Abstract
African American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared with other racial and ethnic populations, highlighting the need to identify causative factors. Here, in the Genomics of Chronic Allograft Rejection cohort, pretransplant blood RNA sequencing revealed a cluster of four consecutive missense single-nucelotide polymorphisms (SNPs), within the leukocyte immunoglobulin-like receptor B3 (LILRB3) gene, strongly associated with death-censored graft loss. This SNP cluster (named LILRB3-4SNPs) encodes missense mutations at amino acids 617–618 proximal to a SHP1/2 phosphatase-binding immunoreceptor tyrosine-based inhibitory motif. The LILRB3-4SNPs cluster is specifically enriched within AA individuals and exhibited a strong association with death-censored graft loss and estimated glomerular filtration rate decline in the AA participants from multiple transplant cohorts. In two large Biobanks (BioMe and All-of-Us), the LILRB3-4SNPs cluster was associated with the early onset of end-stage renal disease and acted synergistically with the apolipoprotein L1 (APOL1) G1/G2 allele to accelerate disease progression. The SNPs were also linked to multiple immune-related diseases in AA individuals. Last, on multiomics analysis of blood and biopsies, recipients with LILRB3-4SNPs showed enhanced inflammation and monocyte ferroptosis. While larger and prospective studies are needed, our data provide insights on the genetic variation underlying kidney transplant outcomes.
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Data availability
The bulk RNAseq data of pretransplant blood from kidney transplant recipients were deposited in the Gene Expression Omnibus (GEO) under accession numbers GSE112927 (GoCAR), GSE252272 (CTOT19) and GSE281721 (VericiDx), with race (self-reported), sex and LILRB3-4SNPs risk allele information. The scRNAseq data of pre- and post-transplant PBMCs in the GoCAR cohort was deposited in GEO under the accession number GSE252273. The bulk RNAseq data of post-transplant blood from the GoCAR recipients was deposited in GEO under the accession number GSE261408. The targeted sequencing of GoCAR and SIRPA data was deposited in the SRA database with accession number PRJNA1203265. The SNP array data of the GoCAR cohort were published previously15 and can be requested from the corresponding author with a 2–4-week response time upon the data usage agreement. The de-identified individual demographic and clinical data can be requested from the corresponding author with a 2–4-week response time upon the data usage agreement. The genotype and phenotypic data of the BioMe biobank was generated by Regeneron and is not publicly available. However, the data will be available for the purpose of validating the results by contacting the corresponding author with a 2–4-week response time upon an appropriate collaboration and data sharing agreement between the institutes. The genotype and phenotypic data All-of-Us biobank can be accessed through the All-of-Us online workbench (https://www.researchallofus.org).
Code availability
The customized pipeline for allele-specific analysis and codes for generating figures were deposited and are available via GitHub at https://github.com/ZephyrSun13/LILRB3_4SNPs.git. The clinical data for validating these codes can be available from the corresponding author with a 2–4-week response time frame. The versions for each software can be found in the scripts and also described in the Methods.
Change history
15 April 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41591-025-03706-7
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Acknowledgements
We gratefully acknowledge the administration and IT team of Institute of Personalized Medicine at Mount Sinai for facilitating access to genetic and clinical data of BioMe cohort. We also acknowledge the Genomic Resources Core at Weill Cornell Medical School for generating bulk and scRNAseq data on the GoCAR cohort. We thank A. Rose, A. Wang and N. Doshi at VericiDx Inc. for the assistance in providing the RNAseq dataset of the CTOT19 cohort and RNAseq and demographic/clinical data of the VericiDx cohort. We thank Associate IT director, R. Kwan, for generating pathological reports for transplant patients in the BioMe cohort. We acknowledged the great contributions from the participants and NIH staff to build the All-of-Us biobank cohort. This work was supported by NIH 5U01AI070107-03 (B. Murphy) and Biocomputation Fund 02435913 (Weijia Zhang) from the Department of Medicine. This work was also supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Weiguo Zhang received the CAMS Innovation Fund for Medical Sciences (CIFMS 2023-I2M-2-010, 2021-I2M-1-047), Suzhou Municipal Key Laboratory Fund (SZS2023005). Xuewu Zhang received grants from the NIH (R35GM130289) and Welch Foundation (I-1702). The MS data were obtained from an Orbitrap mass spectrometer funded in part by NIH grants NS046593 (H.L.) and 1S10OD025047-01 (H.L.), for the support of proteomics research at Rutgers Newark campus.
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Authors and Affiliations
Contributions
Z.S. performed computational data analysis and was involved in conceptualizing and drafting/editing the paper. Z.Y. performed meta-DEG and network analyses. C.W. led the genomic and functional experiments. W.W. performed genomic and functional experiments. T.R. generated the LILRB3-4SNPs overexpression system. P. Cravedi was involved in data interpretation and edited the paper. F.T. and S.C.W. were involved in pathological and clinical data compiling and interpretation of transplant patients in the BioMe cohort. E.A. supervised the serum MS profiling. D.R.S. and Y.L. supervised the analysis of evolutionary selection of the LILR locus. A. Khan and F.Z. were involved in the All-of-Us biobank data analysis. S.A. performed the immune response assay. S.L. modeled the interactions of LILRB3-SHP2. D.L. guided the ferroptosis experiments. J.F. performed scRNAseq. C.X. managed the clinical data. T.L. and H.L. performed MS experiments on pretransplant serum. T.H.V. performed the genetic association analysis. G.M. performed serum profiling. Q.S. analyzed the evolutionary trait of the LILR locus. A. Kumar and Z.Z. edited the paper. S.F. and K.C. were involved data interpretation and edited the paper. J.O. guided the immune functional experiments in THP-1 cells. K.L. edited the paper. S.C. supervised G.M. in serum profiling and edited the paper. J.X. supervised the RNAseq. P. Connolly was involved in data generation of CTOT19 and VericiDx cohorts. L.G., P.J.O., R.C. and M.C.M. were involved data interpretation and edited the paper. G.N. supervised T.H.V. in the genetic association analysis and edited the paper. J.C.H. supervised J.F. in the scRNAseq analysis/data interpretation and edited the paper. M.K. was involved data interpretation and edited the paper. X.J. supervised D.L. in the ferroptosis analysis and edited the paper. X.Z. supervised S.L. in the structural modeling and edited the paper. K.K. supervised All-of-us biobank data analysis and interpretation and edited the paper; A.C. and F.G.L. supervised data analysis for SIRPA cohort and edited the paper. Weiguo Zhang supervised R.L. in in vitro LILRB3-4SNPs overexpression experiments and edited the paper. S.C. supervised S.A. in the immune response assays and edited the paper. P.S.H. was involved in study design and data interpretation and edited the paper. Weijia Zhang conceptualized and designed this study and drafted/edited the paper. All authors reviewed and approved the manuscript.
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Competing interests
Weijia Zhang reports personal fees from VericiDx and reports the patents (Patents US Provisional Patent Application F&R ref. 27527-0134P01, serial no. 61/951,651, filed March 2014; Method for identifying kidney allograft recipients at risk for chronic injury; US Provisional Patent Application: Methods for Diagnosing Risk of Renal Allograft Fibrosis and Rejection (miRNA); US Provisional Patent Application: Method for Diagnosing Subclinical Acute Rejection by RNA Sequencing Analysis of a Predictive Gene Set; US Provisional Patent Application: Pretransplant prediction of post- transplant acute rejection). M.C.M. receives research support from Natera. P. Cravedi is a consultant for Chinook therapeutics. L.G. is the non-executive Director and Chair of the science advisory board for Verici. The other investigators have no financial interest to declare.
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Extended data
Extended Data Fig. 1 Evaluation and quantification of allele-specific expression in the GoCAR cohort.
a) Overall work flow of eSNP identification and allele expression fraction (AEF) calculation. (b-d) The distribution of AEF of homozygous genotype of reference allele (0/0) (b), heterozygous genotype (0/1) (c) and homozygous genotype of alternative allele (1/1) (d) in the GoCAR cohort. Most alleles showing a balanced expression of reference and alternative alleles (AEF around 50%) while some alleles showed a higher expression of either the reference allele or the alternative allele (AEF > 50% or AEF < 50%), and a few sites exhibited mono-allelic expression at both ends (AEF=0 or AEF=1). This distribution aligns with previous studies on allele-specific expression. (e) The sensitivity and specificity of RNAseq-based genotyping by comparing to the SNP array-based genotyping for heterozygous (upper) and homozygous (lower) calls with various read coverage depths in the GoCAR cohort (n = 153 with both RNA-seq and SNP array data). Each dot represents a sample and box and whiskers plot showing the distribution (thick bar, median; box, 25th to 75th percentile, whiskers reach to the largest/smallest observations within 1.5 box-heights of the box). Overall, the RNAseq-based genotyping strategy achieved over 90% sensitivity and specificity for both heterozygous and homozygous detection with more than 10 reads. With 5-10 reads, we achieved over 75% sensitivity and 100% specificity for heterozygous calls, and 100% sensitivity and over 99% specificity for homozygous calls. These data indicate that our informatic pipeline effectively detected exonic SNPs from RNA-seq data in the GoCAR cohort with high sensitivity and specificity.
Extended Data Fig. 2 Comparison of post-transplant longitudinal eGFR values (mean of the records of each month) of AA kidney transplant patients (ICD Z94.0 or V42.0) carrying the LILRB3-4SNPs variant (‘Risk’) vs reference (‘Non-risk’, blue) allele in the BioMe cohort.
a) longitudinal eGFR values of risk and non-risk AA kidney transplant patients within 48 months after transplant. Bold curves indicate the fitted regression lines for two groups. Comparison (Students’ T test, two-sided) of average eGFR between risk (n=9) and non-risk (n=90) patients within 3 months (b), 3–24 months (c), and 3–48 months (d) after transplant. Each dot represents a sample and the box and whiskers plots showing the distribution (thick bar, median; box, 25th to 75th percentile, whiskers reach to the largest/smallest observations within 1.5 box-heights of the box).
Extended Data Fig. 3 Transcriptomic dysregulation of post-transplant blood and kidneys in AA recipients carrying LILRB3-4SNPs variant (‘Risk’) vs reference (‘Non-risk’) allele.
a) Gene Set Enrichment Analysis (GSEA) enrichment plot of the pathways showing gene upregulation involved in Th17 cell differentiation, T cell receptor signaling and B cell mediated immunity in bulk RNA sequencing of the blood samples collected after 6 months post-transplant in AA recipients with (n=10) vs without (n=10) LILRB3-4SNPs. GSEA analysis was performed on post-transplant blood expression profiles of the recipients with and without the SNP to identify the pathways associated with the SNP (P <0.05). b) UMAP of single cell RNA sequencing of the PBMCs isolated from 6 AA patients (with (n=3) and without (n=3) LILRB3-4SNPs) at 24-month after transplantation. c) Cell proportion of each cell type in two groups. The increased T cell and decreased monocyte populations were detected in the SNPs carrying recipients. d) Function enrichment of DEGs between patients with and without LILRB3-4SNPs in each cell type demonstrating gene dysregulation involved in T/B cell activation and ferroptosis. DEGs in the subpopulation was identified by two-sided Wilcoxon Rank Sum test at P value <0.05. The gene-function enrichment was evaluated with one-sided hypergeometric test. e) Heatmap showing the log2(fold change) of selected DEGs of B, T cell activation and ferroptosis signatures between SNP+ vs SNP- cells in each cell type. f) Dysregulated functions (NES: normalized GSEA enrichment score) in 3-month post-transplant biopsies from 6 AA recipients with (n=3) and without (n=3) the LILRB3-4SNPs. g) GSEA enrichment plot showing down-regulation of ferroptosis-negatively associated genes comparing the patient with (n=3) and without (n=3) LILRB3-4SNPs in 3-month biopsies. The shared transcriptional dysregulation among recipient’s pre- and post- transplant blood, transplanted kidneys implied the persistent inflammation in the blood stream post-transplant causes kidney damage.
Extended Data Fig. 4 In vitro functional analysis of THP-1 macrophage cell line overexpressing LILRB3-4SNPs variant (‘Risk’) or reference (‘Non-risk’) allele.
qPCR on expression changes for immune response (a) and ferroptosis-negatively-associated genes (b) upon LPS stimulation from 0 to 6 h (left) or from 6 to 24 h (right). The heatmap colors (blue color for positive values and brown for negative values, respectively) along with the numbers indicate the average of log2(fold changes) from duplicated biological experiments. Cell viability analysis within 48 h upon LPS stimulation (c) and in conjunction with ferroptosis-inhibitor (lipro-1) treatment (d) in THP-1 cells overexpressing LILRB3-4SNPs variant (n = 4) or reference allele (n = 4) (Student’s t-Test, two sided). The bar plots represent the mean values of the cell viability measurements from four biological replicates and error bars represent one standard deviation. Following a 6 h LPS stimulation, all cell lines exhibited increased expression of crucial inflammatory response markers linked to the LILRB family, including TNFα and IL1β, and the expression of these genes decreased from 6 to 24 h. The cell line overexpressing the variant allele produced greater quantities of TNFα, TNFAIP3 and IL1β upon 6-h LPS treatment, with less attenuation between 6 and 24 h than the reference allele, implicating increased inflammation associated with the SNPs (a). Expression of 5/6 ferroptosis negatively-associated genes in cell line with the SNPs decreased more between 6 and 24 h post LPS treatment, consistent with enhanced ferroptosis at 24 h linked to the SNPs (b). Cell viability analysis demonstrated a reduced viability upon LPS stimulation in cells with the SNPs (c, upper, orange vs green) but not those without the SNPs (c, lower, orange vs green). This phenomenon for the SNPs was reversed by ferroptosis inhibitor, Liproxstatin-1 (Lipro-1, at 0.625 umol) that targets lipid peroxidation (d, orange bar, upper). Lipor-1 had no effect on the cells without SNPs (d, orange bar, lower).
Extended Data Fig. 5 The schematic model of the role of LILRB3 in inflammation and ferroptosis.
Activation of LILRB3 causes binding and activation of SHP1/2 phosphatases that, through crosstalk, limit inflammatory signals initiated by TLR stimuli (for example, LPS) among other stimuli. The expression of the variant LILRB3-4SNPs risk allele reduces the capability of LILRB3’s intracellular ITIM domain to bind to and activate SHP1/2 phosphatases, resulting in amplification of the inflammatory response (for example, TNFα and cytokine release) and JAK/STAT activation, facilitate induction of ferroptosis, ultimately leading to graft damages. Figure created with BioRender.com.
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Sun, Z., Yi, Z., Wei, C. et al. LILRB3 genetic variation is associated with kidney transplant failure in African American recipients. Nat Med (2025). https://doi.org/10.1038/s41591-025-03568-z
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DOI: https://doi.org/10.1038/s41591-025-03568-z
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