Biomedical subjects

Lude Franke

Publications and source records attributed to Lude Franke.

7 recordsLinked to original sources

PubMed2024

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Humans↗

PubMed2006

A strategy to search for common obesity and type 2 diabetes genes.

Worldwide, the incidence of type 2 diabetes is rising rapidly, mainly because of the increase in the incidence of obesity, which is an important risk factor for this condition. Both obesity and type 2 diabetes are complex genetic traits but they also share some nongenetic risk factors. Hence, it is tempting to speculate that the susceptibility to type 2 diabetes and obesity might also partly be due to shared genes. By comparing all of the published genome scans for type 2 diabetes and obesity, five overlapping chromosomal regions for both diseases (encompassing 612 candidate genes) have been identified. By analysing these five susceptibility loci for type 2 diabetes and obesity, using six freely available bioinformatics tools for disease gene identification, 27 functional candidate genes have been pinpointed that are involved in eating behaviour, metabolism and inflammation. These genes might reveal a molecular link between the two disorders.

Animals↗

PubMed2006

Combination of text-mining algorithms increases the performance.

MOTIVATION: Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms. RESULTS: In this paper, we will present data that show that combining different text-mining algorithms significantly improves the results. Not only is CONAN a full-scale approach that will ultimately cover all of PubMed/MEDLINE, we also show that this universality has no effect on quality: our system performs as well as or better than existing systems. AVAILABILITY: The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.

Abstracting and Indexing↗

PubMed2006

Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

Computational Biology↗

PubMed2005

Myosin IXB variant increases the risk of celiac disease and points toward a primary intestinal barrier defect.

Celiac disease is probably the best-understood immune-related disorder. The disease presents in the small intestine and results from the interplay between multiple genes and gluten, the triggering environmental factor. Although HLA class II genes explain 40% of the heritable risk, non-HLA genes accounting for most of the familial clustering have not yet been identified. Here we report significant and replicable association (P = 2.1 x 10(-6)) to a common variant located in intron 28 of the gene myosin IXB (MYO9B), which encodes an unconventional myosin molecule that has a role in actin remodeling of epithelial enterocytes. Individuals homozygous with respect to the at-risk allele have a 2.3-times higher risk of celiac disease (P = 1.55 x 10(-5)). This result is suggestive of a primary impairment of the intestinal barrier in the etiology of celiac disease, which may explain why immunogenic gluten peptides are able to pass through the epithelial barrier.

Amino Acid Sequence↗

PubMed2005

No genetic association of the human prolyl endopeptidase gene in the Dutch celiac disease population.

Celiac disease (CD) is a complex genetic disorder of the small intestine. The DQ2/DQ8 human leucocyte antigen (HLA) genes explain approximately 40% of the genetic component of the disease, but the remaining non-HLA genes have not yet been identified. The key environmental factor known to be involved in the disease is gluten, a major protein present in wheat, barley, and rye. Integrating microarray data and linkage data from chromosome 6q21-22 revealed the prolyl endopeptidase (PREP) gene as a potential CD candidate in the Dutch population. Interestingly, this gene encodes for the only enzyme that is able to cleave the proline-rich gluten peptides. To investigate the role of the human PREP gene as a primary genetic factor in CD, we conducted gene expression, sequence analysis, and genetic association studies of the PREP gene and determined PREP enzyme activity in biopsies from CD patients and controls. Sequence analysis of the coding region of the PREP gene revealed two novel polymorphisms. Genetic association studies using two novel polymorphisms and three known PREP variants excluded a genetic association between PREP and CD. Determination of PREP activity revealed weak but significant differences between treated and untreated CD biopsies (P < 0.05). Our results from the association study indicate that PREP is not a causative gene for CD in the Dutch population. These are further supported by the activity determinations in which we observed no differences in PREP activity between CD patients and controls.

Adolescent↗

PubMed2004

TEAM: a tool for the integration of expression, and linkage and association maps.

The identification of genes primarily responsible for complex genetic disorders is a daunting task. Despite the assignment of many susceptibility loci, there has only been limited success in identifying disease genes based solely on positional information from genome-wide screens. The incorporation of several complementary strategies in a single integrated approach should facilitate and further enhance the efficacy of this search for genes. To permit the integration of linkage, association and expression data, together with functional annotations, we have developed a Java-based software tool: TEAM (tool for the integration of expression, and linkage and association maps). TEAM includes a genome viewer, capable of overlaying karyobands, genes, markers, linkage graphs, association data, gene expression levels and functional annotations in one composite view. Data management, analysis and filtering functionality was implemented and extended with links to the Ensembl, Unigene and Gene Ontology databases to facilitate gene annotation. Filtering functionality can help prevent the exclusion of poorly annotated, but differentially expressed, genes that reside in candidate regions that show linkage or association. Here we demonstrate the program's functionality in our study on coeliac disease (OMIM 212750), a multifactorial gluten-sensitive enteropathy. We performed a combined data analysis of a genome-wide linkage screen in 82 Dutch families with affected siblings and the microarray expression profiles of 18,110 cDNAs in 22 intestinal biopsies.

Celiac Disease↗