Identifying the Transcriptional Response of Cancer and Inflammation-Related Genes in Lung Cells in Relation to Ambient Air Chemical Mixtures in Houston, Texas

A review of disrupted biological response associated with volatile organic compound exposure: Insight into identification of biomarkers

Volatile organic compounds (VOCs) are widespread harmful atmospheric pollutants, which have long been concerned and elucidated to be one of the risks of acute and chronic diseases for human, such as leukemia and cancer. Although numerous scientific studies have documented the potential adverse outcomes caused by VOC exposure, the mechanisms which biological response pathways of these VOC disruption remain poorly understood. Therefore, the identification of biochemical markers associated with metabolism, health effects and diseases orientation can be an effective means of screening biological targets for VOC exposure, which provide evidences to the toxicity assessment of compounds. The current review aims to understand the mechanisms underlying VOCs-elicited adverse outcomes by charactering various types of biomarkers. VOCs-related biomarkers from three aspects were summarized through in vitro, animal and epidemiological studies. i) Unmetabolized and metabolized VOC biomarkers in human samples for assessing exposure characteristics in different communities; ii) Adverse endpoint effects related biomarkers, mainly including (anti)oxidative stress, inflammation response and DNA damage; iii) Omics-based molecular biomarkers alteration in gene, protein, lipid and metabolite aspects associated with biological signaling pathway disorders response to VOC exposure. Further research, advanced machine learning and bioinformation approaches combined with experimental results are urgently needed to ascertain the selection of biomarkers and further illuminate toxic mechanisms of VOC exposure. Finally, VOCs-induced disease causes can be predicted with proven results.

Integrated multi-omics analyses reveal the TM4SF family genes with prognostic and therapeutic relevance in hepatocellular carcinoma

TM4SF family members (TM4SFs) have been shown to be aberrantly expressed in multiple types of cancer. However, a comprehensive investigation of the TM4SFs has yet to be performed in LIHC. The study comprehensively investigated the expression and prognostic value of TM4SFs. Then, a TM4SFs-based risk model and nomogram were constructed for prognostic prediction. Finally, functional loss of TM4SFs was performed to verify the potential role of TM4SFs in LIHC. We found that TM4SFs were significantly up-regulated in LIHC. High expression and hypomethylation of TM4SFs were associated with poor prognosis of LIHC patients. Then, a TM4SFs-based risk model was constructed that could effectively classify LIHC patients into high and low-risk groups. In addition, we constructed a prognostic nomogram that could predict the long-term survival of LIHC patients. Based on immune infiltration analysis, high-risk patients had a relatively higher immune status than low-risk patients. Moreover, the prediction module could predict patient responses to immunotherapy and chemotherapy. Finally, loss-of-function studies showed that TM4SF4 knockdown could substantially suppress the growth, migratory, and invasive abilities of LIHC cells. Targeting TM4SFs will contribute to effective immunotherapy strategies and improve the prognosis of liver cancer patients.

Respirable Metals, Bacteria, and Fungi during a Saharan-Sahelian Dust Event in Houston, Texas

Although airborne bacteria and fungi can impact human, animal, plant, and ecosystem health, very few studies have investigated the possible impact of their long-range transport in the context of more commonly measured aerosol species, especially those present in an urban environment. We report first-of-kind simultaneous measurements of the elemental and microbial composition of North American respirable airborne particulate matter concurrent with a Saharan-Sahelian dust episode. Comprehensive taxonomic and phylogenetic profiles of microbial communities obtained by 16S/18S/ITS rDNA sequencing identified hundreds of bacteria and fungi, including several cataloged in the World Health Organization's lists of global priority human pathogens along with numerous other animal and plant pathogens and (poly)extremophiles. While elemental analysis sensitively tracked long-range transported Saharan dust and its mixing with locally emitted aerosols, microbial diversity, phylogeny, composition, and abundance did not well correlate with the apportioned African dust mass. Bacterial/fungal diversity, phylogenetic signal, and community turnover were strongly correlated to apportioned sources (especially vehicular emissions and construction activities) and elemental composition (especially calcium). Bacterial communities were substantially more dissimilar from each other across sampling days than were fungal communities. Generalized dissimilarity modeling revealed that daily compositional turnover in both communities was linked to calcium concentrations and aerosols from local vehicles and Saharan dust. Because African dust is known to impact large areas in northern South America, the Caribbean Basin, and the southern United States, the microbiological impacts of such long-range transport should be assessed in these regions.

Computational Approach to Link Chemicals in Anthropogenic Smoke Particulate Matter with Toxicity

A weighted chemical coexpression network analysis (WCCNA) was utilized to identify chemicals co-modulated to variable burning of anthropogenic materials and to link chemicals to biological responses (lung toxicity and mutagenicity). Polyaromatic hydrocarbons (PAHs) were co-modulated with increased concentrations in flaming smoke particulate matter (PM) from the burning of plastic-containing materials and showed significant association with increased neutrophil influx, cytokine levels, and mutagenicity. Inorganic elements were co-modulated with increased concentrations in flaming plywood and cardboard smoke PM and showed significant association with increased protein and albumin levels. This study shows the potential for using a computational network analysis to identify and prioritize hazardous chemical components within complex environmental mixtures and provides guidance on key chemical tracers required for intervention research to protect public health from the exposure.

Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke

INTRODUCTION

Wildfires are a threat to public health world-wide that are growing in intensity and prevalence. The biological mechanisms that elicit wildfire-associated toxicity remain largely unknown. The potential involvement of cross-tissue communication via extracellular vesicles (EVs) is a new mechanism that has yet to be evaluated.

METHODS

Female CD-1 mice were exposed to smoke condensate samples collected from the following biomass burn scenarios: flaming peat; smoldering peat; flaming red oak; and smoldering red oak, representing lab-based simulations of wildfire scenarios. Lung tissue, bronchoalveolar lavage fluid (BALF) samples, peripheral blood, and heart tissues were collected 4 and 24 h post-exposure. Exosome-enriched EVs were isolated from plasma, physically characterized, and profiled for microRNA (miRNA) expression. Pathway-level responses in the lung and heart were evaluated through RNA sequencing and pathway analyses.

RESULTS

Markers of cardiopulmonary tissue injury and inflammation from BALF samples were significantly altered in response to exposures, with the greatest changes occurring from flaming biomass conditions. Plasma EV miRNAs relevant to cardiovascular disease showed exposure-induced expression alterations, including miR-150, miR-183, miR-223-3p, miR-30b, and miR-378a. Lung and heart mRNAs were identified with differential expression enriched for hypoxia and cell stress-related pathways. Flaming red oak exposure induced the greatest transcriptional response in the heart, a large portion of which were predicted as regulated by plasma EV miRNAs, including miRNAs known to regulate hypoxia-induced cardiovascular injury. Many of these miRNAs had published evidence supporting their transfer across tissues. A follow-up analysis of miR-30b showed that it was increased in expression in the heart of exposed mice in the absence of changes to its precursor molecular, pri-miR-30b, suggesting potential transfer from external sources (e.g., plasma).

DISCUSSION

This study posits a potential mechanism through which wildfire exposures induce cardiopulmonary responses, highlighting the role of circulating plasma EVs in intercellular and systems-level communication between tissues.

Ferroptosis-Related Proteins Are Potential Diagnostic Molecular Markers for Patients with Preeclampsia

Preeclampsia (PE) is the leading cause of maternal and fetal mortality and morbidity. Early and accurate diagnosis is critical to reduce mortality. Placental oxidative stress has been identified as a major pathway to the development of PE. Ferroptosis, a new form of regulated cell death, is associated with iron metabolism and oxidative stress, and has been suspected to play a role in the pathophysiology of PE, although the mechanism is yet to be elucidated. The identification of potential ferroptosis-related biomarkers is of great significance for the early diagnosis and treatment of PE. A gene expression dataset of peripheral blood samples was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed genes (DEGs) were filtrated with the R package “limma”. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs were then conducted. Ferroptosis-related DEGs were screened by overlapping the ferroptosis-related genes with DEGs. The protein−protein interaction (PPI) network was used to identify the key ferroptosis-related DEGs. Enzyme-linked immunosorbent assay (ELISA) was used to validate changes in the selected key ferroptosis-related DEGs. The correlations between the key genes and clinical and pathological characteristics were analyzed. Finally, the diagnostic value of these key genes for PE was confirmed by a receiver operating characteristic (ROC) curve. A total of 5913 DEGs were identified and 45 ferroptosis-related DEGs were obtained. Besides, ferroptosis-related pathways were enriched by KEGG using DEGs. The PPI network showed that p53 and c-Jun were the critical hub genes. ELISA showed that p53 in the serum of PE patients was higher than that of the control group, while c-Jun was lower than that of the control group. Analysis of the clinicopathological features showed that p53 and c-Jun were correlated with the PE characteristics. Finally, based on the area under curve (AUC) values, c-Jun had the superior diagnostic power (AUC = 0.87, p < 0.001), followed by p53 (AUC = 0.75, p < 0.001). Our study identified that two key genes, p53 and c-Jun, might be potential diagnostic biomarkers of PE.

Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.