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DOE OSTI2021

Exposure to an environmental mixture of polycyclic aromatic hydrocarbons induces hepatic cytochrome P450 enzymes in mice

Cytochrome P450 enzymes (CYPs) play an important role in bioactivating or detoxifying polycyclic aromatic hydrocarbons (PAHs), common environmental contaminants. While it is widely accepted that exposure to PAHs induces CYPs, effectively increasing rates of xenobiotic metabolism, dose- and time-response patterns of CYP induction are not well known. In order to better understand dose- and time-response relationships of individual CYPs following induction, we exposed B6129SF1/J mice to single or repeated doses (2-180 µmol/kg/d) of benzo[a]pyrene (BaP) or Supermix-10, a mixture of the top 10 most abundant PAHs found at the Portland Harbor Superfund Site. In hepatic microsomes from exposed mice, we measured amounts of active CYPs using activity-based protein profiling and total CYP expression using global proteomics. We observed rapid Cyp1a1 induction after 6 hr at the lowest PAH exposures and broad induction of many CYPs after 3 daily PAH doses at 72 hr following the first dose. Using samples displaying Cyp1a1 induction, we observed significantly higher metabolic affinity for BaP metabolism (Km reduced 3-fold), 3-fold higher intrinsic clearance, but no changes to the Vmax. Mice dosed with the highest PAH exposures exhibited 1.7 to 5-fold higher intrinsic clearance rates for BaP compared to controls and higher Vmax values indicating greater amounts of enzymes capable of metabolizing BaP. This study demonstrates exposure to PAHs found at Superfund Sites induces enzymes in dose- and time-dependent patterns in mice. Accounting for specific changes in enzyme profiles, and relative rates of PAH bioactivation and detoxification, and resulting risk will help translate internal dosimetry of animal models to humans and improve risk assessments of PAHs at Superfund sites.

Stoddard, Ethan G.↗

DOE OSTI2025

An Integral Activity-Based Protein Profiling Method for Higher Throughput Determination of Protein Target Sensitivity to Small Molecules

Activity-based protein profiling (ABPP) is a chemoproteomic technique that uses small molecule probes to label active enzymes selectively and covalently in complex proteomes. Competitive ABPP, which involves treatment of the active proteome with an analyte of interest, is especially powerful for profiling how small molecules impact specific protein activities. Advances in higher throughput workflows have made it possible to generate extensive competitive ABPP data across diverse biological samples, making this approach highly appealing for characterizing shared and unique proteins affected by perturbations such as drug or chemical exposures. To use the competitive ABPP approach effectively to understand potential adverse effects of chemicals of concern (CoC), a wide range of concentrations may be needed, particularly for chemicals that lack potency or toxicity data. In this work, we present an integral competitive ABPP method that enables target sensitivity determination for different organophosphate (OP) pesticides as model toxicants. Using previously developed OP-ABPs, we optimized conditions for tandem mass tag (TMT) multiplexing of ABPP samples and compared conventional competitive ABPP involving samples at discrete paraoxon concentrations to pooled samples across that same concentration range. We then expanded our approach to compare protein target sensitivities toward two additional OP pesticides, chlorpyrifos oxon and malaoxon. The results showed that differences in integral intensities for the pooled competition sample can be used to evaluate the relative sensitivity of specific proteins without increasing the overall number of samples. For 8 CoC concentrations of interest, this strategy reduced the number of TMT plexes and the corresponding number of LC–MS/MS analyses 3-fold. In conclusion, we envision the integral ABPP (IABPP) method will provide a means to screen diverse chemicals more rapidly to identify both high and low sensitivity protein targets.

activity-based probes↗

DOE OSTI2026

Chemoproteomic Elucidation of β-Lactam Drug Targets in Mycobacterium abscessus

The pathogen Mycobacterium abscessus (Mab) can cause severe and difficult-to-treat chronic lung infections. Despite the rising incidence and clinical concern of Mab infections, treatment options are limited and often ineffective. Treatment is complicated by Mab’s ability to persist in a nonreplicating, drug-resistant state. Several β-lactam antibiotics are potently bactericidal against Mab but are underutilized because their molecular mechanisms of action against Mab are incompletely understood. In the current study, we used β-lactam-derived activity-based probes and chemoproteomics to report the first comprehensive list of Mab enzymes targeted by β-lactams. We compared β-lactam targets across two Mab subspecies in actively replicating and nonreplicating cultures, using a new carbon starvation model of persistence. We identified 17 targets that were active in every condition tested, seven of which were previously unknown to bind β-lactams. Lastly, we characterized the β-lactamase activity and β-lactam inhibition profiles of nine Mab enzymes, demonstrating that imipenem inhibits these targets more effectively than cefoxitin. These findings demonstrate β-lactam target engagement in persistent Mab and provide clarity on the mechanisms of action of clinically relevant β-lactams in Mab, crucial steps toward fully realizing their potential for treating infections caused by this opportunistic pathogen.

Mycobacterium abscessus↗

DOE OSTI2020

Activity Time-Use Patterns and Experienced Satisfaction: Travel Behavioral Dynamics from the 2010–2013 American Time-Use Survey Data

Central to the theory of activity-based approaches is the role of time-use and its impacts on various decision processes related to individual activity-travel patterns, thereby determining ones’ daily well-being. This research aims at quantifying and investigating the underlying correlation of time-use and happiness using the repeated cross-section data from the 2010-2013 American Time Use Survey Well-Being module. The survey specifically records reported levels of activity satisfaction as measured through – happiness, meaningfulness, tiredness, sadness and stress experienced from activity participation. Using these scores, we first construct a composite degree of activity satisfaction that a person experiences using latent class cluster analysis. A series of regression models are then estimated to jointly analyze episodic duration and satisfaction separately for each of years from 2010-2013, by controlling for socioeconomics, household lifecycle stages (29-groups based on age, gender, marital status and presence of children), activity characteristics and global life and health assessment measures. Overall, model estimation results reveal a strong negative correlation between episodic duration and happiness from individual activity engagement patterns. This correlation pattern is temporally stable for the years 2010-2013, with minor variations. Results also suggest that household lifecycle stage has a major impact on activity time-use and satisfaction behavior. Further, single parent and younger couples (25 years to 45 years) with children are more likely to engage in longer and less happy episodes due to family commitments. The model findings also show a strong correlation between health conditions and activity happiness.

Ravulaparthy, Srinath↗