 0.4% (v/v) DMSO. Control represents microalgal cell sample grown in

542

media without DMSO or BHA.

543 544

Fig. 2: Heat maps of LC-MS metabolomic profiles. C represents control media, D represents

545

media supplemented only with 0.4% (v/v) DMSO, and B represents BHA-spiked media. The

546

number represents biological replicates. A) 24 h; B) 48 h; C) 72 h.

547 548

Fig. 3: PLS-DA of GC-MS metabolomic profiles. A) 24 h; B) 48 h; C) 72 h. Each dot

549

represents one biological sample, while the dots of the same color are biological replicates. Three

550

conditions: control, DMSO control and BHA treatment are indicated by different colors.

551 552

Fig. 4: Hub metabolite and their metabolic profiles as represented by node and edge graph.

553

A): hub metabolites and associated network from module M-24 h-1; B): hub metabolites and

554

associated network from module M-24 h-2；C): hub metabolites and associated network from

555

module M-48 h-1；D): hub metabolites and associated network from module M-48 h-2；E): hub

556

metabolites and associated network from module M-72 h-2.

557 558

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560 561

Supplementary information

562 563

Suppl. Fig. S1: Growth of C. cohnii under various conditions. A) BHA supplementation.

564

Different BHA concentrations were indicted by colors and shown inside the figure. CDMSO

565

represents media supplemented with only 0.4% (v/v) DMSO. B) PG supplementation. Different

566

PG concentrations were indicted by colors and shown inside the figure.

567 568

Suppl. Fig. S2: Lipid accumulation in C. cohnii supplemented with PG. The lipid content in

569

the control without any PG supplement was set as 100%, and the % changes under various

570

concentrations of PG were indicated.

571 572

Suppl. Fig. S3: WGCNA analysis of GC-MS metabolomic profiles. A) 24 h; B) 48 h; and C)

573

72 h. The distinct modules identified at each time point were indicted by the clustering patterns

574

of the red color squares along the diagonal inside the plots. The modules highly associated with

575

BHA treatment (r>0.6 and p-value

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