Phosphate acquisition genes in Prochlorococcus
ecotypes: Evidence for genome-wide adaptation
Adam C. Martiny*, Maureen L. Coleman*, and Sallie W. Chisholm†
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
Edited by Rita R. Colwell, University of Maryland, College Park, MD, and approved June 28, 2006 (received for review February 20, 2006)
The cyanobacterium Prochlorococcus is the numerically dominant
phototroph in the oligotrophic oceans. This group consists of
multiple ecotypes that are physiologically and phylogenetically
distinct and occur in different abundances along environmental
gradients. Here we examine adaptations to phosphate (P) limita-
tion among ecotypes. First, we used DNA microarrays to identify
genes involved in the P-starvation response in two strains belong-
ing to different ecotypes, MED4 (high-light-adapted) and MIT9313
(low-light-adapted). Most of the up-regulated genes under P star-
vation were unique to one strain. In MIT9313, many ribosomal
genes were down-regulated, suggesting a general stress response
P-starvation-induced genes comprise two clusters on the chromo-
some, the first containing the P master regulator phoB and most
known P-acquisition genes and the second, absent in MIT9313,
containing genes of unknown function. We examined the organi-
zation of the phoB gene cluster in 11 Prochlorococcus strains
belonging to diverse ecotypes and found high variability in gene
content that was not congruent with rRNA phylogeny. We hy-
pothesize that this genome variability is related to differences in P
availability in the oceans from which the strains were isolated.
Analysis of a metagenomic library from the Sargasso Sea supports
this hypothesis; most Prochlorococcus cells in this low-P environ-
ment contain the P-acquisition genes seen in MED4, although
a number of previously undescribed gene combinations were
genome evolution ? microarrays ? phoB
rococcus is a significant contributor to these processes, because
it accounts for ?30% of primary productivity in midlatitude
oceans (1). Prochlorococcus is composed of closely related
physiologically distinct cells, enabling proliferation of the group
as a whole over a broad range of environmental conditions (2).
Early observations revealed that there are two genetically and
physiologically distinct types of Prochlorococcus, high-light (HL)
and low-light (LL)-adapted (2), which are distributed differently
in the water column (3, 4). Cells belonging to these two groups
differ not only in light optima and pigmentation (5) but also in
nitrogen (6) and phosphorus (7) utilization capabilities, presum-
ably adaptations that are related to depth-dependent nutrient
The HL and LL groups can be further divided into at least six
clades (two HL- and four LL-adapted) based on the phylogeny
of the 16S?23S rRNA internal transcribed spacer region (8). The
relative abundance of cells belonging to these clades has been
measured in several ocean regions, revealing patterns that agree,
for the most part, with their HL?LL phenotype: HL-adapted
cells dominate the surface mixed layer, and LL-adapted cells
most often dominate in deeper waters (3, 9–12). By combining
physiological studies of isolates and clade abundance in the
ocean, it was recently shown that temperature, in addition to
light, is an important determinant of the ocean-scale abundance
of these six phylogenetic clades (12). Based on the observed
correlations between phylogenetic origin, physiological proper-
he oceans play a key role in global nutrient cycling and
climate regulation. The unicellular cyanobacterium Prochlo-
ties, and environmental distributions, these six clades are con-
sidered ecotypes, i.e., distinct phylogenetic clades with ecolog-
ically relevant physiological differences (2, 13).
A closer examination of physiological properties among cul-
tured isolates reveals variability that is not consistent with their
phylogenetic relationships. For example, some LL-adapted
strains can use nitrite as sole nitrogen source, whereas others
require ammonium (6). Moreover, one HL-adapted strain
(MED4) can grow on organic phosphates as a sole phosphorus
source, whereas another (MIT9312) and a LL-adapted strain
light optima for growth can vary in nutrient assimilation capa-
bilities. This implies that nutrient adaptation has occurred more
mechanism for rapid adaptation to a specific environment is the
acquisition of genes by lateral transfer. Indeed, several key genes
involved in nutrient assimilation in Prochlorococcus are thought
to be of foreign origin (13), and we have recently identified
variable genomic islands in Prochlorococcus, thought to have
arisen by lateral gene transfer (14), that contain a number of
genes involved in nutrient assimilation.
To better understand the relationship between variability in
nutrient acquisition mechanisms, phylogeny, and light adapta-
tion, we undertook a detailed analysis of phosphate (P) acqui-
sition in Prochlorococcus. We first identified P-starvation-
induced genes in HL- and LL-adapted isolates using DNA
microarrays. Having identified these genes, we then analyzed
their distribution among the genomes of 11 phylogenetically
diverse Prochlorococcus strains. Finally, we compared these
findings with the collective P-acquisition gene content of a
natural Prochlorococcus population from the surface waters of
the Sargasso Sea, which is periodically P-limited.
Results and Discussion
Identification of Differentially Expressed Genes Under P Starvation.
To determine genes involved in the P-starvation response in
Prochlorococcus, we subjected strains MED4 (HL-adapted) and
MIT9313 (LL-adapted) to abrupt P limitation and monitored
changes in gene expression. To initially map the time course of
the response, we used quantitative RT-PCR to measure expres-
known to be induced under P-limiting conditions in many
P-starvation response differed significantly between the two
strains. In MED4, the transcript level of pstS began to increase
12 h after cells were resuspended in P-free medium (Fig. 1A) and
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: HL, high-light; LL, low-light; P, phosphate.
Data deposition: Orthologs to genes in the MED4 phoB region reported in this paper have
been deposited in the GenBank database (accession nos. DQ786954–DQ787011 and
*A.C.M. and M.L.C. contributed equally to this work.
†To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
© 2006 by The National Academy of Sciences of the USA
August 15, 2006 ?
vol. 103 ?
increased steadily until P was added at 48 h. This release from
P starvation caused a rapid decline in transcript level, which
reached the control value within 2 h. In MIT9313, which has two
copies of pstS, the expression of one (pstS1) was unresponsive to
P starvation, whereas that of the other (pstS2) was elevated
50-fold by 24 h (Fig. 1B), followed by a decline. The addition of
P to the medium after 48 h appeared to accelerate this decrease.
Despite 94% amino acid sequence identity between the two
copies of pstS in MIT9313, the genes responded very differently
to P starvation. The function of pstS1 is unknown.
We next examined genome-wide differences in gene expres-
sion in response to P starvation between the two strains. In
MED4, a progressive induction of genes was observed over 48 h
after the cells were resuspended in P-free medium. Thirty genes
were significantly up-regulated, and four were down-regulated,
by 48 h (Fig. 2A; Table 1, which is published as supporting
information on the PNAS web site). The general response was
different in MIT9313, where 176 genes were differentially ex-
pressed after 24 h, but most (143) were down-regulated (Fig. 2B
and Table 2, which is published as supporting information on the
PNAS web site). The high fraction of down-regulated genes,
including many ribosomal proteins, could indicate a general
reduction in the metabolic rate of MIT9313 cells (16).
Only seven up-regulated genes were common to both strains
(blue lines with gene names in Fig. 2). Most are orthologs to
Escherichia coli genes implicated in P scavenging, such as the
response regulator (phoB) and the transport system for or-
thophosphate (pstABCS). A porin gene located just down-
stream from phoB (PMM0709 in MED4 and PMT0998 in
MIT9313) was also induced in both strains, and we propose
that this gene encodes phoE, which is known to facilitate
transport of orthophosphate across the outer membrane in
other organisms. In addition to known P-starvation genes,
genes previously unassociated with P starvation were up-
regulated in both strains (Fig. 2 and Tables 1 and 2). Only two
of these genes were common to both MED4 and MIT9313:
gap1, which encodes glyceraldehyde-3-phosphate dehydroge-
nase, and mfs, which encodes a major facilitator superfamily
transporter. Both genes are located just downstream from
phoB, suggesting they play an important but unknown role in
the P-starvation response, as has been suggested (17).
A number of orthologs to genes involved in the P-starvation
response in other bacteria (18) were not induced in either
Prochlorococcus strain, including phoH (whose function is un-
an identifiable phosphonatase or C-P lyase gene suggests that
phnCDE encode a transport system for a different substrate in
Prochlorococcus or may be nonfunctional. Also, genes encoding
polyphosphate utilization (ppK and ppX) did not respond to P
starvation in either strain of Prochlorococcus, although they are
known to respond in some bacteria (19).
Despite similarities between the responses of MED4 and
MIT9313, there were also important differences. MIT9313 lacks
an ortholog to the most highly up-regulated gene in MED4,
phoA, encoding alkaline phosphatase, which cleaves P from
organic compounds. ptrA, which encodes a transcription factor
thought to be involved in the P-starvation response (17), is
up-regulated 8-fold in MED4 (PMM0718), whereas MIT9313
carries only a remnant of this gene (between PMT0998 and -999)
that is not expressed. Similarly, MIT9313 carries a pseudogene
of the sensor kinase phoR (17), which was not up-regulated,
whereas the intact version of this gene was up-regulated in
MED4. Despite the absence of phoR expression, both phoB and
pstABCS, which normally depend on phoR, were induced under
P starvation in MIT9313. Several regulatory genes that do not
have orthologs in MED4 (PMT0265, PMT1357, and PMT2151)
were differentially expressed in MIT9313 (Table 2), and these
may be involved in activating phoB and in turn pstABCS. The
remaining differentially expressed genes are unique to either
strain and are primarily of unknown function. They should be
further examined as potentially important for shaping the
ecotype-specific response to P starvation.
The genes that are differentially expressed under P starvation
are not distributed randomly along the chromosomes of the two
strains (Fig. 3, P ? 0.0001). Fifteen are located in a 21-gene
stretch of the genome in MED4 (PMM0705–PMM0725), which
includes phoB, most of the known P-acquisition genes, and
several transporters. MIT9313 lacks intact orthologs to eight of
these 15 genes, but most of the remaining seven are similarly
located in the ‘‘phoB region.’’ In addition, MED4 contains a
second cluster of up-regulated genes located between PMM1403
and PMM1416, which is part of a variable genomic island (14).
This organization suggests that the gene cluster around phoB is
involved in the uptake of various forms of P, whereas the second
cluster encodes an unknown component of the P-starvation
pended in medium with no added P at 0 h (black lines), compared to cells
resuspended in P-replete medium (orange lines). Arrows indicate P addition
(dashed lines), and pstS2 is ORF PMT0993 (solid lines).
Time course of expression of pstS in Prochlorococcus cells resus-
Differentially expressed genes (q ?0.05) in MED4 (A) and MIT9313 (B). Dark-
are genes up-regulated in only one strain, and light-blue lines are genes
down-regulated in only one strain. Error bars represent one standard devia-
tion of fold change.
Martiny et al. PNAS ?
August 15, 2006 ?
vol. 103 ?
no. 33 ?
Genome Content and Organization of P-Acquisition Genes. Genes
that are differentially expressed in response to P starvation in
MED4 and MIT9313 were more likely to be lost or gained than
randomly selected genes (P ? 0.0001) in the genomes of 11
Prochlorococcus strains. In particular, genes found in the phoB
region in MED4 are often missing or rearranged in the other
genomes (Fig. 4A). Some strains (MED4, NATL1A, NATL2A,
MIT9312, and MIT9301) share many orthologs with MED4,
similarly grouped in a large cluster. In contrast, MIT9303,
MIT9313, SS120, MIT9211, MIT9515, and AS9601 harbor fewer
than half the phoB region genes found in MED4, and many of
these are scattered throughout the genome.
This variability in genome content and architecture of P-
sequence divergence (Fig. 4 A and B). Two HL-adapted strains
belonging to the eMED4 clade (MIT9515 and MED4) share
to 15 MED4 genes from the phoB region. Similarly, three strains
belonging to the eMIT9312 clade (MIT9312, MIT9301, and
AS9601; 99.9% 16S rRNA identity) differ in gene content and
organization relative to the MED4 phoB region. In fact,
MIT9312 is more similar to MED4 and AS9601 to MIT9515 in
terms of P-acquisition gene content (Fig. 4A), which is the
inverse of their rRNA similarity. Thus it is reasonably clear, even
from this limited data set, that the organization of P-acquisition
genes in Prochlorococcus strains is not dictated by phylogenetic
Ordering the genomes by gene content and organization
relative to the MED4 phoB region, as depicted in Fig. 4A, reveals
patterns that suggest that P availability in the waters from which
these strains were isolated could influence genome content.
MED4, the strain with the most-expansive phoB region, was
isolated from surface waters in the northwest Mediterranean
Sea, where the P concentration is typically ?100 nM and has
been shown to limit growth of cyanobacteria (20, 21). NATL1A
and NATL2A, which possess orthologs to most of the MED4
phoB region genes, came from surface waters in the central
North Atlantic Ocean, where surface P levels were between 50
and 150 nM (22) at the time these strains were isolated.
Conversely, the strains with the fewest orthologs to the phoB
region in MED4 (AS9601, MIT9515, and MIT9211) were iso-
lated from ocean regions with high surface P levels (?600 nM;
refs. 23 and 24). The remaining five strains in Fig. 4A contain an
intermediate number of orthologs relative to the phoB region in
MED4. Although they were isolated from regions where P
concentrations are either low (?100 nM throughout the eu-
photic zone in Sargasso Sea) or variable (Gulf Stream; refs. 25
and 26), all came from deep in the euphotic zone (between 90
and 135 m). Light is likely the primary limiting factor for growth
at this depth, perhaps relaxing selective pressure on the P-
acquisition system. Thus, we predict that in P-limited environ-
ments, cells will contain many P-acquisition genes, primarily in
a cluster around phoB.
Frequency of Prochlorococcus P-Acquisition Genes in the Sargasso
Sea. To test this hypothesis, we examined gene stoichiometries in
surface waters of the Sargasso Sea (27), where the P concen-
tration is extremely low (25, 26). Indeed, all genes from the
MED4 phoB region were present at roughly one copy per
Prochlorococcus genome in this population (Fig. 4C). This
includes genes between PMM0717 and PMM0722, which are
largely absent from the other genomes, including ones affiliated
with eMIT9312, the ecotype dominating this wild Prochlorococ-
cus population (based on internal transcribed spacer sequence
analysis from this data set). The abundance of P-acquisition
genes similar to those found in MED4, in a population domi-
nated by eMIT9312 cells, further supports our hypothesis that
the regional environment influences the P-acquisition gene
content of Prochlorococcus cells.
We also analyzed the frequency of occurrence of orthologs to
the second up-regulated cluster in MED4 (spanning PMM1403
to -1416; Fig. 3A) in the Sargasso Sea population. As mentioned
previously, the cluster is present only in MED4 and is located in
a variable genomic island. In the Sargasso Sea, most genes from
this cluster were present in a ratio close to 0.5 compared to core
genes (data not shown), indicating that some, but not all,
Prochlorococcus genomes contained these genes (see also ref
14). We discovered genome fragments containing genes from
this island in proximity to known P-acquisition genes commonly
found around phoB (Fig. 4D). These fragments demonstrated
physical linkage between PMM1406 and phoBR, PMM1416 and
phoA and several other combinations. This association of genes
from two separate P-starvation-induced clusters in the MED4
genome supports the importance of these genes in responding to
In MIT9301 and in several genome fragments from the
Sargasso Sea, we also saw an intriguing linkage between genes
found in the phoB region of MED4 and phosphonate uptake
genes (phnCDE; Fig. 4 A and D). It has been proposed that
phosphonates are an important phosphorus resource in marine
ecosystems (28), but efforts to grow Prochlorococcus on phos-
phonates as a sole P source have been unsuccessful thus far. The
clustering of phosphonate uptake genes and genes up-regulated
under P starvation suggests that some Prochlorococcus lineages
may be capable of using this organic phosphorus source.
Adaptation to P Limitation in Prochlorococcus.Ouranalysisrevealed
genomic variation among Prochlorococcus isolates that is not
consistent with their rRNA-based phylogenetic relationships.
We propose that these differences are related, in part, to the
nutrient regime from which the cells were isolated. However,
other forces are likely shaping genome content as well, such as
phages using outer membrane proteins (e.g., PhoE) as receptors
(29), crosstalk between regulatory circuits (e.g., PhoBR; ref. 30),
and limitation by other factors (e.g., light). Stochastic variation
may also play a role.
Lateral gene transfer may explain the lack of correspondence
between the gene complements of the strains and their phylo-
genetic relationships. The pstS gene is encoded in the genomes
2 for differentially expressed genes; gray indicates genes with no significant
(q ?0.05) change. The data plotted are from the 48-h time point in MED4 and
the 24-h time point in MIT9313, the time of maximal pstS expression in each
Genome position of genes that were differentially expressed under
www.pnas.org?cgi?doi?10.1073?pnas.0601301103 Martiny et al.
of cyanophages that infect Prochlorococcus (31), suggesting a
mechanism for moving genes across phylogenetic clades, and
there is evidence that phoA and other genes involved in nutrient
assimilation have been acquired laterally in some Prochlorococ-
cus lineages (13). Furthermore, we observed that genes clustered
in a variable genomic island in MED4 are up-regulated during
P starvation (14). We were unable to detect any other obvious
events of lateral gene transfer in the phoB region using phylo-
genetic analysis, but we anticipate that these events will become
apparent as the sequences of more genomes from marine
environments become available.
Unlike the P-starvation response, some traits, such as adap-
tations to light and temperature, are consistent with the phy-
logeny of Prochlorococcus (2, 12). One explanation for this
difference is that photosynthesis requires a large protein com-
plex that does not readily incorporate whole genes from foreign
organisms (32, 33), and temperature adaptation can occur
through genome-wide changes in amino acid and membrane
lipid composition (34, 35). In contrast, the acquisition of a few
a cell (e.g., nitrite reductase and alkaline phosphatase). A
simplified calculation (see Materials and Methods) shows that if
a Prochlorococcus cell acquires genes that improve growth rate
by 1%, its progeny will dominate the entire population in an
ocean basin in a few decades. This time scale is comparable to
the observed domain shift in the North Pacific Ocean gyre from
of 11 Prochlorococcus strains. A red star indicates a gene that was significantly up-regulated in MED4 or MIT9313 from the microarray analyses. Gene numbers
refer to PMM0XXX in MED4. Unfilled genes are likely pseudogenes. Color coding of strain names reflects ecotype affiliation shown in B (2). (B) Schematic of the
phylogenetic relationship among different Prochlorococcus ecotypes (9). (C) Gene frequency in small insert libraries from the surface waters of the Sargasso Sea
(27). Error bars indicate standard deviation of abundance based on all 150-bp fragments covering a gene. (D) Examples of genomic variants in the Sargasso Sea,
showing linkage between genes found in the phoB region of MED4 and genes found elsewhere in the MED4 genome. Diagonal lines represent unknown
sequence between two end reads of a clone in the data set.
P-acquisition genes in Prochlorococcus. (A) Genes located in proximity to phoB in MED4 (at the top) and the presence of their orthologs in the genomes
Martiny et al. PNAS ?
August 15, 2006 ?
vol. 103 ?
no. 33 ?
a nitrogen- to a P-controlled state, purportedly fueled by in-
creased nitrogen fixation in this region (36). Considering the
strong feedback between the metabolic activity of Prochlorococ-
cus (and all phytoplankton) and the local nutrient regime (37),
understanding shifts in biogeochemical processes in the oceans.
Materials and Methods
Culture Conditions. Prochlorococcus strains were grown at 22°C in
Pro99 medium (6). Before the experiment, cultures were main-
tained in continuous light in log-phase growth at an irradiance
of 12 ?E m?2?s?1[E, einstein (1 mol of photons)] for MIT9313
(growth rate ? 0.18 d?1), and 30 ?E?m?2?s?1for MED4 (growth
rate ? 0.27 d?1) for ?30 generations. Chlorophyll fluorescence
was monitored on a Synergy HT fluorometer (BioTek,
P-Starvation Time Series. To induce P starvation, triplicate 4-liter
cultures were harvested by centrifugation (10,000 ? g), split in
two, and washed twice in either P-replete (Pro99 with 50 ?M
PO4) or -depleted (Pro99 with no added PO4) medium and
resuspended in 2 liters of the same medium. Samples were taken
for RNA extraction, microarray hybridization, and quantitative
RT-PCR (qRT-PCR) analysis at 0, 4, 12, 24, and 48 h after
resuspension. Additional samples were taken for qRT-PCR at
selected time points. After 48 h, 50 ?M P was added to the
P-depleted cultures to monitor the recovery response.
RNA Extraction. RNA was isolated according to ref. 38. In brief,
cells were harvested by centrifugation (10,000 ? g), resuspended
in storage buffer (200 mM sucrose?10 mM NaOAc, pH 5.2?5
mM EDTA) and stored at ?80°C. Before RNA extraction,
MIT9313 cells were treated with 10 ?g??l lysozyme (Sigma, St.
Louis, MO) for 1 h at 37°C (39). Total RNA was extracted by
using the mirVana miRNA kit (Ambion, Austin, TX). DNA was
removed by using Turbo DNase (Ambion). RNA was concen-
Quantitative RT-PCR. RNA (2–10 ng of total RNA) was reverse-
transcribed by using 100 units of SuperScript II (Invitrogen,
Carlsbad, CA) in the presence of 200 units of SuperaseIN
(Ambion). Primers are described in Table 3, which is published
as supporting information on the PNAS web site. The resulting
cDNA was diluted 5-fold in 10 mM Tris, pH 8. Triplicate
real-time PCRs were performed by using the Qiagen (Valencia,
CA) SYBR green kit and the diluted cDNA as template. The
following program was run on an MJ Research (Cambridge,
MA) Opticon DNA engine: 15 min at 95°C, followed by 40 cycles
of denaturation (95°C, 15 s), annealing (56°C, 30 s), and exten-
sion (72°C, 30 s), followed by 5 min at 72°C. cDNA for pstS was
quantified relative to rnpB by using the ?-? CTmethod (40).
Array Analysis. cDNA synthesis, labeling, and hybridization onto
custom MD4–9313 Affymetrix (Santa Clara, CA) microarrays
was done following the standard Affymetrix protocol. The probe
arrays were scanned, and data visualization was done with
GeneSpring software (Version 7.1; Silicon Genetics, Palo Alto,
CA). Normalization was done by using the Robust Multichip
Average algorithm (41) implemented in GeneSpring. Bayesian
genes using Cyber-T (42). The Bayesian estimate of variance,
which incorporates both the experimental variance for a given
gene and variance of genes with similar expression levels (42),
was calculated by using window sizes of 81 for MED4 and 101 for
MIT9313 and a confidence value of 10 for both strains. A t test
was then performed on log-transformed expression values by
using the Bayesian variance estimate. To account for the mul-
tiple t tests performed, we used the program QVALUE, which
measures significance in terms of the false discovery rate (43).
A gene was identified as differentially expressed if the q value
was ?0.05. Signal intensities of individual probes targeting
intergenic regions and potential miscalled ORFs were extracted
by using Intensity Mapper (Affymetrix).
Tests for Clustering and Selective Loss?Gain of Induced Genes. We
tested whether differentially expressed genes were distributed
randomly along the genome by comparing the gene distance (in
weighted gene distance (d) was calculated by using the following
decay function (adjusted for a circular genome):
d ? ?
j?ni ? nj?l?,
where i, j ? 1, 2, . . . , number of expressed genes, and n ?
position in genome. The second summation is based on a sorted
array to nearest neighbor of ni(i.e., ni? n1? 0). The physical
distance between differentially expressed genes was then com-
pared to the d value of i randomly selected genes (10,000
permutations). We also tried other decay functions (e.g., differ-
ent log bases of ni? nj) as well as using gene order as a measure
for distance instead of actual base-pair difference, but all
summations yielded the same result.
We also tested whether differentially expressed genes in
MED4 (34 genes) and MIT9313 (176 genes) were more com-
monly lost or gained compared to randomly selected genes in the
other Prochlorococcus genomes. We randomly chose 34 genes in
the MED4 genome, counted the total number of orthologs to
10,000 times to generate a distribution. We then tested whether
the total number of orthologs of the 34 differentially expressed
genes in MED4 fell significantly outside this distribution. We
repeated the test using the 176 differentially expressed MIT9313
genes. Orthologs were identified as pairwise best blastp hits. To
further support the ortholog assignments, we constructed phy-
logenetic trees (maximum parsimony) for each gene in the
MED4 phoB region and its putative orthologs.
Blast Analysis of Sargasso Sea Shotgun Library. We examined the
occurrence of genes found in the phoB region of MED4 (be-
tween PMM0705 and PMM0725), in the Sargasso Sea environ-
mental sequence data set sampled in February 2003 (excluding
samples 5, 6, and 7; ref. 27). We used MED4 as the template for
PMM0715 to PMM0722 and MIT9312 for the remaining genes.
the phoB region was first searched (blastn or tblastx; ref. 44)
against the environmental sequence data set. A positive hit was
scored if the environmental sequence and the paired end
recovered Prochlorococcus as best hit when searched against a
database consisting of Prochlorococcus, marine Synechococcus
(WH8102, CC9905, and CC9902), Pelagibacter ubique, and Si-
licibacter pomeroyi. The number of copies of a particular phoB-
region gene in the Sargasso Sea data set was estimated by
averaging the number of hits for 150-bp segments comprising
that gene and normalized against the average occurrence of
known single-copy genes in all sequenced Cyanobacteria: cpeA,
glnA, gyrB, hemA, 16S?23S internal transcribed spacer region
(single copy in HL Prochlorococcus clades), recA, rpl10, rpoB,
rpsD, and tyrS.
Changes in Genotype Frequency as a Function of Relative Fitness. To
calculate how long it might take a new genotype with slightly
improved fitness to overtake a population of Prochlorococcus
cells in an ocean, we used equation 11 from ref. 45:
www.pnas.org?cgi?doi?10.1073?pnas.0601301103 Martiny et al.
ln?x1?t??x2?t?? ? ln?x1?0??x2?0?? ? st, Download full-text
where x1(t) is the fraction of the new genotype, and x2(t) is the
fraction of the ancestral genotype at time t (days). At t ? 0, x1
was set to 10?24, and x2was set at 1, assuming 1024cells in an
ocean basin such as the Sargasso Sea (46). We assumed a growth
rate of 0.5 per day?1(47) for the ancestral genotype and an
increase in growth rate (or relative fitness) of new genotype (s)
of 1%, so s ? 0.005 d?1.
We thank Debbie Lindell for many helpful discussions and Robert
Steen and Trent Rector at Harvard Biopolymer Facility for labeling
RNA and hybridizing the microarrays. We also thank numerous
members of the Chisholm and DeLong labs for helpful comments on
the manuscript. This work was supported in part by a fellowship from
the Danish National Science Foundation (to A.C.M.); a National
Science Foundation Graduate Fellowship (to M.L.C.); and grants from
the National Science Foundation, the Gordon and Betty Moore
Foundation, and the U.S. Department of Energy GTL Program (to
1. Goericke, R. & Welschmeyer, N. A. (1993) Deep-Sea Res. 40, 2283–2294.
2. Moore, L. R., Rocap, G. & Chisholm, S. W. (1998) Nature 393, 464–467.
3. West, N. J. & Scanlan, D. J. (1999) Appl. Environ. Microbiol. 65, 2585–2591.
4. Urbach, E. & Chisholm, S. W. (1998) Limnol. Oceanogr. 43, 1615–1630.
5. Moore, L. R. & Chisholm, S. W. (1999) Limnol. Oceanogr. 44, 628–638.
6. Moore, L. R., Post, A. F., Rocap, G. & Chisholm, S. W. (2002) Limnol.
Oceanogr. 47, 989–996.
7. Moore, L. R., Ostrowski, M., Scanlan, D. J., Feren, K. & Sweetsir, T. (2005)
Aquat. Microbial Ecol. 39, 257–269.
8. Rocap, G., Distel, D. L., Waterbury, J. B. & Chisholm, S. W. (2002) Appl.
Environ. Microbiol. 68, 1180–1191.
9. Ahlgren, N. A., Rocap, G. & Chisholm, S. W. (2006) Environ. Microbiol. 8,
& Chisholm, S. W. (2006) Appl. Environ. Microbiol. 72, 723–732.
11. West, N. J., Schonhuber, W. A., Fuller, N. J., Amann, R. I., Rippka, R., Post,
A. F. & Scanlan, D. J. (2001) Microbiology 147, 1731–1744.
12. Johnson, Z. I., Zinser, E. R., Coe, A., McNulty, N. P., Woodward, E. M. &
Chisholm, S. W. (2006) Science 311, 1737–1740.
13. Rocap, G., Larimer, F. W., Lamerdin, J., Malfatti, S., Chain, P., Ahlgren, N. A.,
Arellano, A., Coleman, M., Hauser, L., Hess, W. R., et al. (2003) Nature 424,
14. Coleman, M. L., Sullivan, M. B., Martiny, A. C., Steglich, C., Barry, K., Delong,
E. F. & Chisholm, S. W. (2006) Science 311, 1768–1770.
15. Scanlan, D. J., Silman, N. J., Donald, K. M., Wilson, W. H., Carr, N. G., Joint,
I. & Mann, N. H. (1997) Appl. Environ. Microbiol. 63, 2411–2420.
16. Nomura, M. (1999) J. Bacteriol. 181, 6857–6864.
17. Scanlan, D. J. & West, N. J. (2002) FEMS Microbiol. Ecol. 40, 1–12.
18. Wanner, B. L. (1996) in Escherichia coli and Salmonella: Cellular and Molecular
Biology, ed. Neidhardt, F. C. (Am. Soc. Microbiol., Washington, DC), pp.
19. Kornberg, A., Rao, N. N. & Ault-Riche, D. (1999) Annu. Rev. Biochem. 68,
20. Marty, J. C., Chiaverini, J., Pizay, M. D. & Avril, B. (2002) Deep-Sea Res. 49,
21. Vaulot, D., LeBot, N., Marie, D. & Fukai, E. (1996) Appl. Environ. Microbiol.
22. Irwin, B. (2000) Nutrient Data from the Atlantic, JGOFS Canada Data Sets
1989–1998 (Marine Enviromental Data Service, Department of Fisheries and
Oceans, Canada). CD-ROM Version 1.0.
23. Coale, K. H., Johnson, K. S., Fitzwater, S. E., Gordon, R. M., Tanner, S.,
Chavez, F. P., Ferioli, L., Sakamoto, C., Rogers, P., Millero, F., et al. (1996)
Nature 383, 495–501.
24. Morrison, J. M., Codispoti, L. A., Gaurin, S., Jones, B., Manghani, V. & Zheng,
Z. (1998) Deep-Sea Res. 45, 2053–2101.
25. Cavender-Bares, K. K., Karl, D. M. & Chisholm, S. W. (2001) Deep-Sea Res.
26. Wu, J., Sunda, W., Boyle, E. A. & Karl, D. M. (2000) Science 289, 759–762.
27. Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D.,
Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) Science
28. Karl, D. M. & Bjo ¨rkman, K. M. (2002) in Biogeochemistry of Marine Dissolved
Organic Matter, eds. Hansell, D. A. & Carlson, C. A. (Academic, London), pp.
29. Ho, T. D. & Slauch, J. M. (2001) J. Bacteriol. 183, 1495–1498.
30. Fisher, S. L., Jiang, W., Wanner, B. L. & Walsh, C. T. (1995) J. Biol. Chem. 270,
31. Sullivan, M. B., Coleman, M. L., Weigele, P., Rohwer, F. & Chisholm, S. W.
(2005) PLoS Biol. 3, e144.
32. Blankenship, R. E. (1992) Photosynth. Res. 33, 91–111.
33. Shi, T., Bibby, T. S., Jiang, L., Irwin, A. J. & Falkowski, P. G. (2005) Mol. Biol.
Evol. 22, 2179–2189.
34. Morgan-Kiss, R. M., Priscu, J. C., Pocock, T., Gudynaite-Savitch, L. & Huner,
N. P. A. (2006) Microbiol. Mol. Biol. Rev. 70, 222–252.
35. Tekaia, F., Yeramian, E. & Dujon, B. (2002) Gene 297, 51–60.
36. Karl, D. M., Letelier, R., Tupas, L., Dore, J. E., Christian, J. & Hebel, D. V.
(1997) Nature 388, 533–538.
37. Redfield, A. C. (1934) in James Johnstone Memorial Volume, ed. Daniel, R. J.
(Univ. Press of Liverpool, Liverpool, U.K.).
38. Lindell, D., Jaffe, J. D., Johnson, Z. I., Church, G. M. & Chisholm, S. W. (2005)
Nature 438, 86–89.
39. Tolonen, A. C., Aach, J., Lindell, D., Johnson, Z. I., Rector, T., Steen, R.,
Church, G. M. & Chisholm, S. W. (2006) Mol. Syst. Biol., in press.
40. Livak, K. J. & Schmittgen, T. D. (2001) Methods 25, 402–408.
42. Baldi, P. & Long, A. D. (2001) Bioinformatics 17, 509–519.
43. Storey, J. D. & Tibshirani, R. (2003)Proc. Natl. Acad. Sci. USA 100, 9440–9445.
44. Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W.
& Lipman, D. J. (1997) Nucleic Acids Res. 25, 3389–3402.
45. Dykhuizen, D. E. & Hartl, D. L. (1983) Microbiol. Rev. 47, 150–168.
46. Partensky, F., Hess, W. R. & Vaulot, D. (1999) Microbiol. Mol. Biol. Rev. 63,
47. Liu, H., Landry, M. R., Vaulot, D. & Campbell, L. (1999) J. Geophys. Res. 104,
Martiny et al.PNAS ?
August 15, 2006 ?
vol. 103 ?
no. 33 ?