What everybody should know about the rat genome and its online resources

It has been four years since the original publication of the draft sequence of the rat genome. Five groups are now working together to assemble, annotate and release an updated version of the rat genome. As the prevailing model for physiology, complex disease and pharmacological studies, there is an acute need for the rat's genomic resources to keep pace with the rat's prominence in the laboratory. In this commentary, we describe the current status of the rat genome sequence and the plans for its impending 'upgrade'. We then cover the key online resources providing access to the rat genome, including the new SNP views at Ensembl, the RefSeq and Genes databases at the US National Center for Biotechnology Information, Genome Browser at the University of California Santa Cruz and the disease portals for cardiovascular disease and obesity at the Rat Genome Database.     

Evolution of neoplastic cell lineages in Barrett oesophagus.

It has been hypothesized that neoplastic progression develops as a consequence of an acquired genetic instability and the subsequent evolution of clonal populations with accumulated genetic errors. Accordingly, human cancers and some premalignant lesions contain multiple genetic abnormalities not present in the normal tissues from which the neoplasms arose. Barrett oesophagus (BE) is a premalignant condition which predisposes to oesophageal adenocarcinoma (EA) that can be biopsied prospectively over time because endoscopic surveillance is recommended for early detection of cancer. In addition, oesophagectomy specimens frequently contain the premalignant epithelium from which the cancer arose. Neoplastic progression in BE is associated with alterations in TP53 (also known as p53) and CDKN2A (also known as p16) and non-random losses of heterozygosity (LOH). Aneuploid or increased 4N populations occur in more than 90-95% of EAs, arise in premalignant epithelium and predict progression. We have previously shown in small numbers of patients that disruption of TP53 and CDKN2A typically occurs before aneuploidy and cancer. Here, we determine the evolutionary relationships of non-random LOH, TP53 and CDKN2A mutations, CDKN2A CpG-island methylation and ploidy during neoplastic progression. Diploid cell progenitors with somatic genetic or epigenetic abnormalities in TP53 and CDKN2A were capable of clonal expansion, spreading to large regions of oesophageal mucosa. The subsequent evolution of neoplastic progeny frequently involved bifurcations and LOH at 5q, 13q and 18q that occurred in no obligate order relative to each other, DNA-content aneuploidy or cancer. Our results indicate that clonal evolution is more complex than predicted by linear models.     

The neurotrophic receptor TrkB: a drug target in anti-cancer therapy?

Increasing evidence implies altered signaling through the neurotrophic receptor tyrosine kinase TrkB in promoting tumor formation and metastasis. TrkB, sometimes in conjunction with its primary ligand BDNF, is often overexpressed in a variety of human cancers, ranging from neuroblastomas to pancreatic ductal adenocarcinomas, in which it may allow tumor expansion and contribute to resistance to anti-tumor agents. In vitro, TrkB acts as a potent suppressor of anoikis (detachment-induced apoptosis), which is associated with the acquisition of an aggressive tumorigenic and metastatic phenotype in vivo. In view of its predicted contribution to tumorigenicity and metastasis in humans, TrkB corresponds to a potential drug target, and preclinical models have already been established. The encouraging results of pharmacological Trk inhibitors in tumor xenograft models suggest that TrkB inhibition may represent a promising novel anti-tumor therapeutic strategy. This hypothesis is currently being evaluated in clinical trials. Here, we will discuss the latest developments on TrkB in these contexts as well as highlight some critical questions that remain to be addressed for evaluating TrkB as a therapeutic target in cancer. Received 12 October 2005; received after revision 19 December 2005; accepted 11 January 2006     

Betaine homocysteine S-methyltransferase: just a regulator of homocysteine metabolism?

Betaine homocysteine methyltransferase (BHMT), a Zn²⁺-dependent thiolmethyltransferase, contributes to the regulation of homocysteine levels, increases in which are considered a risk factor for cardiovascular diseases. Most plasma homocysteine is generated through the liver methionine cycle, in which BHMT metabolizes approximately 25% of this non-protein amino acid. This process allows recovery of one of the three methylation equivalents used in phosphatidylcholine synthesis through transmethylation, a major homocysteine-producing pathway. Although BHMT has been known for over 40 years, the difficulties encountered in its isolation precluded detailed studies until very recently. Thus, the last 10 years, since the sequence became available, have yielded extensive structural and functional data. Moreover, recent findings offer clues for potential new functions for BHMT. The purpose of this review is to provide an integrated view of the knowledge available on BHMT, and to analyze its putative roles in other processes through interactions uncover to date. Received 26 May 2006; received after revision 3 July 2006; accepted 24 August 2006     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).     

Common loss-of-function variants of the epidermal barrier protein filaggrin are a major predisposing factor for atopic dermatitis

Atopic disease, including atopic dermatitis (eczema), allergy and asthma, has increased in frequency in recent decades and now affects approximately 20% of the population in the developed world. Twin and family studies have shown that predisposition to atopic disease is highly heritable. Although most genetic studies have focused on immunological mechanisms, a primary epithelial barrier defect has been anticipated. Filaggrin is a key protein that facilitates terminal differentiation of the epidermis and formation of the skin barrier. Here we show that two independent loss-of-function genetic variants (R510X and 2282del4) in the gene encoding filaggrin (FLG) are very strong predisposing factors for atopic dermatitis. These variants are carried by approximately 9% of people of European origin. These variants also show highly significant association with asthma occurring in the context of atopic dermatitis. This work establishes a key role for impaired skin barrier function in the development of atopic disease.     

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.     

Automating sequence-based detection and genotyping of SNPs from diploid samples

The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47-90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use.