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The genomic signature of selection at alcohol dehydrogenase (ADH1B) is described, focusing on that polymorphism and East Asian populations. The results have provided evidence that selection has operated on the ADH1B gene in East Asian populations to increase one haplotype of the gene to high frequency.

Linkage disequilibrium plays an important role in genetic implication of alcoholism. In research of both alcoholics and controls, portions of chromosomes in ADH2 are significantly different between the two groups. However, portions of chromosomes in ADH3 are not notably different between alcoholics and controls. Functional ADH3 polymorphism is driven by ADH2 disequilibrium.

This paper concerns the manner in which combinatorial mating proteins of the fungus, Schizophyllum commune, recognize one another to form complexes that regulate target gene expression. In Schizophyllure, tightly linked Y and Z mating-type genes do not promote development in the combinations present in haploid strains (i.e., self combinations). When the Yand Z genes from two different mating types are brought together by the fusion of two haploid cells, the Y and Z proteins from different mating types recognize one another as nonself, form a complex and activate development. Several Y and Z alleles are present in the population and all nonself combinations of Y and Z alleles are equally functional. We have made chimeric genes among Y1, Y3, Y4 and Y5 and examined their mating-type specificities by transformation and mating tests. These studies show that the specificity of Y protein recognized by Z protein is encoded within a short region of N-terminal amino acids. The critical region is not precisely the same in each Y protein and in each Y-Z protein interaction. For Y3 protein compared with Y4 protein, the critical residues are in an N-terminal region of 56 amino acids (residues 17-72), with 40% identity and 65% similarity. Two-hybrid studies show that: the first 144 amino acids of Y4 protein are sufficient to bind Z3 and Z5 proteins, but not Z4 protein, and proteins deleted of the Y4 specificity region do not bind Z3, Z4 or Z5 protein. Thus the specificity determinant of the Y protein is essential for protein-protein recognition, Y-Z protein binding and mating activity.

Abstract Background The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally. Results We present the results from a preliminary study of the distribution one would expect for analyzing sets of genes extracted from Drosophila, S. cerevisiae, Wormbase, and Gramene databases using the Gene Ontology Database. Conclusions We found that the estimated distribution is not regular and is not predictable outside of a particular set of genes. Permutation-based simulations may be necessary to determine the confidence in results of such analyses.

Genes influencing resting energy expenditure (REE) and respiratory quotient (RQ) represent candidate genes for obesity and the metabolic syndrome because of the involvement of these traits in energy balance and substrate oxidation. We aim to explore the molecular basis for individual variation in REE and fuel partitioning as reflected by RQ. We performed microarray studies in human vastus lateralis muscle biopsies from 40 healthy subjects with measured REE and RQ values. We identified 2,392 and 1,115 genes significantly correlated with REE and RQ, respectively. Genes correlated with REE and RQ encompass a broad array of functions, including carbohydrate and lipid metabolism. gene expression, mitochondrial processes, and membrane transport. Microarray pathway analysis revealed that REE was positively correlated with upregulation of G protein-coupled receptor signaling (meet criteria/total genes: 65 of 283) involved in autonomic nervous system functions, including those receptors mediating adrenergic, dopamine, γ-aminobutyric acid (GABA), neuropeptide Y (NPY), and serotonin action (meet criteria/total genes: 46 of 176). Reduced REE was associated with an increase in genes participating in ubiquitin-proteasome-dependent proteolytic pathways (58 of 232). Serine-type peptidase activity (9 of 76) was positively correlated with RQ, while genes involved in the protein phosphatase type 2A complex (4 of 9), mitochondrial function and cellular respiration (38 of 315), and unfolded protein binding (19 of 97) were associated with reduced RQ values and a preference for lipid fuel metabolism. Individual variations in whole body REE and RQ are regulated by differential expressions of specific genes and pathways intrinsic to skeletal muscle. [ABSTRACT FROM AUTHOR]

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease and affects I in 1,000 individuals. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. There is accumulating evidence suggesting that there are common cellular and molecular mechanisms responsible for cystogenesis in human and murine PKD regardless of the genes mutated, and, in the case of complex metabolomic analysis, the use of a mouse model has distinct advantages for proof of principle over a human study. Therefore, in this study we utilized a urinary metabolomics-based investigation using gas chromatography-time of flight mass spectrometry to demonstrate that the cystic mouse can be discriminated from its wild-type counterpart by urine analysis alone. At day 26 of life, before there is serological evidence of kidney dysfunction, affected mice are distinguishable by urine metabolomic analysis; this finding persists through 45 days until 64 days, at which time body weight differences con- found the results. Using functional score analysis and the KEGG pathway database, we identify several biologically relevant metabolic pathways which are altered very early in this disease, the most highly represented being the purine and galiictose metabolism pathways. In addition, we identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components, once extended to human disease, may prove useful as a noninvasive means of diagnosing cystic kidney diseases and to suggest novel therapeutic approaches. Thus, urine metabolomics has great diagnostic potential for cystic renal disorders and deserves further study. [ABSTRACT FROM AUTHOR]

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high-productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves. [ABSTRACT FROM AUTHOR]

Kidney cancer is the seventh most common cancer in the Western world, its incidence is increasing, and it is frequently metastatic at presentation, at which stage patient survival statistics are grim. In addition, there are no useful biofluid markers for this disease, such that diagnosis is dependent on imaging techniques that are not generally used for screening. In the present study, we use metabolomics techniques to identify metabolites in kidney cancer patients' urine, which appear at different levels (when normalized to account for urine volume and concentration) from the same metabolites in nonkidney cancer patients. We found that quinolinate, 4-hydroxybenzoate, and gentisate are differentially expressed at a false discovery rate of 0.26, and these metabolites are involved in common pathways of specific amino acid and energetic metabolism, consistent with high tumor protein breakdown and utilization, and the Warburg effect. When added to four different (three kidney cancer-derived and one "normal") cell lines, several of the significantly altered metabolites, quinolinate, α-ketoglutarate, and gentisate, showed increased or unchanged cell proliferation that was cell line-dependent. Further evaluation of the global metabolomics analysis, as well as confirmation of the specific potential biomarkers using a larger sample size, will lead to new avenues of kidney cancer diagnosis and therapy.