Publications

Sphingosine 1-phosphate (S1P) from mononuclear phagocytes and platelets signals T cells predominantly through S1P1 G protein-coupled receptors (Rs) to enhance survival, stimulate and suppress migration, and inhibit other immunologically relevant responses. Cellular S1P1 Rs and their signaling functions are rapidly down-regulated by S1P, through a protein kinase C (PKC)-independent mechanism, but characteristics of cell-surface re-expression of down-regulated S1P1 Rs have not been elucidated. T cell chemotactic responses (CT) to 10 and 100 nm S1P and inhibition of T cell chemotaxis to chemokines (CI) by 1 and 3 microm S1P were suppressed after 1 h of preincubation with 100 nm S1P, but recovered fully after 12-24 h of exposure to S1P. Late recovery of down-regulated CT and CI, but not early down-regulation, was suppressed by PKC and PKCepsilon-selective inhibitors and was absent in T cells from PKCepsilon-null mice. The same PKCepsilon inhibitors blocked S1P-evoked increases in T cell nuclear levels of c-Fos and phosphorylated c-Jun and JunD after 24 h, but not 1 h. A mixture of c-Fos plus c-Jun antisense oligonucleotides prevented late recovery of down-regulated CT and CI, without affecting S1P induction of down-regulation. Similarly, S1P-elicited threonine phosphorylation of S1P1 Rs was suppressed by a selective inhibitor of PKCepsilon after 24 h, but not 1 h. Biochemical requisites for recovery of down-regulated S1P1 Rs thus differ from those for S1P induction of down-regulation.

We present a case study using the negative binomial regression model for discrete outcome data arising from a clinical trial designed to evaluate the effectiveness of a prehabilitation program in preventing functional decline among physically frail, community-living older persons. The primary outcome was a measure of disability at 7 months that had a range from 0 to 16 with a mean of 2.8 (variance of 16.4) and a median of 1. The data were right skewed with clumping at zero (i.e., 40% of subjects had no disability at 7 months). Because the variance was nearly 6 times greater than the mean, the negative binomial model provided an improved fit to the data and accounted better for overdispersion than the Poisson regression model, which assumes that the mean and variance are the same. Although correcting the variance and corresponding test statistics for overdispersion is a standard procedure in the Poisson model, the estimates of the regression parameters are inefficient because they have more sampling variability than is necessary. The negative binomial model provides an alternative approach for the analysis of discrete data where overdispersion is a problem, provided that the model is correctly specified and adequately fits the data.