scholarly journals Tobacco-Use Disparity in Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Smoking Status ◽  
Age Groups ◽  
Emergency Situation ◽  
Smoking History ◽  
Normal Lung ◽  
Racial Groups ◽  
Susceptible Population ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale datasets of normal lung tissue to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression. No significant disparities in ACE2 gene expression were found between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in smoker samples compared to non-smoker samples. This indicates the smokers may be more susceptible to 2019-nCov and thus smoking history should be considered in identifying susceptible population and standardizing treatment regimen.

scholarly journals Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Smoking Status ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers

AbstractIn current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn&rsquo;t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (&gt;60 vs &lt;60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker&rsquo;s lung compared to non-smoker&rsquo;s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals Inter-laboratory comparison of gene expression biodosimetry for protracted radiation exposures as part of the RENEB and EURADOS WG10 2019 exercise

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Abend ◽  
S. A. Amundson ◽  
C. Badie ◽  
K. Brzoska ◽  
R. Hargitai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Radiation Exposure ◽  
Dose Rate ◽  
Large Scale ◽  
Biological Response ◽  
Emergency Situation ◽  
Blood Samples ◽  
Dose Rates ◽  
Qrt Pcr ◽  
Edta Blood

AbstractLarge-scale radiation emergency scenarios involving protracted low dose rate radiation exposure (e.g. a hidden radioactive source in a train) necessitate the development of high throughput methods for providing rapid individual dose estimates. During the RENEB (Running the European Network of Biodosimetry) 2019 exercise, four EDTA-blood samples were exposed to an Iridium-192 source (1.36 TBq, Tech-Ops 880 Sentinal) at varying distances and geometries. This resulted in protracted doses ranging between 0.2 and 2.4 Gy using dose rates of 1.5–40 mGy/min and exposure times of 1 or 2.5 h. Blood samples were exposed in thermo bottles that maintained temperatures between 39 and 27.7 °C. After exposure, EDTA-blood samples were transferred into PAXGene tubes to preserve RNA. RNA was isolated in one laboratory and aliquots of four blinded RNA were sent to another five teams for dose estimation based on gene expression changes. Using an X-ray machine, samples for two calibration curves (first: constant dose rate of 8.3 mGy/min and 0.5–8 h varying exposure times; second: varying dose rates of 0.5–8.3 mGy/min and 4 h exposure time) were generated for distribution. Assays were run in each laboratory according to locally established protocols using either a microarray platform (one team) or quantitative real-time PCR (qRT-PCR, five teams). The qRT-PCR measurements were highly reproducible with coefficient of variation below 15% in ≥ 75% of measurements resulting in reported dose estimates ranging between 0 and 0.5 Gy in all samples and in all laboratories. Up to twofold reductions in RNA copy numbers per degree Celsius relative to 37 °C were observed. However, when irradiating independent samples equivalent to the blinded samples but increasing the combined exposure and incubation time to 4 h at 37 °C, expected gene expression changes corresponding to the absorbed doses were observed. Clearly, time and an optimal temperature of 37 °C must be allowed for the biological response to manifest as gene expression changes prior to running the gene expression assay. In conclusion, dose reconstructions based on gene expression measurements are highly reproducible across different techniques, protocols and laboratories. Even a radiation dose of 0.25 Gy protracted over 4 h (1 mGy/min) can be identified. These results demonstrate the importance of the incubation conditions and time span between radiation exposure and measurements of gene expression changes when using this method in a field exercise or real emergency situation.

scholarly journals Quantitative proteomics identify Tenascin-C as a promoter of lung cancer progression and contributor to a signature prognostic of patient survival

2017 ◽  
Vol 114 (28) ◽  
pp. E5625-E5634 ◽  
Author(s):  
Vasilena Gocheva ◽  
Alexandra Naba ◽  
Arjun Bhutkar ◽  
Talia Guardia ◽  
Kathryn M. Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Quantitative Proteomics ◽  
Patient Survival ◽  
Gene Expression Signature ◽  
Lung Tumors ◽  
Smoking History ◽  
Normal Lung ◽  
Primary Tumors ◽  
Tenascin C

The extracellular microenvironment is an integral component of normal and diseased tissues that is poorly understood owing to its complexity. To investigate the contribution of the microenvironment to lung fibrosis and adenocarcinoma progression, two pathologies characterized by excessive stromal expansion, we used mouse models to characterize the extracellular matrix (ECM) composition of normal lung, fibrotic lung, lung tumors, and metastases. Using quantitative proteomics, we identified and assayed the abundance of 113 ECM proteins, which revealed robust ECM protein signatures unique to fibrosis, primary tumors, or metastases. These analyses indicated significantly increased abundance of several S100 proteins, including Fibronectin and Tenascin-C (Tnc), in primary lung tumors and associated lymph node metastases compared with normal tissue. We further showed that Tnc expression is repressed by the transcription factor Nkx2-1, a well-established suppressor of metastatic progression. We found that increasing the levels of Tnc, via CRISPR-mediated transcriptional activation of the endogenous gene, enhanced the metastatic dissemination of lung adenocarcinoma cells. Interrogation of human cancer gene expression data revealed that high TNC expression correlates with worse prognosis for lung adenocarcinoma, and that a three-gene expression signature comprising TNC, S100A10, and S100A11 is a robust predictor of patient survival independent of age, sex, smoking history, and mutational load. Our findings suggest that the poorly understood ECM composition of the fibrotic and tumor microenvironment is an underexplored source of diagnostic markers and potential therapeutic targets for cancer patients.

Immigrant Youth in Germany

2013 ◽  
Vol 18 (3) ◽  
pp. 158-168 ◽  
Author(s):  
Emily Frankenberg ◽  
Katharina Kupper ◽  
Ruth Wagner ◽  
Stephan Bongard
Keyword(s):  
School System ◽  
Large Scale ◽  
Age Groups ◽  
Well Being ◽  
Ethnic Discrimination ◽  
Emotional And Behavioral Problems ◽  
Sociocultural Adaptation ◽  
Age Related ◽  
German School ◽  
Transcultural Adaptation

This paper reviews research on young migrants in Germany. Particular attention is given to the question of how Germany’s history of migration, immigration policies, and public attitude toward migrants influence the transcultural adaptation of children and adolescents from different ethnic backgrounds. We combine past research with the results of new empirical studies in order to shed light on migrants’ psychological and sociocultural adaptation. Studies comparing young migrants and their German peers in terms of psychological well-being, life satisfaction, and mental health outcome suggest higher rates of emotional and behavioral problems among migrants of most age groups. With regard to adolescent populations between the ages of 14 and 17 years, however, the existence of differences between migrants and natives appears to be less clear. Research has also yielded inconsistent findings regarding the time trajectory of transcultural adaptation among adolescents. The coincidence of acculturation and age-related change is discussed as a possible source of these inconsistencies. Further, we provide an overview of risk and protective factors such as conflicting role expectations and ethnic discrimination, which may cause heightened vulnerability to adverse adaptation outcomes in some groups. Large-scale studies have repeatedly shown migrants of all age groups to be less successful within the German school system, indicating poor sociocultural adaptation. Possible explanations, such as the idiosyncrasies of the German school system, are presented. Our own studies contribute to the understanding of young migrants’ adaptation process by showing that it is their orientation to German culture, rather than the acculturation strategy of integration, that leads to the most positive psychological and sociocultural outcomes. The paper concludes by discussing implications for future cross-cultural research on young migrants and by suggesting recommendations for multicultural policies.

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

scholarly journals Predicting COVID-19 mortality with electronic medical records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Hossein Estiri ◽  
Zachary H. Strasser ◽  
Jeffy G. Klann ◽  
Pourandokht Naseri ◽  
Kavishwar B. Wagholikar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Medical Information ◽  
Age Groups ◽  
Prognostic Models ◽  
Smoking History ◽  
Risk Scores ◽  
Gradient Boosting ◽  
Chronic Obstructive ◽  
Clinical Expertise ◽  
History Of

AbstractThis study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65–85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.

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