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Altered Microbiome Leads to Significant Phenotypic and Transcriptomic Differences in a Lipid Accumulating Chlorophyte Lubna Richter, Cresten B. Mansfeldt, Michael M. Kuan, Alexandra Cesare, Stephen T. Menefee, Ruth E. Richardson, and Beth A Ahner Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06581 • Publication Date (Web): 11 May 2018 Downloaded from http://pubs.acs.org on May 13, 2018

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Altered Microbiome Leads to Significant Phenotypic and Transcriptomic Differences in a Lipid Accumulating Chlorophyte

8

T. Menefee†, Ruth E. Richardson‡, and Beth A. Ahner†*

Lubna V. Richter†§, Cresten B. Mansfeldt‡§, Michael M. Kuan†, Alexandra E. Cesare†, Stephen

9 10 11 12 13 14 15

†

Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA

‡

School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA

16 17

§

Authors contributed equally to this work

18 19 20 21 22 23 24

*Correspondence to: B. A. Ahner Tel: 607- 255- 4677; Fax: 607- 255- 3679 Email: [email protected]

25 26 27 28

Keywords: Microalgae, Bacteria, Symbiosis, 16S rRNA, Lipid, Chlorella, Transcriptome

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ABSTRACT

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Given the challenges facing the economically favorable production of products from

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microalgae, understanding factors that might impact productivity rates including growth rates

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and accumulation of desired products, e.g., triacylglycerols (TAG) for biodiesel feedstock,

33

remains critical. Although operational parameters such as media composition and reactor design

34

can clearly effect growth rates, the role of microbe-microbe interactions is just beginning to be

35

elucidated. In this study an oleaginous marine algae Chlorella spp. C596 culture is shown to be

36

better described as a microbial community. Perturbations to this microbial community showed a

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significant impact on phenotypes including sustained differences in growth rate and TAG

38

accumulation of 2.4 and 2.5 fold, respectively. Characterization of the associated community

39

using Illumina 16S ribosomal RNA amplicon and random shotgun transcriptomic analyses

40

showed that the fast growth rate correlated with two specific bacterial species (Ruegeria and

41

Rhodobacter spp). The transcriptomic response of the Chlorella species revealed that the slower

42

growing algal consortium C596-S1 upregulated genes associated with photosynthesis and

43

resource scavenging and decreased the expression of genes associated with transcription and

44

translation relative to the initial C596-R1. Our studies advance the appreciation of the effects

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microbiomes can have on algal growth in bioreactors and suggest that symbiotic interactions are

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involved in a range of critical processes including nitrogen, carbon cycling and oxidative stress.

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INTRODUCTION Select

species

of microalgae biosynthesize and

accumulate high

levels

of

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triacylglycerides (TAG) as a means of energy storage in response to particular environmental

55

stimuli such as nutrient limitation.1 In particular, members of the green algae Chlorella genus

56

have been reported to bioaccumulate high levels of TAG and have therefore been studied for

57

biodiesel production.2-4 Use of such algae species for biofuel production is being explored in

58

part because they can be cultivated on non-arable land using non-potable or saline water

59

resources with high areal productivity.

60

production of biodiesel from algae,5-7 ultimate success will require that algae are grown at near

61

optimal growth rates and within a microbial community that is stable and conducive to lipid

62

production.

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Although challenges remain to achieve sustainable

Bacteria have been shown to be beneficial for algal growth,2,

8

in part via nutrient

64

recycling and exchange. Bacteria can also provide algae with essential vitamins, co-factors 2, 9, 10

65

or growth hormones,11, 12 participate in signal transduction,13 and may help protect algae from

66

pathogens14 in exchange for algae-excreted dissolved organic carbon. Interactions appear to

67

involve complex communication mechanisms15, 16 and may involve bacterial colonization of the

68

algal cell surface.17,

69

communities are structured in nature19 and in photobioreators.17 These studies have focused on

70

the identification of prevalent microbial gene families or metabolic pathways as a means to

71

identify particular symbiotic relationships. Bacteria belonging to alpha-Proteobacteria, beta-

72

Proteobacteria, and Bacteroidia classes (including members of the Flavobacteriales and

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Sphingomonadales orders) have been found to be associated with green algae.20

18

Genomic tools are enabling us to learn more about how these

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Although positive interactions between phototrophic microorganisms and some bacteria

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are well established, and we are only beginning to understand how specific metabolic pathways

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change in algae

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attributes of the algae cell, in particular those of interest for biofuel production. Recent “omics”

78

studies have revealed significant changes in key metabolic pathways when bacteria are

79

introduced or re-introduced to pure cultures of photosynthetic cyanobacteria.21-23 For example,

80

in the presence of the bacterium Alteromonas macleodii, algal (Prochlorococcus) cells

81

upregulated components of their photosynthetic apparatus to increase the export of reduced

82

carbon compounds for use by its heterotrophic partner.23

83

(Synechococcus) expressed membrane transport proteins increased, and vitamin metabolism

84

related proteins decreased in the presence of the bacterium Roseobacter, suggestive of metabolite

85

provision by the bacteria.21

86

in response to variable microbial communities and how this may affect key

In a separate study, algal

Strain C596 is a subtropical marine Chlorella spp. that accumulates lipids (up to 45%

87

TAG by weight) under nitrogen and phosphorous limiting conditions.3,

24

88

versatile growth conditions including across a range of salinities and has the ability to utilize a

89

wide range of carbon substrates for growth in the dark.3 Stock cultures of C596 were not axenic,

90

and during attempts to create axenic stocks, algal cells exhibited lower growth rates that we

91

hypothesized resulted from an altered microbiome in the new consortium. In this study, we

92

created and studied a set of C596 consortia to understand how changes in community structure

93

affect Chlorella growth and lipid accumulation.

94

metatranscriptomic analysis on two consortia with distinct microbiomes to better understand

95

their synergistic roles during co-culture with Chlorella C596.

C596 tolerates

We also conducted a comparative

96



97

METHODS

98

Chlorella consortia growth and plating. Chlorella spp. “SAG 211-18” strain C596 with its

99

initial microbial community3 (designated C596-R1 hereafter) was grown in the synthetic

100

seawater medium Aquil25 under constant illumination (80 µmol photon m-2 s -1, GE Ecolux Cool

101

White bulbs, East Cleveland, OH, USA) at 25˚C.

102

fluorescence (10-AU Fluorometer, Turner Designs, San Jose, CA, USA) and cell counts using a

103

Neubauer hemacytometer (Spencer Bright Line) at 40× magnification with an optical microscope

104

(IX83, Olympus Corp., Center Valley, PA, USA). Various C596 consortia were plated/streaked

105

on solid Aquil medium, and isolated algal colonies were returned to liquid medium as above

106

yielding new consortia (e.g., C596-S1 and C596-S2). To isolate bacterial strains associated with

107

Chlorella, C596-R1 was plated/streaked on solid Aquil medium supplemented with 2, 5, 10 or

108

20 mM of either acetate, sucrose or glycerol. Colony PCR using universal 16S primers (8F: 5’-

109

AGAGTTTGATCCTGGCTCAG- ‘3, 1496R: 5’ -GGCTACCTTGTTACGACTT- ‘3) followed

110

by sequencing were conducted to screen bacterial colonies.

Growth was monitored via chloroplast

111 112

Resuspension experiments. In one set of experiments, C596-R1 was grown till late-exponential

113

phase and harvested by filtration using a 3-µm membrane filter to separate the algae (~5 µm)

114

from the bulk of the bacterial community. Filtrates were then passed through 0.2-µm membrane

115

filters to capture bacteria. These 0.2-µm filters were added to separate mid-exponential phase

116

C596-R1 and -S1 cultures as well as to fresh Aquil media tubes (filter control C596-F1).

117

Membrane filters were removed with sterile forceps after 24 hours, and cultures were monitored

118

for growth thereafter.

119

exponential phase and spent media was filtered through a 0.2-µm membrane. Filtrates were

In a parallel experimental set, C596-R1 culture was grown to late-


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divided in half, one half was autoclaved for 35 minutes. Both halves were then amended with

121

300 µM NaNO3, 10 µM NaH2PO4, trace metals and vitamins stock solutions to final

122

concentrations as recommended for Aquil medium, and pH was verified. Autoclaved filtrate,

123

non-autoclaved filtrate and fresh Aquil media were used to re-suspend pelleted C596-R1 and -S1

124

consortia harvested at mid-exponential phase.

125

florescence measurements.

Growth was monitored via chlorophyll

126 127

Nile red fluorescence. Nile red stain was dissolved in dimethyl sulfoxide (HPLC grade, Sigma

128

Aldrich, St. Louis, MO, USA) to a final concentration of 1 mg/mL, and stored at -20°C in the

129

dark.

130

fluorescence was measured utilizing filters with excitation/ emission wavelengths of 530 ± 25/

131

590 ± 20 nm, respectively. Resulting measurements were used to quantify the relative cellular

132

lipid content26 which was subsequently normalized by the cell concentration (determined

133

microscopically) to determine cellular lipid content. Triplicate measurements were taken at late

134

exponential, early stationary and senescent growth stages.

Algae were stained in 96-well plates as previously reported,26 and the Nile red

135 136

16S rRNA sequencing and microbial community analysis. Biological triplicates of algal

137

consortia were harvested at mid-exponential growth by passing through 0.2-µm membrane filters

138

(Whatman Nuclepore Track-Etch membrane, Sigma-Aldrich). Total RNA was extracted and

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purified using the RNeasy Plant Mini Kit (Qiagen, Germantown, MD, USA),3 and its integrity

140

and concentration were evaluated using a RNA 6000 Nano Kit on an Agilent Bioanalyzer 2100

141

(Agilent Technologies, Santa Clara, CA, USA). Complimentary DNA (cDNA) was generated

142

using an iScript reverse transcriptase and a blend of olig(dt) and random hexamer primers



143

(iScript cDNA Synthesis Kit; Bio-Rad, Hercules, CA, USA). The 16S rRNA gene was amplified

144

using the universal bacterial primers 341F (5’- CCTACGGGNGGCWGCAG -3’) and 805R (5’-

145

GACTACHVGGGTATCTAATCC -3’), with overhang sequences compatible for index

146

attachment as described elsewhere.27 PRIME HotMasterMix (5 PRIME Inc., Gaithersburg, MD,

147

USA) was used in PCR reactions, and cycle conditions were set as previously described.28 The

148

16S rRNA gene amplicon pool was sequenced on an Illumina MiSeq (Cornell Genomics

149

Facility, Ithaca, NY) using a 600-cycle MiSeq Reagent Kit v.3 (Illumina, San Diego, CA, USA).

150

Sequences were processed using the Brazilian Microbiome Project pipeline with modifications

151

as described by Howard et al.28 Briefly, paired-end sequences were merged, primers trimmed,

152

and singleton sequences removed using Mothur v.1.36.1.

153

Taxonomic Units (OTUs) and chimera removal were performed using USEARCH v.7 (Edgar

154

2010). Representative OTU sequences were classified (classify.seqs, cutoff = 80) using the

155

GreenGenes v.13.8 database for 16S rRNA gene sequences, and OTUs that were suspected to

156

not be of bacterial origin were removed. All fastq sequences are available at the NCBI’s

157

Sequence Read Archive (SRA) under the BioProgect PRJNA397076. Statistical analysis were

158

performed using R (v.3.2.1). OTUs were first randomly subsampled to yield an equal number of

159

sequences for all samples (rarefaction curves are shown in Figure S1). Heatmaps were generated

160

via the gplots package in R29 to display the OTU relative abundances. OTUs were clustered

161

hierarchically (average linkage) based on the Bray-Curtis dissimilarity index.

Clustering of 97% Operational

162 163

Random shotgun metatranscriptome sequencing and analyses. For the C596-R1 and -S1

164

consortia, an additional rRNA depletion step was performed on the resulting RNA from the

165

extraction and purification methods described.

This step employed the GeneRead rRNA


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Depletion Kit (Qiagen) specific for eukaryotic rRNA following the manufacturer’s instructions

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to enhance the sequencing coverage of mRNA.

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bacterial rRNA.

There is a potential cross reactivity with

169

Random-hexamer based RNA sequencing, transcript assembly, and analyses that resulted

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in a total of 7,557 C596-annotated transcripts were performed as described elsewhere.3 All raw

171

and processed data files are freely available (NCBI’s Sequence Read Archive accessions

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SRX1282877 and SRX1281663 under the BioProject ID PRJNA294811). The SortMeRNA

173

program30 was used to bin the reads as either of potential rRNA or mRNA origin by comparing

174

the reads to the SILVA Eukaryotic (18S and 28S), SILVA Bacteria (16S and 23S), SILVA

175

Archaea (16S and 23S), and RFAM (5S and 5.8S) databases. The rRNA sequences were then

176

filtered from the processed files using the SILVA RNA databases and assigned to an Operational

177

Taxonomic Unit (OTU).

178

Reads assigned to mRNA transcripts annotated as of Chlorella were analyzed using

179

edgeR31 in the R software suite (v 3.1.2) to determine transcripts that were differentially

180

regulated using the exactTest function. A fold-ratio change of greater than 4.0 or less than 0.25

181

with a multiple-testing corrected p-value of 6.6 x 10-6 (Bonferroni-Holm established corrected

182

threshold) was set to select for the statistically-significant differentially-expressed genes. A

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looser ratio cutoff (fold ratio greater than 2.0 or less than 0.5 with an uncorrected p-value < 0.05)

184

was established to select a subset of genes for a bulk profile analysis. These genes were assigned

185

to KEGG categories32 using a tblast search against the protein database for Chlorella variabilis

186

(BLAST e-value of 10-10), and then the binned transcripts were enumerated to compare relative

187

expression in -S1 and -R1 cells.

188



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RESULTS AND DISCUSSION

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Algal culture variability appears modulated by associated microbiome.

As part of a

191

previous study, we noted that algal cultures generated by inoculation of algal colonies from a

192

plate appeared to be phenotypically different and we hypothesized an altered microbial

193

consortium was responsible.

194

consortium, generated from the initial consortium C596-R1, exhibited a significant decrease in

195

growth rate, 0.7 ± 0.08 d-1 versus 1.3 ± 0.12 d-1 respectively (Figure 1). This difference was

196

maintained in subsequent culture transfers for over one year.

One of the algal cultures, hereafter designated the C596-S1

197 198

Further modification of the microbial background in Chlorella culture. Our attempts to

199

isolate individual bacteria from the C596-R1 consortium using various carbon sources (see

200

Methods) led to the repeated isolation of only one Ruegeria species as confirmed with 16S rRNA

201

gene sequencing. Addition of this Ruegeria strain to cultures of C596-S1 did not stimulate

202

growth (Figure S2). Because only a small fraction of bacteria from an environmental sample can

203

be isolated and cultivated in a laboratory under a single set of growth conditions,33 we next filter-

204

separated the planktonic microbial community (i.e., 0.2 µm < cell size < 3 µm) from the C596-

205

R1 consortium and added the microbial-laden filters to C596-S1 and -R1 consortia (Figure 2A).

206

The addition of the collected community marginally increased the growth rates of both cultures

207

(Figure 2 B & 2C). Triplicate biological controls comprised solely of the filters (collected from

208

500 mL of C596-R1 medium) placed in Aquil media exhibited no measurable algal growth over

209

the period during which we measured increased growth rates in the aforementioned C596-S1 and

210

-R1 cultures. After four days however, the few algae that were present on the filter grew to a

211

sufficient density to be observed, and their growth rate was quantified (Figure 2B). These newly


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generated triplicate Chlorella consortia, designated C596-F1, exhibited a 39% increase in algal

213

growth relative to -R1 (1.67 ± 0.07 d-1 vs. 1.35 ± 0.04 d-1 for C596-R1) and maintained this rate

214

for over six months of subsequent culturing (Figure 2C).

215

To investigate this apparent Chlorella-bacteria symbioses further, triplicate C596-S1 and

216

-R1 consortia pellets were resuspended in nutrient-amended C596-R1 filtrate (Figure 2D) which

217

increased algal growth rates relative to the original C596-S1 and -R1 by 29% and 10%,

218

respectively (Figure 2E & 2F). Resuspension in autoclaved nutrient-amended filtrate or fresh

219

Aquil medium had no effect on Chlorella growth (Figure 2 E & 2F). Both C596-S1 and -R1

220

algal cells benefitted from some growth-promoting factor or factors present in the -R1 filtrate

221

that were destroyed by autoclaving. Importantly, two subsequent transfers of the resulting C596-

222

S1 consortium to Aquil media retained the elevated growth rate (data not shown). It is possible

223

that yet another change in microbial community was generated via resuspension and

224

recombination with small biological agents in subsequent transfers (viruses and/or small

225

bacteria) that passed through the 0.2-µm filters or there was the persistence of a dilute chemical

226

factor(s).34

227 228

Microbial community influences Chlorella cellular lipid content. To investigate whether the

229

composition of the microbial community has an influence on C596 lipid accumulation, Nile red

230

assays were conducted on C596-R1, -S1 and -F1. In all consortia, the cellular lipid content

231

profile was coupled to decreasing nutrient availability over time in the medium, with no

232

measurable lipid accumulation at mid exponential phase (data not shown), detectable levels

233

emerging at late exponential phase and maximum levels in senescence (Figure 3). Algal cells in

234

the C596-S1 consortium, however, displayed the lowest cellular lipid content in all three stages



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relative to -R1 and -F1 cells. Conversely, C596-F1 cells outperformed -R1 and -S1 at each time

236

point; levels were 36% and 58% higher in -F1 cells respectively (p < 0.05) in senescence (Figure

237

3).

238 239

Microbiota analysis reveals pattern of microbial changes that alter phenotype.

To

240

investigate the particular microbial changes in our C596 cultures, extracted RNA was reverse

241

transcribed and 16S rRNA gene sequencing using bacteria-specific primers was employed. Five

242

consortia of C596 were tested: the parent C596-R1, C596-S1 (Figure 1), C596-S2 (a colony

243

culture that maintained the parent growth rate, 1.34 ± 0.03 d-1), C596-F1 (Figure 2A-C) and

244

C596-FS1 (a colony culture of C596-F1 that exhibited a reduced growth rate of 0.84 ± 0.15 d-1).

245

We collected 2,502,720 paired reads ranging from 88,088 to 179,118 reads per sample. The

246

sequencing were then processed for merging and removal of low quality sequencing and

247

singletons. The resulting number of reads after the following elimination of chimeras and non-

248

bacterial OTUs ranged from 30,840 to 1,447 reads per sample with a median OUTs of 7,665. In

249

total, we observed 103 operational taxonomic units (OTUs) from high quality sequence reads,

250

among them 20 OTUs were determined to be of non-bacterial origin. Of the 83 bacterial OTUs,

251

17 OTUs were found to exceed a relative abundance of 1% in one or more of the three biological

252

replicates, of which 14 OTUs belong to the Proteobacteria phylum. Those 17 OTUs were

253

hierarchically grouped based on the average relative abundance of sequencing reads across the

254

tested samples (Figure 4).

255

Higher algal growth rate appears to be linked to the presence of two particular microbes,

256

with the presence of each being important. Both C596-R1 and -S2, which have comparable

257

growth rates (Figure 4), contain an abundant Ruegeria spp. Type 2 (OTU 670577169) and a


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Rhodobacter spp. (Figures 4 and S3). The consortium with the highest algal growth rate, C596-

259

F1, also displays both of these microbes in high abundance, but the relative abundance of the

260

Rhodobacter spp. is greater compared to C596-R1 and -S2 (Figures 4 and S3). In contrast, the

261

microbiome of C596-FS1, which had a lower algal growth rate than all three of these consortia,

262

was predominantly Rhodobacter spp. (99.8% abundance). C596-S1, which was slowest of all,

263

had neither of these OTUs. Notably, both Ruegeria spp. (Type 1: OTU 169121548 and Type 2)

264

were detected among the abundant OTUs, but in the fast growing C596 cultures, only Type 2

265

was present in high abundance. The previously discussed laboratory Ruegeria isolate which did

266

not improve Chlorella growth (Figure S2) was closely related to the less abundant Ruegeria spp.

267

Type 1.

268 269

Metatranscriptome supports the 16S gene-amplicon sequencing results. Random shotgun

270

transcriptomic analyses of the C596-R1 and -S1 algae were conducted via Illumina sequencing

271

of RNA samples harvested at mid-logarithmic phase.

272

performed to enhance the coverage of the mRNA and samples were reverse transcribed using

273

random-hexamer primers. Nevertheless, approximately 85% of the reads for each sample were

274

of rRNA origin which enabled us to screen for 16S rRNA sequences as an additional means to

275

compare the microbial communities of these two isolates.

276

sequences from potential bacterial community members (Figure S4) were separated from those

277

of algal origin (Figure S4). Reads from Rhodobacterales, Rhizobiales, Caulobacterales, and

278

Cytophagales orders were less abundant in the C596-S1 consortium, whereas those from the

279

order Bacillales were greater in -S1 (Figure S4B). Other orders were relatively unchanged as a

280

percent of reads between the two cultures.

Eukaryotic rRNA depletion was

Reads that mapped to rRNA

Consistent with the 16S rRNA gene-amplicon



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sequencing results, the active presence of members of the Rhodobacterales order (which includes

282

Ruegeria spp. and Rhodobacter spp.) is linked to higher growth rates.

283 284

Differential regulation of Chlorella transcripts in response to changes in microbial

285

community. An edgeR analysis with an uncorrected p-value cutoff of < 0.05 and a fold change

286

cutoff of > 2 or < 0.5 was performed to broadly compare transcript expression differences

287

between algal cells from C596-S1 and C596-R1 consortia.

288

differentially expressed, 449 of which fell within standard KEGG categories (Figure 5). Genes

289

encoding for proteins in pathways within the Transcription and Translation categories are less

290

transcribed in C596-S1 relative to -R1 (21 higher in -S1 vs. 48 higher in -R1 and 36 vs. 86 genes

291

respectively), suggesting less active protein biosynthesis in -S1 relative to -R1, which is

292

consistent with a slower growth rate. In particular, genes encoding for proteins in pathways

293

involving RNA degradation, spliceosome activity, ribosome biogenesis, and RNA transport were

294

less abundant in the C596-S1 cells. Consistent with this observation, there was overall lower

295

transcription of genes encoding for proteins involved in amino acid biosynthesis in -S1 compared

296

to -R1, and although DNA replication was generally increased in expression in -S1, gene

297

expression specifically involved in purine metabolism was greater in -R1. Additionally, C596-

298

S1 cells exhibited substantially more expression of genes encoding for proteins within

299

Replication and Repair (24 vs. 15), perhaps reflecting more stress in -S1.

A total of 1878 genes were

300

The potential roles of the bacteria consortia were also explored with this analysis. Within

301

broad metabolism categories, Energy Metabolism (42 higher in -S1 vs. 15 higher in R1) and

302

Lipid Metabolism (32 vs. 6) related genes were more likely to be expressed at a higher level in

303

the slower-growing -S1, suggesting an investment required by -S1 to compensate for metabolites


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no longer provided by the bacterial symbionts. Within the category of Metabolism of Co-factors

305

and Vitamins, the genes involved in the biosynthesis of thiamine (vitamin B1) and biotin

306

(vitamin B7) metabolism were more expressed in -S1, although we include both of these co-

307

factors in our growth medium. We also include cobalamin (vitamin B12), a co-factor known to

308

be provided to algae by bacteria, in our medium. We see no evidence of cobalamin deficiency in

309

either consortium as there is low expression in both -S1 and -R1 of the metE gene (c12716_g1,

310

Table S1), the B12- independent isoform of methionine synthase in C. reinhardtii that is

311

upregulated when B12 is absent.35

312

The application of a more stringent cutoff in the edgeR analysis of significant

313

differentially regulated genes (a fold ratio > 4 or < 0.25; a multiple-testing Bonferroni-Holm

314

corrected p-value < 0.05 which is equivalent to a raw p-value < 6.6 x 10-6) resulted in a subset

315

143 transcripts, 73 of which were “highly” upregulated in C596-S1 (Table S1). Nineteen of

316

these genes displayed significant BLASTx hits (e-value < 10-10 for all hits) to proteins encoded

317

on the C. variabilis NC64A chloroplast or mitochondria,36 whereas only 2 of this type (out of a

318

total of 70 “highly” downregulated gene transcripts) were downregulated in -S1. Consistent with

319

the previous analysis, of those gene transcripts “highly” upregulated in C596-S1, genes encoding

320

photosystem-related proteins are overrepresented (n = 10; Table S2).

321

Also among the restricted list of genes “highly” upregulated in -S1 are several

322

homologues of genes that are known components of a CO2 concentrating mechanism (CCM) in

323

Chlamydomonas reinhardtii. These include the low-CO2 inducible protein LCIC (c94329_g1)

324

and the ABC transporter HLA3 (c10889_g1) (Table S2). In C. reinhardtii, the LCIC gene is

325

expressed under limiting CO2 conditions but their exact function is unknown.37 HLA3, an ATP-

326

binding cassette ABC transporter associated with the HCO3- uptake system, is also upregulated



327

under low CO2 concentrations.38,

39

328

CAH3 (c5724_g1), the enzyme in C. reinhardtii known to catalyze the conversion of HCO3- to

329

CO2 in the thylakoid lumen, did not vary between the transcriptomes of C596-R1 and -S1 (data

330

not shown). However this enzyme is known to be regulated at the post-translational level via

331

phosphorylation.40

Transcript levels of the carbonic anhydrase homologue

332

With respect to nitrogen metabolism, genes encoding for proteins involved in the nitrate

333

assimilation pathway such as nitrate (NO3-) reductase (c11833_g1_i1) and nitrite (NO2-)

334

reductase (c6958_g1_i2) are similarly highly expressed in both C596-R1 and -S1 cells (data not

335

shown). This result is not surprising since our medium contains nitrate that is plentiful during

336

exponential growth. However, we note that a glutamine synthetase gene (c12245_g1) and

337

several genes encoding for proteins involved in secondary nitrogen metabolism, such as an urea

338

active transporter-like protein41 (c13324_g1) and a carbon-nitrogen family hydrolase42

339

(c11710_g1), are upregulated by 4.9, 5.5 and 4.2 fold respectively in the C596-S1 cells (Table

340

S2). This suggests less efficient N recycling by the microbial community in -S1.

341

In addition to facilitating carbonic and nitrogen exchange via the degradation and

342

recycling of carbohydrates, fatty acids and other biomolecules on or near the surface of algal

343

cells, bacteria also consume dissolved oxygen which decreases oxidative stress in the algae.22, 23

344

Transcriptomic evidence of greater oxidative stress in the -S1 cells includes higher expression

345

(5.8 fold) of transcripts encoding for the glutathione reductase43 (c13123_g1, Table S2) and

346

proteins involved in thiamine (vitamin B1) biosynthesis (Figure 5). 44 Increased gene expression

347

in DNA replication and repair in -S1, as well as increased processing of lipids (in the absence of

348

greater TAG accumulation, Figure 5) are also consistent with more oxidative stress in -S1.


Page 16 of 30

Page 17 of 30


349

Similar patterns were observed in cyanobacteria with and without added bacteria, suggesting that

350

the bacterial community can assist the growth of algae by reducing oxidative stress.22

351

Algal cells are known to use chemical signals to recruit bacteria to colonize their

352

surfaces.45, 46 The osmolyte dimethylsulphoniopropionate (DMSP), which is released from algal

353

cells, has recently been shown to serve as a chemoattractant for members of the Roseobacter

354

clade of the Rhodobacterales order, and high concentrations of DMSP can induce a non-motile

355

state in bacteria facilitating biofilm formation.47 Although the final catalytic steps in DMSP

356

production are not characterized,48 it is known that cysteine and methionine are required for the

357

synthesis of DMSP.49

358

upregulation of both the cysteine and methionine metabolism pathways (Figure 5). We speculate

359

that DMSP levels, although typically low in Chlorophytes,50 may be elevated in the -S1 cells in

360

response to the loss of important microbial species from the consortium. Previous studies have

361

noted increased levels of cellular DMSP under nutrient-limiting conditions such as nitrogen, and

362

to a lesser extent CO2 limitation.51

We note in our transcriptome that the C596-S1 cells exhibit an

363

Algal cells may also attempt to recruit bacteria by increasing production of signaling

364

molecules that are anchored in the cell envelope. We see an upregulation of transcripts encoding

365

for general lipid metabolism proteins as well as glycan and carbohydrate metabolism proteins in

366

C596-S1 (Figure 5). Within these broader categories, we note a substantial number of transcripts

367

predicted to be involved in the metabolism of polar lipids are upregulated.

368

phosphoglycerolipids are best known to influence cell membrane structure, they are also known

369

to play a role in plant hormone signal transduction, as are sphingolipids, which can also serve as

370

signal molecules directly.52 Notably, sphingolipids are involved as specific recognition sites for


Although


Page 18 of 30

371

bacterial attachment in animal cells.53 Further work will be needed to determine whether these

372

lipids are also important in symbiotic interactions involving algae.

373 374

IMPLICATIONS

375

Microalgae, in particular lipid-accumulating Chlorella species potentially viable for

376

biofuel feedstock, are well known to have both positive and negative interactions with microbes.

377

Algae are unlikely to remain in pure cultures at the growth scale that will be required for biofuel

378

feedstock production (in open pond or even photobioreactor systems). Particular algae-microbial

379

consortia therefore will exhibit measurable and relevant phenotypic differences. In this study we

380

utilized next-generation sequencing and bioinformatics tools to characterize the algal

381

microbiomes of consortia exhibiting significant differences in algal growth rate and lipid

382

accumulation. We also performed a comparative analysis on algal transcriptomes derived from

383

two of these consortia, the original C596-R13 and the slower growing C596-S1.

384

hypothesized that the expression of genes in various metabolic pathways would help us to

385

identify more precisely which synergistic bacterial interactions have been disrupted in -S1.

We

386

Our work reveals a complex symbiotic relationship between Chlorella C596 cells and its

387

microbiota. Algal cells appear to be carbon limited and stressed when they lose beneficial

388

bacterial partners. Transcriptomic data suggest that cells likely use multiple strategies to recruit

389

bacteria back into a consortium. Our data demonstrate that particular shifts in the microbial

390

community can lead to lower growth rates and less lipid accumulation, which could equate to

391

highly variable productivity and yields for large-scale cultivation facilities.

392

underscores that the performance of axenic algae will not be relevant to production facilities.

393

Clearly, any characterization of lipid-producing algae and/or elucidation of strain-specific


Our work

Page 19 of 30


394

differences in phenotype must be performed with the recognition that microbial consortium are

395

critical to the outcome.

396

phenotype and metabolic activity will also lead to a better understanding of natural ecosystems

397

and may enable future practitioners to manipulate algal phenotypes necessary for the robust large

398

scale algal cultivation required for biofuel feedstocks, including managing the overall growth

399

rate and lipid accumulation.

Understanding how particular microbiological interactions impact

400 401

ACKNOWLEDGMENTS

402

We thank Jenny Kao-Kniffin at Cornell University for providing material for 16S rRNA sample

403

preparation. We also thank Catherine Spirito and Terrence Bell for help in 16S rRNA sample

404

preparation and sequencing analysis. This work was supported by the Department of Energy

405

(DE-EE0003371).

406 407

ASSOCIATED CONTENT



408

Supporting Information

409

Figure S1. Rarefaction curves of the Operational Taxonomic Units (OTUs) detected in MiSeq

410

sequencing of the 16S rRNA of C596-R1, -S1, -S2, -F1, and -FS1 consortia.

411

Figure S2. Effect of one Ruegeria strain isolate on growth of C596-S1. Ruegeria spp. Type 1

412

(OTU169121548) was isolated on solid Aquil medium supplemented with 20 mM acetate. Two

413

colony isolates (A and B) were confirmed by sequencing and independently tested for growth

414

recovery of the C596-S1. Error bars represent the standard error of the mean from triplicate

415

growth experiments.

416

Figure S3. Relative abundance (%) of Rhodobacter spp. and Ruegeria spp. Type 2 (OTU

417

670577169) in C596-F1, -R1, -S2, -FS1, and -S1 cultures. Error bars represent the standard error

418

of the means from triplicate samples.

419

Figure S4. (A) Bar chart displaying the percent of 16S rRNA sequences in the transcriptomes

420

mapping to the chloroplast and mitochondria of Chlorella for the C596-R1 and -S1 cultures;

421

error bars represent the 95% confidence interval based on the biological duplicates. (B) Bar

422

chart displaying 16S rRNA sequences mapping to individual bacterial orders (that represent

423

greater than 0.1% of the total 16S rRNA in either sample); error bars represent the 95%

424

confidence interval based on replicate cultures.

425

Table S1. The identifiers, assembled nucleic acid sequences, annotation descriptions, raw counts

426

and normalized fold changes ratios of all detected algal transcripts with a Bonferonni-Holm

427

adjusted p-value

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