Kit for screening high-risk population with liver injury caused by chemotherapeutic drug methotrexate and application of detection reagent in preparation of methotrexate liver injury prediction product

Abstract

The invention belongs to the technical field of medical detection, and firstly provides a kit for screening high-risk people with liver injury caused by chemotherapeutic drug methotrexate, and a detection reagent for detecting linoleic acid and oxidative metabolite content of human body is arranged in the kit. The invention also provides an application of the detection reagent in preparation of a methotrexate liver injury prediction product, the detection reagent is a reagent for detecting the content of linoleic acid and oxidative metabolites of the linoleic acid of a human body, and the product is a detection kit. In the invention, when the content of the linoleic acid and the oxidative metabolite thereof is relatively high, high liver injury caused after the methotrexate is used by the human body is predicted. According to the method, high-risk people suffering from methotrexate liver injury can be quickly screened, and intervention can be performed in the early stage so as to reduce the risk of suffering from methotrexate liver injury.

Description

Technical Field

[0001] This invention belongs to the field of medical testing technology, specifically relating to a kit for screening high-risk individuals for liver injury caused by the chemotherapy drug methotrexate, and the application of a testing reagent in the preparation of methotrexate liver injury prediction products. Background Technology

[0002] Methotrexate (MTX), a commonly used chemotherapy drug, interferes with DNA and RNA synthesis in tumor cells by inhibiting dihydrofolate reductase and is widely used in the treatment of diseases such as acute lymphoblastic leukemia, osteosarcoma, and rheumatoid arthritis. Recent studies have shown that high-dose MTX induces significant diurnal rhythm changes in liver injury, and more than 50% of genes in the liver exhibit diurnal rhythmic expression, mainly including genes related to drug-metabolizing enzymes, oxidative stress, and inflammation. When the gut microbiota is absent, the diurnal rhythm changes of host genes disappear or reverse, indicating that the microbiota plays a crucial role in the transmission of diurnal rhythms. The diurnal oscillation of gut microbiota and its metabolites in regulating liver injury has been reported in previous studies. For example, liver ischemia-reperfusion injury caused by surgery performed at night is more severe, and the mechanism is mainly related to the decrease of the gut microbiota metabolite 3,4-DHPPA at night. Fecal microbiota transplantation experiments have confirmed that the functional oscillation of gut microbiota is the main factor driving the diurnal variation of acetaminophen-induced acute liver injury. Therefore, gut microbiota may play an important role in methotrexate-induced diurnal rhythm liver injury, and choosing an appropriate dosing time may reduce adverse reactions and enhance efficacy.

[0003] Patent application CN202510826300.3 discloses a SNP locus associated with the risk of methotrexate-induced liver injury and its application. The SNP is located at 31,356,966 bp on chromosome 6 of the human GRCh38 genome, and its base is C or G. This invention provides an SNP locus associated with methotrexate-induced liver injury, which can effectively predict the risk of methotrexate-induced liver injury and serve as a biomarker for predicting methotrexate-induced liver injury.

[0004] However, there is a lack in this field of kits that can be used simply and efficiently to screen high-risk populations for liver injury caused by the chemotherapy drug methotrexate, as well as a test reagent for the application of methotrexate liver injury prediction products. Summary of the Invention

[0005] The present invention first provides a kit for screening high-risk groups for liver damage caused by the chemotherapy drug methotrexate. The kit includes a detection reagent for detecting the content of linoleic acid and its oxidative metabolites in the human body.

[0006] The present invention also provides an application of a detection reagent in the preparation of a methotrexate liver injury prediction product, wherein the detection reagent is a reagent for detecting the content of linoleic acid and its oxidative metabolites in the human body, and the product is a detection kit.

[0007] In this invention, when the content of linoleic acid and its oxidative metabolites is high, it is predicted that the human body will experience high levels of liver damage after using methotrexate.

[0008] In one specific embodiment, the oxidative metabolites of linoleic acid include one or more of 9,10-EpOME, 9-HpODE, 12,13-DiHOME, 12,13-EpOME, 13-HpODE, 9S-HODE, and 13S-HODE.

[0009] In one specific embodiment, the oxidative metabolites of linoleic acid include one or more of 9-HpODE, 9,10-EpOME, 12,13-EpOME, and 12,13-DiHOME.

[0010] In one specific embodiment, the kit includes a detection reagent for detecting the content of linoleic acid and its oxidative metabolites 9,10-EpOME, 12,13-EpOME and 12,13-DiHOME in the human body.

[0011] In one specific embodiment, the kit is used to detect the levels of linoleic acid and its oxidative metabolites in a patient's fecal or serum sample.

[0012] In one specific embodiment, the kit also includes reagents for detecting intestinal lactobacillus flora.

[0013] In this invention, the level of Lactobacillus is reduced, which predicts a high risk of liver damage after the human body uses methotrexate.

[0014] In one specific implementation, the kit further includes a detection module for liver function indicators ALT and AST.

[0015] In this invention, the kit also includes a detection module for liver function indicators ALT and AST, used for multi-parameter joint analysis to improve the accuracy of liver damage prediction.

[0016] In one specific embodiment, the kit is used for ELISA detection, and the kit includes antibodies labeled with linoleic acid and its oxidative metabolites.

[0017] In one specific embodiment, the kit is used for GC-MS detection, and the kit includes chemical reagents for extracting linoleic acid and its oxidative metabolites and chemical reagents for methyl esterification of linoleic acid and its oxidative metabolites.

[0018] The beneficial effects of this invention include at least the following: 1. This invention uses a methotrexate diurnal rhythmic liver injury model, a pseudo-germ-free mouse model, and a fecal microbiota transplantation mouse model to find that methotrexate-induced liver injury exhibits diurnal variations, showing "mild in the morning and severe in the evening," and is dependent on the gut microbiota. Furthermore, evening administration disrupts the gut microbiota in mice, leading to the accumulation of linoleic acid and its oxidative metabolites, and a decrease in Lactobacillus levels. This indicates that linoleic acid and its oxidative metabolites may be independent risk factors and predictors of methotrexate-induced liver injury. Detecting linoleic acid and its oxidative metabolites in individuals using diagnostic reagents enables early assessment of methotrexate-induced liver injury and provides a theoretical basis for the timing of methotrexate administration. 2. By incorporating diagnostic reagents for linoleic acid and its oxidative metabolites into a kit, this invention enables rapid screening of high-risk individuals for methotrexate-induced liver injury, allowing for early intervention to reduce the risk of this condition. Attached Figure Description

[0019] Figure 1 The pathological damage to the liver of mice treated with the drug in the morning and evening. Figure 1 (a) Liver morphology of mice in group ZT0. (b) Liver morphology of mice in group ZT12. (c) Representative HE staining image of liver from group ZT0 (400×). (d) Representative HE staining image of liver from group ZT12 (400×). (e) Liver pathological score (n=4). Compared with group ZT0: * P < 0.05.

[0020] Figure 2 The results showed the liver biochemical indicators of mice that were administered the drugs in the morning and evening. Figure 2 In the study: (a) ALT level (n = 5). (b) AST level (n = 5). (c) AKP level (n = 6). (d) GSH level in liver homogenate (n = 6). (e) SOD level in liver homogenate (n = 6). (f) MDA level in liver homogenate (n = 6). Compared with the ZT0 group: ns indicates P > 0.05, * P < 0.05, **** P < 0.0001.

[0021] Figure 3 The expression levels of proteins related to oxidative damage in the liver of mice were determined by administering the medication morning and evening. Figure 3 (a) Western blot results of p62, HO-1, GPX4, and actin in liver tissue (illustrated). (b) Hepatic p62 protein expression results (n = 4-5). (c) Hepatic HO-1 protein expression results (n = 5). (d) Hepatic GPX4 protein expression results (n = 5). Compared with the ZT0 group: * P < 0.05, **P < 0.01.

[0022] Figure 4 The pathological damage to the liver of pseudo-germ-free mice after administration of the drugs in the morning and evening. Figure 4 (a) Liver morphology of mice in the antibiotic-ZT0 group. (b) Liver morphology of mice in the antibiotic-ZT12 group. (c) Representative HE staining image (400×) of liver from mice in the antibiotic-ZT0 group. (d) Representative HE staining image (400×) of liver from mice in the antibiotic-ZT12 group. (e) Liver pathological score (n=4). Compared with the antibiotic-ZT0 group: ns indicates P > 0.05.

[0023] Figure 5 Liver biochemical indicators were determined by administering the medication to pseudo-germ-free mice twice a day (morning and evening). Figure 5 In the study: (a) ALT level (n = 5). (b) AST level (n = 6). (c) AKP level (n = 6). (d) GSH level in liver homogenate (n = 6). (e) SOD level in liver homogenate (n = 6). (f) MDA level in liver homogenate (n = 6). Compared with the antibiotic-ZT0 group: ns indicates P > 0.05.

[0024] Figure 6 The expression levels of liver oxidative damage-related proteins were determined in pseudo-germ-free mice after administration of drugs twice daily. Figure 6 (a) Western blot results of p62, HO-1, and GPX4 in liver tissue are illustrated. (b) Hepatic p62 protein expression results (n = 5). (c) Hepatic HO-1 protein expression results (n = 5). (d) Hepatic GPX4 protein expression results (n = 5). Compared with the antibiotic-ZT0 group: ns indicates P > 0.05.

[0025] Figure 7 The pathological damage to the liver of mice after fecal microbiota transplantation was administered in the morning. Figure 7 (a) Liver morphology of RZT0 group mice. (b) Liver morphology of RZT12 group mice. (c) Representative HE staining image of RZT0 group mice (400×). (d) Representative HE staining image of RZT12 group mice (400×). (e) Liver pathological score (n=4). Compared with RZT0 group: *** P < 0.001.

[0026] Figure 8 Liver biochemical indicators were measured in mice that underwent fecal microbiota transplantation in the morning. Figure 8In the middle: (a) ALT level (n = 5). (b) AST level (n = 5). (c) AKP level (n = 6). (d) GSH level in liver homogenate (n = 6). (e) SOD level in liver homogenate (n = 6). (f) MDA level in liver homogenate (n = 6). Compared with the RZT0 group: * P < 0.05, ** P < 0.01.

[0027] Figure 9 The expression levels of liver oxidative damage-related proteins in mice that underwent fecal microbiota transplantation in the morning were determined. Figure 9 (a) Western blot results of p62, HO-1, and GPX4 in liver tissue (illustrated). (b) Hepatic p62 protein expression results (n = 5). (c) Hepatic HO-1 protein expression results (n = 6). (d) Hepatic GPX4 protein expression results (n = 5). Compared with the RZT0 group: * P < 0.05, *** P < 0.001, **** P < 0.0001.

[0028] Figure 10 The Chao1 index and observed_species index of the gut microbiota in mice were determined by administering the medication twice a day (morning and evening). Figure 10 In the middle: (a) Chao1 index (n = 5). (b) observed_species index (n = 5). * P < 0.05.

[0029] Figure 11 The ACE index of intestinal flora in mice administered the drug in the morning and evening. Figure 11 In the case of n=5, * P < 0.05.

[0030] Figure 12 A visual representation of β-diversity analysis. Figure 12 In the diagram: PCoA, n=6.

[0031] Figure 13 This is an orthogonal partial least squares discriminant analysis.

[0032] Figure 14 For SMPDB enrichment analysis.

[0033] Figure 15 This refers to the content of linoleic acid and its oxidative metabolites in the intestine. Figure 15In the middle: (a) linoleic acid; (b) 9,10-EpOME; (c) 9-HpODE; (d) 12,13-DiHOME; (e) 12,13-EpOME; (f) 13-HpODE; (g) 9S-HODE; (h) 13S-HODE (n=6). Compared with group ZT0: * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

[0034] Figure 16 A heatmap showing the correlation between liver damage indicators and differentially expressed gut metabolites. Figure 16 middle: * P < 0.05, ** P < 0.01, *** P < 0.001.

[0035] Figure 17 A heatmap showing the correlation between gut microbiota and gut differential metabolites. Figure 17 (a) Spearman correlation analysis of gut microbiota with cecal differential metabolites (9-HpODE, 13-HpODE, 13S-HODE, linoleic acid, 12,13-EpOME, 9,10-EpOME, 12,13-DiHOME, and 9S-HODE) at the genus level. (b) Spearman correlation analysis of gut microbiota with cecal differential metabolites (9,10-EpOME, 12,13-DiHOME, 12,13-EpOME, 9S-HODE, 13-HpODE, 9-HpODE, linoleic acid, and 13S-HODE) at the species level. * P < 0.05, *** P < 0.001.

[0036] Figure 18 A heatmap showing the correlation between differentially expressed genes in the liver and differentially expressed metabolites in the gut. Figure 18 middle: * P < 0.05, ** P < 0.01. The gut microbiota and cecal differential metabolites included 9-HpODE, linoleic acid, 9S-HODE, 12,13-EpOME, 12,13-DiHOME, 9,10-EpOME, 13-HpODE, and 13S-HODE.

[0037] Figure 19 The liver pathological damage after combined treatment with linoleic acid. Figure 19(a) Liver morphology of mice in the methotrexate group. (b) Liver morphology of mice in the linoleic acid combined group. (c) Representative HE staining image (400×) of livers in the methotrexate group. (d) Representative HE staining image (400×) of livers in the linoleic acid combined group. (e) Liver pathological score (n=4). Compared with the methotrexate group: * P < 0.05.

[0038] Figure 20 These are liver biochemical indicators after combined treatment with linoleic acid. Figure 20 In the liver homogenate group: (a) ALT level (n = 5). (b) AST level (n = 5). (c) AKP level (n = 5). (d) GSH level in liver homogenate (n = 5). (e) SOD level in liver homogenate (n = 5). (f) MDA level in liver homogenate (n = 5). Compared with the methotrexate group: ns indicates P > 0.05. * P < 0.05, ** P < 0.01.

[0039] Figure 21 The expression of genes in the Fapp5 and PPAR signaling pathways in the liver after combined treatment with linoleic acid. Figure 21 In the middle: (a) Fapp5 mRNA (n = 5). (b) Ppara mRNA (n = 5). (c) Ppargc1α mRNA (n = 5). (d) Crot mRNA (n = 6). (e) Cpt2 mRNA (n = 6). (f) Cpt1a mRNA (n = 5). (g) Scd1 mRNA (n = 5). (h) Acox1 mRNA (n = 5). (i) Abcd1 mRNA (n = 6). Compared with the methotrexate group: * P < 0.05, ** P < 0.01, *** P < 0.001. Detailed Implementation

[0040] The mediators of the microbiome's regulation of host responses are not fully understood, but metabolites are considered key factors. Our team, through clinical research, found that in patients with alcoholic hepatitis, compared to those with mild liver injury, patients with moderate liver injury had significantly elevated serum levels of linoleic acid oxidation metabolites 9,10-DiHOME (±)-9,10-dihydroxy-12(Z)-octadecenoic acid and 12,13-DiHOME (±)-12,13-dihydroxy-9Z-octadecenoic acid). Furthermore, patients with alcoholic liver disease had HODE (hydroxyoctadecenoic acid, an oxidation metabolite of linoleic acid) levels 46 times higher than healthy subjects and 4 times higher than those with non-alcoholic fatty liver disease. Our previous research found that nighttime administration of methotrexate led to intestinal linoleic acid accumulation and decreased levels of Lactobacillus, with a pattern of "worse in the morning and worse in the evening." Therefore, we hypothesize that nighttime methotrexate-induced intestinal dysbiosis leads to abnormal intestinal linoleic acid metabolic pathways, and that abnormal linoleic acid metabolism promotes liver injury. Against this backdrop, the main objective of this invention is to develop a novel linoleic acid biomarker detection kit to provide a new detection method for the prevention and diagnosis of methotrexate-induced liver injury.

[0041] Given the existing problems in the technology, such as the insufficient development of biomarkers for methotrexate-induced liver injury, exploring new biomarkers for the prevention and diagnosis of methotrexate-induced liver injury will be beneficial for the early prevention of methotrexate-induced liver injury and provide an effective approach. This invention clarifies the role and mechanism of linoleic acid mediated by gut microbiota in methotrexate-induced diurnal rhythmic liver injury. We propose the following three technical problems: 1) To clarify the effect of methotrexate on the gut microbiota structure, using pseudo-germ-free mouse models and fecal microbiota transplantation models to clarify the role of gut microbiota in methotrexate-induced diurnal rhythmic liver injury. 2) To clarify the role of linoleic acid in methotrexate-induced liver injury, using histopathological staining, serum biochemical detection, and oxidative stress index detection to clarify the role of linoleic acid in methotrexate-induced liver injury. 3) To elucidate the mechanism by which linoleic acid mediates methotrexate-induced diurnal rhythmic liver injury, and to clarify the specific mechanism of linoleic acid in methotrexate-induced diurnal rhythmic liver injury through microbiome, non-targeted metabolomics, and transcriptomics. Detecting linoleic acid levels in fecal or serum samples can enable early assessment of methotrexate-induced liver injury, thereby achieving early, rapid, accurate, and large-scale screening of individuals with methotrexate-induced liver injury.

[0042] The detection results of the linoleic acid metabolites are used to guide individualized methotrexate dosing regimens, such as adjusting the dosing time and dietary regimen to reduce the risk of liver damage.

[0043] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. However, the present invention may be implemented in many different ways as limited and covered by the claims.

[0044] Experimental methods not specifically described in the examples are generally performed under standard conditions or as recommended by the manufacturer. Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0045] Compared with existing technologies, this invention provides a novel biomarker associated with methotrexate-induced liver injury. Using a methotrexate circadian rhythm liver injury model, a pseudo-germ-free mouse model, and fecal microbiota transplantation experiments, this invention employs microbiome and metabolomics techniques to discover that methotrexate-induced liver injury exhibits a diurnal rhythm, characterized by "mild in the morning and severe in the evening," and is dependent on the gut microbiota. Furthermore, administration at night disrupted the gut microbiota structure in mice, leading to the accumulation of linoleic acid and its oxidative metabolites, and a decrease in Lactobacillus levels. This indicates that linoleic acid and its oxidative metabolites may be independent risk factors and predictors of methotrexate, and kits targeting this site can predict and intervene in methotrexate-induced liver injury at an early stage.

[0046] Example 1

[0047] 1. Experimental design and research plan.

[0048] 1.1 Experimental Subjects. Six- to eight-week-old male C57BL / 6J mice, weighing 22-25 g, were selected as research subjects. All mice were purchased from Hunan Slack Jingda Laboratory Animal Co., Ltd. All mice were housed in an SPF-grade laboratory animal research center at an ambient temperature of 25°C, with free access to food and water, and a 12-hour light / dark cycle. Mice were housed and acclimatized at the laboratory animal center for one week prior to the formal experiments. All experimental protocols were approved by the Laboratory Animal Ethics Committee of Xiangya Medical College (Ethics No.: CSU-2024-0195).

[0049] 1.2 Methotrexate-induced diurnal rhythmic liver injury model. This invention used 6-8 week old specific pathogen-free C57BL / 6 male mice as research subjects. After one week of acclimatization, their body weight was measured and they were randomly assigned to groups. Methotrexate was dissolved in a small amount of 0.2% sodium hydroxide and then added to physiological saline to prepare a concentration of 15 mg / ml. Methotrexate (150 mg / kg) was administered at ZT0 (8 AM) and ZT12 (8 PM), respectively. Histopathological staining and Western blotting were used to verify whether methotrexate-induced liver injury exhibited diurnal variation.

[0050] 1.3 Pseudo-germ-free mouse model. Mice were randomly divided into two groups (n = 6): antibiotic-ZT0 and antibiotic-ZT12 groups. Both groups of mice were administered the antibiotic mixture by gavage for 7 consecutive days. On day 8, mice in the antibiotic-ZT0 and antibiotic-ZT12 groups were given a single intraperitoneal injection of methotrexate (150 mg / kg) in ZT0 and ZT12, respectively. Samples were collected 5 days later.

[0051] 1.4 Fecal microbiota transplantation (FMT) mouse model. Mice were randomly divided into two groups (n = 6): RZT0 and RZT12. Mice in both groups were administered an antibiotic mixture by gavage for 7 days, followed by fecal microbiota solution from the RZT0 and RZT12 groups by gavage for 14 days. On day 22, mice in both groups received a single intraperitoneal injection of methotrexate (150 mg / kg) at the RZT0 location. Samples were collected 5 days later.

[0052] 1.5 Linoleic Acid Accumulation Model. Mice were randomly divided into two groups: a methotrexate group and a linoleic acid combined with methotrexate group. Mice in the methotrexate group were administered 0.5% sodium carboxymethyl cellulose every other day for two weeks, followed by a single intraperitoneal injection of methotrexate (150 mg / kg) at 8:00 AM. Mice in the linoleic acid combined with methotrexate group were administered linoleic acid (1 g / kg) every other day for two weeks, followed by a single intraperitoneal injection of methotrexate (150 mg / kg) at 8:00 AM. Samples were collected after 5 days.

[0053] 1.6 Experimental methods.

[0054] 1.6.1 Histopathological staining. After dehydration and paraffin embedding, liver tissue was sectioned to the desired thickness (usually 5 µm) using a microtome. Hematoxylin-eosin staining (HE staining) was then performed, and the tissue was observed under a light microscope. Chiu's score and histopathological score were used to assess liver histopathological damage.

[0055] 1.6.2 Determination of Liver Biochemical Indicators. Blood samples were incubated at 4℃ for 2 h, then centrifuged at 3000 rpm for 15 min, and the supernatant serum was collected. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (AKP) levels in the serum were measured using a Nanjing Jiancheng diagnostic kit.

[0056] 1.6.3 Measurement of liver oxidative stress indicators. 50 mg of liver tissue was mixed with 9 times its volume of physiological saline (450 μL), and 3 large and 2 small pickaxe beads were added. The mixture was then homogenized in a tissue homogenizer for 2 min. The homogenate was then centrifuged at 3000 rpm for 10 min, and the supernatant was collected. Malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) were measured according to the manufacturer's instructions.

[0057] 1.6.4 Western Blot. Extraction and concentration determination of tissue proteins: We prepared a mixed lysis buffer in advance, consisting of RIPA, protease inhibitor, and phosphatase inhibitor at a ratio of 100:1:1. Using the BCA kit, the absorbance was measured at 560 nm using a microplate reader, and the protein concentration was calculated based on the standard curve. Protein denaturation: The tissue protein supernatant was mixed with protein loading buffer at a ratio of 4:1 and denatured at 99℃ for 10 min. After aliquoting, the samples were stored at -80℃. Gel preparation and electrophoresis: A 10% separating gel was prepared according to the kit instructions and flattened using anhydrous ethanol. After the separating gel solidified, a 5% stacking gel was added, and the mixture was incubated at room temperature for 30 min. The prepared separating gel was placed in the electrophoresis tank and clamped. After slowly removing the comb, an appropriate amount of pre-stained protein marker was added as needed, and the sample was loaded at a rate of 25 μg / well. After sample loading, place the electrophoresis tank on the electrophoresis apparatus and check the electrophoresis buffer level. Electrophore at 80 V for 30 min, then continue electrophoresis at 120 V for 60 min. Transfer and blocking: Cut the PVDF membrane to the electrophoretic distance in advance and activate it in methanol for 5 min. Place a white transparent sandwich plate at the bottom, then add transfer cotton, filter paper, PVDF membrane, and separating gel. After removing air bubbles from the gap between the PVDF membrane and separating gel, clamp the sandwich plate, add pre-cooled transfer buffer, and transfer at 400 mA for 30 min. After transfer, place the PVDF membrane in protein-free rapid blocking buffer and block on a shaker at room temperature for 25 min. Antibody incubation and development: After blocking, wash the PVDF membrane with 1×TBST for 10 min, repeating 3 times. Then, place it in a specific primary antibody and incubate overnight on a shaker at 4°C. The next day, wash the PVDF membrane with 1×TBST for 10 min, repeating 3 times. Then, incubate the PVDF membrane with rabbit / mouse secondary antibody at room temperature for 1 h. The PVDF membrane was washed with 1×TBST for 10 min, repeated 3 times. Finally, the PVDF membrane was immersed in developing solution, developed in a developer, and the protein grayscale value was calculated using ImageJ (version 1.46r) software.

[0058] 1.6.5 Fecal 16S rRNA Sequencing Analysis. DNA was extracted from mouse fecal samples using a DNA extraction kit, and PCR products were purified. Libraries were merged and sequenced on the Illumina platform according to the required effective library concentration and data volume. Raw tags were quality filtered using FASTP software to obtain high-quality effective data. Chimeric sequences were removed using the UCHIME algorithm, and sequences with ≥97% similarity were assigned to the same operational taxonomic unit (OTU). α-diversity was used to analyze the species diversity of the samples, including Chao1 and ACE indices. β-diversity analysis was used to evaluate the microbial community structure, and weighted and unweighted UniFrac distances were calculated using QIIME software.

[0059] 1.6.6 Non-targeted metabolomics analysis. Samples stored at -80°C were thawed on ice. 400 μL of a solution containing an internal standard (methanol:water = 7:3, V / V) was added to 20 mg of cecal contents, and the mixture was vortexed for 3 min. The sample was sonicated in an ice-water bath for 10 min, vortexed for 1 min, and then placed at -20°C for 30 min. Subsequently, the sample was centrifuged for 10 min (12000 rpm, 4°C). The precipitate was discarded, and the supernatant was centrifuged for 3 min (12000 rpm, 4°C). 200 μL of the supernatant was used for analysis using liquid chromatography-mass spectrometry (LC-MS). For the two analyses, differential metabolites were determined by VIP values ​​(VIP > 1) and P-values ​​(P < 0.05, t-test). VIP values ​​were extracted from the results of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), which also includes score plots and permutation plots, generated using the R package MetaboAnalystR. Prior to OPLS-DA, the data underwent logarithmic transformation (Log2) and mean normalization. Permutation tests were performed to avoid overfitting. Identified metabolites were annotated and enriched.

[0060] 1.6.7 Liver RNA-seq analysis. (1) RNA extraction, cDNA library preparation and sequencing. Total RNA was extracted using Trizol and evaluated using an Agilent 2100 BioAnalyzer and Qubit Fluorometer. Total RNA samples meeting the following requirements were used for subsequent experiments: RNA integrity value greater than 7.0 and 28S:18S ratio greater than 1.8. RNA-seq library construction and sequencing were performed by Beijing Bio-Tech. Independent libraries were constructed for all experimental replicates and subsequently sequenced and analyzed. Sequencing libraries were constructed using the NEBNext® Ultra™ RNA Non-Strand Specific Library Preparation Kit. Poly(a) tailed mRNA molecules were enriched from 1 μg of total RNA using the NEBNext® High Initiation Poly(a) mRNA Isolation Module Kit. The mRNA was fragmented into fragments of approximately 200 base pairs. Using the mRNA fragments as templates, first-strand cDNA was synthesized using reverse transcriptase and random hexamer primers, followed by second-strand cDNA synthesis using DNA polymerase I and RNase H. End repair was performed on the cDNA fragments, including the addition of a single "A" base, followed by adapter ligation. The products were purified and enriched by PCR to amplify the library DNA. The final library was quantified using the KAPA Library Quantification Kit and an Agilent 2100 Bioanalyzer. After RT-qPCR validation, the library was sequenced at both ends on an Illumina NovaSeq6000 sequencer. (2) RNA-seq data analysis. The raw sequencing data were first assessed for quality using FastQC (version: 0.11.5), and then quality controlled using NGSQC (version: 2.3.3) to remove low-quality sequences. The high-quality reads after quality control were aligned to the genome using HISAT2 (version: 2.1.0) with default parameter settings. Gene expression quantification was performed using StringTie (version: 1.3.3b), and differential expression analysis was performed using DESeq (version: 1.28.0). To identify differentially expressed genes, we performed multiple hypothesis testing on all genes and used the Benjamini-Hochberg method to calculate the false discovery rate to correct for the p-value (i.e., the q-value). The screening criteria for differentially expressed genes (DEGs) were: a fold change in expression ≥2 (|log2FC|≥1) and a q-value ≤0.05. Finally, we performed functional annotation of differentially expressed genes based on the ENSEMBL, NCBI, Uniprot, GO, and KEGG databases to elucidate their potential biological functions and pathways.

[0061] 1.6.8 Statistical Analysis. We used R (version 4.4.3) and GraphPad Prism 9.5.0 for statistical analysis and visualization. Data are expressed as mean ± standard error. Independent samples t-tests were used for comparisons between groups that met the requirements of normality and homogeneity of variance. Dunnett's T3 test or Welch's T test was used for comparisons that did not meet the requirement of homogeneity of variance. The Mann-Whitney U test was used for comparisons that did not conform to a normal distribution. P < 0.05 was considered statistically significant.

[0062] 2. Experimental results.

[0063] 2.1 High-dose methotrexate-induced liver injury exhibits a diurnal rhythm. The livers of mice in the ZT0 group had smooth surfaces, a reddish color, and a soft, elastic texture, while the livers of mice in the ZT12 group showed white necrotic spots, a darker color, poor elasticity, fatty degeneration of hepatocytes, and interstitial hemorrhage, with significantly elevated liver pathological scores (P < 0.05). Figure 1 Compared with the ZT0 group, the ZT12 group mice showed significantly increased levels of ALT, AKP, and MDA (P < 0.05), significantly decreased levels of GSH and SOD (P < 0.0001), and although AST levels were increased, the difference was not statistically significant (P > 0.05). Figure 2 Western blot results showed that, compared with the ZT0 group, the expression of p62, HO-1, and GPX4 proteins in the ZT12 group mice was significantly reduced (P < 0.05 or P < 0.01). Figure 3 The above results indicate that administration of methotrexate at night causes more severe liver damage and may exacerbate hepatic oxidative stress damage by downregulating the p62 / HO-1 signaling pathway.

[0064] 2.2 High-dose methotrexate-induced diurnal rhythmic liver injury is dependent on gut microbiota. There were no significant differences in liver color, appearance, and texture between the antibiotic-ZT0 and antibiotic-ZT12 groups. Both groups of mice exhibited steatosis and apoptotic bodies, and there was no significant difference in histopathological scores (P > 0.05). Figure 4 There were no significant differences in ALT, AST, AKP, GSH, SOD, and MDA levels between the antibiotic-ZT0 and antibiotic-ZT12 groups (P > 0.05). Figure 5 Western blot results showed no significant difference in the expression levels of p62, HO-1, and GPX4 proteins between the antibiotic-ZT0 and antibiotic-ZT12 groups (P > 0.05). Figure 6In the fecal microbiota transplantation model, the livers of mice in the RZT0 group had smooth surfaces, a reddish color, and a soft texture. The livers of mice in the RZT12 group had smooth surfaces, but were whitish in color with white necrotic spots. The hepatocytes were disorganized, exhibiting fatty degeneration, and the pathological score was significantly elevated (P < 0.001). Figure 7 Compared with the RZT0 group, the serum ALT, AST, AKP, and MDA levels in the RZT12 group were significantly increased (P < 0.05 or P < 0.01), while the GSH and SOD levels were significantly decreased (P < 0.01). Figure 8 Western blot results showed that the expression of p62, HO-1, and GPX4 proteins in RZT12 group mice was significantly reduced (P < 0.05 or P < 0.001 or P < 0.0001). Figure 9 The pseudo-germ-free mouse model and fecal microbiota transplantation model suggest that the gut microbiota may be a key mediator of high-dose methotrexate-induced diurnal rhythmic liver injury.

[0065] 2.3 High-dose methotrexate induces diurnal differences in gut microbiota diversity and composition.

[0066] The ZT0 and ZT12 groups of mice had 82 and 47 unique OUTs, respectively, and shared 445 OUTs, with a similarity level of 97%. α-diversity results showed that compared with the ZT0 group, the Chao1 index, Observed_species index, and ACE index were significantly lower in the ZT12 group (P<0.05). Figure 10 and Figure 11 Furthermore, β-diversity results showed a significant separation in the gut microbiota structure between the ZT0 and ZT12 groups of mice. Figure 12 ).

[0067] The effects of high-dose methotrexate on the gut microbiota structure of mice at the phylum, genus, and species levels are shown in Table 1. Compared with the ZT0 group, at the phylum level, mice in the ZT12 group showed decreased abundance of species such as Firmicutes and increased abundance of Bacteroidota. At the genus level, mice in the ZT12 group showed decreased abundance of species such as Odoribacter, Alistipes, Lactobacillus, Helicobacter, and Ligilactobacillus, while increasing abundance of species such as Streptococcus, Dubosiella, and Prevotellaceae_UCG-001. At the species level, mice in the ZT12 group showed decreased abundance of species such as Lactobacillus johnsonii, Lactobacillus muinus, Helicobacter typhlonius, and Lachnospiraceae bacterium COE1, while increasing abundance of species such as Bifidobacterium pseudolongum and Lactobacillus reuteri. Table 1 shows the relative abundance of intestinal flora in mice after administration of high-dose methotrexate in the morning and evening.

[0068] Table 1

[0069] 2.4 Untargeted metabolomics was used to explore the metabolic profile of cecal contents. A total of 3361 metabolites were detected using untargeted metabolomics, with 607 metabolites showing significant differences (250 downregulated, 357 upregulated). OPLS-DA showed that the metabolite profiles of ZT0 and ZT12 were clearly separated. Figure 13 Compared with the ZT0 group, the top 5 upregulated differentially expressed metabolites in the ZT12 group were 6-hydroxytryptophan B (log2FC=3.5), ALLM (calpain inhibitor, log2FC=3.32), Linoleic Acid-d4 (linoleic acid-d4, log2FC=3.30), isoflurane acetate (isofluprednisolone acetate, log2FC=3.18), and chlormadinone acetate (chlormadinone acetate, log2FC=3.15).

[0070] KEGG and SMPDB enrichment analyses showed that differentially expressed metabolites were enriched in metabolic pathways (P=0.037) and α-linolenic acid and linoleic acid metabolic pathways. Figure 14Compared with the ZT0 group, the levels of linoleic acid and its oxidative metabolites (9,10-EpOME, 9-HpODE, 12,13-DiHOME, 12,13-EpOME, 13-HpODE, 9S-HODE, and 13S-HODE) in the cecal contents of mice in the ZT12 group were significantly upregulated (P<0.05). Figure 15 Spearman correlation analysis revealed that linoleic acid, 9,10-EpOME, 12,13-EpOME, and 12,13-DiHOME were significantly positively correlated with liver ALT, AKP, and MDA levels (P < 0.05), while they were significantly negatively correlated with liver GSH and SOD levels (P < 0.05). Figure 16 ).

[0071] The oxidative metabolites of linoleic acid include 9,10-EpOME (9,10-cis-epoxide of linoleic acid), 9-HpODE (9-hydroperoxy-10(E),12(Z)-octadecadienoic acid, generated by the oxidation of linoleic acid under the action of lipoxygenase), 12,13-DiHOME (12,13-dihydroxy-9-octadecadienoic acid), 12,13-EpOME (12,13-epoxyoctadecadienoic acid), 13-HpODE (13(S)-hydroperoxylinoleic acid), 9S-HODE (9S-hydroxy-10E,12Z-octadecadienoic acid), and 13S-HODE (13(S)-hydroxy-9Z,11E-octadecadienoic acid).

[0072] 2.5 RNA-Seq sequencing analysis of differentially expressed genes. The analysis revealed 773 differentially expressed genes in the ZT0 and ZT12 groups. Compared to the ZT0 group, the ZT12 group had 392 upregulated genes and 381 downregulated genes. We screened for differentially expressed genes and performed cluster analysis, finding that the ZT0 and ZT12 samples were effectively distinguishable, and that their gene expression patterns differed significantly. GO and KEGG enrichment analyses showed that the main enriched metabolic pathways were fatty acid metabolism and the PPAR signaling pathway.

[0073] 2.6 Combined analysis of microbiome, metabolomics, and transcriptomics. At the genus level, the correlation analysis results between gut microbiota and differentially expressed as follows: Figure 17 (a) shows that Lactobacillus was negatively correlated with 9,10-EpOME, 12,13-EpOME, and 12,13-DiHOME. At the species level, the correlation analysis results between gut microbiota and differentially expressed as follows: Figure 17(b) shows that Lactobacillus johnsonii is negatively correlated with 9,10-EpOME, 12,13-EpOME and 12,13-DiHOME.

[0074] Spearman correlation analysis showed that linoleic acid was significantly negatively correlated with Lpl, Insig2, and Apoa4 (P < 0.05), and significantly positively correlated with Bdh2 (P < 0.05). 9,10-EpOME, 12,13-EpOME, and 12,13-DiHOME were significantly negatively correlated with Avpr1a, and significantly positively correlated with Fapp5, Ptgr1, and Acacb (P < 0.05). Figure 18 Fabp5 was the gene with the highest upregulation (Log2 FC = 2.68).

[0075] 2.7 Linoleic acid synergistically exacerbated methotrexate-induced diurnal rhythmic liver injury. Compared with the methotrexate group, the linoleic acid combined group showed more severe liver pathological damage, including disordered hepatocyte arrangement, steatosis, and hepatocyte edema, with a significantly higher liver pathological score (P < 0.05). Figure 19 Furthermore, compared with the methotrexate group, the linoleic acid combined group showed significantly increased serum ALT and AKP levels (P < 0.01), significantly decreased GSH and SOD levels (P < 0.05), and no significant difference in AST and MDA levels (P > 0.05). Figure 20 ).

[0076] Compared with the methotrexate group, the linoleic acid combined group showed significantly increased Fapp5 gene expression in the liver of mice, and significantly decreased mRNA expression levels of Ppara and its downstream genes Ppargc1α, Crot, Cpt2, Cpt1a, Scd1, Acox1, and Abcd1 (P < 0.05). Figure 21 The above results suggest that linoleic acid may synergistically exacerbate methotrexate-induced liver injury through the FABP5 / PPARα signaling pathway.

[0077] Example 2

[0078] This embodiment describes the detection steps for linoleic acid and its oxidative metabolites.

[0079] Method 1: ELISA detection.

[0080] Sample Pretreatment: 1. Serum (plasma): Follow the ELISA sample detection procedure directly. 2. Cell or tissue samples: 1) Cells: Digest the cells, transfer them to centrifuge tubes, centrifuge at 1000 rpm for 5 min, and discard the supernatant; add cells to physiological saline (mL) at a ratio of 500,000:1 into EP tubes, sonicate at 200 W in an ice-water bath for 4 s, with 5 s intervals, repeating 20 times. Then, centrifuge at 10,000 rpm at 4 ℃ for 10 min, carefully aspirate the supernatant, and store at 4 ℃. 2) Tissue: Weigh approximately 1 g of tissue at a ratio of tissue (g): physiological saline (mL) of 1:10, add 10 mL of physiological saline, homogenize in a pre-cooled homogenizer, centrifuge at 10,000 rpm at 4 ℃ for 10 min, carefully aspirate the supernatant, and store at 4 ℃.

[0081] ELISA sample detection steps: The experimental group consisted of 20 μL of serum, plasma, cell, or tissue samples, while the control group consisted of 20 μL of standards (0.625, 0.25, 0.5, 1, 2 ng / mL). For both the experimental and control groups, 20 μL of sample diluent and 40 μL of linoleic acid and its oxidative metabolite-labeled antibody were added. The mixture was gently vortexed and incubated at 37°C for 45 min. The liquid in the wells was discarded. Then, 100 μL of washing buffer was added to each well, and the mixture was gently patted dry. This process was repeated three times. Next, 80 μL of streptavidin-HRP was added to each well, and the mixture was gently vortexed and incubated at 37°C for 30 min. The supernatant was discarded. Then, 100 μL of washing buffer was added to each well, and the mixture was gently patted dry. This process was repeated three times. 40 μL each of substrate A and B were added to each well, and the mixture was gently vortexed and incubated in the dark for 15 min. Finally, 40 μL of stop solution was added to each well. Immediately after adding the stop solution, the OD value of each well was measured at 450 nm.

[0082] Method 2: GC-MS detection.

[0083] 1. Serum. 1) Fatty acid extraction: Take 100 μL of plasma or serum sample, add 10 μL of heptadecanoic acid internal standard solution (5 mg / mL), then add 200 μL of methyl tert-butyl ether (MTBE) and 40 μL of methanol (MTBE:water:methanol = 10:5:2), vortex for 30 seconds, centrifuge at 3000 rpm for 15 min, and collect the upper organic phase. 2) Fatty acid methyl esterification: Add 400 μL of methanol and 60 μL of trimethylsilyl diazomethane to the upper organic phase, let stand at room temperature for 15 min, and use the inability to fade the yellow color of the solution as an indication that methyl esterification is complete. Finally, blow the liquid dry with nitrogen, add 1 mL of n-hexane to fully reconstitute, transfer to a 2 mL sample vial, and store at -20℃ or directly perform GC-MS analysis.

[0084] 2. Tissue. 1) Fatty acid extraction: Take 20 mg of tissue sample, add 200 μL of physiological saline for homogenization, then add 10 μL of heptadecanoate internal standard solution (5 mg / mL), 400 μL of methyl tert-butyl ether (MTBE), and 80 μL of methanol sequentially, maintaining the volume ratio of MTBE, water, and methanol in the mixed solution at 10:5:2; vortex for 30 seconds, sonicate for 10 min, and then centrifuge at 3000 rpm for 15 min. Carefully transfer the upper MTBE extract phase to a new EP tube. 2) Fatty acid methyl esterification: Add 400 μL of methanol and 60 μL of trimethylsilyl diazomethane to the extract, and let it stand at room temperature for 15 min. If the yellow color of the solution persists, methyl esterification is complete. Finally, dry the derivatized liquid with nitrogen, add 1 mL of n-hexane to fully reconstitute, transfer the solution to a 2 mL injection bottle, and store at -20℃ or directly perform GC-MS analysis.

[0085] Chromatographic conditions: An HP-88 column was used with high-purity helium as the carrier gas, and split injection mode was employed. The column temperature program was set as follows: initial temperature 160℃ and held for 3 min; then increased to 240℃ at a rate of 2.5℃ / min; and held at this temperature for 15 min. The injector and detector temperatures were maintained constant at 240℃, the ion source temperature was 230℃, and the electron impact energy was 70 eV. Finally, quantitative analysis was performed by calculating the ratio of the target analyte peak area to the internal standard peak area.

[0086] In summary, this invention first provides a kit for screening high-risk individuals for liver injury caused by the chemotherapy drug methotrexate. The kit includes a detection reagent for measuring the levels of linoleic acid and its oxidative metabolites in the human body. This invention also provides the application of this detection reagent in the preparation of a methotrexate liver injury prediction product. The detection reagent is for measuring the levels of linoleic acid and its oxidative metabolites in the human body, and the product is a detection kit. In this invention, high levels of linoleic acid and its oxidative metabolites predict a high risk of liver injury after methotrexate use. This invention enables rapid screening of high-risk individuals for methotrexate-induced liver injury, allowing for early intervention to reduce the risk of developing methotrexate-induced liver injury.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A kit for screening high-risk individuals for liver injury caused by the chemotherapy drug methotrexate, characterized in that, The kit includes a detection reagent for detecting the content of linoleic acid and its oxidative metabolites in the human body.

2. The application of a detection reagent in the preparation of methotrexate-induced liver injury prediction products, characterized in that, The detection reagent is used to detect the content of linoleic acid and its oxidative metabolites in the human body, and the product is a detection kit.

3. The application according to claim 2, characterized in that, The oxidative metabolites of linoleic acid include one or more of 9,10-EpOME, 9-HpODE, 12,13-DiHOME, 12,13-EpOME, 13-HpODE, 9S-HODE, and 13S-HODE.

4. The application according to claim 2, characterized in that, The oxidative metabolites of linoleic acid include one or more of 9-HpODE, 9,10-EpOME, 12,13-EpOME, and 12,13-DiHOME.

5. The application according to claim 4, characterized in that, The kit includes detection reagents for detecting the content of linoleic acid and its oxidative metabolites 9,10-EpOME, 12,13-EpOME and 12,13-DiHOME in the human body.

6. The application according to claim 2, characterized in that, The kit is used to detect the levels of linoleic acid and its oxidative metabolites in fecal or serum samples from patients.

7. The application according to any one of claims 2 to 6, characterized in that, The kit also includes reagents for detecting intestinal Lactobacillus flora.

8. The application according to any one of claims 2 to 6, characterized in that, The kit also includes detection modules for liver function indicators ALT and AST.

9. The application according to any one of claims 2 to 6, characterized in that, The kit is used for ELISA detection and includes antibodies labeled with linoleic acid and its oxidative metabolites.

10. The application according to any one of claims 2 to 6, characterized in that, The kit is used for GC-MS detection and includes chemical reagents for extracting linoleic acid and its oxidative metabolites, and chemical reagents for methyl esterification of linoleic acid and its oxidative metabolites.

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