Structural Characterization of Zn(II)-, Co(II)-, and Mn(II)-loaded Forms of the argE-encoded \u3cem\u3eN\u3c/em\u3e-acetyl-L-ornithine Deacetylase from \u3cem\u3eEscherichia coli\u3c/em\u3e

The Zn, Co, and Mn K-edge extended X-ray absorption fine structure (EXAFS) spectra of the N-acetyl-l-ornithine deacetylase (ArgE) from Escherichia coli, loaded with one or two equivalents of divalent metal ions (i.e., [Zn(II)_(ArgE)], [Zn(II)Zn(II)(ArgE)], [Co(II)_(ArgE)], [Co(II)Co(II)(ArgE)], [Mn(II)_(ArgE)], and [Mn(II)Mn(II)(ArgE)]), were recorded. The Fourier transformed data (FT) for [Zn(II)_(ArgE)], [Zn(II)Zn(II)(ArgE)], [Co(II)_(ArgE)] and [Co(II)Co(II)(ArgE)] are dominated by a peak at 2.05 Å, that can be fit assuming five or six light atom (N,O) scatterers. Inclusion of multiple-scattering contributions from the outer-shell atoms of a histidine-imidazole ring resulted in reasonable Debye–Waller factors for these contributions and a slight reduction in the goodness-of-fit value (f′). Furthermore, the data best fit a model that included a M–M vector at 3.3 and 3.4 Å for Zn(II) and Co(II), respectively, suggesting the formation of a dinuclear site. Multiple scattering contributions from the outer-shell atoms of a histidine-imidazole rings are observed at ~ 3 and 4 Å for Zn(II)- and Co(II)-loaded ArgE suggesting at least one histidine ligand at each metal binding site. Likewise, EXAFS data for Mn(II)-loaded ArgE are dominated by a peak at 2.19 Å that was best fit assuming six light atom (N,O) scatterers. Due to poor signal to noise ratios for the Mn EXAFS spectra, no Mn–Mn vector could be modeled. Peak intensities for [M(II)_(ArgE)] vs. [M(II)M(II)(ArgE)] suggest the Zn(II), Co(II), and Mn(II) bind to ArgE in a cooperative manner. Since no structural data has been reported for any ArgE enzyme, the EXAFS data reported herein represent the first structural glimpse for ArgE enzymes. These data also provide a structural foundation for the future design of small molecules that function as inhibitors of ArgE and may potentially function as a new class of antibiotics

A quantitative linkage score for an association study following a linkage analysis

BACKGROUND: Currently, a commonly used strategy for mapping complex quantitative traits is to use a genome-wide linkage analysis to narrow suspected genes to regions on a scale of centiMorgans (cM), followed by an association analysis to fine map the genetic variation in regions showing linkage. Two important questions arise in the design and the resulting inference at the association stage of this sequential procedure: (1) how should we design an efficient association study given the information provided by the previous linkage study? and (2) can an association in a linkage region explain, in part, the detected linkage signal? RESULTS: We derive a quantitative linkage score (QLS) based on Haseman-Elston regression (Haseman and Elston 1972) and make use of this score to address both questions. In designing an association study, the selection of a subsample from the linkage study sample can be guided by the linkage information summarized in the QLS. When heterogeneity exists, we show that selection based on the QLS can increase the proportion of sample individuals from the subpopulation affected by a disease allele and therefore greatly improves the power of the association study. For the resulting inference, we frame as a hypothesis test the question of whether a linkage signal in a region can be in part explained by a marker allele. A simple one sided paired t-statistic is defined by comparing the two sets of QLSs obtained with/without modeling a marker association: a significant difference indicates that the marker can at least partly account for the detected linkage. We also show that this statistic can be used to detect a spurious association. CONCLUSION: All our results suggest that a careful examination of QLSs should be helpful for understanding the results of both association and linkage studies

Two-stage analysis strategy for identifying the IgM quantitative trait locus

Genetic association studies offer an opportunity to find genetic variants underlying complex human diseases. Various tests have been developed to improve their power. However, none of these tests is uniformly best and it is usually unclear at the outset what test is best for a specific dataset. For example, Hotelling's T2 test is best for normally distributed data, but it can lose considerable power when normality is not met. To achieve satisfactory power in most cases, without compromising the overall significance level, we propose to adopt a two-stage adaptive analysis strategy – several statistics are compared on a portion of the samples at the first stage and the most powerful statistic is then used for the remaining samples. We evaluated this procedure by mapping the quantitative trait locus of IgM with the simulated data in Genetic Analysis Workshop 15 Problem 3. The results show that the gain in power of the two-stage adaptive analysis procedure could be considerable when the initial choice of test statistic is wrong, whereas the loss is relatively small in the case that the optimal test chosen initially is correct

Comparison of a unified analysis approach for family and unrelated samples with the transmission-disequilibrium test to study associations of hypertension in the Framingham Heart Study

Population stratification is one of the major causes of spurious associations in association studies. A unified association approach based on principal-component analysis can overcome the effect of population stratification, as well as make use of both family and unrelated samples combined to increase power (family-case-control, or FamCC). In this study, we compared FamCC and the transmission-disequilibrium test (TDT) using data on hypertension, systolic blood pressure, and diastolic blood pressure in the Framingham Heart Study. Our study indicated FamCC has reasonable type I error for both the unrelated sample and the family sample for all three traits. For these three traits, we found results from FamCC were inconsistent with those from the TDT. We discuss the reasons for this inconsistency. After correcting for multiple tests, we did not detect any significant single-nucleotide polymorphisms by either FamCC or the TDT

Genetic Inactivation of Pyruvate Dehydrogenase Kinases Improves Hepatic Insulin Resistance Induced Diabetes

Pyruvate dehydrogenase kinases (PDK1-4) play a critical role in the inhibition of the mitochondrial pyruvate dehydrogenase complex especially when blood glucose levels are low and pyruvate can be conserved for gluconeogenesis. Under diabetic conditions, the Pdk genes, particularly Pdk4, are often induced, and the elevation of the Pdk4 gene expression has been implicated in the increased gluconeogenesis in the liver and the decreased glucose utilization in the peripheral tissues. However, there is no direct evidence yet to show to what extent that the dysregulation of hepatic Pdk genes attributes to hyperglycemia and insulin resistance in vivo. To address this question, we crossed Pdk2 or Pdk4 null mice with a diabetic model that is deficient in hepatic insulin receptor substrates 1 and 2 (Irs1/2). Metabolic analyses reveal that deletion of the Pdk4 gene had better improvement in hyperglycemia and glucose tolerance than knockout of the Pdk2 gene whereas the Pdk2 gene deletion showed better insulin tolerance as compared to the Pdk4 gene inactivation on the Irs1/2 knockout genetic background. To examine the specific hepatic effects of Pdks on diabetes, we also knocked down the Pdk2 or Pdk4 gene using specific shRNAs. The data also indicate that the Pdk4 gene knockdown led to better glucose tolerance than the Pdk2 gene knockdown. In conclusion, our data suggest that hepatic Pdk4 may be critically involved in the pathogenesis of diabetes

A method to correct for population structure using a segregation model

To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical p-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed

Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis

There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (~2.9 cM compared to ~6.7 cM apart) provided slightly less information content (~0.83 compared to ~0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan

Linkage studies of catechol-O-methyltransferase (COMT) and dopamine-beta-hydroxylase (DBH) cDNA expression levels

The COMT and DBH genes are physically located at chromosomes 22q11 and 9q34, respectively, and both COMT and DBH are involved in catecholamine metabolism and are strong candidates for certain psychiatric and neurological disorders. Although the genetic determinants for both enzymes' activities have been widely studied, their genetic involvement on gene mRNA expression levels remains unclear. In this study we performed quantitative linkage analysis of COMT and DBH cDNA expression levels, identifying transcriptional regulatory regions for both genes. Multiple Haseman-Elston regression was used to detect both additive and interactive effects between two loci. We found that the master transcriptional regulatory region 20q13 had an additive effect on the COMT expression level. We also found that chromosome 19p13 showed both additive and interactive effects with 9q34 on DBH expression level. Furthermore, a potential interaction between COMT and DBH was indicated

Linkage analysis of alcohol dependence using both affected and discordant sib pairs

The basic idea of affected-sib-pair (ASP) linkage analysis is to test whether the inheritance pattern of a marker deviates from Mendelian expectation in a sample of ASPs. The test depends on an assumed Mendelian control distribution of the number of marker alleles shared identical by descent (IBD), i.e., 1/4, 1/2, and 1/4 for 2, 1, and 0 allele(s) IBD, respectively. However, Mendelian transmission may not always hold, for example because of inbreeding or meiotic drive at the marker or a nearby locus. A more robust and valid approach is to incorporate discordant-sib-pairs (DSPs) as controls to avoid possible false-positive results. To be robust to deviation from Mendelian transmission, here we analyzed Collaborative Study on the Genetics of Alcoholism data by modifying the ASP LOD score method to contrast the estimated distribution of the number of allele(s) shared IBD by ASPs with that by DSPs, instead of with the expected distribution under the Mendelian assumption. This strategy assesses the difference in IBD sharing between ASPs and the IBD sharing between DSPs. Further, it works better than the conventional LOD score ASP linkage method in these data in the sense of avoiding false-positive linkage evidence