Abstract

D-serine, present only in trace amounts in humans, is now considered a biomarker of chronic kidney disease (CKD). the primary kidney disease in CKD is heterogeneous and its diagnosis requires a renal biopsy. In this study, we examined whether the in vivo dynamics of D-serine (as indicated by its blood and urine levels) reflect the origin of renal disease. Renal biopsies were performed on patients with six renal diseases in the same center. The levels of D-serine and L-serine were determined by two-dimensional high-performance liquid chromatography. The association between the origin of renal disease and the in vivo dynamics of d-serine was examined using multivariate cluster analysis. Unlike non-CKD patients, D-serine dynamics were widely distributed in CKD patients. Plasma D-serine levels play a key role in the detection of renal disease, while the combination of plasma and urine D-serine levels can distinguish the origin of CKD, especially in lupus nephritis. the in vivo dynamics of D-serine are important in predicting the development of renal disease. Monitoring of D-serine can guide the specific treatment of the origin of renal disease.

Keywords: D-Serine ;Kidney biopsy ; Diagnosis ;Lupus nephritis; Chronic kidney disease ; Biomarker;Cistanche tubulosa

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Introduction

Chronic kidney disease (CKD) is a global problem with over 10 million patients in Japan and 850 million worldwide.CKD is often defined as a chronic abnormality in kidney structure or function, including glomerular filtration rate (GFR), and is heterogeneous in origin. The origins of CKD include immune, diabetic, aging-related, rare refractory, and of unknown etiology. The prognosis and treatment of CKD vary depending on Origin varies, and pathological examination of kidney biopsy specimens often provides key information for diagnosis and assessment of disease activity. Although renal biopsy is an important procedure, it carries risks and is only performed when its benefits outweigh the risks. Therefore, biomarkers that can help diagnose kidney disease are under investigation.

D-serine is now emerging as a biomarker for kidney disease. d-serine is one of the d-amino acids, the mirror enantiomer (chiral) of serine, which, unlike the abundant l-serine, is present in only trace amounts in nature. To accurately measure the amount of d-serine in human samples, a two-dimensional high-performance liquid chromatography (2D-HPLC) system was used. 2D-HPLC system enhances the accurate measurement of trace amounts of d-serine in human blood. The level of D-serine in the blood provides critical clinical information because it correlates with GFR, one of the kidney functions, and also reflects the renal prognosis of CKD patients. In addition, urinary D-serine excretion provides additional information for the detection of renal disease. Therefore, assessment of the in vivo kinetics of D-serine by measuring the level of D-serine in blood and urine excretion is useful for monitoring renal function and disease viability.

These facts raise the hypothesis of whether renal diseases may affect these dynamics differently depending on their origin. If so, monitoring the dynamics of D-serine in vivo may be useful in diagnosing the origin of CKD. The present study evaluated d-serine dynamics in CKD patients undergoing renal biopsy and examined the potential of D-serine in determining the origin of renal disease.

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Materials and methods

Study population

We prospectively recruited consecutive patients who underwent their first renal biopsy for diagnostic and/or therapeutic purposes at the Department of Kidney Diseases and Hypertension, Osaka General Medical Center from 2006 to 2016. Biopsy specimens were routinely analyzed using light, immunofluorescence, and electron microscopy procedures. Clinical and pathological diagnoses were established by consensus among experienced nephrologists and pathologists. We extracted common causes of renal disease by referring to the histological classification scheme of glomerular diseases published by the World Health Organization in 1995. The origins of renal diseases include IgA nephritis (IgAN), microscopic change disease (MCD), membranous nephropathy (MN), diabetic nephropathy (DN), hypertensive nephropathy (HT), and lupus nephritis (LN). Ten patients from the study cohort were included in this study for each kidney disease origin. The inclusion criteria for each disease were as follows: IgAN, urinary protein range 0.5 - 1.5 g/gCre and eGFR >60 mL/min/1.73 m2; MCD, MN, meeting criteria for nephrotic syndrome; DN, history of diabetes mellitus, urinary albumin/creatinine ratio 30 mg /gCre, eGFR 30ml/min/1.73 m2; HN, hypertension history, eGFR > 30 mL/min/1.73 m2. eGFR> 60; LN, meeting the 2003 International Society of Nephrology (ISN) / Renal Pathology Society (RPS) classification criteria for SLE. Exclusion criteria included (i) cases in which prednisolone and/or immunosuppressive drugs had been initiated, (ii) cases in which renal biopsy showed less than 10 glomeruli, (iii) cases of acute kidney injury (AKI), and (iv) cases of secondary MN. Clinical demographics, laboratory data, SLE disease activity index at the time of LN (SLE- dai), and plasma and urine samples were collected at the time of renal biopsy. Plasma and urine d-serine levels were measured prior to renal biopsy. Reference data for non-CKD were obtained from a previous report. The study protocol was approved by the Ethics Committee of Osaka General Medical Center (#29- S0606) and NIBIOHN (#236). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki, and all participants gave written informed consent.

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GFR equations and kidney clearance calculation

Estimated GFR (eGFR) was calculated using the Japanese GFR equation based on serum creatinine (eGFRcreat):

eGFRcreat(mL∕min∕1.73 m2)= 194 × Cr−1.094× age−0.287× 0.739 (if female)

Serum and urine creatinine were determined enzymatically. Urinary fixed-point levels of chiral amino acids were adjusted by creatinine. The excretion fraction (FE, %) was calculated by dividing the substrate clearance by the creatinine clearance, as follows:

Where Us and Ps represent urine and plasma substrate levels, respectively.FE is the ratio of substrate excreted in urine after glomerular filtration.FE low and high indicates tubular reabsorption and excretion, respectively.

Sample preparation

Sample preparation from human plasma and urine was performed and modified as described previously. Twenty times the volume of methanol was added to the sample and it (10 μL of the supernatant obtained from the methanol homogenate) was placed in a shaded brown tube for NBD derivatization (1.0 μL of plasma was used for the reaction). After drying under reduced pressure, 20 μL of 200 mM sodium borate buffer (pH 8.0) and 5 μL of fluorescent labeling reagent [40 mM 4-fuoro-7-nitro-2,1,3-benzoxadiazole (nnd -f) in anhydrous MeCN] were added and heated at 60 °C for 2 min. 2 μL of the reaction mixture was taken by adding 0.1% (v/v) aqueous TFA solution (75 μL) for 2D-HPLC.

Determination of serine enantiomers by 2D‑HPLC

The enantiomers of serine were quantified using a 2D-HPLC platform. Briefly, the NBD derivatives of amino acids were subjected to reverse-phase chromatography (Singularity RP column, 1.0 mm i.d. × 50 mm; KAGAMI Inc., Osaka, Japan) using an aqueous mobile phase containing MeCN and formic acid for gradient elution. For the separate determination of d-serine and l-serine, the serine fraction was automatically collected using a multi-loop valve and transferred to a p-anti-selective column (Singularity CSP-001S, 1.5 mm i.d. × 75 mm; KAGAMI Inc .). The D-serine and l-serine were then separated on a two-dimensional basis using an enantioselective column. The mobile phase was a MeOH-MeCN mixture containing formic acid, and the NBD -amino acids were detected fluorescently with two photomultiplier tubes under 530 nm excitation. The target peak was quantified by scaling the standard peak shape.

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Data processing and multivariate analysis

Data were centered on the median and log-transformed for multivariate analysis. Unsupervised principal component analysis (PCA) or supervised orthogonal partial least squares analysis (OPLS) was used to analyze the relationship between chiral amino acids and clinical parameters. Principal component analysis geometrically projects complex high-dimensional data onto low-dimensional data called principal components. Loading plots visualize the degree of influence of each variable on the principal component, as well as the correlation between each variable. The score value of each observation is plotted on the score plot to demonstrate the clustering of observations. The OPLS-DA method was applied to test the discriminable distribution of the variables based on the predictor variables. The ft-optimality of the OPLS models was assessed using R² Y and Q² Y values. R² Y indicates the percentage of variance in the examined variables explained by the model, while Q² Y indicates the predictive performance of the model. R² Y and Q² Y greater than 0.5 indicate good ft-optimality, and more than 20 replacement trials were performed for each OPLS model to validate the model internally. In the development of the LN model, we selected one immutable factor, gender, and removed it from the correlation analysis.

Discussion

In this study, we demonstrate the utility of d-serine monitoring in the diagnosis of the origin of renal disease. d-serine dynamics in vivo depend on the various origins of renal disease and have the potential to differentiate the origin of renal disease. The results of this study contribute to the accurate diagnosis of the origin of renal disease and to the correct diagnosis of cases requiring renal biopsy. d-Serine differential expression in different renal diseases has important implications because they may contribute to the discovery of novel pathophysiology of renal disease.

The current study has several limitations that need to be recognized in order to interpret the results. The limited number of patients with each disease, the prognosis of these patients has not been studied, and these characteristics may obscure further relationships between d-serine and kidney disease. Due to the limited number of participants, subgroup analysis is necessary for diseases with a broad spectrum of disease, such as IgAN and DN. Even with these limitations, this study clearly identifies the origin of renal disease and patients would benefit from monitoring d-serine for diagnosis.

In summary, assessing the in vivo dynamics of d-serine is useful for the diagnosis of primary renal disease. Monitoring of d-serine may guide specific treatment of renal disease, particularly of LN origin. This study opens a new direction for precision medicine using d-serine measurements.

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