Human Kidney Tubule-Specific Gene Expression Based Dissection of Chronic Kidney Disease Traits.
Human Kidney Tubule-Specific Gene Expression Based Dissection of Chronic Kidney Disease Traits.
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DOI：
10.1016/j.ebiom.2017.09.014
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发表时间：
2017-10
期刊：
影响因子：
11.1
通讯作者：
Susztak K
中科院分区：
文献类型：
--
作者：
Beckerman P;Qiu C;Park J;Ledo N;Ko YA;Park AD;Han SY;Choi P;Palmer M;Susztak K
慢性肾脏病(CKD)有多种表型表现，包括结构改变(如纤维化)和功能改变(如肾小球滤过率和蛋白尿)。基因表达谱作为精确医学方法的一种重要的新工具，近年来得到了广泛的应用。在这里，我们使用无偏见和直接的方法来了解基因表达如何捕捉糖尿病和高血压CKD患者的不同CKD表现。来自95个显微解剖的人类肾脏样本的转录组数据被用于初步分析，这些样本具有一系列的人口统计学、功能和结构变化。从41个样本中获得的数据可用于验证。利用无偏加权基因共表达网络分析(WGCNA)，我们鉴定了16个共表达的基因模块。我们发现，与EGFR密切相关的模块主要编码具有代谢功能的基因。主要编码T细胞受体和胶原途径的基因群与纤维化程度的相关性最强，提示这两种表型表现可能有不同的潜在机制。然后使用线性回归模型来确定其表达与结构性(纤维化)或功能性(EGFR)表现显著相关的基因，并主要证实了WGCNA的结果。我们的结论是，基因表达是纤维化的一个非常敏感的传感器，因为即使在调整了EGFR和其他临床参数后，1654个基因的表达也与纤维化相关。GFR和基因表达之间的关联主要是通过纤维化来调节的。总之，我们基于转录组的CKD特征剖析表明，基因表达和肾功能之间的关联是由结构变化介导的，可能存在导致肾功能下降和纤维化发展的不同途径。肾脏标本的基因表达分析表明，基因表达与EGFR的关系是由纤维化介导的，免疫相关途径与纤维化发展的相关性最强，代谢途径与EGFR的相关性最强，慢性肾脏疾病以功能改变(肾小球滤过率，EGFR)和结构改变(主要是肾纤维化)为特征。分析了人类肾脏样本的基因表达谱，以了解这两种表现之间的关系。我们发现，基因表达和EGFR之间的联系是由纤维化介导的，这表明纤维化是功能性肾脏衰退的关键决定因素，也是一个潜在的治疗靶点。基因表达分析还表明，纤维化与免疫途径密切相关，EGFR与代谢途径密切相关，突出了肾脏疾病结构和功能表现之间的潜在机制差异。
Chronic kidney disease (CKD) has diverse phenotypic manifestations including structural (such as fibrosis) and functional (such as glomerular filtration rate and albuminuria) alterations. Gene expression profiling has recently gained popularity as an important new tool for precision medicine approaches. Here we used unbiased and directed approaches to understand how gene expression captures different CKD manifestations in patients with diabetic and hypertensive CKD. Transcriptome data from ninety-five microdissected human kidney samples with a range of demographics, functional and structural changes were used for the primary analysis. Data obtained from 41 samples were available for validation. Using the unbiased Weighted Gene Co-Expression Network Analysis (WGCNA) we identified 16 co-expressed gene modules. We found that modules that strongly correlated with eGFR primarily encoded genes with metabolic functions. Gene groups that mainly encoded T-cell receptor and collagen pathways, showed the strongest correlation with fibrosis level, suggesting that these two phenotypic manifestations might have different underlying mechanisms. Linear regression models were then used to identify genes whose expression showed significant correlation with either structural (fibrosis) or functional (eGFR) manifestation and mostly corroborated the WGCNA findings. We concluded that gene expression is a very sensitive sensor of fibrosis, as the expression of 1654 genes correlated with fibrosis even after adjusting to eGFR and other clinical parameters. The association between GFR and gene expression was mostly mediated by fibrosis. In conclusion, our transcriptome-based CKD trait dissection analysis suggests that the association between gene expression and renal function is mediated by structural changes and that there may be differences in pathways that lead to decline in kidney function and the development of fibrosis, respectively. Gene expression analysis of kidney samples shows the relationship between gene expression and eGFR is mediated by fibrosis Immune related pathways show the strongest correlation with fibrosis development Metabolic pathways show a strong correlation with eGFR Chronic kidney disease is characterized by functional changes (glomerular filtration rate, eGFR) and structural changes (mainly renal fibrosis). Gene expression profiles of human kidney samples were analyzed to understand the relationship between these two manifestations. We found that the association between gene expression and eGFR is mediated by fibrosis, suggesting that fibrosis is a crucial determinant of functional kidney decline, and a potential therapeutic target. Gene expression analysis also indicates that fibrosis strongly correlates with immune pathways, and eGFR with metabolic pathways, highlighting potential mechanistic differences between structural and functional manifestations of kidney disease.
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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
13.2
作者：
Heerspink, Hiddo J. Lambers;Tighiouart, Hocine;Inker, Lesley A.
通讯作者：
Inker, Lesley A.
DOI：
10.2215/cjn.03530708
发表时间：
2009-02-01
影响因子：
9.8
作者：
Bolignano, Davide;Lacquaniti, Antonio;Buemi, Michele
通讯作者：
Buemi, Michele
DOI：
10.2215/cjn.09241010
发表时间：
2011-04-01
影响因子：
9.8
作者：
Botev, Rossini;Mallie, Jean-Pierre;Schueck, Otto
通讯作者：
Schueck, Otto
DOI：
10.1007/bf01490515
发表时间：
1977-01-01
期刊：
KLINISCHE WOCHENSCHRIFT
影响因子：
--
作者：
FISCHBACH, H;MACKENSEN, S;BOHLE, A
通讯作者：
BOHLE, A