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Cytochrome P450 monooxygenase (P450) enzymes metabolize critical endogenous chemicals and oxidize nearly all xenobiotics. Dysregulated P450 activities lead to altered capacity for drug metabolism and cellular stress. The effects of mixed exposures on P450 expression and activity are variable and elusive. A high-fat diet (HFD) is a common exposure that results in obesity and associated pathologies including hepatotoxicity. Herein, we report the effects of cigarette smoke on P450 activities of normal weight and HFD induced obese mice. Activity-based protein profiling results indicate that HFD mice had significantly decreased P450 activity, likely instigated by proinflammatory chemicals, and that P450 enzymes involved in detoxification, xenobiotic metabolism, and bile acid synthesis were effected by HFD and smoke interaction. Smoking increased activity of all lung P450 and coexposure to diet effected P450 2s1. We need to expand our understanding of common exposures coupled to altered P450 metabolism to enhance the safety and efficacy of therapeutic drug dosing.

Quantum dots (QDs) are engineered semiconductor nanoparticles with unique physicochemical properties that make them potentially useful in clinical, research and industrial settings. However, a growing body of evidence indicates that like other engineered nanomaterials, QDs have the potential to be respiratory hazards, especially in the context of the manufacture of QDs and products containing them, as well as exposures to consumers using these products. The overall goal of this study was to investigate the role of mouse strain in determining susceptibility to QD-induced pulmonary inflammation and toxicity. Male mice from 8 genetically diverse inbred strains (the Collaborative Cross founder strains) were exposed to CdSe-ZnS core-shell QDs stabilized with an amphiphilic polymer. QD treatment resulted in significant increases in the percentage of neutrophils and levels of cytokines present in bronchoalveolar lavage fluid (BALF) obtained from NOD/ShiLtJ and NZO/HlLtJ mice relative to their saline (Sal) treated controls. Cadmium measurements in lung tissue indicated strain-dependent differences in disposition of QDs in the lung. Total glutathione levels in lung tissue were significantly correlated with percent neutrophils in BALF as well as with lung tissue Cd levels. Our findings indicate that QD-induced acute lung inflammation is mouse strain dependent, that it is heritable, and that the choice of mouse strain is an important consideration in planning QD toxicity studies. These data also suggest that formal genetic analyses using additional strains or recombinant inbred strains from these mice could be useful for discovering potential QD-induced inflammation susceptibility loci., (Copyright © 2015 Elsevier Inc. All rights reserved.)

In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages., (© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.)

Background: Toxicity testing the rapidly growing number of nanomaterials requires large scale use of in vitro systems under the presumption that these systems are sufficiently predictive or descriptive of responses in in vivo systems for effective use in hazard ranking. We hypothesized that improved relationships between in vitro and in vivo models of experimental toxicology for nanomaterials would result from placing response data in vitro and in vivo on the same dose scale, the amount of material associated with cells., Methods: Balb/c mice were exposed nose-only to an aerosol (68.6 nm CMD, 19.9 mg/m(3), 4 hours) generated from of 12.8 nm superparamagnetic iron oxide particles (SPIO). Target cell doses were calculated, histological evaluations conducted, and biomarkers of response were identified by global transcriptomics. Representative murine epithelial and macrophage cell types were exposed in vitro to the same material in liquid suspension for four hours and levels of nanoparticle regulated cytokine transcripts identified in vivo were quantified as a function of measured nanoparticle cellular dose., Results: Target tissue doses of 0.009-0.4 μg SPIO/cm(2) in lung led to an inflammatory response in the alveolar region characterized by interstitial inflammation and macrophage infiltration. In vitro, higher target tissue doses of ~1.2-4 μg SPIO/ cm(2) of cells were required to induce transcriptional regulation of markers of inflammation, CXCL2 & CCL3, in C10 lung epithelial cells. Estimated in vivo macrophage SPIO nanoparticle doses ranged from 1-100 pg/cell, and induction of inflammatory markers was observed in vitro in macrophages at doses of 8-35 pg/cell., Conclusions: Application of target tissue dosimetry revealed good correspondence between target cell doses triggering inflammatory processes in vitro and in vivo in the alveolar macrophage population, but not in the epithelial cells of the alveolar region. These findings demonstrate the potential for target tissue dosimetry to enable the more quantitative comparison of in vitro and in vivo systems and advance their use for hazard assessment and extrapolation to humans. The mildly inflammogentic cellular doses experienced by mice were similar to those calculated for humans exposed to the same material at the existing permissible exposure limit of 10 mg/m(3) iron oxide (as Fe).

Airway inflammation has a pathophysiological role in asthma. Eosinophils, which are commonly increased in asthmatic airways, express eosinophil peroxidase and thereby produce hypobromite and bromotyrosine. Bromotyrosine is believed to be a specific marker for eosinophil activity, but developing an antibody against monobromotyrosine, the predominant brominated tyrosine residue found in vivo has proven difficult. We evaluated whether a 3-bromobenozoic acid hapten antigen produced antibodies that recognized halogenated tyrosine residues. Studies with small-molecule inhibitors or brominated or chlorinated protein suggested that a mouse monoclonal antibody (BTK-94C) selectively bound free and protein mono- and dibromotyrosine and, to a lesser degree, chlorotyrosine, and thus was designated a general halotyrosine antibody. We evaluated if this antibody had potential for characterizing human asthma using an enzyme-linked immunosorbent assay (ELISA) microarray platform to examine the halogenation of 23 proteins in three independent sets of sputum samples (52 samples total). In 15 healthy control or asthmatic subjects, ICAM, PDGF and RANTES had greater proportional amounts of halogenation in asthmatic subjects and the halogenation signal was associated with the severity of exercise-induced airway hyperresponsiveness. In 17 severe asthma patients treated with placebo or mepolizumab to suppress eosinophils, drug-related decreases in halogenation were observed with p values ranging from 0.006 to 0.11 for these 3 proteins. Analysis of 20 subjects that either had neutrophilic asthma or were healthy controls demonstrated a broad increase in halotyrosine (possibly chlorotyrosine) in neutrophilic asthmatics. Overall, these results suggest that an ELISA utilizing BTK-94C could prove useful for assessing airway inflammation in asthma patients., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Although the potential human health impacts from exposure to engineered nanoparticles (ENPs) are uncertain, past epidemiological studies have established correlations between exposure to ambient air pollution particulates and the incidence of pneumonia and lung infections. Using amorphous silica and superparamagnetic iron oxide (SPIO) as model high production volume ENPs, we examined how macrophage activation by bacterial lipopolysaccharide (LPS) or the lung pathogen Streptococcus pneumoniae is altered by ENP pretreatment. Neither silica nor SPIO treatment elicited direct cytotoxic or pro-inflammatory effects in bone marrow-derived macrophages. However, pretreatment of macrophages with SPIO caused extensive reprogramming of nearly 500 genes regulated in response to LPS challenge, hallmarked by exaggerated activation of oxidative stress response pathways and suppressed activation of both pro- and anti-inflammatory pathways. Silica pretreatment altered regulation of only 67 genes, but there was strong correlation with gene sets affected by SPIO. Macrophages exposed to SPIO displayed a phenotype suggesting an impaired ability to transition from an M1 to M2-like activation state, characterized by suppressed IL-10 induction, enhanced TNFα production, and diminished phagocytic activity toward S. pneumoniae. Studies in macrophages deficient in scavenger receptor A (SR-A) showed SR-A participates in cell uptake of both the ENPs and S. pneumonia and co-regulates the anti-inflammatory IL-10 pathway. Thus, mechanisms for dysregulation of innate immunity exist by virtue that common receptor recognition pathways are used by some ENPs and pathogenic bacteria, although the extent of transcriptional reprogramming of macrophage function depends on the physicochemical properties of the ENP after internalization. Our results also illustrate that biological effects of ENPs may be indirectly manifested only after challenging normal cell function. Nanotoxicology screening strategies should therefore consider how exposure to these materials alters susceptibility to other environmental exposures.

Smoking and obesity are each well-established risk factors for cardiovascular heart disease, which together impose earlier onset and greater severity of disease. To identify early signaling events in the response of the heart to cigarette smoke exposure within the setting of obesity, we exposed normal weight and high fat diet-induced obese (DIO) C57BL/6 mice to repeated inhaled doses of mainstream (MS) or sidestream (SS) cigarette smoke administered over a two week period, monitoring effects on both cardiac and pulmonary transcriptomes. MS smoke (250 μg wet total particulate matter (WTPM)/L, 5 h/day) exposures elicited robust cellular and molecular inflammatory responses in the lung with 1466 differentially expressed pulmonary genes (p < 0.01) in normal weight animals and a much-attenuated response (463 genes) in the hearts of the same animals. In contrast, exposures to SS smoke (85 μg WTPM/L) with a CO concentration equivalent to that of MS smoke (~250 CO ppm) induced a weak pulmonary response (328 genes) but an extensive cardiac response (1590 genes). SS smoke and to a lesser extent MS smoke preferentially elicited hypoxia- and stress-responsive genes as well as genes predicting early changes of vascular smooth muscle and endothelium, precursors of cardiovascular disease. The most sensitive smoke-induced cardiac transcriptional changes of normal weight mice were largely absent in DIO mice after smoke exposure, while genes involved in fatty acid utilization were unaffected. At the same time, smoke exposure suppressed multiple proteome maintenance genes induced in the hearts of DIO mice. Together, these results underscore the sensitivity of the heart to SS smoke and reveal adaptive responses in healthy individuals that are absent in the setting of high fat diet and obesity.

The co-occurrence of environmental factors is common in complex human diseases and, as such, understanding the molecular responses involved is essential to determine risk and susceptibility to disease. We have investigated the key biological pathways that define susceptibility for pulmonary infection during obesity in diet-induced obese (DIO) and regular weight (RW) C57BL/6 mice exposed to inhaled lipopolysaccharide (LPS). LPS induced a strong inflammatory response in all mice as indicated by elevated cell counts of macrophages and neutrophils and levels of proinflammatory cytokines (MDC, MIP-1γ, IL-12, RANTES) in the bronchoalveolar lavage fluid. Additionally, DIO mice exhibited 50% greater macrophage cell counts, but decreased levels of the cytokines, IL-6, TARC, TNF-α, and VEGF relative to RW mice. Microarray analysis of lung tissue showed over half of the LPS-induced expression in DIO mice consisted of genes unique for obese mice, suggesting that obesity reprograms how the lung responds to subsequent insult. In particular, we found that obese animals exposed to LPS have gene signatures showing increased inflammatory and oxidative stress response and decreased antioxidant capacity compared with RW. Because signaling pathways for these responses can be common to various sources of environmentally induced lung damage, we further identified biomarkers that are indicative of specific toxicant exposure by comparing gene signatures after LPS exposure to those from a parallel study with cigarette smoke. These data show obesity may increase sensitivity to further insult and that co-occurrence of environmental stressors result in complex biosignatures that are not predicted from analysis of individual exposures., (Copyright © 2012. Published by Elsevier Inc.)

Principal Component Analysis (PCA) is a common exploratory tool used to evaluate large complex data sets. The resulting lower-dimensional representations are often valuable for pattern visualization, clustering, or classification of the data. However, PCA cannot be applied directly to many -omics data sets generated by newer technologies such as label-free mass spectrometry due to large numbers of non-random missing values. Here we present a sequential projection pursuit PCA (sppPCA) method for defining principal components in the presence of missing data. Our results demonstrate that this approach generates robust and informative low-dimensional data representations compared to commonly used imputation approaches.

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process., Objective: To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme., Methods: The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection., Results: The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort., Conclusions: Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Increasingly, it is recognized that understanding and predicting nanoparticle behavior is often limited by the degree to which the particles can be reliably produced and adequately characterized. Two examples that demonstrate how sample preparation methods and processing history may significantly impact particle behavior are: 1) an examination of cerium oxide (ceria) particles reported in the literature in relation to the biological responses observed and 2) observations related that influence synthesis and aging of ceria nanoparticles. Examining data from the literature for ceria nanoparticles suggests that thermal history is one factor that has a strong influence on biological impact. Thermal processing may alter many physicochemical properties of the particles, including density, crystal structure, and the presence of surface contamination. However, these properties may not be sufficiently recorded or reported to determine the ultimate source of an observed impact. A second example shows the types of difficulties that can be encountered in efforts to apply a well-studied synthesis route to producing well-defined particles for biological studies. These examples and others further highlight the importance of characterizing particles thoroughly and recording details of particle processing and history that too often are underreported.

Francisella tularensis causes the zoonosis tularemia in humans and is one of the most virulent bacterial pathogens. We utilized a global proteomic approach to characterize protein changes in bronchoalveolar lavage fluid from mice exposed to one of three organisms, F. tularensis ssp. novicida, an avirulent mutant of F. tularensis ssp. novicida (F.t. novicida-ΔmglA), and Pseudomonas aeruginosa. The composition of bronchoalveolar lavage fluid (BALF) proteins was altered following infection, including proteins involved in neutrophil activation, oxidative stress, and inflammatory responses. Components of the innate immune response were induced including the acute phase response and the complement system; however, the timing of their induction varied. F. tularensis ssp. novicida infected mice do not appear to have an effective innate immune response in the first hours of infection; however, within 24 h, they show an upregulation of innate immune response proteins. This delayed response is in contrast to P. aeruginosa infected animals which show an early innate immune response. Likewise, F.t. novicida-ΔmglA infection initiates an early innate immune response; however, this response is diminished by 24 h. Finally, this study identifies several candidate biomarkers, including Chitinase 3-like-1 (CHI3L1 or YKL-40) and peroxiredoxin 1, that are associated with F. tularensis ssp. novicida but not P. aeruginosa infection.

A disposable sensor for the determination of cotinine in human serum was developed based on immunochromatographic test strip and quantum dot label. In this assay, cotinine linked with quantum dot competes with cotinine in sample to bind to anti-cotinine antibody in the test strip and the quantum dots serve as signal vehicles for electrochemical readout. Some parameters governing the performance of the sensor were optimized. The sensor shows a wide linear range from 1 ng mL(-1) to 100 ng mL(-1) cotinine with a detection limit of 1.0 ng mL(-1). The sensor was validated with spiked human serum samples and it was found that this method was reliable in measuring cotinine in human serum. The results demonstrate that this sensor is rapid, accurate, and less expensive and has the potential for point of care (POC) detection of cotinine and fast screening of tobacco smoke exposure., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The majority of in vitro studies characterizing the impact of engineered nanoparticles (NPs) on cells that line the respiratory tract were conducted in cells exposed to NPs in suspension. This approach introduces processes that are unlikely to occur during inhaled NP exposures in vivo, such as the shedding of toxic doses of dissolved ions. ZnO NPs are used extensively and pose significant sources for human exposure. Exposures to airborne ZnO NPs can induce adverse effects, but the relevance of the dissolved Zn(2+) to the observed effects in vivo is still unclear. Our goal was to mimic in vivo exposures to airborne NPs and decipher the contribution of the intact NP from the contribution of the dissolved ions to airborne ZnO NP toxicity. We established the exposure of alveolar type II epithelial cells to aerosolized NPs at the air-liquid interface (ALI) and compared the impact of aerosolized ZnO NPs and NPs in suspension at the same cellular doses, measured as the number of particles per cell. By evaluating membrane integrity and cell viability 6 and 24 h post-exposure, we found that aerosolized NPs induced toxicity at the ALI at doses that were in the same order of magnitude as doses required to induce toxicity in submersed cultures. In addition, distinct patterns of oxidative stress were observed in the two exposure systems. These observations unravel the ability of airborne ZnO NPs to induce toxicity without the contribution of dissolved Zn(2+) and suggest distinct mechanisms at the ALI and in submersed cultures.

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values., Results: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs., Availability: https://www.biopilot.org/docs/Software/RMD.php, Contact: bj@pnl.gov, Supplementary Information: Supplementary material is available at Bioinformatics online.

Background: Nitric oxide is a physiological regulator of endothelial function and hemodynamics. Oxidized products of nitric oxide can form nitrotyrosine, which is a marker of nitrative stress. Cigarette smoking decreases exhaled nitric oxide, and the underlying mechanism may be important in the cardiovascular toxicity of smoking. Even so, it is unclear if this effect results from decreased nitric oxide production or increased oxidative degradation of nitric oxide to reactive nitrating species. These two processes would be expected to have opposite effects on nitrotyrosine levels, a marker of nitrative stress., Objective: In this study, we evaluated associations of cigarette smoking and chronic obstructive pulmonary disease (COPD) with nitrotyrosine modifications of specific plasma proteins to gain insight into the processes regulating nitrotyrosine formation., Methods: A custom antibody microarray platform was developed to analyze the levels of 3-nitrotyrosine modifications on 24 proteins in plasma. In a cross-sectional study, plasma samples from 458 individuals were analyzed., Results: Average nitrotyrosine levels in plasma proteins were consistently lower in smokers and former smokers than in never smokers but increased in smokers with COPD compared with smokers who had normal lung-function tests., Conclusions: Smoking is associated with a broad decrease in 3-nitrotyrosine levels of plasma proteins, consistent with an inhibitory effect of cigarette smoke on endothelial nitric oxide production. In contrast, we observed higher nitrotyrosine levels in smokers with COPD than in smokers without COPD. This finding is consistent with increased nitration associated with inflammatory processes. This study provides insight into a mechanism through which smoking could induce endothelial dysfunction and increase the risk of cardiovascular disease.

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.

This paper explores some of the fundamental and practical issues related to the behavior of nanoparticles in the environment and their potential impacts on human health. In our research we have explored the reactive behaviors of nanoparticles with contaminants in the environment, how nanoparticle change in response to their environment and time, and how nanoparticles interact with biological systems of various types. It has become apparent that researchers often underestimate the difficulties of preparing and delivering well characterized nanoparticles for specific types of testing or applications. Difficulties arise in areas that range from not understanding what imparts the "nano" character of a particle to not knowing the impacts of minor species on the properties of high surface area materials. Some of our adventures and misadventures serve as examples of some of these issues as they relate to providing well defined particles for biological studies.

In October 2010, a group of experts met as part of the transatlantic think tank for toxicology (t4) to exchange ideas about the current status and future of safety testing of nanomaterials. At present, there is no widely accepted path forward to assure appropriate and effective hazard identification for engineered nanomaterials. The group discussed needs for characterization of nanomaterials and identified testing protocols that incorporate the use of innovative alternative whole models such as zebrafish or C. elegans, as well as in vitro or alternative methods to examine specific functional pathways and modes of action. The group proposed elements of a potential testing scheme for nanomaterials that works towards an integrated testing strategy, incorporating the goals of the NRC report Toxicity Testing in the 21st Century: A Vision and a Strategy by focusing on pathways of toxic response, and utilizing an evidence-based strategy for developing the knowledge base for safety assessment. Finally, the group recommended that a reliable, open, curated database be developed that interfaces with existing databases to enable sharing of information.

Background: The difficulty of directly measuring cellular dose is a significant obstacle to application of target tissue dosimetry for nanoparticle and microparticle toxicity assessment, particularly for in vitro systems. As a consequence, the target tissue paradigm for dosimetry and hazard assessment of nanoparticles has largely been ignored in favor of using metrics of exposure (e.g. μg particle/mL culture medium, particle surface area/mL, particle number/mL). We have developed a computational model of solution particokinetics (sedimentation, diffusion) and dosimetry for non-interacting spherical particles and their agglomerates in monolayer cell culture systems. Particle transport to cells is calculated by simultaneous solution of Stokes Law (sedimentation) and the Stokes-Einstein equation (diffusion)., Results: The In vitro Sedimentation, Diffusion and Dosimetry model (ISDD) was tested against measured transport rates or cellular doses for multiple sizes of polystyrene spheres (20-1100 nm), 35 nm amorphous silica, and large agglomerates of 30 nm iron oxide particles. Overall, without adjusting any parameters, model predicted cellular doses were in close agreement with the experimental data, differing from as little as 5% to as much as three-fold, but in most cases approximately two-fold, within the limits of the accuracy of the measurement systems. Applying the model, we generalize the effects of particle size, particle density, agglomeration state and agglomerate characteristics on target cell dosimetry in vitro., Conclusions: Our results confirm our hypothesis that for liquid-based in vitro systems, the dose-rates and target cell doses for all particles are not equal; they can vary significantly, in direct contrast to the assumption of dose-equivalency implicit in the use of mass-based media concentrations as metrics of exposure for dose-response assessment. The difference between equivalent nominal media concentration exposures on a μg/mL basis and target cell doses on a particle surface area or number basis can be as high as three to six orders of magnitude. As a consequence, in vitro hazard assessments utilizing mass-based exposure metrics have inherently high errors where particle number or surface areas target cells doses are believed to drive response. The gold standard for particle dosimetry for in vitro nanotoxicology studies should be direct experimental measurement of the cellular content of the studied particle. However, where such measurements are impractical, unfeasible, and before such measurements become common, particle dosimetry models such as ISDD provide a valuable, immediately useful alternative, and eventually, an adjunct to such measurements.

Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing observations in LC-MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error or nonrandom mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values across experimental groups. We pair the G-test results, evaluating independence of missing data (IMD) with an analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use three LC-MS data sets to demonstrate the robustness and sensitivity of the IMD-ANOVA approach.

A portable fluorescence biosensor with rapid and ultrasensitive response for protein biomarker has been built up with quantum dots and a lateral flow test strip. The superior signal brightness and high photostability of quantum dots are combined with the promising advantages of a lateral flow test strip and result in high sensitivity and selectivity and speed for protein detection. Nitrated ceruloplasmin, a significant biomarker for cardiovascular disease, lung cancer, and stress response to smoking, was used as model protein biomarker to demonstrate the good performances of this proposed quantum dot-based lateral flow test strip. Quantitative detection of nitrated ceruloplasmin was realized by recording the fluorescence intensity of quantum dots captured on the test line. Under optimal conditions, this portable fluorescence biosensor displays rapid responses for nitrated ceruloplasmin with the concentration as low as 1 ng/mL. Furthermore, the biosensor was successfully utilized for spiked human plasma sample detection in a wide dynamic range with a detection limit of 8 ng/mL (S/N = 3). The results demonstrate that the quantum dot-based lateral flow test strip is capable of rapid, sensitive, and quantitative detection of nitrated ceruloplasmin and hold a great promise for point-of-care and in field analysis of other protein biomarkers.

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of morbidity and mortality in the United States and cigarette smoking is a primary determinant of the disease. COPD is characterized by chronic airflow limitation as measured by the forced expiratory volume in one second (FEV(1)). In this study, the plasma proteomes of 38 middle-aged or older adult smokers with mild to moderate COPD, with FEV(1) decline characterized as either rapid (RPD, n = 20) or slow or absent (SLW, n = 18), were interrogated using a comprehensive high-throughput proteomic approach, the accurate mass and time (AMT) tag technology. This technology is based upon a putative mass and time tag database (PMT), high-resolution LC separations and high mass accuracy measurements using FT-ICR MS with a 9.4-T magnetic field. The peptide and protein data were analyzed using three statistical approaches to address ambiguities related to the high proportion of missing data inherent to proteomic analysis. The RPD and SLW groups were differentiated by 55 peptides which mapped to 33 unique proteins. Twelve of the proteins have known roles in the complement or coagulation cascade and, despite an inability to adjust for some factors known to affect lung function decline, suggest potential mechanistic biomarkers associated with the rate of lung function decline in COPD. Whether these proteins are the cause or result of accelerated decline will require further research.

Three new sandwich immunoassays for detection of nitrated biomarker have been established with potential applications in biomedical studies and clinical practice. In this study, nitrated human fibrinogen, a potential oxidative stress biomarker for several pathologies, was chosen as the target. To improve the sensitivity and overcome the interference caused by the complexity of human biofluids, we developed three sandwich strategies using various combinations of primary antibody and secondary antibody. All three strategies demonstrated high sensitivity and selectivity towards nitrated forms of fibrinogen in buffer, but their performances were dramatically reduced when tested with human plasma and serum samples. Systematically optimizations were carried out to investigate the effects of numerous factors, including sampling, coating, blocking, and immunoreactions. Our final optimization results indicate that two of these strategies retain sufficient sensitivity and selectivity for use as assays in human physiological samples. Specifically, detection limits reached the pM level and the linear response ranges were up to nM level with a correlation coefficient>0.99. To our best knowledge, this is the first example of using an electrochemical immunoassay for a nitrated biomarker in a physiological fluid. This novel approach provides a rapid, sensitive, selective, cost efficient and robust bioassay for detection of oxidative stress in pathology and for clinical applications. Moreover, the sandwich strategies developed in this paper can be readily used to establish effective methods targeting other nitration biomarkers.

Chronic obstructive pulmonary disease (COPD), characterized by chronic airflow limitation, is a serious public health concern. In this study, we used proton nuclear magnetic resonance ((1)H NMR) spectroscopy to identify and quantify metabolites associated with lung function in COPD. Plasma and urine were collected from 197 adults with COPD and from 195 without COPD. Samples were assayed using a 600 MHz NMR spectrometer, and the resulting spectra were analyzed against quantitative spirometric measures of lung function. After correcting for false discoveries and adjusting for covariates (sex, age, smoking) several spectral regions in urine were found to be significantly associated with baseline lung function. These regions correspond to the metabolites trigonelline, hippurate and formate. Concentrations of each metabolite, standardized to urinary creatinine, were associated with baseline lung function (minimum p-value = 0.0002 for trigonelline). No significant associations were found with plasma metabolites. Urinary hippurate and formate are often related to gut microflora. This could suggest that the microbiome varies between individuals with different lung function. Alternatively, the associated metabolites may reflect lifestyle differences affecting overall health. Our results will require replication and validation, but demonstrate the utility of NMR metabolomics as a screening tool for identifying novel biomarkers of pulmonary outcomes.

Oxidative modifications of protein tyrosines have been implicated in multiple human diseases. Among these modifications, elevations in levels of 3,4-dihydroxyphenylalanine (DOPA), a major product of hydroxyl radical addition to tyrosine, has been observed in a number of pathologies. Here we report the first proteome survey of endogenous site-specific modifications, i.e. DOPA and its further oxidation product dopaquinone in mouse brain and heart tissues. Results from LC-MS/MS analyses included 50 and 14 DOPA-modified tyrosine sites identified from brain and heart, respectively, whereas only a few nitrotyrosine-containing peptides, a more commonly studied marker of oxidative stress, were detectable, suggesting the much higher abundance for DOPA modification as compared with tyrosine nitration. Moreover, 20 and 12 dopaquinone-modified peptides were observed from brain and heart, respectively; nearly one-fourth of these peptides were also observed with DOPA modification on the same sites. For both tissues, these modifications are preferentially found in mitochondrial proteins with metal binding properties, consistent with metal-catalyzed hydroxyl radical formation from mitochondrial superoxide and hydrogen peroxide. These modifications also link to a number of mitochondrially associated and other signaling pathways. Furthermore, many of the modification sites were common sites of previously reported tyrosine phosphorylation, suggesting potential disruption of signaling pathways. Collectively, the results suggest that these modifications are linked with mitochondrially derived oxidative stress and may serve as sensitive markers for disease pathologies.

The cellular interactions and pathways of engineered submicro- and nano-scale particles dictate the cellular response and ultimately determine the level of toxicity or biocompatibility of the particles. Positive surface charge can increase particle internalization, and in some cases can also increase particle toxicity, but the underlying mechanisms are largely unknown. Here we identify the cellular interaction and pathway of positively charged submicrometer synthetic amorphous silica particles, which are used extensively in a wide range of industrial applications, and are explored for drug delivery and medical imaging and sensing. Using time lapse fluorescence imaging in living cells and other quantitative imaging approaches, it is found that heparan sulfate proteoglycans play a critical role in the attachment and internalization of the particles in alveolar type II epithelial cell line (C10), a potential target cell type bearing apical microvilli. Specifically, the transmembrane heparan sulfate proteoglycan, syndecan-1, is found to mediate the initial interactions of the particles at the cell surface, their coupling with actin filaments across the cell membrane, and their subsequent internalization via macropinocytosis. The observed interaction of syndecan molecules with the particle prior to their engagement with actin filaments suggests that the particles initiate their own internalization by facilitating the clustering of the molecules, which is required for the actin coupling and subsequent internalization of syndecan. Our observations identify a new role for syndecan-1 in mediating the cellular interactions and fate of positively charged submicrometer amorphous silica particles in the alveolar type II epithelial cell, a target cell for inhaled particles.

Concerns about the potential adverse health effects of engineered nanoparticles stems in part from the possibility that some materials display unique chemical and physical properties at nanoscales which could exacerbate their biological activity. However, studies that have assessed the effect of particle size across a comprehensive set of biological responses have not been reported. Using a macrophage cell model, we demonstrate that the ability of unopsonized amorphous silica particles to stimulate inflammatory protein secretion and induce macrophage cytotoxicity scales closely with the total administered particle surface area across a wide range of particle diameters (7-500 nm). Whole genome microarray analysis of the early gene expression changes induced by 10- and 500-nm particles showed that the magnitude of change for the majority of genes affected correlated more tightly with particle surface area than either particle mass or number. Gene expression changes that were particle size-specific were also identified. However, the overall biological processes represented by all gene expression changes were nearly identical, irrespective of particle diameter. Direct comparison of the cell processes represented in the 10- and 500-nm particle gene sets using gene set enrichment analysis revealed that among 1009 total biological processes, none were statistically enriched in one particle size group over the other. The key mechanisms involved in silica nanoparticle-mediated gene regulation and cytotoxicity have yet to be established. However, our results suggest that on an equivalent nominal surface area basis, common biological modes of action are expected for nano- and supranano-sized silica particles.

High-throughput (HTP) technologies offer the capability to evaluate the genome, proteome, and metabolome of an organism at a global scale. This opens up new opportunities to define complex signatures of disease that involve signals from multiple types of biomolecules. However, integrating these data types is difficult due to the heterogeneity of the data. We present a Bayesian approach to integration that uses posterior probabilities to assign class memberships to samples using individual and multiple data sources; these probabilities are based on lower-level likelihood functions derived from standard statistical learning algorithms. We demonstrate this approach on microbial infections of mice, where the bronchial alveolar lavage fluid was analyzed by three HTP technologies, two proteomic and one metabolomic. We demonstrate that integration of the three datasets improves classification accuracy to approximately 89% from the best individual dataset at approximately 83%. In addition, we present a new visualization tool called Visual Integration for Bayesian Evaluation (VIBE) that allows the user to observe classification accuracies at the class level and evaluate classification accuracies on any subset of available data types based on the posterior probability models defined for the individual and integrated data.

Human bone lead content has been demonstrated to be related to socioeconomic status, occupation and other social and environmental correlates. Skeletal tissue samples from 135 individuals from an early nineteenth century Philadelphia cemetery (First African Baptist Church) were studied by electrothermal atomic absorption spectrometry and X-ray fluorescence for lead content. High bone lead levels led to investigation of possible diagenetic effects. These were investigated by several different approaches including distribution of lead within bone by X-ray fluorescence, histological preservation, soil lead concentration and acidity as well as location and depth of burial. Bone lead levels were very high in children, exceeding those of the adult population that were buried in the cemetery, and also those of present day adults. The antemortem age-related increase in bone lead, reported in other studies, was not evidenced in this population. Lead was evenly deposited in areas of taphonomic bone destruction. Synchrotron X-ray fluorescence studies revealed no consistent pattern of lead microdistribution within the bone. Our conclusions are that postmortem diagenesis of lead ion has penetrated these archaeological bones to a degree that makes their original bone lead content irretrievable by any known method. Increased bone porosity is most likely responsible for the very high levels of lead found in bones of newborns and children., (Copyright 2008 Wiley-Liss, Inc.)

Bronchoalveolar lavage fluid (BALF) contains proteins derived from various pulmonary cell types, secretions and blood. As the characterization of the BALF proteome will be instrumental in establishing potential biomarkers of pathophysiology in the lungs, the objective of this study was to contribute to the comprehensive collection of Mus musculus BALF proteins using high resolution and highly sensitive micro-capillary liquid chromatography (microLC) combined with state-of-the-art high resolution mass spectrometry (MS). BALF was collected from ICR and C57BL/6 male mice exposed to nose-only inhalation to either air or cigarette smoke. The tandem mass spectra were analyzed by SEQUEST for peptide identifications with the subsequent application of accurate mass and time tags resulting in the identification of 1797 peptides with high confidence by high resolution MS. These peptides covered 959 individual proteins constituting the largest collection of BALF proteins to date. High throughput monitoring profiles of this extensive collection of BALF proteins will facilitate the discovery and validation of biomarkers that would elucidate pathogenic or adaptive responses of the lungs upon toxic insults.

The growing commerce in micro- and nanotechnology is expected to increase human exposure to submicrometer and nanoscale particles, including certain forms of amorphous silica. When inhaled, these particles are likely to reach the alveoli, where alveolar type II epithelial cells that are distinguished by apical microvilli are found. These cells play critical roles in the function of the alveoli and participate in the immune response to amorphous silica and other particles by releasing chemokines. The cellular interactions of the particles, which drive the cellular responses, are still unclear. Adverse effects of nanoparticles have been attributed, in part, to the unique properties of materials at the nanoscale. However, little is known about the cellular interactions of individual or small nanoparticle aggregates, mostly because of their tendency to agglomerate under experimental conditions. Here we investigate the interaction and internalization pathway of individual precipitated amorphous silica particles with specific surface properties and size, by following one particle at a time. We find that both 100 and 500 nm particles can take advantage of the actin turnover machinery within filopodia and microvilli-like structures to advance their way into alveolar type II epithelial cells. This pathway is strictly dependent on the positive surface charge of the particle and on the integrity of the actin filaments, unraveling the coupling of the particle with the intracellular environment across the cell membrane. The retrograde pathway brings a new mechanism by which positive surface charge supports particle recruitment, and potential subsequent toxicity, by polarized epithelial cells bearing microvilli.

The rapid growth in the use of in vitro methods for nanoparticle toxicity assessment has proceeded with limited consideration of the unique kinetics of these materials in solution. Particles in general and nanoparticles specifically, diffuse, settle, and agglomerate in cell culture media as a function of systemic and particle properties: media density and viscosity and particle size, shape, charge and density, for example. Cellular dose then is also a function of these factors as they determine the rate of transport of nanoparticles to cells in culture. Here we develop and apply the principles of dosimetry in vitro and outline an approach for simulation of nanoparticle particokinetics in cell culture systems. We illustrate that where equal mass concentrations (mug/ml) imply equal doses for dissimilar materials, the corresponding particle number or surface area concentration doses differ by orders of magnitude. More importantly, when rates of diffusional and gravitational particle delivery are accounted for, trends and magnitude of the cellular dose as a function of particle size and density differ significantly from those implied by "concentration" doses. For example, 15-nm silver nanoparticles appear approximately 4000 times more potent than micron-sized cadmium oxide particles on a cm(2)/ml media basis, but are only approximately 50 times more potent when differences in delivery to adherent cells are considered. We conclude that simple surrogates of dose can cause significant misinterpretation of response and uptake data for nanoparticles in vitro. Incorporating particokinetics and principles of dosimetry would significantly improve the basis for nanoparticle toxicity assessment, increasing the predictive power and scalability of such assays.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

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.

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.

Scientific and societal interest in the analysis of aggregate toxicity derives from the fact that people are seldom exposed to single chemicals, but rather to multiple agents from different sources and even to mixtures of agents from a single source. Many descriptive terms and mathematical, graphical, and statistical models have been used to evaluate the toxicity of simple mixtures. It is not very easy to distinguish clearly the intrinsic differences, distinctions and limitations of these models when applied to characterizing interactive toxicity. A series of experiments were performed to illustrate model-dependent consistencies and differences in interactive toxicity. Cultured murine renal cortical cells, target cells for metal toxicity, were treated with selected concentrations of one metal or binary mixtures of metals to give conditions of dose-additivity, response additivity, or with only one toxic member of the binary mixture. The cytotoxicity was determined at 24h by lactate dehydrogenase release. The data were analyzed graphically and mathematically by (a) Carter's statistical isobologram, (b) Barton's non-linear, and (c) Kodell and Pounds' linear models to characterize the interaction. These models were compared and contrasted for robustness, and consistency using these common data sets. The models gave generally consistent conclusions, but each model has limitations and strengths for assessing particular mixtures scenarios. This comparison illustrates the complexity of extrapolating conclusions between models, and difficulty of public health assessment from exposures to multiple chemicals in the environment.

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.

Mammary ductal cells are the origin for 70-80% of breast cancers. Nipple aspirate fluid (NAF) contains proteins directly secreted by the ductal and lobular epithelium in non-lactating women. Proteomic approaches offer a largely unbiased way to evaluate NAF as a source of biomarkers and are sufficiently sensitive for analysis of small NAF volumes (10-50 microl). In this study, we initially evaluated a new process for obtaining NAF and discovered that this process resulted in a volume of NAF that was suitable for analysis in approximately 90% of subjects. Proteomic characterization of NAF identified 64 proteins. Although this list primarily includes abundant and moderately abundant NAF proteins, very few of these proteins have previously been reported in NAF. At least 15 of the NAF proteins identified have previously been reported to be altered in serum or tumor tissue from women with breast cancer, including cathepsin D and osteopontin. In summary, this study provides the first characterization of the NAF proteome and identifies several candidate proteins for future studies on breast cancer markers in NAF.

Blood serum is a complex body fluid that contains various proteins ranging in concentration over at least 9 orders of magnitude. Using a combination of mass spectrometry technologies with improvements in sample preparation, we have performed a proteomic analysis with submilliliter quantities of serum and increased the measurable concentration range for proteins in blood serum beyond previous reports. We have detected 490 proteins in serum by on-line reversed-phase microcapillary liquid chromatography coupled with ion trap mass spectrometry. To perform this analysis, immunoglobulins were removed from serum using protein A/G, and the remaining proteins were digested with trypsin. Resulting peptides were separated by strong cation exchange chromatography into distinct fractions prior to analysis. This separation resulted in a 3-5-fold increase in the number of proteins detected in an individual serum sample. With this increase in the number of proteins identified we have detected some lower abundance serum proteins (ng/ml range) including human growth hormone, interleukin-12, and prostate-specific antigen. We also used SEQUEST to compare different protein databases with and without filtering. This comparison is plotted to allow for a quick visual assessment of different databases as a subjective measure of analytical quality. With this study, we have performed the most extensive analysis of serum proteins to date and laid the foundation for future refinements in the identification of novel protein biomarkers of disease.

Chronic ingestion of arsenite-contaminated drinking water causes skin, bladder, and liver cancer. The mechanism of arsenite-induced carcinogenesis is unknown. Arsenite is known to disrupt mitosis and to delay transit through M phase in normal diploid fibroblasts. SV40-transformed human fibroblasts were observed to be hypersensitive to the cytotoxic and cytostatic effects of NaAsO(2) compared with normal diploid fibroblasts in concentration-response experiments. Five to 20 microM NaAsO(2) induced cytostasis in cycling normal diploid fibroblasts but not overt lethality in quiescent normal diploid fibroblasts. High concentrations of arsenite were overtly lethal in both cycling and quiescent cells. The IC50 for cycling SV40-transformed fibroblasts was 3.8 and 4.8 microM for the SV40-transformed lines GM4429 and GM0637, respectively, whereas, in cycling normal diploid fibroblasts (GM0024), the IC50 was 24.7 microM. Microscopic examination of NaAsO(2)-treated SV40-transformed fibroblasts suggested a concentration-dependent accumulation of cells in mitosis undergoing apoptosis. Treatment of SV40-transformed fibroblasts with 0-10 microM NaAsO(2) caused a concentration-dependent inhibition of cell proliferation, accumulation of cells having G2/M DNA contents, and increases in the mitotic index. Phase microscopy, annexin V binding, and electron microscopy demonstrated that arrested mitotic cells underwent apoptosis. These results indicate that SV40-transformation sensitizes cells to arsenite-induced mitotic arrest and induction of apoptosis in the mitotic cells., ((c)2002 Elsevier Science (USA))

The level of osteocalcin in serum is lower in lead-intoxicated children than in their normal counterparts. To explain this clinical observation, we investigated the mechanism of action of lead on vitamin D3-dependent osteocalcin production. Lead (5-20 microM) blocked the stimulating effects of vitamin D3 on osteocalcin production in cultured rat osteosarcoma cells (ROS 17/2.8). It is often suggested that activation of protein kinase C (PKC) is a critical mediator of the toxic actions of lead. Treatment of ROS cells with Gö6976, an inhibitor of PKC alpha and beta isozymes, produced similar effects as lead on vitamin D3-dependent osteocalcin production, while activation of PKC by phorbol-12-myristate-13-acetate (TPA) did not reverse or mimic this effect of lead. Thus activation of PKC is not consistent with the actions of lead on vitamin D3-dependent osteocalcin production. Measurement of PKC enzyme activity showed that 10 microM lead treatment does not activate or inhibit the activity of PKC in ROS cells. Western blot analysis indicated that lead treatment does not translocate PKC alpha, beta, or zeta from cytosol to membrane. Therefore, we concluded that PKC does not mediate the cellular toxicity of lead on vitamin D3-dependent osteocalcin production., (©2002 Elsevier Science (USA).)

Arsenite treatment has been found to induce clinical remission in patients with acute promyelocytic leukemia. Although the potential therapeutic value of arsenite may lie in triggering apoptosis, it has not been established that cytotoxicity is the sole mechanism of action. We have used a myelomonocytic leukemia cell line (U937) to characterize the concentration-dependent effects of arsenite on cell growth, viability, apoptosis, and differentiation. Arsenite has multiple effects on U937 cells. Low concentrations of arsenite (i.e., < or = 1 microM) potentiate vitamin-D(3)-induced differentiation. Two markers of monocyte differentiation, Mac-1 expression and nitroblue tetrazolium reduction, are increased in arsenite-exposed, D(3)-costimulated cells. Concentrations of arsenite >10 microM rapidly induce the death of cells irrespective of cell cycle phase. Intermediate concentrations of arsenite (i.e., 5 to 10 microM) are cytostatic initially. Cell cycle analysis using elutriated, synchronous cell populations revealed that intermediate concentrations of arsenite delay both G(1) and G(2) transit. G(2) cells appear to be most sensitive to arsenite, in that transit through G(2)/M is more delayed than transit through G(1), and apoptosis is induced in these cells as they emerge from an aberrant G(2)/M. Arsenite-induced apoptosis was caspase-3 dependent. Arsenite-mediated cytotoxicity was reduced in the presence of the broad caspase inhibitor Z-Val-Ala-DL-Asp-fluoromethylketone; however, caspase inhibition did not reverse arsenite-induced cytostasis. Thus, arsenite has multiple effects on U937 cells that are dependent on concentration and cell cycle phase. Specifically, cell cycle transit and differentiation are more sensitive to arsenite than is the induction of apoptosis.

Recent longitudinal studies with human subjects and nonhuman primates using high-precision stable lead isotopes show that lead is mobilized from the maternal skeleton during pregnancy and the postpartum period. We have now calculated the cumulative lead release (lead flux in micrograms) mobilized from the skeleton during these periods by means of analysis of monthly PbB samples from recent immigrants to Australia. Results included a statistically significant inverse relationship (P = .006) between the lead flux and the time of conception after the arrival of the subjects in Australia. By using an area-under-the-curve approach to determine the added lead inputs to blood during pregnancy and nursing versus a baseline value, the net lead release to blood varied from 0.9 to 10.1 microg/d, which is equivalent to 0.3 to 4.03 mg of lead. With group PbB concentrations usually less than 3 microg/dL, the observed releases imply a high skeletal turnover of greater than 10% and possibly greater than 30% in some subjects during pregnancy and the postpartum period. These elevated rates in some subjects may partly arise from low daily calcium intakes, being one half to two thirds of that of recommended daily requirements. The lead flux calculated from a cumulative approach was compared with other approaches: first-order kinetics, bone turnover, bone x-ray fluorescence measurements, and the International Commission for Radiological Protection lead pharmacokinetic model. Calculated lead releases and remaining bone lead concentrations would likely not be detectable by current x-ray fluorescence methods.

The maintenance of intracellular Ca2+ homeostasis is critical to many cellular functions that rely on the calcium ion as a messenger. While attempting to characterize the effects of lead on intracellular calcium levels ([Ca2+]i) in LLC-MK2 Rhesus Monkey kidney cells, we observed that treatment with the metal chelating drug, meso-2,3-dimer-captosuccinic acid (DMSA) evoked transient increases in [Ca2+]i. Changes in [Ca2+]i were monitored using the Ca2+ indicator dye Fura-2 and a dual wavelength fluorescence imaging system. In the presence of 2 mM extracellular Ca2+, DMSA treatment caused a concentration-dependent (15-500 microM) transient increase in [Ca2+]i returning to baseline levels within 30-60 s. Pharmacologic concentrations of DMSA (30 microM) stimulated a three-fold increase in [Ca2+]i, which was spatiotemporally comparable to Ca2+ transients induced by other calcium agonists. Depletion of inositol trisphosphate (IP3)-sensitive [Ca2+]i stores with the smooth endoplasmic reticulum calcium-ATPase (SERCA) inhibitor thapsigargin did not prevent DMSA-elicited increases in [Ca2+]i, suggesting that Ca2+ mobilized by DMSA was either extracellular or from an non-IP3 releasable Ca2+ pool. Treatment with glutathione, cysteine, or 2-mercaptoethanol caused similar but not identical calcium transients. Adenosine-5'-trisphosphate (ATP) also elicited transient increases in [Ca2+]i similar to those of DMSA. No transient increases in [Ca2+]i were elicited by DMSA or ATP in the absence of extracellular calcium. These data indicate that DMSA and other sulfhydryl compounds trigger an influx of extracellular calcium, suggesting a previously unobserved and unanticipated interaction between DMSA and the Ca2+ messenger system.