IgM and IgD B cell receptors differentially respond to endogenous antigens and control B cell fate.

Naive B cells co-express two BCR isotypes, IgM and IgD, with identical antigen-binding domains but distinct constant regions. IgM but not IgD is downregulated on autoreactive B cells. Because these isotypes are presumed to be redundant, it is unknown how this could impose tolerance. We introduced the Nur77-eGFP reporter of BCR signaling into mice that express each BCR isotype alone. Despite signaling strongly in vitro, IgD is less sensitive than IgM to endogenous antigen in vivo and developmental fate decisions are skewed accordingly. IgD-only Lyn-/- B cells cannot generate autoantibodies and short-lived plasma cells (SLPCs) in vivo, a fate thought to be driven by intense BCR signaling induced by endogenous antigens. Similarly, IgD-only B cells generate normal germinal center, but impaired IgG1+ SLPC responses to T-dependent immunization. We propose a role for IgD in maintaining the quiescence of autoreactive B cells and restricting their differentiation into autoantibody secreting cells

A generalized linear mixed model for longitudinal binary data with a marginal logit link function

Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis [Biometrika 90 (2003) 765--775] proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall's $\tau$. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS390 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Simple Methods of Determining Confidence Intervals for Functions of Estimates in Published Results

Often, the reader of a published paper is interested in a comparison of parameters that has not been presented. It is not possible to make inferences beyond point estimation since the standard error for the contrast of the estimated parameters depends upon the (unreported) correlation. This study explores approaches to obtain valid confidence intervals when the correlation is unknown. We illustrate three proposed approaches using data from the National Health Interview Survey. The three approaches include the Bonferroni method and the standard confidence interval assuming (most conservative) or (when the correlation is known to be non-negative). The Bonferroni approach is found to be the most conservative. For the difference in two estimated parameter, the standard confidence interval assuming yields a 95% confidence interval that is approximately 12.5% narrower than the Bonferroni confidence interval; when the correlation is known to be positive, the standard 95% confidence interval assuming is approximately 38% narrower than the Bonferroni. In summary, this article demonstrates simple methods to determine confidence intervals for unreported comparisons. We suggest use of the standard confidence interval assuming if no information is available or if the correlation is known to be non-negative

A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness

For analyzing longitudinal binary data with nonignorable and non-monotone missing responses, a full likelihood method is complicated algebraically, and often requires intensive computation, especially when there are many follow-up times. As an alternative, a pseudo-likelihood approach has been proposed in the literature under minimal parametric assumptions. This formulation only requires specification of the marginal distributions of the responses and missing data mechanism, and uses an independence working assumption. However, this estimator can be inefficient for estimating both time-varying and time-stationary effects under moderate to strong within-subject associations among repeated responses. In this article, we propose an alternative estimator, based on a bivariate pseudo-likelihood, and demonstrate in simulations that the proposed method can be much more efficient than the previous pseudo-likelihood obtained under the assumption of independence. We illustrate the method using longitudinal data on CD4 counts from two clinical trials of HIV-infected patients

Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data

In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women, instead of a single outcome variable, there are multiple binary outcomes (e.g., abnormal heart rate, abnormal blood pressure, abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated using generalized estimating equations (GEE) (Liang and Zeger, 1986), and consistent estimates can be obtained under the assumption of a missing completely at random (MCAR) mechanism. When the missing data mechanism is missing at random (MAR), that is the probability of missing a particular outcome at a time-point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models using a single modified GEE based on an EM-type algorithm. The proposed method is motivated by the longitudinal study of cardiac abnormalities in children born to HIV-infected women and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that under an MAR mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided the correlation model has been correctly specified, whereas estimates from standard GEE can lead to substantial bias

Testing for independence in J × K contingency tables with complex sample survey data

Summary: The test of independence of row and column variables in a (J × K) contingency table is a widely used statistical test in many areas of application. For complex survey samples, use of the standard Pearson chi-squared test is inappropriate due to correlation among units within the same cluster

Ets1 and IL17RA cooperate to regulate autoimmune responses and skin immunity to Staphylococcus aureus

IntroductionEts1 is a lymphoid-enriched transcription factor that regulates B- and Tcell functions in development and disease. Mice that lack Ets1 (Ets1 KO) develop spontaneous autoimmune disease with high levels of autoantibodies. Naïve CD4 + T cells isolated from Ets1 KO mice differentiate more readily to Th17 cells that secrete IL-17, a cytokine implicated in autoimmune disease pathogenesis. To determine if increased IL-17 production contributes to the development of autoimmunity in Ets1 KO mice, we crossed Ets1 KO mice to mice lacking the IL-17 receptor A subunit (IL17RA KO) to generate double knockout (DKO) mice.MethodsIn this study, the status of the immune system of DKO and control mice was assessed utilizing ELISA, ELISpot, immunofluorescent microscopy, and flow cytometric analysis of the spleen, lymph node, skin. The transcriptome of ventral neck skin was analyzed through RNA sequencing. S. aureus clearance kinetics in in exogenously infected mice was conducted using bioluminescent S. aureus and tracked using an IVIS imaging experimental scheme.ResultsWe found that the absence of IL17RA signaling did not prevent or ameliorate the autoimmune phenotype of Ets1 KO mice but rather that DKO animals exhibited worse symptoms with striking increases in activated B cells and secreted autoantibodies. This was correlated with a prominent increase in the numbers of T follicular helper (Tfh) cells. In addition to the autoimmune phenotype, DKO mice also showed signs of immunodeficiency and developed spontaneous skin lesions colonized by Staphylococcus xylosus. When DKO mice were experimentally infected with Staphylococcus aureus, they were unable to clear the bacteria, suggesting a general immunodeficiency to staphylococcal species. gd T cells are important for the control of skin staphylococcal infections. We found that mice lacking Ets1 have a complete deficiency of the gd T-cell subset dendritic epidermal T cells (DETCs), which are involved in skin woundhealing responses, but normal numbers of other skin gd T cells. To determine if loss of DETC combined with impaired IL-17 signaling might promote susceptibility to staph infection, we depleted DETC from IL17RA KO mice and found that the combined loss of DETC and impaired IL-17 signaling leads to an impaired clearance of the infection.ConclusionsOur studies suggest that loss of IL-17 signaling can result in enhanced autoimmunity in Ets1 deficient autoimmune-prone mice. In addition, defects in wound healing, such as that caused by loss of DETC, can cooperate with impaired IL-17 responses to lead to increased susceptibility to skin staph infections

A weighted combination of pseudo-likelihood estimators for longitudinal binary data subject to non-ignorable non-monotone missingness

For longitudinal binary data with non-monotone non-ignorably missing outcomes over time, a full likelihood approach is complicated algebraically, and with many follow-up times, maximum likelihood estimation can be computationally prohibitive. As alternatives, two pseudo-likelihood approaches have been proposed that use minimal parametric assumptions. One formulation requires specification of the marginal distributions of the outcome and missing data mechanism at each time point, but uses an “independence working assumption,” i.e., an assumption that observations are independent over time. Another method avoids having to estimate the missing data mechanism by formulating a “protective estimator.” In simulations, these two estimators can be very inefficient, both for estimating time trends in the first case and for estimating both time-varying and time-stationary effects in the second. In this paper, we propose use of the optimal weighted combination of these two estimators, and in simulations we show that the optimal weighted combination can be much more efficient than either estimator alone. Finally, the proposed method is used to analyze data from two longitudinal clinical trials of HIV-infected patients

Recent advances in the structural molecular biology of Ets transcription factors: interactions, interfaces and inhibition

The Ets family of eukaryotic transcription factors is based around the conserved Ets DNA-binding domain. Although their DNA-binding selectivity is biochemically and structurally well characterized, structures of homodimeric and ternary complexes point to Ets domains functioning as versatile protein-interaction modules. In the present paper, we review the progress made over the last decade to elucidate the structural mechanisms involved in modulation of DNA binding and protein partner selection during dimerization. We see that Ets domains, although conserved around a core architecture, have evolved to utilize a variety of interaction surfaces and binding mechanisms, reflecting Ets domains as dynamic interfaces for both DNA and protein interaction. Furthermore, we discuss recent advances in drug development for inhibition of Ets factors, and the roles structural biology can play in their future

An extension of the Wilcoxon rank sum test for complex sample survey data: Extension of Wilcoxon Rank Sum Test

In complex survey sampling, a fraction of a finite population is sampled. Often, the survey is conducted so that each subject in the population has a different probability of being selected into the sample. Further, many complex surveys involve stratification and clustering. For generalizability of the sample to the finite population, these features of the design are usually incorporated in the analysis. While the Wilcoxon rank sum test is commonly used to compare an ordinal variable in bivariate analyses, no simple extension of the Wilcoxon rank sum test has been proposed for complex survey data. With multinomial sampling of independent subjects, the Wilcoxon rank-sum test statistic equals the score test statistic for the group effect from a proportional odds cumulative logistic regression model for an ordinal outcome. Using this regression framework, for complex survey data, we formulate a similar proportional odds cumulative logistic regression model for the ordinal variable, and use an estimating equations score statistic for no group effect as an extension of the Wilcoxon test. The proposed method is applied to a complex survey designed to produce national estimates of the health care use, expenditures, sources of payment, and insurance coverage