The evolutionary dynamics of functional modules and the extraordinary plasticity of regulons: the Escherichia coli perspective

Using profiles of phylogenetic profiles (P-cubic) we compared the evolutionary dynamics of different kinds of functional associations. Ordered from most to least evolutionarily stable, these associations were genes in the same operons, genes whose products participate in the same biochemical pathway, genes coding for physically interacting proteins and genes in the same regulons. Regulons showed the most plastic functional interactions with evolutionary stabilities barely better than those of unrelated genes. Further regulon analyses showed that global regulators contain less evolutionarily stable associations than local regulators. Genes co-repressed by global regulators had a higher evolutionary conservation than genes co-activated by global regulators. However, the reverse was true for genes co-repressed and co-activated by local regulators. Of all the regulon-related associations, the relationship between regulators and their target genes showed the most evolutionary stability. Different negative data sets built to contrast against each of the analysed kinds of modules also differed in evolutionary conservation revealing further underlying genome organization. Applying P-cubic analyses to other genomes might help visualize genome organization, understand the evolutionary importance and plasticity of functional associations and compare the quality of data sets expected to reflect functional interactions, such as those coming from high-throughput experiments.     

Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer''s disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.     

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool

To date, genome-wide association studies have identified thousands of statistically-significant associations between genetic variants, and phenotypes related to a myriad of traits and diseases. A key goal for human-genetics research is to translate these associations into functional mechanisms. Popular gene-set analysis tools, like MAGMA, map variants to genes they might affect, and then integrate genome-wide association study data (that is, variant-level associations for a phenotype) to score genes for association with a phenotype. Gene scores are subsequently used in competitive gene-set analyses to identify biological processes that are enriched for phenotype association. By default, variants are mapped to genes in their proximity. However, many variants that affect phenotypes are thought to act at regulatory elements, which can be hundreds of kilobases away from their target genes. Thus, we explored the idea of augmenting a proximity-based mapping scheme with publicly-available datasets of regulatory interactions. We used MAGMA to analyze genome-wide association study data for ten different phenotypes, and evaluated the effects of augmentation by comparing numbers, and identities, of genes and gene sets detected as statistically significant between mappings. We detected several pitfalls and confounders of such “augmented analyses”, and introduced ways to control for them. Using these controls, we demonstrated that augmentation with datasets of regulatory interactions only occasionally strengthened the enrichment for phenotype association amongst (biologically-relevant) gene sets for different phenotypes. Still, in such cases, genes and regulatory elements responsible for the improvement could be pinpointed. For instance, using brain regulatory-interactions for augmentation, we were able to implicate two acetylcholine receptor subunits involved in post-synaptic chemical transmission, namely CHRNB2 and CHRNE, in schizophrenia. Collectively, our study presents a critical approach for integrating regulatory interactions into gene-set analyses for genome-wide association study data, by introducing various controls to distinguish genuine results from spurious discoveries.     

A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies

The rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.     

Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons

Since operons are unstable across Prokaryotes, it has been suggested that perhaps they re-combine in a conservative manner. Thus, genes belonging to a given operon in one genome might re-associate in other genomes revealing functional relationships among gene products. We developed a system to build networks of functional relationships of gene products based on their organization into operons in any available genome. The operon predictions are based on inter-genic distances. Our system can use different kinds of thresholds to accept a functional relationship, either related to the prediction of operons, or to the number of non-redundant genomes that support the associations. We also work by shells, meaning that we decide on the number of linking iterations to allow for the complementation of related gene sets. The method shows high reliability benchmarked against knowledge-bases of functional interactions. We also illustrate the use of Nebulon in finding new members of regulons, and of other functional groups of genes. Operon rearrangements produce thousands of high-quality new interactions per prokaryotic genome, and thousands of confirmations per genome to other predictions, making it another important tool for the inference of functional interactions from genomic context.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.     

The Tree versus the Forest: The Fungal Tree of Life and the Topological Diversity within the Yeast Phylome

A recurrent topic in phylogenomics is the combination of various sequence alignments to reconstruct a tree that describes the evolutionary relationships within a group of species. However, such approach has been criticized for not being able to properly represent the topological diversity found among gene trees. To evaluate the representativeness of species trees based on concatenated alignments, we reconstruct several fungal species trees and compare them with the complete collection of phylogenies of genes encoded in the Saccharomyces cerevisiae genome. We found that, despite high levels of among-gene topological variation, the species trees do represent widely supported phylogenetic relationships. Most topological discrepancies between gene and species trees are concentrated in certain conflicting nodes. We propose to map such information on the species tree so that it accounts for the levels of congruence across the genome. We identified the lack of sufficient accuracy of current alignment and phylogenetic methods as an important source for the topological diversity encountered among gene trees. Finally, we discuss the implications of the high levels of topological variation for phylogeny-based orthology prediction strategies.     

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

Functional Interactions between Unlinked Muscle Genes within Haploinsufficient Regions of the Drosophila Genome

Mutations in 13 genes affecting muscle development in Drosophila have been examined in pairwise combinations for evidence of genetic interactions. Heterozygous combinations of mutations in five genes, including the gene coding for myosin heavy chain, result in more severe phenotypes than respective single heterozygous mutant controls. The various mutant interactions include examples showing allele-specific intergenic interactions, gene specific interactions, and allele-specific intragenic complementations, suggesting that some interactions result from the manner in which mutant gene products associate. Interactions that result from alterations in ``+' gene copy number were also uncovered, suggesting that normal myofibril development requires that the relative amounts of respective gene products produced be tightly regulated. The importance of the latter parameter is substantiated by the finding that all five interacting loci map to disperse haploinsufficient or haplolethal regions of the genome. The implications of the present findings are discussed in relation to pursuing the phenomena involving genetic interactions to identify new genes encoding interacting myofibrillar proteins, to examine the nature of intermolecular interactions in mutant and normal development and to decipher the quantitative and temporal regulation of a large family of functionally related gene products.     

LinkNMF: Identification of histone modification modules in the human genome using nonnegative matrix factorization

Histone modifications are ubiquitous processes involved in various cellular mechanisms. Systemic analysis of multiple chromatin modifications has been used to characterize various chromatin states associated with functional DNA elements, gene expression, and specific biological functions. However, identification of modular modification patterns is still required to understand the functional associations between histone modification patterns and specific chromatin/DNA binding factors. To recognize modular modification patterns, we developed a novel algorithm that combines nonnegative matrix factorization (NMF) and a clique-detection algorithm. We applied it, called LinkNMF, to generate a comprehensive modification map in human CD4 + T cell promoter regions. Initially, we identified 11 modules not recognized by conventional approaches. The modules were grouped into two major classes: gene activation and repression. We found that genes targeted by each module were enriched with distinguishable biological functions, suggesting that each modular pattern plays a unique functional role. To explain the formation of modular patterns, we investigated the module-specific binding patterns of chromatin regulators. Application of LinkNMF to histone modification maps of diverse cells and developmental stages will be helpful for understanding how histone modifications regulate gene expression. The algorithm is available on our website at biodb.kaist.ac.kr/LinkNMF.     

A domain interaction map based on phylogenetic profiling

Phylogenetic profiling is a well established method for predicting functional relations and physical interactions between proteins. We present a new method for finding such relations based on phylogenetic profiling of conserved domains rather than proteins, avoiding computationally expensive all versus all sequence comparisons among genomes. The resulting domain interaction map (DIMA) can be explored directly or mapped to a genome of interest. We demonstrate that the performance of DIMA is comparable to that of classical phylogenetic profiling and its predictions often yield information that cannot be detected by profiling of entire protein chains. We provide a list of novel domain associations predicted by our method.     

The Human Obesity Gene Map: The 2002 Update

This is the ninth update of the human obesity gene map, incorporating published results through October 2002 and continuing the previous format. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome‐wide scans and various animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. For the first time, transgenic and knockout murine models exhibiting obesity as a phenotype are incorporated (N = 38). As of October 2002, 33 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and the causal genes or strong candidates have been identified for 23 of these syndromes. QTLs reported from animal models currently number 168; there are 68 human QTLs for obesity phenotypes from genome‐wide scans. Additionally, significant linkage peaks with candidate genes have been identified in targeted studies. Seven genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 222 studies reporting positive associations with 71 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. More than 300 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http:obesitygene.pbrc.edu .     

From Arabidopsis thaliana to Brassica napus: development of amplified consensus genetic markers (ACGM) for construction of a gene map

The evolution of genomes can be studied by comparing maps of homologous genes which show changes in nucleic acid sequences and chromosome rearrangements. In this study, we developed a set of 32 amplified consensus gene markers (ACGMs) that amplified gene sequences from Arabidopsis thaliana and Brassica napus. Our methodology, based on PCR, facilitated the rapid sequencing of homologous genes from various species of the same phylogenetic family and the detection of intragenic polymorphism. We found that such polymorphism principally concerned intron sequences and we used it to attribute a Brassica oleracea or Brassica rapa origin to the B. napus sequences and to map 43 rapeseed genes. We confirm that the genetic position of homologous genes varied between B. napus and A. thaliana. ACGMs are a useful tool for genome evolution studies and for the further development of single nucleotide polymorphism suitable for use in genetic mapping and genetic diversity analyses.     

癌症中DNA甲基化基因模块筛选

肿瘤的发生受遗传学和表观遗传学修饰的共同影响。DNA甲基化是一种重要的表观遗传修饰,在癌症的发生与发展中起着重大的作用。因此找到癌症的甲基化标记物在癌症的诊断和治疗中具有重大意义。本文利用权重基因共表达网络分析的方法（WGCNA）筛选出甲基化基因模块,并分析模块向量基因,进行功能注释,最后对基因模块进行功能分析,得到DNA甲基化与肿瘤间的关系。结果显示,这些甲基化异常的基因模块与癌症的发生有着显著的关联。同时还发现某些甲基化异常的基因模块与多种癌症的发生都有着显著的关联。     

How much can we learn about the function of bacterial rRNA modification by mining large-scale experimental datasets?

Modification of ribosomal RNA is ubiquitous among living organisms. Its functional role is well established for only a limited number of modified nucleotides. There are examples of rRNA modification involvement in the gene expression regulation in the cell. There is a need for large data set analysis in the search for potential functional partners for rRNA modification. In this study, we extracted phylogenetic profile, genome neighbourhood, co-expression and phenotype profile and co-purification data regarding Escherichia coli rRNA modification enzymes from public databases. Results were visualized as graphs using Cytoscape and analysed. Majority linked genes/proteins belong to translation apparatus. Among co-purification partners of rRNA modification enzymes are several candidates for experimental validation. Phylogenetic profiling revealed links of pseudouridine synthetases with RF2, RsmH with translation factors IF2, RF1 and LepA and RlmM with RdgC. Genome neighbourhood connections revealed several putative functionally linked genes, e.g. rlmH with genes coding for cell wall biosynthetic proteins and others. Comparative analysis of expression profiles (Gene Expression Omnibus) revealed two main associations, a group of genes expressed during fast growth and association of rrmJ with heat shock genes. This study might be used as a roadmap for further experimental verification of predicted functional interactions.     

Phylogenomic analysis of gene co‐expression networks reveals the evolution of functional modules

Molecular evolutionary studies correlate genomic and phylogenetic information with the emergence of new traits of organisms. These traits are, however, the consequence of dynamic gene networks composed of functional modules, which might not be captured by genomic analyses. Here, we established a method that combines large‐scale genomic and phylogenetic data with gene co‐expression networks to extensively study the evolutionary make‐up of modules in the moss Physcomitrella patens, and in the angiosperms Arabidopsis thaliana and Oryza sativa (rice). We first show that younger genes are less annotated than older genes. By mapping genomic data onto the co‐expression networks, we found that genes from the same evolutionary period tend to be connected, whereas old and young genes tend to be disconnected. Consequently, the analysis revealed modules that emerged at a specific time in plant evolution. To uncover the evolutionary relationships of the modules that are conserved across the plant kingdom, we added phylogenetic information that revealed duplication and speciation events on the module level. This combined analysis revealed an independent duplication of cell wall modules in bryophytes and angiosperms, suggesting a parallel evolution of cell wall pathways in land plants. We provide an online tool allowing plant researchers to perform these analyses at http://www.gene2function.de .