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Genetics of mortality in the bean beetle Callosobruchus maculatus.

Variation in life span is the central focus of the evolutionary biology of aging, and it is generally acknowledged that life expectancy at sexual maturity has a genetic component (Lints 1983; Pearl 1922; Rose and Charlesworth 1981a; Tantawy and Rakha 1964). However, little is known about the age-specific patterns of mortality responsible for these longevity differences. Without such knowledge, it is difficult to understand the underlying determinants of life span. Particularly, it is not possible to infer if the genetic variation relates to differences in senescence (progressive somatic degeneration that causes age-dependent increases in mortality and decreases in reproduction), or to other factors, such as temporary costs of reproduction (Partridge 1987), levels of age-independent robustness, or the onset of senescent degeneration (Sacher 1977).

Because the cause of death cannot be observed in most evolutionary studies of aging, our understanding of underlying mechanisms will be advanced by considering the response of age-specific mortality to experimental and genetic manipulation. For the facultatively aphagous beetle Callosobruchus maculatus, we have explored the effect of egg production on mortality (Tatar et al. 1993). Egg laying produces changes which we interpret as effects on the age of onset of senescence, while mating itself increases the rate of senescence. Differences in mortality patterns between the sexes also exist (Tatar and Carey 1994). Females have greater life expectancy than males, but most of the difference is due to the relatively late onset of age-dependent mortality among females and secondarily to differences in their rate of change of age-dependent mortality (greater in males). We are also making these sorts of comparisons when adults are supplied with surplus nutrients. We find that patterns of age-specific mortality can change with or without effects on the relative life expectancy of the cohorts (M. Tatar unpubl. data). Our thesis throughout these and the current study is that such demographic distinctions are useful in evaluating the predictions of evolutionary theory of aging; Williams (1957, p. 408) and Medawar (1955) both suggested quantifying senescence as the rate of change in age-specific mortality.

There is little data on the genetics of mortality as a function of age, even though our evolutionary understanding of aging requires the elucidation of such heritable variation (Reznick 1985). Johnson (1990) and Johnson and Lithgow (1992) found that the mutant age-1 in the nematode Caenorhabditis elegans decreased the rate of change in mortality. Among the [F.sub.1] hybrids of highly inbred lineages of Drosophila melanogaster, Curtsinger et al. (1992) observed differences in both the patterns and the rates of change in mortality. Rose (1984) suggested that the "B" and "O" lines of D. melanogaster which had been selected for early and late reproduction did not differ with respect to age-dependent changes in mortality. Here, our purpose is to determine if heritable variation exists for age-specific mortality in the bean beetle, Callosobruchus maculatus. To quantify mortality, we estimate the slope and intercept parameters of the Gompertz mortality model from full-sib families and treat these as family values in a parent-offspring regression.


Demography of Mortality

Age-specific mortality is the fraction of those alive at age x dying in the interval x to x + 1 (Chaing 1984). We can summarize the trajectory of age-specific mortality as a function of age by the two-parameter Gompertz model (see Finch et al. 1990; Promislow 1991). This model postulates that the force of mortality [[Mu].sub.x], which is the instantaneous age-specific mortality, increases exponentially as a function of age x, [[Mu].sub.x] = a[e.sup.bx]. Then ln [[Mu].sub.x] = ln a + bx and ln [[Mu].sub.x] plotted against age x is linear with intercept ln a and slope b.

The Gompertz parameters, estimated from cohorts, have physiological interpretations with respect to individuals. The intercept ln a is the vulnerability parameter (Sacher 1977) or the initial mortality rate (Finch 1990). Genetic variation in ln a may reflect differences in the baseline vigor of individuals. Alternatively, ln a may indicate the age of onset of the aging process, and its variation may correspond to differences in the relationship between the chronological and physiological age of individuals. While these alternatives are difficult to distinguish based on demographic data alone, they both concern the level of physiological susceptibility to decay. Life-table modification as a function of environmental influences, such as radiation, privation, or manipulation of reproduction, are associated with changes in this parameter (Finch 1990; Sacher 1977; Tatar et al. 1993).

The slope of the ln [[Mu].sub.x] plot is the demographic rate of senescence and is an index of physiological senescence. It is thought to at least partly reflect the progression of somatic degeneration of individuals (Strehler and Mildvan 1960). Gavrilov and Gavrilova (1991) observed variation in parameter b among populations of humans and Drosophila, although it is thought to be relatively invariant within species by Finch (1990).

The System and Procedures

The bean beetle Callosobruchus maculatus is a facultatively aphagous pest of stored legumes. Females oviposit on the testa of seeds. Upon hatching, larvae burrow into the seed to complete development through pupation. Adults emerge after 3-4 wk, immediately capable of mating and reproducing. Without food or water, the life expectancy of virgins is 12 d for males and 17 d for females. Mated females can lay about 120 eggs (Mitchell 1990).

The stock originated from a warehouse near San Francisco, California (Fox 1993a) and was reared on organic azuki beans (Vigna angularis) for 11 generations prior to this study, and maintained without food or water at 25 [degrees] C, continuous light, and relative humidity [less than] 25%. The culture was propagated by transferring several hundred adults to 500 mL of fresh beans over a 3-d period near the peak of emergence. This produced a generation time of 4 to 5 wk.

Twenty virgin females were randomly selected from the stock population and paired with single males. Each pair was isolated in a petri dish and given enough azuki beans to ensure that eggs were deposited at a density of no more than one per seed. Nineteen females oviposited, and these eggs form the parent generation (generation one). Beans with eggs were placed individually into the cells of 24-cell tissue-culture dishes. These dishes were randomized relative to family and maintained under the standard rearing conditions within a single incubator. Each day, the dishes for emerging adults were examined and rotated front to back and vertically throughout the incubator. The entire cohort of 19 families emerged over a 5-d period. Upon emergence, individuals were weighed to 0.1 mg and transferred to 35 x 15 mm plastic petri dishes, where they remained unmated and without food or water. Mortality was recorded daily. The male and female life tables generated from the sibs of each family in generation one form the parental phenotypes. Generation one contained 1676 individuals.

To produce the offspring generation (generation two), a random female was selected from each family of generation one at emergence and paired singly with a male from the stock population. These pairs were maintained individually in petri dishes with ample azuki beans. These eggs formed the offspring-sibships for each of the 19 families and were handled through emergence and as adults in the same way as in generation one. The male and female life tables generated from the sibs of each family in generation two form the offspring phenotypes. A total of 1957 individuals were in generation two.


Genetics of Individual Longevity and Body Size

The heritabilities and phenotypic and genetic correlations for individual longevity and body size based on our single mating full-sib design are calculated for each generation (Becker 1985). These covariances include 1/2 additive, 1/4 dominance, and epistatic genetic variances, and all the maternal effects. Both life span and body size exhibit moderate, significant heritabilities within the range reported for bruchid beetles (Moller et al. 1989; Nomura and Yonezawa 1990; [TABULAR DATA OMITTED] Tanaka 1993; Tucic et al. 1991). In contrast to Moller et al., we find little evidence for an important effect of body size on life span. The phenotypic correlations are small yet significant; the genetic correlations are not significant. We proceed with the analysis of mortality without further consideration of body size.


Genetics of Mortality

In generations one and two, age-specific mortality, [q.sub.x], is calculated for each family, by sex (see Carey 1993) and the force of mortality is approximated by [[Mu].sub.x] = -ln(1 - [q.sub.x]) (Elandt-Johnson and Johnson 1980). The Gompertz parameters ln a and b are estimated by least-squares linear regression of ln [[Mu].sub.x] versus age x, weighting by [square root of] [n.sub.x] to correct for the dependence of the variance on sample size (Elandt-Johnson and Johnson 1980). Regression as opposed to maximum-likelihood estimation is adequate here, since the parameter estimates are little affected by the lack of independence of errors in the dependent variable [[Mu].sub.x]. It is also necessary to fit this model over the ages where mortality is age-dependent. In a previous analysis of the present data with respect to gender, we found that the mortality prior to age 5 d in males and 11 d in females was age-independent (Tatar and Carey 1994). These deaths (total of 17) are excluded from the present analyses. The goodness-of-fit of the Gompertz model is adequate. The coefficient of determination for the 76 plots has a mean value of 0.74 with a standard deviation of 0.11.

The Gompertz parameters are treated as family values to evaluate the heritable variation from parent-sibship-offspring-sibship correlations. Unfortunately, the estimation of age-specific mortality requires large numbers of individuals in each family life-table where sibs experience independent environmental effects, while estimation of genetic parameters requires many families. Because of this design trade-off, we only aim to detect heritable variation for the mortality parameters and do not attempt to estimate heritabilities.

The slope parameter for females is significantly correlated across generations, while for males the value is positive but not significant. Neither female nor male intercept parameters have significant correlations. The significant correlation for the slope parameter among females in figure 1 may result from the extreme values for two families. The correlation is not significant when these points are excluded. The power of these statistical tests is constraining as well. Our main conclusion must be stated tentatively: there is evidence for heritable variation for the demographic rate of senescence in females, but there is none for initial vulnerability in either sex. The observed phenotypic variation in initial vulnerability is assigned to environmental effects.



Our analysis of mortality as a quantitative trait provides insight into the genetic basis of the factors that affect life span. Fitting the pattern of age-specific mortality to a two-parameter Gompertz model imposes qualitative categories on the mortality mechanisms, initial vulnerability, or age of senescence onset versus the rate of senescence. The slope parameter for the rate of change in age-specific mortality exhibits significant heritable variation among female Callosobruchus maculatus. To the extent that this demographic rate (estimated from familial cohorts) reflects individual physiological senescence, there is the potential for selection to act on the senescence process. No such heritability was detected for the intercept parameter.

A distinction between life span and mortality is implicit in the debate concerning selection experiments for delayed reproduction in Drosophila melanogaster (Partridge 1987, 1992; Reznick 1985, 1992a,b). Lines of flies selected for reproduction late in adult life have increased longevity (Luckinbill et al. 1984; Rose and Charlesworth 1981b). This correlated response is interpreted as evidence for changes in senescence due to variation in alleles that have antagonistic effects on fitness across age classes. Partridge and Andrews (1985), and Partridge et al. (1986, 1987) challenge this interpretation based on their observations of how mating affects mortality risks in both males and females. They argue that the increased longevity in late reproducing lines could result from reductions in mating frequency that simultaneously reduce fecundity and mortality acutely associated with reproduction.

Partridge raised the general point that differences in life span can be caused by various mortality mechanisms, only one of which is senescence. Experiments by Luckinbill et al. (1988) and by Service (1989) address a specific part of this issue. They found that differences in life span persist when flies from the selected lines are held as virgins or are mated; mating frequency is not the cause of the divergence between the lines. While these studies eliminate a specific alternative cause of death, they do not deal with the more general issue of identifying the mortality mechanism. Particularly, do the lines differ in rates of senescence where senescence is measured by the slope of mortality? Life-span measurements alone do not contain this information, but it is available by directly estimating mortality.

A related problem is the relevance of phenotypic plasticity for reproductive costs to the understanding of the evolution of life histories. Reznick (1992a) argues that the mechanisms responsible for phenotypic plasticity need not reflect how genetic constraints affect life-history evolution. Others contend that the mechanisms may be similar so that the study of phenotypic plasticity is informative about the evolutionary trajectory of life histories (Bell and Koufopanou 1986; Partridge 1992; Partridge and Barton 1993). Chippindale et al. (1993) address this issue with lines of D. melanogaster selected for timing of reproduction and for desiccation resistance, which are then exposed to various levels of adult nutrition. They observe that diet restriction affects life history in ways that are similar to selection; that is, life expectancy is extended while early reproduction is decreased. However, it is unclear if the genetic and the environmental manipulations are influencing the same causes of death. For instance, genetic manipulations may alter the rate of change of age-specific mortality, while diet restriction reduces acute and temporary costs associated with egg laying (Partridge et al. 1986) or changes the age of onset of demographic senescence but not its rate (Tatar et al. 1993). All of these can lead to increases in life span. Inferences about the underlying mechanism can be strengthened if the manipulations affect the patterns of mortality in similar ways.

At least three caveats temper our results. First, the significant correlation among the slope parameters of females depends on two extreme values. However, these families are not unusual in terms of female life expectancy, and because our sample size in terms of families is limited, they cannot be statistically treated as outliers. Generally, this problem is an outcome of the antagonism between the sample size requirements of demographic and genetic analyses. Our small sample size also limits the power to detect real differences in the intercept parameter.

Second, the experimental design does not exclude the possibility of maternally transmitted factors. Maternal factors can affect life-history traits in insects (Mousseau and Dingle 1991). For C. maculatus, Messina (1990) reports that variation in adult size largely results from nongenetic maternal effects, while Fox (1993b) has found that maternal age influences development time, but not larval survivorship or adult size. The consequences of these maternal effects on adult longevity are unclear, especially since only a weak phenotypic relationship between adult size and life span was observed in the current study. Nonetheless, it would have been better to use males from the parental generation to propagate the offspring generation, or a half-sib analysis with families acting as populations.

Third, heritable variation for the demographic rate of senescence is compatible with at least two interpretations. It may reflect genetic variation for the rate of physiological senescence among families. Alternatively, there may be familial variation for the plasticity of mortality. Some families may be more sensitive to environmental influences on mortality than others. Given environmental heterogeneity, sensitive families will have a broader distribution of life spans than families with more canalized genotypes, and these differences will be reflected in variation in the rate of change of age-specific mortality (Vaupel and Yashin 1985). The force of mortality will then exhibit genetic variation among families. This problem essentially concerns the potential for the heritability of phenotypic plasticity, rather than for senescence itself (Stearns et al. 1991). It highlights a central challenge for quantifying senescence from demographic data: To what extent does actuarial senescence, as measured at the cohort level, reflect the aging processes of individuals?


We thank A. Hoffmann, M. Turelli, J. Curtsinger, T. Famula, and C. Fox for constructive conversations, and A. Hoffmann, P. Service, and two anonymous reviewers for comments on the manuscript. Support was provided by the National Institute of Aging grant #AG08761-01 (J.R.C. and M.T.), the Sloan Foundation, and a Jastro-Shields Graduate Research Scholarship (M.T.).


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Author:Tatar, Marc; Carey, James R.
Date:Aug 1, 1994
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