High-resolution patterns of meiotic recombination across the human major histocompatibility complex

Definitive characteristics of meiotic recombination events over large (i.e., >1 Mb) segments of the human genome remain obscure, yet they are essential for establishing the haplotypic structure of the genome and for efficient mapping of complex traits. We present a high-resolution map of recombination at the kilobase level across a 3.3-Mb interval encompassing the major histocompatibility complex (MHC). Genotyping of 20,031 single sperm from 12 individuals resulted in the identification and fine mapping of 325 recombinant chromosomes within genomic intervals as small as 7 kb. Several principal characteristics of recombination in this region were observed: (1) rates of recombination can differ significantly between individuals; (2) intense hot spots of recombination occur at least every 0.8 Mb but are not necessarily evenly spaced; (3) distribution in the location of recombination events can differ significantly among individuals; (4) between hot spots, low levels of recombination occur fairly evenly across 100-kb segments, suggesting the presence of warm spots of recombination; and (5) specific sequence motifs associate significantly with recombination distribution. These data provide a plausible model for recombination patterns of the human genome overall.     

Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts

Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2×) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance.     

Detection of differential gene flow from patterns of quantitative variation

A major goal in anthropological genetics is the assessment of the effects of different microevolutionary forces. Harpending and Ward (1982) developed a model that aids in this effort by comparing observed and expected heterozygosity within populations in a local region. The expected heterozygosity within a population is a function of the total heterozygosity of the entire region and the distance of the population from the regional mean centroid of allele frequencies. Greater than average gene flow from an external source will result in a population having greater heterozygosity than expected. Less than average gene flow from an external source will result in a population having less heterozygosity than expected. We extend the Harpending-Ward model to quantitative traits using an equal and additive effects model of inheritance. Here the additive genetic variance within a population is directly proportional to heterozygosity, and its expectation is directly proportional to the genetic distance from the centroid. Under certain assumptions the expectations for phenotypic variances are similar. Observed and expected genetic or phenotypic variance can then be compared to assess the effects of differential external gene flow. When the additive genetic covariance matrix or heritabilities are not known, the phenotypic covariance matrix can be used to provide a conservative application of the model. In addition, we develop new methods for estimation of the genetic relationship matrix (R) from quantitative traits. We apply these models to two data sets: (1) six principal components derived from twenty dermatoglyphic ridge count measures for nine villages in Nepal and (2) ten anthropometric measurements for seven isolated populations in western Ireland. In both cases both the univariate and multivariate analyses provide results that can be directly interpreted in terms of historically known patterns of gene flow.     

Protein arrays for gene expression and molecular interaction screening

The array format has revolutionised biomedical experimentation and diagnostics, enabling ordered high-throughput analysis. During the past decade, classic solid phase substrates, such as microtitre plates, membrane filters and microscopic slides, were turned into high-density, chip-like structures. The concept of the arrayed library was central to this development which now extends from DNA to protein. The new and versatile protein array technology allows high-throughput screening for gene expression and molecular interactions. As a major platform for functional genomics, it is already on its way into medical diagnostics.     

Contrasting patterns of floral and molecular variation across a cline in Mimulus aurantiacus

Abstract Steep clines in ecologically important traits may be caused by divergent natural selection. However, processes that do not necessarily invoke ongoing selection, such as secondary contact or restricted gene flow, can also cause patterns of phenotypic differentiation over short spatial scales. Distinguishing among all possible scenarios is difficult, but an attainable goal is to establish whether scenarios that imply selection need to be invoked. We compared the extent of morphological and genetic differentiation between geographically structured red and yellow floral races of Mimulus aurantiacus (bush monkeyflower; Phrymacea). Flower color was assessed in a common garden as well as in the field to determine whether variation was genetic and to quantify the extent of geographical differentiation. Population genetic differentiation at marker loci was measured for both chloroplast and nuclear genomes, and the degree of population structure within and among the floral races was evaluated. Flower color shows both a strong genetic basis and a sharp geographic transition, with pure red-flowered populations in western San Diego County and pure yellow-flowered populations to the east. In the zone of contact, both pure and intermediate phenotypes occur. Patterns of genetic differentiation at marker loci are far less pronounced, as little of the variation is partitioned according to the differences in flower color. Phenotypic differentiation (Q_ST) between populations with different flower colors is much greater than neutral genetic differentiation (F_ST). When comparisons are made between populations of the same flower color, the opposite trend is evident. Limited neutral genetic structure between the floral races, combined with sharp differentiation in flower color, is consistent with the hypothesis that current or recent natural selection maintains the cline in flower color.     

Hunting patterns and geographic profiling of white shark predation

Interspecific associations can arise for varied reasons including reduced predation risk and improved foraging success. In the case of bird–primate associations, birds typically appear to follow primate groups to harvest insects flushed by primates' movements. However, while previous studies have linked temporal changes in bird–primate associations to environmental conditions, few have assessed the additional effects of bird activity patterns and primate group behaviour and none have disentangled their potentially interdependent effects. Here, we test the hypothesis that foraging opportunities can drive interspecific associations in a previously undescribed bird–primate association between rock kestrels Falco rupicolus and chacma baboons Papio ursinus in central Namibia. Data were collected from two baboon groups and associated kestrels using instantaneous scan sampling during full-day follows over a 7-month field period, and analysed using generalized linear mixed models. We found that kestrel associations with baboons vary with season, show diurnal cycles and are more frequent when the baboons are in open desert habitat, engaged in travel foraging and in a large group. These patterns are statistically independent and consistent with the hypothesis that the kestrel–baboon association is driven by the foraging opportunities acquired by the kestrels. As the baboons do not appear to gain any benefits nor incur any costs from the association, we conclude that the kestrels are likely to be commensal with the baboons.     

Protein-protein interaction investigated by steered molecular dynamics: the TCR-pMHC complex

We present a novel steered molecular dynamics scheme to induce the dissociation of large protein-protein complexes. We apply this scheme to study the interaction of a T cell receptor (TCR) with a major histocompatibility complex (MHC) presenting a peptide (p). Two TCR-pMHC complexes are considered, which only differ by the mutation of a single amino acid on the peptide; one is a strong agonist that produces T cell activation in vivo, while the other is an antagonist. We investigate the interaction mechanism from a large number of unbinding trajectories by analyzing van der Waals and electrostatic interactions and by computing energy changes in proteins and solvent. In addition, dissociation potentials of mean force are calculated with the Jarzynski identity, using an averaging method developed for our steering scheme. We analyze the convergence of the Jarzynski exponential average, which is hampered by the large amount of dissipative work involved and the complexity of the system. The resulting dissociation free energies largely underestimate experimental values, but the simulations are able to clearly differentiate between wild-type and mutated TCR-pMHC and give insights into the dissociation mechanism.     

PADGE: analysis of heterogeneous patterns of differential gene expression.

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644-648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269-2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/.     

Finding consistent gene transmission patterns on large and complex pedigrees

A heuristic algorithm for finding gene transmission patterns on large and complex pedigrees with partially observed genotype data is proposed. The method can be used to generate an initial point for a Markov chain Monte Carlo simulation or to check that the given pedigree and the genotype data are consistent. In small pedigrees, the algorithm is exact by exhaustively enumerating all possibilities, but, in large pedigrees, with a considerable amount of unknown data, only a subset of promising configurations can actually be checked. For that purpose, the configurations are ordered by combining the approximative conditional probability distribution of the unknown genotypes with the information on the relationships between individuals. We also introduce a way to divide the task into subparts, which has been shown to be useful in large pedigrees. The algorithm has been implemented in a program called APE (Allelic Path Explorer) and tested in three different settings with good results.     

Discovering molecular pathways from protein interaction and gene expression data

In this paper, we describe an approach for identifying 'pathways' from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.     

Beyond phytohaemagglutinin: assessing vertebrate immune function across ecological contexts

1. Over the past decade, there has been a substantial increase in interest in the immune system and the role it plays in the regulation of disease susceptibility, giving rise to the field of eco-immunology. 2. Eco-immunology aims to understand changes in host immune responses in the broader framework of an organism's evolutionary, ecological and life-history contexts. 3. The immune system, however, is complex and multifaceted and can be intimidating for the nonimmunologist interested in incorporating immunological questions into their research. Which immune responses should one measure and what is the biological significance of these measures? 4. The focus of this review is to describe a wide range of eco-immunology techniques, from the simple to the sophisticated, with the goal of providing researchers with a range of options to consider incorporating in their own research programs. 5. These techniques were chosen because they provide relatively straightforward, biologically meaningful assessments of immune function, many of which can be performed across a range of ecological contexts (i.e. field vs. laboratory) and in a wide range of vertebrate animals without relying on species-specific reagents. 6. By incorporating assessments of immune function into their specific research questions, animal ecologists will gain a more comprehensive understanding of organism-environment interactions.     

Use of Relibase for retrieving complex three-dimensional interaction patterns including crystallographic packing effects

Relibase is a database system that has been specially designed to handle protein-ligand data. Included within Relibase is a tool that can be used to systematically analyse protein-ligand interaction patterns specified by three-dimensional (3D) constraints, revealing favorable combinations of interacting functional groups and their preferred interaction geometries. This paper describes the Relibase 3D query tools, including novel extensions (Relibase+) for handling crystallographic packing effects. Examples illustrating the broad range of functionality for defining 3D interaction patterns and the application of such queries in drug design comprise carbonyl-carbonyl interactions, zinc binding site environments, and ligand-ligand interactions in the crystal packing.     

Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

Schistosoma mansoni: molecular characterization of Alkaline Phosphatase and expression patterns across life cycle stages

Here we describe the cloning and characterization of the Schistosoma mansoni Alkaline Phosphatase (SmAP), previously identified in the tegument of adult worms. SmAP encodes a complete sequence composed of 536 amino acids containing an N-terminal signal peptide, five N-glycosylation sites, and a GPI anchor signal, similar to that described for mammalian orthologs. Real-time RT-PCR and Western blot experiments suggest a rapid translation as soon as cercariae are transformed into schistosomula. Immunolocalization analysis shows that the protein is widely distributed in the worm tissues, with increased concentration in the vitelline glands of female parasites. Furthermore, the surface localization of this enzyme was quantitatively supported by its enzymatic activity in live ex vivo or cultured parasites throughout the life cycle stages. The fact that cercariae accumulate large amounts of SmAP mRNA, which rapidly translates into protein upon schistosomula transformation, indicates it may have an important role in host invasion.