Reduced ability to recover from spindle disruption and loss of kinetochore spindle assembly checkpoint proteins in oocytes from aged mice

Currently, maternal aging in women, based on mouse models, is thought to raise oocyte aneuploidy rates, because chromosome cohesion deteriorates during prophase arrest, and Sgo2, a protector of centromeric cohesion, is lost. Here we show that the most common mouse strain, C57Bl6/J, is resistant to maternal aging, showing little increase in aneuploidy or Sgo2 loss. Instead it demonstrates significant kinetochore-associated loss in the spindle assembly checkpoint protein Mad2 and phosphorylated Aurora C, which is involved in microtubule–kinetochore error correction. Their loss affects the fidelity of bivalent segregation but only when spindle organization is impaired during oocyte maturation. These findings have an impact clinically regarding the handling of human oocytes ex vivo during assisted reproductive techniques and suggest there is a genetic basis to aneuploidy susceptibility.     

Properties of autonomous 3′→5′ exonucleases

Autonomous 3′→5′ exonucleases (AE) are not bound covalently to DNA polymerases, but they are often included into the replicative complexes. Intracellular AE overproduction in bacteria results in sharp suppression of mutagenesis, whereas inactivation of these enzymes in bacteria and fungi leads to an increase in mutagenesis frequency by 2–3 orders of magnitude. Correction of DNA polymerase errors in vitro occurs after addition of AE to the incubation medium. This correction is clearly manifested under conditions of mutational stress (during induced but not spontaneous mutagenesis), for instance, with an imbalance of dNTPs — error-prone conditions. At equimolar dNTP (error-free conditions), the correction is relatively weak. The gene knockout of both alleles of the major AE gene in mice does not influence spontaneous mutagenesis though a substantial increase could be expected. The frequency of induced mutagenesis has not been yet measured, though the inactivation of AE could increase the frequency of mutagenesis. Complete inactivation of the major AE leads to inflammatory myocarditis and a 5-fold reduction of life span of mice. Dominant heterozygous mutations were found in various loci of the AE gene, which caused the development of Aicardi-Goutieres (autosomal recessive encephalopathy) syndrome, familial chilblain lupus, systemic lupus erythematosus, retinal vasculopathy, and cerebral leukodystrophy. In the nucleus, AE have a corrective function, but after transition into cytoplasm these enzymes destroy aberrant DNA that appears during replication and thereby save the cells from autoimmune diseases. Depending on their intracellular localization, AE carry out various biological functions but employ the same mechanism of the catalyzed reactions.     

Models for Bounded Systems with Continuous Dynamics

Summary .  Models for natural nonlinear processes, such as population dynamics, have been given much attention in applied mathematics. For example, species competition has been extensively modeled by differential equations. Often, the scientist has preferred to model the underlying dynamical processes (i.e., theoretical mechanisms) in continuous time. It is of both scientific and mathematical interest to implement such models in a statistical framework to quantify uncertainty associated with the models in the presence of observations. That is, given discrete observations arising from the underlying continuous process, the unobserved process can be formally described while accounting for multiple sources of uncertainty (e.g., measurement error, model choice, and inherent stochasticity of process parameters). In addition to continuity, natural processes are often bounded; specifically, they tend to have nonnegative support. Various techniques have been implemented to accommodate nonnegative processes, but such techniques are often limited or overly compromising. This article offers an alternative to common differential modeling practices by using a bias-corrected truncated normal distribution to model the observations and latent process, both having bounded support. Parameters of an underlying continuous process are characterized in a Bayesian hierarchical context, utilizing a fourth-order Runge–Kutta approximation.     

Testing Hardy–Weinberg Equilibrium and Homogeneity of Hardy–Weinberg Disequilibrium using Complex Survey Data

Summary For studies on population genetics, the use of representative random samples of the target population can avoid ascertainment bias. Genetic variation data from over a hundred genes were collected in a U.S. nationally representative sample in the Third National Health and Nutrition Examination Survey (NHANES III). Surveys such as the NHANES have complex stratified multistage cluster sample designs with sample weighting that can inflate variances and alter the expectations of test statistics. Thus, classical statistical tests of Hardy–Weinberg equilibrium (HWE) and homogeneity of HW disequilibrium (HHWD) for simple random samples are not suitable for data from complex samples. We propose using Wald tests for HWE and generalized score tests for HHWD that have been modified for complex samples. Monte Carlo simulation studies are used to investigate the finite sample properties of the proposed tests. Rao–Scott corrections applied to the tests were found to improve their type I error properties. Our methods are applied to the NHANES III genetic data for three loci involved in metabolizing lead in the body.     

Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation

In most analyses of large-scale genomic data sets, differentialexpression analysis is typically assessed by testing for differencesin the mean of the distributions between 2 groups. A recentfinding by Tomlins and others (2005) is of a different typeof pattern of differential expression in which a fraction ofsamples in one group have overexpression relative to samplesin the other group. In this work, we describe a general mixturemodel framework for the assessment of this type of expression,called outlier profile analysis. We start by considering thesingle-gene situation and establishing results on identifiability.We propose 2 nonparametric estimation procedures that have naturallinks to familiar multiple testing procedures. We then developmultivariate extensions of this methodology to handle genome-widemeasurements. The proposed methodologies are compared usingsimulation studies as well as data from a prostate cancer geneexpression study.     

Mapping the premigration distribution of eastern Monarch butterflies using community science data

Knowing the distribution of migratory species at different stages of their life cycle is necessary for their effective conservation. For the Monarch butterfly (Danaus plexippus), although its overwintering distribution is well known, the available information on premigration distribution is limited to the studies estimating the natal origins of overwintering Monarchs in Mexico (i.e., postmigration data). However, the premigration distribution and the natal origins of overwintering Monarchs can be equivalent only if we assume that migrating Monarchs have the same mortality rate irrespective of their origins. To estimate Monarchs’ premigration distribution, we used data reported by community scientists before Monarchs start their fall migration, that is, before migration mortality, and controlled for sampling bias. Our premigration distribution map indicated that Minnesota, Texas, and Ontario are the states/provinces with the highest abundance of Monarch in North America. Although this higher estimated abundance can be related to the large sizes of these states/provinces, this information is still important because it identifies the management jurisdictions with the largest responsibility for the conservation of the premigration population of Monarchs. Our premigration distribution map will be useful in future studies estimating the rates, distribution, and causes of mortality in migrating Monarchs.     

Automated extraction of backbone deuteration levels from amide H/2H mass spectrometry experiments

A Fourier deconvolution method has been developed to explicitly determine the amount of backbone amide deuterium incorporated into protein regions or segments by hydrogen/deuterium (H/D) exchange with high-resolution mass spectrometry. Determination and analysis of the level and number of backbone amide exchanging in solution provide more information about the solvent accessibility of the protein than do previous centroid methods, which only calculate the average deuterons exchanged. After exchange, a protein is digested into peptides as a way of determining the exchange within a local area of the protein. The mass of a peptide upon deuteration is a sum of the natural isotope abundance, fast exchanging side-chain hydrogens (present in MALDI-TOF H/2H data) and backbone amide exchange. Removal of the components of the isotopic distribution due to the natural isotope abundances and the fast exchanging side-chains allows for a precise quantification of the levels of backbone amide exchange, as is shown by an example from protein kinase A. The deconvoluted results are affected by overlapping peptides or inconsistent mass envelopes, and evaluation procedures for these cases are discussed. Finally, a method for determining the back exchange corrected populations is presented, and its effect on the data is discussed under various circumstances.     

Size correction: comparing morphological traits among populations and environments

Morphological relationships change with overall body size and body size often varies among populations. Therefore, quantitative analyses of individual traits from organisms in different populations or environments (e.g., in studies of phenotypic plasticity) often adjust for differences in body size to isolate changes in allometry. Most studies of among population variation in morphology either (1) use analysis of covariance (ANCOVA) with a univariate measure of body size as the covariate, or (2) compare residuals from ordinary least squares regression of each trait against body size or the first principal component of the pooled data (shearing). However, both approaches are problematic. ANCOVA depends on assumptions (small variance in the covariate) that are frequently violated in this context. Residuals analysis assumes that scaling relationships within groups are equal, but this assumption is rarely tested. Furthermore, scaling relationships obtained from pooled data typically mischaracterize within-group scaling relationships. We discuss potential biases imposed by the application of ANCOVA and residuals analysis for quantifying morphological differences, and elaborate and demonstrate a more effective alternative: common principal components analysis combined with Burnaby’s back-projection method.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

内生真菌种类和母本基因型对内生真菌-禾草共生体叶形状和叶面积的影响

Epichloë内生真菌感染能够影响宿主植物的生长发育, 但关于内生真菌感染对宿主植物叶形状和叶面积的研究很少。该研究以羽茅(Achnatherum sibiricum)为实验材料, 采用长宽系数计算和扫描测定叶面积相结合的方法探究内生真菌种类和羽茅母本基因型对羽茅-内生真菌共生体叶形状和叶面积的影响。结果表明: 内生真菌感染与否、内生真菌种类和宿主母本基因型对反映叶形状的叶校正系数、叶片长度、宽度和长宽比均无显著影响, 经计算与验证, 确定了羽茅叶片的校正系数为0.594 9。采用该校正系数及叶长宽计算的叶面积与实测叶面积无显著差异, 且二者均未受到内生真菌感染与否、内生真菌种类或宿主植物母本基因型的显著影响。