Biological pathways and comparison with biopsy signals and cellular origin of peripheral blood transcriptomic profiles during kidney allograft pathology
Authors: Elisabet Van Loon, Baptiste Lamarthée, Henriette de Loor, Amaryllis H. Van Craenenbroeck, Sophie Brouard, Richard Danger, Magali Giral, Jasper Callemeyn, Claire Tinel, Álvaro Cortés Calabuig, Priyanka Koshy, Ben Sprangers, Dirk Kuypers, Wilfried Gwinner, Dany Anglicheau, Pierre Marquet, Maarten Naesens
Journal: Kidney International (2022)

Kidney transplant injury processes are associated with molecular changes in kidney tissue, primarily related to immune cell activation and infiltration. How these processes are reflected in the circulating immune cells, whose activation is targeted by strong immunosuppressants, is poorly understood. To study this, we analyzed the molecular alterations in 384 peripheral blood samples from four European transplant centers, taken at the time of a kidney allograft biopsy, selected for their phenotype, using RNA-sequencing. In peripheral blood, differentially expressed genes in 136 rejection and 248 no rejection samples demonstrated upregulation of glucocorticoid receptor and nucleotide oligomerization domain-like receptor signaling pathways. Pathways enriched in antibody-mediated rejection (ABMR) were strongly immune-specific, whereas pathways enriched in T cell-mediated rejection were less immune related. In polyomavirus infection, upregulation of mitochondrial dysfunction and interferon signaling pathways was seen. Next, we integrated the blood results with transcriptomics of 224 kidney allograft biopsies which showed consistently upregulated genes per phenotype in both blood and biopsy. In single-cell RNASeq (scRNASeq) analysis of seven kidney allograft biopsies, the consistently overexpressed genes in ABMR were mostly expressed by infiltrating leukocytes in the allograft. Similarly, in peripheral blood scRNASeq analysis, these genes were overexpressed in ABMR in immune cell subtypes. Furthermore, overexpression of these genes in ABMR was confirmed in independent cohorts in blood and biopsy. Thus, our results highlight the immune activation pathways in peripheral blood leukocytes at the time of kidney allograft pathology, despite the use of current strong immunosuppressants, and provide a framework for future therapeutic interventions. Kidney transplant injury processes are associated with molecular changes in kidney tissue, primarily related to immune cell activation and infiltration. How these processes are reflected in the circulating immune cells, whose activation is targeted by strong immunosuppressants, is poorly understood. To study this, we analyzed the molecular alterations in 384 peripheral blood samples from four European transplant centers, taken at the time of a kidney allograft biopsy, selected for their phenotype, using RNA-sequencing. In peripheral blood, differentially expressed genes in 136 rejection and 248 no rejection samples demonstrated upregulation of glucocorticoid receptor and nucleotide oligomerization domain-like receptor signaling pathways. Pathways enriched in antibody-mediated rejection (ABMR) were strongly immune-specific, whereas pathways enriched in T cell-mediated rejection were less immune related. In polyomavirus infection, upregulation of mitochondrial dysfunction and interferon signaling pathways was seen. Next, we integrated the blood results with transcriptomics of 224 kidney allograft biopsies which showed consistently upregulated genes per phenotype in both blood and biopsy. In single-cell RNASeq (scRNASeq) analysis of seven kidney allograft biopsies, the consistently overexpressed genes in ABMR were mostly expressed by infiltrating leukocytes in the allograft. Similarly, in peripheral blood scRNASeq analysis, these genes were overexpressed in ABMR in immune cell subtypes. Furthermore, overexpression of these genes in ABMR was confirmed in independent cohorts in blood and biopsy. Thus, our results highlight the immune activation pathways in peripheral blood leukocytes at the time of kidney allograft pathology, despite the use of current strong immunosuppressants, and provide a framework for future therapeutic interventions. Despite excellent short-term outcomes of kidney transplantation, allograft survival in the long term is still hampered by transplant-specific diseases, most importantly allograft rejection and polyomavirus-associated nephropathy.1Lamb K.E. Lodhi S. Meier-Kriesche H.U. Long-term renal allograft survival in the United States: a critical reappraisal.Am J Transplant. 2011; 11: 450-462Crossref PubMed Scopus (667) Google Scholar, 2Coemans M. Süsal C. Döhler B. et al.Analyses of the short- and long-term graft survival after kidney transplantation in Europe between 1986 and 2015.Kidney Int. 2018; 94: 964-973Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar, 3Van Loon E. Bernards J. Van Craenenbroeck A.H. Naesens M. The causes of kidney allograft failure: more than allo-immunity. A viewpoint paper.Transplantation. 2020; 104: e46-e56Crossref PubMed Scopus (24) Google Scholar, 4Van Loon E. Senev A. Lerut E. et al.Assessing the complex causes of kidney allograft loss.Transplantation. 2020; 104: 2557-2566Crossref PubMed Scopus (23) Google Scholar In kidney allograft rejection, graft-infiltrating cells are activated primarily in lymphoid organs before traveling and infiltrating the graft, despite the use of strong immunosuppressants.5Stewart B.J. Ferdinand J.R. Clatworthy M.R. Using single-cell technologies to map the human immune system—implications for nephrology.Nat Rev Nephrol. 2020; 16: 112-128Crossref PubMed Scopus (24) Google Scholar Apart from high-dose steroids and T cell–depleting agents such as thymoglobulin, there is no sufficiently efficacious therapy for rejection, partly because the primary pathophysiological processes behind this immune escape are poorly understood. In polyomavirus-associated nephropathy, acquired immunodeficiency permits reactivation of latent polyomavirus in the urinary tract epithelium and infection of kidney tubular epithelial cells, with direct viral toxic effects on the cells and activation of an antiviral host response, leading to tubulo-interstitial inflammatory infiltrates in the graft.In the past decade, transcriptional studies in kidney transplant biopsies have improved our pathophysiological insights into rejection. As immune cell infiltrates in the graft tissue originate from immune activation outside the kidneys (i.e., activation in lymphoid tissue), one hypothesis is that molecular changes seen in kidney allografts at the time of rejection or virus-associated nephropathy are also present in circulating immune cells. We recently illustrated the potential of identifying biologically relevant genes and pathways in peripheral blood at the time of antibody-mediated rejection.6Van Loon E. Gazut S. Yazdani S. et al.Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: a multicentre, prospective study.EBioMedicine. 2019; 46: 463-472Abstract Full Text Full Text PDF PubMed Scopus (37) Google ScholarIn this study, we used RNA-sequencing for whole transcriptome screening in a set of peripheral blood samples selected for rejection and polyomavirus nephropathy, and we compare this to the transcriptomics of heterogeneous control samples without these diseases. We hypothesized that the transcriptional changes in peripheral blood can unveil the key pathways activated in circulating peripheral blood immune cells in kidney transplant recipients, despite the use of powerful immunosuppressants, facilitate their noninvasive diagnosis, and provide potential targets for new therapies.MethodsStudy design, patient population, and sample collectionPeripheral blood samples were selected from an existing biobank prospectively collected as part of the BIOMARGIN (BIOMarkers of Renal Graft Injuries, www.biomargin.eu, ClinicalTrials.gov, NCT02832661) and ROCKET (Reclassification using OmiCs integration in KidnEy Transplantation) multicenter studies performed in 4 European transplant centers (Hôpital Necker, Paris, France; University Hospitals Leuven, Leuven, Belgium; Medizinische Hochschule, Hannover, Germany, and Centre Hospitalier Universitaire Limoges, Limoges, France). Samples were prospectively and consecutively collected at the time of kidney allograft biopsies, between June 2011 and August 2016. In the 4 clinical centers, protocol kidney allograft biopsies were performed at 3, 12, and sometimes 24 months after transplantation, according to local center practice, in addition to clinically indicated biopsies (biopsies at the time of graft dysfunction). All biopsies were used for histologic diagnosis according to the Banff 2019 classification,7Loupy A. Haas M. Roufosse C. et al.The Banff 2019 Kidney Meeting Report (I): updates on and clarification of criteria for T cell- and antibody-mediated rejection.Am J Transplant. 2020; 20: 2318-2331Crossref PubMed Scopus (197) Google Scholar whereas in some of the concomitant biopsy samples (n = 95), additional molecular analysis was performed. Single-end, 50–base pair (bp) sequencing was performed after RNA extraction and quality control of the peripheral blood samples, at Genomics Core facility, KU Leuven, Leuven, Belgium, using an Illumina HiSeq 4000 device, after library preparation using the Lexogen QuantSeq mRNA kit according to manufacturer instructions. Microarray gene expression data of 224 renal allograft biopsy samples (including 95 matched samples) from the same 4 European centers were available for data integration. Details on sample collection, RNA extraction, and the microarray platform used were published previously8Callemeyn J. Lerut E. de Loor H. et al.Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies.J Am Soc Nephrol. 2020; 31: 2168-2183Crossref PubMed Scopus (28) Google Scholar (Gene Expression Omnibus database under the accession number GSE147089). Previously published human single-cell data from 7 kidney allograft biopsies were used to determine the corresponding cell types expressing the antibody-mediated rejection (ABMR) genes observed in both blood and biopsy. The associated raw counts or matrices were downloaded from the Gene Expression Omnibus (GEO, GSE145927, https://www.ncbi.nlm.nih.gov/geo)9Malone A.F. Wu H. Fronick C. et al.Harnessing expressed single nucleotide variation and single cell RNA sequencing to define immune cell chimerism in the rejecting kidney transplant.J Am Soc Nephrol. 2020; 31: 1977-1986Crossref PubMed Scopus (29) Google Scholar and the Kidney Precision Medicine Project (https://atlas.kpmp.org/repository). Cell enrichment analysis was performed using the xCell online tool, which is a computational method used to investigate changes in cell distribution based on bulk transcriptomic profiles. We validated the observed ABMR signals obtained in this study in 2 independent publicly available datasets—1 set of 403 biopsy samples (GSE36059), and 1 set of 16 blood samples (GSE120649), with available rejection phenotyping. To further explore the expression of the ABMR signals in the peripheral blood leukocytes at the time of ABMR, scRNAseq analysis was performed on 12 peripheral blood samples from 6 ABMR and 6 stable patients (4 with biopsy, 2 without biopsy) from Nantes University Hospital, Nantes, France.Additional information on patient population, sample collection, RNA extraction, histologic diagnosis, RNA-sequencing and differential gene expression, integration with biopsy microarray data, validation in publicly available datasets, and single-cell RNASeq experiments can be found in the Supplementary Methods.Statistical analysisVariables with normal distribution are displayed as mean ± SD. Median and range are given for variables not normally distributed. For variance analysis of xCell enrichment scores in different groups, a 2-way analysis of variance was used. To correct for multiple testing, a false discovery rate (FDR) correction was applied during differential gene expression and pathway analysis, and for comparison of xCell enrichment scores. A 2-tailed FDR-corrected P value of <0.05 was considered significant for differentially expressed genes and xCell enrichment scores, and an FDR-corrected P value of < 0.20 was considered significant for Ingenuity Pathway Analysis (IPA) pathway analyses. For all other analyses, a 2-tailed nominal P value of < 0.05 was considered statistically significant. R (version 4.0, R Development Core Team), SAS (version 9.4; SAS Institute), and GraphPad Prism (version 9; GraphPad Software) were used for data analysis and presentation.ResultsPatient and biopsy characteristicsFrom 365 patients, 384 peripheral blood samples taken at the time of a concomitant allograft biopsy for genome-wide expression analysis were included in a case–control study design, as follows: 136 rejection cases (n = 86 with histology of ABMR [ABMRh], and n = 68 with T cell–mediated rejection [TCMR]; of which n = 18 had concomitant ABMRh and TCMR in the biopsy); 21 polyoma-virus associated nephropathy (PVAN) cases; and 227 heterogeneous control biopsies (absence of rejection and PVAN). Patient demographics and clinical characteristics are provided in Table 1; detailed histologic characteristics are given in Supplementary Table S1. Details on the biopsy samples used for microarray gene expression (n = 224) have been provided previously.8Callemeyn J. Lerut E. de Loor H. et al.Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies.J Am Soc Nephrol. 2020; 31: 2168-2183Crossref PubMed Scopus (28) Google ScholarTable 1Characteristics of the patients and biopsiesVariableValueTransplant characteristics (N = 365 patients) Recipient age at transplantation, yr49.3 ± 15.5 Recipient age at time of biopsy, yr51.9 ± 14.6 Recipient sex (male/female)217/148 (59.4/40.6) Repeat transplantation61 (16.7) Recipient ethnicity (European/Asian/African/other)aDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.309/6/11/36 (84.7/1.6/3.0/9.9) Donor age, yraDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.49.5 ± 16.5 Donor sex (male/female)aDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.169/182 (46.3/49.9) Deceased/living donoraDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.279/82 (76.4/22.5) Heart-beating/non–heart-beating donor247/32 (67.7/8.8) Cold ischemia time, haDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.12.9 ± 8.5Biopsy characteristics (N = 384 biopsies) Indication/protocol biopsy141/243 (36.7/63.3) Time after transplantation, d361 (4 – 12,564) Time after transplantation, yr< 1203 (52.9)> 1181 (47.1) MDRD eGFR, (ml/min per 1.73 m2) at biopsy41.9 ± 18.3 Proteinuria (g/g creatinine) at biopsyaDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.0.16 (0.0–8.0)Immunosuppression at biopsy Cyclosporine38 (9.9) Tacrolimus323 (84.1) Mycophenolate54 (85.9) Azathioprine7 (1.8) mTOR inhibitor43 (11.2) Corticosteroids353 (91.9) OtherbOther: belatacept (n = 5) / eculizumab (n = 9).14 (3.6)Histologic diagnosis Control biopsy (no rejection, no PVAN)227 (59.1)T-cell mediated rejection (including mixed) Borderline changes42 (10.9) Grade 1 or 226 (6.8) Histology of antibody-mediated rejection (incl. mixed)86 (22.4) Mixed rejection18 (4.7) Interstitial fibrosis/tubular atrophy grade0200 (52.1)170 (18.2)270 (18.2)344 (11.5)Polyomavirus-associated nephropathy21 (5.5)Polyoma viremiaaDonor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.60 (15.6)CIT, cold ischemia time; eGFR, estimated glomerular filtration rate; MDRD, modification of diet in renal disease; mTOR, Mechanistic target of rapamycin; PVAN, polyoma-virus associated nephropathy.Values are mean (median) ± SD (minimum – maximum), or n (%).a Donor age missing in n=12; CIT missing in n = 14; proteinuria missing in n = 5; donor sex missing in n = 14; ethnicity unknown in n = 3; deceased/living unknown in n = 4; polyoma viremia missing in n = 57.b Other: belatacept (n = 5) / eculizumab (n = 9). Open table in a new tab Differential gene expression, pathway enrichment analysis, and upstream regulatorsIn the blood samples from patients with any rejection (n = 136) versus no rejection (n = 248), ZEB2, DGKH, PATL2, IL18R1, and SLAMF7 were the top 5 most significant differentially expressed genes (DEGs; Figure 1a; Supplementary Table S2). Pathway enrichment analysis on the 319 differentially expressed genes (nominal P < 0.005) revealed glucocorticoid receptor signaling, hepatic fibrosis/hepatic stellate cell activation, and Th1 and Th2 activation pathways as the top 3 canonical pathways (Figure 2a). Additionally, in competitive set enrichment analysis using gene set enrichment analysis (GSEA), the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway was the most upregulated (normalized enrichment score [NES] 2.65, P = 0.003), followed by the Janus kinase signal transducer and activator of transcription (Jak-STAT) signaling pathway (NES 2.21, P = 0.003) and Th17 cell differentiation (NES 2.19, P = 0.003; Supplementary Figure S1). The top 5 most contributing genes to the NOD-like receptor signaling pathway were GBP5, STAT1, GBP1, GBP2, and CASP5. Predicted upstream factor analysis uncovered interferon regulatory factors and T cell activating genes as the most activated upstream regulators (Supplementary Table S3; Supplementary Figure S2). In a sensitivity analysis comparing any rejection versus pristine biopsies (without any lesions; n = 110), similar significant DEGs were found (top 10 DEGs are shown in Supplementary Table S4). When looking at differential expression for clinical (n = 78) versus subclinical rejection (n = 58), no DEG showed a significant difference, with adjusted P value < 0.05.Figure 2Top 10 canonical pathways for the different phenotype comparisons obtained using Ingenuity Pathway Analysis. The orange line denotes the false discovery rate (FDR) P value 0.2 level of significance. The top 5 contributing features are shown per pathway. ABMRh, histology of antibody-mediated rejection; ARE, antioxidant response element; CXCR, C-X-C C-X-C chemokine receptor; fMLP, N-Formylmethionyl-leucyl-phenylalanine; GM-CSF, Granulocyte-macrophage colony-stimulating factor; HLA–DSA, human leukocyte antigen–donor-specific antibody; IL, interleukin; ILK, Integrin-linked kinase; iNOS, Inducible nitric oxide synthase; LXR, Liver X Receptor; NF, nuclear factor; MSP-RON, macrophage-stimulating protein - recepteur d'origine nantais; NFAT, Nuclear factor of activated T cells; PKR, protein kinase R; PPAR, Peroxisome proliferator-activated receptor; PVAN, polyoma-virus associated nephropathy; RXR, retinoid X receptor; STAT, signal transducer and activator of transcription; TCMR, T cell–mediated rejection; Th, T helper; VDR, vitamin D receptor.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Comparing ABMRh (n = 86) versus no ABMRh (n = 298), GBP5, STAT1, GBP1, GBP4, and PATL2 were the top 5 most significant DEGs (Figure 1b; Supplementary Table S5). Primary immunodeficiency signaling, protein ubiquitination, and antigen presenting pathways were identified as the top 3 canonical pathways (Figure 2b). Competitive set enrichment analysis with GSEA again identified the NOD-like receptor signaling pathway as the top upregulated pathway (NES 2.71, P = 0.008; Supplementary Figure S3). Interferon-regulatory factors were the most activated upstream regulators (Supplementary Table S6; Supplementary Figure S4). In a sensitivity analysis comparing ABMRh versus pristine biopsies, similar significant DEGs were found (top 10 DEGs are shown in Supplementary Table S7).Comparing TCMR (n = 68) versus no TCMR (n = 316), IFI27 (down), DGKH, RSAD2 (down), IFI44L (down), and IL18R1 were the top 5 most significant DEGs (Figure 1c; Supplementary Table S8). Hypercytokinemia/hyperchemokinemia in pathogenesis of influenza (down-regulated), interferon signaling (down-regulated), and interleukin (IL)-10 signaling were identified as the top 3 canonical pathways (Figure 2c). Competitive set enrichment analysis using GSEA identified endocytosis as the most upregulated enriched pathways (NES 1.78, P = 0.03), and viral infections were significantly downregulated (Supplementary Figure S5). Interferon regulatory factors were strong inhibitory upstream regulators (Supplementary Table S9; Supplementary Figure S6). In a sensitivity analysis comparing TCMR versus pristine biopsies, few DEGs were found, with only DGKH, MIAT, NKG7, and ZEB2 with FDR < 0.05 (top 10 DEGs are shown in Supplementary Table S10).When comparing pure ABMRh (n = 68) versus pure TCMR (n = 50; excluding mixed rejection cases), STAT1, PARP14, RSAD2, APOL6, and GBP5 were the most significant DEGs, all upregulated in the ABMRh group. Genes that were more upregulated in TCMR included B and T cell–related genes (CD79A, BLNK, LSP1, TCL1A, and IGHM; Supplementary Table S11).When we compared human leukocyte antigen–donor-specific antibody (HLA–DSA)–positive samples (n = 100) versus HLA–DSA negative samples (n = 284), CD27 (down), MT-ND1 (down), SEC24A, SLC2A1, and BIRC2 were the top 5 significant DEGs (Supplementary Table S12; Figure 1d). Nuclear factor of activated T cells (NFAT)–signaling pathways were identified as the top canonical pathways (Figure 2d). The interferon-α group, Krüppel-like Factor 3 (KLF3), IL-5, X-box binding protein 1 (XBP1), and interferon-γ (IFN-γ) were the most strongly activated upstream regulators (Supplementary Table S13; Supplementary Figure S7).Comparing PVAN (n = 21) versus no PVAN (n = 363, including rejection), MT-ND1, MT-ND2, IFI27, MT-ND4, and MT-ATP6 were the top 5 significant DEGs (Supplementary Table S14; Figure 1e). Oxidative phosphorylation and mitochondrial dysfunction were identified as the top canonical pathways (Figure 2e). GSEA demonstrated extracellular matrix–receptor interaction, glycerolipid metabolism, and antigen processing and presentation as the top 3 upregulated pathways (Supplementary Figure S8). INFL1 and DAP3 (involved in mediating IFN-γ–induced cell death) were the most activated upstream regulators (Supplementary Table S15; Supplementary Figure S9). In a sensitivity analysis comparing PVAN versus pristine biopsies, similar significant DEGs were found (top 10 DEGs are shown in Supplementary Table S16).For polyomavirus viremia (with or without nephropathy; n = 60), compared to absence of viremia (n = 267), LY6E, IFI27, MT-ND2, MT-ND4, and IFI44L were the top 5 significant DEGs (Supplementary Table S17; Figure 1f). Hypercytokinemia/hyperchemokinemia in the pathogenesis of influenza, oxidative phosphorylation, and interferon signaling were identified as the top 3 upregulated canonical pathways (Figure 2f). Interferons were the most activated upstream regulators (Supplementary Table S18; Supplementary Figure S10). In a sensitivity analysis comparing viremia versus control (n = 174, excluding rejection), similar significant DEGs were found (the top 10 are shown in Supplementary Table S19). No peripheral blood transcriptomic differences were found for polyoma viremia with versus without nephropathy after FDR adjustment.Integration with biopsy microarray dataNext, we compared, per rejection phenotype, the DEGs from blood with the DEGs from 224 biopsies. Integrative meta-analysis revealed that the top 10 overexpressed genes consistent across blood and biopsy samples in any rejection, versus no rejection, were GBP5, CCL4, C1QA, FCGR1B, CRTAM, GBP1, NKG7, SLAMF7, KLRD1, and STAT1 (Figure 3). These genes were consistent across the different ranking methods (Supplementary Table S20). Similarly, the top 10 differentially expressed genes in ABMRh versus no ABMRh, in blood and biopsy samples, were GBP5, CCL4, GBP1, C1QA, CRTAM, FCGR1B, C1QB, GBP4, AIM2, and SLAMF7 (Figure 3; Supplementary Table S20). In TCMR versus no TCMR, C1QA, VSIG4, LCN2, KCNJ2, IL18R1, CCL4, FKBP5, C1QB, CST7, and BCAT1 were the top 10 overexpressed genes when integrating blood and biopsy differential expression data (Figure 3; Supplementary Table S20). Given that only 3 PVAN cases were present in the biopsy dataset, no integration was performed for the PVAN phenotype. The top consistently enriched upregulated pathways from GSEA across the different rejection phenotypes in blood and biopsies are represented in Supplementary Table S21.Figure 3The top 10 overexpressed features across biopsy microarray data and peripheral blood RNAseq data for any rejection, histology of antibody-mediated rejection (ABMRh), and T cell–mediated rejection (TCMR). The scatter plot shows the distribution of the individual genes, supplemented with a violin plot demonstrating overall distribution with included median and interquartile range.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Cellular origin of consistently overexpressed genes in the kidney allograftTo assess the cellular origin of these consistently overexpressed genes in the kidney allograft, we used publicly available single-cell data from kidney allograft biopsies. Briefly, scRNASeq was performed on 2 kidney allograft biopsies with ABMR, and 5 healthy surveillance biopsies. After quality control and filtering was completed, 33,216 cells were detected, and unsupervised clustering revealed 14 clusters corresponding to the main kidney cell subtypes but also infiltrating immune cells (Supplementary Figure S11A–C). We evaluated the expression of genes corresponding to the top 10 consistently overexpressed genes in ABMR from both blood and biopsy (see above, GBP5, CCL4, GBP1, C1QA, CRTAM, FCGR1B, C1QB, GBP4, AIM2, and SLAMF7) across the kidney structural cells and infiltrated immune cells (Supplementary Figure S11D). The genes of interest were mainly expressed by the infiltrated leukocytes in the kidney—that is, the antigen-presenting cells and lymphocytes.Cell-type enrichment analysisWe found that the peripheral blood signals were consistent with the biopsy signals at the time of rejection. Moreover, these differentially expressed molecules were expressed by the leukocyte pool in the allograft. Therefore, we hypothesized that transcriptomic changes in the peripheral blood reflect changes in circulating immune cells. To further investigate this possibility, we conducted a cell enrichment analysis using xCell. Cell enrichment scores did not discriminate among the different phenotypes (Figure 4). A comparison of the different immune cell types across the different phenotypes showed few significant differences between enrichment scores after FDR adjustment (Supplementary Figure S12). In mixed rejection versus control, there was an overrepresentation of Th2 cells and an underrepresentation of Th1 cells. In PVAN versus control, basophils and Th1 cells were overrepresented, and Th2 cells were underrepresented (Supplementary Figure S12). In contrast, cell enrichment scores based on the biopsy transcriptomic data did discriminate rejection phenotypes from no rejection (Figure 4). In the biopsy data, the most significantly overrepresented cell types in the rejection phenotypes were basophils, CD4+ and CD8+ central and effector memory T cells, dendritic cells, monocytes, NK cells, and Th2 cells. In contrast, naïve CD4+ and CD8+ T cells, and T regulatory cells were significantly underrepresented in all rejection phenotypes (Supplementary Figure S12). The significant differences between pure ABMRh and pure TCMR included the overrepresentation of NK cells in ABMRh and the overrepresentation of B-cells, CD4+ T cells (including CD4+ memory T cells), and CD8+ central memory T cells in TCMR (FDR P < 0.05).Figure 4Cell enrichment scores in the different phenotypes of kidney allograft pathology as obtained from blood (upper panel) and biopsy transcriptomic data. Heatmaps of the cell enrichment scores, ordered by hierarchical clustering. In the blood samples, cell enrichment scores did not discriminate among the different phenotypes, whereas in the biopsy samples, discrimination of rejection versus no-rejection phenotypes based on their cell enrichment scores in immune cells is seen. ABMRh, histology of antibody-mediated rejection; CD, cluster of differentiation; DC, dendritic cell; HLA–DSA, human leukocyte antigen–donor-specific antibody; NK, natural killer cell; NKT, natural killer T cell; PVAN, polyoma-virus associated nephropathy; Tcm, Tem, TCMR, T cell–mediated rejection; Th, T helper cell.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Validation of ABMR signals in independent cohortsFinally, we validated observed ABMR signals from this study in independent cohorts: one