Assessment of DGAT1 and LEP gene polymorphisms in three Nelore (Bos indicus) lines selected for growth and their relationship with growth and carcass traits

The aim of this study was to analyze LEP and DGAT1 gene polymorphisms in 3 Nelore lines selected for growth and to evaluate their effects on growth and carcass traits. Traits analyzed were birth, weaning, and yearling weight, rump height, LM area, backfat thickness, and rump fat thickness obtained by ultrasound. Two SNP in the LEP gene [LEP 1620(A/G) and LEP 305(T/C)] and the K232A mutation in the DGAT1 gene were analyzed. The sample consisted of 357 Nelore heifers from 2 lines selected for yearling weight and a control line, established in 1980, at the Estacao Experimental de Zootecnia de Sertaozinho (Sertaozinho, Brazil). Three genotypes were obtained for each marker. Differences in allele frequencies among the 3 lines were only observed for the DGAT1 K232A polymorphism, with the frequency of the A allele being greater in the control line than in the selected lines. The DGAT1 K232A mutation was associated only with rump height, whereas LEP 1620(A/G) was associated with weaning weight and LEP 305(T/C) with birth weight and backfat thickness. However, more studies, with larger data sets, are necessary before these makers can be used for marker-assisted selection.

High homologous gene conservation despite extreme autopolyploid redundancy in sugarcane

P>Modern sugarcane (Saccharum spp.) is the leading sugar crop and a primary energy crop. It has the highest level of `vertical` redundancy (2n = 12x = 120) of all polyploid plants studied to date. It was produced about a century ago through hybridization between two autopolyploid species, namely S. officinarum and S. spontaneum. In order to investigate the genome dynamics in this highly polyploid context, we sequenced and compared seven hom(oe)ologous haplotypes (bacterial artificial chromosome clones). Our analysis revealed a high level of gene retention and colinearity, as well as high gene structure and sequence conservation, with an average sequence divergence of 4% for exons. Remarkably, all of the hom(oe)ologous genes were predicted as being functional (except for one gene fragment) and showed signs of evolving under purifying selection, with the exception of genes within segmental duplications. By contrast, transposable elements displayed a general absence of colinearity among hom(oe)ologous haplotypes and appeared to have undergone dynamic expansion in Saccharum, compared with sorghum, its close relative in the Andropogonea tribe. These results reinforce the general trend emerging from recent studies indicating the diverse and nuanced effect of polyploidy on genome dynamics.

Divergence Involving Global Regulatory Gene Mutations in an Escherichia coli Population Evolving under Phosphate Limitation

Many of the important changes in evolution are regulatory in nature. Sequenced bacterial genomes point to flexibility in regulatory circuits but we do not know how regulation is remodeled in evolving bacteria. Here, we study the regulatory changes that emerge in populations evolving under controlled conditions during experimental evolution of Escherichia coli in a phosphate-limited chemostat culture. Genomes were sequenced from five clones with different combinations of phenotypic properties that coexisted in a population after 37 days. Each of the distinct isolates contained a different mutation in 1 of 3 highly pleiotropic regulatory genes (hfq, spoT, or rpoS). The mutations resulted in dissimilar proteomic changes, consistent with the documented effects of hfq, spoT, and rpoS mutations. The different mutations do share a common benefit, however, in that the mutations each redirect cellular resources away from stress responses that are redundant in a constant selection environment. The hfq mutation lowers several individual stress responses as well the small RNA-dependent activation of rpoS translation and hence general stress resistance. The spoT mutation reduces ppGpp levels, decreasing the stringent response as well as rpoS expression. The mutations in and upstream of rpoS resulted in partial or complete loss of general stress resistance. Our observations suggest that the degeneracy at the core of bacterial stress regulation provides alternative solutions to a common evolutionary challenge. These results can explain phenotypic divergence in a constant environment and also how evolutionary jumps and adaptive radiations involve altered gene regulation.

Limited global diversity of the Plasmodium vivax merozoite surface protein 4 gene

Merozoite surface proteins (MSPs) of the malaria parasites are major candidates for vaccine development targeting asexual blood stages. However, the diverse antigenic repertoire of these antigens that induce strain-specific protective immunity in human is a major challenge for vaccine design and often determines the efficacy of a vaccine. Here we further assessed the genetic diversity of Plasmodium vivax MSP4 (PvMSP4) protein using 195 parasite samples collected mostly from Thailand, Indonesia and Brazil. Overall, PvMSP4 is highly conserved with only eight amino acid substitutions. The majority of the haplotype diversity was restricted to the two short tetrapeptide repeat arrays in exon 1 and 2, respectively. Selection and neutrality tests indicated that exon 1 and the entire coding region of PvMSP4 were under purifying selection. Despite the limited nucleotide polymorphism of PvMSP4, significant genetic differentiation among the three major parasite populations was detected. Moreover, microgeographical heterogeneity was also evident in the parasite populations from different endemic areas of Thailand. (C) 2009 Elsevier B.V. All rights reserved.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.