Patent Application
20100248251
1. Technical Field
This document relates to methods and materials involved in tissue rejection (e.g., organ rejection) and detecting tissue rejection.
2. Background Information
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.
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 expressed by, for example, cytotoxic T lymphocytes (CTL). The term “CTL associated transcripts” or “CATs” as used herein refers to transcripts that are expressed by activated CTL in culture at a level greater than the level of expression in normal kidney tissue. The description provided herein also is based, in part, on the discovery that the expression levels of CATs 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 the nucleic acids listed in Table 4 or Table 5 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 CATs 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 general, this description features a method for detecting tissue rejection. The method includes determining whether or not tissue transplanted into a mammal contains cells that express at least two of the nucleic acids listed in Table 4 or Table 5, 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. The tissue can be a kidney. The method can include determining whether or not the tissue contains cells that express at least five of the nucleic acids. The method can include determining whether or not the tissue contains cells that express at least ten of the nucleic acids. The method can include determining whether or not the tissue contains cells that express 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. The determining step can include 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 embodiment, the description features a method for detecting tissue rejection. The method includes determining whether or not a sample contains cells that express at least two of the nucleic acids listed in Table 4 or Table 5, 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. The tissue can be a kidney. The method can include determining whether or not the sample contains cells that express at least five of the nucleic acids. The method can include determining whether or not the sample contains cells that express at least ten of the nucleic acids. The method can include determining whether or not the sample contains cells that express 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. The determining step can include measuring the level of polypeptide expressed from the at least two nucleic acids. The sample can be a sample obtained from the tissue within ten days of the tissue being transplanted into the mammal. The sample can be a sample obtained from the tissue 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 embodiment, this description 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 comprise a sequence from nucleic acid selected from the group consisting of the nucleic acids listed in Table 4 and Table 5. 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 comprise a sequence from 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 contain glass. The at least 20 nucleic acid molecules can contain a sequence present in a human.
In another embodiment, this description 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 4 or Table 5 are detected in a sample, wherein the sample is from a transplanted tissue. The computer-readable storage medium can further comprise instructions stored thereon for causing a programmable processor to determine whether one or more of the nucleic acids listed in Table 4 or Table 5 is expressed at a greater level in the sample than in a control sample of non-transplanted tissue.
This description 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 4 or Table 5 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 adapted 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.
Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.
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 one or more CATs or that express one or more of the nucleic acids listed in Table 4 or Table 5.
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 CATs or that express one or more of the nucleic acids listed in Table 4 or Table 5. 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 CAT refers to a transcript that is expressed by activated CTL in culture at a level greater than the level of expression in normal kidney tissue. Examples of CATs include, without limitation, the nucleic acids listed in Table 4 and/or Table 5. Additional examples of CATs can be identified using the procedures described herein. For example, the procedures described in Example 1 and Example 3 can be used to identify CATs other than those listed in Tables 4 and 5.
Any suitable process can be used to determine whether a particular transcript is classified as a CAT. In some embodiments, for example, a process can include determining whether a transcript is expressed in CTL and/or MLR at a level that is at least three (e.g., at least four, at least five, at least six, or at least seven) times higher than the level at which the transcript is expressed in normal kidney cells.
The expression of any number of CATs or nucleic acids listed in Table 4 or Table 5 can be evaluated to determine whether or not transplanted tissue is being or is likely to 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 4 or Table 5 can be used. In some embodiments, determining that a nucleic acid listed in Table 4 or Table 5 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 4 or Table 5 at 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 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 4 or Table 5 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 4 or Table 5) 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 4 or Table 5 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.
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 is being or is likely to 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 a nucleic acid listed in Table 4. 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 one or more CATs or expressing one or more nucleic acids listed in Table 4.
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 4. For the purpose of this document, a sequence is considered present within a nucleic acid listed in Table 4 when the sequence is present within either the coding or non-coding strand. For example, both sense and anti-sense oligonucleotides designed to human CD2 nucleic acid are considered present within CD2 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 11, 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 4. 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 4. A nucleic acid molecule of an array provided herein can contain a sequence present within a nucleic acid listed in Table 4, where that sequence contains one or more (e.g., one, two, three, four, or more) mismatches. Similarly, 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 5. A nucleic acid molecule of an array provided herein can contain a sequence present within a nucleic acid listed in Table 5, 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. Pat. Nos. 5,744,305 and 5,143,854 can be used to make nucleic acid arrays.
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 4 or Table 5 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”).
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 4 or Table 5 (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.
Kidney rejection is mediated by infiltration of cytotoxic T lymphocytes (CTL) and diagnosed by histologic Banff lesions such as tubulitis. Using Affymetrix microarrays, the relationship between the evolution of pathologic lesions and the transcriptome in normal mouse kidneys, CBA isografts, CBA into C57Bl/6 allografts at days 5 to 42, and kidneys rejecting in B cell deficient hosts was evaluated. Histology was dominated by early infiltrate of mononuclear cells and slower evolution of severe tubulitis. A set of CATs was identified as having high expression in a CTL clone and day 4 mixed lymphocyte culture, while being absent in normal kidney. This set of CATs was fully expressed in rejecting kidneys at day 5, representing about 14 to 20 percent of the transcriptome of rejecting kidney. The expression persisted through day 42. Lack of mature B cells had little effect on expression of the set of CATs. In addition, expression of the identified set of CATs was established before diagnostic Banff lesions were observed and remained consistent through day 42 despite massive alterations in the pathology. Thus, the expression of the identified set of CATs in rejecting organs indicates the state of effector T cell infiltration, and can establish the diagnosis of T cell mediated rejection earlier and more securely than pathologic criteria.
Materials and Methods
Mice
Male CBA/J (CBA), C57Bl/6 (B6), B6.129P2-Igh-Jtm1Cgn(Igh-j), and B6.129S2-Igh-6tm1,Cgn(Igh-6) mice were obtained from Jackson Laboratory (Bar Harbor, Me.) and maintained in the Health Sciences Laboratory Animal Services at the University of Alberta. All maintenance and experiments conformed to approved animal care protocols. CBA (H-2K, I-Ak) into C57Bl/6 (B6; H-2KbDb, I-Ab) mice strain combinations were studied across full MHC and non-MHC disparities. To ensure robust findings, two different types of IghKO mice, which were previously shown to have similar phenotypes as hosts for allografts (Jabs et al., Am. J. Transplant, 3(12):1501-1509 (2003)), were used.
Renal Transplantation
Donor mice of 9-11 weeks of age were anaesthetized, and the right kidney was removed through a midline abdominal incision and preserved in cold lactate Ringer's solution. Host mice were similarly anaesthetized, and the right native kidney excised. The donor kidney was anastomosed heterotopically to the aorta, inferior vena cava, and bladder on the right side, without removing the host's left kidney (non life-supporting kidney transplantation). Recovered mice were killed at day 5, 7, 14, 21, or 42 post-transplant, following anaesthesia and cervical dislocation. Kidneys were removed, snap frozen in liquid nitrogen, and stored at −70° C. No mice received immunosuppressive therapy. Kidneys with technical complications or infection at the time of harvesting were removed from the study.
Mixed Leukocyte Reaction (MLR)
CTL effectors were generated by co-culturing C57BL/61 responder splenocytes with mitomycin C-treated (5 μg/mL, Sigma Chemicals, St. Louis, Mo.) CBA splenocytes in complete RPMI 1640 medium (10% FCS, 1% antibiotic-antimycotic; Life Technologies, Grand Island, N.Y.), 1% nonessential amino acids, 1% sodium pyruvate (Flow Laboratories, McLean, Va.), and 50 μM β-ME at a concentration of 3×106 cells/mL. Cultures were kept at 37° C., 5% CO2 in 25 cm2 cell culture flasks standing upright for 4 days. Cytolytic activity was confirmed by a 51Cr release assay.
CTL Culture
A CTL clone, C57/B6 anti C3H, was generated by co-culturing C57Bl/6 splenocytes with irradiated (2500 rads) C3H splenocytes at a 1:1 ratio for 3 days in RPMI 1640 medium (same composition as for the 4-day MLR). CTLs were purified using Ficoll gradient and cultured for another 4 days. Re-stimulation was performed at a 1:14 ratio for 3 days. After purification, cells were used for RNA extraction. Cytolytic activity was confirmed by a 51Cr release assay.
RNA Preparation
Total RNA was extracted from individual kidneys by the guanidinium-caesium chloride method (transplants) or by Trizol extraction (4-day MLR and CTL cultures), and RNA yields were measured by UV absorbance. Quality was assessed by the absorbance ratio, by agarose gel electrophoresis, and, in select samples, by Affymetrix T3 Test arrays (Affymetrix, Santa Clara, Calif.). For microarray analysis, equal amounts of RNA from 3 mice (20-25 μg each) were pooled and purified using the RNeasy Mini Kit (Quiagen, Ont. Canada). dsDNA and cRNA synthesis, hybridization to MOE 430A oligonucleotide arrays (Affymetrix), washing, and staining were carried out according to the manufacturer's manual. See, e.g., Affymetrix Technical Manual, 2003 version downloaded from Affymetrix's website.
Real-Time RT-PCR
To confirm the microarray results, expression of selected genes was assessed by TaqMan real-time RT-PCR. Two micrograms of RNA were transcribed using M-MLV reverse transcriptase and random primers. All TaqMan probe/primer combinations were designed using Primer Express software version 1.5 or purchased as Assay on demand (PE Applied Biosystems). cDNA was amplified in a multiplex system using murine hypoxanthine phosphoribosyltransferase (HPRT) cDNA as the control. Quantification of gene expression was performed utilizing the ABI prism 7700 Sequence Detection System (PE Applied Biosystems) as described elsewhere (Heid et al., Genome Research, 6(10):986-994 (1996)). Fold change over control kidney was determined using the ΔCt or ΔΔCt methods as described by the manufacturer.
Sample Designation and Analysis
Normal control kidneys were from CBA mice (NCBA). Allografts rejecting in wild-type hosts (B6) at day 5, 7, 14, 21, and 42 post transplant were designated WT D5, WT D7, WT D14, WT D21, and WT D42, respectively. Corresponding isografts were designated Iso D5, Iso D7, and Iso D21. Allografts rejecting in mature B cell deficient B6 hosts studied at days 7 and 21 were designated IghKO D7 and IghKO D21. Mixed leukocyte reaction, day 4, was designated as d4MLR and CTL clone, day 4, was designated as CTL. Two biological replicates (each consisting of RNA pooled from 3 mice) were tested in the following groups: WT D7, WT D14, WT D21, WT D42, Iso D7, and IghKO D7. Biological triplicates were analyzed in NCBA, WT D5, IghKO D21 (2 arrays with RNA pooled from 3 Igh-6 hosts, and 1 array with RNA pooled from 3 Igh-j hosts), and a single analysis was done in Iso D5, Iso D21, d4MLR, and CTL. Before processing for mRNA studies, every kidney was examined histologically to exclude kidneys with infection or surgical complication (global early infarction).
Initial data analysis was performed using Microarray Suite Expression Analysis 5.0 software (Affymetrix). Software default conditions were used to flag transcripts as present, marginal, or absent and to calculate the absolute signal strength. Total fluorescence for each array was globally scaled to a target value of 500. GeneSpring™ software (Version 6.1, Silicon Genetics, CA, USA) was used for further analyses. Following data importation, intensity values below 20 were adjusted to a value of 20, a per chip normalization was performed to the 50th percentile, and a per gene normalization was performed using NCBA or CTL as control samples. Replicate samples were expressed as mean normalized value for further analysis. For unsupervised hierarchical cluster analysis, similarity measurements were based on distance and visualized by a tree diagram (Eisen et al., Proc. Natl. Acad. Sci., 95(25):14863-14868 (1998)). CATs were defined as CTL associated transcripts having a signal that was increased at least five-fold in CTL and MLR culture compared to the signal in normal kidney (significant by ANOVA; p<0.05), and that were “absent” (by Affymetrix GCOS software default conditions) in normal CBA kidney.
A second, more refined algorithm, used RMA (robust multichip analysis). In this process, CATs were identified based on (1) a signal less than 50 in normal kidneys in all three strains (CBA, B6, and Balb/c), (2) a signal at least 5 times higher in CTL, MLR, and CD8 as compared to normal kidneys, significantly higher (p(fdr)<0.01) in MLR vs. normal kidney, and at least 2 times higher in wild type allografts (CBA into B6) at day 5 and significant (p(fdr)<0.01) compared to normal kidney.
CATs were analyzed using a K-means cluster algorithm based on expression data normalized to the CTL clone.
Results
Pathological Lesions in Rejecting Kidneys
Histology of CBA kidney allografts in B6 hosts has been described elsewhere (Jabs et al., Am. J. Transplant., 3(12):1501-1509 (2003) and Halloran et al., Am. J. Transplant., 4(5):705-712 (2004)). Isografts at 5 (
Affymetrix Microarray Analysis and Validation
The global gene expression correlated well in biological replicates from two independent pools of three kidneys (NCBA: r=0.96; Iso D7: r=0.96; WT D5: r=0.92; WT D7: r=0.96; WT D14: r=0.98; WT D21: r=0.86; WT D42: r=0.90). The results for WT D5 transplants are presented in
Hierarchical Clustering of the Global Gene Expression in Rejecting Kidneys, Isografts, CTL, and d4 MLR
Unsupervised hierarchical cluster analysis was used to compare overall gene expression between control kidneys, isografts, allografts rejecting in WT and IghKO hosts, d4MLR, and the allostimulated CTL clone. The resulting dendrogram (
CD Antigen Transcript Expression
Expression of CD gene transcripts as a reflection of cellular infiltration was analyzed. Transcripts were selected by searching a master table for “CD antigen.” Genes having an expression level that was increased greater than two fold at least at one time point during rejection in allografts were chosen and compared to other samples.
The expression of thirty-three CD transcripts was increased at least two fold in wild-type allografts as compared to the expression levels observed in NCBA kidney (Table 2). Twenty-one of these had high expression in d4MLR and CTL (increased more than 5 fold as compared to NCBA). High expression of these transcripts in rejecting kidney is consistent with CTL infiltration at D5, which increases at D7 and stabilizes thereafter. CD2f10 and CD 14 were increased in rejecting allografts with no expression in d4MLR or CTL, suggesting that they represent infiltrating activated macrophages, which are poorly represented in d4MLR and absent in CTL. The relatively high CD68 expression in all rejecting grafts supports this view. The B cell specific transcripts CD79a and CD79b appeared late in rejection at days 14, 21, and 42 in wild-type but not in IghKO hosts, consistent with late recruitment of antibody-producing cells to the graft. The analysis of CD transcripts is consistent with an early and sustained CTL/macrophage infiltrate in wild-type and IghKO hosts, and with late B cell infiltration in wild-type hosts.
48
112
97
424
153
41
343
65
990
287
161
67
71
82
272
187
247
132
The table contains the signal strength for controls and fold changes for the transplants. (−) indicates that a given gene was not upregulated; bolded signal values indicate that a transcript was classified as present. In case of multiple probe sets querying the same gene, data obtained from probe sets with suffixes _s_at and _x_at were not considered, and a probe set displaying the most robust signal was selected.
Eighteen CD transcripts were present in normal kidney, perhaps reflecting immature dendritic cells in the interstitium (Austyn et al., J. Immunol., 152:2401-2410 (1994)). Expression of CD transcripts was similar between CTL and d4MLR. In addition, d4MLR contained the B cell specific transcripts CD79a and CD79b. Macrophage transcript CD14 was not expressed in CTL or d4MLR, while macrophage transcript CD68 was expressed at a low level in both.
Expression of CATs in Rejecting Mouse Kidney Allografts
CATs were defined by high expression in both the CTL clone and in d4MLR but rated as “absent” in normal kidney. This algorithm identified 287 CATs. Expression of CATs was lower in d4MLR than in the CTL clone (mean 91±59 percent, median 87 percent). Compared to NCBA and isografts, the CATs were strongly expressed in rejecting WT allografts (
To determine whether the pattern of CAT expression is consistent in vivo, the consistency of expression of individual CATs in various experimental conditions was analyzed. By non-parametric regression analysis, the expression of CATs correlated in all conditions, indicating robust maintenance of CAT expression in vivo and in vitro (Table 3). The d4MLR correlated well with the diluted MLR (r=0.91), despite the 80 percent decrease in signal, and slightly less well with the CTL clone (r=0.81; p<0.001). In rejecting transplants, the CAT signals exhibited a striking correlation among all days in wild-type hosts (r=0.90-0.96), indicating that most CATs displayed predictable and stable levels of expression in vivo in all rejecting kidneys. The correlations of d4MLR with the rejecting transplants at all days were considerably less (r=0.70-0.78; p<0.001), indicating significant differences between the relative transcript levels in vivo and in vitro. Expression in the CTL clone correlated least with that in the transplants (r=0.66-0.74; p=n.s.). Thus, the relative level of expression of individual CATs was similar in vitro between CTL and d4MLR, and was similar in vivo under all conditions in rejecting transplants, but was somewhat different in vivo compared to in vitro.
To further investigate expression patterns of individual CATs, a k-means cluster analysis of CATs was performed based on their expression level in wild-type allografts relative to the CTL clone. The 287 CATs were clustered into five clusters (
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Mus musculus adult
Expression of CATs in Allografts Rejecting in B-Cell Deficient Hosts
Whether the absence of B cells affects T-cell mediated rejection was analyzed by comparing CAT expression in kidneys rejecting in wild-type hosts to those rejecting in IghKO hosts at day 7 and day 21. The level of expression of CATs in grafts rejecting in IghKO hosts was highly correlated with that in wild-type hosts (D7: r=0.98; D21: r=0.98). The mean expression of the five clusters of CATs was also similar in IghKO versus wild-type hosts (
In summary, the relationship between the pathologic Banff lesions of kidney rejection and the transcriptome, particularly in the CTL-associated transcripts, was studied. The interstitial infiltrate was established by day 5 and stable after day 7, whereas tubulitis and arteritis evolved slowly and progressively, being absent at day 5 and fully developed only after 14 days. The transcriptome changed markedly by day 5, with appearance of T cell and macrophage CD antigen transcripts. A set of CATs present in d4MLR and in a CTL clone but absent in normal kidney were identified. The CATs appeared in the transplants with a mean signal intensity about one fifth of that in the CTL clone, and was independent of B cells and alloantibody. In addition, CAT expression was essentially constant from day 5 through 42, despite massive changes in the histopathology. Thus, CTL transcripts appear early in rejecting kidneys, before the diagnostic Banff lesions, and persist for at least 6 weeks, providing a robust measurement of this aspect of rejection. This permits separation of the effectors of rejection, CTL, from the downstream consequences, parenchymal deterioration and pathologic lesions. In addition, CAT expression provides an approximation of the effector T cell burden and activity in rejecting kidneys. The interpretation of the CAT expression does not depend on the assumption that CATs are expressed exclusively in CTL, although it is likely that CTL account for most CAT expression.
The CD transcripts provide an overview of leukocyte population changes, and support the concept of a CTL and macrophage infiltrate with late B cell infiltration indicated by the histologic analysis. There is no real “gold standard” unbiased assessment of the composition of the infiltrate in rejecting transplants: both immunostaining of sections and cell isolation have potential for errors. Nevertheless, the arrays' estimates are fully compatible with estimates based on these methods. CD transcripts with high expression in CTL and d4MLR increased early during rejection and persisted throughout the time course, consistent with CTL infiltration and supporting the contention that CATs in the rejecting kidneys reflect transcripts in effector T cells. The macrophage markers CD14 and CD68 were present in rejecting kidneys, with low expression in CTL and d4MLR, consistent with macrophage infiltration. B cell markers CD79A and CD79B were present in d4MLR but not CTL, and appeared late in rejection, reflecting late B cell infiltration. There were few CD4+ cells in the infiltrate by immunostaining, and CD4 expression in the microarrays was low, in keeping with rejection being mainly driven by CD8+CTL.
The constancy of CAT expression over weeks establishes a new concept of T cell mediated rejection, namely that CTL generated from secondary lymphoid organs create and maintain a constant state in which the parenchyma progressively changes, yielding the pathologic lesions. The surprising stability of CAT levels over time suggests that the CTLs in the graft are occupying a finite “space,” similar to other emerging concepts of space in the secondary lymphoid organs (Stockinger et al., Immunology, 111(3):241-247 (2004)). The differences in the regression coefficients indicate that relative expression of individual CATs was consistent over time in vivo, although somewhat altered relative to the patterns of expression in vitro in the d4MLR and CTL clone. The moderate differences in relative expression of transcripts in the in vivo grafts versus the in vitro conditions may reflect different stimuli for CTL in these conditions (e.g., CD44). Other cells may also be recruited to express selected CAT in vivo: transcripts in cluster 5 exhibited high expression in vivo, perhaps reflecting IFN-γ effects (e.g., STAT1). The algorithm defining CATs, however, may exclude most IFN-γ inducible genes.
B cells do appear late in kidney rejection in this model but have no critical role, either as antigen presenting cells or alloantibody producing cells. Grafts in IghKO hosts exhibited very similar CAT expression to those in wild-type hosts by regression analysis, with slightly higher mean CAT expression at day 7 and lower at day 21. The small decline in CAT expression at day 21 in B cell deficient hosts suggest a role of B cells as second line antigen presenting cells sustaining CTL generation in secondary lymphoid organs.
The sustained expression of transcripts associated with cytotoxicity (e.g., perforin, granzymes A and B) in rejecting grafts raises the question of the role of cytotoxic mechanisms. Typical lesions develop in mice lacking perforin or granzyme A plus granzyme B (Halloran et al., Am. J. Transplant., 4(5):705-712 (2004)). Fas ligand (Tnfsf6) is expressed in CTL and rejecting grafts, but is not necessary for organ rejection across MHC disparities (Larsen et al., Transplant, 60(3):221-224 (1995)). Thus, the alterations in the parenchyma could reflect non-cytotoxic CTL and macrophage products, acting either by direct engagement or by indirect actions, e.g., extracellular matrix alterations triggering secondary changes in the epithelium. On the other hand, the lytic mechanisms such as perforin, granzymes, and Fas ligand could contribute to homeostasis, through fratricide of T cells (Huang et al., Science, 286(5441):952-954 (1999)) or interactions with antigen presenting cells (Ludewig et al., Eur. J. Immunol., 31(6):1772-1779 (2001)).
CAT expression can be used in estimating the burden of CTL in rejecting grafts, by analogy with viral load measurements in viral diseases. Moreover, although CD8− CTL were used as the basis of the effector T cell signature, the definition of CATs probably includes most transcripts in CD4+ effector T cells. Less is known about effector CD4+ T cells in rejection, perhaps because CD8+ effectors develop more rapidly after short term stimulation (Seder and Ahmed, Nat. Immunol., 4(9):835-842 (2003)). CD4+ T cells may play a bigger role in human kidney allograft rejection than in mice, although in human rejection CD8+ T cells predominate (Hancock et al., Transplant, 35(5):458-463 (1983)). CD4+ effectors that home to inflammatory sites share many properties with CD8+ effectors, e.g., IFN-γ production, expression of P-selectin ligand and CXCR3, absence of CCR7 (Campbell et al., Nat. Immunol., 2(9):876-881 (2001)). Other transcript sets can be developed to reflect distinct events in a disease state, e.g., IFN-γ inducible transcripts or macrophage-associated transcripts.
Data obtained from the mouse model were compared to the gene expression data obtained from human kidney biopsies from nine living donor controls, seven recipients with histologically confirmed acute rejection, five recipients with renal dysfunction without rejection on biopsy, and 10 protocol biopsies carried out more than one year post-transplant in patients with good transplant function and normal histology. Microarray data from these biopsies were obtained from a database available on the World Wide Web at scrips.edu/services/dna_array/. Flechner et al., Halloran laboratory Reference Manager #18134: Am. J. Transplant., 4(9):1475-1489 (2004)). Raw data were normalized as described herein for the mouse data, using the donor biopsies as controls. In GeneSpring, a homology database was created for the mouse and human data, and gene lists of interest were then used for supervised hierarchical clustering of the human biopsy samples.
CTL Gene Expression in Human Kidney Transplant Biopsies
The following was performed to determine whether or not the transcriptome pattern observed in mouse CTL and in rejecting mouse kidney reflects the rejection process in human transplant kidneys. A set of human kidney biopsies was analyzed based on the CTL signature identified in the mouse model. The database includes biopsies of normal kidneys (healthy donor biopsies), control biopsies of well functioning kidney transplants, rejecting transplants, and transplants with dysfunction but no rejection. The expression of CTL genes identified in mice in a published database of human renal transplants was examined. Of the 284 mouse CTL transcripts, 164 corresponding transcripts in the human database were identified. Supervised hierarchical cluster analysis based on the CTL transcripts separated the rejecting transplants from the other samples. In rejecting transplants, gene expression of CTL transcripts was increased compared to normal transplants with dysfunction but no rejection. Compared to donor biopsies, control biopsies of well functioning transplants had decreased expression of a subset of CTL transcripts, possibly due to immunosuppressive treatment. Another subset of transcripts exhibited increased expression in control biopsies, indicating some CTL activity in the transplant; however, expression levels were much lower than in rejecting kidneys. A class prediction model based on two classes (rejection-no rejection) identified 19 of the 21 samples correctly based on the expression of CTL transcripts in transplant biopsies (using the 100 best predictor genes (Fisher's Exact Test) and K-nearest neighbors (K=4)). The two samples that could not be classified were diagnosed as “borderline rejection” (AR5) and “tubular nephropathy” (NR5) based on histologic criteria.
In a first analysis of human kidney biopsies, the set of CTL genes identified in the mouse model exhibited striking upregulation in rejecting kidneys and permitted identification of samples from rejecting transplants without further refinement, indicating that the transcriptome patterns observed in rejecting mouse kidney reflect the rejection process in human transplant kidneys. Although this analysis includes only a limited number of human biopsies and may require verification and further refinement in a large patient population, this is a first indication that analysis of the CTL pattern in the transcriptome of kidney biopsies can be used as a diagnostic tool. Addition of other elements of the transcriptome to the CTL gene set may improve the diagnostic power, therefore allowing refinement of the gene set and reduction of the number of transcripts required for a diagnosis. The clinical application of this knowledge can involve either a microarray system using large numbers of genes or an RT-PCR system, depending on an evaluation of sensitivity, specificity, cost, and practicability. Based on the observation in the mouse model that transcriptome changes occur early before tubulitis develops, this approach can be more sensitive and quantitative than evaluation by histopathology and could be developed for use as an endpoint in clinical trials.
A second, more refined algorithm was used to identify CATs. This method involved RMA (robust multichip analysis). CATs were identified based on the following: a signal of less than 50 in normal kidneys in all three strains (CBA, B6, and Balbc); five times higher in CTL, MLR, and CD8 compared to normal kidneys; significantly (p (fdr)<0.01) higher in MLR versus normal kidney; two times increased in wild type allografts (CBA into B6) at day 5 compared to normal kidney; and significant in comparison to normal kidney (p(fdr)<0.01). This algorithm produced a list of 332 CATs, 91 of which were included in the original list of 287 CATs. The new list was checked for polymorphisms that would have been excluded if there had been any polymorphisms (5× difference between the strains or genes that are known to be highly polymorphic e.g., TCR, NKR, Ig, MHC). The list of 332 CATs is provided in Table 5.
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.
This application is a divisional of U.S. application Ser. No. 11/434,389, filed May 15, 2006, which claims the benefit of priority from U.S. Provisional Application Ser. No. 60/681,340, filed May 16, 2005. The disclosures of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.
| Number | Date | Country | |
|---|---|---|---|
| 60681340 | May 2005 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11434389 | May 2006 | US |
| Child | 12797364 | US |
