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.

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.