TRANSPLANTATION CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application Serial No. 60/683,737, filed May 23, 2005.

TECHNICAL FIELD

This document relates to methods and materials involved in tissue rejection (e.g., organ rejection) and detecting tissue rejection.

BACKGROUND

The transplantation of tissue from one mammal to another has been used for years to save lives and to improve the quality of lives. For example, the first successful kidney transplant was performed in the mid-1950s between identical twin brothers. Since then, donors have grown to include not only close relatives but also distant relatives, friends, and total strangers. In some cases, the recipient may reject the transplanted tissue. Thus, tissue rejection is a concern for any recipient of transplanted tissue. If a doctor is able to recognize early signs of tissue rejection, anti-rejection medication often can be used to reverse tissue rejection.

SUMMARY

This document relates to methods and materials involved in detecting tissue rejection (e.g., organ rejection). More particularly, this document relates to methods and materials involved in the early detection of tissue rejection (e.g., kidney rejection) and the assessment of a mammal's probability of rejecting tissue such as a transplanted organ. For example, this document provides nucleic acid arrays that can be used to diagnose tissue rejection in a mammal. Such arrays can allow clinicians to diagnose tissue rejection early based on a determination of the expression levels of nucleic acids that are differentially expressed in tissue being rejected as compared to control tissue not being rejected. The differential expression of such nucleic acids can be detected in tissue being rejected prior to the emergence of visually-observable, histological signs of tissue

rejection. Early diagnosis of patients rejecting transplanted tissue (e.g., a kidney) can help clinicians determine appropriate treatments for those patients. For example, a clinician who diagnoses a patient as rejecting transplanted tissue can treat that patient with medication that suppresses tissue rejection (e.g., immunosuppressants). The description provided herein is based, in part, on the discovery of nucleic acids that are differentially expressed in tissue being rejected as compared to control tissue that is not being rejected. Such nucleic acids can be nucleic acids that are induced by, for example, gamma interferon (IFN-K). The term "gamma interferon induced transcripts" or "GITs" as used herein refers to transcripts that are expressed in kidneys of mammals treated with IFN-K at a level at least 2-fold greater than the level of expression in normal kidney tissue. In some embodiments, a "GIT" is identified based expression that is increased at least two-fold in response to IFN-K in normal kidneys of one or more particular strains (e.g., B6, CBA, and/or BALB/c) as compared to the level of expression in untreated normal kidney. The term "rejection induced transcripts" or "RITs" as used herein refers to transcripts that are elevated at least 2-fold in WT kidney allografts at day 5 post transplant in WT hosts vs. normal kidneys. In some embodiments, a "RIT" is identified based on expression that is increased at least two-fold in WT allografts from one or more particular strains (e.g., B6, CBA, and/or BALB/c) as compared to the level of expression in normal kidney. The term "injury and repair-induced transcripts" or "IRITs" refers to transcripts that are increased at least two-fold in isografts at least once between day 1 and day 21, as compared to normal kidney, and also are increased at least two-fold in CBA allografts at day 5 as compared to normal kidneys.

The term "gamma interferon and rejection induced transcripts" or "GRITs" as used herein refers to IFN-K and rejection-inducible transcripts. These transcripts are (a) expressed at a level at least 2-fold greater in kidney tissue of mammals treated with

IFN-K than in kidney tissue of untreated mammals, (b) elevated at least 2-fold in tissue from WT kidney allografts at day 5 post transplant in WT hosts as compared to normal kidney tissue, and (c) expressed at levels at least 2-fold lower in kidney tissue from IFN-K-deficient (GKO) D5 allografts as compared to WT D5 allografts. Thus, the expression of GRITs is affected by the presence or absence of IFN-K in allografts. The

term "GRIT-like" transcripts as used herein refers to transcripts that are (a) expressed at a level at least 2-fold greater in kidney tissue of mammals treated with IFN-K than in kidney tissue of untreated mammals, (b) elevated at least 2-fold in tissue from WT kidney allografts at day 5 post transplant in WT hosts as compared to normal kidney tissue, and (c) not lower or even increased when IFN-K is absent in GKO D5 allografts compared to WT D5 allografts. GRIT-like transcripts, despite being inducible by rIFN-K, are increased in allografts by mechanisms largely independent of IFN-K.

The term "transcript" as used herein refers to an mRNA identified by one or more numbered Affymetrix probe sets, while a "unique transcript" is an mRNA identified by only one probe set. The term "true interferon gamma dependent and rejection-induced transcripts" or "tGRITs" refers to rejection-induced transcripts that are IFN-.K-dependent in rejection, and also are unique transcripts that are increased at least 2-fold by rIFN-K. The term "occult interferon gamma dependent and rejection-induced transcripts" or "oGRITs" refers to GRITs that are unique transcripts, but that are not 2-fold induced by rIFN-K in normal kidneys.

The description provided herein also is based, in part, on the discovery that the expression levels of RITs can be used to distinguish transplanted tissue that is being rejected from transplanted tissue that is not being rejected. For example, the expression levels of nucleic acids listed in Table 2, Table 7, and/or Table 1 1 can be assessed in transplanted tissue to determine whether or not that transplanted tissue is being rejected. In addition, the description provided herein is based, in part, on the discovery that the expression levels of RITs (e.g., those listed in Table 2, Table 7, and/or Table 1 1) can be used to distinguish transplanted tissue that is being rejected from transplanted tissue that is not being rejected at a time point prior to the emergence of any visually-observable, histological sign of tissue rejection (e.g., tubulitis for kidney rejection). In some embodiments, expression levels of GRITs or GRIT-like transcripts, including, for example, those listed in Tables 4, 5, and 9 can be assessed to determine whether or not transplanted tissue is being rejected or to distinguish transplanted tissue that is being rejected from transplanted tissue that is not being rejected.

In one aspect, this document features a method for detecting tissue rejection. The method can include determining whether or not tissue transplanted into a mammal contains cells that express at least two of the nucleic acids listed in Table 2 or Table 1 1 at elevated levels, wherein the presence of the cells indicates that the tissue is being rejected. The mammal can be a human. The tissue can be kidney tissue or a kidney. The method can include determining whether or not the tissue contains cells that express at least five of the nucleic acids, at least ten of the nucleic acids, or at least twenty of the nucleic acids. The determining step can include measuring the level of mRNA expressed from the at least two nucleic acids or measuring the level of polypeptide expressed from the at least two nucleic acids. The method can include determining whether or not the tissue contains cells that express at least two of the nucleic acids at a level greater than the average level of expression exhibited in cells from control tissue that has not been transplanted.

In another aspect, this document features a method for detecting tissue rejection. The method can include determining whether or not a sample contains cells that express at least two of the nucleic acids listed in Table 2 or Table 1 1 at elevated levels, wherein the sample contains cells, was obtained from tissue that was transplanted into a mammal, and was obtained from the tissue within fifteen days of the tissue being transplanted into the mammal, and wherein the presence of the cells indicates that the tissue is being rejected. The mammal can be a human. The tissue can be kidney tissue or a kidney. The method can include determining whether or not the sample contains cells that express at least five of the nucleic acids, at least ten of the nucleic acids, or at least twenty of the nucleic acids. The determining step can include measuring the level of mRNA expressed from the at least two nucleic acids or measuring the level of polypeptide expressed from the at least two nucleic acids. The sample can be obtained from the tissue within ten days of the tissue being transplanted into the mammal or within five days of the tissue being transplanted into the mammal. The method can include determining whether or not the sample contains cells that express at least two of the nucleic acids at a level greater than the average level of expression exhibited in cells from control tissue that has not been transplanted.

In another aspect, this document features a nucleic acid array containing at least 20 nucleic acid molecules, wherein each of the at least 20 nucleic acid molecules has a different nucleic acid sequence, and wherein at least 50 percent of the nucleic acid molecules of the array contain a sequence from nucleic acid selected from the group consisting of the nucleic acids listed in Table 2 and Table 1 1. The array can contain at least 50 nucleic acid molecules, wherein each of the at least 50 nucleic acid molecules has a different nucleic acid sequence. The array can contain at least 100 nucleic acid molecules, wherein each of the at least 100 nucleic acid molecules has a different nucleic acid sequence. Each of the nucleic acid molecules that contain a sequence from a nucleic acid selected from the group can contain no more than three mismatches. At least 75 percent of the nucleic acid molecules of the array can contain a sequence from nucleic acid selected from the group. At least 95 percent of the nucleic acid molecules of the array can contain a sequence from nucleic acid selected from the group. The array can include glass. The at least 20 nucleic acid molecules can contain a sequence present in a human.

In another aspect, this document features a computer-readable storage medium having instructions stored thereon for causing a programmable processor to determine whether one or more nucleic acids listed in Table 2 or Table 1 1 are detected in a sample, wherein the sample is from a transplanted tissue. The computer-readable storage medium can further have instructions stored thereon for causing a programmable processor to determine whether one or more of the nucleic acids listed in Table 2 or Table 1 1 is expressed at a greater level in the sample than in a control sample of non-transplanted tissue.

This document also features an apparatus for determining whether a transplanted tissue is being rejected. The apparatus can include one or more collectors for obtaining signals representative of the presence of one or more nucleic acids listed in Table 2 or Table 1 1 in a sample from the transplanted tissue, and a processor for analyzing the signals and determining whether the tissue is being rejected. The one or more collectors can be configured to obtain further signals representative of the presence of the one or more nucleic acids in a control sample from non-transplanted tissue.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG 1 is a depiction of selection criteria used for identification of GRITs and GRIT-like transcripts. WT D5 - wild type allografts, GKO D5 - IFN-K-deficient allografts.

FIG 2 A is a depiction of an unsupervised hierarchical clustering of GRITs. FIG. 2B is a graph showing an expression time course of GRITs in injury and rejection. FIG. 2C is a graph showing the signal strength of IFN-K over the time course of in injury and rejection. Clustering was based on distance as the similarity measure. ISO - isografts, WT - allografts, NCBA - control kidneys.

FIG 3A is a depiction of an unsupervised hierarchical clustering of GRIT-like transcripts. FIG. 3B is a graph showing an expression time course of GRIT-like transcripts in injury and rejection. Normalized values are shown. Clustering was based on distance as the similarity measure. ISO - isografts, WT - allografts, NCBA - control kidneys.

FIGS. 4A, 4B, and 4C are graphs showing expression profiles of IFN-K, GRITs, and GRIT-like transcripts, respectively, early after transplantation. ISO - isografts, WT- allografts, GKO D5 - IFN-K deficient grafts, NCBA - control kidneys.

FIG. 5A is a depiction of an unsupervised hierarchical clustering of injury-induced RIT. FIG. 5B is a graph showing an expression time course of RIT in injury and rejection. Normalized values are shown. Clustering was based on distance as the similarity measure. ISO - isografts, WT - allografts, NCBA - control kidneys. FIG. 6A is a graph showing expression profiles of acute phase markers in isografts and allografts. FIG. 6B is a graph showing the expression profiles of Tgfbi and the average expression profile of injury-induced RIT in isografts and allografts. Normalized values are shown in both graphs. ISO - isografts, WT - allografts, NCBA - control kidneys. FIG. 7A is a graph showing an expression profile of Tgfbi superimposed on that for injury-induced RIT. FIG 7B is a graph showing an expression profile of STAT-I superimposed on that for injury-induced RIT.

FIG 8A is a diagram of an algorithm for identifying GRITs. FIG. 8B is a diagram of an algorithm for identifying RITs. FIG. 8C is a diagram of an algorithm for identifying tGRITs and oGRITs.

FIG. 9 is a diagram of an algorithm for identifying IRITs.

DETAILED DESCRIPTION

This description provides methods and materials involved in detecting tissue rejection (e.g., organ rejection). For example, this description provides methods and materials that can be used to diagnose a mammal (e.g., a human) as having transplanted tissue that is being rejected. A mammal can be diagnosed as having transplanted tissue that is being rejected if it is determined that the tissue contains cells that express elevated levels of one or more RITs or that express elevated levels one or more of the nucleic acids listed in Table 2, Table 7, or Table 1 1. In some embodiments, a mammal can be diagnosed as having transplanted tissue that is being rejected if it is determined that the tissue contains cells that express elevated levels of one or more GRITs, GRIT-like, true GRIT, or occult GRIT transcripts including, without limitation, those listed in Tables 4, 5, 9, or 10, respectively.

The methods and materials provided herein can be used to detect tissue rejection in any mammal such as a human, monkey, horse, dog, cat, cow, pig, mouse, or rat. In

addition, the methods and materials provided herein can be used to detect rejection of any type of transplanted tissue including, without limitation, kidney, heart, liver, pancreas, and lung tissue. For example, the methods and materials provided herein can be used to determine whether or not a human who received a kidney transplant is rejecting that transplanted kidney.

Any type of sample containing cells can be used to determine whether or not transplanted tissue contains cells that express one or more RITs or that express one or more of the nucleic acids listed in Table 2, Table 7, or Table 1 1 at elevated levels. Similarly, any type of sample containing cells can be used to determine whether or not transplanted tissue contains cells that express one or more GRITs, GRIT-like, true GRIT, or occult GRIT transcripts, or that express one or more of the nucleic acids listed in Table 4, Table 5, Table 9, or Table 10 at elevated levels. For example, biopsy (e.g., punch biopsy, aspiration biopsy, excision biopsy, needle biopsy, or shave biopsy), tissue section, lymph fluid, blood, and synovial fluid samples can be used. In some embodiments, a tissue biopsy sample can be obtained directly from the transplanted tissue. In some embodiments, a lymph fluid sample can be obtained from one or more lymph vessels that drain from the transplanted tissue. A sample can contain any type of cell including, without limitation, cytotoxic T lymphocytes, CD4 ⁺ T cells, B cells, peripheral blood mononuclear cells, macrophages, kidney cells, lymph node cells, or endothelial cells. As explained herein, a RIT refers to a transcript that is elevated at least 2-fold in

WT kidney allografts at day 5 post transplant in WT hosts vs. normal kidneys. Examples of RITs include, without limitation, those listed in Tables 2, 7, and 1 1. A GRIT refers to an IFN-K and rejection induced transcript that is (a) expressed at a level at least 2-fold greater in kidney tissue of mammals treated with IFN-K than in kidney tissue of untreated mammals, (b) elevated at least 2-fold in tissue from WT kidney allografts at day 5 post transplant in WT hosts as compared to normal kidney tissue, and (c) expressed at levels at least 2-fold lower in kidney tissue from IFN-K-deficient (GKO) D5 allografts as compared to WT D5 allografts. Examples of GRITs include, without limitation, the nucleic acids listed in Table 4. A GRIT-like transcript refers to a transcript that is (a) expressed at a level at least 2-fold greater in kidney tissue of mammals treated with

IFN-K than in kidney tissue of untreated mammals, (b) elevated at least 2-fold in tissue

from WT kidney allografts at day 5 post transplant in WT hosts as compared to normal kidney tissue, and (c) not lower or even increased when IFN-K is absent in GKO D5 allografts compared to WT D5 allografts. Examples of GRIT-like transcripts include, without limitation, those listed in Table 5. Additional examples of RITs, GRITs, and GRIT-like transcripts can be identified using the procedures described herein. For example, the procedures described in Example 1 can be used to identify RITs, GRITs, and GRIT-like transcripts other than those listed in Tables 2, 4, 5, and 7.

A tGRIT refers to a unique transcript that is rejection-induced and IFN-K- dependent in rejection, and also is increased at least two-fold by rIFN-K. Examples of tGRITs include, without limitation, those listed in Table 9. An oGRIT refers to a GRIT that is a unique transcript, but that is not induced at least 2-fold by rIFN-K in normal kidneys. Examples of oGRITs include, without limitation, those listed in Table 10. An IRIT refers to a transcript that is increased at least two-fold in isografts at least once between day 1 and day 21 , as compared to normal kidney, and also increased at least two- fold in CBA allografts at day 5 as compared to normal kidneys. Examples of IRITs include, without limitation, those listed in Table 11. The procedures described in Example 2 can be used to identify RITs, IRITs, GRITs, true GRITs, and occult GRITs other than those listed in Tables 9, 10, and 1 1.

The expression of any number of RITs, IRITs, GRITs, GRIT-like transcripts, tGRITs, oGRITs, or nucleic acids listed in Tables 2, 4, 5, 7, 9, 10, and/or 1 1 can be evaluated to determine whether or not transplanted tissue will be rejected. For example, the expression of one or more than one (e.g., two, three, four, five, six, seven, eight, nine, ten, 15, 20, 25, 30, 40, 50, 75, 100, or more than 100) of the nucleic acids listed in Table 2 can be used. In some embodiments, determining that a nucleic acid listed in Table 2 is expressed in a sample at a detectable level can indicate that the transplanted tissue will be rejected. In some embodiments, transplanted tissue can be evaluated by determining whether or not the tissue contains cells that express a nucleic acid listed in Table 2 at an elevated level, i.e., a level that is greater than the average expression level observed in control cells obtained from tissue that has not been transplanted. Typically, a nucleic acid can be classified as being expressed at a level that is greater than the average level observed in control cells if the expression levels differ by at least 1-fold {e.g., 1.5-fold, 2-

fold, 3-fold, or more than 3-fold). Control cells typically are the same type of cells as those being evaluated. In some cases, the control cells can be isolated from kidney tissue that has not been transplanted into a mammal. Any number of tissues can be used to obtain control cells. For example, control cells can be obtained from one or more tissue samples (e.g., at least 5, 6, 7, 8, 9, 10, or more tissue samples) obtained from one or more healthy mammals (e.g., at least 5, 6, 7, 8, 9, 10, or more healthy mammals).

Any suitable method can be used to determine whether or not a particular nucleic acid is expressed at a detectable level or at a level that is greater than the average level of expression observed in control cells. For example, expression of a particular nucleic acid can be measured by assessing mRNA expression. mRNA expression can be evaluated using, for example, northern blotting, slot blotting, quantitative reverse transcriptase polymerase chain reaction (RT-PCR), real-time RT-PCR, or chip hybridization techniques. Methods for chip hybridization assays include, without limitation, those described herein. Such methods can be used to determine simultaneously the relative expression levels of multiple mRNAs. Alternatively, expression of a particular nucleic acid can be measured by assessing polypeptide levels. For example, polypeptide levels can be measured using any method such as immuno-based assays (e.g., ELISA), western blotting, or silver staining.

The methods and materials provided herein can be used at any time following a tissue transplantation to determine whether or not the transplanted tissue will be rejected. For example, a sample obtained from transplanted tissue at any time following the tissue transplantation can be assessed for the presence of cells expressing elevated levels of a nucleic acid listed in Table 2. In some cases, a sample can be obtained from transplanted tissue 1, 2, 3, 4, 5, 6, 7, 8, or more hours after the transplanted tissue was transplanted. In some cases, a sample can be obtained from transplanted tissue one or more days (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, or more days) after the transplanted tissue was transplanted. Typically, a sample can be obtained from transplanted tissue 2 to 7 days (e.g., 5 to 7 days) after transplantation and assessed for the presence of cells expressing elevated levels of one or more RITs or expressing elevated levels of one or more nucleic acids listed in Table 2.

This description also provides nucleic acid arrays. The arrays provided herein can be two-dimensional arrays, and can contain at least 10 different nucleic acid molecules (e.g., at least 20, at least 30, at least 50, at least 100, or at least 200 different nucleic acid molecules). Each nucleic acid molecule can have any length. For example, each nucleic acid molecule can be between 10 and 250 nucleotides (e.g., between 12 and 200, 14 and 175, 15 and 150, 16 and 125, 18 and 100, 20 and 75, or 25 and 50 nucleotides) in length. In addition, each nucleic acid molecule can have any sequence. For example, the nucleic acid molecules of the arrays provided herein can contain sequences that are present within the nucleic acids listed in Table 2, Table 4, Table 5, Table 7, Table 9, Table 10, and/or Table 1 1. For the purpose of this document, a sequence is considered present within a nucleic acid listed in, for example, Table 2 when the sequence is present within either the coding or non-coding strand. For example, both sense and anti-sense oligonucleotides designed to human Abpl nucleic acid are considered present within Abpl nucleic acid.

Typically, at least 25% (e.g., at least 30%, at least 40%, at least 50%, at least 60%, at least 75%, at least 80%, at least 90%, at least 95%, or 100%) of the nucleic acid molecules of an array provided herein contain a sequence that is (1) at least 10 nucleotides (e.g., at least 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or more nucleotides) in length and (2) at least about 95 percent (e.g., at least about 96, 97, 98, 99, or 100) percent identical, over that length, to a sequence present within a nucleic acid listed in Table 2, Table 4, Table 5, Table 7, Table 9, Table 10, or Table 1 1. For example, an array can contain 100 nucleic acid molecules located in known positions, where each of the 100 nucleic acid molecules is 100 nucleotides in length while containing a sequence that is (1) 30 nucleotides in length, and (2) 100 percent identical, over that 30 nucleotide length, to a sequence of one of the nucleic acids listed in Table 2. A nucleic acid molecule of an array provided herein can contain a sequence present within a nucleic acid listed in Table 2, where that sequence contains one or more (e.g., one, two, three, four, or more) mismatches.

The nucleic acid arrays provided herein can contain nucleic acid molecules attached to any suitable surface (e.g., plastic or glass). In addition, any method can be use to make a nucleic acid array. For example, spotting techniques and in situ synthesis

techniques can be used to make nucleic acid arrays. Further, the methods disclosed in U.S. Patent Nos. 5,744,305 and 5,143,854 can be used to make nucleic acid arrays.

Computer-readable medium and an apparatus for predicting rejection This disclosure further provides a computer-readable storage medium configured with instructions for causing a programmable processor to determine whether a transplanted tissue is being or is likely to be rejected. The determination of whether a transplanted tissue is being or will be rejected can be carried out as described herein; that is, by determining whether one or more of the nucleic acids listed in Table 2 or Table 1 1 is detected in a sample (e.g., a sample of the tissue), or is expressed at a level that is greater than the level of expression in a corresponding tissue that is not transplanted. The processor also can be designed to perform functions such as removing baseline noise from detection signals.

Instructions carried on a computer-readable storage medium (e.g., for detecting signals) can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. Alternatively, such instructions can be implemented in assembly or machine language. The language further can be compiled or interpreted language.

The nucleic acid detection signals can be obtained using an apparatus (e.g., a chip reader) and a determination of tissue rejection can be generated using a separate processor (e.g., a computer). Alternatively, a single apparatus having a programmable processor can both obtain the detection signals and process the signals to generate a determination of whether rejection is occurring or is likely to occur. In addition, the processing step can be performed simultaneously with the step of collecting the detection signals (e.g., "real- time").

Any suitable process can be used to determine whether a transplanted tissue is being or is likely to be rejected. In some embodiments, for example, a process can include determining whether a pre-determined number (e.g., one, two, three, four, five, six, seven, eight, nine, ten, 15, 20, 25, 30, 40, 50, 75, 100, or more than 100) of the nucleic acids listed in Table 2 or Table 1 1 is expressed in a sample (e.g., a sample of transplanted tissue) at a detectable level. If the number of nucleic acids that are expressed

in the sample is equal to or exceeds the pre-determined number, the transplanted tissue can be predicted to be rejected. If the number of nucleic acids that are expressed in the sample is less than the pre-determined number, the transplanted tissue can be predicted to not be rejected. The steps of this process (e.g., the detection, or non-detection, of each of the nucleic acids listed in Table 2 or Table 1 1) can be carried out in any suitable order. In some embodiments, a process can include determining whether a pre-determined number of the nucleic acids listed in Table 2 or Table 1 1 is expressed in a sample at a level that is greater than the average level observed in control cells (e.g., cells obtained from tissue that has not been transplanted. If the number of nucleic acids having increased levels of expression in the sample is equal to or exceeds the pre-determined number, the transplanted tissue can be predicted to be rejected. If the number of nucleic acids having increased expression levels in the sample is less than the pre-determined number, the transplanted tissue can be predicted to not be rejected. Again, the steps of this process can be carried out in any suitable order. Also provided herein, therefore, is an apparatus for determining whether a transplanted tissue is being or is likely to be rejected. An apparatus for determining whether tissue rejection will occur can include one or more collectors for obtaining signals from a sample (e.g., a sample of nucleic acids hybridized to nucleic acid probes on a substrate such as a chip) and a processor for analyzing the signals and determining whether rejection will occur. By way of example, the collectors can include collection optics for collecting signals (e.g., fluorescence) emitted from the surface of the substrate, separation optics for separating the signal from background focusing the signal, and a recorder responsive to the signal, for recording the amount of signal. The collector can obtain signals representative of the presence of one or more nucleic acids listed in Table 2 or Table 11 (e.g., in samples from transplanted and/or non-transplanted tissue). The apparatus further can generate a visual or graphical display of the signals, such as a digitized representation. The apparatus further can include a display. In some embodiments, the apparatus can be portable.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

Example 1 - Interactions between IFN-K-induced transcripts and injury effects

The roles of IFN-γ were investigated in a mouse kidney allograft model that develops the pathologic lesions that are diagnostic in human graft rejection. Basically, a comparison of mouse kidney pathology to the mouse transcriptome was used to guide understanding of the relationship of lesions to transcriptome changes in human rejection. Recombinant IFN-K (rIFN-γ) was administered to WT mice to identify the GITs in the kidney and examined how the GITs changed during graft rejection, comparing VVT to IFN-γ deficient grafts. These experiments have provided insight into some of the complex relationships between IFN-γ inducibility during rejection and in tissue injury and regulation of GITs by non IFN-γ dependent factors during kidney transplantation.

Materials and Methods

Mice: Male CBA/J (CBA) and C57B1/6 (B6) mice were obtained from the Jackson Laboratory (Bar Harbor, ME). IFN-K deficient mice (BALB/c.GKO) and (B6.129S7-IFNg ^tmlTs ; B6.GKO) were bred in the Health Sciences Laboratory Animal Services at the University of Alberta. Mice maintenance and experiments were in conformity with approved animal care protocols. CBA (H-2K, I-A ^k ) into C57B1/6 (B6; H- 2K ⁵ D ⁶ , 1-A ^b ) mice strain combinations, BALB/c.GKO into B6.GKO were studied across full MHC and non-MHC disparities. Renal transplantation: Renal transplantation was performed as a non life- supporting transplant model. Recovered mice were killed at day 5, 7, 14, 21 or 42 post- transplant. Kidneys were removed, snap frozen in liquid nitrogen and stored at -70 ⁰ C. No mice received immunosuppressive therapy. Kidneys with technical complications or infection at the time of harvesting were removed from the study. Treatment with recombinant IFN-K: CBA mice were injected i.p with 300,000

I.U. of recombinant INF-K at 0 and 24 hours. rIFN-K was a generous gift from Dr. T. Stewart at Genentech (South San Francisco, CA). Mice were sacrificed after 48 hours. Microarrays: High-density oligonucleotide Genechip 430A and 430 2.0 arrays, GeneChip T7-Oligo(dT) Promotor Primer Kit, Enzo BioArray High Yield RNA

Transcript Labeling Kit, IVT Labeling KIT, GeneChip Sample Cleanup Module, IVT cRNA Cleanup Kit were purchased from Affymetrix (Santa Clara, Ca). RNeasy Mini Kit was from Qiagen (Ont, Canada), Superscript II, E. coli DNA ligase, E. coli DNA polymerase I, E. coli Rnase H, T4 DNA polymerase, 5X second strand buffer, and dNTPs were from Invitrogen Life Technologies.

RNA preparation and hybridization: Total RNA was extracted from individual kidneys using the guanidinium-cesium chloride method and purified RNA using the RNeasy Mini Kit (Quiagen, Ont. Canada). RNA yields were measured by UV absorbance. The quality was assessed by calculating the absorbance ratio at 260 nm and 280 nm, as well as by using an Agilent BioAnalyzer to evaluate 18S and 28S RNA integrity.

For each array, RNA from 3 mice was pooled. RNA processing, labeling and hybridization to MOE430A or MOE430 2.0 arrays was carried out according to the protocols included in the Affymetrix GeneChip Expression Analysis Technical Manual (available on the World Wide Web at affymetrix.com). cRNA used for Moe 430 2.0 arrays was labeled and fragmented using an IVT Labeling Kit and IVT cRNA Cleanup Kit.

Sample designation: Normal control kidneys were obtained from CBA mice and designated as NCBA. Allografts rejecting in wild type hosts (B6) at day 5 through day 42 post transplant were designated as WT D5, D7, D14, D21 and D42, respectively.

Corresponding isografts were designated Iso D5, D7 and D21. Kidneys from mice treated with recombinant IFN-K and harvested after 48h were designated rIFN-K. BALB/c- GKO kidneys (deficient in IFN-K) rejecting in IFN-K - deficient B6 hosts at day 5 were designated as GKO D5 and corresponding isografts were designated ISO.GKO D5. The following samples (each consisting of RNA pooled from 3 mice) were analyzed by the Moe 430A arrays: two biological replicates of Iso D7, WT (D7, D14, D21 and D42); three replicates for NCBA and WT D5, single samples for Iso D5 and D21. Samples analyzed by the Moe 430 2.0 were NCBA (three replicates), WT D5, GKO D5, ISO.GKO D5 and rIFN-K (two replicates each). Sample analysis: Microarray Suite Expression Analysis 5.0 software was used for analysis of Moe 430A arrays (MAS 5.0, Affymetrix), and Gene Chip Operating software

(GCOS 2.0, Affymetrix) was used for analysis of Moe 430 2.0 arrays to calculate absolute signal strength and transcripts flagging. Normalization per chip and per gene (GeneSpring™ 7.2, Agilent, Palo Alto Ca) and to the control samples (NCBA) were described previously. The mean normalized value for further analysis of replicate samples.

Transcripts of interest were selected based on 2-fold differences and significance by Welch's t-test (Anova parametric test, variances not assumed equal). Groups of selected transcripts were then compared for individual time points using the univariate analysis of variance (Unianova with Bonferroni post hoc tests, for log transformed normalized data, SSPS 1 1.0 statistical package).

Hierarchical cluster analysis was performed using GeneSpring 7.2. Data were log transformed and similarity of transcript expression between experimental groups and between individual transcripts was visualized by a condition and gene tree diagram. Similarity measurements were based on distance. Trajectory clustering (expression pattern comparison, 0.95 correlation coefficient) was performed using the "find similar gene" feature in GeneSpring package.

Results

Terminology: rIFN-K induced transcripts (GITs) were identified as those increased 48 hours after two injections of rIFN-K, spaced 24 hours apart. Rejection induced transcripts (RITs) were identified as those increased in allografts at day 5. Injury- induced transcripts were identified as those induced in isografts at days 5 and/or 7. The rejection induced transcripts thus include effects of transplant-related stress as well as alloimmune related changes. Identification of transcripts induced by IFN-K in vivo in rejecting kidney allografts: Identification of If ⁷ N-K induced transcripts in kidney allografts was based on data obtained from the Moe 430 2.0 arrays, with cytotoxic T lymphocyte associated transcripts (CATs) deleted from all lists to avoid overlap. First, the rIFN-K-induced transcripts were identified: 342 transcripts flagged present and increased 2-fold in normal kidneys from mice treated with rIFN-K (significant by ANOVA) (Table 1). RITs were then selected, defined as transcripts that were elevated 2-fold in WT allografts at day 5

post transplant in WT hosts vs. normal kidneys (significant by Anova). 2040 transcripts, flagged present in the allografts, fulfilled these criteria (Table 2). To determine how many of these transcripts were IFN-K inducible, they were compared to the GITs. This comparison yielded 163 common transcripts that were induced by rIFN-K treatment and increased in rejecting kidneys at day 5. Thus, 179 GITs were not significantly induced in rejecting kidneys by these criteria, in spite of the strong IFN-K response in the allograft.

Validation of IFN-K induced transcripts in mouse kidney allografts: To verify that the increased expression of 163 transcripts in day 5 allografts was at least partially dependent on IFN-K, IFN-K-deficient kidney allografts grafted into IFN-K-deficient hosts were studied. In these grafts, neither the donor nor the host cells can make IFN-K. After removing CATs, 570 transcripts were expressed at least 2-fold lower in GKO D5 compared to WT D5 (significant by ANOVA), indicating that the expression of these transcripts was affected by the presence or absence of IFN-K in allografts (Table 3). Of the 163 previously defined rIFN-K- and rejection-induced transcripts, 74 were decreased in GKO D5, indicating that they were at least partially dependent on IFN-K in WT D5 allografts. These were termed IFN-K and rejection-inducible transcripts (GRITs). On the other hand, 89 transcripts, despite being rIFN-K-inducible, were not lower or were even increased when IFN-K was absent in GKO D5 allografts compared to WT D5 allografts. These were termed GRIT-like transcripts. Thus, the GRIT-like transcripts, despite being inducible by rIFN-K, were increased in allografts by mechanisms largely independent of IFN-K. The algorithm used for transcript selection is shown in Figure 1.

Functional classification of GRIT and GRIT-like: The list of GRITs (Table 4) summarizes local effects of IFN-K on transcription in the isografts and the allografts, as well as systemic effects on the normal rejecting kidney. The transcripts represent genes for several major classes of proteins: (a) MHCs and their related factors (B2m Psmb8-9, Tapbp) and other ubiquitination/proteolysis-related factors (Parpl4, PsmblO, Ubd, UbIl); (b) guanylate binding proteins (Gbp2), interferon-induced GTPases (Igtp, Iigpl, Tgtp) and other so called IFN-K-induced proteins (Ifil and Ifi47); (c) cytokines and chemokines: Ccl5, Ccl8, Cxcl 9, CxcllO, interleukin-18 binding protein (I118bp), Artsl ; (d) other immune functions: complement components (CIr, CIs, C2); and (e)

transcription factors and activators: Irf7 (ISRE sites), Statl (GAS sites), class II transactivator C2ta.

The list of GRIT-like transcripts (Table 5) includes complement components (Clqb, Clqg, Serpingl); cytokine, chemokine and receptor related transcripts (Tnfsfl 3b, Ccr5, Cxcll4, Socs2); some interferon-induced transcripts (Ifi27, Ifitm l , Ifitmό); and Tgfbi, a transcript whose expression is regulated by Tgfbl .

Expression profiles of GRIT and GRIT-like in the isografts and the allografts: The time course of changes in these transcripts post transplant was studied by querying a previously established database from MOE 430A arrays containing the expression values of all transcripts in isografts and allografts, at different times post transplant. Previously identified GRITs were "translated" (using the GeneSpring translation feature) to Moe 430A arrays, and these increased at least 2 fold (significant by Anova) in WT D5 allografts vs NCBA were selected. This permitted creation of a final list of 59 GRITs (Table 4) and 42 GRIT-like transcripts (Table 5). The lower number of transcripts was due to a lack of certain probe sets interrogating Riken sequences in Moe 430A arrays, and perhaps also to the lower sensitivity of the M430A arrays.

Analysis of the expression time course of GRITs and GRIT-like transcripts in isografts and allografts permitted comparison of the impact of transplant-related stress and/or injury on rejection. First, unsupervised clustering of all isografts and allografts was performed based on GRITs and GRIT-like lists (Figure 2 and Figure 3). Kidney samples were grouped into two clusters. One powerful cluster included all of the allografts, indicating strong and consistent expression of the GRITs and GRIT-like transcripts in all allografts at all times tested. Surprisingly, ISO D5 clustered with all allografts for the GRITs (Figure 2). ISO D21 co-clustered with NCBA, indicating recovery of kidneys from injury. ISO D7 was more similar to this group. On the other hand, GRIT-like list yielded good separation of the all isografts from the allografts, clustering ISO D5 and ISO D7 in one subgroup (Figure 3). This indicated a stronger relation of GRIT expression in ISO D5 to rejecting allografts, compared to GRIT-like expression. Time course of GRIT expression parallels IFN-K expression: The time course of

GRIT expression supported the cluster organization and the conclusion that there were

differences in regulation of GRITs (Figure 2) versus GRIT-like transcripts (Figure 3). In the isografts, expression of many GRITs peaked at D5 post transplant and sharply declined at D7 and D21. Thirty-nine and 1 1 GRITs were increased at least 2-fold at ISO D5 and ISO D7, respectively. Mean expression of GRITs in ISO D5 samples was higher than in ISO D7 and D21 (p<0.01). GRIT expression in the allografts, however, was sustained throughout the observation period, with no significant differences among the samples except for a small peak at Dl 4 (p<0.05). In addition, GRIT expression in the allografts showed 4-fold higher mean expression compared to ISO D5 (p<0.02, corrected for multiple comparisons). For the comparison, Figure 2A demonstrates the time course of IFN-K expression in the isografts D5 throughout D21 and the allografts day 5 throughout day 42. Isograft IFN-K transcript levels peaked at D5 and declined from D7 on (Figures 2B and 2C). However, IFN-K transcript levels were 5-fold higher in WT D5 allografts compared to ISO D5, and remained high at all allograft time points, with some increase at day 14. Thus GRIT expression parallels that of IFN-K.

IFN-K expression also was assessed in WT allografts at early times post transplant. IFN-K signal strength increased about 8 fold in D5 compared to D3 allografts (Figure 4A), indicating that the IFN-K expression was established at D5 post transplant. Time course of GRIT-like transcripts parallels TGF- 31 expression: GRIT-like transcripts were analyzed over the time course shown in Figure 3A, and showed consistent expression in all allografts. However, they differed in isografts: there was no statistically significant difference between their mean expression at ISO D5 and ISO D7 (Figure 3B). 17 GRIT-like allografts were increased at least 2-fold either in ISO D5 or ISO D7 samples. Mean GRIT-like expression was 2 fold higher compared to ISO D5 or ISO D7 (p<0.02, corrected for multiple comparisons). However, GRIT-like mean expression in the isografts and the allografts was lower by 2-fold compared to GRITs.

The appearance of Tgfbi in the GRIT-like list suggests that Tgfbl may be playing a role in the regulation of some of these transcripts. Moreover, the GRIT-like transcripts, by definition, are not significantly reduced in the absence of IFN-K, indicating that they are induced by other factors, one candidate being Tgf-θl . The expression profile of Tgfbi

was reminiscent of Tgfbl , i.e., it demonstrated a similar peak in ISO D7 and strong increase in the allografts, like many of the GRIT-like transcripts.

The expression time course of GRIT/GRIT-like in WT allografts at early times post transplant demonstrated a step-wise increase from day 3 to day 4 to day 5 post transplant (Figures 4B and 4C).

Expression of GRITs and GRIT-like in the isografts differ in response to IFN-K: To confirm that the elevated expression of GRIT in wild type isografts is dependent on IFN-K, the expression of GRITs was assessed in the IFN-K-deficient D5 isografts (ISO.GKO D5) by Moe 430 2.0 arrays and compared to GRIT expression in wild type isografts D5. Transcripts that were increased at least 2-fold in WT D5 vs to ISO.GKO D5 were translated to Moe 430A arrays. It was observed that 36 out of 59 GRITs (Table 4) were expressed at least 2-fold higher in WT D5 isografts compared to ISO.GKO D5. Notably, only 10 GRIT-like transcripts fulfilled these criteria (Table 5). Thus, the majority (66%) of GRITs and only a fraction (25%) of GRIT-like transcripts seemed to be dependent on IFN-K produced in the isografts.

Regulation of the expression of injury and rejection induced transcripts that are not IFN- K regulated by these criteria: Due to the uniformity with which the GRITs and GRIT-like transcripts were increased in the isografts (albeit to a varying degree in ISO D5 and ISO D7 samples), the analysis of transplant stress/injury to rejection was extended by analyzing all transcripts flagged present and elevated 2-fold either in isografts at days 5 or 7 and increased 2-fold in WT D5 (significant by Anova). GRITs, GRIT-like and CATs were eliminated from this list. Moreover, transcripts were detected that were decreased 2-fold or more in GKO D5 compared to WT D5, but were not affected by rIFNK administration. One hundred ten of these transcripts translated to Moe 430A arrays (Table 6) and were eliminated from the RIT list.

The analysis yielded many transcripts that behaved like the GRIT-like transcripts, with a peak at day 7 and consistent high expression in all allografts. As listed in Table 7, 147 injury-induced RITs met these conditions in Moe 430A database. Unsupervised clustering based on this list grouped ISO D7 samples with the allografts (Figure 5A). Expression pattern of these transcripts confirmed the clustering result. Injury-induced RITs peaked in ISO D7 isografts rather than ISO D5 (Figure 5B; significant by Anova).

One hundred forty RITs were increased 2-fold or more at ISO D7, including the acute- phase response markers: serum amyloids 1-3 and ceruloplasmin, while 54 were increased at ISO D5. The mean expression of RITs was more elevated and sustained in the allografts compared to ISO D7 and ISO D5, with a peak in WT D14 allografts (Figure 5B, significant by Anova). A Medline-assisted literature search revealed that expression of the injury-induced RITs transcripts could be dependent on either IFN-K or Tgfbl, or both. IFN-K control was reported for Vim, Ccl2, Arrb2 (also reported as a possible marker of human heart rejection), ceruloplasmin, FnI, Fos, Socs3, Timpl , Ncf2, Fcgr3, Plek, and Caspl2. Tgfbl control was reported for transcripts Tgfbi, collagen type Ia2 and 3al, Socs3, Lox, Cspg2, FnI, Postn (periostin, homologous to Tgfbi) (Table 8).

The expression profiles of these transcripts were then analyzed, and it was observed that the average expression pattern of injury-induced RITs was significantly similar to the Tgfbi expression profile, as assessed by the trajectory profiling i.e. "find similar gene(s)" feature of GeneSpring (Figures 6A and 6B). Next, the expression profiles of a selected prototypic GRIT (Statl, which was elevated in isografts) and a prototypic GRIT-like transcript (Tgfbi) were compared to the expression profiles of injury-induced RITs. Using the trajectory clustering, 50 transcripts were found with patterns similar to that of Tgfbi (Figure 7A), and 43 transcripts were found with patterns similar to that of STAT-I (Figure 7B). RITs showing Stat-1-like profiles had substantial overlap (39 transcripts) with Tgfbi-like profiles.

Table 1 rIFN-K-induced transcripts (GITs)

Table 2

Rejection-induced transcripts (RITs)

Table 3

Transcripts decreased in GKO D5

92

Table 4

GRIT expression in WT and GKO grafts

Table 5

GRIT-like transcript expression in WT and GKO grafts

Table 6

Transcripts decreased in GKO D5 but not induced by rIFNK

ii:

Table 7 Injury-induced RITs in rejection

Table 8

Functional annotation of injury-induced RIT

K) O

Bold letters indicate similar effects of cytokines, whereas italics indicate opposite effects.

Example 2 - RITs and GRITs identified using a second algorithm A second, more refined algorithm was used to identify RITs and GRITs. This method involved RMA (robust multichip analysis).

Revised GRITs algorithm Statistical analysis: Raw microarray data was pre-processed using an RMA method (Bioconductor 1.7; R version 2.2). Microarrays for control and treatment groups were preprocessed separately for each mouse strain combination. After preprocessing, data sets were subjected to variance-based filtering, i.e., all probe sets that had an interquartile range of less than 0.5 (Iog2 units), across all chips, were removed. Filtered data were then used for transcript selection. To be selected, a transcript was required to have a corrected p-value of 0.01 or lower, and had to be increased at least two-fold vs. appropriate controls. Corrected p-values (q-values) were calculated using the "limma" package (fdr adjustment method), which was uses an empirical Bayes method for assigning significance. The mean normalized value for replicate samples was used for further analysis. Finally, the data were imported into the GeneSpring™ 7.2 (Agilent, Palo Alto, Ca) for further analyses and creation of transcript lists.

Selection and removal of transcripts associated with cytotoxic T cells: The previously defined CTL associated transcripts (CAT) selection was refined, using the transcriptome of CD8+ cells isolated from allo.CBA D5 into B6 allografts, and the CTL cell line transciptome. Microarray data were normalized (GCOS/GeneSpring) to normal B6 kidneys. Transcripts expressed in the CTL line and in CD8 cells isolated from day 5 allografts (P flags in both samples) were selected based on their > 5-fold expression vs. normal NCBA kidney. This selection yielded 1849 probe sets. These CTL and CD8- associated transcripts (expanded CATs) were removed from all transcript lists prior to any analyss. An exception was made for Psmb8, Psmb9 and Ccl5 transcripts. Although prominently expressed in CTL, they also were expressed in rIFNg treated kidneys and/or a macrophage cell line.

Selection and removal of transcripts related to strain differences, somatically rearranged genes, and NK receptors: All probe sets showing differences in the basal signal (either 5-fold increased or decreased) between norma! CBA, B6 and BALB/c kidneys were selected by the RMA-based method. These probe sets then were removed

from the final transcript lists to reduce the influence of strain differences. Transcripts expressed by somatically rearranged genes, i.e., immunoglobulin genes, also were removed. In addition, transcripts for NK receptors of the KIr family were removed. Development of the unique transcript lists: The term "transcript" refers to an mRNA identified by one or more numbered Affymetrix probe sets, while a "unique transcript" refers to an mRNA identified by only one probe set; these show the highest fold change of expression in the allografts at day 5 post-transplant. Certain probe sets representing the same transcript could appear in more than one list. These were arbitrarily kept only in the first list in which they appeared (e.g., tGRITs), and were eliminated from other lists (e.g., oGRITs, see below). AU transcript name abbreviations use Entrez Gene nomenclature, which is available on the World Wide Web at ncbi.nlm.nih.gov/entrez). IFN-K - dependent rejection induced transcripts (GRITs) The algorithms for transcript selection (applied after removing CATs and strain- differing transcripts) are shown in Figures 8A, 8B, and 8C. Fifty-eight transcripts were identified that were rIFN-K-dependent in normal kidneys of CBA, B6 and BALB/c mice.

Transcripts increased at least 2-fold in day 5 allografts were termed "rejection- induced." The inflammatory changes at day 5 did not fulfill the histologic criteria for rejection (tubulitis), but the patterns established at day 5 were highly conserved as rejection lesions evolved. 1319 unique rejection-induced transcripts were identified in D5 kidney allografts of B6, CBA, and BALB/c strains (Figure 8B).

Rejection-induced transcripts that were IFN-K-dependent in rejection were identified by studying allografts in IFN-K-deficient (GKO) hosts. Kidney allografts from wild-type BALB/c into B6 (allo.BALB/c) were compared to BALB/c.GKO donors (H- 2d) transplanted into B6.GKO (H-2b) recipients (allo.GKO D5). 443 rejection-induced transcripts were identified that were at least 2-fold (signal ratio) greater when IFN-K was present than when it was absent. Of these, 55 transcripts (47 of which were unique transcripts) also were increased by rIFN-K (Figure 8C, Table 9), and thus were labeled true interferon gamma dependent rejection-induced transcripts (tGRITs). The remaining transcripts, 270 of which were unique transcripts, were not 2-fold induced by rIFN-K in normal kidneys, but nevertheless were IFN-K dependent in rejecting kidneys (Figure 8C).

To reflect their lower inducibility by rIFN-K, these transcripts were termed "occult" GRITs (oGRITs; Table 10).

The "tGRIT" and "oGRIT" terms used in this example are equivalent to the "GRIT" term used in Example 1, i.e., the GRIT category includes tGRITs and oGRITs. On average, there was 70% overlap between the GRITs identified by the RMA method with the (GCOS/GeneSpring) method described in Example 1.

Injury and repair induced transcripts (IRITs) algorithm

Transcript selection: Samples preprocessing, normalization and data filtering was done using the RMA-based method. Data also were corrected for the tGRITs, oGRITs and new CTL associated transcripts before transcript selection. The following algorithm was used (Figure 9): transcripts were required to be increased in at least one of the isografts day 1 - day 21 (> 2-fold, p(fdr) = 0.01, where "fdr" is the false discovery rate), and also in allo.CBA D5 (> 2-fold, p(fdr) = 0.01). Transcripts satisfying these criteria were selected, and the overlapping 372 transcripts were termed injury and repair-induced transcripts (IRITs). The final list, corrected for polymorphisms, contained 303 unique (highest expression in allo.CBA D5 vs NCBA) IRITs (Table 1 1).

The IRITs listed in Table 1 1 show a substantial overlap with the RITs, the injury- induced RITs and the GRIT-like lists of transcripts established as described in Example 1. In addition, the IRITs recapitulate the Tgfbl effect on the transcriptome of rejecting mouse kidneys, similarly to GRIT-like and injury-induced RITs.

Table 9

Refined true gamma interferon-dependent rejection-induced transcripts (tGRIT)

Table 10

Refined occult gamma interferon-dependent rejection-induced transcripts (oGRIT)

Table 11

Refined injury and repair-induced transcripts (IRIT)

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.