Publications

Exacerbated sensitivity to mechanical stimuli that are normally innocuous or mildly painful (mechanical allodynia and hyperalgesia) occurs during inflammation and underlies painful diseases. Proteases that are generated during inflammation and disease cleave protease-activated receptor 2 (PAR2) on afferent nerves to cause mechanical hyperalgesia in the skin and intestine by unknown mechanisms. We hypothesized that PAR2-mediated mechanical hyperalgesia requires sensitization of the ion channel transient receptor potential vanilloid 4 (TRPV4). Immunoreactive TRPV4 was coexpressed by rat dorsal root ganglia (DRG) neurons with PAR2, substance P (SP) and calcitonin gene-related peptide (CGRP), mediators of pain transmission. In PAR2-expressing cell lines that either naturally expressed TRPV4 (bronchial epithelial cells) or that were transfected to express TRPV4 (HEK cells), pretreatment with a PAR2 agonist enhanced Ca2+ and current responses to the TRPV4 agonists phorbol ester 4alpha-phorbol 12,13-didecanoate (4alphaPDD) and hypotonic solutions. PAR2-agonist similarly sensitized TRPV4 Ca2+ signals and currents in DRG neurons. Antagonists of phospholipase Cbeta and protein kinases A, C and D inhibited PAR2-induced sensitization of TRPV4 Ca2+ signals and currents. 4alphaPDD and hypotonic solutions stimulated SP and CGRP release from dorsal horn of rat spinal cord, and pretreatment with PAR2 agonist sensitized TRPV4-dependent peptide release. Intraplantar injection of PAR2 agonist caused mechanical hyperalgesia in mice and sensitized pain responses to the TRPV4 agonists 4alphaPDD and hypotonic solutions. Deletion of TRPV4 prevented PAR2 agonist-induced mechanical hyperalgesia and sensitization. This novel mechanism, by which PAR2 activates a second messenger to sensitize TRPV4-dependent release of nociceptive peptides and induce mechanical hyperalgesia, may underlie inflammatory hyperalgesia in diseases where proteases are activated and released.

The relative contributions of HLA alleles and T-cell receptors (TCRs) to the prevention of mutational viral escape are unclear. Here, we examined human immunodeficiency virus type 1 (HIV-1)-specific CD8(+) T-cell responses restricted by two closely related HLA class I alleles, B*5701 and B*5703, that differ by two amino acids but are both associated with a dominant response to the same HIV-1 Gag epitope KF11 (KAFSPEVIPMF). When this epitope is presented by HLA-B*5701, it induces a TCR repertoire that is highly conserved among individuals, cross-recognizes viral epitope variants, and is rarely associated with mutational escape. In contrast, KF11 presented by HLA-B*5703 induces an entirely different, more heterogeneous TCR beta-chain repertoire that fails to recognize specific KF11 escape variants which frequently arise in clade C-infected HLA-B*5703(+) individuals. These data show the influence of HLA allele subtypes on TCR selection and indicate that extensive TCR diversity is not a prerequisite to prevention of allowable viral mutations.

The outcome of drug therapy is often unpredictable, ranging from beneficial effects to lack of efficacy to serious adverse effects. Variations in single genes are 1 well-recognized cause of such unpredictability, defining the field of pharmacogenetics (see Glossary). Such variations may involve genes controlling drug metabolism, drug transport, disease susceptibility, or drug targets. The sequencing of the human genome and the cataloguing of variants across human genomes are the enabling resources for the nascent field of pharmacogenomics (see Glossary), which tests the idea that genomic variability underlies variability in drug responses. However, there are many challenges that must be overcome to apply rapidly accumulating genomic information to understand variable drug responses, including defining candidate genes and pathways; relating disease genes to drug response genes; precisely defining drug response phenotypes; and addressing analytic, ethical, and technological issues involved in generation and management of large drug response data sets. Overcoming these challenges holds the promise of improving new drug development and ultimately individualizing the selection of appropriate drugs and dosages for individual patients.