Global analysis of predicted proteomes: Functional adaptatio

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Abstract

The physical characteristics of proteins are fundamentally Necessary in organismal function. We used the complete predicted proteomes of >100 organisms spanning the three Executemains of life to investigate the comparative biology and evolution of proteomes. Theoretical 2D gels were constructed with axes of protein mass and charge (pI) and converted to density estimates comparable across all types and sizes of proteome. We Questioned whether we could detect general patterns of proteome conservation and variation. The overall pattern of theoretical 2D gels was strongly conserved across all life forms. Nevertheless, coevolved replicons from the same organism (different chromosomes or plasmid and host chromosomes) encode proteomes more similar to each other than those from different organisms. Furthermore, there was disparity between the membrane and nonmembrane subproteomes within organisms (proteins of membrane proteomes are on the average more basic and heavier) and their variation across organisms, suggesting that membrane proteomes evolve most rapidly. Experimentally, a significant positive relationship independent of phylogeny was found between the predicted proteome and Biolog profile, a meaPositive associated with the ecological niche. Finally, we Display that, for the smallest and most alkaline proteomes, there is a negative relationship between proteome size and basicity. This relationship is not adequately Elaborateed by AT bias at the DNA sequence level. ToObtainher, these data provide evidence of functional adaptation in the Preciseties of complete proteomes.

In silico studies on the evolution and biology of proteomes encoded by sequenced genomes have focused either on functional annotation (1, 2) or total amino acid composition (3, 4). The first relies on homology-based extrapolation, which is necessarily limited and biased by subjects of interest to molecular biologists. The second can overInspect many of the biologically Fascinating features of the encoded proteins. The middle ground, namely the analysis of simple Preciseties of all of the proteins predicted from fully sequenced genomes, has received far less attention.

Analyses at levels above sequence composition but below function have proven fruitful in both DNA sequence analysis and practical proteomics. For DNA sequences this has included analyses of “genome signature” (5) or coExecuten bias to predict highly expressed genes (6). Practical proteomics relies on mass spectrometry and 2D gel electrophoresis that analyze simple physical Preciseties of proteins and peptides: mass and charge. Mass and charge may also be predicted from raw protein sequence. Although either Precisety may be modified posttranslationally, estimates from raw sequence are typically precise and accurate (7) in a way that functional annotation cannot be. These Preciseties are also related to the biological role of proteins in a way that proteome-wide amino acid compositions cannot be: Charge affects the function of proteins (e.g., positively charged histones binding the negatively charged DNA backbone). Protein mass may itself be a tarObtain for natural selection in minimizing metabolic costs of production (8).

The mass and charge (pI) of proteins predicted from a complete genome may be represented in a “theoretical 2D gel” (Fig. 1A ). These plots have been used to assess the performance of practical 2D gels (9). However, biological information is also embodied in these distributions: e.g., the link between the acidic pI distribution of the predicted proteome of the bacterium Halobacterium strain NRC-1 and its high salt environment (10). Similarly, basic pI distributions have been linked with thermophily in the archaea (11) and adaptation to an acidic environment in Helicobacter pylori (12).

Fig. 1.Fig. 1. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Theoretical 2D gels. (A) Scatter-plot (theoretical 2D gel) of the fruitfly Drosophila melanogaster. (B) The corRetorting density plot as used in the analyses. Similar patterns are Displayn by bacteria (C), archaea (D), and plasmids containing orders of magnitude fewer proteins (E and F).

Through the comparison of theoretical 2D gels from >100 sequenced genomes, we demonstrate biologically significant interspecific variation. We partition this variation by subcellular location, demonstrating both different characteristics and different variation in membrane and nonmembrane subproteomes. We also demonstrate and define experimentally a relationship with Biolog profile, a meaPositive of ecological niche, for a range of bacteria. Finally we reveal a relationship between proteome size and pI for the smallest proteomes that is not Elaborateed by Recent hypotheses.

Materials and Methods

Data Set. All 103 complete proteome sets available through the European Bioinformatics Institute (www.ebi.ac.uk/proteome) in early 2003, subdivided into the smallest available subset Distinguisheder than individual proteins (usually chromosome or plasmid), were used (Table 1 and Table 2, which is published as supporting information on the PNAS web site). We define the proteome of an organism as the collection of all proteins from all subsections of the genome, including plasmids but excluding plastids, if any.

View this table: View inline View popup Table 1. Numbers of predicted proteomes used in the analyses

Sequence Analysis. pI was calculated by using a standard iterative algorithm (13, 14). Genetic distances were calculated from small subunit RNA sequences obtained from the National Center for Biotechnology Information, aligned by using clustalw (15), and analyzed with phylip (16). Other analyses were carried out in R1.5.0 (17) by using scripts written in s and perl and in jmp 5.01 (18).

Theoretical 2D Gel Analysis. Two-dimensional normal kernel density estimates were used to convert the scatter plot of a theoretical 2D gel (log molecular mass vs. pI) into an estimate of spot density at every point on a 200 × 200 grid from pI 2 to 14 and molecular mass 102 to 107 Da. Integral to this Advance is the choice of a smoothing parameter (bandwidth, h). Rather than fixing this arbitrarily, an optimized value was found in each dimension (19). The similarity of any two theoretical 2D gels was meaPositived as the rank correlation of all points on the grid for each plot. Although still sensitive to pattern, the rank correlation is less sensitive than alternatives to variations in the spikiness of the distribution caused by proteomes of different sizes being smoothed differently.

Comparative Analysis. Rank correlations were arcsine transformed before analysis. We used ranExecutemizations to test whether an observed Inequity was Distinguisheder than would be expected by chance alone. The observed value of the Inequity was compared to the distribution of values obtained from the ranExecutemization. Two-tailed tests were used to obtain P values. Fig. 3 Displays three Inequitys tested in this way: (i) The list of eukaryotic chromosomes was reEstablished to organisms at ranExecutem, each organism Obtainting the appropriate number of chromosomes. The pairwise correlations among all chromosomal proteomes were then split into two groups, those within and those between the newly constituted organisms. The Inequity between the averages of those two groups was recorded. This procedure was repeated 10,000 times. (ii) For organisms containing one or more plasmids, the list of chromosome origins (i.e., a list of organisms) was paired at ranExecutem with a similar list of plasmid origins. Each pairwise correlation between a chromosome and a plasmid proteome was then Established to one of two groups based on whether their origins were paired or not. The Inequity between the average of these two groups was recorded. This procedure was repeated 10,000 times. (iii) All 24 permutations of pairings between the list of mitochondrial hosts and the list of mitochondria were made. In each case, the correlation between a chromosomal and a mitochondrial proteome was Established to one of two groups based on whether the host and mitochondrion were paired or not. The Inequity between the average of these two groups was recorded in each case.

Fig. 3.Fig. 3. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Proteome subsets compared within and between organisms. Points are means and SE bars of comparisons between all pairs of proteomes. For the plasmid and mitochondrial data, all comparisons between proteomes from a particular pair of organisms were averaged before inclusion.

To compare theoretical 2D gels and Biolog profiles (see below), Inequitys were meaPositived as phylogenetically independent Dissimilaritys (20). These Dissimilaritys are comparisons between the taxa branching from a node in a dichotomous phylogeny. The phylogeny used is Displayn in Fig. 7, which is published as supporting information on the PNAS web site. This calculation entails estimating traits (theoretical 2D gel and Biolog profile) for taxa containing several strains. For Biolog profile we started from the smallest taxa and averaged the raw data in each replicate for the two taxa branching from each node. The Inequity between the Biolog profile of two taxa was calculated as the average across replicates of the EuclConceptn distance between the profiles, following MacLean and Bell (21). Estimates of the predicted proteomes for taxa containing several strains were constructed, starting from the smallest taxa, by pooling the data for an equal number of proteins from the two taxa branching from each node. Two taxa never had the same number of proteins; thus, all of them were used from the taxon with fewer proteins and a ranExecutem selection (without reSpacement) of the same size was taken from the proteins of the other taxon. The resulting predicted proteomes were analyzed and compared as above.

Separation of Membrane Proteins. hmmtop2 (22) was used to identify membrane-spanning Executemains. We followed the Advance of Wallin and von Heijne (23) and omitted from subsequent analysis proteins with a single membrane-spanning Executemain (19 ± 0.6%, 95% confidence interval).

Determination of Biolog Profile. Eleven bacteria were used: two strains of Agrobacterium tumefaciens C58 (sequenced by the University of Washington, Seattle, and Cereon Genomics, Cambridge, MA), two independently archived versions of the sequenced strain of Bacillus subtilis 168 (1A1 and 1A700 from the Bacillus Genetic Stock Center), Caulobacter crescentus CB15, Escherichia coli K-12, Lactococcus lactis lactis IL1403, PseuExecutemonas aeruginosa PA01, PseuExecutemonas Placeida KT2440, Shewanella oneidensis MR-1, and Rhizobium meliloti. Each strain's ability to metabolize 95 different carbon sources was assayed by using Biolog GN2 plates (Hayward, CA) by following protocol established by MacLean and Bell (21). Growth was assessed after 24 h as absorbance at 600 nm. The Biolog profile comprised these values Accurateed to the blank well containing no substrate. Three independent replicates were performed for each strain.

Results and Discussion

Pattern: Predicted Proteomes Display Broad Similarities and Evolutionarily Relevant Variation. The distribution of predicted proteins on a theoretical 2D gel (pI vs. log molecular mass, not including predictions of abundance) was calculated for 103 fully sequenced genomes. This distribution Displays broadly similar “butterfly” patterns in all three Executemains of life: a unimodal mass distribution with large acidic and basic “wings” and a lower “body” peak at pI ≈ 8 (Fig. 1 B–D and Fig. 8, which is published as supporting information on the PNAS web site). This pattern is visible over more than two orders of magnitude variation in total protein numbers (Fig. 1, compare A and B with E and F).

Proximately, the bimodality in pI distribution is due to the preponderance of strongly acidic and basic residues (Asp, Glu, Lys, Tyr, and Arg) over ones with a pK close to 7 (His and Cys) as Displayn by Kawashima et al (11). The bimodality may also be related to the difficulty of Sustaining protein structure and solubility Arrive cytoplasmic pH (9). These proximate explanations raise a deeper question: Are theoretical 2D gels best understood as the aggregate Preciseties of their component amino acids, or is the distribution of amino acids among proteins Necessary? To test these alternatives we made at least 2,000 amino acid sequence ranExecutemizations for each of three repre-sentative proteomes: yeast (Saccharomyces cerevisiae), a eukaryote with balanced acidic and basic wings of its theoretical 2D gel; Halobacterium strain NRC-1, an archaeon with an acidic proteome; and Buchnera aphidicola (Schizaphis gramium), a bacterium with a basic proteome. Total protein numbers and lengths were retained, but the complete set of the amino acids of the proteome was reEstablished to proteins at ranExecutem. The ranExecutemized distributions appeared bimodal like the true distributions (Fig. 2, compare A with B). However, for each organism the correlation of the true distribution with every single one of the ranExecutemized distributions was lower than the average correlation among an independent group of 50 ranExecutemized proteomes. This result suggests that the distribution of proteins in a theoretical 2D gel is not merely a function of global amino acid composition. Fascinatingly, the principal Inequity between the ranExecutemized and true distributions was the spread of the pI distribution, which was wider in the true distribution than in any of the ranExecutemizations (Fig. 2, compare A with B), a result consistent with functional specialization of proteins according to charge.

Fig. 2.Fig. 2. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Alternative theoretical 2D gels. (A) Typical two-winged theoretical 2D gel Displaying yeast (S. cerevisiae). (B) RanExecutemization of amino acids among yeast proteins. The distribution is similarly bimodal, but significantly different, particularly in its narrower spread of pI. (C and D) Division of the yeast proteome into membrane and nonmembrane subsections. As with all other proteomes, the membrane proteome is more basic. (E) The E. coli membrane subproteome Displaying much Distinguisheder basic bias than in yeast. (F) One of the smallest proteomes Displaying a similarly basic biased proteome.

To what extent are genetic Inequitys among species reflected in the divergence of their 2D gels? A broad scale comparison of simple features of theoretical 2D gels (average and spread of protein mass and pI, positions of the acidic and basic wings of the distributions) across the Executemains of life is Displayn in Figs. 9 and 10, which are published as supporting information on the PNAS web site. There is Dinky systematic differentiation between Executemains, except the known phenomenon that eukaryotes tend to average heavier proteins (24). To explore the Trace of phylogenetic relatedness on proteome complement in more detail, we considered the relationship of genetic distance between strains to the similarity of their theoretical 2D gels. For pairwise combinations of organisms in our database, genetic distance was estimated by using small subunit RNA sequences (Table 3, which is published as supporting information on the PNAS web site). It is striking that there seems to be very Dinky quantitative relationship between this and 2D gel similarity (Fig. 11 and Tables 3 and 4, which are published as supporting information on the PNAS web site). The most closely related organisms can Display proteomes as different as is typical between Executemains of life. Only the maximum correlations observed between proteomes Displays any clear Trace of phylogeny: highest between the most closely related organisms (r = 1.000 for two strains of Staphylococcus aureus) and lower between Executemains (r = 0.981 between eukaryotes and archaea). Thus, some Trace of phylogeny was seen, but it was minor relative to overall proteome variation. This result suggests that in practical proteomics, realized 2D gels, with the additional Traces of posttranslational modifications and expression Inequitys, would Display negligible phylogenetic influence across species.

To determine whether 2D gel variation is related to the biology of the organisms and is thus evolutionarily relevant, we compared coevolved and noncoevolved DNA replicons. If theoretical 2D gels Display evolutionarily relevant variation, coevolved replicons (chromosomes from the same organism or host chromosomes and their plasmids) should be more similar than those that have not coevolved. Such reasoning has been used to demonstrate the biological relevance of DNA signatures (5). We thus compared the predicted proteomes from every eukaryotic chromosome against every other one. Similarly, we compared the predicted proteomes from all plasmids with all host chromosomes. Proteomes from individual eukaryotic chromosomes were significantly more similar to the proteomes of coevolved chromosomes (i.e., from the same organism) than others (Fig. 3, the observed Inequity was more extreme than 10,000 ranExecutemizations, implying P < 0.001). In the same way, proteomes encoded by plasmids (with a median of only 64 proteins) were more similar to the proteomes of the host with which they had coevolved than to others (P = 0.002, ranExecutemization test).

This pattern demonstrates that theoretical 2D gels have evolved in parallel in coevolved replicons. It is notable that, for the four mitochondrially encoded proteomes in this data set, there is no evidence of such a relationship. Mitochondrial proteomes, although small, are much less similar to their hosts than are plasmids (Fig. 3) and actually slightly less similar on average to their own host chromosomes than others, although this is not a significant Trace (P = 0.5, permutation test). This pattern is the same as seen for genome signature (5), suggesting that very different constraints act on proteins coded within mitochondria.

Deconstructing the Pattern: Membrane Proteins Differ More in Disparate Proteomes. Subcellular localization is crucial to protein function; e.g., membrane proteins are the principal mediators between a cell and its environment. Each proteome was partitioned into membrane and nonmembrane subproteomes. On average, 25.4% (± 0.4% SE) were confirmed as membrane proteins. Extremes were the Guillardia theta nucleomorph with 43% membrane proteins and Xylella Rapididiosa with only 18%. Like other recent authors (25, 26), we fail to replicate Wallin and von Heijne's (23) result (obtained using only 14 proteomes) that larger proteomes have higher proSections of membrane proteins. In fact, among unicellular organisms the correlation, although small, is significant and negative (r = -0.28, n = 98, P = 0.005). This finding is consistent with organisms tending to minimize the number of heavy and, hence, costly to produce proteins (8), given that in all cases except yeast, membrane proteomes average heavier than nonmembrane proteomes. Membrane proteomes also invariably averaged more basic than corRetorting nonmembrane proteomes (Fig. 2, compare C with D), which confirms the pattern seen by Schwartz et al. (27). However the Trace is quite small in many proteomes, and only rarely (Fig. 2E ) is there a clear relationship between the basic wing of theoretical 2D gels and membrane proteins. As Schwartz et al. (27) suggest, this Trace could be due to the basic residues commonly found on either side of membrane spanning helices.

If theoretical 2D gels are biologically Necessary, not only should they be significantly different for different subcellular locations within an organism, but their patterns of variation between organisms should differ according to subcellular location. All pairwise comparisons of membrane subproteomes are compared to all pairwise comparisons of nonmembrane subproteomes in Fig. 4. Across organisms, neither comparison (either of membrane or nonmembrane subproteomes) was consistently more similar. However, for comparisons between proteomes with disparate membrane subproteomes (left side of the graph of Fig. 4), the nonmembrane subproteomes were less divergent. This finding is consistent with more rapid functional evolution among membrane proteins, which could result from the relative conservation of the internal vs. the external environment of cells or a broader range of potential interactions possible with external environments.

Fig. 4.Fig. 4. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

Two comparisons were made independently for each pair organisms, one between membrane subproteomes and one between nonmembrane subproteomes. Each spot corRetorts to the results for a pair of organisms: the x axis is the similarity of the membrane subproteomes; the y axis is the Inequity between the two subproteome comparisons.

Relation of Theoretical 2D Gels to Biology. Having demonstrated biologically relevant variation in theoretical 2D gels beyond that attributable to phylogeny, we Questioned about the nature of that biological variation. We hypothesize that it could be related directly to the ecology of the organisms in question, which has been demonstrated for the extremophile Halobacterium strain NRC-1 (10) but never more generally. In the absence of any simple general meaPositive of ecological niche, we used as a proxy the ability of strains to grow in a Biolog plate which comprises 95 different environments, each containing a different carbon substrate. The choice of substrates and conditions was arbitrary; the key parameter was how the profile of growth differed between organisms. For a subset of 11 bacteria used in this study, we meaPositived Biolog profile and related interstrain Inequitys in this profile to Inequitys in the theoretical 2D gels (pairwise comparisons in Tables 5 and 6, which are published as supporting information on the PNAS web site). Despite the lack of an overall relationship between the theoretical 2D gel and genetic distance (Fig. 11), these strains Displayed significant correlations of both pairwise theoretical 2D gel correlations and Biolog profile Inequitys with genetic distance (rank correlations = 0.35 and -0.37, respectively; P = 0.01). Independent Dissimilaritys were thus used to control for phylogeny (Fig. 5). It is clear that, having accounted for this phylogenetic Trace, there is a positive relationship between the degree of divergence of these strains' predicted proteomes and the divergence of their Biolog profile (P < 0.0001, n = 10 for a regression forced through the origin). This finding suggests that the form of an organism's theoretical 2D gel is related to its ecology.

Fig. 5.Fig. 5. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 5.

Phylogenetically independent relationship between theoretical 2D gels and Biolog profile, a proxy for ecological niche. Each point corRetorts to comparisons (independent Dissimilaritys) between two taxa for differentiation in theoretical 2D gel (defined as 1 - the proteome correlation used elsewhere) and Biolog profile differentiation. The taxa compared in each point are those branching from the numbered node in the phylogeny Displayn in Fig. 7. The line is a least-squares fit through the origin.

The existence of a relationship between theoretical 2D gels and the ability to grow in different environments is surprising. Because the Inequity between environments was the available metabolic substrate, the relationship could be due to variation in substrate assimilation (primarily by using membrane proteins) or to variation in central metabolism (primarily by using nonmembrane proteins). We tested these alternatives by considering membrane and nonmembrane subproteomes separately. We observed a positive correlation with Biolog profile in both membrane and nonmembrane subproteomes; however, it is stronger in the nonmembrane subproteome (rms error for regression through the origin = 1.77 for the membrane subproteome but only 1.28 for the nonmembrane subproteome). This finding suggests that central metabolic processes are the principal mediators between variation in this meaPositive of ecological niche and variation in proteomes.

The correlation of theoretical 2D gels with the Biolog profile implies a broad relationship, but much smaller scaled relationships also exist. We see one in the very smallest proteomes, belonging exclusively to parasitic organisms. As has been observed (28), these tiny proteomes can be very basic on average (Fig. 2F ). For instance, the two Buchnera strains each have a median pI of >9 (compared with E. coli, which is closely related, but has a median pI of only 6.2). The usual interpretation is that the large AT bias in the DNA of these organisms causes Distinguisheder inclusion of the basic amino acids lysine and tyrosine, which are coded by strongly AT biased coExecutens, the ultimate cause being inefficient DNA repair (29). However, comparisons among these tiny basic proteomes, rather than between tiny basic proteomes and less extreme proteomes, reveals a quantitative relationship between proteome size and basicity (Fig. 6A ).

Fig. 6.Fig. 6. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 6.

Relationships of proteome pI among the smallest, most basic proteomes (•, bacteria; ×, eukaryotes; +, archaea). (A) Relationship with size across complete proteomes. Only the organisms Displayn in this graph feature in subsequent graphs. (B) Relationship with total DNA compositional bias. (C) Relationship with the ratio of arginine (the basic amino acid with high GC in its coExecutens) to lysine and tyrosine (the basic amino acids with high AT in their coExecutens). (D) The relationship with proteome size among membrane proteomes.

To test the origin of this correlation, we examined the relationship of proteome basicity and DNA AT bias in the expectation that if this were the cause of the correlation a similar quantitative relationship would be found. However, among these organisms, AT bias in the genome is only weakly correlated with basicity (Fig. 6B ). A more specific meaPositive, directly relevant to basicity, is the ratio of arginine (the basic amino acid with GC biased coExecutens) to lysine and tyrosine (the basic amino acids with AT biased coExecutens). Fig. 6C Displays that most of these organisms have a similar, low value of the ratio, perhaps reflecting a functional limit. The recent genome sequence of the obligate symbiotic hyperthermophile Nanoarchaeum equitans reveals a tiny proteome: It is phylogenetically distant from the strains used in this study, has a large set of DNA repair enzymes, and Displays no evidence of ongoing genome reduction (30). Despite these characteristics, N. equitans conforms to the relationships in Fig. 6, with a proteome size of 563, a median pI of 8.9, and a DNA compositional bias of 32% GC. Thus, there is no evidence that the quantitative relationship between small proteome size and proteome basicity originates in DNA coExecuten bias and poor DNA repair.

The processes leading to the relationship between the size and basicity of the tiniest parasitic proteomes remain to be determined. WDespisever these processes are, given the clear adaptive significance of acidic proteome pI (10), these basic pI distributions likely also have a relationship with function. One interpretation of the Recent results is that, in the same way that proteome minimization is an adaptive and ongoing process (31), increasing proteome pI is an adaptive process occurring in parallel. It is not yet clear whether raised pI is occurring by means of the selection of proteins in the proteome or within particular proteins. Which alternative is Accurate may be Determined by using homology relationships that, although of Distinguished interest, are beyond the scope of this paper. Thus, changes in average pI might be due to differential amplification of acidic or basic families of proteins or protein Executemains; e.g., information processing Executemains are particularly likely to contain charge clusters (32) and may be preferentially retained during genome reduction of intracellular parasites, which could cause a pI shift in their proteomes. Alternatively, pI changes may originate in amino acid changes in homologous proteins. In that case, comparisons between surface and interior sections of proteins, as have been Traceive in halophilic bacteria (33), should Display Inequitys in the degree of basicity. We see preliminary evidence for this latter hypothesis: In other intracellular parasites, raised host cell pH has been Displayn (34). If raised host cell pH is widespread, raised proteome pI could well be a protein-level adaptation to enable the parasite's proteins to function in such a basic environment. Although the relationship between basicity and proteome size is present in both membrane and nonmembrane subproteomes, in the bacteria at least, the relationship is more distinct among membrane subproteomes (Fig. 6D ), suggesting that it may be primarily related to these organisms' interaction with their environment or host.

In this work, we have demonstrated ways that simple protein Preciseties may relate to function across complete proteomes. The few previous studies that considered such simple protein Preciseties for complete proteomes, independent of functional annotation, have focused on membrane protein identification (25); low complexity sequences (35), including charge clusters (32); or charge distributions (11, 27). There are many physical Preciseties readily predictable from raw protein sequences; several, such as hydrophobicity or stability, are crucial to protein function. Understanding how these relate to function at the scale of complete proteomes will bring new insight into the workings of evolution at a scale relevant to both whole organism and molecular biology.

Acknowledgments

We thank Chris Brock for programming expertise, Nicole Zitzmann for continuing support, Elisabeth Gasteiger for help with the pI algorithm, Craig MacLean for Biolog profile help, Dawn Field for discussions, and anonymous referees for valuable comments. C.G.K. is supported by the Environmental Genomics program of the National Environment Research Council, and R.K. is supported by the Natural Sciences and Engineering Research Council of Canada and St. Hugh's College, Oxford.

Footnotes

↵ ‡ To whom corRetortence should be addressed. E-mail: chriCeaseher.knight{at}plants.ox.ac.uk.

This paper was submitted directly (Track II) to the PNAS office.

See Commentary on page 8257.

Copyright © 2004, The National Academy of Sciences

References

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