Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Whole-genome sequencing of the efficient industrial fuel-ethanol fermentative Saccharomyces cerevisiae strain CAT-1

The Saccharomyces cerevisiae strains widely used for industrial fuel-ethanol production have been developed by selection, but their underlying beneficial genetic polymorphisms remain unknown. Here, we report the draft whole-genome sequence of the S. cerevisiae strain CAT-1, which is a dominant fuel-ethanol fermentative strain from the sugarcane industry in Brazil. Our results indicate that strain CAT-1 is a highly heterozygous diploid yeast strain, and the similar to 12-Mb genome of CAT-1, when compared with the reference S228c genome, contains similar to 36,000 homozygous and similar to 30,000 heterozygous single nucleotide polymorphisms, exhibiting an uneven distribution among chromosomes due to large genomic regions of loss of heterozygosity (LOH). In total, 58 % of the 6,652 predicted protein-coding genes of the CAT-1 genome constitute different alleles when compared with the genes present in the reference S288c genome. The CAT-1 genome contains a reduced number of transposable elements, as well as several gene deletions and duplications, especially at telomeric regions, some correlated with several of the physiological characteristics of this industrial fuel-ethanol strain. Phylogenetic analyses revealed that some genes were likely associated with traits important for bioethanol production. Identifying and characterizing the allelic variations controlling traits relevant to industrial fermentation should provide the basis for a forward genetics approach for developing better fermenting yeast strains.

MUC1 gene polymorphism in three Nelore lines selected for growth and its association with growth and carcass traits

The objective of this study was to describe the VNTR polymorphism of the mucin 1 gene (MUC1) in three Nelore lines selected for yearling weight to determine whether allele and genotype frequencies of this polymorphism were affected by selection for growth. In addition, the effects of the polymorphism on growth and carcass traits were evaluated. Birth, weaning and yearling weights, rump height, Longissimus muscle area, backfat thickness, and rump fat thickness, were analyzed. A total of 295 Nelore heifers from the Beef Cattle Research Center, Instituto de Zootecnia de Sertozinho, were used, including 41 of the control line, 102 of the selection line and 152 of the traditional. The selection and traditional lines comprise animals selected for higher yearling weight, whereas control line animals are selected for yearling weight close to the average. Five alleles were identified, with allele 1 being the most frequent in the three lines, especially in the lines selected for higher means for yearling weight. Heterozygosity was significantly higher in the control line. Association analyses showed significant effects of allele 1 on birth weight and weaning weight while the allele 3 exert significant effects on yearling weight and back fat thickness. Despite these findings, application of this marker to marker-assisted selection requires more consistent results based on the genotyping of a larger number of animals in order to increase the accuracy of the statistical analyses.

Segmental dataset and whole body expression data do not support the hypothesis that non-random movement is an intrinsic property of Drosophila retrogenes

Background: Several studies in Drosophila have shown excessive movement of retrogenes from the X chromosome to autosomes, and that these genes are frequently expressed in the testis. This phenomenon has led to several hypotheses invoking natural selection as the process driving male-biased genes to the autosomes. Metta and Schlotterer (BMC Evol Biol 2010, 10:114) analyzed a set of retrogenes where the parental gene has been subsequently lost. They assumed that this class of retrogenes replaced the ancestral functions of the parental gene, and reported that these retrogenes, although mostly originating from movement out of the X chromosome, showed female-biased or unbiased expression. These observations led the authors to suggest that selective forces (such as meiotic sex chromosome inactivation and sexual antagonism) were not responsible for the observed pattern of retrogene movement out of the X chromosome. Results: We reanalyzed the dataset published by Metta and Schlotterer and found several issues that led us to a different conclusion. In particular, Metta and Schlotterer used a dataset combined with expression data in which significant sex-biased expression is not detectable. First, the authors used a segmental dataset where the genes selected for analysis were less testis-biased in expression than those that were excluded from the study. Second, sex-biased expression was defined by comparing male and female whole-body data and not the expression of these genes in gonadal tissues. This approach significantly reduces the probability of detecting sex-biased expressed genes, which explains why the vast majority of the genes analyzed (parental and retrogenes) were equally expressed in both males and females. Third, the female-biased expression observed by Metta and Schltterer is mostly found for parental genes located on the X chromosome, which is known to be enriched with genes with female-biased expression. Fourth, using additional gonad expression data, we found that autosomal genes analyzed by Metta and Schlotterer are less up regulated in ovaries and have higher chance to be expressed in meiotic cells of spermatogenesis when compared to X-linked genes. Conclusions: The criteria used to select retrogenes and the sex-biased expression data based on whole adult flies generated a segmental dataset of female-biased and unbiased expressed genes that was unable to detect the higher propensity of autosomal retrogenes to be expressed in males. Thus, there is no support for the authors' view that the movement of new retrogenes, which originated from X-linked parental genes, was not driven by selection. Therefore, selection-based genetic models remain the most parsimonious explanations for the observed chromosomal distribution of retrogenes.

Selection of Effective Stone Tools by Wild Bearded Capuchin Monkeys

Appreciation of objects` affordances and planning is a hallmark of human technology. Archeological evidence suggests that Pliocene hominins selected raw material for tool making [1, 2]. Stone pounding has been considered a precursor to tool making [3, 4], and tool use by living primates provides insight into the origins of material selection by human ancestors. No study has experimentally investigated selectivity of stone tools in wild animals, although chimpanzees appear to select stones according to properties of different nut species [5, 6]. We recently discovered that wild capuchins with terrestrial habits [7] use hammers to crack open nuts on anvils [8-10]. As for chimpanzees, examination of anvil sites suggests stone selectivity [11], but indirect evidence cannot prove it. Here, we demonstrate that capuchins, which last shared a common ancestor with humans 35 million years ago, faced with stones differing in functional features (friability and weight) choose, transport, and use the effective stone to crack nuts. Moreover, when weight cannot be judged by visual attributes, capuchins act to gain information to guide their selection. Thus, planning actions and intentional selection of tools is within the ken of monkeys and similar to the tool activities of hominins and apes.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

Selection of early-maturing sweet orange cultivars by performance indexes

The objective of this work was to select adequate early-maturing sweet orange cultivars for the fresh fruit market and for industrial processing using performance indexes. Performance indexes for citrus were established from data collected in an experiment carried out in the southwest region of the state of Sao Paulo, involving 12 early-maturing sweet orange cultivars. New results were obtained by identifying cultivars with superior characteristics. In a comparison with 'Hamlin' sweet orange, a standard early-maturing cultivar, 'Valencia 2' and 'Salustiana' were considered better materials for the fresh fruit market, whereas 'Westin' sweet orange was identified as a superior cultivar for orange juice processing.

The Selection Exerted by Oil Contamination on Mangrove Fungal Communities

Mangrove ecosystems are tropical environments that are characterized by the interaction between the land and the sea. As such, this ecosystem is vulnerable to oil spills. Here, we show a culture-independent survey of fungal communities that are found in the sediments of the following two mangroves that are located on the coast of Sao Paulo State (Brazil): (1) an oil-spill-affected mangrove and (2) a nearby unaffected mangrove. Samples were collected from each mangrove forest at three distinct locations (transect from sea to land), and the samples were analyzed by quantitative PCR and internal transcribed spacer (ITS)-based PCR-DGGE analysis. The abundance of fungi was found to be higher in the oil-affected mangrove. Visual observation and correspondence analysis (CA) of the ITS-based PCR-DGGE profiles revealed differences in the fungal communities between the sampled areas. Remarkably, the oil-spilled area was quite distinct from the unaffected sampling areas. On the basis of the ITS sequences, fungi that are associated with the Basidiomycota and Ascomycota taxa were most common and belonged primarily to the genera Epicoccum, Nigrospora, and Cladosporium. Moreover, the Nigrospora fungal species were shown to be sensitive to oil, whereas a group that was described as "uncultured Basidiomycota" was found more frequently in oil-contaminated areas. Our results showed an increase in fungal abundance in the oil-polluted mangrove regions, and these data indicated potential fungal candidates for remediation of the oil-affected mangroves.

FIG CLONES SELECTION OF cv. ROXO DE VALINHOS OBTAINED BY IRRADIATED BUDS

The fig tree (Ficus carica L.) is a fruit tree of great world importance and, therefore, the genetic improvement becomes an important field of research for the crop improvement, being necessary to gather information on this species, mainly regarding its genetic variability so that appropriate propagation projects and management are made. However, the fig, in Brazil, is all produced from only one cultivar, Roxo de Valinhos, which produces seedless fruit, making impossible the conventional breeding. So, the fig breeding through induced mutagenic becomes a very important research line, greatly contributing to the fig culture development. The objective of this study was to select fig plants formed by cuttings treated with gamma ray. The plants used were obtained from buds of the cv. Roxo de Valinhos. The cuttings were irradiated with gamma rays in an irradiator Gamma Cell at 10 cm from the tip of the cutting, at doses of 30 Gy with dose rate of 238 Gy/h. The experiment consisted of 450 treatments, where each formed plant was a treatment. The treatments were numbered sequentially from 1 to 450 and spaced 2.5 x 1.5 m. It was evaluated the vegetative and the fruits characteristics, and the incidence of major crop pests and diseases. The analysis data showed that there is genetic variability among treatments and that the plants under numbers 1, 5, 20, 79, 164, 189, 194, 201, 221, 214, 258, 301, 322, 392, 433 and 440 are probably genetic mutants that should be tested as commercial orchards.