High urea induces depression and LTP impairment through mTOR signalling suppression caused by carbamylation

Wang, Hongkai; Huang, Baoxin; Wang, Weiling; Li, Jinfang; Chen, Yi; Flynn, Trevor; Zhao, Meng; Zhou, Zhiming; Lin, Xiaojing; Zhang, Yinan; Xu, Mengmeng; Li, Keqiong; Tian, Kuan; Yuan, Dezhi; Zhou, Peng; Hu, Ling; Zhong, Dandan; Zhu, Shuai; Li, Jing; Chen, Dilong; Wang, Kejian; Liang, Jianhui; He, Qihua; Sun, Jianbin; Shi, Jie; Li, Yan; Sands, Jeff M.; Xie, Zhengwei; Lian, Xuemei; Xu, Duan; Ran, Jianhua; Yang, Baoxue

doi:10.1016/j.ebiom.2019.09.049

Cited by 35 publications

(31 citation statements)

References 71 publications

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“…Based on this hypothesis, we infer that the UT-B expressed by astrocytes near the vessels, by cells around the third ventricle, and in the cerebral pia mater plays a crucial role in facilitating the transport of urea into the blood or cerebrospinal fluid. In UT-B knockout mice, urea accumulation in the brain results in depressive-like behavior (Li et al, 2011;Wang et al, 2019), which also supports this inference. The substantia nigra staining is different from the cell staining.…”

Section: Discussionsupporting

confidence: 58%

Expression of Urea Transporter B in Normal and Injured Brain

et al. 2021

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Urea transporter B (UT-B) is a membrane channel protein widely distributed in mammals, and plays a significant physiological role by regulating urea and water transportation in different tissues. More and more studies have found that UT-B is related to neurological diseases, including myelinopathy and depression. When urea accumulates in the brains of UT-B knockout mice, the synaptic plasticity of neurons is reduced, and the morphology and function of glial cells are also changed. However, the distribution and expression change of UT-B remain unclear. The purpose of this study is to determine the expression characteristics of UT-B in the brain. Through single-cell RNA sequencing, UT-B was found to express universally and substantially throughout the various cells in the central nervous system except for endothelial and smooth muscle cells. UT-B was detected in the third cerebral ventricular wall, granule cell layer of the dentate gyrus, and other parts of the hippocampal, cerebral cortex, substantia nigra, habenular, and lateral hypothalamic nucleus by immunohistochemistry. Compared with the membrane expression of UT-B in glial cells, the subcellular localization of UT-B is in the Golgi apparatus of neurons. Further, the expression of UT-B was regulated by osmotic pressure in vitro. In the experimental traumatic brain injury model (TBI), the number of UT-B positive neurons near the ipsilateral cerebral cortex increased first and then decreased over time, peaking at the 24 h. We inferred that change in UT-B expression after the TBI was an adaptation to changed urea levels. The experimental data suggest that the UT-B may be a potential target for the treatment of TBI and white matter edema.

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Section: Discussionsupporting

confidence: 58%

Expression of Urea Transporter B in Normal and Injured Brain

et al. 2021

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“…bimmer also infers a DCE of lower urea on "worrier / anxious feelings" that does not survive correction for multiple testing as a TCE (G = 0.011, p = 0.062). This lends additional evidence urea levels can have psychological consequences [45,46,47].…”

Section: Bimmer Identifies Concentrated Sub-network With Correlatedmentioning

confidence: 77%

Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization

Brown

Knowles

2020

Preprint

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Inference of directed biological networks from observational genomics datasets is a crucial but notoriously difficult challenge. Modern population-scale biobanks, containing simultaneous measurements of traits, biomarkers, and genetic variation, offer an unprecedented opportunity to study biological networks. Mendelian randomization (MR) has received attention as a class of methods for inferring causal effects in observational data that uses genetic variants as instrumental variables, but MR methods rely on assumptions that limit their application to complex traits at the biobank-scale. Moreover, MR estimates the total effect of one trait on another, which may be mediated by other factors. Biobanks include measurements of many potential mediators, in principle enabling the conversion of MR estimates into direct effects representing a causal network. Here, we show that this can be accomplished by a flexible two stage procedure we call bidirectional mediated Mendelian randomization (bimmer). First, bimmer estimates the effect of every trait on every other. Next, bimmer finds a parsimonious network that explains these effects using direct and mediated causal paths. We introduce novel methods for both steps and show via extensive simulations that bimmer is able to learn causal network structures even in the presence of non-causal genetic correlation. We apply bimmer to 405 phenotypes from the UK biobank and demonstrate that learning the network structure is invaluable for interpreting the results of phenome-wide MR, while lending causal support to several recent observational studies. * bb2991@columbia.edu † dak2173@columbia.edu causal effects, MR methods must make strong assumptions that limit their ability to be applied at the biobank-scale. Perhaps the most controversial assumption is that the SNP only effects B through A (i.e. there is no horizontal pleiotropy). Recent methods such as Egger regression and the mode-based-estimator are able to relax this assumption, instead assuming there is no correlated pleiotropy or modal pleiotopy, respectively [11,12]. Another approach, the latent causal variable (LCV) model, is able to detect causality under arbitrarily-structured pleiotropy [13]. However, the quantity that LCV calculates is not interpretable as the causal effect size of A on B. Most MR studies also presuppose the direction of effect, specifying one phenotype as the outcome and the other as the exposure. Pre-specifying the effect direction is sound when the outcome is clearly biologically downstream of the exposure, but in some cases it is better to learn the direction of the effect from the data. Some researchers have instead used bidirectional MR [14,15], which tests for an effect in each direction, or gwas-pw [16], which infers the effect direction from the data. However, the utility of these approaches for complex traits, which might contain non-causal genetic correlation, is questionable [13].In mimicking a randomized controlled trial, MR estimates the total causal effect (TCE) of A on B [17]. This effec...

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“…The increased cytokine levels and alteration of neurotransmitters by uremia, and comorbid vitamin b12 deficiency and anemia, and genetic predisposition are some known biological factors [12][13][14]. Based on animal and human studies, urea accumulation in the brain is an independent factor causing depression, bypassing psychosocial stress [13].…”

Section: Discussionmentioning

confidence: 99%

Depression Impairs Level of Functioning in Chronic Kidney Disease Inpatients: A Case-Control Study

Virani¹,

Shah²,

Haneef³

et al. 2021

Cureus

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To evaluate the difference in demographics and clinical correlates during hospitalization for chronic kidney disease (CKD) between patients with depression and those without depression, and its impact on the severity of illness and in-hospital mortality. MethodsWe conducted a case-control study and included 2,296 adult inpatients (age ≥18 years) with a primary discharge diagnosis of CKD using the nationwide inpatient sample (NIS). We used propensity score matching to extract the cases i.e., CKD inpatients with depression (N = 1,264) and the controls i.e. CKD inpatients without depression (N = 1,032). The matching was done based on demographic characteristics of age at admission, sex, race, and median household income. Our outcomes of interest are the severity of illness and all-cause in-hospital mortality. All patient refined drg (APR-DRG) are allocated using health information systems software by the NIS and the severity of illness within each base APR-DRG was classified into minor, moderate, or major loss of body functions. Binomial logistic regression analysis was conducted to find the odds ratio (OR) of association for major loss of function in CKD inpatients with depression, and this model was adjusted for potential confounders of congestive heart failure (CHF), coronary artery disease (CAD), diabetes, hypertension, obesity, and tobacco abuse, and utilization of hemodialysis. ResultsA higher proportion of CKD inpatients with depression had a statistically significant higher prevalence of major loss of function (49.8% vs. 40.3% in non-depressed). There was a statistically significant difference with higher utilization of hemodialysis in CKD inpatients with depression (76.2% vs. 70.7% in nondepressed). The all-cause in-hospital mortality rate was lower in CKD inpatients with depression (2.1% vs. 3.5% in non-depressed). After controlling the logistic regression model for potential comorbidities and utilization of hemodialysis, depression was associated with increased odds (OR 1.46; 95% CI 1.227 -1.734) for major loss of function versus in non-depressed CKD inpatients ConclusionComorbid depression increases the likelihood of major loss of functioning in CKD inpatients by 46%. Treating depression can allow patients to better cope emotionally and physically with CKD and other comorbidities and significantly improve the patient's quality of life (QoL) and health outcome.

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High urea induces depression and LTP impairment through mTOR signalling suppression caused by carbamylation

Cited by 35 publications

References 71 publications

Expression of Urea Transporter B in Normal and Injured Brain

Expression of Urea Transporter B in Normal and Injured Brain

Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization

Depression Impairs Level of Functioning in Chronic Kidney Disease Inpatients: A Case-Control Study

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