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Macrophages are pivotal innate immune cells which exhibit high phenotypic plasticity and can exist in different polarization states dependent on exposure to external stimuli. Numerous methods have been employed to simulate macrophage polarization states to test their function in vitro. However, limited research has explored whether these polarization methods yield comparable populations beyond key gene, cytokine, and cell surface marker expression. Here, we employ an unbiased comprehensive analysis using data organized through the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, which compiles all RNAseq data deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In silico analyses were carried out demonstrating that commonly employed macrophage polarization methods generate distinct gene expression profiles in macrophage subsets that remained poorly described until now. Our analyses confirm existing knowledge on broad macrophage polarization, while expanding nuanced differences between M2a and M2c subsets, suggesting non-interchangeable stimuli for M2a polarization. Furthermore, we characterize divergent gene expression patterns in M1 macrophages following standard polarization protocols, indicating significant subset distinctions. Consequently, equivalence cannot be assumed among polarization regimens for in vitro macrophage studies, particularly in simulating diverse pathogen responses. [ABSTRACT FROM AUTHOR]

Background: Exposure to military burn pit smoke during deployment is associated with different respiratory and non-respiratory diseases. However, information linking smoke exposure to human pulmonary health is lacking. This study examined the effects of simulated burn pit smoke condensates on human airway epithelial cells (HAECs) from twelve donors (smokers/non-smokers, biological female/male) cultured at an air-liquid interface and exposed to condensates from three simulated burn pit waste materials (cardboard, plywood, and plastic) incinerated at two combustion conditions: smoldering and flaming. Cellular gene expression was analyzed using bulk RNA sequencing, and basolateral media cytokine levels were assessed using multiplex immunoassay. Results: Flaming smoke condensates caused more significant differentially expressed genes (DEGs) with plywood flaming smoke being the most potent in altering gene expression and modulating cytokine release. Cardboard and plywood flaming condensates primarily activated detoxification pathways, whereas plastic flaming affected genes related to anti-microbial and inflammatory responses. Correlation analysis between smoke condensate chemicals and gene expression to understand the underlying mechanism revealed crucial role of oxygenated polycyclic aromatic hydrocarbons (PAHs) and aluminum, molybdenum, and silicon elements; IL6 expression was positively correlated with most PAHs. Stratification of data based on HAEC donor demographics suggests that these affect gene expression changes. Enrichment analysis indicated similarity with several deployment-related presumptive and reported diseases, including asthma, emphysema, and cancer of different organs. Conclusions: This study highlights that simulated burn pit smoke exposure of HAECs causes gene expression changes indicative of deployment-related diseases with more pronounced effects seen in smokers and females. Future studies are needed to further characterize how sex and smoking status affect deployment-related diseases. [ABSTRACT FROM AUTHOR]

Summary: Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098, Introduction The past decade has witnessed a paradigm shift in the environmental sciences as studies have shifted from examining specific exposures to attempting to comprehensively measure and characterize the broad [...]

Per- and polyfluoroalkyl substances (PFAS) have emerged as high-priority contaminants due to their ubiquity and pervasiveness in the environment. Numerous PFAS co-occur across sources of drinking water, including areas in North Carolina (NC) where some detected concentrations are above the Environmental Protection Agency's health advisory levels. Although evidence demonstrates that PFAS exposure induces harmful effects in the liver, the involvement of extracellular vesicles (EVs) as potential mediators of these effects has yet to be evaluated. This study set out to evaluate the hypothesis that PFAS mixtures induce dose-dependent release of EVs from liver cells, with exposures causing differential loading of microRNAs (miRNAs) and PFAS chemical signatures. To test this hypothesis, a defined PFAS mixture was prioritized utilizing data collected by the NC PFAS Testing Network. This mixture contained 3 substances, perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexanoic acid (PFHxA), selected based upon co-occurrence patterns and the inclusion of both short-chain (PFHxA) and long-chain (PFOA and PFOS) substances. HepG2 liver cells were exposed to equimolar PFAS, and secreted EVs were isolated from conditioned media and characterized for count and molecular content. Exposures induced a dose-dependent release of EVs carrying miRNAs that were differentially loaded upon exposure. These altered miRNA signatures were predicted to target mRNA pathways involved in hepatic fibrosis and cancer. Chemical concentrations of PFOS, PFOA, and PFHxA were also detected in both parent HepG2 cells and their released EVs, specifically within a 15-fold range after normalizing for protein content. This study therefore established EVs as novel biological responders and measurable endpoints for evaluating PFAS-induced toxicity. [ABSTRACT FROM AUTHOR]