Core C: Genomics and Analytical Chemistry
The overall goal of the Berkeley Superfund Basic Research Program (SBRP) is to apply functional genomics, proteomics, transcriptomics, and nanotechnology to better detect arsenic, mercury, benzene, polycyclic aromatic hydrocarbons, trichloroethylene, and other Superfund priority chemicals in the environment; evaluate their effects on human health, especially the health of susceptible populations such as children; and remediate their presence and reduce their toxicity. The individual research projects use functional genomics, proteomics, and transcriptomics in their studies. Their success largely depends on the effective handling and management of biological samples, as well as access to and expertise in the latest “-omic” technologies. Thus, detailed collection and storage protocols have been designed and core facilities are being provided for the cytogenetic, genotyping, gene expression and proteomic analyses to be undertaken in the research projects. Specific Core activities include: 1) processing, maintaining and storeing biological samples and cell lines; 2) providing facilities and methodologies for cytogenetic analysis; 3) providing facilities for gene expression profiling using Affymetrix, Illumina, and custom array technologies; 4) providing facilities for proteomic analyses using various mass spectrometric technologies; and, 5) providing facilities and methodologies for the analysis of genetic polymorphisms by Taqman-based and bead array technologies using the ABI 7900 Sequence Detection System and Illumina Bead Station platforms.
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The Toxicogenomics Laboratory Core provides a centralized source of specialized facilities and equipment, services, well-tested collection and storage protocols, and expert technical support using the latest “-omics” technologies and analytical instruments for project investigators. These services greatly enhance the ability of project investigators to achieve their overall goals.
The goal of this laboratory core is to provide the infrastructure and core expertise for Projects 1-4 to achieve their goals. The overall goal of the Program is to apply functional genomics, proteomics, transcriptomics, and nanotechnology to better detect arsenic, mercury, benzene, polycyclic aromatic hydrocarbons, trichloroethylene, and other Superfund priority chemicals in the environment; evaluate their effects on human health, especially the health of susceptible populations such as children; and remediate their presence and reduce their toxicity. Projects 1 through 4 use functional genomics, proteomics, and transcriptomics in their studies. Further, Projects 1 and 3 are epidemiological studies that require sophisticated sample processing so that these technologies can be applied. The success of Projects 1-4 largely depends on the effective handling and management of biological samples, as well as access to and expertise in the latest “-omic” technologies. Thus, detailed collection and storage protocols have been designed and core facilities provided for the cytogenetic, genotyping, gene expression and proteomic analyses proposed in Projects 1-4.
Important accomplishments so far
The core has processed, maintained and stored biological samples and cell lines; provided facilities and methodologies for cytogenetic analysis (study of chromosome structure); provided facilities for gene expression profiling using Affymetrix, Illumina, and custom array technologies; provided facilities for proteomic analyses using various mass spectrometric technologies; and provided facilities and methodologies for the analysis of genetic polymorphisms by Taqman-based and bead array technologies using the ABI 7900 Sequence Detection System and Illumina Bead Station platforms.
Accomplishments for the last year
We conducted protein expression profiling in arsenic target cell lines to help us identify candidate biomarkers of response to arsenic, thereby helping to elucidate the mechanisms of arsenic toxicity. The most significantly altered peptides were chosen for protein identification via mass spectrometry. To follow up, we performed analyses to confirm the identified expression changes of 3 proteins, HSPA5, MCM6 and GNB1, which are involved in protein folding, DNA replication and signal transduction, respectively. For this, we treated human HK-2 kidney cells and HOK-16B keratinocytes to both inorganic arsenic and MMA, a toxic, methylated form of arsenic found as a common metabolite in the human body.
What we plan to do next
Future experiments will involve 2D DIGE analysis on cells exposed for multiple time points and multiple biologically relevant concentrations of arsenic to determine the specificity of response to treatment, followed by measurement of expression changes in urine specimens from children exposed to high and low levels of arsenic.
- Zhang L, Bassig BA, Mora JL, Vermeulen R, Ge Y, Curry JD, Hu W, Shen M, Qiu C, Ji Z, Reiss B, McHale CM, Liu S, Guo W, Purdue MP, Yue F, Li L, Smith MT, Huang H, Tang X, Rothman N, Lan Q (2013) Alterations in serum immunoglobulin levels in workers occupationally exposed to trichloroethylene. Carcinogenesis. Jan16. [Epub ahead of print]. PMID: 23276795. (PMC Journal- In Progress). [PDF]
- Smith MT, Zhang L, McHale CM, Skibola CF, Rappaport SM (2011) Benzene, the exposome and future investigations of leukemia etiology. Chem Biol Interact. Jun 30;192(1-2):155-9. PMID: 21333640. [PDF]
- Lightfoot TJ, Roman E, Smith MT, Skibola CF (2010) Acute lymphoblastic leukaemia in children – is there a role for MTHFR? Br J Haematol. Feb 8; PMID: 20148884. [PDF]
- Vlaanderen J, Moore LE, Smith MT, Lan Q, Zhang L, Skibola CF, Rothman N, Vermeulen R (2010) Application of OMICS technologies in occupational and environmental health research; current status and projections. Occup Environ Med. Feb; 67(2):136-43. PMID: 19933307. [PDF]
- Lightfoot TJ, Johnston WT, Painter D, Simpson J, Roman E, Skibola CF, Smith MT, Allan JM, Taylor GM (2010) Genetic variation in the folate metabolic pathway and risk of childhood leukemia. Blood. Jan 25; PMID: 20101025. [PDF]
- McHale CM, Zhang L, Lan Q, Li G, Hubbard AE, Forrest MS, Vermeulen R, Chen J, Shen M, Rappaport SM, Yin S, Smith MT, Rothman N (2009) Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms. Genomics. Apr; 93(4):343-9. PMID: 19162166. PMCID: PMC2693268. [PDF]
- Scelo G, Metayer C, Zhang L, Wiemels JL, Aldrich MC, Selvin S, Month S, Smith MT, Buffler PA (2009) Household exposure to paint and petroleum solvents, chromosomal translocations, and the risk of childhood leukemia. Environ Health Perspect. Jan; 117(1):133-9. PMID: 19165400. [PDF]
- Wiemels JL, Hofmann J, Kang M, Selzer R, Green R, Zhou M, Zhong S, Zhang L, Smith MT, Marsit C, Loh M, Buffler P, Yeh RF (2008) Chromosome 12p deletions in TEL-AML1 childhood acute lymphoblastic leukemia are associated with retrotransposon elements and occur postnatally. Cancer Res. Dec 1; 68(23):9935-44. PMID: 19047175. PMCID: PMC2597307. [PDF]
- Hegedus CM, Skibola CF, Warner M, Skibola DR, Alexander D, Lim S, Dangleben NL, Zhang L, Clark M, Pfeiffer RM, Steinmaus C, Smith AH, Smith MT, Moore LE (2008) Decreased urinary beta-defensin-1 expression as a biomarker of response to arsenic. Toxicol Sci. Nov; 106(1):74-82. PMID: 18511430. PMCID: PMC2563143. [PDF]
- Paynter RA, Skibola DR, Skibola CF, Buffler PA, Wiemels JL, Smith MT (2006) Accuracy of multiplexed Illumina platform-based single-nucleotide polymorphism genotyping compared between genomic and whole genome amplified DNA collected from multiple sources. Cancer Epidemiol Biomarkers Prev. Dec; 15(12):2533-6. PMID: 17164381. [PDF]
- Aldrich MC, Zhang L, Wiemels JL, Ma X, Loh ML, Metayer C, Selvin S, Feusner J, Smith MT, Buffler PA (2006) Cytogenetics of Hispanic and White children with acute lymphoblastic leukemia in California. Cancer Epidemiol Biomarkers Prev. Mar; 15(3):578-81. PMID: 16537719. [PDF]
- Birkner MD, Hubbard AE, van der Laan MJ, Skibola CF, Hegedus CM, Smith MT (2006) Issues of processing and multiple testing of SELDI-TOF MS proteomic data. Stat Appl Genet Mol Biol. 5:Article11. PMID: 16646865. [PDF]
- Hegedus CM, Gunn L, Skibola CF, Zhang L, Shiao R, Fu S, Dalmasso EA, Metayer C, Dahl GV, Buffler PA, Smith MT (2005) Proteomic analysis of childhood leukemia. Leukemia. Oct; 19(10):1713-8. PMID: 16136170. [PDF]
- Smith MT, McHale CM, Wiemels JL, Zhang L, Wiencke JK, Zheng S, Gunn L, Skibola CF, Ma X, Buffler PA (2005) Molecular biomarkers for the study of childhood leukemia. Toxicol Appl Pharmacol. Aug 7; 206(2):237-45. PMID: 15967214. [PDF]
- Forrest MS, Lan Q, Hubbard AE, Zhang L, Vermeulen R, Zhao X, Li G, Wu YY, Shen M, Yin S, Chanock SJ, Rothman N, Smith MT (2005) Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers. Environ Health Perspect. Jun; 113(6):801-7. PMID: 15929907. PMCID: PMC1257610. [PDF]
- Moore LE, Pfeiffer R, Warner M, Clark M, Skibola C, Steinmous C, Alguacil J, Rothman N, Smith MT, Smith AH (2005) Identification of biomarkers of arsenic exposure and metabolism in urine using SELDI technology. J Biochem Mol Toxicol. 19(3):176. PMID: 15977200. [PDF]