B Cell Activation in Insulin Resistance and Obesity

Our group has demonstrated that inflammatory diseases such as type 2 diabetes (DM), inflammatory bowel disease (IBD), and periodontal disease (PD) are associated with altered B cell function that may contribute to disease pathogenesis. B cells were found to be highly activated with characteristics of inflammatory cells. Obesity is a pre-disease state for cardiovascular disease and type 2 diabetes and is considered a state of chronic inflammation. Therefore, we sought to better characterize B cell function and phenotype in obese patients. We demonstrate that (Toll-like receptor) TLR4 and CD36 expression by B cells is elevated in obese subjects, suggesting increased sensing of lipopolysaccharide (LPS) and other TLR ligands. These ligands may be of microbial, from translocation from a leaky gut, or host origin. To better assess microbial ligand burden and host response in the bloodstream, we measured LPS binding protein (LBP), bacterial/permeability increasing protein (BPI), and high mobility group box 1 (HMGB1). Thus far, our data demonstrate an increase in LBP in DM and obesity indicating increased responses to TLR ligands in the blood. Interestingly, B cells responded to certain types of LPS by phosphorylating extracellular-signal-regulated kinases (ERK) 1/2. A better understanding of the immunological state of obesity and the microbial and endogenous TLR ligands that may be activating B cells will help identify novel therapeutics to reduce the risk of more dangerous conditions, such as cardiovascular disease.

A Local Circuit Model of Learned Straitial and Dopamine Cell Responses under Probabilistic Schedules of Reward

Before choosing, it helps to know both the expected value signaled by a predictive cue and the associated uncertainty that the reward will be forthcoming. Recently, Fiorillo et al. (2003) found the dopamine (DA) neurons of the SNc exhibit sustained responses related to the uncertainty that a cure will be followed by reward, in addition to phasic responses related to reward prediction errors (RPEs). This suggests that cue-dependent anticipations of the timing, magnitude, and uncertainty of rewards are learned and reflected in components of the DA signals broadcast by SNc neurons. What is the minimal local circuit model that can explain such multifaceted reward-related learning? A new computational model shows how learned uncertainty responses emerge robustly on single trial along with phasic RPE responses, such that both types of DA responses exhibit the empirically observed dependence on conditional probability, expected value of reward, and time since onset of the reward-predicting cue. The model includes three major pathways for computing: immediate expected values of cures, timed predictions of reward magnitudes (and RPEs), and the uncertainty associated with these predictions. The first two model pathways refine those previously modeled by Brown et al. (1999). A third, newly modeled, pathway is formed by medium spiny projection neurons (MSPNs) of the matrix compartment of the striatum, whose axons co-release GABA and a neuropeptide, substance P, both at synapses with GABAergic neurons in the SNr and with the dendrites (in SNr) of DA neurons whose somas are in ventral SNc. Co-release enables efficient computation of sustained DA uncertainty responses that are a non-monotonic function of the conditonal probability that a reward will follow the cue. The new model's incorporation of a striatal microcircuit allowed it to reveals that variability in striatal cholinergic transmission can explain observed difference, between monkeys, in the amplitutude of the non-monotonic uncertainty function. Involvement of matriceal MSPNs and striatal cholinergic transmission implpies a relation between uncertainty in the cue-reward contigency and action-selection functions of the basal ganglia. The model synthesizes anatomical, electrophysiological and behavioral data regarding the midbrain DA system in a novel way, by relating the ability to compute uncertainty, in parallel with other aspects of reward contingencies, to the unique distribution of SP inputs in ventral SN.