WO2018085897A1 - Transplant rejection assay - Google Patents

Abstract

The present disclosure relates to methods of detecting organ transplant rejection based on the level of gene expression in a sample isolated from an organ transplant recipient. In particular, the present disclosure relates to methods of grading organ transplant rejection. The present disclosure also relates to methods of resolving inconclusive pathology to detect and/or grade transplant rejection.

Description

TRANSPLANT REJECTION ASSAY

FIELD OF THE INVENTION

The present disclosure relates to methods of detecting organ transplant rejection based on the level of gene expression in a sample isolated from an organ transplant recipient. In particular, the present disclosure relates to methods of grading organ transplant rejection. The present disclosure also relates to methods of resolving inconclusive pathology to detect and/or grade transplant rejection. BACKGROUND OF THE INVENTION

Tissue, including organ, transplantation is an important medical procedure to replace diseased or traumatized tissue in a recipient patient with donor tissue from a genetically or histocompatibility matched donor. The success or otherwise of a transplantation procedure is currently determined mainly by an invasive histological examination of biopsied material. However, histological examination has a high degree of sampling error, is invasive, has low sensitivity, can potentially cause tissue damage, requires a high degree of technical expertise and is consequently expensive.

For example, endomyocardial biopsy remains the gold standard for detection of rejection following heart transplantation. Patients require hospital admission and general anaesthesia to undergo the invasive procedure. The procedure is expensive and current protocols specify 10 heart biopsies for routine surveillance in the 12 months post-transplant alone. These biopsies not only represent considerable difficulty for the patients, but also significant cost and strain on the healthcare system.

Immuno-suppression is the standard of care after all organ transplantation. Detecting transplant rejection represents an important component of clinical management after organ transplant as it allows treating physicians to administer appropriate immuno-suppressive therapy. Accordingly, there remains an unmet need for accurate and efficient methods of detecting organ transplant rejection in patients. SUMMARY OF THE INVENTION

The present inventors have identified that expression levels of particular gene(s) are informative markers of transplant rejection. Accordingly, in one example, the present disclosure relates to a method of detecting organ transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKSl, CD8B and ATPlAl in a sample isolated from a subject, wherein organ transplant rejection is detected based on the expression level of the gene(s) in the sample.

The present inventors have also identified that the analytes of the present disclosure are informative markers of transplant rejection grade. Thus, in another example, the present disclosure relates to a method of grading organ transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKS1, CD8B and ATP1A1 in a sample isolated from a subject, wherein organ transplant rejection is graded based on the expression level of the gene(s) in the sample.

For example, the heart transplant rejection may be grade OR, grade 1R, grade 2R or grade 3R rejection. In another example, the heart transplant rejection may be grade OR or grade 1R rejection.

In another example, when performing the methods of the present disclosure, the expression level of two genes selected from the group consisting of MKS1, CD8B and ATP1A1 are determined. In another example, the expression level of MKS1, CD8B and ATP1A1 are determined. In another example, the methods further comprise determining the expression level of a gene selected from the group shown in Table 1.

In an example, the sample is selected from the group consisting of biopsy material, blood (including whole blood), peripheral blood mononuclear cells, blood plasma, blood serum, white blood cells, B cells, dendritic cells, granulocytes, innate lymphoid cells (ILCs), megakaryocytes, monocytes/macrophages, natural killer (NK) cells, platelets, red blood cells (RBCs), T cells, thymocytes. In one example, the sample is whole blood. In another example, the sample is peripheral blood mononuclear cells. In another example, the sample is biopsy material. For example, the biopsy material may be obtained from an endomyocardial biopsy.

In another example, the expression level of gene(s) are determined in at least 2 samples. For example, gene expression levels may be determined in samples obtained pre-transplant and post-transplant. In an example, gene expression levels can subsequently be compared to determine a change in gene expression between the samples. In this example, organ transplant rejection is detected based on a change in the expression level of the gene(s) in the sample.

In another example, gene expression levels may be determined in two samples selected from the group consisting of whole blood, peripheral blood mononuclear cells and biopsy material. For example, gene expression levels may be determined in whole blood and biopsy material. In another example, gene expression levels may be determined in peripheral blood mononuclear cells and biopsy material. In another example, gene expression levels may be determined in one or more cell populations isolated from whole blood and biopsy material. For example, gene expression levels may be determined in B-cells and T-cells isolated from whole blood and biopsy material.

In an example, the expression level of gene(s) are determined using RNA or protein extracted from the sample(s). For example, the expression level of gene(s) may be determined using circulating cell free RNA extracted from the sample(s). In an example, the RNA is cellular RNA.

In an example, the subject is a child, adolescent or adult. In an example, the subject is a child or adolescent. In an example, the subject is a child. In an example, the subjects age is about 4 months - 18 years.

In an example, the level of the gene expression is determined by whole genome sequencing, next generation sequencing, NanoString technology, droplet digital PCR, quantitative RT-PCR, mass spectrometry, immunohistochemistry.

The methods of the present disclosure may be used to detect transplant rejection in various organs. In an example, the organ is a heart, kidney, lung, pancreatic islet, liver, intestine or skin transplant.

For example, the organ may be a heart. Thus, in this example, the present disclosure also relates to a method of detecting heart transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKSl, CD8B and ATPlAl in a sample isolated from a subject, wherein heart transplant rejection is detected based on the expression level of the gene(s) in the sample. In another example, the present disclosure relates to a method of grading heart transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKSl, CD8B and ATPlAl in a sample isolated from a subject, wherein heart transplant rejection is graded based on the expression level of the gene(s) in the sample. For example, the heart transplant rejection may be grade OR, grade 1R, grade 2R or grade 3R rejection. In another example, the heart transplant rejection may be grade OR or grade 1R rejection.

In another example, the methods of the present disclosure relate to a method of treating organ transplant rejection comprising, detecting transplant rejection using the above exemplified methods and administering immunosuppressive therapy. In another example, the methods of the present disclosure relate to a method of treating organ transplant rejection comprising, staging transplant rejection using the above exemplified methods and administering immunosuppressive therapy. In another example, the present disclosure provides a kit for performing the above exemplified methods, the kit comprising probes and/or primers specific for an analyte of a gene selected from the group consisting of MKSl, CD8B and ATPlAl, and/or an analyte of a gene selected from the group shown in Table 1. In another example, the present disclosure provides a microarray for performing the above exemplified methods, the microarray having probes and/or primers able to determine the level of an analyte of a gene selected from the group consisting of MKSl, CD8B and ATPlAl, and/or an analyte of a gene selected from the group shown in Table 1.

In another example, the present disclosure relates to a method of resolving an inconclusive pathological assessment of an endomyocardial biopsy obtained from a heart transplant subject, the method comprising, determining the expression level of a gene in sample obtained from the subject, the gene being selected from the group consisting of MKSl, CD8B and ATPlAl, wherein the inconclusive pathological assessment is resolved based on the expression level of the gene(s) in the sample.

In another example, the present disclosure relates to a method of detecting or monitoring heart transplant rejection in a subject, the method comprising:

i) performing a pathological assessment of an endomyocardial biopsy obtained from a subject to determine the grade of transplant rejection;

ii) determining the expression level of a gene in a sample obtained from the subject, the gene being selected from the group consisting of MKSl, CD8B and

ATPlAl;

wherein when the pathological assessment of the endomyocardial biopsy is inconclusive for heart transplant rejection, heart transplant rejection is detected based on the expression level of the gene(s) in the sample.

In another example, the present disclosure relates to a method of screening for a compound which regulates the expression of MKSl, CD8B or ATPlAl, the method comprising:

(a) contacting a cell with a candidate compound,

(b) determining if the candidate compound modifies the expression of MKSl, CD8B or ATPlAl . For example, the methods may be used to screen for therapeutics suitable for treating transplant rejection.

Any example herein shall be taken to apply mutatis mutandis to any other example unless specifically stated otherwise.

The present disclosure is not to be limited in scope by the specific examples described herein, which are intended for the purpose of exemplification only. Functionally-equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.

Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

The disclosure is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

Figure 1. Gene expression levels of immunoglobulin gene clusters on chromosomes 2,

14, 22 (top) and T cell receptor gene clusters α, β, γ, δ (bottom) as determined by RNA- seq.

Figure 2. GENAS analysis of gene expression profiles belonging to grade 1R and 2R rejection samples.

Figure 3. Left: 3D scatter plot of transcript levels (logCPM) of CD8B, MKS1 and ATP1 Al in grade 0 and 1R rejections. DETAILED DESCRIPTION OF THE INVENTION

General Techniques and Selected Definitions

Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., molecular biology, biochemistry, pathology, diagnostics, genetics, physiology, and clinical studies).

Unless otherwise indicated, the molecular and statistical techniques utilized in the present disclosure are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T.A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D.M. Glover and B.D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F.M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J.E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

As used in this specification and the appended claims, terms in the singular and the singular forms "a," "an" and "the," for example, optionally include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to "an analyte" optionally includes one or more analytes.

As used herein, the term "about", unless stated to the contrary, refers to +/- 10%, more preferably +/- 5%, more preferably +/- 1%, of the designated value.

The term "and/or", e.g., "X and/or Y" shall be understood to mean either "X and Y" or "X or Y" and shall be taken to provide explicit support for both meanings or for either meaning.

Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

The methods of the present disclosure can be performed in vitro. For example, the methods of the present disclosure can be performed as an in vitro assay. The term "assay" is used in the context of the present disclosure to refer to an investigative (analytic) procedure or method for qualitatively assessing or quantitatively measuring the presence or amount or the functional activity of a target. In an example, a method or assay according to the present disclosure may be incorporated into a treatment regimen. For example, a method of treating a condition in a subject in need thereof may comprise performing an assay that embodies the methods of the present disclosure. In an example, a clinician or similar may wish to perform or request performance of an assay according to the present disclosure before administering or modifying treatment to a patient. For example, a clinician may perform or request performance of an assay according to the present disclosure on a transplant recipient before electing to administer or modify therapy such as immunosuppressive therapy. Organ transplant rejection

The term "organ transplant rejection" is used in the context of the present disclosure to refer to the biological process whereby transplanted tissue is rejected by a recipient's immune system. In one example, the methods of the present disclosure relate to the detection of organ transplant rejection in a subject. In an example, the methods of the present disclosure can detect acute rejection and chronic rejection. In another example, the methods detect acute rejection. In another example, the methods detect chronic rejection.

In another example, the methods of the present disclosure can detect the grade of organ transplant rejection. Organ transplant rejection can be classified by grade of rejection with higher grades being associated with more severe or prolonged rejection. Rejection grade can be further characterised using histological markers. Exemplary markers include infiltrating T cells which may be accompanied by infiltrating eosinophils, plasma cells, and neutrophils; structural compromise of tissue anatomy; and, injury to blood vessels.

Exemplary "organ transplants" assessed by the methods of the present disclosure can include heart, kidney, lung, pancreatic islet, liver, intestine or skin transplant. Other exemplary transplants include limb transplants such as leg, arm, hand or foot or appendage transplants such as a toe, nose or ear.

In an example, the methods of the present disclosure encompass detecting heart transplant rejection. In another example, the methods of the present disclosure encompass detecting the grade of heart transplant rejection. Heart transplant rejection grades are generally classified based on pathological assessment of endomyocardial biopsy (e.g. ISHLT-2004). Rejection grades include grade 0 (no rejection), grade 1 (interstitial and/or perivascular infiltrate with up to 1 focus of myocyte damage), grade 2 (two or more foci of infiltrate with associated myocyte damage) and grade 3 (diffuse infiltrate with multifocal myocyte damage; +/- edema; +/- haemorrhage; +/- vasculitis (Stewart et al. 2005).

In an example, the methods of the present disclosure can detect grade 0 heart transplant rejection. In another example, the methods of the present disclosure can detect grade 1 heart transplant rejection. In another example, the methods of the present disclosure can detect grade 2 heart transplant rejection. In another example, the methods of the present disclosure can detect grade 3 heart transplant rejection. In another example, the methods of the present disclosure can detect grade 0 and grade 1 heart transplant rejection. For example, the methods of the present disclosure can distinguish grade 1 from grade 2 rejection. For example, grade 1 from grade 2 rejection can be distinguished based on expression of MKS1. In another example, the methods of the present disclosure can detect grade 0, grade 1 and grade 2 heart transplant rejection. In another example, the methods of the present disclosure can detect grade 0, grade 1, grade 2 and grade 3 heart transplant rejection. Gene expression analysis

The methods of the present disclosure rely on determining expression level of gene(s) that are informative markers of transplant rejection. In an example, the expression level of MKS1 (NCBI gene id# 54903) is determined. In another example, the expression level of CD8B (NCBI gene id# 926) is determined. In another example, the expression level of ATP1A1 (NCBI gene id# 476) is determined. In other examples, the expression level of more than one gene can be determined. For example, the level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20 genes can be determined.

Accordingly, in an example, the expression level of MKS1 and CD8B are determined. In another example, the expression level of MKS1 and ATP1A1 are determined. In another example, the expression level of CD8B and ATP1A1 are determined. In another example, the expression level of MKS1, CD8B and ATP1A1 are determined.

In an example, the methods of the present disclosure can further comprise determining the expression level of any one or more of the genes listed in Table 1.

Table 1. Exemplary genes indicative of transplant rejection

As one of skill in the art would appreciate, gene expression levels can be determined by assessing various analytes. The term "analyte" is used in the context of the present disclosure to refer to a molecule whose presence in a sample provides a quantitative or qualitative measure of gene expression. Exemplary analytes informative of gene expression levels include RNA and protein. In an example, the level of gene expression is determined by assessing an RNA species. For example, the level of gene expression can be determined by assessing mRNA. In an example, the level of gene expression can be determined by assessing cellular RNA. In another example, the level of gene expression can be determined by assessing circulating cell free RNA. In another example, the level of gene expression can be determined by assessing a combination of cellular RNA and circulating cell free RNA. In another example, the level of gene expression can be determined by assessing protein. Exemplary analytes are shown in Table 1. Sample preparation and analysis

Transplant rejection is assessed using the methods of the present disclosure on a sample isolated from a subject. Thus, in one example, the methods of the present disclosure relate to determining the expression level of a gene in a sample obtained from a transplant recipient. It is considered that terms such as "sample", "biological sample" and "specimen" are terms that can, in context, be used interchangeably in the present disclosure. In an example, the sample is isolated from a human. For example, the sample can be isolated from a child, adolescent or adult human subject. In an example, the subject's age ranges from about 3 months to about 90 years. In another example, the subject's age ranges from about 3 months to about 70 years. In another example, the subject's age ranges from about 3 months to about 50 years. In another example, the subject's age ranges from about 3 months to about 40 years. In another example, the subject's age ranges from about 3 months to about 30 years. In another example, the subject's age ranges from about 3 months to about 25 years. In another example, the subject's age ranges from about 3 months to about 18 years. In another example, the subject's age ranges from about 3 months to about 15 years. In another example, the subject's age ranges from about 3 months to about 10 years.

In an example, the subject is a heart transplant recipient. In other examples the subject is a kidney, lung, pancreatic islet, liver, intestine or skin transplant recipient. In other examples, the subject is the recipient of a limb transplant such as leg, arm, hand or foot. In another example, the subject is the recipient of an appendage transplant such as a toe, nose or ear. In the present disclosure, any material can be used as the above-mentioned sample so long as it can be collected from a subject and gene expression can be analysed according to the methods of the present disclosure. For example, the sample may be biopsy material. In an example, the sample can be biopsy material obtained from an endomyocardial biopsy. An overview of endomyocardial biopsy and examples of performing the procedure are provided in From et al. 2011.

In another example, the sample is a blood sample (e.g. isolated venous blood). The term "blood sample" is used in the context of the present disclosure to refer to a sample of whole blood or a sub-population of cells in whole blood. Sub-populations of cells in whole blood include platelets, red blood cells (erythrocytes), platelets and white blood cells (i.e., peripheral blood leukocytes, which are made up of neutrophils, lymphocites, eosinophils, basophils and monocytes). These five types of white blood cells can be further divided into two groups, granulocytes (which are also known as polymorphonuclear leukeocytes and include neutrophils, eosinophils and basophils) and mononuclear leukocytes (which include monocytes and lymphocytes). Lymphocytes can be further divided into T-cells, B-cells and NK cells. The blood sample may be treated to remove whole cells such as by centrifugation, affinity chromatography (e.g. immunoabsorbent means) and filtration.

In an example, the sample can comprise a specific cell type or mixture of cell types isolated directly from the subject or purified from a sample obtained from the subject. For example, the sample may be peripheral blood mononuclear cells (pBMC). In another example, any of the above referenced cell types can be purified from a blood sample and assessed using the methods of the present disclosure. For example, gene expression levels can be assessed in T-cells. In another example, gene expression levels can be assessed in B-cells. In another example, gene expression levels can be assessed in granulocytes. In another example, gene expression levels can be assessed in T-cells, B-cells and NK cells obtained from a subjects blood sample. In another example, gene expression levels can be assessed in T-cells, B-cells and granulocytes obtained from a subjects blood sample. In another example, gene expression levels can be assessed in B-cells and T-cells obtained from a subjects blood sample.

Various methods of purifying sub-populations of cells are known in the art. For example, pBMC can be purified from whole blood using various known Ficoll based centrifugation methods (e.g. Ficoll-Hypaque density gradient centrifugation). Other cell populations can be immunoselected based on their expression of various cell surface markers using techniques such as fluorescence-activated cell sorting (FACS) or magnetic bead based separation techniques. For example, T-cells can be selected based on their expression of CD3 and CD4 and/or CD8, B-cells can be selected based on their expression of CD 19 and NK cells can be selected based on their expression of CD 16 and/or CD56. In other examples, monocytes can be purified based on their expression of CD14. In another example, T-cells and granulocytes are purified based on expression of CD3 and CD 19, T-cells being CD3+CD19+ and granulocytes being CD3-CD19-.

In an example, more than one sample may be obtained from the subject. In an example, at least two samples are obtained from the subject. For example, samples may be obtained pre and post-transplant. In another example, when a subject presents to the clinic for a biopsy of a transplanted organ, biopsy material and whole blood can be obtained from the subject. In this example, pBMC can be purified from the whole blood sample. In other examples, at least three or at least four samples can be obtained from the subject. In these examples, the level of gene expression can be determined in each sample.

In other examples, multiple daughter samples can be produced from an original parent sample. For example, at least two or at least three cell populations can be purified from a whole blood sample with the level of an analyte being determined in each daughter sample.

In another example, the biological sample is purified or processed to remove circulating cell free nucleic acids before isolating analytes from cells. For example, the plasma can be purified from the biological sample using centrifugation. In another example, a whole blood sample can be obtained from a subject and the serum can be removed via centrifugation after clotting.

Nucleic acids can be extracted from the cells in the biological sample for analysis. In an example, the nucleic acids extracted from the cells is an RNA species such as mRNA. Proteins can also be extracted from the cells in the biological sample for analysis. Various methods of isolating analytes, in particular RNA and protein from cells are known to those of skill in the art. In general, known methods involve disruption and lysis of the starting material. For RNA isolation, disruption and lysis is generally followed by the removal of proteins and other contaminants and finally recovery of the RNA. For example, techniques involving organic phenol based extraction, filter-based spin baskets or magnetic particles have been used for many years to extract and isolate RNA. One example of RNA isolation is exemplified below (e.g. TRIzol; Gibco/BRL). However, there are various other commercially available kits for RNA extraction (e.g. Life technologies; Qiagen). Purity and concentration of RNA can be assessed by various methods, for example, spectrophotometry. Exemplary methods of protein isolation and purification include precipitation, chromatography or electrophoresis. Purified proteins can then be concentrated using various lyophilisation or ultrafiltration based approaches. Again, there are various commercially available kits suitable for protein extraction (e.g. BioRad; Qiangen). Purity and concentration of protein can be assessed by various methods, for example, SDS page or Surface Plasmon Resonance.

Determining gene expression level

Gene expression levels can be determined using various methods known in the art. The appropriate methods will vary depending on the nature of the analyte. One of skill in the art would appreciate that the DNA, RNA, protein or other sequences required to determine the level of expression of the genes discussed above can be obtained from publically available databases. For example, relevant sequence information can be obtained by querying the NCBI database

(https://www.ncbi.nlm.nih.gov/gene) using the NCBI gene identifiers provided above for MKS1 (NCBI gene id# 54903), CD8B (NCBI gene id# 926) or ATP1 Al (NCBI gene id# 476).

In another example, relevant sequence information can be obtained from the gene sequence of MKS1 (SEQ ID NO: 1), CD8B (SEQ ID NO: 2) or ATP1A1 (SEQ ID NO: 3). Accordingly, in an example, gene expression levels can be determined based on the level of an analyte expressed from a gene having the sequence shown in SEQ ID NO: 1, SEQ ID NO: 2 or SEQ ID NO: 3. In an example, gene expression levels can be determined based on the level of an analyte expressed from a gene having the sequence shown in SEQ ID NO: 1. In another example, gene expression levels can be determined based on the level of an analyte expressed from a gene having the sequence shown in SEQ ID NO: 2. In another example, gene expression levels can be determined based on the level of an analyte expressed from a gene having the sequence shown in SEQ ID NO: 3.

The level of an RNA analyte can be determined via amplification reaction using primers which target a desired region of the RNA transcript. One of skill in the art can readily design primers that target RNA analytes according to the present disclosure based on methods described in the art. For example, primer selection for long-range PCR is described in US 10/042,406 and US 10/236,480; for short-range PCR, US 10/341,832 provides guidance with respect to primer selection. There are also publicly available programs such as "Oligo" or PrimerQuest (Integrated DNA Technologies) that are available for primer design. With such available primer selection and design software and the publicly available human genome sequence, one of skill in the art can construct primers to amplify analytes according to the present disclosure.

It is not intended that the primers of the disclosure be limited to generating an amplicon of any particular size. For example, the primers used to amplify the analytes described herein are not limited to amplifying the entire transcript region. The primers can generate an amplicon of any suitable length for detection. In some embodiments, analyte amplification produces an amplicon at least 100 nucleotides in length, or alternatively, at least 200 nucleotides in length. Amplicons of any size can be detected using the various technologies described herein. Differences in base composition or size can be detected by conventional methods such as electrophoresis.

It will be appreciated that amplification is not a requirement for analyte detection. In an example, determining the level of RNA comprises assessing the RNA with a quantitative amplification-independent detection means. For example, the level of an analyte can be determined using methods such as whole genome sequencing (WGS), next generation sequencing (NGS), massive parallel sequencing and NanoString technology. One of skill in the art can determine the level of an analyte based on its signal strength (e.g. as measured by quantitative amplification or via amplification-independent NGS or NanoString technology). For example DNA concentration can be calculated using the formula described in Lo et al. Am J HumGenet. 62:768 1998.

Various methods of determining protein analyte levels are also known in the art. Exemplary methods include mass spectrometry and immunohistochemistry. In other examples, gel electrophoresis, ELISA, immunoblot, chromatography or multiplex systems can be incorporated into the methods of the present disclosure to determine the level of an analyte. In another example, a protein analyte of interest can be purified before determining the respective level of gene expression in a sample. Levels of purified protein can be quantified using simple colorimetric assay such as Bradford assay. Detecting organ transplant rejection

When performing the methods of the present disclosure, organ transplant rejection is detected based on the expression level of gene(s) in a sample. In an example, organ transplant rejection is detected based on an increase in gene expression.

In another example, organ transplant rejection is detected based on a decrease in gene expression. In the context of the present disclosure, reference to an "increase" or a

"decrease" in the expression level of a gene refers to expression of the gene at a greater or lesser level, respectively, than normal level of expression of the gene. In an example, a genes expression is increased when its expression level is increased at least two fold relative to the normal level of expression. Conversely, a genes expression is decreased when its level of expression is decreased at least two fold relative to the normal level of expression. In other examples, increases or decreases in the level of expression are associated with at least a three fold, at least a four fold, at least a five fold change in expression level relative to the normal level of expression. In an example, the normal level of expression can be determined based on a pre-determined reference level of expression generated following assessment of a control panel of samples. For example, the normal level of expression can be determined based on a sample or series of samples obtained from a transplant recipient prior to receiving an organ transplant.

In an example, an increase in the expression level of MKSl indicates organ transplant rejection. In another example, an increase in the expression level of MKSl indicates grade 1 organ transplant rejection. In another example, an increase in the expression level of CD8B indicates organ transplant rejection. In another example, an increase in the expression level of CD8B indicates grade 1 organ transplant rejection. In an example, an increase in the expression level of ATPlAl indicates organ transplant rejection. In another example, an increase in the expression level of ATPlAl indicates grade 1 organ transplant rejection. In another example, an increase in the expression level of MKSl, CD8B or ATPlAl and a change in the expression level of any one of ADSL, LETMD1, MPI or TEX261 consistent with Table 1 indicates organ transplant rejection. In another example, an increase in the expression level of MKSl, CD8B or ATPlAl and a change in the expression level of any one of TRBV5-1, TRBV11-2, TRBV20-1 or TRBV19 consistent with Table 1 indicates organ transplant rejection. In another example, an increase in the expression level of MKSl, CD8B or ATPlAl and a change in the expression level any one of ADSL, LETMD1, MPI or TEX261 consistent with Table 1 indicates grade 1 organ transplant rejection. In another example, an increase in the expression level of MKSl, CD8B or ATPlAl and a change in the expression level of any one of TRBV5-1, TRBVl l-2, TRBV20-1 or TRBV19 consistent with Table 1 indicates grade 1 organ transplant rejection.

In another example, a log fold change (logFC) in MKSl of at least 0.4 indicates organ transplant rejection. In another example, a logFC in MKSl of at least 0.5 indicates organ transplant rejection. In another example, a logFC in MKSl of at least 0.55 indicates organ transplant rejection. In another example, a logFC in MKSl of at least 0.6 indicates organ transplant rejection. In another example, a logFC in MKSl of at least 0.7 indicates organ transplant rejection. In another example, a logFC in MKSl consistent with Table 1 indicates organ transplant rejection.

In another example, a logFC in MKSl of at least 0.4 indicates grade 1 organ transplant rejection. In another example, a logFC in MKSl of at least 0.5 indicates grade 1 organ transplant rejection. In another example, a logFC in MKSl of at least 0.55 indicates grade 1 organ transplant rejection. In another example, a logFC in MKSl of at least 0.6 indicates grade 1 organ transplant rejection. In another example, a logFC in MKSl of at least 0.7 indicates grade 1 organ transplant rejection. In another example, a logFC in MKSl consistent with Table 1 indicates grade 1 organ transplant rejection.

In another example, a logFC in ATP1A1 of at least 0.4 indicates organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.5 indicates organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.55 indicates organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.6 indicates organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.7 indicates organ transplant rejection. In another example, a logFC in ATP1 Al consistent with Table 1 indicates organ transplant rejection.

In another example, a logFC in ATP1A1 of at least 0.4 indicates grade 1 organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.5 indicates grade 1 organ transplant rejection. In another example, a logFC in ATP1A1 of at least0.55 indicates grade 1 organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.6 indicates grade 1 organ transplant rejection. In another example, a logFC in ATP1A1 of at least 0.7 indicates grade 1 organ transplant rejection. In another example, a logFC in ATP1A1 consistent with Table 1 indicates grade 1 organ transplant rejection.

In another example, a logFC in CD8B of at least 1.8 indicates organ transplant rejection. In another example, a logFC in CD8B of at least 1.9 indicates organ transplant rejection. In another example, a logFC in CD8B of at least 1.98 indicates organ transplant rejection. In another example, a logFC in CD8B of at least 2.0 indicates organ transplant rejection. In another example, a logFC in CD8B of at least 2.1 indicates organ transplant rejection. In another example, a logFC in CD8B consistent with Table 1 indicates organ transplant rejection.

In another example, a logFC in CD8B of at least 1.8 indicates grade 1 organ transplant rejection. In another example, a logFC in CD8B of at least 1.9 indicates grade 1 organ transplant rejection. In another example, a logFC in CD8B of at least 1.98 indicates grade 1 organ transplant rejection. In another example, a logFC in CD8B of at least 2.0 indicates grade 1 organ transplant rejection. In another example, a logFC in CD8B of at least 2.1 indicates grade 1 organ transplant rejection. In another example, a logFC in CD8B consistent with Table 1 indicates grade 1 organ transplant rejection.

Monitoring

By performing the methods of the present disclosure over at least two time points, the disclosed methods can be used to monitor organ transplant rejection in a subject over time. For example, the methods of the present disclosure can be used to monitor organ transplant rej ection in a subj ect following heart transplant.

In an example, the methods of the present disclosure can be performed daily. In another example, the methods of the present disclosure can be performed weekly. In another example, the methods of the present disclosure can be performed bi-monthly.

In another example, the methods of the present disclosure can be performed monthly. In another example, the methods of the present disclosure can be performed every two months, every three months, every four months, every six months. In another example, the methods of the present disclosure can be performed yearly.

In another example, the methods of the present disclosure can be performed in conjunction with a tissue biopsy. For example, the methods of the methods of the present disclosure can be performed in conjunction with an endomyocardial biopsy. In this example, the methods of the methods of the present disclosure can be performed about 10 times in the first 12 months post organ transplant.

In another example, the methods of the present disclosure can be used to monitor treatment. For example, the methods of the present disclosure can be used to predict treatment failure. In these examples, immunosuppressive therapy can be modified accordingly.

In an example, an increase in the expression level of MKS1 indicates immunosuppressive therapy should be increased. In another example, an increase in the expression level of CD8B indicates immunosuppressive therapy should be increased. In an example, an increase in the expression level of ATP1A1 indicates immunosuppressive therapy should be increased.

Method of Treatment

In an example, the methods of the present disclosure may be incorporated into an assay for use in determining a suitable treatment regimen. For example, the present disclosure provides a method of treating organ transplant rejection in a subject, the method comprising monitoring the level of gene expression in samples obtained from the transplant recipient over time, wherein immunosuppressive therapy is administered to the transplant recipient or the transplant recipient's immunosuppressive therapy is modified based on the level of gene expression in the sample. For example, the present disclosure provides a method of treating organ transplant rejection in a subject, the method comprising detecting the expression level of a gene selected from the group consisting of MKS1, CD8B and ATP1A1 in a sample obtained from a transplant recipient, wherein therapy is administered to the transplant recipient or the transplant recipient's therapy is modified based on the level of gene expression in the sample. Exemplary therapies comprise immunotherapy, antibody therapy or other pharmacological agents. Exemplary pharmacological agents include glucocorticoids, Immunophilin-binding drugs, T and/or B cell depleting antibodies, intravenous gammaglobulin, C5 inhibitors and proteasome inhibitors. Exemplary therapeutic modifications include increasing or decreasing therapy. For example, therapy can be modified based on the changes in gene expression discussed above.

Inconclusive pathological assessment

Previously, it has been difficult to resolve an inconclusive pathological assessment to detect transplant rejection. It has now been found that an inconclusive pathological assessment can be resolved to determine clinical status by determining the level of gene expression according to the present disclosure.

As used herein, an "inconclusive pathological assessment" refers to a pathological assessment that is inconclusive for transplant rejection and therefore is not informative for reaching a diagnosis or grading of transplant rejection. For example, in the context of assessing heart transplant rejection, rejection is generally assessed by two independent pathologists. Subtle histological changes can often make it difficult for pathologists to reach consensus. Accordingly, an inconclusive pathological assessment of heart transplant rejection encompasses an assessment that identifies indicators of rejection that are not sufficiently pronounced to reach a diagnosis of organ transplant rejection.

As used herein, the term "resolving" refers to the resolution of an inconclusive pathological assessment to detect organ transplant rejection. In an example, an inconclusive pathological assessment can be resolved to detect grade of organ transplant rejection. Reflexive Testing

The disclosed methods can be performed as a reflexive test. A "reflexive test" refers to a subsequent test (e.g., a second test) that is undertaken based upon the results obtained in a previous test (e.g., a first test). When detecting organ transplant rejection, pathological assessment of a sample can lead to a desire to test for another target. In the context of the present disclosure, the desire to test for another target (i.e. determine a level of gene expression according to the present disclosure) is driven by a pathological assessment that is inconclusive for organ transplant rejection. Adjunctive Testing

The methods of the present disclosure can also be performed as an adjunctive test. A test that provides information that adds to or assists in the interpretation of the results of other tests, and provides information useful for resolving an inconclusive earlier assessment may be classified as an adjunctive test. In a clinical setting, a pathological assessment may be requested to determine whether a subjects immune system is rejecting a transplanted organ. However, the pathological assessment may be inconclusive. Therefore, to assist in detecting organ transplant rejection, the methods of the present disclosure are performed to detect a level of gene expression as an adjunct to the pathological assessment of transplant rejection. In this context, the level of gene expression indicates organ transplant rejection, resolving the inconclusive pathological assessment.

In performing adjunctive testing it is envisaged that the pathological assessment can be performed at or about the same time as determining the level of gene expression. However, these steps may be performed separately. These steps may also be preformed on daughter samples obtained from the same parent sample. For example, daughter samples may be obtained from endomyocardial biopsy material with one sample being sent for pathology and the other for gene expression analysis.

Computer implemented method

It is envisaged that the methods of the present disclosure may be implemented by a system such as a computer implemented method. For example, the system may be a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as "processor") connected to a memory. The memory may be a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM. Software, that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the level of an analyte in a biological sample form a subject; receiving data indicating the level of analyte in the sample; processing the data to detect transplant rejection based on the level of gene expression; outputting findings.

For example, the memory may comprise program code which when executed by the processor causes the system to determine the measure of transplant rejection, or receive data indicating the measure of transplant rejection in the subject; process the data to measure transplant rejection based on the level of analyte isolated from the sample; reporting the measure of transplant rejection.

In another example, the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information. For example, the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.

In an example, the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network. For example, the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network. In other embodiments, the system may be isolated from direct user interaction.

In another example, performing the methods of the present disclosure to detect transplant rejection in a subject by determining the level of gene expression enables establishment of a diagnostic or prognostic rule based on the level of gene expression. For example, the diagnostic or prognostic rule can be based on the measure of the analyte relative to a control.

In another example, the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between measures of analytes and rejection grade observed in training data (with known rejection grade) to infer relationships which are then used to predict the rejection grade of subjects with unknown status. An algorithm is employed which provides an index of probability that, for example:

rejection has increased in severity; or

rejection has decreased in severity.

In an example, the algorithm performs a multivariate or univariate analysis function. In another example, the present disclosure relates to a method of allowing a user to determine the status, prognosis and/or treatment response of an organ transplant recipient, the method including (a) receiving data indicating the level of an analyte in a sample obtained from the subject; b) processing the data to determine the measure of transplant rejection in the subject; and c) outputting the status, prognosis and/or treatment response of a subject.

Compositions/kits

In one example, the present disclosure relates to a kit comprising PCR primer pairs specifically configured to amplify the analytes outlined in the present disclosure for use in the methods of the present disclosure. For example, the kit can comprise probes and/or primers and/or antibodies specific for analytes shown in Table 1.

In an example, the kit components may be packaged in or with a suitable solvent or in lyophilised form. The kit components may optionally be packaged in a suitable container with written instructions for performing the methods of the present disclosure. In an example, the present disclosure relates to the use of the primers disclosed herein in the manufacture of a non-invasive in vitro diagnostic assay for performing a method of the present disclosure. Screening methods

In another example, the methods of the present disclosure relate to screening for a compound that regulates gene expression of MKS1, CD8B or ATP1A1. In an example, these methods encompass, screening for compounds that decrease MKS1,

CD8B or ATP1A1 gene expression. In another example, these methods encompass screening for therapeutics suitable for the treatment of organ transplant rejection in a subject. For example, these methods can encompass screening for therapeutics suitable for the treatment of heart transplant rejection in a subject.

As the skilled person would appreciate, there are a wide variety of different screening procedures which could be adapted to screen candidate compounds. For example, candidate compounds may be screened using in vitro and in vivo assay systems.

In an exemplary in vitro assay system, cells are contacted with candidate compound and assessed to determine if the candidate compound modifies gene expression of MKS1, CD8B or ATP1A1. Exemplary cells can be derived from mammalian tissue or organ. For example, cells can be derived from heart, kidney, lung, pancreatic islet, liver, intestine or skin. In another example, cells are stem cells such as induced pluripotent stem cells. In another example, cells are cardiomyoblasts. For example, cells can be H9c2 cells. In other examples, cells are of human origin and are derived from the above referenced tissues and organs.

In an exemplary in vivo assay system, compounds are administered to an animal and assessed to determine if the candidate compound modifies the gene expression of MKS1, CD8B or ATP1A1. Compounds may be administered systemically or directly to a particular organ of interest.

In an example, high throughput screening methods are used which involve providing a library containing a large number of candidate compounds. Such libraries are then screened in one or more assays to identify those library members (e.g. particular chemical species or subclasses) that impart a desired level of modification to gene expression.

High throughput screening systems are commercially available and typically automate entire procedures, including all sample and reagent pipetting, liquid dispensing, timed incubations, and final readings of the microplate in detectors appropriate for the assay. These configurable systems provide rapid start up as well as a high degree of flexibility and customization. The manufacturers of such systems (e.g. Invitrogen, PerkinElmer, Bayer Pharma etc.) provide detailed protocols for use.

In an example, cells can be exposed to a solution or suspension of a candidate compound in cell culture media. For example, the compound can be dissolved in cell culture media if the compound is water soluble or water-immiscible. Otherwise, a suitable substrate may be soaked in the compound and placed over cells in culture. For the screening of a library of volatile candidate compounds, cells can be exposed to air or other gas mixtures comprising the compound(s).

In performing the methods of the present disclosure a plurality of candidate compounds can be contacted with a cell. For example, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 1,000, at least 10,000, at least 100,000 or more candidate compounds can be contacted with cells.

The term "candidate compound" is used in the context of the present disclosure to refer to an agent to be screened for modifying gene expression. Candidate compounds may include, for example, small molecules such as small organic compounds (e.g., organic molecules having a molecular weight between about 50 and about 2,500 Da), peptides or mimetics thereof, ligands including peptide and non- peptide ligands, polypeptides, nucleic acid molecules such as aptamers, peptide nucleic acid molecules, and components, combinations, and derivatives thereof. In an example, candidate compounds can be screened to determine whether they modify expression of more than one gene. For example, candidate compounds can be screened to determine if they modify expression of one or more of MKS1, CD8B or ATP1A1. In another example, candidate compounds are screened to determine if they modify expression of genes shown in Table 1. Various methods suitable for determining whether a candidate compound modifies gene expression are discussed above.

In an example, utility of candidate compounds that modify expression of MKS1, CD8B or ATP1A1 for treating organ transplant rejection can be assessed. Again, candidate compounds may be screened using in vitro and in vivo assay systems representative of transplant rejection. Various systems are known in the art. Models of hyper acute rejection are reviewed in Baldwin et al. (2010). An exemplary model of acute rejection is discussed in Nozaki et al. (2007). Heart transplant models are described in Cony et al. (1973); Schenk et al. (2008); Kwun et al. (2011). Kidney transplant models are reviewed in Ge and Gong (2011). Skin transplant models are described in Capla et al. (2006) and Lindenblatt et al. (2008). Various other models are known in the art and suitable for assessing utility of candidate compounds that modify expression of MKS1, CD8B or ATP1 Al for treating organ transplant rejection. EXAMPLES

EXAMPLE 1 - Materials and Methods

RNA extraction

Total RNA (TRNA) was isolated from the blood samples utilizing Trizol Reagent (GIBCO/BRL) following the manufacturer's protocol. The quality of total RNA was assessed by Agilent 2100 Bioanalyzer (version A.02.01 S1232, Agilent Technologies). RNA with an OD ratio of 1.99-2.0 at 260/280 was assessed.

Oligonucleotide arrays

101 hybridizations were performed on the Human HG-U133A GeneChip Set (Affymetrix, Santa Clara, CA, http://www.affymetrix.com) using RNA samples obtained from the blood samples of subjects (prior to transplantation and at each biopsy) and one reference sample (Stratagene) as a universal control. Arrays were performed following manufacturer's instructions. Data analysis

Data analysis was performed using two independent programs, GeneChip and GeneSpring in a standard fashion as previously described (Konstantinov et al. 2005; Konstantinov et al. 2004a, Arab et al. 2007; Konstantinov et al. 2004b). To identify differentially expressed transcripts, pairwise comparison analyses were carried out using MicroArray Suite Version 5 (MASv5; Affymetrix). This approach, which is based on the Mann-Whitney pairwise comparison test, allows the ranking of results by concordance, as well as the calculation of significance (P value) of each identified change in gene expression. Statistically significant genes (P<0.05) were selected for further analysis. Statistically significant changes in mean expression values were determined by importing the data from MASv5 into GeneSpring 5 (Silicon Genetics, Redwood City, CA). In short, a stepwise process was followed, first with normalizations. A per-chip followed by per-gene normalization was performed in order to facilitate direct comparison of biological differences. The 50th percentile of all measurements was used as a positive control for each sample; each measurement for each gene was divided by this synthetic positive control. The bottom tenth percentile was used as a test for correct background subtraction. Each gene was normalized to itself by making a synthetic positive control for that gene, and dividing all measurements for that gene by the positive control. This synthetic control was the median of the gene's expression values over all the samples. Next, a second method of filtering was performed using Affymetrix data and p-value with cut off of <0.005 in all conditions generated a number of genes which were used for further analysis.

Hierarchical clustering

Following initial filtering of genes based on the presence call, a second filtering based on P < 0.005, and the third filtering based on differential expression, genes were clustered and ordered using a hierarchical clustering algorithm that employs an average linkage method in GeneSpring 5. Experimental design, gene lists, hierarchical trees, microarray hybridization, and statistical analysis were performed in compliance with a Minimum Information about a Microarray Experiment (MIAME) protocol.

Microarray validation

Real-time quantitative polymerase chain reaction (qPCR) validation was performed on randomly selected individual target genes with highest and most consistent change on microarray platform. EXAMPLE 2 - Pilot Study

Samples from 5 patients were assessed via microarray. Data was analysed, in both cross-sectional fashion (before transplantation, n=5 samples and after transplantation, n=5 samples) and longitudinal fashion in 1 of these patients (5 consecutive samples in 1 patient, i.e., 1 before transplantation and 4 after transplantation).

Gene expression after heart transplantation was predominantly suppressed compared to preoperative state, particularly, genes encoding immunity proteins, which was consistent with induced immunosuppression.

Despite personal variability in gene expression in human samples the cross sectional study revealed 16 genes that changed significantly in all 5 patients after heart transplantation, whereas a longitudinal assessment of gene expression demonstrated 12 genes that changed significantly in all 4 consecutive samples taken from 1 patient compared to baseline.

The cross-sectional arm of the study was designed to reveal expression pattern common to all individuals being consistent with induction of an immunosuppressive state, while the longitudinal arm of the study was designed to develop a personalized assessment of individual genomes prior to transplantation, during immuno-suppressed state and at rejection episodes. Both approaches are complementary. The 16 genes that consistently changed with more than 100% fold change in all 5 patients after transplantation revealed predominant suppression of gene expression pattern after transplantation, whereas gene expression involved in early non-specific inflammatory response was increased.

Consistent suppression of gene expression of immunoglobulin (IG) genes was observed in all patients after heart transplantation. Thus, our preliminary results demonstrated that gene expression pattern can be consistently identified before and after heart transplantation across all individual patients and is consistent with an induction of the immuno-suppression therapy. For example, tumour necrosis factor receptor super family 17 (T FRSF17) is expressed on human B-lymphocytes and the over expression of its gene has previously observed in rejection following kidney transplantation. Expression of T FRSF17 was consistently decreased in all patients after transplantation and induction of immuno-suppression.

A longitudinal assessment of genome-wide gene expression in 1 patient demonstrated that an early grade of rejection (Grade I) can be identified. EXAMPLE 3 - Predicting Transplant Rejection

106 blood samples and corresponding endomyocardial biopsies were obtained from 18 subjects that had received a heart transplant rejection. Venous blood samples (5 ml each) were obtained immediately after induction of anaesthesia prior to heart transplantation and then immediately after induction of anaesthesia prior to each endomyocardial biopsy during 1st year after transplantation (9 biopsies).

The mean age at transplantation was 19.3 years (median 12 years, range: 4 months - 17.5 years). Pre-operative interpersonal variances in gene expression, including age differences, were negated by post-transplantation immunosuppression.

Immunosuppression was associated with downregulation of immunoglobulin genes, whilst rejection was associated with upregulation of T cell receptor genes (Figure 1). Grade 1R and 2R rejections showed correlated gene expression patterns (Figure 2), and were pooled and compared with no rejection to discover differentially expressed (DE) genes (Table 2).

Drop-one-out cross validation using the top 3 DE genes (MKS1, CD8B,

ATPlAl) and k=17 correctly predicted 86% (68/79) of grade 1R rejection vs grade 0 (Figure 3). Using the top gene MKS1 yielded 81% correction prediction of grade 1R vs 0. Similarly, MKS1 and k=9 yielded 81.8% correction prediction of grade 2R+1R vs 0. Accordingly, allograft rejection after heart transplantation can be predicted in blood using genomic approach despite wide age range.

Table 2.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the disclosure as shown in the specific embodiments without departing from the spirit or scope of the disclosure as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

The present application claims priority from AU 2016904635 filed 14 November 2017, the disclosure of which is incorporated herein by reference.

All publications discussed above are incorporated herein in their entirety.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present disclosure. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each claim of this application.

REFERENCES

Arab et al. (2007) J Thorac Cardiovasc Surg, 134, 74-81.

Ausubel et al. (editors) (1988) Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (including all updates until present).

Baldwin et al. (2010) Am J Transplant, 10, 1135-42.

Brown (editor) (1991), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press.

Capla et al. (2006) Plast Reconstr Surg, 117, 836-44.

Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

Cony et al. (1973) Transplantation, 16, 343-50.

From et al. (2011) Mayo Clin Proc, 86, 1095 - 1102.

Ge and Gong (2011) Exp Clin Transplant, 9, 287-94.

Glover and Hames (editors) (1995 and 1996) DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press.

Harlow and Lane (editors) (1988) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory.

Konstantinov et al. (2004a) J Thorac Cardiovasc Surg, 127, 746-54.

Konstantinov et al. (2004b) Physical Genomics, 19, 143-50.

Konstantinov et al. (2005) J Thorac Cadiovasc Surg, 130, 1326-32.

Kwun et al. (2011) J Immunol, 187, 3997-4006.

Lindenblatt et al. (2008) Plast Reconstr Surg. 122, 1669-80.

Lo et al. (1998) Am J HumGenet. 62, 768.

Nozaki et al. (2007) J Immunol., 179, 5238-45.

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Claims

1. A method of detecting organ transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKSl, CD8B and ATP1A1 in a sample isolated from a subject, wherein organ transplant rejection is detected based on the expression level of the gene(s) in the sample.

2. A method of grading organ transplant rejection in a subject, the method comprising, determining the expression level of a gene selected from the group consisting of MKSl, CD8B and ATP1A1 in a sample isolated from a subject, wherein organ transplant rejection is graded based on the expression level of the gene(s) in the sample.

3. The method of claim 1 or claim 2, wherein the expression level of two genes selected from the group consisting of MKSl, CD8B and ATP1A1 are determined.

4. The method of claim 1 or claim 2, wherein the expression level of MKSl, CD8B and ATP1 Al are determined.

5. The method according to any one of claims 1 to 4, further comprising determining the expression level of a gene selected from the group shown in Table 1.

6. The method according to any one of claims 1 to 5, wherein the sample is selected from the group consisting of biopsy material, blood (including whole blood), peripheral blood mononuclear cells, blood plasma, blood serum, white blood cells, B cells, dendritic cells, granulocytes, innate lymphoid cells (ILCs), megakaryocytes, monocytes/macrophages, natural killer (NK) cells, platelets, red blood cells (RBCs), T cells, thymocytes.

7. The method according to any one of claims 1 to 5, wherein the sample is whole blood.

8. The method according to any one of claims 1 to 5, wherein the sample is peripheral blood mononuclear cells.

9. The method according to any one of claims 1 to 5, wherein the sample is biopsy material.

10. The method of claim 9, wherein the biopsy material is obtained from an endomyocardial biopsy.

11. The method according to any one of claims 1 to 10, wherein the expression level of gene(s) is determined in at least 2 samples.

12. The method of claim 11, wherein the two samples are selected from the group consisting of whole blood, peripheral blood mononuclear cells and biopsy material.

13. The method according to any one of claims 1 to 12, wherein the expression level of gene(s) is determined using RNA or protein extracted from the sample(s).

14. The method according to any one of claims 1 to 7, 11 or 12, wherein the expression level of gene(s) is determined in circulating cell free RNA extracted from the sample(s).

15. The method of claim 13, wherein the RNA is cellular RNA.

16. The method according to any one of claims 1 to 15, wherein the subject is a child or adolescent.

17. The method of claim 16, wherein the subjects age is about 4 months - 18 years.

18. The method according to any one of claims 1 to 17, wherein the level of the gene expression is determined by whole genome sequencing, next generation sequencing, NanoString technology, droplet digital PCR, quantitative RT-PCR, mass spectrometry, immunohistochemistry.

The method according to any one of claims 1 to 18, wherein the organ is a heart, kidney, lung, pancreatic islet, liver, intestine or skin transplant.

The method according to any one of claims 1 to 18, wherein the organ is a heart.

The method according to any one of claims 2 to 20, wherein the organ transplant rejection is grade OR, grade 1R, grade 2R or grade 3R rejection.

The method according to claim 21, wherein the organ transplant rejection is grade OR or grade 1R rejection.

A method of treating organ transplant rejection comprising, detecting/staging transplant rejection using the methods according to any one of claims 1 to 22 and administering immunosuppressive therapy.

A kit for performing the methods according to any one of claims 1 to 23, the kit comprising probes and/or primers specific for an analyte of a gene selected from the group consisting of MKS1, CD8B, ATP1A1 and/or an analyte of a gene selected from the group shown in Table 1.

A microarray for performing the methods according to any one of claims 1 to 23, the microarray having probes and/or primers able to determine the level of an analyte of a gene selected from the group consisting of MKS1, CD8B, ATP1A1 and/or an analyte of a gene selected from the group shown in Table 1.

A method of resolving an inconclusive pathological assessment of an endomyocardial biopsy obtained from a heart transplant subject, the method comprising, determining the expression level of a gene in sample obtained from the subject, the gene being selected from the group consisting of MKS1, CD8B and ATP1A1, wherein the inconclusive pathological assessment is resolved based on the expression level of the gene(s) in the sample.

A method of detecting or monitoring heart transplant rejection in a subject, the method comprising:

i) performing a pathological assessment of an endomyocardial biopsy obtained from a subject to determine the grade of transplant rejection; ii) determining the expression level of a gene in a sample obtained from the subject, the gene being selected from the group consisting of MKSl, CD8B and ATP1A1;

wherein when the pathological assessment of the endomyocardial biopsy is inconclusive for heart transplant rejection, heart transplant rejection is detected based on the expression level of the gene(s) in the sample.

28. A method of screening for a compound which regulates the expression of MKSl, CD8B or ATP1 Al, the method comprising:

(a) contacting a cell with a candidate compound,

(b) determining if the candidate compound modifies the expression of MKSl, CD8B or ATP1A1.

29. The steps, features, integers, compositions and/or compounds disclosed herein or indicated in the specification of this application individually or collectively, and any and all combinations of two or more of said steps or features.