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Intragraft Molecular Pathways Associated with Tolerance Induction in Renal Transplantation

Lorenzo Gallon,1,2 James M. Mathew ,2,3,4 Sai Vineela Bontha,5 Catherine I. Dumur,6 Pranav Dalal,2,3 Lakshmi Nadimpalli,2,3 Daniel G. Maluf,5 Aneesha A. Shetty,1,2 Suzanne T. Ildstad,7,8,9 Joseph R. Leventhal,2,3 and Valeria R. Mas 5

Departments of 1Medicine-Nephrology, 3Surgery, 4Microbiology-Immunology and 2Comprehensive Transplant Center, Northwestern University, Chicago, Illinois; 5Translational Genomics Transplant Laboratory, Department of Surgery, University of Virginia, Charlottesville, Virginia; 6Molecular Diagnostics Laboratory, Department of Pathology, 7 Virginia Commonwealth University, Richmond, Virginia; and Departments of Surgery, 8Physiology, and 9Immunology, Institute for Cellular Therapeutics, University of Louisville, Louisville, Kentucky

ABSTRACT The modern immunosuppression regimen has greatly improved short-term allograft outcomes but not long-term allograft survival. Complications associated with immunosuppression, specifically nephrotoxi- city and infection risk, significantly affect graft and patient survival. Inducing and understanding pathways BASIC RESEARCH underlying clinical tolerance after transplantation are, therefore, necessary. We previously showed full donor chimerism and immunosuppression withdrawal in highly mismatched allograft recipients using a bioengineered stem cell product (FCRx). Here, we evaluated the expression and microRNA expres- sion profiles in renal biopsy samples from tolerance-induced FCRx recipients, paired donor organs before implant, and subjects under standard immunosuppression (SIS) without rejection and with acute rejection. Unlike allograft samples showing acute rejection, samples from FCRx recipients did not show upregulation of T cell– and B cell–mediated rejection pathways. pathways differed slightly between FCRx samples and the paired preimplantation donor organ samples, but most of the functional gene networks overlapped. Notably, compared with SIS samples, FCRx samples showed upregulation of involved in pathways, like B cell signaling. Additionally, prediction analysis showed inhibition of proinflammatory regulators and activation of anti-inflammatory pathways in FCRx samples. Furthermore, integrative analyses (microRNA and gene expression profiling from the same biopsy sample) identified the induction of regulators with demonstrated roles in the downregulation of inflammatory pathways and maintenance of tissue homeostasis in tolerance-induced FCRx samples compared with SIS samples. This pilot study highlights the utility of molecular intragraft evaluation of pathways related to FCRx-induced tolerance and the use of integrative analyses for identifying upstream regulators of the affected down- stream molecular pathways.

J Am Soc Nephrol 29: 423–433, 2018. doi: https://doi.org/10.1681/ASN.2017030348

Utilization of more effective immunosuppression Received March 29, 2017. Accepted September 7, 2017. (IS) has been successful at inhibiting acute rejection J.M.M. and S.V.B. contributed equally to this work. and improved the short-term outcome of allogeneic Published online ahead of print. Publication date available at 1 organ transplantation. However, the required life- www.jasn.org. long use of IS leads to significant morbidities, in- Correspondence: Dr. Lorenzo Gallon Northwestern University cluding nephrotoxicity, infection, malignancies, Feinberg School of Medicine, Arkes Family Pavilion, Suite 19, 676 and cardiovascular disease, thus adversely affecting North St. Clair Street, Chicago, IL 60611-2923. Email: l-gallon@ the long-term survival of both graft and recipient.2–5 northwestern.edu Ten-year survival of living donor kidney allografts is Copyright © 2018 by the American Society of Nephrology

J Am Soc Nephrol 29: 423–433, 2018 ISSN : 1046-6673/2902-423 423 BASIC RESEARCH www.jasn.org only about 48%,6 and attempts at reducing the burden of IS have Significance Statement not been particularly fruitful to date.5 Therefore, a safe and re- liable approach to induce allograft tolerance remains an impor- Hematopoietic stem cell (HSC) chimerism produces tolerance to tant objective for organ transplantation. transplanted tissues; this has been achieved in highly mismatched It has been known for over 60 years that hematopoietic stem kidney allograft recipients using FCRx, a bioengineered stem cell product that contains donor HSCs and unique facilitating cells. This cell chimerism is associated with tolerance to transplanted study examined gene and miRNA expression for the first time tissues and cells.7 Application of this approach has recently in renal biopsies from tolerance-induced FCRx recipients, paired been reported by a number of transplant centers.8–18 Except preimplantation donors, and subjects receiving standard immuno- for the few patients who had received HLA-identical bone suppression. Although gene expression pathways associated with marrow transplants, became fully chimeric, and subsequently rejection were not upregulated in tolerant biopsies, these biopsies showed upregulation of genes involved in B cell receptor signaling, 2,19,20 received kidney transplants, no fully chimeric and toler- activation of anti-inflammatory pathways, and inhibition of proin- ant patients from HLA disparate and unrelated donors have flammatory regulators when compared with nonrejecting subjects been reported until recently. on standard immunosuppression. Results support potential of this We have shown that full donor chimerism and total IS tolerance induction strategy (through active immunoregulation) to withdrawal can be attained with minimal toxicity in highly improve long-term kidney allograft survival. mismatched related and unrelated kidney allograft recipi- ents.12–14,17,18 This was achieved through the use of a bioengi- and 1094 downregulated) were observed (false discovery rate neered stem cell product containing donor hematopoietic stem ,0.05 and fold change [FC] $1.5). The top canonical pathways cells and a unique population of cells, termed facilitating cells downregulated in FCRx and upregulated in R were associated (the total product termed FCRx), accompanied by reduced in- with immune response spanning cellular differentiation, antigen tensity conditioning. Aside from this, only a limited number of presentation, signaling, and effector functions (Table kidney transplant recipients have been reported over the past 2). Specifically, principal pathways downregulated (FC=211.3 two decades as being operationally tolerant (i.e., off all IS), to 22.7) in the FCRx group included antigen presentation path- mostly through nonadherence, and retaining allograft function. way (i.e., HLA-A, HLA-B, B2M, CD74, CIITA, HLA-DRB1, Therefore, it is paramount that the mechanisms by which these TAP1,andTAP2) (Supplemental Figure 4A), graft-versus-host patients have developed donor-specific tolerance be analyzed. In disease signaling (i.e., CD86, multiple HLA genes, PRF1), IFN this report, we have evaluated the characteristic molecular sig- signaling (IFIT2, IFNGR1, IRF1, and OAS1), allograft rejection natures that portray the graft with FCRx-induced tolerance. signaling (CD40, CD86, PRF1, TRGV9,andFAS)(Supplemental Figure 4B), CD28 signaling in T helper cells (CD4, FCEGR1G, LCP2, CD3E,andTRGV9), and dendritic cell maturation RESULTS (FCER1G and HLA-DR) among others. Top downregulated genes included those for immunologic diseases (P value range Patient Demographics and Samples 8.89E-72 to 2.80E-18; including 650 DEGs), inflammatory re- Three groups of patients with kidney transplants distributed sponse (P value range 43.42E-71 to 46E-18; including 575 into those who were under FCRx-induced tolerant protocol DEGs), and inflammatory disease (P value range 3.37E-60 to (FCRx; n=7), diagnosed with rejection (R; n=10), and without 1.64E-18; with 423 DEGs). Conversely, a small number of acute rejection but under standard immunosuppression (SIS; signaling pathways were upregulated in FCRx samples, with n=10; six samples were used in microarray and four were used PPARa/RXRa activation, ketogenesis, and glutathione-mediated in quantitative RT-PCR [qRT-PCR] to compare in both assays detoxification being the top ones, suggestive of active immune against FCRx) were evaluated. Additional information on the regulation and cellular homeostasis. patients is given in Table 1 and Supplemental Material. The biopsy samples were also compared against paired normal DEGs in Biopsies from FCRx versus SIS Recipients kidney preimplantation allograft biopsy samples from FCRx A comparison analysis was performed between FCRx and stable (preimplantation donor [D]; n=5) and SIS (SISD; n=2). SISrenalallograftbiopsiestoidentifyspecificmolecularpathways associated with induced tolerance in the kidney graft. From this Validation of FFPE versus Fresh Frozen Samples analysis, a molecular profile of 1509 probe sets representing 1372 We validated our microarray assays using paired FFPE and DEGs (529 downregulated and 843 upregulated) was identified frozen biopsies and obtained comparable results (Supplemen- (P#0.05; FC$1.5). The top upregulated genes (FC.2.0) in tal Figures 1–3). Hence, subsequent studies of differentially FCRx samples include LFNG, APBA3, PARP12, HK1,and expressed genes (DEGs) were performed with FFPE samples. NR2F2, whereas downregulated genes (FC.3.5) include EN- PEP, GATM, SLC5A12, KL,andITM2B. Top network functions DEGs in Tolerant (FCRx) versus Acutely Rejecting associated with these genes included cellular assembly and or- Transplants ganization, nervous system development and function, and cell From the comparison of the two sample sets (FCRx versus R), a death and survival (score =44); molecular transport, RNA traf- total of 1713 differentially expressed probe sets (619 upregulated ficking, and RNA post-transcriptional modification (score =42);

424 Journal of the American Society of Nephrology J Am Soc Nephrol 29: 423–433, 2018 www.jasn.org BASIC RESEARCH

Table 1. Patient characteristics at baseline PRA Time to Patient Age at Cause of Type of HLA Rejection in IS at Creatinine Sex Race before Induction Biopsy, ID Tx, yr ESRD Tx Mismatch Biopsy Time of Biopsy at Biopsy Tx mo after Tx R R1 36 M Hisp DM SPK 3/6 34, 0 Al 11 1B ACR FK/MMF 1.25 R2 35 M Hisp MPGN LRD 4/6 15, 53 Al 4 1B ACR FK/MMF 2.1 R4 22 M Cau MCD LRD 3/6 0, 0 Al 6 2B ACR FK/MMF 1.44 R8 72 F AA HTN CKT 5/6 68, 60 Al 0.4 2A ACR FK/MMF 0.98 R9 51 M AA IgAN LURD 6/6 0, 0 Simulect 0.2 2A ACR FK/MMF/Pred 7.2 R11 45 M AA HTN LRD 2/6 0, 0 Al 9 1B ACR FK/MMF 1.1 R12 39 M Cau PKD CKT 3/6 67, 16 Al 3 1B ACR FK/MMF 0.9 R13 41 F Cau DM LRD 4/6 14, 0 Al 10 2B ACR FK/MMF 0.7 R14 43 F AA HTN/DM CKT 5/6 24, 1 Al 6 2B ACR FK/MMF 1.56 R15 53 M Cau HTN CKT 4/6 45, 12 Al 5 2A ACR FK/MMF 0.76 SIS SIS1 39 F Hisp PKD LRD 5/6 0, 0 Al 12 None FK/MMF 0.75 SIS2 56 F Cau PKD LRD 3/6 0, 0 Al 12 None FK/MMF 1.08 SIS3 66 F Cau Sclero- LURD 4/6 32, 41 Al 12 None FK/MMF/Pred 0.98 derma SIS4 55 M Cau HTN LURD 2/6 0, 0 Al 12 None FK/MMF 1.44 SIS5 68 F Hisp Unknown LURD 6/6 0, 39 Al 12 None FK/MMF 0.86 SIS8 62 F Cau DM CKT 0/6 94, 31 Simulect 12 None FK/MMF 1.0 SIS6a 23 F AA DM SPK 6/6 60, 0 Al 13 None FK/MMF 1.55 SIS7a 67 M Cau HTN LURD 3/6 12, 0 Al 12 None FK/MMF 1.4 SIS9a 62 M Cau PKD LURD 5/6 0, 0 Al 11 None FK/MMF 1.3 SIS10a 55 M Hisp DM CKT 3/6 0, 0 Al 12 None FK/MMF 1.2 Stem cell FCRx1 46 F Cau PKD LURD 5/6 15, 0 None 12 None None 0.95 FCRx2 42 M Cau PKD LURD 5/6 23, 0 None 25 None None 1.35 FCRx3 39 F Cau Reflux LURD 2/6 9, 0 None 12 None None 0.87 FCRx4 40 M Cau Chronic GN LURD 5/6 0, 0 None 24 None None 1.35 FCRx5 44 M AA IgAN LRD 3/6 0, 0 None 25 None None 1.48 FCRx6 46 M Cau PKD LRD 4/6 4, 4 None 13 None None 1.53 FCRx7 19 F Asian MPGN LRD 2/6 0, 0 None 12 None None 1 ID, identification; Tx, transplant; PRA, panel reactive ; M, man; Hisp, Hispanic; DM, diabetes mellitus; SPK, simultaneous kidney pancreas transplant;Al, alemtuzumab (campath-1h); ACR, acute cellular rejection; FK, tacrolimus; MMF, mycophenolate mofetil or mycophenolic acid; MPGN, membranoproliferative GN; LRD, living related donor kidney transplant; Cau, white; MCD, medullary cystic disease; F, woman; AA, black; HTN, hypertension; CKT, cadaveric kidney transplant; IgAN, IgA nephropathy; LURD, living unrelated donor kidney transplant; Pred, prednisone; PKD, polycystic kidney disease. aSamples used for independent validation. and cell death and survival, embryonic development, and cellu- pro-B cells, and granulocytes (Supplemental Table 1). The top lar development (score =42). predicted upstream activators with activation z score .2but The analysis of canonical pathways associated the DEGs in significant overlapping (P,0.05) included EIF4E, IFNA2,and FCRx versus SIS showed 158 significant signaling cascades TGFB1 (Supplemental Table 2A). Additionally, the analysis of (P,0.05). Among these, B cell receptor signaling was identified top regulatory networks identified 20 regulator complexes as one of the top significant canonical pathways with positive with positive consistency scores in FCRx; they and their func- predicted activation (P,0.001); the upregulated genes included tional correlates are listed in Supplemental Table 2B. CD79A, BAD, CFL1, CREB3, ETS1, MAP3K3, MAPK3, Additionally, cell type enrichment analysis21 showed that MAPK11, MAPK12, NFATC4, NFKBIA, PIK3CD, INPP5J, the DEGs in the FCRx recipients were augmented in kidney BCL6, CARD10, IKBKB, PRKCQ, INPPL1, VAV2, FGFR1,and cells and immune cells, like B cells, T cells, and BDCA4+ den- TCF3 (Figure 1). The downstream molecular analysis predicted dritic cells (Supplemental Table 3). positive transcriptional activity and therefore, pathway activation in tolerant samples (z score =1.633). The list of the top ten sig- Molecular Profile Associated with Induction of nificant canonical pathways (z score .1.6) is shown in Table 3. Tolerance—Comparison with Baseline Preimplantation Analysis of the immune-related cellular functions that were Donor Tissue upregulated in FCRx revealed predicted activation of immune A gene expression analysis was performed comparing paired functions of proliferation, survival, and recruitment in T cells, renal biopsy samples collected at preimplantation (D) and then

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Table 2. Downregulated canonical pathways associated with the FCRx group against rejection Ingenuity Canonical Pathways Present Molecules (%) Downregulated Upregulated P Value Antigen presentation pathway 25/38 (66) 25 0 ,0.001 Type 1 diabetes mellitus signaling 40/110 (37) 36 4 ,0.001 Graft-versus-host disease signaling 19/48 (40) 19 0 ,0.001 Autoimmune thyroid disease signaling 19/47 (40) 19 0 ,0.001 Allograft rejection signaling 22/84 (26) 22 0 ,0.001 Cdc42 signaling 37/167 (22) 35 2 ,0.001 IFN signaling 12/36 (33) 12 0 ,0.001 Dendritic cell maturation 45/190 (24) 41 4 ,0.001 Crosstalk between dendritic cells and natural killer cells 30/89 (34) 30 0 ,0.001 OX40 signaling pathway 24/89 (27) 24 0 ,0.001 CD28 signaling in T helper cells 19/118 (16) 19 0 ,0.001 T helper cell differentiation 15/71 (21) 15 0 ,0.001 Fcg receptor–mediated phagocytosis in 14/93 (15) 14 0 ,0.001 and monocytes Remodeling of epithelial adherence junctions 12/68 (18) 12 0 ,0.001 Regulation of actin-based motility by Rho 13/91 (14) 13 0 ,0.001 Present molecules for each canonical pathway are broken down as downregulated and upregulated. Representative percentages of present versus total molecules per canonical pathways are shown between parentheses. after establishing chimerism and tolerance (FCRx). Twenty of shown in Figure 2A. Additionally, using DEGs between FCRx 23 functional networks for DNA replication, recombination, and R (because R represents the extreme negative signature repair, energy production, and nucleic acid opposite to tolerance), a principal component analysis was were found to be overlapped between FCRx and D samples used for classifying samples (Figure 2B). Figure 2B shows that (not shown). However, the analysis also identified 355 (1) there was high homogeneity among R samples, (2)there probe sets representing 327 DEGs, with 143 genes downregu- were some levels of heterogeneity among D and FCRx samples, lated and 184 genes upregulated in FCRx samples (P,0.001; and (3) FCRx samples have more alike with D than R samples. FCminimal61.5). and pathway analysis identi- Similar findings were observed when the same set of DEGs was fied eight associated network functions (Table 4). The used in a supervised cluster analysis to evaluate how samples top canonical pathways associated with tolerance induction (including SIS samples) associate together (Figure 2C). included inosine-59-phosphate biosynthesis II (P,0.001), telomere extension by telomerase (P=0.001), and axonal guid- Confirmation of DEGs Identified by SensationPlus with ance signaling (P=0.003). The prediction analysis for up- RT-qPCR Assays stream regulators showed inhibition of a number of effector We further validated the results from Sensation Plus assays molecules in FCRx samples (Table 5). Analysis using the described above by performing Taqman qRT-PCR assays database for annotation, visualization, and integrated discov- for the relative levels of the top three DEGs (Supplemental ery22 showed that many of the DEGs were involved, especially Figure 7). in glycan biosynthesis and metabolism (Supplemental Figure 5). Cell type enrichment analysis revealed augmentation of Distinct Sets of MicroRNAs Are Highly Expressed in CD34+ cells and CD19+ B cells among other immune cell FCRx Biopsies types (Supplemental Table 4). Because microRNAs (miRNAs) are important regulatory ele- S-score analyses of two SIS grafts and their paired preim- ments in gene expression, their profiles were tested in renal plantation donor biopsies (SIS1 versus D6 and SIS2 versus D7) biopsies from FCRx, SIS, and R as well as in paired D (Figure 3). showed that 50 genes (70 probe sets) and 65 genes (84 probe Compared with all other groups, the biopsies from the sets) were significantly differentially expressed in SIS1 versus patients on FCRx showed unique upregulation of three D6 and SIS2 versus D7, respectively. Twenty-one of these DEGs miRNAs: miR-31–5p, miR-9–5p, and miR-125b-5p (Figure were common in both individual comparisons (Supplemental 3D). In addition, FCRx biopsies, when compared with Table 5), whereas 29 and 44 genes were unique for SIS1 versus SIS and R groups, showed upregulation of four (miR-5p, D6 and SIS2 versus D7, respectively (Supplemental Figure 6). miR-26a-5p, miR574–3p, and miR-Let-7e-5p) and six (miR- 187–3p, miR-148–3p, miR-126–3p, miR-23b-3p, miR-147a, DEGs in FCRx: Multifactorial Comparison with D, SIS, and miR-383–5p) miRNAs, respectively. miR-21–5p and and R miR-223–5p were downregulated in FCRx in comparison A comparison of the gene expression profiles of the FCRx with R and in miR-143–5p in comparison with paired D against paired donors (D), SIS, and R sample groups using biopsies. The significance of these miRNA expressions was a 62.0 (P value =0.001) FC cutoff resulted in the Venn diagram established by integrative network analysis (below).

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Figure 1. Activation of the B cell receptor signaling canonical pathway in FCRx versus SIS: a schematic representation. Red indicates upregulation, and green indicates downregulation. Prediction of pathway activity is represented in blue (inhibition) and orange (ac- tivation). Color gradient intensities indicate magnitudes for expression and activation trends.

Integration of miRNA and Gene Expression Profiles in target genes involved in (1)inflammatory pathways (NFATC3, FCRx Group MAF, PIK3C2A,andITGAV), (2) metabolism (GATM, Integration analysis of the differentially expressed miRNAs SUCLG2,andGCLM), and (3) mitochondrial fatty acid oxi- showed a corresponding differential expression of their target dation (ACADM and ALDH1B1). Similarly, the miRNAs up- genes in the biopsies. The upregulated miRNAs in FCRx in regulated in FCRx versus R biopsies (Supplemental Figure 9) comparison with SIS group (Supplemental Figure 8) had their inhibited their target genes involved in various inflammatory

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Table 3. Top significant canonical pathways differentially regulated between FCRx and SIS Ingenuity Canonical Pathways Present Molecules (%) Downregulated Upregulated P Value B cell receptor signaling 25/190 (13.2) 4 21 ,0.001 kinase A signaling 41/396 (10.3) 16 25 ,0.001 iNOS signaling 9/45 (20) 1 8 0.002 SAPK/JNK signaling 15/104 (14.5) 6 9 0.002 Mouse embryonic stem cell pluripotency 15/106 (14.1) 3 12 0.002 4–1BB signaling in T lymphocytes 7/32 (21.9) 1 6 0.003 CD27 signaling in lymphocytes 9/53 (17) 3 6 0.005 LPS/IL-1–mediated inhibition of RXR function 23/221 (10.4) 15 8 0.01 Dopamine-DARPP32 feedback in cAMP signaling 18/163 (11) 7 11 0.01 IL-22 signaling 5/24 (20.8) 0 5 0.01 Present molecules for each canonical pathway are broken down as downregulated and upregulated. Representative percentages of present versus total molecules per canonical pathways are shown between parentheses. iNOS, inducible ; SAPK, stress-activated protein kinases; JNK, jun amino-terminal kinases. pathways, like antigen presentation, cytotoxic T cell–mediated showing a distinct profile. We also observed a distinct miRNA of target cells, NF-kB signaling, dendritic cell mat- profile among the same renal biopsies and used integrative uration, and various cytokine signaling pathways. A few of the approaches to show their possible role as upstream regulators genes from these pathways that were downregulated in FCRx of differential gene expression. and part of integrated dataset included HLA-DOA, TAP2, Our first comparison between FCRx and R samples, as ex- STAT3, CASP8, PYCARD, PRF-1,andTNFRSF1B. pected, showed major differences in immune pathways with activated antigen presentation, immune cell trafficking, and inflammatory responses in the R samples as described DISCUSSION previously.26,27 Although these differences were anticipated, it could be concluded from this analysis that, in addition Development of tolerance protocols requires assays or bio- to absence of clinical acute rejection as reflected by normal markers that distinguish tolerant from nontolerant recipients histologic findings, there was no molecular evidence of sub- to be established. Also, understanding the plausible mecha- clinical alloactivation after weaning from IS in the FCRx nisms of tolerance by identifying key associated molecular tolerance–induced samples. pathways is necessary for progression in the field. Herein, mo- The comparison of FCRx with SIS samples showed a com- lecular pathways in the allografts of a unique set of renal trans- paratively smaller set of DEGs indicative of slightly different plant recipients who display induced tolerance (with high mechanisms by which tolerance is achieved through active level of donor chimerism) after FCRx treatment protocol immune regulation. Enrichment analysis indicated that the and successful weaning of IS are shown. DEGs in biopsies downregulated genes, mostly of metabolic function, were from subjects under SIS without rejection and with R as well within the FCRx kidney cells, indicating use of tissue-specific as from D biopsies were compared using microarray-based protective mechanisms. For instance, the downregulated genes gene expression assay on FFPE tissue RNA (Sensation Plus). included SLC5A12 and ENPEP, which were reported to be This method was previously used exclusively in cancer-related associated with kidney injury.28,29 SLC5A12 had been shown studies23–25 andshowedasignificant analytic performance to increase reabsorption of monocarboxylates, which have correlation with classic gene expression microarray assays. utilization that is involved renal acidosis and in- Significant differences in gene expression profiles were ob- hibitor toxicity.28 Conversely, enrichment analysis of upregu- served among the three patient groups, with the FCRx biopsies lated genes in FCRx versus SIS showed augmentation in

Table 4. Associated network functions in tolerance compared with paired donor samples Top Associated Network Functions Score Focus Molecules Carbohydrate metabolism, lipid metabolism, post-translational modification 50 29 Embryonic development, organismal development, tissue morphology 50 29 Cellular assembly and organization, neurologic disease, cell death and survival 36 23 Cellular development, hematologic system development and function, hematopoiesis 32 21 Connective tissue disorders, developmental disorder, respiratory disease 29 20 Cell morphology, developmental disorder, gastrointestinal disease 29 20 Cell death and survival, cellular development, dermatological diseases and conditions 29 20 Cell death and survival, embryonic development, gene expression 27 19

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Table 5. Predicted activity of upstream regulators in tolerant FCRx samples in the transplanted and tolerant kidneys, versus D biopsies similar to those that we observed in Upstream Regulator Predicted Activity Activation z Score P Value of Overlap another group of nonchimeric-induced MYD88 Inhibited 22.952 ,0.001 tolerant HLA-identical kidney transplant EGF Inhibited 22.930 ,0.001 recipients.11,33 Additionally, the enrich- + TLR3 Inhibited 22.574 ,0.001 ment of CD34 cell–specific genes is con- TNF Inhibited 22.244 ,0.001 sistent with the notion that the CD34+ cells IL13 Inhibited 22.003 0.002 used for the tolerance induction maybe TLR9 Inhibited 22.397 0.002 homing to the allograft. Similar enrich- PTGS2 Inhibited 22.540 0.003 ment of genes of glycan biosynthesis path- 2 NF-kB (complex) Inhibited 2.132 0.003 way is suggestive of their crucial role in cell 2 , GDNF Inhibited 2.182 0.01 adhesion, differentiation, cell survival, and PDGF BB Inhibited 22.397 ,0.01 signal transduction processes.34 SFTPA1 Activated 2.236 ,0.01 CD24 Activated 2.000 ,0.01 Several studies have shown that toler- Tlr Inhibited 22.408 0.01 ance (induced, operational, immunologic, TLR4 Inhibited 22.236 0.01 or “prope”) was accomplished through ERK Inhibited 22.275 0.01 activation and differentiation of B cell sub- IGF1 Inhibited 23.333 0.02 types and regulatory T cells.11,35–42 How- FOSL1 Activated 2.000 0.02 ever, these studies were mostly limited to Cg Inhibited 22.227 0.03 analysis of gene expression profiles in the Creb Inhibited 22.156 0.03 PBMCs. Specifically, for operationally tol- 2 TLR2 Inhibited 2.195 0.03 erant kidney transplant recipients, cellular 2 EGFR Inhibited 2.746 0.04 and molecular profiles associated with PI3K (complex) Inhibited 22.014 0.04 B and T cells populations were reported IRF8 Inhibited 22.195 0.05 exclusively in peripheral blood.11,36,39,43 Brouard et al.44 reported a set of 33 genes immune cell subsets, particularly of B cell–related pathways. preferentially related to T cell activation and the TGF-a sig- Upregulation of signaling pathways mediated by B cell receptor, naling pathway. Newell et al.36 proposed a three-gene signa- APRIL, PI3k, and B cell activating factor was suggestive ture (IGKV1D-13,IGKV4–1,andIGLL1) that may predict of active immune regulation in B cells, supporting a role of tolerance in operationally tolerant kidney transplant recipi- development, survival (antiapoptosis), and commitment of B ents and then further corroborated this in an independent cells in the FCRx samples. Similarly, FLT3 signaling pathway study.45 Although the above three gene–based signature was upregulation is indicative of activation of BDC4A+ plasmacy- not observed in these gene array studies performed on the toid dendritic cells,30 which were also identified as enriched in renal allografts, induction of tolerance in FCRx recipients FCRx recipients. These plasmacytoid dendritic cells are known was also found to be associated with modulation of molecular to induce regulatory T and B cells critical for immune homeo- mechanisms involved in activation and survival of B cells. stasis.31,32 Also, the predicted activation of anti-inflammatory Deregulated B cell molecular pathways in peripheral blood pathways (RAR activation) and the inhibition of proinflamma- were also shown to correlate with increases in total B cells tory upstream regulators (NF-kB complex) were observed in and subpopulations of B cells (mainly naïve and transitional) FCRx versus SIS group. Integration of ex vivo miRNA and in most operationally tolerant kidney transplant recipi- gene expression profiles also supported their mechanistic role ents.33,36,39,46 These changes together with amplified regula- in achieving metabolic and immune homeostasis in patients on tory T cells have been proposed as a crucial component for FCRx in the absence of IS regimen. induction of tolerance.33,42 Interim results from the current It is reasonable to expect a steady state of gene expression for cohort of FCRx also showed an increase from baseline in the functional operations in the normal kidney (i.e.,Dsamples) circulating regulatory T cells and B cells through 5 years of and a comparable state in the same organ after development of transplantation.13 transplant tolerance (i.e., in the FCRx biopsies). However, it is We have shown for the first time the molecular pathways interesting to note that some differences were observed in the occurring in the allografts of patients with FCRx-induced tol- gene expression profile between the FCRx samples versus erance. Although the study is restricted in sample size, a com- paired kidney D biopsies. Thus, on the one hand, 20 out of mon limitation in transplant tolerance studies, we have used 23 cellular functional networks were found to be overlapped, stringent cutoffs for the molecular analysis and well selected and on the other hand, 143 genes, primarily those associated patient groups. The extended analysis using various compar- with innate immunity and inflammatory pathways, were isons with preimplant biopsies, biopsies with R, and those on downregulated in the FCRx samples. This would suggest SIS helped better understand the possible molecular pathways that active regulatory/suppressive mechanisms are occurring in this unique set of patient biopsy samples. Moreover, this is

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Figure 2. Comparison analyses among DEGs stratifying FCRx as being in between D and SIS, and distinctly different from rejection (R) samples. (A) Venn diagram including differential probe sets from each pairwise comparison. (B) Principal component analysis using DEGs from FCRx versus R comparison. (C) Supervised hierarchical clustering analysis. the first study using integration of two biologic layers (mRNA study groups (including FCRx versus R, FCRx versus SIS, and and miRNA) to better understand regulation of pathway’sex- FCRx versus D) to identify DEGs. S-score method was used for the pression. Although using FFPE samples might entail to loss of identification of significantly and differentially expressed probe sets certain information when compared with samples stored in in SISD versus D comparisons (SIS1 versus D6 and SIS2 versus D7) RNALater, the insights from this study together with further from two individually analyzed renal transplant recipients.47 For sta- longitudinal analysis of the patient biopsies will help under- tistical significance, a P value #0.05 was considered significant after a standing of molecular pathways and signatures that need to be controlled false discovery rate of ,5% and an FC of $1.5. monitored to track continuation of tolerance in these patients. miRNA Profiling and Integration with Gene Expression Part of the total isolated RNA from the FFPE renal biopsies was used CONCISE METHODS for pathway-directed miRNA profiling. Eighty-four different miRNAs previously reported to be associated with immunopathology were Gene Expression Profiling studied using qRT-PCR arrays (n=12; Qiagen) and compared be- Total RNAwas isolated from FFPE renal biopsy samples. Briefly, three tween FCRx and each of the remaining groups (paired donors, re- sections of 10-mm thickness were obtained from each FFPE block, jection, and SIS). The results were then analyzed using a web-based and total RNA was isolated using the High Pure RNA Paraffin Kit tool from SABiosciences. Volcano plots were obtained to report the (Roche) following the manufacturer’s instructions. Then, the total significantly and differentially expressed miRNA. RNA was labeled with SensationPlus FFPE Amplification and WT Labeling Kit SensationPlus and used for Affymetrix GeneChip Statistical Analyses HG-U133 2.0 microarray hybridization. Normalized signals were Data were analyzed as the mean6SD. Parametric (paired t tests) and generated using RMA. Pairwise ANOVAs were performed among nonparametric (Mann–Whitney U test/Wilcoxon signed test) tests

430 Journal of the American Society of Nephrology J Am Soc Nephrol 29: 423–433, 2018 www.jasn.org BASIC RESEARCH

Figure 3. Differential expression of miRNA demonstrating unique profile in FCRx, when compared to D, SIS and R groups. (A) Volcano plot showing differential expression of miRNA between FCRx and paired donor groups. (B) Volcano plot showing differential expression of miRNA between FCRx and SIS. (C) Volcano plot showing differential expression of miRNA between FCRx and R. (D) Figure showing miRNAs that are differentially expressed in FCRx biopsies versus other indicated groups (i.e.,miR-31–5p, miR-9–5p, and miR125b-5p are uniquely upregulated in FCRx); other miRNAs shown in red are upregulated and those shown in green are downregulated in FCRx when compared with indicated groups. were used among compared groups. Significance was established at two- determine their differential effect on ex vivo gene expressions. sided a-levels of ,0.05 using statistical software (SAS, Inc., Cary, NC). Furthermore, the pathways and integrative networks in which the gene sets were involved were defined using either IPA or Panther Biologic Interpretation analysis. Gene ontology and pathways analysis of gene expression profiles, identified as significantly differentially expressed, were performed Validation of Gene Expression Profiling from FFPE using the Ingenuity Pathway Analysis (IPA) tool (www.ingenuity. Samples com). Spreadsheets containing probe set identification and FC mag- The methods for paired comparisons of fresh frozen versus FFPE nitudes were uploaded to the IPA tool. A P value of ,0.05 was con- biopsies as well as microarrays versus qRT-PCR are described in Sup- sidered significant. Molecular and cellular functions activity trends plemental Material. were predicted and analyzed using IPA calculated z scores. Addition- ally, the database for annotation, visualization, and integrated dis- ACKNOWLEDGMENTS covery was used for pathway analysis,22 and CTen software was used for cell type enrichment analysis.21 The FCRx subjects were enrolled in a clinical trial (trial registry FDA- Data obtained from the renal biopsy samples for miRNAs were IDE 13947; ClinicalTrials.gov identifier NCT00497926) supported, in analyzed by miRNA target profiling for data integration and to part, by Regenerex, LLC and Novartis Pharmaceuticals.

J Am Soc Nephrol 29: 423–433, 2018 Transplant Tolerance Markers 431 BASIC RESEARCH www.jasn.org

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J Am Soc Nephrol 29: 423–433, 2018 Transplant Tolerance Markers 433 SUPPLEMENTARY MATERIALS

SUPPLEMENTARY METHODS:

Patient groups:

All protocols were approved by the Northwestern University (NWU) and University of

Louisville Institutional Review Boards. Informed written consent was obtained for all donors and recipients. For the FCRx therapy group, conditioning regimen, kidney transplantation, maintenance immunosuppression, and infusion of bioengineered FDA-regulated HSC product enriched for facilitating cells (FCRx) (IDE 13947) were reported previously (12-14).

For the standard immunosuppression group, induction therapy with alemtuzumab and

short course of was used for all except one patient, who underwent basiliximab induction.

Tacrolimus (8-10 ng/mL) and MMF (1-1.25 g orally twice daily) were used as maintenance

immunosuppression. Adjustments in immunosuppression dose were made for adverse events.

Renal biopsy samples:

This study utilized FFPE allograft kidney biopsy samples from KTx recipients diagnosed

with acute cellular rejection (R; n = 10) and without acute rejection under standard

immunosuppression (SIS; n = 10, 6 samples used for the microarray analyses and RT-qPCR

reactions, and 4 for additional independent validation of genes using RT-qPCR) as well as from

FCRx induced tolerant recipients (FCRx; n = 7) and paired pre-implantation donor allograft biopsy

samples from FCRx (D; n = 5) and SIS (SISD; n = 2).

Validation of gene expression profiling from FFPE samples:

In order to assess the performance characteristics of gene expression profiling from possibly degraded specimens, such as FFPE archival samples, we run paired fresh-frozen: FFPE in triplicates of two different tissue types from archival samples (collected and banked in 2009), on HG-U133Plus 2.0 arrays. For the fresh-frozen samples, total RNA was isolated from 10-µm thick frozen tissue sections using the MagMAX™-96 for Microarrays Total RNA Isolation Kit

(InvitrogenTM Life Technologies, Carlsbad, CA), in an automated fashion using the magnetic particle processors MagMAXTM Express. For the paired FFPE samples, total RNA was isolated as described above.

Validation of array reactions using RT-qPCR reactions for top up- and down–regulated

genes:

To validate the results from the microarrays, top genes (up- and down –regulated in unique

and common analysis) were validated using RT-qPCR reactions as previously described (40).

Additionally, two genes differentially expressed between FCRx and SIS samples were validated

(a) using same samples (FCRx, n =7 vs. SIS; n = 6); and (b) using same RNA used for microarray reactions from FCRx (n =7) and an independent set of SIS samples (n= 4).

SUPPLEMENTARY RESULTS

Successful validation of FFPE samples using gene expression analysis.

As most of the studies were carried out using formalin fixed paraffin embedded (FFPE)

biopsy samples in which molecular messages can be degraded, we performed quality control

analyses using paired FFPE and frozen samples. Highly reproducible results were obtained between paired fresh-frozen vs. FFPE samples (Fig. S1). A gene signature of 241 probe sets created from FFPE samples (Fig. S2A) showed a near 70% overlap with a similar gene signature created from fresh-frozen samples (Fig. S2B). The 241 probe set signature was able to cluster together in replicates for both FFPE (r=0.991) and paired fresh-frozen (r=0.994) samples as well as to distinguish different biological phenotypes (Fig. S3). Thus, the analyses showed that despite a lower number of genes being captured in the FFPE samples, the overall critical top pathways and functions were comparable, and hence FFPE samples were used for subsequent studies of differentially expressed genes (DEGs).

A B

Fig. S1. Scatterplots of RMA-normalized probe set summary means.

(A) Pearson’s correlation of the mean values for the paired FFPE: fresh-frozen samples for tissue type 1 (Sample 1), and (B) for tissue type 2 (Sample 2). The identity line is shown in red.

A B

Fig. S2. Differentially expressed genes between biological types. Volcano plots for (A) FFPE and (B) fresh-frozen comparisons, where red points correspond to significantly altered probe sets for each comparison.

Fig. S3. Supervised hierarchical clustering analysis. The heat map and agglomerative dendrogram illustrate the correlation between triplicates of paired FFPE: fresh-frozen samples based on 241 differentially expressed probe sets between the two different biological types. The dendrogram correlation distance bar among tissue types (Biological Types) is shown. Green: down-regulation; Red: up-regulation; FFPE: formalin-fixed, paraffin-embedded; FF: fresh-frozen.

Fig. S4A. Principal canonical pathways downregulated in FCRx vs. R samples.

A. Antigen presentation pathway. Green: downregulated.

Fig. S4B. Principal canonical pathways downregulated in FCRx vs. R samples.

B. Pathway design for allograft rejection signaling in association with differentially expressed

genes robustly related with absence of acute rejection in the FCRx samples.

Table S1. Immune response related cellular functions of FCRx upregulated genes versus SIS Cellular Molecules z-score p-Value Function recruitment of ACKR1,APOA1,CAT,CD14,CXCL8,CXCL9,CYP1A2,DPP4,FA 3.32 4.97E- granulocytes STK,GRK6,HSPA1A/HSPA1B,IKBKB,IL1RL1,IRF3,KDM6B,KIT 02 LG,MDK,PIK3CD,PRKCQ,PTN,RASGRP2,SELPLG,SMAD3,S OD2,ST3GAL4,THBS1,TRAF3IP2,VAV2

proliferation of ACLY,ADA,AHNAK,APOA1,ARIH2,BABAM1,BAX,BBC3,BCL2, 2.614 2.54E- blood cells BCL6,BCLAF1,BHLHE40,BMI1,BMP4,CASP8,CAT,CD14,CD2 02 4,CD27,CD58,CD79A,CD83,CDKN1B,CEBPB,CSF2RA,CTSZ, CXCL8,DGCR8,DGKA,DIAPH1,DLG1,DPP4,DRD2,EPO,EPO R,ERCC1,ETS1,ETV6,FGFR1,FUBP1,GADD45A,GADD45B,G P2,GRM5,GSTP1,HES1,HMGA1,HOXB3,HSF1,HSPA1A/HSP A1B,ID2,IFNGR1,IKBKB,IKZF2,IL1RL1,IL23A,IMPDH1,ITGB1, JUNB,JUND,KITLG,KLF9,LAT2,LPIN1,MAF,MAPK11,MAPK3, MARCH7,MDK,MED1,MICA,MSN,MYH10,NDFIP1,NFATC3,N FKBIA,NOTCH3,PBX1,PIK3CD,PKN1,PLAU,PRKAR1A,PRKC Q,PRNP,PTN,PTPN11,RARA,RHBDF2,RNF128,SLAMF1,SMA D3,SPHK2,SPN,ST3GAL2,STAT1,STAT5B,TCF12,TCF3,TG,T HBS1,TNFRSF12A,TNFRSF21,TOB1,TRAF2,TRIM33,VAV2,V EGFA,ZBTB32 cell viability of BCL2,CADM1,CD27,CSF2RA,EPO,ETV6,FGFR1,GRK6,KITL 2.6 1.26E- leukocyte cell G,MCL1,MYBL2,PTPN1,YWHAZ 02 lines

growth of APP,BBC3,BMP4,CD79A,CDKN1B,IFNGR1,IKBKB,IMPDH1,K 2.449 1.51E- lymphoid organ ITLG,LFNG,MAP3K3,MAPK3,NFKBIA,PIK3CD,PRKCQ,PTGE 02 S2,RNF128,SMAD3,STAT5B,TCF12,TCF3 production of ADA,EPO,EPOR,ETS1,ID2,KITLG,NFKBIA,THRA 2.433 3.38E- hematopoietic 03 progenitor cells quantity of T ADA,APOA1,APP,ARID5A,ATP6AP2,BAD,BAX,BBC3,BCL2,B 2.224 2.67E- lymphocytes CLAF1,BHLHE40,CASP8,CD27,CD79A,CD83,CDKN1B,DDX5 03 8,DGKA,DIAPH1,DNMT1,DNMT3A,,EEF1D,ETS1,ETV6, FGFR1,GADD45A,GADD45B,GALNT1,GRK6,HDAC3,HELLS, HIVEP2,ID1,ID2,IFNGR1,IKBKB,IL1RL1,IL23A,IRAK1,KITLG, MAF,MAPK3,MCL1,MDK,MR1,MYBL2,NBN,NDFIP1,NFATC3, NFKBIA,NOTCH3,PBX1,PIK3CD,PRKCQ,PRNP,SELPLG,ST1 4,ST3GAL2,STAT1,STAT5B,TCF12,TCF3,TCF4,TG,THBS1,T HRA,TNFRSF21,TOB1,TRAF2,TRAF3IP2,TRIB2,UPF1,VAV2, VEGFA,ZBTB17 development of EPO,EPOR,IFNGR1,KITLG,STAT1,VEGFA 2.219 3.75E- burst-forming 05 erythroid cells survival of pro-B BCL2,HLF,PML,STAT5B,TCF3 2.191 8.33E- lymphocytes 03 cell movement ACKR1,ADA,ADAM15,ADD2,APBA3,APOA1,APP,AQP3,BAX, 2.055 4.1E-02 BCL2,BMP4,CAT,CD14,CD200,CD207,CD58,CKLF,CREB3,C SF2RA,CTSZ,CX3CL1,CXCL8,CXCL9,CYP1A2,DAXX,DCTN2 ,DIAPH1,DPP4,DRD2,EGFL7,ENTPD1,EPO,EPS8,ETS1,ETV 6,FASTK,FCER1A,FGFR1,FLNA,G6PC,GALNT1,GNA11,GNA S,GPSM3,GRK6,HP,HSPA1A/HSPA1B,IFNGR1,IKBKB,IL1RL 1,IL23A,IRF3,ITGB1,KDM6B,KITLG,KNG1,LUM,MAPK3,MDK, MMP14,NARS,NDST1,NFE2L2,NFKBIA,NOTCH3,PA2G4,PIK 3CD,PLAU,PLXND1,PRKCQ,PRNP,PTN,PTPN1,PTPN11,PVR ,RALGDS,RAMP1,RAP1A,RAP1GAP,RASGRP2,RGS3,SELPL G,SLAMF1,SLAMF8,SMAD3,SOD2,SPHK2,SPN,ST3GAL4,ST AT2,THBS1,TRAF3IP2,VAMP7,VASP,VAV2,VEGFA proliferation of BAX,BCL2,CD24,CDKN1B,EPO,HSPA1A/HSPA1B,IL23A,PRK 2.035 2.02E- activated T CQ,SLAMF1,SMAD3,STAT5B 02 lymphocytes

Table S2A. Upstream regulators with significant predicted activation in FCRx vs. SIS Upstream Regulator (*) Molecule Type Activation z- p-value of overlap score EIF4E translation regulator 2.6 0.013 CD5 transmembrane receptor 2 0.238 MYCN regulator 3.1 0.00273 IRF3 transcription regulator 2.9 0.00289 IRF7 transcription regulator 2.8 0.232 EBF1 transcription regulator 2.8 0.00054 IRF5 transcription regulator 2.6 0.0867 ETS1 transcription regulator 2.4 0.00452 POU2F2 transcription regulator 2.3 0.0114 NEUROG1 transcription regulator 2.2 0.388 KLF11 transcription regulator 2.2 0.0189 IRF8 transcription regulator 2 0.0379 IRF1 transcription regulator 2 0.432 GMNN transcription regulator 2 1 SOX1 transcription regulator 2 0.488 SOX3 transcription regulator 2 1 STAT5B transcription regulator 2 0.0158 TP73 transcription regulator 2 0.00113 NFATC2 transcription regulator 2 0.144 RUNX3 transcription regulator 2 0.238 PTF1A transcription regulator 2 0.0868 FGF8 2.8 0.22 IGF1 growth factor 2.4 3.7E-05 BMP2 growth factor 2.2 0.00798 TGFB1 growth factor 2 1.8E-17 IFNA2 cytokine 3 6.9E-05 IFNB1 cytokine 2.9 0.00125 IFNG cytokine 2.4 6.9E-05 IFNL1 cytokine 2.2 5.5E-05 IFNA1/IFNA13 cytokine 2 0.0188 FHIT 2.4 0.00981 MGEA5 enzyme 2.4 2.2E-07 PIN1 enzyme 2.2 0.164 IRS1 enzyme 2.2 0.00024 EHHADH enzyme 2 0.0121 HSD17B4 enzyme 2 0.0603 Fcor enzyme 2 0.00358 ERBB2 kinase 2.9 3.5E-08 EPHB4 kinase 2.1 0.0135 Smad complex 2 0.018 NR5A2 ligand-dependent nuclear 2.2 0.049 receptor miR-200b-3p mature microrna 2 0.0769 FSHR g-protein coupled receptor 2 0.0135 alpha group 3.2 0.00121 IFN Beta group 2.4 0.00072 IFN type 1 group 2.3 0.00067 Insulin group 2.1 0.00645 Akt group 2.1 2.8E-05 Calmodulin group 2 0.312 Ins1 other 3.4 0.00017 MAVS other 2.4 0.164 MUC1 other 2.2 0.00018 (*) All upstream regulators are predicted as activated

Table S2B. Regulator effects associated with FCRx vs. SIS gene profile Consisten Regulators Target Molecules in Dataset Diseases & Functions cy Score EPHB4 6.124 BMP4,CSF2RA,KITLG,PAX3,STAT5B,TB cell X2 transformation,development of genitourinary system,organismal death,proliferation of blood cells EIF4E,Ins1,INS 5.774 BCL2,CDKN1B,CEBPB,ERCC1,FGF18,M proliferation of hepatocytes R,Insulin,IRF8, ED1,NFE2L2,NFKBIA,PML,THBS1,TNFR MGEA5,PKD1, SF12A,VEGFA PPARGC1A EIF4E 4.596 BAD,BCL2,CDC34,CEBPB,MCL1,NFKBI cell death of melanoma cell A,NOL3,PA2G4 lines,invasion of cells,proliferation of epithelial cells ETS1,GFI1 3.328 BMP4,CXCL8,DIAPH1,ID2,IKBKB,JUNB, Growth MAPK3,NFKBIA,PLAU,PML,SMAD3,STA Failure,transactivation T1,TRAF2 mir-122,miR- 2.858 MAPK11,PKM,PTPN1,RAD21,SLC7A1,T cell viability 122-5p RPV6 IRS1 2.673 BMP6,CEBPB,G6PC,ID2,LAMA4,MAPK1 cell movement of endothelial 2,MMP14,MYBL2,NR2F1,PAX3,PCK1,PD cells,cell movement of tumor GFA,THRA,VEGFA cell lines,cell survival,concentration of D- glucose,neonatal death,quantity of T lymphocytes,transactivation of RNA IFNL1,IRF3,MU 2.593 AHNAK,BAD,CD83,CXCL8,DDX58,HER cell cycle C1 C6,IFI44,IFITM1,IKBKB,JUNB,NFKBIA,P progression,neoplasia of LAU,PML,PPP2R3A,SOD2,SORL1,SP10 epithelial cells,Renal Cancer 0,STAT1 and Tumors,urinary tract cancer EBF1,POU2F2 2.5 BCL6,PIK3CD,SCD,TCF3 hypoplasia of organ NR5A2 2.449 APOA1,BCL2,CEBPB,JUNB,LHB,TDGF1 growth of tumor,transcription of RNA mir-133 2.236 DNMT1,FSCN1,GSTP1,HCN2,MMP14 migration of tumor cell lines,organismal death ESR1,IFN 2.186 AHNAK,ATP2B1,BAX,BCL2,BHLHE40,C Renal Cancer and Beta,IFNA2,IF ALD1,CDKN1B,CXCL8,DDX58,ENPEP,F Tumors,urinary tract cancer NB1,Interferon GFR1,FLNA,G6PD,GLS,GNAS,HSP90AA alpha,MUC1 1,HSPA1A/HSPA1B,IFITM1,IGFBP5,IKB KB,ITGB1,KL,MCRS1,MR1,MUC5AC,NF KBIA,NUP153,PALLD,PDE4A,PSD4,PTP RT,RAD21,RARA,RHEB,SMC3,SOD2,SU LT1C2,THBS1,UBA7,VCL,VEGFA EBF1 2.041 ACACB,BCL6,EIF4EBP1,INPPL1,PTPN1, size of body STAT1 Ins1 2 CDKN1B,CEBPB,E2F4,ERCC1,G6PD,JU proliferation of fibroblast cell NB,MAPK3,PRLR,VEGFA lines,proliferation of hepatocytes IFNA1/IFNA13 1.732 IFITM1,STAT1,UBE2L6 invasion of tumor cell lines IFN Beta,IFN 1.491 APOL2,ATF5,BAX,BCL2,BCL6,BMP4,CA neoplasia of epithelial type SP8,CD83,CDKN1B,CEBPB,CHMP5,CX cells,Renal Cancer and 1,IFNA2,Interfe 3CL1,CXCL8,CYP1A2,DDX58,DPP4, Tumors,tumorigenesis of ron 4,ENPEP,EPB41L3,GLS,GNAS,HERC6, genital tumor,urinary tract alpha,MUC1 HSPA1A/HSPA1B,IFI44,IFITM1,IKBKB,IT cancer GB1,KDM5A,MCL1,MCRS1,N4BP1,NFK BIA,OGFR,PLAU,PML,PTBP1,RNF31,SO D2,SP100,STAT1,STAT2,TRANK1,TRIB2 ,UBA7,VEGFA COL18A1,mir- 1.429 ACADM,AUH,BAD,BCL2,CYP1A2,ETS1, epithelial-mesenchymal 122,TFAM F11,FGFR1,HK1,ID1,KNG1,MAP3K3,MA transition,necrosis of X,MCL1,PFKP,PKM,PLAU,PTPN1,RAD2 malignant 1,SPOCK1,STAT1,THBS1,TNPO2,VEGF tumor,tumorigenesis of A genital tumor ESR1,IFN 1.209 ABCA4,ACTR2,ADD3,AHNAK,ANAPC5, genitourinary Beta,IFN type ATP11A,ATP2A3,ATP2B1,ATP6V1A,BAX carcinoma,Renal Cancer 1,IFNA2,IFNB1 ,BAZ2A,BCL2,BCL6,BCLAF1,BHLHE40, and Tumors,tumorigenesis ,MUC1,PKD1 BMP4,BRAP,CA2,CALD1,CASP8,CD14, of genital tumor,urinary tract CDKN1B,CKB,CNP,CPE,CX3CL1,CXCL8 cancer ,CYFIP2,CYP1A2,CYP2C9,DAB2,DDX58 ,DIAPH1,DLG1,DPP4,ENPEP,EPB41L3, EZH1,FBN1,FGFR1,FLNA,FMR1,FOXC1, G6PC,G6PD,GABBR2,GATM,GLS,GNAS ,GPRASP1,GULP1,HERC6,HSP90AA1,H SPA2,IDH1,IFI44,IFITM1,IFNGR1,IFRD1, IGFBP5,IKBKB,IL1RL1,IRF3,JUNB,KDM4 B,KDM5A,KL,KNG1,KPNA3,KTN1,LANC ,LHB,MADD,MAN1A1,MAPK11,MAPK 8IP3,MAST2,MINK1,MR1,MUC5AC,MYH 10,MYO6,N4BP1,NFIX,NFKBIA,NOTCH3 ,NUP153,NUP210,PAH,PALLD,PCDH9,P DCD4,PDE4A,PDZK1,PLCB1,PLCE1,PP P5C,PRKAR1A,PRLR,PSD4,PTPRT,RAD 21,RAMP3,RARA,RDX,RGS3,RHEB,RR BP1,SEC23IP,SEMA4C,SH2B1,SLC25A3 6,SLC2A2,SLC7A1,SMC3,SOD2,STAT1, STAT2,STX6,SULT1C2,TBCD,TEAD4,T GOLN2,THBS1,THBS3,THBS4,TMF1,TN FAIP2,TNPO1,TRAF2,TRIM38,UBA7,VC L,VEGFA IFN type 1.134 ADA,APP,BAX,BBC3,BCL2,BCL6,CAD,C non-Hodgkin disease 1,IGF1,IL1RN, CNA2,CDKN1B,CEBPB,CXCL8,DNMT1, MYCN,RB1 DNMT3A,ETV6,FBN1,ID1,ID2,IFI44,IFI6,I GFBP7,MAF,MCL1,NBN,NFKBIA,SP100, STAT1,STAT2,VEGFA TCF7L2 0.354 BMP4,CDKN1B,HSPA2,ID4,LAMP1,PTP development of genital N11,QKI,VEGFA organ PKD1 0.218 DDX3Y,DPEP1,EIF4EBP1,EZH1,FGFR1, tumorigenesis of epithelial GATM,GLS,GPRASP1,IDH1,KNG1,MINK neoplasm 1,PAH,PCDH7,PCDH9,PCK1,PRLR,RGS 3,SEMA4C,SLC16A4,SLC2A2,TRAF2

Table S3. Cell type enrichment for differentially expressed genes between FCRx and SIS Cell Type Enrichment adjusted-P value Kidney 0.000176064 CD33+ Myeloid 0.005398789 CD8+ T Cells 0.007401828 BDCA4+ Dentritic Cells 0.007764024 CD4+ T Cells 0.009844623 CD19+ B cells 0.036294635

Table S4. Cell type enrichment for differentially expressed genes between FCRx and D Cell type Enrichment adjusted-P value CD56+ NK Cells 0.001628

CD14+ Monocytes 0.006796

CD19+ B cells 0.008276

CD105+ Endothelial 0.010358

CD33+ Myeloid 0.018414

CD34+ 0.024458

Fig. S5. KEGG pathway for metabolism showing involvement of listed genes in Glycan biosynthesis and metabolism The differentially expressed genes between FCRx and D were plotted on KEGG pathway (using DAVID )for metabolism and the genes (red stars) were found to be involved in glycan biosynthesis.

Fig. S6. Venn diagrams from FCRX vs. D and SIS vs. D pairwise comparisons. The numbers of unique and common differentially expressed genes between single SIS vs. D comparisons are shown.

Table S5. Genes differentially expressed in common between SIS vs. D Fold-change Symbol Gene Name (SIS vs. D) PDK4 pyruvate dehydrogenase kinase, isozyme 4 -14.1 EGR1 early growth response 1 -13.9 DUSP1 dual specificity phosphatase 1 -9.0 FOSB FBJ murine osteosarcoma viral homolog B -7.0 CYR61 cysteine-rich, angiogenic inducer, 61 -6.5 FOS FBJ murine osteosarcoma viral oncogene homolog -5.7 ZFP36 ZFP36 ring finger protein -5.0 GADD45B growth arrest and DNA-damage-inducible, beta -4.3 JUN jun proto-oncogene -4.1 IER2 immediate early response 2 -3.9 KLF6 Kruppel-like factor 6 -3.2 RHOB ras homolog family member B -2.7 FOSL2 FOS-like antigen 2 -2.2 CEBPD CCAAT/enhancer binding protein (C/EBP), delta -2.1 KLF9 Kruppel-like factor 9 -1.8 JUND jun D proto-oncogene -1.4 HLA-B major histocompatibility complex, class I, B 2.1 CYCS cytochrome c, somatic 3.8 CD24 CD24 molecule 5.2 IGF2 insulin-like growth factor 2 (somatomedin A) 6.5 solute carrier family 12 (sodium/potassium/chloride transporters), SLC12A1 10.3 member 1

Confirmation of DEGs identified by SensationPlus with RT-qPCR assays.

To further validate the global gene expression profile comparisons described above using

SensationPlus assays, the relative levels of the top 3 DEGs were quantified using Taqman RT-

qPCR assays. As shown in Fig. S7, there was concordance between the two assays with the

selected genes demonstrating comparable expression trends albeit with different levels of

statistical significance and dynamic ranges. Moreover, the two differentially expressed genes

tested between SIS and FCRx (referred as a T in the Fig. 7), were validated for same RNA

samples used for arrays (Fig. 7A) and evaluating same FCRx RNA samples with an independent

set of SIS samples (Fig. 7B) [SIS samples used for second validation are described in Table-1 as labeled with (*)].

A B

Fig. S7. Comparison and validation of selected genes. Taqman RT-qPCR assays were performed to validate the differential expression of CXCL10, LYZ, and EGR1. Expression trends observed by RT-qPCR (black bars) were compared to those obtained by microarrays (gray bars) in the comparisons mentioned in the graphic. (#) trend of significance; (*) p < 0.05; (**) p < 0.01;

(***) p < 0.001. T refers to FCRx sample group.

Fig. S8. Integrative networks showing miRNA differentially expressed from FCRx vs. SIS and their targets. The 7 significantly upregulated miRNA in FCRx target 198 genes to be differentially down regulated as shown in the network generated by the miRNA target profiler using Ingenuity pathway analysis software. The color codes are represented in the legend present in the figure.

Fig. S9. Integrative networks showing miRNA differentially expressed from FCRx vs. R and their targets. Seven of the 10 significantly upregulated miRNA in FCRx target 75 differentially down regulated genes as shown in the network generated by the miRNA target profiler using Ingenuity pathway analysis software. The color codes are represented in the legend present in the figure. SIGNIFICANCE STATEMENT

Hematopoietic stem cell (HSC) chimerism pro- duces tolerance to transplanted tissues; this has been achieved in highly mismatched kidney allograft recipients using FCRx, a bioengineered stem cell product that contains donor HSCs and unique fa- cilitating cells. This study examined gene and miRNAexpression for the first time in renal biopsies from tolerance-induced FCRx recipients, paired preimplantation donors, and subjects receiving standard immunosuppression. Although gene ex- pression pathways associated with rejection were not upregulated in tolerant biopsies, these biopsies showed upregulation of genes involved in B cell receptor signaling, activation of anti-inflammatory pathways, and inhibition of proinflammatory reg- ulators when compared with nonrejecting subjects on standard immunosuppression. Results support potential of this tolerance induction strategy (through active immunoregulation) to improve long-term kidney allograft survival.