An exome-wide study of renal operational tolerance

Abstract

Background: Renal operational tolerance is a rare and beneficial state of prolonged renal allograft function in the absence of immunosuppression. The underlying mechanisms are unknown. We hypothesized that tolerance might be driven by inherited protein coding genetic variants with large effect, at least in some patients.

Methods: We set up a European survey of over 218,000 renal transplant recipients and collected DNAs from 40 transplant recipients who maintained good allograft function without immunosuppression for at least 1 year. We performed an exome-wide association study comparing the distribution of moderate to high impact variants in 36 tolerant patients, selected for genetic homogeneity using principal component analysis, and 192 controls, using an optimal sequence-kernel association test adjusted for small samples.

Results: We identified rare variants of HOMER2 (3/36, FDR 0.0387), IQCH (5/36, FDR 0.0362), and LCN2 (3/36, FDR 0.102) in 10 tolerant patients vs. 0 controls. One patient carried a variant in both HOMER2 and LCN2. Furthermore, the three genes showed an identical variant in two patients each. The three genes are expressed at the primary cilium, a key structure in immune responses.

Conclusion: Rare protein coding variants are associated with operational tolerance in a sizable portion of patients. Our findings have important implications for a better understanding of immune tolerance in transplantation and other fields of medicine.ClinicalTrials.gov, identifier: NCT05124444.

Keywords: Homer2; IQCH; LCN2; NGAL; exome sequencing; operational tolerance; primary cilium; renal transplantation.

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

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).

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).