J.1439 0485.2011.00479.x


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J.1439 0485.2011.00479.x

  1. 1. Marine Ecology. ISSN 0173-9565REVIEW ARTICLEWhat can we learn from genomics approaches in marineecology? From sequences to eco-systems biology!Thomas Mock & Amy KirkhamSchool of Environmental Sciences, University of East Anglia, Norwich, UKKeywords AbstractAdaptation; algae; bacteria; biogeochemistry;biological oceanography; evolution; genomics; The application of genomic approaches to marine biota has profoundly alteredmarine ecology; metagenomics; microbes; our understanding of life in the oceans, especially regarding concepts of adap-phytoplankton. tation, speciation and evolution. The avalanche of genomic data has provided an unbiased view of marine biology that has never been seen before. In partic-Correspondence ular, comparative and metagenomic approaches with microbes from differentThomas Mock, School of EnvironmentalSciences, University of East Anglia, Norwich, biogeochemical marine provinces provided the first insights into how theyUK. acquire and discard genes as needed, even across kingdom boundaries, inE-mail: t.mock@uea.ac.uk response to their environment. These data clearly reveal that marine microbes have remarkable abilities to change their genomes according to both environ-Accepted: 5 July 2011 mental stresses and biotic interactions. Thus, it is most likely that the flux of energy and matter in the marine system is reflected by the presence or absencedoi:10.1111/j.1439-0485.2011.00479.x of genes and proteins in marine organisms, which could provide novel tools to understand biogeochemical processes of global significance. However, the chal- lenge is to put the reductionistic knowledge gained by genomics and metage- nomics into the larger contexts of cellular systems and ecosystems to identify emergent properties that could not have been predicted by breaking down the whole into its individual parts. approaches, such as systems ecology, for tackling emer-Introduction gent properties are not yet as developed for marineIn 1977, Eugen P. Odum wrote: ‘Ecology must combine systems as they are for their terrestrial counterparts (e.g.holism with reductionism if applications are to benefit Odum 1983; Chapin et al. 2002). However, these emer-society’. His ideas with respect to greater attention to hol- gent properties, based on complex, interacting marineism in science and technology were ground-breaking communities, are the basis of global-scale processes suchbecause an important consequence of hierarchical organi- as cycling of energy and matter. Thus, there is a need forzation in ecosystems is that as components or subsets are more systems-oriented science in the field of Marinecombined to produce larger functional wholes, new prop- Research to tackle the challenges of the 21st century.erties emerge that were not present or not evident at the The HMS Challenger Expedition, a 4-year voyagenext level below (Odum 1977). For example, the interac- which circumnavigated the globe between December 1872tions between prey organisms and their predators in the and May 1876, largely founded the basis for Marine Ecol-same environment cannot be explained fully even by the ogy and Biological Oceanography. This expeditionmost sophisticated models of their population dynamics provided a wealth of novel information (compiled into a(e.g. Davidson et al. 2010). Thus, their population 50-volume report which can be found in the Encyclopædiadynamics are the consequence of emergent properties Britannica) about the oceans and their organisms butbased on interactions of members of the whole commu- still only a first glimpse into the marine ecosystem.nity of organisms with each other and their environment Uncountable expeditions followed and many more takeand the physical constraints imposed by it. Holistic place each year to explore the largest but least knownMarine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 131
  2. 2. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamhabitat on earth. The tendency towards holistic but inter- current holistic concepts to understand ecosystem func-disciplinary approaches, which tend to lack deep under- tioning.standing of the parts that shape the whole, is likely dueto the size of the marine system and difficulties in access- How Do Genomic Approaches Influence Marineing it. It might be expected that the difficulties of access- Ecology?ing, sampling and studying marine organisms would havedriven the development of new technology. However, this Marine Ecology, like other ecological disciplines, is drivenhas not been the case compared to the parallel develop- by hypotheses derived from observing the interactions ofment of other scientific disciplines (e.g. medicine, chemis- organisms and their communities with the environmenttry, physics) over the same period of time. Reasons for (e.g. Odum 1977; Putman & Wratten 1984). Therefore,this may be manifold, but it seems that Marine Ecology ecology uses a deductive system to explain relationshipsand Biological Oceanography entered a new era approxi- between organisms and their environment and thus seeksmately two decades ago by applying new technology to to explain their evolution. In contrast, genomics is notold questions. One of the most significant examples was primarily driven by hypotheses but by technology. In fact,the discovery of the hydrothermal vents on the deep technological developments such as high throughputocean floor at fracture zones along mid-oceanic ridges sequencing technologies (e.g. 454 GS-Titanium, Illumina,(e.g. Van Dover 1990; Tunnicliffe & Flower 1996). The SOLID) over the last decade have significantly advanceddevelopment of new submarines enabled their discovery, genomic disciplines (e.g. Schuster 2008). Genomics useswhich even led to new hypotheses about the evolution of an inductive approach per se and therefore instead gener-life on earth (e.g. Martin et al. 2008). About a decade ates hypotheses.after its discovery, this fascinating ecosystem was At first sight, genomics and ecology seem an odd cou-described using genome-enabled technologies such as me- ple with unbridgeable gaps and contradictory approachestagenomics and proteomics. These technologies revealed from two entirely different standpoints. All genome pro-the key molecular mechanisms underpinning how the jects with marine organisms so far have provided novelcharacteristic symbiotic communities of this ecosystem and ground-breaking insights into their biology and evo-generate energy (sulphur oxidation) and produce organic lution (e.g. Gloeckner et al. 2003; Rocap et al. 2003;carbon compounds [via the reductive tricarboxylic acid Armbrust et al. 2004; Palenik et al. 2007; Bowler et al.cycle (TCA) and Calvin cycles] for growth (e.g. Markert 2008; Worden et al. 2009; Cock et al. 2010) but there iset al. 2007; Grzymski et al. 2008). still a long way to go before genomic information The development of new methods to sample and ana- becomes integrated into holistic ecological concepts suchlyse marine systems in combination with the use of gen- as the flux of energy and matter through the marine eco-ome-enabled technology has, in our opinion, initiated a system. There are two reasons for this. Firstly, we are stillnew era in Marine Ecology. This is because new sampling in the discovery phase where we try to identify novel sin-strategies allow us to explore new ecosystems, and gle genes and gene families, and ascertain their impor-genomics has the potential to fill gaps in our understand- tance for the biology and evolution of organisms.ing of diversity and functioning of marine communities. Secondly, we haven’t yet begun to think about how toThus, we will soon be able to use this new understanding select and integrate these sequence data into overridingof the components and functioning of complex marine ecosystem models, such as models to explain the biogeog-communities combined with concepts of cycling energy raphy of microbial communities linked to biogeochemicaland matter to establish a systems approach to identify ´ ´ cycling of elements (e.g. LeQuere et al. 2005; Followsemergent properties that influence global-scale processes. et al. 2007). We will solve this problem as soon as we This review will try to explain why the reductionist become able to use genomic information to model notapproach, through the use of genome-enabled technolo- only evolution but also the earth system as a whole basedgies in Marine Ecology, has already revolutionized our on emergent properties that are more than the sum of aunderstanding of marine organisms, their evolution and parts list (e.g. Raes & Bork 2008; DeLong 2009).adaptation to the environment. Perhaps, it will also help The following sections give examples of how the induc-us to find ways to combine these new data with broader tive approaches used by genomics are able to transformcommunity- and ecosystem-related concepts such as bio- Marine Ecology. This review will focus on marinegeochemical cycling (e.g. Crombach & Hogeweg 2009). microbes because most of the genomic information weHowever, the nature of revolutions is that they have currently have about marine organisms comes frommajor implications for many different fields. One possi- sequencing prokaryotic and eukaryotic microbes and theirble consequence may be that we have to rethink our microbial communities.132 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  3. 3. Mock & Kirkham What can we learn from genomics approaches in marine ecology?Influence of Genomics on Our Understanding ofthe Evolution of Marine Organisms CoreBacteria SharedIt took about 7 years from sequencing the first genomes Orphanof bacteria (Haemophilus influenzae, Mycoplasma genitali-um) in 1995 (Fleischmann et al. 1995; Fraser et al. 1995)to the first genome sequences from the sea (e.g. Dufresneet al. 2003; Rocap et al. 2003; Palenik et al. 2006). Beforethese genomes from various marine Prochlorococcusstrains became available, all of the classification studieswere based on phylogenetic marker genes (e.g. Woese1987; Ludwig & Schleifer 1994), such as the small subunitof ribosomal RNA (i.e. 16S), to define a species or taxonbecause most bacteria lack taxonomically useful morpho-logical features. These very first genome sequences frombacteria revolutionized our view of evolution and diver-sity, and gave rise to the new discipline, ComparativeGenomics (e.g. Koonin & Wolf 2008). Fig. 1. Common and rare genes in 10 aquatic archaea and bacterial Although the sequenced Prochlorococcus strains are genomes (Pelagiobacter ubique (SAR11) HTCC1062, Halorubrum lacusprofundi ATCC49239, Methanococcoides burtonii DSM6242,identical based on the criterion of more than 97% Prochlorococcus MIT9313, Pseudoalteromonas haloplanktis TAC125,sequence identity of the small subunit of ribosomal RNA, Rhodopirellula baltica SH1, Roseobacter sp. GAI101, Silicibacter sp.they showed remarkable differences in genome size, GC TM1040, Sphingopyxis alaskensis RB2256, Synechococcus sp.content, the number of genes and gene composition WH8102) based on prokaryotic clusters of orthologous genes (COGs).(Rocap et al. 2003). These differences reflect adaptation Core set = COGs present in 9 or 10 genomes; shared set = COGsto different ecological niches in the pelagic environment, present in 2–8 genomes; orphan set = COGs present in 1 genomedetermining the relative fitness of each ecotype and hence only.regulating their distribution and abundance in the ocean.Thus, there is much higher genetic diversity than previ- The major modes of transmission of genetic materialously concluded using only selected marker genes. This that determine the diversity of genes, and therefore phylo-diversity is the consequence of different modes of evolu- genetic patterns, are vertical and horizontal gene transfertion, and hence adaptation, which ultimately shapes bio- (HGT) (Table 1). Transfer of genetic material to off-geochemical processes in the ocean. Consequently, spring, or the inheritance of genes by subsequent genera-understanding the evolution of marine microbes is crucial tions, is the essential basis of vertical gene transfer. HGTfor our understanding of the cycling of elements and had been recognized (Woese 1987) as a mode of geneticgases in the ocean. transfer long before the first bacterial genomes were Based on comparative analyses of many more sequenced sequenced (e.g. Smith et al. 1992; Syvanen 1994; Ochmanprokaryotic genomes published over the last few years (e.g. et al. 2000). However, it was viewed as a rare phenome-Palenik et al. 2006), we can identify general patterns of non occurring only under specific circumstances, such asgenome organization, function and evolution. The gene the development of resistance to antibiotics among differ-space of prokaryotes can be subdivided into three groups: ent strains of bacteria (e.g. Aminov & Mackie 2007).(i) the set of universal or nearly universal core genes that Sequencing of bacterial genomes, their comparative analy-have orthologues in all species and strains of bacteria and sis and especially sequencing of ocean metagenomes hasarchaea, (ii) moderately common genes (shell ⁄ shared shown that HGT is at least as important as vertical genegenes), and (iii) orphan genes, which are genes that have transfer for the evolution of prokaryotes. This was per-no detectable similarity to any other available protein haps the most important contribution of comparativesequence (Koonin & Wolf 2008) (Fig. 1). Less than 5% of and meta-genomics to our understanding of prokaryoticall genes in any prokaryotic organism belong to the core evolution. Recent marine metagenome and transcriptomegroup of genes, which are basically house-keeping genes; projects have impressively revealed that key genes, such asover 50% belong to the shell group; and the rest belong to those from the rhodopsin family, were horizontallygroups of specific genes that only occur in particular spe- transferred among very different microbial organismscies or in a small number of closely related strains from (Archaea, Bacteria) to benefit the whole community (e.g.the same species (Koonin & Wolf 2008) (Fig. 1). Venter et al. 2004). A type of vector for this massiveMarine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 133
  4. 4. What can we learn from genomics approaches in marine ecology? Mock & KirkhamTable 1. A summary of the main forces that shape genomes of subject to adaptation to the environment. Purifying selec-marine microbes (prokaryotes and eukaryotes). tion is especially intense in large populations that live inForces that shape genomes relatively stable environments under resource limitationof marine microbes Prokaryotes Eukaryotes (e.g. tropical ocean) and leads to genome streamlining with extensive loss of genes. Examples are SAR11 (Pelagio-genome degradation XX X bacter ubiquis) (Giovannoni et al. 2005) and cyanobacteriagene duplication XXX XXXgene loss XXX XXX of the genus Prochlorococcus (Rocap et al. 2003). Character-HGT XXX X istic features of these genomes are not necessarily a smallnon-orthologous gene displacement XX X genome size but compactness, indicated by a strong reduc-endosymbiotic gene transfer 0 XXX tion of intergenic space and a lack of pseudogenes. How-operon shuffling XXX X ever, despite their evolution under strong selectionreplication fusion XXX X pressure, the genomes of Prochlorococcus have features thattransposition XX XX are not compatible with streamlining, such as genomicsexual recombination X XXepigenetics XX XXX islands. This finding emphasizes the interplay of bothgene fusion XX XXX opposing processes on the evolution of Prochlorococcus,conservation of gene order X X which is assumed to be common in many more prokary-mutations XXX XX otes (Koonin & Wolf 2008). In contrast, genome complex-operonization XX 0 ification is likely to occur only in those prokaryotes thatregulation X XXX live in complex and variable environments (on or withinXXX = strong impact; XX = medium impact; X = weak impact; substrates, such as rocks and sediments respectively) where0 = minor effect or not present; HGT = horizontal gene transfer. they persist in small populations and ⁄ or are exposed to severe population bottlenecks (Lynch & Conery 2003). One example is the genome of Rhodopirellula baltica, an impor-transfer of genes, besides conjugation, transduction or tant planctomycete involved in degradation of complextransformation, seems to be phages (e.g. Canchaya et al. organic carbon compounds (e.g. polysaccharides) produced2003) because of their dominance in marine systems and by photosynthetic organisms. Its genome is 7.1 Mb encod-mobility of genetic material (e.g. Angly et al. 2006). The ing 7325 open reading frames and 72 RNA genes (Gloeck-pool of marine phage genes is still poorly explored but it ner et al. 2003).is assumed that a major fraction of orphans in bacterial The opposing effects of genome contraction by stream-and archaeal genomes may be derived from the bacterio- lining and degradation (e.g. symbionts, parasites) andphage gene pool (e.g. Koonin & Wolf 2008). expansion by complexification are reflected in the size The discovery of the importance of HGT is the major distribution of bacteria genomes, which seems to bereason why our view of prokaryotic evolution has become bimodal (Koonin & Wolf 2008) (Fig. 2). The first peak isincreasingly modular. The genome space can be consid- at about 2 Mb and the second peak is at about 5 Mb,ered to look like a composite of ‘LEGOÒ’ blocks with an reflecting streamlining and complexification, respectivelyalmost unlimited degree of combination, at least for the (Fig. 2). The smallest known genome size in free-livingshell and orphan genes. However, there is increasing bacteria (SAR11) is 1.3 Mb encoding 1354 open readingevidence that even core genes, including those used as frames (Giovannoni et al. 2005). Most of these genes rep-traditional markers for vertical gene transfer (e.g. resent the essential metabolism for building the cell andDNA-dependent RNA polymerase) are subject to HGT acquiring nutrients for growth (Giovannoni et al. 2005).(Iyer et al. 2004). The upper limit is less defined and the reason for it is still Genomics has significantly changed our view of pro- under debate. One of the largest bacterial genomeskaryotic evolution and hence the classification of pro- sequenced so far comes from the soil bacteria Sorangiumcesses that effect their evolution. There are basically two cellulosum with a size of 13 Mb (Schneiker et al. 2007).opposite outcomes of the evolution of prokaryotic The reasons for the bimodal genome size distributiongenomes: complexification and genome streamlining (e.g. remain enigmatic and may imply that intermediate habi-Koonin & Wolf 2008). According to theories by Lynch & tats between stable (resource limited) and unstable (vari-Conery (2003), complexification is an intrinsic process in able resources) might be less common, which doesn’tall genomes and is caused by gene duplications, HGT and seem to be the case. It might also be the case that theother processes leading to a higher number of genes and bimodal distribution of bacterial genome sizes reflects thehence is not adaptive (Table 1). However, complexifica- sampling strategy of bacteria for genome sequencing.tion will be fixed under purifying selection (here consid- Perhaps we under-sample bacteria from habitats withered as loss of genes and not alleles) and is therefore intermediate disturbance levels.134 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  5. 5. Mock & Kirkham What can we learn from genomics approaches in marine ecology? marine genome sequencing projects. Algae are a polyphy- Genome complexfication letic assemblage of photosynthetic eukaryotes (Falkowski 0.3 Genome streamlining et al. 2004). We now have at least one genome sequence 0.2 1st peak 2nd peak from each major algal taxon available (about 15 in total) Genome 0.1 1.0 5.0 10.0 size, Mbp (Parker et al. 2008), which is little compared to the num- 0 ber of available prokaryotic genomes (about 1000 in P.u. S.m. S.a. S.p. R.b. total) but it has given us a glimpse of the major forces P.m. H.l. R.d. shaping the evolution of algae. M.b. P.h. Vertical evolutionFig. 2. Distribution of genome sizes among bacteria (dotted line) in The engulfment of a photosynthetic cyanobacterium by arelation to complexification and genome streamlining as major resultsof various evolutionary forces (Koonin & Wolf 2008). The horizontal unicellular eukaryote about 1.2 billion years ago was theline shows genome sizes on a logarithmic scale and the dotted line first crucial step in the evolution of algae (Falkowski et al.shows the distribution of genome sizes among bacteria with two dis- 2004). The endosymbiont became the chloroplast, with atinct peaks. One peak is at around 2 Mb and the other at 5 Mb. The highly reduced cyanobacterial genome due to either genegenome sizes of 10 aquatic Archaea and Bacteria were mapped onto transfer into the host genome or gene loss. This singlethe logarithmic scale (P.u. = Pelagiobacter ubique (SAR11) endosymbiotic event (primary endosymbiosis) is hypothe-HTCC1062, S.m. = Synechococcus sp. WH8102, P.m. = Prochlorococ- sized to have given rise to the three major eukaryoticcus MIT9313, M.b. = Methanococcoides burtonii DSM6242, S.a. =Sphingopyxis alaskensis RB2256, H.l. = Halorubrum lacusprofundi photosynthetic lineages – the green, red and glaucophyteATCC49239, P.h. = Pseudoalteromonas haloplanktis TAC125, S.p. = lineages. In the ocean, chlorophytic algae (uni- and multi-Silicibacter sp. TM1040, R.d. = Roseobacter sp. GAI101, R.b. = Rho- cellular) and prasinophytes (unicellular) are the majordopirellula baltica SH1). This graph has been adapted from Koonin & algal classes derived from the green lineage (FalkowskiWolf (2008). et al. 2004). Prasinophytes have a global distribution and include the world’s smallest eukaryotes such as the Correlation analyses between the abundance of genes in picoeukaryote Ostreococcus, which has a cell diameter oftheir corresponding orthologous clusters (COGs) with 1 lm (Derelle et al. 2006). Prasinophytes resemble agenome size have revealed that genes coding for tran- diverse group of paraphyletic lineages diverging at thescription, translation and cell division show no depen- base of the Chlorophyta (Worden et al. 2009). All otherdence on genome size, whereas genes encoding for marine algal lineages such as diatoms, dinoflagellates,metabolic processes increase proportionally with genome chrysophytes, phaeophytes and haptophytes, are derivedsize (Koonin & Wolf 2008). Interestingly, genes encoding from additional endosymbiotic events (e.g. secondary andfor regulation of transcription and signal transduction tertiary endosymbiosis) between autotrophic and hetero-scale with the square of the total number of genes. Thus, trophic eukaryotic organisms (Parker et al. 2008). Thebigger genomes such as R. baltica have an excess of regu- reason for the occurrence of these multiple endosymbioticlators and signal transduction proteins. This could be one events is not understood yet but is hypothesized to be afactor limiting the upper size of bacterial genomes result of whole ocean denitrification (e.g. anoxic events),because genome growth would become unsustainable at as occurred in the period of the Triassic and Toarcianthe point where more than one regulator is added per (lower Jurassic c. 175–185 Ma years ago) (Falkowski et al.gene (Lynch 2007). 2004). A lack of nitrogen in the water column may have provided a strong selection pressure for the evolution of new partnerships and therefore increased the occurrenceAlgae of endosymbiotic events. In fact, the first fossil record ofMarine algae contribute about 40–50% of global primary diatoms is at around the period of the Toarcian (Falkow-productivity despite the fact that their photosynthetic bio- ski et al. 2004). However, molecular clock approachesmass represents only about 0.2% of that on land (Field with diatoms have indicated that the fossil record under-et al. 1998). Thus, their biomass turnover is very high estimates the age of this group, and that diatoms mostcompared to land plants, making them a very dynamic likely pre-date the Triassic (Kooistra & Medlin 1996).pool of organic carbon that can be more easily influenced Thus, it remains to be seen whether the suggested denitri-by environmental changes and vice versa. A significant fication events can in fact be linked with the origin ofcontribution of algae to the global carbon cycle accompa- endosymbiotic events.nied by their dynamic growth make them key players in These partnerships between different organisms haveour understanding of global biogeochemical cycles of left traces in extant genomes because of massive gene gainelements. Consequently, algae were an early target for and endosymbiotic gene transfer, but also gene loss,Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 135
  6. 6. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamwhich was known before the first genomes from algal spe- Comparative genomics with marine algae from thecies were sequenced (e.g. Martin et al. 1998; Cavalier- green lineage have also provided novel insights into theirSmith 1999). However, the unbiased view provided by evolution (e.g. Derelle et al. 2006; Palenik et al. 2007;whole genome sequences and their comparison enabled Worden et al. 2009). Most genome sequences have comeground-breaking discoveries, which re-shaped our view of from the monophyletic marine order Mamiellales, includ-the evolution of these organisms. ing the picoeukaryotes Ostreococcus spp. and Micromonas Many algal taxa derived from multiple endosymbiotic spp. Their genomes are much less diverged from eachevents retained at least parts of the plastid genome of the other compared with the sequenced chromalveolate ge-engulfed photosynthetic eukaryote but the nucleus was nomes, which probably reflects their less complex evolu-either reduced to a nucleomorph, as in the case of cryp- tionary history. For instance, 18 of 20 chromosomes intomonades and chlorarachniohytes, or entirely lost (e.g. the two sequenced Ostreoccocus genomes from Ostreocco-Douglas et al. 2001; Gilson et al. 2006). Nucleomorph cus tauri and Ostreoccocus lucimarinus are conserved withgenome sequences from both groups of organisms reveal a strong synteny between them (Derelle et al. 2006). Fur-strong compaction and a very limited number of genes thermore, both species share 97% of their catalogued(£464 protein coding genes) (Douglas et al. 2001). The genes. Although the two sequenced strains of Micromonasintergenic space is greatly reduced and genes sometimes pusilla (RCC299, CCMP1545) are more diverged fromoverlap. These features suggest that nucleomorph each other, indicated by less strong synteny and onlygenomes have been streamlined over time. Most of their sharing 90% of their catalogued genes (Worden et al.genes encode for nucleomorph functions (e.g. replication) 2009), they are still much less diverged than the diatomsbut some still encode for plastid function. The other algal P. tricornutum and T. pseudonana, which share only 57%lineages have either transferred the genes from the of their genes without any synteny (Bowler et al. 2008).nucleus of the engulfed photosynthetic eukaryote to the However, the pennate and centric diatom lineagesnucleus of the host organism or they have lost them. The diverged about 90 million years ago, which pre-dates thechromalveolate hypothesis (Cavalier-Smith 1999) pro- separation between the Micromonas clades (65 millionposes that the engulfed photosynthetic eukaryote was a years ago) (Slapeta et al. 2006). In contrast to diatom ge-red alga, which gave rise to all extant chromists nomes, prasinophyte genomes, like oligotrophic bacterial(stramenopiles, haptophytes, cryptophytes) and alveolates genomes, show features of strong compaction (Worden(ciliates, dinoflagellates, apicomplexa). However, recent et al. 2009). This may be associated with their occurrencedata based on phylogenomic analysis of the genomes of in fairly low-nutrient habitats. SAR11 and Prochlorococcusthe diatoms Thalassiosira pseudonana and Phaeodactylum live in oligotrophic environments and also have verytricornutum in comparison with other recently sequenced streamlined genomes (see above). Thus, low nutrientmarine algae revealed that >70% of genes derived either marine environments seem to shape eukaryotic genomesfrom red or green sources actually are of green (not red) similarly to bacterial genomes, but without changing thelineage provenance, in contradiction to the chromalveo- general architecture of eukaryotic genomes (Fig. 3). Gen-late hypothesis (Moustafa et al. 2009). However, so far ome compaction in prasinophytes is indicated by genethere have been many more genomes sequenced from fusions, a reduction of the intergenic space, reducedmembers of the green lineage than the red lineage, which intron length and number per gene (Worden et al. 2009).biases this study towards identifying genes with homo-logues in organisms from the green lineage. The largestproportion of theses green genes (c. 40%) has a phyloge- Genome complexificationnetic affiliation with Prasinophytes (Ostreococcus and Genome streamliningMicromonas from the Mamiellales clades) of which some Genome 10.0 50.0 100.0 500.0 size, Mbpwere shared with Chlorophyta and Streptophyta (Mousta-fa et al. 2009). Interestingly, some of these diatom greengenes are also shared with apicomplexans, plastid-lacking O.t. P.t. A.a. F.c. E.h. M.p. T.p. P.m.ciliates and the haptophyte Emiliania huxleyi. These dataindicate that a large proportion of these green genes areof ancient provenance and pre-dates the split of crypto- Fig. 3. Interplay of the results of evolutionary forces that shape eukaryotic microalgal genomes. The horizontal line shows genomephytes and haptophytes from other chromalveolates. This sizes. The genome sizes of 10 aquatic microalgal species have beenprovides evidence that a prasinophyte-like endosymbiont mapped onto the sale (O.t. = Ostreococcus tauri, M.p. = Micromonasis the common ancestor of chromalveolates, and must pusilla, P.t. = Phaeodactylum tricornutum, T.p. = Thalassiosira pseudo-have pre-dated the red-algal one because the most recent nana, A.a. = Aureococcus anophagefferens, F.c. = Fragilariopsis cylin-endosymbiont provides the plastid (Moustafa et al. 2009). drus, E.h. = Emiliania huxleyi, P.m. = Pseudonitzschia multiseries).136 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  7. 7. Mock & Kirkham What can we learn from genomics approaches in marine ecology? Phylogenomic comparison between both Micromonas high number of prokaryotic genes in eukaryotic genomesspecies, other prasinophytes, chlorophytes and strepto- indicates very close relationships between both groups ofphytes have revealed that specific genes, thought to have organisms over a long evolutionary period of time. Inevolved later in higher plants, in fact were already present fact, microscopical and physiological studies of interac-in the proto-prasinophyte ancestor but were lost in chlo- tions between bacteria and diatoms had already indicatedrophytes. One example concerns transcription factors a variety of intimate relationships from commensalism tofrom the YABBY family that were thought to be associ- symbiosis before these molecular data became availableated with leaf development in higher plants but which (Schmid 2003). The majority of the prokaryotic genes inhave been found in the Micromonas genomes (Worden P. tricornutum are shared with T. pseudonana, indicatinget al. 2009). Thus, sequencing genomes from phylogeneti- that acquisition of prokaryotic genes took place no latercally related branches of the tree of life might improve than the divergence of the clade containing the pennatesour understanding of gene gain and loss and hence the and the clade containing T. pseudonana. This does not,mechanisms by which species have radiated. however, represent one of the earlier diverging clades among extant diatoms (e.g. Sims et al. 2006). Most of theHorizontal evolution diatom genes of prokaryotic origin belong to the shellBefore information was available from whole genome and group and have functions related to mitochondrialcDNA sequencing, there was a strong belief that all major metabolism, including genes encoding proteins from theinnovations in algal genomes were due to endosymbiosis, urea cycle (e.g. carbamoyl transferase, carbamate kinase)recombination, mutation and selection. These forces are (Bowler et al. 2008). Additional novel features have arisenstill key for our understanding of algal evolution in gen- from novel domain combinations containing orthologueseral, but HGT, previously only considered to be relevant reported in bacteria. Many of these are related to two-to prokaryotes, has recently also been recognized component signalling systems such as the aureochrome(Table 1). The genome of Ostreococcus tauri has revealed blue-light photoreceptors that contain orthologues ofhow significant HGT can be for the overall genome struc- LovHK and other light-dependent histidine kinasesture (Derelle et al. 2006). Two chromosomes (2 and 19) reported in bacteria (Bowler et al. 2008).in O. tauri differ structurally from the other 18 by having In addition, organisms from the green lineage seem toa significantly reduced GC content. They also contain the be gene donors for HGT to diatoms because only aboutmajority of transposable elements (TE) in the genome, 85% of the green genes identified in diatom genomes canwhich might have contributed to the genetic variability at be tracked to the ancestor of diatoms and other stra-least in these two chromosomes. Only 18% of peptide- menopiles. Thus, about 255 genes in the genomes ofencoding genes on chromosome 19 of O. tauri are related T. pseudonana and P. tricornutum seem to have beento the green lineage (Derelle et al. 2006). The others acquired via HGT from prasinophyte-like algae, which isresemble proteins from various origins but mainly bacte- about 2% of gene models for each of the two diatomsria. Interestingly, most of them (84%) belong primarily to (Moustafa et al. 2009). Most of them, in contrast to genesthe group of surface membrane proteins. Based on these of prokaryotic origin, seem to be involved in primaryfeatures, it seems reasonable to assume that at least chro- metabolism of the plastid such as carotenoid synthesis,mosome 19 is of different origin from the rest of the gen- the Calvin cycle and electron transport. Thirty percent ofome and therefore most likely acquired via HGT from a the proteins from the carotenoid biosynthesis pathway,prokaryote-like organism (Derelle et al. 2006). for instance, are very closely related to green algal homo- Prokaryotes also seem to be the major donor organisms logues and some of them branch with members of thefor horizontally acquired genes in diatoms. The diatom prasinophyte group. One of these is the enzyme zeaxan-P. tricornutum has about 587 genes (more than 5% of the thin epoxidase (ZEP), which converts zeaxanthin intototal number of genes in the genome) acquired via HGT violaxanthin, the precursor of fucoxanthin (Frommoltfrom prokaryotic organisms (Bowler et al. 2008). Most of et al. 2008).them show strong similarity to bacterial genes but some HGT between viruses and their hosts are well known inare also similar to Archaeal genes. Recent analysis of long bacteria but the first evidence for the existence of HGTterminal repeat (LTR) retrotransposons in the diatom between a DNA virus and a eukaryotic alga has onlyP. tricornutum indicated that they significantly contrib- recently been obtained (Monier et al. 2009). An entireuted to intraspecific genetic variability in this species metabolic pathway involved in sphingolipid biosynthesis(Maumus et al. 2009). Meiosis has never been observed seems to have been transferred from the large DNA virusfor P. tricornutum or O. tauri in cultures, and intraspe- of the coccolithophore Emiliania huxleyi to its host. Sevencific diversity, therefore, might have been significantly genes were transferred over time successively changing theinfluenced by rearrangements due to TEs. The unusually lipid metabolisms of the host, probably to give the virus aMarine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 137
  8. 8. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamselective advantage. The end product of this pathway is oceanographic frontal zones. However, purifying selectionceramide and sphingolipids, which induce metacaspases seems to act much more strongly on bacteria than onfor activating apoptosis of the host and thus promote the eukaryotic algae, as streamlining in bacteria is much morereproduction of the virus. pronounced (Figs 2 and 3). One reason for this might be Another case of algal–virus gene transfer occurred related to the more complex evolution of algae (e.g. sec-between O. tauri and its OtV5 DNA virus (Derelle et al. ondary and tertiary endosymbiosis), which per se leads to2008). However, the transfer was from the host to the a high degree of polymorphisms as observed in diatomvirus and only one gene seemed to have been affected by genomes (e.g. Armbrust et al. 2004). A higher degree ofthe event. The transferred gene encodes proline dehydro- polymorphism makes purifying selection less effective andgenase, which was found to be very similar in the host may be favoured by positive selection because maintain-and virus. Its role in promoting the reproduction of the ing polymorphic alleles (i) prevents loss of self-incompati-virus is unclear but proline is generally involved in stress bility loci (e.g. Clark & Kao 1991) and (ii) possiblyresponse in many different organisms. improves survival in the fluctuating ocean environment. Despite the discovery of horizontal gene transfer in Thus, highly variable environments such as coastal inter-eukaryotic microbes, the majority of the gene space, based tidal zones seem to select for organisms with a higheron eukaryotic clusters of orthologous genes (KOGs), degree of polymorphisms and heterozygosity in general.seems much more determined by endosymbiotic gene Highly diverse sets of genes, of which many seem to havetransfer compared to prokaryotes (Fig. 4). The core genes been acquired via HGT and ⁄ or endosymbiotic gene trans-dominate all KOGs in eukaryotic genomes unlike in fer events in the case of chromalveolates, are advanta-prokaryotic genomes (Fig. 1). geous in that they provide organisms with the necessary To summarize, the general forces that cause genome set of tools when the environment is changing.evolution in marine pro- and eukaryotes (Table 1) seem Selection for fast growth is inversely related to genomevery similar, with genome streamlining being observed in size and cell size in both pro- and eukaryotes (e.g. Kooninorganisms primarily living in low-nutrient environments & Wolf 2008). Although a strong negative correlationand genome complexification in highly variable nutrient- between genome size ⁄ gene number and growth rate isrich environments such as coastal systems, sediments or observed in prokaryotes (with some exceptions, e.g. SAR11), this relationship is not as quite as clear in micro- bial eukaryotes. Very small eukaryotes such as Ostreococ- cus have smaller and more compact genomes than larger microalgae but they don’t necessarily grow faster. One Core explanation might be that, in general, many microalgal Shared genomes show a strong compaction (reduced intergenic Orphan space) in comparison with higher plants and animals. Furthermore, a dominance of rRNA over DNA in micro- algae might allow fast cell division in combination with a high rate of protein synthesis (Hessen et al. 2009). The elemental costs for growth in microalgae also seem to be reduced compared to higher plants and animals by a high C:N:P stoichiometry of 106:16:1 (Redfield ratio) in most phytoplankton, whereas the ratio in a typical animal tissue is 50:7:1, indicating a higher demand for N and P. Influence of Genomics on Our Understanding of Acclimation and Adaptation of Marine Organisms Acclimation, the ability to respond to short-term fluctua-Fig. 4. Common and rare genes in 10 aquatic microalgal genomes tions of the environment, is important in marine ecosys-(Aureococcus anophagefferens, Emiliania huxleyi, Fragilariopsis cylin- tems characterized by strong fluctuations, such asdrus, Micromonas pusilla, Monosigia brevicolis, Ostreococcus lucimari- seasonal temperate ecosystems, polar ecosystems with sea-nus, Ostreococcus tauri, Phaeodactylum tricornutum, Thalassiosirapseudonana) based on eukaryotic clusters of orthologous genes sonal sea ice cover, coastal and frontal zones. As explored(KOGs). Core set = KOGs present in 9 or 10 genomes; shared set = - in the previous section organisms from these environ-KOGs present in 2–8 genomes; orphan set = KOGs present in 1 gen- ments tend to increase the complexity their genomes byome only. HGT, gene and genome duplications, which allows them138 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  9. 9. Mock & Kirkham What can we learn from genomics approaches in marine ecology?to diversify on an intraspecies level and therefore to processes described, how do acclimatory processes, medi-occupy many different niches in these complex environ- ated by changes of gene expression, influence the abilityments. An important unknown factor is the time-scale of to adapt?genome complexification. Genetic adaptation is consid- It is appealing to suggest that short-term phenotypicered to be a long-term process of genetic change that change may become constitutive if either the environ-influences phenotypic acclimation (short term). However, mental change persists (e.g. ocean acidification) or mech-the application of combined genomic and post-genomic anisms exist that allow a rapid development of a geneticapproaches to microbes has already revealed that (i) basis. Waddington (1953) was the first to propose thegenetic adaptation can also occur on short-time scales idea of genetic accommodation and we think genomics(e.g. Herring et al. 2006; Gresham et al. 2008; Brookfield and postgenomics have already provided evidence that2010) and (ii) that genetic accommodation, which is the similar processes might occur in marine microbes (e.g.inheritance of a non-genetic response (a phenomenon Steglich et al. 2008; Weinberg et al. 2009). However, itthat changes the final outcome of a gene or chromosome remains to be seen how much these processes contributewithout changing the underlying DNA sequence, such as to the adaptability of organisms to their environment.epigenetics), might occur and impact adaptation (e.g. Those oceanographic regimes that are characterized byBossdorf et al. 2008). rapid and significant changes such as coastal seas would The major force for both acclimation and adaptation is possibly select for genetic accommodation.a changing environment (abiotic or biotic). A species Environmental regime shifts between feast and faminemight show a suite of acclimatory and adaptive changes characterize highly dynamic coastal seas and are thereforefollowing a new environmental challenge. The use of frequently used to describe the responses of microbes andnext-generation sequencing is now allowing us to identify shed light on the relationship between acclimation andhow quickly the genetic information can change in exper- adaptation. One important mechanism to develop aimental populations through resequencing their genomes. genetic basis from a phenotypic change is transposition, aMoreover, when accompanied with whole-genome tran- process by which a chromosomal segment is transferredscriptome studies, sequence analysis will reveal how to a new position in the genome (e.g. McClintock 1948).phenotypic changes are linked to the genotype. Evidence for this mechanism has been found in marine In general, the speed of adaptive evolution is predicted bacteria and diatoms. The planktomycete Rhodopirellulato be high in microbial populations because of their large baltica induces expression of transposases under short-population sizes (e.g. Collins & Bell 2006; Gresham et al. term (<300 min) heat, cold and salinity stress, indicating2008). There are two reasons for this. Firstly, large popu- re-arrangement of the genome as a way to adapt quicklylations have an increased supply of beneficial mutations to extreme environments (Wecker et al. 2009). Transpos-in each generation, which increases the rate at which able elements contribute 6.4% of the Phaeodactylumadvantageous mutations occur; and secondly, large popu- tricornutum genome. About 6% of the P. tricornutum TEslations have increased access to these mutations. How- belong to the class of long terminal repeat (LTR) retro-ever, this doesn’t necessarily mean that large populations transposons (Ty1 ⁄ Copia), typical of retroviruses whichhave a higher fitness, they just take larger steps to attain replicate through reverse transcription of an mRNA inter-fitness. Data from evolution experiments with microbes mediate. Phaeodactylum tricornutum cells cultivated underusing next-generation sequencing approaches have short-term nitrate limitation and exposure to diatom-revealed an interesting relationship between molecular derived reactive aldehydes, which are known to inducechange and improved fitness (e.g. Gresham et al. 2008; stress responses and cell death, demonstrate strong tran-Brookfield 2010). Whereas the rate of genomic molecular scription activity of LTR retrotransposons, indicating acti-change seems constant over time, the strongest changes in vation of TE insertions under these conditions (Maumusfitness occur early after an environmental change. This et al. 2009). In fact, different insertion patterns of theseindicates that more advantageous changes spread quickly LTR elements could be shown in metagenome datasetsin populations, whereas less advantageous changes spread and therefore point to an important contribution of LTRlater and have a smaller impact on fitness (Brookfield retrotransposons in generating intraspecific genetic2010). Intraspecific competition among clones from the variability (Maumus et al. 2009).same population compete against each other and may Another important mechanism of genetic accommoda-therefore contribute to the initial speed of spread of early tion is epigenetic change through heritable methylation,mutations. So one question seems to be interesting to histone modifications or through the regulation of geneaddress in the above-mentioned context: If the fate of expression by non-coding RNAs. Unfortunately, almoststrains in populations after a significant change in their no data are available yet about the influence of methyla-environment occurs early and relatively quickly by the tion on acclimation and adaptation for marine organisms;Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 139
  10. 10. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamhowever, there is very early evidence of the influence of been lost in the low light adapted strains. The high-lightDNA-binding proteins such as histones for acclimation, adapted strains have lost both the nitrate and nitriteadaptation and evolution. For instance, transcriptome reductase genes, whereas a nitrite reductase has beenanalysis of haploid and diploid cells of the haptophyte retained in the low-light adapted strains. A genome-wideEmiliania huxleyi showed that the H4 histone is only comparison of Synechococcus strains from coastal andpresent and expressed in the genome of diploid cells, so open ocean waters revealed the functional blueprints ofmight therefore have a specific function for gene expres- complexification and streamlining, respectively (Paleniksion regulation unique to diploid cells (von Dassow et al. et al. 2006). The coastal strain had a much bigger genome2009). Both cell types have very different phenotypes with with nearly double the number of response-regulator anddifferent metabolisms (e.g. only diploid cells calcify) and histidine kinase genes to sense changes of the moreoccupy different oceanographic niches, and it therefore dynamic coastal environment. Furthermore, it had manyremains to be shown whether the histone H4 makes a sig- more genes encoding proteins in metal transport andnificant contribution to either acclimation or adaptation metabolism, including metallothioneins, ferritin, cyto-of diploid E. huxleyi cells. chromes and proteins from the P-450 family. However, it There is increasing evidence that marine microbes also is still unclear whether the greatly enhanced transport andhave the ability to regulate gene expression, translation storage capacity for metals in the coastal strain is due toand protein function by using non-coding RNAs a greater need for metals, the need to respond to excess(ncRNAs). These molecules have no protein-coding func- metal levels, or exposure to episodic fluctuations in metaltion and their genomic positions are in intergenic regions: concentration.introns or antisense to protein-coding genes. NcRNAs The picture of niche differentiation is less clear forcan be short (<50 bp) or long (>500 bp) and are eukaryotic microbes in the ocean because we still onlyinvolved in responses to environmental cues (e.g. Mattick know a very limited number of their genome sequences& Makunin 2006). NcRNAs have been found in Prochlo- and most of them were isolated from very similar habitatsrococcus (Steglich et al. 2008) and Thalassiosira (Mock (e.g. Palenik et al. 2007; Bowler et al. 2008). For instance,et al. 2008), being differentially expressed under various both sequenced Ostreococcus species (tauri and lucimarinus)conditions including light quality and nutrient stress. The have been isolated from coastal habitats (the coast of Brit-overall importance of these ncRNAs for marine microbes tany, France and the coast of California, US) and they sharewas shown by their identification in metagenome datasets 95% of their catalogued genes (Palenik et al. 2007).(Shi et al. 2009). However, it remains to be seen how Although the two sequenced isolates of Micromonasmuch they in fact contribute to the adaptation of (CCMP1545, RCC299) came from very different habitatsmicrobes to different marine habitats and niches. (CCMP1545 is from eutrophic, temperate waters off the The boundaries between acclimation and adaptation in coast of England and RCC299 is from oligotrophic, tropicalthe era of genomics have become less clear, which should waters in the South-West Pacific), they still share aboutremind us that biology responds to environmental forcing 90% of their predicted genes and few of the unique genesin a dynamic way. Thus, all the heritable information in can be related to adaptation to their specific environmentsgenomes is only a snapshot in the picture of evolution (Worden et al. 2009). However, it could be shown thatbut it provides us with data on how the sequenced organ- strain CCMP1545 (from more eutrophic waters) was miss-isms are adapted to current environmental conditions. ing specific transporter gene families, including some Perhaps one of the greatest outcomes of sequencing related to nitrogen uptake. A very similar picture is emerg-genomes from marine prokaryotes was the realization that ing from a comparison of the two sequenced diatomphysico-chemical conditions of the pelagic marine envi- genomes (Thalassiosira pseudonana, a centric diatom, andronment (e.g. light and nutrient gradients) have shaped Phaeodactylum tricornutum, a pennate), which also sharebacterial genomes and hence their evolution. The first evi- the same type of coastal marine habitat (Bowler et al.dence came from sequencing different Prochlorococcus and 2008). Although their genomes differ significantly (indi-Synechococcus ecotypes (Rocap et al. 2003; Palenik et al. cated by the fact that 40% of their genes are not shared),2006). Some of the sequenced Prochlorococcus strains are there are no striking differences that can help to explainadapted to a high-light environment that is limited by niche differentiation in coastal areas. Pennate diatoms arenitrate and nitrite, whereas other strains have lost the more widely distributed in benthic habitats whereas centricability to cope with high light but can grow on nitrite diatoms dominate pelagic habitats. A higher number of(Rocap et al. 2003). The high-light-adapted strains gene families in the genome of P. tricornutum may suggesthave twice as many genes encoding putative high-light- that the more recently evolved pennate diatoms have moreinducible proteins and they possess photolyase genes to specialized functions, perhaps to cope with the heterogene-cope with damage by ultraviolet radiation, which have ity and dynamic of coastal benthic systems (Bowler et al.140 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  11. 11. Mock & Kirkham What can we learn from genomics approaches in marine ecology?2008). However, some more specific studies on single genes activity (e.g. carbon fixation, respiration and uptake mea-in diatoms have already provided strong evidence for niche surements) in situ, (ii) quantification of major resourcesdifferentiation. One of these is the study by Peers & Price [e.g. NO3, PO4, Si(OH)4, Fe], and (iii) identification and(2006) who showed a replacement of iron containing cyto- enumeration of as many members of the community aschrome C6 with the copper-containing plastocyanine for possible. Data from these studies were then used to modelphotosynthetic electron transport in the oceanic diatom biogeochemical cycles from small to global scales (e.g.Thalassiosira oceanica. This diatom lives in an iron-limited ´ ´ LeQuere et al. 2005; Follows et al. 2007). Metagenomicocean and is probably able to thrive there because it has and metatranscriptomic studies have impressively revealedlowered its iron requirement for growth by preferably using the shortcomings and biases of the above-mentionedproteins that require copper, a metal that is less scarce in approaches. The major shortcoming is that the vastthese environments. It is most likely that the gene encoding majority of microbes in the ocean (prokaryotes but alsothe plastocyanine protein was acquired via horizontal gene eukaryotes) are unknown and therefore their metabolismtransfer from either a cyanobacterium or any member of is also unknown. The types of metabolism found in athe green lineage. It seems that prokaryotic marine community have significant influences on resource cyclingmicrobes were much more carefully selected for genome and thus govern all kinds of linkages between the organ-sequencing to investigate niche adaptation than their isms of a coexisting (sympatric) community (e.g. Trippeukaryotic counterparts. Thus, we need to focus on a wider et al. 2010). Random sampling of sequence data fromdiversity of eukaryotic microbes from very different marine combined community members (shotgun sequencing ofhabitats for the next round of genome sequencing projects. the metagenome) has provided unbiased insights into theSequencing the genome of the diatom Fragilariopsis cylin- functional diversity of microbial communities with manydrus (http://genome.jgi-psf.org/Fracy1/Fracy1.home.html) surprises that have changed our understanding of marineseems promising in that regard because F. cylindrus is a microbial communities (e.g. Venter et al. 2004; Tripppsychrophile from the iron-limited Southern Ocean. et al. 2010). The first metagenome projects were largely discovery-based, whereas later ones have begun with com- parative approaches between communities and biomesInfluence of Genomics on our Understanding of (Dinsdale et al. 2008). A key finding that came from theMarine Ecosystem Functioning first large-scale marine metagenome projects was the dis-Acclimation, adaptation and evolution, as described in covery of proteorhodopsin proteins, light-driven protonprevious sections, are based on interactions of organisms pumps identified in the Sargasso Sea metagenome ofwith the environment and among organisms themselves mainly heterotrophic prokaryotic communities (Venter(e.g. HGT). The nature of these abiotic and biotic interac- et al. 2004). Proteorhodopsins had been characterizedtions is defined by resources that link organisms with the from bacteria before but their significance remainedenvironment and to each other and therefore define the unrecognized until this study. A recent study of a Vibriofunction of marine communities. Thus, an important strain has revealed that proteorhodopsins increase long-question to address, in order to understand ecosystem term survival when the cells are starved in seawaterfunctioning, is how resource cycling influences the link- ´ exposed to light (Gomez-Consarnau et al. 2010). Anotherages between different species of the same community. example from a metagenome project that has changedMetagenomic and transcriptomic studies have already our view of resource cycling is the discovery of Archaeashown their ability to facilitate the identification of link- that oxidize ammonia to nitrite and therefore play anages between different species of the same community by important part in the global nitrogen cycle (e.g. Francisfunctional analysis of their gene repertoire (e.g. Dinsdale et al. 2007). Before this, bacteria were thought to be theet al. 2008; Vieira-Silva & Rocha 2010). This section will only oxidizers of ammonia but models couldn’t matchsummarize how genomics-enabled approaches have led us the rates of ammonia oxidation with the abundance andto a new understanding of the functioning of whole com- activity of putative ammonia oxidizers until the discoverymunities in an environmental context, and will assess of archaean ammonia oxidation. An ammonia monooxy-whether a systems approach for marine communities genase gene was identified next to a gene encoding the(eco-systems biology) might become feasible in the near small-subunit ribosomal RNA for Archaea. This mono-future (Raes & Bork 2008). oxygenase gene could therefore be assigned as being from an archaean genome. If a community is dominated by a smaller number of species, their genomic fragments fromCommunity–Environment Interactions metagenomes can be assigned to species because completeInteractions of communities with the environment have genomes can be assembled if the sequencing depth isbeen investigated by (i) bulk measurements of their large enough. This is more difficult for eukaryotes (e.g.Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 141
  12. 12. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamprotists) because the genomes are much more complex implying distinct environmental preferences (e.g. Rusch(e.g. introns, intergenic space, heterozygosity between sis- et al. 2007). However, some ecotypes have been found inter chromosomes) and much bigger (e.g. Armbrust et al. a wide range of locations (Pelagibacter ubique) and multi-2004; Bowler et al. 2008). However, as promising as these ple ecotypes have been found simultaneously in a singleapproaches seem to be, at least for prokaryotes, they are sample (Rusch et al. 2007). One attempt to explain whyfar from trivial and have only moved the problem of some microbes are very widely distributed across differentunder-sampling to the next level because we are still only ecosystems stems from the dynamics of ecosystemscratching the surface of complex microbial communities changes and their effects of shaping the gene spaceif we do not sequence deeply enough. Promising new (Rusch et al. 2007). We can’t assume that there is oneapproaches for closing the gaps in complex communities Darwinian daemon with fitness under all environmentalinclude targeted sequencing of selected populations (by conditions. Such an organism wouldn’t be sustainableusing flow cytometry) and single-cell genome sequencing from an energetic point of view. However, we might beof isolated unknown organisms (Woyke et al. 2009). able to assume that there is a super organism composed However, it requires more than just sequencing a com- of multiple variants, even within an ecotype, allowingmunity at a single point in time and space to identify the coexistence of all individuals under small and mediumforces that have shaped functional diversity in relation to variability of the environment. This kind of populationenvironmental constraints. Metatranscriptomics represents would be similar to a multicellular organism, where dif-population- or community-wide gene expression influ- ferent specialized cell types help the organism to functionenced by these constraints and therefore is able to identify as a whole when subjected to variable external conditions,those genes that are important to allow the community under the control of internal homeostasis. The crypticto thrive under the many integrated conditions that diversity of widely distributed microbes in the oceandefine the physico-chemical niche (Frias-Lopez et al. might act as a buffer, as suggested by Rusch et al. (2007),2008). However, those genes are likely to be swamped by similar to regulatory mechanisms of homeostasis in mul-genes necessary for the maintenance of basic cellular ticellular organisms. If the variability of the ecosystemmachinery enabling growth and metabolisms such as becomes too strong (e.g. significant seasonality) or reachesgenes involved in transcription and translation, photosyn- a certain tipping point where individual organisms fromthesis and respiration, which have already been shown in the same population can no longer buffer externalthe first metatranscriptome studies (e.g. Frias-Lopez et al. changes, smaller populations develop at or above the eco-2008; Moran 2009). Nevertheless, the regulation of these type level with more distinct functional diversity. Individ-core genes for metabolism also depends on environmental uals from these populations tend to have larger genomesconditions, so comparative metatranscriptomics and a because they may be less buffered by a cooperating com-link to species-specific transcriptomes might reveal munity of different individuals and therefore need to havetheir significance for the regulation of growth in a given a higher functional diversity per individual (Koonin &environment. Wolf 2008). HGT manifested in hypervariable genomic Comparative metagenomics of samples from different islands is the source of novel physiology and thereforehabitats enabled the first insights into their communities’ generates the ability to adapt to well-defined physicalenvironment-specific adaptations. The first global study (temperature, nutrient limitations) niches in the environ-of this kind for the marine system was the Sorcerer II ment. One consequence is that communities from veryGlobal Ocean Sampling (GOS) expedition (e.g. Rusch different habitats tend to have a higher degree of differ-et al. 2007). A later study enlarged the scope and also ences in their functional diversification, with a lowerincluded non-marine systems (Dinsdale et al. 2008). The degree of overlap. This has been illustrated by theGOS dataset encompasses 41 marine locations from the comparison of prokaryotic metagenomes from nine veryNorthwest Atlantic through to the Eastern Tropical Paci- different biomes (subterranean, hypersaline, marine,fic. Total DNA was primarily extracted from material freshwater, coral, microbialites, fish, terrestrial animals,size-fractionated using filters with a pore size between 0.1 mosquito) (Dinsdale et al. 2008). All the biomesand 0.8 lm. DNA was cloned and subjected to shotgun were dominated by sequences for genes involved withsequencing. About 6.25 Gb of sequences were obtained carbohydrate and amino acid metabolism, which seem tofrom the 41 locations. Sequence analysis revealed a strong belong to the core genome of all communities regardlessintra-species diversity caused by many subtypes (ecotypes) of their habitat. The next most abundant sequences wereof dominant bacterial strains. A comparison of environ- involved with virulence, protein metabolisms, respirationmental conditions (primarily temperature) indicates that and photosynthesis. A canonical discriminant analysis ofthis diversity is organized into genetically isolated popula- the metabolic profiles showed that metagenomestions that have overlapping but independent distributions, were highly predictive of metabolic potential within an142 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH
  13. 13. Mock & Kirkham What can we learn from genomics approaches in marine ecology?ecosystem. In contrast, such differentiation wasn’t exert strong selection pressures on individual populationsobserved on the basis of 16S rRNA gene sequences, sug- similar to the impact of abiotic perturbations. Most ofgesting that, at least for bacteria, different ecosystems can- these interactions among organisms are mediated by thenot be distinguished by their taxa. Thus, one important cell surface via receptors for signalling molecules, trans-outcome of this comparison between functional and phy- porters, channels, and extracellular enzymes. Interestingly,logenetic marker genes is that there seems to be a high tax- these groups of outer membrane proteins are among theonomic evenness in microbial communities from different most quickly evolving genes in Prochlorococcus, evidencedecosystems opposing a low functional evenness. The by a genome-wide comparison of 12 genomes (Kettlerauthors of this study suggested that the environment, in et al. 2007). Furthermore, an analysis of membrane pro-fact, selects for genes but not organisms. This means that teins from the GOS databank revealed that the distribu-genetic sweeps favour particular gene frequencies regard- tion of different classes correlates with oceanographicless of their taxonomic background (Dinsdale et al. 2008). variables and human factors such as pollution and climateThe variation in gene content between ecosystems is change. Therefore, membrane proteins seem to be indica-assumed to occur by means of HGT and not by changing tors for different biogeochemical regimes in the oceantaxa. These findings reflect our observations at the organ- (Patel et al. 2010). They seem to be under strong diversi-ism level but they also have strong implications for future fication selection because they serve as sites for communi-studies on linking ecosystems with communities. At least cation or attachment, or as recognition sites for predatorsfor prokaryotes and their viruses, it no longer seems or phages. Furthermore, the observed variation amongappropriate to look only at phylogenetic marker genes if these genes might reflect different biotic interactions inquestions of evolution and adaptation are addressed in an each of the habitats from which these strains were iso-environmental context. Eukaryotic microbes of the ocean lated. Community-specific metatranscriptomics frommay be less prone to act as a free-floating functional gene these habitats would help to answer the question of howpool (Fig. 4) and therefore a stronger relationship the diversity of membrane proteins contributes to bioticbetween the presence of taxa and functional capabilities interactions. This is because interactions between Prochlo-might exist. However, this assumption is difficult to test rococcus and phages, for instance, would be seen in awithout similar studies to that of Dinsdale et al. (2008) community-wide expression profile. Additionally, differ-for eukaryotic microbial communities. ences between habitats with similar communities could be identified by a comparative metatranscriptome approach. Another mechanism of interactions among differentCommunity–Community Interactions communities is allelopathy, a chemical interference com-One consequence of interactions between communities petition for resources (e.g. Hulot & Huisman 2004).and the environment is a change in community structure, Many laboratory experiments with marine plankton havewhich affects the interactions within a community and revealed allelopathic effects caused by diverse compounds,among different communities. Unlike metagenomics, including toxins, that lead to a dominance of compound-metatranscriptomics of natural communities enables us to producing species by defending a plankter against para-track down genes that are susceptible to short-term envi- sites, pathogens or even predators (e.g. Jonsson et al.ronmental perturbations because responses are manifested 2009). Some of these released substances are also assumedfirstly in gene expression changes and later in shifts of to kill the opponent. There are some doubts about thesecommunity composition (e.g. Frias-Lopez et al. 2008; interactions in natural populations because the cell densi-Gilbert et al. 2008; Hewson et al. 2009; Gifford et al. ties, and therefore the concentrations of these allelopathic2011). Thus, metagenomes represent the long-term conse- substances, are likely to be too low under well-mixedquence of environmental perturbations, whereas meta- conditions to have any effect on competitors or parasitestranscriptomes give us information on how these new (e.g. Jonsson et al. 2009). However, they may play a sig-steady states have been reached over time and space. nificant role in direct cell–cell interactions under high cellCharacterization of gene expression dynamics in natural densities. Signalling processes most likely play an impor-communities therefore leads to the identification of (i) tant role in allelopathic interactions because they mediatecommunity-wide regulatory responses over different taxa, recognition of substances and subsequent reactions. Thus,(ii) key metabolic pathways, and (iii) genes indicative of these processes occur on a short time scale (lifespan ofcommunication within and among communities (Moran individual organism) and may cause transcriptome2009). Consequently, metatranscriptomics is tailor-made changes in the affected organisms that reflect the naturefor the study of community interactions, including vari- of these interactions. However, it remains to be seenous sources of mortality (e.g. programmed cell death, whether meta-omics approaches such as metatranscripto-grazing), allelopathy and symbiosis. These interactions mics in combination with meta-metabolomics will be ableMarine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH 143
  14. 14. What can we learn from genomics approaches in marine ecology? Mock & Kirkhamto identify the genes and metabolites responsible for these and language for the integration of sequences from gen-interactions. ome-enabled technologies into a holistic concept. The current approach still primarily consists of the collection of sequences by sequencing more and more species, pop-Eco-systems biology ulations and communities. The connectivity of theseAt the beginning of this paper, we cited Eugen P. Odum sequences (e.g. signalling, communication, spatial andwho emphasized in 1977 that Ecology must combine hol- temporal variation) is tackled by approaches such asism with reductionism to identify emergent properties in metatranscriptomics and meta-metabolomics. Thus, itcomplex ecological systems. Interestingly, Systems Biol- seems that we are close to reconstructing larger ecologicalogy, as a supposedly new trend in bioscience research, networks at the molecular level in a similar manner tohas similar ambitions to those set out by Odum but is cellular systems biology (e.g. Raes & Bork 2008). Thefocused only on single organisms and not ecosystems. bottleneck will be to identify those new properties thatThe overarching goal for Systems Biology is the model- arise from relationships between parts (sequences) andling and discovery of emergent properties from interac- the whole (e.g. cycling of elements) that could not havetions giving rise to the function and behaviour of an been predicted by breaking down the whole into its indi-organism. Reductionistic genomic approaches have been vidual parts. Modern physics provides an analogy for thevery successful in identifying the parts of a whole, which, challenges that lie ahead in our field. The discovery ofparticularly in marine sciences, has led to new insights quantum relationships revolutionized atomistic reduc-and hypotheses about the function of organisms. How- tionism in the sense that it provided holism. Two differ-ever, the initial excitement about the discovery of new ent quantum entities (e.g. different polarizations of twogenes and pathways must be replaced, at some point, by photons) can create a new property if they interact,proof of their significance for the organism as a whole, which can’t be predicted by describing both separately.and also even further for populations and communities A similar kind of emergent property might exist in cellu-in an environmental context. Reductionist genomic lar systems and complex ecosystems because they followapproaches alone are not able to provide satisfying the same fundamental principles but in the biologicalanswers for this holistic view because they can’t put sin- contexts of energy transport and matter to sustain life.gle genes or proteins into the larger context of regulatory However, it remains to be seen whether we will be ablenetworks that lead to emergent properties of the system to identify the ‘quantum mechanics’ of Eco-systems biol-(e.g. Raes & Bork 2008). The following example will ogy to identify emergent properties leading to a newexplain what is meant by emergent properties in the mechanistic understanding of the ocean based on geno-marine system. Global biogeochemical cycling of elements mic data.(e.g. C, N, Si, P, S, Fe) is an emergent property arisingfrom the interactions of different microbial cells (e.g. Acknowledgementsviruses, bacteria, algae) in the ocean. Thus, we won’tunderstand this complex process that shapes our planet We are grateful to Andrew Toseland who helped with theunless we identify (i) most of the players, (ii) their biol- genome-wide analysis of clusters of orthologous genesogy, and (iii) their interactions with each other and the (COG ⁄ KOG) from selected aquatic prokaryotes andenvironment. Reductionist genomic approaches have eukaryotes. We thank Chris Bowler for the invitation tohelped to identify who is there and what they are doing, contribute this review article.but the big challenge that lies ahead is how to use thisinformation to better understand the outcome of com- Referencesmunity interactions. An eco-systems biology approachseems tailor-made for tackling this challenge but we have Aminov R.I., Mackie R.I. (2007) Evolution and ecology ofbarely begun to apply a Systems Biology approach for antibiotic resistance genes. FEMS Microbiology Letters, 271,any marine organism. Therefore, does it seem reasonable 147–161.to begin with an Eco-systems approach? We think so Angly F.E., Felts B., Breitbart M., Salamon P., Edwards R.A.,because, as we have seen by comparison to Ecology, it is Carlson C., Chan A.M., Haynes M., Kelley S., Liu H.,just a matter of scaling. Principles that apply to the orga- Mahaffy J.M., Mueller J.E., Nulton J., Olson R., Parsons R.,nization of single organisms (e.g. compartmentalization, Rayhawk S., Suttle C.A., Rohwer F. (2006) The marinecommunication, energy conversion, replication) are not viromes of four oceanic regions. PLoS Biology, 4, e368. Armbrust E.V., Berges J.A., Bowler C., Green B.R., Martinezvery different from principles that apply to populations D., Putnam N.H., Zhou S., Allen A.E., Apt K.E., Bechneror even communities of different populations on a global M., Brzezinski M.A., Chaal B.K., Chiovitti A., Davis A.K.,scale. However, we have not yet found the right tools144 Marine Ecology 33 (2012) 131–148 ª 2011 Blackwell Verlag GmbH