McDonald, K.L., and Auer, M. (2006) High-pressure freezing ...cmore.soest.hawaii.edu/summercourse/2013/documents/... · Instead, I will prog-nosticate brieﬂy on how recent trends

Acknowledgements

I would like to thank Phil Hugenholtz, Falk Warnecke, Bern-hard Knierim, Brandon Van Leer (FEI), Tom Goddard(UCSF), Monica Lin and Mitalee Desai for their help insample preparation, 3D FIB/SEM imaging, 3D visualization ofthe depicted mixed microbial community.

This work was supported by the Director, Office of Science,Office of Basic Energy Sciences, of the U.S. Department ofEnergy under Contract No. DE-AC02-05CH11231.

References

Dohnalkova, A.C., Marshall, M.J., Areay, B.W., Williams,K.H., Buck, E.C., and Fredrickson, J.K. (2011) Imaginghydrated microbial extracellular polymers: comparativeanalysis by electron microscopy. Appl Environ Microbiol77: 1254–1262.

McDonald, K.L., and Auer, M. (2006) High-pressure freezing,cellular tomography and structural cell biology. Biotech-niques 41: 137–141.

Palsdottir, H., Remis, J.P., Schaudinn, C., O’Toole, E., Lux,R., Shi, W., et al. (2009) Three-dimensional macromolecu-lar organization of cryo-fixed Myxococcus xanthus biofilms,as revealed by electron microscopic tomography. J Bacte-riol 191: 2077–2082.

Microbial Earth: the motion picture

Edward F. DeLong, Department of Civil and Environmen-tal Engineering and Department of Biological Engineer-ing, Massachusetts Institute of Technology, Cambridge,MA 02139, USA.

Imagine you win the lottery and your prize is to travelwith Sir David Frederick Attenborough (OM, CH, CVO,CBE, FRS, FZS, FSA), to train in the art of craftingpopular Nature documentaries. In your travels with themaster, you are awed by the raw violence of great whitesdevouring sea lions, by the smooth stealth of a huntinglioness, by the speed and grace of the gazelle thatevades her, and by the unimaginable diversity of plantand animal life in the rainforests and coral reefs. You areequally awed by Attenborough’s uncanny skill and craftin capturing the essence of nature and nurture, and thebeauty, savagery, vastness and variety, which connectshis audience emotionally to natural history in a deep,intuitive and visceral way.

Now it is your turn, a microbial ecologist having justtrained with the great Sir David. The BBC gives you megabucks to produce a 12-part series, ‘Microbial Earth’. So,how are YOU going to connect in the same emotional,visceral and intuitive ways as Attenborough? Will youshow the savagery of exoenzyme hydrolysis attacking adying diatom bloom, the grace and beauty of runs andtumbles in a chemotactic sensory path, the vicious jab ofa Type III pilus, or complex food chain dynamics thatrecycle carbon and energy between microbes and sedi-ments? Do you think you will get Joe Public’s rapt atten-tion in these efforts? Hmmm – it is really NOT as easy atask as Attenborough has!

Admittedly, there are some relatively straightforwardbridges to be built. After all, videos of ciliates feeding ontheir bacterial prey, food vacuoles bulging as they gorge,portrays a microbial predator–prey dynamic easily trans-latable into what people see, know and can intuit. Butwhat about those savage exoenzymes, buzzing electrontransport chains, vicious Type III secretion systems, inti-mate symbioses and vast biogeochemical cycles and gra-dients? These are not so visceral, intuitive or emotionallyaccessible, nor arguably so easily portrayed to capturethe general public’s excitement and imagination. Part ofthe challenge is that humans simply do not have theintuition, instincts or aesthetic appreciation of microscopic

Fig. 1. Focused ion beam scanning electron microscopy (FIB/SEM)of a mixed microbial community reveals distinct ultrastructuralfeatures inside and between cells. Upon 3D segmentation and 3Drendering, the 3D organization can be examined in exquisite detail,probing for the presence and 3D organization of macromolecularmachines to cellular and community 3D organization.

2 Crystal ball

© 2013 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology Reports, 5, 1–16

and invisible form, function and interactions (Stahl, 1993;Woese, 1994). The majesty, diversity, impact and com-plexity of the vast microbial world is not so easily visual-ized, captured and communicated to the general public –even with the best artists and animators on the planet atyour disposal. While the task is certainly not hopeless,and there is great progress to be made, do you really thinkthat today, you could easily top Attenborough’s appeal forthe public’s excitement and attention, in your microbialdocumentary? (More power to you if your answer is yes –please do it!)

But I digress. My gaze into the crystal ball today is notreally about one-upping Attenborough. Instead, I will prog-nosticate briefly on how recent trends have influenced ourappreciation of microbial natural history today, and wherewe may be heading towards in the future: namely towardsa much more deep and realistic four-dimensional motionpicture of microbial natural history in the wild!

To understand the present, look to the past. Tounderstand the future, look to the present

It goes with saying that over the past 20 years our per-spective on microbial natural history has advanced sig-nificantly thanks to the emerging cultivation-independentparadigm (Pace, 1997), as well as advances in moretraditional microbiological approaches. From microbialgenomics and microarrays, to more recently developed‘next generation’ sequencing techniques, there havebeen great gains in the scope, scale and economy ofmicrobial ‘omics’ data acquisition, and the molecular rea-douts of microbial community structure, function anddynamics that they bring. These advances in turn havebrought new insights into the nature of microbial genomeevolution, the mechanisms of microbial populationdynamics, global maps of microbial taxon distribution andabundance, and the distributions of microbial genes,gene expression and proteins in the environment. TheWhole Earth Catalogue of microbes, genomes and genesis fleshing out impressively, at levels unimaginable onlyjust a few years ago. Some may still lament the ‘big data’problem, complain that we are drowning in data, and quipthat information is not knowledge. Of course, there arestill great challenges, but the future is bright. While wemay be swimming in a sea of big data, as we swim weare learning new ‘strokes’, including new and improvedsampling techniques, high-density data archiving capa-bilities, statistical methods and computational modellingapproaches. These newfound capabilities are now facili-tating unprecedented views into the natural history ofmicrobial communities and ecosystems, at a scope andscale never-before imaginable.

So, at this juncture, what can we predict about thetrajectory of future new views of the natural microbial

world? One thing seems fairly certain: we soon will movebeyond static surveys, snapshot modes and simplermodels of the past. This in part will be driven by inte-grated pictures of in situ microbial community interac-tions and dynamics, obtained by ‘filming’ the minute-by-minute microbial activities at high biological resolution, atmore and more realistic and relevant spatial and tempo-ral scales. This likely will involve the integration of manynew and developing technologies including scaled downmicrofluidic and nanoscale technologies; automatedsampling and sensing coupled with high biological reso-lution ‘omic’-based approaches; high-speed microscopicvisualization and chemical approaches (for example,miniaturized flow cytometers and mass spectrometers);and quantitative mapping of the multiple (omic) readoutsof indigenous microbial ‘biosensors’ (aka, microbial com-munity members), onto other biological environmentalvariations.

Already some of these new motion pictures are begin-ning to be released, albeit the technologies still needmuch improvement, and short film clips are only justnow becoming available. The autobiographic human gutmicrobial community drama entitled ‘Our humans andus’ is now being filmed (Caporaso et al., 2011). Instal-ments of ‘Seasons of our lives: my years in marine pico-plankton’ is also being filmed (Gilbert et al., 2009). The‘Bloom and Bust’ series, documenting phytoplanktonsuccessional events, is also being made in severalinstalments (Rinta-Kanto et al., 2012; Teeling et al.,2012). And again in the sea, a film entitled ‘A day in thelives of marine picoplankton’, shot with automated,Lagrangian sampling and high-resolution communitytranscriptome profiling (Ottesen et al., 2011), is alsobeing filmed (coming soon to a theatre near you,Ottesen et al., 2012).

These four-dimensional movies of the natural micro-bial world will increasingly employ remote and continu-ous sampling and sensing at both micro and macroscales. Sometimes they will be achievable in real time,and sometimes not. And it goes without saying they willrequire advanced computational, statistical and model-ling approaches, to fully develop the plot line and storyof the microbial motion picture in the wild. The dailydrama and natural historical details of the minutes, days,weeks, months and years in the ‘lives’ of microbial com-munities that remain obscure at present, will soon comeinto much sharper focus. With these new perspectivesfuture microbial natural historians are likely to havemuch richer stories to tell. Microbial natural histories willsoon rival the stories told by Sir David Attenborough, ashigh-resolution, four-dimensional microbial motion pic-tures more clearly reveal the drama, majesty and inti-mate interactions that occur each day on the microbialSerengeti.

Crystal ball 3


References

Caporaso, J.G., Lauber, C.L., Costello, E.K., Berg-Lyons, D.,Gonzalez, A., Stombaugh, J., et al. (2011) Moving picturesof the human microbiome. Genome Biol 12: R50.

Gilbert, J.A., Field, D., Swift, P., Newbold, L., Oliver, A.,Smyth, T., et al. (2009) The seasonal structure of microbialcommunities in the Western English Channel. EnvironMicrobiol 11: 3132–3139.

Ottesen, E.A., Marin, R., 3rd, Preston, C.M., Young, C.R.,Ryan, J.P., Scholin, C.A., and DeLong, E.F. (2011) Metat-ranscriptomic analysis of autonomously collected and pre-served marine bacterioplankton. ISME J 5: 1881–1895.

Ottesen, E.A., Young, C.R., Eppley, J.M., Ryan, J.P., Chavez,F.P., Scholin, C., and DeLong, E.F. (2012) Pattern andsynchrony of gene expression among sympatric marinemicrobial populations. Proc Natl Acad Sci USA (in press).

Pace, N.R. (1997) A molecular view of microbial diversity andthe biosphere. Science 276: 734–740.

Rinta-Kanto, J.M., Sun, S., Sharma, S., Kiene, R.P., andMoran, M.A. (2012) Bacterial community transcription pat-terns during a marine phytoplankton bloom. Environ Micro-biol 14: 228–239.

Stahl, D.A. (1993) The natural history of microorganisms.ASM News 59: 609–613.

Teeling, H., Fuchs, B.M., Becher, D., Klockow, C., Gardebre-cht, A., Bennke, C.M., et al. (2012) Substrate-controlledsuccession of marine bacterioplankton populationsinduced by a phytoplankton bloom. Science 336: 608–611.

Woese, C.R. (1994) Microbiology in transition. Proc NatlAcad Sci USA 91: 1601–1603.

The next big thing in cyanobacteria

Robert Haselkorn, Department of Molecular Genetics &Cell Biology, The University of Chicago, 920 East 58Street, Chicago, IL 60637, USA.

It is roughly 20 years since the discovery of the tran-scription factor HetR in Anabaena. In that period itbecame clear that the HetR protein alone could not beresponsible directly for the expression of the 1500 or sogenes needed to turn a vegetative cell fixing carbon intoan anaerobic factory fixing nitrogen. With the solution ofthe X-ray structure of the HetR dimer and studies of itsbinding to a single palindrome in the Anabaena genomeand its regulation by the peptide RGSGR, we are on thecusp of understanding the cascade it directs. The urgentlyneeded information now is the catalogue of auxiliary pro-teins that associate with HetR to direct it to additional DNAsites, the mechanism by which HetR turns on transcrip-tion, and the details of the cascade of genes whoseexpression is unleashed by HetR.

A different set of questions has been posed in connec-tion with the study of toxins produced by cyanobacteria.What functions do these compounds carry out? Why arethey made in the first place? During the past year or two thenumber and character of toxins produced by cyanobacteriahas expanded significantly. Originally we were concernedwith the microcystins, cyclic heptapeptides that bind irre-

versibly to protein phosphatases. Microcystins are madeby very large synthetic complexes containing multipledomains, each of which binds an activated amino acid,modifies it and joins it to another, using thioester chemistry.This system is termed non-ribosomal peptide synthesis(NRPS). Not all the NRPS products are cyclic; some arelinear and at least one has a lipid side-chain that promotesattachment to cholesterol-containing membranes. Andnow, as a result of genome gazing, another large family ofpeptides has been uncovered, this time made by ordinaryribosomal peptide synthesis (Wang et al., 2011). Onestrain of Anabaena has enough genes to encode hundredsof protein precursors, which are processed into tetrapep-tides, cyclized and exported. Some of these are proteaseinhibitors. Finally, there is a family of alkaloids called ana-toxins, made by a series of three polyketide synthases(Cadel-Six et al., 2009). Anatoxin binds to the mammaliannicotinic acetylcholine receptor, causing paralysis.

In the cases of the microcystins and anatoxins, theknown targets are eukaryotic, metazoan, even mamma-lian. The question then arises: what was the original func-tion of these toxins if their contemporary targets arose abillion years later? Could there have been targets amongthe prokaryotes that occupied related niches when thecyanobacteria were the most advanced organisms onearth? JP Changeux and PJ Corringer asked this questionseveral years ago and found that the cyanobacteriumGloeocapsa contains an acetylcholine receptor. Thisprotein is pentameric and its X-ray structure is almostidentical to that of the pentameric AChR from Torpedo.Expressed in Xenopus oocytes, it functions as a protonpump. It remains to be shown that it binds anatoxin(Corringer et al., 2012).

These observations lead to the following prediction: thenear and mid-term future will see significant attention tothe evolutionary significance of the cyanobacterial toxins:are they signalling molecules, do they play a role in nichecompetition? Can they be tamed and made useful inmedicine or, in the case of anatoxin, basic research on thefunctions of acetylcholine receptors?

References

Cadel-Six, S., Iteman, I., Peyraud-Thomas, C., Mann, S.,Ploux, O., and Méjean, A. (2009) Identification of apolyketide synthase coding sequence specific foranatoxin-a producing Oscillatoria. Appl Environ Microbiol75: 4909–4912.

Corringer, P.J., Poitevin, F., Prevost, M.S., Sauguet, L.,Delarue, M., and Changeux, J.P. (2012) Structure andpharmacology of pentameric receptor channels: from bac-teria to brain. Structure 20: 941–956.

Wang, H., Fewer, D.P., and Sivonen, K. (2011) Genomemining demonstrates the widespread occurrence of geneclusters encoding bacteriocins in cyanobacteria. PLoSONE 6: e22384.

4 Crystal ball


Elephants in the room: protists and the importanceof morphology and behaviour

Patrick J. Keeling, Canadian Institute for AdvancedResearch, Botany Department, University of BritishColumbia, 3529-6270 University Boulevard, Vancouver,BC, Canada V6T 1Z4.

A couple of years ago I found out I was not a micro-biologist after all. I always thought I was, and eventold strangers that is what I did, if they ever asked.But at a meeting of the American Society for Microbiol-ogy, I learned that my definition of a ‘microbe’ was notparticularly representative. This is because I work onprotists. Protists are microbial eukaryotes (more orless – we cannot quite decide on a definition), they arefound in most of the environments you would expect tofind other kinds of microbes (which is to say, every-where), they are abundant, extraordinarily diverse, and(among my friends, anyway) generally consideredto be ecologically important. They do come up some-times in conversation, or even arguments, such as‘who is the most important primary producer?’, or‘are viruses or grazers more important for nutrientcycling?’. But protists are too often excluded frommicrobial ecosystem models or assessments of theircomposition; even studies that assess a complete‘microbiome’ more often than not ignore the microbialeukaryotes.

Before I am written off as a whinging specialist who isfeeling marginalized, let me state that there are goodreasons for this gap in our knowledge; they reflect inter-esting reasons that go back to fundamental differences inbiology. Indeed, the problems associated with a thoroughunderstanding of microbial eukaryotic ecology are sostark, that my prediction for the next year is not that we willsolve these problems, or even make progress. My predic-tion (or perhaps wishful thinking) is that the ‘eukaryoticquestion’ will increasingly emerge as an elephant in theroom, which is an elegant idiom to describe our failure tograsp the role of so many large microbes that are rightunder our noses.

Bigger yes, but also different

I would like to discuss two reasons why protists have notentered the mainstream of conventional high-throughputenvironmental microbiology. The first of these is trivialand well understood: their genomes are bigger andorganized differently. We know that new sequencingtechnologies have had a major impact in our under-standing of the diversity and ecological roles of bacteria,archaea and viruses, for example, by allowing whole-community metagenomic surveys. To include protists inthese surveys is easy – simply do not filter them out!

However, we also know that nuclear genome sizeswould require epic sequencing and analysis budgets thatare simply not practical. Moreover, we cannot accrue thesame benefits for protists, even if we could sequenceenough, because their genomes are fragmented, repeat-rich, and lack functionally related gene clustering, all ofwhich limit the inferences we can make about individualgenomes and metabolic networks from metagenomicsby limiting our ability to link genes to other genes in agenome.

But there is another less discussed, but infinitely moreinteresting problem. Bacterial and archaeal diversity issubstantially manifested at the level of metabolism.Accordingly, the sequence of a bacterial or archaealgenome can go a long way to describing what thatorganism ‘does’ in the community, because we havedeveloped reasonable ways to translate the informationin a genome into predictions about that organism’smetabolic actions in the environment. This is not thecase for eukaryotes: although microbial eukaryotesharbour a sizable metabolic diversity, they are distin-guished from other microbial life in that they manifesta great deal more diversity at the levels of morphologyand behaviour. Indeed, morphology and behaviourhave a much greater effect on what most protists ‘do’in the environment than do their metabolic capacities(photosynthesis being an obvious exception). Unfortu-nately, the manifestation of these properties is muchmore complex than a straightforward gene–proteincorrespondence, and we are accordingly much worseat translating the information in a genome into predic-tions about what an organism looks like or how itbehaves.

To illustrate this problem, imagine four dinoflagellateprotists living in the same marine environment: one is afree-living benthic autotroph, one is an intracellular para-site of gastropods, one is an obligate photosyntheticsymbiont of cnidarians, and one is a heterotrophicgrazer feeding on bacteria and eukaryotic algae. Nowimagine we have sequenced whole genomes and wholetranscriptomes for all four of these organisms. How easywould it be to reconstruct these interactions? Theanswer is, it would be virtually impossible, even withthese miraculous quantities of molecular data. We couldrecognize that two were photosynthetic, but this mighteven mislead us to assume they shared a similar niche,when in reality the two forming intracellular relationshipswith invertebrates might share more in common. Thisfailure is because the most important characteristics thatdistinguish these organisms and their activities arederived from poorly understood coordinated actions ofthousands of gene products, and worse still, subtletiesof regulation and epigenetics relating to thousands ofgenes.

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Organisms DO matter – how do we study them?

They say that if you have a hammer, everything looks likea nail, and right now our biggest hammer is sequencing.Getting more sequence data from eukaryotes at the envi-ronmental level is a technical problem that can, and soonwill be, solved. The most revolutionary solution will be thearrival of routine single-cell genomics and transcriptomics.Despite all we have learned through metagenomicapproaches, cells do matter in the final analysis becausebiological activities are compartmentalized and howthe metabolism of a community is partitioned makes adifference; a community is not just the sum of itsenzymes, and seeing how functions are distributed acrossa community will change how we interpret them. Single-cell genomics will therefore be a boon to all environmentalmicrobiology. And for eukaryotes, single-cell transcriptom-ics in particular will give us a first inroad to their otherwiseintractable genomes when it can be automated acrossnatural communities.

How we interpret environmental sequence data fromeukaryotes is another problem altogether. If the predic-tive power of even genome-wide sequence data is criti-cally limited by our inability to infer characteristics ofmorphology and behaviour from it, then how do we inte-grate protists into a detailed picture of a microbial com-munity that is primarily based on such data? Certainlybeing able to predict what an organism is like based onits close relatives will continue to be important, butrequires a lot of ‘model’ systems scattered around thetree of eukaryotes to be truly effective. The real answerlikely lies in a re-emergence, and indeed a reinvention,of arts like cultivation, ultrastructural characterization,identification and observation of live cells within theirnatural community, and field microscopy – some ofwhich are badly under-appreciated at present. Our chal-lenge is therefore not to put away our hammer, but toplace more emphasis on the need for other tools too (infact, I once watched a graduate student hammering ascrew, so perhaps there is even greater depth to thisneed). It is not always obvious how these tools will be asadapted to a high-throughput approach as genomicmethods were, but advances in imaging and cell sortingopen a host of possibilities. So, to some extent, the wayforward involves integrating existing methods rather thaninventing new ones (e.g. linking high-throughput imagingwith single-cell sorting would allow morphology to belinked with genomic data).

In summary then, it is my hope that in the coming yearsmicrobial eukaryotes emerge a bit from the shadows oftheir smaller cousins. Luring them out into the open willrequire more than protists simply ‘catching up’ with exist-ing methods: we must improve the integration of protistswith our understanding of other members of microbial

communities by coordination and deliberate efforts toreconstruct entire microbiomes, including all membersand their interactions. The genomic revolution hasallowed astonishing advances, but perhaps this onlymeans that it needs to be grounded in biology more thanever.

Adopting modularity of metabolism as a guidingparadigm may lead to better accounting andunderstanding of the unseen majority of life:exercised with focus on the nitrogen cycle

Martin G. Klotz, Evolutionary and Genomic MicrobiologyLaboratory, Department of Biology, The University ofNorth Carolina, Charlotte, NC 28223, USA.

Obligate aerobic, chemolithotrophic and predominantlyautotrophic ammonia-oxidizing bacteria (‘AOB’) clusterwithin two distant monophyletic groups: the betaproteo-bacterial family Nitrosomonadaceae and the purple sulfurbacterial genus Nitrosococcus of the Gammaproteobac-teria. Yet, these two distant groups seemingly live identi-cal catabolic lifestyles, posing challenging evolutionaryquestions that have awaited answers for several decades.Long generation times of the AOB and their infamousrecalcitrance to transformation, as well as cloning andrecombinant expression of their genes, have preventedextensive molecular genetic experimentation to verifytheir catabolic pathways. Thus, the opportunity in 1999 tosequence and annotate the genome of a bacterium oncethought to be the ultimate representative for aerobic nitro-gen biology created a lot of buzz and expectations;however, it took almost 4 years from the isolation of ‘pureenough’ genomic DNA to reporting the results (Chainet al., 2003). Aside from the exhilarating experience offinding all the genes necessary to make a living cell andthe previously implicated inventory for it being an AOB,little could be gleaned from the genome to answer press-ing questions on the evolution of nitrification as a processor the obligate nature of the ammonia-oxidizing lifestyle.This initial genome analysis was soon followed by addi-tional sequencing projects, including other AOB and obli-gate aerobic chemolithotrophic nitrite-oxidizing bacteria(‘NOB’), that were facilitated by the then fully establishedDOE Joint Genome Institute (JGI) and initially coordinatedby a group of Principal Investigators (PIs) supported byfunding from the US National Science Foundation for aResearch Coordination Network. The outcome of thisendeavour was tremendous: Principal Investigators withdifferent interests and expertise as well as at differentlevels of advancement in their careers came together andwitnessed the power of genuine collaboration, whichincluded the immersion of postdocs, graduate and evenundergraduate students (http://nitrificationnetwork.org).

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Along the way, the JGI, working closely with project PIs,developed the mastery of assembling extensive, complexand repetitive contigs into complete polished genomes.Last but not least, we all learned quickly that unfinished(and even finished) genomes generated many, if notmore, unanswered questions than they provided answersto about the biology and origin of the organisms underinvestigation. In case of the AOB and NOB, metabolic (insilico) reconstruction of their genomes revealed that noneof the genes implicated in ‘nitrification’ were actuallyunique to nitrifying bacteria and that the very few identifiedunique (exclusively present) genes were encoding pro-teins that – to this day – are not implicated in a ‘process-specific’ lifestyle (Arp et al., 2007; Klotz and Stein, 2008;2011; Lücker et al., 2010; Campbell et al., 2011; Stein andKlotz, 2011; Simon and Klotz, 2013; Sorokin et al., 2012).Furthermore, additional ground-breaking work discoveredthat obligate aerobic ammonia-oxidizing Thaumarchaea(Könneke et al., 2005) as well as methane-oxidizingVerrucomicrobia (Hou et al., 2008; Islam et al., 2008) andProteobacteria (Stein & Yung, 2003 and references inthere) also nitrify and that anaerobic AOB oxidize nitrite(Strous et al., 2006). Yet another ‘process group’, theanaerobic methanotrophs (Ettwig et al., 2008) appear toencode many of the genes implicated in the obligate life-styles of ammonia and nitrite oxidizers (Ettwig et al.,2010; Luesken et al., 2012). A critical observer of thisrecent ‘omics-driven’ progress in our understanding ofmicrobial nitrogen transformations may then wonder whyso many non-unique gene markers remain widely used aspreferred targets to assess residence, abundance, diver-sity and distribution of Bacteria and Archaea that drivevarious aspects of the nitrogen biogeochemical cycle. Theanswer to this conundrum is multifold and needs to belooked at within a historical framework: early work on thebiology of the nitrogen cycle was process-oriented and‘cohorts’ of microbes that contributed to one or anotherof these processes were understood as dedicated facili-tators of these processes: Nitrifiers, Denitrifiers, Ammoni-fiers and Nitrogen fixers. In addition, predominantenvironmental conditions associated with these proc-esses were used as qualifiers (i.e. oxic vs. hypoxic andanoxic) and extended to the metabolic lifestyle of theparticipating microbes (i.e. aerobic vs. anaerobic). As anatural progression of process analysis, start and end-points became the foci of research, which resulted in anartificial categorization of which cohort ‘owned’ which sub-strate and end-product and which step was (rate-) limitingto the entire process. Some of these processes wereentirely facilitated by individual microbial isolates (i.e.denitrification), whereas others required the sequentialparticipation of more than one microbe (i.e. nitrification).With increasing technical advances, genetic methods ofthe mid 1990s allowed for identification of some molecular

inventories involved in these processes [i.e. the genesencoding ammonia monooxygenase (AMO), the firstenzyme in the nitrification process] that had up until thenbeen elusive. In contrast, denitrification genes (i.e. nirS,encoding nitrite reductase) encoded by many chemoor-ganoheterotrophs such as Escherichia coli and genesencoding dinitrogen fixation inventory in the alphaproteo-bacterial order Rhizobiales were already well character-ized in the 1980s. Equipped with this new geneticinformation, phylogenetic analysis found that evolutionaryrelationships were congruent between the gene encodingone of the subunits of AMO, amoA, and the small subunitribosomal genes of AOB (Rotthauwe et al., 1997). At thesame time, physiological studies led to the belief that theAmoA protein contained the active site to AMO (Hymanand Arp, 1992). Because ammonium is the starting pointof the nitrification process, there was consensus that theevolutionary history of nitrification as well as the abun-dance and distribution of nitrifying microbes could beunderstood solely by tracking the amoA gene and study-ing the AmoA subunit of AMO. Although it soon becameclear that AMO is a representative of a much larger familyof membrane-bound monooxygenases that includes par-ticular methane monooxygenase (Klotz and Norton,1998), AMO (amoA and AmoA, in particular) has beenfaithfully regarded as the beacon of nitrification. A similarreasoning was applied in the study of other processesnotwithstanding the fact that, for instance, ammonification(also known as ‘dissimilatory reduction of nitrate to ammo-nium’, DNRA) and canonical denitrification (dissimilatoryreduction of nitrate to dinitrogen) share inventory facilitat-ing the reduction of nitrate to nitrite. There was thus hopethat the growing availability of genomes would provide theopportunity to construct the ‘core genome elements’ of themicroorganisms that were typical facilitators of specificbiogeochemical process, i.e. the ‘cohort.’ Unfortunately,the reality of genome information has not brought uscloser to defining our preconceived functional cohorts andinventories, but rather has presented a much broader,less specific, portrayal of genome evolution. However,increased availability of sequenced genomes during thelast two decades facilitated two major improvements forenvironmental microbiology: (i) an increased number ofsequence variants of ‘functional signature genes’ routinelyused to detect specific microbial cohorts, and (ii) severalnovel or improved functional signature genes includingthose that detect newly discovered players in geochemi-cal cycles, such as the obligate anaerobic ammonia-oxidizing (Strous et al., 2006) and methane-oxidizingbacteria (Ettwig et al., 2010), obligate aerobic chemolitho-trophic ammonia-oxidizing Thaumarchaeota (Könnekeet al., 2005; Walker et al., 2010; Spang et al., 2012),and new phylotypic representatives of NOB that utilizethe same but sequence-divergent inventory (i.e. the

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Nitrospira-type vs. the Nitrobacter-type nitrite oxidationmodule; Lücker et al., 2010; Sorokin et al., 2012). Both ofthese improvements allowed for improved design ofprimers and probes in PCR and FISH hybridizations.

In the early days of genome sequencing, whichincluded our work on Nitrosomonas europaea and otherammonia- and nitrite-oxidizing bacteria, our expectationwas that the number of ‘unknown’, ‘hypothetical’ and‘conserved hypothetical’ protein-encoding open readingframes per genome would ‘shrink’ as the number ofsequenced genomes increased. Today, nearly 20 yearsafter TIGR presented the inaugural complete bacterialgenome of Haemophilus influenzae R20 (Fleischmannet al., 1995) and with a sequencing capacity that canproduce several fully sequenced microbial genomes in asingle day, we are still waiting for the curve of ‘hypotheti-cals’ versus the number of sequenced genomes to turnfrom exponential to asymptotic. How does this challengeour quest of understanding abundance and diversity ofmicrobial populations and the changing structure of theircommunities? I believe this means that our practised cat-egorization of metabolism based in principle on chemoor-ganoheterotrophic pathways of E. coli that considers anydeviation as an exception is fundamentally flawed.Rather, we need to understand the presently knownacquisition of catabolic potential as additional examples ofmany versions of yet unknown metabolic diversity.

Contemporary wizardry of analysing signature macro-molecules (DNA, RNA, proteins) seems to have much incommon with computing and computer-based modelling:output is ultimately dependent on the information andtheoretical framework of (implicated) input. The latter isusually a mix of experimentally proven and unprovenhypotheses connected by a pinch of wishful thinking. Weare beginning to acknowledge and understand that oneof the major problems in environmental microbiology isactually of semantic nature in that ‘process’, ‘organism’and ‘implicated molecular inventory’ were usually unam-biguously correlated, such as in the case of the Zumftian‘canonical denitrifiers’ (anaerobic organoheterotrophicbacteria that reduce nitrate to dinitrogen; Zumft, 1997),the ‘nitrifiers’ (ammonia- and nitrite-oxidizing bacteria)and the ‘ammonifiers’ (anaerobic reducers of nitrate toammonium).

This problematic situation was likely created by animmature marriage of key questions asked by microbialecologists (Who is there? What is everyone doing?) aswell as by physiologists (What are the sources of Energy,Reductant and Carbon?) followed by times during which‘bride and groom’ did not effectively communicate. Anadditional chasm has formed by a focus on the ‘uncul-tured majority’ using functional gene markers in molecularmicrobial ecology studies versus the detailed physiologi-cal and biochemical examination of ‘model organisms’

that are able to grow under defined laboratory conditionsand survive experimental manipulation. To this day, thereis ongoing debate over the relevance of cultured microor-ganisms to big environmental processes: for instance,can the study of a single model organism such as N. eu-ropaea define the process of ammonia oxidation?However, the dawn of evolutionary and genomic microbi-ology affords us the realization that metabolism ismodular, a conclusion built on sound molecular evolution-ary theory and confirmed with every newly sequencedand annotated genome. Evolutionary and genomic micro-biology also informs us that these metabolic modulesarose by birth and fortuitous combination (horizontal genetransfer) and have persisted and adapted as forced byfunctional pressures (‘use it or lose it’) thereby providingthe basis for functional niche adaptation. We have knowncollectively for quite some time that metabolism (in par-ticular, catabolism) of environmental microbes revolvesaround highly reactive and toxic intermediates. Forinstance, nitrite, nitric oxide radicals, hydroxylamine,hydrazine (rocket fuel) and nitrous oxide (laughing gas) inthe N-cycle are requisite metabolic intermediates. We arealso informed by evolutionary and genomic analyses thatthe genomes of these microbes encode multiple, function-ally redundant, overlapping yet distinct inventories thatregulate the half lives of reactive metabolic intermediatesand facilitate their transformations. For example, atpresent, we know more than five evolutionarily unrelatedclasses of nitric oxide reductases, some of which existingin several evolutionarily related variations.

The study of function and origin (evolution) of biogeo-chemical processes with an emphasis on the startingpoint (as determined by the sources of Energy, Reductantand Carbon) neglected that selection for high-throughputtoxin-producing machines (such as the alcohol andaldehyde-producing initial steps in chemolithotrophiccatabolism) could not occur without the pertinent detoxi-fication as well as energy- and reductant-extracting inven-tories already in place (Klotz and Stein, 2008; 2011;Tavormina et al., 2011). Likewise, using the genes encod-ing these usually substrate-promiscuous toxin-producingmachines as indicators for the prediction of redox parti-tioning and flow in microbial communities to explain meta-bolic capacity and ecosystem function is likely missing thetarget, unless the system is functionally stratified.Although phylogenetic studies of process start pointinventory are important (i.e. the superfamily of copper-dependent membrane monooxygenases, Cu-MMOs;Tavormina et al., 2011), sound phylogenetic, protein struc-tural and functional analyses of the end-point detox,energy- and reductant-extracting inventory (Bergmannet al., 2005; Klotz et al., 2008; Kartal et al., 2011a,b; Kernet al., 2011; Simon & Klotz, 2013 and references therein)paired with comparative genome analysis (Arp et al.,

8 Crystal ball


2007; Bartossek et al., 2012; Hu et al., 2012; Speth Daanet al., 2012) continue to be just as crucial for understand-ing the function and origin of metabolic modules. Mycrystal ball reveals that this new paradigm and theincreasing collaboration between molecular ecologistsand molecular (omics-informed) physiologists will lead tocontinued successful environmental microbiological appli-cations including the development of primers that targetgenes encoding detox, energy- and reductant-extractinginventory (Schmid et al., 2008; Attard et al., 2010; Li et al.,2010; Harhangi et al., 2012) and the inclusion of morephenotypically variable isolates in physiological andgenomic studies. A case in point is the still elusive inven-tory that extracts energy and reductant in ammonia-oxidizing Thaumarchaeota, which is needed to crisplydistinguish between those Thaumarchaeota that supporttheir growth by the oxidation of ammonia to nitrite (the‘AOA’) and those that express functional AMO for otherpurposes (the amo-encoding Archaea; ‘AEA’) (Hatzen-pichler, 2012 and references therein). In the end, we mayyet achieve a more reliable correlation between the physi-cal world and the unseen majority of life that sustains andchanges it.

Acknowledgements

My thanks go to all colleagues in the Nitrification Networkand the Organization for Methanotroph Genome Analysis(OMeGA) for past, present and future discussions and spe-cifically to those who gave me the opportunity to co-authorcollaborative work. In particular, I would like to thank Lisa Y.Stein (UA-Edmonton) for continuing critical and motivatingdiscussions, collaboration and friendship, and for a criticalreading of this crystal ball contribution.

References

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Attard, E., Poly, F., Commeaux, C., Laurent, F., Terada, A.,Smets, B.F., et al. (2010) Shifts between Nitrospira- andNitrobacter-like nitrite oxidizers underlie the response ofsoil potential nitrite oxidation to changes in tillage practices.Environ Microbiol 12: 315–326.

Bartossek, R., Spang, A., Weidler, G., Lanzen, A., and Sch-leper, C. (2012) Metagenomic analysis of ammonia oxidiz-ing archaea affiliated with the soil group. Front Microbiol 3:e208 (201–214).

Bergmann, D.J., Hooper, A.B., and Klotz, M.G. (2005) Struc-ture and sequence conservation of hao cluster genes ofautotrophic ammonia-oxidizing bacteria: evidence for theirevolutionary history. Appl Environ Microbiol 71: 5371–5382.

Campbell, M.A., Nyerges, G., Kozlowski, J.A., Poret-Peterson, A.T., Stein, L.Y., and Klotz, M.G. (2011) Model ofthe molecular basis for hydroxylamine oxidation and

nitrous oxide production in methanotrophic bacteria. FEMSMicrobiol Lett 322: 82–89.

Chain, P., Lamerdin, J., Larimer, F., Regala, W., Lao, V.,Land, M., et al. (2003) Complete genome sequence of theammonia-oxidizing bacterium and obligate chemolithoau-totroph Nitrosomonas europaea. J Bacteriol 185: 2759–2773.

Ettwig, K.F., Shima, S., van de Pas-Schoonen, K.T., Kahnt,J., Medema, M.H., Op den Camp, H.J M., et al. (2008)Denitrifying bacteria anaerobically oxidize methane in theabsence of Archaea. Environ Microbiol 10: 3164–3173.

Ettwig, K.F., Butler, M.K., Le Paslier, D., Pelletier, E., Mang-enot, S., Kuypers, M.M.M., et al. (2010) Nitrite-drivenanaerobic methane oxidation by oxygenic bacteria. Nature464: 543–548.

Fleischmann, R.D., Adams, M.D., White, O., Clayton, R.A.,Kirkness, E.F., Kerlavage, A.R., et al. (1995) Whole-genome random sequencing and assembly of Haemo-philus influenzae Rd. Science 269: 496–512.

Harhangi, H.R., Le Roy, M., van Alen, T., Hu, B.-l., Groen, J.,Kartal, B., et al. (2012) Hydrazine synthase, a unique phy-lomarker with which to study the presence and biodiversityof anammox bacteria. Appl Environ Microbiol 78: 752–758.

Hatzenpichler, R. (2012) Diversity, physiology, and niche dif-ferentiation of ammonia-oxidizing archaea. Appl EnvironMicrobiol 78: 7501–7510.

Hou, S., Makarova, K., Saw, J., Senin, P., Ly, B., Zhou, Z.,et al. (2008) Complete genome sequence of the extremelyacidophilic methanotroph isolate V4, Methylacidiphiluminfernorum, a representative of the bacterial phylum Verru-comicrobia. Biol Direct 3: 26–51.

Hu, Z., Speth, D.R., Francoijs, K.-J., Quan, Z.-X., and Jetten,M. (2012) Metagenome analysis of a complex communityreveals the metabolic blueprint of anammox bacteriumCandidatus Jettenia asiatica. Front Microbiol 3: e366 (361–369).

Hyman, M.R., and Arp, D.J. (1992) 14C2H2- and 14CO2-labeling studies of the de novo synthesis of polypeptides byNitrosomonas europaea during recovery from acetyleneand light inactivation of ammonia monooxygenase. J BiolChem 267: 1534–1545.

Islam, T., Jensen, S., Reigstad, L.J., Larsen, O., and Birke-land, N.-K. (2008) Methane oxidation at 55°C and pH 2 bya thermoacidophilic bacterium belonging to the Verrucomi-crobia phylum. Proc Natl Acad Sci USA 105: 300–304.

Kartal, B., Geerts, W., and Jetten, M.S.M. (2011a) Cultivation,detection, and ecophysiology of anaerobic ammonium-oxidizing bacteria. Methods Enzymol 486: 89–108.

Kartal, B., Maalcke, W.J., de Almeida, N.M., Cirpus, I.,Gloerich, J., Geerts, W., et al. (2011b) Molecular mecha-nism of anaerobic ammonium oxidation. Nature 479: 127–130.

Kern, M., Klotz, M.G., and Simon, J. (2011) The Wolinellasuccinogenes mcc gene cluster encodes an unconven-tional respiratory sulphite reduction system. Mol Microbiol82: 1515–1530.

Klotz, M.G., and Norton, J.M. (1998) Multiple copies ofammonia monooxygenase (amo) operons have evolvedunder biased AT/GC mutational pressure in ammonia-oxidizing autotrophic bacteria. FEMS Microbiol Lett 168:303–311.

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Klotz, M.G., and Stein, L.Y. (2008) Nitrifier genomics andevolution of the nitrogen cycle. FEMS Microbiol Lett 278:146–156.

Klotz, M.G., and Stein, L.Y. (2011) Genomics of ammonia-oxidizing bacteria and insights to their evolution. In Nitrifi-cation. Ward, B.B., Arp, D.J., and Klotz, M.G. (eds).Washington, D.C.: ASM Press, pp. 57–93.

Klotz, M.G., Schmid, M.C., Strous, M., op den Camp, H.J.,Jetten, M.S., and Hooper, A.B. (2008) Evolution of anoctahaem cytochrome c protein family that is key to aerobicand anaerobic ammonia oxidation by bacteria. EnvironMicrobiol 10: 3150–3163.

Könneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B.,Waterbury, J.B., and Stahl, D.A. (2005) Isolation of anautotrophic ammonia-oxidizing marine archaeon. Nature437: 543–546.

Li, M., Hong, Y., Klotz, M.G., and Gu, J.D. (2010) A compari-son of primer sets for detecting 16S rRNA and hydrazineoxidoreductase genes of anaerobic ammonium-oxidizingbacteria in marine sediments. Appl Microbiol Biotechnol86: 781–790.

Lücker, S., Wagner, M., Maixner, F., Pelletier, E., Koch, H.,Vacherie, B., et al. (2010) A Nitrospira metagenome illumi-nates the physiology and evolution of globally importantnitrite-oxidizing bacteria. Proc Natl Acad Sci USA 107:13479–13484.

Luesken, F.A., Wu, M.L., Op den Camp, H.J.M., Keltjens, J.T.,Stunnenberg, H., Francoijs, K.-J., et al. (2012) Effect ofoxygen on the anaerobic methanotroph Candidatus‘Methylomirabilis oxyfera’: kinetic and transcriptional analy-sis. Environ Microbiol 14: 1024–1034.

Rotthauwe, J.H., Witzel, K.P., and Liesack, W. (1997) Theammonia monooxygenase structural gene amoA as a func-tional marker: molecular fine-scale analysis of naturalammonia-oxidizing populations. Appl Environ Microbiol 63:4704–4712.

Schmid, M.C., Hooper, A.B., Klotz, M.G., Woebken, D., Lam,P., Kuypers, M.M., et al. (2008) Environmental detection ofoctahaem cytochrome c hydroxylamine/hydrazine oxidore-ductase genes of aerobic and anaerobic ammonium-oxidizing bacteria. Environ Microbiol 10: 3140–3149.

Simon, J., and Klotz, M.G. (2013) Diversity and evolution ofbioenergetic systems involved in microbial nitrogen com-pound transformations. Biochim Biophys Acta (BBA)-Bioenergetics 1827: 114–135.

Sorokin, D.Y., Lucker, S., Vejmelkova, D., Kostrikina, N.A.,Kleerebezem, R., Rijpstra, W.I.C., et al. (2012) Nitrificationexpanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J 6:2245–2256.

Spang, A., Poehlein, A., Offre, P., Zumbrägel, S., Haider, S.,Rychlik, N., et al. (2012) The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insightsinto metabolic versatility and environmental adaptations.Environ Microbiol 14: 3122–3145.

Speth Daan, R., Hu, B., Bosch, N., Keltjens, J., Stunnenberg,H., and Jetten, M. (2012) Comparative genomics of twoindependently enriched Candidatus Kuenenia stuttgartien-sis anammox bacteria. Front Microbiol 3: e307 (301–307).

Stein, L.Y., and Klotz, M.G. (2011) Nitrifying and denitrifying

pathways of methanotrophic bacteria. Biochem Soc Trans39: 1826–1831.

Stein, L.Y., and Yung, Y.L. (2003) Production, isotopic com-position, and atmospheric fate of biologically producednitrous oxide. Annu Rev Earth Planet Sci 31: 329–356.

Strous, M., Pelletier, E., Mangenot, S., Rattei, T., Lehner, A.,Taylor, M.W., et al. (2006) Deciphering the evolution andmetabolism of an anammox bacterium from a communitygenome. Nature 440: 790–794.

Tavormina, P.L., Orphan, V.J., Kalyuzhnaya, M.G., Jetten,M.S.M., and Klotz, M.G. (2011) A novel family of functionaloperons encoding methane/ammonia monooxygenase-related proteins in gammaproteobacterial methanotrophs.Environ Microbiol Rep 3: 91–100.

Walker, C.B., de la Torre, J.R., Klotz, M.G., Urakawa, H.,Pinel, N., Arp, D.J., et al. (2010) Nitrosopumilus maritimusgenome reveals unique mechanisms for nitrification andautotrophy in globally distributed marine crenarchaea. ProcNatl Acad Sci USA 107: 8818–8823.

Zumft, W.G. (1997) Cell biology and molecular basis of deni-trification. Microbiol Mol Biol Rev 61: 522–616.

The bioavailability of essential trace metals and itsmodification by microbes

François M. M. Morel, Department of Geosciences, GuyotHall, Princeton University, Princeton, NJ 08544, USA.

As cofactors of metalloenzymes, metals play key rolesin the metabolism and growth of microorganisms. This iswidely appreciated in the case of Fe, which is used inmyriad redox enzymes, but it is also true of other metalssuch as Zn, Cu and Mo, among others, which catalysekey processes such as protein degradation, methane oxi-dation and N2 fixation.

The bioavailability of trace metals thus influences theflow of energy and nutrients in ecosystems with importantconsequences for biogeochemical processes and com-munity structure. For example, Fe limits primary produc-tion in large oceanic regions (Martin et al., 1994), whileMo limits N2 fixation in some tropical forests (Barronet al., 2009).

Metal-binding compounds, some from exogenoussources, some produced by the organisms themselves,control the bioavailability of trace metals. The best-knownexample is that of siderophores produced by microbes tobind and take up Fe (Sandy and Butler, 2009). The toxicityof other metals such as Cu or Cd is generally decreasedby complexation with organic compounds. In oligotrophicenvironments where they can be used (such as the openocean), electrochemical techniques have shown that thebulk of essential metals is bound to strong complexingagents. But the nature, origin and function of thesechelators remains one of the most vexed questions inenvironmental microbiology. In most instances, we do noteven know the nature of metal complexing agents inhighly controlled conditions such as in culture media

10 Crystal ball


where their presence and function is usually masked bythe addition of artificial chelators such as EDTA.

The major obstacle to unravelling the question of metalbioavailability is simply analytical: how to identify andquantify in very complex media compounds of unknownstructure that complex trace metals at very low concen-tration. This limitation is being overcome by the enormousprogress in high-sensitivity high-resolution mass spec-trometry which is able to identify very large numbers ofcompounds in complex mixtures with increasingly betteraccuracy and lower limits of detection. This progress inhigh-resolution LC-MS/MS technology is essentiallyresponsible for the emergence of fields like proteomicsand metabolomics within the last decade and a half. But itshould also allow identification of metal-binding com-pounds in culture media and natural samples. As withmuch of the emerging technologies, the problem of ana-lytical detection is replaced by one of data analysis as thecompounds of interest must be identified among the hun-dreds of thousands of individual species revealed by theinstruments over the course of a single LC-MS run. Asalready exemplified in a few studies (Velasquez et al.,2011), the distinctive isotopic distributions of individualmetals can be used to distinguish novel metal complexesamong a forest of unrelated compounds. Analysis of frag-mentation patterns of individual compounds, comple-mented by additional analytical information, will revealconserved metal binding structural features. It should alsogradually provide spectral libraries of matched MS/MSspectra for compounds bearing these motifs, greatlyimproving the bioinformatics necessary for identifyingnovel metal chelating agents. The age of ‘chelomics’ isnearly upon us.

Our crystal ball may principally reflect our optimism, butas we begin to identify and characterize metal complexingagents in cultures and in nature, we foresee a sea changein our understanding of the bioavailability of trace metals,an important facet of the interactions between microbesand their environment.

Acknowledgements

I thank Xinning Zhang-Paulot, Oliver Baars, Jeffra Schaefer,David Perlman and Anne Kraepiel for advice anddiscussions.

References

Barron, A.R., Wurzburger, N., Bellenger, J.P., Wright, S.J.,Kraepiel, A.M.L., and Hedin, L.O. (2009) Molybdenum limi-tation of asymbiotic nitrogen fixation in tropical forest soils.Nat Geosci 2: 42–45. doi: 10.1038/NGEO366.

Martin, J.H., Coale, K.H., Johnson, K.S., Fitzwater, S.E.,Gordon, R.M., Tanner, S.J., et al. (1994) Testing the ironhypothesis in ecosystems of the equatorial Pacific Ocean.Nature 371: 123–129. doi: 10.1038/371123a0.

Sandy, M., and Butler, A. (2009) Microbial iron acquisition:marine and terrestrial siderophores. Chem Rev 109: 4580–4595. doi: 10.1021/cr9002787.

Velasquez, I., Nunn, B.L., Ibisanmi, E., Goodlett, D.R.,Hunter, K.A., and Sander, S.G. (2011) Detection ofhydroxamate siderophores in coastal and Sub-Antarcticwaters off the South Eastern Coast of New Zealand.Mar Chem 126: 97–107. doi: 10.1016/j.marchem.2011.04.003.

Electrical interactions of bacteria

Ken Nealson, Department of Earth Sciences, University ofSouthern California, Los Angeles, CA 90089, USA.

The crystal ball has always been a poor weaponfor me – I am a far better marksman with the retro-spectroscope! That being said, it is always fun to have alook at what might be, and it is an honour to be askedto say a few words. Based on what I have seen andheard in the last year, I suspect that the electrical(redox) charge of surfaces, and electrical interactionsbetween cells (of the same and different species) aregoing to be an area of great interest and impact in thecoming years.

In the past few years, it has become apparent thatextracellular electron transport to insoluble electronacceptors (EAs), as well as to soluble EAs that becomeinsoluble or toxic upon reduction, is commonly done bymicrobes: being a well-characterized process in bacteria,and less well so in Archaea. Much less well-appreciatedare the recent findings from many laboratories that bac-teria can take up electrons from insoluble electron donors,using these electrons as a source of energy. Along withthese observations are the more subtle issues involvedwith attachment, growth and biofilm formation: issues thatare almost certainly closely related to, and controlled by,various methods of sensing and responding to surfacecharge.

My crystal ball says that there will be many new dis-coveries of electrical interactions of bacteria with in-soluble substrates, be they other bacteria, insoluble min-erals, charged electrodes, or even eukaryotic cells, all ofwhich have a charge that changes as a function of pH.Thus, we have a lot to learn: (i) how do bacteria senseand respond to charged surfaces; (ii) how is thisresponse regulated, and what are the consequences ofthe response; and (iii) what are the ecological implica-tions of these interactions? Unless I miss my bet, we willfind that such behaviour is far more common than weanticipated, and that there is an entire area of microbialecology dealing with the response to surface charge,and the ensuing extracellular electron transfer: an areathat will range from syntrophy, symbiosis and pathogen-esis, on one hand, to geobiology, corrosion and materialscience on the other.

Crystal ball 11


Combating global proliferation of harmfulcyanobacterial blooms by integrating conceptualand technological advances in a water managementtoolbox

Hans W. Paerl, Institute of Marine Sciences, University ofNorth Carolina at Chapel Hill, 3431 Arendell Street, More-head City, NC 28557, USA.

Nutrient enrichment (eutrophication) of freshwater eco-systems has promoted global proliferation of cyanobacte-rial harmful (toxic) algal blooms (CyanoHABs). Thisproblem is exacerbated by global warming (Paerl andHuisman, 2008), and threatens the use and sustainabilityof some of the world’s largest lakes and drinking waterreservoirs. Particularly affected are rapidly developingregions, typified by China’s third largest lake, Taihu, apreviously pristine lake supplying the drinking waterneeds of over 12 million people, and a key regionalfishing, tourism and cultural resource (Fig. 1).

Taihu, and other large lake ecosystems, have becomethe ‘poster children’ for CyanoHAB expansion in denselypopulated regions. Experimental work has demonstratedthat excessive inputs of both nitrogen (N) and phosphorus(P) are responsible for the proliferation and persistence oftoxic CyanoHABs in Taihu (Xu et al., 2010). These resultschallenge the previous paradigm that only P reductionsare needed to control CyanoHABs, which was based onthe assumption that numerous diazotrophic genera can fixatmospheric nitrogen (N2), thus supplying ecosystem Ndemand (Schindler et al., 2008). However, numerousstudies have shown that this assumption does not holdtrue for freshwater and marine ecosystems (i.e. N inputssupplied by N2 fixation fall far short of ecosystem Ndemands) (Nixon, 1995; Paerl and Scott, 2010). Hence,eutrophication in these systems can be further acceler-

ated by additional N inputs, especially if they containsufficient amounts of P stored in sediments (Conley et al.,2009). Indeed, eutrophic systems worldwide exhibit thecapacity to absorb even more N and increase their trophicstate and CyanoHAB dominance. It is crucial to under-stand how input reductions in total, as well as specific Nand P substrates, shape phytoplankton communities, andto do so while accounting for climactic variations that areknown to favour CyanoHABs.

While managing these nutrients often requires engi-neering solutions, implementation can only be success-ful if it is ecologically constrained so that the resultingmicrobial taxa are desirable (e.g. nontoxic species).There is a need to define N and P reduction thresholdsfavouring bloom abatement in order to clarify the selec-tive effects of anthropogenic N and P forms, includingdetermining how selective nutrient reductions impacttoxin-producing versus non-toxic cyanobacterial genera.

The challenge is to combine environmental multi-disciplinary approaches to combat CyanoHABs overgeological, climatic and hydrological gradients. To do this,we must combine rapid, sensitive and (from a biodiversityperspective) meaningful identification and characteriza-tion techniques with spatio-temporal delineation ofthe effects nutrient enrichment exerts on CyanoHABexpansion.

Aquatic microbial ecologists have developed in situ bio-assays and whole lake assessments of phytoplanktonresponses to nutrient enrichment and reductions. Theseapproaches have utilized general and taxon-specific bio-chemical and molecular techniques, including phytoplank-ton group-specific diagnostic photopigment indicators andgenetic markers capable of detecting quantifying taxa-specific responses to nutrient manipulations. Theseassays can corroborate or expand information gainedfrom conventional microscopic observations, and tradi-tional biomass indicators such as chlorophyll a (total algalbiomass), c-phycocyanin (total cyanobacteria), particulateC and dry weight. In addition, great inroads are beingmade to better understand the most troublesome aspectof CyanoHAB proliferation due to nutrient over-enrichment, their toxicity. Toxin producers can now bedistinguished and quantified using a suite of molecularapproaches, both amplification-based (myriad PCRassays) and in situ (e.g. fluorescence-based hybridizationassays or shotgun metagenomics). Coupling these taxa-specific assays to nutrient enrichment experiments hashelped identify relationships between basin-specific nutri-ent loads and the selective stimulation and proliferation oftoxin-producing CyanoHABs such as Microcystis spp.(Otten et al., 2012).

From an environmental management perspective,there is a need to ‘scale up’ local experimental results tothe ecosystem level, including large lakes and coastal

Fig. 1. A toxic cyanobacterial (Microcystis spp.) bloom in LakeTaihu, China (photo: Hans Paerl).

12 Crystal ball


environments to gauge regional responses to nutrientenrichment and climatic variability. Aircraft or satellite-based remote sensing has proven to be a powerful, highlyuseful means of relating small-scale experimental resultsto whole ecosystem responses. It has also helped clarifycausal relationships between environmental, anthropo-genic and climate parameters and CyanoHABs, and pre-dicting bloom potential under future change scenarios.Traditional approaches to collecting data to assess thedynamics of CyanoHABs involve direct observation bylight microscopy on shipboard or mooring, or laboratoryexperiments, such as taxonomic analysis or pigmentextraction. Advances in autonomous sensing (fluoromet-ric, spectrometric) analyses can now provide real-timemeasurements of water quality. Remote sensing providesobservations at large coverage and high frequency. Mul-tispectral satellite images have been used for assessingharmful algal blooms including CyanoHABs (Schofieldet al., 1999). These images can discriminate CyanoHABs’distinct potentially toxic algal groups from other phyto-plankton by observing subtle but detectable absorbancecharacteristics of diagnostic photopigments (e.g. phyco-cyanin, zeaxanthin). Landsat, MODIS, MERIS and Quick-Bird data (Wheeler et al., 2012) have been used to assesscyanobacteria in US lakes. These platforms can comple-ment ground-level measurements of diagnostic photopig-ments, making them highly useful in extrapolating ground-truthed data. Complementary optical water quality (e.g.turbidity, chlorophyll a, coloured organic matter and tem-perature) has been measured using remote sensing ofabsorption, reflectance and emission of light by a sub-stance. Satellite imagery, however, has spatial and tem-poral resolution limitations. Techniques such as imagefusion, in which two or more images are combined into asingle image, can be used in combination with waveletinformation, regression trees and spatial/temporal adap-tive reflectance fusion model (STAR-FM) to extractmaximum amounts of information to help characterizeCyanoHABs (Singh, 2011). Lastly, recent advances inhyperspectral imagery show promise in detecting toxicalgal species and associated water quality parameters.Hyperspectral imagery provides a specific reflectancedifference in algal bloom types based on taxa-specificphotopigments that absorb in characteristic and highlyspecific wavelengths. At present, hyperspectral imagery isvery expensive and site-specific because it is dependenton flyovers of highly specialized aircraft (e.g. hyperionaboard the EO-1 high altitude aircraft) (Lunetta et al.,2009). The streamlining of this technology will facilitatethe application of hyperspectral imagery to water qualitymeasurements in the near future.

Understanding the linkage between human- and cli-matically driven CyanoHABs and developing effectivemeans to control these events will require combining envi-

ronmental microbiology techniques with remote andin-system sensing technologies that can capture andquantify environmental forcing features and the microbialresponses over a range of watershed, basin, regional andglobal scales. The good news is that individually, thesetechnologies and approaches are largely ready for wide-scale deployment. The challenge now is to couple them ina manner that will enable us to capture and quantify thecause and effect relationships and thresholds in a non-linear, event-driven, hydrologically variable, warmingworld. Current and evolving empirical, statistical and infer-ential modelling techniques will help address these chal-lenges and achieve the ultimate goal of safe, sustainableaquatic ecosystems.

Acknowledgements

I thank J.T. Scott, M. McCarthy, W. Lewis and W. Wurtsbaughfor helpful discussions and the US National Science Founda-tion for support of much of the work discussed.

References

Conley, D.J., Paerl, H.W., Howarth, R.W., Boesch, D.F.,Seitzinger, S.P., Havens, K.E., et al. (2009) Controllingeutrophication: nitrogen and phosphorus. Science 323:1014–1015.

Lunetta, R.S., Knight, J.F., Paerl, H.W., Streicher, J.J.,Peierls, B.L., Gallo, T., et al. (2009) Measurement of watercolor using AVIRIS Imagery to assess the potential for anoperational monitoring capability in the Pamlico SoundEstuary, USA. Int J Remote Sens 30: 3291–3314.

Nixon, S.W. (1995) Coastal marine eutrophication: a defini-tion, social causes, and future concerns. Ophelia 41: 199–219.

Otten, T.G., Xu, H., Qin, B., Zhu, G., and Paerl, H.W. (2012)Spatiotemporal patterns and ecophysiology of toxigenicMicrocystis blooms in Lake Taihu, China: implications forwater quality management. Environ Sci Technol 46: 3480–3488.

Paerl, H.W., and Huisman, J. (2008) Blooms like it hot.Science 320: 57–58.

Paerl, H.W., and Scott, J.T. (2010) Throwing fuel on the fire:synergistic effects of excessive nitrogen inputs and globalwarming on harmful algal blooms. Environ Sci Technol 44:7756–7758.

Schindler, D.W., Hecky, R.E., Findlay, D.L., Stainton, M.P.,Parker, B.R., Paterson, M., et al. (2008) Eutrophication oflakes cannot be controlled by reducing nitrogen input:results of a 37 year whole ecosystem experiment. ProcNatl Acad Sci USA 105: 11254–11258.

Schofield, O., Grzymski, J., Bissett, W.P., Kirkpatrick, G.J.,Millie, D.F., Moline, M., and Roesler, C.S. (1999) Opticalmonitoring and forecasting systems for harmful algalblooms: possibility or pipe dream? J Phycol 35: 1477–1496.

Singh, D. (2011) Generation and evaluation of gross primaryproductivity using Landsat data through blending withMODIS data. Int J Appl Earth Obs Geoinf 13: 59–69.

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Wheeler, S.M., Morrissey, L.A., Levine, S.N., Livingston,G.P., and Vincent, W.F. (2012) Mappingcyanobacterialblooms in Lake Champlain’s Missisquoi Bay using Quick-Bird and MERIS satellite data. J Great Lakes Res 38:68–75.

Xu, H., Paerl, H.W., Qin, B., Zhu, G., and Gao, G. (2010)Nitrogen and phosphorus inputs control phytoplanktongrowth in eutrophic Lake Taihu, China. Limnol Oceanogr55: 420–432.

Unweaving the evolutionary fabric of symbioticdigestion in termites

Claire L. Thompson and Andreas Brune, Department ofBiogeochemistry, Max Planck Institute for TerrestrialMicrobiology, Karl-von-Frisch-Strasse 10, 35043Marburg, Germany.

More than a century ago, the contents spilling out of apunctured termite gut reminded the naturalist JosephLeidy of ‘the turning out of a multitude of persons fromthe door of a crowded meeting-house’ (Leidy, 1881). Wenow know that this dense community of microorganismsbreaks down lignocellulose and converts it to fermenta-tion products that drive the metabolism of their host.However, the intestinal microbial community of a termitereflects more than just its day-to-day activities. Indeed,there are indications that elements of the gut microbiotaare tightly woven into the evolutionary fabric of both ver-tebrate and invertebrate hosts (Ley et al., 2008; Colmanet al., 2012). As descendants of omnivorous cock-roaches that lived more than 130 million years ago, ter-mites have gone on to become dietary specialists, ableto degrade lignocellulose more rapidly and efficientlythan any other organism known. Despite fundamentaldifferences in host diet, the gut microbiota of cock-roaches and termites have many bacterial lineages incommon, and bacterial symbionts of termite gut flagel-lates appear to be derived from free-living relatives thatwere already present in the ancestor of termites (Nodaet al., 2009; Schauer et al., 2012).

However, the evolutionary origin of most lineages, thebasis for the complexity of the intestinal community, andthe fundamental changes associated with the loss of thecellulolytic flagellates in the evolutionary higher termitesare still unclear. High-throughput sequencing technolo-gies now allow a detailed census of the meeting-houseattendees and the teasing out of phylogenetic patternsacross a broad range of host species. A far more chal-lenging task is to understand what the meeting is about.Gazing into the crystal ball, we predict that future studieswill reveal the functions of individual populations withinthe termite gut community. Metagenomic analysis hasalready provided insights into the nature of the bacteriainvolved in cellulose digestion in wood-feeding higher ter-mites (Warnecke et al., 2007), and a survey of hydroge-

nase genes has indicated that the microorganismsresponsible for hydrogen turnover differ between termitesand cockroaches (Ballor and Leadbetter, 2012). However,the specific roles of the gut microbiota in the majority oftermite species, particularly those specialized on lignocel-lulosic diets at advanced stages of humification, includingsoil organic matter, remain to be clarified. New tools fromthe burgeoning fields of functional genomics and meta-transcriptomics will permit identification of the microorgan-isms responsible for the degradation processes as well asthe metabolic pathways involved.

Of equal importance will be to understand the interac-tions of the microbiota with their host. Such interactionsin vertebrates are under intensive study. Studies withgerm-free mammals have shown that the gut microbiotais crucial for the complete post-natal maturation of thehost and has a profound influence on immune develop-ment (Mazmanian et al., 2005). Apart from Drosophilamelanogaster, comparatively little is known about theimmune systems of insects, including termites, and theinteraction of the gut microbiota with the immunesystem. We envisage the arrival of genome sequencesof several termite species and new approaches usinggerm-free cockroaches that will shed light on thecomplex host–microbe interactions occurring within theguts of these insects.

References

Ballor, N.R., and Leadbetter, J.R. (2012) Analysis of exten-sive [FeFe] hydrogenase gene diversity within the gutmicrobiota of insects representing five families of Dictyop-tera. Microb Ecol 63: 586–595.

Colman, D.R., Toolson, E.C., and Takacs-Vesbach, C.D.(2012) Do diet and taxonomy influence insect gut bacterialcommunities? Mol Ecol 21: 5124–5137.

Leidy, J. (1881) The parasites of termites. J Acad Nat SciPhila 8: 425–447.

Ley, R.E., Hamady, M., Lozupone, C., Turnbaugh, P.J.,Ramey, R.R., Bircher, J.S., et al. (2008) Evolution ofmammals and their gut microbes. Science 320: 1647–1651.

Mazmanian, S.K., Liu, C.H., Tzianabos, A.O., and Kasper,D.L. (2005) An immunomodulatory molecule of symbioticbacteria directs maturation of the host immune system.Cell 122: 107–118.

Noda, S., Hongoh, Y., Sato, T., and Ohkuma, M. (2009)Complex coevolutionary history of symbiotic Bacteroidalesbacteria of various protists in the gut of termites. BMC EvolBiol 9: 158.

Schauer, C., Thompson, C.L., and Brune, A. (2012) Thebacterial community in the gut of the cockroach Shel-fordella lateralis reflects the close evolutionary relatednessof cockroaches and termites. Appl Environ Microbiol 78:2758–2767.

Warnecke, F., Luginbühl, P., Ivanova, N., Ghassemian, M.,Richardson, T.H., Stege, J.T., et al. (2007) Metagenomic

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and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450: 560–565.

Correlation analysis in microbial ecology: can weinfer causation after all?

William Van Treuren2 and Rob Knight1,2,3,4, 1Department ofComputer Science, University of Colorado at Boulder,Boulder, CO 80309, USA. 2BioFrontiers Institute, Univer-sity of Colorado at Boulder, Boulder, CO 80309, USA.3Department of Chemistry & Biochemistry, University ofColorado at Boulder, Boulder, CO 80309, USA. 4HowardHughes Medical Institute, Boulder, CO 80309, USA.

It is by now a canard that ‘correlation does not implycausation’. However, researchers and clinicians increas-ingly need to mine feature-rich datasets to create hypoth-esis about mechanisms of disease and targets ofintervention in those diseases. Counter-intuitively, onestrategy that has emerged to serve this need is correlationanalysis, with the goal of extracting a subset of meaning-ful features [operational taxonomic units (OTUs), metabo-lites, etc.] that can be investigated with higher confidencethat this subset is important to the overall structure of thedata, and provide explanations at a deeper level.

The motivation for this redux in correlation techniques isthat improvements in data acquisition through genomesequencing, nuclear magnetic resonance and mass spec-trometry have expanded the scale at which microbialcommunities can be surveyed much faster than the cor-responding computational techniques for analysing andinterpreting the data. Discovering meaningful correlationsin datasets with tens of thousands of features and hun-dreds of millions of observations is, to say the least,challenging. Many high-profile microbial ecology papersinclude networks and heatmaps to suggest correlationsin their data. These correlation analyses include co-occurrence analysis (which OTUs or metabolites arefound in the same samples?) and covariance analysis(which OTUs are found together with which metabolites?)and occasionally correlations of OTUs and/or metaboliteswith time (Xia et al., 2011). The same challenges apply inusing these correlation techniques for mixed multi-levelomic datasets (e.g. which transcripts correlate with whichmetabolites?). A key challenge is the compositional natureof the data: normalizing to a sum can introduce correla-tions among many components that should be uncorre-lated. Although many researchers have recentlydeveloped independent methods for assessing correla-tions in compositional data, including SparCC (Friedmanand Alm, 2012), CoNet (Faust et al., 2012), Family WiseError Rate strategies (e.g. Romano et al., 2008) and basicdistance metric strategies, no consensus on techniquehas been reached nor have these methods been bench-marked against one another.

To make progress, consensus must be reached aboutwhich correlation analysis methods are most appropriatefor which data types and experimental designs. Very littlecomparative work has been done identifying whichmethods are most effective in which parts of the corre-lation space, leading to a profusion of different methodsas well as replication of some known bad strategies. Forexample, applying basic distance metrics (Spearmanrank correlations, Euclidean distance, etc.) and choosingthe most extreme linkages often fails because the prob-ability of the most extreme links being true does not differfrom the probability that less extreme links are true. Simi-larly, because there is no metric for deducing how manycorrelations one should expect, high numbers of falsepositives obscure meaningful correlations and can leadto inaccurate interpretations of the data (Lovell et al.,2010).

Development of a suite of techniques verified to beboth precise and accurate will greatly assist both hypoth-esis generation and data explanation, especially throughthe development of causal models. In dysbioses,whether at the scale of our own gut or of entire ecosys-tems, knowing which organisms correlate, co-vary anddepend on one another could have radical implicationsfor correcting the ecological imbalance. For example,identifying members of the community that are centrallyor critically located within the metabolic network of a dys-biotic community via correlation analysis of multiplelevels of omic data could provide new targets for inter-vention, and, coupled with sensitivity analysis and Baye-sian network inference techniques, improved predictionsabout causality. Recent advances in treatment of refrac-tory Clostridium difficile infections using faecal commu-nity isolates (Lawley et al., 2012) demonstrate how morerobust network analyses could be deployed. A provenway to analyse co-occurrences among metabolites andtaxa could significantly reduce the time from hypothesisto treatment. Using microbial communities for environ-mental remodelling (remediation, extraction, etc.) relieson keeping those communities operating efficiently androbust to invasion – both of which could be greatlyassisted by knowing the co-occurrence and covariancepatterns.

References

Faust, K., Sathirapongsasuti, J.F., Izard, J., Segata, N.,Gevers, D., Raes, J., and Huttenhower, C. (2012) Microbialco-occurrence relationships in the human microbiome.PLoS Comput Biol 8: e1002606. doi: 10.1371/journal.pcbi.1002606.

Friedman, J., and Alm, E.J. (2012) Inferring correlation net-works from genomic survey data. PLoS Comput Biol 8:e1002687. doi: 10.1371/journal.pcbi.1002687.

Lawley, T.D., Clare, S., Walker, A.W., Stares, M.D., Connor,

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T.R., Raisen, C., et al. (2012) Targeted restoration of theintestinal microbiota with a simple, defined bacteriotherapyresolves relapsing Clostridium difficile disease in mice.PLoS Pathog 8: e1002995. doi: 10.1371/journal.ppat.1002995.

Lovell, D., Müller, W.M., Taylor, J., Zwart, A., and Helliwell, C.(2010) Caution! Compositions! Can constraints on omicsdata lead analyses astray? CSIRO: 1–44.

Romano, J.P., Shaikh, A.M., and Wolf, M. (2008) Control ofthe false discovery rate under dependence using the boot-strap and subsampling. Test 17: 417–442.

Xia, L.C., Steele, J.A., Cram, J.A., Cardon, Z.G., Simmons,S.L., Vallino, J.J., et al. (2011) Extended local similarityanalysis (eLSA) of microbial community and other timeseries data with replicates. BMC Syst Biol 5 (Suppl. 2):S15.

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