Gene expression analysis in post-embryonic pericardial cells of Drosophila

Increasing evidence suggests conservation of cardiovascular molecules between vertebrates and invertebrates. Vertebrate Rudhira, an evolutionary conserved WD40 protein is expressed during primitive erythropoiesis, neoangiogenesis and tumors. We report here the expression profile of the Drosophila ortholog of Rudhira (DRudh) in the fly life cycle. DRudh is expressed specifically in all post-embryonic pericardial cells (PCs) and garland cells (GCs). This is the first report of a cytoplasmic marker highly specific to post-embryonic PCs. Embryonic PCs belong to three distinct genetic classes based on Odd-skipped (Odd), Even-skipped (Eve) and Tinman (Tin) expression. To identify which among these three classes of PCs expresses DRudh in post-embryonic stages, we analyzed expression of embryonic PC markers in the post-embryonic stages. Unlike in the embryo all larval PCs show an identical gene expression profile. While Odd and Eve expression is mutually exclusive in the embryonic PCs, these two markers are co-expressed in larval PCs but show a distinct subcellular localization. Tin is not expressed in any post-embryonic PC. Additionally larval PCs also express the GATA factor, Serpent (Srp) and the extracellular matrix protein, Pericardin (Prc). While PC number is known to decrease post-embryogenesis, which of the Odd or Eve lineage embryonic PCs persists is not known. Co-expression of the two distinct lineage markers only in post-embryonic stages indicates a complex temporal regulation of gene expression in PCs.     

Evolutionary systems biology: links between gene evolution and function

The recent accumulation of genome-wide data on various facets of gene expression, function and evolution stimulated the emergence of a new field, evolutionary systems biology. Many significant correlations were detected between variables that characterize the functioning of a gene, such as expression level, knockout effect, connectivity of genetic and protein-protein interaction networks, and variables that describe gene evolution, such as sequence evolution rate and propensity for gene loss. The first attempts on multidimensional analysis of genomic data yielded composite variables that describe the 'status' of a gene in the genomic community. However, it remains uncertain whether different functional variables affect gene evolution synergistically or there is a single, dominant factor. The number of translation events, linked to selection for translational robustness, was proposed as a candidate for such a major determinant of protein evolution. These developments show that, although the methodological basis of evolutionary systems biology is not yet fully solidified, this area of research is already starting to yield fundamental biological insights.     

Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions

Organismic evolution requires that variation at distinct hierarchical levels and attributes be coherently integrated, often in the face of disparate environmental and genetic pressures. A central part of the evolutionary analysis of biological systems remains to decipher the causal connections between organism-wide (or genome-wide) attributes (e.g., mRNA abundance, protein length, codon bias, recombination rate, genomic position, mutation rate, etc) as well as their role-together with mutation, selection, and genetic drift-in shaping patterns of evolutionary variation in any of the attributes themselves. Here we combine genome-wide evolutionary analysis of protein and gene expression data to highlight fundamental relationships among genomic attributes and their associations with the evolution of both protein sequences and gene expression levels. Our results show that protein divergence is positively coupled with both gene expression polymorphism and divergence. We show moreover that although the number of protein-protein interactions in Drosophila is negatively associated with protein divergence as well as gene expression polymorphism and divergence, protein-protein interactions cannot account for the observed coupling between regulatory and structural evolution. Furthermore, we show that proteins with higher rates of amino acid substitutions tend to have larger sizes and tend to be expressed at lower mRNA abundances, whereas genes with higher levels of gene expression divergence and polymorphism tend to have shorter sizes and tend to be expressed at higher mRNA abundances. Finally, we show that protein length is negatively associated with both number of protein-protein interactions and mRNA abundance and that interacting proteins in Drosophila show similar amounts of divergence. We suggest that protein sequences and gene expression are subjected to similar evolutionary dynamics, possibly because of similarity in the fitness effect (i.e., strength of stabilizing selection) of disruptions in a gene's protein sequence or its mRNA expression. We conclude that, as more and better data accumulate, understanding the causal connections among biological traits and how they are integrated over time to constrain or promote structural and regulatory evolution may finally become possible.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

Background  

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.     

Asymmetric functional divergence of duplicate genes in yeast

Most duplicate genes are eliminated from a genome shortly after duplication, but those that remain are an important source of biochemical diversity. Here, I present evidence from genome-scale protein-protein interaction data, microarray expression data, and large-scale gene knockout data that this diversification is often asymmetrical: one duplicate usually shows significantly more molecular or genetic interactions than the other. I propose a model that can explain this divergence pattern if asymmetrically diverging duplicate gene pairs show increased robustness to deleterious mutations.     

Exploring pleiotropy using principal components

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.     

A gene's ability to buffer variation is predicted by its fitness contribution and genetic interactions

Background

Many single-gene knockouts result in increased phenotypic (e.g., morphological) variability among the mutant''s offspring. This has been interpreted as an intrinsic ability of genes to buffer genetic and environmental variation. A phenotypic capacitor is a gene that appears to mask phenotypic variation: when knocked out, the offspring shows more variability than the wild type. Theory predicts that this phenotypic potential should be correlated with a gene''s knockout fitness and its number of negative genetic interactions. Based on experimentally measured phenotypic capacity, it was suggested that knockout fitness was unimportant, but that phenotypic capacitors tend to be hubs in genetic and physical interaction networks.

Methodology/Principal Findings

We re-analyse the available experimental data in a combined model, which includes knockout fitness and network parameters as well as expression level and protein length as predictors of phenotypic potential. Contrary to previous conclusions, we find that the strongest predictor is in fact haploid knockout fitness (responsible for 9% of the variation in phenotypic potential), with an additional contribution from the genetic interaction network (5%); once these two factors are taken into account, protein-protein interactions do not make any additional contribution to the variation in phenotypic potential.

Conclusions/Significance

We conclude that phenotypic potential is not a mysterious “emergent” property of cellular networks. Instead, it is very simply determined by the overall fitness reduction of the organism (which in its compromised state can no longer compensate for multiple factors that contribute to phenotypic variation), and by the number (and presumably nature) of genetic interactions of the knocked-out gene. In this light, Hsp90, the prototypical phenotypic capacitor, may not be representative: typical phenotypic capacitors are not direct “buffers” of variation, but are simply genes encoding central cellular functions.     

Proprotein convertases [corrected] are responsible for proteolysis and inactivation of endothelial lipase

Plasma lipoprotein metabolism is tightly regulated by several members of the triglyceride lipase family, including endothelial lipase (EL) and lipoprotein lipase (LPL). Our previous work suggested that EL is proteolytically processed. In this report, we have used a combination of epitope tagging, mutagenesis, and N-terminal sequencing to determine the precise location of the cleavage site within EL. The cleavage occurs immediately after the sequence RNKR, a known recognition sequence for the proprotein convertase (PC) family. We demonstrate that some PCs, but not all, can proteolytically cleave EL at this site and thereby directly regulate EL enzymatic activity through modulating EL cleavage. Furthermore, specific knockdown of individual PCs proves that PCs are the proteases that cleave EL in human endothelial cells. Interestingly, a homologous site in LPL is also cleaved by PCs. This action is unusual for PCs, which are traditionally known as activators of pro-proteins, and highlights a potential role of PCs in lipid metabolism through their proteolytic processing of lipases.     

Data integration workflow for search of disease driving genes and genetic variants

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/     

Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables

Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest—the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers—rs1042173 in SLC6A4 and rs3809865 in ITGB3.