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Hierarchical partitioning of metazoan protein conservation profiles provides new functional insights.

ABSTRACT: The availability of many complete, annotated proteomes enables the systematic study of the relationships between protein conservation and functionality. We explore this question based solely on the presence or absence of protein homologues (a.k.a. conservation profiles). We study 18 metazoans, from two distinct points of view: the human's and the fly's. Using the GOrilla gene ontology (GO) analysis tool, we explore functional enrichment of the "universal proteins", those with homologues in all 17 other species, and of the "non-universal proteins". A large number of GO terms are strongly enriched in both human and fly universal proteins. Most of these functions are known to be essential. A smaller number of GO terms, exhibiting markedly different properties, are enriched in both human and fly non-universal proteins. We further explore the non-universal proteins, whose conservation profiles are consistent with the "tree of life" (TOL consistent), as well as the TOL inconsistent proteins. Finally, we applied Quantum Clustering to the conservation profiles of the TOL consistent proteins. Each cluster is strongly associated with one or a small number of specific monophyletic clades in the tree of life. The proteins in many of these clusters exhibit strong functional enrichment associated with the "life style" of the related clades. Most previous approaches for studying function and conservation are "bottom up", studying protein families one by one, and separately assessing the conservation of each. By way of contrast, our approach is "top down". We globally partition the set of all proteins hierarchically, as described above, and then identify protein families enriched within different subdivisions. While supporting previous findings, our approach also provides a tool for discovering novel relations between protein conservation profiles, functionality, and evolutionary history as represented by the tree of life.

SUBMITTER: Witztum J

PROVIDER: S-EPMC3942430 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Hierarchical partitioning of metazoan protein conservation profiles provides new functional insights.

Witztum Jonathan J Persi Erez E Horn David D Pasmanik-Chor Metsada M Chor Benny B

PloS one 20140304 3

The availability of many complete, annotated proteomes enables the systematic study of the relationships between protein conservation and functionality. We explore this question based solely on the presence or absence of protein homologues (a.k.a. conservation profiles). We study 18 metazoans, from two distinct points of view: the human's and the fly's. Using the GOrilla gene ontology (GO) analysis tool, we explore functional enrichment of the "universal proteins", those with homologues in all 1 ...[more]

PMID: 24594619

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Project description:BackgroundHierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software "hier.part package" has been developed for running HP in R software. Its authors highlight a "minor rounding error" for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation.Methodology/principal findingsUsing numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position.Conclusions/significanceHP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given.

Project description:Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets.Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression.We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression.

Project description:Cellular membranes are laterally organized into domains of distinct structures and compositions by the differential interaction affinities between various membrane lipids and proteins. A prominent example of such structures are lipid rafts, which are ordered, tightly packed domains that have been widely implicated in cellular processes. The functionality of raft domains is driven by their selective recruitment of specific membrane proteins to regulate their interactions and functions; however, there have been few general insights into the factors that determine the partitioning of membrane proteins between coexisting liquid domains. In this work, we used extensive coarse-grained and atomistic molecular dynamics simulations, potential of mean force calculations, and conceptual models to describe the partitioning dynamics and energetics of a model transmembrane domain from the linker of activation of T cells. We find that partitioning between domains is determined by an interplay between protein-lipid interactions and differential lipid packing between raft and nonraft domains. Specifically, we show that partitioning into ordered domains is promoted by preferential interactions between peptides and ordered lipids, mediated in large part by modification of the peptides by saturated fatty acids (i.e., palmitoylation). Ordered phase affinity is also promoted by elastic effects, specifically hydrophobic matching between the membrane and the peptide. Conversely, ordered domain partitioning is disfavored by the tight molecular packing of the lipids therein. The balance of these dominant drivers determines partitioning. In the case of the wild-type linker of activation of T cells transmembrane domain, these factors combine to yield enrichment of the peptide at Lo/Ld interfaces. These results define some of the general principles governing protein partitioning between coexisting membrane domains and potentially explain previous disparities among experiments and simulations across model systems.

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Hierarchical partitioning of metazoan protein conservation profiles provides new functional insights.

Publications

Hierarchical partitioning of metazoan protein conservation profiles provides new functional insights.

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