Engineering topics

Pounds, Joel G.

Publications and source records attributed to Pounds, Joel G..

14 records

DOE OSTI2019

Modulation of susceptibility to lung bacterial infection by engineered nanomaterials: In vitro and in vivo correspondence based on macrophage phagocytic function

The vast diversity of engineered nanoparticles (ENPs) under development is driving demand for new approaches for predictive toxicological and hazard analysis, placing increased importance on rapid in vitro (cellular) test systems for nanotoxicology. While the potential health effects of engineered nanomaterials in humans remain uncertain, evidence links exposure to nanoscale particulates from ambient and occupational sources with increased susceptibility to lung infections, including pneumonia. Here, we applied an in vitro assay amenable to high throughput to evaluate the effects of pretreatment with 16 different ENP types on the ability of macrophages to phagocytize Streptococcus pneumoniae, the leading cause of community-acquired pneumonia. Hierarchical clustering analysis of the results revealed three major classes of ENPs, ranging from metal oxide ENPs which induce macrophage cytotoxicity and robustly inhibit phagocytic function (e.g., CuO, CoO), to ENPs that had no detectable adverse effects (e.g. SiO2, TiO2). Importantly, some ENPs that lacked bioactivity by conventional cytotoxicity measures, (e.g., Fe3O4) were found to significantly suppress macrophage phagocytic function. Lung infection studies in mice using three ENPs, representing the low, medium and high toxic groups, showed excellent correspondence with in vitro predictions of their inhibitory effects on lung clearance/survival of S. pneumonia in vivo. Structure-activity modeling and gene expression analysis suggest that the potential of an ENP to enhance susceptibility to S. pneumoniae is driven by its propensity to induce oxidative stress and reprogram macrophage activation responses. Our results demonstrate the important concept that adverse biological consequences of nanomaterials may not always be directly mediated, but can be manifested by altering susceptibility to other common environmental exposures. The current study warrants careful consideration of these biological endpoints of susceptibility from both a mechanistic and hazard assessment framework for engineered nanoparticles.

engineered nanoparticle, pneumonia susceptibility,↗

DOE OSTI2018

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

Cytochrome P450 monooxygenases (P450) are key to the metabolism of myriad endogenous chemicals and xenobiotics, including the majority of therapeutic drugs. Dysregulated P450 activities can lead to altered drug metabolism and toxicity, oxidative stress, and inflammation; all physiological states frequently charged as the impetus for various chronic pathologies. We characterized the impact of common xenobiotic exposures, specifically high-fat diet and active or passive cigarette smoke, on the functional capacity of hepatic and pulmonary P450s. We employed an activity-based protein profiling approach to characterize the identity and activity level of measured individual P450 isoforms. Our results confirm expectations of significant alterations in pulmonary P450s due to cigarette smoke, but now reveal the repressive impact of high-fat diet-induced obesity on many hepatic P450s activities, and the dynamic alterations due to concomitant diet and smoke exposures on liver and lung P450 activities impacting drug metabolism and pathways of inflammation.

Sadler, Natalie C.↗

DOE OSTI2015

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. - Highlights: • Quantum dot acute lung inflammation was evaluated in a multi-strain mouse model. • QD disposition differed across 8 Collaborative Cross (CC) founder strains. • Neutrophil and cytokine levels in BALF were also mouse strain dependent. • NOD/ShiLtJ, NZO/HlLtJ, and A/J were more sensitive to QDs than C57BL/6J mice. • The cytokines KC and Mip1α were strongly correlated with Cd and BALF neutrophils.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Metabolite signatures in hydrophilic extracts of mouse lungs exposed to cigarette smoke revealed by 1 H NMR metabolomics investigation

Herein, 1 H-NMR metabolomics are carried out to evaluate the changes of metabolites in lungs of mice exposed to cigarette smoke. It is found that the concentrations of adenosine derivatives (i.e. ATP, ADP and AMP), inosine and uridine are significantly fluctuated in the lungs of mice exposed to cigarette smoke compared with those of controls regardless the mouse is obese or regular weight. The decreased ATP, ADP, AMP and elevated inosine predict that the deaminases in charge of adenosine derivatives to inosine derivatives conversion are altered in lungs of mice exposed to cigarette smoke. Transcriptional analysis reveals that the concentrations of adenosine monophosphate deaminase and adenosine deaminase are different in the lungs of mice exposed to cigarette smoke, confirming the prediction from metabolomics studies. We also found, for the first time, that the ratio of glycerophosphocholine (GPC) to phosphocholine (PC) is significantly increased in the lungs of obese mice compared with regular weight mice. The ratio of GPC/PC is further elevated in the lungs of obese group by cigarette smoke exposure. Since GPC/PC ratio is a known biomarker for cancer, these results may suggest that obese group is more susceptible to lung cancer when exposed to cigarette smoke.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

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. In summary, 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.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

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.

Protein Quantitation, proteomics↗

DOE OSTI2014

A Halotyrosine Antibody that Detects Increased Protein Modifications in Asthma Patients

Background-Airway inflammation plays an important pathophysiological role in asthma. Eosinophils produce hypobromite and bromotyrosine while neutrophils produce hypochlorite and chlorotyrosine. Objective-To evaluate halotyrosine modifications of individual airway proteins as a marker of inflammation in asthma using an antibody-based assay. Methods-We developed a novel monoclonal antibody (BTK-94C) that binds halogenated tyrosine residues, and used this antibody in a custom enzyme-linked immunosorbent assay (ELISA) microarray platform to examine halotyrosine levels in 23 proteins in three independent sets of sputum samples (52 samples total). Results-In 15 subjects with either no asthma, or with asthma characterized by high or low sputum eosinophil counts, we found associations between increased halotyrosine levels of at least three proteins and severity of airway hyperresponsiveness (AHR). Treatment with mepolizumab in 17 patients with sputum eosinophilia markedly reduced the sputum eosinophilia and significantly reduced halotyrosine levels in one sputum protein. Further analysis of 10 subjects with neutrophilic asthma and 10 health controls demonstrated a broad increase in halotyrosine in the patients with airway neutrophilia. Conclusions-Significantly higher levels of halotyrosine are associated with asthma in the asthma phenotypes we examined. The halotyrosine levels correlated with indirect AHR in the form of exercise-induced bronchoconstriction. Clinical Implication-An antibody-based assay for tyrosine halogenation in specific proteins may prove useful for assessing airway inflammation in asthma. Capsule Summary-An antibody to measure protein monobrominated tyrosine and other halotyrosine modifications was developed and used to evaluate halogenation in specific proteins in the airways for the first time. Associations were found between levels of halotyrosine and exercise-induced bronchoconstriction, and eosinophil and neutrophil inflammation in sputum from asthma patients. The ELISA developed here can be easily translated into a clinical test.

Asthma, sputum, halotyrosine, airway hyperresponsi↗

DOE OSTI2014

Comparative Iron Oxide Nanoparticle Cellular Dosimetry and Response in Mice by the Inhalation and Liquid Cell Culture Exposure Routes

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 (target cell dose). Methods: Balb/c mice were exposed nose-only to an aerosol of 12.8 nm (68.6 nm CMD, 19.9 mg/m3, 4 hours) super paramagnetic iron oxide particles, target cell doses were calculated and biomarkers of response anchored with histological evidence 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 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/cm2 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/ cm2 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 advance their use for hazard assessment and extrapolation to humans. The mildly inflammogentic cellular doses experienced by mice were similar those calculated for humans exposed to the same at the existing permissible exposure limit of 10 mg/m3 iron oxide (as Fe).

Dosimetry↗

DOE OSTI2013

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

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.

Biomarker Identification↗

DOE OSTI2013

Genetic Algorithms and Classification Trees in Feature Discovery: Diabetes and the NHANES database

This paper presents a feature selection methodology that can be applied to datasets containing a mixture of continuous and categorical variables. Using a Genetic Algorithm (GA), this method explores a dataset and selects a small set of features relevant for the prediction of a binary (1/0) response. Binary classification trees and an objective function based on conditional probabilities are used to measure the fitness of a given subset of features. The method is applied to health data in order to find factors useful for the prediction of diabetes. Results show that our algorithm is capable of narrowing down the set of predictors to around 8 factors that can be validated using reputable medical and public health resources.

Heredia-Langner, Alejandro↗

DOE OSTI2013

Dysregulation of Macrophage Activation Profiles by Engineered Nanoparticles

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. Finally, nanotoxicology screening strategies should therefore consider how exposure to these materials alters susceptibility to other environmental exposures.

Kodali, Vamsi↗

DOE OSTI2013

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.

heart transcriptome↗

DOE OSTI2013

Sequential Projection Pursuit Principal Component Analysis – Dealing with Missing Data Associated with New -Omics Technologies

We present a new version of sequential projection pursuit Principal Component Analysis (sppPCA) that has the capability to perform PCA on large multivariate datasets that contain non-random missing values. We demonstrate that sppPCA generates more robust and informative low-dimensional representations of the data than imputation-based approaches and improved downstream statistical analyses, such as clustering or classification. A Java program to run sppPCA is freely available at https://www.biopilot.org/docs/Software/sppPCA.

Webb-Robertson, Bobbie-Jo M.↗

DOE OSTI2013

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. - Highlights: ► Obesity modulates inflammatory markers in BAL fluid after LPS exposure. ► Obese animals have a unique transcriptional signature in lung after LPS exposure. ► Obesity elevates inflammatory stress and reduces antioxidant capacity in the lung. ► Toxicant-specific biomarkers predict exposure independent of systemic inflammation.

60 APPLIED LIFE SCIENCES↗