Changes in Urine Proteome After Living Kidney Donation: A Prospective Cohort Study

Introduction

Living kidney transplantation is increasing due to the shortage of deceased donors, better outcomes with living-donor transplantation as compared to deceased-donor transplantation, and favorable data on the safety of kidney donors.^1,2 However, the safety of living organ donors is the primary concern in renal transplantation. Previous studies have shown a risk of ESKD and/or mortality in kidney donors similar to that in the general population.^3,4 However, recent studies that compared kidney donors with healthy non-donors showed a higher incidence of ESKD, increased long-term risk for cardiovascular and all-cause mortality.^5,6

The healthy single kidney after contralateral nephrectomy undergoes adaptive mechanisms to compensate for the function of the other kidney, increasing renal plasma flow, glomerular filtration rate (GFR), and intraglomerular pressure. This leads to the hyperfiltration and hypertrophy of the remnant kidney. The adaptive changes after nephrectomy may play an important role in determining long-term kidney function. With pressure to approve more donors as transplant waiting lists increase, it is imperative to better understand the renal adaptive process in the kidneys of donors.

Proteomics is increasingly used for biomarker discovery. Changes in the kidney and urogenital tract can be detected with changes in the urinary proteome.⁷ Urine proteomics is a useful platform to identify urinary excreted proteins in different stages of disease or therapy and to determine their quantity and function.⁸ It is likely that the urinary proteome would change after living kidney donation. In this study, we explored the changes in the urinary proteome after living kidney donation and their possible association with renal adaptations in kidney donors.

Materials and Methods

This study was a prospective observational cohort conducted at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The study was approved by the Institute Ethics Committee (INT/IEC/2021/SPL-437).

Prospective living kidney donors who were scheduled for elective donor nephrectomy at PGIMER, Chandigarh, were eligible for screening. Participants aged >18 years were included, and participants with a history or diagnosis of hypertension, urinary tract stone disease, heart disease, chronic liver disease, chronic lung disease, or malignancy were excluded. Participants were enrolled after informed consent.

Demographic, clinical, and biochemical characteristics were recorded at enrolment. Fifty milliliters of the second morning spot urine sample was collected at baseline. The samples were collected in sterile containers containing protease inhibitors [100 mM sodium azide, 10 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM leupeptin], centrifuged at 2500 rpm for 15 min, and the supernatant was stored at -80°C until further analysis. Those who underwent living donor nephrectomy were followed up at 6 months after enrollment. Clinical course and biochemical characteristics were again recorded, and repeat urine samples were collected, processed, and stored as described above.

Proteomic analyses in urine samples collected at baseline (pre-nephrectomy) and follow-up (6 months after donor nephrectomy) were performed by liquid chromatography-mass spectrometry (LC-MS) using a Thermo Scientific™ Orbitrap Fusion™ Tribrid™ mass spectrometer as described by Court et al.⁹ and detailed in Appendix 1 in the supplementary material. The raw files acquired by the MS system were processed using the Thermo Scientific™ Proteome Discoverer™ 3.2 software, where proteins were normalized based on the total peptide amount. The list of proteins quantified by LC-MS included their accession numbers, abundance in each sample (raw and normalized), abundance ratio (pre-nephrectomy to post-nephrectomy), and abundance-ratio-adjusted p-value. False discovery rates (FDRs) at the peptide level and protein level were set to less than 0.01. DEPs with a log2 fold change were selected for further analysis if p<0.05.

The gene ontology enrichment analysis was done utilizing the Metascape online database. The DEPs were analyzed to detect their involvement in protein-protein interaction (physical interaction) and the link between the proteins that participated in the same reaction of a pathway (pathway relatedness). The Molecular Complex Detection (MCODE)¹⁰ algorithm was implemented to identify densely connected network components.

Results

We screened 60 prospective living kidney donors between January 2021 and October, 2021; 35 participants were excluded due to various reasons, 25 were enrolled, and out of these, 20 completed the follow-up visit at 6 months [Figure 1].

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Figure 1: Study enrolment and follow-up.

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Proteomic analyses

The result of LC-MS/MS analysis of processed urine samples identified 3149 proteins in total. The DEPs with log2 fold change (pre-donation to post-donation) were considered. A total of 350 DEPs (201 downregulated and 149 upregulated) with the log2 fold change were found to be significant with respect to post-kidney transplant [Figure 2, Supplementary Tables 1 and 2]. DEPs include several cell signaling pathways and proteins involved in immune regulation, cellular proliferation and migration, followed by downregulated proteins in intracellular transport (cytoskeletal structural proteins), immune and inflammatory responses, metabolic processes, and protein processing, etc. Upregulated proteins particularly related to renal function include β-2 microglobulin (B2M), α-1-microglobulin/bikunin precursor (AMBP), urinary retinol binding protein-2 (RBP2), hepcidin, and platelet-derived growth factor receptor alpha (PDGFRA). Downregulated proteins include proteins like galectin-10 (CLC), coagulation factor 13- β-chain (F13B), Meprin A subunit alpha (MEP1A), and CD55.

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Figure 2: Volcano plot showing significantly upregulated (Green area) and significantly downregulated (Pink area) proteins in comparative analysis. (The vertical lines correspond to log2 fold changes in up and down expression, whereas the horizontal line represents a P value of 0.05).

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Gene ontology enrichment analysis of DEPs

The hierarchical clustering and pathways enrichment analysis for upregulated proteins is shown in Figure 3. Of note in these clusters were growth factor binding, glycosaminoglycan (heparan sulfate) metabolism, regulation of endocytosis, regulation of TGF-β receptor signaling pathway, VEGFA-VEGFR2 Signaling and TGF-β Smad signaling. Hierarchical clustering and pathway enrichment analysis for downregulated proteins are shown in Figure 4. It shows involvement of endomembrane system organization, carboxylic acid metabolic process, RHO GTPase signaling, vesicle-mediated transport, response to endoplasmic reticulum stress, regulation of innate immune response, and lipid biosynthesis as top clusters. The top biological processes for upregulated and downregulated proteins are shown in Figures 3 and 4, respectively. Different classes of proteins are differentially expressed, with immune system proteins showing the highest expression, followed by metabolite interconversion enzymes, protein-modifying enzymes, and membrane traffic proteins.

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Figure 3: Top enrichment clusters and biological processes of upregulated proteins.

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Figure 4: Top enrichment clusters and biological processes of downregulated proteins.

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The MCODE networks detected for differentially expressed proteins (DEPs), as well as protein-protein interaction and pathway relatedness, are shown in Supplementary Figure 1. Important pathways include RHO GTPases signaling, transmembrane receptor protein tyrosine kinase signaling pathway, MAPK signaling pathway, cellular responses to stress, and Vesicle-mediated transport.

Discussion

In the current study, we sought to identify proteomic changes that may be involved in renal adaptation after kidney donation. We found a total of 350 DEPs, out of which 149 proteins were upregulated, and 201 proteins were downregulated in the post-kidney donation. Gene ontology enrichment analysis indicates that these DEPs are involved in diverse molecular and biological functions and pathways.

Further, we noted that 6 months after donation, the mean eGFR decreased by 24%. These findings are consistent with the literature, which reported a 15-40% decrease in GFR post-kidney donation.^11,12 We also found an increase in the size of the remnant kidney, which is expected due to compensatory hypertrophy of the remaining kidney after donation.

A significant drop in hemoglobin level in donors post-kidney donation was noted. These findings are consistent with the study by Kasiske et al., who observed a significant decline in hemoglobin levels after 6 months of kidney donation.¹³ Another study conducted by Shah et al. showed that 11.8% donors developed post-donation anemia on 20 months of follow-up.¹⁴ They also found pre-donation anemia and retained kidney mass were important determinants of post-donation anemia. Nutritional deficiencies, postoperative blood loss, loss of kidney mass, and loss of renal function may be possible mechanisms of this decrease in hemoglobin.

Upregulated proteins that are particularly related to renal function are β-2 microglobulin, retinol binding protein, PDGF-R-α, and α-1-microglobulin/bikunin precursor. These results are supported by a recent review¹⁵ conducted on proteomics studies in transplant recipients, which highlighted the importance of these proteins along with members of the CXCR3 family and NGAL. β-2 microglobulin, a low molecular weight protein, is filtered by the glomerulus and utilized as a urinary biomarker for kidney damage. α-1-microglobulin, an immunomodulatory protein, appears to be a promising marker for evaluating tubular function; its levels were upregulated in our study and have been shown previously in urinary proteomics studies of AKI.¹⁶ Daniels et al.¹⁷ reported PDGF-C as a marker of AKI recovery, and similarly, levels of PDGF-R-α were upregulated in our study.

Similar to our study, other studies have shown downregulation of biomarkers such as coagulation factor 13 β chain and MEP1A in AKI.¹⁸ Coagulation factor 13 β chain is a glycoprotein acting as a carrier and inhibitor in the blood coagulation cascade. A study has shown the damaging nature of MEP1A in the kidney during AKI, and meprin inhibition has been shown to provide protective roles and improve renal function.¹⁹ Its levels being downregulated in our study shows the renal recovery in remanent kidney after donation. Galectin-10, which is a marker of eosinophils, required for eosinophil differentiation and granulogenesis²⁰ was found to be downregulated in our study.

Along with this, a key factor in complement-mediated kidney injury is the expression and function of complement regulatory proteins.²¹ Normally, the kidney is protected from complement-mediated injury by circulating and membrane-bound regulatory proteins such as decay-accelerating factor (CD55), which inhibits C3 convertase, a central component of the complement cascade.²¹ CD55 accelerates the decay of C3 and C5 convertases, thereby limiting complement-mediated damage.²² Brodsky et al.²³ have shown that higher CD55 expression, particularly in C4d-negative allografts, is associated with improved graft function, lower serum creatinine levels, and better allograft survival. Therefore, CD55 upregulation may represent an adaptive mechanism in kidney donors after nephrectomy.

We also observed increased expression of hepcidin, a key regulator of iron homeostasis in the body. Increased hepcidin expression blocks iron absorption in the intestine and macrophage iron recycling by inhibiting ferroportein and divalent metal transporter 1 (DMT1), ultimately leading to restricted erythropoiesis and anaemia.²⁴ Hepcidin expression is increased in a chronic inflammatory state and is responsible for anemia of chronic disease. An increase in immune system proteins in urine after kidney donation might suggest an inflammatory milieu in the donor, which could be responsible for elevated urinary hepcidin levels. Increased urine hepcidin levels have been demonstrated to play a renoprotective role in response to renal tubular injury by facilitating intracellular iron sequestration, hence reducing oxidative stress, free radical damage, and renal injury.²⁵

The enrichment analysis of upregulated DEPs shows involvement of some interesting clusters, including Growth factor binding, Glycosaminoglycan (GAG) binding, regulation of endocytosis, VEGFA–VEGFR2 signaling, and TGF-β SMAD signaling. The GAGs are the key component of the ECM, attached to core proteins forming glycoproteins.²⁶ The complex, linear, acidic polysaccharides heparin and heparan sulfate are members of the GAG family.²⁷ It has been demonstrated that heparin and heparan sulfate (HS) interact with numerous important proteins regulating a variety of biological processes, such as angiogenesis, inflammation, cell division, viral infectivity, and development.²⁷ Overall, HS-GAG is involved in inflammation, mesangial proliferation and expansion, and other processes involved in the glomerulus.^28-31

Vascular endothelial growth factor-A (VEGFA) plays a crucial role in the angiogenesis^32,33 and the regulation and physiological function of VEGF is complex in AKI.³⁴ Huang et al.³⁴ have demonstrated increased expression of VEGFA during the hyperacute injury phase and the AKI-CKD transition period, thus revealing the dual role in regulating early injury and late fibrosis. VEGFA is reported to be elevated during progression to AKI.³⁵ However, studies examining VEGFA expression during the transition from AKI to CKD have shown widely varying results in favor of the renoprotective role of VEGFA.^S1,S2 These inconsistencies across different time points make it difficult to clearly define the expression pattern of VEGFA in the kidney.

TGF-β has an established role in the pathogenesis of renal fibrosis in both experimental and human kidney disease by promoting ECM production and inhibiting its degradation.^S3-S5 The in vivo activity of TGF-β in the tubular epithelial cells leads to partial EMT, proliferation, cell cycle arrest, apoptosis, and tubular atrophy, which ultimately contributes to the kidney fibrosis through interactions with nearby interstitial cells.^S6-S8 TGF-β signaling is elevated in CKD and plays a key role in fibrosis, as shown in multiple experimental models.^S9 Multiple studies with transgenic mice overexpressing TGF-β1 have elucidated the role in the development of glomerulosclerosis, fibrosis, and kidney dysfunction.^S10

The enrichment analysis of downregulated proteins shows involvement of endomembrane system organization, carboxylic acid metabolic process, response to endoplasmic reticulum stress, regulation of innate immune response, lipid biosynthesis process, signaling by receptor tyrosine kinase, signaling by RHO GTPase cycle, etc. RHO GTPases regulate cell behavior by activating a number of downstream effectors that regulate cytoskeletal organization, intracellular trafficking, and transcription.^S11

The protein-protein interaction and pathway relatedness indicate the clusters of membrane-related processes (MCODE_2), immune-related pathways (MCODE_1), cytoskeletal remodeling and cell migration (MCODE_7) that include the RHO and CDC42 GTPase cycle, which are essential for tissue repair and immune cell dynamics.

The current study has its strengths, as it was conducted in well-characterized kidney donors. Despite being a proof-of-concept study with a relatively small patient sample size, we successfully developed methods for high-quality urine proteomics in renal donors within a clinical setting. Nevertheless, our study also has limitations, such as a small number of subjects, a shorter duration of follow-up, and a lack of validation of the identified proteins in a larger cohort to firmly assert the role of these proteins and their pathophysiological role in kidney adaptations post-nephrectomy.

To conclude, we observed DEPs that may have a role as kidney injury or repair indicators depicted by proteins like β-2 microglobulin, PDGF-R-α, RBP, coagulation factor 13 β chain, MEP1A, galectin-10, and CD55. Although these observations would need validation, the study uncovers possible biological processes and pathways involved in adaptation after unilateral nephrectomy.