Altered Microbiome Leads to Significant Phenotypic and

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Environmental Processes

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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Environmental Science & Technology

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

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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

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§

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]

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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,

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

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

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

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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

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

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

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

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

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

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

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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

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

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

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

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

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resources with high areal productivity.

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

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

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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

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

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

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

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

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algal cell surface.17,

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

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

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

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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

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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”

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

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

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

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

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carbon compounds for use by its heterotrophic partner.23

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

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

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provision by the bacteria.21

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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%

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

24

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

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

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

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

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

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affect Chlorella growth and lipid accumulation.

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metatranscriptomic analysis on two consortia with distinct microbiomes to better understand

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their synergistic roles during co-culture with Chlorella C596.

C596 tolerates

We also conducted a comparative

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METHODS

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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for growth thereafter.

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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

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

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

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

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consortia harvested at mid-exponential phase.

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florescence measurements.

Growth was monitored via chlorophyll

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

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

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dark.

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

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

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

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

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exponential, early stationary and senescent growth stages.

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

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

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

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(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

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

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

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



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

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

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

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

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

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

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

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Sequences were processed using the Brazilian Microbiome Project pipeline with modifications

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

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and singleton sequences removed using Mothur v.1.36.1.

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

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

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

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

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

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

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

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

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

Clustering of 97% Operational

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

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

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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

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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

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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

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

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

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

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

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Taxonomic Unit (OTU).

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

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

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

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

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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)

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

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

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

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expression in -S1 and -R1 cells.

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RESULTS AND DISCUSSION

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Algal culture variability appears modulated by associated microbiome.

As part of a

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

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

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consortium was responsible.

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

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

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maintained in subsequent culture transfers for over one year.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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factor(s).34

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

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

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

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

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

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

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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

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

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3).

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Microbiota analysis reveals pattern of microbial changes that alter phenotype.

To

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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tested samples (Figure 4).

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

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

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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-

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

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

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

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

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

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

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

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

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Type 1.

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

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

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of RNA samples harvested at mid-logarithmic phase.

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

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

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

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compare the microbial communities of these two isolates.

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

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

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

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

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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

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

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Differential regulation of Chlorella transcripts in response to changes in microbial

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A total of 1878 genes were

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

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

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

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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

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

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(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

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

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

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

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upregulated when B12 is absent.35

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

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

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

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

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

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

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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

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

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

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

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

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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-

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



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under low CO2 concentrations.38,

39

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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

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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


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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.

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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

Altered Microbiome Leads to Significant Phenotypic and

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