Dee Aud

Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma

The prevalence and severity of allergic asthma continue to rise, lending urgency to the search for environmental triggers and genetic substrates. Using microarray analysis of pulmonary gene expression and single nucleotide polymorphism–based genotyping, combined with quantitative trait locus analysis, we identified the gene encoding complement factor 5 (C5) as a susceptibility locus for allergen-induced airway hyperresponsiveness in a murine model of asthma. A deletion in the coding sequence of C5 leads to C5-deficiency and susceptibility. Interleukin 12 (IL-12) is able to prevent or reverse experimental allergic asthma. Blockade of the C5a receptor rendered human monocytes unable to produce IL-12, mimicking blunted IL-12 production by macrophages from C5-deficient mice and providing a mechanism for the regulation of susceptibility to asthma by C5. The role of complement in modulating susceptibility to asthma highlights the importance of immunoregulatory events at the interface of innate and adaptive immunity in disease pathogenesis.

Pamapimod, a Novel p38 Mitogen-Activated Protein Kinase Inhibitor: Preclinical Analysis of Efficacy and Selectivity

P38α is a protein kinase that regulates the expression of inflammatory cytokines, suggesting a role in the pathogenesis of diseases such as rheumatoid arthritis (RA) or systemic lupus erythematosus. Here, we describe the preclinical pharmacology of pamapimod, a novel p38 mitogen-activated protein kinase inhibitor. Pamapimod inhibited p38α and p38β enzymatic activity, with IC50 values of 0.014 ± 0.002 and 0.48 ± 0.04 μM, respectively. There was no activity against p38δ or p38γ isoforms. When profiled across 350 kinases, pamapimod bound only to four kinases in addition to p38. Cellular potency was assessed using phosphorylation of heat shock protein-27 and c-Jun as selective readouts for p38 and c-Jun NH2-terminal kinase (JNK), respectively. Pamapimod inhibited p38 (IC50, 0.06 μM), but inhibition of JNK was not detected. Pamapimod also inhibited lipopolysaccharide (LPS)-stimulated tumor necrosis factor (TNF) α production by monocytes, interleukin (IL)-1β production in human whole blood, and spontaneous TNFα production by synovial explants from RA patients. LPS- and TNFα-stimulated production of TNFα and IL-6 in rodents also was inhibited by pamapimod. In murine collagen-induced arthritis, pamapimod reduced clinical signs of inflammation and bone loss at 50 mg/kg or greater. In a rat model of hyperalgesia, pamapimod increased tolerance to pressure in a dose-dependent manner, suggesting an important role of p38 in pain associated with inflammation. Finally, an analog of pamapimod that has equivalent potency and selectivity inhibited renal disease in lupus-prone MRL/lpr mice. Our study demonstrates that pamapimod is a potent, selective inhibitor of p38α with the ability to inhibit the signs and symptoms of RA and other autoimmune diseases.

Mechanisms of Disease: transcription factors in inflammatory arthritis

Studies on the pathogenesis of inflammatory arthritides have begun to delve into the molecular and cellular mechanisms behind the development of these diseases, and transcription factors, as key regulators of immune-effector-cell development and function, have received growing attention. Their involvement in immune cells, such as T and B lymphocytes, macrophages and neutrophils, and cells from diseased tissues, such as synoviocytes, has been investigated, revealing dominant roles for members of the nuclear factor κB family, signal-transducer and activator of transcription family, and activator protein 1 family. This review summarizes recent findings and current knowledge regarding the roles of transcription factors in inflammatory arthritis, as evidenced by both biological and genetic studies, and discusses the relevance of these findings for anti-inflammatory therapies.

SNP Discovery and Genotyping

The identification of genes affecting complex traits (i.e., biological traits affected by several genetic and environmental factors) is a very difficult and challenging task (1, 2, 3). For many complex traits, the observable variation between individuals is quantitative; hence, loci affecting such traits are generally termed quantitative trait loci (QTLs). In contrast with monogenic traits, it is impossible to identify all the genomic regions responsible for complex trait variation without additional information on how these regions segregate (1,4). A key development in complex trait analysis was the establishment of large collections of molecular/genetic markers. With the discovery of a large amount of single nucleotide polymorphisms (SNPs) in human and model organisms, correlating SNP markers with phenotype in a segregating population has become a useful tool in QTL studies (5). In both linkage and association mapping, the development of high-throughput methods to discover and genotype polymorphism markers has enabled whole-genome scanning to detect individual loci possible (2).