Biological Sciences

Reflecting their exceptional potential to advance a range of biomedical, aeronautic, and other industrial products, carbon nanotube (CNT) production and the potential for human exposure to aerosolized CNTs are increasing. CNTs have toxicologically significant structural and chemical similarities to asbestos (AB) and have repeatedly been shown to cause pulmonary inflammation, granuloma formation, and fibrosis after inhalation/instillation/aspiration exposure in rodents, a pattern of effects similar to those observed following exposure to AB. To determine the degree to which responses to single-walled CNTs (SWCNT) and AB are similar or different, the pulmonary response of C57BL/6 mice to repeated exposures to SWCNTs, crocidolite AB, and ultrafine carbon black (UFCB) were compared using high-throughput global high performance liquid chromatography fourier transform ion cyclotron resonance mass spectrometry (HPLC-FTICR-MS) proteomics, histopathology, and bronchoalveolar lavage cytokine analyses. Mice were exposed to material suspensions (40 micrograms per mouse) twice a week for 3 weeks by pharyngeal aspiration. Histologically, the incidence and severity of inflammatory and fibrotic responses were greatest in mice treated with SWCNTs. SWCNT treatment affected the greatest changes in abundance of identified lung tissue proteins. The trend in number of proteins affected (SWCNT [376] > AB [231] > UFCB [184]) followed the potency of these materials in three biochemical assays of inflammation (cytokines). SWCNT treatment uniquely affected the abundance of 109 proteins, but these proteins largely represent cellular processes affected by AB treatment as well, further evidence of broad similarity in the tissue-level response to AB and SWCNTs. Two high-sensitivity markers of inflammation, one (S100a9) observed in humans exposed to AB, were found and may be promising biomarkers of human response to SWCNT exposure.

Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification [J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29 (1999) 1181-1189]. The analysis of these data involves the use of appropriate multivariate statistical methods; Principal Component Analysis (PCA) has been documented as a valuable pattern recognition technique for 1H NMR spectral data [J.T. Brindle, H. Antti, E. Holmes, G. Tranter, J.K. Nicholson, H.W. Bethell, S. Clarke, P.M. Schofield, E. McKilligin, D.E. Mosedale, D.J. Grainger, Nat. Med. 8 (2002) 1439-1444; B.C. Potts, A.J. Deese, G.J. Stevens, M.D. Reily, D.G. Robertson, J. Theiss, J. Pharm. Biomed. Anal. 26 (2001) 463-476; D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; L.C. Robosky, D.G. Robertson, J.D. Baker, S. Rane, M.D. Reily, Comb. Chem. High Throughput Screen. 5 (2002) 651-662]. Prior to PCA the raw data is typically processed through four steps; (1) baseline correction, (2) endogenous peak removal, (3) integration over spectral regions to reduce the number of variables, and (4) normalization. The effect of the size of spectral integration regions and normalization has not been well studied. The variability structure and classification accuracy on two distinctly different datasets are assessed via PCA and a leave-one-out cross-validation approach under two normalization approaches and an array of spectral integration regions. The first dataset consists of urine from 15 male Wistar-Hannover rats dosed with ANIT measured at five time points, mimicking drug-induced cholangiolitic hepatitis [D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; J.P. Shockcor, E. Holmes, Curr. Top. Med. Chem. 2 (2002) 35-51; N.J. Waters, E. Holmes, A. Williams, C.J. Waterfield, R.D. Farrant, J.K. Nicholson, Chem. Res. Toxicol. 14 (2001) 1401-1412]. The second data is serum samples from young male C57BL/6 mice subjected to instillation of pancreatic elastase producing emphysema type symptoms [C. Kuhn, S.Y. Yu, M. Chraplyvy, H.E. Linder, R.M. Senior, Lab. Invest. 34 (1976) 372-380; C. Kuhn, R.M. Senior, Lung 155 (1978) 185-197]. This study indicates that independent of the normalization method the classification accuracy achieved from metabonomic studies is not highly sensitive to the size of the spectral integration region. Additionally, both datasets scaled to mean zero and unity variance (auto-scaled) have higher variability within classification accuracy over spectral integration window widths than data scaled to the total intensity of the spectrum. Of the top 10 latent variables for the ANIT dataset the auto-scale normalization has standard deviations larger than the total-scale in seven cases. In the case of the elastase all standard deviations are larger for the auto-scaling.