WO2019241526A1

WO2019241526A1 - Genetic and serological assays for improved donor/recipient matching

Info

Publication number: WO2019241526A1
Application number: PCT/US2019/036999
Authority: WO
Inventors: Krzysztof KIRYLUK; Ali GHARAVI; Nicholas STEERS
Original assignee: Columbia University in the City of New York
Current assignee: Columbia University in the City of New York
Priority date: 2018-06-14
Filing date: 2019-06-13
Publication date: 2019-12-19
Anticipated expiration: 2020-12-14

Abstract

The present disclosure provides a genetic marker for donor/recipient matching that can decrease the risk of transplant rejection. Genomic collision at a novel histocompatibility locus encoding LIMS 1 antigen is associated with a higher risk of transplant rejection and production of anti-LIMS 1 antibodies.

Description

GENETIC AND SEROLOGICAL ASSAYS FOR IMPROVED

DONOR/RECIPIENT MATCHING

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 62/684,884 (filed on June 14, 2018), which is incorporated herein by reference in its entirety.

SEQUENCE LISTING

The present application contains a Sequence Listing which has been filed electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on June 13, 2019, is named 0l00l_006667_WO0_ST25.txt and is 3 KB in size.

GOVERNMENT LICENSE RIGHTS

This invention was made with government support under Grant No. UL1TR001873 awarded by the NIH National Center for Advancing Translational Sciences, and Grant No. T35- DK093430 and T32-DK108741 Supplement awarded by the National Institute of Diabetes and Digestive and Kidney Diseases. The government has certain rights in the invention.

FIELD OF THE INVENTION

The present invention relates to genetic and serological assays for donor/recipient matching before transplantation and for evaluating transplant rejection after transplantation.

BACKGROUND OF THE INVENTION

Transplant and graft rejection is a life-threatening condition that occurs when the recipient’s immune system begins to attack the donor tissue. This can lead to many additional side effects and, without treatment, may lead to the loss of the transplanted organ.

Approximately 20% of the kidney waiting list in the United States consists of candidates whose allografts have failed.¹ Two primary causes of graft failure are cell-mediated rejection (CMR) and antibody-mediated rejection (ABMR). Acute rejection is one of the strongest predictors of decreased allograft survival. Although human genetic variation and donor-recipient genetic interactions are likely to be involved in the determination of allograft outcomes, there have been few well-designed and adequately powered genetic investigations in this area.

Current techniques to mitigate rejection are blood type and tissue type matching pre surgery (based on human leukocyte antigens) followed by a post operation regimen of immunosuppressive medications and invasive check-up biopsies. For kidney transplants, the diagnosis of allograft rejection involves kidney allograft biopsy, which represents an invasive procedure that carries risks of bleeding and other complications. There are no other reliable non- invasive means to diagnose allograft rejection and stratify kidney transplant recipients at risk of rejection.

Even though the dramatic improvements in short-term outcomes after transplantation have not resulted in similar improvements in long-term graft survival, new genetic strategies for organ matching may offer practical means by which rejection can be avoided and graft survival can be prolonged.^2,3 Human leukocyte antigen (HLA) matching has been studied extensively, with an emphasis on donor- specific HLA alloantibodies.^2,4-6 However, the incidence of donor- specific HLA alloantibodies among all allograft recipients is relatively low (15 to 25%).^7- ¹⁰ Increasing evidence suggests an important role for minor histocompatibility antigens that result in immune activation without HLA-directed response. The clinical significance of minor histocompatibility antigens is highlighted by the observation that recipients of allografts from HLA-identical siblings require immunosuppression despite perfect HLA matching, and there are survival differences when analyses are stratified according to panel -reactive antibody titers.^3,11 It is estimated that approximately 56% of allograft failures can be attributed to immunologic reactions, with 38% of the reactions being against non-HLA factors and only 18% being due to HLA mismatch.¹² In the past decade, several minor histocompatibility antigens have been successfully identified.^13-18 Angiotensin II type 1 (ATi) receptor is perhaps the best-studied example, with preexisting and acquired anti- AT i receptor antibodies predicting allograft rejection and failure.^15,19-21 Nevertheless, the overall incidence of known alloantibodies to minor histocompatibility antigens is low, which suggests that there are contributions from additional antigens.¹⁴ SUMMARY

The present disclosure provides for a method for identifying a transplant donor for a subject in need of a transplant, where the subject is homozygous for rs893403-G allele and/or is homozygous for CNVR915.1 -deletion. The method may comprise the steps of: (a) obtaining at least one donor nucleic acid sample from at least one potential donor; (b) detecting in the at least one donor nucleic acid sample (i) the presence or absence of a G allele at single nucleotide polymorphism (SNP) rs893403, and/or (ii) the presence or absence of CNVR915.1 -deletion; and (c) identifying a transplant donor for the subject if the transplant donor is homozygous for rs893403-G allele and/or is homozygous for CNVR915.1 -deletion.

The present disclosure provides for a method for identifying a transplant donor for a subject in need of a transplant, where the subject is homozygous for a variant or polymorphism in linkage disequilibrium with rs893403. The present disclosure also provides for a method for identifying a transplant donor for a subject in need of a transplant, where the subject is homozygous for any of the following variants.

The method may comprise the steps of: (a) obtaining at least one donor nucleic acid sample from at least one potential donor; (b) detecting in the at least one donor nucleic acid sample the presence or absence of an allele of a variant/polymorphism (e.g., single nucleotide polymorphism (SNP)) as described herein; and (c) identifying a transplant donor for the subject if the transplant donor is homozygous for the allele of the variant/polymorphism.

The method may comprise the steps of: (a) obtaining at least one donor nucleic acid sample from at least one potential donor; (b) detecting in the at least one donor nucleic acid sample the presence or absence of an allele of a variant/polymorphism (e.g., single nucleotide polymorphism (SNP)) in linkage disequilibrium with rs893403; and (c) identifying a transplant donor for the subject if the transplant donor is homozygous for the allele of the

variant/polymorphism.

The present method may further comprise transplanting an organ, tissue or cell from the transplant donor to the subject.

The present disclosure also provides for a method for detecting transplant rejection in a subject who has received a transplant from a donor or assessing the subject’s risk of transplant rejection towards a transplant from a donor. The method may comprise the steps of: (a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor; (b) detecting in the recipient nucleic acid sample and the donor nucleic acid sample (i) the presence or absence of a G allele at single nucleotide polymorphism (SNP) rs893403, or (ii) the presence or absence of CNVR915.1 -deletion; and (c) diagnosing that the subject has transplant rejection or an increased risk of transplant rejection, if (i) the subject is homozygous for rs893403-G allele, and the donor is not homozygous for rs893403-G allele; or (ii) the

subject/recipient is homozygous for CNVR915.1 -deletion, and the donor is not homozygous for CNVR915.1 -deletion.

In step (c) of the present method, the subject may be diagnosed to have transplant rejection or an increased risk of transplant rejection when the subject/recipient and donor have a LIMS 1 locus“High-Risk” or“Genomic Collision” genotype combination.

The present disclosure provides for a method for detecting transplant rejection in a subject who has received a transplant from a donor or assessing the subject’s risk of transplant rejection towards a transplant from a donor. The method may comprise the steps of: (a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor; (b) detecting in the recipient nucleic acid sample and the donor nucleic acid sample the presence or absence of an allele of a variant/polymorphism (e.g., SNP) in linkage disequilibrium with rs893403 (or an allele of a variant/polymorphism (e.g., SNP) as described herein); and (c) diagnosing that the subject has transplant rejection or an increased risk of transplant rejection, if the subject/recipient is homozygous for the allele, and the donor is not homozygous for the allele.

The present disclosure provides for a method for treating a subject with transplant rejection or an increased risk of transplant rejection where the subject has received a transplant from a donor. The method may comprise the steps of: (a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor; (b) detecting in the recipient nucleic acid sample and the donor nucleic acid sample (i) the presence or absence of a G allele at single nucleotide polymorphism (SNP) rs893403, or (ii) the presence or absence of CNVR915.1 -deletion; and (c) treating the subject for transplant rejection or an increased risk of transplant rejection, if (i) the subject/recipient is homozygous for rs893403-G allele, and the donor is not homozygous for rs893403-G allele; or (ii) the recipient is homozygous for

CNVR915.1 -deletion, and the donor is not homozygous for CNVR915.1 -deletion. In step (c), at least one immunosuppressant may be administered to the subject. In one embodiment, the subject is homozygous for rs893403-G allele, and the donor is homozygous for rs893403-A allele or is rs893403-AG.

In another embodiment, the subject is homozygous for CNVR915.1 -deletion, and the donor is heterozygous for CNVR915.1 -deletion or lacks CNVR915.1 -deletion.

In step (c) of the present method, the subject may be treated for transplant rejection or an increased risk of transplant rejection, when the subject/recipient and donor have a LIMS 1 locus “High-Risk” or“Genomic Collision” genotype combination.

The present disclosure provides for a method for treating a subject with transplant rejection or an increased risk of transplant rejection where the subject has received a transplant from a donor. The method may comprise the steps of: (a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor; (b) detecting in the recipient nucleic acid sample and the donor nucleic acid sample the presence or absence of an allele of a variant/polymorphism (e.g., SNP) in linkage disequilibrium with rs893403 (or an allele of a variant/polymorphism (e.g., SNP) as described herein); and (c) treating the subject for transplant rejection or an increased risk of transplant rejection, if the subject is homozygous for the allele, and the donor is not homozygous for the allele. In step (c), at least one

immunosuppressant may be administered to the subject.

LIMS 1 locus“High-Risk” or“Genomic Collision” genotype combination may be defined as:

(i) recipient rs893403-GG, and donor rs893403-AA or rs893403-AG, or

(ii) recipient CNVR915.1 -del/del, and donor CNVR9l5.l-wt/wt or CNVR9l5.l-del/wt.

LIMS1 locus“Low-Risk” or“Non-collision” genotype combination may be defined as:

(i) recipient rs893403-AA regardless of the donor genotype,

(ii) recipient rs893403-AG regardless of the donor genotype,

(iii) donor rs893403-GG regardless of the recipient genotype,

(iv) recipient CNVR9l5.l-del/wt regardless of the donor genotype,

(v) recipient CNVR9l5.l-wt/wt regardless of the donor genotype, or

(vi) donor CNVR915.1 -del/del regardless of the recipient genotype.

Non-limiting examples of variants/polymorphisms that may be used in the present methods include: rsl0084l99, rs427l73l, rs7596l99, rs2683806, rsl464406, rsl l l23694, rs2460947, rs2460944, rsl474220, rs2683798, rs2683797, rs2049l5l, rs2049l50, rs2l39807, rs25776l2, rs2577599, rs27l8764, rs27l8765, rs2l 18448, rs2l 18450, rs2577627, rsl0084l99, rs2718740, rsl978839, rsl469966, rs920264, rs2577588, rs27l8733, rsl3023059, rs27l8725, rs2718724, rs27l8722, rs2718721, rs2577625, rs2465952, rs2l64829, rsl370592, rs2577598, rs2718729, rs893403, rs4676202, rs27l8738, rs257760l, rs2577600, rs826683, rs826687, rsl 14636875, rs826688, rs826690, rs82669l, rs826693, rs3469l824, rs826694, rs826698, rs70956266, rs826677, rs826678, rs826679, rsl 1691112, rsl2998958, rs376l36l63, and rs865444.

Also encompassed by the present disclosure is a method for detecting transplant rejection or an increased risk of transplant rejection in a subject who has received a transplant. The method may comprise the steps of: (a) obtaining a sample from the subject; (b) determining level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample; (c) comparing the level obtained in step (b) with the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a control sample; and (d) diagnosing that the subject has transplant rejection or an increased risk of transplant rejection, if the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases by at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample.

The present disclosure provides for a method for treating a subject with transplant rejection or an increased risk of transplant rejection, the method comprising the steps of: (a) obtaining a sample from the subject; (b) determining level of anti-LIMS 1 antibodies and/or anti- GCC2 antibodies in the sample; (c) comparing the level obtained in step (b) with the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a control sample; and (d) treating the subject for transplant rejection or an increased risk of transplant rejection, if the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases by at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample.

In step (d), at least one immunosuppressant may be administered to the subject.

Also encompassed by the present disclosure is a method for detecting transplant rejection or an increased risk of transplant rejection in a subject who has received a transplant. The method may comprise the steps of: (a) obtaining a sample from the subject; (b) determining level of LIMS 1 mRNA and/or GCC2 mRNA in the sample; (c) comparing the level obtained in step (b) with the level of the LIMS 1 mRNA and/or GCC2 mRNA in a control sample; and (d) diagnosing that the subject has transplant rejection or an increased risk of transplant rejection, if the level of the LIMS 1 mRNA and/or GCC2 mRNA obtained in step (b) decreases by at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample.

The present disclosure provides for a method for treating a subject with transplant rejection or an increased risk of transplant rejection, the method comprising the steps of: (a) obtaining a sample from the subject; (b) determining level of LIMS 1 mRNA and/or GCC2 mRNA in the sample; (c) comparing the level obtained in step (b) with the level of the LIMS 1 mRNA and/or GCC2 mRNA in a control sample; and (d) treating the subject for transplant rejection or an increased risk of transplant rejection, if the level of the LIMS 1 mRNA and/or GCC2 mRNA obtained in step (b) decreases by at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample. In step (d), at least one

immunosuppressant may be administered to the subject.

In one embodiment, the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies is determined by enzyme-linked immunosorbent assay (ELISA).

The sample may be a plasma, serum or blood sample.

The transplant may be a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a bone marrow transplant, a portion thereof, or a combination thereof. The transplant may be a tissue transplant.

The control sample may be from a healthy subject or a plurality of healthy subjects. The control sample may be from a subject who has received a transplant without rejection or from a plurality of subjects who have received a transplant without rejection.

The transplant rejection may comprise acute cellular rejection (ACR) and/or antibody- mediated rejection (ABMR). The transplant rejection may be hyperacute rejection. The transplant rejection may be acute rejection. The transplant rejection may be chronic transplant rejection.

In one embodiment, the subject is human.

In the present methods, the subject’s existing immunosuppressive regimen may be modified or maintained.

BRIEF DESCRIPTION OF THE FIGURES

Figures 1A-1C. Discovery Phase. Figure 1A shows our strategy for selecting high- priority deletions for tagging and typing in the discovery cohort. A total of 44 of 50 deletions were successfully tagged and genotyped in the discovery cohort; 6 of 50 deletion-tagging single nucleotide polymorphisms (SNPs) were either monomorphic or failed our genotype quality- control analysis. Annotations were based on the human reference genome hgl8 (accessed in July 2010). Copy-number polymorphisms (CNPs) were common copy-number variants (CNVs with an allele frequency of >1%). MAF denotes minor allele frequency. Figure 1B shows the probability-probability plot for the genetic screen for rejection in the discovery cohort of 705 recipients under a recessive model. The circles represent P values for 44 successfully typed common deletions; the horizontal dashed lines represent significance thresholds of 0.05

(unadjusted analysis) and 0.0011 (Bonferroni-corrected for 44 independent tests). The dash- dotted line indicates the expectation under the null hypothesis, and the shaded area corresponds to a 95% confidence interval for the null hypothesis of no association. The top SNP (rs893403) represents a near-perfect tag (r2 = 0.98) for a common l.5-kb deletion (CNVR915.1) on chromosome 2ql2.3. Figure 1C shows the genomic characteristics of the 44 CNP-tagging SNPs that were tested in the discovery phase. Plus-minus values are means +SD. Details are provided in Table 4.

Figures 2A-2D. Effects of rs893403 on the Rejection-free Allograft Survival in Study Cohorts. Figure 2A shows the results in the discovery phase (involving 705 kidney transplant recipients [the Columbia cohort] who had either a nonrisk genotype [upper] or a risk genotype [lower]). Tick marks indicate censored data. Figure 2B shows the results in the replication phase, which involved a stratified analysis of three other cohorts (Belfast, TransplantFines, and Torino) that included a total of 2004 donor-recipient pairs. The P values correspond to the minimally adjusted model, with adjustment for cohort only (if applicable). Figure 2C shows the results in all the cohorts combined, which involved a stratified analysis of the four cohorts (i.e., 2709 kidney transplants [in 705 recipients from the discovery cohort and 2004 donor-recipient pairs from the replication cohorts]). Figure 2D shows the estimated hazard ratios (with 95%

confidence intervals) of rejection in each of the four cohorts individually, in all the replication cohorts, and in all the cohorts combined. The effects were estimated before (solid circles [recipient only]) and after (open circles [donor-recipient pairs]) accounting for donor compatibility in order to show that the inclusion of genetic information from the donors resulted in consistently improved hazard ratio estimates.

Figures 3A-3G. Detection of Anti-LIMSl Antibodies in Kidney Transplant

Recipients at Genetic Risk for Rejection. Figure 3A shows the change in intensity (x axis) as compared with the -log P value (y axis) for the top-ranking proteins on the basis of the mean signal intensity in a protein array; the change is calculated as a ratio of the mean normalized intensity in the high-risk rejection group to the mean normalized intensity of all other groups (termed“fold change”). The findings suggest the presence of anti-LIMSl reactivity in high-risk recipients with rejection. Figure 3B shows the normalized intensity levels for LIMS1 on the protein array for the comparison between the high-risk rejection group and all other groups (P = 0.002); the horizontal lines represent the group means. Figure 3C shows the results of anti- LIMS 1 total IgG seroreactivity studies with the use of an enzyme-linked Figure 2

immunosorbent assay that were performed in 318 persons across seven genotype- and phenotype-discordant groups. The results are shown as the change in the optical density (OD), defined as a ratio of the measured OD for each sample to the mean OD of the same 5 normalization controls (serum samples obtained from healthy persons) that were used on each plate. These studies included 52 controls who had not undergone transplantation (Control), 37 recipients who were homozygous for the risk allele and did not have rejection (Risk-NR), 31 recipients who were homozygous for the risk allele and had rejection (Risk-R; solid circles), 50 recipients who were heterozygous for the risk allele and did not have rejection (Het-NR), 50 recipients who were heterozygous for the risk allele and had rejection (Het-R), 63 recipients who were homozygous for the non-risk-associated allele and did not have rejection (Hom-NR), and 35 recipients who were homozygous for the non-risk-associated allele and had rejection (Hom- R). Total IgG seroreactivity was detected only in recipients with a high-risk genotype who had rejection. Horizontal lines represent group means, and the dotted line represents 3 SD above the mean for the control group. Figures 3D through 3G show the anti-LIMSl reactivity of IgGl, IgG2, IgG3, and IgG4 subclasses, respectively; the results show predominant IgG2 and IgG3 responses. An asterisk indicates a P value of less than 0.001 and a dagger a P value of less than 0.01 for the comparisons of the group of recipients with a high-risk genotype who had rejection as compared with all other groups. Figures 4A-4D. Receiver Operating Characteristic (ROC) curves for anti-LIMSl IgG (Figure 4A), IgG2 (Figure 4B), IgG3 (Figure 4C), and IgG4 (Figure 4D) demonstrate superior diagnostic performance of IgG2 and IgG3 subclasses: Kidney transplant recipients with rejection and a high-risk genotype compared to all other groups. AUC = area under ROC curve.

DETAILED DESCRIPTION

The present disclosure provides a new genetic marker for donor/recipient matching that can decrease the risk of transplant rejection. Genomic collision at a novel histocompatibility locus encoding LIMS 1 antigen is associated with a higher risk of transplant rejection and production of anti-LIMSl antibodies. The risk of rejection may be modified by genetic matching based on rs893403 genotype. This technology can serve as a diagnostic tool for better selection of transplant donor tissues/organs and to determine risk factors for patients who have already received transplants.

Also encompassed by the present disclosure are methods for assessing transplant rejection by assaying the levels of anti-LIMSl antibodies and/or anti-GCC2 antibodies in a sample (e.g., a plasma or serum sample) taken from a patient who has received a transplant, such as a kidney transplant. The levels of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample can be used for assessing the onset or severity of transplant rejection, or as an indicator of the efficacy of a therapeutic intervention for treating transplant rejection. Based on the levels of the anti-LIMSl antibodies and/or anti-GCC2 antibodies, transplant rejection may be diagnosed or predicted, and then the subject may be treated. For patients under an immunosuppressive therapy, based on the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies levels, the therapeutic intervention may be continued when it is effective, or altered if ineffective or insufficient.

The method may also identify a transplant recipient at risk for transplant rejection or delayed graft function. As such, the methods of the present disclosure can impact the way transplant recipients are treated (before, during, and/or after a transplantation procedure). For example, patients identified as having a high risk of transplant rejection can be treated more aggressively with, for example, immunosuppressants or other therapeutic agents. Patients identified as low risk may be treated less aggressively (e.g., with minimal or no

immunosuppressants).

The present methods can predict or diagnose transplant rejection in a subject before transplantation or who has received a transplant.

The present method can be used to identify individuals at high risk of rejection by performing a composite genetic test of donors and recipients (e.g., LIMS1 genetic mismatch), and/or performing a serological test that detects anti-LIMS 1 antibodies and/or anti-GCC2 antibodies. The method can be non-invasive, and lead to early clinical detection of rejection events. The present method can also provide pre-emptive donor-recipient matching based on the composite genetic tests of recipients and their prospective donors.

In one embodiment, the recipient’s inheritance of variants that disrupt genes expressed in the kidney predisposes to allosensitization and rejection. We specifically test the impact of large copy number variants (CNVs) that intersect gene transcripts, since such variants may have a profound impact on gene function and expression. In a genome-wide screen of 50 high priority deletion polymorphisms, we identified a novel MHA locus on chromosome 2ql2.3 that significantly increased the risk of rejection by altering kidney gene expression of LIMS1. We demonstrate that donor-recipient genomic incompatibility at this locus is associated with the production of anti-LIMS 1 antibodies and increased risk of kidney allograft rejection.

In one embodiment, LIMS1 antigen (encoded within the locus marked by rs893403) is a viable target for determining histocompatibility between the donor and recipient.

In one embodiment, mismatched LIMS1 genotype has an about 63% increased risk of rejection.

The present method may be used for genetic screen for donor/recipient matching, and for detecting risk of rejection or assessing rejection after transplantation. The present disclosure provides for a minimally invasive technique to identify signs of rejection, or risk of rejection in transplant recipients. The method can guide patient-specific, anti-rejection medicine regimens, and provide additional targets for antibody depletion therapies.

In another embodiment, similar genetic incompatibilities can arise as a result of donor- recipient differences in variants that alter protein immunogenicity, by changing either protein structure, localization, or expression. For example, in a“genomic collision” scenario, an allograft recipient is homozygous for a common deletion polymorphism and receives an allograft (e.g., a kidney allograft) from a donor that caries at least one normal allele. The product of the gene(s) affected by these polymorphisms can become targets of the recipient’s immune system when expressed in an allograft.

In the setting of transplantation, genomic incompatibilities between a donor and recipient may lead to allosensitization against novel antigens. For example, recessive inheritance of gene- disrupting variants may represent a risk factor for allograft rejection. In one embodiment, donor-recipient genomic incompatibility at the LIMS 1 locus is associated with the production of anti-LIMS 1 antibodies and increased risk of allograft rejection (e.g., kidney allograft rejection).

In one embodiment, genetic matching is defined by both a transplant recipient and donor being homozygous for a gene-intersecting deletion.

“Genomic collision” may be defined as a specific donor-recipient genotype combination in which a recipient homozygous for a gene-intersecting deletion receives a transplant from a non-homozygous donor. In one embodiment,“genomic collision” (high-risk) donor/recipient genotype combination is defined as recipient homozygosity for the rs893403-G allele in the absence of donor homozygosity for the same allele. “Collision risk” may be defined by recipient homozygosity in the absence of donor homozygosity.

In one embodiment, the present method comprises a composite donor-recipient genetic test. For example, DNA samples are obtained from both the recipient and the potential or actual donor. The genotype at either rs893403 or CNVR915.1, or any proxies for these loci (such as variants which are in linkage disequilibrium with these loci) is determined in both donor and recipient DNA using any available DNA typing or sequencing technology. The“genomic collision” (high-risk) donor-recipient genotype combination may be determined as recipient homozygosity for the rs893403-G allele (or CNVR915.1 -deletion or any other polymorphism in linkage disequilibrium with the rs893403-G allele) in the absence of donor homozygosity for the same allele(s).

(i) recipient rs893403-GG, and donor rs893403-AA or rs893403-AG, or

(i) recipient rs893403-AA regardless of the donor genotype,

(ii) recipient rs893403-AG regardless of the donor genotype,

(iii) donor rs893403-GG regardless of the recipient genotype,

(iv) recipient CNVR9l5.l-del/wt regardless of the donor genotype,

(v) recipient CNVR9l5.l-wt/wt regardless of the donor genotype, or

(vi) donor CNVR915.1 -del/del regardless of the recipient genotype. CNVR915.1 -del refers to the deletion allele. CNVR9l5.l-wt refers to the wildtype allele (without the deletion).

Any other polymorphism in linkage disequilibrium with rs893403 may be used to replace rs893403 and CNVR915.1. Non-limiting examples of variants/polymorphisms that may be used in the present methods include: rsl0084l99, rs427l73l, rs7596l99, rs2683806, rsl464406, rsl 1123694, rs2460947, rs2460944, rsl474220, rs2683798, rs2683797, rs2049l5l, rs2049l50, rs2l39807, rs25776l2, rs2577599, rs27l8764, rs27l8765, rs2l 18448, rs2l 18450, rs2577627, rsl0084l99, rs27l8740, rsl978839, rsl469966, rs920264, rs2577588, rs27l8733, rsl3023059, rs2718725, rs27l8724, rs27l8722, rs2718721, rs2577625, rs2465952, rs2l64829, rsl370592, rs2577598, rs27l8729, rs893403, rs4676202, rs27l8738, rs257760l, rs2577600, rs826683, rs826687, rsl 14636875, rs826688, rs826690, rs82669l, rs826693, rs3469l824, rs826694, rs826698, rs70956266, rs826677, rs826678, rs826679, rsl 1691112, rsl2998958, rs376l36l63, and rs865444.

In certain embodiments, a recipient will receive organ or tissue transplant from a donor where matched,“low-risk”, or“non-collision” recipient-donor genotype combination is established.

In another embodiment, the present method comprises assaying the presence or level of alloantibodies. The presence or level of alloantibodies may be assayed using protein arrays, ELISA, and/or western blots.

For example, anti-LIMS 1 antibodies (and/or anti-GCC2 antibodies) serological test may be used to detect circulating anti-LIMS 1 antibodies (and/or anti-GCC2 antibodies) in sera of kidney transplant recipients. Positive test indicates the presence of anti-LIMS 1 (and/or anti- GCC2) humoral response that is associated with allograft rejection risk (e.g., kidney allograft rejection risk).

Any suitable assay may be used to assay the presence or level of alloantibodies (e.g., anti- LIMS 1 antibodies and/or anti-GCC2 antibodies), including, but not limited to, ELISA, immunodiffusion, immunoblotting techniques, immunofluorescence, immunohistochemistry, immunocytochemistry, immunoprecipitation, heamagglutination, enzyme immunoassays, ELISpot, flow cytometry and flow cytometry for multiplex bead-based assays such as Luminex- type assays. The present disclosure provides for a method for detecting transplant rejection in a subject who has received a transplant from a donor, or a method for assessing the subject’s risk of transplant rejection towards an allograft from a donor.

In certain embodiments, the method contains the following steps: (a) obtaining a sample (e.g., a plasma or serum sample, or other samples as discussed herein) from the subject; (b) assaying the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample; and (c) comparing the level obtained in step (b) with the level of the anti-LIMS 1 antibodies and/or anti- GCC2 antibodies in a control sample. The subject is determined (or diagnosed to undergo transplant rejection, or diagnosed) to have an increased risk of transplant rejection, if the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases by at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample.

The present methods may treat a subject with transplant rejection or an increased risk of transplant rejection. When diagnosed with transplant rejection, the subject may be treated with at least one immunosuppressant. Alternatively, when transplant rejection is predicted (or when an increased risk of transplant rejection is diagnosed), the subject may be treated with at least one immunosuppressant.

In certain embodiments, the method contains the following steps: (a) obtaining a sample (e.g., a plasma or serum sample, or other samples as discussed herein) from the subject; (b) assaying the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample; (c) comparing the level obtained in step (b) with the level of the anti-LIMS 1 antibodies and/or anti- GCC2 antibodies in a control sample; and (d) treating the subject for transplant rejection or an increased risk of transplant rejection, if the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases at least or about 10%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 1 fold, at least or about 1.2 folds, at least or about 1.5 fold, at least or about 2 folds, at least or about 3 folds, or at least or about 4 folds, compared to its level in the control sample. The alloantibodies may be any isotype, including IgG (e.g., IgGl, IgG2, IgG3, IgG4), IgM, IgA (IgAl, IgA2), IgD and/or IgE. In one embodiment, the antibodies are IgG2 and/or IgG3.

There may be a number of different isoforms for each of these proteins/polypeptides discussed in this disclosure, provided herein are the general accession numbers, NCBI Reference Sequence (RefSeq) accession numbers, GenBank accession numbers, and/or UniProt numbers to provide relevant sequences. The proteins/polypeptides may also comprise other sequences.

The level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in the sample may increase or decrease by about 1% to about 100%, about 5% to about 90%, about 10% to about 80%, about 5% to about 70%, about 5% to about 60%, about 10% to about 50%, about 15% to about 40%, about 5% to about 20%, about 1% to about 20%, about 10% to about 30%, at least or about 5%, at least or about 10%, at least or about 15%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 100%, about 10% to about 90%, about 12.5% to about 80%, about 20% to about 70%, about 25% to about 60%, or about 25% to about 50%, about 2 fold, about 3 fold, about 4 fold, about 5 fold, about 6 fold, about 7 fold, about 8 fold, about 9 fold, about 10 fold, at least or about 1.1 fold, at least or about 1.2 fold, at least or about 1.3 fold, at least or about 1.4 fold, at least or about 1.5 fold, at least or about 1.6 fold, at least or about 1.8 fold, at least or about 2 fold, at least or about 2.5 fold, at least or about 3 fold, at least or about 3.5 fold, at least or about 5 fold, at least or about 10 fold, at least or about 15 fold, at least or about 20 fold, at least or about 50 fold, at least or about 100 fold, at least or about 120 fold, from about 2 fold to about 500 fold, from about 1.1 fold to about 10 fold, from about 1.1 fold to about 5 fold, from about 1.5 fold to about 5 fold, from about 2 fold to about 5 fold, from about 3 fold to about 4 fold, from about 5 fold to about 10 fold, from about 5 fold to about 200 fold, from about 10 fold to about 150 fold, from about 10 fold to about 20 fold, from about 20 fold to about 150 fold, from about 20 fold to about 50 fold, from about 30 fold to about 150 fold, from about 50 fold to about 100 fold, from about 70 fold to about 150 fold, from about 100 fold to about 150 fold, from about 10 fold to about 100 fold, from about 100 fold to about 200 fold, compared to the level(s) in the control sample.

The level of the LIMS 1 mRNA and/or GCC2 mRNA in the sample may decrease or increase by about 1% to about 100%, about 5% to about 90%, about 10% to about 80%, about 5% to about 70%, about 5% to about 60%, about 10% to about 50%, about 15% to about 40%, about 5% to about 20%, about 1% to about 20%, about 10% to about 30%, at least or about 5%, at least or about 10%, at least or about 15%, at least or about 20%, at least or about 30%, at least or about 40%, at least or about 50%, at least or about 60%, at least or about 70%, at least or about 80%, at least or about 90%, at least or about 100%, about 10% to about 90%, about 12.5% to about 80%, about 20% to about 70%, about 25% to about 60%, or about 25% to about 50%, about 2 fold, about 3 fold, about 4 fold, about 5 fold, about 6 fold, about 7 fold, about 8 fold, about 9 fold, about 10 fold, at least or about 1.1 fold, at least or about 1.2 fold, at least or about 1.3 fold, at least or about 1.4 fold, at least or about 1.5 fold, at least or about 1.6 fold, at least or about 1.8 fold, at least or about 2 fold, at least or about 2.5 fold, at least or about 3 fold, at least or about 3.5 fold, at least or about 5 fold, at least or about 10 fold, at least or about 15 fold, at least or about 20 fold, at least or about 50 fold, at least or about 100 fold, at least or about 120 fold, from about 2 fold to about 500 fold, from about 1.1 fold to about 10 fold, from about 1.1 fold to about 5 fold, from about 1.5 fold to about 5 fold, from about 2 fold to about 5 fold, from about 3 fold to about 4 fold, from about 5 fold to about 10 fold, from about 5 fold to about 200 fold, from about 10 fold to about 150 fold, from about 10 fold to about 20 fold, from about 20 fold to about 150 fold, from about 20 fold to about 50 fold, from about 30 fold to about 150 fold, from about 50 fold to about 100 fold, from about 70 fold to about 150 fold, from about 100 fold to about 150 fold, from about 10 fold to about 100 fold, from about 100 fold to about 200 fold, compared to the level(s) in the control sample.

The control sample may be from a patient who has received a transplant without rejection or a plurality of patients who have received a transplant without rejection. The control sample may be from a healthy subject or a plurality of healthy subjects.

The samples may include, but are not limited to, serum, plasma, blood, whole blood and derivatives thereof, cardiac tissue, bone marrow, urine, cerebrospinal fluid (CSF), myocardium, endothelium, skin, hair, hair follicles, saliva, oral mucus, vaginal mucus, sweat, tears, epithelial tissues, semen, seminal plasma, prostatic fluid, excreta, ascites, lymph, bile, kidney, and other tissues, organs, as well as other samples or biopsies. In one embodiment, the biological sample is plasma or serum.

The level or amount of alloantibodies in a subject’s sample can be compared to a reference level or amount of the alloantibodies present in a control sample. The control sample may be from a patient or patients without undergoing transplant rejection, or a healthy subject or subjects. In other embodiments, a control sample is taken from a patient prior to transplant or treatment with a therapeutic intervention, or a sample taken from an untreated patient. In certain embodiments, a control sample is from transplant recipients without transplant rejection.

Reference levels for a polypeptide can be determined by determining the level of a polypeptide in a sufficiently large number of samples obtained from normal, healthy control subjects to obtain a pre-determined reference or threshold value. A reference level can also be determined by determining the level of the polypeptide in a sample from a patient prior to transplant.

Reference (or calibrator) level information and methods for determining reference levels can be obtained from publicly available databases, as well as other sources.

The transplant may be an allograft or a xenograft. An allograft is a transplant of an organ, tissue, bodily fluid or cell from one individual to a genetically non-identical individual of the same species. A xenograft is a transplant of an organ, tissue, bodily fluid or cell from a different species.

The transplant maybe any organ or tissue transplant, including, but not limited to, a kidney transplant, a heart transplant, a liver transplant, a pancreas transplant, a lung transplant, an intestine transplant, a skin transplant, a bone marrow transplant, a small bowel transplant, a trachea transplant, a cornea transplant, a limb transplant, and a combination thereof.

The present methods may diagnose or predict any type of transplant rejection, including, but not limited to, hyperacute rejection, acute rejection, and/or chronic rejection.

The present methods may determine/detect the presence, type and/or severity of the transplant rejection.

Also encompassed by the present disclosure is a method for assessing efficacy of an immunosuppressant therapy for transplant rejection in a patient. The method may contain the following steps: (a) obtaining a first sample from the patient before initiation of the therapy (or at a first time point after initiation of the therapy); (b) assaying the levels of alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) in the first sample; (c) obtaining a second sample from the patient after initiation of the therapy (or at a second time point after initiation of the therapy); (d) assaying the levels of the alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) in the second sample; (e) comparing the levels of step (b) with the levels of step (d). If the level of alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) obtained in step (d) decreases (e.g., by about 1% to about 100%, about 5% to about 90%, about 10% to about 80%, about 5% to about 70%, about 5% to about 60%, about 10% to about 50%, about 15% to about 40%, about 5% to about 20%, about 1% to about 20%, about 10% to about 30%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 10% to about 90%, about 12.5% to about 80%, about 20% to about 70%, about 25% to about 60%, or about 25% to about 50%, about 2 fold, about 3 fold, about 4 fold, about 5 fold, about 6 fold, about 7 fold, about 8 fold, about 9 fold, about 10 fold, at least or about 1.1 fold, at least or about 1.2 fold, at least or about 1.3 fold, at least or about 1.4 fold, at least or about 1.5 fold, at least or about 1.6 fold, at least or about 1.8 fold, at least or about 2 fold, at least or about 5 fold, at least or about 10 fold, at least or about 15 fold, at least or about 20 fold, at least or about 50 fold, at least or about 100 fold, at least or about 120 fold, from about 2 fold to about 500 fold, from about 1.1 fold to about 10 fold, from about 1.1 fold to about 5 fold, from about 1.5 fold to about 5 fold, from about 2 fold to about 5 fold, from about 3 fold to about 4 fold, from about 5 fold to about 10 fold, from about 5 fold to about 200 fold, from about 10 fold to about 150 fold, from about 10 fold to about 20 fold, from about 20 fold to about 150 fold, from about 20 fold to about 50 fold, from about 30 fold to about 150 fold, from about 50 fold to about 100 fold, from about 70 fold to about 150 fold, from about 100 fold to about 150 fold, from about 10 fold to about 100 fold, from about 100 fold to about 200 fold), compared to its (or their) level obtained in step (b), the therapy is considered to be effective.

The present disclosure provides for a method for assessing efficacy of an

immunosuppressant therapy for transplant rejection in a patient. The method may contain the following steps: (a) obtaining a first sample from the patient before initiation of the therapy (or at a first time point after initiation of the therapy); (b) assaying the levels of LIMS1 mRNA and/or GCC2 mRNA in the first sample; (c) obtaining a second sample from the patient after initiation of the therapy (or at a second time point after initiation of the therapy); (d) assaying the levels of the LIMS 1 mRNA and/or GCC2 mRNA in the second sample; (e) comparing the levels of step (b) with the levels of step (d). If the level of LIMS 1 mRNA and/or GCC2 mRNA obtained in step (d) increases (e.g., by about 1% to about 100%, about 5% to about 90%, about 10% to about 80%, about 5% to about 70%, about 5% to about 60%, about 10% to about 50%, about 15% to about 40%, about 5% to about 20%, about 1% to about 20%, about 10% to about 30%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 10% to about 90%, about 12.5% to about 80%, about 20% to about 70%, about 25% to about 60%, or about 25% to about 50%, about 2 fold, about 3 fold, about 4 fold, about 5 fold, about 6 fold, about 7 fold, about 8 fold, about 9 fold, about 10 fold, at least or about 1.1 fold, at least or about 1.2 fold, at least or about 1.3 fold, at least or about 1.4 fold, at least or about 1.5 fold, at least or about 1.6 fold, at least or about 1.8 fold, at least or about 2 fold, at least or about 5 fold, at least or about 10 fold, at least or about 15 fold, at least or about 20 fold, at least or about 50 fold, at least or about 100 fold, at least or about 120 fold, from about 2 fold to about 500 fold, from about 1.1 fold to about 10 fold, from about 1.1 fold to about 5 fold, from about 1.5 fold to about 5 fold, from about 2 fold to about 5 fold, from about 3 fold to about 4 fold, from about 5 fold to about 10 fold, from about 5 fold to about 200 fold, from about 10 fold to about 150 fold, from about 10 fold to about 20 fold, from about 20 fold to about 150 fold, from about 20 fold to about 50 fold, from about 30 fold to about 150 fold, from about 50 fold to about 100 fold, from about 70 fold to about 150 fold, from about 100 fold to about 150 fold, from about 10 fold to about 100 fold, from about 100 fold to about 200 fold), compared to its (or their) level obtained in step (b), the therapy is considered to be effective.

An effective therapy may be continued, or discontinued if the patient’ s condition has improved and is no longer in need of treatment. An ineffective treatment may be altered or modified, or replaced with other treatment.

The present methods can include the steps of measuring the level of alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) in a sample from a patient receiving a therapeutic intervention, and comparing the measured level to a reference level or the level of alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) in a control sample. The measured level of the alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) is indicative of the therapeutic efficacy of the therapeutic intervention.

Based on the measured alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) levels, therapy may be continued or altered, e.g., by change of dose or dosing frequency, or by addition of other active agents, or change of therapeutic regimen altogether.

The present invention also encompasses a method of predicting or assessing the level of severity of transplant rejection in a patient. In one embodiment, the method comprises measuring the level of alloantibodies (e.g., anti-LIMS 1 antibodies and/or anti-GCC2 antibodies) in a biological sample from a patient; and comparing the measured level to a reference level or the level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in a control sample, wherein the measured level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) is indicative of the level of severity of transplant rejection in the patient. In other embodiments, an increase or decrease (as described herein) in the level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) is indicative of the level of severity of transplant rejection in the patient.

Another aspect of the disclosure is a kit containing a reagent for measuring the level of alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in a biological sample, instructions for measuring alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies), and instructions for evaluating or monitoring transplant rejection in a patient based on the level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies).

The present disclosure also provides for a kit containing a reagent for measuring the mRNA level (e.g., LIMS 1 mRNA and/or GCC2 mRNA) in a biological sample, instructions for measuring mRNA level (e.g., LIMS1 mRNA and/or GCC2 mRNA), and instructions for evaluating or monitoring transplant rejection in a patient based on the level of the mRNA (e.g., LIMS1 mRNA and/or GCC2 mRNA).

Also encompassed by the disclosure are kits for assessing or predicting the severity or progression of transplant rejection in a subject. The kit may comprise a reagent for measuring alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in a biological sample, and instructions for assessing severity or progression of transplant rejection based on the level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies).

The kit may comprise a reagent for measuring mRNA level (e.g., LIMS1 mRNA and/or GCC2 mRNA) in a biological sample, and instructions for assessing severity or progression of transplant rejection based on the mRNA level (e.g., LIMS1 mRNA and/or GCC2 mRNA).

LIMS1

LIM zinc finger domain containing 1 (LIMS1) is a protein that in humans is encoded by the LIMS1 gene. The NCBI Reference Sequence (RefSeq) accession numbers for human LIMS 1 mRNA may include NM_00l 193482, NM_00l 193483, NM_00l 193484, NM_00l 193485 and NM_00l 193488. The NCBI Reference Sequence (RefSeq) accession numbers for human LIMS1 protein may include NP 001180411, NP_00l 180412, NP_00l 180413, NP_00l 180414, and NP_00l 180417. The NCBI Reference Sequence (RefSeq) accession numbers for murine LIMS1 mRNA may include NM_00l 193303, NMJ326148, NM_20l242, NM 001346676 NM_00l359l 15. The NCBI Reference Sequence (RefSeq) accession numbers for murine LIMS1 protein may include NP_00l 180232, NPJ301333605, NPJ380424, NP_957694 and NPJ301346044.

One of the single-nucleotide polymorphisms (SNPs) of LIMS1, rs893403, resides on chromosome 2ql2.3 in the intronic portion of LIMS1. This gene encodes LIM zinc finger domain containing- 1, a protein involved in cell adhesion and integrin signaling through its interaction with integrin-linked kinase found in focal adhesion plaques. This SNP was initially selected as it tags a common l.5-kb deletion CNVR915.1 downstream of the LIMS1 gene (r2=0.98 in the HapMap CEU population). CNVR915.1 was originally annotated to intersect LOC100288532, a pseudogene in hgl8 that was removed in subsequent releases of the human genome.

Using our new tissue- specific FUN-LDA functional scoring method based on 127 Roadmap and ENCODE tissues and cell types25, we also performed analyses of the deleted sequence, as well as all known variants in linkage disequilibrium (LD) with rs893403 outside of the deletion region. We did not detect any potentially functional variants within the deletion region by FUN-LDA. However, there were several other potentially functional variants in LD (r2>0.8) with rs893403. This includes rsl0084l99, which resides within the LIMS1 transcription start site (TSS) and has FUN-LDA posterior probability 1.0 across all 127 tissues.

We also note that rs893403 regulates mRNA expression of GCC2, a peripheral membrane protein of unclear function. We demonstrate that GCC2 protein is expressed in the cytoplasm of kidney proximal tubule cells.

Genotyping of polymorphic variants can be carried out using any suitable methodology known in the art.

Techniques which may be used for genotyping single nucleotide polymorphisms include ligation detection reaction (Day et ah, Genomics 29, 152 62 (1995)), mass spectrometry, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), single nucleotide primer extension and DNA chips or microarrays (see review by Schafer et ah, Nature Biotechnology, Vol 16, pp33-39 (1998)). The use of DNA chips or microarrays may enable simultaneous genotyping at many different polymorphic loci in a single individual or the simultaneous genotyping of a single polymorphic locus in multiple individuals. SNPs may also be scored by DNA sequencing.

In addition to the above, SNPs are commonly scored using PCR-based techniques, such as PCR-SSP using allele- specific primers (Bunce et al., Tissue Antigens, 1995; 50: 23-31). This method generally involves performing DNA amplification reactions using genomic DNA as the template and two different primer pairs, the first primer pair comprising an allele- specific primer which under appropriate conditions is capable of hybridizing selectively to the wild type allele and a non allele-specific primer which binds to a complementary sequence elsewhere within the gene in question, the second primer pair comprising an allele- specific primer which under appropriate conditions is capable of hybridizing selectively to the variant allele and the same non allele- specific primer. Further suitable techniques for scoring SNPs include PCR ELISA and denaturing high performance liquid chromatography (DHPLC).

If the SNP results in the abolition or creation of a restriction site, genotyping can be carried out by performing PCR using non-allele specific primers spanning the polymorphic site and digesting the resultant PCR product using the appropriate restriction enzyme (also known as PCR-RFLP). Restriction fragment length polymorphisms, including those resulting from the presence of a single nucleotide polymorphism, may be scored by digesting genomic DNA with an appropriate enzyme then performing a Southern blot using a labelled probe corresponding to the polymorphic region (Molecular Cloning: A Laboratory Manual, Sambrook, Fritsch and Maniatis, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y.).

“Genotyping" of any given polymorphic variant may comprise screening for the presence or absence in the genome of the subject of both the normal or wild type allele and the variant or mutant allele, or may comprise screening for the presence or absence of either individual allele, it generally being possible to draw conclusions about the genotype of an individual at a polymorphic locus having two alternative allelic forms just by screening for one or other of the specific alleles.

It is within the scope of the present disclosure to perform genotyping of polymorphisms or polymorphic variants within multiple genes, wherein at least one of the genes is LIMS1. Such a "panel screen" of multiple genes may be used to simultaneously analyze multiple

polymorphisms in the same human subject. In one embodiment, genotyping of multiple polymorphisms in a single subject sample may be carried out simultaneously, for example with the use of a microarray or "gene chip".

"Multiple" should be taken to mean two or more, three or more, four or more, five or more, six or more etc.

Genotyping may be carried out in vitro, and can be performed on an isolated sample containing genomic DNA prepared from a suitable sample obtained from the subject under test. For example, genomic DNA is prepared from a sample of whole blood or tissue, or any suitable sample as described herein, according to standard procedures which are well known in the art. If genomic sequence data for the individual under test in the region containing the SNP is available, for example in a genomic sequence database as a result of a prior genomic sequencing exercise, then genotyping of the SNP may be accomplished by searching the available sequence data.

In the case of genetic variants which have a detectable effect on the mRNA transcripts transcribed from a given gene, for example variants which cause altered splicing or which affect transcript termination or which affect the level or mRNA expression, then as an alternative to detecting the presence of the variant at the genomic DNA level, the presence of the variant may be inferred by evaluating the mRNA expression pattern using any suitable technique. Similarly, in the case of genetic variants which have a detectable effect on the protein products encoded by a gene, for example variants which cause a change in primary amino acid sequence, structure or properties of the encoded protein, the presence of the variant may be inferred by evaluating the sequence, structure or properties of the protein using any convenient technique.

The level of the DNA or RNA (e.g., mRNA) molecules may be determined/detected using routine methods known to those of ordinary skill in the art. The level of the nucleic acid molecule may be determined/detected by nucleic acid hybridization using a nucleic acid probe, or by nucleic acid amplification using one or more nucleic acid primers.

Nucleic acid hybridization can be performed using Southern blots, Northern blots, nucleic acid microarrays, etc.

For example, the DNA in a sample may be evaluated by a Southern blot. Similarly, a Northern blot may be used to detect an mRNA. In one embodiment, mRNA is isolated from a given sample, and then electrophoresed to separate the mRNA species. The mRNA is transferred from the gel to a solid support. Labeled probes are used to identify or quantity the nucleic acids.

In certain embodiments, labeled nucleic acids are used to detect hybridization. Complementary nucleic acids may be labeled by any one of several methods typically used to detect the presence of hybridized polynucleotides. One method of detection is the use of autoradiography. Other labels include ligands that bind to labeled antibodies, fluorophores, chemiluminescent agents, enzymes, and antibodies which can serve as specific binding pair members for a labeled ligand.

Nucleic acid microarray technology, which is also known as DNA chip technology, gene chip technology, and solid-phase nucleic acid array technology, may be based on, but not limited to, obtaining an array of identified nucleic acid probes on a fixed substrate, labeling target molecules with reporter molecules (e.g., radioactive, chemiluminescent, or fluorescent tags such as fluorescein, Cye3-dUTP, or Cye5-dUTP, etc.), hybridizing target nucleic acids to the probes, and evaluating target-probe hybridization. Jackson et al. (1996) Nature Biotechnology, 14: 1685- 1691. Chee et al. (1995) Science, 274: 610-613.

The sensitivity of the assays may be enhanced through use of a nucleic acid

amplification system that multiplies the target nucleic acid being detected.

Nucleic acid amplification assays include, but are not limited to, the polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time RT-PCR, quantitative RT-PCR, etc.

Measuring or detecting the amount or level of mRNA in a sample can be performed in any manner known to one skilled in the art and such techniques for measuring or detecting the level of an mRNA are well known and can be readily employed. A variety of methods for detecting mRNAs have been described and may include, Northern blotting, microarrays, real time PCR, RT-PCR, targeted RT-PCR, in situ hybridization, deep- sequencing, single-molecule direct RNA sequencing (RNAseq), bioluminescent methods, bioluminescent protein reassembly, BRET (bioluminescence resonance energy transfer)-based methods, fluorescence correlation spectroscopy and surface-enhanced Raman spectroscopy (Cissell, K. A. and Deo, S. K. (2009) Anal. Bioanal. Chem., 394:1109-1116).

The methods of the present invention may include the step of reverse transcribing RNA when assaying the level or amount of an mRNA.

Anti-LIMSl antibodies, anti-GCC2 antibodies

The present application measures the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a biological sample. In certain embodiments, the sample is a body fluid. For example, the body fluid can include, but are not limited to, serum, plasma, blood, whole blood and derivatives thereof, urine, tears, saliva, sweat, cerebrospinal fluid (CSF), oral mucus, vaginal mucus, seminal plasma, semen, prostatic fluid, excreta, ascites, lymph, bile, and amniotic fluid. In certain embodiments, the biological sample is plasma or serum.

In certain embodiment, samples can include, but are not limited to, kidney tissue, cardiac tissue, bone marrow, myocardium, endothelium, skin, hair, hair follicles, epithelial tissues, as well as other samples or biopsies. In certain embodiments, the biological sample is kidney tissue.

The sample may be obtained at any time point after the transplant procedure, such as about 10 minutes, about 30 minutes, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 5 hours, about 6 hours, about 7 hours, about 8 hours, about 10 hours, about 12 hours, about 15 hours, about 18 hours, about 20 hours, about 22 hours, about 1 day, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 1 week, about 2 weeks, about 3 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 1 year, about 2 years, about 3 years, about 5 years or longer following the transplantation procedure. The time point may also be earlier or later.

The level or amount of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a patient sample can be compared to a reference level or amount of the anti-LIMS 1 antibodies present in a control sample. The control sample may be from a patient who has received a transplant without rejection or a plurality of patients who have received a transplant without rejection. The control sample may be from a healthy subject or a plurality of healthy subjects. In other embodiments, a control sample is taken from a patient prior to treatment with a therapeutic intervention or a sample taken from an untreated patient (e.g., a patient who has not received a transplant and/or an immunosuppressant therapy). Reference levels for anti-LIMS 1 antibodies and/or anti-GCC2 antibodies can be determined by determining the level of anti-LIMS 1 antibodies and/or anti- GCC2 antibodies in a sufficiently large number of samples obtained from a patient or patients who have received a transplant without transplant rejection, or normal, healthy control subjects to obtain a pre-determined reference or threshold value. A reference level can also be determined by determining the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a sample from a patient prior to treatment with the therapeutic intervention. Reference (or calibrator) level information and methods for determining reference levels can be obtained from publicly available databases, as well as other sources. (See, e.g., Bunk, D. M. (2007) Clin. Biochem. Rev., 28(4): 131-137; and Remington: The Science and Practice of Pharmacy, Twenty First Edition (2005)).

Protein-based assays

The level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies can be detected and/or quantified by any of a number of methods well known to those of skill in the art. The anti- LIMS 1 antibodies and/or anti-GCC2 antibodies may be detected by, for example, mass spectrometry (e.g., LC-MS/MS) and Western blot. The methods may include various immunoassays such as enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), immunohistochemistry, antibody sandwich capture assay, immunofluorescent assay, Western blot, enzyme-linked immunospot assay (EliSpot assay), precipitation reactions (in a fluid or gel), immunodiffusion, Immunoelectrophoresis, radioimmunoassay (RIA), competitive binding protein assays, chemiluminescent assays, and the like. Also included are analytic biochemical methods such as electrophoresis, capillary electrophoresis, high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyperdiffusion chromatography, liquid chromatography-tandem mass spectrometry, and the like. U.S. Patent Nos. 4,366,241; 4,376,110; 4,517,288; and 4,837,168. Methods in Cell Biology Volume 37: Antibodies in Cell Biology, Asai, ed. Academic Press, Inc. New York (1993); Basic and Clinical Immunology 7th Edition, Stites & Terr, eds. (1991).

The level of anti-LIMS 1 antibodies may be detected by using molecules (e.g., polypeptides, etc.) that bind to the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies. For example, the binding polypeptide may be the LIMS1 protein (e.g., recombinant LIMS1 protein), or may be an antibody or antibody fragment, such as an Fab, F(ab)₂, F(ab’)₂, Fd, or Fv fragment of an antibody. Any of the various types of antibodies can be used for this purpose, including, but not limited to, polyclonal antibodies, monoclonal antibodies, humanized antibodies, human antibodies (e.g., generated using transgenic mice, etc.), single chain antibodies (e.g., single chain Fv (scFv) antibodies), heavy chain antibodies and chimeric antibodies. The antibodies can be from various species, such as rabbits, mice, rats, goats, chickens, guinea pigs, hamsters, horses, sheep, llamas etc.

In certain embodiments, ELISA is used to detect and/or quantify anti-LIMS 1 antibodies in a sample. The ELISA can be any suitable methods, including, but not limited to, direct ELISA, sandwich ELISA, and competitive ELISA.

Presence or level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies can also be assayed using immunodiffusion, immunoblotting techniques, immunofluorescence,

immunohistochemistry, immunocytochemistry, immunoprecipitation, heamagglutination, enzyme immunoassays, ELISpot, flow cytometry and flow cytometry for multiplex bead-based assays such as Luminex-type assays.

In certain embodiments, Western blot (immunoblot) is used to detect and quantify anti- LIMS 1 antibodies and/or anti-GCC2 antibodies in a sample. The technique may comprise separating sample proteins by gel electrophoresis, transferring the separated proteins to a suitable solid support, and incubating the sample with the antibodies that specifically bind the anti- LIMS 1 antibodies and/or anti-GCC2 antibodies.

The disclosure further includes protein microarrays (including antibody arrays) for the analysis of levels of a plurality of alloantibodies (e.g., including LIMS1 antibodies and/or anti- GCC2 antibodies). Protein microarray technology, which is also known as protein chip technology and solid-phase protein array technology, is well known to those of ordinary skill in the art. Protein microarray may be based on, but not limited to, obtaining an array of identified peptides or proteins on a fixed substrate, binding target molecules or biological constituents to the peptides, and evaluating such binding. See, e.g., MacBeath et ah, Printing Proteins as Microarrays for High-Throughput Lunction Determination, Science 289(5485): 1760- 1763,

2000. In some embodiments, one or more control peptide or protein molecules are attached to the substrate.

The polypeptides that may be used to assay the level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies may be derived also from sources other than antibody technology. Lor example, such binding agents can be provided by degenerate peptide libraries which can be readily prepared in solution, in immobilized form or as phage display libraries. Combinatorial libraries also can be synthesized of peptides containing one or more amino acids. Libraries further can be synthesized of peptides and non-peptide synthetic moieties. The anti-LIMS 1 antibodies and/or anti-GCC2 antibodies can be used to screen peptide libraries, including phage display libraries, to identify and select peptide binding partners of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies. Yeast two-hybrid screening methods also may be used to identify polypeptides that bind to the anti-LIMS l antibodies and/or anti-GCC2 antibodies.

These assays of determining/detecting the presence and/or level of anti-LIMS l antibodies and/or anti-GCC2 antibodies may include use of a label(s). The labels can be any material having a detectable physical or chemical property. Thus, a label is any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means. Such labels may include, but are not limited to, a fluorescent label, a radiolabel, a chemiluminescent label, an enzyme, a metallic label, a bioluminescent label, a chromophore, biotin etc. For example, a fluorescently labeled or radiolabeled antibody that selectively binds to a polypeptide of the invention may be contacted with a tissue or cell to visualize the

polypeptide. In some aspects of the invention, a label may be a combination of the foregoing molecule types.

The level, amount, abundance or concentration of anti-LIMS 1 antibodies and/or anti- GCC2 antibodies may be measured. The measurement result may be an absolute value or may be relative (e.g., relative to a reference protein or polypeptide, etc.)

In one embodiment, a difference (increase or decrease) in the measured level of the anti- LIMS 1 antibodies and/or anti-GCC2 antibodies relative to the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the control sample (e.g., a sample in at least one patient who has received a transplant without rejection, in the patient prior to treatment, at a different time point during treatment, or an untreated patient) or a pre-determined reference value is indicative of the therapeutic efficacy of the therapeutic intervention (e.g., an immunosuppressant therapy). In one embodiment, a reduction or decrease in the measured level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies relative to the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the control sample (e.g., a sample in the patient prior to treatment or an untreated patient) or pre-determined reference value can be indicative of the therapeutic efficacy of the therapeutic intervention. For instance, in such embodiments, when the level of anti-LIMS l antibodies and/or anti-GCC2 antibodies is decreased when compared to the level in a control sample or pre-determined reference value in response to a therapeutic intervention, the decrease is indicative of therapeutic efficacy of the therapeutic intervention.

Transplant rejection The present method may be used to assess the transplant status or outcome, including, but not limited to, transplant rejection, transplant function (including delayed graft function), non rejection based allograft injury, transplant survival, chronic transplant injury, or titer

pharmacological immunosuppression. In some embodiments, the non-rejection based allograft injury may include ischemic injury, virus infection, peri-operative ischemia, reperfusion injury, hypertension, physiological stress, injuries due to reactive oxygen species and/or injuries caused by pharmaceutical agents. The transplant status or outcome may comprise vascular

complications or neoplastic involvement of the transplanted organ.

In some embodiments, the methods described herein are used for diagnosing or predicting transplant status or outcome (e.g., transplant rejection). In some embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) to determine whether a subject is undergoing transplant rejection. In some embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) for diagnosis or prediction of transplant rejection. In some embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) for determining an immunosuppressive regimen for a subject who has received a transplant. In some embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) to predict transplant survival in a subject that have received a transplant. The invention provides methods of diagnosing or predicting whether a transplant in a transplant recipient will survive or be lost.

In certain embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) to diagnose or predict the presence of long-term graft survival. In some embodiments, the methods described herein are used to detect and/or quantify alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) for diagnosis or prediction of non-rejection based transplant injury. The present methods may be used to diagnose graft-versus-host-disease (GVHD).

As used herein the term "diagnose" or "diagnosis" of a transplant status or outcome includes predicting or diagnosing the transplant status or outcome, determining predisposition to a transplant status or outcome, monitoring treatment of transplant patient, diagnosing a therapeutic response of transplant patient, and prognosis of transplant status or outcome, transplant progression, and response to a particular treatment.

The transplant maybe any organ or tissue transplant, including, but not limited to, a heart transplant, a kidney transplant, a liver transplant, a pancreas transplant, a lung transplant, an intestine transplant, a skin transplant, a bone marrow transplant, a small bowel transplant, a trachea transplant, a cornea transplant, a limb transplant, and a combination thereof.

The present methods may determine the presence, type and/or severity of the transplant rejection. Transplant rejection includes a partial or complete immune response to a transplanted cell, tissue, organ, or the like on or in a recipient of said transplant due to an immune response to a transplant. A transplant can be rejected through either a cell-mediated rejection (CMR) or antibody-mediated rejection (AMR). The rejection may be acute cellular rejection (ACR). The rejection may be T-cell-mediated rejection.

The present methods may diagnose or predict any type of transplant rejection, including, but not limited to, hyperacute rejection, acute rejection, and/or chronic rejection. Hyperacute rejection can occur within minutes or hours to days following transplantation and may be mediated by a complement response in recipients with pre-existing antibodies to the donor. In hyperacute rejection, antibodies are observed in the transplant vasculature very soon after transplantation, possibly leading to clotting, ischemia, and eventual necrosis and death. Acute rejection occurs days to months or even years following transplantation. It can include a T-cell mediated response and is identified based on presence of T-cell infiltration of the transplanted tissue, structural injury to the transplanted tissue, and injury to the vasculature of the transplanted tissue. Chronic rejection occurs months to years following transplantation and is associated with chronic inflammatory and immune response against the transplanted tissue. Chronic rejection may also include chronic allograft vasculopathy, which is associated with fibrosis of vasculature of the transplanted tissue. U.S. Patent No. 8,637,038. Fibrosis is a common factor in chronic rejection of all types of organ transplants. Chronic rejection can typically be described by a range of specific disorders that are characteristic of the particular organ. For example, in heart transplants or transplants of cardiac tissue, such as valve replacements, such disorders include fibrotic atherosclerosis; in lung transplants, such disorders include fibroproliferative destruction of the airway (bronchiolitis obliterans); in kidney transplants, such disorders include obstructive nephropathy, nephrosclerorsis, tubulointerstitial nephropathy; and in liver transplants, such disorders include disappearing bile duct syndrome. Chronic rejection can also be characterized by ischemic insult, denervation of the transplanted tissue, hyperlipidemia and hypertension associated with immunosuppressive drugs.

In some embodiments, the invention provides methods of determining whether a patient or subject is displaying transplant tolerance. The term "transplant tolerance" includes when the subject does not reject a graft organ, tissue or cell(s) that has been introduced into/onto the subject. In other words, the subject tolerates or maintains the organ, tissue or cell(s) that has been transplanted.

Graft-versus-host-disease (GVHD) is the pathological reaction that occurs between the host and grafted tissue. The grafted or donor tissue dominates the pathological reaction. GVHD can be seen following stem cell and/or solid organ transplantation. GVHD occurs in

immunocompromised subjects, who when transplanted, receive "passenger" lymphocytes in the transplanted stem cells or solid organ. These lymphocytes recognize the recipient's tissue as foreign. Thus, they attack and mount an inflammatory and destructive response in the recipient. GVHD has a predilection for epithelial tissues, especially skin, liver, and mucosa of the gastrointestinal tract. GVHD subjects are immunocompromised due the fact that prior to transplant of the graft, the subject receives immunosuppressive therapy.

Certain embodiments of the invention provide methods of predicting transplant survival in a subject that has received a transplant. The invention provides methods of diagnosing or predicting whether a transplant in a transplant patient or subject will survive or be lost. In certain embodiments, the invention provides methods of diagnosing or predicting the presence of long term graft survival. Long-term graft survival refers to graft survival for at least about 5 years beyond current sampling, despite the occurrence of one or more prior episodes of acute rejection. In certain embodiments, transplant survival is determined for patients in which at least one episode of acute rejection has occurred. As such, these embodiments provide methods of determining or predicting transplant survival following acute rejection.

Therapeutic intervention Based on the present methods, transplant rejection may be diagnosed or predicted (a risk of transplant rejection assessed), and then the subject may be treated with a therapy for the rejection, such as an immunosuppressant therapy.

An immunosuppressant, also referred to as an immunosuppressive agent, can be any compound that decreases the function or activity of one or more aspects of the immune system, such as a component of the humoral or cellular immune system or the complement system.

Non-limiting examples of immunosuppressants include, (1) antimetabolites, such as purine synthesis inhibitors (such as inosine monophosphate dehydrogenase (IMPDH) inhibitors, e.g., azathioprine, mycophenolate, and mycophenolate mofetil), pyrimidine synthesis inhibitors (e.g., lefhmomide and terifhmomide), and antifolates (e.g., methotrexate); (2) calcineurin inhibitors, such as tacrolimus, cyclosporine A, pimecrolimus, and voclosporin; (3) TNF-alpha inhibitors, such as thalidomide and lenalidomide; (4) IL-l receptor antagonists, such as anakinra; (5) mammalian target of rapamycin (mTOR) inhibitors, such as rapamycin (sirolimus), deforolimus, everolimus, temsirolimus, zotarolimus, and biolimus A9; (6) corticosteroids, such as prednisone; and (7) antibodies to any one of a number of cellular or serum targets (including anti-lymphocyte globulin and anti-thymocyte globulin).

Non-limiting exemplary cellular targets and their respective inhibitor compounds include, but are not limited to, complement component 5 (e.g., eculizumab); tumor necrosis factors (TNFs) (e.g., infliximab, adalimumab, certolizumab pegol, afelimomab and golimumab); IL-5 (e.g., mepolizumab); IgE (e.g., omalizumab); BAYX (e.g., nerelimomab); interferon (e.g., faralimomab); IL-6 (e.g., elsilimomab); IL-12 and IL-13 (e.g., lebrikizumab and ustekinumab); CD3 (e.g., muromonab-CD3, otelixizumab, teplizumab, visilizumab); CD4 (e.g., clenoliximab, keliximab and zanolimumab); CDl la (e.g., efalizumab); CD18 (e.g., erlizumab); CD20 (e.g., afutuzumab, ocrelizumab, pascolizumab); CD23 (e.g., lumiliximab); CD40 (e.g., teneliximab, toralizumab); CD62L/L-selectin (e.g., aselizumab); CD80 (e.g., galiximab); CDl47/basigin (e.g., gavilimomab); CD154 (e.g., ruplizumab); BLyS (e.g., belimumab); CTLA-4 (e.g., ipilimumab, tremelimumab); CAT (e.g., bertilimumab, lerdelimumab, metelimumab); integrin (e.g., natalizumab); IL-6 receptor (e.g., tocilizumab); LFA-l (e.g., odulimomab); and IL-2

receptor/CD25 (e.g., basiliximab, daclizumab, inolimomab).

The present disclosure provides for methods of evaluating and/or monitoring the efficacy of a therapeutic intervention (e.g., an immunosuppressant therapy) for treating transplant rejection. These methods can include the step of measuring the level of alloantibodies (e.g., anti- LIMS1 antibodies), or a panel of alloantibodies (e.g., anti-LIMSl antibodies), in a biological sample from a patient who has received a transplant. In some embodiments, the level of alloantibodies (e.g., anti-LIMSl antibodies) in the biological sample is compared to a reference level, or the level of the alloantibodies (e.g., anti-LIMSl antibodies) in a control sample. The control sample may be taken from the patient at a different time point after transplantation, or from the patient before initiation of the therapeutic intervention (e.g., an immunosuppressant therapy), or from the patient at a different time point after initiation of the therapeutic intervention (e.g., an immunosuppressant therapy). The measured level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) is indicative of the therapeutic efficacy of the therapeutic intervention. In some cases, a decrease in the level of the

alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) is indicative of the efficacy of the therapeutic intervention. In some embodiments, a change in the measured level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) relative to a sample from the patient taken prior to treatment or earlier during the treatment regimen is indicative of the therapeutic efficacy of the therapeutic intervention.

In certain embodiments, the method comprises detecting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

12, 13, 14, 15, 16, 17, 18, 19, 20 or more alloantibodies (e.g., including anti-LIMSl antibodies and/or anti-GCC2 antibodies) described herein. When the levels of a panel of alloantibodies are determined/detected in the patient sample, the patient sample may be classified as indicative of effective or non-effective intervention on the basis of a classifier algorithm. For example, samples may be classified on the basis of threshold values as described, or based upon mean and/or median alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) levels in one population or versus another (e.g., a population of healthy controls or a population of patients having received a transplant without rejection, or levels based on effective versus ineffective therapy).

Various classification schemes are known for classifying samples between two or more classes or groups, and these include, without limitation: Principal Components Analysis, Naive Bayes, Support Vector Machines, Nearest Neighbors, Decision Trees, Logistic, Artificial Neural Networks, Penalized Logistic Regression, and Rule-based schemes. In addition, the predictions from multiple models can be combined to generate an overall prediction. Thus, a classification algorithm or "class predictor" may be constructed to classify samples. The process for preparing a suitable class predictor (reviewed in Simon (2003) British Journal of Cancer (89) 1599-1604).

The present invention also provides methods for modifying a treatment regimen comprising detecting the level of alloantibodies (e.g., anti-LIMSl antibodies) in a biological sample from a patient receiving the therapeutic intervention and modifying the treatment regimen based on an increase or decrease in the level of the alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in the biological sample. The methods for modifying the treatment regimen of a therapeutic intervention may comprise the steps of: (a) detecting the level of at least one alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) in a biological sample from a patient receiving the therapeutic intervention; and (b) modifying the treatment regimen based on an increase or decrease in the level of the at least one alloantibodies (e.g., anti-LIMSl antibodies) in the biological sample. In some embodiments, the method comprises detecting 2, 3, 4, 5, 6, 7, 8, 9, 10 or more alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) described herein. In certain embodiments, the levels of less than 50, less than 30, or less than 20 alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) are detected.

Modifying the treatment regimen can include, but is not limited to, changing and/or modifying the type of therapeutic intervention, the dosage at which the therapeutic intervention is administered, the frequency of administration of the therapeutic intervention, the route of administration of the therapeutic intervention, as well as any other parameters that would be well known by a physician to change and/or modify. For example, where one or more alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) decrease (or increase) during therapy or match reference levels, the therapeutic intervention is continued. In embodiments where one or more alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) do not decrease (or increase) during therapy or match reference levels, the therapeutic intervention is modified. In another embodiment, the information regarding the increase or decrease in the level of at least one alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) can be used to determine the treatment efficacy, as well as to tailor the treatment regimens of therapeutic interventions.

In one embodiment, the present methods are used for the titration of a subject's immunosuppression. Additionally, the present method can be utilized to determine whether the response to drug therapy indicates resolution of rejection risk. It can also be used to test whether the reduction of drug therapy increases the risk of rejection and whether drug therapy, if discontinued, should be resumed. This helps avoiding over-medication and/or under-medication of a given patient and duration of treatment can be tailored to the needs of the patient. The titration of immunosuppression can be after organ transplantation, or during a viral or bacterial infection. Further, the titration can be during a viral or bacterial infection after a subject has undergone organ transplantation. The method can include monitoring the response of a subject to one or more immunosuppressive agents, the withdrawal of an immunosuppressive agent, an antiviral agent, or an anti-bacterial agent.

Information gained by the methods described herein can be used to develop a

personalized treatment plan for a transplant recipient. Accordingly, the disclosure further provides methods for developing personalized treatment plans for transplant recipients. The methods can be carried out by, for example, carrying out any of the methods of alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) analysis described herein and, in consideration of the results obtained, designing a treatment plan for the patient whose transplant is assessed. If the levels of alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies) indicate that the patient is at risk for an undesirable clinical outcome (e.g., transplant rejection, developing delayed graft function, or compromised graft function), the patient is a candidate for treatment with an effective amount of an immunosuppressant. Depending on the level of alloantibodies (e.g., anti-LIMSl antibodies and/or anti-GCC2 antibodies), the patient may require a treatment regime that is more aggressive than a standard regime, or it may be determined that the patient is best suited for a standard regime. When so treated, one can treat or prevent transplant rejection (or, at least, prolong the time the transplanted organ functions adequately). Conversely, a different result (i.e., a different level of alloantibodies (e.g., anti- LIMS 1 antibodies and/or anti-GCC2 antibodies)) may indicate that the patient is not likely to experience an undesirable clinical outcome. In that event, the patient may avoid

immunosuppressants. U.S. Patent No. 8,741,557.

Samples

Sampling methods are well known by those skilled in the art and any applicable techniques for obtaining biological samples of any type are contemplated and can be employed with the methods of the present invention. (See, e.g., Clinical Proteomics: Methods and

Protocols, Vol. 428 in Methods in Molecular Biology, Ed. Antonia Vlahou (2008).)

The samples may be drawn before, during or after transplantation. The samples may be drawn at different time points during transplantation, and/or be drawn at different time points after transplantation.

When the sample is drawn after transplantation, it can be obtained from the subject at any point following transplantation. In some embodiments, the sample is obtained about 1 week, about 2 weeks, about 3 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, at least 1, 2, 3, or 6 months following transplantation. In some embodiments, the sample is obtained least 1, 2, 3, 4, 6 or 8 weeks following transplantation. In some embodiments, the sample is obtained at least 1, 2, 3, 4, 5, 6, or 7 days following transplantation. In some embodiments, the sample is obtained at least 10 minutes, 30 minutes, 1 hour, 6 hours, 12 hours, 18 hours or 24 hours after transplantation. In other embodiments, the sample is obtained at least one week following transplantation. In some embodiments, one or more alloantibodies (e.g., anti-LIMS l antibodies and/or anti-GCC2 antibodies) are measured between 1 and 8 weeks, between 2 and 7 weeks, at 1, 2, 3, 4, 5, 6, 7 or 8 weeks following transplantation.

Kits

Another aspect of the disclosure is a kit containing a reagent or reagents for measuring anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a biological sample, instructions for measuring the anti-LIMS l antibodies and/or anti-GCC2 antibodies, and/or instructions for evaluating or monitoring transplant rejection in a patient based on the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies, and/or instructions for assessing an immunosuppressant therapy in a patient.

The present disclosure provides for a kit containing a reagent or reagents for measuring LIMS 1 mRNA level and/or GCC2 mRNA level in a biological sample, instructions for measuring the LIMS 1 mRNA level and/or GCC2 mRNA level, and/or instructions for evaluating or monitoring transplant rejection in a patient based on the LIMS 1 mRNA level and/or GCC2 mRNA level, and/or instructions for assessing an immunosuppressant therapy in a patient.

In certain embodiments, the kit comprises LIMS 1 protein (e.g., recombinant LIMS 1 protein) and/or GCC2 protein. In certain embodiments, the kit comprises primers and/or probe for genetic testing according to the present methods as described herein.

Any of the compositions described herein may be comprised in a kit. In one embodiment, the kit contains a reagent for measuring anti-LIMS 1 antibodies and/or anti-GCC2 antibodies levels in a biological sample, instructions for measuring anti-LIMS 1 antibodies and/or anti- GCC2 antibodies levels, and instructions for evaluating or monitoring transplant rejection in a patient based on the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies levels. In another embodiment, the kit contains a reagent for measuring LIMS 1 protein and/or GCC2 protein in a biological sample, instructions for measuring the LIMS 1 protein and/or GCC2 protein, and instructions for evaluating or monitoring transplant rejection in a patient based on the level of the LIMS 1 protein and/or GCC2 protein.

The kit may also be customized for determining the efficacy of therapy for transplant rejection.

The components of the kits may be packaged either in aqueous media or in lyophilized form. The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted. Where there is more than one component in the kit, the kit also will generally contain a second, third or other additional container into which the additional components may be separately placed (e.g., sterile, pharmaceutically acceptable buffer and/or other diluents). However, various combinations of components may be comprised in a vial. The kits of the present invention also will typically include a means for containing the nucleic acids, and any other reagent containers in close confinement for commercial sale. Such containers may include injection or blow molded plastic containers into which the desired vials are retained.

When the components of the kit are provided in one and/or more liquid solutions, the liquid solution may be an aqueous solution. The components of the kit may also be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container means.

Such kits may also include components that preserve or maintain the reagents or that protect against their degradation. Such components may be protease inhibitors or protect against proteases. Such kits generally will comprise, in suitable means, distinct containers for each individual reagent or solution.

A kit will also include instructions for employing the kit components as well the use of any other reagent not included in the kit. Instructions may include variations that can be implemented.

The following are examples of the present invention and are not to be construed as limiting.

Example 1 Genomic Mismatch at LIMS1 Locus and Kidney Allograft Rejection

Current kidney transplants carry risk of rejection by the recipient’s immune system and determining this risk is difficult and invasive. This technology presents a new genetic marker for donor/recipient matching that can dramatically decrease risk of transplant rejection. Genomic collision at a novel histocompatibility locus encoding LIMS 1 antigen is associated with a higher risk of transplant rejection and production of anti-LIMSl antibodies. The risk of rejection can be modified by genetic matching based on rs893403 genotype. This technology provides new targets for both assessing risk in transplant recipients and improving tissue matching between donors and recipients to lower rejection risks.

In the context of kidney transplantation, genomic incompatibilities between donor and recipient may lead to allosensitization against new antigens. We hypothesized that recessive inheritance of gene-disrupting variants may represent a risk factor for allograft rejection. We hypothesized that the recipient’s inheritance of variants that disrupt kidney genes predisposes the recipient to allosensitization and rejection. In this study, we tested common copy-number polymorphisms that intersect genes, since such variants have a profound effect on gene function and expression.²² Using a genetic association screen of high-priority deletions, we sought to identify specific loci associated with kidney allograft rejection.

We performed a two-stage genetic association study of kidney allograft rejection. In the first stage, we performed a recessive association screen of 50 common gene-intersecting deletion polymorphisms in a cohort of kidney transplant recipients. In the second stage, we replicated our findings in three independent cohorts of donor-recipient pairs. We defined genomic collision as a specific donor-recipient genotype combination in which a recipient who was homozygous for a gene-intersecting deletion received a transplant from a nonhomozygous donor. Identification of alloantibodies was performed with the use of protein arrays, enzyme-linked immunosorbent assays, and Western blot analyses. In the discovery cohort, which included 705 recipients, we found a significant association with allograft rejection at the LIMS 1 locus represented by rs893403 (hazard ratio with the risk genotype vs. nonrisk genotypes, 1.84; 95% confidence interval [Cl], 1.35 to 2.50; P=9.8xl0^-5). This effect was replicated under the genomic-collision model in three independent cohorts involving a total of 2004 donor-recipient pairs (hazard ratio, 1.55; 95% Cl, 1.25 to 1.93;

P=6.5xl0^-5). In the combined analysis (discovery cohort plus replication cohorts), the risk genotype was associated with a higher risk of rejection than the nonrisk genotype (hazard ratio, 1.63; 95% Cl, 1.37 to 1.95; P=4.7xl0^-8). We identified a specific antibody response against LIMS 1, a kidney-expressed protein encoded within the collision locus. The response involved predominantly IgG2 and IgG3 antibody subclasses.

We found that the LIMS 1 locus appeared to encode a minor histocompatibility antigen. Genomic collision at this locus was associated with rejection of the kidney allograft and with production of anti-LIMS l IgG2 and IgG3.

Methods

Study Design and Clinical Outcome

The study design had two stages. The first stage (the discovery phase) involved a screen of 50 high-priority copy-number polymorphisms (see below) in kidney allograft recipients who had undergone transplantation at the Columbia University Irving Medical Center in New York. Signals that reached a P value of less than 0.05 were advanced to the second stage (the replication phase). The replication phase involved genotyping of the top signals from the discovery phase in additional cohorts involving donor-recipient pairs. The primary outcome in the genetic association study was the first rejection in a time-to-event analysis, defined as a rejection event (antibody-mediated rejection or T-cell-mediated rejection) that occurred between the date of transplantation and the date of allograft biopsy showing such an event.

Discovery Phase

We used a publicly available catalogue of known copy-number variants generated with the use of 2.1 M NimbleGen comparative genome hybridization arrays.^23,24 From this data set, a total of 3266 copy-number variants were mapped to the human reference genome hgl8 (accessed July 2010). To optimize the power of this study, we selected only copy-number polymorphisms that had a global minor allele frequency of more than 10%, corresponding to the expected homozygosity rates of more than 1% (Table 2). Next, we intersected this set with a transcribed segment of the genome to identify potential gene-disrupting variants. A total of 180 gene-intersecting copy-number polymorphisms met our criteria, of which 87 (48%) were deletions. On the basis of the HapMap3 data, we found a single-nucleotide polymorphism (SNP) that was an informative tag (r²>0.8) for 50 (57%) of the identified deletions. These were prioritized for targeted genotyping in the discovery cohort, which involved 705 kidney recipients who had undergone transplantation at Columbia

University and had been followed for a mean of 8.6 years (Table 3).

For the 50 genotyped SNPs, we performed strict quality-control analysis of genotypes that included per-SNP and per-recipient genotyping rates of more than 95%, elimination of monomorphic SNPs, and elimination of markers that significantly deviated from the Hardy- Weinberg equilibrium within each ethnic group. In total, 44 SNPs passed all the quality-control filters (Figure 1, and Table 4).

Replication Phase

The replication cohorts (Table 5) included the Belfast cohort (387 donor-recipient pairs; mean follow-up, 9.2 years; overall rejection rate, 24%), the TransplantLines Genetics cohort (833 donor-recipient pairs; mean follow-up, 6.6 years; overall rejection rate, 36%), and the Torino cohort (784 donor-recipient pairs; mean follow-up, 9.6 years; overall rejection rate,

22%), providing a total of 2004 donor-recipient pairs for analysis. The genotype quality-control and analytical procedures that we used for replication were similar to those used in the discovery phase (see the Methods section).

Molecular Analyses

Detailed methods regarding the deletion breakpoint mapping, functional genomic annotations, sequence motif analysis, tissue immunohistochemical and in situ hybridization studies, detection of anti-LIMSl antibodies, and cell-culture experiments are provided in the Methods section. Testing for expression quantitative trait loci (eQTL) was conducted with the use of Genotype-Tissue Expression (GTEx) and the Nephrotic Syndrome Study Network (NEPTUNE) data sets. Detection of anti-LIMSl antibodies was performed by protein arrays (ProtoArray, Invitrogen) and confirmed by means of enzyme-linked immunosorbent assay and Western blots.

Statistical Analysis The association of genetic predictors and baseline covariates with the primary outcome was tested with the use of the cause-specific hazards models by treating death and allograft loss as censoring events. In recipient-only analyses, the deletion-tagging alleles were coded under a recessive model in which a risk genotype was defined according to a recipient’s homozygosity for a deletion-tagging allele. The full models were additionally adjusted for age, sex, ethnic group, donation type (living or cadaveric donor), HLA mismatch, and sensitization factors. We prespecified a Bonferroni-adjusted significance threshold for statistical significance in the discovery phase (alpha level of 0.05÷44, or l.lxlO^-3). On the basis of the observed distribution of P values, we estimated a positive false discovery rate (Q value) for each of the tested polymorphisms. In the combined donor-recipient analyses, we defined genomic collision as a specific donor-recipient genotype combination in which a recipient who was homozygous for a deletion-tagging allele received a transplant from a nonhomozygous donor. We defined genome wide significance as an alpha level of 5.0xl0^-8, as generally accepted for genetic association studies.

Study Design: The study was designed in two stages. Stage 1 (the discovery phase) involved a genome-wide screen of 50 high priority common copy number polymorphisms (CNPs) in 705 kidney allograft recipients transplanted at Columbia University Irving Medical Center (CUIMC); signals reaching nominal P-value < 0.05 were advanced to the replication phase. We carried out power calculations for the discovery cohort under the assumption of a recessive model, nominal replication threshold alpha = 0.05, and perfect LD between a tag-SNP and a deletion allele

(Table 2). This calculation demonstrates limited power at MAFs below 10%, motivating our MAF > 10% criterion for the selection of candidate deletions. Stage 2 (the replication phase) involved genotyping of the top signals from Stage 1 in additional cohorts, totalling N=2,004 full DR pairs.

Clinical Outcomes and Statistical Methods: The primary outcome was time-to-first-rejection, defined from the date of first transplant to the date of first biopsy demonstrating a rejection event, including both antibody mediated rejections (ABMR) and T-cell mediated rejections (TCMR). We used Kaplan-Meier survival analysis (for univariate analysis) and Cox proportional hazards model (for multivariate analysis) to model these outcomes. Multivariate modeling of clinical covariates was done using variables that were nominally associated with outcome on univariate analysis ( P < 0.05). Such covariates were then subjected to stepwise selection using a BIC-guided selection. In the recipient-only analysis, the multivariate model was used to test recipient’s genotype coded under a recessive model, with risk genotype defined by

homozygosity for the deletion-tagging allele. In the full DR pair analysis, the risk genotype (“collision genotype”) was defined by recipient homozygosity in the absence of donor homozygosity for the deletion-tagging allele. Under this coding, any DR pair with a donor genotype homozygous for the deletion-tagging allele was coded as non-risk regardless of the paired recipient genotype, including when recipient genotype was not available. Similar to recipient-only analyses, the“collision genotype” was used as one of the predictors in the multivariate Cox proportional hazards model to derive adjusted effect estimates and

P-values. Statistical analyses were performed using R base and survival packages (R v3.4, CRAN).

Stage 1 ( Discovery ) Methods: The clinical characteristics of the CUIMC discovery cohort are summarized in Table 3. The association screen was performed using a tag-SNP approach based on the filtering strategy depicted in Figure 1. In total, we identified 50 common deletions perfectly tagged by a SNP at r2>0.8. These 50 high priority candidate SNPs were genotyped in a cohort of 705 kidney transplant recipients recruited by the Columbia Chronic Kidney Disease (CKD) Bio-bank; genomic DNA was extracted from whole blood (QuickGene-6lOL, Kurabo) and individuals SNPs were typed using KASP (Kompetitive Alelle Specific PCR) assay by LGC Genomics. Strict genotype quality control (QC) analysis was performed, including per-SNP and per- individual genotyping rates >95% and elimination of SNPs deviating from Hardy- Weinberg equilibrium within each ethnic group. In total, 44 SNPs passed all QC filters (Figure 1). To test for effects of deletion homozygosity, we used a time-to-event survival analysis under a recessive model with and without adjustment for relevant clinical covariates. We applied a Bonferroni- corrected alpha to declare statistical significance.

Stage 2 (Replication) Methods: The replication cohorts included the Belfast cohort (N=387 full DR pairs), the TransplantLines Genetics cohort⁴³ (N=833 full DR pairs, trial registration number NCT03272841) and the Torino cohort (N=784 full DR pairs), providing a total of 2,004 full donor-recipient pairs for analysis. The clinical characteristics of the replication cohorts are presented in Table 5. Targeted genotyping was performed using KASP assay (TransplantLines cohort), Sequenom iPLEX MassARRAY® (Belfast cohort) and by direct Sanger sequencing (Torino cohort). The QC assessment included genotyping rate >95% in the entire cohort and passing Hardy- Weinberg equilibrium test (P>0.05) in both donor and recipient groups separately. In addition, we compared genotype frequencies of rs893403 between donors and recipients within and across all replication cohorts. As summarized in Table 5, genotype frequencies were nearly identical in donors and recipients within each European cohort, and comparable across cohorts, assuring against potential genotyping errors or bias. The statistical models applied to the replication cohorts were the same as in the discovery analysis. The combined statistical analyses across replication cohorts were stratified by cohort membership. Because in our replication cohorts the majority of rejection events occurred within the first year post-transplant, we also compared alternative statistical models that do not rely on the assumption of proportional hazards. This included non-parametric (log-rank) and logistic-regression-based tests, as summarized in Table 8. These additional analyses demonstrate that our model choices have no effect on the overall conclusions of our tests, and our results remain statistically significant regardless of the specific model assumptions.

Deletion Breakpoint Mapping and Sequence Motif Analysis: We fine-mapped the CNVR915.1 deletion breakpoints using whole genome sequence data from 503 Europeans sequenced through the 1000 Genomes Project⁴⁴. Briefly, based on the genotype of rs893403, we classified each individual into AA (N=l76), AG (N=245), or GG (N=82) groups. We randomly selected 50 individuals for each group and merged all individual BAM files into a single BAM file for each group to visualize the coverage using Integrative Genomics Viewer (IGV)⁴⁵. ETsing BEDtools (coverage command)⁴⁶, we extracted the region and defined the breakpoints by analyzing depth of coverage in the rs893403-GG group. Sequence alignments by rs893403 genotype were conducted showing the presence of l.5-kb deletion in the region. The deletion was further confirmed by 3 -primer PCR analysis of individuals with AG and GG genotype, and the precise boundaries were mapped to chr2: 109310555-109312110 (hgl9, 1556 base pairs deleted). The deleted sequence was next interrogated for various regulatory motifs and features using UCSC Genome Browser⁴⁷ and a variety of other data types, including Human mRNAs⁴⁸, Human ESTs⁴⁹, predicted Retroposed Genes (UCSC Genes V5), C/D and H/ACA Box snoRNAs, scaRNAs, and microRNAs from snoRNABase⁵⁰ and miRBase⁵¹, tRNA Genes predicted by using tRNAscan-SE v. l.23⁵²; TargetScan miRNA Regulatory Sites⁵³; repeat sequences by

RepeatMasker (http://www.repeatmasker.org); conservation metrics such as GERP⁵⁴; and Transcription Factor Binding Sites computed with the Transfac Matrix Database (v7.0) from Biobase (http://gene-regulation.com/pub/databases.html).

PCR-based Deletion Confirmation: The CNVR915.1 deletion was confirmed by genomic DNA quantitative PCR in participant samples with rs893043-AG and GG genotypes and primer walking PCR method was adapted to confirm deletion boundaries based on the presence of a PCR product between close primer pairs. The below primer pair detected a single band PCR product and Sanger sequencing of this amplicon using the same primer pair identified the deletion breakpoints:

• CNV915.1-F: 5’ - AAAGACCTCAAATCAATAGCCTG-3’ (SEQ ID NO: 2)

• CNVR915.1-R: 5’- GGACATTTAGGCTGCTTCTG-3’ (SEQ ID NO: 3)

The precise deletion boundaries were mapped to chr2: 109,310,555- 109,312, 110 (hgl9, 1,556 base pairs deleted). Next, a simple 3-primer PCR was designed to enable rapid deletion typing:

• CNP-F1: 5’-TGTTTGTTGTTAAGGTCTCTATTG-3’ (SEQ ID NO: 4)

• CNP-F2: 5’-ATACAGATGTGCAAATACCTCTTACAG-3’ (SEQ ID NO: 5)

• CNP-R: 5’-AAATGACAGTGGTAATCCTTACCTATC-3’ (SEQ ID NO: 6)

Functional Annotation: We used a broad range of methods to perform functional annotations of the LIMS1 locus. First, we annotated rs893403, all variants in strong linkage disequilibrium with rs893403 (defined by r2>0.8 based on Europeans in 1000 Genomes phase 3), and the

CNVR915.1 deletion region using tissue- specific annotations available through the ENCODE and Roadmap Epigenomes projects^{55 57}, including conservation, chromatin state segmentation, DNAse hypersensitivity sites and protein-DNA binding sites; we used HaploReg2 to manually query individual variants⁵⁸. Because neither ENCODE nor Roadmap includes adult kidney cells, we performed histone tail modification analysis of human kidney proximal tubule cell line (GSE49637) followed by ChromHMM segmentation⁵⁶ to define tubule- specific regulatory elements. Second, we used our recently proposed unsupervised tissue-specific functional scoring method for non-coding variants⁴⁰. This method, called FUN-LDA, is based on the Latent Dirichlet Allocation (LDA) model, a generative probabilistic model widely used in the topic modeling literature that allows joint modeling of data from multiple tissues. Using FUN-LDA, we computed the posterior probability for each variant in the region to be functional separately for 127 different tissues and cell types. Based on FUN-LDA analysis of all Roadmap and ENCODE data, we detected no predicted functional variants within the CNVR915.1 deletion region. In contrast, in the analysis of all variants in LD with rs893403 we detected several variants with high probability of being functional, including rsl0084l99 with high scores across nearly all 127 tissues and types (avg. posterior FUN-LDA probability 1.0).

Testing for eQTL Effects of rs893403: Using Genotype-Tissue Expression (GTEx) data, we tested for significant effect of rs893403 on mRNA levels of all genes located within lMb window of the index SNP. We detected a strong and significant cis-eQTL effects of rs893403 on two genes, LIMS1 and GCC2 across many tissues. Because GTEx does not include kidney tissue, we next tested rs893403 against transcriptomic data from manually micro-dissected human glomerular and tubulointerstitial compartments in 166 participants of the Nephrotic Syndrome Study Network (NEPTUNE) study^{41, 59, 60}. Briefly, transcript quantification was performed with Affymetrix 2.1 ST arrays; after normalization of expression levels across genes using robust multi-array average (RMA)⁶¹ and derivation of PEER factors as previously described^{62, 63}, rs893403 was tested for eQTL effects with transcripts within a l-Mb window using linear regression under additive genotype coding with adjustments for age, sex, PEER factors and the first 4 principal components of ancestry.

Detection of Anti-LIMSl Alloantibodies by Protein Arrays: Sera were screened against 9,114 human proteins displayed on the Human Protein Microarray (ProtoArray®, Invitrogen) according to manufacturer's instructions. Serum was diluted at 1:250. The reactivity of the serum to proteins on the ProtoArray were detected using Alexa Fluor 647 goat anti-human IgG antibody, and Alexa Fluor® 647-labeled anti-V5 antibody was used to detect the control protein gradients that are printed on each of the sub-arrays. The arrays were read on a Molecular Devices GenePix 4000B scanner and analyzed using Genepix Pro7 software and protein prospector.

Standard pre-processing was applied to the microarrays to account for technical variability and protein prospector was used to normalize the samples using a linear model and calculate the M- scores. Z-scores were calculated as the number of standard deviations of the signal derived from the signal of the mean and a Z score >2.5 was considered positive, on the condition this was concurrent with both protein spots on the microarray.

Detection of Anti-LIMSl Alloantibodies by ELISA: LIMS 1 protein (ab 116807) was diluted to 2 pg/l lml in carbonate bicarbonate buffer and coated on an Immulon H2 plate overnight at 4°C. Plates were washed x3 with 200 pl of washing buffer (lxPBS, 0.05% tween) and blocked for two hours with blocking buffer (lxPBS, 0.05% tween, 1% fish gelatin). Plates were washed x3 with 200 mΐ of washing buffer, serum was diluted 1:1000 in washing buffer, 100 mΐ was added per well and incubated for two hours at room temperature. Plates were washed x7 with 200 mΐ of washing buffer, before the addition of the detection antibodies diluted according to

manufacturer’s instructions [anti-Human IgG-HRP (ab97l60) 1:20,000, anti-human IgGl -biotin (Sigma B6775) 1:5,000, anti-human IgG2-biotin (Sigma B3393) 1:20,000, anti-human IgG3- biotin (Sigma B3523) 1:20,000, and anti-human IgG4-biotin (Sigma B3648) 1:20,000]. Plates were washed x7 with 200 mΐ of washing buffer. For the development of the total IgG plates,

TMB peroxidase substrate and Peroxidase substrate Sol B were mixed at a 1:1 ratio at room temperature and 100 mΐ was added to the plate, and the reaction was stopped by adding 100 mΐ of 2M H2S04, left on the bench for 20 minutes before reading at 450 nm. For the IgG subclasses, we added anti-biotin-HRP antibody (abl922l) diluted 1:20000 in lxPBS, 0.05% tween and incubated at room temperature for 1 hour, then the plates were washed x7 with 200 mΐ of washing buffer. To develop the plates TMB peroxidase substrate and Peroxidase substrate Sol B were mixed at a 1:1 ratio and 100 mΐ was added to the plate at room temperature, and the reaction was stopped by adding 100 mΐ of 2M H₂S0₄, left on the bench for 20 minutes before reading at 450 nm. We used two sets of controls; for the first control to ensure that the LIMS1 protein bound to the plate, we used control antibody, mouse anti-LIMSl (LSBio LS-C169391) in a serial dilution, that was developed using anti-mouse IgGl-HRP labeled (ab97240). Our second control was serum taken from normal healthy controls which were non-reactive to LIMS 1. These samples were used as normalization controls between the plates. The reactivity of individual serum samples was measured as a fold-change in OD compared to the average for the normalization controls (Figure 3).

Western Blots: The protein blots were performed using standard procedures - 0.25 ug of reduced LIMS1 protein was run on a 10% SDS-PAGE gel and then transferred to nitrocellulose. The membrane was blocked with fish gelatin, before probed with subject’s plasma (1:1000 dilution). Membranes were washed 5 times in 1 x PBS 0.1% tween, before the addition of the secondary antibody (anti-human IgG, 1:10,000) incubated for 1 hour before washed 5 times in 1 x PBS 0.1% tween, and developed using Immobilon Western HRP substrate Luminol Reagent

(Millipore).

Immunohistochemistry: Tissue antibody staining was performed with the use of mouse monoclonal (IgGl) to human LIMS1 (LSBio LS-C169391) as well as rabbit polyclonal IgG antibody to human GCC2 (Genetex GTX51372) on paraffin-embedded tissues with the use of heat-induced antigen retrieval. The following human tissues were sectioned and examined: kidney, liver, heart, lung, pancreas, and skin.

RNAscope® in Situ Hybridization: RNA in situ hybridization was performed using the

RNAscope® 2.5 HD Duplex (Chromogenic) Detection Kit for Human (Advanced Cell

Diagnostics, Cat. No. 322435) on formalin-fixed paraffin-embedded (FFPE) nephrectomy tissue sections. The 5mM sections were cut from tumor- free regions of the nephrectomy, which underwent 15 minutes of warm ischemia followed by 2 hours of cold ischemia from handling procedures. In situ hybridization was performed according to the manufacturer’s protocol, RNAscope® 2.5 HD Duplex Kit User Manual for FFPE human samples. The following probes were used for both single- and dual-channel detection: Hs-LIMSl-C2 (Cat. No. 546401-C2), Hs- AQP2-C1 (Cat No. 434861), and Hs-SLC26A4-Cl (Cat No. 423311). Bright field images were captured using the Olympus 1X73 Inverted Microscope under low (lOOx) and high (600x) magnifications.

Flow cytometry: Cells were stained using standard flow cytometry protocols. Briefly, for the staining of LIMS1, cells were resuspended in flow buffer (1 x PBS, 2 % FBS and blocked using Human TruStain FcX (BioLegend), before incubation with the anti-LIMSl antibody (LSBio LSC169391, dilution 1:200, manufacturer’s recommendation), and incubated for 40 minutes at 4°C. Cells were washed twice in flow buffer, and incubated with anti-mouse IgGl (Alexa Fluor 488, BioLegend) for 30 minutes at 4°C. Cells were washed twice with flow buffer and fixed using 1% formaldehyde in PBS. Cells were analyzed on an LSR II flow cytometer (BD

Biosciences) and using FCS Express 6 Flow (De Novo Software).

Microscopic Evaluation ofLIMSl Protein in Human Kidney Cells: Human Renal Cortical Epithelial Cells (HRCE, Lonza, catalog no: CC-2554) and HEK-293 cells (ATCC® CRL- 1573™) were grown on to sterile cover slips overnight and were washed x3 in PBS, before being fixed in 4% paraformaldehyde solution (methanol free) for 15 minutes at room temperature.

Cells were washed x3 in PBS, followed by 20 min incubation in PBS, 0.1% Triton xlOO, and washed x3 in PBS. Cells were initially incubated with anti-LIMSl antibody (1:200 dilution in PBS, 1% BSA, manufacturer’s instructions) for 60 minutes, and washed x2 in PBS. Cells were incubated with anti-mouse IgGl (Alexa Fluor 488) and DAPI for 45 minutes, before washed 4 times in PBS. Cells were attached to a slide using vector shield (Vector Labs) and stored at 4oC. Images were taken using Nikon Al confocal microscope at 600x, images were processed using ImageJ (NIH).

Microscopic Evaluation of the effect anti-LIMSl antibodies on kidney cells. Human Renal Cortical Epithelial Cells and HEK-293 cells were grown on to sterile cover slips. Cells were treated with anti-LIMS 1 antibody (3 pg/ml) or a control antibody (mouse IgG, 3 pg/ml) overnight. Both antibodies were filter washed using sterile PBS before use on the cells. Cells were washed x3 in PBS, and fixed in 4% paraformaldehyde solution (methanol free) for 15 minutes at room temperature. Cells were washed x3 in PBS, followed by 20 min incubation in PBS, 0.1% Triton xlOO, and washed x3 in PBS. Cells were stained with phalloidin (Alexa Fluor 594, Invitrogen) and DAPI (BioLegend) for 45 minutes at room temperature, before washed 4 times in PBS. Cells were attached to a slide using vector shield (Vector Labs) and stored at 4oC. Images were taken on an Olympus 1X73 microscope at 600x, images were processed using ImageJ (NIH).

Cytotoxicity Assay: Cell cytotoxicity was measured using the LDH-Cytotoxicity Assay (Abeam) following manufacturer’s instructions. Briefly, HEK-293 cells and HRCE cells and were treated with anti-LIMS 1 antibody (3 pg/ml) or a control antibody (mouse IgG, 3 pg/ml) overnight. Both antibodies were filter washed using sterile PBS before use on the cells. Cell supernatants were removed from the culture plate, centrifuges at 250g to remove cellular debris and used in the LDH-Cytotoxicity Assay. The OD was measured using a Bio-Tek Powerwave XS reader, absorbance of samples was measured at 490 nm and the reference wave length was 630 nm.

The primary outcome in genetic association studies was time-to-first-rejection, defined from the date of transplant to the date of allograft biopsy demonstrating a rejection event, including both Antibody Mediated Rejections (ABMR) and/or Acute Cellular Rejections (ACR). The association of genetic predictors and baseline covariates with the primary outcome was tested using the cause-specific hazards models by treating death and allograft loss as censoring events.

Results

Discovery Phase

To test for the effect of deletion homozygosity on the risk of rejection, we used time-to- event survival analysis under a recessive model for the deletion-tagging alleles. In the Kaplan- Meier analysis, we observed that a single SNP (rs893403) surpassed a Bonferroni-corrected significance threshold and reached a false discovery rate of 0.3%. Kidney transplant recipients who were homozygous for the deletion-tagging allele had an approximately 84% higher risk of rejection than those who did not have this genotype (hazard ratio, 1.84; 95% confidence interval [Cl], 1.35 to 2.50; P=9.8xl0^-5) (Figure 2A). This effect was robust to adjustments for ethnic group (adjusted hazard ratio, 1.80; 95% Cl, 1.32 to 2.45; P=2.0xl0^-4) as well as for age, sex, donation type, HLA-mismatch status, and sensitization risk factors, including history of transplantation, transfusion, and pregnancy (adjusted hazard ratio, 1.83; 95% Cl, 1.31 to 2.53; P=2.9xl0^-4). The top SNP, rs893403, represented a near-perfect tag for a l.5-kb deletion on chromosome 2ql2.3 (CNVR915.1, r²=0.98).

Replication Phase

We genotyped rs893403 in kidney transplant recipients and their matched donors from three international kidney transplant cohorts (the Belfast, TransplantLines, and Torino cohorts). After standard genotype quality control was assessed, there were 2004 donor-recipient pairs available across the three replication cohorts. For each replication cohort, we first applied the same approach as in the discovery phase, using the recipient’s homozygosity for the deletion tagging allele as a predictor in the model of rejection. Despite the diversity of our replication cohorts, we found a direction-consistent effect of the risk genotype in all three cohorts (Table 6), and a combined meta-analysis confirmed that recipients with the risk genotype had a higher risk of acute rejection than those with a nonrisk genotype independently of age, sex, donation type, HLA-mismatch status, and transplantation center (adjusted hazard ratio, 1.41; 95% Cl, 1.14 to 1.74; P=l.6xl0^-3). In the combined stratified analysis of the discovery and replication cohorts, a high-risk genotype conveyed a risk of rejection that was 50% higher than that observed with a nonrisk genotype (adjusted hazard ratio, 1.50; 95% Cl, 1.26 to 1.79; P=5.4xl0^-6). The significance of this association surpassed our prespecified Bonferroni- adjusted threshold (alpha level of l.lxlO^-3).

Subsequently, we used the genetic information from the donor-recipient pairs to test the genomic-collision hypothesis. The risk of genomic collision was defined according to recipient homozygosity in the absence of donor homozygosity. In findings consistent with our hypothesis, the effect estimates became larger after the analysis accounted for donor genotypes within each cohort as directly compared with recipient-only analyses (Table 1). The effect estimates were direction-consistent and similar in magnitude in each of the three replication cohorts, with adjusted hazard ratios of 1.77 (95% Cl, 1.06 to 2.93; P=2.8xl0^-2) in the Belfast cohort, 1.58 (95% Cl, 1.14 to 2.19; P=5.7xl0^-3) in the TransplantLines cohort, and 1.53 (95% Cl, 1.05 to 2.23; P=2.6xl0^-2) in the Torino cohort. In the combined stratified analysis of all the replication data sets, the donor-recipient pairs with the collision genotype had a risk of allograft rejection that was 58% higher than the risk among pairs without the collision genotype, and this effect was significant (adjusted hazard ratio, 1.58; 95% Cl, 1.27 to 1.97; P=5.lxl0^-5) (Figure 2B).

Next, we performed a pooled analysis of all four cohorts, which involved 2709 transplants (in 705 unmatched recipients from the discovery phase and in 2004 donor-recipient pairs from the replication phase). We note that the inclusion in this analysis of the Columbia cohort, which lacked donor genotype data, resulted in an expected predictor misclassification frequency of 1.7% in the combined data set, biasing the result slightly toward the null. Despite this limitation, the collision genotype was associated with the rejection risk, reaching

genomewide significance (hazard ratio, 1.63; 95% Cl, 1.37 to 1.95; P=4.7xl0^-8). The genomic- collision model was superior to recipient-only recessive or additive models (Table 7).

The effect of the collision genotype was robust to multivariate adjustment for age, race, ethnic group, and HLA-mismatch status (adjusted hazard ratio, 1.63; 95% Cl, 1.36 to 1.95;

P=9.4xl0^-8) (Figure 2C and 2D) and was consistent under alternative statistical models (Table 8). The association was driven predominantly by T-cell-mediated rejection, the most common type of rejection, but other rejection types also contributed. This effect was three times as high as the risk due to per- allele HLA mismatch in the same model (adjusted hazard ratio, 1.21; 95% Cl, 1.16 to 1.28; P=l.9xl0^-14). Although our study was not adequately powered to test for the association with allograft failure, we observed a non-significantly higher risk of failure in the collision genotype group than in the noncollision genotype group (adjusted hazard ratio, 1.12; 95% Cl, 0.90 to 1.39; P=0.32).

Functional Annotation of the 2ql2.3 Locus

The top SNP, rs893403, resides on chromosome 2ql2.3 in the intronic portion of LIMS1. This gene encodes a protein that is involved in cell adhesion and integrin signaling found in focal adhesion plaques. The risk allele, rs893403-G, is frequent in persons of European and African ancestry but absent in persons of East Asian ancestry and tags a common l.5-kb deletion

(CNVR915.1) that is downstream of LIMS1 (r²=0.98 in the HapMap European population). CNVR915.1 was originally annotated to intersect LOC100288532, a gene in the human reference genome hgl8 that was removed in subsequent releases of the human genome. Our deletion breakpoint mapping and detailed functional annotations of the region indicated that the rs893403-G risk allele was associated with lower messenger RNA (mRNA) expression of LIMS1 and GCC2, the neighboring gene, across multiple GTEx project tissues than was the alternative allele. Furthermore, rs893403 has a direction-consistent cis-eQTL effect on the LIMS1 mRNA level in the kidney tubulointerstitium. (Details are provided in Table 9.)

Using immunohistochemical studies, we confirmed that LIMS1 was strongly expressed in human kidneys and other commonly transplanted organ tissues, such as heart and lung. Within the kidney, LIMS 1 staining is strongest in the distal nephron, including the basolateral surface of distal tubules in the cortex and medulla, the medullary thick ascending limb of the loop of Henle, and medullary collecting ducts. The proximal-to-distal gradient of LIMS 1 expression was consistent with human kidney single nuclear RNA sequencing data and was confirmed by means of RNAscope in situ hybridization. Moreover, cell-surface LIMS 1 was induced by hypoxia in HEK293 cell lines, a finding consistent with previously reported hypoxia- induced LIMS1 gene expression in cultured endothelial cells.²⁵ In contrast, GCC2 was detected predominantly in the cytoplasmic compartment of proximal tubules (most strongly in S3) and in kidney vascular smooth muscle. (Details are provided in Table 11.)

Seroreactivity against LIMS1 in Recipients with High-Risk Genotype

To obtain an unbiased characterization of alloantibody response in kidney recipients with a high-risk genotype, we used ProtoArray protein arrays to screen serum specimens that had been obtained from recipients. These protein arrays capture the human proteome with 9375 immobilized recombinant human proteins. We tested serum specimens that had been obtained from 16 recipients, including 8 persons with rejection (4 recipients with a high-risk genotype and 4 with a low-risk genotype) and 8 controls who had not had rejection. The seroreactivity to proteins was detected with antihuman IgG as an increased intensity normalized to control protein gradients printed on each array. Despite the small sample size in this experiment, the LIMS 1 protein ranked l4th among the 9375 proteins (0.l5th percentile) on the array according to the mean intensity in the group of recipients with a high-risk genotype who had allograft rejection. Among the 14 top intensity signals, LIMS1 seroreactivity was most specific to the high-risk rejection group (P=0.002 for high-risk genotype with rejection vs. all other groups) (Figure 3 A and 3B). No other proteins that were encoded within a l-Mb window of rs893403 showed significant seroreactivity, although the GCC2 protein was not captured on protein arrays. Finally, we detected a lower-intensity signal for the LIMS2 protein, which shares 92% sequence identity with LIMS1,²⁶ thus suggesting potential cross-reactivity (P=0.007).

To confirm the protein array findings, we obtained serum specimens from 318 transplant recipients across seven genotype- and phenotype-discordant groups. We detected highly specific IgG reactivity toward LIMS 1 in the kidney recipients with a high-risk genotype who had allograft rejection but not in any other control group (Figure 3C). Next, we investigated whether an antibody targeting LIMS1 could be injurious. Treatment of cultured human kidney cortical epithelial cells expressing the LIMS1 protein with mouse antihuman LIMS1 antibody disrupted the normal organization of F-actin filaments and showed significant cytotoxicity on lactate dehydrogenase assay as compared with a nonspecific control antibody.

IgG subclass analysis showed that the anti-LIMSl response was predominantly of IgG2 and IgG3 subtype, but weaker IgG4 reactivity was also detected (Figure 3D-3G, and Figure 4). Of 31 recipients with a high-risk genotype who had rejection, 26 (84%) and 23 (74%) tested positive for anti-LIMSl reactivity by IgG2 and IgG3, respectively. Overall, 29 recipients (94%) tested positive by either IgG2 or IgG3 subtype. IgG2 and IgG3 comprise only a small percentage of total IgG (up to 30% and 8%, respectively), and there were no detectable differences between groups in levels of IgGl, the main IgG subclass, which explains why total IgG had overall lower reactivity than these subtypes.

Genomic Annotation of the 2ql2.3 Locus: The top SNP, rs893403, was initially selected as it tags a common l.5-kb deletion CNVR915.1 downstream of the LIMS1 gene (r2=0.98 in the CEU population). CNVR915.1 was originally annotated to intersect LOC100288532, a pseudogene in hgl8 that was removed in subsequent releases of the human genome. Using pooled sequencing read data for the European participants of the 1000 Genomes Project, we first re-mapped the precise breakpoints of this deletion. We next interrogated the deleted sequence for presence of microRNAs, pseudogenes, or regulatory elements. We confirmed the presence of several repeated elements as well as a single conserved retroposed gene ( retro-COX7B ), but no other coding elements. Additionally, interrogation of the ENCODE and Roadmap datasets revealed no obvious regulatory elements within the region. Because neither one of these datasets includes adult kidney cells, we also performed histone tail modification analysis of human kidney proximal tubule cell line that suggested the possibility of a weak tubule- specific enhancer spanning across this region, but no other functional segments. Using our new tissue- specific FUN-LDA scoring method based on 127 tissues and cell types⁴⁰, we performed analyses of the deleted sequence and all known variants in LD (r2>0.8) with rs893403. We did not detect any potentially functional variants within the deletion region by FUN-LDA. However, there were several potentially functional variants outside of the deletion region (Table 10), including rsl0084l99, which resides within the LIMS1 transcription start site and has FUN-LDA posterior probability 1.0 across all 127 tissues.

Analysis ofeQTL Effects: We next tested for expression QTL effects of rs893403. Although kidney tissue eQTL data is not available in Genotype-Tissue Expression (GTEx) project, data supported strong effect of rs893403 on the mRNA levels of LIMS1 and nearby GCC2 gene across multiple tissues. In each case, the risk allele was associated with lower mRNA levels for both genes. To test for a similar effect in kidney tissue, we used transcriptomic data from laser- microdissected human kidney tissue compartments of N=l66 NEPTUNE study participants⁴¹.

We detected a direction-consistent cis-eQTL effect with the deletion-tagging allele associated with reduced LIMS1 mRNA levels in the tubulointerstitium (Beta -0.28, P=0.0l4). Notably, LIMS1 transcript represented the most significant association among all genes within l-Mb window of the rs893403 in the tubulointerstitial compartment, including GCC2 (Beta -0.09, P=0.l9). Compared to rs893403, the TSS variant prioritized by our FUN-LDA analysis exhibited a stronger effect on LIMS1 expression in the tubulointerstitium (rsl0084l99, Beta -0.29, P=0.0l2). In summary, our annotations suggest that rs893403 or another variant in LD, such as rsl0084l99, is associated with gene expression of LIMS1 in human kidney tubules and other organs.

Induction of in vitro Cytotoxicity in Human Kidney Cells by Anti-LIMSl Antibodies: By immunofluorescence staining, we demonstrated that LIMS 1 protein is present in both HEK-293 cell line and primary human renal cortical epithelial (HRCE) cells. Overnight culture of the HRCE cells with the mouse anti-human LIMS 1 antibodies disrupted the normal arrangement of actin filaments in the epithelial cells compared to the control antibody and was cytotoxic to kidney epithelial cells compared to the control antibody as determined by measuring LDH in the supernatant (8.2 + 3.1% compared to the control antibody 1.3 + 0.3%, p < 0.01). Similar cytotoxic effect was also observed in HEK-293 cells (8.7 + 2.6% compared to the control antibody 3.2 + 1.2%, p < 0.01). Cell Surface Detection ofLIMSI Protein in vitro under Hypoxic Conditions: Previous report demonstrated that LIMS1 mRNA expression was upregulated by hypoxia in arterial endothelial cells⁴². We hypothesized that under similar conditions, we would be able to detect the LIMS1 protein on the cell surface in cultured kidney cells. Following culture of the cells in hypoxic conditions, there was a significant increase in Mean Florescent Intensity (MFI) of LIMS 1 measured by Flow Cytometry on the surface of the HEK-293 cells (isotype control MFI 585, normal culture conditions MFI 695 + 52, hypoxic conditions MFI 978 + 17, P O.Ol) compared to the control conditions. This demonstrates LIMS1 protein was detected on the cell membranes under hypoxic conditions. Under normal conditions, LIMS1 appears to be absent on the cell surface as there was not a significant increase in the specific LIMS 1 MFI compared to the isotype control. A similar trend was observed for HRCE cells, although not statistically significant.

Discussion

In this study, we examined a genomic-collision scenario in which an allograft recipient was homozygous for a deletion polymorphism and received a kidney allograft from a donor who had at least one normal allele. In the analysis of four large kidney transplant cohorts, we found that the genomic collision at chromosome 2ql2.3 led to a risk of rejection that was nearly 60% higher than the risk among donor-recipient pairs with noncollision genotypes. The risk associated with the collision genotype is equivalent to a mismatch of three of six HLA alleles, which is both clinically significant and potentially modifiable by genetic testing and matching. The genomic collision at chromosome 2ql2.3 would be expected to occur in approximately 12 to 15% of transplants from unrelated donors among persons of European and African ancestry but would be very rare among persons of East Asian ancestry.

In our study, the collision genotype was associated with the presence of anti-LIMS 1 antibodies. We also found that the risk genotype was associated with a lower kidney mRNA level of LIMS1 and that LIMS 1 protein was induced on the cell surface under hypoxic conditions. The recessive model potentially supports a loss-of-function effect, and our data point to LIMS1 as the most likely culprit gene, but the precise causal variant underlying this locus is still unclear. We note that rs893403 also regulates mRNA expression of GCC2, encoding a protein of unclear function. We found that the GCC2 protein is expressed in proximal tubule cells. Further genotype- specific analysis of expression patterns of genes within the LIMS1 locus may be useful, especially in the context of hypoxic injury and other forms of injury.

Several other lines of evidence suggest that genomic incompatibilities beyond the traditional ABO and HLA loci are predictive of allograft rejection. For example, female recipients of organs from male donors are at greater risk for poor graft outcomes, probably because of sensitization to minor histocompatibility antigens encoded on the Y chromosome.^{27 31} The occurrence of aggressive post-transplantation antiglomerular basement membrane disease in persons affected by the Alport syndrome owing to collagen IV mutations, including loss-of- function variants, exemplifies another proof of concept for this phenomenon.^32,33

Taken together, our results provide support for genomic collision at chromosome 2ql2.3 contributing to the risk of allograft rejection and point to LIMS1 as a potential minor

histocompatibility antigen encoded by this locus. In addition, we found that the LIMS 1 protein was expressed in other commonly transplanted tissues, such as the heart and lung, but follow-up studies will be useful in determining whether our findings are generalizable to other organs. The reverse of our hypothesis has previously been tested in the context of bone marrow

transplantation: the immune system of a donor who was homozygous for a gene-disrupting deletion may recognize epitopes that are encoded by that gene in the tissues of a recipient, leading to graft-versus-host disease.²² A similar mechanism may also apply to other types of variants that were not examined in this study, such as loss-of-function variants, variants altering the expression of immunogenic proteins, or missense variants that create new immunogenic epitopes. A population-based sequencing study has shown that a large proportion of persons are natural“human gene knockouts” (i.e., have two copies of loss-of-function variants in the same gene) for a number of nonessential genes.³⁴ Given our hypothesis, we speculate that such persons may be at risk for rejection if they receive an allograft expressing an intact protein.

To our knowledge, the LIMS1 locus has not been detected in previous genome- wide association studies of kidney transplant rejection. We suspect that this is probably due to the limited sample size of earlier studies and to the fact that limited research has been done in testing the genomic-collision model. Previous studies have involved recipient-only analyses or a standard additive genotype coding scheme.^{35 37} On the basis of the observed minor allele frequency and the pooled additive effect estimate for the rs893403-G allele in our cohorts, we estimated that our study would have no more than 3.5% power to detect this locus at a Bonferroni-corrected alpha level under additive coding in our discovery cohort. We also estimated that in a recipient-only genome- wide association study under an additive model, a minimum of 13,000 kidney transplant recipients would need to be enrolled for the study to have 80% power for detection of this locus at a genome- wide significant alpha level of

5xl0^-8 (assuming a minor allele frequency of 0.50 and a rejection rate of 33%). Although large- scale efforts in genome- wide association studies of kidney transplantation are under way,^38,39 the largest discovery study of allograft rejection that we are aware of involved only 2094 kidney transplants.³⁷ It remains to be seen whether the combination of genomic profiling with proteome- wide antibody screens can be used effectively to uncover new histocompatibility antigens and potentially improve the precision of organ matching.

Glossary:

• Single Nucleotide Variant (SNV): genetic variation at a single base pair position in the genome; usually involves a substitution of one base pair for another.

• Single Nucleotide Polymorphism (SNP): Refers to SNV that is common in a given population, conventionally defined by a population frequency greater than 1%.

• Copy Number Variant (CNV): is a phenomenon in which a segment of the genome is either missing (deletion) or is repeated (duplication); the number of copies (segment repeats) can also vary between individuals.

• Copy Number Polymorphism (CNP): refers to a CNV that is common in a given population, conventionally defined by a population frequency greater than 1%.

• Deletion Polymorphism: refers to a deletion type of CNP; i.e. common deletion of a genomic segment with population frequency greater than 1%.

• Expression Quantitative Trait Locus (eQTL): a genetic variant that is associated with mRNA expression levels; cis-eQTLs (or local eQTLs) are genetic variants associated with transcript levels of nearby genes; trans-eQTLs (or distant eQTLs) are associated with transcript levels of distant genes (e.g. located on a different chromosome). Table 1. Cox Proportional-Hazards Association Analysis of LIMS1 Collision Genotype with Allograft Rejection in a Time-to-Event Analysis, According to Cohort.

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* The analysis was adjusted for cohort only (if applicable).

**The analysis was adjusted for recipient’s age, sex, race, ethnic group, HLA-mismatch status, and cohort (if applicable).

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Table 2. The study power

The power for the discovery phase was calculated for a range of expected effect sizes (HR 1.50-2.00) and MAFs (from 10% to n

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50%), assuming a recessive model, a perfectly tagged causal variant, average rejection rate of 35%, and a nominal replication

m threshold a=0.05 used for selection of markers for replication. The power for the joint analysis of discovery and replication was O

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10 calculated using the Bonferroni-corrected significance threshold (a=l.1x10-3), other assumptions as above. Only polymorphisms with 05

50 MAF>l0% were selected for genotyping based on these calculations. 50

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Table 3. Baseline characteristics of the Columbia Discovery Cohort: N=705 kidney transplant recipients recruited to the Columbia

CKD Bio-bank in the years 2008-2012.

Cohort Characteristics _ N (%)

Ethnicity (self-report):

White (%) 336 (47.7%)

Black (%) 115 (16.3%)

Hispanic (%) 200 (28.4%) O

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East Asian (%) 40 (5.7%) n

Male-to-Female ratio: 1.5 O

Mean Age (range): 46 (5-84) O

Mean follow-up time (years): 8.6 05

High PRA (%): 79 (11.3%) 50

50

Family History of Renal Disease (%) 163 (24.7%)

Biopsy Diagnosis of Rejection (%) 234 (33.2%)

Borderline (%) 68 (9.6%)

T Cell Mediated Rejection (TCMR) (%) 138 (19.6%) O Grade IA (%) 62 (8.7%) O

Grade IB (%) 47 (6.6%) Ό

Grade IIA (%) 22 (3.1%)

Grade IIB (%) 4 (0.6%) os

Grade III (%) 3 (0.4%)

Antibody Mediated Rejection (ABMR) 24 (3.4%)

(%)

Chronic Active ABMR (%) 2 (0.3%)

History of Pregnancies (%) 196 (28.2%)

History of Transfusions (%) 304 (47.2%)

as Previous Transplants (%) 129 (18.5%)

Donor Status:

Living Related Donor (%) 273 (39.1%)

Living Unrelated Donor (%) 121 (17.3%)

Deceased Donor (%) 304 (43.6%)

Primary Diagnosis:

Diabetic Nephropathy 123 (17.4%)

Hypertensive Nephropathy 97 (13.7%)

Glomerulonephritis 255 (36.2%)

Kidney Malformations 31 (4.4%)

Cystic Diseases 73 (10.4%)

Other 60 (8.5%)

Unknown 66 (9.4%) n

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Table 4. The deletions and their tag-SNPs genotyped in the discovery cohort

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* R2 estimated based on Europeans in HapMap; # deletion length in base pairs (bp).

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5 Table 5. Baseline Clinical Characteristics of the Replication Cohorts

Cohort characteristics TransplantLines Belfast Cohort Torino Cohort (N=784)

Cohort (N=833) (N=387)

Mean Age (range) 47 (15-74) 41 (2-76) 49 (12-76)

Male-to-Female ratio 1.4 1.7 1.9

Mean follow-up time (years) 6.6 years 9.2 years 9.6 years

White/European Race 833 (100%) 387 (100%) 784 (100%)

Maximum PRA >30% (%) 111 (13.4%) 50 (13.0%) 107 (13.6%)

Biopsy Diagnosis of Rejection (%) 300 (36.0%) 92 (23.8%) 174 (22.2%)

n History of Pregnancies (%) NA 89 (23.0%) 165 (21.0%) H

History of Transfusions (%) NA NA 399 (50.9%) tno o Previous Transplants (%) 94 (10.5%) NA 46 (5.9%)

Donor Status: o

Deceased Donor (%) 678 (81.4%) 387 (100%) 784 (100%)

Living Donor (%) 155 (18.6%) 0 (0%) 0 (0%)

Primary Diagnosis:

Diabetic Nephropathy 28 (3%) NA 34 (4%)

Hypertensive Nephropathy 78 (9%) NA 47 (6%) O

Glomerulonephritis 192 (23%) NA 286 (36%) O

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Kidney Malformations 19 (2%) NA 25 (3%)

Cystic Diseases 138 (17%) NA 141 (18%)

Other 275 (33%) NA 172 (22%)

Unknown 103 (12%) NA 79 (10%)

Risk Genotypes:

Recipient rs893403 G allele frequency 0.402 0.390 0.450

Donor rs893403 G allele frequency 0.406 0.390 0.459

Recipient rs893403 GG genotype frequency 0.152 0.176 0.188

Donor-Recipient rs893403 collision frequency 0.120 0.142 0.149

Table 6. Recipients-only Cox proportional hazards association analysis of LIMS1 risk genotype with time-to-first-rejection in the

discovery, replication, and all cohorts combined.

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* adjusted for cohort only (if applicable)

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Table 7. Comparison of alternative genetic models demonstrates the best fit of the“genomic collision” model. Note: A small number DR pairs with donors homozygous for rs893403-G but missing recipient genotype were classified as non-risk in the main study, but

o were excluded from this analysis in order to make the models comparable, i.e. all three models have identical number of observations,

facilitating direct comparisons of statistical metrics of goodness-of-fit.

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5 * All cohorts combined, excluding any transplant with missing recipient genotype and adjusted for cohort only

^L Genomic collision risk coded as recipient homozygosity for rs893403-G in the absence of donor homozygosity

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Table 8. Comparison of alternative tests for association of collision genotype with rejection: non-parametric (log-rank) and logistic - 10 regression-based statistical tests produce comparable results to Cox models.

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Table 9. Frequency of rs893403-G risk allele in the 1000 Genomes Populations.

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Table 10. ANNOVAR annotation of all variants in LD (r2>0.8) with rs893403.

CHR BP SNPs Functonal Gene Annot n

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2 109310556 BI_GS_DEL 1 _B 5_P0360_299 intergenic LIMSl(dist=6854),RANBP2(dist=2538l) n

2 109138677 rs2258404 ncRNA_intronic GCC2- AS 1 o

2 109139209 rs2718694 ncRNA_intronic GCC2- AS 1 o

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109316789 rs7575335 intergenic LIMS 1 (dist= 13087),RANBP2(dist= 19148) so so so

2 109066344 rs2460944 intronic GCC2

2 109066799 rs 1474220 intronic GCC2

2 109067975 rs2683798 intronic GCC2

2 109069008 rs2683797 intronic GCC2

2 109070634 rs2718749 intronic GCC2

2 109071433 rs3083l73 intronic GCC2

2 109076377 rs2049l5l intronic GCC2

2 109076457 rs2049l50 intronic GCC2

2 109076856 rs2l39807 intronic GCC2

2 109078616 rs9789664 intronic GCC2

2 109079902 rs25776l2 intronic GCC2

2 109088868 rsl898557 intronic GCC2

2 109089404 rs2243888 intronic GCC2

2 109090111 rs2718704 intronic GCC2

2 109095886 rs25776l6 intronic GCC2

2 109099477 rs25776l3 intronic GCC2

2 109099865 rs 1542025 intronic GCC2

2 109104464 rsl0l844l7 intronic GCC2

2 109107041 rs579l9394 intronic GCC2

2 109114691 rs2378l49 intronic GCC2

2 109116846 rs2003996 intronic GCC2

2 109118132 rs2718761 intronic GCC2

2 109120418 rs243825l intronic GCC2

2 109122204 rs30983l9 intronic GCC2

2 109217351 2: 109217351 :TCTCTTC:T intronic LIMS1

2 109217358 2:l092l7358:TCTTC:T intronic LIMS1

2 109260044 rs70956267 intronic LIMS1

2 109065858 rs2460947 intronic GCC2

2 109310555 rs 10202224 intergenic LIMSl(dist=6853),RANBP2(dist=25382) 2 109310556 BI_GS_DEL 1 _B 5_P0360_299 intergenic LIMSl(dist=6854),RANBP2(dist=2538l)

2 109102534 rs2577586 intronic GCC2

2 109108460 rs2577599 intronic GCC2

2 109112655 rs2917988 intronic GCC2

2 109088996 rs2577622 intronic GCC2

2 109117975 rs2917983 intronic GCC2

2 109128310 rsl 11963733 ncRNA_intronic GCC2- AS 1 2 109038026 rs 13022595 intergenic SULT 1 C4(dist=32600),GCC2(dist=27551) 2 109042040 rs59274398 intergenic SULTlC4(dist=36614),GCC2(dist=23537) 2 109042493 rsl 829601 intergenic SULTlC4(dist=37067),GCC2(dist=23084) 2 109042578 rsl 829599 intergenic SULTlC4(dist=37152),GCC2(dist=22999) 2 109043961 rs4271731 intergenic SULT lC4(dist=38535),GCC2(dist=21616) 2 109044008 rs 1915487 intergenic SULT lC4(dist=38582),GCC2(dist=21569) 2 109047386 rsl0170784 intergenic SULT 1 C4(dist=41960),GCC2(dist= 18191)

OO

2 109049305 rs35256991 intergenic SULT 1 C4(dist=43879),GCC2(dist= 16272) 2 109058119 rs2683808 intergenic SULTlC4(dist=52693),GCC2(dist=7458) 2 109059760 rs2718759 intergenic SULT 1 C4(dist=54334) ,GCC2(dist=5817) 2 109060047 rs2718758 intergenic SULT 1 C4(dist=54621 ) ,GCC2(dist=5530) 2 109060094 rs2176959 intergenic SULTlC4(dist=54668),GCC2(dist=5483) 2 109060227 rs2139811 intergenic SULTlC4(dist=54801),GCC2(dist=5350) 2 109062290 rsl 464406 intergenic SULT 1 C4(dist=56864) ,GCC2(dist=3287) 2 109128129 rs62148145 ncRNA_intronic GCC2- AS 1 2 109048145 rs 147976738 intergenic SULTlC4(dist=42719),GCC2(dist=17432) 2 109097214 rs35817047 intronic GCC2

2 109119111 rs2438253 intronic GCC2

2 109119112 rs2438252 intronic GCC2

2 109057701 rs59698498 intergenic SULT 1 C4(dist=52275) ,GCC2(dist=7876) 2 109128319 rsl 12503499 ncRNA_intronic GCC2-AS1 2 109127984 rs62148112 ncRNA_intronic GCC2- AS 1 2 109128217 rs200858383 ncRNA_intronic GCC2- AS 1

2 109312728 rs760662l intergenic LIMSl(dist=9026),RANBP2(dist=23209) 2 109128296 rs73954373 ncRNA_intronic GCC2- AS 1

2 109128231 rs201904559 ncRNA_intronic GCC2- AS 1

2 109128260 rs73954372 ncRNA_intronic GCC2- AS 1

2 109239869 rs826688 intronic LIMS1

2 109045638 rs2l39809 intergenic SULT 1 C4(dist=40212),GCC2(dist= 19939) 2 109048620 rs 147362369 intergenic SULT lC4(dist=43194),GCC2(dist= 16957) 2 109057011 rs35997658 intergenic SULTlC4(dist=5l585),GCC2(dist=8566) 2 109217352 rsl 1123709 intronic LIMS1

2 109084523 rs55803l50 intronic GCC2

2 109305944 rs865444 intergenic LIMSl(dist=2242),RANBP2(dist=29993) 2 109305465 rs376l36l63 intergenic LIMSl(dist=l763),RANBP2(dist=30472) 2 109145808 rs2118446 ncRNA_intronic GCC2- AS 1

2 109134387 rs246595l ncRNA_intronic GCC2- AS 1

2 109150714 rs 10084199 upstream GCC2- AS 1 ,LIMS 1 (dist=97) 2 109170306 rs 1469966 intronic LIMS1

2 109062693 rs2718755 intergenic SULT 1 C4(dist=57267) ,GCC2(dist=2884) 2 109064563 rsl 1123694 intergenic SULT 1 C4(dist=59137) ,GCC2(dist= 1014) 2 109128632 rs2718764 ncRNA_intronic GCC2- AS 1

2 109042679 rsl829598 intergenic SULTlC4(dist=37253),GCC2(dist=22898) 2 109045009 rs7596l99 intergenic SULTlC4(dist=39583),GCC2(dist=20568) 2 109049182 rs 10200997 intergenic SULTlC4(dist=43756),GCC2(dist=l6395) 2 109060980 rs2683806 intergenic SULT 1 C4(dist=55554) ,GCC2(dist=4597) 2 109131824 rs2953739 ncRNA_intronic GCC2- AS 1

2 109131828 rs2917971 ncRNA_intronic GCC2- AS 1

2 109050406 rs 140686654 intergenic SULT 1 C4(dist=44980),GCC2(dist= 15171) 2 109166974 rs 10084394 intronic LIMS1

Table 11. Immunohistochemistry staining patterns for LIMS1 and GCC2 in human kidney tissue compartments. Supportive imaging data provided in Figures S3 and S4.

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* basolateral subcellular staining pattern

** punctate cytoplasmic subcellular distribution, strongest in S3 segment of the proximal tubule

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Example 2 Anti-LIMSl Antibodies Serological Test

This test can be used to detect circulating anti-LIMS 1 antibodies in sera of kidney transplant recipients. Positive test indicates the presence of anti-LIMS 1 humoral response that is associated with kidney allograft rejection risk.

Materials:

Recombinant LIMS1 protein (Abeam abl 16807), Immulon H-2B ELISA plates, Anti-human IgG antibody-HRP (Abeam ab97l60), Anti-human IgGl antibody-Biotin (Sigma B6775), Anti human IgG2 antibody-Biotin (Sigma B3398), Anti-human IgG3 antibody-Biotin (Sigma B3523), Anti-human IgG4 antibody-Biotin (Sigma B3648), Anti-LIMSl antibody-HRP (LSBio

C169391), Anti-Mouse IgGl-HRP (Abeam ab 97240), Anti-biotin antibody-HRP (Abeam abl 9221), Spectrphotometer plate reader (read at 450 nm).

Protocol:

1: Prepare a lx solution of carbonate-bicarbonate buffer.

2: Dilute 2.2 pg of LIMS 1 protein into 11 ml solution of lx carbonate-bicarbonate buffer.

3: Add 100 pl of the diluted protein to each ELISA well.

4: Incubate the LIMS1 coated ELISA plates overnight in a 4°C refrigerator.

5: Wash plates three times (3x) with 250 pl of wash buffer (1 x PBS, 0.05% tween), ensure all the liquid is removed from each of the wells.

6: Block plates for 2 hours with 250 pl of blocking buffer (1 x PBS, 0.05% tween, 1% fish gelatin) and incubate at room temp on the bench. Cover plate.

7: Wash plates three times (3x) with 250 pl of wash buffer, ensure all the liquid is removed from each of the wells.

8a: Dilute serum to 1:1000 with the blocking buffer, and add 100 pl of the diluted serum to each well. This is done in duplicates or triplicates. Cover plate and incubate at room temperature for 2 hours. 8b: Using the mouse derived LIMS1 antibody, generate serial dilutions of this control antibody starting at 1:2000 (through 1: 128,000) leaving the last well blank (no antibody). Add 100 pl of the diluted control antibody to each well, cover plate and incubate at room temperature for 2 hours. 8c: Use serum from a minimum of 5 healthy controls, diluted to 1: 1000 with blocking buffer, and add 100 pl of the diluted serum to each well. This is done in duplicates or triplicates. Cover plate and incubate at room temperature for 2 hours. 9: Wash plates ten times (10 x) with 250 mΐ of wash buffer (1 x PBS, 0.05% tween), ensure all the liquid is removed from each of the wells.

For measurement of total IgG:

10: Dilute the anti-human IgG-HRP 1: 20000 using lxPBS, 0.05% tween. Dilute the anti-mouse IgGl-HRP (for LIMS1 control) 1: 20000 using lxPBS, 0.05% tween. Add 100 mΐ to each well. Cover plate and incubate at room temperature for 2 hours.

11: Wash plates ten times (10 x) with 250 mΐ of wash buffer (1 x PBS, 0.05% tween), ensure all the liquid is removed from each of the wells.

12: Mix the TMB peroxidase substrate and Peroxidase substrate Sol B at a 1:1 ratio.

13: Add 100 mΐ of the developing solution to the plate, and incubate at room temperature until the plate develops.

14: Stop the reaction by adding 100 mΐ of 2M H₂S04, leave on the benchtop rotator for 20 minutes before reading

15: Read ELISA plate at 450 nm using a spectrophotometer plate reader.

Calculations:

A positive result is calculated by the OD of the sample that is above the average OD of the healthy control serum plus 3 times the standard deviation.

References

1. Magee JC, Barr ML, Basadonna GP, et al. Repeat organ transplantation in the United States, 1996-2005. Am J Transplant 2007; 7: 1424-33.

2. Sellares J, de Lreitas DG, Mengel M, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant 2012; 12: 388-99.

3. Opelz G. Non-HLA transplantation immunity revealed by lymphocytotoxic antibodies. Lancet 2005; 365: 1570-6.

4. Lachmann N, Terasaki PI, Budde K, et al. Anti-human leukocyte antigen and donor- specific antibodies detected by Luminex posttransplant serve as biomarkers for chronic rejection of renal allografts. Transplantation 2009; 87: 1505-13. 5. Eng HS, Bennett G, Chang SH, et al. Donor human leukocyte antigen specific antibodies predict development and define prognosis in transplant glomerulopathy. Hum Immunol 2011; 72: 386-91.

6. Willicombe M, Brookes P, Sergeant R, et al. De novo DQ donor- specific antibodies are associated with a significant risk of antibody-mediated rejection and transplant glomerulopathy. Transplantation 2012; 94: 172-7.

7. Everly MJ, Rebellato LM, Haisch CE, et al. Incidence and impact of de novo donor- specific alloantibody in primary renal allografts. Transplantation 2013; 95:410-7.

8. Hourmant M, Cesbron-Gautier A, Terasaki PI, et al. Frequency and clinical implications of development of donor- specific and non-donor- specific HLA antibodies after kidney

transplantation. J Am Soc Nephrol 2005; 16: 2804-12.

9. Mohan S, Palanisamy A, Tsapepas D, et al. Donor- specific antibodies adversely affect kidney allograft outcomes. J Am Soc Nephrol 2012; 23: 2061-71.

10. Loupy A, Jordan SC. Transplantation: donor- specific HLA antibodies and renal allograft failure. Nat Rev Nephrol 2013; 9: 130-1.

11. Joosten SA, van Kooten C. Non-HLA humoral immunity and chronic kidney graft loss. Lancet 2005; 365: 1522-3.

12. Terasaki PI. Deduction of the fraction of immunologic and non-immunologic failure in cadaver donor transplants. Clin Transpl 2003; 449-52.

13. Joosten SA, Sijpkens YW, van Ham V, et al. Antibody response against the glomerular basement membrane protein agrin in patients with transplant glomerulopathy. Am J Transplant 2005; 5: 383-93.

14. Dragun D, Catar R, Philippe A. Non-HLA antibodies against endothelial targets bridging allo- and autoimmunity. Kidney Int 2016; 90: 280-8.

15. Amico P, Honger G, Bielmann D, et al. Incidence and prediction of early antibody-mediated rejection due to non-human leukocyte antigen-antibodies. Transplantation 2008; 85: 1557-63.

16. Sumitran-Holgersson S, Wilczek HE, Holgersson J, Soderstrom K. Identification of the nonclassical HLA molecules, mica, as targets for humoral immunity associated with irreversible rejection of kidney allografts. Transplantation 2002; 74: 268-77. 17. Li L, Sigdel T, Vitalone M, Lee SH, Sarwal M. Differential immunogenicity and clinical relevance of kidney compartment specific antigens after renal transplantation. J Proteome Res 2010; 9: 6715-21.

18. Hess SM, Young EF, Miller KR, et al. Deletion of naive T cells recognizing the minor histocompatibility antigen HY with toxin-coupled peptide-MHC class I tetramers inhibits cognate CTL responses and alters immunodominance. Transpl Immunol 2013; 29: 138-45.

19. Dragun D, Miiller DN, Hinrich Brasen J, et al. Angiotensin II type l-receptor activating antibodies in renal-allograft rejection. N Engl J Med 2005; 352: 558-69.

20. Banasik M, Boratyhska M, Koscielska-Kasprzak K, et al. Non-HLA antibodies: angiotensin II type 1 receptor (anti- AT 1R) and endothelin-l type A receptor (anti-ETAR) are associated with renal allograft injury and graft loss. Transplant Proc 2014; 46: 2618-21.

21. Jackson AM, Sigdel TK, Delville M, et al. Endothelial cell antibodies associated with novel targets and increased rejection. J Am Soc Nephrol 2015; 26: 1161-71.

22. McCarroll SA, Bradner JE, Turpeinen H, et al. Donor-recipient mismatch for common gene deletion polymorphisms in graft-versus-host disease. Nat Genet 2009; 41: 1341-4.

23. Conrad DF, Pinto D, Redon R, et al. Origins and functional impact of copy number variation in the human genome. Nature 2010; 464: 704-12.

24. Craddock N, Hurles ME, Cardin N, et al. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature 2010; 464: 713-20.

25. Manalo DJ, Rowan A, Lavoie T, et al. Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-l. Blood 2005; 105: 659-69.

26. Braun A, Bordoy R, Stanchi F, et al. PINCH2 is a new five LIM domain protein, homologous to PINCH and localized to focal adhesions. Exp Cell Res 2003; 284: 239-50.

27. Candinas D, Gunson BK, Nightingale P, Hubscher S, McMaster P, Neuberger JM. Sex mismatch as a risk factor for chronic rejection of liver allografts. Lancet 1995; 346: 1117-21.

28. Prendergast TW, Furukawa S, Beyer AJ III, Browne BJ, Eisen HJ, Jeevanandam V. The role of gender in heart transplantation. Ann Thorac Surg 1998; 65: 88-94.

29. Roberts DH, Wain JC, Chang Y, Ginns LC. Donor-recipient gender mismatch in lung transplantation: impact on obliterative bronchiolitis and survival. J Heart Lung Transplant 2004; 23: 1252-9. 30. Gratwohl A, Dohler B, Stem M, Opelz G. H-Y as a minor histocompatibility antigen in kidney transplantation: a retrospective cohort study. Lancet 2008; 372: 49-53.

31. Kim SJ, Gill JS. H-Y incompatibility predicts short-term outcomes for kidney transplant recipients. J Am Soc Nephrol 2009; 20: 2025-33.

32. Kashtan CE. Renal transplantation in patients with Alport syndrome. Pediatr Transplant 2006; 10: 651-7.

33. Pedchenko V, Bondar O, Fogo AB, et al. Molecular architecture of the Goodpasture autoantigen in anti-GBM nephritis. N Engl J Med 2010; 363: 343-54.

34. Sulem P, Helgason H, Oddson A, et al. Identification of a large set of rare complete human knockouts. Nat Genet 2015; 47: 448-52.

35. Ghisdal L, Baron C, Lebranchu Y, et al. Genome-wide association study of acute renal graft rejection. Am J Transplant 2017; 17: 201-9.

36. O’Brien RP, Phelan PJ, Conroy J, et al. A genome-wide association study of recipient genotype and medium-term kidney allograft function. Clin Transplant 2013; 27: 379-87.

37. Hemandez-Fuentes MP, Franklin C, Rebollo-Mesa I, et al. Long- and short-term outcomes in renal allografts with deceased donors: a large recipient and donor genome- wide association study. Am J Transplant 2018; 18: 1370-9.

38. International Genetics & Translational Research in Transplantation Network (iGeneTRAiN). Design and implementation of the International Genetics and Translational Research in

Transplantation Network. Transplantation 2015; 99: 2401-12.

39. Li YR, van Setten J, Verma SS, et al. Concept and design of a genome-wide association genotyping array tailored for transplantation- specific studies. Genome Med 2015; 7: 90.

40. Backenroth D, He Z, Kiryluk K, et al. FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and

Applications. Am J Hum Genet 2018;102:920-42.

41. Gillies CE, Putler R, Menon R, et al. An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. Am J Hum Genet 2018;103:232-44.

42. Manalo DJ, Rowan A, Lavoie T, et al. Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-l. Blood 2005;105:659-69. 43. Eisenga MF, Gomes-Neto AW, van Londen M, et al. Rationale and design of

TransplantLines: a prospective cohort study and biobank of solid organ transplant recipients.

BMJ Open 20l8;8:e024502.

44. Genomes Project C, Abecasis GR, Auton A, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56-65.

45. Robinson JT, Thorvaldsdottir H, Winckler W, et al. Integrative genomics viewer. Nat Biotechnol 2011;29:24-6.

46. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26:841-2.

47. Fujita PA, Rhead B, Zweig AS, et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res 20l l;39:D876-82.

48. Benson DA, Cavanaugh M, Clark K, et al. GenBank. Nucleic Acids Res 20l3;4l:D36-42.

49. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update.

Nucleic Acids Res 2004;32:D23-6.

50. Lestrade L, Weber MJ. snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res 2006;34:Dl58-62.

51. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res 2008;36:Dl54-8.

52. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997;25:955-64.

53. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005;120:15-20.

54. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol 2010; 6:el00l025.

55. Roadmap Epigenomics C, Kundaje A, Meuleman W, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015; 518:317-30.

56. Ernst J, Kellis M. ChromHMM: automating chromatin- state discovery and characterization. Nature methods 2012; 9:215-6.

57. Hoffman MM, Buske OJ, et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nature methods 2012; 9:473-6. 58. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic acids research 20l2;40: D930-4.

59. Gadegbeku CA, Gipson DS, Holzman LB, et al. Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach. Kidney Int 2013; 83:749-56.

60. Cohen CD, Frach K, Schlondorff D, Kretzler M. Quantitative gene expression analysis in renal biopsies: a novel protocol for a high-throughput multicenter application. Kidney international 2002; 61: 133-40.

61. Lockstone HE. Exon array data analysis using Affymetrix power tools and R statistical software. Briefings in bioinformatics 20l l:bbq086.

62. Stegle O, Parts L, Durbin R, Winn J. A Bayesian framework to account for complex non- genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol 2010; 6:el000770.

63. Parts L, Stegle O, Winn J, Durbin R. Joint genetic analysis of gene expression data with inferred cellular phenotypes. PLoS Genet 20l l;7:el00l276.

Sequences

rs893403 (allelePos=50l ; totalLen=l00l; alleles='C/T' - NCBI dbSNP)

SEQ ID NO. 1 : (Y: C or T)

CCACAATATT CCCATTTGGT GGAGCCTTCC AGAGCTCCCC CAGGGAGGCA GTGGAAGCTG TTTTCTCCTC TGGGATCCTA CTGAACTTTA GACAAATCTA TTTTGTTGCA CTTGCTGTAT TGTGTGTTGT TTATACGTTG GTTGCTCTTC CTCACTGAAA TGTGAGCTCT TCAAGCCTGG CAACAGTAGG TACCTAAGAA ATGCTTCTTG AATGGTTGAA TTCAGTTCAG AAGCTTCATA CAAACATGAT AGTGCTAATA AAATTGT AAA AGTACCTTAC AAATTGTGAT TTTTTCTTAC TAACGTATAC ACACATGCAC ATATACTGAA ATCCGCCCCC CTTCCAAAAA AAGTCCTCGG TTCAGTGACA GAGTTTGCTG GTGAATCCAG CTGGCTTCAC CTTGGTGAAG AATCCCACCT TGGTGAATTT TTAAAAATGT AAAGTCTGAC CTTCAGCTTC TGAAGCTGAG CAATGAATGT TGTCAGCACC CTAAGGTAGA Y

CTGCCACTCA AGCATCCTCA TTCACCAACT TTTAGAACTG ACGTGTAAAA CCACAACTCC A ACTT GCA A A CT A A A AT AT C TAGCTTTCAT AAGTAATGTG TTCTCTCCTA GACACAGGTC A A AT G A A A AC CTAAGTTATA ATT AGAAGGT TGTCTGGTGT GCCTGAATTC AT A A A A AT AG TTATACAGTC TGAATACTCA GTGACTACAA CAGCCAGATT CACCTCCCCC AAACTGGGGG CAGAGCTGGC

AGCAGGAACC TGGTACTTGG GCCCGAGGGC CACGCTGCCT GGCCCCCAGG CCTGGTTGCC AGCTTCACCT CTGTGACTGC CCATACAACT GCTGCTTCCA TGCTCACCGG GCCTTTCAGA TGGTCATGTC CTTTCTTTAC ATACACATCC AGAGCCACTC CATCCCCTTT AGGAGACTAC AGAGCTCTAC TCCTGTGTCA AGTTTGAAGT TTCACTCTCA GAGGATAATC TGACTTCTTT TCTTTCCAAG

All references cited herein are incorporated by reference to the same extent as if each individual publication, database entry (e.g. UniProt, Genbank sequences or GenelD entries), patent application, or patent, was specifically and individually indicated to be incorporated by reference. This statement of incorporation by reference is intended by Applicants, pursuant to 37 C.F.R. § 1.57(b)(1), to relate to each and every individual publication, database entry (e.g. UniProt, Genbank sequences or GenelD entries), patent application, or patent, each of which is clearly identified in compliance with 37 C.F.R. § 1.57(b)(2), even if such citation is not immediately adjacent to a dedicated statement of incorporation by reference. The inclusion of dedicated statements of incorporation by reference, if any, within the specification does not in any way weaken this general statement of incorporation by reference. Citation of the references herein is not intended as an admission that the reference is pertinent prior art, nor does it constitute any admission as to the contents or date of these publications or documents.

The scope of the present invention is not limited by what has been specifically shown and described hereinabove. Those skilled in the art will recognize that there are suitable alternatives to the depicted examples of materials, configurations, constructions and dimensions. Numerous references, including patents and various publications, are cited and discussed in the description of this invention. The citation and discussion of such references is provided merely to clarify the description of the present invention and is not an admission that any reference is prior art to the invention described herein. All references cited and discussed in this specification are incorporated herein by reference in their entirety. Variations, modifications and other implementations of what is described herein will occur to those of ordinary skill in the art without departing from the spirit and scope of the invention. While certain embodiments of the present invention have been shown and described, it will be obvious to those skilled in the art that changes and modifications may be made without departing from the spirit and scope of the invention. The matter set forth in the foregoing description is offered by way of illustration only and not as a limitation.

Claims

What is claimed is:

1. A method for identifying a transplant donor for a subject in need of a transplant, wherein the subject is homozygous for rs893403-G allele and/or is homozygous for CNVR915.1- deletion, the method comprising:

(a) obtaining at least one donor nucleic acid sample from at least one potential donor;

(b) detecting in the at least one donor nucleic acid sample (i) the presence or absence of a G allele at single nucleotide polymorphism (SNP) rs893403, and/or (ii) the presence or absence of CNVR915.1 -deletion; and

(c) identifying a transplant donor for the subject if the transplant donor is homozygous for rs893403-G allele and/or is homozygous for CNVR915.1 -deletion.

2. The method of claim 1, further comprising transplanting an organ, tissue or cell from the transplant donor to the subject.

3. A method for detecting transplant rejection in a subject who has received a transplant from a donor or assessing the subject’s risk of transplant rejection towards a transplant from a donor, the method comprising:

(a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor;

(b) detecting in the recipient nucleic acid sample and the donor nucleic acid sample (i) the presence or absence of a G allele at single nucleotide polymorphism (SNP) rs893403, or (ii) the presence or absence of CNVR915.1 -deletion; and

(c) diagnosing that the subject has transplant rejection or an increased risk of

transplant rejection, if (i) the subject is homozygous for rs893403-G allele, and the donor is not homozygous for rs893403-G allele; or (ii) the subject is homozygous for CNVR915.1 -deletion, and the donor is not homozygous for CNVR915.1 -deletion.

4. A method for treating a subject with transplant rejection or an increased risk of transplant rejection where the subject has received a transplant from a donor, the method comprising the steps of: (a) obtaining a recipient nucleic acid sample from the subject, and a donor nucleic acid sample from the donor;

(c) treating the subject for transplant rejection or an increased risk of transplant rejection, if (i) the subject is homozygous for rs893403-G allele, and the donor is not homozygous for rs893403-G allele; or (ii) the subject is homozygous for CNVR915.1 -deletion, and the donor is not homozygous for CNVR915.1 -deletion.

5. The method of claims 3 or 4, wherein the subject is homozygous for rs893403-G allele, and the donor is homozygous for rs893403-A allele or is rs893403-AG.

6. The method of claims 3 or 4, wherein the subject is homozygous for CNVR915.1- deletion, and the donor is heterozygous for CNVR915.1 -deletion or lacks CNVR915.1 -deletion.

7. A method for detecting transplant rejection or an increased risk of transplant rejection in a subject who has received a transplant, the method comprising the steps of:

(a) obtaining a sample from the subject;

(b) determining level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample;

(c) comparing the level obtained in step (b) with the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in a control sample; and

(d) diagnosing that the subject has transplant rejection or an increased risk of

transplant rejection, if the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases by at least 10% compared to its level in the control sample.

8. A method for treating a subject with transplant rejection or an increased risk of transplant rejection, the method comprising the steps of:

(a) obtaining a sample from the subject; (b) determining level of anti-LIMS 1 antibodies and/or anti-GCC2 antibodies in the sample;

(d) treating the subject for transplant rejection or an increased risk of transplant rejection, if the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies obtained in step (b) increases by at least 10% compared to its level in the control sample.

9. The method of claims 7 or 8, wherein the increase in the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies is at least 20%.

10. The method of claims 7 or 8, wherein the increase in the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies is at least 30%.

11. The method of claims 7 or 8, wherein the increase in the level of the anti-LIMS 1 antibodies and/or anti-GCC2 antibodies is at least 40%.

12. A method for detecting transplant rejection or an increased risk of transplant rejection in a subject who has received a transplant, the method comprising the steps of:

(a) obtaining a sample from the subject;

(b) determining level of LIMS1 mRNA and/or GCC2 mRNA in the sample;

(c) comparing the level obtained in step (b) with the level of the LIMS 1 mRNA and/or GCC2 mRNA in a control sample; and

transplant rejection, if the level of the LIMS 1 mRNA and/or GCC2 mRNA obtained in step (b) decreases by at least 10% compared to its level in the control sample.

13. A method for treating a subject with transplant rejection or an increased risk of transplant rejection, the method comprising the steps of:

(a) obtaining a sample from the subject;

(b) determining level of LIMS1 mRNA and/or GCC2 mRNA in the sample; (c) comparing the level obtained in step (b) with the level of the LIMS 1 mRNA and/or GCC2 mRNA in a control sample; and

(d) treating the subject for transplant rejection or an increased risk of transplant rejection, if the level of the LIMS 1 mRNA and/or GCC2 mRNA obtained in step (b) decreases by at least 10% compared to its level in the control sample.

14. The method of claims 12 or 13, wherein the decrease in the level of the LIMS1 mRNA and/or GCC2 mRNA is at least 30%.

15. The method of claims 12 or 13, wherein the decrease in the level of the LIMS1 mRNA and/or GCC2 mRNA is at least 50%.

16. The method of claims 12 or 13, wherein the decrease in the level of the LIMS1 mRNA and/or GCC2 mRNA is at least 70%.

17. The method of claims 4, 8 or 13, wherein in step (d) at least one immunosuppressant is administered to the subject.

18. The method of any of claims 1, 3, 4, 7, 8, 12 and 13, wherein the sample is a plasma, serum or blood sample.

19. The method of any of claims 1, 3, 4, 7, 8, 12 and 13, wherein the transplant is a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a bone marrow transplant, a portion thereof, or a combination thereof.

20. The method of any of claims 1, 3, 4, 7, 8, 12 and 13, wherein the transplant is a tissue transplant.

21. The method of any of claims 7, 8, 12 and 13, wherein the control sample is from a healthy subject or a plurality of healthy subjects.

22. The method of any of claims 7, 8, 12 and 13, wherein the control sample is from a subject who has received a transplant without rejection or from a plurality of subjects who have received a transplant without rejection.

23. The method of any of claims 3, 4, 7, 8, 12 and 13, wherein the transplant

rejection comprises acute cellular rejection (ACR) and/or antibody-mediated rejection (ABMR).

24. The method of any of claims 3, 4, 7, 8, 12 and 13, wherein the transplant rejection is hyperacute rejection.

25. The method of any of claims 3, 4, 7, 8, 12 and 13, wherein the transplant rejection is acute rejection.

26. The method of any of claims 3, 4, 7, 8, 12 and 13, wherein the transplant rejection is chronic transplant rejection.

27. The method of any of the preceding claims, wherein the subject is human.

28. The method of any of the preceding claims, wherein the subject’s existing

immunosuppressive regimen is modified or maintained.

29. The method of claims 7 or 8, wherein the level of the anti-LIMSl antibodies and/or anti- GCC2 antibodies is determined by enzyme-linked immunosorbent assay (ELISA).

30. The method of claims 7 or 8, wherein the anti-LIMSl antibodies are IgG2 and/or IgG3.