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Ingestion of arsenic (As) through contaminated drinking water results in increased risks of skin, lung, kidney, and bladder cancers. Due to its association with kidney and bladder cancers, we hypothesized that analysis of the urinary proteome could provide insight into the mechanisms of As toxicity. Urine from participants in a cross-sectional As biomarker study conducted in Nevada, classified as having either high (or= 100 microg total urinary As/l) or low exposure (100 microg total urinary As/l) was analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Two polypeptides, 2.21 and 4.37 kDa, were significantly decreased in the high exposure group (p0.05) and were limited to men when stratified by sex. To replicate these findings, urine from participants in a second As study in Chile was analyzed and results confirmed the decrease of the 4.37 kDa polypeptide as well as a 4.76 kDa polypeptide among highly exposed men. These peaks were identified and confirmed as human beta-defensin-1 (HBD-1) peptides. In a separate in vitro experiment, gene expression analysis of As-treated cell lines demonstrated reduced HBD1 mRNA confirming that the observed decrease in HBD-1 resulted from As exposure. HBD-1 is an antimicrobial peptide constitutively expressed in multiple tissues including epithelial cells of the respiratory and urogenital systems. Recent studies support its role as a tumor suppressor gene for urological cancers suggesting that decreased HBD-1 levels may play a role in the development of cancers associated with As exposure. Further studies are warranted to investigate the role of HBD-1 in As-related toxicity.

Interleukin (IL)-6 is a circulatory, pleiotropic cytokine with multiple roles in the immune system. Both IL-6 and the IL6 -174G>C promoter polymorphism have been linked to various diseases associated with inflammation. However, the mechanism by which the polymorphism influences disease risk is unclear. We postulated that serum proteome analysis of individuals with different IL6 -174G>C genotypes would provide insight on genotype-phenotype associations of this polymorphism and its role in disease susceptibility. Serum from a random sample of control participants in an ongoing population-based case-control study of non-Hodgkin lymphoma was pooled by IL6 genotype and used to screen for the optimal SELDI-TOF MS arrays for analysis. We report differences in serum protein expression of individuals with specific genotypes based on pooled and individual sample analysis. In particular, we report an association of the -174C allele with increased apolipoprotein C-I (ApoC-I). Additionally, we corroborate previous findings of an association of the -174C allele with lower autoantibodies to heat shock protein 60 and confirm the absence of any association between the IL6 -174G>C genotype and serum IL-6 levels. This study illustrates that proteome analysis can enhance our understanding of genotype-phenotype relationships. Additional studies are needed to clarify the interaction between the IL6 -174G>C polymorphism and ApoC-I.

A new data filtering method for SELDI-TOF MS proteomic spectra data is described. We examined technical repeats (2 per subject) of intensity versus m/z (mass/charge) of bone marrow cell lysate for two groups of childhood leukemia patients: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). As others have noted, the type of data processing as well as experimental variability can have a disproportionate impact on the list of "interesting'' proteins (see Baggerly et al. (2004)). We propose a list of processing and multiple testing techniques to correct for 1) background drift; 2) filtering using smooth regression and cross-validated bandwidth selection; 3) peak finding; and 4) methods to correct for multiple testing (van der Laan et al. (2005)). The result is a list of proteins (indexed by m/z) where average expression is significantly different among disease (or treatment, etc.) groups. The procedures are intended to provide a sensible and statistically driven algorithm, which we argue provides a list of proteins that have a significant difference in expression. Given no sources of unmeasured bias (such as confounding of experimental conditions with disease status), proteins found to be statistically significant using this technique have a low probability of being false positives.

Childhood acute lymphoblastic and myeloid leukemias are stratified into molecular and cytogenetic subgroups important for prognosis and therapy. Studies have shown that gene expression profiles can discriminate between leukemia subtypes. Thus, proteome analysis similarly holds the potential for characterizing different subtypes of childhood leukemia. We used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze cell lysates from childhood leukemia cell lines as well as pretreatment leukemic bone marrow derived from childhood leukemia cases. Comparison of the acute myeloid leukemia (AML) cell line, Kasumi, and the biphenotypic myelomonocytic cell line, MV4;11, with the acute lymphoblastic leukemia (ALL) cell lines, 697 and REH, revealed many differentially expressed proteins. In particular, one 8.3?kDa protein has been identified as a C-terminal truncated ubiquitin. Analysis of childhood leukemia bone marrow showed differentially expressed proteins between AML and ALL, including a similar peak at 8.3?kDa, as well as several proteins that differentiate between the ALL t(12;21) and hyperdiploid subtypes. These results demonstrate the potential for proteome analysis to distinguish between various forms of childhood leukemia. Future analyses are warranted to validate these findings and to investigate the role of the C-terminal truncated ubiquitin in the etiology of ALL.