An exome-wide study of renal operational tolerance

Introduction

Renal transplantation is considered the best treatment option for end-stage renal disease. However, graft survival comes at the price of life-long immunosuppression that may cause cardiovascular disease, cancer and infection. Exceptional patients will however maintain long-lasting allograft function, in spite of complete discontinuation of immunosuppression (1–3). The absence of destructive immunological response toward the allograft, contrasting with global immunocompetence and a normal response against other immunological challenges is referred to as 'operational tolerance'. Renal operational tolerance is associated with prolonged allograft and patient survival (1, 2, 4, 5). The discontinuation of immunosuppression in tolerant patients generally results from patient non-compliance or from medical decisions grounded on a life-threatening infection or cancer. However, under current transplantation protocols, and despite non-adherence being so common (6) spontaneous tolerance is very rarely observed. In a recent European survey, clinicians identified tolerance in 3/10,000 transplanted patients (2). Because non-compliance is usually undisclosed and in the absence of reliable biomarkers, most tolerant kidney recipients are discovered by chance, suggesting that the incidence of tolerance may be underestimated. Inducing renal tolerance in human recipients is possible through strategies of combined kidney and bone marrow transplantation, resulting in transient chimerism (7). Unfortunately, these strategies are challenging and far from routine clinical implementation. Thus, a better understanding of the molecular pathways and mechanisms involved in spontaneous tolerance might help design innovative tolerance induction protocols and medications.

Most studies of renal operational tolerance to date aimed at identifying reliable biomarkers for the detection of patients with reduced immunosuppression needs or for monitoring tolerance induction protocols [for review, see (3)]. Such studies included antigen-specific functional assays, circulating cell immuno-phenotyping, transcriptomic analyses and mechanistic studies. All together they showed that operational tolerance relies on the expansion of diverse alloreactive immune cells with inhibitory phenotypes and suppressive properties that maintain graft protection against inflammation injury. It remains unclear however whether the changes observed were causes or consequences of tolerance and currently, nothing explains why a kidney recipient initially receiving standard immunosuppression will eventually develop a form of immunoquiescence toward an alloreactive graft.

Because various extreme phenotypes are enriched in trait-causing alleles (8–14), we hypothesized that some tolerant patients may carry inherited, high impact gene variants allowing tolerance to develop. We reasoned that even Mendelian or near-Mendelian inheritance of tolerance would go unnoticed, because the phenotype is only revealed by transplantation, which is never performed experimentally in asymptomatic relatives of transplanted patients. As most high impact variants are found in protein-coding sequences of the genome (15), we sampled one of the world's largest DNA collections of tolerant patients (n = 40) and performed an exome-wide association study.

Materials and methods

Recruitment and data analysis

Patients were recruited from August 22, 2015 through October 29, 2019 and originated from transplantation centers in 12 different European countries (Austria, Belgium, Czech Republic, France, Germany, Holland, Italy, Lithuania, Romania, Spain, Switzerland, Turkey). Data analysis was performed from February 7, 2018 until September 29, 2021. The study protocol was approved at Erasme-ULB Ethics Committee under the refence 2015/379) and all participants signed a written informed consent form approved by their local institutional review board. This paper adheres to the Helsinki Declaration of 1975, as revised in 2013. The trial was registered in ClinicalTrials.gov under the access number NCT05124444.

Eligibility criteria

Tolerant patients (i) were transplanted with an allogenic kidney; (ii) were 18 y or older; (iii) maintained good allograft function (serum creatinine < 1.7 mg/dL and proteinuria ≤ 1 g/day or /g creatinine; or values above these limits but stable with a maximum variability of 20%) in the absence of immunosuppression for at least 1 year. Patients with a history of bone marrow/other solid organ transplantation or who had been included in a protocol of tolerance induction were not eligible. Control patients were (i) healthy persons or persons with a condition unrelated to kidney or immunological disease; (ii) of European ancestry according to the investigator. All participants signed a written informed consent form approved by their local institutional review board.

DNA samples

Following a large European survey involving 256 renal transplantation centers in 28 countries (2), we collected DNA samples and clinical data from 40 tolerant patients originating from 12 countries. Twenty-two patients' DNA samples were available at Nantes University, FR (IRB protocol number: RC14_0431) and 18 other patients were prospectively sampled as part as the TOlerance MOlecular and Genome-wide studies with Renal Allograft recipient Material [TOMOGRAM study, launched by the ERA-EDTA DESCARTES working group (http://wwwa.era-edtaworkinggroups.org/en-US/group/descartes)]. The samples originating from the Nantes University biocollection were sequenced over 100 base pairs from both ends on an Illumina HiSEq2000 (Aros Applied Biotechnology, DK) with a mean depth of 68 ± 7. The remaining 18 patients DNAs were paired-end sequenced over 125 base pairs on an Illumina HiSeq 1500 platform (Brightcore, BE) with a mean depth of 121 ± 28. The exome sequencing data from the 40 tolerant patients were compared with the dataset of 197 in house controls previously sequenced the same way as the TOMOGRAM cases. Details on DNA sequencing and variant analysis are available in the Supplementary material. Characteristics of tolerant patients are given in Supplementary Table 1.

Principal component analysis

PCA of exome data is a commonly used multivariate analysis that reduces the data's dimensionality while their covariance remains preserved. This analysis reduced the high dimensional dataset to a small number of dimensions termed principal components. Here we performed a principal component decomposition of the whole set of exome data (n = 85,899 variants after applying the filters described above), without LD pruning, to identify outliers due to ancestry using the function snpgds PCA of the R Bioconductor package SNPRelate v1.12.2.2.

Case-control analysis

The rarity of operational tolerance—our cohort of 40 patients is one of the world largest—(3) represents a major obstacle to the exhaustive study of its underlying genetics. However, extreme phenotypes are often driven by highly penetrant genetic variants of large effect making small (9, 13) or very small samples (16–18) sufficient to identify the causative gene. We reasoned that our limited number of patients would allow for discovering such variants, if present, and may encourage building larger cohorts in the future. To demonstrate our hypothesis, we built a pilot study comparing 40 tolerant patients to 197 controls from the general population with the objective to demonstrate enrichment of variants with large effect in the tolerant patients. This design called “single extreme vs. control” has proven to be an effective and reasonable method to increase the study power without resorting to a high number of, less accessible, exome-sequenced patients with the opposite phenotype (19). Healthy controls are specially relevant when we study a rare trait because the contamination in the control population will remain low with little or no impact on the study power (18). Finally, given the high number of genes/variants tested, we reported both false discovery rates (FDRs) and p-values following Bonferroni correction as measures of statistical significance after correction for multiple hypothesis testing (20).

In practice, we filtered exome data for moderate to high-impact variants affecting exonic, gene promoter or splice-site regions (for details see Supplementary material) and performed a principal component analysis (PCA) to identify outliers by ancestry. We studied exome-wide association using an optimal sequence kernel association test with small sample adjustment (aSKAT-O) which behaves well over a wide range of allele frequencies (21). This test can detect differences in both rare and common variants but upweights the contribution of rare variants that are likely trait-causing. The aSKAT-O can furthermore be used as a burden test or a variance component test by changing the kernel weight (Burden configuration when Rho = 1; SKAT configuration when Rho = 0) (Supplementary Table 2). We introduced principal components (PCs) 1 to 3 as covariables into our aSKAT-O model in order to correct for a possible population stratification artifact and an additional PC issued from the comparison of patients according to the sequencing machine in order to correct for a possible batch effect. We performed both by-gene analysis, with the aim to capture allelic heterogeneity, and by-variant analysis.

Control of the variants

All genes and variants arising from aSKAT-O analyses had to pass three control steps to ensure they were true and not resulting from a batch effect due to the use of two different sequencers. Firstly, all variants were carefully examined (at the level of the BAM files) for any irregularity in the alignment processes. All variants but one (rs529437974—lack of material) were confirmed by Sanger sequencing. Secondly, a new aSKAT-O was performed with the sequencer as an outcome (instead of the phenotype) and the genes or variants predicting the sequencer were excluded (Supplementary Figure 1). Thirdly, we performed a new SKAT analysis comparing our controls (n = 192) to the tolerant patients after exclusion of the 9 non-European (3 Maghrebin, 6 Turkish) (n = 31) in order to rule out a spurious genetic association due to population stratification. Of note, three variants shared by a pair of tolerant patients each were further investigated to rule out a family relationship between the carriers that could bias the genotype-phenotype association. Patients carrying the same variants were compared for the proportion of single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) > 5%) (Supplementary Table 2).

Results

Principal component analysis

The filtration steps (see Study Flow Chart in Figure 1) yielded 85,899 moderate to high impact variants in 16,390 genes. PCA of exome data excluded 5 outliers by ancestry out of 197 controls and 4 (T26, T43, T53, and T63) out of 40 tolerant patients (Figure 2).

FIGURE 1

Figure 1. Study flow chart. In house controls were selected for absence of history of renal dysfunction and for homogeneity in principal component analysis.

FIGURE 2

Figure 2. Principal component analysis (PCA) of 85.899 variants. PCA is used to reduce the dimensionality of a data set with many variables/features while minimizing the information loss and thus capturing as much of the data's variability as possible. The principal components are linear functions of the variables in the original data set. Principal components are identified by solving an eigenvalue/eigen vector problem. (A–C) Each dot represents a patient and is distributed in function of the 2 first principal components. Blue dots represent controls, orange dots represent tolerant patients sequenced on the AROS platform (Nantes cohort), green dots represent patients sequenced on the BIGRE platform (TOMOGRAM cohort). (D) % of variance explained by each principal component before (left) and after (right) the removal of 4 tolerant patient outliers (T26, T43, T53, and T63).

Per gene and per variant comparisons

We compared the distribution of variants within each gene (n = 16,390) in the 36 tolerant patients vs. 192 controls (Table 1, Figure 3). We identified two genes HOMER2 (entrez ID 9455) and IQCH-AS1 (entrez ID 100506686); FDR = 0.0387 and 0.0362, respectively; p = 0.1160 and 0.1086, respectively. Three patients (8%) carried a variant in HOMER2. The same variant (rs79448007, p.Glu266Gly) was found in two patients (T38 and T62, Table 1). Five patients (14%) carried a IQCH-AS1 gene variant, two of whom shared the same variant (p = 0.03 for the null hypothesis of random selection among the tolerant group, Supplementary methods) (T23 and T28, Table 1). No control subject carried a moderate or highly damaging variant in these two genes. IQCH-AS1 encodes a long non-coding RNA, and variants were filtered in because the complementary DNA strand encompasses two protein-coding genes: IQCH (most of IQCH-AS1 length) and C15orf61. These two genes remain significant after excluding the 9 tolerant patients of foreign origin (data not shown). A third gene, LCN2 (entrez ID 3934) showed a weaker association (FDR = 0.102; p = 0.4847), with three patients (8%) carrying a variant vs. 0 controls. Again, two patients shared the same LCN2 variant (T38 and T54, Table 1). Both LCN2 variants were located 9 base pairs apart, in the 20 amino-acid long signal peptide of the encoded NGAL protein, suggesting the possibility of a shared functional effect. Of note, one patient (T38) carried both a LCN2 and a HOMER2 variant. The 8 variants identified in the 10 patients were rare [i.e., allele frequency < 1% (22)] single nucleotide substitutions. All three genes encode proteins that affect primary cilia function (p = 0.01; Supplementary methods). We ruled out close ancestry in patients sharing a same variant by quantifying their SNPs homology (Supplementary Table 3). The clinical characteristics of the tolerant patients with genetic variants are given in Table 2. Four out of the 10 tolerant patients had received a fully-matched graft for loci HLA A, B and DR. Interestingly, patient T38 who carried variants in both HOMER2 and LCN2 had remained tolerant over an especially long time (173 months at the time of this analysis). SNP array analysis in a subgroup of 19 tolerant patients (from Nantes) detected no copy number variants (CNVs) over any of the three loci (Supplementary methods).

TABLE 1

Table 1. Characteristics of the variants associated with tolerance through per-gene aSKAT-O.

FIGURE 3

Figure 3. Gene-by-gene comparison of tolerant patients and controls. The distribution of exome variants with predicted moderate or high impact was compared in 36 kidney allograft tolerant recipients and 192 unrelated controls of European ancestry, using SKAT-O adjusted for small sample size. Results are presented as –log₁₀ quantile-quantile plots of observed P-values (Y axis) vs. expected P-values (X axis) if variants were equally distributed in cases and controls, when Rho is set to 0 (SKAT-like test, left panel) or Rho is set to 1 (Burden–like test, right panel). Five genes (3 on the left panel, 2 on the right panel) were not considered significant because of an artefactual signal produced by the sequencing machine (see Supplementary Figure 1).

TABLE 2

Table 2. Characteristics of the variant-bearing tolerant patients identified through per-gene aSKAT-O.

We then used the aSKAT-O to compare the frequency of each variant (n = 85,899) in the 36 tolerant vs. 192 controls, and did not identify any variant with statistical significance.

Discussion

Overall, our results show a statistically significant association of spontaneous renal tolerance with moderate to high impact coding variants in HOMER2 and IQCH/C15orf61, and a likely association with LCN2. HOMER2 is an actin-binding, scaffolding protein expressed at various specialized primary cilia (23), and a critical regulator of intracellular Ca²⁺ signaling (24). Homer2 and 3 compete with calcineurin, resulting in a negative regulation of T-cell activation and Homer2,3-deficient mice develop an autoimmune-like pathology (25). IQCH encodes the IQ motif-containing protein H also known as SPATA17 (spermatogenesis associated 17), a cilia and flagella-associated protein involved in spermatogenesis (26). IQ motif containing proteins bind calmodulin (27), the key transducer of intracellular calcium signals in T cells, and alter its binding to calcium. C15orf61 (chromosome 15 open reading frame 61) is an uncharacterized coding gene located immediately downstream of IQCH in a non-imprinted portion of chromosome 15. LCN2 is expressed at the primary cilium (28, 29). It encodes NGAL (neutrophil gelatinase-associated lipocalin), an iron-trafficking protein involved in multiple immunological processes (30–32), including common variable immunodeficiency (33), but its role in transplantation remains obscure (34, 35). Furthermore, HOMER2, IQCH, and NGAL are all cilia-associated proteins. The T cell cilium is viewed as a specialized compartment that assembles at the plasma membrane upon TCR binding and interaction with the antigen-presenting cell or target cell, producing the so-called “immune synapse” (36, 37). The immune synapse is a place of intense TCR/CD3 recycling which directly impacts on the duration and intensity of TCR signaling. This process is dependent upon actin polymerization and intracellular calcium release and is critical for the development of T cell full effector potential (38, 39). A spurious association between renal tolerance and the primary cilium resulting from a cluster of ciliopathies in our cohort appears unlikely. While 2/10 variant-carrying tolerant patients suffered from a recognized ciliopathy (Table 2), none of the genes above are associated with a renal disease, either in the OMIM database or in a large whole-exome study (40). Furthermore, our results are consistent with the reported association of tolerance with a coding variant in PARVG, encoding an actin-binding protein (41), and also with a GWAS of the mirror phenotype, acute rejection of renal allograft, that showed a signal close to the CCDC67 locus (42), encoding a key protein for ciliogenesis (43). Finally, accumulating reports of immunological disorders linked to dysfunctional actin dynamics at the immune synapse further support the causality of the association (44, 45). It might seem contradictory that patients carrying putative tolerance variants would still be immunocompetent. It is however well-known that transplantation tolerance in primates cannot be achieved by blocking a single mechanism (46, 47). It is therefore likely that renal allograft tolerance results from a combination of the hereditary predisposition that we showed here, and the use of multiple immunosuppressive drugs at the time of transplantation. To our knowledge, this is the first unbiased, exome-wide study of tolerance in solid organ transplantation. While we identified genetic variants in only a subset of our patients (10/36, 27%), indicating causal heterogeneity, other mechanisms, e.g., epigenetic changes, might possibly involve the same molecules, or other molecules in the same pathway, like Homer1 as suggested elsewhere (48). The unusually high proportion (25%) of donor-recipient pairs that were fully matched for the HLA A, B and DR loci, also argues for a possible contribution of donor-recipient genetic interactions (49).

Our study has several limitations. First, because documented cases of allograft tolerance in renal transplantation are extremely rare, we could not replicate our results in an independent cohort. Our study stems from a near-complete survey of more than 218,000 renal transplant recipients in 28 countries, where tolerance was reported in 3 per 10,000 renal recipients (2). Contrasting with other successful genetic projects on common phenotypes (diabetes, chronic kidney disease, and many more), our recruitment was necessarily limited. We designed our study specifically to overcome this issue (SKAT adjusted for small samples, FDR, enlarged control group), resulting in several significant rare variants in biologically relevant genes. While a plethora of GWA studies for many kinds of traits has called for stringent guidelines on p-values and replication cohorts, the number of tolerant patients needed to meet these standards is simply not feasible. Nevertheless, the FDRs of ~0.03 are highly convincing, indicating only a 3% chance that the results are false discoveries (20). Second, while our study allowed to unmask a few cases of possibly Mendelian tolerance—akin to studies of rare Mendelian diseases where only a few patients can uncover the underlying gene (16, 17)—it was underpowered to detect heterogeneous and/or polygenic factors in the remaining patients. We may also have missed variants of large effect as we did not cover every exon of the genome, partly because of the limits of exome capture kits (50). Finally, in the context of a rare trait and a posteriori identification we must acknowledge that data collection could not be exhaustively controlled. Nevertheless, this is the first exome-wide study of tolerance and provides a springboard for candidate gene studies and meta-analyses.

In conclusion, our work strongly suggests that spontaneous renal allograft tolerance stems from moderate to high impact, coding variants of primary cilium genes in at least a sizeable subgroup of patients (10/36, 27%). Further research should confirm our results and investigate the mechanisms involved.

Data availability statement

The bam files of the 38 tolerant patients who consented to further use of their data by all bona fide researchers are deposited in the European Genome-Phenome Archive (EGAS00001007154 and 155).

Ethics statement

The studies involving human participants were reviewed and approved by Erasme-ULB Ethics Committee, Université Libre de Bruxelles, Brussels, Belgium. The patients/participants provided their written informed consent to participate in this study.

Author contributions

DA and MA conceived the study. AM and OV implemented the biocollection of the TOMOGRAM study with the support of the DESCARTES ERA-EDTA board members and MG the biocollection of Nantes University with the support of the DIVAT Consortium. AM, OV, MG, MS, JP, MM, and the RENAL TOLERANCE INVESTIGATORS managed IRB approval process and collected patient data and samples. AM and MG reviewed patients' phenotypes. JD, RBS, ET, SVD, and CV obtained IRB-approved and consent from control patients. AM, CP, and VJ performed DNA extraction, preparation of the samples, and Sanger validation of the variants. BC, DC, and DD created patient libraries, sequenced the samples, and prepared them for CNV analysis. JS performed variant calling and annotation. LG, JS, DC, GS, SD, VJ, IP, OV, and PH contributed significant intellectual content. CO performed the principal component analysis and the case-control study under the guidance of ME. AM, CO, RD, ME, DA, SB, and MA analyzed the data and wrote the manuscript. AM, MA, SB, and DA supervised all aspects of the project. All authors revised the manuscript. All authors contributed to the article and approved the submitted version.

Group members of the Renal Tolerance Investigators

Maria Aguilar Rodríguez¹, Friederike Bachmann², Rajendra Bahadur Shahi³, Frederike Bemelman⁴, Luboslav Bena⁵, Luigi Biancone³, Laura Braun⁷, Klemens Budde⁸, Alejandro Camargo-Salamanca⁹, Katia Clemente¹⁰, Hulya Colak¹¹, Adrian Covic¹², Jacques Degreve³, Philippe Gatault¹³, François Glowacki¹⁴, Karine Hadaya^15,16, Marc Hazzan¹⁴, Bénédicte Janbon^15,16, Christophe Legendre¹⁷, Umberto Maggiore¹⁸, Marius Miglinas¹⁹, Anja Mühlfeld²⁰, Maarten Naesens²¹, Christian Noël¹⁴, Rainer Oberbauer²², Evangeline Pillebout²³, Gian Benedetto Piredda²⁴, Francesco Pisani¹⁰, Ana Ramírez Puga²⁵, Tomas Reischig²⁶, Francisco González-Roncero¹, Søren Schwartz Sørensen²⁷, Daniel Seron Micas²⁸, Nurhan Seyahi²⁹, Dimitrie Siriopol¹², Goce Spasovski³⁰, Jean-François Subra³¹, Erik Teugels³, Serhan Tuǧlular³², Sonia Van Dooren³³, Catheline Vilain^34,35, Florence Villemain³¹, Xavier Warling³⁶, Bruno Watschinger²² and Laurent Weekers³⁷

¹Hospital Universitario Virgen del Rocío, Unidad de Gestión Clínica de Urología y Nefrología, Sevilla, Spain

²Charité University Medicine Berlin, Campus Virchow Klinikum, Nierentransplantationsambulanz, Berlin, Germany

³Laboratory of Medical and Molecular Oncology (LMMO), Vrije Universiteit Brussel (VUB), Brussels, Belgium

⁴Renal Transplant Unit, Department of Nephrology, Academic Medical Center, Amsterdam, Netherlands

⁵Transplant Centre, University Hospital Louis Pasteur, Košice, Slovakia

⁶Department of Medical Sciences, University of Torino, Torino, Italy

⁷Hôpital de jour de Néphrologie, Service de Néphrologie et Transplantation Rénale, Nouvel Hôpital Civil, CHU, Strasbourg, France

⁸Charité - Universitätsmedizin Berlin, Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, Berlin, Germany

⁹Colombiana de Trasplantes, Bogotá, Colombia

¹⁰U.O.C. Trapianti D'Organo, L'Aquila, Italy

¹¹Department of Nephtology, Tepecik Training and Research Hospital, Izmir, Turkey

¹²Dr. CI Parhon University Hospital, Lasi, Romania

¹³Service Néphrologie - Immunoclinique, CHRU, Hôpital Bretonneau, Tours, France

¹⁴Nephrology Department, University Hospital of Lille, Lille, France

¹⁵Department of Nephrology and Hypertension, Geneva University Hospitals, Geneva, Switzerland

¹⁶Transplantation rénale, CHU Grenoble, Grenoble, France

¹⁷Service de Néphrologie-Transplantation, Hôpital Necker, Université de Paris, Paris, France

¹⁸Nephrology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy

¹⁹Nephrology Center, Medical Faculty, Vilnius University, Hospital Santaros Klinikos, Vilnius, Lithuania

²⁰Division of Nephrology, Uniklinik RWTH Aachen, Aachen, Germany

²¹Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium

²²Department of Internal Medicine III, Nephrology, Medical University Vienna, AKH Wien, Vienna, Austria

²³Service de Néphrologie, Hôpital Saint-Louis, Paris, France

²⁴Kidney Transplant Azienda Ospedaliera G. Brotzu, Kidney Diseases, Cagliari, Italy

²⁵Universitario Insular de Gran Canaria, Servicio de nefrología, Las Palmas, Spain

²⁶Department of Internal Medicine I, Charles University Teaching Hospital Pilsen, Pilsen, Czechia

²⁷Department of Nephrology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

²⁸Servicio de Nefrología, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain

²⁹Department of Nephrology, İstanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul, Turkey

³⁰Department of Nephrology, Medical Faculty, University of St. Cyril and Methodius, Skopje, Macedonia

³¹Service de Néphrologie-Dialyse-Transplantation, CHU, Angers, France

³²Marmara School of Medicine Hastanesi (Nephrology), S.B. Marmara Üniversitesi EAH Mimar Sinan Cd., Istanbul, Turkey

³³Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium

³⁴ULB Center of Human Genetics, Department of Genetics, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium

³⁵Department of Genetics, Hôpital Universitaire des Enfants Reine Fabiola, ULB Center of Human Genetics, Université Libre de Bruxelles, Brussels, Belgium

³⁶Department of Nephrology, Centre Hospitalier Régional de la Citadelle, Liège, Belgium

³⁷Nephrology Department, Avenue de l'Hôpital 1, CHU of Liege, Liège, Belgium

Funding

AM was supported by the Fonds Erasme; the Fonds de la Recherche Scientifique Médicale (FRSM, PDR 23670170); the Fonds Carine VYGHEN; and the Fonds Horlait-Dapsens). MA was supported by the Belgian FNRS (T.0174.15); and Fonds Erasme. RD was supported by a Marie Skłodowska-Curie fellowship (IF-EF) from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 706296 and the ANR project KTD-innov (ANR-17-RHUS-0010) thanks to the French government financial support managed by the French National Research Agency (ANR) within investments into the future program. This work was performed in the context of the IHU-Cesti project (ANR-10-IBHU-005), the DHU Oncogreffe, the LabEx IGO program (no. ANR-11-LABX-0016), the ANR project PRELUD (ANR-18-CE17-0019) and the ANR project BIKET (ANR-17-CE17-0008). The IHU-Cesti project was also supported by Nantes Métropole and Région Pays de la Loire. The laboratory received funding from the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No. 754995. OV was supported by the Ministry of Health of the Czech Republic (NV19-06-00031). TOMOGRAM was also supported logistically and financially by the ERA-EDTA – DESCARTES working group, the Department of Nephrology at Antwerp University Hospital, Belgium and the Institute for Clinical and Experimental Medicine (IKEM) in Prague, Czech Republic. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Open access funding provided by University of Geneva.

Acknowledgments

We are in debt to the patients, clinicians, and study nurses, who took part in this study and especially to the members of the DESCARTES ERA-EDTA working group and the DIVAT consortium for mounting the biocollection. We thank Ms. Prokopova and Mr. Snelders for administrative support, Eric Trepo for interesting discussion, the cytogenetics unit of Erasme Hospital, Brussels, Belgium for preparing cell lines, as well as the biological resource centre for biobanking from Nantes (Nantes Université, CHU Nantes, Centre de resources biologiques (CRB), F-44000 Nantes, France) (BRIF: BB-0033-00040).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2022.976248/full#supplementary-material

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