KR20200039654A - 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 biomarker for noninvasively diagnosing acute rejection of kidney transplantation and a use thereof, and more particularly, to: a marker composition for diagnosing acute rejection of kidney transplant, the marker composition comprising, as a marker, mRNA of at least one gene selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and a combination thereof; a method for providing information necessary for diagnosing acute rejection of kidney transplantation using the marker composition; a method for providing information necessary for diagnosing acute rejection of kidney transplantation using the marker composition; a diagnostic composition comprising an agent for measuring the expression level of the marker; and a diagnostic kit comprising a quantitative device for the marker. mRNA of 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, and thus may be used for conveniently diagnosing acute rejection of kidney transplantation in a noninvasive way.

Description

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

The present invention relates to a non-invasive urine transcript for diagnosing acute kidney transplantation reaction and its use, specifically one selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof Comprising a marker composition for diagnosing acute kidney rejection reaction comprising the mRNA of the above gene as a marker, a method of providing information necessary for diagnosing acute kidney transplantation using the marker composition, and an agent measuring the expression level of the marker It relates to a diagnostic composition, and a diagnostic kit comprising a quantification device for the marker.

The body's immune system acts to destroy protein components that invade the body, such as bacteria and viruses, and cause disease. However, this immune system cannot distinguish between the transplanted kidney tissue and foreign proteins such as bacteria or viruses, and attacks the transplanted kidney by considering it as an external invading material. This process by which the immune system attacks the transplanted kidney is called kidney transplant rejection.

Acute rejection reactions after kidney transplantation are generally asymptomatic, but if left untreated, urinary poisoning-related symptoms such as loss of appetite, decreased urine volume, swelling, and shortness of breath may occur early after transplantation as it is associated with loss of function of the transplanted kidney. Therefore, if an acute rejection reaction is suspected after transplantation, early diagnosis and treatment through biopsy is recommended. However, a biopsy that directly collects tissue has a disadvantage of increasing the burden on the patient in terms of cost, time, or recovery.In addition, as acute rejection reaction occurs as a large amount of time is required for diagnosis, the treatment time is absolutely insufficient and the recovery rate There was a problem that it was difficult to improve.

Accordingly, various methods for easily diagnosing acute kidney transplant rejection have been developed, for example, a method for diagnosing kidney allograft rejection by comparing the expression level of a specific gene contained in blood has been developed. Patent No. 2011-0020853). However, as the above method also accompanies the inconvenience of blood collection, the development of a non-invasive method overcoming this is required.

Under this background, the present inventors made intensive research efforts to develop a method for non-invasively diagnosing acute rejection in kidney transplant patients. As a result, C1QB, CD3ε, CXCL9, IP-10, Tim-3 and PSMB9 contained in the urine of patients. It was confirmed that the level of mRNA of 6 kinds of genes was significantly increased in patients with acute rejection reaction, thereby completing the present invention.

One object of the present invention is C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and a marker for diagnosing acute kidney transplantation reaction comprising the mRNA of one or more genes selected from the group consisting of a combination thereof as a marker It is to provide a composition.

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

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

Another object of the present invention is to provide a kit for diagnosing acute kidney transplant rejection, including a quantitative device for the marker.

As an aspect for achieving the above object, the present invention comprises the mRNA of one or more genes selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof as a marker, kidney It provides a marker composition for diagnosing acute transplant rejection.

The term "acute kidney transplant rejection" is a phenomenon in which the transplanted kidney is collapsed by a specific immune response, and since it develops within about 1 week to 3 months after transplantation, it is a chronic disease that gradually develops after several months to several years. It is distinct from rejection.

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

As used herein, the term "C1QB" refers to a gene encoding the complement C1q chain B protein, which is a component of the C1q complex in charge of the complement system. The protein is complement subcomponent C1q chain B; complement component 1, q subcomponent, B chain; Also named as complement component 1, q subcomponent, beta polypeptide, etc., its nucleotide sequence can be obtained from known databases such as NCBI's GenBank (eg GenBank Accession No. NM_000491.4, etc.).

The term "CD3ε" of the present invention refers to a gene encoding a CD3-epsilon polypeptide protein constituting a cluster of differentiation 3 (CD3) T cell co-receptor. The protein is CD3-epsilon; CD3e antigen, epsilon polypeptide (TiT3 complex); It is also named CD3e molecule, epsilon (CD3-TCR complex), etc., and its nucleotide sequence can be obtained from known databases such as NCBI's GenBank (eg, GenBank Accession No. NM_000733.3, etc.).

As used herein, the term "CXCL9" refers to a gene encoding a C-X-C motif chemokine ligand 9 protein belonging to the CXC chemokine family. The protein is gamma-interferon-induced monokine; monokine induced by gamma interferon; It is also named small-inducible cytokine B9, and its nucleotide sequence can be obtained from known databases such as GenBank of NCBI (eg, GenBank Accession No. NM_002416.2, etc.).

The term "IP-10" of the present invention refers to a gene encoding a C-X-C motif chemokine 10 protein belonging to the CXC chemokine family. The protein is CXCL10; Interferon gamma-induced protein 10; It is also named small-inducible cytokine B10, and its nucleotide sequence can be obtained from known databases such as GenBank of NCBI (eg, GenBank Accession No. NM_001565.3, etc.).

The term "Tim-3" of the present invention refers to a gene encoding a TIM-3 protein that regulates macrophage activation. The protein is HAVCR2; Hepatitis A virus cellular receptor 2; T-cell membrane protein 3; T cell immunoglobulin mucin 3; TIM-3; TIM3; TIMD-3; It is also named as TIMD3 and the like, and its nucleotide sequence can be obtained from a known database such as GenBank of NCBI (eg, GenBank Accession No. NM_032782.4, etc.).

The term "PSMB9" of the present invention refers to a gene encoding a proteasome subunit beta type-9 protein constituting a proteasome. The protein is also named 20S proteasome subunit beta-1i, etc., and its nucleotide sequence can be obtained from known databases such as GenBank of NCBI (eg, 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 kidney transplant rejection, and in the present specification, the mRNA of the gene may be referred to as'urine transcript' or'transcriptome'. have.

An example of a method of discovering mRNA markers of the six genes, that is, urine transcriptome markers, will be described in detail with reference to FIG. 1:

First, meta-analysis using a known data set was performed to find a transcript with a specifically increased expression level when an acute rejection reaction occurred in a kidney transplant patient. Subsequently, the discovered transcript was used as a target for RNA samples of a candidate group including a urine sample of a patient who was stable after a kidney transplant and a urine sample of a patient with an acute rejection reaction after a kidney transplant, and the expression level of the discovered transcript was determined. As a result, when an acute rejection reaction occurred in a kidney transplant patient, a transcript with a specific increase in expression level was developed as an AR marker gene. The AR prediction model was developed by statistically processing the expression level of the developed transcript by a regression analysis method. In order to check whether the developed AR prediction model can be used to diagnose whether acute rejection reaction will occur in kidney transplant patients, urine samples of stable patients after kidney transplantation and urine samples of patients with acute rejection reaction after kidney transplantation were analyzed. The expression level of the developed AR marker gene was measured for the RNA sample of the included validation 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 discovered gene, that is, a urine transcriptome marker is described as follows:

The expression level of the urine transcript is substituted into the AR prediction model developed using logistic regression analysis. Then, by comparing the AUC (Area Under the Curve) value obtained through ROC (Receiver Operating Characteristic) analysis, and confirming the sensitivity and specificity of the urine transcriptome marker, the effect of the marker is verified.

In another aspect, the present invention is the mRNA level of one or more genes selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof from a sample of an individual suspected of acute kidney transplant rejection. It provides a method of providing information necessary for diagnosing acute kidney transplant rejection, comprising the step of measuring.

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

The term "individual" of the present invention may include, without limitation, mammals, farmed fish, etc., including rats, livestock, humans, etc., which are likely to develop an acute kidney transplant rejection reaction.

The term "diagnosis" of the present invention is to determine the susceptibility of an object to a specific disease or disorder, to determine whether an object currently has a specific disease or disorder, or to a specific disease or disorder. It includes determining the prognosis of an affected object, or therametrics (eg, monitoring the condition of the object to provide information about treatment efficacy).

The sample is not particularly limited thereto, but may be urine separated from a kidney transplant subject.

In the present invention, the step of measuring the mRNA level of a gene may be performed by any method known to those skilled in the art. As a specific example, PCR, ligase chain reaction (LCR), transcription amplification, self-maintained sequence replication, and nucleic acid-based sequence amplification (NASBA) methods may be used, but are not limited thereto. At this time, since the base sequences of the six genes according to the present invention are known in databases such as NCBI, those skilled in the art can use appropriate means required for measuring the mRNA level.

For the purposes of the present invention, the method of providing information necessary for diagnosing acute kidney transplant rejection comprises determining that the measured mRNA level of the gene is higher than that of a normal control sample, determining as acute kidney transplant rejection. It may contain additionally. In this case, the normal control group may be a stable graft function (STA) group showing a stable prognosis among individuals who have undergone a kidney transplant.

In a specific embodiment of the present invention, the mRNA expression level of the C1QB, CD3ε, CXCL9, IP-10, Tim-3, and PSMB9 transcripts contained in the urine sample of a kidney transplant subject is compared with the normal group (STA), It was confirmed that there was a statistically significant increase in acute rejection (AR) (FIG. 2). It is determined that the mRNA of the six genes detected in a urine sample can be usefully used as a marker for diagnosing acute kidney transplant rejection, and an acute kidney transplant rejection reaction occurred when the expression level of the mRNA was increased. It suggests that you can do it.

In addition, for the purposes of the present invention, the method may further include the step of correlating the result of quantitative analysis of the mRNA level of each gene with the diagnosis of acute kidney transplant rejection.

In this case, the step of associating may be performed by combining the results of quantitative analysis of the mRNA level of each gene. That is, since the mRNA of each gene is also detected in the urine of a normal person or a kidney transplant patient with a stable prognosis, it may not be easy to diagnose an acute kidney transplant rejection reaction only with a fractional level of the mRNA of the gene. By combining and analyzing the levels of the mRNA of the gene, it is possible to more accurately diagnose acute kidney transplant rejection.

As such, a conventional statistical analysis method may be used as a method of combining and analyzing the results of quantitative analysis of the mRNA level of each gene. At this time, the statistical analysis method that can be used is not particularly limited as long as it can diagnose acute kidney transplant rejection, but as an example, a linear or nonlinear regression analysis method (Lasso panelized logistic regression, Forward stepwise selection, Logistic regression, etc.) ; Linear or nonlinear classification analysis method; Analysis of variance (ANOVA) method; Neural network analysis method; Genetic analysis method; Support vector machine analysis method; Hierarchical analysis or clustering analysis method; Hierarchical algorithm using decision tree, or kernel principal components analysis method; Markov Blanket analysis method; RFE (recursive feature elimination) analysis method; The front or rear FS (floating search) analysis method can be used alone or in combination.

In addition, 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, the regression analysis model of the following [Equation 1] may be obtained by combining the mRNA levels of the CXCL9 gene and the Tim-3 gene by a linear or nonlinear regression analysis method.

[Equation 1]

In the above 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, and when the result obtained by applying the mRNA levels of the CXCL9 gene and the Tim-3 gene to Equation 1 is higher than 0.476, acute rejection reaction after kidney transplantation It can be predicted that this will occur, and if the result is lower than 0.476, it can be predicted that an acute rejection reaction will not occur after a kidney transplant.

As another specific example of the combination of the detection results, it may be a regression analysis model of the following [Equation 2] obtained by combining the mRNA levels of the CXCL9 gene, the Tim-3 gene, and the PSMB9 gene by a linear or nonlinear regression analysis method.

[Equation 2]

In the above 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 cutoff value of the regression analysis model of [Equation 2] is 0.457, and when the result obtained by applying the mRNA levels of the CXCL9 gene, Tim-3 gene, and PSMB9 gene to Equation 2 is higher than 0.457, after kidney transplantation It can be predicted that an acute rejection reaction will occur, and if the result value is lower than 0.457, it can be predicted that an acute rejection reaction will not occur after a kidney transplant.

As another specific example of the combination of the detection results, the following obtained by combining the mRNA levels of the C1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene, and PSMB9 gene by a linear or nonlinear regression analysis method. It can be the regression analysis model of equation 3].

[Equation 3]

In the above formula,

C1QB represents the mRNA level of the C1QB gene,

CD3ε represents the mRNA level of the CD3ε 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 cutoff value of the regression analysis model of [Equation 3] is 0.518, obtained by applying the mRNA levels of the 1QB gene, CD3ε gene, CXCL9 gene, IP-10 gene, Tim-3 gene, and PSMB9 gene to the equation 3 If the result value is higher than 0.518, it can be predicted that an acute rejection reaction will occur after a kidney transplant, and if the result value is lower than 0.518, it can be predicted that an acute rejection reaction will not occur after a kidney transplant.

In yet another aspect, the present invention comprises an agent for measuring the mRNA expression level of one or more genes selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof. , Provides a composition for diagnosing acute kidney transplant rejection.

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

The term of the present invention, "an agent for measuring the expression level of the mRNA of a gene" refers to an agent capable of specifically binding to and 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 is not limited thereto, and those skilled in the art will be able to select an appropriate agent for the purpose of the invention.

The agent may be directly or indirectly labeled to measure the expression level of the gene. Specifically, the label may include a ligand, a bead, a radionuclide, an enzyme, a substrate, a cofactor, an inhibitor, a fluorescent material, a chemiluminescent material, a magnetic particle, a hapten, and a dye, but is limited thereto. It doesn't. As a specific example, the ligand includes biotin, avidin, streptavidin, and the like, the enzyme includes luciferase, peroxidase, and beta galactosidase, and the fluorescent material includes fluorescein, coumarin, rhodamine, Phycoerythrin and sulforodamic acid chloride (Texas red), and the like, but are not limited thereto. Most of the known labels can be used as such detectable labels, and those skilled in the art will be able to select an appropriate label for the purpose of the invention.

The term'primer' is a base sequence having a short free 3'terminal hydroxyl group, which can form a complementary template and a base pair, and is a starting point for template strand copying. It refers to a short sequence that functions as. In the present invention, the primers used to amplify the mRNA of the gene are appropriate conditions in an appropriate buffer (e.g., 4 different nucleoside triphosphates and a polymerization agent such as DNA, RNA polymerase or reverse transcriptase) and an appropriate temperature. It may be a single-stranded oligonucleotide that can serve as a starting point for template-directed DNA synthesis under, and the appropriate length of the primer may vary depending on the purpose of use. The primer sequence need not be completely complementary to a polynucleotide of the mRNA of the gene or a polynucleotide that is complementary thereto, and can be used as long as it is sufficiently complementary to hybridize.

The term'probe' refers to a labeled nucleic acid fragment or peptide capable of specific binding to an mRNA or protein. Specific examples include oligonucleotide probes, single stranded DNA probes, double stranded DNA probes, RNA probes, oligonucleotide probes, polypeptide probes, etc. Can be made with

Another aspect is for diagnosing acute kidney transplant rejection, comprising a quantitative device for mRNA of one or more genes selected from the group consisting of C1QB, CD3ε, CXCL9, IP-10, Tim-3, PSMB9, and combinations thereof. Kits are provided.

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

The quantitative device included in the diagnostic kit of the present invention may measure the mRNA expression level of the six kinds of genes. As a specific example, the RT-PCR kit may be used, but as long as the expression level of the mRNA of the gene can be measured, it is not limited thereto.

In this case, the RT-PCR kit may be a kit including essential elements necessary to perform RT-PCR. For example, the RT-PCR kit includes, in addition to each primer specific for the gene, a test tube or other suitable container, reaction buffer (varies in pH and magnesium concentration), deoxynucleotides (dNTPs), didioxynucleotides (ddNTPs). ), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC-water, sterile water, and the like. In addition, a primer pair specific to a gene used as a quantitative control may be included.

The mRNA of the C1QB, CD3ε, CXCL9, IP-10, Tim-3 and/or PSMB9 genes according to the present invention is present at high levels in the urine of patients with acute kidney transplant rejection, so a non-invasive method for acute kidney transplant rejection It can be used for simple diagnosis.

1 is a schematic diagram schematically showing the overall progress of the research process of the present invention, showing a meta-analysis for detecting a biomarker from urine of a patient with acute kidney transplant reaction and the development of an acute rejection response (AR) prediction model. It is shown schematically.
2 shows the results of analyzing the expression levels of CD3εe, C1QB, CXCL9, Foxp3, IDO1, IP-10, PSMB9, PTPRC and Tim-3 transcripts for RNA extracted from urine samples of a candidate group (training set). It is a graph showing.
3 is a fluorescence micrograph showing the result of detecting the expression level of CXCL9 transcript in the glomerular region and interstitial region of a kidney biopsy sample from an STA patient, a kidney biopsy sample from an ACR patient, and a kidney biopsy sample from an AMR patient.
4 is a graph showing the ROC curve of an AR prediction model developed using some or all of the six types of transcripts discovered in the present invention.
5 is a graph showing the results of analyzing the expression levels of C1QB, CD3ε, CXCL9, IP-10, Tim-3, and PSMB9 transcripts for the validation group RNA.
6 is a graph showing the results of analyzing the expression levels of 6 types of transcripts in the verification group using the 3 types of AR prediction models provided by the present invention.
7 is a graph showing the results of performing decision curve analysis for three predictive models.

Hereinafter, the present invention will be described in more detail through 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 patients and preparation of samples

To discover mRNA markers of genes for diagnosing acute rejection reactions in kidney transplant patients, 6 centers (Kangdong Kyunghee University Hospital, Kyungpook National University Hospital, Kyunghee Medical Center, Samsung Seoul Hospital, 315 urine samples were collected from kidney transplant patients who underwent transplant biopsy at Seoul St. Mary's Hospital and Inje University Paik Hospital) and transplant patients with stable transplant function (eGFR ≥ 50 ml/min/1.73㎡) for a long period of more than 10 years. At this time, all the patients were treated with standard immunosuppressants including calcineurin inhibitor (taclolimus or cyclosporine), mTOR inhibitor, prednisone and mycophenolate mofetil.

The clinical characteristics of the patient who collected the urine sample were analyzed and the results are as follows (Table 1).

The 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 injury; Other graft damage

BKVN, BK viral nephropathy; BK virus nephropathy

KT, kidney transplantation; Kidney transplant

HLA, human leukocyte antigen; Human leukocyte antigen

eGFR, estimated glomerular filtration rate; Glomerular filtration rate

PCR, protein-to-creatinine ratio; Creatine ratio in protein

ATN, acute tubular necrosis; Acute coronary necrosis

GN, glomerulonephritis; Glomerulonephritis

CNI, calcineurin inhibitor; Calcineurin inhibitor

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

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

Thereafter, the quantification (260nm absorbance) and purity (260nm and 280nm absorbance ratio) of RNA were measured using a NanoDrop® ND-2000 UV spectrophotometer (Thermo Scientific).

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

Total RNA extracted from the 315 urine samples was divided into 98 candidate groups (training set) and 217 validation groups (validation sets), and was used as a sample for deriving and verifying acute rejection markers.

Example 2: Discovery of acute rejection (AR) marker gene

Gene expression profiles of 442 stable patients and 212 acute rejection patients were collected from the public data set of the Gene Expression Omnibus database, and meta-analysis was performed on them to derive 6 candidate genes for AR markers.

In addition, by analyzing the results of known studies, three additional candidate genes for AR markers were derived.

Real-time PCR was performed on the 9 derived candidate genes, and AR marker genes were finally selected.

Example 2-1: Meta-analysis

Meta-analysis was performed through the GeneMeta method (Choi et al, Bioinformatics, 2003, i84-i90). Specifically, it was calculated using the GeneMeta R package, and permutation was performed 1000 times. Meanwhile, the GeneMeta method is a method for calculating effect size and statistical importance by integrating data of multiple gene transcripts (mRNA), and the effect size is calculated as an overall average through a random effect model. And, the statistical importance is a method obtained from a permutation test over a large number of data. In the present invention, rather than a fixed-effect model that finds the strongest signal in a candidate group (training set), a random-effect model that finds a signal in the entire population was used, and the effect in the transcriptome analysis is effective. The GeneMeta method was used because the permutation test method, which is known to be excellent, was used.

First, in order to collect data on urine transcripts related to kidney transplantation through meta-analysis, we searched a data set related to kidney transplant rejection from the gene expression ombinus, and redundancy among them. Four data sets were selected in consideration of and representativeness, from which a total of 654 gene expression profiles were collected, including 442 stable graft function (STA) and 212 acute rejection patients (AR). The collected gene expression profile was measured by the common platform Affymetrics Gene Chip.

Among the 654 urine transcripts, in order to select an important gene (transcript), the meta-analysis expression level change (Fold Change) was calculated as a weighted sum of the expression level change in each data set, and the weight ( weight) was set to be proportional to the sample size of each data set. Specifically, the meta-analysis FDR (False Discovery Rate) was calculated by GeneMeta's permutation test, and the selection criteria were set to FDR<0.001 and FC>2.

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

Meanwhile, three candidate genes for AR markers (CD3ε, Foxp3, and Tim-3) were additionally derived by analyzing the results of existing studies related to marker genes whose expression level increases during acute rejection after kidney transplantation (Reference: Huraib. , S, Goldberg, et al., Am J Kidney Dis, 14: 13-17, 1989; Beckingham, IJ, et al., Br J Urol, 73: 13-15, 1994; Furness, PN, et al., Kidney Int, 60: 1998-2012, 2001; Li, B, Hartono, et al., N Engl J Med, 344: 947-954, 2001; Ding, R, Li, et al., Transplantation, 75: 1307- 1312, 2003; Tatapudi, RR, et al., Kidney Int, 65: 2390-2397, 2004; Muthukumar, T, et al., N Engl J Med, 353: 2342-2351, 2005; Renesto, PG, et al ., Am J Transplant, 7: 1661-1665, 2007; Suthanthiran, M, et al., N Engl J Med, 369: 20-31, 2013; Seo, JW, et al., PLoS One, 12: e0180045, 2017; Solez, K, et al., Am J Transplant, 8: 753-760, 2008; Odgers, DJ, et al., AMIA Joint Summits on Translational Science proceedings AMIA Joint Summits on Translational Science, 2016: 176-183, 2016; Youden, WJ, Cancer, 3: 32-35, 1950; Robin, X, et al., BMC bioinformatics, 12: 77, 2011; Vickers, AJ, et al., Medical decision making : an international journal of the Society for 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 levels of the 9 types of AR marker candidate genes derived in Example 2-1 were confirmed in the 98 candidate group RNAs classified in Example 1, and the AR marker genes were finally selected. At this time, the 98 candidate groups included 46 stable graft function (STA) samples including long-term graft survival (LGS) samples, and 35 acute T cell-mediated rejection reactions (acute Cellular-mediated rejection (ACR) samples and 17 Acute Antibody-Mediated Rejection (AMR) samples were included.

First, 10 μl of each candidate RNA was used as a template, and complementary DNA (cDNA) was synthesized using the 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 (quality control) of the sample. In addition, a reaction product containing 1 µl of reverse transcription product, 1x TaqMan Universal PCR mastermix, no AmpErase UNG, and 1 µl primer mix was used during the reaction.

Subsequently, the ABI StepOnePlus real-time PCR system (Applied Biosystems) was used to amplify each of the synthesized cDNAs, and for this, 40 reactions were performed at 95°C for 10 minutes, 95°C for 15 seconds, and 60°C for 60 seconds. I did. The expression level of each transcript was calculated using the comparative threshold cycle (CT) method, which is a method of correcting the Ct (threshold cycle) value of the integrated human standard RNA (universal human reference RNA) and 18S rRNA (Fig. 2). At this time, the sequences or types of primers and probes used in the analysis are shown in Tables 3 and 4 below.

Type of transcript 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

Type of transcript order CD3ε Sense: 5'-AAGAAATGGGTGGTATTACACAGACA-3' (SEQ ID NO: 1)
Reverse: 5'-TGCCATAGTATTTCAGATCCAGGAT-3' (SEQ ID NO: 2)
Probe: 5'-FAM-CCATCTCTGGAACCACAGTAATATTGACATGCC-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-TACAATGAAAAAGAAGGGTGAGAA-MGB-3' (SEQ ID NO: 6)

2 shows the results of analyzing the expression levels of CD3εe, C1QB, CXCL9, Foxp3, IDO1, IP-10, PSMB9, PTPRC and Tim-3 transcripts for RNA extracted from urine samples of a candidate group (training set). As shown in Fig. 2, compared to the STA sample, the expression levels of the seven transcripts C1QB, CD3ε, CXCL9, Foxp3, IP-10, Tim-3 and PSMB9 were statistically significantly increased in the AR sample. , It was confirmed that the expression levels of the transcripts of IDO1 and PTPRC did not show much difference between the STA and AR samples.In addition, the expression level of the Foxp3 transcript was statistically significantly increased, but it was constant in most samples. Since it did not show the level of expression, it was analyzed that it is not suitable for the AR diagnosis prediction model.

Accordingly, it was found that 6 types of transcripts C1QB, CD3ε, CXCL9, IP-10, Tim-3, and PSMB9 can be used for AR diagnosis in kidney transplant patients.

Example 2-3: in situ Verification through hybridization analysis

From the results of Example 2-2, it was confirmed that 6 types of transcripts C1QB, CD3ε, CXCL9, IP-10, Tim-3, and PSMB9 can be used for AR diagnosis in kidney transplant patients, in fact, the transcripts We tried to verify whether the expression level of is increased specifically for AR patients through in situ hybridization analysis using CXCL9 transcripts. At this time, the in situ hybridization analysis was performed using an RNAscope 2.5 HD BROWN assay kit (Advanced Cell Diagnostics, Hayward, California).

Roughly, the glomerulus and interstitium of the kidney biopsy sample of the normal person (NP), the kidney biopsy sample of the ACR patient, and the kidney biopsy sample of the AMR patient are fixed with formalin, and a known microtechnical method is used. Thus, each tissue flake was produced. CXCL9 oligonucleotide probe was added to each of the tissue slices, hybridized at 40° C. for 2 hours, treated with a preamplifier, 30 minutes at 40° C., and treated with a signal enhancer, followed by 15 minutes at 40° C. , After treatment with an amplifier, 30 minutes at 40° C. and a label probe, followed by sequential reaction at 40° C. for 15 minutes. Thereafter, a signal amplifier was treated at room temperature, and the HRP binding labeling process was performed for 30 minutes and 15 minutes. Finally, staining with DAB (3,3-diaminobenzidine) and counter-staining with hematoxylin, the hybridization signal was confirmed with a fluorescence microscope (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 micrograph showing the results of detecting the expression level of CXCL9 transcript in the glomerular region and interstitial region of a kidney biopsy sample from an STA patient, a kidney biopsy sample from an ACR patient, and an AMR patient.

As shown in FIG. 3, it was confirmed that the CXCL9 transcript was not expressed in the kidney tissues of normal humans, but was expressed in the kidney tissues of ACR patients and AMR patients.

Therefore, it was found that the 6 types of transcripts identified in Example 2-2 can be used as a marker for AR diagnosis in kidney transplant patients.

Example 3: Development of a predictive model for acute rejection reaction

Various statistical analysis methods were performed using all or part of the six types of transcripts discovered in Example 2-2, to develop various AR prediction models and to confirm the effectiveness of these models.

Example 3-1: Prediction model using two transcripts

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

[Equation 1]

In the above formula,

CXCL9 represents the mRNA level of the CXCL9 gene,

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

Example 3-2: Prediction 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 discovered in Example 2-2:

[Equation 2]

In the above 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: Prediction model using 6 transcripts

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

[Equation 3]

In the above formula,

C1QB represents the mRNA level of the C1QB gene,

CD3ε represents the mRNA level of the CD3ε 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 predictive model

The ROC curves represented by the three predictive models developed in Examples 3-1 to 3-3 were analyzed (FIG. 4).

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

Cutoff point, sensitivity, specificity, and AUC confirmed by analyzing the ROC curve of FIG. 4 are shown in Table 5.

ROC curve analysis result of AR prediction model Model Cutoff point Semsitivity
(%) Specificity
(%) AUC (95% CI) 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, when the value obtained through the model indicates a value higher than the cutoff point, it can be predicted that AR will occur, and if the value obtained through the model indicates a value lower than the cutoff point, it is assumed that AR will not develop. It is predictable.

Example 4: Checking the effect of an AR prediction model using a validation set

Using the 217 validation set RNAs classified in Example 1, the expression level of 6 AR marker genes (C1QB, CXCL9, IDO1, IP-10, PSMB9 and PTPRC) was measured, and the measured expression The level was applied to the AR prediction model developed in Example 3 to check whether the AR prediction model was usable.

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

Example 4-1: RNA expression level verification

The expression levels of 6 types of transcripts were compared by performing the same method as in Example 2-2, except that 217 validation group RNAs were used instead of candidate RNAs (FIG. 5).

5 is a graph showing the results of analyzing the expression levels of C1QB, CD3ε, CXCL9, IP-10, Tim-3, and PSMB9 transcripts for the validation group RNA.

As shown in FIG. 5, it was confirmed that all of the six transcripts showed higher expression levels in the AR and BKVN samples than in the STA sample (NP/LGS), and relatively lower expression levels in the OGI sample than in the AR sample.

In addition, it was confirmed that C1QB, PSMB9 and Tim-3 transcripts showed higher expression levels in OGI samples than in STA samples (NP/LGS), but CD3ε, CXCL9 and IP-10 transcripts were STA samples (NP/LGS). It was confirmed that there was no significant difference with and, specifically, it was confirmed that the IP-10 transcript exhibited the highest expression level in BKVN.

Through the above results, the six transcripts of C1QB, CD3, CXCL9, IP-10, Tim-3 and PSMB9 can be usefully used for AR diagnosis in kidney transplant patients, and additionally CD3ε, CXCL9 and IP-10 transcripts It was found that can be used to differentiate between AR and other disease groups.

Example 4-2: Validation of the effect of the AR prediction model

In the validation group RNA, the expression levels of C1QB, CD3ε, CXCL9, IP-10, Tim-3 and PSMB9 transcripts were measured, and the measured expression levels were applied to the model developed in Example 3, and then each model indicated The ROC curve was analyzed (Fig. 6).

6 is a graph showing the results of analyzing the expression levels of 6 types of transcripts in the verification group using the 3 types of AR prediction models provided by the present invention.

As shown in FIG. 6, it was confirmed that in all three models, the STA sample exhibited a value lower than the Cutoff point, while the AR sample exhibited a higher value than the Cutoff point.

Example 4-3: Decision curve analysis

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

7 is a graph showing the results of performing decision curve analysis for three predictive models.

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

Through the above results, 6 types of urine transcripts according to the present invention and 3 types of AR prediction models using the same can diagnose acute rejection reactions of kidney transplant patients with high reliability, sensitivity, and specificity. It was found that it can be used as a diagnostic tool for conducting a biopsy.

From the above description, those skilled in the art to which the present invention pertains will understand that the present invention can be implemented in other specific forms without changing the technical spirit or essential features thereof. In this regard, the embodiments described above are illustrative in all respects and should be understood as non-limiting. The scope of the present invention should be construed that all changes or modifications derived from the meaning and scope of the claims to be described later rather than the above detailed description and equivalent concepts are included in the scope of the present invention.

<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-D1 <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 (14)

A composition for diagnosing acute rejection of kidney transplant, comprising an agent for measuring the expression level of mRNA of a gene composed of a combination of CXCL9 and Tim-3 from urine samples of a suspected kidney transplant.
The composition of claim 1, wherein the composition further comprises an agent that measures the expression level of the mRNA of the PSMB9 gene.
A method for providing information necessary for diagnosing an acute rejection of a kidney transplant, comprising measuring the expression level of mRNA of a gene consisting of a combination of CXCL9 and Tim-3 from a urine sample of a suspected kidney transplant.
The method of claim 3, wherein measuring the expression level of the mRNA of the gene further comprises measuring the expression level of the mRNA of the PSMB9 gene.
The method of claim 3, wherein the method further comprises determining a kidney transplantation acute rejection when the measured mRNA level of the gene is higher than that of a normal control sample.
The method of claim 3, wherein the method further comprises associating a result obtained by measuring the mRNA level of the gene with a diagnosis of acute rejection of a kidney transplant.
The method of claim 6, wherein the step of associating is performed by combining the results obtained by measuring the level of mRNA of the gene.
The method of claim 7, wherein the combination of results is a linear or nonlinear regression method; Linear or nonlinear classification analysis methods; ANOVA method; Neural network analysis method; Genetic analysis method; Support vector machine analysis method; Hierarchical analysis or clustering analysis method; Hierarchical algorithm using decision tree, or Kernel principal components analysis method; Markov Blanket analysis method; RFE (recursive feature elimination) analysis method; Front or rear FS (floating search) analysis method; And an analysis method selected from the group consisting of combinations thereof.
The method of claim 7, wherein the combination of results is obtained by combining the mRNA levels of the CXCL9 gene and the Tim-3 gene by a linear or nonlinear regression analysis method, and is represented in the form of a regression model of [Equation 1]. Way:
[Equation 1]

In the above formula,
CXCL9 represents the mRNA level of the CXCL9 gene,
Tim3 represents the mRNA level of the Tim-3 gene.
The method of claim 9,
The cutoff value of the regression model in [Equation 1] is 0.476, and when the result obtained by applying the mRNA levels of the CXCL9 gene and the Tim-3 gene to Equation 1 is higher than 0.476, an acute rejection reaction after kidney transplantation The method can be predicted to develop, and if the result is lower than 0.476, a method for predicting that an acute rejection reaction will not occur after kidney transplantation.
The method of claim 7,
The combination of the above results is expressed in the form of a regression model of [Equation 2] obtained by combining mRNA levels of CXCL9 gene, Tim-3 gene, and PSMB9 gene by a linear or nonlinear regression method:
[Equation 2]

In the above formula,
CXCL9 represents the mRNA level of the CXCL9 gene,
Tim3 represents the mRNA level of the Tim-3 gene,
PSMB9 represents the mRNA level of the PSMB9 gene.
The method of claim 11,
The cutoff value of the regression model of [Equation 2] is 0.457. If the result obtained by applying the mRNA levels of the CXCL9 gene, Tim-3 gene and PSMB9 gene to Equation 2 is higher than 0.457, after the kidney transplantation A method for predicting that an acute rejection reaction will occur, and when the result is lower than 0.457, acute rejection reaction after kidney transplantation may be predicted not to develop.
A kit for diagnosing acute rejection of kidney transplant, comprising a quantitative device for mRNA of a gene composed of a combination of CXCL9 and Tim-3 from urine samples of a suspected kidney transplant.
The kit of claim 13, wherein the kit further comprises a quantitation device for mRNA of the PSMB9 gene.

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