Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.
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ABSTRACT:
SUBMITTER: Kastenmuller G
PROVIDER: S-EPMC2690999 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe genomes of worldwide poultry breeds divergently selected for performance and other phenotypic traits may also be affected by, and formed due to, past and current admixture events. Adaptation to diverse environments, including acclimation to harsh climatic conditions, has also left selection footprints in breed genomes.ResultsUsing the Chicken 50K_CobbCons SNP chip, we genotyped four divergently selected breeds: two aboriginal, cold tolerant Ushanka and Orloff Mille Fleur, one egg-type Russian White subjected to artificial selection for cold tolerance, and one meat-type White Cornish. Signals of selective sweeps were determined in the studied breeds using three methods: (1) assessment of runs of homozygosity islands, (2) FST based population differential analysis, and (3) haplotype differentiation analysis. Genomic regions of true selection signatures were identified by two or more methods or in two or more breeds. In these regions, we detected 540 prioritized candidate genes supplemented them with those that occurred in one breed using one statistic and were suggested in other studies. Amongst them, SOX5, ME3, ZNF536, WWP1, RIPK2, OSGIN2, DECR1, TPO, PPARGC1A, BDNF, MSTN, and beta-keratin genes can be especially mentioned as candidates for cold adaptation. Epigenetic factors may be involved in regulating some of these important genes (e.g., TPO and BDNF).ConclusionBased on a genome-wide scan, our findings can help dissect the genetic architecture underlying various phenotypic traits in chicken breeds. These include genes representing the sine qua non for adaptation to harsh environments. Cold tolerance in acclimated chicken breeds may be developed following one of few specific gene expression mechanisms or more than one overlapping response known in cold-exposed individuals, and this warrants further investigation.
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Project description:This analysis is a follow-up to an earlier investigation of 182 genes selected as likely candidate genetic variations conferring susceptibility to anorexia nervosa (AN). As those initial case-control results revealed no statistically significant differences in single nucleotide polymorphisms, herein, we investigate alternative phenotypes associated with AN. In 1762 females, using regression analyses, we examined the following: (i) lowest illness-related attained body mass index; (ii) age at menarche; (iii) drive for thinness; (iv) body dissatisfaction; (v) trait anxiety; (vi) concern over mistakes; and (vii) the anticipatory worry and pessimism versus uninhibited optimism subscale of the harm avoidance scale. After controlling for multiple comparisons, no statistically significant results emerged. Although results must be viewed in the context of limitations of statistical power, the approach illustrates a means of potentially identifying genetic variants conferring susceptibility to AN because less complex phenotypes associated with AN are more proximal to the genotype and may be influenced by fewer genes.
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Project description:Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).
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Project description:BackgroundMicroorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which focus on the evolutionary relationships of organisms, classifications of the genomes based on alternative points of view may help advance our understanding of the delicate relationships of organisms.ResultsWe have developed a proteome-based method for classifying microbial species. This classification method uses a set of probes comprising short, highly conserved amino acid sequences. For each genome, in silico translation is performed to obtained its proteome, based on which a probe-set frequency pattern is generated. Then, the probe-set frequency patterns are used to cluster the proteomes/genomes.ConclusionsFeatures of the proposed method include a high running speed in challenge of a large number of genomes, and high applicability for classifying organisms with incomplete genome sequences. Moreover, the probe-set clustering method is sensitive to the metabolic phenotypic similarities/differences among species and is thus supposed potential for the classification or differentiation of closely-related organisms.
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