Identification and metabolite profiling of chemical activators of lipid accumulation in green algae

55 Pages·2017·14.73 MB·English

Plant Physiology Preview. Published on June 26, 2017, as DOI:10.1104/pp.17.00433

1
2
3
4
5 Identification and metabolite profiling of chemical activators of lipid
6 accumulation in green algae
7
8
9
10 Nishikant Wase, Boqiang Tu, James W. Allen, Paul Black and Concetta DiRusso*
11
12
13
14 Department of Biochemistry, University of Nebraska, Lincoln, NE 68588, USA
15
16
17 *Address correspondence: to [email protected]
18
19
20
21 Author Contributions
22 C.D. and N.W. conceived the original compound selection and screening plan. N.W., J.W.A.
23 and B.T. performed the research. C.D., N.W., J.W.A. and B.T. analyzed the data and C.D., N.W.,
24 J.W.A. and P.N.B. wrote the article and prepared the figures and tables.
25
26 Short Title:
27 Metabolite profiles of algal lipid activators
28
29 One sentence summary:
30 Small compound activators of lipid accumulation in algae are distinguished by differential
31 effects on lipid and polar metabolite profiles.

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
Copyright 2017 by the American Society of Plant Biologists
32 ABSRACT
33
34 Microalgae are proposed as feedstock organisms useful for producing biofuels and co-
35 products. However, several limitations must be overcome before algae-based production
36 is economically feasible. Among these is the ability to induce lipid accumulation and
37 storage without affecting biomass yield. To overcome this barrier, a chemical genetics
38 approach was employed in which 43,783 compounds were screened against
39 Chlamydomonas reinhardtii and 243 compounds were identified that increase
40 triacylglyceride (TAG) accumulation without terminating growth. Identified compounds
41 were classified by structural similarity and 15 were selected for secondary analyses
42 addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein
43 and starch concentrations. TAG accumulation was verified using GC-MS quantification
44 of total fatty acids and targeted TAG and galactolipid (GL) measurements using LC-
45 MRM/MS. These results demonstrated TAG accumulation does not necessarily proceed
46 at the expense of GL. Untargeted metabolite profiling provided important insights into
47 pathway shifts due to 5 different compound treatments and verified the anabolic state of
48 the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle,
49 tricarboxylic acid cycle and amino acid biosynthetic pathways. Metabolite patterns were
50 distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil
51 accumulation in algae. The efficacy of these compounds was also demonstrated in 3 other
52 algal species. These lipid inducing compounds offer a valuable set of tools for delving
53 into the biochemical mechanisms of lipid accumulation in algae and a direct means to
54 improve algal oil content independent of the severe growth limitations associated with
55 nutrient deprivation.
56
57
58 Keywords: algae, biofuels, bioproducts, high throughput screening, lipids, metabolite
59 profiles
60
2

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
61 INTRODUCTION
62
63 Currently economic barriers must be overcome for the commercial adoption of
64 microalgae for next generation biofuel production despite their distinct advantages. These
65 include rapid growth and the ability to accumulate 20-40% of dry weight as lipids, a potential for
66 100-fold more oil per acre than soybeans or other oil-seed bearing plants, and an ability to thrive
67 in poor quality water in a large variety of environmental conditions (Jones and Mayfield, 2012;
68 Scranton et al., 2015). Algae also fix CO into biomass during photosynthesis, thus addressing
2
69 concerns about the generation of carbon emissions. Additionally, a wide range of byproducts
70 useful for biotechnological applications are produced in algae, notably replacing an increasing
71 amount of the docosahexaenoic acid (DHA, C22:6 ω3) market as the supply of fish oil dwindles
72 (Morita et al., 2006; Song et al., 2015).
73 Lipid accumulation in algae normally requires an environmental stress, particularly
74 nutrient deprivation of nitrogen, sulfur or certain metals, as algae do not appreciably synthesize
75 oil during rapid growth (Guarnieri et al., 2011; Cakmak et al., 2012). Nutrient limitation is
76 sometimes achieved during normal growth when cultures reach saturation density when nitrogen
77 becomes limiting and triacylglyceride (TAG) rich lipid droplets become visible and measurable
78 (Msanne et al., 2012; Wang et al., 2012). This is normally preceded by, or commensurate with,
79 cessation of protein synthesis, degradation of chlorophylls and photosynthetic enzymes including
80 RuBisCO, and a dramatic reduction in chloroplast membrane lipids (Wase et al., 2014; Allen et
81 al., 2015). Therefore, the commercial production of algae for fuel and coproducts is limited by
82 the antagonism between reproductive growth and oil accumulation. Solving this problem
83 requires further insight into activating metabolic pathways leading to lipid storage while
84 avoiding the obstruction of cell growth or division.
85 Important information on lipid synthetic pathways has been derived by comparison of
86 algal and plant genomes (Awai et al., 2006; Benning, 2008). Such comparisons have led to the
87 conclusion that fatty acid synthesis in algae occurs primarily, if not exclusively, in the
88 chloroplast (reviewed in (Hu et al., 2008)). Fatty acid synthesis uses metabolic substrates derived
89 from photosynthetic CO fixation, which supplies chloroplast-specific complex lipid synthesis.
2
90 De novo synthesized fatty acids must also be trafficked outside the chloroplast to support extra-
91 plastid lipid synthesis (Awai et al., 2006). Involved in this process is an interwoven, intracellular
3

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
92 system of acyl chain incorporation into glycerolipids including the Kennedy Pathway, acyl chain
93 editing and lipid remodeling reactions. During N starvation, this system is coopted for TAG
94 synthesis, which competes with membrane lipid synthesis for available acyl-CoA. In algae,
95 abiotic stress-induced TAG synthesis mainly occurs through the sequential acylation of glycerol
96 using fatty acids derived from both de novo synthesis and membrane glycerolipids, which are
97 continually synthesized and degraded (Allen et al., 2015). The side chain composition of newly
98 synthesized TAG, however, differs significantly from membrane lipid fatty acid composition in
99 having higher levels of saturated and monounsaturated long chain fatty acids, supporting a
100 greater role for de novo synthesized acyl chain incorporation (reviewed in (Hu et al., 2008)).
101 Additionally, 13C-CO labeling provides evidence that a large majority of the TAG side chains
2
102 are derived directly from photosynthetic CO fixation and de novo fatty acid synthesis (Allen et
2
103 al., 2015; Allen et al., 2017). These recent advancements indicate that, although membrane
104 glycerolipid synthesis, degradation, and acyl editing are intricately involved in TAG
105 accumulation as a consequence of the stress-induced cessation of growth, it may be possible to
106 extricate TAG synthesis from these reactions.
107 In previous studies, we used a combinatorial proteomics and metabolomics approach to
108 help define metabolic and regulatory mechanisms responsible for TAG synthesis, especially
109 related to the central metabolism during nitrogen starvation (Wase et al., 2014). These studies
110 indicated that the TCA cycle acts as a central hub for maintaining equilibrium in the supply and
111 demand of carbon skeletons, channeling excess carbon precursors as citrate into lipid synthesis
112 (Wase et al., 2014). Under these conditions growth is halted, biosynthetic activities are
113 minimized, and excess carbon is channeled to lipids. Thus, the yield of biomass is compromised,
114 which ultimately also limits lipid production and thus minimizes feasibility for use in biofuels.
115 Here we report a chemical genetics study using small molecule activators of lipid
116 synthesis that were identified by high throughput screening. Several of these lipid activators
117 were employed to probe pathways permissive to both algal growth and TAG accumulation
118 (McCourt and Desveaux, 2010; Wase et al., 2015). For screening, low density algal cultures
119 were treated with compounds from a large chemical library for 72 h, and both growth and lipid
120 accumulation were assessed (Wase et al., 2015). A collection of 243 active compounds were
121 identified and verified that fell into 5 structurally-related groups. Novel secondary screens were
122 conducted with 15 of these compounds to examine impacts on growth and chloroplast integrity,
4

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
123 as well as total lipid, protein, and starch contents. All but one compound accumulated TAG
124 without producing an apparent stress response. To further these analyses, metabolite profiles
125 resulting from treatment with a subset of 5 compounds inducing distinct phenotypic responses in
126 Chlamydomonas reinhardtii were conducted. These data provide valuable insight into changes in
127 central carbon and amino acid metabolism associated with lipid accumulation in algae. To
128 confirm that TAG accumulation is not limited to C. reinhardtii, we also tested the selected
129 compounds in three different freshwater chlorophycean algae: Chlorella sorokiniana, Chlorella
130 vulgaris and Tetrachlorella alterens. Lipid-inducing compounds discovered by chemical genetic
131 screening represent useful tools for identifying metabolic reactions and regulatory factors that
132 can affect both lipid and biomass accumulation, and thus are of use in the commercial production
133 of algal biofuels and other high value co-products.
134
135
136 RESULTS
137
138 Selection of lipid storage activators
139
140 The screening protocol used was previously devised and tested using a small compound
141 library designed for this purpose (Wase et al., 2015) and is outlined in Supplemental Fig. S1.
142 The primary selection required two phenotypic tests; [1] the lipophilic dye Nile Red was used to
143 identify treated cells that accumulated neutral lipids, and [2] assessment of growth and
144 accumulated cellular mass using spectrophotometric measurements at OD . Compounds were
600
145 added to a final concentration of 10 µM at low cell density and cells were cultured for 72 h prior
146 to analyses. In the current screening experiments, a control based normalization approach was
147 employed where the data were standardized to a negative control in terms of lipid accumulation
148 (i.e., cells cultured without compound in Tris-acetate-phosphate media (TAP) with vehicle
149 (DMSO)). For quality control analysis (QC), a Z-factor was calculated for each analysis plate to
150 determine the separation between the positive control for lipid accumulation (cells grown in TAP
151 media without nitrogen (N-)) and the negative control (cells grown in standard TAP media with
152 nitrogen (N+)) to measure the signal range (Zhang et al., 1999). The mean Z-factor was robust at
5

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
153 0.78 ± 0.08 over 124 plates that were used to screen 43,783 compounds (Fig.1A). The QC
154 analysis is given in supplemental data 1 (Supplemental Fig. S2-S4).
155 Estimation of the change in biomass after 72h treatment compared with the initial
156 inoculum revealed that 35 of the 43,783 compounds severely blunted growth (i.e., final OD ≤
600
157 0.2) (Fig. 1B). Approximately 3% of the compounds (1,294) had a moderate inhibitory effect on
158 growth whereby the cells achieved an average optical density ≤ 0.32. Treatment with 26,656
159 compounds resulted in an average of 0.45 OD after three days of treatment, which was
600
160 comparable to the controls.
161 Following the elimination of compounds that limited growth, a fold-change analysis of
162 Nile Red intensity as an indicator of neutral lipid accumulation was completed (Fig. 1C). The
163 primary hit list included 367 compounds that induced lipid accumulation to ≥ 2.5-fold levels
164 over untreated controls to yield a hit rate of 0.8%. To confirm the primary screening results,
165 these compounds were retested at over a range of concentrations from 0.25 to 30 μM (Fig. 1D
166 and Supplemental Table S3). Fluorescence microscopy was used to verify these compounds
167 induced lipid body accumulation in cells (data not shown). The final hits from the primary
168 screen consisted of 243 compounds that gave 2.5-fold induction at one or more concentrations
169 tested; 124 compounds that yielded less than 2.5-fold lipid induction were not considered for
170 further analysis (Supplemental Fig. S2, and PubChem AID 1159536).
171
172 Structural models for lipid-inducing compounds
173
174 To gain insight into the structural relatedness of the hit compounds, we performed
175 chemoinformatics analysis using cytoscape ChemViz plugin (Wallace et al., 2011). Structural
176 similarity of the compounds were based on Estate bit fingerprint descriptors (Hall and Kier,
177 1995). To construct a network similarity graph, a Tanimoto similarity cutoff of 0.70 was used
178 for edge creation (0 representing dissimilar compounds and 1.0 representing identical
179 compounds) and visualized using Cytoscape v2.8.2 (Yeung et al., 2002). Data from three
180 treatment concentrations, 10, 15 and 30 μM were mapped and a pie chart was painted at each
181 node. The fold change value for data acquired at the 30 μM concentration was used to adjust the
182 node size (Fig. 2).
6

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
183 The analysis using the Cytoscape ChemViz plugin provided a structure-activity
184 relationship in the form of a network similarity graph based on the structural similarities of the
185 compounds and their ability to induce lipid synthesis. For further analysis of the compounds to
186 identify structurally similar molecular framework /scaffolds Scaffold hunter was used (Wetzel et
187 al., 2009). The structures of the 243 active molecules were imported into Scaffold hunter v2.3.0
188 and a new database was created. Using chemical fingerprinting analysis of the 243 active small
189 molecules, we constructed a model to predict the active structural class of the lipid accumulating
190 compounds. The chemical space was organized by abstracting the molecular structures so that a
191 set of structurally similar molecules can be represented by a single structure referred to as the
192 molecular framework, or scaffold, that is obtained from a molecule by removing side chains,
193 generating a hierarchy of scaffolds sharing a common molecular framework. For structural
194 comparison of the active molecules, first Estate Bit chemical fingerprints were calculated and
195 hierarchical clustering was performed by the Ward’s linkage method. The distance was
196 calculated using the Tanimoto coefficient of 0.70 noted above (Hall and Kier, 1995). Based on
197 the hierarchical clustering, we identified several major and minor structural classes of
198 compounds that illustrate related structure and activity. Using at least 3 compounds per cluster,
199 we identified 18 different structural molecular frameworks (scaffolds) and 45 singletons (Fig. 2A
200 and 2B). The major common structural features included benzene (45 members), piperazine (114
201 members), morpholine (32 members), piperidine (28 members), adamantane (14 members), and
202 cyclopentane (11 members). Further comparisons of the most active compounds in terms of lipid
203 accumulation led to the selection of 15 that were divided into 5 related structural groups.
204 Compounds in group 1 (GR-1) shared a piperidine moiety, group 2 (GR-2) a benzyl piperizine
205 moiety, group 3 (GR-3) a nitrobenzene moiety, group 4 (GR-4) a phenylpiperizine moiety and
206 group 5 (GR-5) an adamantane moiety (Fig. 3).
207 To visually assess the effect of compounds on the lipid accumulating phenotype, cells
208 were stained with Nile Red and images captured using both brightfield and fluorescent confocal
209 microscopy (Fig. 3). As expected, all compounds induced the accumulation of lipid bodies. For
210 some compound treatments, granular particles were also observed that may be starch deposits.
211
212 Effect of selected compounds on growth, photosynthetic pigments, total starch and protein
213
7

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
214 The 15 highest performing compounds in terms of lipid accumulation and growth were
215 further evaluated using secondary screens that assessed impact on growth and selected cellular
216 metabolite pools. Cells in early-log phase (0.1-0.2 OD ) were treated with compounds at a
600
217 single concentration of 30 μM. Incubation was continued for 72 h and the change in the optical
218 density was monitored daily (Fig. 4A). At 30 µM, some growth reduction was observed for most
219 compounds after 48 h compared with controls. One compounds WD10784 had a more
220 pronounced reduction of growth at 30 µM, so the concentration of this compounds was reduced
221 to 10 µM for secondary analyses. This concentration was sufficient to induce the maximal lipid
222 accumulation and was less growth restrictive. Importantly, unlike during N starvation, total
223 protein levels were not significantly reduced by treatment with any of the selected lipid
224 activating compounds (Fig. 4B). Protein levels were slightly but significantly increased after
225 treatment with WD10264, WD10872, WD10615, and WD20542.
226 During N limitation, which induces the accumulation of lipids, the levels of starch also
227 increases, indicating the storage of carbon as both sugars and fats (Longworth et al., 2012).
228 These changes in the macromolecular pools are associated with the stress response induced by N
229 limitation and the redirection of carbon to storage compounds including lipids and starch (Wase
230 et al., 2014). Treatment with the selected lipid inducing compounds had a variable impact on
231 accumulation of starch; 8 compounds had no significant impact on starch accumulation while 7
232 compounds significantly increased starch levels compared to the untreated control cells (Fig.
233 5A). The compounds that did not alter starch levels were primarily from structural groups 1 and
234 3. The most common structural feature of the compounds that induced starch levels was the
235 piperazine moiety, thus suggesting that this structural feature is important in inducing this effect.
236 To utilize carbon efficiently and satisfy energy demands, algae must maintain adequate
237 chlorophyll and carotenoids levels. We have previously shown that during nitrogen starvation,
238 there is a gradual decrease in the chlorophyll:carotenoid ratio (Wase et al., 2014). Quantification
239 of photosynthetic and total carotenoid pigments showed treatment with most compounds had no
240 significant effect (Fig. 5B-5D). However, treatment for 72h with compounds WD10784 and
241 WD10615 reduced total carotenoids, chlorophyll a (chl a) and chlorophyll b (chl b) by ≥50 %,
242 relative to control levels (Fig. 5B-5D). There was also a small but significant decrease in both
243 carotenoid and chl b quantities after treatment with WD10738, WD10599, and WD20542, while
244 chl a was unaffected.
8

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
245
246 Fatty acid analysis of compound treated cells
247
248 Following compound treatment of Chlamydomonas, lipids were extracted and the fatty
249 acid composition determined using GC-MS (Table 1). The total amount of fatty acids
250 accumulated varied with compound treatment from 1.3-fold (WD40157) to 4.4-fold (WD30030)
251 control cells. For all compound treatments, there were significantly higher levels of C16:0,
252 C18:0, C18:2cis∆9,12 and C18:3cis∆9,12,15 compared with untreated control cells (given as µg/5x106
253 cells). For example, compound WD40844 (Table 1, line 3) resulted in the accumulation of 3.7-
254 fold more C16:0 as compared to untreated control cells; C16:0 was increased 3.8-fold with
255 WD30030 treatment, 3.0-fold with WD20067, 3.2-fold with WD10461, 2.4-fold with WD20542,
256 3.7-fold with WD40844, and 2.6-fold with WD10784. The fatty acid profiles for compound
257 treated cells differed somewhat from those measured after nitrogen starvation (Msanne et al.,
258 2012; Wase et al., 2014). For example, during nitrogen starvation, the levels of the
259 polyunsaturated fatty acid C16:4 increase, whereas for most of the lipid accumulating
260 compounds the levels of this fatty acid were not significantly different from the controls. The
261 opposite was true for C18:3.
262
263 Complex lipid analysis after selected compound treatments
264
265 In order to gain further insight into the biochemical shifts that result during treatment of
266 Chlamydomonas with compounds from the different structural classes that promoted lipid
267 accumulation, we performed metabolite analyses of cultures treated with WD10784 from GR-1
268 (piperidine moiety), WD10461 from GR-2 (benzyl piperizine moiety), WD30030 from GR-3
269 (nitrobenzene moiety), and WD20067 and WD20542 from GR-5 (adamantane moiety). A
270 common component of the algal abiotic stress response is the reduction of chlorophyll content
271 and concurrent degradation of chloroplast membrane lipids, specifically mono-
272 galactosyldiacylglycerol (MGDG) which can collapse these membranes into irreversible, non-
273 biological states as chloroplasts are dimensionally reduced (Guschina and Harwood, 2009;
274 Goncalves et al., 2013; Urzica et al., 2013). The galactolipid (GL) content of cells was therefore
275 analyzed in relation to TAG levels to assess both the quantitative extent of TAG accumulation
9

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
276 and possible abiotic stress due to the compound treatments (Fig. 6A-6C). In all cases, the
277 compounds increased the TAG content ranging from 2.7 ± 0.6- to 5.5 ± 3.0-fold greater than
278 algae grown without compounds but with vehicle (DMSO) (Fig. 6A). The total GL content of
279 cells treated with WD30030, WD20542, WD10784, and WD10461, was not statistically
280 significant by one-way analysis of variance (p > 0.05) (Fig 6B). By contrast, treatment with
281 WD20067 reduced total GL by 0.3 ± 0.04 fold. Therefore, with the exception of WD20067, the
282 compounds tested increased TAG content without causing the chloroplast membrane lipid
283 degradation typical of abiotic stress.
284
285 Changes in polar metabolite levels in response to compound treatment
286
287 Untargeted metabolomics was employed using GC-MS to broadly assess impacts of the 5
288 selected compounds on cellular polar metabolites. Identified metabolites were reported if present
289 in at least 7 of 9 samples per treatment group, resulting in a list of 125 compounds. These were
290 deconvoluted and aligned using the eRah R-package. Putative identification of the compounds
291 was made using both the MassBank and Golm metabolome databases, which resulted in 98
292 unique metabolite identifications. The metabolite IDs were mapped with the KEGG compound
293 IDs and classified according to KEGG compound biological role classifications (Table 2).
294 Metabolites annotated but not assigned a role in the KEGG databases were denoted as
295 unclassified and unannotated metabolites were classified as unknown.
296 For the first quantitative evaluation, normalized and pareto scaled ion intensities for the
297 125 metabolites and 54 samples (9 replicates for each condition) were analyzed using
298 unsupervised multivariate statistics (principal component analysis, PCA) to globally compare
299 biochemical traits between the control and compound treated groups. The resultant score
300 scatterplot showed that the principal factors PC1 and PC2 discriminated between the metabolite
301 profiles in accordance with the compound treatment (Fig 7A). PC 1 had an explained variance of
302 22.2% and mainly discriminated the metabolite profiles of the untreated control from compound
303 treated samples. With the combination of PC2 (explained variance 20.6%), all the treated groups
304 were clustered away from the untreated control and 42.8% of the cumulative variance was
305 explained among the 6 different treatments. A clear separation between the control and
306 compound treated samples was observed, however, data for compounds WD30030, WD10461
10

Downloaded from on April 5, 2019 - Published by www.plantphysiol.org
Copyright © 2017 American Society of Plant Biologists. All rights reserved.