Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein–protein interaction network

Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene’s propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein–protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.     

Gene prioritization through genomic data fusion

The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.     

No evidence for linkage between familial hypertriglyceridemia and apolipoprotein B,apolipoprotein C-III or lipoprotein lipase genes

Familial hypertriglyceridemia has been suggested to be an autosomal dominant condition with age-dependent penetrance, but so far the underlying defective gene has not been elucidated. We examined the possible role of three candidate gene loci by linkage analysis in six Finnish families with familial clustering of hypertriglyceridemia. The probands were initially recruited from a group of hyperlipidemic outpatients after measurement of serum triglyceride concentrations exceeding 2.00 mmol/l on two occasions. Altogether, 71 subjects were included in the linkage analyses. Bior multiallelic DNA polymorphisms were used as markers for the apolipoprotein B gene (chromosome 2), lipoprotein lipase gene (chromosome 8), and apolipoprotein A-I/C-III/A-IV gene cluster (chromosome 11). Linkage analysis was performed by applying two alternative phenotyping models, one adopting quantitative serum triglyceride concentrations and another using qualitative classification of the subjects into hypertriglyceridemic, normotriglyceridemic, and borderline hypertriglyceridemic groups. Using either approach, the cumulative lod scores of each of the three candidate genes in the six families were less than -2.0 at the recombination fraction 0.0. These results suggest that none of the candidate genes investigated is involved in familial clustering of hypertriglyceridemia in our study.     

Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm.

Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.     

LOD Score Exclusion Analyses for Candidate QTLs using Random Population Samples

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes as putative QTLs using random population samples. Previously, we developed an LOD score exclusion mapping approach for candidate genes for complex diseases. Here, we extend this LOD score approach for exclusion analyses of candidate genes for quantitative traits. Under this approach, specific genetic effects (as reflected by heritability) and inheritance models at candidate QTLs can be analyzed and if an LOD score is < or = -2.0, the locus can be excluded from having a heritability larger than that specified. Simulations show that this approach has high power to exclude a candidate gene from having moderate genetic effects if it is not a QTL and is robust to population admixture. Our exclusion analysis complements association analysis for candidate genes as putative QTLs in random population samples. The approach is applied to test the importance of Vitamin D receptor (VDR) gene as a potential QTL underlying the variation of bone mass, an important determinant of osteoporosis.     

Computational gene knockout reveals transdisease-transgene association structure

Genome-wide association studies for a variety of diseases are identifying increasing numbers of candidate genes. Now we are confronted with the fact that some genes are common candidates across diseases. Thus there is a strong need to develop a hypothesis formulation methodology to comprehend multifaceted associations between genes and diseases. We have developed a computational method for building transdisease-transgene association structure. By introducing the basic rationale underlying the gene knockout approach as an information processing procedure to a network constructed on the basis of hyperlinks between disease and gene pages listed in the Online Mendelian Inheritance in Man (OMIM) database, relations of genes with diseases are computationally quantified. We did successively eliminate gene pages (called "computational gene knockout" in this paper) expected to contribute to metabolic syndrome, and catalogued each association with various disease pages. We thereby apply a co-clustering method to the gene-disease relations to obtain an association structure by classifying diseases and genes simultaneously. Observing an association structure between over 100 diseases and their related genes, we then found that the structure revealed gene classes that were commonly associated with diseases as well as gene classes that were selectively associated with a specific disease class.     

SCA db: spinocerebellar ataxia candidate gene database

The positional candidate gene approach accelerates the discovery of genes involved in disease. However, the properties of such disease genes are very diverse and the sample size of known disease genes is too small and does not warrant success by the use of a machine-learning approach. A user-defined scoring system may thus help to determine the priority of candidate genes. Spinocerebellar ataxia (SCA) is a good model to test this approach because most SCA subtypes are caused by an expansion of short tandem repeats (STRs). The SCA db is a candidate gene database for SCA, which collected 3185 genes for 17 types of SCA. Those SCA subtypes that have known disease genes can be used as positive controls to optimize the parameters. The users may browse the candidate genes of a given SCA subtype by using the default parameters. The known disease genes were found to be the top three candidates using the default parameters. Alternatively, the users may score the candidate genes by changing the weight or the scores on the basis of their own working hypothesis. AVAILABILITY: This database is available at http://ymbc.ym.edu.tw/sca/     

利用果蝇模型研究人类心脏早期发育的分子机理(英文)

近年来 ,果蝇心脏特化的遗传机制已初步研究清楚 ,但控制人类心脏早期发育的基因尚待鉴定。因为调控果蝇和脊椎动物早期心脏细胞命运定型的途径具有保守性 ,果蝇是一种探讨人类心脏早期发育的分子机理的理想动物模式。为此目的 ,我们采用P转座子和EMS诱变技术建立了约 3 0 0 0个隐性致死基因平衡系。通过心脏前体细胞特异性抗体免疫组化筛选 ,我们检出 2 0 0余个表现心脏突变表型的平衡致死系。我们进一步利用RNAi技术对一些基因的功能进行了初步的研究 ,证明这些基因表现RNAi的突变表型 ,该类突变表型与基因突变时表现的表型相似 ,即心管呈缺陷型或无心脏前体细胞形成。利用果蝇和人类基因组计划获得的成果 ,我们从果蝇心脏侯选基因中初步克隆和鉴定了 5 0个人类同源基因 ,其中 2 0个是新基因。Northen印迹分析表明 ,一部分人类基因在心脏组织中有表达 ,从而为研究这些基因在人类心脏早期发育中的作用提供了信息。目前 ,我们正在建立转基因果蝇 ,以此为模型研究这些基因是否对心肌细胞发生或心肌功能起调控作用。产生心肌细胞突变类型的基因如果类似于人类心脏病综合症 ,则可以作为人类心脏疾病侯选基因作进一步的分析。     

In silico gene prioritization by integrating multiple data sources

Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.     

Demystification of Chester porphyria: a nonsense mutation in the Porphobilinogen Deaminase gene

The porphyrias arise from predominantly inherited catalytic deficiencies of specific enzymes in heme biosynthesis. All genes encoding these enzymes have been cloned and several mutations underlying the different types of porphyrias have been reported. Traditionally, the diagnosis of porphyria is made on the basis of clinical symptoms, characteristic biochemical findings, and specific enzyme assays. In some cases however, these diagnostic tools reveal overlapping findings, indicating the existence of dual porphyrias with two enzymes of heme biosynthesis being deficient simultaneously. Recently, it was reported that the so-called Chester porphyria shows features of both variegate porphyria and acute intermittent porphyria. Linkage analysis revealed a novel chromosomal locus on chromosome 11 for the underlying genetic defect in this disease, suggesting that a gene that does not encode one of the enzymes of heme biosynthesis might be involved in the pathogenesis of the porphyrias. After excluding candidate genes within the linkage interval, we identified a nonsense mutation in the porphobilinogen deaminase gene on chromosome 11q23.3, which harbors the mutations causing acute intermittent porphyria, as the underlying genetic defect in Chester porphyria. However, we could not detect a mutation in the coding or the promotor region of the protoporphyrinogen oxidase gene that is mutated in variegate porphyria. Our results indicate that Chester porphyria is neither a dual porphyria, nor a separate type of porphyria, but rather a variant of acute intermittent porphyria. Further, our findings largely exclude the possibility that a hitherto unknown gene is involved in the pathogenesis of the porphyrias.     

Candidate gene prioritization

Candidate gene identification is typically labour intensive, involving laboratory experiments required to corroborate or disprove any hypothesis for a nominated candidate gene being considered the causative gene. The traditional approach to reduce the number of candidate genes entails fine-mapping studies using markers and pedigrees. Gene prioritization establishes the ranking of candidate genes based on their relevance to the biological process of interest, from which the most promising genes can be selected for further analysis. To date, many computational methods have focused on the prediction of candidate genes by analysis of their inherent sequence characteristics and similarity with respect to known disease genes, as well as their functional annotation. In the last decade, several computational tools for prioritizing candidate genes have been proposed. A large number of them are web-based tools, while others are standalone applications that install and run locally. This review attempts to take a close look at gene prioritization criteria, as well as candidate gene prioritization algorithms, and thus provide a comprehensive synopsis of the subject matter.     

A systems biology approach to genetic studies of complex diseases

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.     

High blood pressure: hunting the genes.

High blood pressure is a disease of unknown cause. Family history of the disease indicates higher risk, but it is not known which genes are involved or how they interact with environmental influences to produce the disorder. Molecular biology offers an approach to problems that have not so far been solved by classical physiology or biochemistry. By analysing polymorphic variation in chromosome markers such as minisatellite sequences, or by restriction fragment polymorphism analysis of candidate genes, attempts are being made to link genetic variations with hypertension. In genetically hypertensive rats, hypertension is associated with a polymorphism of the renin gene and with other loci on chromosomes 10 and 18. The role of these loci in human hypertension remains to be determined. Other genes such as sodium-lithium countertransport may be involved. Environmental factors such as stress or salt intake could influence the rate or timing of expression of certain genes and thus result in hypertension.     

Recent advances in the genetic investigation of osteoarthritis

Osteoarthritis (OA) demonstrates considerable clinical heterogeneity, generating heated debate over whether OA is a single disease or a complex mix of disparate diseases and concerning which tissues are principally involved in disease initiation and progression. Epidemiological studies have demonstrated a major genetic component to OA risk. However, these studies have also revealed differences in risk between males and females and for disease at different skeletal sites. This observation has resulted in the concept of genes for specific sites rather than a generalised OA phenotype. Recent breakthroughs have shed considerable light on the nature of OA genetic susceptibility. Many candidate genes have been confirmed, such as the interleukin-1 gene cluster and the oestrogen alpha-receptor gene ESR1. Genome-wide linkage scans have revealed several regions harbouring novel loci, some of which are beginning to yield their genes.     

Evaluation of candidate genes in the absence of positional information: a poor bet on a blind dog!

More than 350 inherited diseases have been reported in dogs and at least 50% of them have human counterparts. To remove the diseases from dog breeds and to identify canine models for human diseases, it is necessary to find the mutations underlying them. To this end, two methods have been used: the functional candidate gene approach and linkage analysis. Here we present an evaluation of these in canine retinal diseases, which have been the subject of a large number of molecular genetic studies, and we show the contrasting outcomes of these approaches when dealing with genetically heterogeneous diseases. The candidate gene approach has led to 377 published results with 23 genes. Most of the results (66.6%) excluded the presence of a mutation in a gene or its coding region, while only 3.4% of the results identified the mutation causing the disease. On the other hand, five linkage analysis studies have been done on retinal diseases, resulting in three identified mutations and two mapped disease loci. Mapping studies have relied on dog research colonies. If this favorable application of linkage analysis can be extended to dogs in the pet population, success in identifying canine mutations could increase, with advantages to veterinary and human medicine.     

Molecular psychogenetics of deviant aggressive behavior in humans

The review considers the known candidate gene loci that are involved in the dopamine, serotonin, and androgen systems and are associated with human deviant aggressive behavior. Both positive and negative correlations with deviant aggressive behavior have been observed for almost all of the candidate gene loci. Many genes of the neurotransmitter and androgen system and intricate interactions among them may influence the propensity to aggression. Further studies should focus not only on individual gene polymorphisms, but also on complex interactions among the alleles of all candidate genes that have functionally important polymorphisms affecting their expression and function. A complex analysis should be performed to study the association of the homozygous genotypes at all candidate gene markers with various forms of human deviant aggressive behavior. The approach will make it possible to assess the individual reactivity to various environmental stimuli that provoke aggression and to develop a means of predicting and preventing deviant aggressive behavior in humans.     

Mapping a Mendelian form of intracranial aneurysm to 1p34.3-p36.13

The identification of pathways that underlie common disease has been greatly impacted by the study of rare families that segregate single genes with large effect. Intracranial aneurysm is a common neurological problem; the rupture of these aneurysms constitutes a frequently catastrophic neurologic event. The pathogenesis of these aneurysms is largely unknown, although genetic and environmental factors are believed to play a role. Previous genomewide studies in affected relative pairs have suggested linkage to several loci, but underlying genes have not been identified. We have identified a large kindred that segregates nonsyndromic intracranial aneurysm as a dominant trait with high penetrance. Genomewide analysis of linkage was performed using a two-stage approach: an analysis of ~10,000 single-nucleotide polymorphisms in the 6 living affected subjects, followed by the genotyping of simple tandem repeats across resulting candidate intervals in all 23 kindred members. Analysis revealed significant linkage to a single locus, with a LOD score of 4.2 at 1p34.3-p36.13 under a dominant model with high penetrance. These findings identify a Mendelian form of intracranial aneurysm and map the location of the underlying disease locus.     

Integration of text- and data-mining using ontologies successfully selects disease gene candidates

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (±18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.