Current Genomics - Volume 7, Issue 1, 2006

Migraine is a complex debilitating neurovascular disease affecting approximately 15% of the Western populations. Familial clustering, twin studies and segregation analyses suggest that migraine has a significant genetic component, but the number of genes involved remains unclear. The progress in migraine genetics has recently jumped ahead with the identification of genes responsible for Familiar Hemiplegic Migraine (FHM), a rare subtype of migraine with aura showing autosomal dominant mode of inheritance. Nevertheless, the knowledge about common types of migraine has been particularly rewarding and recently, seven loci with significant linkage to migraine with or without aura have been identified on 1q31, 4q24, 6p12.2-21.1, 11q24, 14q21.2-q22.3, 15q11-q13 and Xq24-28, suggesting the presence of migraine susceptibility genes in these regions. Identification of genes predisposing to the more common and genetically complex forms of migraine has been complicated by clinical and genetic heterogeneity of the disease. The major challenge in the coming years facing biomedical research of migraine is the identification of disease-susceptibility genes and the understanding of how migraine risk can be influenced by the interaction of these variants with each other and with specific environmental factors in order to provide individuals with clinically-useful diagnostic, prognostic and therapeutic information. This paper briefly summarizes the previous knowledge and highlights some recent developments in the complex genetic nature of migraine.

As predicted, in the post-genomic era microarray technology has a major impact on our understanding of complex gene expression patterns and circuit function in the brain. We increasingly appreciate that, due to the phenotypic and transcript complexity, brain transcriptome profiling data are multifaceted and are best interpreted in the context of the cellular diversity of the studied brain region. However, despite advances made over the past five years, biological interpretation of massive microarray datasets remains a significant challenge. Although we are becoming more efficient in separating "true" transcriptome differences from experimental noise, verification of microarray data and anatomical localization of expression changes to neuronal subpopulations will continue to be an integral part of brain microarray experiments.

The prolongation of skeletal muscle strength in aging and neuromuscular disease has been the objective of numerous studies employing a variety of approaches. To date however, efforts to prevent or attenuate age- or disease-related muscle degeneration have been largely unsuccessful. Cell-based therapies have been stalled by the difficulty in obtaining sufficient numbers of autologous myoblasts and by inefficient incorporation into host muscle. Administration of growth hormone prevents age-related loss of muscle mass, but has failed to increase muscle strength. In this context, where direct therapeutic approaches to redress the primary disease are still suboptimal, it may be more effective to focus on strategies for improving skeletal muscle function. Experimental models of muscle growth and regeneration have implicated Insulin-like Growth Factor-1 (IGF-1) as an important mediator of anabolic pathways in skeletal muscle cells. Two major IGF-1 transcripts are characterized: the locally acting isoform with an autocrine/paracrine action and the circulating isoform with endocrine effects. The physiological differences between the function of local and circulating isoform of IGF-1 are not completely established. However the selective expression of the muscle-specific IGF-1 isoform avoids hypertrophic effects on distal organs such as the heart, and eliminates risk of possible neoplasms induced by inappropriate high expression levels of circulating IGF-1. In this review we discuss the roles of IGF-1 isoforms in myogenesis and the potential therapeutic role of local IGF-1 isoform on muscle aging and diseases.

The functional analysis of plant genes employs various comprehensive approaches that include transcriptome analysis using microchips, gene knockout, RNA interference, and various experimental models. We have proposed a novel experimental model approach for plant functional genomics. It is based on creating and studying transgenic plants that express bacterial genes which are functionally similar to plant genes. The validity of this approach owes to the similarity of basic pro- and eukaryotic metabolic pathways and gene networks controlling the functioning of these organisms under normal conditions and in response to abiotic and biotic stresses. Our studies using molecular biology, physiology and biochemistry methods have demonstrated adequateness of the proposed strategy. It allows the modeling of processes taking place in the plant cell and differential assessment of contribution of individual enzymes. Our results indicate that the proposed approach is highly effective for functional genomics, namely, for determining the function of a gene product in vivo. The experimental data can be further used as a basis for elaboration of gene networks controlling particular physiological processes and stress responses of plants to biotic and abiotic environmental factors.

Genome sequences from the pufferfishes Takifugu rubripes (Fugu) and Tetraodon nigroviridis, the zebrafish Danio rerio and the medaka Oryzias latipes together with genomic data from various other fish species have opened an important era of comparative genomics shedding a new light on the structure and evolution of vertebrate genomes. For instance, comparative analysis of fish genomes has revealed that the ancestral bony vertebrate genome was composed of 12 chromosomes, has confirmed the occurrence of at least one event of genome duplication in the early history of vertebrates and has allowed the identification of conserved regulatory and coding sequences in the human genome. Importantly, major differences have been observed between teleost fish and mammalian genomes. There is now convincing evidence that all teleosts are derived from a common tetraploid fish ancestor. This tetraploidization event arose about 320-350 million years ago in the ray-finned fish lineage, followed by rediploidization and retention of hundreds of duplicate pairs. Divergent evolution of the resulting duplicates has been proposed to be involved in the species richness observed in teleost fishes. Fish genomes also contain many more families of transposable elements than mammals and birds. Finally, while the mammalian and bird lineages possess major sex determination systems with sex chromosomes conserved in very divergent species, fishes have very frequently switched between sex determination mechanisms and repeatedly created novel sex chromosomes during evolution. Hence, teleost fishes display a high level of genomic plasticity, which might be related to the astonishing biodiversity observed in these animals.

Because of its paramount importance in many biological and biomedical aspects, epistasis, expressed as the suppression or enhancement of a gene by the effect of an unrelated gene, has received a resurgence of interest in recent years. One of the most powerful analytical approaches for detecting epistasis is based on the genetic mapping of interacting quantitative trait loci (QTL) that often present long chromosomal segments. Current high-throughput technologies for genotyping single nucleotide polymorphisms (SNPs) to construct the haplotype map or HapMap for the entire human genome are shaping our prospects into the role of epistasis. In this article, we have developed a new statistical model for refining QTL structure into individual nucleotides and estimating and testing epistasis between different DNA nucleotides throughout the HapMap. This model detects quantitative trait nucleotides (QTN) for complex diseases. It is founded on the SNP-based haplotype blocking theory, constructed within the context of maximum likelihood and implemented with the EM algorithm. The model provides a quantitative framework for testing the additive x additive, additive x dominance, dominance x additive and dominance x dominance interaction effects between different QTN sequences from haplotype blocks. The model was used to detect sequence-sequence interactions between two candidate genes, BAR-1 and BAR-2, for human obesity in 155 subjects sampled from a natural population. This model will have many implications for the detection of specific DNA sequence variants that interactively contribute to the genetic architecture of complex diseases.