As evolutionary biologists, we have long been concerned with the evolutionary character problem by other names - in particular as constraints or trade-offs. We have been hopeful that the evolutionary character problem could be solved by whole organism approaches, such as those I have discussed above. While these approaches can be informative, they have many pitfalls which can only be avoided by thorough, multidisciplinary approaches such as those being taken with wing-polymporphic insects (Zera and Denno, 1997). This suggests the desirability of exploiting technical advances which will simplify the process of indentifying the functional wiring underlying evolution.
Most efforts by evolutionary biologists to understand evolutionary characters have been
directed at the important but crude question of whether there is a pleiotropic relationship between a pair of traits. However, theory makes clear that it is not simply the existence of a tradeoff which is
important to validate a particular evolutionary scenario, but the precise quantitative nature of the
tradeoff. For example, current modeling efforts on the reproductive effort problem seek to incorporate growth and age and size at maturity into the reproductive effort model in a more general way (Sibly et al., 1985; Kozlowski, 1992; Bernardo, 1993). All such models assume that age and size at maturity are in effect one trait, with a strong positive relationship between them, while reproductive fitness is enhanced both by being early, and by being large. Two different growth curves have been used to model deterministic growers. One set of models takes a von Bertalanffy growth equation as an assumption (Roff, 1984; Stearns and Koella, 1986; Berrigan and Koella, 1994, reviewed by Day and Taylor, 1997). The von Bertalanffy model assumes that there is a maximum possible size that an organism can achieve, and that growth rates diminish as this asymptotic size is approached. An alternative is to model growth as a power function of mass (Roff, 1983; Kozlowski and Weigert, 1987; Kozlowski and Weiner, 1996). Here there is no maximum size, so additional growth always results in larger size. Cessation of growth is assumed to be due to the shunting of resources to
reproduction, away from growth. In each case, there is a tight tradeoff-driven relationship between age and size at maturity, dictated by the growth model. Only the shape of this relationship differs between the models. Day and Taylor (1997) showed that even with such similar models, the effect of a change in growth rate on the optimal age at maturity differs between them. When juvenile and adult mortality are held constant, the von Bertalanffy-based models will predict that an increase in growth rate will usually lead to a decrease in age at maturity, as an individual will simply approach its maximum size more rapidly. The power function based models predict that a fast growing individual should always mature later, as it would be more willing to pay the mortality costs of additional growth.
As evolutionary biologists we really need to know the precise nature of the evolutionary
characters which underlie life history transitions. To get this information, we will have to depend more heavily on studies of gene function and interrelationships. Genomic analyses ultimately hold the promise of a complete understanding of the functional wiring of organisms. QTL mapping holds the promise of identifying which loci out of all the ones in the functional wiring actually vary within and among
populations. Explicitly evolutionary studies will ultimately be necessary to tell us which of the loci that vary are important in effecting evolutionary transitions.
Genomics
A fundamentally new tool which will ultimately prove the key to developing the detailed wiring diagram underlying potential life history transitions are the complete descriptions of gene sequences which the various genome projects have produced (e.g. Fleischmann et al., 1995; Clayton et al., 1997; C.
elegans Sequencing Consortium, 1998). Just how much we have to understand is emphasized by the high proportion of protein coding sequences found which have no known function. For example 56%
of the over 6400 proteins coded for by the yeast genome had no known function at the time the sequence was completed (Clayton et al., 1997). Substantial efforts are now being directed at understanding the functions of these unknown proteins (James, 1997). Evolutionary studies which make use of whole genomes are in their infancy, but already yield some intriguing results relevant to our study of evolutionary characters. For example, comparisons of the genome complements of pathogenic and non-pathogenic species suggests hypotheses about which pathways are necessary for a pathogenic life history (Huynen and Bork, 1998). In the longer term, application of comparative genomic methods to suites of taxa differing in life histories will prove a powerful source of hypotheses concerning the
characters responsible for evolutionary transitions.
A complementary approach which nibbles at the edges of this ignorance are mutagenesis screens which have been used in model systems to identify large numbers of genes with particular classes of mutant phenotypes. For example, mutant screens in the nematode worm Caenorhabditis elegans have led to the discovery of at least eight genes which are capable of affecting mortality rates (Hekimi et al., 1998). Consideration of what we have learned from these mutations highlights both the possibilities and the limitations of this approach. These genes seem to fall into two categories with independent effects on mortality (Lakowski and Hekimi, 1996), and so identify two evolutionary characters which can affect lifespan. One set of genes affects the entry of worms into a resting stage, known as a dauer larva. Lifespan of a dauer larva can be up to six months, compared to a normal life span of 15 days. The genes identified seem to be part of the signalling pathway that turns on the dauer phenotype when conditions deteriorate. The existence of these genes is predictable from the natural history of the organism, the fact that it has an inducible resting stage. However, when the constitutively dauer genotypes are placed at a temperature too high for induction of the morphological dauer larva phenotype, lifespan is still lengthened considerably. This has revealed unexpected complexity to the basis of the phenotype which would otherwise have been difficult to find. Otherwise, these loci are not very informative, since they do not help identify the physiological basis of the alterations to lifespan.
The second class of genes affecting mortality rates are the clock genes, so called because they seem to affect a host of traits that have a temporal component (Hekimi et al., 1998). The effects on lifespan seem to be highly correlated with effects on development rate. The genes act maternally, suggesting that they are involved in setting the rate of living of worms early in life, and that this rate is
difficult to change later. One of the clock genes has been cloned, and shows extensive homology with a yeast gene which is involved in regulating the switch from growth on glucose to growth on
nonfermentable carbon sources. The implication is that in the worm, the clock genes are involved in activating a fundamental energetic pathway, with mutants depriving worms of the energy the pathway supplies, with the pleiotropic effect of slowing the rate of living, and decreasing mortality rate. This suggests that the clock genotype is a classic example of an antagonistically pleiotropic one, where the fitness advantages of increased metabolism on fitness early in life more than compensates for the gain in fitness late in life.
Possession of the entire sequence of the C. elegans genome will catalyze the identification of the missing elements of these known pathways. What is known is tantalizing rather than conclusive, but clearly reveals the power of the genomic approach to the identification of evolutionary characters.
QTL mapping
Quantitative trait locus (QTL) mapping seeks to find the genetic location of segregating variation, usually in artificially constructed populations. To do the mapping, the population is first constructed by crossing stocks which differ in the phenotype of interest. In addition, the two stocks are characterized for genetic markers at previously mapped marker loci. The QTL mapping is then carried out by looking for correlations between the markers and the phenotype in the descendants of the original cross. The technique is beginning to be widely applied to life history traits by evolutionary biologists (Mitchell-Olds, 1995) and plant (Stuber, 1995) and animal breeders (Haley, 1995).
While ultimately the goal is to identify the specific loci responsible for the mapped variation, this
is rarely possible today using QTL mapping alone, given the large numbers of unknown loci in the genome, and the small numbers of markers employed in QTL mapping (Mackay and Fry, 1996). A related problem is that a single QTL containing region may harbor several loci affecting the trait.
Successes in identifying the specific loci involved have only come by comparing QTL maps with previously mapped loci which are known to influence the trait of interest, which are called candidate genes (Doebley et al., 1995, 1997; Long et al., 1995; Mackay and Fry, 1996). Presumably the information necessary to resolve these issues will increase in model organisms as the genomic data increase.
Even without knowledge of the function of a QTL, its identification can delineate the existence of evolutionary characters. For example, Mitchell-Olds (1996) mapped two genes controlling flowering time in Arabidopsis thaliana. Both gene regions also had correlated effects on plant size, such that late flowering plants were large. This reflects a classic time vs. size tradeoff as postulated in many life history theories. Interestingly, the strains crossed to initiate mapping did not vary in flowering time, but each carried one early and one late allele whose effects approximately canceled out.
A second example of the usefulness of QTL mapping for the identification of evolutionary characters are maps of the differences between primitive cultivars of maize, and its wild progenitor teosinte (Doebley and Stec, 1993; Doebley et al., 1995). The two strains are extremely different in large numbers of traits, so different that they were originally classified as different genera (Iltis, 1983).
Most of the morphological differences reflect increased reproductive effort and harvestability in the cultivar, both of great practical value to agriculturalists. The list of major changes includes at least a dozen traits in the ear and the overall architecture of the plant. Remarkably, only five regions of the
genome seem to control the lion’s share of the differences in all of these traits. In some cases, the correlated effects of a single locus seem sensibly related to each other. For example, the seed of teosinte is protected by a hard outer glume, covering the opening of the thick cupule in which the seed sits. A mutation at a single locus, tga1, withdraws the protective cupule and softens the glume to the edible form found in maize (Dorweiler et al., 1993). However, two other loci jointly determine a bewildering variety of seemingly unrelated traits, including the conversion of the long lateral branches of teosinte to the short ones of maize, the conversion of the terminal inflorescence on the lateral branches from male to female, an increase in the number of seeds/ear, rearrangement of the seeds along the ear, increase in seed size, and for increasing the tendency of ears to stay intact during handling (Doebley et al., 1995). One of these, tb1, has been identified and cloned (Doebley et al., 1995, 1997). All of this evidence suggests the presence of a functional pathway whose nature could never have been predicted a priori.
The conservation of gene order in the grasses enables the comparison of QTL positions between maize, rice and sorghum, and remarkably these two gene regions also have effects on mass per seed in crosses between wild and cultivated strains in all three species (Paterson et al., 1995).
Correspondence of regions influencing other traits, such as day-length response is also apparent, suggesting that the same evolutionary characters have been involved in independent domestication events.
Phenomics
Very soon, we will have a catalog of all of the genes in a wide sample of model multicellular organisms.
This has already lead to a shift in attention away from genomics, to the decipherment of the biological roles of the proteins a genome is capable of constructing. The set of proteins in an organism has been dubbed its proteome (Kahn, 1995). Understanding the proteome will occupy more reductionist scientists such as biochemists and developmental biologists for some time. Within a few years or decades at the most we can anticipate that the proteome too will be deciphered, that is we will know what all the genes do, and which biochemical or developmental pathways they are organized into.
There is another domain which we need to understand, and that is the potential effects of variation in the proteome on the phenotype (see Figure 1). This is the same task I have suggested we need to pursue to promote our understanding of evolution, the decipherment of evolutionary characters.
I suggest the term phenomics for this task, to suggest that this is ultimately as important as the genome projects themselves. The task of phenomics is to understand the implications of functional architecture for biology. Some pathways are likely to be important for evolution, while others rarely are. The ones which underlie major evolutionary transitions, or respond to phenotypic selection most readily, or cause the most genetic load should receive the most attention from all sorts of biologists. Other pathways may be ignored as a first approximation. These are crucial tasks which evolutionary biologists should
embrace as one for which their conceptual training makes them uniquely suited.
Acknowledgements: Thanks to Günter Wagner, Tom Miller, Alice Winn, Locke Rowe and Ellie Larsen for comments on previous drafts.
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