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Integration of genomic and proteomic biosignatures improves the discrimination of response to nanoparticles in a mouse model.

Authors
Webb-Robertson-B; Waters-K; Varnum-S; Teeguarden-J; Jacobs-J; Zangar-R; Kisin-E; Murray-A; Shvedova-A; Pounds-J
Source
Toxicologist 2009 Mar; 108(1):53
NIOSHTIC No.
20035236
Abstract
Health risks associated with contact and consumption of nanomaterials is largely unknown and a topic of considerable interest to both manufacturers and consumers. We compared the pulmonary response to repeated exposure to singlewalled carbon nanotubes, crocidolite asbestos, and ultrafine carbon black in a mouse model. Biological signatures of exposure were derived using both genome and proteome profiles though high-content analytical platforms, including microarray, global mass spectrometry-based proteomics, and multiplexed protein ELISA microarray. The goal of this study was to derive biomarkers of response from these data streams and to evaluate the screening potential of integrated biosignatures for hazard assessment. We developed a biosignature integration approach that fuses the multiple data sources at a molecular level. Probability models were derived for individual datasets by using partial least squares discriminant analysis and transformed into likelihood values associated with the probability that a particular sample was exposed to one of the defined particles or the control group. The screening potential of each biosignature was then assessed using the probability model to give insight into particle classes that biomarkers may be derived from each data source. Finally, Bayesian statistics were applied to the likelihood probability models to fuse all data streams into a single model. We find that the probability models associated with individual data types can only successfully separate less than 90% of the samples with cross-validation. However, the integrated probability model can nearly perfectly classify all samples. Using this approach, we identified a panel of biosignatures for each particle class with statistical power to predict response to particulates.
Keywords
Airborne-dusts; Airborne-particles; Biomarkers; Biological-distribution; Biological-effects; Biological-function; Biological-systems; Biological-transport; Exposure-assessment; Exposure-levels; Exposure-methods; Laboratory-animals; Lung-disorders; Lung-irritants; Lung-function; Molecular-biology; Particle-aerodynamics; Particulate-dust; Pulmonary-disorders; Pulmonary-function; Particulates; Pulmonary-clearance; Pulmonary-system; Pulmonary-system-disorders; Respiratory-hypersensitivity; Respiratory-irritants; Respiratory-system-disorders; Statistical-analysis; Nanotechnology
CAS No.
7440-44-0; 12001-28-4; 1332-21-4
Publication Date
20090301
Document Type
Abstract
Fiscal Year
2009
NTIS Accession No.
NTIS Price
Issue of Publication
1
ISSN
1096-6080
NIOSH Division
HELD
Priority Area
Construction; Manufacturing
Source Name
The Toxicologist. Society of Toxicology 48th Annual Meeting and ToxExpo, March 15-19, 2009, Baltimore, Maryland
State
WA; WV
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