Initial and ongoing tobacco smoking elicits vascular damage and distinct inflammatory response linked to neurodegeneration

Unburned Tobacco Smoke Affects Neuroinflammation-Related Pathways in the Rat Mesolimbic System

Tobacco use disorder represents a significant public health challenge due to its association with various diseases. Despite awareness efforts, smoking rates remain high, partly due to ineffective cessation methods and the spread of new electronic devices. This study investigated the impact of prolonged nicotine exposure via a heat-not-burn (HnB) device on selected genes and signaling proteins involved in inflammatory processes in the rat ventral tegmental area (VTA) and nucleus accumbens (NAc), two brain regions associated with addiction to different drugs, including nicotine. The results showed a reduction in mRNA levels for PPARα and PPARγ, two nuclear receptors and anti-inflammatory transcription factors, along with the dysregulation of gene expression of the epigenetic modulator KDM6s, in both investigated brain areas. Moreover, decreased PTEN mRNA levels and higher AKT phosphorylation were detected in the VTA of HnB-exposed rats with respect to their control counterparts. Finally, significant alterations in ERK 1/2 phosphorylation were observed in both mesolimbic areas, with VTA decrease and NAc increase, respectively. Overall, the results suggest that HnB aerosol exposure disrupts intracellular pathways potentially involved in the development and maintenance of the neuroinflammatory state. Moreover, these data highlight that, similar to conventional cigarettes, HnB devices use affects specific signaling pathways shaping neuroinflammatory process in the VTA and NAc, thus triggering mechanisms that are currently considered as potentially relevant for the development of addictive behavior.

Channels of Communication: Extracellular Vesicles in Environmental Stress and Human Disease

Extracellular vesicles offer great promise for revealing mechanisms and serving as biomarkers in studies of exposure effects on neurological, respiratory, reproductive, and other physiological systems-and they require only a simple blood draw.

Seminar: Extracellular Vesicles as Mediators of Environmental Stress in Human Disease

BACKGROUND

Extracellular vesicles (EVs), membrane-bound particles containing a variety of RNA types, DNA, proteins, and other macromolecules, are now appreciated as an important means of communication between cells and tissues, both in normal cellular physiology and as a potential indicator of cellular stress, environmental exposures, and early disease pathogenesis. Extracellular signaling through EVs is a growing field of research for understanding fundamental mechanisms of health and disease and for the potential for biomarker discovery and therapy development. EVs are also known to play important roles in mediating the effects of exposure to environmental stress.

OBJECTIVES

This seminar addresses the application of new tools and approaches for EV research, developed in part through the National Institutes of Health (NIH) Extracellular RNA Communication Program, and reflects presentations and discussions from a workshop held 27-28 September 2021 by the National Institute of Environmental Health Sciences (NIEHS) and the National Center for Advancing Translational Sciences (NCATS) on "Extracellular Vesicles, Exosomes, and Cell-Cell Signaling in Response to Environmental Stress." The panel of experts discussed current research on EVs and environmental exposures, highlighted recent advances in EV isolation and characterization, and considered research gaps and opportunities toward identifying and characterizing the roles for EVs in environmentally related diseases, as well as the current challenges and opportunities in this field.

DISCUSSION

The authors discuss the application of new experimental models, particularly organ-on-chip (OOC) systems and in vitro approaches and how these have the potential to extend findings in population-based studies of EVs in exposure-related diseases. Given the complex challenges of identifying cell-specific EVs related to environmental exposures, as well as the general heterogeneity and variability in EVs in blood and other accessible biological samples, there is a critical need for rigorous reporting of experimental methods and validation studies. The authors note that these efforts, combined with cross-disciplinary approaches, would ensure that future research efforts in environmental health studies on EV biomarkers are rigorous and reproducible. https://doi.org/10.1289/EHP12980.

Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls

Introduction

Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches.

Methods

Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree.

Results

Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data.

Discussion

Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.