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The oxidation of iron is a process withwhich we are all familiar. The surfaceof Mars, the rust on our cars, the blood

in our veins — all bear the conspicuous redcolour that marks the oxidation of iron fromits metallic (Fe),or divalent (Fe2�), form to atrivalent cationic species (Fe3�). At atmos-pheric pressure, the process of iron oxida-tion in the presence of oxygen depends onthe partial pressure of gaseous oxygen (oftenreferred to in geological literature as oxygenfugacity). Earth scientists have used thesame thermodynamic formulations, withgreat success, to calibrate the oxidation stateof iron in the rocks and minerals of Earth’supper mantle, at much higher pressures andtemperatures1. Now Frost et al.2 (page 409 ofthis issue) show that, when it comes to themore extreme pressure conditions in the lowermantle, we must abandon our conventionalthinking about oxidation state.

Geology students are taught that certainminerals — such as (Mg,Fe)Al2O4,or ‘spinel’— serve as sensors of oxidation state becausetheir Fe3�/Fe2� ratios are known, experi-mentally and thermochemically, to dependon oxygen partial pressure. From such ‘geo-barometry’,we have learnt that Earth’s uppermantle is much too oxidized to be in chemi-cal equilibrium with metallic iron1 — theabundance of Fe3� is too great for metalliciron to be stable. In contrast, we are quiteconfident that the mantle as a whole was inchemical equilibrium with metallic iron asthe planet’s core formed (when Earth wasless than 50 million years old)3. How theuppermost mantle, the part we can sample,became so oxidized is unknown.

The portion of the mantle at depthsgreater than 660 km, referred to as thelower mantle, is thought to be dominated(70–80% by weight) by a dense, magne-sium–iron silicate, (Mg,Fe,Al)(Si,Al)O3.This mineral has a stable ‘perovskite’ crystalstructure (a structure well known in ceram-ics to have an array of rich and unusual prop-erties). The oxygen content of the lowermantle is unknown because we have nodirect rock samples,but it is usually assumedto be similar to that of the upper mantle. In1997, McCammon4 reported the surprisingdiscovery that magnesium–silicate perov-skites synthesized in the laboratory can havevery high Fe3�/Fe2� ratios — much higher,for example, than are preserved by spinel inthe relatively oxidized upper mantle. How-ever, the relationship between the oxygen

partial pressure and the Fe3�/Fe2� ratio inperovskite remained unclear because of thedifficulty of knowing (let alone controlling)the oxygen content in experiments atextreme conditions.

Frost et al.2 have performed experimentsin which they brought magnesium per-ovskite into chemical equilibrium withmetallic iron under pressure–temperatureconditions typical of the uppermost lowermantle. They made the remarkable discov-ery that, with very low oxygen content intheir samples, the perovskite synthesizedcan have surprisingly high Fe3�/Fe2� ratios— similar, in fact, to the ratios measured inperovskites synthesized at very high oxygencontents.This decoupling of oxygen contentfrom the Fe3�/Fe2� ratio shatters our tra-ditional view of oxidation state based onoxygen partial pressure.

This result leads Frost et al.2 to predictthat a free iron-metal phase should exist inEarth’s lower mantle — only 1% by weight,but nevertheless an important component.Ironically, this metal phase forms because ofthe high Fe3�/Fe2� ratio in perovskite,ratherthan being excluded by it. For a mineral toachieve a high Fe3�/Fe2� ratio, oxygen mustbe available to convert FeO into Fe2O3 (Fe2�

to Fe3�). This could happen in the mantle ifsome chemical reaction serves as an oxygenpump: for example, the reduction of CO2 toform C and O2. An alternative mechanism

Earth science

Putting the squeeze on oxidation Michael J. Walter

The results of experiments conducted under the extreme temperatureand pressure conditions of Earth’s lower mantle suggest that, at thesedepths, low oxygen content is no barrier to iron oxidation.

proposed by Frost et al. is that magnesiumperovskite has such an affinity for Fe3�thatit is energetically favourable for FeO inperovskite to ‘self-reduce’ by the reaction3Fe2�O ➝ Fe (metal) � 2Fe3�O1.5. In thiscase,no external oxygen pump is needed anda free iron-metal phase forms out of thenecessity for mass balance.

The lower mantle constitutes a large vol-ume of the Earth, about 58%, so even a smallamount of metallic iron represents a sub-stantial reservoir — several per cent of allmetallic iron in the planet. The lower mantlemay first have formed because of the increasein pressure as the solid Earth grew, or it mayhave crystallized from a deep magma ocean.The core formed very early in Earth’s historyby the segregation of metal from silicate, alarge-scale differentiation event that substan-tially decreased the Fe/O ratio of the silicatemantle. This ratio, dictated by metal–silicateequilibrium, would have been considerablyhigher than the current Fe/O ratio in theupper mantle. Frost et al.2 calculate that ifabout 10% of the perovskite-manufacturedfree-metal phase were mechanically re-moved from the lower mantle throughinteraction with descending plumes of ironduring core segregation, the mantle couldhave acquired its present Fe/O ratio. Thus,Frost and colleagues’ discovery may explainthe vexing question of why the upper mantleseems to have had such a high oxidation statefor the past 4 billion years5. ■

Michael J. Walter is at the Institute for Study of theEarth’s Interior, Okayama University, Misasa,Tottori 682-0193, Japan.e-mail: walter@misasa.okayama-u.ac.jp 1. O’Neill, H. S. C. & Wall, J. V. J. Petrol. 28, 1169–1191 (1987).

2. Frost, D. J. et al. Nature 428, 409–412 (2004).

3. Walter, M. J. et al. in Origin of the Earth and Moon (eds Canup,

R. M. & Righter, K.) 265–290 (Univ. Arizona Press, Tucson, 2000).

4. McCammon, C. Nature 387, 694–696 (1997).

5. Canil, D. Nature 389, 842–845 (1997).

Medicine

Profile of a tumourOlli Kallioniemi

Molecular profiling — the comprehensive analysis of genes, RNAs andproteins — is having a radical effect on our understanding of cancer. Thenext step is to convert these findings into better diagnosis and treatment.

The scale of cancer research projectshas increased enormously over thepast decade or so, presenting scientists

with the opportunity to investigate the roleof all human genes in cancer. This changehas been wrought largely by advances inhigh-throughput technologies for analysingthe human genome, transcriptome andproteome — genes, messenger RNAs andproteins — and in bioinformatic and statis-tical methods for exploring the gigabytes ofdata that are generated. As more is learnt

about the genes and signalling pathways thatpromote the proliferation of cancer cells,prevent their death or allow their spread toother organs, it should become feasible todevelop new methods of diagnosis, as well astreatments that are tailor-made to fit thespecific molecular patterns of individualpatients.But realizing these ideas is proving achallenge,as a recent meeting* made clear.

In one approach for converting the

*CNIO Symposium: The Molecular Taxonomy of Cancer. Madrid,

Spain, 3–6 February 2004.

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knowledge obtained from ‘omics’techniquesinto clinical application, researchers areattempting to use microarrays — a means ofanalysing patterns of gene expression bylooking at the mRNAs present — to deter-mine the appropriate treatment for differentpatients. For instance, microarray profilingof breast tumours, using a set of 70 informa-tive genes, has revealed gene-expression sig-natures that are associated with a good or apoor prognosis1,2. In these studies, roughly40% of early-stage breast cancers turned outto have a ‘good’ signature, associated withonly a 15% risk of metastasis (the spread oftumour cells) and a 5% risk of death by 10years after diagnosis. According to currentclinical practice, most patients with early-stage breast cancer receive adjuvant therapy(chemotherapy or endocrine therapy aftersurgery). But less than 1% of the patientswith a ‘good prognosis’ signature would belikely to benefit, and the treatments oftenhave harmful side effects (discussed by L.Van’t Veer, Netherlands Cancer Institute,Amsterdam)2. So researchers now suggestusing the ‘poor’ signature to guide theadministration of adjuvant therapy to thosefor whom it would be most useful.

Indeed, gene-expression profiling isalready being introduced clinically as a diag-nostic procedure in academic centres in theNetherlands (L. Van’t Veer), with similarprogrammes being launched in the UnitedStates.The profiles have been validated retro-spectively in several cohorts of patients.Prospective validation, however, can only beachieved after 5–10 years. Also — as several

speakers discussed — some technical issueshave yet to be resolved, including how to pro-cure and process samples and to ensurereproducibility and quality control. Never-theless, various analysis platforms and shortlists of diagnostic genes are being developedto predict the prognosis associated with othertypes of tumours, such as lymphomas (L.Staudt, NCI, Bethesda, and M. Piris, CNIO,Madrid), or to predict the likelihood of par-ticular endpoints, such as metastasis (T.Golub, Broad Institute, Cambridge, Massa-chusetts). Predicting a patient’s response tospecific types of treatment would, of course,be the most important application.But it maytake years for the approach to meet with regu-latory approval and be extended from acade-mic centres to mainstream practice.

Another strand of research involves theproteomic profiling of blood serum. Thiscould soon complement — if not replace —the use of established tumour ‘markers’ incancer screening and early diagnosis (E.Petricoin, FDA, Bethesda). Whereas specifictumour markers are often used singly, pro-teomic serum profiling involves the use ofmass spectrometry to identify up to 15,000peaks, representing proteins and proteinfragments that are defined by their mass-to-charge ratios3. The sensitivity and specificityof such profiles has exceeded 90% for thediagnosis of lung cancer, and approached100% for ovarian cancer (E.Petricoin).Mostof the peaks have still to be identified, andseveral investigators argued that panels ofspecific immunoassays, which use antibod-ies that recognize particular proteins, should

be developed for diagnosis instead. But it isthe fragments, not intact proteins, that areoften the most informative diagnostically,and there may be dozens — if not hundreds— of such fragments, possibly existing incomplexes with one another and with otherserum proteins. That makes the develop-ment of specific assays demanding.

A further application of proteomics con-cerns the analysis of signalling pathways, forexample by studying the levels of specificphosphorylated proteins — a key feature ofmany pathways — in tumour samples (E.Petricoin). Analysis of these signalling pro-files is being incorporated into many clinicaltrials to identify biological markers that indi-cate which tumours are likely to respond totreatment. Proteomic tumour profiling isoften carried out from specific cell types,acquired by microdissection of the tumours.Such studies could also help us to understandthe tumour microenvironment, where manydifferent cell types and interactions mayaffect tumour behaviour. For example, inbreast cancer, characteristics of the stromalfibroblast cells adjacent to the tumour mightchange, and contribute to, cancer progres-sion (R.Weinberg,MIT) — much as does thegrowth of new blood vessels.

Moving on to studies of gene sequence(genomics), mutations in roughly 1% ofhuman genes (291 genes in total) werereported to contribute to cancer (M. Strat-ton, Sanger Centre, Hinxton)4. Twenty-seven of these genes, more than would beexpected by chance, encode protein kinases— enzymes that phosphorylate other pro-

In January this year, NASA’s Stardustspacecraft flew within 237 km of thecomet Wild2. Stardust took 72close-up shots during the flyby,described as an “unqualifiedsuccess” at the Lunar and PlanetaryScience Conference in Houston,Texas, last week. After a fewmonths’ analysis, the Stardust teamhas now released stunning picturesof the comet’s 5-km nucleus.

The Wild2 comet is ‘fresh’. It isbelieved to have spent billions ofyears in the Kuiper belt, out beyondNeptune, until gravitationalencounters kicked it into orbit closerto the Sun. In 1974, a closeencounter with Jupiter knockedWild2 into its present orbit, insidethat planet’s. Since then, Wild2 haspassed close to the Sun only fivetimes and so has suffered lessexposure to solar radiation thanother comets. Its surface bears a

well-preserved record of its earlyhistory in the Kuiper belt.

The short-exposure image at thecentre of this composite shows thecometary surface. The surface ismarkedly different from those of thecomets Halley and Borrelly, and ofJupiter’s ice-rich moons Callisto andGanymede: instead of a continuumof crater size, Wild2’s craters aremostly large; some are likely to beimpact craters, others not. A long-exposure image taken 10 secondslater reveals a glowing halo of gas,thrown out from the nucleus in asmany as 20 highly collimated jets.

Stardust’s mission doesn’t endthere. The spacecraft is nowheading for Earth once more,carrying particles from the comet’scoma that were trapped during theflyby in centimetre-size aerogelcells. The samples will arrive on 15January 2006. Alison Wright

Planetary science

Stardust’s comet memories NA

SA/J

PL

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teins. As several approved cancer drugs areinhibitors of tumour-associated kinases,such as BCR/ABL, KIT, HER-2 and the epi-dermal growth factor receptor, the searchfor mutated kinases is important. Anothermutated kinase is BRAF, identified in a large-scale DNA-sequencing project to be alteredin melanomas (reviewed in ref. 4). But anongoing project to sequence the genesencoding 478 kinases in a panel of breastcancers has yet to replicate the success of theBRAF example (M.Stratton).

A new way to discover the roles of particu-lar genes in cancer is to use cultured cancercells and the technology of RNA interferenceto decrease gene expression. Several academ-ic centres and corporations are developinglibraries of RNA interference reagents thatreduce the expression of any of the 30,000human genes (G. Hannon, Cold Spring Har-bor Laboratory; see also page 375). Theselibraries will make it possible to identify thefunctions of genes that control crucial prop-erties of cancer,and could therefore be attrac-tive drug targets. Moreover, several otherapproaches to the development of cancertreatments are being launched.When specific

target proteins are not known, gene-expres-sion profiles may be used to screen for drugsthat modulate cancer-cell behaviour (T.Golub)5. For example, the expression patternof five genes distinguishes leukaemic cellsfrom normal blood cells and can be used asa readout to search for drugs that induceleukaemic cells to differentiate5.

Roughly 500 new anticancer drugs are inclinical trials,and many more should soon beunder development as a result of new tech-nologies. At the same time, advances in themolecular profiling of tumours could help todefine diagnostic patterns that identify thosepatients most likely to benefit from the newtreatments. Although numerous challengesstill lie ahead, the future of personalizedcancer treatment looks promising. ■

Olli Kallioniemi is at the Medical BiotechnologyUnit, VTT Technical Research Centre of Finland,and the University of Turku, FIN-20521 Turku,Finland.e-mail: olli.kallioniemi@vtt.fi1. van’t Veer, L. J. et al. Nature 415, 530–536 (2002).

2. Van de Vijver, M. J. et al. N. Engl. J. Med. 347, 1999–2009 (2002).

3. Alexe, G. et al. Proteomics 4, 766–783 (2004).

4. Futreal, P. A. et al. Nature Rev. Cancer 4, 177–183 (2004).

5. Stegmaier, K. et al. Nature Genet. 36, 257–263 (2004).

produced either by fluctuations in anoscillatory spike-generator or by activity-dependent changes in threshold. Long inter-vals followed by short ones (and vice versa)result in negative interval correlations.

The essence of spike generation is thatinputs too weak to trigger a response aresummed,giving a potential; when the poten-tial reaches a threshold, a spike is generated.Chacron et al.1 use a ‘perfect integratormodel’5 for both spike-generation schemes,with a random threshold drawn from a uni-form distribution. When the threshold isreached the potential is reset, either by anamount dependent on the magnitude of thethreshold just reached (to produce serialcorrelation), or by a random amount (togenerate a spike train with the same inter-spike-interval density but no correlation).

The advantage of using this simplemodel is that the spectra, coherence andinformation transmission rates (mutualinformation rates) can all be calculated, aswell as estimated through computer simula-tion. Chacron et al. demonstrate that a nega-tive serial correlation between spikes reducesthe spike-train spectrum and coherence atvery low frequencies, although at middle-range frequencies the coherence is increased.Serial correlation also enhances the ability ofthe model to transmit information by reduc-ing low-frequency noise compared with therenewal process. Such effects have been seenin more realistic neural models6, and can bequantified in real neural spike trains; but thecomplexity of both of these situations hadmeant that the mechanism for improvedinformation transfer was unclear.

Whether or not this increase in the infor-mation transmission rate is exploited inneural systems is an open question, as bio-logical evolution produces systems that workwell enough and are robust, without neces-sarily being optimally efficient. The nervoussystem responds to a spike train in real timeand does not process it as an indefinitesequence. But the effect of correlation on thetransmission rate in a single spike trainmight transfer to correlations between mul-tiple spike trains: variability between differ-ent neurons could be correlated, throughcommon inputs and feedback7,and couplingwithin a population could lead to a similarreduction in variability by noise shaping8. ■

Arun V. Holden is in the School of BiomedicalSciences, University of Leeds, Leeds LS2 9JT, UK.e-mail: arun@cbiol.leeds.ac.uk1. Chacron, M. J., Lindner, B. & Longtin, A. Phys. Rev. Lett. 92,

080601 (2004).

2. Borst, A. & Theunissen, F. E. Nature Neurosci. 2, 947–957 (1999).

3. Jaramillo, F. & Wisenfeld, K. Nature Neurosci. 1, 384–388 (1998).

4. Ratnam, R. & Nelson, M. E. J. Neurosci. 20, 6672–6683 (2000).

5. Stein, R. B., French, A. S. & Holden, A.V. Biophys. J. 12, 295–322

(1972).

6. Chacron, M. J., Longtin, A. & Maler, L. J. Neurosci. 21,

5328–5343 (2001).

7. Azouz, R. & Gray, C. M. J. Neurosci. 19, 2209–2223 (1999).

8. Marr, D. J., Chow, C. C., Gerstner, W., Adams, R. W. & Collins,

J. J. Proc. Natl Acad. Sci. USA 96, 10450–10455 (1999).

Anerve cell is an example of a systemwhose excitation codes and transmitsdynamic information.When a stimu-

lus is above a given threshold, the neuronresponds by generating an action potential,so that over time it fires irregular sequencesof all-or-nothing spike responses. The ioniccell mechanisms that generate the spike areunderstood,but there are many mechanismsfor coding the spike train, and our knowl-edge of these mechanisms ranges from wellestablished to speculative. In Physical ReviewLetters, Chacron et al.1 show, using a simpleaction-potential model, that correlationsbetween sequential interspike intervals canshape the noise spectrum — and that thisshaping can increase the transmission ofinformation, because it reduces the noisespectrum at low frequencies.

Information theory is essentially linear2:a reaction is directly proportional to anaction. So it might be expected that the con-straints imposed by interval correlationswould reduce the transmission of informa-tion. However, spike generation is a stronglynonlinear,excitable process with a threshold,and such systems can behave counter-intuitively. A good example is stochastic

resonance3, in which additive noise canincrease, rather than decrease, the efficiencyof information transmission.

A spike train can be represented by thesequence of intervals between spikes; this ischaracterized by the interval statistics (inthe time domain by probability distributionsand correlations, and in the frequencydomain by spectral densities).Chacron et al.1

consider two schemes of spike generation.The first produces a ‘renewal process’ thathas no memory of the excitation because thesystem resets itself each time a spike is gener-ated. Here, there is no correlation betweensuccessive spike intervals. The probabilitydistributions for higher-order intervals (say,between one spike and the third spike follow-ing) and the ‘autocorrelation’ (the probabili-ty of a spike occurring after some other spike,irrespective of how many intervening spikesthere were) can be calculated directly fromthe interspike-interval probability density.

The second scheme generates a non-renewal spike train, with correlationsbetween adjacent intervals. Spike trains ofsensory neurons4 with a constant stimulusoften show dependencies between neigh-bouring intervals; such dependencies are

Signal processing

Neural coding by correlation?Arun V. Holden

Noise limits the efficiency of information transfer. But correlations of theintervals between signal pulses can reduce low-frequency noise andthereby increase the transfer of information.

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