Synthesis and structural properties of the first macrocyclic selenoether complex of arsenic(III): a rare example of exo and endo coordination in a single species.
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Journal Article↗
Biomedical subjects
N J Hill
Publications and source records attributed to N J Hill.
Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes.
In general, common diseases do not follow a Mendelian inheritance pattern. To identify disease mechanisms and etiology, their genetic dissection may be assisted by evaluation of linkage in mouse models of human disease. Statistical modeling of multiple-locus linkage data from the nonobese diabetic (NOD) mouse model of type 1 diabetes has previously provided evidence for epistasis between alleles of several Idd (insulin-dependent diabetes) loci. The construction of NOD congenic strains containing selected segments of the diabetes-resistant strain genome allows analysis of the joint effects of alleles of different loci in isolation, without the complication of other segregating Idd loci. In this article, we analyze data from congenic strains carrying two chromosome intervals (a double congenic strain) for two pairs of loci: Idd3 and Idd10 and Idd3 and Idd5. The joint action of both pairs is consistent with models of additivity on either the log odds of the penetrance, or the liability scale, rather than with the previously proposed multiplicative model of epistasis. For Idd3 and Idd5 we would also not reject a model of additivity on the penetrance scale, which might indicate a disease model mediated by more than one pathway leading to beta-cell destruction and development of diabetes. However, there has been confusion between different definitions of interaction or epistasis as used in the biological, statistical, epidemiological, and quantitative and human genetics fields. The degree to which statistical analyses can elucidate underlying biologic mechanisms may be limited and may require prior knowledge of the underlying etiology.
Animals↗
The psychometric function: I. Fitting, sampling, and goodness of fit.
The psychometric function relates an observer's performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function's parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (or lapses). We show that failure to account for this can lead to serious biases in estimates of the psychometric function's parameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditional chi2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods.
Bias↗
The psychometric function: II. Bootstrap-based confidence intervals and sampling.
The psychometric function relates an observer's performance to an independent variable, usually a physical quantity of an experimental stimulus. Even if a model is successfully fit to the data and its goodness of fit is acceptable, experimenters require an estimate of the variability of the parameters to assess whether differences across conditions are significant. Accurate estimates of variability are difficult to obtain, however, given the typically small size of psychophysical data sets: Traditional statistical techniques are only asymptotically correct and can be shown to be unreliable in some common situations. Here and in our companion paper (Wichmann & Hill, 2001), we suggest alternative statistical techniques based on Monte Carlo resampling methods. The present paper's principal topic is the estimation of the variability of fitted parameters and derived quantities, such as thresholds and slopes. First, we outline the basic bootstrap procedure and argue in favor of the parametric, as opposed to the nonparametric, bootstrap. Second, we describe how the bootstrap bridging assumption, on which the validity of the procedure depends, can be tested. Third, we show how one's choice of sampling scheme (the placement of sample points on the stimulus axis) strongly affects the reliability of bootstrap confidence intervals, and we make recommendations on how to sample the psychometric function efficiently. Fourth, we show that, under certain circumstances, the (arbitrary) choice of the distribution function can exert an unwanted influence on the size of the bootstrap confidence intervals obtained, and we make recommendations on how to avoid this influence. Finally, we introduce improved confidence intervals (bias corrected and accelerated) that improve on the parametric and percentile-based bootstrap confidence intervals previously used. Software implementing our methods is available.
Confidence Intervals↗
Wild-type and met-65-->Leu variants of human cystatin A are functionally and structurally identical.
The solution structure of an N-terminally truncated and mutant form (M65L(2-98)) of the human cysteine protease inhibitor cystatin A has been reported that reveals extensive structural differences when compared to the previously published structure of full-length wild-type (WT) cystatin A. On the basis of the M65L(2-98) structure, a model of the inhibitory mechanism of cystatin A was proposed wherein specific interactions between the N- and C-terminal regions of cystatin A are invoked as critical determinants of protease binding. To test this model and to account for the reported differences between the two structures, we undertook additional structural and mechanistic analyses of WT and mutant forms of human cystatin A. These show that modification at the C-terminus of cystatin A by the addition of nine amino acids has no effect upon the affinity of papain inhibition (K(D) = 0.18+/-0.02 pM) and the consequences of such modification are not propagated to other parts of the structure. These findings indicate that perturbation of the C-terminus can be achieved without any measurable effect on the N-terminus or the proteinase binding loops. In addition, introduction of the methionine-65 --> leucine substitution into cystatin A that retains the N-terminal methionine (M65L(1-98)) has no significant effect upon papain binding (K(D) = 0.34+/-0.02 pM). Analyses of the structures of WT and M65L(1-98) using (1)H NMR chemical shifts and residual dipolar couplings in a partially aligning medium do not reveal any evidence of significant differences between the two inhibitors. Many of the differences between the published structures correspond to major violations by M65L(2-98) of the WT constraints list, notably in relation to the position of the N-terminal region of the inhibitor, one of three structural motifs indicated by crystallographic studies to be involved in protease binding by cystatins. In the WT structure, and consistent with the crystallographic data, this region is positioned adjacent to another inhibitory motif (the first binding loop), whereas in M65L(2-98) there is no proximity of these two motifs. As the NMR data for both WT9C and M65L(1-98) are wholly consistent with the published structure of WT cystatin A and incompatible with that of M65L(2-98), we conclude that the former represents the most reliable structural model of this protease inhibitor.
Amino Acid Substitution↗
The NOD Idd9 genetic interval influences the pathogenicity of insulitis and contains molecular variants of Cd30, Tnfr2, and Cd137.
Previous analyses of NOD mice have shown that some genes control the development of both insulitis and diabetes, while other loci influence diabetes without reducing insulitis. Evidence for the existence of a gene only influencing diabetes, Idd9 on mouse chromosome 4, is provided here by the development of a novel congenic mouse strain, NOD.B10 Idd9. NOD.B10 Idd9 mice display profound resistance to diabetes even though nearly all develop insulitis. Subcongenic analysis has demonstrated that alleles of at least three B10 genes, Idd9.1, Idd9.2, and Idd9.3 are required to produce Idd9-mediated diabetes resistance. Candidate genes with amino acid differences between the NOD and B10 strains have been localized to the 5.6 cM Idd9.2 interval (Tnfr2, Cd30) and to the 2.0 cM Idd9.3 interval (Cd137).
Alleles↗
Congenic mapping of the type 1 diabetes locus, Idd3, to a 780-kb region of mouse chromosome 3: identification of a candidate segment of ancestral DNA by haplotype mapping.
Type 1 diabetes in the nonobese diabetic (NOD) mouse arises as a consequence of T cell-mediated destruction of the insulin-producing beta cells of the pancreas. Although little is known of the events that initiate and subsequently drive beta-cell destruction it is clear that the entire process is under complex genetic control. At present 19 loci have been mapped that influence the development of diabetes either at the level of initiation of insulitis or at the level of progression from insulitis to overt diabetes, or both. Previously, we have mapped one of these loci, Idd3, to a 0.35-cM interval on proximal mouse chromosome 3. In the present study we have narrowed the map position of this locus to an interval of 0.15 cM by a combination of novel congenic strains and an ancestral haplotype analysis approach. We have constructed a physical contig in bacterial artificial chromosome (BAC) clones across the minimal interval. Restriction mapping of the BAC contig placed the maximum size of the Idd3 interval at 780 kb between the markers D3Nds36 and D3Nds76. To refine further the Idd3 interval we developed a series of novel single nucleotide polymorphisms (SNPs) and carried out haplotype analysis on DNA from mouse strains known to carry either Idd3 susceptibility or protective alleles. This haplotype analysis identified a 145-kb segment of ancestral DNA between the microsatellite marker D3Nds6 and the SNP 81.3. One haplotype of this ancestral segment of DNA is found in mouse strains carrying an Idd3 susceptibility allele and another is found in mouse strains carrying an Idd3 protective allelle. Within the 780-kb congenically defined interval this 145-kb segment represents the most likely location for Idd3. The Il2 gene, which encodes the cytokine interleukin 2 (IL2), maps to this interval and is a strong candidate for Idd3. To investigate whether sequence variation exists in the promoter region of the Il2 gene, which might alter its expression, we sequenced the promoter region of the Il2 gene from mouse strains carrying either an Idd3 susceptibility or resistance allele. Two sequence variants were identified, neither of which fell in known regulatory elements within the Il2 promoter. In agreement with this observation steady-state Il2 mRNA levels showed no variation between susceptible and resistant mouse strains. These data suggest that the profound protection from diabetes seen in congenic mice carrying an Idd3 protective allele is unlikely to be due to differences in the level of expression of the Il2 gene. Instead, all of the current data support our hypothesis that Idd3 corresponds to amino acid variation at the amino terminus of Il2.
Alleles↗
Detection of incremental and decremental bars at different locations across Mach bands and related stimuli.
Two-alternative forced-choice procedures were used to measure the detectability of bright and dark bars at various locations across luminance patterns that produced Mach bands. Detection performance was significantly affected by both dark and bright Mach bands: poor detection performance was observed at locations near, but not in, the Mach bands; relatively good detection performance at locations within the Mach bands was caused by reliable changes in the width, depth, or symmetry of the bands produced by the signal bars. The changes were apparent with signals of lower luminance than that needed for detection in the plateau regions far from the bands, but, because the cues were not sufficiently reliable to allow errorless performance, unusually shaped psychometric functions were obtained.
Adult↗
NOD Idd5 locus controls insulitis and diabetes and overlaps the orthologous CTLA4/IDDM12 and NRAMP1 loci in humans.
A genome scan for B10-derived loci that reduce the frequency of diabetes and insulitis in NOD mice demonstrated a large region (34 cM) of linkage on the proximal end of chromosome 1. This locus was designated Idd5 and encompassed candidate genes including Il1r1, Il1r2, Stat1, Stat4, Nramp1, and Bcl2. In the current study, we have confirmed the existence of Idd5 by developing a series of congenic mouse strains that are resistant to diabetes and determined that Idd5 is actually two genes located within a 9.4-cM interval. Idd5.1 is in the proximal 1.5-cM portion of the interval and contains the candidates Casp8, Cflar (FLIP), Cd28, and Cd152 (CTLA4). Idd5.1 overlaps the orthologous CTLA4/IDDM12 locus in humans. Idd5.2 is in the distal 5.1-cM portion of the 9.4-cM interval and contains the candidates Nramp1, which has a functional polymorphism between NOD and B10, and Cmkar2 (CXCR2, interleukin [IL]-8 receptor alpha). Candidate genes eliminated by this analysis include Il1r1, Ilr2, Zap70, Orch5, Stat1, Stat4, Bcl2, Cmkar4 (CXCR4), and Il10. On its own, the Idd5 locus provides a significant amount of protection from diabetes (50% reduction from parental frequency) and when combined with another resistance locus (Idd3 on chromosome 3), provides nearly complete protection from diabetes and insulitis.
Abatacept↗
The role of Gly-4 of human cystatin A (stefin A) in the binding of target proteinases. Characterization by kinetic and equilibrium methods of the interactions of cystatin A Gly-4 mutants with papain, cathepsin B, and cathepsin L.
The importance of the evolutionarily conserved Gly-4 residue for the affinity and kinetics of interaction of cystatin A with several cysteine proteinases was assessed by site-directed mutagenesis. Even the smallest replacement, by Ala, resulted in approximately 1000-, approximately 10- and approximately 6000-fold decreased affinities for papain, cathepsin L, and cathepsin B, respectively. Substitution by Ser gave further 3-8-fold reductions in affinity, whereas the largest decreases, >10(5)-fold, were observed for mutations to Arg and Glu. The kinetics of inhibition of papain by the mutants with small side chains, Ala and Ser, were compatible with a one-step bimolecular reaction similar to that with wild-type cystatin A. The decreased affinities of these mutants for papain and cathepsin L were due exclusively to increased dissociation rate constants, but the reduced affinities for cathepsin B were due also to decreased association rate constants. The latter finding indicates that the intact N-terminal region serves as a guide directing cystatin A to the active site of cathepsin B, as has been proposed for cystatin C. The kinetics of binding of the mutants with charged side chains, Arg and Glu, to papain were consistent with a two-step binding mechanism, in which the mutant side chains are accommodated in the complex by a conformational change. The NMR solution structure of the Ala and Trp mutants showed only minor changes compared with wild-type cystatin A, indicating that the large reductions in affinity for proteinases are not due to altered structures of the mutants. Instead, a side chain larger than a hydrogen atom at position 4 affects the interaction with the proteinase most likely by interfering with the binding of the N-terminal region.
Binding, Competitive↗
Localization of two insulin-dependent diabetes (Idd) genes to the Idd10 region on mouse chromosome 3.
Multiple genes control the development of autoimmune diabetes both in humans and in the nonobese diabetic (NOD) strain of mouse. Previously, three insulin-dependent diabetes (Idd) genes, Idd3, Idd10, and Idd17, were localized to mouse Chromosome (Chr) 3. The B10- or B6-derived resistance alleles at Idd10 and Idd3 together provide the NOD mouse with nearly complete protection from diabetes. In the present study, the 10.2-cM region encoding Idd10 was defined further with newly developed congenic strains. A locus, located in the centromeric 2.1 cM of the 10.2 cM region, contributed to the Idd10 trait. However, this locus did not account for the full effect of Idd10, suggesting the presence of a second gene in the distal portion of the 10.2-cM region. This second gene is designated as Idd18 and is localized to a 5.1-cM region. The resolution of the originally defined Idd3 locus into at least four separate loci, Idd3, Idd10, Idd17, and Idd18, illustrates the complex polygenic nature of diabetes.
Animals↗
Genetic analysis of chromosome 2 in type 1 diabetes: analysis of putative loci IDDM7, IDDM12, and IDDM13 and candidate genes NRAMP1 and IA-2 and the interleukin-1 gene cluster. IMDIAB Group.
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Chromosomes, Human, Pair 2↗
Congenic mapping of the insulin-dependent diabetes (Idd) gene, Idd10, localizes two genes mediating the Idd10 effect and eliminates the candidate Fcgr1.
The development of autoimmune diabetes in the nonobese diabetic (NOD) mouse is under the control of multiple insulin-dependent diabetes (Idd) genes. The Idd3 gene, originally defined as a broad peak of linkage on mouse chromosome 3, was subsequently identified as two genes, Idd3 and Idd10, separated by at least 20 cM. The resistance alleles of Idd3 and Idd10 individually confer only partial protection from diabetes but, in combination, result in profound resistance to disease due to an epistatic genetic interaction. In this study, we used newly developed congenic strains to further localize Idd10. Surprisingly, we found that Idd10 itself comprises at least two linked loci: Idd10 and the newly designated Idd17. Idd17 was localized to a 1.1-cM region between D3Mit26 and D3Mit40, proximal to Fcgr1, a candidate gene encoding the high affinity Fc receptor for IgG. Idd10 was localized to a 10-cM region between D3Mit213 and D3Mit106, distal to Fcgr1. Thus, Fcgr1 was excluded as a candidate for either Idd10 or Idd17, despite the fact that the NOD strain expresses a mutant form of the receptor. Interestingly, although Idd10 and Idd17 participate in a genetic interaction with each other, Idd10 but not Idd17 participates in the genetic interaction with Idd3. Our study on chromosome 3 begins to reveal the extent of the polygenic nature of autoimmune diabetes, and demonstrates that the use of congenic strains is an effective mapping strategy, even in the dissection of multiple, linked genes with subtle effects.
Alleles↗
Mapping of the IDDM locus Idd3 to a 0.35-cM interval containing the interleukin-2 gene.
Currently, 16 loci that contribute to the development of IDDM in the NOD mouse have been mapped by linkage analysis. To fine map these loci, we used congenic mapping. Using this approach, we localized the Idd3 locus to a 0.35-cM interval on chromosome 3 containing the Il2 gene. Segregation analysis of the known variations within this interval indicated that only one variant, a serine-to-proline substitution at position 6 of the mature interleukin-2 (IL-2) protein, consistently segregates with IDDM in crosses between NOD and a series of nondiabetic mouse strains. These data, taken together with the immunomodulatory role of IL-2, provide circumstantial evidence in support of the hypothesis that Idd3 is an allelic variation of the Il2 gene, or a variant in strong linkage disequilibrium.
Amino Acid Sequence↗
Mapping the diabetes polygene Idd3 on mouse chromosome 3 by use of novel congenic strains.
Development of novel congenic mouse strains has allowed us to better define the location of the diabetogenic locus, Idd3, on Chromosome (Chr) 3. Congenic strains were identified by use of published and newly developed microsatellite markers, their genomes fingerprinted by a rapid, fluorescence-based approach, and their susceptibility to type 1 diabetes evaluated. The maximum interval containing Idd3 is now approximately 4 cM.
Animals↗
Nursing in Northern Ireland.
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Hospitals, Psychiatric↗