CN116904586A - Application of reagent for detecting plasma-derived exosome lncRNA in preparation of diagnostic reagent for detecting kidney injury - Google Patents
Application of reagent for detecting plasma-derived exosome lncRNA in preparation of diagnostic reagent for detecting kidney injury Download PDFInfo
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- CN116904586A CN116904586A CN202311167229.XA CN202311167229A CN116904586A CN 116904586 A CN116904586 A CN 116904586A CN 202311167229 A CN202311167229 A CN 202311167229A CN 116904586 A CN116904586 A CN 116904586A
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Abstract
The invention discloses application of a reagent for detecting plasma-derived exosome lncRNA in preparation of a diagnostic agent for detecting kidney injury, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID being NONRATT 021116.2. The lncRNA derived from exosomes can be applied to preparation of kidney injury biomarkers, particularly can be used for detecting kidney injury caused by exogenous compounds (such as gentamicin sulfate), has high detection sensitivity and simple detection method and reagent, can be widely applied to detecting kidney injury, has reliable detection results, and can be used for detecting kidney injury singly or jointly evaluating kidney injury in combination with other indexes.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to application of a reagent for detecting plasma-derived exosome lncRNA in preparation of a diagnostic agent for detecting kidney injury.
Background
Drug-induced kidney injury is one of the major factors currently leading to drug development failure or withdrawal of commercially available drugs. Kidneys are susceptible to drug damage for the following reasons: 1) the kidneys receive 20-25% of resting cardiac output, which exposes them to more circulating drug than other organ systems, 2) the tubules concentrate the filtrate, thereby exposing them to higher concentrations of drug, 3) the transporter can further increase the intracellular concentration of drug, 4) the tubules have higher energy requirements, making them susceptible to nephrotoxic damage. Therefore, drug-induced kidney damage is common in drug development. Unfortunately, traditional serum markers of kidney injury, such as creatinine or urea nitrogen, are not highly sensitive or specific. For example, the measurement of creatinine is affected by non-creatinine substances, including glucose, uric acid, ketone bodies, hemoglobin, etc., which may lead to false positive increases in serum creatinine values. Serum creatinine may also vary due to non-renal factors independent of renal function, such as age, sex, muscle mass, nutritional status, and the like. Intake of creatine supplements or cooked meat may result in increased serum creatinine and protein-restricted diets may result in decreased serum creatinine. Strenuous exercise may increase creatinine by increasing muscle breakdown. In addition, serum creatinine is insensitive to loss of kidney reserves, and changes in serum creatinine are small after one kidney is lost or donated. BUN is also altered by non-renal factors such as protein intake, catabolic state, upper gastrointestinal bleeding, blood volume state, etc.
To address the problem of insufficient sensitivity and specificity of traditional biomarkers, the predictive safety test alliance (PSTC) organized drug-induced kidney injury biomarker studies, and after the PSTC nephrotoxicity working group submitted drug toxicity studies and biomarker performance analysis to the FDA and european medicines agency (EMEA), 7 kidney injury biomarkers have been defined for non-clinical and clinical drug development to help guide safety assessment. Renal injury molecule-1 (KIM-1), albumin, total protein, B2M, clusterin, TFF-3, and cystatin C are considered by the FDA and EMEA as highly sensitive and specific urinary biomarkers for monitoring drug-induced renal injury in preclinical studies and clinical trials. However, the above-mentioned kidney biomarkers are all obviously raised after the injury occurs to the viscera, so as to achieve the effect of diagnosing the injury, however, sometimes serious events may occur when the injury is heavy, and many injuries are irreversible, so that the exploration of new biomarkers with higher sensitivity and specificity and the early detection of kidney injury is particularly critical.
Exosomes are one type of extracellular vesicles, which are divided into three types: apoptotic bodies, microbubbles, and exosomes. The process of exosome production involves the dual invagination of the plasma membrane and the formation of intracellular multivesicular bodies (MVB) containing endoluminal vesicles (ILV). ILV is finally fused to plasma membrane by MVB and secreted in exosome form with a diameter of 40-160 nm by exocytosis. The density of exosomes is 1.15-1.19 g/mL. The tetrameric transmembrane proteins CD63, CD9 and CD81 found on their surfaces are often used as surface biomarkers for exosomes. Meanwhile, most cells secrete exosomes, which are widely present in various body fluids such as blood, saliva, urine, cerebrospinal fluid, etc. Exosomes transport various molecules from the parent cell to other cells, including proteins, DNA, mRNA/miRNA, lncRNA, and the like. The exosomes are similar in composition to the parent cell and thus are capable of providing specific information for the parent cell that can be tracked. Scanning Electron Microscopy (SEM), transmission electron microscopy, dynamic light scattering and nanoparticle tracking analysis are widely used to measure physical characteristics of exosomes, such as vesicle size, distribution and concentration.
Long non-coding RNAs (lncRNA) are a class of non-coding RNA molecules that lack an open reading frame that are more than 200 ribonucleotides in length, have no ability to code for proteins, or have limited coding functions. At the beginning, it was considered as "noise" of genome transcription, a by-product of RNA polymerase II transcription, with no biological effect. Through intensive studies, lncRNA has been found to be involved in important regulatory processes in cells, such as: regulate cell differentiation, aging, proliferation, apoptosis, necrosis, and tumor development. It was found that lncRNA expression is more cell specific than mRNA expression, although lncRNA is prone to expression at low levels compared to messenger RNA, suggesting that lncRNA may be a key regulator of cell fate. Meanwhile, studies show that lncRNA is closely related to kidney related diseases and injuries. However, the lncRNA database is very large (up to 172,216) and no lncRNA closely related to kidney injury, especially caused by exogenous compounds (e.g., gentamicin sulfate), has been developed for clinical detection.
Numerous studies have reported that, under pathological conditions, the expression levels of many exosomes lncRNA are significantly different from normal control groups, indicating that exosomes can selectively package, secrete and transport lncRNA and specifically exert biological functions. Exosomes can protect lncRNA from rnase degradation, so they can be stably present in body fluids.
Disclosure of Invention
The invention aims to overcome the defects that a traditional Chinese medicine physical kidney injury biomarker has low sensitivity, a detection method is too complicated, a reagent is expensive, kidney injury needs to be evaluated jointly by combining other indexes, and medicine physical kidney injury cannot be predicted timely and accurately, and the like, and provides application of long-chain non-coding RNA (lncRNA) from exosome in preparation of a diagnosis agent for detecting kidney injury, a kit containing the same, application of a reagent for detecting long-chain non-coding RNA (lncRNA) from exosome in preparation of a diagnosis agent for detecting kidney injury, and application of a kit or chip containing the reagent. The application can take the lncRNA as a kidney injury biomarker, particularly can be used for detecting kidney injury caused by exogenous compounds (such as gentamicin sulfate), has high detection sensitivity and simple detection method and reagent, can be widely applied to timely detecting the drug-induced kidney injury, can timely find the drug-induced kidney injury in early stage, has reliable detection result, and can be used for singly detecting or jointly evaluating the kidney injury in combination with other indexes.
The inventor conducts long-term research on kidney injury, and through a large number of experiments, the inventor unexpectedly discovers that lncRNA derived from exosomes can be used as a specific biomarker of drug-induced kidney injury caused by exogenous compounds (such as gentamicin sulfate), is highly expressed in plasma of drug-induced kidney injury caused by exogenous compounds, can exert the application of the lncRNA in preparing kidney injury biomarkers, and can detect kidney injury caused by exogenous compounds.
In order to solve the technical problems, the invention provides application of plasma-derived exosome lncRNA in preparation of a diagnostic agent for detecting kidney injury, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID of NONRATT 021116.2.
In certain embodiments, the lncRNA is used as the sole biomarker for kidney injury, or in combination with a conventional biomarker for kidney injury.
In certain embodiments, the biomarker of conventional kidney injury is selected from serum Creatinine (CREA), urea nitrogen (BUN), kidney injury molecule-1 (Kim-1).
In certain embodiments, the biomarker is characterized and/or quantified by detecting the transcriptional level of the lncRNA in a sample.
The detection of the transcription level according to the present invention may be RNA whole transcriptome sequencing, real-time fluorescent quantitative PCR detection or an expression profiling chip, as is conventional in the art.
In certain embodiments, the detecting uses RNA whole transcriptome sequencing; and/or, the sample is plasma.
In certain embodiments, the kidney injury is a kidney injury caused by an exogenous compound.
In certain embodiments, the exogenous compound is gentamicin sulfate.
In another aspect of the invention, a kit is provided, comprising reagents for detecting lncRNA; the lncRNA comprises lncRNA of which NONCODE TRANSCRIPT ID is NONRATT 021116.2.
In certain embodiments, the kit further comprises at least one of a microporous filter membrane, sodium chloride, and gentamicin sulfate.
In certain embodiments, the reagent is a reagent that detects the level of transcription of the lncRNA in a sample.
In another aspect of the invention, a chip is provided, the chip having disposed thereon a reagent comprising a reagent for detecting lncRNA; the lncRNA comprises lncRNA of which NONCODE TRANSCRIPT ID is NONRATT 021116.2.
In certain embodiments, the reagent is a reagent that detects the level of transcription of the lncRNA in a sample.
In a further aspect the invention provides the use of an agent for detecting the expression level of the lncRNA transcript level of NONCODE TRANSCRIPT ID to NONRATT021116.2, a kit according to the invention or a chip according to the invention for the manufacture of a product for detecting kidney damage.
In certain embodiments, the kidney injury is a kidney injury caused by an exogenous compound.
In certain embodiments, the exogenous compound is gentamicin sulfate.
In another aspect, the invention provides a method for detecting kidney damage for non-diagnostic purposes, the method comprising detecting the transcriptional level of lncRNA in a test sample, the lncRNA comprising lncRNA having NONCODE TRANSCRIPT ID of NONRATT 021116.2.
Another aspect of the present invention provides a prediction system for risk of kidney injury, the prediction system comprising a detection module and an analysis and judgment module; the detection module detects the transcription level of the lncRNA in the sample to be detected and transmits the transcription level data to the analysis and judgment module; the analysis judging module is used for constructing a prediction model by using the transcription level data obtained through PCR processing, respectively predicting the probability of sample kidney injury and the probability of non-kidney injury, judging whether the transcription level data accords with preset judging conditions or not so as to predict the risk of sample kidney injury, and outputting a prediction result; the judging condition is the probability of kidney injury and the probability of non-kidney injury;
outputting a prediction result of "having a risk of kidney injury" when the transcription level data satisfies the judgment condition; when the transcription level data does not meet the judging condition, namely the probability of kidney injury is smaller than that of non-kidney injury, outputting a prediction result as 'without kidney injury risk';
The marker comprises lncRNA of which NONCODE TRANSCRIPT ID is NONRATT 021116.2.
In certain embodiments, the kidney injury is a kidney injury caused by an exogenous compound.
In certain embodiments, the exogenous compound is gentamicin sulfate.
Another aspect of the invention provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the invention or performs the functions of the prediction system according to the invention.
Another aspect of the invention provides an electronic device comprising a memory storing a computer program for executing the computer program to perform the steps of the method according to the invention or to perform the functions of the prediction system according to the invention.
In another aspect, the invention provides an application of a reagent for detecting plasma-derived exosome lncRNA in preparing a diagnostic reagent for detecting kidney injury, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID of NONRATT021116.2, and the kidney injury is caused by gentamicin sulfate.
In certain embodiments, the lncRNA is used as the sole biomarker for kidney injury, or in combination with a conventional biomarker for kidney injury.
In certain embodiments, the biomarker of conventional kidney injury is selected from serum Creatinine (CREA), urea nitrogen (BUN), kidney injury molecule-1 (Kim-1).
In certain embodiments, the biomarker is characterized and/or quantified by detecting the transcriptional level of the lncRNA in a sample.
The detection of the transcription level according to the present invention may be RNA whole transcriptome sequencing, real-time fluorescent quantitative PCR detection or an expression profiling chip, as is conventional in the art.
In certain embodiments, the detecting uses RNA whole transcriptome sequencing; and/or, the sample is plasma.
In certain embodiments, the agent is a primer that specifically amplifies the exosome lncRNA, the nucleotide sequence of the primer is shown as SEQ ID No. 1 and SEQ ID No. 2.
In another aspect, the invention provides the use of a kit for the manufacture of a product for the detection of renal injury caused by gentamicin sulphate, the kit comprising a reagent for the detection of plasma derived exosomes lncRNA as described in the present invention.
In another aspect, the invention provides the use of a chip for the manufacture of a product for detecting kidney damage caused by gentamicin sulphate, the chip having disposed thereon an agent for detecting plasma derived exosomes lncRNA as described herein before.
Another aspect of the present invention provides a prediction system for risk of kidney injury, the prediction system comprising a detection module and an analysis and judgment module; the detection module detects the transcription level of the lncRNA in the sample to be detected and transmits the transcription level data to the analysis and judgment module; the analysis judging module is used for constructing a prediction model by using the transcription level data obtained through PCR processing, respectively predicting the probability of sample kidney injury and the probability of non-kidney injury, judging whether the transcription level data accords with preset judging conditions or not so as to predict the risk of sample kidney injury, and outputting a prediction result; the judging condition is the probability of kidney injury and the probability of non-kidney injury;
outputting a prediction result of "having a risk of kidney injury" when the transcription level data satisfies the judgment condition; when the transcription level data does not meet the judging condition, namely the probability of kidney injury is smaller than that of non-kidney injury, outputting a prediction result as 'without kidney injury risk';
The lncRNA comprises lncRNA of which NONCODE TRANSCRIPT ID is NONRATT 021116.2.
Another aspect of the invention provides a computer readable storage medium storing a computer program, which when executed by a processor, performs the functions of the prediction system according to the invention,
or a step of realizing a method for detecting kidney injury, the method comprising detecting the transcription level of lncRNA in a sample to be detected, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID being NONRATT021116.2, and the kidney injury is kidney injury caused by gentamicin sulfate.
Another aspect of the invention provides an electronic device comprising a memory storing a computer program and a processor for executing the computer program to perform the functions of the prediction system according to the invention,
or a step of realizing a method for detecting kidney injury, the method comprising detecting the transcription level of lncRNA in a sample to be detected, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID being NONRATT021116.2, and the kidney injury is kidney injury caused by gentamicin sulfate.
In another aspect, the invention provides a primer for specifically amplifying lncRNA, wherein NONCODE TRANSCRIPT ID of the lncRNA is NONRATT021116.2, and the nucleotide sequence of the primer is shown as SEQ ID NO. 1 and SEQ ID NO. 2. In certain embodiments, the lncRNA is an exosome-derived lncRNA.
On the basis of conforming to the common knowledge in the field, the conditions can be arbitrarily combined to obtain each preferred embodiment of the invention.
The reagents and materials used in the present invention are commercially available.
The invention has the positive progress effects that:
the invention discloses a method for preparing a kidney injury biomarker by applying lncRNA from exosome, which is particularly used for detecting kidney injury caused by exogenous compounds (such as gentamicin sulfate), has high detection sensitivity and simple detection method and reagent, can be widely applied to detecting kidney injury, has reliable detection result, and can be used for detecting kidney injury singly or jointly evaluating kidney injury in combination with other indexes.
Drawings
FIG. 1 is a ROC graph of lncRNA.
FIG. 2 is a graph showing changes in serum creatinine, urea nitrogen, kim-1 activity in gentamicin sulfate dosed rats in the first rat kidney injury model, wherein ﹡ represents P <0.05 compared to vehicle group.
Fig. 3 is a schematic diagram of a system for predicting risk of kidney injury.
Fig. 4 is a schematic structural diagram of an electronic device.
FIG. 5 is a graph showing changes in serum creatinine, urea nitrogen, kim-1 activity in gentamicin sulfate dosed rats in a second rat kidney injury model, wherein ﹡ represents P <0.05 compared to vehicle group.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention. The experimental methods, in which specific conditions are not noted in the following examples, were selected according to conventional methods and conditions, or according to the commercial specifications.
Example 1 search for renal injury biomarkers
1.1 Establishment of kidney injury model
1.1.1 Experimental reagent, instrument and experimental animal
(1) Positive drug: gentamicin sulfate (lot number: WXBD5416V, purchased from Sigma-Aldrich);
(2) Vehicle control: 0.9% sodium chloride solution (lot number 21111304B, available from Anhui Shuanghe pharmaceutical Co., ltd.);
CREA and BUN detection kit manufactured by Nippon and light pure medicine industry Co., ltd
Kim-1 detection kits are manufactured by r & d systems;
the HITACHI 7060 full-automatic biochemical analyzer was purchased from japan HITACHI industries, ltd.
(3) Experimental animal
10 male SD rats, 10 female rats, SPF grade, weight 180-320 g, 6-15 weeks old, purchased from Beijing Vetong Lihua laboratory animal technology Co., ltd. [ license number: SCXK (Beijing) 2021-0011]. The animals are marked by a chip and a cage plate, 5 animals are fed in SPF-grade animal houses of Shanghai Yinuo biotechnology Co Ltd, the temperature of the animal houses is controlled at 22-26 ℃, the humidity is controlled between 40-70%, the ventilation times per minute are more than or equal to 15 times, the bright and dark illumination period is 12 hours/12 hours, the animals eat freely, SPF rats subjected to cobalt 60 irradiation sterilization maintain feed provided by Australian feed Co Ltd in Beijing, and the animals drink self-made deionized water freely through drinking water bottles.
1.1.2 Experimental method
1.1.2.1 Preparation of positive medicine
Preparing gentamicin sulfate solution: 480mg of gentamicin sulfate was weighed, transferred to a beaker and added with an appropriate amount of 0.9% sodium chloride injection. Stirring with glass rod, adding rotor, turning on magnetic stirrer, dispersing thoroughly, turning off, adding 0.9% sodium chloride injection to required volume (12 mL), mixing to ensure uniformity, and filtering with microporous membrane of 0.22 μm to ensure sterility. And (3) placing the mixture at the temperature of 2-8 ℃ in a dark place for standby. The preparation process needs to be protected from light.
1.1.2.2 Animal test dose setting
Grouping with random granule design, 20 rats were randomly assigned to 2 groups according to body weight: the vehicle control group (0.9% sodium chloride solution) and the administration group (80 mg/kg gentamicin sulfate) are shown in Table 1.
Table 1 experimental dose design
1.1.2.3 Administration and visual inspection
The administration routes of the vehicle control group and the gentamicin sulfate administration group are oral gastric lavage administration, and the single administration has the administration capacity of 10 mL/kg. The dosing volume was calculated from the last measured body weight.
14 days after administration, anesthesia is carried out by adopting a 40mg/mL sultai+5 mg/mL ranolazine injection mixed preparation, and a small part of the whole blood collected by the abdominal aorta is placed in a separation gel vacuum blood collection tube for serum separation: 3500 rpm,4 ℃,5 min, sucking serum, split charging into an EP tube, and storing in a refrigerator at-80 ℃ for detecting kidney function indexes. Most of them are placed in EDTA-K 2 In a vacuum blood collection tube for separating plasma: 800g,4 ℃ for 10min, sucking the upper plasma layer, sub-packaging in an EP tube, and storing in a refrigerator at-80 ℃ for performing the whole transcriptome sequencing of the exosome RNA.
1.1.2.4 Serum biochemical detection result
The experiment mainly detects the changes of blood indexes such as serum Creatinine (CREA), urea nitrogen (BUN) and kidney injury molecule-1 (Kim-1) commonly used in preclinical and clinical kidney injury evaluation. As the results in fig. 2 show, serum Creatinine (CREA), urea nitrogen (BUN), and kidney injury molecule-1 (Kim-1) were significantly elevated in serum of rats in the dosing group compared to the vehicle control group (P < 0.05) 14 days after the administration of 80 mg/kg gentamicin sulfate.
1.1.2.5 Histopathological examination results
The kidney histopathological examination shows that the rats in the solvent control group have no obvious abnormality, and the male and female rats in the administration group have focal inflammatory cell infiltration and different degrees of basophilic tubules and tubular tubules.
1.1.2.6 Conclusion(s)
In this part of the experiment, 80 mg/kg gentamicin sulfate and 0.9% sodium chloride were administered by intramuscular injection to SD rats, and serum biochemical detection and histopathological examination were performed by taking blood after 14 days to examine the condition of gentamicin sulfate-induced kidney injury.
In the serum biochemical test, CREA, BUN, kim-1 was significantly elevated in animals of the APAP group of 80 mg/kg 14 days after administration. Pathological histological examination mainly revealed kidney cell necrosis to varying degrees with inflammatory cell infiltration and basophilic tubule lesions.
In conclusion, the SD rat successfully induces the kidney injury of the rat after intramuscular injection of gentamicin sulfate, so that a model of the kidney injury induced by the gentamicin sulfate is established, and the gentamicin sulfate can be used for screening and verifying kidney injury biomarkers.
1.2 Exosome RNA complete transcriptome sequencing
1.2.1 Exosome extraction
Exosomes in the samples were isolated using exoRNeasy Serum/Plasma Maxi kit (Qiagen) and operated according to standard protocols provided by the manufacturer.
The method comprises the following steps:
1) Taking out 500uL plasma sample at-80deg.C, thawing in water bath at 25deg.C;
2) 13000g, centrifugation at 4℃for 10 min
3) Adding Buffer XBP according to a sample volume of 1:1, and reversing the above steps for 5 times;
4) Transferring the sample and Buffer XBP mixed solution to exoEasy spin column, and centrifuging at 500g and 4 ℃ for 1min; discarding the waste liquid at the bottom;
5) Add 3.5m Buffer XWP to exoEasy spin column, centrifuge 5000g for 5 min at 4deg.C; discarding the waste liquid at the bottom;
6) Transferring spin column into a new collection tube;
7) Adding 200uL Buffer XE,5000g 4 ℃ for centrifugation for 5 minutes, and collecting the bottom exosomes into a 1.5mL centrifuge tube;
8) Split charging exosomes, and preserving at-80deg.C.
1.2.2 Identification of exocrine related indicators
1.2.2.1 Identification of exosome markers WB
Taking a certain amount of PBS heavy suspension of exosomes, adding an equal volume of RIPA (strong) lysate for carrying out lysis to extract protein, and then carrying out protein concentration measurement and WB detection of exosome markers.
1.2.2.2 Exosome nanoparticle tracking detection (NTA)
1) Taking a frozen sample, thawing in a water bath at 25 ℃, and placing on ice;
2) Exosome samples were taken and diluted with 1 XPBS and used directly for NTA detection.
3) Instrument information for testing
Instrument name: nanometer particle size particle tracking analyzer
Production company: PARTICLE METRIX
Instrument model: zetaVIEW S/N17-310
Analysis software version: zetaView 8.04.02
1.2.3 RNA quality identification
The initial sample of the sequencing experiment is total RNA, the total RNA of the exosomes is obtained from a QIAGEN exoRNeasy Midi Kit (Cat No./ID: 77144) kit, the quality inspection is carried out by the Agilent 4200 tape station, and the RNA which is qualified in the quality inspection can be subjected to subsequent full transcriptome sequencing.
1.2.4 Library construction and quality inspection
And (3) separating the exosomes, extracting the obtained total RNA, constructing a library by using a super-sensitive trace sample chain specific kit aiming at the exosome RNA, and detecting the concentration of the constructed library by using a Qubit 2.0 Fluorometer and detecting the library size by using Agilent 2100.
1.2.5 Sequencing on machine
Qualified libraries were tested and were prepared for Illumina sequencing with a sequencing strategy of PE150. The sequencing rationale is sequencing-by-synthesis (SBS, sequencing by Synthesis): and (3) loading the flow cell with the cluster, adding four fluorescence-labeled dNTPs, DNA polymerase and a joint primer into the flow cell for amplification, and when each sequencing cluster extends a complementary strand, releasing corresponding fluorescence by adding one fluorescence-labeled dNTP, wherein a sequencer captures a fluorescence signal and converts the optical signal into a sequencing peak through computer software, so that the sequence information of the fragment to be detected is obtained.
1.2.6 Data quality control
After obtaining the sequencing Raw Data (Raw Data), the Data is filtered, the adaptor sequence is removed, the low-quality reads are processed, the sequencing quality is evaluated, and the high-quality Data (Clean Data) is obtained through repeated inspection. Comparing the clear Data with a reference genome, and quantitatively analyzing the known mRNA and the lncRNA; reads on the unalignment analyzed circular RNAs using ACFS.
1.2.7 Data preprocessing
The original sequencing data contains sequencing linker sequences, and some reads contain unqualified conditions such as lower-quality bases, lower-quality ends of reads and the like, which can influence the reliability of subsequent analysis results, so that the original data is preprocessed to remove the linker sequences and the low-quality reads. The data preprocessing software uses Seqtk.
The method mainly comprises the following steps:
1) Removing the linker sequence contained in reads;
2) Removing bases with 3' end mass Q below 20, i.e. base error rate below 0.01, wherein q= -10log (error_ratio);
3) Removing reads with a length less than 25;
4) Ribosome RNA reads of the belonging species is removed.
And preprocessing the original data to obtain clear reads for subsequent analysis.
1.2.8 Comparison analysis
The pretreated sequencing sequences were subjected to genome mapping analysis using HISAT2 software. Typically we aligned clean no RNA reads to the genome, which is also the basis for subsequent analysis. HISAT2 adopts a global and local search method, can perform mapping efficiently, can effectively compare the speed reads in the RNA Seq sequencing data, sets default parameters for parameters, and refers to genome version Rnor_6.0.95, and the result is a BAM file.
1.2.9 lncRNA quantification and differential analysis
1.2.9.1 Novel lncRNA prediction
Comparing annotation information obtained by mapping with reference annotation (NONCODE and Ensembl databases) by using cuffcompact in cufflinks (version: 2.1.1) to obtain new transcripts which cannot be matched with known annotation genes, extracting { i, u, x } transcripts to perform lncRNA prediction, wherein the method comprises the following specific steps:
1) The transcription length is more than 200bp and exon > 2;
2) Covering fragment counts > 3;
3) Predicted ORF < 300;
4) PhyoCSF (currently limited to human, rat, and Pfam, CPC, CNCI predictions), intersection of 4 (or 3) predictions, selects transcripts with PhyoCSF score <0& Pfam alignment insignificant & CPC score <0& CNCI score <0 as potential lncRNAs.
5) The same sequence as the knownlncrna was removed as compared to the known lncRNA.
1.2.9.2 lncRNA expression quantification
The predicted novel lncRNA and NONCODE databases (version: NONCODE 2016; http:// www.noncode.org /) as well as the known lncRNA in the Ensembl database were quantitated using Stringtie (version: 1.3.0). Wherein MSTRG has a head ID of NOVEL lncRNA, NON has a head ID of lncRNA known in the database, and ENS has a head ID of lncRNA known in the Ensembl database.
1.2.9.3 lncRNA differential expression analysis
Edge was used for sample-to-sample differential lncRNA analysis. And volcanic and Heatmap plots were drawn for the differential lncRNAs.
1.2.9.4 lncRNA target gene prediction and target gene enrichment analysis
The interaction relationship between lncRNA and mRNA is classified into cis (cis) and trans (trans), and the mRNA that has an interaction relationship with lncRNA is called a target gene of lncRNA.
High throughput sequencing gives a relatively large number of differential genes, ranging from hundreds to thousands of possible. To better understand the biological functions that these differential genes may perform in cells or the signal pathways that may be perturbed, we annotated and enriched the Gene on log and KEGG databases for differential genes.
1.3 Whole transcriptome sequencing results
1.3.1 Summary of the invention
Statistical significance of the differences in the samples drawn by cluster analysis (P<0.05 Log) and log 2 FC (Fold changes) values>More than 2 times and at least one set of count minima>10 lncRNA. The results show (fold change in part lncRNA is listed in table 2): log of the dosing group compared to the vehicle control group 2 FC (abs, absolute value) varies by more than 3 times and p<15 of 0.05 were upregulated lncRNAs;5 times more varied and p <5 of 0.05 were upregulated lncRNAs. Suggesting that these differentially expressed lncrnas may be closely related to the occurrence, progression, and molecular regulation of drug-induced kidney injury. Wherein the expression level of lncRNA (NONCODE TRANSCRIPT ID: NONRATT 021116.2) in the plasma exosomes of the rats in the administration group is 64.92 times that in the vehicle control group.
TABLE 2 fold change in part lncRNA
1.3.2 GO analysis results
The differentially expressed genes were analyzed for GO using Fisher's exact test. Fisher's exact test calculation to obtain p-value, and multiple hypothesis test correction to obtain q-value. GO entries with q-value less than 0.05 were screened as significantly enriched GO entries. GO functional annotation analysis found that target gene mRNAs of differentially expressed lncRNAs focused mainly on biological processes: a steady state of fatty acids; molecular function: tRNA methyltransferase Activity, SNAP receptor Activity.
1.3.3 KEGG analysis results
KEGG analysis was further performed on differentially expressed lncRNAs predictive of target gene mRNAs. Similar to GO classification statistics, the number of differentially expressed genes on each path major class of KEGG was counted. KEGG analysis found that differentially expressed lncRNAs predicted target gene mRNAs focused mainly on the body system: the immune system, the endocrine system; metabolic pathways: an integral metabolic pathway; cell passage: signal transduction, transport and dissociation. It is suggested that differentially expressed lncRNAs may be involved in the regulation of these pathways by modulating predicted target gene mRNAs.
1.4 Secondary establishment of rat Kidney injury model
10 male SD rats and 10 female rats were purchased from Beijing Vetong Lihua laboratory animal technology Co., ltd. [ license number: SCXK (Beijing) 2021-0011], the construction of a secondary rat kidney injury model was performed according to the method of step 1.1 described above.
1.4.1 administration and visual inspection
The administration routes of the vehicle control group and the gentamicin sulfate administration group are oral gastric lavage administration, and the single administration has the administration capacity of 10 mL/kg. The dosing volume was calculated from the last measured body weight.
14 days after administration, anesthesia is carried out by adopting a 40mg/mL sultai+5 mg/mL ranolazine injection mixed preparation, and a small part of the whole blood collected by the abdominal aorta is placed in a separation gel vacuum blood collection tube for serum separation: 3500 rpm,4 ℃,5 min, sucking serum, split charging into an EP tube, and storing in a refrigerator at-80 ℃ for detecting kidney function indexes. Most of them are placed in EDTA-K 2 In a vacuum blood collection tube for separating plasma: 800g,4 ℃ for 10min, sucking the upper plasma layer, sub-packaging in an EP tube, and storing in a refrigerator at-80 ℃ for performing the whole transcriptome sequencing of the exosome RNA.
1.4.2 Serum biochemical detection result
The experiment mainly detects the changes of blood indexes such as serum Creatinine (CREA), urea nitrogen (BUN) and kidney injury molecule-1 (Kim-1) commonly used in preclinical and clinical kidney injury evaluation. As shown in the results of fig. 5, serum Creatinine (CREA), urea nitrogen (BUN), and kidney injury molecule-1 (Kim-1) were significantly elevated in serum of rats in the dosing group compared to the vehicle control group (P < 0.05) 14 days after the administration of 80 mg/kg gentamicin sulfate.
1.4.3 Histopathological examination results
The kidney histopathological examination shows that the rats in the solvent control group have no obvious abnormality, and the male and female rats in the administration group have focal inflammatory cell infiltration and different degrees of basophilic tubules and tubular tubules.
1.4.4 Conclusion(s)
In this part of the experiment, 80 mg/kg gentamicin sulfate and 0.9% sodium chloride were administered by intramuscular injection to SD rats, and serum biochemical detection and histopathological examination were performed by taking blood after 14 days to examine the condition of gentamicin sulfate-induced kidney injury.
In the serum biochemical test, CREA, BUN, kim-1 of 80 mg/kg gentamicin sulfate group animals was significantly elevated 14 days after administration. Pathological histological examination mainly revealed kidney cell necrosis to varying degrees with inflammatory cell infiltration and basophilic tubule lesions.
In conclusion, the rat kidney injury is successfully induced after the gentamicin sulfate is given by intramuscular injection of SD rats, so that a model of kidney injury induced by the gentamicin sulfate is established, and the gentamicin sulfate can be used for verifying kidney injury biomarkers.
1.5 Real-time quantitative PCR verification
The total exosome RNA was obtained using a QIAGEN exoRNeasy Midi Kit (Cat No./ID: 77144) kit using the sample as the plasma collected after the second molding in step 1.4 described above.
1.5.1 Reagent(s)
Reverse transcription reagent: TOYOBO ReverTra Ace qPCR RT Kit
Quantitative PCR reagent ABI Power SYBR Green PCR Master Mix
Primer synthesis: shanghai Bioengineering Co Ltd
1.5.2 Instrument:
ABI 7500 real-time fluorescence quantitative PCR system of quantitative PCR instrument
1.5.3 Experimental procedure
1.5.3.1 First Strand Synthesis of cDNA
1) RNA was removed from the-80℃refrigerator, thawed at 4℃and then the reaction solution was prepared in a 0.2ml PCR tube as shown in Table 3, wherein X 1 And X 2 The value of (2) varies depending on the concentration of RNA extracted from each sample, and the volume to be used is converted based on the concentration.
TABLE 3 reaction solution System
2) The PCR tube was placed in a PCR apparatus, and the procedure was run: incubation at 37deg.C for 15 min, denaturation at 98deg.C for 5 min, and incubation at 4deg.C.
1.5.3.2 SYBR Green qPCR
1) The reaction solution was prepared in a 0.2ml PCR tube as shown in Table 4, wherein X 3 The value of (2) varies depending on the cDNA concentration of the sample, and the volume to be taken is converted based on the concentration.
TABLE 4 reaction solution System
In Table 4, the upstream primer sequence of NONRATT021116.2 is (SEQ ID NO: 1): CTAAAGCGCTCTGCCTCACA, NONRATT021116.2 has the downstream primer sequence (SEQ ID NO: 2): CTTCCAGCTACCCCAGAACCT. The upstream primer sequence of the reference gene GAPDH is (SEQ ID NO: 3): TGGCCTCCAAGGAGTAAGAAAC the downstream primer sequence of the reference gene GAPDH is (SEQ ID NO: 4): GGCCTCTCTCTTGCTCTCAGTATC.
2) The reaction solution was added to a 96-well plate
3) The 96-well plate was placed in an ABI 7500 real-time fluorescent quantitative PCR instrument. Running a program: 50. incubating for 2min at the temperature; 95 ℃ for 10min;40 cycles: 95 ℃,15 seconds, 60 ℃ and 1min.
1.6 ROC curve
The ROC (Receiver operating characteristic curves) curve was plotted using MedCalc (version 20.2). The ROC profile of lncRNA is shown in fig. 1.
Auc=0.722, sensitivity 66.67% and specificity 83.33%, which shows that the lncRNA has a certain significance in distinguishing rats in the dosing group from rats in the control group.
1.7 Specificity verification
1.7.1 Experimental reagent, instrument and experimental animal
(1) Positive drug: acetaminophen (lot number: D2114266, available from Shanghai Ala Biotechnology Co., ltd.);
(2) Vehicle control: 0.5% sodium carboxymethyl cellulose (0.5% cmc-Na);
CREA and BUN detection kit manufactured by Nippon and light pure medicine industry Co., ltd
Kim-1 detection kits are manufactured by r & d systems;
the HITACHI 7060 full-automatic biochemical analyzer was purchased from japan HITACHI industries, ltd.
(3) Experimental animal
10 male SD rats, 10 female rats, SPF grade, weight 180-320 g, 6-9 weeks old, purchased from Beijing Vetong Lihua laboratory animal technology Co., ltd. [ license number: SCXK (Beijing) 2021-0011]. The animals are marked by a chip and a cage plate, 5 animals are fed in SPF-grade animal houses of Shanghai Yinuo biotechnology Co Ltd, the temperature of the animal houses is controlled at 22-26 ℃, the humidity is controlled between 40-70%, the ventilation times per minute are more than or equal to 15 times, the bright and dark illumination period is 12 hours/12 hours, the animals eat freely, SPF rats subjected to cobalt 60 irradiation sterilization maintain feed provided by Australian feed Co Ltd in Beijing, and the animals drink self-made deionized water freely through drinking water bottles.
1.7.2 Experimental method
1.7.2.1 Preparation of positive medicine
Preparation of Acetaminophen (APAP) suspension: 3.75. 3.75 g acetaminophen was weighed separately, transferred to glass bottles and added with the appropriate amount of 0.5% CMC-Na (W/W). Stirring with glass rod, turning on a homogenizer for full dispersion, turning off, adding 0.5% CMC-Na to required volume, mixing, preparing 1250mg/kg suspension, ensuring uniformity, and standing at room temperature in dark place. The preparation process needs to be protected from light.
1.7.2.2 Animal test dose setting
Grouping with random granule design, 20 rats were randomly assigned to 2 groups according to body weight: the vehicle control group (0.5% CMC-Na), the dosing group (1250 mg/kg acetaminophen) are shown in Table 5.
Table 5 experimental dose design
1.7.2.3 Administration and visual inspection
The administration routes of the vehicle control group and the acetaminophen administration group are oral and gastric administration, and the administration capacity of single administration is 10 mL/kg. The dosing volume was calculated from the last measured body weight.
After 24 hours following dosing, anesthesia was performed with a 40mg/mL sultai+5 mg/mL xylazine injection mix, and a small portion of the abdominal aortic collection whole blood was placed in a gel separation vacuum tube for serum separation: 3500 rpm,4 ℃,5 min, sucking serum, split charging into an EP tube, and storing in a refrigerator at-80 ℃ for detecting liver function indexes. Most of them are placed in EDTA-K 2 In a vacuum blood collection tube for separating plasma: 800g,4 ℃,10min, the upper plasma was pipetted into EP tubes and stored in a-80 ℃ freezer for subsequent PCR validation.
1.7.2.4 Serum biochemical detection result
The experiment mainly detects blood indexes such as serum Creatinine (CREA), urea nitrogen (BUN) and kidney injury molecule-1 (Kim-1) which are commonly used in preclinical and clinical kidney injury evaluation. 1250 After 24 hours of administration of mg/kg acetaminophen, CREA, BUN, kim-1 was slightly elevated in the serum of the rats of the administration group compared to the vehicle control group, but there was no significant difference.
1.7.2.5 PCR verification
The specific process is the same as 1.5. The PCR validation results showed that the lncRNA was not elevated in the liver injury model, thus showing that it has good specificity.
1.8 Summary
The lncRNA sequencing result of the plasma exosomes of the rats in the experiment shows that the quantity of the lncRNA which is differentially expressed in the exosomes is more, wherein the expression quantity of the lncRNA (NONCODE TRANSCRIPT ID: NONRATT 021116.2) in the plasma exosomes of the rats in the administration group is 64.92 times that in the solvent control group, and the expression difference multiple is very high, which indicates that the lncRNA (NONCODE TRANSCRIPT ID: NONRATT 021116.2) can be used as a kidney injury biomarker for detecting kidney injury caused by exogenous compounds (such as gentamicin sulfate).
After secondary molding, the real-time fluorescence quantitative PCR is used for verification, and the verification result shows that: the expression level of lncRNA (NONCODE TRANSCRIPT ID: NONRATT 021116.2) in the plasma exosomes of the rats in the administration group was 8.25 times (p < 0.05) that in the vehicle control group. This demonstrates that the use of nonrat 021116.2 as a biomarker can be detected in time with reliable detection results, demonstrating that the use of nonrat 021116.2 as a biomarker has good specificity for detection of kidney damage caused by exogenous compounds (e.g., gentamicin sulfate).
The ROC curve was used to examine whether lncRNA (nonode TRANSCRIPT ID: NONRATT 021116.2) could better distinguish between rats in the dosing group and rats in the control group. Area under the curve auc=0.750, showing that the lncRNA has a certain discrimination capability.
The specificity of lncRNA (NONCODE TRANSCRIPT ID: NONRATT 021116.2) was examined by modeling rat liver injury using acetaminophen. The PCR validation results showed that the lncRNA was not elevated in the liver injury model, thus showing that it has good specificity.
Example 2 System for predicting risk of kidney injury
System 61 for predicting risk of kidney injury: the data processing module 52 and the judging and outputting module 53 further include a data collecting module 51 (fig. 3).
The data collection module 51 is configured to collect data on the expression level of the biomarker combinations in the patient's kidney damaged tissue sample and transmit the data to the data processing module.
The data processing module 52 is configured to analyze the expression level data of the received or input biomarker combinations according to the data analysis method described in example 4 to obtain a calculation result. Wherein the expression level data of the biomarker combinations can be collected by the data collection module 51, and the expression level data of the biomarker combinations can also be obtained from other sources.
The judging and outputting module 53 is configured to judge whether the calculated result meets a preset judging condition, that is, the probability of risk of suffering from kidney injury is greater than or equal to the probability of risk prediction without kidney injury, so as to predict risk of kidney injury, and output a predicted result; in the judging and outputting module, when the expression level data meets the judging condition that the probability of the risk of suffering from the kidney injury is greater than or equal to the probability of not suffering from the kidney injury prediction, outputting a prediction result as 'having the risk of the kidney injury'; and outputting a predicted result as 'no risk of kidney injury' when the expression level data does not meet the judgment condition and the probability of risk of kidney injury is smaller than the predicted probability of no risk of kidney injury.
Example 3 electronic device
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (e.g., may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the method for predicting risk of kidney injury in embodiment 1 of the present invention when executing the computer program.
Fig. 4 shows a schematic diagram of the hardware structure of the present embodiment, and the electronic device 4 specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the data analysis method of embodiment 1 of the present application, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Embodiment 4 computer-readable storage Medium
An embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for predicting risk of kidney injury in embodiment 1 of the present invention.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method for predicting risk of kidney damage in embodiment 1 of the invention, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
Claims (11)
1. Use of a reagent for detecting plasma-derived exosome lncRNA in the preparation of a diagnostic reagent for detecting kidney injury, wherein the lncRNA comprises lncRNA with nonnode TRANSCRIPT ID being NONRATT021116.2, and the kidney injury is gentamicin sulfate-induced kidney injury.
2. The use of claim 1, wherein the lncRNA is used as the sole biomarker for kidney injury or in combination with a biomarker for conventional kidney injury.
3. The use according to claim 2, wherein the biomarker of regular kidney injury is selected from at least one of serum creatinine, urea nitrogen, and kidney injury molecule-1.
4. The use of claim 2, wherein the biomarker is characterized and/or quantified by detecting the transcript level of the lncRNA in a sample.
5. The use of claim 4, wherein the detection uses RNA whole transcriptome sequencing; and/or, the sample is plasma.
6. The use according to claim 5, wherein the reagent is a primer for specifically amplifying the exosome lncRNA, the nucleotide sequence of the primer is shown as SEQ ID No. 1 and SEQ ID No. 2.
7. Use of a kit for the preparation of a product for the detection of kidney injury, wherein the kidney injury is caused by gentamicin sulphate, the kit comprising a reagent for the detection of plasma-derived exosomes lncRNA as defined in any one of claims 1 to 6.
8. Use of a chip on which an agent for detecting plasma-derived exosomes lncRNA as defined in any one of claims 1-6 is provided for the preparation of a product for detecting kidney damage, wherein the kidney damage is caused by gentamicin sulphate.
9. A prediction system for kidney injury risk, wherein the prediction system comprises a detection module and an analysis and judgment module; the detection module detects the transcription level of the lncRNA in the sample to be detected and transmits the transcription level data to the analysis and judgment module; the analysis judging module is used for constructing a prediction model by using the transcription level data obtained through PCR processing, respectively predicting the probability of sample kidney injury and the probability of non-kidney injury, judging whether the transcription level data accords with preset judging conditions or not so as to predict the risk of sample kidney injury, and outputting a prediction result; the judging condition is the probability of kidney injury and the probability of non-kidney injury;
Outputting a prediction result of "having a risk of kidney injury" when the transcription level data satisfies the judgment condition; when the transcription level data does not meet the judging condition, namely the probability of kidney injury is smaller than that of non-kidney injury, outputting a prediction result as 'without kidney injury risk';
the lncRNA comprises lncRNA of which NONCODE TRANSCRIPT ID is NONRATT021116.2, and the kidney injury is caused by gentamicin sulfate.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the functions of the prediction system of claim 9,
or a step of realizing a method for detecting kidney injury, the method comprising detecting the transcription level of lncRNA in a sample to be detected, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID being NONRATT021116.2, and the kidney injury is kidney injury caused by gentamicin sulfate.
11. An electronic device comprising a memory storing a computer program and a processor for executing the computer program to perform the functions of the predictive system of claim 9,
Or a step of realizing a method for detecting kidney injury, the method comprising detecting the transcription level of lncRNA in a sample to be detected, wherein the lncRNA comprises lncRNA with NONCODE TRANSCRIPT ID being NONRATT021116.2, and the kidney injury is kidney injury caused by gentamicin sulfate.
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