| Literature DB >> 29602296 |
Louise C Evans1, Alex Dayton1, Chun Yang1, Pengyuan Liu1,2, Theresa Kurth1, Kwang Woo Ahn3, Steve Komas4, Francesco C Stingo5, Purushottam W Laud6, Marina Vannucci5, Mingyu Liang1,2, Allen W Cowley1.
Abstract
Studies exploring the development of hypertension have traditionally been unable to distinguish which of the observed changes are underlying causes from those that are a consequence of elevated blood pressure. In this study, a custom-designed servo-control system was utilized to precisely control renal perfusion pressure to the left kidney continuously during the development of hypertension in Dahl salt-sensitive rats. In this way, we maintained the left kidney at control blood pressure while the right kidney was exposed to hypertensive pressures. As each kidney was exposed to the same circulating factors, differences between them represent changes induced by pressure alone. RNA sequencing analysis identified 1,613 differently expressed genes affected by renal perfusion pressure. Three pathway analysis methods were applied, one a novel approach incorporating arterial pressure as an input variable allowing a more direct connection between the expression of genes and pressure. The statistical analysis proposed several novel pathways by which pressure affects renal physiology. We confirmed the effects of pressure on p-Jnk regulation, in which the hypertensive medullas show increased p-Jnk/Jnk ratios relative to the left (0.79 ± 0.11 vs. 0.53 ± 0.10, P < 0.01, n = 8). We also confirmed pathway predictions of mitochondrial function, in which the respiratory control ratio of hypertensive vs. control mitochondria are significantly reduced (7.9 ± 1.2 vs. 10.4 ± 1.8, P < 0.01, n = 6) and metabolomic profile, in which 14 metabolites differed significantly between hypertensive and control medullas ( P < 0.05, n = 5). These findings demonstrate that subtle differences in the transcriptome can be used to predict functional changes of the kidney as a consequence of pressure elevation.Entities:
Keywords: inflammation; metabolism; perfusion pressure; renal transcriptome
Mesh:
Year: 2018 PMID: 29602296 PMCID: PMC6032288 DOI: 10.1152/physiolgenomics.00034.2018
Source DB: PubMed Journal: Physiol Genomics ISSN: 1094-8341 Impact factor: 3.107