Biomarker for diagnosing drug-induced liver injury

Abstract

The invention relates to a biomarker for diagnosing drug-induced liver injury. The biomarker is ceramide and / or sphingomyelin. The invention verifies that effective information can be provided for accurate and specific diagnosis of patients with drug-induced liver injury through detection of ceramide and sphingomyelin in blood and combined diagnosis of ceramide and sphingomyelin, and has a good application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to biomarkers for diagnosing drug-induced liver injury. Background Art

[0002] Drug-induced liver injury (DILI) refers to liver damage caused by the use of drugs, including conventional drugs and Chinese herbal medicines. Although many DILI cases can improve after hospitalization, severe cases can escalate to acute liver failure and may lead to death. The current diagnosis of DILI relies on a detailed clinical history, traditional liver function indicators, and the exclusion of other causes of liver disease. It can manifest as acute and chronic liver disease of various causes, with clinical symptoms including fever, nausea, vomiting, jaundice, dark urine, itching, and right upper abdominal pain, and the symptoms of DILI caused by different drugs are different. Therefore, doctors have high requirements for clinical experience and lack of accuracy in diagnosing DILI based on clinical symptoms and medical history.

[0003] Although traditional indicators are helpful for the diagnosis and prognosis of DILI, the biggest problem is the lack of specificity because they are also present in the kidneys, heart, brain, skeletal muscle, and red blood cells. For example, serum ALT also increases after strenuous exercise and in patients with muscle diseases (such as muscular dystrophy); elevated serum ALP levels can also be seen in hyperparathyroidism, leukemia, Hodgkin's lymphoma, congestive heart failure, ulcerative colitis, and rickets; the predictive ability of Hy's law varies depending on the causative drug. In addition, some clinical scales (such as CIOMS / RUCAM) can assist in exclusionary diagnosis, but they still have great limitations due to their high subjectivity and variability between observers.

[0004] Metabolome is a general term for small molecule chemicals involved in metabolism. Traditionally, the purpose of studying metabolome is to identify biomarkers for disease diagnosis and prediction. In recent years, the scope of metabolome has expanded to metabolites that are generally considered to be common cellular building blocks, including amino acids that make up proteins, pyrimidine and purine bases used in nucleic acid biosynthesis, and phospholipids involved in cell membrane formation. Metabolomics can be defined as measuring the levels of all cellular metabolites and has become a powerful new tool for discovering new low-molecular-weight biomarkers. The analytical platforms commonly used in metabolomics research are nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectrometry (GC-MS). The utility of this group has been demonstrated by the identification of new markers for colorectal cancer, diabetes, acute myocardial ischemia, and endometrial cancer. Therefore, metabolomics has a very broad prospect in the search for disease diagnostic markers. So far, metabolomics has been successfully applied to the study of various liver diseases, such as hepatocellular carcinoma, cholangiocarcinoma, primary biliary cirrhosis and primary sclerosing cholangitis. These studies have deepened our understanding of the pathogenesis of liver diseases and are beneficial to the diagnosis, monitoring and treatment of diseases. Therefore, metabolomics is feasible and applicable in screening biomarkers for DILI patients, and is an important direction worthy of in-depth research.

[0005] The sphingolipid metabolic pathway is an important intracellular pathway composed of biologically active sphingolipids, especially sphingomyelin and ceramide, and their corresponding enzymes. The balance between sphingomyelin and ceramide in liver tissue is strictly regulated and is essential for maintaining liver function. Sphingomyelin can be hydrolyzed by sphingomyelinase to produce ceramide, which can be further metabolized to sphingosine by ceramidase. Sphingomyelin acts as a reservoir for ceramide production and participates in membrane structure. Ceramide is generally associated with pro-apoptotic and anti-proliferative effects. In hepatocytes, ceramide accumulation can disrupt mitochondrial function, leading to oxidative stress and death. In addition, ceramide-mediated inhibition of lipid synthesis pathways may affect hepatocyte function and cause liver dysfunction in liver regeneration. In hepatic stellate cells, ceramide-mediated signal transduction may stimulate the expression of fibrogenic cytokines and extracellular matrix proteins, leading to the progression of liver fibrosis. Elevated ceramide levels also activate the inflammatory response of Kupffer cells and promote the production of inflammatory cytokines and chemokines, leading to liver inflammation and damage. In liver sinusoidal endothelial cells, ceramide-mediated endothelial dysfunction may lead to liver injury, fibrosis, and impaired liver regeneration. The sphingolipid pathway also plays a key role in regulating the function of hepatic lymphocytes, contributing to immune surveillance and immune-mediated liver disease. However, little attention has been paid to the changes in sphingolipid metabolism and sphingomyelin and ceramide in the setting of DILI, and further research is needed.

[0006] In summary, the current diagnosis of DILI mainly relies on excluding other liver diseases, because there are no universally accepted specific biomarkers to detect and predict DILI. This diagnostic method is often time-consuming and easily delays treatment, which may lead to worsening of the patient's condition or even death. Therefore, finding and identifying DILI early diagnostic markers with high sensitivity, strong specificity, and non-invasiveness to guide clinicians to take treatment measures as soon as possible, thereby improving the treatment effect of patients and improving the prognosis and quality of life of patients, is a scientific problem that needs to be urgently solved in this field. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a biomarker for the diagnosis of drug-induced liver injury. It is verified that by detecting ceramide and sphingomyelin in the blood and their combined diagnosis, effective information can be provided for the accurate and specific diagnosis of patients with drug-induced liver injury, and it has good application prospects.

[0008] The present invention provides biomarkers for diagnosing drug-induced liver injury, wherein the biomarkers are ceramide and / or sphingomyelin.

[0009] The present invention also provides a detection product comprising a biomarker for diagnosing drug-induced liver injury.

[0010] Preferably, the detection product includes a test kit, a test paper or a chip.

[0011] Preferably, the sample used for the detection is a peripheral blood sample of a patient.

[0012] Preferably, the test product is detected by liquid chromatography tandem mass spectrometry LC-MS / MS.

[0013] The present invention uses DILI patients and healthy human serum for liquid chromatography tandem mass spectrometry (LC-MS / MS) detection to screen out differentially expressed ceramide and sphingomyelin, and uses ELISA to perform large sample verification in plasma, and finds that ceramide and sphingomyelin are differentially expressed in the blood of DILI patients and healthy people and have certain diagnostic value, especially when the two are combined for diagnosis, the diagnostic efficiency is improved. Therefore, it is believed that serum ceramide and sphingomyelin detected based on ELISA technology can be used as an accurate diagnostic method for DILI.

[0014] The present invention uses ceramide and sphingomyelin in combination as diagnostic markers for detecting DILI. Compared with healthy people, the area under the ROC curve is 0.97, and compared with other liver injury patients, the area under the ROC curve is 0.82, which is significantly higher than the sensitivity and specificity of a single marker, providing a new way for the early diagnosis of DILI. The expression of the metabolic markers of the present invention in DILI blood is significantly higher than that of the physical examination control population, so it can be used as an early screening for DILI and can be well applied in clinical practice.

[0015] Beneficial Effects

[0016] The present invention verifies that the detection of ceramide and sphingomyelin in the blood and their combined diagnosis can provide effective information for the accurate and specific diagnosis of patients with drug-induced liver injury, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 : It is a classification effect diagram of the metabolomics data of the three groups of subjects in the embodiment; wherein A is a scatter plot of the OPLS-DA scores of the three groups of subject data for paired comparison; B is a scatter plot of the PLS-DA scores of the three groups of subjects as a whole; C is a permutation test diagram corresponding to A.

[0018] Figure 2 : It is the metabolic characteristics of DILI in the embodiment; A and B are the volcano map and heat map of the differential metabolites between DILI and HC groups, respectively; C is the Venn diagram of the differential metabolites; D is the heat map of specific candidate metabolites unique to DILI; E is the heat map of DILI-specific candidate metabolites.

[0019] Figure 3 The figure is a validation of the efficacy of the biomarkers in the embodiment; A and B are box plots comparing the serum concentrations of ceramide and sphingomyelin of all subjects, respectively; C and E are ROC curves drawn using the training set data for the above two biomarkers to compare the DILI group with the HC group and the DILI group with the NDILI group, respectively; D and F are ROC curves predicted using the validation set data for comparing the DILI group with the HC group and the DILI group with the NDILI group, respectively.

[0020] Figure 4 It is the overall flow chart of the research experiment in the embodiment. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0022] Example 1

[0023] (1) Subject recruitment

[0024] Clinical sample collection: The research subjects (subjects) of this project were divided into healthy people who underwent physical examinations at Tongji Hospital affiliated to Tongji University, patients diagnosed with DILI, and control patients with other liver diseases. Blood samples were collected from the above subjects; and the clinical data of the subjects were collated.

[0025] The inclusion criteria are as follows: 1) Patients with drug-induced liver injury (DILI): ① with a clear history of exposure to hepatotoxic drugs; ② excluding liver function damage caused by other causes; ③ excluding patients with alanine aminotransferase (ALT) ≤ 3 times the normal value and alkaline phosphatase (ALP) ≤ 2 times the normal value; ④ RUCAM score scale ≥ 5 points. 2) Healthy subjects (HC): healthy people with similar overall age and gender to the collected DILI patients were selected as the control group. 3) Patients with non-drug-induced liver injury (NDILI): autoimmune hepatitis, viral hepatitis, cirrhosis and other diseases that cause liver damage.

[0026] The exclusion criteria are as follows: First, exclude those who obviously do not meet the requirements or cannot be observed and counted: ① Short hospitalization time: less than 3 days of hospitalization, unable to complete relevant auxiliary examinations and detailed observation of liver function index changes; ② Unclear medication: The specific medication situation cannot be determined in combination with the case; ③ Too complicated medication: The patient usually uses a variety of drugs and cannot explain the specific medication time and method; ④ Biochemical indicators do not meet the diagnostic criteria; Alanine aminotransferase (ALT) is less than three times the normal value and alkaline phosphatase (ALP) is less than two times the normal value; ⑤ The specific liver function before medication cannot be determined. The included cases were comprehensively evaluated using the RUCAM scoring scale: those with a score below 5 were excluded, and those with a score greater than or equal to 5 were retained for further analysis.

[0027] (2) Metabolite extraction and liquid chromatography tandem mass spectrometry (LC-MS / MS) analysis

[0028] Take out the sample from the -80℃ freezer and thaw it on ice until there is no ice in the sample (all subsequent operations are performed on ice); after the sample is thawed, vortex for 10 seconds to mix, and transfer 50μL of the sample to the corresponding numbered centrifuge tube; add 300μL of 20% acetonitrile methanol internal standard extract, vortex for 3min, and centrifuge at 12000r / min for 10min at 4℃; after centrifugation, transfer 200μL of the supernatant to another corresponding numbered centrifuge tube, and let it stand in a -20℃ refrigerator for 30min; centrifuge again at 12000r / min for 3min at 4℃, and transfer 180μL of the supernatant to the inner liner tube of the corresponding injection bottle for analysis on the machine.

[0029] Two LC / MS methods were used for all samples. One aliquot was analyzed under positive ion conditions and eluted from a T3 column (Waters ACQUITY Premier HSS T3 Column 1.8 μm, 2.1 mm*100 mm) using 0.1% formic acid in water as solvent A and 0.1% formic acid in acetonitrile as solvent B. The elution gradient was as follows: 5% to 20% in 2 minutes, then to 60% in the next 3 minutes, then to 99% in 1 minute and held for 1.5 minutes, then returned to 5% mobile phase B in 0.1 minutes and held for 2.4 minutes. The analysis conditions were as follows: column temperature 40 °C; flow rate 0.4 mL / min; injection volume 4 μL. Another aliquot was analyzed under negative ion conditions with the same elution gradient as in the positive mode.

[0030] Data acquisition was performed in information-dependent acquisition (IDA) mode using Analyst TF 1.7.1 software (Sciex, Concord, ON, Canada). Source parameters were set as follows: ion source gas 1 (GAS1) was 50 psi; ion source gas 2 (GAS2) was 50 psi; curtain gas (CUR) was 25 psi; temperature (TEM) was 550 °C; declustering voltage (DP) was 60 V or -60 V in positive and negative modes, respectively; ion spray voltage floating (ISVF) was 5000 V or -4000 V in positive and negative modes, respectively. TOF MS scan parameters were set as follows: mass range 50–1000 Da; accumulation time 200 ms; dynamic background subtraction was on. The product ion scan parameters were set as follows: mass range 25–1000 Da; accumulation time 40 ms; collision energy 30 or -30 V in positive and negative modes, respectively; collision energy extension 15; resolution UNIT; charge state 1 to 1; intensity 100 cps; isotopes within 4 Da were excluded; mass tolerance 50 ppm; the maximum number of candidate ions monitored per cycle was 18. (3) ELISA detection of ceramide and sphingomyelin

[0031] The ceramide and sphingomyelin detection kits were purchased from Shanghai ELISA Biotechnology Co., Ltd. The concentrations of ceramide and sphingomyelin in human serum were detected according to the instructions of the ELISA kit. The detection range of the ceramide kit is 5μmol / L–160μmol / L; the minimum detection concentration is less than 1.0μmol / L; there is no cross-reaction with other soluble structural analogs; the intra-plate coefficient of variation is less than 10%, and the inter-plate coefficient of variation is less than 15%. The minimum detection concentration of the sphingomyelin kit is less than 10pg / mL; there is no cross-reaction with other soluble structural analogs; the intra-plate and inter-plate coefficients of variation are both less than 15%.

[0032] (4) Data processing and identification of differential metabolites

[0033] 1) Data preprocessing

[0034] The raw data from the mass spectrometer were converted into mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peaks with a missing rate of >50% in each group of samples were filtered, and the blank values ​​were filled by KNN, and the peak area was corrected by the SVR method. The corrected and screened peaks were identified by searching the laboratory's self-built database and integrating public libraries (HMDB, https: / / hmdb.ca / and KEGG, https: / / www.kegg.jp / ). Finally, the substances with a comprehensive score of more than 0.5 and a QC sample CV value of less than 0.3 were extracted and identified, and then the positive and negative modes were merged (retaining the substances with the highest qualitative grade and the smallest CV value) to obtain the preprocessed data.

[0035] 2) Baseline characteristics:

[0036] The medication status of the 12 patients with drug-induced liver injury who underwent non-targeted metabolomics testing is shown in Table 1, and the clinical information of the 24 subjects who underwent non-targeted metabolomics testing is shown in Table 2. 55 DILI patients, 14 NDILI patients, and 10 healthy controls (HC) were included in the ELISA test. The clinical characteristics of the three groups are shown in Table 3. Compared with healthy controls, many traditional indicators changed significantly when liver function was impaired (P<0.05), but there were no significant differences in the baseline characteristics between DILI and NDILI, except for albumin (P>0.05). However, albumin decreases in both hepatocellular damage and malnutrition, so it lacks specificity.

[0037] Table 1 Pathogenic drugs in 12 patients with drug-induced liver injury detected by non-targeted metabolomics

[0038]

[0039] Table 2 Clinical information of 24 subjects tested by non-targeted metabolomics

[0040]

[0041]

[0042] Table 2 (continued)

[0043] type age gender SOD TBIL DBIL TBA PA TP ALB GLOB A / G ALT AST AST GGT ALP LDH CK GR HC 55 male / 19.4 3.2 / / 76.7 41.6 27.6 1.50 24.5 20.5 / 31.1 97.2 197.3 110.1 / HC 56 female 167.5 22.9 3.8 3.7 0.317 81.6 50.2 31.4 1.60 15.0 20.4 2.7 18.3 100.0 169.1 46.4 /

[0044] TCM: patients with drug-induced liver injury caused by traditional Chinese medicine; WM: patients with drug-induced liver injury caused by western medicine; NDILI: patients with non-drug-induced liver injury; HC: healthy controls; SOD: superoxide dismutase; TBIL: total bilirubin; DBIL: direct bilirubin; TBA: total bile acid; PA: prealbumin; TP: total protein; ALB: albumin; GLOB globulin; A / G: albumin-globulin ratio; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ASTm: mitochondrial isozyme of aspartate aminotransferase; GGT: gamma-glutamyl transpeptidase; ALP: alkaline phosphatase; LDH: lactate dehydrogenase; CK: creatine kinase; GR: glutathione reductase.

[0045] Table 3 Clinical characteristics of 79 subjects

[0046]

[0047] Table 3 Clinical characteristics of 79 subjects

[0048]

[0049] SD: standard deviation; P1: DILI vs NDILI; P2: DILI vs HC; SOD: superoxide dismutase; TBIL: total bilirubin; DBIL: direct bilirubin; TBA: total bile acid; PA: prealbumin; TP: total protein; ALB: albumin; GLOB globulin; A / G: albumin-globulin ratio; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ASTm: mitochondrial isozyme of aspartate aminotransferase; GGT: gamma-glutamyl transpeptidase; ALP: alkaline phosphatase; LDH: lactate dehydrogenase; CK: creatine kinase; GR: glutathione reductase.

[0050] 3) Metabolomics data analysis and screening of candidate DILI biomarkers

[0051] A total of 853 metabolites were detected by LC-MS. Partial least squares discriminant analysis (PLS-DA) was used to perform preliminary analysis on each group of samples, and the results showed that the classification effect of each group of samples was good, such as Figure 1 B. The three groups were then combined in pairs to establish orthogonal partial least squares discriminant analysis (OPLS-DA) methodology models to further explain the changes in metabolites between the groups ( Figure 1 A). The model was evaluated using a permutation test to demonstrate the validity and stability of the PLS-DA model ( Figure 1C). The evaluation was performed by 7-cycle interactive validation and 20-cycle response ranking tests. The variable projection importance (VIP)>1, P<0.05, FC>1.4 or <0.8 were used as screening conditions, and 244 differential metabolites between DILI and HC groups were identified as candidate biomarkers ( Figure 2 A, B). Subsequently, 207 differential metabolites were screened out between the NDILI and HC groups under the same screening conditions, and a Venn diagram was made with the above-mentioned candidate markers ( Figure 2 C), selected 172 differential metabolites unique to DILI ( Figure 2 D) KEGG pathway analysis was performed and the most significantly enriched pathway (sphingolipid metabolism pathway) was selected ( Figure 2 E) The differential metabolites in this pathway were used as candidate biomarkers.

[0052] 4) Selection and validation of DILI biomarkers

[0053] Among the candidate markers, the metabolite with the largest multiple difference between patients with drug-induced liver injury and healthy controls was selected: sphingomyelin (SM) (|log2FC|=1.22, t-test P value=2.54×10 -6 ), and ceramide, a key metabolite in the sphingolipid metabolism pathway, as potential diagnostic biomarkers for DILI. The concentrations of SM and ceramide in the serum of all subjects were detected by enzyme-linked immunosorbent assay (ELISA), and it was found that the concentrations of the two metabolites were significantly different in DILI compared with the other two groups ( Figure 3 A, B). In order to establish a diagnostic model for DILI, the subjects were randomly divided into 70% training set and 30% validation set. The training set was used to calculate the area under the ROC curve (AUC) value for the above two metabolites and their combination using logistic regression. Compared with the HC group, the AUC values ​​of ceramide, sphingomyelin, and the combination of the two in the DILI group were 0.93, 0.86, and 0.97, respectively ( Figure 3 C), the above model was used to verify the validation set, and the AUC values ​​were 0.86, 0.86, and 0.90 respectively ( Figure 3 D). Compared with the DILI group, the AUC values ​​of ceramide, sphingomyelin, and the combination of the two were 0.71, 0.71, and 0.82, respectively ( Figure 3 E), the above model was used to verify the validation set, and the AUC values ​​of sphingomyelin and the combination of the two were greater than 0.7 ( Figure 3 F). The diagnostic effect of the traditional liver function modeling using TBIL, ALT, AST, and ALP was poor compared with the above two markers (Table 4).

[0054] Table 4 AUC values ​​of the two biomarkers of this application and traditional liver function indicators in the training set data

[0055] substance AUC(DILIvsHC) AUC(DILIvsNDILI) Ceramide 0.93 0.71 Sphingomyelin 0.86 0.71 TBIL 0.68 0.50 ALT 0.99 0.66 AST 0.98 0.54 ALP 0.83 0.59 Ceramide + Sphingomyelin 0.97 0.82

[0056] TBIL: total bilirubin; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: alkaline phosphatase

[0057] AUC: Area under the ROC curve.

Claims

1. A biomarker for the diagnosis of drug-induced liver injury, characterized in that: The biomarkers are ceramide and / or sphingomyelin.

2. A detection product comprising the biomarker for diagnosing drug-induced liver injury as described in claim 1.

3. The detection product according to claim 2, characterized in that: The detection product includes a test kit, a test paper or a chip.

4. The detection product according to claim 2, characterized in that: The sample used in the test is a peripheral blood sample from a patient.

5. The detection product according to claim 2, characterized in that: The test product is detected by liquid chromatography tandem mass spectrometry LC-MS / MS.

Images