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Endurance exercise elicits temporal and sexual dimorphic multi-omics remodeling of liver metabolism revealed by MoTrPAC
The mechanisms by which exercise modulate liver metabolism, a central regulator of systemic metabolism, are poorly understood. Leveraging data from MoTrPAC, we analyzed liver adaptations across 1, 2, 4, and 8 weeks of exercise in male and female rats using multi-omic approaches. Female livers displayed a progressive increase in oxidative phosphorylation (OXPHOS) complexes (at the protein level), while male livers showed an increase in acetylation of OXPHOS, TCA cycle, and fatty acid oxidation enzymes. Exercise also enhanced liver cholesterol and bile acid synthesis, reducing liver lipid metabolites in males after 8 weeks of exercise. Male rats had higher fecal cholesterol and cholic acid levels, indicating a sex-specific mechanism of lipid excretion with exercise. Moreover, 8 weeks of training reduced markers related to hepatic stellate cell activation and fibrosis in both sexes. This study highlights the sexual dimorphic and temporal molecular signatures by which exercise modulates liver metabolism to provide hepatoprotective effects.
X-ray fluorescence and XANES spectroscopy revealed diverse potassium chemistries and colocalization with phosphorus in the ectomycorrhizal fungus Paxillus ammoniavirescens
Ectomycorrhizal (ECM) fungi play a major role in forest ecosystems and managed tree plantations. Particularly, they facilitate mineral weathering and nutrient transfer towards colonized roots. Among nutrients provided by these fungi, potassium (K) has been understudied compared to phosphorus (P) or nitrogen (N). The ECM fungus Paxillus ammoniavirescens is a generalist species that interacts with the root of many trees and can directly transfer K to them, including loblolly pine. However, the forms of K that ECM fungi can store is still unknown. Here, we used synchrotron potassium X-ray fluorescence (XRF) and K-edge X-ray Absorption Near Edge Structure (XANES) spectroscopy on P. ammoniavirescens growing in axenic conditions to investigate the K chemistries accumulating in the center and the edge of the mycelium. We observed that various K forms accumulated in different part of the mycelium, including K-nitrate (KNO 3 ), K-C-O compounds (such as K-tartrate K 2 (C 4 H 4 O 6 ) and K-oxalate (K 2 C 2 O 4 )), K-S and K-P compounds. Saprotrophic fungi have been shown to excrete carboxylic acids, which in turn play a role in soil mineral weathering. Our finding of several K counter-ions to carboxylic acids may suggest that, besides their direct transfer to colonized roots, K ions can also be involved in the production of compounds necessary for sourcing nutrients from their surrounding environment by ECM fungi. Additionally, this work reveals that XANES spectroscopy can be used to identify the various forms of K accumulating in biological systems.
Subject-specific multi-scale modeling of the fate of inhaled aerosols
Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.
Analysis of biokinetic parameters reveals patterns in mercury accumulation across aquatic species
Mercury (Hg) is a potent neurotoxicant and poses a risk to human health through the ingestion of Hg-contaminated fish. Mercury, especially in its organic form methylmercury (MeHg), biomagnifies up food chains such that even small aqueous concentrations of Hg can result in significant concentrations of total Hg in fish. Understanding the ecological and human health risks associated with Hg and MeHg exposure requires an understanding of the factors that affect its bioaccumulation in aquatic species. We compiled estimates of three biokinetic parameters: uptake rate (k u ), assimilation efficiency (AE), and efflux rate (k e ). These parameters describe contaminant uptake from aqueous (k u ) and dietary (AE) exposure and the rate of excretion (k e ). We found parameter values for 38 and 34 different species of fish and aquatic invertebrates, respectively, and collected 502 parameter values in total. Here, we used a machine learning technique to establish the relationships between experimental and physiological variables and these parameter values. We found differences in which variables were associated with biokinetic parameter values for fish and aquatic invertebrates. The form of Hg was the most impactful variable, influencing values of all parameters except k u for invertebrates, for which aqueous exposure time was the only significant predicator variable. The parameter k e were the only values significantly influenced by more than one variable, with water type (freshwater, brackish, or marine), organism weight, and form of Hg significantly impacting parameter values for fish and/or invertebrates. To our knowledge, this study represents the most extensive review of biokinetic parameters of Hg and MeHg accumulation in aquatic organisms. Environmental parameters found to significantly impact Hg and MeHg bioaccumulation in past studies were not identified as important in our analyses across aquatic ecosystems and species. Our dataset and analysis reveal novel patterns that may help us better understand and manage Hg bioaccumulation.
Building a predictive model for polycyclic aromatic hydrocarbon dosimetry in organotypically cultured human bronchial epithelial cells using benzo[ a ]pyrene
The airway epithelium is a primary route of exposure for inhaled toxicants, and organotypic culture models represent an important advancement for toxicity testing compared to simple in vitro models that may lack metabolic capability and multicellular structure/communication associated with the bronchial epithelium in vivo. A quantitative understanding of chemical dosimetry is key for interpreting and extrapolating study results; however, dosimetry is understudied in organotypic models limiting ability to predict toxicity. We developed a dosimetry model for primary human bronchial epithelial cells (HBECs) cultured at the air-liquid interface (ALI) using benzo[a]pyrene (BAP), a representative polycyclic aromatic hydrocarbon. Dose and time course evaluation of metabolite formation and enzyme activity and expression were utilized to parameterize a cellular dosimetry model to improve the utility of ALI-HBECs for assessing chemical risk. Dosimetry analysis demonstrated absorption of BAP into cells and an increase in Phase 1 and 2 metabolites over time that correlated with regulation of metabolizing enzymes. BAP was cleared from cells by 48 h after exposure, and the primary metabolites generated in ALI-HBECs were BAP-3-phenol, BAP-4,5-dihydrodiol, BAP-7,8-dihydrodiol, BAP-9,10-dihydrodiol, BAP-7,8,9,10-tetrol, BAP-3-phenol-glucuronide, BAP-4,5-dihydrodiol-glucuronide, and BAP-9,10-dihydrodiol-glucuronide. The resulting dosimetry model described BAP and 7,8-dihydrodiol toxicokinetics in ALI-HBECs and suggested active excretion of 7,8-dihydrodiol. Overall, this study demonstrates metabolic competency of ALI-HBECs for BAP metabolism, demonstrates the usefulness of complex in vitro systems for human-relevant toxicity data, and exhibits how in silico models can be utilized for understanding the dosimetry of test compounds to aid in in vitro to human extrapolation of toxicity data for risk assessments.
RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design
Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.
Modeling Plutonium Decorporation in a Female Nuclear Worker Treated with Ca-DTPA after Inhalation Intake
The present work models plutonium (Pu) biokinetics in a female former nuclear worker. Her bioassay measurements are available at the US Transuranium and Uranium Registries. The worker was internally exposed to a plutonium-americium mixture via acute inhalation at a nuclear weapons facility. She was medically treated with injections of 1 g Ca-DTPA on days 0, 5, and 14 after the intake. Between days 0 and 20, fecal and urine samples were collected and analyzed for 239 Pu and 241 Am. Subsequently, she was followed up for bioassay monitoring over 14 y, with additional post-treatment urine samples collected and analyzed for 239 Pu. The uniqueness of this dataset is due to the availability of: (1) both early and long-term bioassay data from a female with plutonium intake; (2) data on chelation therapy for a female; and (3) fecal measurement results. Chelation therapy with Ca- and/or Zn-salts of DTPA is known to aid in reducing the internal radiation dose by enhancing the excretion of plutonium and americium from the body. Such enhancement affects plutonium biokinetics in the human body, posing a challenge to the internal dose assessment. The current radiation dose assessment practice is to exclude the data affected by Ca-DTPA from the analysis. The present analysis is the first to explicitly model the chelation-affected bioassay data in a female by using a newly developed chelation model. Thus, the bioassay data collected during and after the Ca-DTPA administrations were used for biokinetic modeling and dose assessment. The Markov Chain Monte Carlo method was used to investigate model parameter uncertainty, based on the bioassay data and assumed prior probability distributions. A χ 2 /nData (number of data points) ≈ 1 was observed in this study, which indicates self-consistency of the data with the model. Results of this study show that the worker’s 239 Pu intake was 12 Bq, with a committed effective dose to the whole-body of 1.2 mSv and a committed equivalent dose to the bone surfaces, liver, and lungs of 37.8, 9.1, and 0.8 mSv, respectively. This study also discusses the worker’s dose reduction due to chelation treatment.
Dynamic Rhizodeposition in the Woody Perennial Populus trichocarpa
Plants undergo physiological and metabolic changes that release specific molecules into the surrounding soil, a process collectively known as rhizodeposition. These compounds play crucial roles in plant‐microbe‐soil interactions, such as supporting plant development and resilience in changing environments. Under nutrient‐limited conditions, these plant‐derived compounds modify the rhizosphere environment, mobilizing otherwise inaccessible nutrients and recruiting stress‐adaptive microbial communities that support stress resilience. Currently, the chemical diversity of rhizodeposition has yet to be fully realized but is expected to be a complex mixture that includes soluble organic compounds excreted from root cells, along with products of root cell turnover, sloughed‐off root cap and border cells, and mucilage. Here, we developed a methodological and conceptual framework for an in‐depth measurement of rhizodeposition through critical advancements in untargeted metabolomics. Further, this approach provided foundational insights into the dynamic changes in rhizodeposition for the woody perennial Populus trichocarpa and rhizodeposit profiles varying by genotype, time, location, and environment. More broadly, this study provides a framework that will help formulate the next steps to effectively study rhizodeposition.
Activity of Cannabidiol on Ex Vivo Amino Acid Fermentation by Bovine Rumen Microbiota
Amino-acid-fermenting bacteria are wasteful organisms within the rumens of beef cattle that remove dietary amino nitrogen by producing ammonia, which is then excreted renally. There are currently no on-label uses for the control of this microbial guild, but off-label use of broad-spectrum antimicrobials has shown efficacy, which contributes to antimicrobial resistance. Plant-derived antimicrobials supplemented into the diets of cattle may offer worthwhile alternatives. This study sought to investigate the role of cannabidiol (CBD) as a terpenophenolic antimicrobial. Ex vivo cell suspensions were harvested from the rumen fluid of Angus × Holstein steers in non-selective media with amino acid substrates. The suspensions were treated with five concentrations of CBD (860 μg mL−1–0.086 μg mL−1) and incubated (24 h), after which ammonia production and viable number of cells per substrate and treatment were measured. The data demonstrated a ~10–15 mM reduction in ammonia produced at the highest concentration of CBD and negligible changes in the viable number of amino-acid-fermenting bacteria. CBD does not appear to be a biologically or economically viable terpenophenolic candidate for the control of amino acid fermentation in beef cattle.
Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022
Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.