Disposable Soma Theory and the Evolution of Maternal Effects on Ageing

Maternal effects are ubiquitous in nature and affect a wide range of offspring phenotypes. Recent research suggests that maternal effects also contribute to ageing, but the theoretical basis for these observations is poorly understood. Here we develop a simple model to derive expectations for (i) if maternal effects on ageing evolve; (ii) the strength of maternal effects on ageing relative to direct environmental effects; and (iii) the predicted relationships between environmental quality, maternal age and offspring lifespan. Our model is based on the disposable soma theory of ageing, and the key assumption is thus that mothers trade off their own somatic maintenance against investment in offspring. This trade-off affects the biological age of offspring at birth in terms of accumulated damage, as indicated by biomarkers such as oxidative stress or telomere length. We find that the optimal allocation between investment in maternal somatic investment and investment in offspring results in old mothers and mothers with low resource availability producing offspring with reduced life span. Furthermore, the effects are interactive, such that the strongest maternal age effects on offspring lifespan are found under low resource availability. These findings are broadly consistent with results from laboratory studies investigating the onset and rate of ageing and field studies examining maternal effects on ageing in the wild.     

A Study of Maternal and Maternal Interaction Effects in Diallel Crosses

The analysis of diallel crosses by including the components due to maternal effect and maternal interaction effects have been presented for Griffing method—1 (random effect model) and Griffing method—3 (fixed and random effect model). Wherever exact test of significance is not possible, testing procedure using Satterthwaite (1946) approximation has been presented.     

The Effects of Fetal Gender on Maternal and Fetal Insulin Resistance

Objective

Gender plays a role in the development of a number of cardiovascular and metabolic diseases and it has been suggested that females may be more insulin resistant in utero. We sought to assess the relationship between infant gender and insulin resistance in a large pregnancy cohort.

Study Design

This is a secondary analysis of a cohort from the ROLO randomized control trial of low GI diet in pregnancy. Serum insulin, glucose and leptin were measured in early pregnancy and at 28 weeks. At delivery cord blood C-peptide and leptin were measured. A comparison of maternal factors, fetal biometry, insulin resistance and leptin was made between male and female offspring. A multivariate regression model was built to account for the possible effects of maternal BMI, birthweight and original study group assignment on findings.

Results

A total of 582 women were included in this secondary analysis, of whom 304 (52.2%) gave birth to male and 278 (47.8%) gave birth to female infants. Compared to male infants at birth, female infants were significantly lighter, (3945 ± 436 vs. 4081± 549g, p<0.001), shorter in length (52.36 ± 2.3 vs. 53.05 ± 2.4cm, p<0.001) and with smaller head circumferences (35.36 ± 1.5 vs. 36.10 ± 1.1cm, p<0.001) than males. On multiple regression analysis, women pregnant with female fetuses were less insulin resistant in early pregnancy, i.e. had lower HOMA indices (B = -0.19, p = 0.01). Additionally female fetuses had higher concentrations of both cord blood leptin and C-peptide at birth when compared to male offspring (B = 0.38, p<0.001 and B = 0.31, p = 0.03 respectively).

Conclusion

These findings suggest gender is a risk factor for insulin resistance in–utero. Additionally, carrying a female fetus decreases the risk of insulin resistance in the mother, from as early as the first trimester.     

The Effects of Evolution are Local: Evidence from Experimental Evolution in Drosophila

One of the enduring temptations of evolutionary theory is theextrapolation from short-term to long-term, from a few speciesto all species. Unfortunately, the study of experimental evolutionreveals that extrapolation from local to general patterns ofevolution is not usually successful. The present article supportsthis conclusion using evidence from the experimental evolutionof life-history in Drosophila. The following factors demonstrablyundermine evolutionary correlations between functional characters:inbreeding, genotype-by-environment interaction, novel fociof selection, long-term selection, and alternative genetic backgrounds.The virtual certainty that at least one of these factors willarise during evolution shreds the prospects for global theoriesof the effects of adaptation. The effects of evolution apparentlydon't generalize, even though evolution is a global process.     

Multivariate Mutation-Selection Balance with Constrained Pleiotropic Effects

A multivariate quantitative genetic model is analyzed that is based on the assumption that the genetic variation at a locus j primarily influences an underlying physiological variable yj, while influence on the genotypic values is determined by a kind of "developmental function" which is not changed by mutations at this locus. Assuming additivity among loci the developmental function becomes a linear transformation of the underlying variables y onto the genotypic values x, x = By. In this way the pleiotropic effects become constrained by the structure of the B-matrix. The equilibrium variance under mutation-stabilizing selection balance in infinite and finite populations is derived by using the house of cards approximation. The results are compared to the predictions given by M. Turelli in 1985 for pleiotropic two-character models. It is shown that the B-matrix model gives the same results as Turelli's five-allele model, suggesting that the crucial factor determining the equilibrium variance in multivariate models with pleiotropy is the assumption about constraints on the pleiotropic effects, and not the number of alleles as proposed by Turelli. Finally it is shown that under Gaussian stabilizing selection the structure of the B-matrix has effectively no influence on the mean equilibrium fitness of an infinite population. Hence the B-matrix and consequently to some extent also the structure of the genetic correlation matrix is an almost neutral character. The consequences for the evolution of genetic covariance matrices are discussed.     

3种药物对老化卵母细胞4种母源mRNA水平的影响

利用Real-Time PCR技术定量分析了绵羊(Ovis aries)卵母细胞在生长-成熟-老化过程4种母源mRNA(Mater、Zar1、Dnmt1、GDF9)发生的动态变化,以及3种药物(咖啡因、DTT和矾酸盐)对老化卵母细胞母源mRNA水平的影响。结果显示,除Dnmt1在12 h开始下降外,卵母细胞中其他3个基因的表达量都是从生发泡期逐渐下降,且在体外培养至24 h降到最低,然后随着卵母细胞的老化又逐渐上升。其中GDF9和Mater 2个基因在30 h组的卵母细胞中的表达量显著高于24 h组卵母细胞(P0.05)。30 h卵母细胞中Zar1和Dnmt1的转录水平与24 h卵母细胞差异不显著(P0.05)。除Zar1外,咖啡因和DTT都显著降低了老化卵母细胞中其他3个基因的表达量(P0.05),且所有基因表达量与24 h组卵母细胞差异不显著(P0.05)。矾酸盐处理后的卵母细胞其4种母源mRNA水平都是最高,除Zar1外其他3个基因表达量都显著高于24 h组(P0.05)。结论是,Mater、Zar1、Dnmt1、GDF9 4种母源mRNA水平与卵母细胞质量成反比,咖啡因和DTT能在一定程度上延缓卵母细胞的衰老,改善卵母细胞质量;而矾酸盐则加速了卵母细胞老化进程,降低了卵母细胞质量。     

Inclusion of Dominance Effects in the Multivariate GBLUP Model

New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.     

Parameterization of Multivariate Random Effects Models for Categorical Data

Alternative parameterizations and problems of identification and estimation of multivariate random effects models for categorical responses are investigated. The issues are illustrated in the context of the multivariate binomial logit-normal (BLN) model introduced by Coull and Agresti (2000, Biometrics 56, 73-80). We demonstrate that the BLN model is poorly identified unless proper restrictions are imposed on the parameters. Moreover, estimation of BLN models is unduly computationally complex. In the first application considered by Coull and Agresti, an identification problem results in highly unstable, highly correlated parameter estimates and large standard errors. A probit-normal version of the specified BLN model is demonstrated to be underidentified, whereas the BLN model is empirically underidentified. Identification can be achieved by constraining one of the parameters. We show that a one-factor probit model is equivalent to the probit version of the specified BLN model and that a one-factor logit model is empirically equivalent to the BLN model. Estimation is greatly simplified by using a factor model.     

Long-Lasting Effects of Maternal Condition in Free-Ranging Cervids

Causes of phenotypic variation are fundamental to evolutionary ecology because they influence the traits acted upon by natural selection. One such cause of phenotypic variation is a maternal effect, which is the influence of the environment experienced by a female (and her corresponding phenotype) on the phenotype of her offspring (independent of the offspring’s genotype). While maternal effects are well documented, the longevity and fitness impact of these effects remains unclear because it is difficult to follow free-living individuals through their reproductive lifetimes. For long-lived species, it has been suggested that maternal effects are masked by environmental variables acting on offspring in years following the period of dependence. Our objective was to use indirect measures of maternal condition to determine if maternal effects have long-lasting influences on male offspring in two species of cervid. Because antlers are sexually selected, we used measures of antler size at time of death, 1.5–21.5 years after gestation to investigate maternal effects. We quantified antler size of 11,000 male elk and mule deer born throughout the intermountain western US (6 states) over nearly 30 years. Maternal condition during development was estimated indirectly using a suite of abiotic variables known to influence condition of cervids (i.e., winter severity, spring and summer temperature, and spring and summer precipitation). Antler size of male cervids was significantly associated with our indirect measure of maternal condition during gestation and lactation. Assuming the correctness of our indirect measure, our findings demonstrate that antler size is a sexually selected trait that is influenced–into adulthood–by maternal condition. This link emphasizes the importance of considering inherited environmental effects when interpreting population dynamics or examining reproductive success of long-lived organisms.     

Estimation of Variance Components Based on a Diallel Model Involving Maternal and Maternal Interaction Effects

For a diallel model (TOPHAM, 1966) estimators of variance components are obtained by adopting the general approach outlined by SEELY (1969, 1970, 1971), SEELY and ZYSKIND (1971) and YUAN (1977) in the case of unbalanced data.     

Association between Maternal Smoking during Pregnancy and Low Birthweight: Effects by Maternal Age

Background

Maternal smoking during pregnancy has been consistently related to low birthweight. However, older mothers, who are already at risk of giving birth to low birthweight infants, might be even more susceptible to the effects of maternal smoking. Therefore, this study aimed to examine the modified association between maternal smoking and low birthweight by maternal age.

Methods

Data were obtained from a questionnaire survey of all mothers of children born between 2004 and 2010 in Okinawa, Japan who underwent medical check-ups at age 3 months. Variables assessed were maternal smoking during pregnancy, maternal age, gestational age, parity, birth year, and complications during pregnancy. Stratified analyses were performed using a logistic regression model.

Results

In total, 92641 participants provided complete information on all variables. Over the 7 years studied, the proportion of mothers smoking during pregnancy decreased from 10.6% to 5.0%, while the prevalence of low birthweight did not change remarkably (around 10%). Maternal smoking was significantly associated with low birthweight in all age groups. The strength of the association increased with maternal age, both in crude and adjusted models.

Conclusions

Consistent with previous studies conducted in Western countries, this study demonstrates that maternal age has a modifying effect on the association between maternal smoking and birthweight. This finding suggests that specific education and health care programs for older smoking mothers are important to improve their foetal growth.     

The Effects of Supercritical Carbon Dioxide Processing on Progesterone Dispersion Systems: a Multivariate Study

The aim of this work was to investigate the effects of supercritical carbon dioxide (SC-CO₂) processing on the release profiles of progesterone (PGN) and Gelucire 44/14 dispersion systems. A fractional factorial design was conducted for optimization of the particles from gas-saturated suspension (PGSS) method and formulation parameters and evaluating the effects of three independent responses: PGSS process yield, in vitro dissolution extent after 20 min (E₂₀) and t_1/2 for prepared PGN dispersion systems. The experimental domain included seven factors measured at two levels to determine which factors represent the greatest amount of variation, hence the most influence on the resulting PGN dispersion systems. Variables tested were temperature (A) and pressure (B) of the supercritical fluid, sample loading (C), SC-CO₂ processing time (D), sonication (E), drug-to-excipient ratio (F) and orifice diameter into the expansion chamber (G). The analysis of variance showed that the factors tested had significant effects on the responses (p value <0.05). It was found that the optimum values of the PGSS process are higher pressure (186 bar), higher temperature (60°C), a longer processing time (30 min) and lower PGN-to-excipient ratio of 1:10. The corresponding processing yield was 94.7%, extent of PGN dissolution after 20 min was 85.6% and the t_1/2 was 17.7 min. The results suggest that Gelucire 44/14-based dispersion systems might represent a promising formulation for delivery of PGN. The preparation of PGN-loaded Gelucire 44/14 dispersion systems from a PGSS method can be optimized by factorial design experimentation.Key words: factorial design experiment, in vitro dissolution, optimization, particles from gas-saturated suspensions (PGSS), process yield