American journal of pharmacogenomics : genomics-related research in drug development and clinical practice最新文献

Attention deficit-hyperactivity disorder (ADHD) is a very common and heterogeneous childhood-onset psychiatric disorder, affecting between 3% and 5% of school age children worldwide. Although the neurobiology of ADHD is not completely understood, imbalances in both dopaminergic and noradrenergic systems have been implicated in the origin and persistence of core symptoms, which include inattention, hyperactivity, and impulsivity. The role of a genetic component in its etiology is strongly supported by genetic studies, and several investigations have suggested that the dopamine transporter gene (DAT1; SLC6A3 locus) may be a small-effect susceptibility gene for ADHD. Stimulant medication has a well-documented efficacy in reducing ADHD symptoms. Methylphenidate, the most prescribed stimulant, seems to act mainly by inhibiting the dopamine transporter protein and dopamine reuptake. In fact, its effect is probably related to an increase in extracellular levels of dopamine, especially in brain regions enriched in this protein (i.e. striatum). It is also important to note that dopamine transporter densities seem to be particularly elevated in the brain of ADHD patients, decreasing after treatment with methylphenidate. Altogether, these observations suggest that the dopamine transporter does play a major role in ADHD. Among the several polymorphisms already described in the SLC6A3 locus, a 40 bp variable number of tandem repeats (VNTR) polymorphism has been extensively investigated in association studies with ADHD. Although there are some negative results, the findings from these reports indicate the allele with ten copies of the 40 bp sequence (10-repeat allele) as the risk allele for ADHD. Some investigations have suggested that this polymorphism can be implicated in dopamine transporter gene expression in vitro and dopamine transporter density in vivo, even though it is located in a non-coding region of the SLC6A3 locus. Despite all these data, few studies have addressed the relationship between genetic markers (specifically the VNTR) at the SLC6A3 locus and response to methylphenidate in ADHD patients. A significant effect of the 40 bp VNTR on response to methylphenidate has been detected in most of these reports. However, the findings are inconsistent regarding both the allele (or genotype) involved and the direction of this influence (better or worse response). Thus, further investigations are required to determine if genetic variation due to the VNTR in the dopamine transporter gene is able to predict different levels of clinical response and palatability to methylphenidate in patients with ADHD, and how this information would be useful in clinical practice.

Chemical genomics is concerned with the effects of both genetic variation and chemical perturbation on the cellular effects of small molecules. Chemical genomics relies on selecting biological networks for study, such as those represented by different cell types or disease models, in order to build the desired specificity into the experimental design. The most relevant network property for such experiments is the global connectivity of all cellular proteins comprising the functional ensemble, as illustrated by case studies of the evolution of cyclooxygenase inhibitors and heat-shock protein modulators. Recent examples of chemical genomic profiling, particularly of different cell types, highlight the power of carefully planned experimental approaches in chemical genomics. These new approaches demonstrate the use of the genome to find new targets or new modes of biological interaction.

The involuntary collection of DNA into databanks for insurance and identification purposes has been well-explored, as has the voluntary use of such repositories of DNA information for the construction of databases for medical research. There is a little-investigated fourth manifestation of such databanks, however, a voluntary, non-medical, consumer-oriented one. Specifically, DNA information is now being marketed in the commodity consumer market as a way of establishing both genealogical relatedness and identity per se, including religious, racial, and ethnic identity. In this article the development of such identity databases is discussed, and the ethical consequences of the accumulation and dissemination of such information are briefly explored.

The is a double-stranded RNA-activated protein kinase (PKR) has been largely investigated for its key role in viral host defense. Although best characterized by its function in mediating the antiviral and antiproliferative effects of interferon (IFN), PKR is also implicated in transcriptional regulation, cell differentiation, signal transduction, and tumor suppression. However, recent findings identifying PKR as an important effector of apoptosis have led to an increased interest in PKR modulation as an antitumor strategy. PKR can either be up-regulated through direct induction by the transcription factor E2F-1, or it can be activated through direct protein-protein interactions with the melanoma differentiation-associated gene-7 (MDA7, IL-24). Additionally, the intracellular formation of double-stranded RNA by transfection with antisense RNA complementary to tumor-specific RNA sequences can induce PKR activation and apoptosis selective to these tumor cells. The growing application of viral vector-based gene therapies and oncolytic, replicating viruses that must elude viral defense in order to be effective, has also drawn attention to PKR. Oncolytic viruses, like the attenuated herpes simplex virus R3616, the vesicular stomatitis virus, or reovirus, specifically replicate in tumor cells only because the viral host defense in the permissive cells is suppressed. In this article we review the role of PKR as an effector of apoptosis and a target for tumor treatment strategies and discuss the potential of PKR-modifying agents to treat patients with cancer. Targeted gene therapy against cancer can be approached by activation of PKR with the down-regulation of protein synthesis and induction of apoptosis, or by suppression of PKR with the propagation of oncolytic virus. Since the PKR pathway can be modified by many routes, antitumor therapies combining oncolytic virus, gene therapies, and chemotherapy with PKR modifiers are likely to emerge in the near future as therapeutic options in the treatment of patients with cancer.

It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.

Given the biological complexity of viral infections, the variability of the host response, and the safety concerns related to viral-mediated gene transfer, recent studies have made use of DNA mircoarrays to integrate multi-layered experimental approaches aimed at completely clarifying virus-host interactions. Particular attention has been given to those viruses that are implicated in clinical use and/or in life-threatening diseases. Examples of such use can be divided into three main categories, including: (i) the use of microarrays to study viral expression; (ii) the use of microarrays to analyze the host response to viral infection; and (iii) the use of microarrays to characterize the host response to viral vector-mediated transduction. Significant information on virus- and viral vector-host interactions can be obtained with the microarray approach, including the recognition of master pathways of virally-induced responses, the identification of new target genes for specific viruses, and indications on the molecular toxicity of specific gene transfer vectors currently used for gene therapy trials (in particular, adeno-associated viruses and adenovirus-derived vectors). We predict that the development of accessible repositories containing most of the DNA microarray data on viral infections will certainly help to elucidate the puzzling pictures of different viral infections. This will be crucially important for the correct handling of viral diseases and the intelligent amelioration of viral vectors for gene therapy.

There is currently a broad effort to produce genome-wide high-density linkage disequilibrium (LD) maps with single nucleotide polymorphisms. The hope is that the resulting maps can be exploited to find genes that affect the onset and severity of at least some common human diseases. These maps may also be useful for identifying genes that affect drug response or the likelihood of drug toxicities. The goal of this review is to provide a broad overview of some of the key concerns motivating the design of a major international project called the International Haplotype Map Project. The process of map production requires the identification of very large numbers of polymorphic sites, implementation of facile, highly accurate and inexpensive genotyping production pipelines, and provision for public access to the genotype data. Great progress has been made recently in genotyping methods and these advances are allowing very large-scale data collection. A major goal of these efforts is to enable the selection of subsets of markers that capture useful genetic information in short genomic intervals, while optimally reducing the number of markers that must be genotyped. Standard measures of LD provide a starting point but may not fully capture the complexity of the information inherent in the data. Extremely dense genotype data in several broadly representative populations (European, Chinese, Japanese, and Yoruba) should yield important insights into the genetic structure of most genes. Further study is required to determine how broadly applicable the data will be to other population groups. Significant challenges lie ahead in determining the best methods for the selection of markers in disease/phenotype studies, large-scale genotyping, and analysis of the resulting genetic data.

Eating disorders such as anorexia nervosa and bulimia nervosa involve complex and interacting mechanisms. Formal genetic studies suggest that there is a substantial genetic influence for these disorders. Animal models of eating disorders are scarce. Candidate gene studies have initially focused on the serotonergic and other central neurotransmitter systems and on genes involved in body weight regulation. Most of the studies, including meta-analysis, have yielded negative results; only a single positive finding has been replicated independently. Recently, systematic genome-wide scans based on families with two or more individuals with an eating disorder (anorexia nervosa or bulimia nervosa) revealed initial linkage regions on chromosomes 1, 3, and 4 (anorexia nervosa) and 10p (bulimia nervosa). Fine mapping of one of these regions led to the identification of genes where an association with anorexia nervosa was detected. Currently treatment of patients with eating disorders can not rely on results of molecular genetic studies.

The advent of structural genomics presents new challenges to the archive of biomacromolecular structures--the Protein Data Bank (PDB). As technologies involved in structure determination have advanced, both the number and size of structures available in the PDB have increased rapidly. The structural genomics initiatives are creating a large amount of data that needs to be tracked, archived, and made easily available. The PDB has developed tools to facilitate the rapid deposition of data produced by the structural genomics initiatives and has created databases to track the progress of the work.