Building a foundation for structure‐based cellulosome design for cellulosic ethanol: Insight into cohesin‐dockerin complexation from computer simulation

The organization and assembly of the cellulosome, an extracellular multienzyme complex produced by anaerobic bacteria, is mediated by the high‐affinity interaction of cohesin domains from scaffolding proteins with dockerins of cellulosomal enzymes. We have performed molecular dynamics simulations and free energy calculations on both the wild type (WT) and D39N mutant of the C. thermocellum Type I cohesin‐dockerin complex in aqueous solution. The D39N mutation has been experimentally demonstrated to disrupt cohesin‐dockerin binding. The present MD simulations indicate that the substitution triggers significant protein flexibility and causes a major change of the hydrogen‐bonding network in the recognition strips—the conserved loop regions previously proposed to be involved in binding—through electrostatic and salt‐bridge interactions between β‐strands 3 and 5 of the cohesin and α‐helix 3 of the dockerin. The mutation‐induced subtle disturbance in the local hydrogen‐bond network is accompanied by conformational rearrangements of the protein side chains and bound water molecules. Additional free energy perturbation calculations of the D39N mutation provide differences in the cohesin‐dockerin binding energy, thus offering a direct, quantitative comparison with experiments. The underlying molecular mechanism of cohesin‐dockerin complexation is further investigated through the free energy profile, that is, potential of mean force (PMF) calculations of WT cohesin‐dockerin complex. The PMF shows a high‐free energy barrier against the dissociation and reveals a stepwise pattern involving both the central β‐sheet interface and its adjacent solvent‐exposed loop/turn regions clustered at both ends of the β‐barrel structure.     

Quantifying the genetic component of phenotypic variation in unpedigreed wild plants: tailoring genomic scan for within-population use

The study of adaptive genetic variation in natural populations is central to evolutionary biology. Quantitative genetics methods, however, are hardly applicable to long-lived organisms, and current knowledge on adaptive genetic variation in wild plants mostly refers to annuals and short-lived perennials. Studies on long-lived species are essential to explore possible life-history correlates of genetic variation, selection, and trait heritability. In this paper, we propose a method based on molecular markers to quantify the genetic basis of individual phenotypic differences in wild plants under natural conditions. Rather than focusing on inferring individual relatedness to estimate the heritability of phenotypic traits, we directly estimate the proportion of observed phenotypic variance that is statistically accounted for by genotypic differences between individuals. This is achieved by (i) identifying loci that are correlated across individuals with the phenotypic trait of interest by means of an amplified fragment length polymorphism (AFLP)-based explorative genomic scan, and (ii) fitting multiple regression and linear random effect models to estimate the effects of genotype, environment and genotype × environment on phenotypes. We apply this method to estimate genotypic and environmental effects on cumulative maternal fecundity in a wild population of the long-lived Viola cazorlensis monitored for 20 years. Results show that between 56–63% (depending on estimation method) of phenotypic variance in fecundity is accounted for by genotypic differences in 11 AFLP loci that are significantly related to fecundity. Genotype × environment effects accounted for 38% of fecundity variance, which may help to explain the unexpectedly high levels of genetic variance for fecundity found.     

From heritability to probability

Can a heritability value tell us something about the weight of genetic versus environmental causes that have acted in the development of a particular individual? Two possible questions arise. Q1: what portion of the phenotype of X is due to its genes and what portion to its environment? Q2: what portion of X’s phenotypic deviation from the mean is a result of its genetic deviation and what portion a result of its environmental deviation? An answer to Q1 provides the full information about X’s development, while an answer to Q2 leaves out a large portion unexplained—that portion which corresponds to the phenotypic mean. Q1 is unanswerable, but I show it is nevertheless legitimate under certain quantitative genetics models. With regard to Q2, opinions in the philosophical and biological literature differ as to its legitimacy. I argue that not only is it legitimate, but in particular, under a few simplifying assumptions, it allows for a quantitative probabilistic answer: for normally distributed quantitative traits with no G-E correlation or statistical G × E interaction, we can assess the probability that X’s genes had a greater effect than its environment on its deviation from the mean population value. This probability is expressed as a function the heritability and the individual’s phenotypic value; we can also provide a quantitative probabilistic answer to Q2 for an arbitrary individual where the probability is a function only of heritability.     

动物学定量与定性资料分析中的常见错误

合理选择正确的统计方法是提高科研工作质量和可信度的关键.然而有些科研工作所采用的统计方法往往不合理,甚至是错误的.例如,常常忽视定量资料中t-检验和方差分析的前提条件--正态性、方差齐性.对单因素多水平的定量资料直接使用组间两两比较的t-检验;对定性资料中列联表的分析一律通用Pearson χ~2检验,包括对Pearson χ~2检验结果的理解出现偏差,从而做出错误的推论.本文对国内一些动物学研究论文中所采用的具体统计方法提出辨析.     

Quantification of blockiness in pectins—A comparative study using vibrational spectroscopy and chemometrics

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045 cm⁻¹) spectrum and one significant area (1250-1400 cm⁻¹) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B > B > B-R > R > re-methylated pectin.     

IS INBREEDING DEPRESSION LOWER IN MALADAPTED POPULATIONS? A QUANTITATIVE GENETICS MODEL

Despite abundant empirical evidence that inbreeding depression varies with both the environment and the genotypic context, theoretical predictions about such effects are still rare. Using a quantitative genetics model, we predict amounts of inbreeding depression for fitness emerging from Gaussian stabilizing selection on some phenotypic trait, on which, for simplicity, genetic effects are strictly additive. Given the strength of stabilizing selection, inbreeding depression then varies simply with the genetic variance for the trait under selection and the distance between the mean breeding value and the optimal phenotype. This allows us to relate the expected inbreeding depression to the degree of maladaptation of the population to its environment. We confront analytical predictions with simulations, in well-adapted populations at equilibrium, as well as in maladapted populations undergoing either a transient environmental shift, or gene swamping in heterogeneous habitats. We predict minimal inbreeding depression in situations of extreme maladaptation. Our model provides a new basis for interpreting experiments that measure inbreeding depression for the same set of genotypes in different environments, by demonstrating that the history of adaptation, in addition to environmental harshness per se, may account for differences in inbreeding depression.     

EASY AND FLEXIBLE BAYESIAN INFERENCE OF QUANTITATIVE GENETIC PARAMETERS

There has been a tremendous advancement of Bayesian methodology in quantitative genetics and evolutionary biology. Still, there are relatively few publications that apply this methodology, probably because the availability of multipurpose and user-friendly software is somewhat limited. It is here described how only a few rows of code of the well-developed and very flexible Bayesian software WinBUGS ( Lunn et al. 2000 ) can be used for inference of the additive polygenic variance and heritabilty in pedigrees of general design. The presented code is illustrated by application to an earlier published dataset of Scots pine.     

THE EVOLVABILITY OF GROWTH FORM IN A CLONAL SEAWEED

Although modular construction is considered the key to adaptive growth or growth‐form plasticity in sessile taxa (e.g., plants, seaweeds and colonial invertebrates), the serial expression of genes in morphogenesis may compromise its evolutionary potential if growth forms emerge as integrated wholes from module iteration. To explore the evolvability of growth form in the red seaweed, Asparagopsis armata, we estimated genetic variances, covariances, and cross‐environment correlations for principal components of growth‐form variation in contrasting light environments. We compared variance–covariance matrices across environments to test environmental effects on heritable variation and examined the potential for evolutionary change in the direction of plastic responses to light. Our results suggest that growth form in Asparagopsis may constitute only a single genetic entity whose plasticity affords only limited evolutionary potential. We argue that morphological integration arising from modular construction may constrain the evolvability of growth form in Asparagopsis, emphasizing the critical distinction between genetic and morphological modularity in this and other modular taxa.     

Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets

Summary .  This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein–Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine ( Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability.