Identification of different types of chronic kidney disease: urinary protein and peptide markers

for more information:ali.ma@wecistanche.com

Part Ⅰ：Urinary Protein and Peptide Markers in Chronic Kidney Disease

Natalia Chebotareva, Anatoliy Vinogradov & et al.

1. Introduction

According to The Kidney Disease: Improving Global Outcomes (KDIGO)criteria, chronic kidney disease (CKD) is defined as an abnormality in kidney structure or function present for more than 3 months, with health implications [1,2]. CKD (chronic kidney disease) is an independent risk factor for death, cardiovascular disease, end-stage renal disease, and acute kidney injury [3-7] and has a global prevalence of 11-13%[8]. CKD (chronic kidney disease) is a socially significant problem due to the high risk of early disability from the disease and the need for high-cost treatments in the case of end-stage renal failures, such as hemodialysis, peritoneal dialysis, and kidney transplants [9,10]. The three most common causes of CKD (chronic kidney disease) are diabetes mellitus, hypertension, and glomerulonephritis, especially with nephrotic syndrome [11]. Kidney diseases can have similar clinical symptoms and may range from mild and benign to progressive with rapid end-stage renal disease development. The severity of the clinical manifestations, however, does not always correspond to the severity of renal damage, which can be determined by renal biopsy [12]. The majority of patients undergo a single kidney biopsy to determine the morphological form of kidney disease. In sporadic cases, the biopsy is repeated to assess the effectiveness of therapy and prognosis. However, the assessment of the regression of nephropathic activity is crucial for the dynamic assessment of treatment, including the treatment's effectiveness, optimization, and prognosis.

Kidney diseases: chronic kidney disease (CKD)

Click to cistanche side effects and Cistanche products

Urine proteomic analysis is a much safer option compared to a biopsy and has good potential for developing non-invasive diagnostic methods. Urine analysis has several advantages compared to blood proteomic analysis [13]. Firstly, the urine proteome is not very complicated and mainly contains proteins and peptides of renal origin (up to 70%). On the contrary, kidney damage markers comprise only a small fraction of the highly diverse plasma/serum proteome, making their analysis in the latter challenging. Secondly, it is much easier to normalize the concentration of a protein biomarker in the urine than in the blood—for example, based on the concentration of creatinine [14]. Thirdly, urine collection is simple and non-invasive. Finally, urine samples are stable at a temperature of -20°C and are suitable for proteomic analysis even after years of storage[15]. The aforementioned advantages of urine make it a popular subject for the search for protein markers for various pathologies[16]. These pathologies include renal and genitourinary pathologies and pathologies associated with proteinuria, such as kidney diseases [17-19; bladder, prostate, and ovarian cancers [20-23]; diabetic nephropathy [24]; and pre-eclampsia [25-27]. Urinary protein markers have also been described for colon and lung cancers [28,29], cholangiocarcinoma [30, cardiovascular diseases [31, autoimmune diseases [32], and infectious diseases [33]. Nevertheless, the urine proteome should be most informative for renal pathologies and may present a fingerprint of different kidney diseases [34-39] (Table 1).

However, despite a large number of studies, there are still no reliable kidney-disease-specific biomarkers that can be accurately reproduced in different studies. The various factors affecting proteome composition include the collection conditions and regime (morn-ing, daily, variability over several days, etc.), physical activity, nutrition, the anatomical features of the urinary tract(the absence of one kidney, etc.), sex, and age [40-43]. All of these factors should be taken into account when comparing the results of different studies. In general, combining the markers of specific nephropathies outlined in various studies could facilitate better progress in the creation of highly specific differentiating panels for possible clinical use after multi-stage prospective validation [44].

Table 1. Urine proteome studies in different types of nephropathies

Mass spectrometry(MS)-based approaches, which feature a high multiplexing capacity, are the most unbiased and sensitive instruments and have already provided most of the currently known information about urine peptide and protein contents in different nephropathies, as well as potential biomarker panels for various diseases [37-39]. A number of MS methods have been successfully applied (Table 1). The most commonly used approaches include matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF), capillary electrophoresis(CE), and liquid chromatography(LC) MS. The most advanced approaches with isobaric or tandem mass tags for relative and absolute quantitation (iTRAQ and TMT) facilitate the identification of markers among commonly present proteins and peptides when their amounts vary significantly. In general, the listed untargeted MS approaches are the most appropriate for the primary search of potential biomarkers, whereas targeted MS and immunoassays can be used for further validation.

This review summarizes data from numerous studies of the urine proteome in nephropathies associated with CKD (chronic kidney disease), with a focus on recent studies from 2015 to 2021. The electronic databases MEDLINE, PubMed, and Cochrane were searched using key-words such as"proteomics","peptidomics","biomarkers","chronic kidney disease", "urine","membranous nephropathy""IgA nephropathy","focal segmental glomerulosclerosis""minimal-change disease","diabetic nephropathy", and"lupus nephritis". The reference lists of articles were also investigated to explore related literature. The bibliographic information of 1030 retrieved articles was analyzed, and papers with irrelevant or unreliable information, those unavailable in full text, and those not in English were deleted. After deleting all duplicate references, 69 articles remained.

2. Chronic Kidney Disease (CKD)

Several urinary proteome studies have considered the CKD (chronic kidney disease) group of pathologies without subdivisions. Harald Mischak's group is the leader in MS studies on the urinary peptidome and proteome. This group described 1580 native urinary peptides, showing that 73% were unique for urine and proving the clinical value of native urinary peptide markers for diagnosing several diseases, including those associated with kidney damage[14,77,78].

Rossing K. et al. developed the first panel consisting of 65 urinary proteins, including collagen fragments, serum albumin, o1-antitrypsin(A1AT), and uromodulin, which differentiated diabetic nephropathy in 97% of cases, showing high sensitivity and specificity among 148 types 2 DM and DN patients [72]. The panel was further successfully validated by Alkhalaf A.et al. [79].

Good D.M.et al. analyzed urine samples from 476 patients with CKD (chronic kidney disease)(mostly diabetic nephropathy) and 379 controls and developed a classifier based on 273 urinary peptides(CKD (chronic kidney disease)273) in the form of a composite CKD (chronic kidney disease) biomarker [45]. The panel contained fragments of collagen type I and ⅢI α-chains(181 peptides), reflecting the extracellular matrix turnover and reduced protease activity in situ. CKD (chronic kidney disease) patients also demonstrated increased urinary excretion of plasma proteins and their fragments (e.g., A1AT, serum albumin, o-hemoglobin chain, and α-fibrinogen chain), kidney-specific proteins (uromodulin, gamma-chain Nat/K+-ATPase, and membrane-associated progesterone receptor component 1), and proteins excreted by the tubules, which may reflect chronic damage to the glomerular filtration barrier, increased glomerulosclerosis, and interstitial fibrosis. In a blinded study, the CKD (chronic kidney disease) 273 classifier made it possible to differentiate patients with CKD (chronic kidney disease) of various etiologies with 85.5% sensitivity and 100% specificity and predict the mortality in type 2 DM with microalbuminuria [45,80,81]. These results were further validated by Schanstra .P.et al.[18] and Pontillo C.et al. [46 who confirmed this classifier's value as a predictor of renal function deterioration and demonstrated a decrease in the glomerular filtration rate of <60 mL/min over 5 years of monitoring. Zürbig P et alshowed that the CKD273 classifier could predict the development of diabetic nephropathy 1.5 years before the onset of microalbuminuria. Argiles A.et al. used the CKD (chronic kidney disease) 273 classifier on 53 patients with CKD (chronic kidney disease) and differentiated patients according to their degrees of impairment of renal function and the risk of end-stage CKD (chronic kidney disease) or death [17].

Catanese L.et al. developed the FPP_29BH classifier, which contains 29 specific fibro-sis biomarkers for patients with various immune and non-immune kidney diseases. The patients with renal fibrosis showed an increase in urinary proteases (cathepsin D, matrix metalloproteinase 2, collagenase 3, and matrix metalloproteinase-14), α-2-HS-glycoprotein, or fetuin-A, as well as 19 different collagen peptide fragments of eight different collagen chains with differential intensities between patients with high and low degrees of fibrosis [47].

Since CKD (chronic kidney disease) is an umbrella term for several conditions that affect the kidneys, many of the aforementioned markers are not disease-specific. A study of 1180 urine samples by Siwy J.et al.showed that many markers remain the same in different nephropathies and reflect common pathological processes [54]. However, this large-scale study identified a number of specific markers. Three fragments of clusterin were shown to be increased in diabetic nephropathy,β-2-microglobulin was decreased in minimal-change disease, and an S100-A9 protein fragment distinguished lupus nephritis [54]. Other specific proteomics and peptidomics changes in various CKD (chronic kidney disease) types, including minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), IgA nephropathy (IgAN), diabetic nephropathies (DN), and lupus nephritis (LN), are reviewed below.

3. Minimal Change Disease and Focal Segmental Glomerulosclerosis

Minimal-change disease (MCD) and primary focal glomerulosclerosis (FSGS) are diseases with primary podocyte injury (primary podocytopathies), which is manifested as high proteinuria and nephrotic syndrome 82,83]. However, morphological studies of a kidney biopsy in the early stages of FSGS can miss segmental sclerosis in individual glomeruli and may misclassify the disease as MCD (Minimal-change disease) [84]. Primary FSGS pathogenesis is associated with circulating permeability factors (such as soluble urokinase-type plasminogen activator receptor (SuPAR), corticotrophin-like protein-1, and anti-CD40 antibody CD-80 expression), which leads to the development of nephrotic syndrome [85-91]. In general, compared to FSGS, MCD (Minimal-change disease) has a more favorable prognosis regarding the progression of renal dysfunction; FSGS is more likely to develop therapy resistance and result in rapid renal dysfunction and is also more likely to need an aggressive and persistent therapeutic strategy [83,92,93]. In addition, the presence of secondary FSGS complicates diagnosis and disease treatment. Due to its non-immune nature, this form of the disease does not require immunosuppressive therapy [1.

Several studies have aimed at identifying proteomic differences between these two nephropathies. In particular, it was shown that the calretinin and UBA52 levels were higher in FSGS [4849], while the 39S ribosomal protein L17 was higher in MCD (Minimal-change disease) [48](Table 2). Significantly higher levels of cathepsin B, cathepsin C, and annexin A3 were shown in cases of the collapsing variant of FSGS (characterized by glomerular collapse and a rapid loss of renal function) than in MCD (Minimal-change disease), MN, and other FSGS variants [94]. Several potential markers specific only for FSGS include increased levels of cadherin-like 26, RNase A family 1, DIS3-like exonuclease 1[50], matrix-remodeling protein 8[51], CD59,insulin-like growth factor-binding protein7, and roundabout homolog 4[52], as well as a decrease in the polymeric immunoglobulin receptor and Golgi-associated olfactory signaling regulator [54]or the complete absence of dipeptidase 1 (DPEP1)[52]. Increased CD14 levels were found to be specific only for MCD (Minimal-change disease) [50] and were not identified in any other nephropathy(Table 2). At the same time, increases in transferrin and histatin-3 may distinguish both FSGS and MCD (Minimal-change disease) [48] from other types of kidney disease.

Among the revealed potential markers, the overrepresentation of ribonuclease 2 and underrepresentation of haptoglobin may suggest the worst FSGS prognosis, whereas apolipoprotein A1 and matrix-remodeling protein 8(MXRA8)showed significant changes between steroid-sensitive and steroid-resistant forms of FSGS[51].

In general, the presence of most of the aforementioned proteins in the urine and increases in their levels may reflect massive cell death and the release of intracellular contents during the podocytes' separation from the glomerular membrane. These results may also suggest special roles for immunity, inflammation, and apoptosis in the development of FSGS. Cell proliferation, differentiation, and death may be involved in MCD (Minimal-change disease) development [95]. Dynamic studies performed using a focal segmental glomerulosclerosis rat model (adriamycin(ADR)-induced nephropathy) revealed a gradual increase in afamin and ceruloplasmin, as well as a gradual decrease in cadherin-2 and aggrecan core protein in FSGS, and suggested that decreased levels of fetuin-B,α-1-microglobulin, and α-2-HS-glycoprotein may be promising markers for the early detection of FSGS [96]. Other promising markets include CD44, MXRA8, cathepsins, and apolipoprotein A1. CD44 reflects the activation of parietal epithelial cells, which triggers glomerulosclerosis. MXRA8 cathepsins are involved in fibrosis accumulation and disease progression. Apolipoprotein A1 reflects oxidative stress, is associated with hyperlipidemia, and represents one of the pathogenetic factors in the development of FSGS.

Kidney disease assay: Urine proteomic analysis

CLICK HERE TO PART Ⅱ