Methods for identifying renal allograft rejection genes in urine and the utility of measuring them

Abstract

The present disclosure provides methods for preparing and analyzing biological samples from renal transplant recipients, including measuring the amount of nucleic acid or protein expressed from a preselected target gene indicative of a renal disease state, including a nucleic acid or protein derived from a urine sample from the renal transplant recipient. These methods allow for the assessment of renal transplant rejection and renal transplant rejection states, such as T-cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or a combined TCMR and ABMR disease state.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 443,873, filed February 7, 2023, which is incorporated herein by reference in its entirety. [Background technology]

[0002] In the United States, more than 26 million people suffer from chronic kidney disease (CKD), defined by persistent albuminuria and / or reduced glomerular filtration rate. Given this prevalence, kidney transplantation accounts for the largest number of transplants per year. Kidney transplantation also remains the most preferred treatment for end-stage renal disease. Renal transplant allograft rejection can lead to a high risk of graft dysfunction, an increased likelihood of chronic failure, and eventual graft loss.

[0003] As of December 2018, there were 229,887 patients in the United States with functioning kidney transplants, or 678 recipients per million people, representing a 40% increase since 2008. A record 24,273 kidney transplants were performed in the United States in 2019. In 2020, the average cost of a kidney transplant was US$442,500. The cost of the transplant hospital stay, including the procedure itself, was the most expensive item, accounting for 34% of the total cost, even as transplant rates are predicted to increase. Furthermore, as of 2018, approximately 95,000 patients were waiting for a kidney transplant in the United States, and more than half of listed candidates die or are removed from the list before transplantation. Therefore, maintaining the function of transplanted organs is paramount to recipient health, containing care costs, and protecting the limited organ supply to meet the needs of an ever-increasing patient population.

[0004] Graft failure primarily occurs due to causes other than acute rejection. El-Zoghby et al., Am J Transplant, 9(3):527-35 (2009), found that 330 grafts were lost among 1,317 conventional kidney recipients. Unraveling the disease pathogenesis remains complex. The abnormal presence of plasma-derived proteins and related factors likely causes tubulointerstitial damage and may amplify the kidney's inherent susceptibility to dysfunction. Specific inflammatory cells, both resident and recruited circulating inflammatory cells, can participate in the phagocytosis of damaged cells and matrix after injury. However, if the inflammatory response is not adequately "regulated," it can become uncontrolled, and renal macrophage populations can become detrimental through apoptosis and the secretion of proinflammatory cytokines (e.g., IL-1β, IL-18, TNF-α, and CXC chemokines), which can stimulate further inflammation and eventual rejection.

[0005] Allograft rejection can result in severe impairment of graft function and poor survival outcomes. It is generally classified as either cellular, i.e., T cell-mediated rejection (TCMR), or humoral, i.e., antibody-mediated rejection (ABMR), although patients may also present with a combination of both (mixed). Both rejection conditions are triggered by the recognition of alloantigens on the allograft by recipient T cells. Furthermore, inadequate suppression of T cells due to nonadherence to medications or intentional reduction of immunosuppressants is an important cause of allograft rejection.

[0006] Despite a robust and clear understanding of the initiating events of CKD, early graft loss is common. As a result, there is a pressing need for early, sensitive, and accurate determination of renal rejection status to improve graft rejection monitoring, management, and treatment strategies before it is too late to reverse the rejection process. The present disclosure provides this need. Summary of the Invention

[0007] In one aspect, the present disclosure relates to a method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or the risk of renal transplant rejection, the method comprising: (a) extracting nucleic acids from the urine sample of the renal transplant recipient, wherein the extracted nucleic acids comprise one or more mRNAs and / or miRNAs of target genes associated with renal transplant rejection; (b) preparing a composition of nucleic acids from the nucleic acids extracted in step (a) by isolating the mRNAs and / or miRNAs and removing contaminating molecules, wherein optionally, preparing the composition comprises reverse transcribing complementary DNA (cDNA) from the nucleic acids extracted in step (a); and (c) measuring the amount of one or more mRNAs and / or miRNAs and generating one or more transplant rejection scores from the measured amount of the one or more mRNAs and / or miRNAs, wherein the one or more transplant rejection scores provide a quantitative value for renal transplant rejection risk or the presence or absence of renal transplant rejection.

[0008] In some embodiments, the method includes generating two or more transplant rejection scores, each transplant rejection score based on a different subset of mRNAs and / or miRNAs.

[0009] In some embodiments, the one or more transplant rejection scores are generated using predictive models, machine learning-based methods, and / or artificial intelligence methods.

[0010] In some embodiments, one or more transplant rejection scores are calculated using methods such as logistic regression (LogReg), t-test, violin plot, random forest (RE), neural networks, decision tree machine learning analysis, decision tree classification techniques, analysis of variance (ANOVA), Bayesian networks, boosting and adaboost, bootstrap aggregation (or bagging) algorithms, classification and regression trees (CART), boosted CART, recursive partitioning trees (RPART), Curds and Whey (CW), Curds and Kernel-based machine algorithms such as Whey-Lasso, Principal Component Analysis (PCA), Factor Rotation or Factor Analysis, Discriminant Analysis, Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Quadratic Discriminant Analysis, Discriminant Function Analysis (DFA), Factor Rotation or Factor Analysis, Genetic Algorithms, Hidden Markov Models, Kernel Density Estimation, Kernel Partial Least Squares Algorithm, Kernel Matching Pursuit Algorithm, Kernel Fisher Discriminant Analysis Algorithm, Kernel Principal Component Analysis Algorithm, Linear Regression and Generalized Linear Models, Forward Linear Stepwise Regression, Lasso (or LASSO) Shrinkage and Selection Method, Elasticity The data may be generated using methods such as KNN (Knearest Neighbor Method), KNN (Lasso and Elastic Net Regularized Generalized Linear Models), KNN (Knearest Neighbor Method ...

[0011] In some embodiments, the one or more transplant rejection scores are generated using logistic regression (LogReg), random forest (RE), neural network, or decision tree machine learning analysis.

[0012] In some embodiments, one or more mRNAs and / or miRNAs are considered by using eight separate machine learning classifier methods based on six determined kidney disease states.

[0013] In some embodiments, the performance of the assay for determining renal transplant rejection or risk of renal transplant rejection is measured and characterized by an AUC value of about 0.6 to about 0.99, about 0.7 to about 0.99, about 0.8 to about 0.99, about 0.9 to about 0.99, about 0.7 to about 0.79, about 0.8 to about 0.89, about 0.6 to about 0.89, or about 0.6 to about 0.79.

[0014] In some embodiments, the AUC value is from about 0.8 to about 0.99.

[0015] In some embodiments, the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk that is predictive of clinical assessment of kidney disease state and that can distinguish between two or more kidney disease states.

[0016] In some embodiments, the kidney disease state comprises non-rejection, T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or a mixed TCMR and ABMR disease state.

[0017] In some embodiments, the TCMRs further comprise molecularly defined TCMRs (mTCMRs) and / or potential TCMRs (pTCMRs).

[0018] In some embodiments, ABMR further comprises molecularly defined ABMR (mABMR) and / or potential ABMR (pABMR).

[0019] In some embodiments, the one or more transplant rejection scores include a first transplant rejection score based on a set of mRNAs and / or miRNAs associated with TCMR, and a second transplant rejection score based on a set of mRNAs and / or miRNAs associated with ABMR.

[0020] In some embodiments, the one or more transplant rejection scores comprise a transplant rejection score based on a set of mRNAs and / or miRNAs associated with inflammation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with allograft rejection, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with T cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with B cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with cytokine response, and / or a transplant rejection score based on a set of mRNAs and / or miRNAs associated with chemokine response.

[0021] In some embodiments, the methods disclosed herein involve collecting urine samples from a transplant recipient at different times and generating one or more transplant rejection scores from each urine sample.

[0022] In some embodiments, urine samples are collected from the transplant recipient before, concurrently with, and / or after transplant.

[0023] In some embodiments, the risk of transplant rejection is based on two or more transplant rejection scores generated at different times, and a change in the two or more transplant rejection scores indicates a change in the kidney disease state.

[0024] In some embodiments, the kidney transplant recipient is administered treatment for a determined kidney disease state or kidney transplant rejection.

[0025] In some embodiments, the treatment comprises an anti-rejection or immunosuppressant agent.

[0026] In some embodiments, a change in one or more transplant rejection scores identifies the presence, absence, or degree of a therapeutic response to a treatment.

[0027] In some embodiments, treatment is determined based on one or more transplant rejection scores or a change in one or more transplant rejection scores.

[0028] In some embodiments, the nucleic acid comprises cellular nucleic acid, extracellular nucleic acid, and / or nucleic acid obtained from extracellular vesicles.

[0029] In some embodiments, the method comprises isolating cells from the urine sample and extracting nucleic acids from the cells.

[0030] In some embodiments, the method further comprises isolating the extracellular vesicles and extracting nucleic acids from the extracellular vesicles.

[0031] In some embodiments, the cDNA is amplified before determining the amount.

[0032] In some embodiments, the extracted nucleic acid comprises one or more mRNAs.

[0033] In some embodiments, the extracted nucleic acid comprises one or more miRNAs.

[0034] In some embodiments, the extracted nucleic acids include one or more mRNAs and one or more miRNAs.

[0035] In some embodiments, preparing a composition of the extracted nucleic acid or fraction thereof in step (a) comprises amplification of cDNA derived from the nucleic acid.

[0036] In some embodiments, the amplification comprises performing multiplex targeted amplification of cDNA at 10-50,000 target loci in a single reaction volume.

[0037] In some embodiments, the amplification comprises universal amplification.

[0038] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using quantitative PCR, real-time PCR, digital PCR, or sequencing.

[0039] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using multiplex quantitative PCR, multiplex real-time PCR, and / or multiplex digital PCR.

[0040] In some embodiments, the sequencing comprises next-generation whole genome sequencing.

[0041] In some embodiments, the abundance of one or more mRNAs and / or miRNAs is measured by using a microarray.

[0042] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using molecular barcodes and microscopic imaging (such as NanoString nCounter®).

[0043] In some embodiments, the amount of one or more mRNAs and / or miRNAs is determined by measuring the absolute copy number of one or more mRNAs and / or miRNAs per amount of total nucleic acid in the urine sample.

[0044] In some embodiments, the one or more mRNAs and / or miRNAs are associated with antibody-mediated transplant rejection (AMTR), T-cell-mediated transplant rejection (TMTR), apoptotic pathways, cytokines, antimicrobial responses, and / or inflammatory cell responses.

[0045] In some embodiments, the one or more mRNAs and / or miRNAs associated with the antimicrobial response are C-X-C motif chemokine ligand (CXCL) type genes.

[0046] In some embodiments, the one or more mRNAs [Table 1-1] [Table 1-2] [Table 1-3] [Table 1-4] [Table 1-5] and combinations thereof.

[0047] In some embodiments, the one or more miRNAs [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4] [Table 2-5] and combinations thereof.

[0048] In some embodiments, the one or more mRNAs [Table 3] and combinations thereof.

[0049] In some embodiments, the one or more miRNAs [Table 4] and combinations thereof.

[0050] In some embodiments, the one or more mRNAs [Table 5] and combinations thereof.

[0051] In some embodiments, the one or more miRNAs [Table 6] and combinations thereof.

[0052] In some embodiments, the one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, The gene is expressed from a gene selected from the group consisting of Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

[0053] In some embodiments, the one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lc k, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

[0054] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

[0055] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

[0056] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, PSMB10, B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

[0057] In some embodiments, the one or more mRNAs are not expressed from a gene selected from the group consisting of PDCD1, MARCHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

[0058] In some embodiments, the one or more mRNAs are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

[0059] In some embodiments, one or more miRNAs are miR-186-5p, miR-665, miR-873-5p.1, miR-543, miR-330-3p, miR-362-5p / 500b-5p, miR-217, miR-140-5p, miR-193-3p, miR-382-5p, miR-140-3p.2, miR-653-5p, miR-455-3p.2, miR-145-5p, miR-491-5p, miR-23-3p, miR-375, miR-129-5p, miR-96-5p / 1271-5p, miR-182-5p, miR-371-5p, miR-203a-3p.1, miR-494-3p, miR-146-5p, miR-140-3p.1, miR-125-5p, miR-346, miR-760, miR-185-5p, miR-325-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-423-5p, miR-496.1, miR-155-5p, miR-142-3p.2, miR-24-3p, miR-874-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-181-5p, miR-142-5p, miR-130-3p / 301-3p / 454-3p, miR-21-5p / 590-5p, miR-103-3p / 107, miR-137, miR-340-5p, miR-490-3p, miR-143-3p, miR-409-3p, miR-27-3p, miR-138-5p, miR-485-5p, miR-328-3p, miR-326, miR-148-3p / 152-3p, miR-9-5p, miR-31-5p, miR-452-5p / 892-3p, miR-202-5p, miR-29-3p, miR-338-3p, miR-26-5p, let-7-5p / 98-5p, miR-196-5p, miR-30-5p, miR-142-3p.1, miR-19-3p, miR-411-3p, miR-493-5p, miR-218-5p, miR-203a-3p2, miR-495-3p, miR-425-5p, miR-135-5p, miR-154-3p / 487-3p, miR-223-3p, miR-219-5p, miR-670-3p, miR-216b-5p, miR-200bc-3p / 429, miR-320, m iR-216a-5p, miR-141-3p / 200a-3p, miR-144-3p, miR-128-3p, miR-455-3p.1, miR-219a-2-3p, miR-873-5p.2, miR-448, miR-183-5p.2, miR-374-5p miR-505-3p.1, miR-433-3p, miR-377-3p, miR-365-3p, miR-124-3p.1, miR-410-3p, miR-199-3p, miR-22-3p, miR-129-3p, miR-383-5p.1, miR-1-3p / 206, miR-296-5p, miR-299-3p, miR-212-5p, miR-331-3p, miR-378-3p, miR-136-5p, miR-1193, miR-505-3p.2, miR-302c-3p.2 / 520-3p, miR-421, miR -499a-5p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-124-3p.2 / 506-3p, miR-34-5p / 449-5p, miR-376c-3p, miR-139-5p, miR-221-3p / 222-3p, miR-5 04-5p.1、miR-335-5p、miR-101-3p.1、miR-431-5p、miR-489-3p、miR-369-3p、miR-330-3p.2、miR-18-5p、miR-28-5p / 708-5p、miR-133a-3p.2 / 133b、 miR-205-5p, miR-199-5p, miR-455-5p, miR-126-3p.2, miR-7-5p, miR-483-3p.2, miR-668-3p, miR-1306-5p, miR-150-5p, miR-296-3p, miR-204-5p / 211-5p, miR-3064-5p, miR-532-5p, miR-876-5p, miR-501-3p / 502-3p, miR-33-5p, miR-153-3p, miR-214-5p, miR-655-3p, miR-342-3p, miR-133a-3p.1. It is selected from the group consisting of miR-411-5p.1, miR-496, miR-411-5p.2, miR-582-5p, miR-381-3p, miR-188-5p, miR-383-5p.2, miR-486-5p, miR-183-5p.1, miR-208-3p, miR-193a-5p, miR-101-3p.2, miR-542-3p, miR-190-5p, miR-299-5p, miR-154-5p, miR-802, miR-323-3p, miR-532-3p, miR-224-5p, miR-339-5p, miR-194-5p, miR-149-5p, miR-493-3p, miR-382-3p, miR-132-3p / 212-3p, miR-1197, miR-99-5p / 100-5p, miR-877-5p, miR-483-3p.1, miR-10-5p, miR-361-5p, miR-539-3p, miR-191-5p, miR-329-3p / 362-3p, miR-122-5p, miR-379-5p, miR-376-3p, miR-1298-5p, miR-451, miR-210-3p, miR-1224-5p, miR-324-5p, miR-544a-5p, miR-488-3p, miR-758-3p, miR-151-3p, miR-875-5p, miR-134-5p, miR-192-5p / 215-5p, and miR-127-3p.

[0060] In some embodiments, one or more miRNAs are miR-96-5p / 1271-5p, miR-493-5p, miR-183-5p.2, miR-150-5p, miR-7-5p, miR-653-5p, miR-200bc-3p / 429, miR-212-5p, miR-1298-5p, miR-137, miR-758-3p, miR-325-3p, miR-542-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-495-3p, miR-30-5p, miR-873-5p.1, miR-146-5p, miR-505-3p.1, miR-539-3p, miR-216a-5p, miR-216b-5p, miR-340-5p, miR-361-5p, miR-338-3p, miR-217, miR-9-5p, miR-219a-2-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-199-3p, miR-1-3p / 206, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-142-5p, miR-302c-3p.2 / 520-3p, miR-326, miR-760, miR-138-5p, miR-27-3p, miR-145-5p, miR-142-3p.2, miR-101-3p.2, miR-182-5p, miR-203a-3p.2, miR-140-3p.1, miR-183-5p.1, miR-144-3p, miR-101-3p.1, miR-330-3p, miR-224-5p, miR-148-3p / 152-3p, miR-485-5p, miR-122-5p, miR-155-5p, miR-320, miR-23-3p, miR-124-3p.2 / 506-3p, miR-135-5p, miR-381-3p, miR-26-5p, miR-1224-5p, miR-192-5p / 215-5p, miR-1249-3p, miR-125-5p, miR-483-3p.2, miR-668-3p, miR-223-3p, miR-655-3p, miR-382-5p, miR-130-3p / 301-3p / 454-3p, miR-19-3p, miR-582-5p, miR-194-5p, miR-802, miR-483-3p.1, miR-382-3p, miR-129-5p, miR-3064-5p, miR-873-5p.2, miR-499a-5p, miR-128-3p, miR-532-5p, miR-296-5p, miR-744-5p, miR-425-5p, miR-218-5p, and miR-496.1.

[0061] In some embodiments, the method further includes measuring the amount of donor-derived cell-free DNA in a sample obtained from the transplant recipient; extracting cell-free DNA from the sample obtained from the transplant recipient, wherein the extracted cell-free DNA comprises donor-derived cell-free DNA and recipient-derived cell-free DNA; performing targeted amplification of the extracted DNA at 50 to 50,000 target loci in a single reaction volume; sequencing the amplified DNA by high-throughput sequencing to obtain sequencing reads; measuring the amount of donor-derived cell-free DNA based on the sequencing reads; and generating a transplant rejection score indicative of transplant rejection based on whether the measured amount of donor-derived cell-free DNA, or a function thereof, exceeds a cutoff threshold for the amount of cell-free DNA indicative of transplant rejection, wherein transplant rejection is determined based on both the one or more transplant rejection scores from the measured amounts of one or more mRNAs and / or miRNAs and the transplant rejection score determined based on the measured amount of donor-derived cell-free DNA.

[0062] In some embodiments, measuring the amount of mRNA and / or miRNA comprises amplifying at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target nucleic acid molecules, 2-10, 200-100, 50-500, or 50-2000 target nucleic acid molecules using at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target RNA molecules, 2-10, 200-100, 50-500, or 50-2000 pairs of forward and reverse PCR primers.

[0063] In some embodiments, the one or more mRNAs and / or miRNAs are determined by text mining a database.

[0064] In some embodiments, the abundance of one or more mRNAs and / or miRNAs is measured relative to a housekeeping gene.

[0065] In another aspect, the present disclosure relates to a method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or the risk of renal transplant rejection, the method comprising: (a) extracting nucleic acids from the urine sample of the renal transplant recipient, wherein the extracted nucleic acids comprise one or more RNA molecules associated with the risk of renal transplant rejection; (b) preparing a composition of nucleic acids from the extracted nucleic acids from step (a) by isolating the RNA molecules and removing contaminating molecules, wherein optionally, preparing the composition comprises reverse transcribing the RNA molecules to synthesize cDNA; and (c) measuring the amount of RNA molecules associated with the risk of renal transplant rejection in the composition of nucleic acids and generating one or more transplant rejection scores from the measured amount of the one or more RNA molecules, wherein the one or more transplant rejection scores provide a quantitative value for the risk of renal transplant rejection or the presence or absence of renal transplant rejection.

[0066] In some embodiments, the one or more RNA molecules are mRNA or miRNA.

[0067] In some embodiments, the present disclosure relates to a method for preparing a composition of proteins from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or the risk of renal transplant rejection, the method comprising: (a) extracting proteins from the urine sample of the renal transplant recipient, wherein the extracted proteins are associated with the risk of renal transplant rejection; (b) preparing a composition of proteins from the proteins extracted in step (a) by removing contaminating molecules; and (c) measuring the amount of protein in the composition and generating one or more transplant rejection scores from the measured amount, wherein the one or more transplant rejection scores provide a quantitative value for the risk of renal transplant rejection or the presence or absence of renal transplant rejection.

[0068] In some embodiments, the measuring step is based on two or more transplant rejection scores, each transplant rejection score being based on a different subset of proteins.

[0069] In some embodiments, the one or more transplant rejection scores are generated using predictive models, machine learning-based methods, and / or artificial intelligence methods.

[0070] In some embodiments, the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk that is predictive of clinical assessment of kidney disease state and that can distinguish between two or more kidney disease states.

[0071] In some embodiments, the kidney disease state comprises non-rejection, T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or a mixed TCMR and ABMR disease state.

[0072] In some embodiments, the TCMRs further comprise molecularly defined TCMRs (mTCMRs) and / or potential TCMRs (pTCMRs).

[0073] In some embodiments, ABMR further comprises molecularly defined ABMR (mABMR) and / or potential ABMR (pABMR).

[0074] In some embodiments, the one or more transplant rejection scores comprise a first transplant rejection score based on a set of proteins associated with TCMR and a second transplant rejection score based on a set of proteins associated with ABMR.

[0075] In some embodiments, the one or more transplant rejection scores comprise a transplant rejection score based on a set of proteins associated with inflammation, a transplant rejection score based on a set of proteins associated with allograft rejection, a transplant rejection score based on a set of proteins associated with T cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with B cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with cytokine response, and / or a transplant rejection score based on a set of proteins associated with chemokine response.

[0076] In some embodiments, the methods of the present disclosure include collecting urine samples from a transplant recipient at different times and generating one or more transplant rejection scores from each urine sample.

[0077] In some embodiments, urine samples are collected before, concurrently with, and / or after transplantation.

[0078] In some embodiments, the risk of transplant rejection is based on multiple transplant rejection scores generated at different time points, and a change in one or more transplant rejection scores indicates a change in kidney disease status.

[0079] In some embodiments, the kidney transplant recipient is administered treatment for a determined kidney disease state or kidney transplant rejection.

[0080] In some embodiments, a change in one or more transplant rejection scores identifies the presence, absence, or degree of a therapeutic response to a treatment.

[0081] In some embodiments, treatment is determined based on one or more transplant rejection scores or a change in one or more transplant rejection scores.

[0082] In some embodiments, the treatment comprises an anti-rejection or immunosuppressant agent.

[0083] In some embodiments, the methods of the present disclosure further comprise isolating cells from the urine sample and extracting proteins from the cells.

[0084] In some embodiments, the methods of the present disclosure further comprise isolating the extracellular vesicles and extracting proteins from the extracellular vesicles.

[0085] In some embodiments, the one or more proteins are [Table 7-1] [Table 7-2] [Table 7-3] [Table 7-4] [Table 7-5] and combinations thereof.

[0086] In some embodiments, the one or more proteins are [Table 8] and combinations thereof.

[0087] In some embodiments, the one or more proteins are [Table 9] and combinations thereof.

[0088] In some embodiments, the one or more proteins are CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal , Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

[0089] In some embodiments, the one or more proteins are CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lc k, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

[0090] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

[0091] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

[0092] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, PSMB10, B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

[0093] In some embodiments, the one or more proteins are not expressed from a gene selected from the group consisting of PDCD1, MARCHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

[0094] In some embodiments, the one or more proteins are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

[0095] In some embodiments, the disclosed method further includes (a) measuring the amount of donor-derived cell-free DNA in a sample obtained from the transplant recipient and extracting cell-free DNA from the sample obtained from the transplant recipient, wherein the extracted cell-free DNA comprises donor-derived cell-free DNA and recipient-derived cell-free DNA; (b) performing targeted amplification of the extracted DNA at 50 to 50,000 target loci in a single reaction volume; (c) sequencing the amplified DNA by high-throughput sequencing to obtain sequencing reads, measuring the amount of donor-derived cell-free DNA based on the sequencing reads, and generating a score indicative of transplant rejection based on whether the measured amount of donor-derived cell-free DNA or a function thereof exceeds a cutoff threshold for the amount of cell-free DNA indicative of transplant rejection, wherein transplant rejection is determined based on both one or more scores based on the amount of measured protein and the score determined based on the measured amount of donor-derived cell-free DNA. [Brief explanation of the drawings]

[0096] [Figure 1] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 2] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 3] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 4] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 5] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 6]1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 7] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 8] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 9] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 10] 1 is a graphic depiction of exemplary mRNAs identified from the urine rejection literature that effectively distinguish kidney rejection states. [Figure 11] 1 is a graphic depiction of exemplary mRNAs from CXCL-type genes (antimicrobial response genes) identified by text mining that are useful for determining kidney rejection status. [Figure 12] 1 is a graphic depiction of exemplary mRNAs from housekeeping genes that do not distinguish between rejection status. [Figure 13] 1 is a graphic depiction of exemplary mRNAs from housekeeping genes that do not distinguish between rejection status. [Figure 14] 1 is a graphic depiction of exemplary mRNAs identified from PaxGene® RNA blood collection tubes, of which only a small number of genes effectively distinguish rejection status. [Figure 15] 1 is a graphic depiction of exemplary mRNAs identified from PaxGene® RNA blood collection tubes, of which only a small number of genes effectively distinguish rejection status. [Figure 16] 1 is a graphic depiction of exemplary mRNAs identified from PaxGene® RNA blood collection tubes, of which only a small number of genes effectively distinguish rejection status. [Figure 17A]1 is a graphical depiction of the KEGG gene ontology analysis of genes published in Akalin versus genes identified herein enriched in urine samples. GO analysis of Akalin genes is shown. [Figure 17B] 1 is a graphical depiction of the KEGG gene ontology analysis of genes identified herein enriched in urine samples versus genes published in Akalin. 2 shows the GO analysis of genes identified herein that were enriched in urine samples. [Figure 18A] 1 is a graphical depiction of the biochemical process-based gene ontology analysis of genes published in Akalin versus genes identified herein enriched in urine samples. GO analysis of Akalin genes is shown. [Figure 18B] 1 is a graphic depiction of a biochemical process-based gene ontology analysis of genes published in Akalin versus genes identified herein enriched in urine samples. 2 shows a GO analysis of genes identified herein in urine samples. [Figure 19] MicroRNA markers (urine miRNA jmp) found in urine from 207 patients are shown. GSE128348_MBITZ1-CTOT2-Urine-Biopsy-Associated. DETAILED DESCRIPTION OF THE INVENTION

[0097] The present disclosure relates to a method for identifying renal allograft rejection genes in urine and the use of their measurements for accurate and specific detection of renal rejection status. A method for accurately and specifically detecting different states of renal rejection can lead to novel preventive, diagnostic, and therapeutic approaches. Two major types of renal allograft rejection have been recognized: T-cell-mediated (TCMR) and antibody-mediated (ABMR). Additionally, renal rejection status can be determined as probable TCMR (pTCMR), probable ABMR (pABMR), and mixed (both TCMR and ABMR). TCMR and ABMR differ in etiology, pathology, and prognosis, require tailored treatment, and cannot be distinguished based on clinical data or histology alone. Therefore, the method herein provides a method for identifying renal rejection status based on measuring the amount of specific RNA or protein in urine. The identification of differentially expressed mRNAs, proteins, or miRNAs in urine samples from transplant recipients with different renal rejection status allowed the construction of molecular classifiers capable of distinguishing between renal rejection statuses, as further described below.

[0098] In one aspect, the present disclosure relates to a method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or the risk of renal transplant rejection, the method comprising: (a) extracting nucleic acids from the urine sample of the renal transplant recipient, wherein the extracted nucleic acids comprise one or more mRNAs and / or miRNAs of target genes associated with renal transplant rejection; (b) preparing a composition of nucleic acids from the nucleic acids extracted in step (a) by isolating the mRNAs and / or miRNAs and removing contaminating molecules, wherein optionally, preparing the composition comprises reverse transcribing complementary DNA (cDNA) from the nucleic acids extracted in step (a); and (c) measuring the amount of one or more mRNAs and / or miRNAs and generating one or more transplant rejection scores from the measured amount of the one or more mRNAs and / or miRNAs, wherein the one or more transplant rejection scores provide a quantitative value for renal transplant rejection risk or the presence or absence of renal transplant rejection.

[0099] In some embodiments, the method includes generating two or more transplant rejection scores, each transplant rejection score based on a different subset of mRNAs and / or miRNAs.

[0100] In another aspect, the present disclosure relates to a method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or the risk of renal transplant rejection, the method comprising: (a) extracting nucleic acids from the urine sample of the renal transplant recipient, wherein the extracted nucleic acids comprise one or more RNA molecules associated with the risk of renal transplant rejection; (b) preparing a composition of nucleic acids from the extracted nucleic acids from step (a) by isolating the RNA molecules and removing contaminating molecules, wherein optionally, preparing the composition comprises reverse transcribing the RNA molecules to synthesize cDNA; and (c) measuring the amount of RNA molecules associated with the risk of renal transplant rejection in the composition of nucleic acids and generating one or more transplant rejection scores from the measured amount of the one or more RNA molecules, wherein the one or more transplant rejection scores provide a quantitative value for the risk of renal transplant rejection or the presence or absence of renal transplant rejection.

[0101] In some embodiments, the present disclosure relates to a method for preparing a composition of proteins from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or risk of renal transplant rejection, the method comprising: (a) extracting proteins from the urine sample of the renal transplant recipient, wherein the extracted proteins are associated with risk of renal transplant rejection; (b) preparing a composition of proteins from the proteins extracted in step (a) by removing contaminating molecules; and (c) measuring the amount of protein in the composition and generating one or more transplant rejection scores from the measured amounts, wherein the one or more transplant rejection scores provide a quantitative value for risk of renal transplant rejection or the presence or absence of renal transplant rejection. In some embodiments, the measuring step is based on two or more transplant rejection scores, each based on a different subset of proteins. In some embodiments, the one or more transplant rejection scores include a first transplant rejection score based on a set of proteins associated with TCMR and a second transplant rejection score based on a set of proteins associated with ABMR.

[0102] In some embodiments, RNA, such as mRNA or microRNA (miRNA), cell-free DNA, or protein, is isolated from a urine sample of a kidney transplant recipient, and expression products from a set of genes determined to be capable of distinguishing between different rejection states are measured. In some embodiments, miRNAs that bind to mRNA expressed from the set of genes are measured. The examples presented herein illustrate that mRNAs found in urine can distinguish between different kidney rejection states. In particular, mRNAs and miRNAs useful for detecting and distinguishing between kidney rejection states are listed in Table 1, or more preferably, Table 6. Alternatively, it is contemplated herein that apoptosis pathway genes expressing mRNAs found in urine may also be useful for detecting and distinguishing between kidney rejection states. Illustrative examples of apoptosis pathway genes determined herein to be useful for detecting and distinguishing between kidney rejection states are listed in Table 9. It is also contemplated herein that proteins expressed from the genes listed in Tables 1, 6, and 9 may also be useful for detecting and distinguishing between kidney rejection states.

[0103] The determination of target genes useful for detecting and distinguishing renal rejection states is described in more detail in Example 1. Briefly, target genes are determined by cross-referencing target genes from the MMDx® Diagnostic System with genes expressed in urine samples and shown to be associated with specific renal rejection states. A machine learning approach is used to test the performance of these genes in detecting renal rejection states in silico by using the MMDx® Diagnostic System. Methods for measuring RNA and determining a rejection score based on these measurements to assess renal rejection are further described below.

[0104] Determination of different renal rejection or disease states can inform clinical treatment of renal transplant recipients, for example, with anti-rejection agents to treat the rejection state. In some embodiments, the renal transplant recipient is administered treatment for the determined renal disease state or renal transplant rejection. In some embodiments, the treatment includes an anti-rejection or immunosuppressant. In some embodiments, a change in one or more transplant rejection scores identifies the presence, absence, or degree of a therapeutic response to the treatment. In some embodiments, the treatment is determined based on one or more transplant rejection scores or a change in one or more transplant rejection scores.

[0105] An "anti-rejection agent" is any substance administered to a subject for the purpose of preventing or ameliorating a rejection state. Anti-rejection agents include, but are not limited to, azathioprine, cyclosporine, FK506, tacrolimus, mycophenolate mofetil, anti-CD25 antibodies, anti-thymocyte globulin, rapamycin, ACE inhibitors, perillyl alcohol, anti-CTLA4 antibodies, anti-CD40L antibodies, antithrombin III, tissue plasminogen activator, antioxidants, anti-CD154, anti-CD3 antibodies, thymoglobulin, OKT3, corticosteroids, or combinations thereof. A "baseline treatment regimen" is understood to include anti-rejection agents administered at baseline after transplantation. The baseline treatment regimen may be modified by the temporary or chronic addition of other anti-rejection agents or by a temporary or chronic increase or decrease in the dose of one or all of the baseline anti-rejection agents. For TCMR, initial treatment traditionally includes pulse methylprednisolone at 250-500 mg daily for 3-5 days or T-cell depletion. For ABMR, traditional treatment may be plasma exchange and intravenous Ig with or without rituximab, or more recently, treatment for ABMR includes depletion of antibody-producing B cells or plasma cells, depletion of antibodies (DSA), and / or subsequent inhibition of complement-regulated graft injury.

[0106] A method for determining and monitoring kidney rejection based on measuring RNA (or miRNA binding to mRNA) in urine samples. In one aspect, the present disclosure relates to methods for preparing a composition of complementary DNA (cDNA) from RNA extracted from a urine sample of a kidney transplant recipient, useful for determining kidney rejection. In some embodiments, no amplification or pre-amplification is performed on the extracted RNA prior to measuring the amount by quantitative PCR, microarray, or sequencing.

[0107] In some embodiments, when RNA levels are measured by sequencing, a sequencing library is prepared from the cDNA, and preparing the sequencing library includes attaching adapters to the cDNA, for example, by ligation. In some embodiments, the cDNA fragments are repaired and the generated blunt ends are filled in. In some embodiments, adapters are added to the cDNA fragments by blunt-end ligation. In some embodiments, adapters are added to the cDNA by sticky-end ligation to generate a sequence library of cDNA. In some embodiments, measuring the amount of mRNA and / or miRNA comprises amplifying at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target nucleic acid molecules, 2-10, 200-100, 50-500, or 50-2000 target nucleic acid molecules using at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target RNA molecules, 2-10, 200-100, 50-500, or 50-2000 pairs of forward and reverse PCR primers.

[0108] In one aspect, the disclosure herein relates to a method for preparing a composition of amplified complementary DNA (cDNA) from RNA extracted from a urine sample of a renal transplant recipient, useful for assessing renal transplant rejection, the method comprising: (a) extracting RNA from the urine sample of the renal transplant recipient; (b) preparing a composition of amplified cDNA from the extracted RNA by performing multiplexed targeted amplification of cDNA at 10 to 50,000 target loci in a single reaction volume to detect and quantify the amount of RNA expressed from multiple target genes; and (c) determining whether the amount of RNA target loci, or a function thereof, exceeds a cutoff threshold indicative of renal transplant rejection.

[0109] In some embodiments, the nucleic acid comprises cellular nucleic acid, extracellular nucleic acid, and / or nucleic acid obtained from extracellular vesicles. In some embodiments, the method comprises isolating cells from a urine sample and extracting nucleic acid from the cells. In some embodiments, the method further comprises isolating extracellular vesicles and extracting nucleic acid from the extracellular vesicles. In some embodiments, the RNA is derived from extracellular vesicles (EVs) isolated from a urine sample of a kidney transplant recipient.

[0110] A method for determining and monitoring kidney transplant rejection based on measuring proteins. In one aspect, the present disclosure relates to a method for preparing a composition of proteins from a urine sample of a kidney transplant recipient, useful for determining kidney transplant rejection, the method comprising: (a) extracting proteins from the urine sample of the kidney recipient; (b) detecting and quantifying the amount of protein expressed from a target gene; and (c) determining whether the amount of protein expressed from the target gene, or a function thereof, exceeds a cutoff threshold indicative of kidney transplant rejection.

[0111] In one aspect, the present disclosure relates to a method for preparing a composition of proteins derived from extracellular vesicles (EVs) isolated from a urine sample of a renal transplant recipient, useful for assessing renal transplant rejection, the method comprising: (a) extracting proteins from extracellular vesicles (EVs) isolated from a urine sample of the renal transplant recipient; (b) detecting and quantifying the amount of protein expressed from a target gene; and (c) determining whether the amount of protein, or a function thereof, exceeds a cutoff threshold indicative of renal transplant rejection.

[0112] In one aspect, the present disclosure relates to a method of administering immunosuppressive therapy in a renal transplant recipient, the method comprising: (a) measuring the amount of protein of a target gene; and (b) titrating the dosage of an immunosuppressive therapy according to the amount of protein or a function thereof.

[0113] In some embodiments, the methods herein further include repeating steps (a)-(b) longitudinally for the same kidney transplant recipient and determining longitudinal changes in the amount of the donor-derived protein, donor-derived target protein, or function thereof, and longitudinal changes in the amount of the donor-derived protein, target protein, or function thereof.

[0114] In some embodiments, the methods herein further comprise titrating the dosage of the immunosuppressive therapy in response to longitudinal changes in the donor-derived protein, the donor-derived target protein, or its function.

[0115] Methods for measuring protein amount include, but are not limited to, various sandwich, competitive, or non-competitive assay formats to generate a signal related to the presence or amount of the protein analyte of interest. One agent for detecting the protein of the present invention is, for example, an antibody that can bind to the protein, preferably an antibody with a detectable label. The antibody can be polyclonal, or preferably monoclonal. An intact antibody or its fragment (e.g., Fab or F(ab')2) can be used. The term "labeling" is intended to encompass both direct labeling of the antibody by binding a detectable substance to the antibody, and indirect labeling of the antibody by reactivity with another directly labeled reagent.

[0116] Various formats can be used to determine whether a sample contains a protein that binds to a specific antibody. Examples of such formats include, for example, enzyme-linked immunosorbent assays, radioimmunoassays, Western blot analysis, and ELISA. Numerous formats of antibody arrays have been proposed and described using antibodies. Such arrays typically contain different antibodies with specificity for different proteins intended to be detected. For example, typically, at least 100 different antibodies are used to detect 100 different protein targets, with each antibody specific to one target. In some embodiments, the amount of protein is measured using a mass spectrometry-based approach. In a related aspect, the present invention provides an array comprising a support(s) bearing multiple ligands that specifically bind to multiple proteins. The multiple proteins include at least two, three, four, or five proteins determined to be indicative of a renal rejection state. In some embodiments, the number of proteins in the multiple proteins is less than 1000 or less than 100, and more than 100 or more than 10, respectively. In some embodiments, the multiple ligands are bound to a planar support or beads. In some embodiments, the ligands are different antibodies, and the different antibodies bind to different proteins among the multiple proteins.

[0117] In some embodiments, the target protein is encoded by an RNA target disclosed elsewhere herein.

[0118] Samples containing nucleic acids and methods for obtaining samples and extracting nucleic acids - Patents.com The methods disclosed herein include extracting fragmented or intact RNA from a sample obtained from a kidney recipient. In some embodiments, the methods disclosed herein include collecting urine samples from the transplant recipient at different times and generating one or more transplant rejection scores from each urine sample.

[0119] In some embodiments, urine samples are collected from the transplant recipient before, concurrently with, and / or after transplant.

[0120] In some embodiments, the risk of transplant rejection is based on two or more transplant rejection scores generated at different times, and a change in the two or more transplant rejection scores indicates a change in the kidney disease state.

[0121] In some embodiments, the sample is obtained from the kidney recipient less than 18 months post-transplant, less than 17 months post-transplant, less than 16 months post-transplant, less than 15 months post-transplant, less than 14 months post-transplant, less than 13 months post-transplant, or less than 12 months post-transplant, hi some embodiments, the sample is obtained from the transplant recipient 0-2 months post-transplant, 2-4 months post-transplant, 4-6 months post-transplant, 6-9 months post-transplant, 9-12 months post-transplant, or 12-18 months post-transplant.

[0122] In some embodiments, the sample is obtained from the kidney recipient prior to transplant, e.g., 1, 2, 3, 4, 5, 6, or 7 days prior to transplant. In some embodiments, the urine sample is obtained on the same day as transplant.

[0123] In some embodiments, the methods disclosed herein further include longitudinally measuring the amount of cell-free DNA, RNA, or protein in the same kidney recipient to determine longitudinal changes in the amount of cell-free DNA, RNA, or protein. In some embodiments, the amount of cell-free DNA, RNA, or protein is the total amount of cell-free DNA, RNA, or protein derived from the donor organ. In some embodiments, the amount of RNA or protein expressed from a target gene is measured. In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured relative to a housekeeping gene.

[0124] In some embodiments, the urine sample can be extracellular vehicles, cells, or free-floating RNA, DNA, or protein obtained from urine.

[0125] Nucleic acids and methods for extracting or concentrating nucleic acids The method disclosed herein includes extracting nucleic acids from a sample derived from a subject. The nucleic acids can be cell-free DNA, cellular DNA, DNA extracted from exosomes, cell-free RNA, cellular RNA, or RNA extracted from exosomes. The term "RNA" as used herein refers to any type of RNA, including messenger RNA (mRNA) or small non-coding RNA (sncRNA) such as microRNA (miRNA). In some embodiments, the extracted nucleic acids include one or more miRNAs.

[0126] In some embodiments, the extracted nucleic acids include one or more mRNAs and one or more miRNAs. In some embodiments, the RNA can be cell-free, cellular, or exosomal RNA. In some embodiments, the RNA includes small non-coding RNAs (sncRNAs). In some embodiments, the sncRNAs include microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs), or other RNAs (miscRNAs). In some embodiments, the cell-free sncRNAs are derived from exosomes or microspores.

[0127] In some embodiments, the methods of the present disclosure further comprise isolating cells from the urine sample and extracting proteins from the cells.

[0128] In some embodiments, the methods of the present disclosure further comprise isolating the extracellular vesicles and extracting proteins from the extracellular vesicles.

[0129] In some embodiments, nucleic acids are extracted by using size exclusion. In some embodiments, cell-free DNA or RNA is isolated from cellular DNA or RNA based on size. In some embodiments, nucleic acids are isolated by using affinity chromatography.

[0130] In some embodiments, nucleic acids are preferentially enriched. Nucleic acids may be preferentially enriched by using preferential enrichment at loci or target sites. Such preferential enrichment refers to any method whereby the proportion of nucleic acid molecules corresponding to loci in a nucleic acid mixture after enrichment is higher than the proportion of nucleic acid molecules corresponding to loci in the nucleic acid mixture before enrichment. The method may include selective amplification of nucleic acid molecules corresponding to loci. The method may include removing nucleic acid molecules that do not correspond to loci. The method may include a combination of methods. Enrichment is defined as the proportion of nucleic acid molecules corresponding to loci or targets in the mixture after enrichment divided by the proportion of nucleic acid molecules corresponding to loci or targets in the mixture before enrichment. Preferential enrichment may be performed at multiple loci. In some embodiments of the present disclosure, the enrichment is greater than 20. In some embodiments of the present disclosure, the enrichment is greater than 200. In some embodiments of the present disclosure, the enrichment is greater than 2,000. When preferential enrichment is performed at multiple loci, the enrichment may refer to the average enrichment of all loci in the set of loci.

[0131] Preferential enrichment of nucleic acids depends on the ability of primers or oligos to randomly hybridize to target nucleic acids or nucleic acids and be extended in a polymerase reaction. The term "hybridization" includes reactions in which one or more nucleic acids or polynucleotides react to form a complex stabilized through hydrogen bonds between the bases of nucleotide residues. Hydrogen bonds can occur through Watson-Crick base pairing, Hoogsteen binding, or any other sequence-specific manner. The complex may contain two strands forming a duplex structure, three or more strands forming a multi-stranded complex, a single self-hybridizing strand, or any combination of these. A hybridization reaction may constitute a step in a more extensive process, such as the initiation of a PCR reaction, a primer extension reaction, or the enzymatic cleavage of a polynucleotide by a ribozyme.

[0132] As used herein, the terms "hybridize" and "hybridization" refer to the annealing of a complementary sequence to a target nucleic acid, i.e., the ability of two nucleic acid polymers (polynucleotides) containing complementary sequences to anneal through base pairing. The terms "annealed" and "hybridized" are used interchangeably throughout and are intended to encompass any specific and reproducible interaction between a complementary sequence and a target nucleic acid, including the binding of regions with only partial complementarity. Certain bases not normally found in natural nucleic acids may be included in the nucleic acids of the present invention, such as inosine and 7-deazaguanine. Those skilled in the art of nucleic acid technology can empirically determine duplex stability, taking into account several variables, including, for example, the length of the complementary sequence, the base composition and sequence of the oligonucleotide, the ionic strength, and the incidence of mismatched base pairs. Nucleic acid duplex stability is measured by the melting temperature, or "Tm." The Tm of a particular nucleic acid duplex under specified conditions is the temperature at which, on average, half of the base pairs have dissociated.

[0133] Hybridization reactions can be performed under conditions of different "stringency." The stringency of a hybridization reaction includes the difficulty with which any two nucleic acid molecules hybridize to each other. Under stringent conditions, nucleic acid molecules that are at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, or 100% identical to each other remain hybridized to each other, while molecules with lower percent identity cannot remain hybridized. When hybridization occurs in an antiparallel configuration between two single-stranded polynucleotides, this reaction is called "annealing," and the polynucleotides are described as "complementary." A double-stranded polynucleotide can be "complementary" or "homologous" to another polynucleotide if hybridization can occur between one of the strands of the first polynucleotide and the second polynucleotide. "Complementarity" or "homology" can be quantified in terms of the proportion of bases in opposing strands that are predicted to hydrogen bond with each other according to generally accepted base-pairing rules.

[0134] The term " stringency " refers to the temperature, ionic strength and other compound conditions that nucleic acid hybridization is carried out under.Under " high stringency " conditions, nucleic acid base pairing only occurs between nucleic acid fragments that have a high frequency of complementary base sequences.Therefore, when it is desired that nucleic acids that are not completely complementary to each other are hybridized or annealed, " medium " or " low " stringency conditions are often required.It is well known in the art that many equivalent conditions can be used to include medium or low stringency conditions.

[0135] Amplification refers to the technique of increasing the copy number of nucleic acid molecules.Selective amplification can refer to the technique of increasing the copy number of specific nucleic acid molecules or nucleic acid molecules corresponding to specific regions of nucleic acid molecules.It can also refer to the method of increasing the copy number of specific targeting molecules of target nucleic acid molecules or target regions of nucleic acid molecules more than the copy number of non-targeting molecules or regions of nucleic acid molecules.

[0136] Selective amplification can be a method of preferential enrichment. A universal priming sequence refers to a DNA sequence that can be added to a population of target DNA molecules, for example, by ligation, PCR, or ligation-mediated PCR. Once added to a population of target molecules, primers specific to the universal priming sequence can be used to amplify the target population using a single amplification primer pair. The universal priming sequence is typically not related to the target sequence. A universal adapter, or "ligation adapter" or "library tag," is a DNA molecule containing a universal priming sequence that can be covalently attached to the 5' and 3' ends of a population of target double-stranded DNA molecules. Addition of the adapter provides universal priming sequences at the 5' and 3' ends of the target population, where PCR amplification can be performed, and a single amplification primer pair is used to amplify all molecules from the target population. Targeting refers to a method used to selectively amplify or otherwise preferentially enrich molecules of DNA corresponding to a set of loci in a mixture of DNA.

[0137] Specific nucleic acids may be enriched by using hybrid capture. In some embodiments, preferentially enriching RNA with a plurality of biomarkers comprises obtaining a set of hybrid capture probes, hybridizing the hybrid capture probes to RNA in the sample, and physically separating the hybridized RNA from the sample of RNA from non-hybridized RNA. In some embodiments, preferentially enriching sncRNAs, such as miRNAs, with a plurality of biomarkers comprises obtaining a set of hybrid capture probes, hybridizing the hybrid capture probes to miRNAs in the sample, and physically separating the hybridized miRNAs from the sample of RNA from non-hybridized RNA. In some embodiments, preferentially enriching preselected mRNAs comprises obtaining a set of hybrid capture probes, hybridizing the hybrid capture probes to mRNAs in the sample, and physically separating the hybridized mRNAs from the sample of RNA from non-hybridized RNA.

[0138] In some embodiments, the term target locus refers to a specific target gene or any nucleic acid structure of interest, such as a genetic abnormality. Specifically, genetic abnormalities include, but are not limited to, overexpression of a gene (e.g., an oncogene) or a panel of genes, underexpression of a gene (e.g., a tumor suppressor gene such as p53 or RB) or a panel of genes, alternative splice variants of a gene or a panel of genes, gene copy number variations (CNV) (e.g., DNA double minute chromosomes), nucleic acid modifications (e.g., methylation, acetylation, and phosphorylation), single nucleotide polymorphisms (SNPs), chromosomal rearrangements (e.g., inversions, deletions, and duplications), mutations of a gene or a panel of genes (insertion, deletion, duplication, missense, nonsense, synonymous, or any other nucleotide change) (which often ultimately affect the activity and function of the gene product, leading to alternative transcriptional splice variants and / or changes in gene expression levels), or any combination of the above. In some embodiments, preferentially enriching nucleic acids in a sample at a plurality of polymorphic loci includes obtaining a plurality of pre-circularized probes, each probe targeting one of the polymorphic loci and the 3' and 5' ends of the probes designed to hybridize to a region of a nucleic acid sequence that is a small number of bases away from a polymorphic site at the locus, where the small number is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21-25, 26-30, 31-60, or a combination thereof; hybridizing the pre-circularized probes to nucleic acids from the sample; using a DNA polymerase to fill in gaps between the ends of the hybridized probes; circularizing the pre-circularized probes; and amplifying the circularized probes.

[0139] In some embodiments, preferentially enriching nucleic acids at a plurality of polymorphic loci includes obtaining a plurality of ligation-mediated PCR probes, each PCR probe targeting one of the polymorphic loci and wherein the upstream and downstream PCR probes are designed to hybridize to a region of DNA on one strand of DNA that is only a small number of bases away from the polymorphic site of the locus, where the small number is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21-25, 26-30, 31-60, or a combination thereof; hybridizing the ligation-mediated PCR probes to nucleic acids from the first sample; using a nucleic acid polymerase to fill gaps between the ends of the ligation-mediated PCR probes; ligating the ligation-mediated PCR probes; and amplifying the ligated ligation-mediated PCR probes.

[0140] In some embodiments, preferentially enriching nucleic acids at a plurality of polymorphic loci comprises obtaining a plurality of hybrid capture probes that target the polymorphic loci, hybridizing the hybrid capture probes to nucleic acids in the sample, and physically removing some or all of the unhybridized nucleic acids from the first nucleic acid sample.

[0141] In some embodiments, the hybrid capture probes are designed to hybridize to regions on either side of the polymorphic site but not overlapping. In some embodiments, the hybrid capture probes are designed to hybridize to regions on either side of the polymorphic site but not overlapping, and the length of the capture probes on either side can be selected from the group consisting of less than about 120 bases, less than about 110 bases, less than about 100 bases, less than about 90 bases, less than about 80 bases, less than about 70 bases, less than about 60 bases, less than about 50 bases, less than about 40 bases, less than about 30 bases, and less than about 25 bases. In some embodiments, the hybrid capture probes are designed to hybridize to regions overlapping with the polymorphic site, and the plurality of hybrid capture probes includes at least two hybrid capture probes for each polymorphic locus, and each hybrid capture probe is designed to be complementary to a different allele at the polymorphic locus.

[0142] In some embodiments, preferentially enriching nucleic acids at a plurality of target or polymorphic loci includes obtaining a plurality of inner forward primers, each primer targeting one of the target or polymorphic loci, wherein the 3' end of the inner forward primer is designed to hybridize to a DNA region upstream of the target or polymorphic site and is separated from the polymorphic site by a small number of bases, wherein the small number of bases is selected from the group consisting of 1, 2, 3, 4, 5, 6-10, 11-15, 16-20, 21-25, 26-30, or 31-60 base pairs; and optionally, wherein each primer is designed to hybridize to a DNA region upstream of the target or polymorphic locus and a 3' end of the inner forward primer is separated from the polymorphic site by a small number of bases, wherein the small number of bases is selected from the group consisting of 1, 2, 3, 4, 5, 6-10, 11-15, 16-20, 21-25, 26-30, or 31-60 base pairs. or targeting one of the polymorphic loci, obtaining a plurality of inner reverse primers designed such that the 3' ends of the inner reverse primers hybridize to a nucleic acid region upstream of the target or polymorphic site and are separated from the target locus or polymorphic site by a small number of bases, where the small number of bases is selected from the group consisting of 1, 2, 3, 4, 5, 6-10, 11-15, 16-20, 21-25, 26-30, or 31-60 base pairs; hybridizing the inner primers to the nucleic acid; and amplifying the nucleic acid using polymerase chain reaction to form an amplicon.

[0143] In some embodiments, the method also includes obtaining a plurality of outer forward primers, each targeting one of the polymorphic loci and the outer forward primer designed to hybridize to a nucleic acid region upstream of the inner forward primer; optionally obtaining a plurality of outer reverse primers, each targeting one of the polymorphic loci and the outer reverse primer designed to hybridize to a nucleic acid region immediately downstream of the inner reverse primer; hybridizing the first primer to the nucleic acid; and amplifying the nucleic acid using polymerase chain reaction.

[0144] In some embodiments, the method also includes obtaining a plurality of outer reverse primers, each targeting one of the polymorphic loci and designed to hybridize to a nucleic acid region immediately downstream of the inner reverse primer; optionally obtaining a plurality of outer forward primers, each targeting one of the polymorphic loci and designed to hybridize to a nucleic acid region upstream of the inner forward primer; hybridizing the first primer to the nucleic acid; and amplifying the DNA using polymerase chain reaction.

[0145] In some embodiments, preparing the first sample further comprises adding universal adaptors to nucleic acids in the first sample and amplifying the nucleic acids in the first sample using polymerase chain reaction, wherein at least a portion of the amplified amplicons are less than 100 bp, less than 90 bp, less than 80 bp, less than 70 bp, less than 65 bp, less than 60 bp, less than 55 bp, less than 50 bp, or less than 45 bp, and wherein the portion is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 99%.

[0146] In some embodiments, amplifying nucleic acids is carried out in one or more separate reaction volumes, each separate reaction volume comprising more than 100 different forward and reverse primer pairs, more than 200 different forward and reverse primer pairs, more than 500 different forward and reverse primer pairs, more than 1,000 different forward and reverse primer pairs, more than 2,000 different forward and reverse primer pairs, more than 5,000 different forward and reverse primer pairs, more than 10,000 different forward and reverse primer pairs, more than 20,000 different forward and reverse primer pairs, more than 50,000 different forward and reverse primer pairs, or more than 100,000 different forward and reverse primer pairs.

[0147] In some embodiments, preparing the sample further comprises dividing the sample into multiple portions, with the nucleic acids in each portion preferentially enriched at a subset of the multiple polymorphic loci. In some embodiments, the inner primers are selected by identifying primer pairs that are likely to form undesired primer duplexes and removing at least one of the identified primer pairs that are likely to form undesired primer duplexes from the multiple primers. In some embodiments, the inner primers include a region designed to hybridize either upstream or downstream of the targeted polymorphic locus, and optionally include a universal priming sequence designed to enable PCR amplification. In some embodiments, at least some of the primers further include a random region that varies for each individual primer molecule. In some embodiments, at least some of the primers further include a molecular barcode.

[0148] In some embodiments, the method includes (a) performing a multiplex polymerase chain reaction (PCR) on a nucleic acid sample containing target loci to simultaneously amplify at least 1,000 different target loci in a single reaction volume using (i) at least 1,000 different primer pairs, or (ii) at least 1,000 target-specific primers and either a universal primer or a tag-specific primer, to generate amplification products containing target amplicons, and (b) sequencing the amplification products. In some embodiments, the method does not include using a microarray.

[0149] In some embodiments, the method includes (a) performing a multiplex polymerase chain reaction (PCR) on a cell-free DNA sample containing target loci to simultaneously amplify at least 1,000 different target loci in a single reaction volume using (i) at least 1,000 different primer pairs, or (ii) at least 1,000 target-specific primers and either a universal primer or a tag-specific primer, to generate amplification products containing target amplicons, and (b) sequencing the amplification products. In some embodiments, the method does not include using a microarray.

[0150] In some embodiments, mRNA is isolated by using a probe that hybridizes to the polyA tail of the mRNA molecule.

[0151] Target genes and loci and protein targets The nucleic acids may include target loci or target genes indicative of an immune response or various diseases or conditions described elsewhere herein. In some embodiments, the target loci include one or more distinct sets of target loci. In some embodiments, the target loci include a set of target genes associated with a kidney rejection state, such as non-rejection, T-cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or a mixed TCMR and ABMR disease state. In some embodiments, the TCMR further includes a molecularly defined TCMR (mTCMR) and / or a potential TCMR (pTCMR). In some embodiments, the ABMR further includes a molecularly defined ABMR (mABMR) and / or a potential ABMR (pABMR).

[0152] In some embodiments, the target gene or set of target genes (including the mRNA, miRNA, or protein expressed by or associated with the target genes) is associated with inflammation, allograft rejection, T cell activation and / or differentiation, B cell activation and / or differentiation, cytokine response, and / or chemokine response. In some embodiments, the target gene or set of target genes (including the mRNA, miRNA, or protein expressed by or associated with the target genes) is associated with apoptosis.

[0153] In some embodiments, the one or more mRNAs and / or miRNAs are associated with antibody-mediated transplant rejection (AMTR), T-cell-mediated transplant rejection (TMTR), apoptotic pathways, cytokines, antimicrobial responses, and / or inflammatory cell responses.

[0154] In some embodiments, the one or more mRNAs and / or miRNAs associated with the antimicrobial response are C-X-C motif chemokine ligand (CXCL) type genes.

[0155] In some embodiments, the one or more mRNAs [Table 10-1] [Table 10-2] [Table 10-3] [Table 10-4] [Table 10-5] and combinations thereof.

[0156] In some embodiments, the one or more miRNAs [Table 11-1] [Table 11-2] [Table 11-3] [Table 11-4] [Table 11-5] and combinations thereof.

[0157] In some embodiments, the one or more mRNAs [Table 12] and combinations thereof.

[0158] In some embodiments, the one or more miRNAs [Table 13] and combinations thereof.

[0159] In some embodiments, the one or more mRNAs [Table 14] and combinations thereof.

[0160] In some embodiments, the one or more miRNAs [Table 15] and combinations thereof.

[0161] In some embodiments, the one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, The gene is expressed from a gene selected from the group consisting of Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

[0162] In some embodiments, the one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lc k, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

[0163] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

[0164] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

[0165] In some embodiments, the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, PSMB10, B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

[0166] In some embodiments, the one or more mRNAs are not expressed from a gene selected from the group consisting of PDCD1, MARCHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

[0167] In some embodiments, the one or more mRNAs are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

[0168] In some embodiments, one or more miRNAs are miR-186-5p, miR-665, miR-873-5p.1, miR-543, miR-330-3p, miR-362-5p / 500b-5p, miR-217, miR-140-5p, miR-193-3p, miR-382-5p, miR-140-3p.2, miR-653-5p, miR-455-3p.2, miR-145-5p, miR-491-5p, miR-23-3p, miR-375, miR-129-5p, miR-96-5p / 1271-5p, miR-182-5p, miR-371-5p, miR-203a-3p.1, miR-494-3p, miR-146-5p, miR-140-3p.1, miR-125-5p, miR-346, miR-760, miR-185-5p, miR-325-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-423-5p, miR-496.1, miR-155-5p, miR-142-3p.2, miR-24-3p, miR-874-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-181-5p, miR-142-5p, miR-130-3p / 301-3p / 454-3p, miR-21-5p / 590-5p, miR-103-3p / 107, miR-137, miR-340-5p, miR-490-3p, miR-143-3p, miR-409-3p, miR-27-3p, miR-138-5p, miR-485-5p, miR-328-3p, miR-326, miR-148-3p / 152-3p, miR-9-5p, miR-31-5p, miR-452-5p / 892-3p, miR-202-5p, miR-29-3p, miR-338-3p, miR-26-5p, let-7-5p / 98-5p, miR-196-5p, miR-30-5p, miR-142-3p.1, miR-19-3p, miR-411-3p, miR-493-5p, miR-218-5p, miR-203a-3p.2, miR-495-3p, miR-425-5p, miR-135-5p, miR-154-3p / 487-3p, miR-223-3p, miR-219-5p, miR-670-3p, miR-216b-5p, miR-200bc-3p / 429, miR-320, m iR-216a-5p, miR-141-3p / 200a-3p, miR-144-3p, miR-128-3p, miR-455-3p.1, miR-219a-2-3p, miR-873-5p.2, miR-448, miR-183-5p.2, miR-374-5p miR-505-3p.1, miR-433-3p, miR-377-3p, miR-365-3p, miR-124-3p.1, miR-410-3p, miR-199-3p, miR-22-3p, miR-129-3p, miR-383-5p.1, miR-1-3p / 206, miR-296-5p, miR-299-3p, miR-212-5p, miR-331-3p, miR-378-3p, miR-136-5p, miR-1193, miR-505-3p.2, miR-302c-3p.2 / 520-3p, miR-421, miR -499a-5p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-124-3p.2 / 506-3p, miR-34-5p / 449-5p, miR-376c-3p, miR-139-5p, miR-221-3p / 222-3p, miR-5 04-5p.1、miR-335-5p、miR-101-3p.1、miR-431-5p、miR-489-3p、miR-369-3p、miR-330-3p.2、miR-18-5p、miR-28-5p / 708-5p、miR-133a-3p.2 / 133b、 miR-205-5p, miR-199-5p, miR-455-5p, miR-126-3p.2, miR-7-5p, miR-483-3p.2, miR-668-3p, miR-1306-5p, miR-150-5p, miR-296-3p, miR-204-5p / 211-5p, miR-3064-5p, miR-532-5p, miR-876-5p, miR-501-3p / 502-3p, miR-33-5p, miR-153-3p, miR-214-5p, miR-655-3p, miR-342-3p, miR-133a-3p.1. It is selected from the group consisting of miR-411-5p.1, miR-496, miR-411-5p.2, miR-582-5p, miR-381-3p, miR-188-5p, miR-383-5p.2, miR-486-5p, miR-183-5p.1, miR-208-3p, miR-193a-5p, miR-101-3p.2, miR-542-3p, miR-190-5p, miR-299-5p, miR-154-5p, miR-802, miR-323-3p, miR-532-3p, miR-224-5p, miR-339-5p, miR-194-5p, miR-149-5p, miR-493-3p, miR-382-3p, miR-132-3p / 212-3p, miR-1197, miR-99-5p / 100-5p, miR-877-5p, miR-483-3p.1, miR-10-5p, miR-361-5p, miR-539-3p, miR-191-5p, miR-329-3p / 362-3p, miR-122-5p, miR-379-5p, miR-376-3p, miR-1298-5p, miR-451, miR-210-3p, miR-1224-5p, miR-324-5p, miR-544a-5p, miR-488-3p, miR-758-3p, miR-151-3p, miR-875-5p, miR-134-5p, miR-192-5p / 215-5p, and miR-127-3p.

[0169] In some embodiments, one or more miRNAs are miR-96-5p / 1271-5p, miR-493-5p, miR-183-5p.2, miR-150-5p, miR-7-5p, miR-653-5p, miR-200bc-3p / 429, miR-212-5p, miR-1298-5p, miR-137, miR-758-3p, miR-325-3p, miR-542-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-495-3p, miR-30-5p, miR-873-5p.1, miR-146-5p, miR-505-3p.1, miR-539-3p, miR-216a-5p, miR-216b-5p, miR-340-5p, miR-361-5p, miR-338-3p, miR-217, miR-9-5p, miR-219a-2-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-199-3p, miR-1-3p / 206, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-142-5p, miR-302c-3p.2 / 520-3p, miR-326, miR-760, miR-138-5p, miR-27-3p, miR-145-5p, miR-142-3p.2, miR-101-3p.2, miR-182-5p, miR-203a-3p.2, miR-140-3p.1, miR-183-5p.1, miR-144-3p, miR-101-3p.1, miR-330-3p, miR-224-5p, miR-148-3p / 152-3p, miR-485-5p, miR-122-5p, miR-155-5p, miR-320, miR-23-3p, miR-124-3p.2 / 506-3p, miR-135-5p, miR-381-3p, miR-26-5p, miR-1224-5p, miR-192-5p / 215-5p, miR-1249-3p, miR-125-5p, miR-483-3p.2, miR-668-3p, miR-223-3p, miR-655-3p, miR-382-5p, miR-130-3p / 301-3p / 454-3p, miR-19-3p, miR-582-5p, miR-194-5p, miR-802, miR-483-3p.1, miR-382-3p, miR-129-5p, miR-3064-5p, miR-873-5p.2, miR-499a-5p, miR-128-3p, miR-532-5p, miR-296-5p, miR-744-5p, miR-425-5p, miR-218-5p, and miR-496.1.

[0170] In some embodiments, the one or more proteins are [Table 16-1] [Table 16-2] [Table 16-3] [Table 16-4] [Table 16-5] and combinations thereof.

[0171] In some embodiments, the one or more proteins are [Table 17] and combinations thereof.

[0172] In some embodiments, the one or more proteins are [Table 18] and combinations thereof.

[0173] In some embodiments, the one or more proteins are CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal , Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

[0174] In some embodiments, the one or more proteins are CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lc k, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

[0175] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

[0176] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

[0177] In some embodiments, the one or more proteins are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, PSMB10, B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

[0178] In some embodiments, the one or more proteins are not expressed from a gene selected from the group consisting of PDCD1, MARCHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

[0179] In some embodiments, the one or more proteins are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

[0180] Samples and methods for isolating nucleic acids from samples In some embodiments, the nucleic acid sample comprises fragmented or digested nucleic acid. In some embodiments, the nucleic acid sample comprises DNA such as genomic DNA, cDNA, cell-free DNA (cfDNA), cell-free mitochondrial DNA (cfmDNA), cell-free DNA derived from nuclear DNA (cfnDNA), cellular DNA, or mitochondrial DNA.

[0181] In some embodiments, the nucleic acid sample comprises RNA such as cfRNA, cellular RNA, cytoplasmic RNA, coding cytoplasmic RNA, non-coding cytoplasmic RNA, mRNA, miRNA, mitochondrial RNA, rRNA, or tRNA. In some embodiments, the nucleic acid sample comprises DNA from a single cell, 2 cells, 3 cells, 4 cells, 5 cells, 6 cells, 7 cells, 8 cells, 9 cells, 10 cells, or more than 10 cells. In some embodiments, the nucleic acid sample is a substantially cell-free urine sample. In some embodiments, the target locus is a fragment of human nucleic acid found in the human genome. In some embodiments, the target locus comprises or consists of a single nucleotide polymorphism (SNP).

[0182] In some embodiments, the method includes isolating or purifying DNA and / or RNA. There are several standard procedures known in the art to achieve such a goal. In some embodiments, the sample may be centrifuged to separate the various layers. In some embodiments, DNA or RNA may be isolated using filtration. In some embodiments, DNA or RNA preparation may involve amplification, separation, chromatographic purification, liquid separation, isolation, preferential enrichment, preferential amplification, target amplification, or any of several other techniques known in the art or described herein. In some embodiments for DNA isolation, RNase is used to degrade RNA. In some embodiments for RNA isolation, DNase (such as DNase I from Invitrogen, Carlsbad, CA, USA) is used to degrade DNA. In some embodiments, an RNeasy™ Mini Kit (Qiagen) is used to isolate RNA according to the manufacturer's protocol. In some embodiments, small RNAs are isolated using the mirVana™ PARIS kit (Ambion, Austin, Texas, USA) according to the manufacturer's protocol (Gu et al., J. Neurochem. 122:641-649, 2012, the entirety of which is incorporated herein by reference). RNA concentration and purity can optionally be determined using Nanovue (GE Healthcare, Piscataway, NJ, USA), and RNA integrity can optionally be measured using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, Calif., USA) (Gu et al., J. Neurochem. 122:641-649, 2012, the entirety of which is incorporated herein by reference). In some embodiments, TRIZOL or RNAlater™ (Ambion) is used to stabilize RNA during storage.

[0183] In some embodiments, adapters are added to generate a sequencing library. Prior to ligation, the sample DNA may be blunt-ended, and then a single adenosine base is added to the 3' end. In some embodiments, the ligation of the adapter to the nucleic acid is cohesive end ligation. Prior to ligation, the DNA may be cleaved using a restriction enzyme or some other cleavage method. During ligation, the 3-prime adenosine of the sample fragment and the complementary 3-prime tyrosine overhang of the adapter can increase ligation efficiency. In some embodiments, adapter ligation is performed using a ligation kit found in the AGILENT SURESELECT™ kit.

[0184] In some embodiments, the library is amplified using universal primers. In one embodiment, the amplified library is fractionated by size separation or by using products such as AGENCOURT AMPURE™ beads or other similar methods. In some embodiments, PCR amplification is used to amplify the target loci. In some embodiments, the amplified DNA is sequenced (such as by sequencing using an ILLUMINA IIGAX™ or HiSeq sequencer). In some embodiments, the amplified DNA is sequenced from each end of the amplified DNA to reduce sequencing errors. If a sequence error exists at a particular base when sequencing from one end of the amplified DNA, there is less likely to be a sequence error in the complementary base when sequencing from the other end of the amplified DNA (compared to multiple sequencing from the same end of the amplified DNA).

[0185] In some embodiments, miRNAs can be separated from RNA fragments caused by degradation because degraded RNAs lose their terminal phosphorylation groups. miRNAs retain their terminal phosphorylation groups. Adapters can ligate to phosphorylated miRNA ends, but adapters do not ligate to unphosphorylated RNA species, such as degraded mRNA. Adapters can include sequences that allow primer binding to support reverse transcription, selectively generating complementary DNA (cDNA) from RNA molecules produced by target genes of interest.

[0186] As non-limiting examples, a locus can be a single nucleotide polymorphism, an intron, or an exon. In some embodiments, a locus can include an insertion, deletion, or rearrangement.

[0187] In some embodiments, floating DNA or RNA is isolated. Free-floating or cell-free DNA typically exists in fragments of approximately 160 nucleotides in length. In some embodiments, free-floating DNA is isolated using EDTA-2Na tubes after centrifugation to remove cell debris and platelets. Plasma samples can be stored at -80°C until DNA is extracted, for example, using a QIAamp™ DNA Mini Kit (Qiagen, Hilden, Germany) (e.g., Hamakawa et al., Br J Cancer. 2015;112:352-356). However, samples can be derived from other sources, and nucleic acid molecules from any organism can be used in this method. In some embodiments, DNA derived from bacteria and / or viruses can be used to analyze true sequence variants within mixed populations, particularly in environmental and biodiversity sampling.

[0188] Many kits and methods for generating libraries of nucleic acid molecules for subsequent sequencing are known in the art. Kits specifically adapted for preparing libraries from small nucleic acid fragments, particularly circulating cell-free DNA, can be useful for implementing the methods provided herein. For example, the NEXTflex™ Cell-Free Kit (Bioo Scientific, Austin, Texas) or the Natera Library Prep Kit (Natera, San Carlos, California). Such kits are typically modified to include adapters customized for the amplification and sequencing steps of the methods provided herein. Adapter ligation can also be performed using commercially available kits, such as the ligation kit found in the Agilent SureSelect™ Kit (Agilent, Santa Clara, California).

[0189] The sample nucleic acid molecule is composed of natural or unnatural ribonucleotides or deoxyribonucleotides linked via phosphodiester bonds. Furthermore, the sample nucleic acid molecule is composed of a nucleic acid fragment targeted for sequencing. The sample nucleic acid molecule can be or contain a nucleic acid fragment of at least 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotides in length. In any of the embodiments disclosed herein, the sample nucleic acid molecules or nucleic acid fragments may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 nucleotides in length at the lower end of the range and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 nucleotides in length at the upper end of the range. can be between 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 and 10,000 nucleotides in length. In some embodiments, the nucleic acid molecule can be a fragment of genomic DNA and can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 nucleotides in length at the lower end of the range and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 nucleotides in length at the upper end of the range. can be between 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, and 10,000 nucleotides in length. For clarity, nucleic acids initially isolated from biological tissues, fluids, or cultured cells can be much longer than the sample nucleic acid molecules processed using the methods herein.As discussed herein, for example, such initially isolated nucleic acid molecules can be fragmented to generate nucleic acid fragments before use in the methods herein. In some embodiments, the nucleic acid molecule and the nucleic acid fragment can be identical. The sample nucleic acid molecule or sample nucleic acid fragment can include a target locus containing the nucleotide(s) being queried, particularly a single nucleotide polymorphism or single nucleotide mutation. In any of the disclosed embodiments, the target locus can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotides in length and can include part or all of the sample nucleic acid molecule and / or sample nucleic acid fragment. In other embodiments, the target locus is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500 nucleotides in length at the lower end of the range and 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500 nucleotides in length at the higher end of the range. In some embodiments, the target loci of different sample nucleic acid molecules may be at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, 99.9%, or 100% identical. In some embodiments, target loci of different sample nucleic acid molecules can share at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, 99.9% or 100% sequence identity.

[0190] In some embodiments, the entire sample nucleic acid molecule is a sample nucleic acid fragment. For example, in certain embodiments, the entire nucleic acid molecule can be a sample nucleic acid fragment, for example, in which an adapter is directly linked to the end of the sample nucleic acid molecule, or to a nucleic acid(s) linked to the end of the sample nucleic acid molecule, or to a primer that binds to a sequence at the end of the sample nucleic acid fragment, or, as further described herein, an adapter such as a universal adapter is added thereto. In other embodiments, for example, in certain embodiments in which an adapter is added to the sample nucleic acid molecule as part of a primer that targets an internal binding site at the end of the sample nucleic acid molecule, a portion of the sample nucleic acid molecule can be a sample nucleic acid fragment targeted for downstream sequencing. For example, at least 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% of the sample nucleic acid molecule can be a nucleic acid fragment.

[0191] In some embodiments, the sample nucleic acid molecules are a mixture of nucleic acids isolated from natural sources, with some sample nucleic acid molecules having identical sequences, some sharing sequences with at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, or 99% sequence identity, and some having less than 50%, 40%, 30%, 20%, 10%, or 5% sequence identity across a range from a lower limit of 20, 25, 50, 75, 100, 125, 150, 200, or 250 nucleotides to an upper limit of 50, 75, 100, 125, 150, 200, 250, 300, 400, or 500 nucleotides. Such sample nucleic acid molecules may be nucleic acid samples isolated from mammalian tissues or bodily fluids, such as humans, without enriching for certain sequences over others. In other embodiments, target sequences, such as those from a gene of interest, may be enriched before performing the methods provided herein.

[0192] Methods for identifying target genes for constructing a molecular classifier of kidney rejection status Identification of one or more mRNAs and / or miRNAs derived from target genes associated with kidney transplant rejection and found to be present in urine samples from subjects with known kidney rejection status can be achieved by text mining databases. Artificial intelligence can be used to text mine and predict known target genes of interest for kidney transplant rejection and kidney health.

[0193] In some embodiments, the one or more transplant rejection scores are generated using predictive models, machine learning-based methods, and / or artificial intelligence methods.

[0194] In some embodiments, one or more transplant rejection scores are calculated using methods such as logistic regression (LogReg), t-test, violin plot, random forest (RE), neural networks, decision tree machine learning analysis, decision tree classification techniques, analysis of variance (ANOVA), Bayesian networks, boosting and adaboost, bootstrap aggregation (or bagging) algorithms, classification and regression trees (CART), boosted CART, recursive partitioning trees (RPART), Curds and Whey (CW), Curds and Kernel-based machine algorithms such as Whey-Lasso, Principal Component Analysis (PCA), Factor Rotation or Factor Analysis, Discriminant Analysis, Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Quadratic Discriminant Analysis, Discriminant Function Analysis (DFA), Factor Rotation or Factor Analysis, Genetic Algorithms, Hidden Markov Models, Kernel Density Estimation, Kernel Partial Least Squares Algorithm, Kernel Matching Pursuit Algorithm, Kernel Fisher Discriminant Analysis Algorithm, Kernel Principal Component Analysis Algorithm, Linear Regression and Generalized Linear Models, Forward Linear Stepwise Regression, Lasso (or LASSO) Shrinkage and Selection Method, Elasticity The data may be generated using methods such as KNN (Knearest Neighbor Method), KNN (Lasso and Elastic Net Regularized Generalized Linear Models), KNN (Knearest Neighbor Method ...

[0195] In some embodiments, the one or more transplant rejection scores are generated using logistic regression (LogReg), random forest (RE), neural network, or decision tree machine learning analysis.

[0196] In some embodiments, the one or more mRNAs and / or miRNAs are considered by using eight separate machine learning classifier methods based on six determined kidney disease states. In some embodiments, the one or more transplant rejection scores include a first transplant rejection score based on a set of mRNAs and / or miRNAs associated with TCMR and a second transplant rejection score based on a set of mRNAs and / or miRNAs associated with ABMR. In some embodiments, the one or more transplant rejection scores include a transplant rejection score based on a set of mRNAs and / or miRNAs associated with inflammation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with allograft rejection, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with T cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with B cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with cytokine response, and / or a transplant rejection score based on a set of mRNAs and / or miRNAs associated with chemokine response. In some embodiments, the one or more transplant rejection scores comprise a transplant rejection score based on a set of proteins associated with inflammation, a transplant rejection score based on a set of proteins associated with allograft rejection, a transplant rejection score based on a set of proteins associated with T cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with B cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with cytokine response, and / or a transplant rejection score based on a set of proteins associated with chemokine response.

[0197] In some embodiments, the performance of the assay for determining renal transplant rejection or risk of renal transplant rejection is measured and characterized by an AUC value of about 0.6 to about 0.99, about 0.7 to about 0.99, about 0.8 to about 0.99, about 0.9 to about 0.99, about 0.7 to about 0.79, about 0.8 to about 0.89, about 0.6 to about 0.89, or about 0.6 to about 0.79.

[0198] In some embodiments, the AUC value is from about 0.8 to about 0.99.

[0199] In some embodiments, the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk that is predictive of clinical assessment of kidney disease state and that can distinguish between two or more kidney disease states.

[0200] Determining a set of target genes useful for determining a transplant rejection score provides a quantitative value for the risk of kidney transplant rejection or the presence or absence of kidney transplant rejection.

[0201] The set of target genes found to be differentially expressed in urine across different renal rejection states can be used to build a machine learning classifier to distinguish between different renal rejection or disease states. In particular, the classifier is built to distinguish between T cell-mediated (TCMR) or antibody-mediated renal rejection (ABMR). Furthermore, the classifier can determine probable TCMR (pTCMR) or probable ABMR (pABMR), or a mixed state of TCMR and ABMR (referred to as "mixed").

[0202] Classifiers can also be used to determine kidney rejection status characterized by apoptosis, cytotoxic T cell infiltration, cytokine response, chemokine response, parenchymal deterioration, atrophy, fibrosis, or a combination thereof.

[0203] For example, a molecular classifier for ABMR can be constructed by selecting multiple differentially expressed genes in ABMR and other renal rejection states. A molecular classifier for TCMR can be constructed by selecting multiple differentially expressed genes in TCMR and other renal rejection states.

[0204] The performance of a classifier can be determined by calculating the area under the curve (AUC) of a receiver operating characteristic (ROC) curve, which plots the true positive rate (sensitivity) and the false positive rate (specificity). In some embodiments, performance in determining renal transplant rejection or risk of renal transplant rejection is measured and characterized by an AUC value of about 0.6 to about 0.99, about 0.7 to about 0.99, about 0.8 to about 0.99, about 0.9 to about 0.99, about 0.7 to about 0.79, about 0.8 to about 0.89, about 0.6 to about 0.89, or about 0.6 to about 0.79.

[0205] Example 1 provides further details of this process and an illustrative example thereof.

[0206] Combining cell-free DNA and RNA measurements to assess and / or monitor transplant rejection In some embodiments, the methods herein include (i) measuring the amount of donor-derived cell-free DNA in a sample obtained from the renal transplant recipient, and extracting cell-free DNA from the sample obtained from the renal transplant recipient, wherein the extracted cell-free DNA comprises donor-derived cell-free DNA and recipient-derived cell-free DNA; (ii) performing targeted amplification of the extracted DNA at 50 to 50,000 target loci in a single reaction volume; (iii) sequencing the amplified DNA by high-throughput sequencing to obtain sequencing reads, and determining the amount of donor-derived cell-free DNA based on the sequencing reads. and determining kidney transplant rejection based on whether the amount of donor-derived cell-free DNA, or a function thereof, exceeds a cutoff threshold for cell-free DNA amount indicative of kidney transplant rejection, wherein kidney transplant rejection is determined based on (a) whether the amount of donor-derived cell-free DNA, or a function thereof, exceeds a cutoff threshold for cell-free DNA amount indicative of kidney transplant rejection, and (b) one or more transplant rejection scores that provide a quantitative value for kidney transplant rejection risk or the presence or absence of kidney transplant rejection, determined from measuring mRNA and / or miRNA in a urine sample from the kidney transplant recipient as described elsewhere herein. In some embodiments, the amount of one or more mRNA and / or miRNA is measured relative to housekeeping genes.

[0207] In some embodiments, the combination of the amount of mRNA target selected from a group of preselected targets and the amount of cfDNA in sample indicates transplant rejection or kidney transplant status.In another aspect, the rejection risk of transplant recipient can be determined based on the amount of miRNA, which provides a quantitative value of kidney transplant rejection risk or status, and the amount of cell-free DNA, which indicates kidney transplant rejection.In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured relative to housekeeping genes.

[0208] Determining the risk of rejection in transplant recipients In some embodiments, the rejection risk or kidney disease status of a kidney transplant recipient is determined using logistic regression, random forest, or decision tree machine learning analysis. In some embodiments, the machine learning analysis incorporates as a parameter the amount of RNA (mRNA or miRNA from a target gene) in a sample from the transplant recipient, or a function thereof. In some embodiments, the machine learning analysis incorporates as a parameter the number of RNA / DNA reads, or a function thereof. In some embodiments, the machine learning analysis incorporates as a parameter the estimated ratio of donor-derived RNA to total RNA. In some embodiments, the machine learning analysis incorporates as a parameter the amount of cell-free DNA, the number of cell-free DNA reads, or the estimated ratio of cell-free DNA to total cell-free DNA in a sample from the transplant recipient. In some embodiments, the machine learning analysis incorporates as a parameter the amount of a plurality of proteins derived from the kidney graft. In some embodiments, the machine learning analysis further incorporates as a parameter the amount of total cell-free DNA in a sample from the transplant recipient, or a function thereof. In some embodiments, the machine learning analysis further incorporates as a parameter the number of total cell-free DNA reads, or a function thereof.

[0209] Machine learning is disclosed in WO2020 / 018522, entitled "Methods and Systems for calling Ploidy States using a Neural Network," filed July 16, 2019 as PCT / US2019 / 041981, which is incorporated herein by reference in its entirety.

[0210] In some embodiments, the cutoff thresholds or rejection scores referred to herein take into account the patient's weight, BMI, or blood volume. In some embodiments, the cutoff threshold or rejection score takes into account one or more of donor genome copies per volume of plasma, cell-free DNA yield per volume of plasma, donor height, donor weight, donor age, donor sex, donor ethnicity, donor organ mass, donor organ, living versus deceased donor, familial relationship (or lack thereof) between donor and recipient, recipient height, recipient weight, recipient age, recipient sex, recipient ethnicity, creatinine, eGFR (estimated glomerular filtration rate), cfDNA methylation, DSA (donor-specific antibodies), KDPI (Kidney Donor Profile Index), medications (immunosuppressants, steroids, blood thinners, etc.), infections (BKV, EBV, CMV, UTI), recipient and / or donor HLA allele or epitope mismatch, Banff classification of renal allograft pathology, and legitimate versus surveillance or protocol biopsy.

[0211] The methods disclosed herein may have at least 50% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 50% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 60% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 70% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 80% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 90% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have at least 99% sensitivity and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have a sensitivity of 70-99% and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have a sensitivity of 80-99% and a 95% confidence interval in determining renal rejection or disease state. The methods disclosed herein may have a sensitivity of 70-89% and a 95% confidence interval in determining renal rejection or disease state.

[0212] Some embodiments use either a fixed threshold of donor nucleic acids per urine volume, or a non-fixed threshold, such as adjusted or scaled as described herein. The method for determining this may be based on building an algorithm that maximizes performance using a training data set. Other data, such as patient weight, age, or other clinical factors, may also be taken into account.

[0213] In some embodiments, the method further includes determining the occurrence or likelihood of transplant rejection using the amount of donor-derived cell-free DNA in the urine sample. In some embodiments, the occurrence or likelihood of transplant rejection is determined by comparing the amount of donor-derived cell-free DNA to a cutoff threshold, and the cutoff threshold is adjusted or scaled according to the amount of total cell-free DNA. In some embodiments, the cutoff threshold is a function of the number of donor-derived cell-free DNA reads.

[0214] In some embodiments, the method includes applying a scale or dynamic threshold metric that takes into account the amount of total cfDNA in the sample to more accurately assess transplant rejection. In some embodiments, the method further includes flagging the sample if the amount of total cell-free DNA is above a predetermined value. In some embodiments, the method further includes flagging the sample if the amount of total cell-free DNA is below a predetermined value.

[0215] RNA, DNA, or protein may be extracted from a sample from the transplant recipient, the sample including urine.

[0216] In some embodiments, the machine learning analysis further incorporates time since transplant as a parameter. In some embodiments, the machine learning analysis further incorporates age of the transplant recipient and / or transplant donor as a parameter. In some embodiments, the machine learning analysis further incorporates gender of the transplant recipient and / or transplant donor as a parameter.

[0217] In some embodiments, the transplant recipient's risk of rejection is determined with a sensitivity of at least 0.81, or at least 0.82, or at least 0.83, or at least 0.84, or at least 0.85, or at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90. In some embodiments, the transplant recipient's risk of rejection is determined with a specificity of at least 0.81, or at least 0.82, or at least 0.83, or at least 0.84, or at least 0.85, or at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90. In some embodiments, the transplant recipient's risk of rejection is determined by an area under the curve (AUC) of at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90, or at least 0.91, or at least 0.92, or at least 0.93, or at least 0.94, or at least 0.95.

[0218] In some embodiments, the rejection status of the transplant recipient is determined with a sensitivity of at least 0.81, or at least 0.82, or at least 0.83, or at least 0.84, or at least 0.85, or at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90. In some embodiments, the rejection status of the transplant recipient is determined with a specificity of at least 0.81, or at least 0.82, or at least 0.83, or at least 0.84, or at least 0.85, or at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90. In some embodiments, the rejection status of the transplant recipient is determined by an area under the curve (AUC) of at least 0.86, or at least 0.87, or at least 0.88, or at least 0.89, or at least 0.90, or at least 0.91, or at least 0.92, or at least 0.93, or at least 0.94, or at least 0.95.

[0219] Method for measuring the amount of nucleic acid In some embodiments, the amount of RNA is measured by quantitative PCR. In some embodiments, the amount of RNA is measured by real-time PCR. In some embodiments, the amount of RNA is measured by digital PCR. In some embodiments, the amount of RNA is measured by sequencing, such as high-throughput sequencing, next-generation sequencing, or sequencing by synthesis.

[0220] In some embodiments, the amount of nucleic acid (e.g., RNA and / or DNA) from the donor is determined by using ratiometric and / or machine learning artificial intelligence comparisons at single or multiple time points. In some embodiments, the amount of mRNA from the donor is determined by using ratiometric and / or machine learning artificial intelligence comparisons at single or multiple time points. In some embodiments, the amount of miRNA from the donor is determined by using ratiometric and / or machine learning artificial intelligence comparisons at single or multiple time points.

[0221] In some embodiments, the amount of RNA or cell-free DNA is measured by quantitative PCR. In some embodiments, the amount of mRNA is measured by quantitative PCR. In some embodiments, the amount of miRNA is measured by quantitative PCR. In some embodiments, the quantitative PCR comprises real-time PCR or digital PCR.

[0222] In some embodiments, the amount of mRNA or cell-free DNA is measured by massively multiplexed PCR (mmPCR) to obtain amplicons containing the biomarkers and sequencing the amplicons.

[0223] In some embodiments, the amount of nucleic acid (eg, mRNA, miRNA, or cell-free DNA) is measured by using a microarray.

[0224] In some embodiments, the amount of nucleic acid (e.g., mRNA, miRNA, or cell-free DNA) is measured by using molecular barcodes and microscopic imaging (such as NanoString nCounter®).

[0225] In some embodiments, amplifying RNA comprises performing a reverse transcriptase to obtain complementary DNA (cDNA). In some embodiments, preparing a composition of extracted nucleic acid or a fraction thereof in step (a) comprises amplifying cDNA derived from the nucleic acid.

[0226] In some embodiments, the amplification comprises performing multiplex targeted amplification of cDNA at 10-50,000 target loci in a single reaction volume.

[0227] In some embodiments, the amplification comprises universal amplification.

[0228] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using quantitative PCR, real-time PCR, digital PCR, or sequencing.

[0229] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using multiplex quantitative PCR, multiplex real-time PCR, and / or multiplex digital PCR.

[0230] In some embodiments, the sequencing comprises next-generation whole genome sequencing.

[0231] In some embodiments, the abundance of one or more mRNAs and / or miRNAs is measured by using a microarray.

[0232] In some embodiments, the amount of one or more mRNAs and / or miRNAs is measured by using molecular barcodes and microscopic imaging (such as NanoString nCounter®).

[0233] In some embodiments, the amount of one or more mRNAs and / or miRNAs is determined by measuring the absolute copy number of one or more mRNAs and / or miRNAs per amount of total nucleic acid in the urine sample.

[0234] In some embodiments, the amount of nucleic acid is measured by target amplification. In some embodiments, the amount of a specific mRNA target is measured by target amplification. In some embodiments, the target amplification comprises PCR. In some embodiments, the primers for target amplification comprise 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 pairs of forward and reverse PCR primers. In some embodiments, target amplification is performed using 500 to 20,000, 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 primer pairs in a single reaction. This involves performing amplification at 100-20,000, 500-20,000, 1,000-10,000, 200-500, 500-1,000, 1,000-2,000, 2,000-5,000, 5,000-10,000, 10,000-20,000, or 20,000-50,000 target loci to obtain amplification products.

[0235] In some embodiments, the target amplification comprises nested PCR. In some embodiments, the primers for target amplification comprise a first universal primer and 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 target specific primers. and a second universal primer and 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 internal target-specific primers. In some embodiments, target amplification is performed using a first universal primer and 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 target-specific primers. The amplification method includes using a nucleotide sequence to perform amplification at 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 target loci in a single reaction volume to obtain amplification products.In some embodiments, target amplification is performed using a second universal primer and 10 to 50,000, 100 to 50,000, 200 to 50,000, 500 to 20,000, or 1,000 to 10,000, 200 to 500, 500 to 1,000, 1,000 to 2,000, 2,000 to 5,000, 5,000 to 10,000, 10,000 to 20,000, or 20,000 to 50,000 inner target-specific primers. Using primers, amplification is performed at 10-50,000, 100-50,000, 200-50,000, 500-20,000, or 1,000-10,000, 200-500, 500-1,000, 1,000-2,000, 2,000-5,000, 5,000-10,000, 10,000-20,000, or 20,000-50,000 target loci in a single reaction volume to obtain amplification products. In some embodiments, the methods disclosed herein comprise PCR amplification of at least 10, at least 100, at least 500, at least 1000, at least 2000 biomarkers from 10-1000, 100-10000, 200-50000, or 500-20000 RNA biomarkers using at least 10, at least 100, at least 500, at least 1000, at least 2000, 10-1000, 100-10000, 200-50000, 500-20000 pairs of forward and reverse PCR primers. In some embodiments, step (b) comprises amplifying at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, or at least 100 target RNA molecules from 2-10, 200-100, 50-500, or 50-2000 target RNA molecules using at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, or at least 100 target RNA molecules from 2-10, 200-100, 50-500, or 50-2000 pairs of forward and reverse PCR primers.

[0236] In some embodiments, the method further comprises adding a tag to the amplification product prior to performing high-throughput sequencing, the tag comprising a sequencing-compatible adapter. In some embodiments, the method further comprises adding a tag to the extracted RNA prior to performing target amplification, the tag comprising an adapter for amplification. In some embodiments, the tag comprises a sample-specific barcode, and the method further comprises pooling the amplification products from multiple samples prior to high-throughput sequencing and sequencing the pool of amplification products together in a single run during high-throughput sequencing.

[0237] In some embodiments, the amount of nucleic acid is determined by, for example, using tracer nucleic acid or internal calibration nucleic acid.The terms " tracer nucleic acid " or " internal calibration nucleic acid " are used interchangeably and refer to the composition of nucleic acid, the length, sequence, nucleotide composition, amount or biological origin of which is known in advance.Tracer can be added to biological sample from human subject to help estimate the amount of total RNA or cfDNA in said sample.Tracer can also be added to reaction mixture other than biological sample itself.

[0238] Performance of the method for determining transplant rejection or rejection status when combining measurements of specific urinary target genes and the amount of donor RNA or DNA. In one aspect, the methods for determining kidney transplant rejection status described herein can be combined with measuring transplant rejection risk based on determining the amount of donor-derived RNA (dd-RNA) or cell-free DNA (dd-cfDNA) in a biological sample from a kidney transplant recipient.

[0239] In some embodiments, the method has a sensitivity of at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 98% in identifying acute rejection (AR) versus non-AR at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0240] In some embodiments, the method has a specificity of at least 60%, or at least 65%, or at least 70%, or at least 75%, or at least 80%, or at least 85%, or at least 90% in identifying AR versus non-AR at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0241] In some embodiments, the method has an area under the curve (AUC) of at least 0.8, or 0.85, or at least 0.9, or at least 0.95 in identifying AR versus non-AR at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0242] In some embodiments, the method has a sensitivity of at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 98% in identifying AR versus normal stable allograft (STA) at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0243] In some embodiments, the method has a specificity of at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 98% in identifying AR versus STA at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0244] In some embodiments, the method has an AUC of at least 0.8, or 0.85, or at least 0.9, or at least 0.95, or at least 0.98, or at least 0.99 in identifying AR versus STA at a cutoff threshold of 1% dd-RNA or dd-cfDNA and a 95% confidence interval.

[0245] In some embodiments, the method has a sensitivity determined by a limit of blank (LoB) of 0.5% or less and a limit of detection (LoD) of 0.5% or less. In some embodiments, the LoB is 0.23% or less and the LoD is 0.29% or less. In some embodiments, the sensitivity is further determined by the limit of quantitation (LoQ). In some embodiments, the LoQ may be 10-fold greater than the LoD, the LoQ may be 5-fold greater than the LoD, the LoQ may be 1.5-fold greater than the LoD, the LoQ may be 1.2-fold greater than the LoD, the LoQ may be 1.1-fold greater than the LoD, or the LoQ may be equal to or greater than the LoD. In some embodiments, the LoB is 0.04% or less, the LoD is 0.05% or less, and / or the LoQ is equal to the LoD.

[0246] In some embodiments, the method has an accuracy determined by evaluating a linearity value obtained from a linear regression analysis of the measured donor fraction as a function of the corresponding attempted spike level, the linearity value being R 2 is the value of R 2 In some embodiments, the value of R 2 The value is 0.999. In some embodiments, the method has an accuracy determined by calculating a slope and intercept value using linear regression for the measured donor fraction as a function of the corresponding attempted spike level, where the slope value is about 0.9 to about 1.2 and the intercept value is about -0.0001 to about 0.01. In some embodiments, the slope value is about 1 and the intercept value is about 0.

[0247] In some embodiments, the method has a precision determined by calculating the coefficient of variation (CV), wherein the CV is less than about 10.0%. The CV is less than about 6%. In some embodiments, the CV is less than about 4%. In some embodiments, the CV is less than about 2%. In some embodiments, the CV is less than about 1%.

[0248] In some embodiments, the AR is antibody-mediated rejection (ABMR). In some embodiments, the AR is T-cell-mediated rejection (TCMR).

[0249] In some embodiments, the cutoff threshold is an estimated percentage of RNA target to total RNA or a function thereof. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% RNA (e.g., mRNA or miRNA). In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% of cell-free DNA or a combination of cell-free DNA and RNA. In some embodiments, the cutoff threshold is adjusted depending on the type of organ to be transplanted. In some embodiments, the cutoff threshold is adjusted depending on the number of organs to be transplanted.

[0250] In some embodiments, the cutoff threshold is the estimated percentage of donor-derived RNA relative to total RNA or a function thereof. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% RNA. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% of cell-free DNA or a combination of cell-free DNA and RNA.

[0251] In some embodiments, the cutoff threshold is an estimated percentage of the amount of a preselected mRNA relative to total mRNA, or a function thereof. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% RNA. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% cell-free DNA or a combination of cell-free DNA and mRNA. In some embodiments, the cutoff threshold is adjusted depending on the type of organ being transplanted. In some embodiments, the cutoff threshold is adjusted depending on the number of organs being transplanted.

[0252] In some embodiments, the cutoff threshold is the estimated proportion of donor-derived preselected mRNA targets relative to total mRNA, or a function thereof. In some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% RNA.

[0253] In some embodiments, the cutoff threshold is an estimated percentage or function of the amount of the preselected protein relative to total protein, hi some embodiments, the cutoff threshold is 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, or 2.0% protein.

[0254] In some embodiments, the cutoff threshold is proportional to the absolute value of the donor-derived RNA concentration. In some embodiments, the cutoff threshold is the copy number of donor-derived RNA or a function thereof. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA per volume unit of blood sample. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA per volume unit of blood sample multiplied by the body weight, BMI, or blood volume of the transplant recipient.

[0255] In some embodiments, the cutoff threshold is proportional to the absolute value of the donor-derived RNA concentration. In some embodiments, the cutoff threshold is the copy number of donor-derived RNA or a function thereof. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA per volume unit of blood sample. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of RNA per volume unit of sample multiplied by body weight or BMI.

[0256] In some embodiments, the cutoff threshold is proportional to the absolute value of the concentration of donor-derived protein. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of protein. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of protein per volume unit of blood sample. In some embodiments, the cutoff threshold is expressed as the amount or absolute amount of protein per volume unit of blood sample multiplied by the weight, BMI, or blood volume of the transplant recipient.

[0257] definition As used herein, the term "single nucleotide polymorphism (SNP)" refers to a single nucleotide that may differ between the genomes of two members of the same species. The use of this term does not imply any restriction on the frequency with which each variant occurs.

[0258] In some embodiments, for example, the sequence refers to a DNA or RNA sequence or a gene sequence. It may refer to the primary physical structure of a DNA or RNA molecule or strand in an individual. It may refer to the sequence of nucleotides present in the DNA or RNA molecule, or the complementary strand of the DNA or RNA molecule. It may refer to the information contained in the DNA or RNA molecule as its in silico representation.

[0259] "Baseline level of gene expression" includes the expression level of a particular gene in a healthy subject or a subject with a well-functioning graft. Baseline levels of gene expression include gene expression levels in subjects without acute rejection. Baseline levels of gene expression can be paper values ​​or can be baseline levels of gene expression from control samples of healthy subjects or subjects with well-functioning grafts.

[0260] A "gene product" includes a peptide, polypeptide, or structural RNA produced when a gene is transcribed and / or translated. mRNA that encodes a peptide or polypeptide can be translated to produce the peptide or polypeptide, whereas structural RNA (e.g., rRNA) is not translated.

[0261] As used herein, the term "gene expression level" refers to quantifying gene expression. In some embodiments, to accurately assess whether increased mRNA or rRNA is significant, it is preferable to "normalize" gene expression to accurately compare expression levels between samples, i.e., to a baseline level to which gene expression is compared. Quantification of gene expression can be achieved by methods known in the art, such as reverse transcription polymerase chain reaction (RT-PCR) and TAQMAN® assays. Gene expression can also be quantified by directly detecting proteins, peptides, or structural RNA gene products in various assay formats known to those skilled in the art. For example, proteins and peptides can be detected by assays such as enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), immunofluorescence, immunoprecipitation, equilibrium dialysis, immunodiffusion, immunoblotting, mass spectrometry, and other techniques. See, e.g., Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 188; Weir, DM, Handbook of Experimental Immunology, 1986, Blackwell Scientific, Boston.

[0262] In some embodiments, for example, a locus refers to a particular region of interest on an individual's DNA or RNA, including, but not limited to, one or more SNPs, potential insertion or deletion sites, or sites of some other associated genetic variation. Disease-associated SNPs can also refer to disease-associated loci.

[0263] In some embodiments, for example, a polymorphic allele, or "polymorphic locus," refers to an allele or locus whose genotype varies between individuals within a given species. Some examples of polymorphic alleles include single nucleotide polymorphisms (SNPs), short tandem repeats, deletions, duplications, and inversions.

[0264] In some embodiments, for example, an allele refers to a nucleotide or nucleotide sequence that occupies a particular locus.

[0265] In some embodiments, for example, genetic data, also "genotype data," refers to data describing aspects of one or more individual's genome. It may refer to one or a set of loci, a partial or entire sequence, a partial or entire chromosome, or the entire genome. It may refer to the identity of one or more nucleotides, which may refer to a series of contiguous nucleotides, or nucleotides from different locations in the genome, or a combination thereof. While genotype data is computational, it is also possible to consider the physical nucleotides in a sequence as chemically encoded genetic data. Genotype data may be referred to as "pertaining to" an individual(s), "of" an individual(s), "at" an individual(s), "from" an individual(s), or "relating to" an individual(s). Genotype data may refer to output measurements from a genotyping platform where these measurements are made on genetic material.

[0266] In some embodiments, for example, genetic material, or "genetic sample," refers to physical matter, such as tissue or urine, from one or more individuals that contains nucleic acid (including, for example, DNA or RNA).

[0267] As used herein, the term "transplantation" refers to the process of removing cells, tissues, or organs, called "transplants" or "grafts," from one individual and placing them (usually several) into a different individual. The individual providing the graft is called the "donor," and the individual receiving the graft is called the "recipient" (or "host"). An organ or graft transplanted between two genetically distinct individuals of the same species is called an "allograft." A graft transplanted between individuals of different species is called a "xenograft."

[0268] As used herein, "transplant rejection" refers to the functional and structural deterioration of an organ due to an active immune response mounted by the recipient, independent of non-immunological causes of organ dysfunction. Acute transplant rejection can result from activation of the recipient's T cells and / or B cells; rejection primarily driven by T cells is classified as T cell-mediated acute rejection (TCMR), and rejection primarily driven by B cells is classified as antibody-mediated rejection (AMR). In some embodiments, the provided methods and compositions can detect and / or predict acute cellular rejection. In some embodiments, the methods can distinguish between different states of kidney rejection, such as TCMR or AMR.

[0269] In some embodiments, for example, allele type data refers to a set of genotype data for a set of one or more alleles. It can refer to graded haplotype data. It can refer to SNP identities, and can refer to nucleic acid sequence data, including insertions, deletions, repeats, and mutations.

[0270] As used herein, "subject" means a mammal, including a "transplant recipient." "Mammal" means any member of the class Mammalia, including, but not limited to, humans, non-human primates such as chimpanzees and other apes and monkey species, livestock such as cows, horses, sheep, goats, and pigs, domestic animals such as rabbits, dogs, and cats, laboratory animals including rodents such as rats, mice, and guinea pigs, and the like. The term "subject" does not denote a particular age or sex. Preferably, the subject is a human patient. In some embodiments, the subject is a human who has received an organ transplant, i.e., a transplant recipient.

[0271] The terms "upregulation," "upregulated," "increased expression," and "higher expression" are used interchangeably herein and refer to an increase or elevation in the amount of a target mRNA or target protein. In some embodiments, "upregulation," "upregulated," "increased expression," and "higher expression" include an increase of 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100% or more above baseline (e.g., control or reference) levels.

[0272] In some embodiments, for example, an allele state refers to the actual state of a gene within a set of one or more alleles. This may refer to the actual state of a gene as described by allele type data.

[0273] In some embodiments, for example, allele ratio or allele ratio refers to the ratio between the amount of each allele at the locus present in sample or individual.If sample is measured by sequencing, allele ratio can refer to the ratio of sequence reads that map to each allele at the locus.If sample is measured by intensity-based measurement method, allele ratio can refer to the ratio of the amount of each allele present at that locus that is estimated by measurement method.

[0274] In some embodiments, for example, the allele count refers to the number of sequences that map to a particular locus, or, if the locus is polymorphic, the number of sequences that map to each of the alleles. If each allele is counted in a binary manner, the allele count will be an integer. If alleles are counted probabilistically, the allele count may be a fraction.

[0275] In some embodiments, for example, a primer, or "PCR probe," refers to a single DNA molecule (DNA oligomer) or a collection of DNA molecules (DNA oligomers) where the DNA molecules are identical or nearly identical, the primer includes a region designed to hybridize to a target polymorphic locus, and includes a priming sequence designed to enable amplification, such as PCR amplification. The primer may also include a molecular barcode. The primer may include a random region that is different for each individual molecule.

[0276] In some embodiments, for example, hybrid capture probe refers to any nucleic acid sequence, which may be modified, that is generated by various methods, such as PCR or direct synthesis, and is intended to be complementary to one strand of a specific target DNA or RNA sequence in a sample.Exogenous hybrid capture probe can be added to the prepared sample and hybridized through a denaturation-annealing process to form a double strand of exogenous-endogenous fragments.These double strands can then be physically separated from the sample by various means.

[0277] In some embodiments, for example, a sequence read refers to data representing the sequence of nucleotide bases measured using clonal sequencing methods. Clonal sequencing can generate sequence data representing a single, clone, or cluster of original DNA or RNA molecules. A sequence read can also have an associated quality score for each base position in the sequence, indicating the likelihood that the nucleotide was called correctly.

[0278] In some embodiments, for example, mapping a sequence read is the process of determining the location of the origin of a sequence read in the genome sequence of a particular organism. The location of the origin of a sequence read is based on the similarity of the nucleotide sequences of the read and the genome sequence.

[0279] In some embodiments, for example, donor-derived DNA or RNA refers to DNA or RNA that was originally part of cells whose genotype was essentially equivalent to that of the transplant donor. The donor can be a human or a non-human mammal (e.g., a pig).

[0280] In some embodiments, for example, DNA or RNA of recipient origin refers to DNA or RNA that was originally part of a cell whose genotype was essentially equivalent to that of the transplant recipient.

[0281] In some embodiments, RNA may refer to messenger RNA (mRNA), small non-coding RNA (sncRNA), transfer RNA (tRNA), or non-protein-coding RNA from a cell. In some embodiments, sncRNA includes microRNA (miRNA), piwi-interacting RNA (piRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), or other RNA (miscRNA). In some embodiments, the RNA is cell-free RNA. In some embodiments, the cell-free RNA is derived from exosomes or microvesicles.

[0282] In some embodiments, amplifying RNA involves reverse transcribing the RNA to produce complementary DNA (cDNA), followed by amplifying the cDNA by amplification methods disclosed elsewhere herein.

[0283] In some embodiments, for example, preferential enrichment of DNA or RNA corresponding to a locus, or preferential enrichment of DNA or RNA at a locus, refers to any technique that results in a higher proportion of DNA or RNA molecules in a post-enrichment DNA or RNA mixture corresponding to a locus than the proportion of DNA or RNA molecules in the pre-enrichment DNA or RNA mixture corresponding to the locus. The technique may include selective amplification of DNA or RNA molecules corresponding to the locus. The technique may include removing DNA or RNA molecules that do not correspond to the locus. The technique may include a combination of methods. Enrichment is defined as the proportion of DNA or RNA molecules in the post-enrichment mixture corresponding to the locus divided by the proportion of DNA or RNA molecules in the pre-enrichment mixture corresponding to the locus. Preferential enrichment may be performed at multiple loci. In some embodiments of the present disclosure, the enrichment is greater than 20. In some embodiments of the present disclosure, the enrichment is greater than 200. In some embodiments of the present disclosure, the enrichment is greater than 2,000. When preferential enrichment is performed at multiple loci, the enrichment may refer to the average enrichment of all loci in the set of loci.

[0284] In some embodiments, for example, amplification refers to techniques that increase the copy number of RNA and / or DNA molecules.

[0285] In some embodiments, for example, selective amplification can refer to a technique that increases the copy number of a specific molecule of RNA and / or DNA or a molecule of RNA and / or DNA corresponding to a specific region of RNA and / or DNA. It can also refer to a technique that increases the copy number of a specific target molecule of RNA and / or DNA or a target region of RNA and / or DNA over non-target molecules or regions of RNA and / or DNA. Selective amplification can be a preferential enrichment method.

[0286] In some embodiments, for example, universal priming sequence refers to a DNA sequence that can be added to a population of target nucleic acid molecules by, for example, ligation, PCR, or ligation-mediated PCR.Once added to a population of target molecules, a single amplification primer pair can be used to amplify the target population using a primer specific to the universal priming sequence.The universal priming sequence does not need to be related to the target sequence.

[0287] In some embodiments, for example, a universal adapter, or "ligation adapter" or "library tag," is a DNA molecule containing a universal priming sequence that can be covalently attached to the 5' and 3' ends of a population of target double-stranded DNA molecules. The addition of the adapter provides universal priming sequences at the 5' and 3' ends of the target population, allowing PCR amplification to be performed, and all molecules from the target population are amplified using a single amplification primer pair.

[0288] In some embodiments, for example, targeting refers to a method used to selectively amplify or otherwise preferentially enrich for DNA or RNA molecules corresponding to a set of loci in a mixture of DNA or RNA.

[0289] "Acute rejection (AR)" is a rejection reaction by the immune system of a tissue transplant recipient when the transplanted tissue is immunologically abnormal. Acute rejection is characterized by the recipient's immune cells infiltrating the transplanted tissue and exerting effector functions to destroy the transplanted tissue. The onset of acute rejection is rapid and typically occurs in humans within a few weeks of transplant surgery. Generally, acute rejection can be inhibited or suppressed by immunosuppressive drugs such as rapamycin, cyclosporin A, and anti-CD40L monoclonal antibodies.

[0290] "Chronic transplant rejection or injury" or "CAI" commonly occurs in humans within months to years after engraftment, even when immunosuppression of acute rejection is successful. Fibrosis is a common factor in chronic rejection of all types of organ transplants. Chronic rejection can usually be described by a series of specific disorders characteristic of specific organs. In kidney transplants, such disorders include obstructive nephropathy, nephrosclerosis, and tubulointerstitial nephropathy. Chronic rejection is also characterized by ischemic injury, denervation of the transplanted tissue, and hyperlipidemia and hypertension associated with immunosuppressive drugs.

[0291] The term "transplant injury" refers to all modes of graft dysfunction, regardless of pathological diagnosis. The term "organ injury" refers to target loci that track organ functional decline, whether the organ is native or a transplant, and regardless of etiology.

[0292] Multiplex Amplification In some embodiments, the method includes performing a multiplex amplification reaction to amplify multiple target loci in one reaction mixture prior to sequencing the selectively enriched RNA or DNA.

[0293] In certain exemplary embodiments, the nucleic acid sequence data is generated by performing high-throughput RNA sequencing of multiple copies of a series of amplicons generated using a multiplex amplification reaction, where each amplicon in the series spans at least one polymorphic locus in a set of polymorphic loci, and each polymorphic locus in the set is amplified. In certain exemplary embodiments, the nucleic acid sequence data is generated by performing high-throughput DNA sequencing of multiple copies of a series of amplicons generated using a multiplex amplification reaction, where each amplicon in the series spans at least one polymorphic locus in a set of polymorphic loci, and each polymorphic locus in the set is amplified. For example, in these embodiments, multiplex PCR may be performed to amplify amplicons across at least 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, or 100,000 polymorphic loci (e.g., SNP loci). The multiplex reaction can be set up as a single reaction or as a pool of different subsets of multiplex reactions. The multiplex reaction methods provided herein, such as the massively multiplexed PCR disclosed herein, provide exemplary processes for performing amplification reactions to help achieve improved multiplexing and, therefore, sensitivity levels.

[0294] In some embodiments, the amplification is performed using direct multiplex PCR, sequential PCR, nested PCR, double nested PCR, one-and-a-half PCR, or a combination of both. sided nested PCR, fully nested PCR, one-sided fully nested PCR, one-sided nested PCR, heminested PCR, heminested PCR, triplex heminested PCR, semi-nested PCR, one-sided semi-nested PCR, reverse semi-nested PCR, or one-sided PCR, as described in U.S. Application Serial No. 13 / 683,604, filed November 21, 2012, U.S. Publication No. 2013 / 0123120, U.S. Application Serial No. 13 / 300,235, filed November 18, 2011, U.S. Publication No. 2012 / 0270212, and U.S. Patent No. 61 / 994,791, filed May 16, 2014, all of which are incorporated by reference in their entireties.

[0295] In some embodiments, multiplex PCR is used. In some embodiments, a method for amplifying target loci in a nucleic acid sample involves (i) contacting the nucleic acid sample with a library of primers that simultaneously hybridize to at least 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, or 100,000 different target loci to generate a single reaction mixture; and (ii) subjecting the reaction mixture to primer extension reaction conditions (such as PCR conditions) to generate amplification products containing target amplicons. In some embodiments, at least 50, 60, 70, 80, 90, 95, 96, 97, 98, 99, or 99.5% of the target loci are amplified. In various embodiments, less than 60, 50, 40, 30, 20, 10, 5, 4, 3, 2, 1, 0.5, 0.25, 0.1, or 0.05% of the amplification products are primer-dimers. In some embodiments, the primers are in solution (e.g., dissolved in a liquid phase rather than a solid phase). In some embodiments, the primers are in solution and not immobilized on a solid support. In some embodiments, the primers are not part of a microarray.

[0296] In certain embodiments, the multiplex amplification reaction is carried out under limiting primer conditions for at least half of the reactions. In some embodiments, limiting primer concentrations are used in 1 / 10, 1 / 5, 1 / 4, 1 / 3, 1 / 2, or all of the reactions in the multiplex reaction. Provided herein are factors to consider when achieving limiting primer conditions in amplification reactions such as PCR.

[0297] In certain embodiments, multiplex amplification reactions may include, for example, 2,500 to 50,000 multiplex reactions, in particular multiplex reactions ranging from 100, 200, 250, 500, 1000, 2500, 5000, 10,000, 20,000, 25,000, and 50,000 at the lower end of the range to 200, 250, 500, 1000, 2500, 5000, 10,000, 20,000, 25,000, 50,000, and 100,000 at the higher end of the range.

[0298] In one embodiment, multiplex PCR assays are designed to amplify potentially heterozygous SNPs or other polymorphic or non-polymorphic loci on one or more chromosomes, and these assays are used in a single reaction to amplify DNA. The number of PCR assays can be 50-200 PCR assays, 200-1,000 PCR assays, 1,000-5,000 PCR assays, or 5,000-20,000 PCR assays (50-200 reactions, 200-1,000 reactions, 1,000-5,000 reactions, 5,000-20,000 reactions, and more than 20,000 reactions, respectively). In one embodiment, a multiplex pool of at least 10,000 PCR assays (10,000 reactions) is designed to amplify potentially heterozygous SNP loci in a single reaction to amplify RNA or cfDNA obtained from a urine sample. The SNP frequency of each locus can be determined by clonal methods or some other methods of sequencing amplicons. In another embodiment, the original cfDNA sample is divided into two samples, and 5,000 parallel assays are performed. In another embodiment, the original cfDNA sample is divided into n samples, and 5,000 parallel assays are performed (approximately 10,000 / n) parallel assays are performed, where n is 2 to 12, or 12 to 24, or 24 to 48, or 48 to 96.

[0299] In one embodiment, the method disclosed herein uses highly efficient, highly multiplexed targeted PCR to amplify DNA, followed by high-throughput sequencing to determine the allele frequency at each target locus.One technique that allows highly multiplexed targeted PCR to be performed in a highly efficient manner involves designing primers that are unlikely to hybridize with each other.PCR probes are typically called primers, and a thermodynamic model of potentially harmful interactions between at least 100, at least 200, at least 500, at least 1,000, at least 2,000, at least 5,000, at least 10,000, at least 20,000, or at least 50,000 potential primer pairs or unintended interactions between primers and sample DNA is created, and then this model is used to select designs that are incompatible with other designs in the pool.Another technique that allows highly multiplexed targeted PCR to be performed in a highly efficient manner is to use a partial or complete nesting approach to targeted PCR. Using one or a combination of these techniques, it is possible to multiplex at least 100, at least 200, at least 500, at least 1,000, at least 2,000, at least 5,000, at least 10,000, at least 20,000, or at least 50,000 primers in a single pool, with the resulting amplified DNA comprising the majority of the DNA, when sequenced, mapping to the target locus. Using one or a combination of these techniques, it is possible to multiplex a large number of primers in a single pool, with the resulting amplified DNA comprising greater than 50%, greater than 80%, greater than 90%, greater than 95%, greater than 98%, or greater than 99% of the DNA molecules mapping to the target locus.

[0300] Bioinformatics methods are used to analyze the genetic data obtained from multiplex PCR. Bioinformatics methods useful and relevant to the methods disclosed herein can be found in U.S. Patent Publication No. 2018 / 0025109, which is incorporated herein by reference.

[0301] High-throughput sequencing In some embodiments, the sequence of the amplicon is determined by performing high-throughput sequencing.

[0302] The genetic data of transplant recipients and / or donors can be converted from molecular to electronic state by measuring the appropriate genetic material using tools and techniques from a group including, but not limited to, genotyping microarrays and high-throughput sequencing. Some high-throughput sequencing methods include Sanger DNA sequencing, pyrosequencing libraries, the ILLUMINA SOLEXA platform, ILLUMINA's genome analyzer, or APPLIED BIOSYSTEM's 454 sequencing platform, HELICOS's TRUE SINGLE MOLECULE SEQUENCING platform, HALCYON MOLECULAR's electron microscope sequencing, PacBio®, Oxford Nanopore®, or any other sequencing method. In some embodiments, high-throughput sequencing is performed on an Illumina NextSeq®. All of these methods physically convert the genetic data stored in a sample of DNA into a set of genetic data that is typically stored in a memory device during processing.

[0303] In some embodiments, the sequence of the selectively enriched DNA is determined by performing microarray analysis. In one embodiment, the microarray may be an ILLUMINA SNP microarray or an AFFYMETRIX SNP microarray.

[0304] In some embodiments, the sequence of selectively enriched DNA is determined by quantitative PCR (qPCR) or digital droplet PCR (ddPCR) analysis. qPCR measures the intensity of fluorescence at a specific time (generally per amplification cycle) to determine the relative amount of target molecules (DNA). ddPCR measures the actual number of molecules (target DNA) because each molecule is in a droplet, thus providing a separate "digital" measurement. This provides absolute quantification because ddPCR measures the sample's positivity rate, i.e., the number of droplets that fluoresce due to proper amplification. This positivity rate accurately indicates the initial amount of template nucleic acid.

[0305] This description is further illustrated by the following examples, which should not be construed as limiting in any way. [Example]

[0306] Example 1: Identification of mRNA targets in urine that can determine kidney rejection This example is merely illustrative and those skilled in the art will appreciate that the invention disclosed herein may be embodied in a variety of other ways.

[0307] The purpose of this example is to demonstrate the identification of mRNA targets in urine samples that can effectively distinguish rejection across different kidney disease states.

[0308] To identify mRNA targets that can effectively distinguish rejection across different kidney disease states, Affymetrix® mRNA expression data for 1,745 samples was obtained from the MMDx® Diagnostic System. The MMDx® Kidney & Heart is a biopsy-based, laboratory-developed test that measures gene expression profiling and provides risk assessment of rejection and damage in transplanted organs. Rejection-related mRNA targets from the MMDx® Diagnostic System were matched with mRNA targets found in the literature to be associated with kidney rejection in urine samples.

[0309] Table 1 (see Tables section herein) shows the mRNAs and corresponding miRNAs that were found to be expressed in the urine samples.

[0310] Specifically, 96% (1,679) of the MMDx® samples were histologically examined and classified using machine learning techniques. mRNAs independently found in the literature were examined through eight separate machine learning (ML) classifiers for six kidney disease states determined by MMDx®. Kidney disease states include antibody-mediated rejection (ABMR), T-cell-mediated rejection (TCMR), possible ABMR (pABMR), possible TCMR (pTCMR), mixed (both ABMR and TCMR), and non-rejection. Of these 1,679 samples, 509 were ABMR, 52 were pABMR, 123 were TCMR, 21 were pTMCR, 69 were mixed (ABMR and TCMR), and 905 were non-rejection (NR).

[0311] The AUC (area under the curve) based on the sensitivity (true positive results) and specificity (false positive results) curves was determined by using eight classifiers to evaluate the performance of mRNA targets across six urinary kidney disease states as determined by MMDx® as shown in Tables 2 and 10 in the Tables section below. Notably, the AUC calculated across the eight classifiers ranged from 0.82 to 0.99 for the disease state comparisons shown.

[0312] A t-test was used to evaluate the performance of mRNA expressed from the target genes to distinguish between different renal rejection states, as shown in Table 3 (see Table section below). Forty-nine target genes that can effectively distinguish between different renal rejection states were identified herein by matching MMDx® genes with genes expressed in urine samples from subjects representing different rejection states and evaluating performance with a t-test. Figures 1-11 show the expression of some of these target genes (PSMB9, GZMB, GNLY, CXCL11, and CXCL9) across different renal rejection states. Control genes showed no variation across different renal rejection states, as shown in Figures 12-13.

[0313] As shown in Table 3 and Figures 14-16, few genes identified in blood samples from the PaxGene® RNA study reported in Akalin et al., Kidney360, 2021, 2(12) 1998-2009 (hereafter referred to as "Akalin") could be used to distinguish between different kidney rejection states. The following genes identified in the blood samples reported in Akalin were found to be particularly ineffective in distinguishing between different kidney rejection stages: PDCD1, MARCHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, and RHOU.

[0314] The 49 target genes identified by cross-referencing of MMDx® and urinary expression data listed in Table 3 are CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6 , Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TB P, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, and IL18BP.

[0315] Further analysis of the performance of target genes identified by cross-referencing MMDx® data with publications of genes differentially expressed in urine samples from subjects with different rejection status was performed by using logistic regression analysis and determination of AUC values ​​as shown in Table 4. The urine 63p23g gene set is CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, and SERPINB12. The urine139p49g gene set is CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, and Tap1. These gene sets were compared with the MMDx®30p39g gene set, which was the top 39 MMDx® probe sets by t-test from the literature (20 for ABMR vs. others, 20 for TCMR vs. others, and 1 missing, resulting in a total of 39 probe sets from 26 genes). See also Table 11 in the Tables section below. In addition, the urinary gene sets were compared with the Akalin gene sets Akalin10p5g, Akalin30p11g, and Akalinp14g, which are established gene signatures reported in Akalin. In particular, Akalin10p5g is a set of five target genes reported in Akalin to predict kidney transplant rejection. Akalin30p11g includes the five target genes from Akalin10p5g and adds genes predictive of heart transplant rejection. Akalinp14g includes 14 genes selected as reference genes; these genes are not predictive of kidney rejection status.As expected, Akalinp14g performed poorly, showing lower AUC values ​​than the targeted gene set. Akalin30p11g performed better than Akalin10p5g, but none of the Akalin gene sets performed at the level of Urine63p23g or Urine139p49g.

[0316] The performance of the target gene set was also evaluated by using the Banff® dataset containing 1208 samples, including 215 ABMR samples, 87 TCMR samples, and 274 NR samples, as shown in Table 5. Consistent with the results from the MMDx® system, the performance of Urine63p23g or Urine139p49g was better than that of the Akalin gene set in the Banff® system.

[0317] A total of 53 genes shown in Table 6 have been found herein to be particularly effective in distinguishing between different kidney rejection conditions. The detection of microRNAs (miRNAs) that bind to the mRNAs produced by these genes can also be used to distinguish between different kidney rejection genes. Table 6 also lists the miRNAs that are predicted to bind to the target gene products.

[0318] Gene ontology (GO) enrichment analysis demonstrated that the Urine63p23g or Urine139p49g gene sets were enriched for genes related to cytokine response / stimulation and immune activation to foreign factors, as shown in Table 7. GO analysis was performed using the following gene sets from the Human Molecular Signature Database (MSigDB) for Gene Set Enrichment Analysis (GSEA): Hallmark allograft rejection gene set (GSEA-MSigDB: 200 genes), Hallmark inflammatory response gene set (GSEA-MSigDB: 200 genes), KEGG allograft rejection gene set (GSEA-MSigDB: 38 genes), and KEGG T cell receptor gene set (GSEA-MSigDB: 108 genes). The Akalin gene set Akalin30p11g was enriched for cytokine response pathways, whereas the Urine63p23g or Urine139p49g gene sets showed a much higher degree of immune function genes. See Table 7. In addition, the Urine63p23g or Urine139p49g gene sets were enriched for allograft and inflammatory genes, whereas the Akalin gene set was not readily matched with the GSEA-MSigDB gene sets, as shown in Table 8 and Figures 17-18.

[0319] Urinary expressed mRNAs appeared to be highly enriched in the MMDx® disease classification data as determined by t-test, one-way anova, and AUC classification.

[0320] It is also contemplated herein that target genes of the apoptotic pathway, such as the genes listed in Table 9 from the hallmark apoptosis gene set, are useful in distinguishing between renal transplant rejection states.

[0321] miRNAs can also be found in urine samples, as shown in Figure 19. Differentiation of different kidney rejection states may also be achieved by using miRNAs, such as those shown in Figure 19 or miRNAs that bind to mRNA expressed from target genes listed in Tables 1 and 6. In some embodiments, one or more miRNAs useful for determining kidney rejection status include miR-186-5p, miR-665, miR-873-5p.1, miR-543, miR-330-3p, miR-362-5p / 500b-5p, miR-217, miR-140-5p, miR-193-3p, miR-382-5p, miR-140-3p.2, miR-653-5p, miR-455-3p.2, miR-145-5p, miR-491-5p, miR-23-3p, miR-375, miR-1 29-5p, miR-96-5p / 1271-5p, miR-182-5p, miR-371-5p, miR-203a-3p.1, miR-494-3p, miR-146-5p, miR-140-3p.1, miR-125-5p, miR-346, miR -760, miR-185-5p, miR-325-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-423-5p, miR-496.1, miR-155-5p, miR-142-3p.2、miR-24-3p、miR-874-3p、miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p、miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p、miR-181-5p、miR-142-5p、mi R-130-3p / 301-3p / 454-3p, miR-21-5p / 590-5p, miR-103-3p / 107, miR-137, miR-340-5p, miR-490-3p, miR-143-3p, miR-409-3p, miR-27-3p, mi R-138-5p, miR-485-5p, miR-328-3p, miR-326, miR-148-3p / 152-3p, miR-9-5p, miR-31-5p, miR-452-5p / 892-3p, miR-202-5p, miR-29-3p, miR- 338-3p, miR-26-5p, let-7-5p / 98-5p, miR-196-5p, miR-30-5p, miR-142-3p.1, miR-19-3p, miR-411-3p, miR-493-5p, miR-218-5p, miR-203a-3p .2, miR-495-3p, miR-425-5p, miR-135-5p, miR-154-3p / 487-3p, miR-223-3p, miR-219-5p, miR-670-3p, miR-216b-5p, miR-200bc-3p / 429, miR -320, miR-216a-5p, miR-141-3p / 200a-3p, miR-144-3p, miR-128-3p, miR-455-3p.1, miR-219a-2-3p, miR-873-5p.2, miR-448, miR-183-5p.2 miR-374-5p, miR-505-3p.1, miR-433-3p, miR-377-3p, miR-365-3p, miR-124-3p.1, miR-410-3p, miR-199-3p, miR-22-3p, miR-129-3p, miR-38 3-5p.1、miR-1-3p / 206、miR-296-5p、miR-299-3p、miR-212-5p、miR-331-3p、miR-378-3p、miR-136-5p、miR-1193、miR-505-3p.2、miR-302c-3p.2 / 520-3p, miR-421, miR-499a-5p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-124-3p.2 / 506-3p, miR-34-5p / 449-5p, miR-376c-3p, miR-139-5p, miR-221-3p / 222-3p, miR-504-5p.1, miR-335-5p, miR-101-3p.1, miR-431-5p, miR-489-3p, miR-369-3p, miR-330 -3p.2、miR-18-5p、miR-28-5p / 708-5p、miR-133a-3p.2 / 133b、miR-205-5p、miR-199-5p、miR-455-5p、miR-126-3p.2、miR-7-5p、miR-483-3p.2、miR-668-3p、miR-1306-5p、miR-150-5p、miR-296-3p、miR-204-5p / 211-5p、miR-3064-5p、miR-532-5p、miR-876-5p、 miR-501-3p / 502-3p, miR-33-5p, miR-153-3p, miR-214-5p, miR-655-3p, miR-342-3p, miR-133a-3p.1, miR-411-5p.1, miR-496.2, miR-411-5p.2, miR-582-5p, miR-381-3p, miR-188-5p, miR-383-5p.2, miR-486-5p, miR-183-5p.1, miR-208-3p, miR-193a-5p, mi R-101-3p.2, miR-542-3p, miR-190-5p, miR-299-5p, miR-154-5p, miR-802, miR-323-3p, miR-532-3p, miR-224-5p, miR-339-5p, miR-194-5p, miR-149-5p, miR-493-3p, miR-382-3p, miR-132-3p / 212-3p, miR-1197, miR-99-5p / 100-5p, miR-877-5p, miR-483-3p.1. It is selected from the group consisting of miR-10-5p, miR-361-5p, miR-539-3p, miR-191-5p, miR-329-3p / 362-3p, miR-122-5p, miR-379-5p, miR-376-3p, miR-1298-5p, miR-451, miR-210-3p, miR-1224-5p, miR-324-5p, miR-544a-5p, miR-488-3p, miR-758-3p, miR-151-3p, miR-875-5p, miR-134-5p, miR-192-5p / 215-5p, and miR-127-3p. Preferably, one or more miRNAs useful for determining the renal rejection state are miR-96-5p / 1271-5p, miR-493-5p, miR-183-5p.2, miR-150-5p, miR-7-5p, miR-653-5p, miR-200bc-3p / 429, miR-212-5p, miR-1298-5p, miR-137, miR-758-3p, miR-325-3p, miR-542-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-495-3p, miR-30-5p, miR-873-5p.1, miR-146-5p, miR-505-3p.1, miR-539-3p, miR-216a-5p, miR-216b-5p, miR-340-5p, miR-361-5p, miR-338-3p, miR-217, miR-9-5p, miR-219a-2-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-199-3p, miR-1-3p / 206, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-142-5p, miR-302c-3p.2 / 520-3p, miR-326, miR-760, miR-138-5p, miR-27-3p, miR-145-5p, miR-142-3p.2, miR-101-3p.2, miR-182-5p, miR-203a-3p.2, miR-140-3p.1, miR-183-5p.1, miR-144-3p, miR-101-3p.1, miR-330-3p, miR-224-5p, miR-148-3p / 152-3p, miR-485-5p, miR-122-5p, miR-155-5p, miR-320, miR-23-3p, miR-124-3p.2 / 506-3p, miR-135 -5p, miR-381-3p, miR-26-5p, miR-1224-5p, miR-192-5p / 215-5p, miR-1249-3p, miR-125-5p, miR-483-3p.2, miR-668-3p, miR-223-3p, miR-655- 3p, miR-382-5p, miR-130-3p / 301-3p / 454-3p, miR-19-3p, miR-582-5p, miR-194-5p, miR-802, miR-483-3p.1, miR-382-3p, miR-129-5p, miR-3064-5p, miR-873-5p.2, miR-499a-5p, miR-128-3p, miR-532-5p, miR-296-5p, miR-744-5p, miR-425-5p, miR-218-5p, and miR-496.1.

[0322] Example 2: Obtaining urine samples and measuring RNA Urine sample Urine samples are collected from patients who have received a donor kidney. In particular, urine samples (approximately 50 ml) from registered kidney transplant recipients may be collected longitudinally on a pre-established collection schedule: before transplantation, 3, 7, 15, and 30 days after transplantation, and 2, 3, 4, 5, 6, 9, and 12 months after transplantation, as well as at the time of any renal allograft biopsy, and before treatment and 2 weeks after biopsy. Urine cell pellets are prepared using standard protocols for urinary cell sedimentation, and the pellets are stored at -80°C. Patient urine samples are collected before, at, and at various time intervals after transplantation surgery. Samples may be matched to biopsies, and urine samples are collected at the time of clinical dysfunction and biopsy, or at the time of protocol biopsy (at which point most patients do not have clinical dysfunction). Additionally, urine samples may be collected serially after transplantation.

[0323] Isolation of nucleic acids from urine samples Nucleic acids such as RNA or DNA, particularly cell-free DNA, mRNA, and microRNA, are extracted from urine samples. Total RNA is isolated from urine cell pellets using commercially available kits for RNA isolation. Typically, urine cell pellets are lysed by adding one volume of lysis buffer and vortexing. After adding an equal volume of 100% ethanol, the sample is transferred to an RNA spin cartridge. The cartridge is then washed three to four times with the wash buffer provided in the kit, and total RNA is eluted from the cartridge with 30 μl of RNase-free water.

[0324] Circulating nucleic acids may be obtained by using the QIAamp™ Circulating Nucleic Acid Kit (Qiagen). Cellular nucleic acids are obtained by isolating cells from urine samples by centrifugation. LabChip™ NGS 5k Kit (Perkin Elmer, Waltham, MA, USA) is used for quantification.

[0325] The quantity (absorbance at 260 nm) and purity (ratio of absorbance at 260 nm and 280 nm) of RNA isolated from the urine cell pellets are measured using, for example, a NanoDrop® ND-1000 UV-Vis spectrophotometer (Thermo Scientific).

[0326] Measurement of target mRNA from urine samples Total RNA is reverse transcribed (RT) into cDNA on the same day as the total RNA is isolated, using, for example, a TaqMan® Reverse Transcription Kit (Cat. No. N808-0234, Applied Biosystems). RT is performed by combining 1.0 μg of total RNA in a 100 μl volume with final concentrations of 1× TaqMan RT buffer, 5.5 mM magnesium chloride, 500 μM each of the four dNTPs, 2.5 μM random hexamers, 0.4 units / μl RNase inhibitor, and 1.25 units / μl MultiScribe® Reverse Transcriptase. Samples are incubated at 25° C. for 10 minutes, 48° C. for 30 minutes, and 95° C. for 5 minutes.

[0327] Multiplex real-time PCR reactions are performed on the cDNA using, for example, the Amplifluor® Universal Detection System (Intergen) and iCycler® (BioRad). The qPCR assay may be performed on an Applied Biosystems 7500 real-time PCR instrument and / or a BioRad CFX, but useful instrument platforms are not limited thereto. The qPCR assay of the present invention can be adapted to function on most real-time PCR instruments. The following PCR conditions may be used, but they can be modified as needed: a 10-minute 95°C denaturation cycle, followed by 32 cycles of two-step qPCR (95°C for 15 seconds, and 60°C for 2 minutes (annealing) and 72°C for 10 minutes (extension)). Additional PCR parameters (i.e., number of cycles, denaturation and annealing / extension times and temperatures) can be investigated to obtain a robust and sensitive qPCR multiplex as described elsewhere herein.

[0328] Quantification of RNA molecules by comparison T law(2 -ΔΔCT This is done using standard methods known in the art, such as the ELISA method.

[0329] In some embodiments, quantitative real-time PCR is used to determine the amount of 6, 12, 24, 48, or 96 target gene loci in one reaction. In some embodiments, single gene-specific oligonucleotide pairs and TaqMan® probes are used to measure RNA molecules. Real-time PCR can be performed on any of the RNA molecules provided herein or their combinations. In particular, quantitative PCR is performed to measure the amount of mRNA expressed by target genes CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, and SERPINB12. The urine139p49g gene set includes CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

[0330] Alternatively, RNA expression can be quantified using microarrays. For example, the Human Genome U219 array consists of over 530,000 probes covering over 36,000 transcripts and variants representing over 20,000 genes mapped through UniGene or RefSeq annotation. The EST and mRNA sequences used in the design were clustered and assembled to create consensus sequences representing alternative splice forms. Each assembly was then analyzed for orientation and evidence of alternative 3' ends. The content was selected to cover all well-annotated genes and transcripts from RefSeq v36 and to rigorously detect alternative 3' ends of those well-annotated genes by utilizing all available EST and mRNA evidence belonging to the same cluster. Over 1,000 probe sets represent transcripts that do not have official gene symbols in UniGene, but are based on predicted RefSeq sequences and UniGene clusters and have sufficient evidence of actual transcription.

[0331] PrimeView Human Genome 96 Array Plates and Trays enable high-throughput expression profiling of 96 samples at a time using established, well-annotated, content-focused probe sets. Sequences used to design the arrays were selected from the UniGene database, RefSeq version 36, and full-length human mRNAs from GenBank.

[0332] For those who underwent biopsy, matched (urine collected from days minus 3 to plus 1 of biopsy) and quality-controlled urine samples may be included, while for the no-biopsy group, all longitudinally collected urine samples that passed quality control may be included.

[0333] Example 3: Prediction of rejection status from RNA measurements In each sample, RNA is measured and matched to rejection status. All statistical tests are two-sided, where applicable. The significance level is set at p<0.05. Data may be analyzed using the Kruskal-Wallis rank sum test followed by Dunn's multiple comparison test with Holm's correction.

[0334] Data is evaluated by the area under the ROC curve (AUC) of the fitted model, as well as the sensitivity and specificity for diagnosing rejection. An AUC of 1.0 indicates perfect agreement. For example, all rejection states have higher scores in the diagnostic signature than all non-rejection states. An AUC of 0.50 indicates that the diagnostic signature's ability to distinguish acute cellular rejection biopsies from biopsies without acute cellular rejection is no different from chance.

[0335] Example 4: Combining RNA and cfDNA measurements The determination of rejection status shown in Examples 1 to 3 may be combined with prediction of rejection status based on measuring cell-free DNA (cfDNA) as described elsewhere. table [Table 19-1] [Table 19-2] [Table 19-3] [Table 19-4] [Table 19-5] [Table 19-6] [Table 19-7] [Table 19-8] Table 19-9 Table 19-10 Table 19-11 Table 19-12 Table 19-13 Table 19-14 Table 19-15 Table 19-16 Table 19-17 Table 19-18 Table 19-19 Table 19-20 Table 19-21 Table 19-22 Table 19-23 Table 19-24 Table 19-25 Table 19-26 Table 19-27 Table 20 Table 21 Table 22 Table 23 Table 24-1 Table 24-2 Table 24-3 Table 25 Table 26 Table 27-1 Table 27-2 Table 27-3 Table 27-4 Table 27-5 Table 28 Table 29 Table 30

Claims

1. 1. A method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or risk of renal transplant rejection, comprising: (a) extracting nucleic acids from the urine sample of the renal transplant recipient, wherein the extracted nucleic acids comprise one or more mRNAs and / or miRNAs of target genes associated with renal transplant rejection; (b) preparing a composition of nucleic acids from the nucleic acids extracted in step (a) by isolating mRNA and / or miRNA and removing contaminating molecules, optionally wherein preparing the composition comprises reverse transcribing complementary DNA (cDNA) from the nucleic acids extracted in step (a); (c) measuring the amount of said one or more mRNAs and / or miRNAs and generating one or more transplant rejection scores from the measured amounts of said one or more mRNAs and / or miRNAs, wherein said one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk or the presence or absence of kidney transplant rejection.

2. 2. The method of claim 1, comprising generating two or more transplant rejection scores, each transplant rejection score being based on a different subset of mRNAs and / or miRNAs.

3. 3. The method of claim 1 or 2, wherein the one or more transplant rejection scores are generated using predictive models, machine learning-based methods, and / or artificial intelligence methods.

4. The one or more transplant rejection scores may be analyzed using methods such as logistic regression (LogReg), t-test, violin plot, random forest (RE), neural networks, decision tree machine learning analysis, decision tree classification techniques, analysis of variance (ANOVA), Bayesian networks, boosting and adaboost, bootstrap aggregation (or bagging) algorithms, classification and regression trees (CART), boosted CART, recursive partitioning trees (RPART), Curds and Whey (CW), Curds and Whey (WWE), and others. Kernel-based machine algorithms such as Whey-Lasso, Principal Component Analysis (PCA), Factor Rotation or Factor Analysis, Discriminant Analysis, Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Quadratic Discriminant Analysis, Discriminant Function Analysis (DFA), Factor Rotation or Factor Analysis, Genetic Algorithms, Hidden Markov Models, Kernel Density Estimation, Kernel Partial Least Squares Algorithm, Kernel Matching Pursuit Algorithm, Kernel Fisher Discriminant Analysis Algorithm, Kernel Principal Component Analysis Algorithm, Linear Regression and Generalized Linear Models, Forward Linear Stepwise Regression, Lasso (or LASSO) Shrinkage and Selection Method, Elastic Net Regularization 3. The method of claim 1 or 2, wherein the model is generated using a classification and selection method, glmnet (Lasso and Elastic Net Regularized Generalized Linear Models), metalearner algorithms, nearest neighbor methods for classification or regression, K-Nearest Neighbor (KNN), nonlinear regression or classification algorithms, neural networks, partial least squares, rule-based classifiers, shrunken centroids (SC), sliced ​​inverse regression, product model data exchange standard, application interpretation structure (StepAIC), super principal component (SPC) regression, support vector machines (SVM) and recursive support vector machines (RSVM), and / or combinations thereof.

5. 3. The method of claim 1 or 2, wherein the one or more transplant rejection scores are generated using logistic regression (LogReg), random forest (RE), neural network, or decision tree machine learning analysis.

6. 3. The method of claim 1 or 2, wherein the one or more mRNAs and / or miRNAs are examined by using eight separate machine learning classifier methods based on six determined kidney disease states.

7. 7. The method of any one of claims 1 to 6, wherein the performance of determining renal transplant rejection or the risk of renal transplant rejection is measured and characterized by an AUC value of about 0.6 to about 0.99, about 0.7 to about 0.99, about 0.8 to about 0.99, about 0.9 to about 0.99, about 0.7 to about 0.79, about 0.8 to about 0.89, about 0.6 to about 0.89, or about 0.6 to about 0.

79.

8. 8. The method of claim 7, wherein the AUC value is from about 0.8 to about 0.

99.

9. 9. The method of any one of claims 1 to 8, wherein the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk that is predictive of clinical assessment of kidney disease state and that can distinguish between two or more kidney disease states.

10. 10. The method of claim 9, wherein the kidney disease state comprises non-rejection, T-cell mediated rejection (TCMR), antibody mediated rejection (ABMR), or a mixed TCMR and ABMR disease state.

11. The method of claim 10, wherein the TCMR further comprises a molecularly defined TCMR (mTCMR) and / or a potential TCMR (pTCMR).

12. 12. The method of claim 10 or 11, wherein the ABMR further comprises a molecularly defined ABMR (mABMR) and / or a potential ABMR (pABMR).

13. 13. The method of any one of claims 10 to 12, wherein the one or more transplant rejection scores comprise a first transplant rejection score based on a set of mRNAs and / or miRNAs associated with TCMR, and a second transplant rejection score based on a set of mRNAs and / or miRNAs associated with ABMR.

14. 14. The method of any one of claims 1 to 13, wherein the one or more transplant rejection scores comprise a transplant rejection score based on a set of mRNAs and / or miRNAs associated with inflammation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with allograft rejection, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with T cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with B cell activation and / or differentiation, a transplant rejection score based on a set of mRNAs and / or miRNAs associated with cytokine response, and / or a transplant rejection score based on a set of mRNAs and / or miRNAs associated with chemokine response.

15. 10. The method of any one of the preceding claims, comprising collecting the urine samples from the transplant recipient at different times and generating the one or more transplant rejection scores from each urine sample.

16. 16. The method of claim 15, wherein the urine sample is collected from the transplant recipient before, concurrently with, and / or after transplant.

17. 17. The method of claim 15 or 16, wherein the risk of transplant rejection is based on two or more transplant rejection scores generated at different times, and a change in the two or more transplant rejection scores indicates a change in kidney disease status.

18. 10. The method of any one of the preceding claims, wherein the renal transplant recipient is administered treatment for the determined renal disease state or renal transplant rejection.

19. 20. The method of claim 18, wherein the treatment comprises an anti-rejection or immunosuppressant agent.

20. 20. The method of claim 18 or 19, wherein a change in one or more transplant rejection scores identifies the presence, absence, or degree of a therapeutic response to said treatment.

21. The method of any one of claims 18 to 20, wherein the treatment is determined based on the one or more transplant rejection scores or a change in one or more transplant rejection scores.

22. 22. The method of any one of claims 1 to 21, wherein the nucleic acid comprises cellular nucleic acid, extracellular nucleic acid, and / or nucleic acid obtained from extracellular vesicles.

23. 23. The method of any one of claims 1 to 22, further comprising isolating cells from the urine sample and extracting nucleic acids from the cells.

24. 24. The method of any one of claims 1 to 23, further comprising isolating extracellular vesicles and extracting nucleic acids from the extracellular vesicles.

25. The method of any one of claims 1 to 23, wherein the cDNA is amplified before determining the amount.

26. 25. The method of any one of claims 1 to 24, wherein the extracted nucleic acids comprise one or more mRNAs.

27. The method of any one of claims 1 to 24, wherein the extracted nucleic acids comprise one or more miRNAs.

28. The method of any one of claims 1 to 24, wherein the extracted nucleic acids comprise one or more mRNAs and one or more miRNAs.

29. 29. The method of any one of claims 1 to 28, wherein preparing a composition of the nucleic acids or fractions thereof extracted in step (a) comprises amplification of cDNA derived from the nucleic acids.

30. 30. The method of claim 29, wherein said amplifying comprises performing multiplex targeted amplification of said cDNA at 10 to 50,000 target loci in a single reaction volume.

31. 31. The method of claim 29 or 30, wherein the amplification comprises universal amplification.

32. 32. The method of any one of claims 1 to 31, wherein the amount of the one or more mRNAs and / or miRNAs is measured by using quantitative PCR, real-time PCR, digital PCR, or sequencing.

33. 33. The method of any one of claims 1 to 32, wherein the amount of one or more mRNAs and / or miRNAs is measured by using multiplex quantitative PCR, multiplex real-time PCR, and / or multiplex digital PCR.

34. 33. The method of claim 32, wherein the sequencing comprises next-generation whole genome sequencing.

35. The method of any one of claims 1 to 31, wherein the amount of the one or more mRNAs and / or miRNAs is measured by using a microarray.

36. 32. The method of any one of claims 1 to 31, wherein the amount of said one or more mRNAs and / or miRNAs is measured by using molecular barcodes and microscopic imaging (such as a NanoString nCounter®).

37. 37. The method of any one of claims 1 to 36, wherein the amount of the one or more mRNAs and / or miRNAs is determined by measuring the absolute copy number of the one or more mRNAs and / or miRNAs per amount of total nucleic acid in the urine sample.

38. 38. The method of any one of claims 1 to 37, wherein the one or more mRNAs and / or miRNAs are associated with antibody-mediated transplant rejection (AMTR), T-cell-mediated transplant rejection (TMTR), apoptotic pathways, cytokines, antimicrobial responses, and / or inflammatory cell responses.

39. 39. The method of claim 38, wherein the one or more mRNAs and / or miRNAs associated with the antimicrobial response are CXC motif chemokine ligand (CXCL) type genes.

40. the one or more mRNAs Table 1-1 Table 1-2 Table 1-3 Table 1-4 Table 1-5 39. The method of any one of claims 1 to 38, wherein the gene is expressed from a gene selected from the group consisting of: and combinations thereof.

41. the one or more miRNAs Table 2-1 Table 2-2 Table 2-3 Table 2-4 Table 2-5 39. The method of any one of claims 1 to 38, wherein the nucleic acid sequence is linked to one or more expression products from a gene selected from the group consisting of:

42. the one or more mRNAs Table 3 39. The method of any one of claims 1 to 38, wherein the gene is expressed from a gene selected from the group consisting of: and combinations thereof.

43. the one or more miRNAs Table 4 and combinations thereof.

44. the one or more mRNAs Table 5 40. The method of any one of claims 1 to 39, wherein the gene is expressed from a gene selected from the group consisting of: and combinations thereof.

45. the one or more miRNAs Table 6 and combinations thereof.

46. The one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, and Cxcl1 39. The method of any one of claims 1 to 38, wherein the gene is expressed from a gene selected from the group consisting of: 0, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

47. The one or more mRNAs are selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, and Tap 1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

48. 39. The method of any one of claims 1 to 38, wherein the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

49. 39. The method of any one of claims 1 to 38, wherein the one or more mRNAs are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

50. The one or more mRNAs are selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, and PSMB10. , B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

51. 51. The method of any one of claims 1-50, wherein the one or more mRNAs are not expressed from a gene selected from the group consisting of PDCD1, MARCHHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

52. 51. The method of any one of claims 1-50, wherein the one or more mRNAs are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

53. The one or more miRNAs are miR-186-5p, miR-665, miR-873-5p.1, miR-543, miR-330-3p, miR-362-5p / 500b-5p, miR-217, miR-140-5p, miR-193-3p, miR-382-5p, miR-140-3p.2, miR-653-5p, miR-455-3p.2, miR-145-5p, miR-491-5p, miR-23-3p, miR-375, miR-129-5p, miR-96-5p / 1271-5p, miR-182-5p, miR-371-5p, miR-203a-3p.1, miR-494-3p, miR-146-5p, miR-140-3p.1, miR-125-5p, miR-346, miR-760, miR-185-5p, miR-325-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-423-5p, miR-496.1, miR-155-5p, miR-142-3p.2, miR-24-3p, miR-874-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-181-5p, miR-142-5p, miR-130-3p / 301-3p / 454-3p, miR-21-5p / 590-5p, miR-103-3p / 107, miR-137, miR-340-5p, miR-490-3p, miR-143-3p, miR-409-3p, miR-27-3p, miR-138-5p, miR-485-5p, miR-328-3p, miR-326, miR-148-3p / 152-3p, miR-9-5p, miR-31-5p, miR-452-5p / 892-3p, miR-202-5p, miR-29-3p, miR-338-3p, miR-26-5p, let-7-5p / 98-5p, miR-196-5p, miR-30-5p, miR-142-3p.1, miR-19-3p, miR-411-3p, miR-493-5p, miR-218-5p, miR-203a-3p.2, miR-495-3p, miR-425-5p, miR-135-5p, miR-154-3p / 487-3p, miR-223-3p, miR-219-5pmiR-670-3p、miR-2116b-5p、miR-200bc-3p / 429、miR-320、miR-2116a-5p、m iR-14411-33p / 2200a-33p、miR-1144-33p、miR-1128-3p、miR-455-3p.11、miR-22119a- 2-33p、miR-873-5p.2、miR-448、miR-1183-5p.2㼒、miR-374-5p、miR-505-3p.1 、miR-433-3p、miR-377-3p、miR-365-3p、miR-124-3p.11、miR-410-3p、miR- 188-33p、miR-222-3p、miR-1229-3p、miR-383-55p.11、miR-1-3p / 2206、miR-2296 -5p、miR-299-3p、miR-212-5p、miR-331-3p、miR-378-3p、miR-136-5p、miR -11193、miR-505-3p.2、miR-302c-3p.2 / 520-3p、miR-421、miR-499a-5p、mi R-302-3p / 372-3p / 373-3p / 520-3p、m iR-124-3p.2 / 506-3p、miR-34-5p / 44 9-5p、miR-376c-3p、miR-1139-5p、miR-2-2221-3p / 2222-3p、miR-5504-55p.1、mi R-3335-5p、miR-1101-3p.11、miR-431-5p、miR-489-3p、miR-369-3p、miR-330 -3p.2、miR-188-5p、miR-228-5p / 708-5p、miR-1333a-3p.2 / 1333b、miR-2205-5p 、miR-199-5p、miR-455-5p、miR-126-3p.2、miR-7-5p、miR-483-3p.2、miR- 668-3p、miR-1306-5p、miR-150-5p、miR-296-3p、miR-204-5p / 211-5p、miR -33064-5p、miR-532-5p、miR-876-5p、miR-501-3p / 502-3p、miR-33-5p、miR -1533-3p、miR-2-2114-5p、miR-655-3p、miR-342-3p、miR-1133a-3p.11、miR-4111 -5p.1、miR-496.2、miR-411-5p.2㼒、miR-582-5p、miR-381-3p、miR-188-5p、miR-383-5p. 2, miR-486-5p, miR-183-5p. 1, miR-208-3p, miR-193a-5p, miR-101-3p. 2, miR-542-3p, miR-190-5p, miR-299-5p, miR-154-5p, miR-802, miR-323-3p, miR-532-3p, miR-224-5p, miR-339-5p, miR-19 4-5p, miR-149-5p, miR-493-3p, miR-382-3p, miR-132-3p / 212-3p, miR-1197, miR-99-5p / 100-5p, miR-877-5p, miR-483-3p. 1, miR-10-5p, miR-361-5p, miR-539-3p, miR-191-5p, miR-329-3p / 362-3p, miR-122 -5p, miR-379-5p, miR-376-3p, miR-1298-5p, miR-451, miR-210-3p, miR-1224-5p, mi The method of any one of claims 1 to 51, wherein the miR-324-5p, miR-544a-5p, miR-488-3p, miR-758-3p, miR-151-3p, miR-875-5p, miR-134-5p, miR-192-5p / 215-5p, and miR-127-3p.

54. The one or more miRNAs are miR-96-5p / 1271-5p, miR-493-5p, miR-183-5p.2, miR-150-5p, miR-7-5p, miR-653-5p, miR-200bc-3p / 429, miR-212-5p, miR-1298-5p, miR-137, miR-758-3p, miR-325-3p, miR-542-3p, miR-25-3p / 32-5p / 92-3p / 363-3p / 367-3p, miR-495-3p, miR-30-5p, miR-873-5p.1, miR-146-5p, miR-505-3p.1, miR-539-3p, miR-216a-5p, miR-216b-5p, miR-340-5p, miR-361-5p, miR-338-3p, miR-217, miR-9-5p, miR-219a-2-3p, miR-15-5p / 16-5p / 195-5p / 424-5p / 497-5p, miR-503-5p, miR-199-3p, miR-1-3p / 206, miR-17-5p / 20-5p / 93-5p / 106-5p / 519-3p, miR-302-3p / 372-3p / 373-3p / 520-3p, miR-142-5p, miR-302c-3p.2 / 520-3p, miR-326, miR-760, miR-138-5p, miR-27-3p, miR-145-5p, miR-142-3p.2, miR-101-3p.2, miR-182-5p, miR-203a-3p.2, miR-140-3p.1, miR-183-5p.1, miR-144-3p, miR-101-3p.1, miR-330-3p, miR-224-5p, miR-148-3p / 152-3p, miR-485-5p, miR-122-5p, miR-155-5p, miR-320, miR-23-3p, miR-124-3p.2 / 506-3p, miR-135-5p, miR-381-3p, miR-26-5p, miR-1224-5p, miR-192-5p / 215-5p, miR-1249-3p, miR-125-5p, miR-483-3p.2, miR-668-3p, miR-223-3p, miR-655-3p, miR-382-5p, miR-130-3p / 301-3p / 454-3p, miR-19-3p, miR-582-5p, miR-194-5p, miR-802,The method of any one of claims 1 to 51, wherein the miR-483-3p.1, miR-382-3p, miR-129-5p, miR-3064-5p, miR-873-5p.2, miR-499a-5p, miR-128-3p, miR-532-5p, miR-296-5p, miR-744-5p, miR-425-5p, miR-218-5p, and miR-496.1 is selected from the group consisting of.

55. (i) measuring the amount of donor-derived cell-free DNA in a sample obtained from the transplant recipient and extracting cell-free DNA from the sample obtained from the transplant recipient, wherein the extracted cell-free DNA comprises donor-derived cell-free DNA and recipient-derived cell-free DNA; (ii) performing targeted amplification of the extracted DNA at 50 to 50,000 target loci in a single reaction volume; 10. The method of claim 9, further comprising: (iii) sequencing the amplified DNA by high-throughput sequencing to obtain sequencing reads; measuring the amount of cell-free DNA derived from the donor based on the sequencing reads; and generating a transplant rejection score indicative of transplant rejection based on whether the measured amount of cell-free DNA derived from the donor or a function thereof exceeds a cutoff threshold for the amount of cell-free DNA indicative of transplant rejection, wherein transplant rejection is determined based on both the one or more transplant rejection scores from the measured amounts of the one or more mRNAs and / or miRNAs and the transplant rejection score determined based on the measured amount of cell-free DNA derived from the donor.

56. 10. The method of any one of the preceding claims, wherein measuring the amount of mRNA and / or miRNA comprises amplifying at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target nucleic acid molecules, 2-10, 200-100, 50-500, or 50-2000 target nucleic acid molecules using at least 2, at least 5, at least 10, at least 20, at least 30, at least 50, at least 100 target RNA molecules, 2-10, 200-100, 50-500, or 50-2000 pairs of forward and reverse PCR primers.

57. 10. The method of any one of the preceding claims, wherein the one or more mRNAs and / or miRNAs are determined by text mining a database.

58. 10. The method of any one of the preceding claims, wherein the amount of said one or more mRNAs and / or miRNAs is measured relative to a housekeeping gene.

59. 1. A method for preparing a composition of nucleic acids from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or risk of renal transplant rejection, comprising: (a) extracting nucleic acid from the urine sample of the renal transplant recipient, wherein the extracted nucleic acid comprises one or more RNA molecules associated with risk of renal transplant rejection; (b) preparing a composition of nucleic acids from the extracted nucleic acids from step (a) by isolating RNA molecules and removing contaminating molecules, optionally wherein preparing the composition comprises reverse transcribing the RNA molecules to synthesize cDNA; (c) measuring the amount of RNA molecules associated with the risk of renal transplant rejection in the composition of nucleic acids and generating one or more transplant rejection scores from the amount of one or more measured RNA molecules, wherein the one or more transplant rejection scores provide a quantitative value for the risk of renal transplant rejection or the presence or absence of renal transplant rejection.

60. 60. The method of Claim 59, wherein the one or more RNA molecules is mRNA or miRNA.

61. 1. A method for preparing a composition of proteins from a urine sample of a renal transplant recipient, useful for determining renal transplant rejection or risk of renal transplant rejection, comprising: (a) extracting proteins from the urine sample of the kidney transplant recipient, wherein the extracted proteins are associated with risk of kidney transplant rejection; (b) preparing a composition of proteins from the proteins extracted in step (a) by removing contaminating molecules; (c) measuring the amount of protein in the composition and generating one or more transplant rejection scores from the measured amounts, wherein the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk or the presence or absence of kidney transplant rejection.

62. 62. The method of claim 61, wherein the measuring step is based on two or more transplant rejection scores, each transplant rejection score being based on a different subset of proteins.

63. 63. The method of claim 61 or 62, wherein the one or more transplant rejection scores are generated using predictive models, machine learning-based methods, and / or artificial intelligence methods.

64. 64. The method of any one of claims 61 to 63, wherein the one or more transplant rejection scores provide a quantitative value of kidney transplant rejection risk that is predictive of clinical assessment of kidney disease state and that can distinguish between two or more kidney disease states.

65. 65. The method of claim 64, wherein the kidney disease state comprises non-rejection, T-cell mediated rejection (TCMR), antibody mediated rejection (ABMR), or a mixed TCMR and ABMR disease state.

66. 66. The method of claim 65, wherein the TCMR further comprises a molecularly defined TCMR (mTCMR) and / or a potential TCMR (pTCMR).

67. 67. The method of claim 65 or 66, wherein the ABMR further comprises a molecularly defined ABMR (mABMR) and / or a potential ABMR (pABMR).

68. 68. The method of any one of claims 65 to 67, wherein the one or more transplant rejection scores comprise a first transplant rejection score based on a set of proteins associated with TCMR, and a second transplant rejection score based on a set of proteins associated with ABMR.

69. 69. The method of any one of claims 61 to 68, wherein the one or more transplant rejection scores comprise a transplant rejection score based on a set of proteins associated with inflammation, a transplant rejection score based on a set of proteins associated with allograft rejection, a transplant rejection score based on a set of proteins associated with T cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with B cell activation and / or differentiation, a transplant rejection score based on a set of proteins associated with cytokine response, and / or a transplant rejection score based on a set of proteins associated with chemokine response.

70. 70. The method of any one of claims 61 to 69, comprising collecting the urine samples from the transplant recipient at different times and generating the one or more transplant rejection scores from each urine sample.

71. 71. The method of claim 70, wherein the urine sample is collected before, concurrently with, and / or after transplantation.

72. 72. The method of claim 71, wherein the risk of transplant rejection is based on multiple transplant rejection scores generated at different times, and a change in one or more transplant rejection scores indicates a change in kidney disease status.

73. 73. The method of any one of claims 61 to 72, wherein the renal transplant recipient is administered treatment for the determined renal disease state or renal transplant rejection.

74. 74. The method of claim 73, wherein a change in one or more transplant rejection scores identifies the presence, absence, or degree of a therapeutic response to said treatment.

75. 75. The method of claim 74, wherein the treatment is determined based on the one or more transplant rejection scores or a change in one or more transplant rejection scores.

76. 76. The method of claim 75, wherein the treatment comprises an anti-rejection or immunosuppressant agent.

77. 77. The method of any one of claims 61 to 76, further comprising isolating cells from the urine sample and extracting proteins from the cells.

78. 78. The method of any one of claims 61 to 77, further comprising isolating extracellular vesicles and extracting proteins from the extracellular vesicles.

79. one or more of said proteins Table 7-1 Table 7-2 Table 7-3 Table 7-4 Table 7-5 79. The method of any one of claims 61 to 78, wherein the gene is expressed from or regulated by a gene selected from the group consisting of:

80. one or more of said proteins Table 8 79. The method of any one of claims 61 to 78, wherein the gene is expressed from a gene selected from the group consisting of: and combinations thereof.

81. one or more of said proteins Table 9 79. The method of any one of claims 61 to 78, wherein the gene is expressed from a gene selected from the group consisting of: and combinations thereof.

82. One or more of the proteins may be CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl1 79. The method of any one of claims 61-78, wherein the gene is expressed from a gene selected from the group consisting of: 0, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, IL18BP, and combinations thereof.

83. One or more of the proteins may be selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, and Tap 100. The method of any one of claims 61-78, wherein the gene is expressed from a gene selected from the group consisting of: IL-1, GZMB, PRF1, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, Calhm6, Klrc4-Klrk1, Psmb10, CD3delta, Map4k1, IL2Ra, TGFB1, FN1, CDH1, PRPF31, HAVCR2, IL18BP, and combinations thereof.

84. 79. The method of any one of claims 61-78, wherein the one or more proteins are expressed from a gene selected from the group consisting of CXCL9, CD3ε, IP-10, LCK, C1QB, PSMB9, Tim-3, Foxp3, FAM26F, ALB, CTB-186G2.1, CXCL11, GNLY, MRC1, RGS1, RP11-1049A21.2, TMEM156, Z98744.3, SERPINB12, C11orf21, AF001548, 18s ribosomal, Basp1, Cd6, Cxcl10, Cxcl9, Inpp5d, Isg20, Lck, Nkg7, Runx3, Tap1, and combinations thereof.

85. 79. The method of any one of claims 61-78, wherein the one or more proteins are expressed from a gene selected from the group consisting of CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, and combinations thereof.

86. One or more of the proteins are CXCL11, CXCL9, GNLY, PSMB9, TAP1, CXCL10, NKG7, ISG20, RUNX3, C11orf21, C1QB, LCK, INPP5D, CD6, CD3ε, MRC1, BASP1, TMEM156, ALB, FOXP3, RGS1, SERPINB12, PRF1, CALHM6, KLRC4-KLRK1, CD74, GZMB, PSMB10 , B2M, STAT1, CD3delta, PYCARD, IL18BP, MAP4K1, IL32, IL2RA, C3, IFNGR1, IRAK2, TGFB1, FN1, NAMPT, SERPINA1, TBP, CDH1, PRPF31, BMP7, CXCL14, HAVCR2, and combinations thereof.

87. 87. The method of any one of claims 61-86, wherein one or more of the proteins is not expressed from a gene selected from the group consisting of PDCD1, MARCHHF8, DCAF12, IL1R2, FLT3, ITGA4, ITGAM, PF4, C6orf25, SEMA7A, RHOU, and combinations thereof.

88. 87. The method of any one of claims 61-86, wherein one or more of the proteins are not expressed from a gene selected from the group consisting of CCDC159, dcaf12, DECR1, ERCC5, EWSR1, FLT3, GABPB2, GPI, IL1R2, ITGA4, ITGAM, MAP3K3, MAPK9, MARCHHF8, NONO, PDCD1, PF4, RHOU, SEMA7A, SRRM1, TBC1D10B, TOP2B, and combinations thereof.

89. (i) measuring the amount of donor-derived cell-free DNA in a sample obtained from the transplant recipient and extracting cell-free DNA from the sample obtained from the transplant recipient, wherein the extracted cell-free DNA comprises donor-derived cell-free DNA and recipient-derived cell-free DNA; (ii) performing targeted amplification of the extracted DNA at 50 to 50,000 target loci in a single reaction volume; 89. The method of any one of claims 61 to 88, further comprising: (iii) sequencing the amplified DNA by high-throughput sequencing to obtain sequencing reads; measuring the amount of cell-free DNA derived from the donor based on the sequencing reads; and generating a score indicative of transplant rejection based on whether the measured amount of cell-free DNA derived from the donor, or a function thereof, exceeds a cutoff threshold amount of cell-free DNA indicative of transplant rejection, wherein transplant rejection is determined based on both the measured amount of the protein and the score determined based on the measured amount of cell-free DNA derived from the donor.

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