This document relates to methods and materials involved in tissue rejection (e.g., organ rejection) and detecting tissue rejection.
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 that are induced by, for example, gamma interferon (IFN-γ). The term “gamma interferon induced transcripts” or “GITs” as used herein refers to transcripts that are expressed in kidneys of mammals treated with IFN-γ 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-γ 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-γ 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-γ 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-γ-deficient (GKO) D5 allografts as compared to WT D5 allografts. Thus, the expression of GRITs is affected by the presence or absence of IFN-γ 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-γ 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-γ is absent in GKO D5 allografts compared to WT D5 allografts. GRIT-like transcripts, despite being inducible by rIFN-γ, are increased in allografts by mechanisms largely independent of IFN-γ.
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-γ-dependent in rejection, and also are unique transcripts that are increased at least 2-fold by rIFN-γ. 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-γ 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 11 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 11) 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 11 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 11 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 11. 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 11 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 11 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 11 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.
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 11. 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 11 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 11. A GRIT refers to an IFN-γ and rejection induced transcript that is (a) expressed at a level at least 2-fold greater in kidney tissue of mammals treated with IFN-γ 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-γ-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-γ 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-γ 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-γ-dependent in rejection, and also is increased at least two-fold by rIFN-γ. 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-γ 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 11.
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 11 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 11. 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 Abp1 nucleic acid are considered present within Abp1 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 2, Table 4, Table 5, Table 7, Table 9, Table 10, or Table 11. 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. Pat. 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 11 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 11 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 11) 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 11 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.
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-γ (rIFN-γ) was administered to WT mice to identify the GITs in the kidney and examined how the GITs changed during graft rejection, comparing WT 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 C57Bl/6 (B6) mice were obtained from the Jackson Laboratory (Bar Harbor, Me.). IFN-γ deficient mice (BALB/c.GKO) and (B6.129S7-IFNgtm1Ts; 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-Ak) into C57Bl/6 (B6; H-2KbDb, I-Ab) 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° 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-γ. CBA mice were injected i.p with 300,000 I.U. of recombinant INF-γ at 0 and 24 hours. rIFN-γ was a generous gift from Dr. T. Stewart at Genentech (South San Francisco, Calif.). 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 HighYield RNA Transcript Labeling Kit, IVT Labeling KIT, GeneChip Sample Cleanup Module, IVT cRNA Cleanup Kit were purchased from Affymetrix (Santa Clara, Calif.). 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, 5× 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-γ and harvested after 48 h were designated rIFN-γ. BALB/c-GKO kidneys (deficient in IFN-7) rejecting in IFN-γ-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-γ (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 Calif.) 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 1.0 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-γ induced transcripts (GITs) were identified as those increased 48 hours after two injections of rIFN-7, 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-γ in vivo in rejecting kidney allografts. Identification of IFN-γ 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-γ-induced transcripts were identified: 342 transcripts flagged present and increased 2-fold in normal kidneys from mice treated with rIFN-γ (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-γ inducible, they were compared to the GITs. This comparison yielded 163 common transcripts that were induced by rIFN-γ 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-γ response in the allograft.
Validation of IFN-γ 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-γ, IFN-γ-deficient kidney allografts grafted into IFN-γ-deficient hosts were studied. In these grafts, neither the donor nor the host cells can make IFN-γ. 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-γ in allografts (Table 3). Of the 163 previously defined rIFN-γ- and rejection-induced transcripts, 74 were decreased in GKO D5, indicating that they were at least partially dependent on IFN-γ in WT D5 allografts. These were termed IFN-γ and rejection-inducible transcripts (GRITs). On the other hand, 89 transcripts, despite being rIFN-γ-inducible, were not lower or were even increased when IFN-γ 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-γ, were increased in allografts by mechanisms largely independent of IFN-γ. The algorithm used for transcript selection is shown in
Functional classification of GRIT and GRIT-like: The list of GRITs (Table 4) summarizes local effects of IFN-γ 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 (Parp14, Psmb10, Ubd, Ub11); (b) guanylate binding proteins (Gbp2), interferon-induced GTPases (Igtp, Iigp1, Tgtp) and other so called IFN-γ-induced proteins (Ifi1 and Ifi47); (c) cytokines and chemokines: Cc15, Cc18, Cxc19, Cxc110, interleukin-18 binding protein (I118 bp), Arts1; (d) other immune functions: complement components (C1r, C1s, C2); and (e) transcription factors and activators: Irf7 (ISRE sites), Stat1 (GAS sites), class II transactivator C2ta.
The list of GRIT-like transcripts (Table 5) includes complement components (C1qb, C1qg, Serping1); cytokine, chemokine and receptor related transcripts (Tnfsf13b, Ccr5, Cxci 14, Socs2); some interferon-induced transcripts (Ifi27, Ifitm1, Ifitm6); and Tgfbi, a transcript whose expression is regulated by Tgfb1.
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 (
Time course of GRIT expression parallels IFN-γ expression: The time course of GRIT expression supported the cluster organization and the conclusion that there were differences in regulation of GRITs (
For the comparison,
IFN-γ expression also was assessed in WT allografts at early times post transplant. IFN-γ signal strength increased about 8 fold in D5 compared to D3 allografts (
Time course of GRIT-like transcripts parallels TGF-β1 expression: GRIT-like transcripts were analyzed over the time course shown in
The appearance of Tgfbi in the GRIT-like list suggests that Tgfb1 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-γ, indicating that they are induced by other factors, one candidate being Tgf-β1. The expression profile of Tgfbi was reminiscent of Tgfb1, 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 (
Expression of GRITs and GRIT-like in the isografts differ in response to IFN-γ: To confirm that the elevated expression of GRIT in wild type isografts is dependent on IFN-7, the expression of GRITs was assessed in the IFN-γ-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-γ produced in the isografts.
Regulation of the expression of injury and rejection induced transcripts that are not IFN-γ 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 rIFNγ 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 (
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 (
M. musculus mRNA for testis-specific
Mus musculus 0 day neonate head cDNA,
Mus musculus 0 day neonate kidney
Mus musculus 0 day neonate kidney
Mus musculus 0 day neonate kidney
Mus musculus 0 day neonate lung cDNA,
Mus musculus 0 day neonate thymus
Mus musculus 10 days neonate
Mus musculus 12 days embryo spinal
Mus musculus 16 days embryo head
Mus musculus 16 days embryo head
Mus musculus 16 days neonate
Mus musculus 16 days neonate thymus
Mus musculus 18-day embryo whole body
Mus musculus 2 days pregnant adult
Mus musculus 3 days neonate thymus
Mus musculus 4 days neonate male
Mus musculus 6 days neonate head
Mus musculus 7 days embryo whole body
Mus musculus adult male bone cDNA,
Mus musculus adult male corpora
Mus musculus adult male medulla
Mus musculus adult male olfactory brain
Mus musculus Corpos mRNA, 3′
Mus musculus NOD-derived CD11c +ve
sapiens], full insert sequence
Mus musculus similar to Aldo-keto
Mus musculus similar to hypothetical
Mus musculus similar to hypothetical
Mus musculus transcribed sequence
Mus musculus transcribed sequence with
Mus musculus transcribed sequence with
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus, clone IMAGE: 4507681,
Mus musculus 3 days neonate thymus
Mus musculus 0 day neonate lung cDNA,
norvegicus}, full insert sequence
Mus musculus transcribed sequences
Mus musculus 16 days neonate thymus
Mus musculus adult male testis cDNA,
Mus musculus 10 days neonate cortex
Mus musculus adult male colon cDNA,
Mus musculus similar to hypothetical
Mus musculus ES cells cDNA, RIKEN
Mus musculus transcribed sequence with
Mus musculus ES cells cDNA, RIKEN
Mus musculus adult male testis cDNA,
Mus musculus adult male small intestine
Mus musculus adult retina cDNA, RIKEN
Mus musculus 0 day neonate head
Mus musculus adult male testis cDNA,
Mus musculus transcribed sequence with
Mus musculus 13 days embryo heart
Mus musculus 0 day neonate eyeball
Mus musculus adult male xiphoid
Mus musculus, clone IMAGE: 5356629,
Mus musculus 0 day neonate eyeball
Mus musculus adult male hypothalamus
Mus musculus 16 days embryo head
Mus musculus 13 days embryo male
Mus musculus adult male olfactory brain
Mus musculus adult male olfactory brain
Mus musculus transcribed sequences
Mus musculus similar to Jun dimerization
Mus musculus transcribed sequences
Mus musculus 12 days embryo
Mus musculus adult male testis cDNA,
Mus musculus adult male testis cDNA,
Mus musculus adult retina cDNA, RIKEN
Mus musculus transcribed sequence with
Mus musculus adult male hypothalamus
Mus musculus adult male spinal cord
Mus musculus 0 day neonate thymus
Mus musculus adult male aorta and vein
Mus musculus 3 days neonate thymus
Mus musculus 13 days embryo male
Mus musculus transcribed sequence with
Mus musculus transcribed sequence with
sapiens]
Mus musculus 2 days neonate thymus
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 9 days embryo whole
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 16 days neonate thymus
Mus musculus 16 days neonate thymus
Mus musculus 16 days neonate thymus
Mus musculus 3 days neonate thymus
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 12 days embryo spinal
Mus musculus transcribed sequences
Mus musculus 13 days embryo male
Mus musculus 10 days neonate medulla
norvegicus], full insert sequence
Mus musculus adult male bone cDNA,
Mus musculus 16 days neonate thymus
Mus musculus adult male testis cDNA,
Mus musculus similar to DYSKERIN
Mus musculus adult male corpus striatum
Mus musculus adult male liver tumor
Mus musculus 18-day embryo whole
Mus musculus adult male hippocampus
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 6 days neonate head
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 9 days embryo whole
Mus musculus adult male tongue cDNA,
Mus musculus 13 days embryo heart
Mus musculus transcribed sequences
Mus musculus 7 days embryo whole
Mus musculus transcribed sequences
Mus musculus 11 days pregnant adult
Mus musculus transcribed sequence with
Mus musculus transcribed sequence with
Mus musculus 0 day neonate lung cDNA,
Mus musculus transcribed sequences
Mus musculus 10 days neonate skin
Mus musculus transcribed sequence with
Mus musculus hypothetical LOC269515
Mus musculus adult male corpora
Mus musculus hypothetical LOC237436
Mus musculus hypothetical LOC237436
Mus musculus similar to hypothetical
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 15 days embryo male
Mus musculus 15 days embryo male
Mus musculus 2 days neonate thymus
musculus], full insert sequence
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
sapiens]
Mus musculus 13 days embryo lung
Mus musculus adult retina cDNA, RIKEN
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus adult male small intestine
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
sapiens]
Mus musculus transcribed sequences
Mus musculus 0 day neonate lung cDNA,
Mus musculus adult male thymus cDNA,
Mus musculus adult male thymus cDNA,
Mus musculus adult male thymus cDNA,
Mus musculus transcribed sequences
Mus musculus cDNA clone MGC: 58382
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus adult male aorta and vein
Mus musculus adult male aorta and vein
Mus musculus mRNA for mKIAA0845
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 0 day neonate thymus
Mus musculus 13 days embryo forelimb
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus 7 days embryo whole
Mus musculus 12 days embryo spinal
Mus musculus 10 days neonate
Mus musculus 16 days neonate thymus
Mus musculus 9.5 days embryo
Mus musculus transcribed sequences
Mus musculus 9 days embryo whole
Mus musculus 9 days embryo whole
Mus musculus 16 days embryo head
Mus musculus 16 days embryo head
Mus musculus transcribed sequence with
Mus musculus 16 days embryo head
Mus musculus 7 days neonate
Mus musculus 7 days neonate
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
M. musculus mRNA for Tcell receptor, V-J
Mus musculus adult male thymus cDNA,
musculus], full insert sequence
Mus musculus adult male thymus cDNA,
musculus], full insert sequence
Mus musculus adult male thymus cDNA,
musculus], full insert sequence
Mus musculus T cell receptor alpha chain
Mus musculus adult male thymus cDNA,
musculus], full insert sequence
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
M. musculus mRNA for testis-specific
M. musculus mRNA for testis-specific
Mus musculus transcribed sequences
Mus musculus 7 days embryo whole
Mus musculus adult female vagina
musculus], full insert sequence
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus Corpos mRNA, 3′
Mus musculus transcribed sequences
Mus musculus, clone IMAGE: 1263252,
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 0 day neonate eyeball
Mus musculus transcribed sequence with
Mus musculus adult male aorta and vein
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 16 days neonate heart
Mus musculus 2 days neonate thymus
Mus musculus 12 days embryo
Mus musculus adult male thymus cDNA,
Mus musculus transcribed sequence with
Mus musculus 2 days pregnant adult
Mus musculus 9 days embryo whole
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus similar to FLJ14075
Mus musculus 0 day neonate skin cDNA,
Mus musculus adult male thymus cDNA,
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
musculus]
Mus musculus transcribed sequences
Mus musculus cDNA clone MGC: 6071
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 0 day neonate eyeball
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus adult male thymus cDNA,
Mus musculus 0 day neonate eyeball
Mus musculus similar to Elongation factor
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
musculus]
Mus musculus transcribed sequences
Mus musculus similar to DYSKERIN
Mus musculus, clone IMAGE: 5066616,
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus mRNA similar to
Mus musculus transcribed sequences
Mus musculus similar to 2-cell-stage,
Mus musculus similar to Ig delta chain C
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus 0 day neonate thymus
Mus musculus transcribed sequences
Mus musculus adult male tongue cDNA,
Mus musculus 18 days pregnant adult
Mus musculus diabetic nephropathy-
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus 16 days neonate heart
Mus musculus adult male cecum cDNA,
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus 3 days neonate thymus
Mus musculus transcribed sequence with
Mus musculus transcribed sequence with
Mus musculus transcribed sequences
Mus musculus adult male aorta and vein
Mus musculus similar to toll-like receptor
Mus musculus transcribed sequences
Mus musculus transcribed sequences
Mus musculus similar to AIM2 protein
Mus musculus transcribed sequences
Mus musculus transcribed sequence with
Mus musculus adult male aorta and vein
Mus musculus, clone IMAGE: 1348774,
Mus musculus similar to surface protein
Mus musculus adult male cortex cDNA,
Mus musculus transcribed sequences
Mus musculus 16 days
Mus musculus adult male
Mus musculus 4 days
MUS MUSCULUS
Mus musculus adult male
Mus musculus 13 days
Mus musculus adult male
Mus musculus adult male
Mus musculus adult male
Mus musculus adult male
Mus musculus adult male
Mus musculus adult male
Mus musculus adult male
Mus musculus transcribed
Mus musculus 16 days
Mus musculus transcribed
Mus musculus 10 days
Mus musculus transcribed
Mus musculus adult male
Mus musculus 13 days
Mus musculus transcribed
Mus musculus transcribed
Mus musculus 2 days
musculus], full insert
Mus musculus transcribed
Mus musculus 13 days
Mus musculus 12 days
Mus musculus transcribed
Mus musculus nucleosome-
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus similar to
Mus musculus 12 days
Mus musculus 3 days
Mus musculus 16 days
Mus musculus transcribed
Mus musculus 0 day neonate
Mus musculus 16 days
Mus musculus transcribed
Mus musculus transcribed
M. musculus mRNA for Tcell
Mus musculus T cell receptor
Mus musculus adult male
musculus], full insert
Mus musculus adult male
musculus], full insert
Mus musculus transcribed
Mus musculus adult female
musculus], full insert
Mus musculus transcribed
Mus musculus 16 days
Mus musculus similar to 2-
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus anti-MUC1
Mus musculus LOC380741
Mus musculus cDNA clone
Mus musculus transcribed
Mus musculus adult male
Mus musculus 0 day neonate
Mus musculus transcribed
Mus musculus similar to
Mus musculus transcribed
Mus musculus 0 day neonate
Mus musculus cDNA clone
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus diabetic
Mus musculus transcribed
Mus musculus transcribed
Mus musculus transcribed
Mus musculus adult male
Mus musculus 9 days embryo
Mus musculus transcribed
Mus musculus adult male
Mus musculus adult male
Mus musculus mRNA similar
Mus musculus adult male
Mus musculus 0 day neonate
Mus musculus adult male
Mus musculus 3 days neonate thymus
Mus musculus 0 day neonate thymus
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yes
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yes
yes
yes
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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 inter-quartile range of less than 0.5 (log2 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, Calif.) 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 transcriptome. 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 analysis. An exception was made for Psmb8, Psmb9 and Cc15 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 normal 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 Klr 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). All transcript name abbreviations use Entrez Gene nomenclature, which is available on the World Wide Web at ncbi.nlm.nih.gov/entrez).
IFN-γ-Dependent Rejection Induced Transcripts (GRITs)
The algorithms for transcript selection (applied after removing CATs and strain-differing transcripts) are shown in
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 (
Rejection-induced transcripts that were IFN-γ-dependent in rejection were identified by studying allografts in IFN-γ-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-γ was present than when it was absent. Of these, 55 transcripts (47 of which were unique transcripts) also were increased by rIFN-γ (
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 (
The IRITs listed in Table 11 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 Tgfb1 effect on the transcriptome of rejecting mouse kidneys, similarly to GRIT-like and injury-induced RITs.
musculus], mRNA sequence
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 claims priority from U.S. Provisional Application Ser. No. 60/683,737, filed May 23, 2005.
Number | Name | Date | Kind |
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5143854 | Pirrung et al. | Sep 1992 | A |
5744305 | Fodor et al. | Apr 1998 | A |
20010051344 | Shalon et al. | Dec 2001 | A1 |
Number | Date | Country | |
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20060269949 A1 | Nov 2006 | US |
Number | Date | Country | |
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60683737 | May 2005 | US |