KR20180132567A - Urinary mRNA for non-invasive differential diagnosis of acute rejection in kidney transplanted patients and uses thereof - Google Patents

Abstract

The present invention relates to a noninvasive biomarker for diagnosing acute rejection to kidney transplantation and a use thereof and, more specifically, relates to a marker composition for diagnosing acute rejection to kidney transplantation, including mRNA of at least one gene selected as a marker from a group comprising C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof, a method of providing necessary information for diagnosing acute rejection to kidney transplantation by using the marker composition, a diagnosing composition including a medicine for measuring an expression level of the marker, and a diagnosing kit including a quantitative device for the marker. According to the present invention, mRNA of C1QB, CD3ε, CXCL9, IP-10, Tim-3, and/or PSMB9 genes exists at a high level in the urine of a patient with acute rejection to kidney transplantation, and thus, the present invention is able to be conveniently used for diagnosing acute rejection to kidney transplantation through a noninvasive method.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a urinary transcript for the diagnosis of acute rejection of a kidney transplant patient,

The present invention relates to a non-invasive urine transcript for the diagnosis of acute rejection of renal transplantation and a use thereof, and more particularly to a urine transcript for diagnosis of renal transplant rejection, A marker composition for diagnosing kidney transplantation acute rejection reaction comprising mRNA of the above gene as a marker, a method for providing information necessary for diagnosing renal transplantation acute rejection using the marker composition, and an agent for measuring the expression level of the marker A diagnostic composition, and a quantification device for the marker.

The body's immune system, such as bacteria and viruses that invade the body to cause disease and destroys protein components that act. However, such an immune system does not distinguish between the transplanted kidney tissue and foreign proteins such as bacteria or viruses, so that the transplanted kidney is regarded as an external intrusion substance and attacked. The process by which the immune system attacks the transplanted kidney is called kidney transplant rejection.

Acute rejection after kidney transplantation is generally asymptomatic. However, if left untreated, it may be associated with loss of function of transplantation kidney, resulting in early signs of uremic symptoms such as anorexia, decreased urination, edema and dyspnea. Therefore, if acute rejection after transplantation is suspected, early diagnosis and treatment through biopsy is recommended. However, biopsy, which takes tissue directly, has a disadvantage in that the burden on the patient is increased in terms of cost, time, or recovery. Further, since the diagnosis is required for a long time, It is difficult to improve the performance.

Accordingly, various methods for easily diagnosing kidney transplantation acute rejection have been developed, for example, a method of diagnosing renal allograft rejection by comparing the expression levels of specific genes contained in blood has been developed Patent No. 2011-0020853). However, the above method is also accompanied with inconvenience of blood collection, and thus it has been required to develop a non-invasive method that overcomes this problem.

Under these circumstances, the present inventors have made extensive efforts to develop a method for diagnosing acute rejection non-invasively in renal transplant recipients. As a result, it has been found that C1QB, CD3 ?, CXCL9, IP-10, Tim-3 and PSMB9 The present inventors completed the present invention by confirming that the level of the mRNA of the six genes of the present invention is significantly increased in acute rejection patients.

One object of the present invention is to provide a marker for diagnosing kidney transplant acute rejection, which comprises the mRNA of one or more genes selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9, To provide a composition.

Another object of the present invention is to provide a method for providing information necessary for diagnosing renal transplant acute rejection using the marker composition.

Another object of the present invention is to provide a composition for diagnosing kidney transplantation acute rejection comprising an agent for measuring the expression level of the marker.

It is still another object of the present invention to provide a kit for the diagnosis of kidney transplantation acute rejection, which comprises a quantification device for the marker.

In one aspect of the present invention, C1QB , CD3? , CXCL9, IP-10, Tim-3, PSMB9  And a combination of at least one gene selected from the group consisting of mRNA With a marker  Diagnosing acute rejection of kidney transplant, including Marker  Lt; / RTI >

The term "kidney transplantation acute rejection" as used herein refers to a phenomenon in which a kidney transplanted by a specific immune response is collapsed, and it develops within a period of about one week to three months after transplantation, Rejection reaction.

In the present invention, mRNA of C1QB, CD3ε, CXCL9, IP-10, Tim-3 or PSMB9 gene can be used as a marker newly diagnosed in the present invention in order to diagnose acute rejection of kidney transplant patients.

The term "C1QB " of the present invention means a gene encoding complement C1q chain B protein, which is a component of the C1q complex responsible for the complement system. The protein is complement subcomponent C1q chain B; complement component 1, q subcomponent, B chain; complement component 1, q subcomponent, and beta polypeptide, and the nucleotide sequence thereof can be obtained from a known database such as NCBI GenBank (e.g., GenBank Accession No. NM_000491.4, etc.).

The term "CD3" of the present invention means a gene encoding CD3-epsilon polypeptide which constitutes a CD3 (cluster of differentiation 3) T cell-assisted receptor. The protein is CD3-epsilon; CD3e antigen, epsilon polypeptide (TiT3 complex); CD3e molecule, epsilon (CD3-TCR complex), and the nucleotide sequence thereof can be obtained from a known database such as NCBI's GenBank (e.g., GenBank Accession No. NM_000733.3 etc.).

The term "CXCL9" of the present invention means a gene encoding the C-X-C motif chemokine ligand 9 protein belonging to the CXC chemokine family. The protein may be gamma-interferon-induced monokine; monokine induced by gamma interferon; small-inducible cytokine B9, and the nucleotide sequence thereof can be obtained from a known database such as NCBI's GenBank (e.g., GenBank Accession No. NM_002416.2, etc.).

The term "IP-10" of the present invention means a gene encoding the C-X-C motif chemokine 10 protein belonging to the CXC chemokine family. Wherein said protein is selected from the group consisting of CXCL10; Interferon gamma-induced protein 10; small-inducible cytokine B10, and the nucleotide sequence thereof can be obtained from a known database such as NCBI's GenBank (e.g., GenBank Accession No. NM_001565.3, etc.).

The term "Tim-3" of the present invention means a gene encoding a TIM-3 protein that regulates macrophage activation. Wherein said protein is selected from the group consisting of HAVCR2; Hepatitis A virus cellular receptor 2; T-cell membrane protein 3; T cell immunoglobulin mucin 3; TIM-3; TIM3; TIMD-3; TIMD3 and the like, and the nucleotide sequence thereof can be obtained from a known database such as NCBI's GenBank (e.g., GenBank Accession No. NM_032782.4, etc.).

The term "PSMB9 " of the present invention means a gene encoding a proteasome subunit beta type-9 protein constituting a proteasome. The protein is also named 20S proteasome subunit beta-1i and its base sequence can be obtained from known databases such as NCBI's GenBank (e.g., GenBank Accession NM_002800.4, NM_148954.2, etc.).

Specifically, the mRNA of each gene may be contained in a urine sample of a patient suspected of acute renal failure rejection. In the present specification, the mRNA of the gene may be named as a 'urine transcript' or a 'transcript' have.

 An example of a method for locating an mRNA marker, i.e., a urine transcript marker, of the above six genes will be described in detail with reference to Fig. 1:

First, a meta-analysis using a known data set was performed to identify transcripts with an increased expression level when acute rejection occurred in kidney transplant patients. Next, the excised transcripts were subjected to the urine samples of the patients who were stable after renal transplantation and the urine samples of the patients who developed acute rejection after renal transplantation, and the expression levels of the excised transcripts As a result, when an acute rejection occurred in a kidney transplant patient, a transcript with a specifically increased level of expression was developed as an AR marker gene. The AR prediction model was developed by statistically processing the expression levels of the transcripts by regression analysis. To determine whether the developed AR prediction model can be used to diagnose acute rejection in renal transplant patients, the urine samples of renal transplant patients and the urine samples of renal transplant patients The expression level of the developed AR marker gene was measured in an RNA sample containing the test group and the measured expression level was applied to the developed AR prediction model to verify the utility of the model.

In addition, an example of a method for verifying the effect of the mRNA marker of the excavated gene, that is, the urine transcript marker, is described as follows:

The expression level of the urine transcript is assigned to an AR prediction model developed using logistic regression analysis. Thereafter, the effectiveness of the marker is verified by comparing the Area Under the Curve (AUC) value obtained through ROC (Receiver Operating Characteristic) analysis and confirming the sensitivity and specificity of the urine transcript marker.

In another aspect, the invention provides a method of treating a patient suffering from renal transplant rejection, CD3? , CXCL9 , IP-10, Tim-3, PSMB9  And combinations thereof. Selected Of one or more genes mRNA  A method for providing information necessary for diagnosis of kidney transplant acute rejection.

The definitions of the terms 'C1QB', 'CD3ε', 'CXCL9', 'IP-10', 'Tim-3', 'PSMB9' and 'kidney transplantation acute rejection' are as described above.

The term "individual" of the present invention may include, without limitation, mammals, aquaculture fish, and the like, including rats, livestock,

The term "diagnosis" of the present invention is intended to include determining the susceptibility of an object to a particular disease or disorder, determining whether an object currently has a particular disease or disorder, Determining the prognosis of a stuck object, or therametrics (e.g., monitoring the status of an object to provide information about the treatment efficacy).

The sample may be, but is not limited to, urine isolated from a kidney transplant recipient.

In the present invention, the above step of measuring the mRNA level of the gene can be used by any method known to a person skilled in the art. Specific examples include, but are not limited to, PCR, ligase chain reaction (LCR), transcription amplification, self-sustained sequence replication, nucleic acid based sequence amplification (NASBA) The nucleotide sequences of the six genes according to the present invention are known in the NCBI database, and those skilled in the art can use appropriate means required for measuring the mRNA level.

For the purpose of the present invention, a method for providing information necessary for diagnosing acute rejection of renal transplantation comprises the step of determining acute renal transplant rejection when the mRNA level of the gene is higher than that of a normal control sample May be further included. At this time, the normal control may be a stable graft function (STA) showing stable prognosis among the kidney transplant recipients.

In a specific embodiment of the present invention, the mRNA expression levels of the C1QB, CD3ε, CXCL9, IP-10, Tim-3 and PSMB9 transcripts contained in the urine samples of the kidney transplant recipients are higher than those of the normal group (STA) And statistically significant increase in the acute rejection (AR) (Fig. 2). This indicates that the mRNAs of the six genes detected in the urine samples can be usefully used as a marker for diagnosis of renal transplant acute rejection, and when the expression level of the mRNA increases, acute kidney transplant rejection It is possible.

In addition, for the purpose of the present invention, the method may further comprise correlating the result of quantitative analysis of the mRNA level of each gene with diagnosis of renal transplantation acute rejection.

At this time, the associating step may be performed by combining results of quantitative analysis of mRNA levels of the respective genes. That is, since the mRNA of each gene is detected in the urine of a normal or stable kidney transplant patient, it may not be easy to diagnose kidney transplant acute rejection only by the fragment level of the mRNA of the gene. Therefore, By analyzing the level of mRNA of the gene in combination, more accurate diagnosis of renal transplant acute rejection can be made.

As a method of analyzing the results obtained by quantitatively analyzing the mRNA levels of the respective genes, a conventional statistical analysis method can be used. In this case, the statistical analysis method that can be used is not particularly limited as long as it can diagnose kidney transplantation acute rejection reaction. For example, a linear or non-linear regression method (Lasso panelized logistic regression, Forward stepwise selection, Logistic regression, etc.) ; Linear or nonlinear classification methods; ANOVA method; Neural network analysis method; Genetic analysis method; Support vector machine analysis method; Hierarchical analysis or clustering analysis methods; A hierarchical algorithm using a decision tree, or a method of analyzing kernel principal components; Markov Blanket analysis method; Recursive feature elimination (RFE) analysis method; Forward or backward FS (floating search) analysis method, etc., can be used alone or in combination.

The combination of the detection results may be performed using a computer algorithm capable of automatically performing the statistical method.

As a specific example of the combination of the detection results, it may be a regression analysis model of [Equation 1] obtained by combining mRNA levels of CXCL9 gene and Tim-3 gene by linear or nonlinear regression analysis method.

[Equation 1]

In this formula,

CXCL9 represents the mRNA level of the CXCL9 gene,

Tim-3 represents the mRNA level of the Tim-3 gene.

The cutoff value of the regression analysis model of Equation (1) is 0.476. When the result obtained by applying the mRNA level of CXCL9 gene and Tim-3 gene to Equation 1 is higher than 0.476, acute rejection after kidney transplantation And the result is lower than 0.476, it can be predicted that acute rejection does not occur after kidney transplantation.

As another specific example of the combination of the detection results, the regression model of the following formula (2), which is obtained by combining the mRNA levels of the CXCL9 gene, the Tim-3 gene and the PSMB9 gene in a linear or nonlinear regression analysis method.

&Quot; (2) "

In this formula,

CXCL9 represents the mRNA level of the CXCL9 gene,

Tim-3 represents the mRNA level of the Tim-3 gene,

PSMB9 represents the mRNA level of the PSMB9 gene.

The cut-off value of the regression analysis model of Equation (2) is 0.457. When the result obtained by applying the mRNA level of CXCL9 gene, Tim-3 gene and PSMB9 gene to Equation 2 is higher than 0.457, It can be predicted that acute rejection will occur, and if the result is lower than 0.457, it can be predicted that acute rejection does not occur after kidney transplantation.

As another specific example of the combination of the detection results, mRNA levels of C1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene and PSMB9 gene are combined by linear or nonlinear regression analysis, Can be a regression analysis model of Eq. (3).

&Quot; (3) "

In this formula,

C1QB represents the mRNA level of the C1QB gene,

CD3? Represents the mRNA level of the CD3? Gene,

CXCL9 represents the mRNA level of the CXCL9 gene,

IP-10 represents the mRNA level of the IP-10 gene,

Tim-3 represents the mRNA level of the Tim-3 gene,

PSMB9 represents the mRNA level of the PSMB9 gene.

The cut-off value of the regression analysis model of the above formula (3) is 0.518, which is obtained by applying the mRNA level of the 1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene and PSMB9 gene to the above equation If the result is higher than 0.518, it can be predicted that the acute rejection will occur after the kidney transplantation. If the result is lower than 0.518, it can be predicted that the acute rejection does not occur after the kidney transplantation.

In yet another aspect, C1QB , CD3? , CXCL9 , IP-10, Tim-3, PSMB9  And a combination of at least one gene selected from the group consisting of mRNA  A composition for the diagnosis of acute kidney transplant rejection, which comprises an agent for measuring expression levels.

The definitions of the terms 'C1QB', 'CD3ε', 'CXCL9', 'IP-10', 'Tim-3', 'PSMB9' and 'kidney transplantation acute rejection' are as described above.

The term "agent for measuring the expression level of the mRNA of the gene" of the present invention means a preparation capable of specifically recognizing the mRNA of the gene or amplifying the mRNA. As a specific example, it may be a primer or a probe that specifically binds to the mRNA, but it is not limited thereto, and a person skilled in the art will be able to select a suitable preparation for the purpose of the invention.

The agent may be directly or indirectly labeled for the expression level of the gene. Specifically, the label may be a ligand, a bead, a radionuclide, an enzyme, a substrate, a cofactor, an inhibitor, a fluorescer, a chemiluminescent material, a magnetic particle, a hapten and a dye. It does not. Specific examples of the ligand include biotin, avidin and streptavidin, and the enzyme includes luciferase, peroxidase, and beta-galactosidase, and the fluorescent substance includes fluorescein, coumarin, rhodamine, But are not limited to, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, fumaric acid, Many of the known markers may be used as such detectable markers, and those skilled in the art will be able to select appropriate markers for the purpose of the invention.

The term 'primer' is a base sequence having a short free 3 'hydroxyl group, which can form a base pair with a complementary template and has a starting point for template strand copy Quot; short sequence " In the present invention, a primer used for amplification of the mRNA of the gene is amplified by PCR using appropriate conditions in an appropriate buffer (for example, four different nucleoside triphosphates and a polymer such as DNA, RNA polymerase or reverse transcriptase) Stranded oligonucleotides that can serve as a starting point for template-directed DNA synthesis under the control of the primers. The appropriate length of the primers may vary depending on the intended use. The primer sequence need not be completely complementary to the polynucleotide of the mRNA of the gene or its complementary polynucleotide, and can be used if it is sufficiently complementary to hybridize.

The term 'probe' refers to a labeled nucleic acid fragment or peptide that can undergo specific binding with mRNA or protein. Specific examples include oligonucleotide probes, single stranded DNA probes, double stranded DNA probes, RNA probes, oligonucleotide peptide probes, polypeptide probes, and the like. .

Another embodiment relates to a method for diagnosing acute rejection of the kidney transplant, comprising a device for quantifying mRNA of one or more genes selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9, Provide a kit.

The definitions of the terms 'C1QB', 'CD3ε', 'CXCL9', 'IP-10', 'Tim-3', 'PSMB9' and 'kidney transplantation acute rejection' are as described above.

The quantification device included in the diagnostic kit of the present invention may be one for measuring the expression level of mRNA of the above-mentioned six genes. As a specific example, it may be an RT-PCR kit, but is not limited thereto, so long as the amount of mRNA expression of the gene can be measured.

At this time, the RT-PCR kit may be a kit including essential elements necessary for performing RT-PCR. For example, an RT-PCR kit can contain, in addition to each primer specific to the gene, a test tube or other appropriate container, a reaction buffer (pH and magnesium concentrations vary), deoxynucleotides (dNTPs), ddNTPs ), Enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC-water, sterile water, and the like. It may also contain a primer pair specific for the gene used as a quantitative control.

Since the mRNA of the C1QB, CD3ε, CXCL9, IP-10, Tim-3 and / or PSMB9 genes according to the present invention is present at a high level in the urine of patients with renal transplant acute rejection, the renal transplantation acute rejection is non- invasive And can be used to diagnose easily.

FIG. 1 is a schematic diagram schematically illustrating the progress of the research process of the present invention. FIG. 1 is a diagram illustrating a process of meta-analysis and development of an acute rejection (AR) prediction model for detecting a biomarker from urine in a kidney transplantation acute rejection patient Graphically.
FIG. 2 shows the results of analysis of the expression levels of CD3εe, C1QB, CXCL9, Foxp3, IDO1, IP-10, PSMB9, PTPRC and Tim-3 transcripts in RNA extracted from urine samples of a training set FIG.
FIG. 3 is a fluorescence microscope photograph showing the results of detecting the expression levels of CXCL9 transcripts in renal biopsy samples of STA patients, renal biopsy samples of ACR patients, and glomerular area and epilepsy of renal biopsy samples of AMR patients.
FIG. 4 is a graph showing an ROC curve of an AR prediction model developed using a part or all of the six types of transcripts uncovered in the present invention.
FIG. 5 is a graph showing the results of analysis of expression levels of C1QB, CD3?, CXCL9, IP-10, Tim-3 and PSMB9 transcripts in the test group RNA.
6 is a graph showing the results of analysis of expression levels of six transcripts in the test group using the three types of AR prediction models provided in the present invention.
FIG. 7 is a graph showing a result of performing a decision curve analysis on a three-kind prediction model. FIG.

Hereinafter, the present invention will be described in more detail with reference to examples. However, these examples are for illustrative purposes only, and the scope of the present invention is not limited to these examples.

Example  1: Selection of patient and preparation of sample

In order to identify the gene mRNA markers for diagnosis of acute rejection in kidney transplant patients, six centers (Kyung Hee University Hospital, Kyungpook National University Hospital, Kyung Hee Medical Center, Samsung Seoul Hospital, 315 urine samples were collected from transplant patients with transplantation biopsy and transplantation stable function (eGFR ≥ 50 ml / min / 1.73 ㎡) for more than 10 years in patients with transplantation biopsy at Seoul St. Mary's Hospital and Inje University Paik Hospital. At this time, all of the patients were treated with a standard immunosuppressive agent including calcineurin inhibitor (tacrolimus or cyclosporine), mTOR inhibitor, prednisone and mycophenolate mofetil.

The clinical characteristics of the patients who collected the urine samples were analyzed as follows (Table 1).

Abbreviations in the above table are as follows

STA, stable patients; Stabilized patient

ACR, acute cellular rejection; Acute cellular rejection

AMR, acute antibody-mediated rejection; Acute antibody-mediated rejection

OGI, other graft injuries; Other graft damage

BKVN, BK viral nephropathy; BK virus nephropathy

KT, kidney transplantation; Kidney transplantation

HLA, human leukocyte antigen; Human leukocyte antigen

eGFR, estimated glomerular filtration rate; Glomerular filtration rate

PCR, protein-to-creatinine ratio; The ratio of creatine in the protein

ATN, acute tubular necrosis; Acute coronary necrosis

GN, glomerulonephritis; Glomerulonephritis

CNI, calcineurin inhibitor; Calcineurin inhibitor

Each of the 315 urine samples thus obtained was centrifuged at 2,000 x g and -4 ° C for 20 minutes, and a precipitate was obtained therefrom.

The obtained precipitates were subjected to a method using PureLink ™ RNA Mini Kit (Invitrogen) to extract total RNA of each sample.

Subsequently, quantitation (260 nm absorbance) and purity (absorbance ratio at 260 nm and 280 nm) of RNA were measured using a NanoDrop ND-2000 UV spectrophotometer (Thermo Scientific).

As a result, the median (25th and 75th percentile) of the total RNA content obtained from 315 samples was 0.345 (0.200-0.807) and the median (25th and 75th percentile) of total RNA purity was 1.95 (1.83-2.09).

The total RNA extracted from the 315 urine samples were divided into 98 training sets and 217 validation sets and used as samples for deriving and verifying acute rejection markers.

Example 2: Acute rejection (AR) marker gene discovery

From the open data set of the Gene Expression Omnibus database, gene expression profiles of 442 stable patients and 212 acute rejection patients were collected and subjected to meta-analysis to derive candidate genes for six AR markers.

In addition, a known gene for the three AR markers was further derived by analyzing the known existing research results.

Real-time PCR was performed on the nine candidate genes so as to finally select an AR marker gene.

Example 2-1: Meta-analysis

Meta-analysis was performed using the GeneMeta method (Choi et al, Bioinformatics, 2003, i84-i90). Specifically, it was calculated using the GeneMeta R package and subjected to 1000 permutations. Meanwhile, the GeneMeta method is a method for calculating the effect size and statistical significance by integrating data of a plurality of gene transcripts (mRNA). The size of the effect is calculated as a total average through a random effect model And statistical significance is obtained from a permutation test over a large number of data. In the present invention, a random-effect model for finding a signal in a whole group is used rather than a fixed-effect model in which a strongest signal is found in a training set. We used the GeneMeta method because we used the well-known permutation test.

First, to collect data on urine transcripts related to kidney transplantation through meta-analysis, a data set related to kidney rejection was retrieved from a gene expression ombinus, Four representative data sets were selected with representative values, from which a total of 654 gene expression profiles were collected, including 442 stable graft functions (STAs) and 212 acute rejection patients (AR). The collected gene expression profile was measured using Affymetrics Gene Chip, a common platform.

In order to select important genes (transcripts) among the above-mentioned 654 urine transcripts, the meta-analysis expression change amount (fold change) was calculated as a weighted sum of change in expression amount in each data set, weight) was set to be proportional to the sample size of each data set. Specifically, meta-analysis FDR (False Discovery Rate) was calculated by GeneMeta's permutation test method, and FDR <0.001 and FC> 2 were set as selection criteria.

As a result, it was confirmed that 137 sets of probes corresponding to a total of 109 unique genes were observed in several data sets. The clarity of the gene tin and the ease of design of the PCR primer were further investigated in the top 10 genes among the 109 genes identified above and finally 6 genes (C1QB, CXCL9, IDO1, IP-10, PSMB9 and PTPRC ) As an AR marker gene (Table 2).

On the other hand, the existing research results related to the marker gene in which expression level is increased during acute rejection after renal transplantation were analyzed to further derive three candidate genes for AR marker (CD3ε, Foxp3 and Tim-3) (Note: Huraib Furness, PN, et al., J. Biol. &Lt; RTI ID = 0.0 &gt; Ding, R, Li, et al., Transplantation, 75: 1307-954, 2001. Kidney Int, 60: 1998-2012, 2001; Li, B, Hartono, et al., N Engl J Med, 344: Reng et al., Kidney Int, 65: 2390-2397, 2004, Muthukumar, T, et al., N Engl J Med, 353: 2342-2351, 2005; Renesto, PG, et al Seo, JW, et al., PLoS One, 12: e0180045, &quot; Am J Transplant, 7: 1661-1665, 2007; Suthanthiran, M, et al., N Engl J Med, 369: 20-31, AMIA Joint Summits on Translational Science proceedings AMIA Joint Summits on Translational Science, 2016: 176-183, &lt; RTI ID = 0.0 &gt;2016; Vickers, AJ, et al., Medical Decision Making: An International Journal of the Society, Vol. 3, pp. Medical Decision Making, 26: 565-574, 2006; Kirk, AD, Transplantation, 87: 953-955, 2009; Kirk, AD, et al., Transpl Int, 18: 2-14, 2005; Sugiyama, K, et al., Exp Clin Transplant, 12: 195-199, 2014; Wang, XZ, et al., Transpl Immunol, 30: 18-23, 2014; Boix, F, et al., Transplant Proc., 48: 2987-2989, 2016; Rabant, M, et al., Am J Transplant, 16: 1868-1881, 2016; Hricik, DE, et al., Am J Transplant, 3: 878-884, 2003; Galichon, P., et al., Am J Transplant, 16: 3033-3040, 2016; Shahbaz, SK, et al., Transpl Immunol, 43-44: 11-20, 2017; Hricik, DE, et al., Am J Transplant, 13: 2634-2644, 2013; Matignon, M, et al., J Am Soc Nephrol, 25: 1586-1597, 2014; Vanhecke, D, et al., Arthritis and Rheumatism, 64: 3706-3714, 2012; Ma, R, Cui, et al., PLoS One, 9: e91250, 2014; Karin, M, et al., Semin Immunol, 12: 85-98, 2000; Ermolaeva, MA, et al., Aging Res Rev, 23: 3-11, 2015; Li, L, Khatri, et al., Am J Transplant, 12: 2710-2718, 2012; Roedder, S, et al., PLoS medicine, 11: e1001759, 2014; Sigdel, TK, et al., PLoS One, 10: e0138133, 2015; Pakfetrat, M, et al., Int J Organ Transplant Med, 6: 77-84, 2015; Hirsch, HH, et al., Transplantation, 79: 1277-1286, 2005; Maehana, T, et al., PLoS One, 11: e0162942, 2016; Kariminik, A., et al., Cell Mol Biol (Noisy-le-grand), 62: 104-108, 2016; Rau, S, Schonermarck, et al., Clin Transplant, 27: E184-191, 2013; Zhou, W, Sharma, et al., Viral Immunol, 20: 379-388, 2007; Li, JY, McNicholas, et al., Am J Transplant, 14: 1183-1190, 2014; Kim, MH, et al., PLoS One, 12: e0190068, 2017)

Example 2-2: Real-time PCR analysis

The expression level of the candidate genes for the nine AR markers derived in Example 2-1 was confirmed in 98 candidate RNAs classified in Example 1, and the AR marker gene was finally selected. At this time, the 98 candidate groups include 46 stable graft function (STA) samples including long-term graft survival (LGS), 35 acute T cell-mediated rejection cellular-mediated rejection (ACR) samples and 17 acute antibody-mediated rejection (AMR) samples.

First, complementary DNA (cDNA) was synthesized by using 10 μl of each candidate RNA as a template and using M-MLV Reverse Transcriptase system. At this time, 18S rRNA was selected as a standard gene, and 18S rRNA and TGF-β1 were selected as a control gene of the sample. Also, in the reaction, a reaction product containing 1 μl of the reverse transcription product, 1 × TaqMan Universal PCR mastermix, no AmpErase UNG, and 1 μl of the primer mix was used.

For the amplification of each of the synthesized cDNAs, an ABI StepOnePlus real-time PCR system (Applied Biosystems) was used. For this, 40 cycles of reaction at 95 ° C for 10 minutes, 95 ° C for 15 seconds, and 60 ° C for 60 seconds Respectively. The expression level of each transcript was calculated using the comparative threshold cycle (CT) method, which is a method of calibrating to the Ct (threshold cycle) value of universal human reference RNA and 18S rRNA 2). The sequences and types of the primers and probes used in the analysis are shown in Tables 3 and 4 below.

Types of Transcripts Catalog number
(Cat. Number) brand
(Brands) C1QB Hs.PT.56a.20374814 IDT CXCL9 Hs.PT.58.27316119 IDT IDO1 Hs.PT.58.924731 IDT PSMB9 Hs.PT.58.23199410 IDT PTPRC Hs.PT.58.38947549 IDT Foxp3 Hs01085834_m1 ABI Tim-3 Hs00958618_m1 ABI 18S rRNA Hs9999901_s1 ABI TGF-b1 Hs00998133_m1 ABI

Types of Transcripts order CD3? Sense: 5'-AAGAAATGGGTGGTATTACACAGACA-3 '(SEQ ID NO: 1)
Reverse: 5'-TGCCATAGTATTTCAGATCCAGGAT-3 '(SEQ ID NO: 2)
Probe: 5'-FAM-CCATCTCTGGAACCACTATAATTGACATGCC-TAMRA-3 '(SEQ ID NO: 3) IP-10 Sense: 5'-TGTCCACGTGTTGAGATCATTG-3 '(SEQ ID NO: 4)
Reverse: 5'-GGCCTTCGATTCTGGATTCA-3 '(SEQ ID NO: 5)
Probe: 5'-FAM-TACAATGAAAAAAAGGGTGAGAA-MGB-3 '(SEQ ID NO: 6)

FIG. 2 shows the results of analysis of the expression levels of CD3εe, C1QB, CXCL9, Foxp3, IDO1, IP-10, PSMB9, PTPRC and Tim-3 transcripts in RNA extracted from urine samples of a training set FIG.

As shown in FIG. 2, the expression levels of the seven kinds of transcripts of C1QB, CD3ε, CXCL9, Foxp3, IP-10, Tim-3 and PSMB9 were significantly increased in the AR samples compared to the STA samples, but IDO1 and PTPRC The expression level of Foxp3 transcripts was not significantly different between the STA and AR samples, and the expression level of Foxp3 transcripts was statistically significantly increased. However, in most of the samples, a certain level of expression level , It is analyzed that it is not suitable for AR diagnosis prediction model.

Thus, it was found that six types of transcripts of C1QB, CD3ε, CXCL9, IP-10, Tim-3 and PSMB9 could be used for AR diagnosis in renal transplant patients.

Example 2-3: in situ  Verification through hybridization analysis

From the results of the above Example 2-2, it was confirmed that six types of transcripts of C1QB, CD3 ?, CXCL9, IP-10, Tim-3 and PSMB9 could be used for AR diagnosis in renal transplant patients. Actually, Was investigated by in situ hybridization analysis using CXCL9 transcripts. At this time, in situ hybridization analysis was performed using RNAscope 2.5 HD BROWN assay kit (Advanced Cell Diagnostics, Hayward, Calif.).

Approximately, renal biopsy samples of normal (NP), renal biopsy samples of ACR patients, and glomerulus and interstitium of renal biopsy specimens of AMR patients are fixed with formalin and used using known microtechnique methods To prepare respective tissue flakes. The CXCL9 oligonucleotide probe was added to each of the above tissue flakes, followed by hybridization at 40 ° C for 2 hours. Then, the preamplifier was treated, treated with a signal enhancer at 40 ° C for 30 minutes, and incubated at 40 ° C for 15 minutes , Amplifiers were treated and reacted sequentially at 40 ° C for 30 minutes and label probes and at 40 ° C for 15 minutes. Subsequently, a signal amplifier was treated at room temperature for 30 minutes and an HRP-conjugated labeling procedure was performed for 15 minutes. Finally, staining with DAB (3,3-diaminobenzidine), staining with hematoxylin, and hybridization signals were confirmed by fluorescence microscopy (Fig. 3). At this time, Hs-PPIB (human peptidylprolyl isomerase B) was used as a positive control and dapB (bacterial dihydrodipicolinate reductase) was used as a negative control.

FIG. 3 is a fluorescence microscope photograph showing the results of detecting the expression levels of CXCL9 transcripts in renal biopsy samples of STA patients, renal biopsy samples of ACR patients, and glomerular area and epilepsy of renal biopsy samples of AMR patients.

As shown in FIG. 3, the CXCL9 transcript was not expressed in normal kidney tissues but was expressed in renal tissues of patients with ACR and patients with AMR.

Thus, it can be seen that the six transcripts identified in Example 2-2 can be used as markers for AR diagnosis in kidney transplant patients.

Example 3: Development of an acute rejection prediction model

Various AR prediction models were developed by performing various statistical analysis methods using all or a part of the six types of transcripts extracted in the embodiment 2-2, and the utility of these models was confirmed.

Example 3-1: Predictive model using two transcripts

The following AR prediction model (2gene-M) was developed using the expression levels of Tim-3 and CXCL9 transcripts and the Lasso panelized logistic regression among the six transcripts excised in Example 2-2:

[Equation 1]

In this formula,

CXCL9 represents the mRNA level of the CXCL9 gene,

Tim-3 represents the mRNA level of the Tim-3 gene.

Example 3-2: Predictive model using three transcripts

The following AR prediction model (3gene-M) was developed using the expression levels of Tim-3, CXCL9 and PSMB9 transcripts and Forward stepwise selection among the six transcripts excised in Example 2-2:

&Quot; (2) &quot;

In this formula,

CXCL9 represents the mRNA level of the CXCL9 gene,

Tim-3 represents the mRNA level of the Tim-3 gene,

PSMB9 represents the mRNA level of the PSMB9 gene.

Example 3-3: Predictive model using 6 transcripts

The following AR prediction model (6gene-M) was developed using the expression level and logistic regression of all six transcripts excised in Example 2-2:

&Quot; (3) &quot;

In this formula,

C1QB represents the mRNA level of the C1QB gene,

CD3? Represents the mRNA level of the CD3? Gene,

CXCL9 represents the mRNA level of the CXCL9 gene,

IP-10 represents the mRNA level of the IP-10 gene,

Tim-3 represents the mRNA level of the Tim-3 gene,

PSMB9 represents the mRNA level of the PSMB9 gene.

Example 3-4: ROC curve analysis of each prediction model

The ROC curves represented by the three kinds of prediction models developed in Examples 3-1 to 3-3 were analyzed (Fig. 4).

FIG. 4 is a graph showing an ROC curve of an AR prediction model developed using a part or all of the six types of transcripts uncovered in the present invention.

Cutoff point, Semsitivity, Specificity, and AUC determined by analyzing the ROC curve of FIG. 4 are shown in Table 5. &lt; tb &gt; &lt; TABLE &gt;

ROC curve analysis result of AR prediction model Model Cutoff point Semsitivity
(%) Specificity
(%) AUC (95% Cl) p-value 2gene-M 0.476 82 78 0.87
(0.79-0.95) <0.001 3gene-M 0.457 84 78 0.90
(0.82-0.97) <0.001 6gene-M 0.518 84 83 0.90
(0.82-0.97) <0.001

In Table 5, if the value obtained through the model shows a value higher than the cutoff point, it can be predicted that the AR will develop, and if the value obtained through the model shows a value lower than the cutoff point, Can be predicted.

Example 4: Validation of an AR prediction model using a validation set

Expression levels of six AR marker genes (C1QB, CXCL9, IDO1, IP-10, PSMB9 and PTPRC) were measured using 217 validation set RNAs classified in Example 1, Level to the AR prediction model developed in the third embodiment to check whether or not the AR prediction model can be used.

At this time, the RNA of the test group was subdivided as follows: 78 STA samples, 38 ACR samples, 11 AMR samples, 15 BK virus nephropathy (BKVN) samples, and 75 other graft injuries (other graft injury, OGI). At this time, STA samples include samples of long-term graft survival (LGS) and normal pathology (NP) samples; The OGI samples include acute tubular necrosis, calcineurin inhibitor toxicity, and glomerulonephritis.

Example 4-1: Verification of RNA expression level

The expression levels of the six transcripts were compared by performing the same method as in Example 2-2, except that 217 validated group RNAs were used instead of candidate RNAs (FIG. 5).

FIG. 5 is a graph showing the results of analysis of expression levels of C1QB, CD3?, CXCL9, IP-10, Tim-3 and PSMB9 transcripts in the test group RNA.

As shown in FIG. 5, all of the six transcripts exhibited higher expression levels in the AR and BKVN samples than the STA samples (NP / LGS), and showed relatively low expression levels in the OGI samples than in the AR samples.

The CD3ε, CXCL9, and IP-10 transcripts were also found in STA samples (NP / LGS), while the C1QB, PSMB9 and Tim-3 transcripts showed higher expression levels in OGI samples than STA samples (NP / LGS) , And the IP-10 transcripts showed the highest expression level in BKVN.

These results indicate that the six types of transcripts of C1QB, CD3, CXCL9, IP-10, Tim-3 and PSMB9 can be useful for the diagnosis of AR in renal transplant patients and further include CD3ε, CXCL9 and IP- Could be used to differentiate between AR and other disease groups.

Example 4-2: Verification of effect of AR prediction model

Expression levels of the C1QB, CD3ε, CXCL9, IP-10, Tim-3 and PSMB9 transcripts were measured in the validated group RNAs and the measured expression levels were applied to the model developed in Example 3, The ROC curve was analyzed (Figure 6).

6 is a graph showing the results of analysis of expression levels of six transcripts in the test group using the three types of AR prediction models provided in the present invention.

As shown in FIG. 6, in all three models, the STA sample showed a lower value than the cutoff point, whereas the AR sample had a higher value than the cutoff point.

Example 4-3: Decision curve analysis [

Decision curve analysis was performed to verify whether the three kinds of AR prediction models developed in Example 3 can be used for clinical use (Fig. 7). The decision curve analysis represents the various benefit probabilities of the acute rejection response (pt) and the net benefit of the minimum expected probability. Therefore, it can be used as a tool to determine the biopsy for the diagnosis of acute rejection.

FIG. 7 is a graph showing a result of performing a decision curve analysis on a three-kind prediction model. FIG.

As shown in FIG. 7, it is confirmed that the three types of AR prediction models provided by the present invention show the largest net benefit in a reasonable probability range of 0.2 to 0.5.

Based on the above results, the six types of urine transcripts according to the present invention and the three types of AR prediction models using the same can diagnose acute rejection in renal transplant patients with high reliability, sensitivity, and specificity, and furthermore, And it can be used as a diagnostic tool for biopsy.

From the above description, it will be understood by those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. In this regard, it should be understood that the above-described embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the present invention should be construed as being included in the scope of the present invention without departing from the scope of the present invention as defined by the appended claims.

<110> University-Industry Cooperation Group of Kyung Hee University <120> Urinary mRNA for non-invasive differential diagnosis of acute          rejection in kidney transplanted patients and uses thereof <130> KPA161526-KR-P1 <150> KR 10-2017-0069212 <151> 2017-06-02 <160> 6 <170> KoPatentin 3.0 <210> 1 <211> 26 <212> DNA <213> Artificial Sequence <220> <223> primer <400> 1 aagaaatggg tggtattaca cagaca 26 <210> 2 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> primer <400> 2 tgccatagta tttcagatcc aggat 25 <210> 3 <211> 33 <212> DNA <213> Artificial Sequence <220> <223> probe <400> 3 ccatctctgg aaccacagta atattgacat gcc 33 <210> 4 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> primer <400> 4 tgtccacgtg ttgagatcat tg 22 <210> 5 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> primer <400> 5 ggccttcgat tctggattca 20 <210> 6 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> probe <400> 6 tacaatgaaa aagaagggtg agaa 24

Claims (19)

Wherein the markers comprise mRNAs of one or more genes selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9 and combinations thereof as markers.
Comprising measuring the mRNA level of one or more genes selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9 and combinations thereof from a sample of suspected renal transplant rejection- Methods of providing information necessary to diagnose transplant acute rejection.
3. The method of claim 2,
Wherein the sample is urine.
3. The method of claim 2,
Wherein said combination is a combination of a CXCL9 gene and a Tim-3 gene.
3. The method of claim 2,
Wherein said combination is a combination of a CXCL9 gene, a Tim-3 gene and a PSMB9 gene.
3. The method of claim 2,
Wherein said combination is a combination of a C1QB gene, a CD3ε gene, a CXCL9 gene, an IP-10 gene, a Tim-3 gene and a PSMB9 gene.
3. The method of claim 2,
Wherein the method further comprises the step of determining a renal transplant acute rejection response if the mRNA level of the gene measured is greater than the mRNA level of a normal control sample.
3. The method of claim 2,
Wherein the method further comprises correlating the result obtained by measuring the mRNA level of the gene with a diagnosis of renal transplant acute rejection.
9. The method of claim 8,
Wherein said associating is performed by combining the results obtained by measuring the level of mRNA of said gene.
10. The method of claim 9,
The combination of the results may be linear or non-linear regression analysis; Linear or nonlinear classification methods; ANOVA method; Neural network analysis method; Genetic analysis method; Support vector machine analysis method; Hierarchical analysis or clustering analysis methods; A hierarchical algorithm using a decision tree, or a method of analyzing kernel principal components; Markov Blanket analysis method; Recursive feature elimination (RFE) analysis method; Front or rear FS (floating search) analysis method; &Lt; / RTI &gt; and a combination thereof.
10. The method of claim 9,
Wherein the combination of the results is performed using a computer algorithm.
10. The method of claim 9,
Wherein the combination of the results is represented by a regression model of the following formula (1) obtained by combining the mRNA levels of the CXCL9 gene and the Tim-3 gene by linear or nonlinear regression analysis:
[Equation 1]

In this formula,
CXCL9 represents the mRNA level of the CXCL9 gene,
Tim-3 represents the mRNA level of the Tim-3 gene.
13. The method of claim 12,
The cutoff value of the regression analysis model of Equation (1) is 0.476. When the result obtained by applying the mRNA level of CXCL9 gene and Tim-3 gene to Equation 1 is higher than 0.476, acute rejection after kidney transplantation , And the result is predicted to be less than 0.476 that the acute rejection does not occur after kidney transplantation.
10. The method of claim 9,
The combination of the above results is represented by the regression model of the following formula (2) obtained by combining the mRNA levels of the CXCL9 gene, the Tim-3 gene and the PSMB9 gene in a linear or nonlinear regression method:
&Quot; (2) &quot;

In this formula,
CXCL9 represents the mRNA level of the CXCL9 gene,
Tim-3 represents the mRNA level of the Tim-3 gene,
PSMB9 represents the mRNA level of the PSMB9 gene.
15. The method of claim 14,
The cut-off value of the regression analysis model of Equation (2) is 0.457. When the result obtained by applying the mRNA level of CXCL9 gene, Tim-3 gene and PSMB9 gene to Equation 2 is higher than 0.457, Wherein an acute rejection reaction can be predicted to occur, and when the result is less than 0.457, it can be predicted that acute rejection will not occur after kidney transplantation.
10. The method of claim 9,
The combination of the above results is a regression analysis model of Equation (3) obtained by combining mRNA levels of C1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene and PSMB9 gene by linear or non- Lt; RTI ID = 0.0 &gt;
&Quot; (3) &quot;

In this formula,
C1QB represents the mRNA level of the C1QB gene,
CD3? Represents the mRNA level of the CD3? Gene,
CXCL9 represents the mRNA level of the CXCL9 gene,
IP-10 represents the mRNA level of the IP-10 gene,
Tim-3 represents the mRNA level of the Tim-3 gene,
PSMB9 represents the mRNA level of the PSMB9 gene.
17. The method of claim 16,
The cut-off value of the regression analysis model of the above formula (3) is 0.518, which is obtained by applying the mRNA level of the 1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene and PSMB9 gene to the above equation The result can be predicted to be acute rejection after kidney transplantation when the result is higher than 0.518, and it can be predicted that acute rejection does not occur after kidney transplantation when the result is lower than 0.518.
A composition for diagnosing acute rejection of kidney transplantation comprising an agent for measuring an expression level of mRNA of at least one gene selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9 and combinations thereof.
A kit for quantifying mRNA of one or more genes selected from the group consisting of C1QB, CD3 ?, CXCL9, IP-10, Tim-3, PSMB9 and combinations thereof.

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