Transcriptomic changes in the nasal epithelium associated with diesel engine exhaust exposure

Drizik, Eduard; Corbett, Sean; Zheng, Yuxin; Vermeulen, Roel; Dai, Yufei; Hu, Wei; Ren, Dianzhi; Duan, Huawei; Niu, Yong; Xu, Jianing; Fu, Wei; Meliefste, Kees; Zhou, Baosen; Zhang, Mengjie; Yang, Jincai; Bassig, Bryan A.; Liu, Hanqiao; Ye, M.; Liu, Gang; Jia, Xiaowei; Meng, Tao; Peng, Bin; Zhang, J.; Silverman, Debra T.; Spira, Avrum; Rothman, Nathaniel; Lenburg, Marc E.; Lan, Qing

doi:10.1016/j.envint.2020.105506

Cited by 19 publications

(20 citation statements)

References 43 publications

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“…We selected nasal epithelium as a local sampling site because in previous studies the quality of nasal epithelial samples has been sufficient for transcriptomic analysis, 29 whereas the quality of sputum samples has varied. 30 Localized (nasal biopsy samples) and systemic (PBMCs) gene expression was profiled after exposure to either flour, isocyanates, or welding fumes.…”

Section: Discussionmentioning

confidence: 99%

Endotyping asthma related to 3 different work exposures

Suojalehto

Ndika

Lindström

et al. 2021

Journal of Allergy and Clinical Immunology

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Section: Discussionmentioning

confidence: 99%

Endotyping asthma related to 3 different work exposures

Suojalehto

Ndika

Lindström

et al. 2021

Journal of Allergy and Clinical Immunology

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“…We selected nasal epithelia as a local sampling site because in previous studies the quality of nasal epithelia samples has been sufficient for transcriptomic analysis [ 21 ], whereas quality of sputum samples varies [ 22 ]. Previous studies have identified IL-17, IL-8, and IL-6 as key cytokines in non-T2 asthma [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic Profiling of Adult-Onset Asthma Related to Damp and Moldy Buildings and Idiopathic Environmental Intolerance

Suojalehto

Ndika

Lindström

et al. 2021

IJMS

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A subset of adult-onset asthma patients attribute their symptoms to damp and moldy buildings. Symptoms of idiopathic environmental intolerance (IEI) may resemble asthma and these two entities overlap. We aimed to evaluate if a distinct clinical subtype of asthma related to damp and moldy buildings can be identified, to unravel its corresponding pathomechanistic gene signatures, and to investigate potential molecular similarities with IEI. Fifty female adult-onset asthma patients were categorized based on exposure to building dampness and molds during disease initiation. IEI patients (n = 17) and healthy subjects (n = 21) were also included yielding 88 study subjects. IEI was scored with the Quick Environmental Exposure and Sensitivity Inventory (QEESI) questionnaire. Inflammation was evaluated by blood cell type profiling and cytokine measurements. Disease mechanisms were investigated via gene set variation analysis of RNA from nasal biopsies and peripheral blood mononuclear cells. Nasal biopsy gene expression and plasma cytokine profiles suggested airway and systemic inflammation in asthma without exposure to dampness (AND). Similar evidence of inflammation was absent in patients with dampness-and-mold-related asthma (AAD). Gene expression signatures revealed a greater degree of similarity between IEI and dampness-related asthma than between IEI patients and asthma not associated to dampness and mold. Blood cell transcriptome of IEI subjects showed strong suppression of immune cell activation, migration, and movement. QEESI scores correlated to blood cell gene expression of all study subjects. Transcriptomic analysis revealed clear pathomechanisms for AND but not AAD patients. Furthermore, we found a distinct molecular pathological profile in nasal and blood immune cells of IEI subjects, including several differentially expressed genes that were also identified in AAD samples, suggesting IEI-type mechanisms.

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“…Diesel engine exhaust (DEE) is one of the primary causes of air pollution worldwide, which can affect the human body. This RNA dataset was collected from those working in diesel engine factories, along with those working without DEE exposure as the control group, for a detailed analysis of genes affected by air pollution [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative Study of Classification Algorithms for Various DNA Microarray Data

Kim

Yoon

Park

et al. 2022

Genes

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Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.

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Transcriptomic changes in the nasal epithelium associated with diesel engine exhaust exposure

Cited by 19 publications

References 43 publications

Endotyping asthma related to 3 different work exposures

Endotyping asthma related to 3 different work exposures

Transcriptomic Profiling of Adult-Onset Asthma Related to Damp and Moldy Buildings and Idiopathic Environmental Intolerance

Comparative Study of Classification Algorithms for Various DNA Microarray Data

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