Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes
Background: Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing tech...
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sg-ntu-dr.10356-1056652019-12-06T21:55:29Z Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes Ma, Lichun Zheng, Jie School of Computer Science and Engineering Biomedical Informatics Lab Single-cell Hyperglycaemia DRNTU::Engineering::Computer science and engineering Background: Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing technology can provide additional information about the molecular mechanisms of T2D at single-cell level. Results: In this work, we analyze three datasets of single-cell transcriptomes to reveal β-cell dysfunction and deficit mechanisms in T2D. Focused on the expression levels of key genes, we conduct discrimination of healthy and T2D β-cells using five machine learning classifiers, and extracted major influential factors by calculating correlation coefficients and mutual information. Our analysis shows that T2D β-cells are normal in insulin gene expression in the scenario of low cellular stress (especially oxidative stress), but appear dysfunctional under the circumstances of high cellular stress. Remarkably, oxidative stress plays an important role in affecting the expression of insulin gene. In addition, by analyzing the genes related to apoptosis, we found that the TNFR1-, BAX-, CAPN1- and CAPN2-dependent pathways may be crucial for β-cell apoptosis in T2D. Finally, personalized analysis indicates cell heterogeneity and individual-specific insulin gene expression. Conclusions: Oxidative stress is an important influential factor on insulin gene expression in T2D. Based on the uncovered mechanism of β-cell dysfunction and deficit, targeting key genes in the apoptosis pathway along with alleviating oxidative stress could be a potential treatment strategy for T2D. Published version 2019-06-13T06:01:42Z 2019-12-06T21:55:29Z 2019-06-13T06:01:42Z 2019-12-06T21:55:29Z 2018 Journal Article Ma, L., & Zheng, J. (2018). Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes. BMC Bioinformatics, 19(S19), 515-. doi:10.1186/s12859-018-2519-1 https://hdl.handle.net/10356/105665 http://hdl.handle.net/10220/48721 http://dx.doi.org/10.1186/s12859-018-2519-1 en BMC Bioinformatics © 2018 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 12 p. application/pdf |
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Single-cell Hyperglycaemia DRNTU::Engineering::Computer science and engineering Ma, Lichun Zheng, Jie Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
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Background: Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing technology can provide additional information about the molecular mechanisms of T2D at single-cell level. Results: In this work, we analyze three datasets of single-cell transcriptomes to reveal β-cell dysfunction and deficit mechanisms in T2D. Focused on the expression levels of key genes, we conduct discrimination of healthy and T2D β-cells using five machine learning classifiers, and extracted major influential factors by calculating correlation coefficients and mutual information. Our analysis shows that T2D β-cells are normal in insulin gene expression in the scenario of low cellular stress (especially oxidative stress), but appear dysfunctional under the circumstances of high cellular stress. Remarkably, oxidative stress plays an important role in affecting the expression of insulin gene. In addition, by analyzing the genes related to apoptosis, we found that the TNFR1-, BAX-, CAPN1- and CAPN2-dependent pathways may be crucial for β-cell apoptosis in T2D. Finally, personalized analysis indicates cell heterogeneity and individual-specific insulin gene expression. Conclusions: Oxidative stress is an important influential factor on insulin gene expression in T2D. Based on the uncovered mechanism of β-cell dysfunction and deficit, targeting key genes in the apoptosis pathway along with alleviating oxidative stress could be a potential treatment strategy for T2D. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ma, Lichun Zheng, Jie |
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Article |
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Ma, Lichun Zheng, Jie |
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Ma, Lichun |
title |
Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
title_short |
Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
title_full |
Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
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Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
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Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
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single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes |
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2019 |
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https://hdl.handle.net/10356/105665 http://hdl.handle.net/10220/48721 http://dx.doi.org/10.1186/s12859-018-2519-1 |
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