Polycystic kidney disease is an inherited degenerative disease in which the uriniferous tubules are replaced by expanding fluid-filled cysts that ultimately destroy organ function. Autosomal dominant polycystic kidney disease (ADPKD) is the most common form, afflicting approximately 1 in 1,000 people. It primarily is caused by mutations in the transmembrane proteins Polycystin-1 (PKD1) and Polycystin-2 (PKD2). The most proximal effects of polycystin mutations leading to cyst formation are not known, but pro-proliferative signaling must be involved for the tubule epithelial cells to increase in number over time. The stress-activated mitogen-activated protein kinase (MAPK) pathway c-Jun N-terminal kinase (JNK) promotes proliferation in specific contexts and is activated in acute and chronic kidney disease. Previous work found evidence of JNK activation in cystic tissues (Le et al., 2005) and others showed that JNK signaling is activated by aberrant expression of PKD1 and PKD2 in cell culture (Arnould et al., 1998; Arnould et al., 1999; Parnell et al., 2002; Yu et al., 2010) but the contribution of JNK signaling to cystic disease in vivo has not been investigated. This body of work describes the use of conditional and germline deletion of Pkd2, Jnk1 and Jnk2 to model ADPKD and JNK signaling inhibition in juvenile and adult mice. Immunoblots and histological staining were used to measure JNK activation and evaluate the effect of JNK deletion on cystic disease. Results show that Pkd2 deletion activated JNK signaling in juvenile and adult mice. Reduction of JNK activity significantly reduced cystic burden in kidneys of juvenile Pkd2 mutant mice. This correlated with reduced tubule cell proliferation and reduced kidney fibrosis. The improvement in cystic phenotype was driven primarily by Jnk1 deletion rather than Jnk2. JNK signaling inhibition in adult Pkd2 mutants significantly reduced liver cysts when mice were aged six months. JNK inhibition reduces the severity of cystic disease caused by the loss of Pkd2 suggesting that the JNK pathway should be explored as a potential therapeutic target for ADPKD.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21008号 / 医博第4354号 / 新制||医||1028(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 長船 健二, 教授 小川 修, 教授 横出 正之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Polycystic kidney disease is a severe genetic disease occurring in both adult and pediatric patients. The basic characteristic of this disease is the development and progressive enlargement of renal cysts gradually replacing functional kidney tissue. This leads to renal failure in many patients. However, renal cysts may also occur in a number of other diseases, including multisystem syndromes. This complicates differential diagnosis in some patients. In our study, we first focused on the diagnosis and characterization of genotypic-phenotypic relationships in patients with polycystic disease arising in childhood, later we extended our study to adult patients and patients with unclear clinical diagnosis. At the same time, we expanded the portfolio of analyzed disorders to a number of diseases in which the phenotype of polycystic kidneys may occur, and noncystic diseases as well. During our project, massive parallel sequencing was used to analyze 149 patients - 128 with cystic and 21 with noncystic clinically diagnosed nephropathies. At the same time, the findings were verified by Sanger sequencing in 176 relatives of our probands. Mutation detection reached 59% in cystic patients, and 43% in non-cystic patients, respectively. In many patients, molecular genetic analysis revealed a different etiology...

Renal phase-contrast magnetic resonance imaging (PC-MRI) is an MRI modality where the phase component of the MR signal is made sensitive to the velocity of water molecules in the kidneys. PC-MRI is able to assess the Renal Blood Flow (RBF), which is an important biomarker in the development of kidney disease. RBF is analyzed with the manual or semi-automatic delineation by experts of the renal arteries in PC-MRI. This is a time-consuming and operator-dependent process. We have therefore trained, validated and tested a fully-automated deep learning model for faster and more objective renal artery segmentation. The PC-MRI data used in model training, validation and testing come from four studies (N=131 subjects). Images were acquired from three manufacturers with different imaging parameters. The best deep learning model found consists of a deeply-supervised 2D attention U-Net with residual skip connections. The output of this model was re-introduced as an extra channel in a second iteration to refine the segmentation result. The flow values in the segmented regions were integrated to provide a quantification of the mean arterial flow in the segmented renal arteries. The automated segmentation was evaluated in all the images that had manual segmentation ground-truths that come from a single operator. The evaluation was completed in terms of a segmentation accuracy metric called Dice Coefficient. The mean arterial flow values that were quantified from the auto-mated segmentation were also evaluated against ground-truth flow values from semi-automatic software. The deep learning model was trained and validated on images with segmentation ground-truths with 4-fold cross-validation. A Dice segmentation accuracy of 0.71±0.21 was achieved (N=73 subjects). Although segmentation results were accurate for most arteries, the algorithm failed in ten out of 144arteries. The flow quantification from the segmentation was highly correlated without significant bias in comparison to the ground-truth flow measurements. This method shows promise for supporting RBF measurements from PC-MRI and can likely be used to save analysis time in future studies. More training data has to be used for further improvement, both in terms of accuracy and generalizability.

Chronic kidney disease (CKD) affects 13.4% of adults in America; and 38% in people aged 65 years or older[1]. In addition, cardiovascular disease (CVD) is the leading cause of death in CKD patients with end-stage kidney disease. CKD is associated with chronic inflammation, which contributes to the progression of CVD[2]. Furthermore, CKD alter apolipoprotein profile and elevate plasma lipid levels. It has been reported that 68.8% of CKD patients are associated with hyperlipidemia[3]. Therefore, hyperlipidemia is the critical risk factor for cardiovascular morbidity and mortality in CKD patients [4, 5]. In addition, trained immunity has been shown to play a critical role in chronic inflammatory diseases[6]. However, whether trained immunity promotes the inflammation in hyperlipidemia-CKD remains unclear. Circulating lipopolysaccharide (LPS) is significantly increased in atherosclerotic and CKD patients[7]. Clinical data indicates that circulating LPS is positively associated with the progression of CKD, and its levels even higher in patients with hemodialysis or dialysis[8]. Studies found that circulating LPS is delivered into cytosol for caspase-4/11 activation[9]. The Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) involving 10,061 patients found that targeting interleukin-1β (IL-1β) innate immunity pathway is significantly lowered the rate of recurrent cardiovascular events independent of lipid-level lowering[10]. Therefore, inhibiting the secretion of proinflammatory cytokine IL-1β has high potential to future development of novel therapeutics for hyperlipidemia-CKD accelerated CVD. Gasdermin D (GSDMD) is cleaved by inflammatory caspase-1 and caspase-4. N-terminal GSDMD binds to plasma membrane forming protein channel [11] and mediates the secretion of IL-1β[12, 13]. We found that caspase-1 activation was significantly decreased in caspase-4/11 deficient high-fat diet (HFD)-CKD mice, indicating that caspase-4 could regulate caspase-1 activation in HFD-CKD. Whether increased cytosolic LPS contribute to the increased vascular inflammation via caspase-4/11-GSDMD-IL-1β pathway remains unknown. In this study, we used HFD fed 5/6 nephrectomy CKD mice in vivo and cytosolic LPS stimulation in human aortic endothelial cell (HAECs) in vitro. We made the following results: 1) Inflammatory pathways are significantly increased in the aorta of HFD-CKD compared to HFD-Sham, normal diet (ND)-CKD, and ND-Sham. 2) Expression levels of endothelial cell activation markers (ICAM1 and VCAM1) are significantly increased in the aorta of HFD-CKD mice compared to HFD-Sham, ND-CKD, and ND-Sham. 3) Caspase-4 activation and N-GSDMD cleavage are significantly increased in the aorta of HFD-CKD mice compared to HFD-Sham, ND-CKD, and ND-Sham and in cytosolic LPS stimulated HAECs. 4) The increased inflammatory pathways and increased expression of adhesion molecules are decreased in the deficiency of caspase-4 in vivo and in the presence of caspase-4 inhibitor and N-terminal GSDMD cleavage inhibitor in vitro. 5) The increased mitochondrial ROS promote endothelial cell activation via caspase-4-GSDMD axis. Taken together, the caspase-4/11-GSDMD axis mediates endothelial cell activation and vascular inflammation in the aorta of HFD-CKD mice compared to controls. Furthermore, the increased endothelial cell activation and vascular inflammation are restored by caspase-4/11 deficiency in the aorta of HFD-CKD mice. These evidence indicate that inhibiting caspase-4/11-GSDMD axis could be a potential therapeutic target for inhibiting vascular inflammation associated with hyperlipidemia-CKD. / Biomedical Sciences

Background: There is a 3-fold risk of developing end stage kidney disease in Non-Hispanic African Americans compared to Non-Hispanic White Americans (Centers for Disease Control and Prevention, 2017). Estimated glomerular filtration rate (eGFR), one of the fundamental algorithms for coordinating treatment for kidney disease which factors in age, race, gender, and levels of creatinine, may pose an issue in this vulnerable population. Currently African Americans receive a correction factor between 1.21 and 1.16 to their eGFR to adjusting the value higher, potentially impacting appropriate kidney disease classification, and delaying beneficial interventions (National Kidney Foundation, 2020). Methods: A systematic literature search of four databases was completed. Eligibility criteria included 1) published in a peer reviewed journal, 2) English language, 3) the use of race correction in calculating eGFR, and 4) a quantitative study design. A total of 47 articles were screened with 17 selected for final review. The Johns-Hopkins Nursing Evidence - Based Practice evidence guide was then used to rate the strength and quality of the evidence. Results: Early evidence of the unreliability of race based eGFR equations emerged in 2008, and the body of evidence continues to grow. Recent studies have found eGFR calculated with no race corrections correlate best with directly measured iothalmate GFR in black patients (Zelnick et al., 2021), and that a potential 1,066,026 Black Americans may be reclassified to a more severe stage of CKD (Bragg-Gresham et al., 2021). Use of the race correction in GFR equations has been poorly supported in studies conducted in Africa and Brazil. For those with HIV, an accurate eGFR is doubly important yet all eGFR equations have marked variability. Some medical facilities have successfully updated to calculating eGFR without the racial coefficient (Shi et al., 2021). Conclusion: Nurses should be aware of the implications of using race correction in eGFR equations, educate their patients on its use, and advocate for those near threshold targets to ensure equitable and timely access to appropriate kidney disease interventions.

Chronic kidney disease greatly raises cardiovascular disease risk. Heart disease and death risk grow proportionately with renal disease progression. Investigate the link between cardiovascular disease prevalence and chronic renal disease severity and mortality using meta-analysis. In this study, 155 publications were found after searching several databases (including PubMed and Google Scholar). 48 studies that matched the inclusion criteria were included in the literature review, however, only 20 were included in the meta-analysis. 17101 people had CKD, while 8883 had CVD or non-CVD. Using the R programming language, a meta-analysis was performed to get a pooled impact of the influence of CVD on the severity of CKD (odds ratio OR), and a funnel plot was also generated to check for publication bias. The outcomes of the meta-analysis indicate that cardiovascular disease has a moderate impact on the severity of chronic kidney disease (OR=2.28, 95% CI, 1.90-2.73). All data will give essential insights into the epidemiology of the cardiovascular disease in chronic kidney disease (CKD), disclose the influence of individual risk variables on bad outcomes, and serve as the platform for future interventional research. Further investigation of the particular (non-traditional) risk factors associated with the renal illness that contribute to accelerated atherosclerosis in this population is necessary to improve the efficacy of cardiovascular treatments for patients with CKD. The purpose of this research is to determine whether and how these variables affect the development of CKD. / <p>Utbytesstudent.</p>

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24795号 / 医博第4987号 / 新制||医||1066(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 小林 恭, 教授 波多野 悦朗, 教授 羽賀 博典 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(社会健康医学) / 甲第24536号 / 社医博第128号 / 新制||社医||12(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 近藤 尚己, 教授 西浦 博, 教授 柳田 素子 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM