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Decidual stromal cells differentiate from endometrial stromal fibroblasts (ESFs) under the influence of progesterone and cyclic adenosine monophosphate (cAMP) and are essential for implantation and the maintenance of pregnancy. They evolved in the stem lineage of placental (eutherian) mammals coincidental with the evolution of implantation. Here we use the well-established in vitro decidualization protocol to compare early (3 days) and late (8 days) gene transcription patterns in immortalized human ESF. We document extensive, dynamic changes in the early and late decidual cell transcriptomes. The data suggest the existence of an early signal transducer and activator of transcription (STAT) pathway dominated state and a later nuclear factor κB (NFKB) pathway regulated state. Transcription factor expression in both phases is characterized by putative or known progesterone receptor (PGR) target genes, suggesting that both phases are under progesterone control. Decidualization leads to proliferative quiescence, which is reversible by progesterone withdrawal after 3 days but to a lesser extent after 8 days of decidualization. In contrast, progesterone withdrawal induces cell death at comparable levels after short or long exposure to progestins and cAMP. We conclude that decidualization is characterized by a biphasic gene expression dynamic that likely corresponds to different phases in the establishment of the fetal–maternal interface. [ABSTRACT FROM AUTHOR]

Humans are one of the few mammalian viviparous species in which pregnancy is extended beyond the luteal phase, the phase during which progesterone is synthesized by the maternal ovary. Instead, it is the fetal placenta that produces progesterone throughout the latter 2 trimesters of human pregnancy. The placenta is developmentally crucial for reproductive success and is the most conspicuous anatomical novelty of placental mammals. However, before it can exert its dual functions as both an endocrine organ and an organ capable of facilitating gas and nutrient exchange, enormous changes must take place within the uterus to not only tolerate the presence of this hemiallogeneic tissue but to also accommodate and support placental development. The most dramatic of these changes is endometrial decidualization, the origin of which coincides in evolutionary history with invasive placentation. This article builds on the observation that the physiological changes that occur during the nonpregnant secretory phase of the uterine cycle in women are remarkably similar to that seen during pregnancy. The fundamental characteristics of human pregnancy (including endometrial decidualization followed several months later by intrauterine inflammation, uterine contractions, and discharge of the decidual lining from the uterine cavity) are present already in the nonpregnant menstrual cycle and are thus independent of the fetus. We hypothesize that many of the physiological defects that lead to complications during pregnancy and parturition are detectable already during spontaneous decidualization in the nonpregnant state and at the onset of menstruation, and can thus be determined before the onset of pregnancy. [ABSTRACT FROM AUTHOR]

To explain the evolution of complex organisms by random mutation, drift, and selection is not a trivial task. This becomes obvious if we imagine an organism in which most genes affect most traits and all mutations are immediately expressed in the phenotype. Most of the mutations will be deleterious. Computer programmers experienced a similar problem when trying to evolve computer programs by introducing random changes to a conventional computer code, realizing that almost all random changes are 'lethal.' Everyone who has done any programming knows that conventional computer languages are very brittle! Real organisms are not organized in this way but rather involve mediation between the genes and the phenotypic traits, namely development, also sometimes called the genotype-phenotype map. This map of genetic effects is structured in a way that enables evolvability, that is, enhances the probability that mutations will improve the performance of the organism. Here we outline two properties of organismal development, namely modularity and robustness. Modularity refers to the situation in which genes affect a restricted number of functionally related phenotypic characters. Robustness describes a situation in which cryptic mutations can accumulate without effect on fitness but can become visible to selection in a new environment or genetic background. We discuss recent empirical evidence in support of both phenomena and their effect on evolvability and also briefly address their evolution. [ABSTRACT FROM AUTHOR]

The mechanisms translating genetic to phenotypic variation determine the distribution of heritable phenotypic variance available to selection. Pleiotropy is an aspect of this structure that limits independent variation of characters. Modularization of pleiotropy has been suggested to promote evolvability by restricting genetic covariance among unrelated characters and reducing constraints due to correlated response. However, modularity may also reduce total genetic variation of characters. We study the properties of genotype-phenotype maps that maximize average conditional evolvability, measured as the amount of unconstrained genetic variation in random directions of phenotypic space. In general, maximal evolvability occurs by maximizing genetic variance and minimizing genetic covariance. This does not necessarily require modularity, only patterns of pleiotropy that cancel on average. The detailed structure of the most evolvable genotype-phenotype maps depends on the distribution of molecular variance. When molecular variance is determined by mutation-selection equilibrium either highly pleiotropic or highly modular genotype-phenotype maps can be optimal, depending on the mutation rate and the relative strengths of stabilizing selection on the characters. [ABSTRACT FROM AUTHOR]

The potential and direction of phenotypic evolution is constrained by the distribution of genetic variation for the traits as described by the phenotypic ( P) and genetic covariance matrices ( G). The rank of the covariance matrix reflects the number of independent variational dimensions of the phenotype. Covariance matrices with less than full rank indicate lack of variation in some directions of the phenotype space and thus are an indication of absolute evolutionary constraints. Because selection acts upon phenotypic variation, the rank of P represents the upper limit of the dimensionality in G, relevant for selection response. The limitations of current methods to estimate matrix rank motivated us to analyze and adjust a bootstrap method and evaluate its performance by simulation. The results show that the modified bootstrap method (ABRE) gives reliable and rather conservative rank estimates when the sample size is sufficient for the number of variables studied (the sample size is at least five-fold the number of variables). Applying the method to various datasets suggests high phenotypic dimensionality in all cases. The analysis thus provides no evidence for absolute evolutionary constraints. [ABSTRACT FROM AUTHOR]

The concept of morphological integration describes the pattern and the amount of correlation between morphological traits. Integration is relevant in evolutionary biology as it imposes constraint on the variation that is exposed to selection, and is at the same time often based on heritable genetic correlations. Several measures have been proposed to assess the amount of integration, many using the distribution of eigenvalues of the correlation matrix. In this paper, we analyze the properties of eigenvalue variance as a much applied measure. We show that eigenvalue variance scales linearly with the square of the mean correlation and propose the standard deviation of the eigenvalues as a suitable alternative that scales linearly with the correlation. We furthermore develop a relative measure that is independent of the number of traits and can thus be readily compared across datasets. We apply this measure to examples of phenotypic correlation matrices and compare our measure to several other methods. The relative standard deviation of the eigenvalues gives similar results as the mean absolute correlation (W.P. Cane, Evol Int J Org Evol 47:844–854, 1993) but is only identical to this measure if the correlation matrix is homogenous. For heterogeneous correlation matrices the mean absolute correlation is consistently smaller than the relative standard deviation of eigenvalues and may thus underestimate integration. Unequal allocation of variance due to variation among correlation coefficients is captured by the relative standard deviation of eigenvalues. We thus suggest that this measure is a better reflection of the overall morphological integration than the average correlation. [ABSTRACT FROM AUTHOR]

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo–devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms. [ABSTRACT FROM AUTHOR]

As perceived by Darwin, evolutionary adaptation by the processes of mutation and selection is difficult to understand for complex features that are the product of numerous traits acting in concert, for example the eye or the apparatus of flight. Typically, mutations simultaneously affect multiple phenotypic characters. This phenomenon is known as pleiotropy. The impact of pleiotropy on evolution has for decades been the subject of formal analysis. Some authors have suggested that pleiotropy can impede evolutionary progress (a so-called ‘cost of complexity’). The plausibility of various phenomena attributed to pleiotropy depends on how many traits are affected by each mutation and on our understanding of the correlation between the number of traits affected by each gene substitution and the size of mutational effects on individual traits. Here we show, by studying pleiotropy in mice with the use of quantitative trait loci (QTLs) affecting skeletal characters, that most QTLs affect a relatively small subset of traits and that a substitution at a QTL has an effect on each trait that increases with the total number of traits affected. This suggests that evolution of higher organisms does not suffer a ‘cost of complexity’ because most mutations affect few traits and the size of the effects does not decrease with pleiotropy. [ABSTRACT FROM AUTHOR]

Reply to: J. Hermisson & A. P. McGregor 456, 10.1038/nature07452 (2008). In our paper on pleiotropic scaling and the cost of complexity, we presented evidence for three findings: first, most genes affect a small number of traits (the degree of pleiotropy is low); second, the total effect of a quantitative trait locus (QTL) increases with the degree of pleiotropy, refuting the constant total effect model; and third, the increase in total effect (defined as , where Ai is the effect on character i, that is, half the difference between the genotypic values of the homozygous genotypes) seems to be stronger than predicted by the superposition model of pleiotropic effects. Hermisson and McGregor point out that the last result could be due to multiple mutations being mapped to the same QTL, but only if these mutations affect overlapping sets of traits. We agree that this is a possibility that we could not address with the data at hand. [ABSTRACT FROM AUTHOR]