High-Fat Diets Alter the Modulatory Effects of Xenobiotics on Cytochrome P450 Activities.

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.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

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.

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

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 model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed 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 at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.

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.

Susceptibility to quantum dot induced lung inflammation differs widely among the Collaborative Cross founder mouse strains.

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.

Comparative iron oxide nanoparticle cellular dosimetry and response in mice by the inhalation and liquid cell culture exposure routes.

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).

A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments.

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.

Diet-induced obesity reprograms the inflammatory response of the murine lung to inhaled endotoxin.

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.

Impaired transcriptional response of the murine heart to cigarette smoke in the setting of high fat diet and obesity.

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.

Proteomic analysis of bronchoalveolar lavage fluid proteins from mice infected with Francisella tularensis ssp. novicida.

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.

Improved quality control processing of peptide-centric LC-MS proteomics data.

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.

Endogenous 3,4-dihydroxyphenylalanine and dopaquinone modifications on protein tyrosine: links to mitochondrially derived oxidative stress via hydroxyl radical.

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.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors.

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.

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size.

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.

Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data.

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.

(1)H nuclear magnetic resonance metabolomics analysis identifies novel urinary biomarkers for lung function.

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.

Rapid and sensitive detection of protein biomarker using a portable fluorescence biosensor based on quantum dots and a lateral flow test strip.

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.

Proteomic biomarkers in plasma that differentiate rapid and slow decline in lung function in adult cigarette smokers with chronic obstructive pulmonary disease (COPD).

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.

ISDD: A computational model of particle sedimentation, diffusion and target cell dosimetry for in vitro toxicity studies.

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.

Syndecan-1 mediates the coupling of positively charged submicrometer amorphous silica particles with actin filaments across the alveolar epithelial cell membrane.

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.