Quantitative proteomics of a B12-dependent alga grown in
coculture with bacteria reveals metabolic tradeoffs required for
mutualism
Katherine E. Helliwell1*, Jagroop Pandhal2*, Matthew B. Cooper1, Joseph Longworth2, Ulrich Johan Kudahl1,
David A. Russo2, Eleanor V. Tomsett1, Freddy Bunbury1, Deborah L. Salmon3, Nicholas Smirnoff 3,
Phillip C. Wright2 and Alison G. Smith1
1Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK; 2Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield,
S1 3JD, UK; 3Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QD, UK
Authors for correspondence:
Jagroop Pandhal
Tel: +44 (0)1223 333952
Email: j.pandhal@sheffield.ac.uk
Alison G. Smith
Tel: +44 (0)1223 333952
Email: as25@cam.ac.uk
Received: 7 June 2017
Accepted: 31 August 2017
New Phytologist (2018) 217: 599–612
doi: 10.1111/nph.14832
Key words: iTRAQ proteomics, Lobomonas
rostrata,Mesorhizobium loti, mutualism,
photosynthesis, vitamin B12.
Summary

 The unicellular green alga Lobomonas rostrata requires an external supply of vitamin B12
(cobalamin) for growth, which it can obtain in stable laboratory cultures from the soil bac-
terium Mesorhizobium loti in exchange for photosynthate. We investigated changes in pro-
tein expression in the alga that allow it to engage in this mutualism.

 We used quantitative isobaric tagging (iTRAQ) proteomics to determine the L. rostrata pro-
teome grown axenically with B12 supplementation or in coculture withM. loti. Data are avail-
able via ProteomeXchange (PXD005046).

 Using the related Chlamydomonas reinhardtii as a reference genome, 588 algal proteins
could be identified. Enzymes of amino acid biosynthesis were higher in coculture than in
axenic culture, and this was reflected in increased amounts of total cellular protein and several
free amino acids. A number of heat shock proteins were also elevated. Conversely, photosyn-
thetic proteins and those of chloroplast protein synthesis were significantly lower in L. rostrata
cells in coculture. These observations were confirmed by measurement of electron transfer
rates in cells grown under the two conditions.

 The results indicate that, despite the stability of the mutualism, L. rostrata experiences stress
in coculture withM. loti, and must adjust its metabolism accordingly.
Introduction
Microalgae are a polyphyletic set of photosynthetic eukaryotes
found across the eukaryotic tree of life which are estimated to be
responsible for c. 50% of global CO2 fixation (Falkowski, 1998).
In both soil and aquatic (marine and freshwater) habitats, algae
exist alongside a wide spectrum of other microbes, including bac-
teria, archaea, fungi and cyanobacteria. This has led to millions
of years of coevolution between contemporaneous species. It is
therefore not surprising that a broad range of interactions
between different microbial players within the ecosystem have
been observed. As well as harmful interactions (Fernandes et al.,
2011), many examples of beneficial associations have been docu-
mented between algae and bacteria, including promotion of algal
growth (Park et al., 2008), support of cellular differentiation
(Matsuo et al., 2005), provision of bactericidal/algaecidal protec-
tion (Hold et al., 2001; Geng et al., 2008; Thiel et al., 2010),
stress tolerance (Xie et al., 2013), and hormonal stimulation
(Ashen et al., 1999; Amin et al., 2015). Underpinning many
mutualistic interactions is nutrient exchange (Cooper & Smith,
2015), where algal photosynthate is exchanged for micronutrients
provided by bacteria, for example by facilitation of iron uptake
(Amin et al., 2009), or provision of the vitamins thiamine (vita-
min B1) (Paerl et al., 2015) or cobalamin (vitamin B12) (Croft
et al., 2005; Wagner-D€obler et al., 2010; Kazamia et al., 2012;
Durham et al., 2015). Vitamins are organic micronutrients,
which perform essential functions within the cell as enzyme
cofactors. Increasingly, it is recognized that many algae, despite
their photosynthetic lifestyles, require an exogenous source of
one or more of these compounds, or their precursors, for growth
(reviewed in Helliwell, 2017). Analysis of environmental samples
has detected the co-occurrence of bacterial producers and algal
requirers of cobalamin in a variety of marine environments (Koch
et al., 2013; Bertrand et al., 2015), and fertilization experiments
have demonstrated that these compounds are limiting for algal
productivity (Koch et al., 2012), providing evidence that algal–
bacterial interactions are likely to be widespread and of consider-
able significance for global net primary production.
Cobalamin is required by over 50% of all microalgal species
surveyed (Croft et al., 2005), but with no phylogenetic*These authors contributed equally to the work.

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Research
relationship between dependent and nondependent species,
implying that this trait has arisen multiple times during algal evo-
lution. The requirement for cobalamin is as a cofactor for B12-
dependent methionine synthase (METH), which is involved in
the synthesis of the amino acid methionine, as well as more gen-
erally in cellular C1 metabolism. Species that do not require vita-
min B12 have an alternative, B12-independent, enzyme, METE
(Helliwell et al., 2011), which performs the same function, albeit
at lower efficiency (Gonzalez et al., 1992; Croft et al., 2005; Hel-
liwell et al., 2015). Several species of algae, such as
Chlamydomonas reinhardtii and Phaeodactylum tricornutum,
encode both isoforms of methionine synthase, and can use
METE when B12 is not available. The presence of an external
supply of the vitamin allows these species to use METH, and at
the same timeMETE gene expression is repressed, in both labora-
tory cultures (Helliwell et al., 2011; Bertrand et al., 2012) and
environmental samples (Bertrand et al., 2015). Long-term expo-
sure to B12 in the environment might therefore lead to loss of the
METE gene, and indeed an metE mutant was generated by an
experimental evolution approach after growth of C. reinhardtii
cells in B12 for c. 500 generations (Helliwell et al., 2015). It is
conceivable, therefore, that interactions with B12-producing bac-
teria in the environment over evolutionary time might lead to the
frequent loss of METE in diverse algal lineages (Kazamia et al.,
2016), which would account for the widespread occurrence of
B12 auxotrophy across the algal lineages.
A major challenge now is to dissect the specific molecular
mechanisms that underpin the exchange of cobalamin, but this is
difficult in the dilute conditions of the aquatic environment. To
study aspects such as signalling, regulation, transporter proteins
and dynamics over time, defined model systems are required. In
our laboratory we developed a model partnership between the
vitamin B12-requiring freshwater green alga Lobomonas rostrata, a
close relative of C. reinhardtii, and the rhizobial bacterium
Mesorhizobium loti, which supplies vitamin B12 in exchange for
fixed carbon (Kazamia et al., 2012). Although a synthetic interac-
tion, the ease with which it formed mirrors that of other artificial
systems, such as the mutualism established between C. reinhardtii
and the yeast Saccharomyces cerevisiae based on the exchange of
carbon and nitrogen (Hom & Murray, 2014). Moreover, physio-
logical experiments with our L. rostrata and M. loti cocultures
under both batch and semicontinuous conditions revealed that
an equilibrium in terms of cell number was established and main-
tained (Kazamia et al., 2012), demonstrating that the partnership
exhibits a degree of regulation. The direct exchange of B12 could
be modelled using growth dynamics of algal–bacterial cocultures
(Grant et al., 2014), and the model implicated specific release
from live bacterial cells, rather than simply release of the vitamin
once the cells had died and lysed. With this observation of true
mutualism between the algae and bacteria, we might expect speci-
fic metabolic changes in L. rostrata when grown in coculture, as
compared with axenic growth with B12 supplementation.
Genome-scale approaches to dissect metabolic shifts occurring
on account of interspecies microbial interactions have been applied
to a variety of different phytoplankton–bacteria coculture systems.
Such work has revealed important insights into alterations in core
metabolism, including amino acid biosynthesis in associations
between cyanobacteria and heterotrophic bacteria (Beliaev et al.,
2014; Biller et al., 2016), as well as sulphur cycling (Durham et al.,
2015), and signalling and production of infochemicals (Amin
et al., 2015) in diatom–bacterial interactions. However, the major-
ity of the studies were at the transcript level, whereas it is proteins
that are responsible for performing cellular processes. A require-
ment for proteomics analyses is the need for databases of peptides
that can be matched to experimental mass spectra. Until recently,
this required annotated genome sequence information of the
organism under study, which is not currently available for
L. rostrata, but as more organisms have been sequenced, this has
resulted in many more shared peptides in the databases. In this
study, we performed a series of preliminary bioinformatics and
proteomics experiments to evaluate the applicability of the global
quantitative proteomics chemical isobaric tags (iTRAQ) method-
ology to our system. Once the feasibility was confirmed, the
approach was used to identify metabolic differences between
L. rostrata cells grown in media supplemented with vitamin B12
and those grown in cultures where B12 is provided by M. loti in a
mutualistic exchange. This has provided novel insights into the
metabolic consequences of mutualism between a photosynthetic
green alga and its heterotrophic bacterial partner.
Materials and Methods
Algal and bacterial strains and cultivation
Lobomonas rostrata (SAG 45-1) was obtained from the Experi-
mental Phycology and Culture Collection of Algae at the Univer-
sity of Goettingen (EPSAG), Germany. It was grown
autotrophically on TP+ medium (Kazamia et al., 2012). Vitamin
B12 was provided as cyanocobalamin (Sigma-Aldrich) at
100 ng l1, as this supports the maximum carrying capacity of
L. rostrata (Kazamia et al., 2012). The L. rostrata–M. loti cocul-
ture was an established coculture that had been maintained over
many generations without a source of organic carbon or vitamin
B12. Cultures were maintained on a 16 : 8 h, light : dark cycle,
with shaking (140 rpm) at 25°C. M. loti (MAFF 303099) was a
gift from Prof. Allan Downie at the John Innes Centre, Norwich,
UK. It was maintained axenically in TP+ with 0.1% v/v glycerol
at 28°C. Cells were harvested by centrifugation, and, if not anal-
ysed immediately, cell pellets were frozen in liquid N2 and stored
at 80°C. Algal cell counts were determined using a Dual
Threshold Beckman Coulter (Z2) Particle Counter and Size
Analyser (Indianapolis, IN, USA). Bacterial cell numbers were
determined by plating on solid media.
Protein preparation and iTRAQ labelling
Full experimental details are provided in Supporting Information
Methods S1. In brief, cultures were harvested at 3000 g for
10 min at 4°C. The resulting cell pellets were washed in 0.5 M
triethylammonium bicarbonate buffer, and then extracted by
grinding in liquid nitrogen and sonication. Insoluble protein was
removed by centrifugation. The soluble protein fraction was
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precipitated using acetone, then the resuspended pellet was
reduced with tris-(2-carboxyethyl)-phosphine, followed by alky-
lation with methyl methanethiosulfonate in isopropanol. Samples
were digested with trypsin and the resulting peptides were either
taken forward for mass spectrometry analysis for preliminary pro-
teomics analysis or labelled with iTRAQ reagents following the
manufacturer’s instructions (AB Sciex, Famingham, MA, USA).
Labels 113, 114, 115 and 116 were used to label biological repli-
cates of L. rostrata cells cultured with B12 supplemented in the
media, and labels 117, 118, 119 and 121 were used for L. rostrata
cells cultured withM. loti.
Hydrophilic interaction liquid chromatography and mass
spectrometry
All eight labelled peptide samples were combined before being
dried in a vacuum concentrator (Concentrator 5301; Eppendorf,
Stevenage, UK). The sample was resuspended in 200 ll of buffer
A (10 mM ammonium formate, 90% ACN, pH 3 (adjusted with
formic acid)) and 100 ll was loaded onto a PolyHydroxyethyl A
column (particle size, 5 lm; length, 20 cm; diameter, 2.1 mm;
pore size, 200A; PolyLC, Colombia, MD, USA) using an Agi-
lent 1100-series HPLC (Agilent, Wokingham, UK). With a flow
of 0.5 ml min1, buffer A was exchanged with buffer B (10 mM
ammonium formate, 10% ACN, pH 4 (adjusted with formic
acid)) to form a linear gradient as follows: 0% B (0–5 min), 0–
15% B (5–7 min), 15% B (7–10 min), 15–60% B (10–50 min),
60–100% B (50–55 min), 100% B (55–65 min), 0% B (65–
75 min). Beginning at 18 min, 22 fractions of 1 min length, fol-
lowed by three fractions of 3 min length were collected and dried
by vacuum centrifugation ready for reverse-phase LC-MS/MS.
Liquid chromatography-tandem mass spectrometry was con-
ducted using an Ultimate 3000 high-performance liquid chro-
matograph (Dionex, Sunnyvale, CA, USA) coupled to a QStar
XL Hybrid electrospray ionization (ESI) quadrupole time-of-
flight tandem mass spectrometer (Applied Biosystems (now
ABSciex), Framingham, MA, USA). Samples were resuspended
in 20 ll buffer A (3% ACN, 0.1% FA) before loading 9 ll onto a
Acclaim PepMap 100 C18 column (particle size, 3 lm; length,
15 cm; diameter, 75 lm; pore size, 100A; Dionex, Sunnyvale,
CA, USA). With a flow of 300 ll min1 buffer A was exchanged
with buffer B (97% ACN, 0.1% FA) to form a linear gradient as
follows. 3% B (05 min), 3–35% B (5–95 min), 35–90% B (95–
97 min), 90% B (97–102 min), 3% B (102–130 min). The mass
detector range was set to 350–1800m/z and operated in positive
ion mode. Peptides with +2, +3, and +4 were selected for frag-
mentation. The mass spectrometry proteomics data have been
deposited to the ProteomeXchange Consortium via the PRIDE
(Vizcaıno et al., 2014) partner repository with the dataset identi-
fier PXD005046.
Protein identification and quantification
The work flow for proteomic identifications is shown in Fig. S1.
Searches were conducted using MASCOT, OMMSA, X!TANDEM,
PEAKS and PROTEINPILOT against the Uniprot reference proteome
for C. reinhardtii (Uniprot ID 3055) and M. loti (Uniprot ID
266835). Each search was conducted with the target-decoy
database method to calculate false discovery rate (FDR; Elias &
Gygi, 2007). The decoy was formed using either reversed
sequences (MASCOT, OMMSA, X!TANDEM and PROTEINPILOT) or
randomized sequence (PEAKS) of the C. reinhardtii and M. loti
proteomes. Searches were restricted to a peptide FDR of 3%
before decoy hits were removed at the peptide spectral match
(PSM) level. PSMs are the individual matches made by the search
engine algorithm between the mass spectrometer’s output pro-
duct ion scan and the potential peptides contained within the
search database. They combine to form the peptide and protein
identifications. PSMs from the five search engines were merged
using an R-based script that also removed PSMs showing dis-
agreement in terms of peptide assignment or protein identifica-
tion between the search engines, as performed previously
(Longworth et al., 2016). Separately, reporter ion intensities for
each PSM were extracted and matched to the merged results. For
quantitation, these reporter ion intensities were extracted fol-
lowed by variance stabilization normalization, isotopic correction
and median correction, and finally averaging by protein. To
avoid using arbitrary thresholds for highlighting differentially
expressed proteins, a t-test was performed between replicate con-
ditions to determine significance with fold change. Transmem-
brane domains were identified using TMHMM server 2.0 (http://
www.cbs.dtu.dk/services/TMHMM/). We identified all putative
transmembrane domains in each of the proteins identified in the
iTRAQ experiment. If a sequence had one or more transmem-
brane domains, this was classified as likely to be membrane-
associated rather than soluble.
Analytical methods
Maximum potential photosynthetic capacity as electron transfer
rate (ETR) was estimated using pulse amplitude modulation
(PAM). Cells were prepared to a density of 2.59 106 cells ml1,
placed in cuvettes and mixed with a magnetic stirrer in order to
maintain an even cell suspension. Instant light response curves of
Chl fluorescence were generated for each sample using a Mini-
PAM (Heinz Walz GmbH, Effeltrich, Germany). The ETR was
calculated as follows:
ETR ¼ Y =1000 PAR  0:5 ETR factor; Eqn 1
where Y/1000 is equal to (Fm
0  F)/Fm0, or ΦPSII, the quantum
yield of photosystem II. The ETR factor described refers to the
fraction of incident photons absorbed by the sample, for which a
default value of 0.83 was used (Maxwell & Johnson, 2000).
Protein was measured using Bradford’s reagent as previously
described (Bradford, 1976) using BSA as a standard. For
amino acid analysis, full experimental details are provided in
the Supporting Information Methods S1. In brief, c. 5 mg of a
freeze-dried sample of cultures were extracted in acetonitrile,
and then re-extracted with 20% methanol spiked with an
internal standard containing stable isotope-labelled amino acids
(L-amino acid mix; Sigma-Aldrich). The supernatants were

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pooled and amino acids were derivatised with an AccQ•Tag
Ultra derivatization kit (Waters Corp., Milford, MA, USA).
HPLC-ESI-MS/MS quantitative analysis of the amino acids
was performed using an Agilent 6420B triple quadrupole
(QQQ) mass spectrometer (Agilent Technologies, Palo Alto,
CA, USA). All ions were scanned in positive ion mode and
given a dwell time of 50 ms. Data analysis was undertaken
using Agilent Mass Hunter Quantitative analysis software for
QQQ (v.B.07.01). Accurate quantification used the stable iso-
tope-labelled internal standards added during sample extrac-
tion, and data were normalized to the DW of the samples.
Reverse transcriptase-quantitative polymerase chain
reaction (RT-qPCR)
RNA was extracted from the cell pellet from 10ml of day 14
(late-log) cultures using RNeasy Plant Mini Kit (Qiagen), treated
with Turbo DNA-freeTM kit (Ambion) to remove genomic DNA,
and reverse-transcribed with the Superscript III First-trand synthe-
sis system for RT-PCR (Invitrogen). Selected C. reinhardtii coding
sequences from the NCBI database were aligned with scaffolds
produced from L. rostrata RNA-seq data (U.J. Kudahl et al., un-
published) in order to find genes of interest; homologues were
found to be 90% similar at the nucleotide level. Primers were
designed with melting temperatures of 61–63°C, and to amplify
products of 150–200 nucleotides (Untergasser et al., 2012), and
the sequences are shown in Table S1. Quantitative PCR reactions
were prepared in 10 ll volumes using SYBR Green JumpStartTM
Taq ReadyMixTM (Sigma-Aldrich). Reactions were performed in a
Rotor-Gene Q (Qiagen) with an initial denaturation at 95°C for
2 min followed by cycling 45 times between 58°C for 30 s and
95°C for 15 s, and finally a melt curve was generated by increas-
ing the temperature from 55 to 95°C in 1°C steps held for 5 s.
Amplification efficiency and cycle threshold values were calculated
with the Rotor-Gene Q software v.2.02. Quantification and nor-
malization were carried out using the Pfaffl model (Pfaffl, 2001)
with three reference genes: Ubiquitin (UBQ), Eukaryotic transla-
tion initiation factor 4A (EIF4A) and Receptor of activated pro-
tein kinase C1 (RACK1) (Mus et al., 2007).
Results
Preliminary bioinformatics and proteomic analyses
To ascertain whether it was feasible to use iTRAQ for global
analysis of the proteome of unsequenced L. rostrata, a series of
preliminary experiments was carried out. The first aim was to
find a suitable reference proteome for matching experimental
spectra sourced from L. rostrata. This process relies on shared
peptides from sequenced organisms being present within existing
protein databases. The closely related freshwater green alga
C. reinhardtii (Uniprot ID 3055) was a prime candidate and also
has the advantage of having a relatively well annotated genome
(https://phytozome.jgi.doe.gov/pz/portal.html). A search against
C. reinhardtii produced 1% PSMs, similar to a previous cross-
species proteomics study (Pandhal et al., 2008), implying that
sufficient proteins could be identified in a global proteomics
study to uncover biological insights into the metabolic processes
of the unsequenced alga.
Our iTRAQ experiment was aimed at identifying proteins
from organisms in coculture, so proteins from both L. rostrata
andM. loti would be present. Therefore, it was important to con-
sider the possibility that shared peptides between the two organ-
isms might influence the results, namely that peptides from
M. loti might be assumed to be from L. rostrata, and thus inter-
fere with protein quantifications. To evaluate this, a theoretical
tryptic digest was undertaken. The identification of shared pep-
tides between the alga and bacteria was done using protein
sequences for M. loti and C. reinhardtii retrieved from Uniprot
(02-07-2016 ). A python script was written to read the protein
sequences and theoretically digest them into tryptic peptides, by
cleaving them after arginine and lysine unless followed by a pro-
line. Peptides between six and 16 amino acids long were identi-
fied and compared, calculating the shared fraction using the total
number of unique peptides in C. reinhardtii. The result was that
0.31% of tryptic peptides within this size range were shared
between C. reinhardtii and M. loti. Given this low number, and
the close phylogenetic relationship between C. reinhardtii and
L. rostrata, it was unlikely that interpretation of the iTRAQ
experiment would be confounded by peptides shared between the
alga and bacterium in the cocultures.
Finally, the iTRAQ approach rests on the relative quantifica-
tion of peptides across samples, with the general assumption that
proteins are present in all samples, whereas in our study compar-
ing cocultures and axenic cultures of L. rostrata, only half would
contain M. loti cells. Moreover, the aim of the experiment was to
look for metabolic differences in L. rostrata under the two condi-
tions, and therefore it would be ideal to minimize spectra sourced
from M. loti, which could potentially reduce the total alga spectra
generated per sample injection. This was potentially problematic,
as the ratio of cell counts for L. rostrata to M. loti cells in stable
cocultures is between 1 : 30 and 1 : 100 (Kazamia et al., 2012),
averaging 1 : 50 in the samples used for the iTRAQ experiment
here. Moreover, proteins from sequenced organisms are more
likely to be identified compared with those from unsequenced
organisms, in bottom-up proteomics experiments. The latter was
confirmed with proteins from axenic cultures of M. loti, where
the PSM was 11% (compared with 1% PSM for L. rostrata).
However, the much larger algal cell volume (c. 500 lm3, com-
pared with c. 0.5 lm3 for the bacteria) means that in the cocul-
ture this would correspond to significantly more algal protein
than bacterial protein, even with the higher number of bacterial
cells. In order to test this, a protein per cell measurement was
made for axenic cultures of the two organisms, and found to be
2.299 107 mg cell1 for L. rostrata and 3.259 1010 mg cell1
for M. loti. At the cell densities in the coculture, this corresponds
to c. 0.45 mg ml1 for L. rostrata protein, compared with
0.033 mg ml1 for M. loti, a 14-fold higher amount. This,
together with the nature of peptide fractionation in bottom-up
mass spectrometry-based proteomics, where there is a bias
towards identification of high-abundance peptides and thus pro-
teins, provided confidence in our approach.
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Overview of the L. rostrata proteome determined by
iTRAQ analysis
Having established the parameters and methodology for the
experiment, four biological replicates of both axenic L. rostrata
supplemented with 100 ng l1 B12 and established cocultures of
L. rostrata and M. loti were grown in autotrophic medium. Cells
were harvested after 14 d when the algae were at mid-to-late
exponential growth phase (Fig. S2), and total soluble protein was
extracted for proteomic analysis. Altogether, 588 proteins were
identified based on one high-confidence peptide hit, and about
half of these (293) had a minimum of two high-confidence pep-
tide hits per protein. A total of 47 proteins were predicted to be
membrane-associated by virtue of the presence of at least one
transmembrane domain. A search of the same mass spectral data
against the M. loti database (Uniprot ID 266835) identified just
four proteins (all based on one high-confidence peptide hit), indi-
cating that the vast majority of proteins identified were of algal
origin. Comparison of proteins from the two different conditions
revealed 153 proteins that were significantly differentially
expressed (P > 0.05). Of these, 70 were present in higher
amounts in L. rostrata when grown with M. loti in comparison to
the axenic condition, and 83 were less abundant. A heat map rep-
resentation of these data showing fold changes between co- and
monoculture illustrates the consistency between individual repli-
cates of the treatments (Fig. S3).
Comparative proteomics reveals fundamental changes in
metabolism of L. rostrata when grown in coculture with
M. loti
To establish how the metabolism of L. rostrata differed between
the two treatments, we used a combined KEGG/Mercator-based
analysis (Kanehisa & Goto, 2000; Lohse et al., 2014) to assign
function to the differentially expressed proteins, and to classify
them into particular cellular processes and metabolic pathways
(Table 1; Fig. 1). This analysis revealed that the metabolic pro-
cesses most affected were those involved in protein metabolism
(38 proteins, 24.8% of total), amino acid metabolism (23 pro-
teins, 15%) and photosynthesis (24 proteins, 15.7%). Within the
first category, which included those involved in general protein
metabolism, translation, targeting and folding, about half were
found in higher abundance in cocultured L. rostrata than in
axenic culture, and half in lower amounts. Several chaperonins
were elevated, along with cytosolic elongation and initiation fac-
tors. Four ribosomal proteins (S19, S20, L12 and L40) were in
higher amounts, although another four (S2, S9, L3 and L8) were
reduced. Strikingly, of the remaining 16 proteins of protein
metabolism that were lower in cocultures, 15 were those of plas-
tid protein synthesis, including several ribosomal proteins and
elongation factors EF-Tu and EF-G.
This effect on chloroplast biology was further reflected in the
substantial number of proteins associated with both the light-
dependent reactions of photosynthesis and CO2 fixation, which
were much lower in the coculture; none were elevated (Table 1;
red boxes in Fig. 2). There is reduced abundance of subunits of
the chloroplast ATP synthase alongside those from the oxygen-
evolving complex of PSII, cytochrome b6f complex (Rieske Fe.S
protein, Re), and soluble electron carriers ferredoxin (Fd) and
ferredoxin-NADP reductase (FNR). Several enzymes in the
Calvin cycle were also in lower abundance, including RuBisCO
activase (RCA1) and two enzymes involved in photorespiration,
hydropyruvate reductase and glycolate dehydrogenase (Chauvin
et al., 2008). In addition, there was a decrease in two carbonic
anhydrases, which catalyse conversion of carbon dioxide to bicar-
bonate, one of which showed the greatest log2-fold change
(2.91) of all detected proteins.
Subunits of soluble starch synthase and ADP-glucose
pyrophosphorylase (STA1, STA6), both involved in starch syn-
thesis, were less abundant in the coculture, whereas starch phos-
phorylase, important for starch remobilization, was higher.
Similarly, enzymes of glycolysis and the tricarboxylic acid (TCA)
cycle also showed a mixture of higher and lower abundance, but
all the subunits of the mitochondrial electron transfer chain that
were detected (three subunits of NADH:ubiquinone oxidoreduc-
tase and two subunits of mitochondrial ATP synthase) were
reduced in cocultures compared with axenic L. rostrata, as were
enzymes involved in fatty acid biosynthesis, enoyl ACP reductase
and the biotin carboxylase subunit of the acetyl-CoA carboxylase
enzyme complex (Ohlrogge & Jaworski, 1997), and three out of
four enzymes of tetrapyrrole biosynthesis, including two of the
Chl branch, light-dependent protochlorophyllide reductase and
geranylgeranyl PP reductase. The impression is that in L. rostrata
grown withM. loti there is a reduction in biosynthesis of the pho-
tosynthetic apparatus and activity.
By contrast, many enzymes of amino acid metabolism were in
higher abundance in cocultured L. rostrata compared with mono-
cultures, with a total of 18 enzymes involved in both the degra-
dation and biosynthesis of amino acids being elevated (Fig. 2).
Among them were anthranilate synthase, involved in the trypto-
phan biosynthesis pathway (Liu et al., 2010), cysteine synthase
(O-acetylserine sulphydrolase, OASTL4), the final enzyme of the
synthesis of cysteine (Tai et al., 2001), methylcrotonoyl-CoA car-
boxylase, used in the degradation of leucine to acetoacetate (Song
et al., 1994), and S-adenosylhomocysteine hydrolase, involved in
the methylation cycle of C1 metabolism (Palmer & Abeles,
1979). Just two proteins involved in amino acid metabolism were
lower in cocultured L. rostrata, N-acetylglutamate kinase
and argininosuccinate lysase, enzymes of the urea cycle and
important for production of fumarate for the TCA cycle (Shar-
gool et al., 1988). Ferredoxin-glutamate synthase (Fd-GOGAT),
involved in nitrate assimilation, was also elevated in cocultures,
as was ATP-sulphurylase involved in S-assimilation, although
adenylylphosphosulfate reductase was reduced. Two other
enzymes of biosynthetic pathways were elevated: 2-C-methyl-D-
erythritol 2,4-cyclodiphosphate synthase, an enzyme of the non-
mevalonate pathway for the production of the isoprenoid precur-
sor isopentenyl-pyrophosphate (Eisenreich et al., 2001); and
THIC, encoding an enzyme of de novo thiamine biosynthesis.
Of particular note in the other functional categories was the
higher abundance in the cocultures of stress-response related pro-
teins, comprising six proteins annotated as heat shock proteins

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Table 1 List of identified proteins altered in abundance in cocultures of Lobomonas rostrata vs axenic cultures
General function
Uniprot
entry Protein ID Protein function
Fold-
change
Protein metabolism A8ISZ1 EF-3 Elongation factor EF-3 2.69
A8HX38 eEF1a1 Eukaryotic translation elongation factor 1 alpha 1 (EC 3.6.5.3) 2.55
A8J597 RPL12 Ribosomal protein L12 2.42
I2FKQ9 CPN60C Mitochondrial chaperonin 60 2.41
A8J8M9 RPS20 Ribosomal protein S20 2.18
A8J7C8 KtRS Lysine–tRNA ligase (EC 6.1.1.6) (Lysyl-tRNA synthetase) 2.00
Q66YD0 VIPP1 Chloroplast vesicle-inducing protein in plastids 1 1.98
A8J5Z0 RPP0 Acidic ribosomal protein P0 1.93
A8I403 RPS19 Ribosomal protein S19 1.89
A8HTK7 TBA1 PsbA translation factor 1.72
A8JIB7 CPN60A Chaperonin 60A 1.72
P25840 HSP70 Heat shock 70 kDa protein 1.70
A8ITH8 CPN60B2 Chaperonin 60B2 1.52
A8JHX9 EFG2 Elongation factor 2 (EC 3.6.5.3) 1.51
A8JCX9 RPL40 Ribosomal protein L40 1.44
A8J9Q1 IPA1 Importin subunit alpha 1.33
A8I232 EIF2G Eukaryotic initiation factor 1.28
A8JIE0 CPN20 Chaperonin 20 1.17
A8IV59 TIIC22 22 kDa translocon at the inner membrane of chloroplasts (Fragment) 1.14
A8J7T7 CEP1 Cysteine endopeptidase 1.16
Q70DX8 S1PRPS1 Plastid ribosomal protein S1 (Ribosomal protein S1 homologue) 1.27
P17746 TUFA Elongation factor Tu, chloroplastic (EF-Tu) 1.30
E3SC57 RPL3 60S ribosomal protein L3 (Fragment) 1.34
Q5QEB2 EFTs Elongation factor Ts 1.34
A8HME4 RPS2 Ribosomal protein S2 1.37
A8IA39 EFG1 Chloroplast elongation factor G (EC 3.6.5.3) 1.41
A8J282 CYN20 Peptidyl-prolyl cis-trans isomerase (EC 5.2.1.8) plastid 1.41
E3SC50 RPS9 40S ribosomal protein S9 (Fragment) 1.42
Q6Y683 RAP41 41 kDa ribosome-associated protein (chloroplast) 1.49
Q6Y682 RAP38 38 kDa ribosome-associated protein (Chloroplast stem-loop-binding protein) 1.52
Q8HTL1 RPL5 50S ribosomal protein L5, chloroplastic 1.52
A8IVK1 RPL8 Ribosomal protein L8 1.53
Q84U21 ERY2 50S ribosomal protein L22, chloroplastic 1.60
Q7Y258 PRPS5 Ribosomal protein S5 (plastid) 1.61
Q8GV23 NAB1 Nucleic acid binding protein (Putative nucleic acid binding protein) 1.90
A8I8Z4 PRPL1 Plastid ribosomal protein L1 1.91
A8IRU6 CYN38 Peptidyl-prolyl cis-trans isomerase, cyclophilin-type (plastid) 1.97
O20032 RPS18 30S ribosomal protein S18, chloroplastic 1.98
A8HWS8 PRPL28 Plastid ribosomal protein L28 2.75
Photosynthetic
electron transfer
A8IV40 Fd Apoferredoxin 1.18
A8I9I9 Re Rieske ferredoxin 1.19
A8J0E4 PSBO Oxygen-evolving enhancer protein 1 of photosystem II 1.21
A8HXL8 ATPC Chloroplast ATP synthase gamma chain 1.25
B7U1J0 ATPA ATP synthase subunit alpha, chloroplastic (EC 3.6.3.14) 1.32
Q6PSL4 HYDG Fe-hydrogenase assembly protein (Hydrogenase assembly factor) 1.36
A8J6Y8 FNR2 Ferredoxin–NADP reductase (EC 1.18.1.2) 1.63
P53991 PETH Ferredoxin–NADP reductase, chloroplastic (FNR) (EC 1.18.1.2) 1.66
Carbon assimilation A8IVM9 GCSP Glycine cleavage system, P protein (EC 1.4.4.2) 1.15
Q6SA05 RCA1 Rubisco activase 1.23
P23489 RA Ribulose bisphosphate carboxylase/oxygenase activase, chloroplastic 1.36
Q0ZAZ1 GDH Glycolate dehydrogenase 1.37
A8HP84 GAP3 Glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.12) (EC 4.3.2.1) 1.47
A8IPI7 HPR1 Hydroxypyruvate reductase 1.51
A8IRK4 SEBP1 Sedoheptulose-1,7-bisphosphatase (EC 3.1.3.37) 1.53
A8IKW6 RPE1 Ribulose-phosphate 3-epimerase (EC 5.1.3.1) 1.57
A8I5A0 CPLD45 Predicted protein 1.58
A8IKQ0 FBP1 Fructose-1,6-bisphosphatase (EC 3.1.3.11) 1.65
Q42690 FBA1 Fructose-bisphosphate aldolase 1, chloroplastic (EC 4.1.2.13) 1.71
A8IRQ1 RPI1 Ribose-5-phosphate isomerase (EC 5.3.1.6) 1.72
Q75NZ2 LCIB Low-CO2 inducible protein LCIB 1.77
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Table 1 (Continued)
General function
Uniprot
entry Protein ID Protein function
Fold-
change
A8JC04 PGK1 Phosphoglycerate kinase (EC 2.7.2.3) 1.81
P93109 CA2 Beta-carbonic anhydrase 1.95
Q39589 BETA-
CA1
Carbonic anhydrase 2.91
Starch metabolism Q2VA40 PHOA Phosphorylase (EC 2.4.1.1) 1.48
Q2VA41 PHOB Phosphorylase (EC 2.4.1.1) 1.42
A8HS14 STA1 ADP-glucose pyrophosphorylase (EC 2.7.7.27) 1.23
Q9LLL6 STA6 ADP-glucose pyrophosphorylase (EC 2.7.7.27) 1.34
O64926 SS Soluble starch synthase (EC 2.4.1.21) 1.81
Glycolysis A8JHR9 GAP1A Glyceraldehyde 3-phosphate dehydrogenase, dominant splicing variant (EC
1.2.1.12)
3.01
A8 IE23 PGI1 Glucose-6-phosphate isomerase (EC 5.3.1.9) 1.22
A8IVR6 PYK2 Pyruvate kinase (EC 2.7.1.40) 1.70
A8JC04 PGK1 Phosphoglycerate kinase (EC 2.7.2.3) 1.81
TCA cycle A8J2S0 CIS2 Citrate synthase (EC 2.3.3.1) 1.82
A8JHC9 CIS1 Citrate synthase 1.49
A8HMQ1 ACH1 Aconitate hydratase (EC 4.2.1.3) 1.41
Q9FNS5 MDH5 NADP-Malate dehydrogenase (EC 1.1.1.82) 1.27
A8HPL8 DLD2 Dihydrolipoamide dehydrogenase (EC 1.8.1.4) 1.58
A8J7F6 DLA2 Dihydrolipoamide acetyltransferase (EC 2.3.1.12) 1.59
Mitochondrial
electron transfer
chain
A8JDV9 ATP3 F1F0 ATP synthase gamma subunit 1.17
Q96550 ATPA ATP synthase subunit alpha 1.32
Q6V9B1 NUOS8 NADH:ubiquinone oxidoreductase subunit 8 (EC 1.6.5.3) 1.32
A8IVJ7 NUOS1 NADH:ubiquinone oxidoreductase 76 kDa subunit 1.49
Q6V9A8 NUO7 NADH:ubiquinone oxidoreductase 49 kD subunit (EC 1.6.5.3) 1.77
Fatty acid
biosynthesis
A8HPL8 DLD2* Dihydrolipoamide dehydrogenase (EC 1.8.1.4) 1.58
A8J7F6 DLA2* Dihydrolipoamide acetyltransferase (EC 2.3.1.12) 1.59
A8JFI7 EAR enoyl ACP reductase 1.92
A8JGF4 BC Biotin carboxylase, acetyl-CoA carboxylase component 1.98
Tetrapyrrole
metabolism
A8I980 ALAD Delta-aminolevulinic acid dehydratase (EC 4.2.1.24) 1.41
A8J7H3 GSA Glutamate-1-semialdehyde aminotransferase (EC 5.4.3.8) 1.23
A8HNE8 CHLP Geranylgeranyl reductase (EC 1.3.1.-) 1.33
A8HPJ2 POR Light-dependent protochlorophyllide reductase (EC 1.3.1.33) 1.59
Amino acid metabolism A8IH03 PSAT Phosphoserine aminotransferase (EC 2.6.1.52) 3.11
A8J786 HIS7 Imidazole glycerol phosphate synthase (Fragment) 2.95
A8J2X6 AGGPR N-acetyl-gamma-glutamyl-phosphate reductase 2.29
A8HXS9 LEU2 Isopropylmalate synthase (EC 2.3.3.13) (Fragment) 2.18
A8IXE0 SAHH S-Adenosylhomocysteine hydrolase (EC 3.3.1.1) 1.99
A8JFR4 OTA1 Ornithine transaminase (EC 2.6.1.13) 1.95
A8JG03 LEU1 Isopropylmalate dehydratase, large subunit 1.94
A8J979 MCCA Methylcrotonoyl-CoA carboxylase alpha subunit 1.85
A8I826 HSK1 Homoserine kinase (EC 2.7.1.39) 1.81
A8IMY5 ANS1 Anthranilate synthase, alpha subunit (Fragment) 1.70
A8J173 ASD1 Aspartate semialdehyde dehydrogenase 1.62
A8J2Z6 ChS1 Chorismate synthase (EC 4.2.3.5) 1.47
A8IX80 AAD1 Acetohydroxyacid dehydratase 1.46
A8ISB0 OASTL4 O-acetylserine sulphydrolase; cysteine synthase (EC 2.5.1.47) 1.42
A8J355 CGS1 Cystathionine gamma-synthase 1.42
A8J3D3 HIS5 Histidinol phosphate aminotransferase 1.41
A8ITU7 PGD1 D-3-phosphoglycerate dehydrogenase (EC 1.1.1.95) 1.37
O22547 ALAS Acetolactate synthase 1.20
A8JIR0 CPS1 Carbamoyl phosphate synthase, large subunit (EC 6.3.5.5) 1.17
P22675 ARG7 Argininosuccinate lyase (ASAL) (EC 4.3.2.1) (Arginosuccinase) 1.26
A8I305 GLN1 Glutamine synthetase (EC 6.3.1.2) 1.43
A8JBL3 NAGS N-acetylglutamate synthase (Fragment) 1.49
Other A8IJL3 MEP-S 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (EC 4.6.1.12) 2.02
A8J841 THICb Hydroxymethylpyrimidine phosphate synthase 2.00
A8HMC0 CRT2 Calreticulin 2, calcium-binding protein 1.53
Q8LRU1 FER1 Pre-apoferritin 1.41
Kinases A8JDN2 ADK3 Adenylate kinase 3 (EC 2.7.4.3) 1.97
A8JH12 – Nucleoside diphosphate kinase 1.97

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(HSPs): four annotated as members of the HSP70 family, the
others as a chloroplast-targeted HSP101 homologue and an
HSP90-like protein. Also elevated were four flagellar-associated
proteins and autophagy protein (APG8), the latter being the
most highly altered relative to axenic L. rostrata (3.84). And
whilst five histones are lower in abundance in coculture, histone
methylase and three other enzymes of DNA metabolism were
found in higher amounts.
Given the observed reduction in amounts of almost all the
proteins of the Calvin cycle, we decided to determine if this was
a result of changes in transcript expression levels. We therefore
carried out RT-qPCR using as template RNA extracted from
cultures grown in conditions identical to those used for pro-
teomics analysis. We were able to identify sequences for eight
proteins involved in carbon fixation by searching an RNAseq
dataset from L. rostrata (U.J. Kudahl et al., unpublished) using
the C. reinhardtii homologues, to allow design of appropriate
PCR primers. We also found L. rostrata homologues for three
of the most elevated proteins, THIC, APG8, and GAP1A,
encoding cytosolic glyceraldehyde 3-phosphate dehydrogenase.
Fig. 3 shows the results in order of the fold-abundance detected
in the iTRAQ experiment, with those lower in coculture than
in monoculture on the left, and those higher on the right. In
general, the trend in the transcript abundances mirrored that of
the proteins, with significant (P < 0.001) up-regulation of
GAP1A and APG8 in coculture (c. three- and fivefold, respec-
tively). The thiamine biosynthesis enzyme THIC was also ele-
vated, although this was not statistically significant. Similarly,
there was significant (P-value < 0.001) down-regulation of phos-
phoglycerate kinase 1(PGK1) and chloroplast glyceraldehyde 3-
phosphate dehydrogenase 3 (GAP3), as well as sedoheptulose-
1,7-bisphosphatase (SEBP1; P < 0.01). The transcript abun-
dances for the other photosynthetic genes were unaltered or
slightly higher in coculture, but not significantly. It should be
Table 1 (Continued)
General function
Uniprot
entry Protein ID Protein function
Fold-
change
Redox proteins O22472 CAT Catalase (EC 1.11.1.6) 2.16
A8HQT1 PDI2 Protein disulfide isomerase 1.54
A8IA77 GSH1 Gamma-glutamylcysteine synthetase (EC 6.3.2.2) 1.54
A8IVV9 Flavodoxin-like protein (Fragment) 1.16
A8IWK2 – Ferredoxin thioredoxin reductase, catalytic chain 2.14
N & S metabolism A8JHB4 GSF1 Ferredoxin-dependent glutamate synthase 1.29
A8IXF1 ATS1 ATP-sulfurylase 1.15
A8J6A7 MET16 Adenylylphosphosulfate reductase 1.27
A8JGD1 GDH2 Glutamate dehydrogenase 1.36
Cell structure/
organization
A8JB85 APG8 Autophagy protein 3.84
A8HW56 CDC48 Flagellar associated protein (EC 3.6.1.3) 2.08
A8J614 FAP42 Flagellar associated protein, adenylate/guanylate kinase-like protein 1.80
A8IFL6 FAP262 Flagellar associated protein 1.70
A8JEQ8 FAP79 Flagellar associated protein 1.31
A8JAV1 IDA5 Actin 1.31
P09205 TUBA2 Tubulin alpha-2 chain 1.90
A8IXZ0 TUB1 Beta tubulin 1 (Beta tubulin 2) 2.11
Stress response Q39603 HSP70B Heat shock protein 70B 2.06
A8IZU0 HSP70C Heat shock protein 70C 1.77
P25840 HSP70 Heat shock 70 kDa protein 1.70
A8I7T1 HSP90B Heat shock protein 90B (Fragment) 1.62
A8I972 CLPB3 ClpB chaperone, Hsp100 family 1.61
A8HYV3 HSP70B Heat shock protein 70B 1.32
DNA and RNA
metabolism
A8JFP3 – Histone methyltransferase 1.34
Q1G2Y1 PNP Chloroplast polyribonucleotide phosphorylase 1.30
A8IZS5 – Glycine-rich RNA-binding protein 1.23
A8I0F5 ORC1 Origin recognition complex subunit 1 1.21
A8JCT1 HBV1 Histone H2B 1.77
A8HWY2 HTR14 Histone H3 (Fragment) 1.88
A8HVA3 HFO4 Histone H4 1.91
A8JIN9 HFO43 Histone H4 1.92
A8HRZ9 H2A Histone H2A 1.95
Transporters A8JGB0 – Arsenite translocating ATPase-like protein (Fragment) 1.44
A8I164 ATPVA1 Vacuolar ATP synthase, subunit A 1.18
A8I268 HLA3 ABC transporter 2.40
Peptides from the quantitative isobaric tagging (iTRAQ) experiment were identified by comparison with the Chlamydomonas reinhardtii proteome
(indicated by the Uniprot ID). A total of 70 proteins were found to be significantly (P < 0.05) more abundant in the cocultures withMesorhizobium loti
(positive values), and 83 were less abundant (negative values). Identified proteins are grouped into functional categories as in Fig. 1, ranked according to
fold-change. Data are available via ProteomeXchange (PXD005046). *Also involved in the tricarboxylic acid (TCA) cycle.
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noted that the CAH1 gene, encoding a carbonic anhydrase, may
encode a different isoform to the proteins detected in the
iTRAQ experiment.
Photosynthetic ETR is reduced in coculture withM. loti
Our observation that several proteins involved in both the light
reactions of photosynthesis and carbon dioxide fixation were less
abundant in L. rostrata cells in coculture with M. loti prompted
us to investigate whether these altered protein abundances
impacted photosynthetic activities of cells in the symbiotic vs
axenic treatments. Accordingly, we made photophysiological
measurements of cells in the two treatments using PAM fluo-
rimetry. Quantification of the quantum yield of PSII and photo-
synthetic ETR provides information concerning the overall
photosynthetic performance of a culture. L. rostrata cultures, with
either 100 ng l1 B12 or M. loti, were grown in identical condi-
tions to those described for the proteomics experiment. Measure-
ments of ETR were made at 7, 11 and 14 d after inoculation into
fresh medium, corresponding to three different growth stages of
the culture (Fig. S2). As can be seen in Fig. 4, at 7 d, there is little
difference in ETR, but at both days 11 and 14, it was signifi-
cantly lower in cells grown in the presence of M. loti, with the
effect becoming more pronounced, so that at day 14 (corre-
sponding to samples used for the proteomics analysis) the rate
was only c. 60% of that in axenic cultures.
L. rostrata cells grown in coculture withM. loti have higher
amounts of amino acids and total protein
The other major group of proteins that were altered were
enzymes of amino acid biosynthesis. Accordingly we measured
total protein in the cultures and found that for both the cell pellet
and the media, the amount in the coculture was approximately
double that in the axenic cultures (Fig. 5a). Whilst the former
contain bacterial cells in addition, the estimated contribution of
the bacterial cells to the total protein is probably < 10% (see ear-
lier). We also determined the profile of free amino acids in the
cells using HPLC-MS (Fig. 5b; note the log scale for the y-axis;
Table S2). The majority of the 15 protein amino acids that could
be identified by this method were elevated in coculture, with up
to twofold changes seen for asparagine (Asn), glutamate (Glu)
and histidine (His). The exceptions were methionine and serine,
for which slight decreases were observed. Given that methionine
is the product of the enzyme in L. rostrata that requires B12
(cobalamin-dependent methionine synthase, METH), this might
indicate B12 limitation, a conclusion supported by elevation of
SAHH in coculture, and the observation that addition of B12 to
the media of cocultures enhances growth of L. rostrata (Kazamia
et al., 2012).
Discussion
In this study we applied quantitative iTRAQ proteomics to
understand the metabolic differences in L. rostrata cells when
grown in media supplemented with vitamin B12 as compared
with cultures in which B12 is provided by M. loti cells. L. rostrata
does not have a sequenced genome, and therefore its proteome is
unknown, and the presence of bacterial cells within the coculture
samples also complicated the analysis. A series of preliminary
experiments were undertaken, an approach that is essential for
these sorts of coculture, cross-species proteomics experiments.
Through these analyses we demonstrated that C. reinhardtii
would provide a suitable reference database by a 1% PSM rate to
L. rostrata peptides; that a low (0.31%) shared tryptic peptide
(length of six to 16 amino acids) between C. reinhardtii and
M. loti minimized interference by shared peptides; and that the
c. 14-fold less bacterial protein compared with algal protein in
the coculture would not be detrimental to the overall algal pro-
tein numbers confidently identified and relatively quantified
using iTRAQ. The value of these preliminary experiments was
successfully demonstrated during the iTRAQ experiment, where
Fig. 1 Overview of annotated functions ascribed to Lobomonas rostrata
proteins. Proteins were extracted from 14 d cultures of L. rostrata grown in
coculture withMesorhizobium loti, or axenically supplemented with
100 ng l1 cyanocobalamin (vitamin B12). (a) Relative proportions of each
protein category determined by the quantitative isobaric tagging (iTRAQ)
experiment to be more abundant in coculture (a total of 70 proteins).
(b) Relative proportions of each protein category found in lower amounts
(a total of 83 proteins). Proteins were categorized into major functional
groups based on combined KEGG/Mercator (Kanehisa & Goto, 2000;
Lohse et al., 2014) analysis. TCA, tricarboxylic acid; N & S, nitrogen &
sulphur; ETC, electron transfer chain.

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we were able to identify 588 L. rostrata proteins with at least one
high-confidence peptide hit. Protein fold changes were compara-
ble to previous iTRAQ studies, where relative values are signifi-
cantly lower than usually seen for transcript-level quantifications.
To avoid using arbitrary thresholds for highlighting differentially
expressed proteins, stringent quantitative statistics were applied
(Noirel et al., 2011) and revealed 153 proteins that were signifi-
cantly differentially expressed (P-value < 0.05). (Table 1; Fig. 1).
Furthermore, whilst transcript expression may not necessarily
reflect protein abundance, qRT-PCR analysis showed that tran-
script and protein expression were correlated in the two treat-
ments for several of the genes we sampled (Fig. 3), providing
further verification of our approach.
Inspection of the classes of protein that were altered revealed a
higher abundance of proteins related to amino acid biosynthesis
(Fig. 2a), and this was accompanied by modest but significant
increases in several protein amino acids in the algal cells (Fig. 5b).
M. loti is dependent on L. rostrata for fixed carbon in the cocul-
tures, but the specific compound (or compounds) that the alga
supplies is currently unknown. It is possible, therefore, that
amino acids are involved, as has been documented for several
other symbiotic interactions. For example, growth of the
heterotrophic bacterium Shewanella W3-18-1 with the cyanobac-
terium Synechococcus sp. PCC 7002 modulates amino acid
metabolism in Shewanella (Beliaev et al., 2014). A reduction in
the expression of genes involved in alanine and methionine
biosynthesis in this bacterium appear to have resulted from
enhanced excretion of these amino acids by the cyanobacterial
partner, indicative of metabolite exchange. Perhaps the best-
known example of amino acids exchange is provided by legume–
rhizobial interactions, where the nitrogen fixed by the symbiotic
bacteria is transferred to their legume partners in the form of
ammonium, some of which is metabolized by the plant and
returned to the bacteria in the form of amino acids in order to
support their growth (Lodwig et al., 2003). There are further
similarities between proteins significantly up-regulated in cocul-
tured L. rostrata and those in the nodule symbiosome, the mem-
brane bordering the nitrogen-fixing root nodule. These include a
Fig. 2 Schematic diagram summarizing changes in primary metabolic enzymes of Lobomonas rostrata in coculture vs in axenic culture with B12
supplementation. Proteins found in higher amounts in coculture are indicated in blue, whereas those that are less abundant are shown in red. Abbreviations
are provided in Table 1.
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60 kDa chaperonin, a homologue of that found in symbiosomes
of Glycine max (Panter et al., 2000), HSP 70 found in symbio-
somes of Lotus japonicus and Medicago truncatula (Wienkoop &
Saalbach, 2003; Catalano et al., 2004), and calreticulin, also
found in L. japonicus (Wienkoop & Saalbach, 2003). The canon-
ical function of chaperonins and HSPs is in assisting correct pro-
tein folding and preventing protein aggregation in cells
undergoing some form of stress (Saibil, 2013). Other proteins
up-regulated in cocultured L. rostrata that are also found in the
symbiosome membrane are D-3-phosphoglycerate dehydroge-
nase involved in amino acid biosynthesis (Wienkoop & Saalbach,
2003), citrate synthase (Saalbach et al., 2002), cysteine synthase
(Wienkoop & Saalbach, 2003) and lysine-tRNA ligase (Catalano
et al., 2004). The prokaryotic isoforms of some of these proteins
have also been found to be present in the symbiosome space (the
area between the plant and bacterial cell membranes in the sym-
biosome) rather than the membrane, indicating that they share a
common symbiotic function between kingdoms (Emerich &
Krishnan, 2014). The exact function these proteins have acquired
in the symbiosome is still largely unknown. In other symbiotic
systems, mostly parasitic, the prokaryotic isoforms of these pro-
teins are thought to be important in the cell-to-cell adhesion of
symbiont and host and it could be that the same is true for the
eukaryotic isoforms (Copley, 2012), although it is known that
cell-to-cell contact is not an obligate requirement for our
L. rostrata andM. loti system (Kazamia et al., 2012).
Perhaps the most striking feature of L. rostrata grown in cocul-
ture with M. loti is that there is an overall reduction in many
chloroplast proteins. First, proteins involved in chloroplast pro-
tein synthesis and import are much lower in coculture, whereas
several of the cytosolic equivalents are elevated. Correlating with
this observation is a reduction in two chloroplast-encoded sub-
units of the ATP synthase. At the same time, many components
of the nucleus-encoded, light-dependent reactions of photosyn-
thesis (Fig. 2), including the Rieske Fe.S subunit (Re) of the
cytochrome complex, ferredoxin (Fd), and Fd-NADP reductase
(FNR), and virtually all of the Calvin cycle enzymes are also
found in lower amounts in the coculture cells. Measurement of
the ETR confirmed that the photosynthetic capacity of L. rostrata
cells grown with M. loti in coculture is significantly lower than
Fig. 3 Reverse transcriptase-quantitative polymerase chain reaction
(RT-qPCR) measurements of transcript abundance. Transcripts of genes
encoding enzymes of photosynthesis that were found in lower abundance
in cocultures, plus three proteins highly elevated in cocultures were
quantified using cDNA prepared from RNA extracted from 14-d-old
cultures. The transcripts labelled on the x-axis are in ascending order of
protein fold-change measured by the quantitative isobaric tagging
(iTRAQ) experiment. The vertical dashed line separates proteins that are
lower in abundance in coculture (left) from those that are higher (right).
Transcript abundance was normalized against three housekeeping genes
(EIF4A, UBQ, RACK1) and then levels in cocultures withMesorhizobium
loti (black columns) are shown relative to the level in axenic Lobomonas
rostrata cultures (grey columns) set as 1. Error bars, + SE; n ≥ 3; P-value
(Student’s t-test): **, P < 0.01; ***, P < 0.001. Abbreviations are as in
Table 1.
Fig. 4 Photosynthetic capacity of Lobomonas rostrata cultures. Pulse
amplitude modulation (PAM) measurements of the electron transfer rate
(ETR) were made to estimate overall photosynthetic capacity. The solid
line shows data from cultures grown axenically with 100 ng l1
cyanocobalamin, while dashed lines show the ETR from cocultures with
Mesorhizobium loti. Rates were measured in samples taken from cultures
at (a) day 7, (b) day 11, and (c) day 14 after inoculation. Cells were diluted
to equivalent cell densities (2.59 106 cells ml1). Data are the mean of
three replicates  SEM. Significant differences in ETR between treatments
(two-sample t-test): *, P ≤ 0.05; **, P ≤ 0.01.

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that in cells grown in axenic culture (Fig. 4). This is in contrast to
a transcriptomics analysis of the cyanobacterium Prochlorococcus
NATL2A grown in coculture with the heterotrophic bacterium
Alteromonas macleodii MIT 1002, where a notable enhancement
of genes encoding photosynthetic apparatus, including subunits
of PSI and Chl biosynthesis proteins, was seen in the cyanobac-
terium. The profound impact that interactions can have on
growth and Chl fluorescence parameters has also been high-
lighted by a systematic physiological study that screened a library
of hundreds of heterotrophic marine bacteria in pairwise cocul-
tures with different Prochlorococcus ecotypes (Sher et al., 2011).
In general, this study identified that ‘enhancing interactions’,
whereby the presence of heterotrophic bacteria enhanced
cyanobacterial growth rate and/or Chl fluorescence, were more
common than the other way round. However, in this work, the
treatment conditions were such that all the necessary nutrients
for growth were present in the medium (in this case Pro99
medium supplemented with pyruvate, acetate, lactate, glycerol,
alongside vitamins) even in the presence of the bacterium, which
is in contrast to our setup in which the coculture treatment
lacked a bacterial carbon source, alongside a vital micronutrient
necessary for L. rostrata growth. Thus, in a scenario in which
growth of both microbial partners is absolutely dependent of the
presence of the other, a fine balance between mutualism and
competition appears to be in play. Whilst L. rostrata growth (and
photosynthetic output) is supported by a B12-synthesizing bac-
terium, it is nevertheless constrained compared with growth in
media where all necessary nutrients are in adequate supply. There
may also be a degree of competition between the two species for
other nutrients in the media (such as nitrogen and trace elements)
and the cocultured alga may dedicate efforts to producing
resource acquisition proteins rather than photosynthesis-related
proteins. In fact, in the closely related algae C. reinhardtii, it is
well known that photosynthetic gene expression declines during
early nutrient deprivation (Zhang et al., 2004; Schmollinger
et al., 2014), which has been suggested to be a result in part of
accumulation of transcripts for rls1, a putative transcriptional
repressor of nuclear-encoded chloroplast proteins (Nedelcu,
2009). This competition may also provide another explanation
for the up-regulation of amino acid biosynthesis enzymes in
cocultured L. rostrata, the cocultured alga requiring more
resource acquisition machinery rather than providing amino acids
to its symbiont (Arrigo, 2005).
A previous study of a mutualistic coculture based on B12
exchange, in this case between the marine diatom Thalassiosira
pseudonana and a roseobacter Ruegeria pomeroyi DSS-3, looked
at transcript-level changes and identified that the likely com-
pound supplied by the alga was the sulphur-containing com-
pound 2,3-dihydroxypropane-1-sulfonate (Durham et al.,
2015). They found no evidence of alterations in transcripts for
enzymes of the photosynthetic machinery, nor any change in
photosynthetic activity. By contrast, the results of our study at
the protein level, which is a more direct indication of cellular
activity, suggest, perhaps counterintuitively, that a photosyn-
thetic organism grown mutualistically with a bacterium does
not increase its photosynthetic activity to support both organ-
isms. Instead, the photosynthetic capacity of the alga is probably
more constrained by its own nutrient status than by the
demands of its partner. In this system, where the nutrient (B12)
status of the alga is effectively set by the bacterium, and vice
versa (in terms of carbon supply), there appears to be a tradeoff
in terms of the algal response. The striking impact that cohabit-
ing bacteria can have on algal physiology, evolution and
metabolism is becoming increasingly recognized. Algae and bac-
teria are among the most abundant organisms on the planet,
and together their metabolism impacts global nutrient cycling
and energy flow. Thus, enhancing our knowledge of the molec-
ular basis of algal–bacterial interactions is critical to establishing
the fundamental core principles governing microbial commu-
nity function in the aquatic biosphere.
Fig. 5 Protein and amino acid content of Lobomonas rostrata cultures. (a) Total protein from the cell pellet or media of 14-d-old cultures of axenic
L. rostrata or of L. rostrata in coculture withMesorhizobium loti. Protein is expressed in pg per algal cell relative to a standard of BSA. (b) Amino acids from
the cell pellet on day 14 were separated by high-performance liquid chromatography (HPLC) and quantified by MS. Amino acids are labelled on the x-axis
in alphabetical order, and quantities are expressed in fg per algal cell on the y-axis. Axenic L. rostrata + 100 ng l1 B12, grey columns; L. rostrata +M. loti,
black columns. Error bars, + SE; n = 4; P-value (Student’s t-test): *, P < 0.05; **, P < 0.01.
New Phytologist (2018) 217: 599–612 
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Acknowledgements
The work in this manuscript was supported by funding from the
Biotechnology and Biological Sciences Research Council
(BBSRC) of the UK (grant BB/I013164/1, a CASE studentship
for M.B.C. joint with PML Applications Ltd, Plymouth, UK,
and a DTP studentship for F.B.); an EU FP7 Marie Curie ITN
Photo.Comm, no. 317184 for U.J.K.; Engineering and Physical
Science Research Council (EPSRC) grant EP/E036252/1; and
the Natural Environment Research Council (NERC) grant
NE/J024767/1.
Author contributions
J.P., K.E.H., P.C.W. and A.G.S. conceived the project and
designed the research; K.E.H., M.B.C., J.L., U.J.K, D.A.R.,
E.V.T., F.B., D.L.S. and N.S. carried out the research; K.E.H.,
M.B.C, U.J.K, F.B., N.S., J.P. and A.G.S. carried out the data
analysis and interpretation; K.E.H., M.B.C., J.P. and A.G.S.
wrote the manuscript, with input from all authors. All authors
read and approved the final manuscript.
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Supporting Information
Additional Supporting Information may be found online in the
Supporting Information tab for this article:
Fig. S1 Proteomics work flow.
Fig. S2 Growth over time of L. rostrata grown in axenic culture
with B12 supplementation (100 ng l
1) versus in coculture with
M. loti.
Fig. S3 Heat map of proteins identified from iTRAQ analysis.
Table S1 List of primers used for reverse transcriptase-
quantitative polymerase chain reaction (RT-qPCR)
Table S2 Optimized values for MS analysis of amino acids
Methods S1 Amino acid analysis.
Please note: Wiley Blackwell are not responsible for the content
or functionality of any Supporting Information supplied by the
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directed to the New Phytologist Central Office.
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