Force generation in RNA polymerase.

RNA polymerase (RNAP) is a processive molecular motor capable of generating forces of 25-30 pN, far in excess of any other known ATPase. This force derives from the hydrolysis free energy of nucleotides as they are incorporated into the growing RNA chain. The velocity of procession is limited by the rate of pyrophosphate release. Here we demonstrate how nucleotide triphosphate binding free energy can rectify the diffusion of RNAP, and show that this is sufficient to account for the quantitative features of the measured load-velocity curve. Predictions are made for the effect of changing pyrophosphate and nucleotide concentrations and for the statistical behavior of the system.     

Physiological Heterothallism and Sexuality in Euascomycetes: A Partial History

Copyright 1998 Academic Press.     

Nociceptive mechanisms modulate ozone-induced human lung function decrements

We have previously suggested that ozone(O₃)-induced pain-relatedsymptoms and inhibition of maximal inspiration are due to stimulationof airway C fibers (M. J. Hazucha, D. V. Bates, and P. A. Bromberg.J. Appl.Physiol. 67: 1535-1541, 1989). If this were so,pain suppression or inhibition by opioid-receptor agonists shouldpartially or fully reverseO₃-induced symptomatic and lung functional responses. The objectives of this study were to determine whether O₃-induced pain limitsmaximal inspiration and whether endogenous opioids contribute tomodulation of the effects of inhaledO₃ on lung function. Theparticipants in this double-blind crossover study were healthyvolunteers (18-59 yr) known to be "weak" (WR;n = 20) and "strong"O₃ responders (SR;n = 42). They underwent either two 2-hexposures to air or two 2-h exposures to 0.42 parts/millionO₃ with moderate intermittentexercise. Immediately afterpost-O₃ spirometry, the WR wererandomly given either naloxone (0.15 mg/kg iv) or saline, whereas SRrandomly received either sufentanil (0.2 µg/kg iv) or saline.O₃ exposure significantly(P < 0.001) impaired lung function.In SR, sufentanil rapidly, although not completely, reversed both thechest pain and spirometric effects (forced expiratory volume in 1 s;P < 0.0001) compared with saline.Immediate postexposure administration of saline or naloxone had nosignificant effect on WR. Plasma -endorphin levels were not relatedto an individual's O₃responsiveness. Cutaneous pain variables showed a nonsignificantweak association with O₃responsiveness. These observations demonstrate that nociceptive mechanisms play a key role in modulatingO₃-induced inhibition ofinspiration but not in causing lack of spirometric response toO₃ exposure in WR.

     

Linkage and association analysis of candidate genes for TB and TNFα cytokine expression: evidence for association with IFNGR1, IL-10, and TNF receptor 1 genes

Tuberculosis (TB) is a growing public health threat globally and several studies suggest a role of host genetic susceptibility in increased TB risk. As part of a household contact study in Kampala, Uganda, we have taken a unique approach to the study of genetic susceptibility to TB by developing an intermediate phenotype model for TB susceptibility, analyzing levels of tumor necrosis factor-α (TNFα) in response to culture filtrate as the phenotype. In the present study, we analyzed candidate genes related to TNFα regulation and found that interleukin (IL)-10, interferon-gamma receptor 1 (IFNGR1), and TNFα receptor 1 (TNFR1) genes were linked and associated to both TB and TNFα. We also show that these associations are with progression to active disease and not susceptibility to latent infection. This is the first report of an association between TB and TNFR1 in a human population and our findings for IL-10 and IFNGR1 replicate previous findings. By observing pleiotropic effects on both phenotypes, we show construct validity of our intermediate phenotype model, which enables the characterization of the role of these genetic polymorphisms on TB pathogenesis. This study further illustrates the utility of such a model for disentangling complex traits. C. C. Whalen and S. K. Iyengar contributed equally as senior authors of this work.     

A systems-biology analysis of feedback inhibition in the Sho1 osmotic-stress-response pathway

BACKGROUND: A common property of signal transduction systems is that they rapidly lose their ability to respond to a given stimulus. For instance in yeast, the mitogen-activated protein (MAP) kinase Hog1 is activated and inactivated within minutes, even when the osmotic-stress stimulus is sustained. RESULTS: Here, we used a combination of experimental and computational analyses to investigate the dynamic behavior of Hog1 activation in vivo. Computational modeling suggested that a negative-feedback loop operates early in the pathway and leads to rapid attenuation of Hog1 signaling. Experimental analysis revealed that the membrane-bound osmosensor Sho1 is phosphorylated by Hog1 and that phosphorylation occurs on Ser-166. Moreover, Sho1 exists in a homo-oligomeric complex, and phosphorylation by Hog1 promotes a transition from the oligomeric to monomeric state. A phosphorylation-site mutation (Sho1(S166E)) diminishes the formation of Sho1-oligomers, dampens activation of the Hog1 kinase, and impairs growth in high-salt or sorbitol conditions. CONCLUSIONS: These findings reveal a novel phosphorylation-dependent feedback loop leading to diminished cellular responses to an osmotic-stress stimulus.     

Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions

MOTIVATION: The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined. RESULTS: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.     

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions

MOTIVATION: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk. RESULTS: While the original MDR method provides a simple binary measure of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group. AVAILABILITY: The program written in R is available.     

Haseman-Elston weighted by marker informativity

In the Haseman-Elston approach the squared phenotypic difference is regressed on the proportion of alleles shared identical by descent (IBD) to map a quantitative trait to a genetic marker. In applications the IBD distribution is estimated and usually cannot be determined uniquely owing to incomplete marker information. At Genetic Analysis Workshop (GAW) 13, Jacobs et al. [BMC Genet 2003, 4(Suppl 1):S82] proposed to improve the power of the Haseman-Elston algorithm by weighting for information available from marker genotypes. The authors did not show, however, the validity of the employed asymptotic distribution. In this paper, we use the simulated data provided for GAW 14 and show that weighting Haseman-Elston by marker information results in increased type I error rates. Specifically, we demonstrate that the number of significant findings throughout the chromosome is significantly increased with weighting schemes. Furthermore, we show that the classical Haseman-Elston method keeps its nominal significance level when applied to the same data. We therefore recommend to use Haseman-Elston with marker informativity weights only in conjunction with empirical p-values. Whether this approach in fact yields an increase in power needs to be investigated further.     

Fine-mapping using the weighted average method for a case-control study

We present a new method for fine-mapping a disease susceptibility locus using a case-control design. The new method, termed the weighted average (WA) statistic, averages the Cochran-Armitage (CA) trend test statistic and the difference between the Hardy-Weinberg disequilibrium test statistic for cases and controls (the HWD trend). The main characteristics of the WA statistic are that it improves on the weaknesses, and maintains the strengths, of both the CA trend test and the HWD trend test. Data from three different populations in the Genetic Analysis Workshop 14 (GAW14) simulated dataset (Aipotu, Karangar, and Danacaa) were first subjected to model-free linkage analysis to find regions exhibiting linkage. Then, for fine-scale mapping, 140 SNPs within the significant linkage regions were analyzed with the WA test statistic on replicates of the three populations, both separately and combined. The regions that were significant in the multipoint linkage analysis were also significant in this fine-scale mapping. The most significant regions that were obtained using the WA statistic were regions in chromosome 3 (B03T3056-B03T3058, p-value < 1 x 10(-10)) and chromosome 9 (B09T8332-B09T8334, p-value 1 x 10(-6)). Based on the results of the simulated GAW14 data, the WA test statistic showed good performance and could narrow down the region containing the susceptibility locus. However, the strength of the signal depends on both the strength of the linkage disequilibrium and the heterozygosity of the linked marker.     

Single- and multilocus allelic variants within the GABA(B) receptor subunit 2 (GABAB2) gene are significantly associated with nicotine dependence

Twelve single-nucleotide polymorphisms (SNPs) in the human gamma-aminobutyric acid type B (GABA(B)) receptor subunit 2 gene (GABAB2) were tested for association with nicotine dependence (ND) in an extensively phenotyped cohort of 1,276 smokers and nonsmokers, representing approximately 404 nuclear families of African American (AA) or European American (EA) origin. The GABAB2 gene encodes a subunit of the GABA(B) receptor for GABA, an inhibitory neurotransmitter involved in the regulation of many physiological and psychological processes in the brain. The gene is located within a region of chromosome 9q22 that showed a "suggestive" linkage to ND. Individual SNP analysis performed using the PBAT-GEE program indicated that two SNPs in the AAs and four SNPs in the EAs were significantly associated with ND. Haplotype analysis using the Family-Based Association Test revealed that, even after Bonferroni correction, the haplotype C-C-G of rs2491397-rs2184026-rs3750344 had a significant positive association with ND in both the pooled and the AA samples. In the EAs, we identified two haplotypes, C-A-C-A and T-A-T-A, formed by SNPs rs1435252-rs378042-rs2779562-rs3750344, that showed a highly significant negative and positive association with ND, respectively. In summary, our findings provide evidence of a significant association of GABAB2 variants with ND, implying that this gene plays an important role in the etiology of this drug addiction.