A biomarker for modulating oxidative stress in a tuberculosis patient

Abstract

The application belongs to the technical field of biology and particularly relates to a biomarker for regulating oxidative stress state of a tuberculosis patient. After being infected with Mycobacterium tuberculosis, a macrophage produces a large amount of active oxygen to kill or inhibit the Mycobacterium tuberculosis, and genes are enriched to oxidation-related functions through GO function; KEGG enrichment analysis finds related pathways and genes upstream and downstream of the active oxygen. CYBB, ITPR1, CAMK2B, GPX3, CAT and SOD2 genes are screened; when the CAMK2B, GPX3 and SOD2 genes are up-regulated and the CYBB and CAT genes are down-regulated, the active oxygen level of the macrophage infected with the Mycobacterium tuberculosis is changed, thereby causing the oxidative stress state to be changed.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a biomarker for regulating oxidative stress in tuberculosis patients. Background Technology

[0002] Tuberculosis (TB) is a chronic, wasting zoonotic disease caused by Mycobacterium tuberculosis and is one of the world's leading causes of death. According to a 2022 report by the World Health Organization (WHO), an estimated 10.6 million people were living with TB in 2021, compared to 10.1 million in 2020. With increasing environmental pollution, the prevalence of drug-resistant TB strains, and the frequent co-infections with HIV / AIDS, the incidence of TB is rising, posing a serious threat to global human health. The situation regarding TB prevention and control remains severe.

[0003] Oxidative stress (OS) refers to an imbalance between the body's oxidative and antioxidant capacities, with a predominance of oxidation leading to neutrophil inflammatory infiltration, increased protease secretion, and the production of large amounts of oxidative intermediates. The balance between reactive oxygen species (ROS) and antioxidants may be inextricably linked to the occurrence and development of diseases. The generation and metabolic imbalance of ROS are associated with multi-system diseases, with respiratory diseases being particularly prevalent.

[0004] After Mycobacterium tuberculosis invades the human body, it resides within macrophages. The main immune response of the human body against Mycobacterium tuberculosis is a cell-mediated immune response, primarily involving CD4 cells. + and CD8 + T cells. Tuberculosis is also closely related to reactive oxygen species (ROS). Clinical studies have found that the overall survival (OS) of tuberculosis patients changed significantly compared to those after anti-tuberculosis treatment. In vitro studies suggest that macrophages produce large amounts of ROS after infection with Mycobacterium tuberculosis (MTB). Research indicates that MTB can be killed or inhibited by ROS. Macrophages are phagocytic cells of the innate immune system, playing a central role in tissue homeostasis and responses to pathogenic stimuli. They can produce large amounts of ROS and reactive nitrogen, leading to inflammatory responses, apoptosis, ferroptosis, autophagy, and other cellular functions, further influencing the progression of tuberculosis. Therefore, regulating host oxidative stress may become a new target for tuberculosis treatment. Summary of the Invention

[0005] In a first aspect, the present invention provides a biomarker for regulating the oxidative stress state of tuberculosis patients. The biomarker is selected from the CYBB, ITPR1, CAMK2B, GPX3, CAT and / or SOD2 genes. The CYBB, ITPR1 and CAMK2B genes are mainly related to the production of reactive oxygen species (ROS) in the oxidative stress state of tuberculosis patients, while the GPX3, CAT and SOD2 genes are mainly related to the clearance of ROS in the oxidative stress state of tuberculosis patients. When the expression of CAMK2B, GPX3 and SOD2 genes is upregulated and the expression of CYBB and CAT genes is downregulated, the ROS level of macrophages infected with Mycobacterium tuberculosis changes, thereby leading to an alteration in the oxidative stress state.

[0006] Furthermore, the tuberculosis includes infection caused by the presence of the pathogen Mycobacterium tuberculosis.

[0007] Furthermore, the Mycobacterium tuberculosis infection includes: primary infection, secondary infection, and extrapulmonary infection.

[0008] Furthermore, the oxidative stress refers to a state of imbalance between the body's oxidative and antioxidant capacities, with a tendency towards oxidation, leading to neutrophil inflammatory infiltration, increased protease secretion, and the production of a large number of oxidative intermediates. The reactive oxygen species are produced by macrophages infected with Mycobacterium tuberculosis, and the reactive oxygen species can kill or inhibit Mycobacterium tuberculosis.

[0009] Secondly, this invention provides a method for screening biomarkers that regulate oxidative stress in tuberculosis patients. The biomarkers are selected from the CYBB, ITPR1, CAMK2B, GPX3, CAT, and / or SOD2 genes. The CYBB, ITPR1, and CAMK2B genes are mainly related to the production of reactive oxygen species (ROS) in tuberculosis patients under oxidative stress, while the GPX3, CAT, and SOD2 genes are mainly related to the clearance of ROS in tuberculosis patients under oxidative stress. When the expression of CAMK2B, GPX3, and SOD2 genes is upregulated, and the expression of CYBB and CAT genes is downregulated, the ROS level in macrophages infected with Mycobacterium tuberculosis changes, thereby leading to an alteration in oxidative stress. The method includes the following steps:

[0010] S1. Culture human monocytes and induce their differentiation into macrophages, while simultaneously culturing Mycobacterium tuberculosis;

[0011] S2. Establish a cell infection model. Macrophages differentiated from human monocytes infected by Mycobacterium tuberculosis obtained in S1 were used as the infection group, and an uninfected group was also set up.

[0012] S3. Extract and identify total RNA from macrophages infected with Mycobacterium tuberculosis obtained in S2, and perform concentration determination, ribonucleic acid sequencing and transcriptomics analysis;

[0013] S4. Based on the transcript abundance, i.e., expression level, during S3, screen for differentially expressed genes between the infected and uninfected groups;

[0014] S5. The differentially expressed genes selected in S4 were compared with human genes related to reactive oxygen species in the GeneCards database using Venn analysis, resulting in 458 differentially expressed genes.

[0015] S6. The 458 differentially expressed genes obtained in S5 were analyzed by GO and KEGG, and 6 genes were screened out by transcript abundance, i.e. expression level, and their expression levels were analyzed.

[0016] Furthermore, in S1, the human mononuclear cells are selected from the human mononuclear leukemia cell line (THP-1 cell line), and the Mycobacterium tuberculosis is selected from the Mycobacterium tuberculosis standard strain H37Rv.

[0017] Furthermore, in S5, when screening differentially expressed genes, after expression level analysis, GO and KEGG libraries are annotated and enriched. GO analysis is used to identify genes related to reactive oxygen species (ROS) function, and KEGG analysis is used to identify differentially expressed genes upstream and downstream of ROS.

[0018] Furthermore, the expression level is calculated by the number of sequences located in the genomic region, which is reflected by the abundance of transcripts. The higher the transcript abundance, the higher the gene expression level.

[0019] Furthermore, the abundance of transcripts refers to the amount of mature mRNA transcribed from a gene, which is determined by the promoter and affected by cell state and promoter strength.

[0020] Furthermore, in S6, the expression level analysis involves reverse transcribing the RNA extracted in S3 into cDNA, designing primers, and detecting the expression level using RT-qPCR. The primer sequences are shown in SEQ ID NO. 1~12.

[0021] CYBB upstream primer: SEQ ID NO.1 ACCGGGTTTATGATATTCCACCT;

[0022] CYBB downstream primer: SEQ ID NO.2 GATTTCGACAGACTGGCAAGA;

[0023] ITPR1 upstream primer: SEQ ID NO.3 ATTGCTGGGGACCGTAATCC;

[0024] ITPR1 downstream primer: SEQ ID NO.4 TCCAATGTGACTCTCATGGCA;

[0025] CAMK2B upstream primer: SEQ ID NO. 5 GCACACCAGGCTACCTGTC;

[0026] CAMK2B downstream primer: SEQ ID NO. 6 GGACGGGAAGTCATAGGCA;

[0027] GPX3 upstream primer: SEQ ID NO.7 GAGCTTGCACCATTCGGTCT;

[0028] GPX3 downstream primer: SEQ ID NO. 8 GGGTAGGAAGGATCTCTGAGTTC;

[0029] CAT upstream primer: SEQ ID NO.9 TGTTGCTGGAGAATCGGGTTC;

[0030] CAT downstream primer: SEQ ID NO. 10 TCCCAGTTACCATCTTCTGTGTA;

[0031] SOD2 upstream primer: SEQ ID NO.11 TTTCAATAAGGAACGGGGACAC;

[0032] SOD2 downstream primer: SEQ ID NO.12 GTGCTCCCACACATCAATCC.

[0033] Thirdly, the present invention provides the use of a biomarker in the preparation of a promoter or inhibitor of oxidative stress in tuberculosis patients, wherein the biomarker is selected from the CYBB, ITPR1, CAMK2B, GPX3, CAT and / or SOD2 genes.

[0034] Furthermore, the promoter or inhibitor also includes pharmaceutically acceptable excipients.

[0035] Furthermore, the excipient is a pharmaceutical excipient that enhances the effect of the promoter or inhibitor, reduces toxicity, and / or alleviates toxic side effects. Attached Figure Description

[0036] Figure 1 The correlation between CFU and ROS in MTB-infected macrophages;

[0037] Figure 2 Differential expression of ROS-related genes in MTB-infected macrophages;

[0038] Figure 3 GO analysis of ROS-related genes in MTB-infected macrophages;

[0039] Figure 4 KEGG analysis of ROS-related genes in MTB-infected macrophages;

[0040] Figure 5 PPI analysis of ROS-related genes in MTB-infected macrophages;

[0041] Figure 6 : Validation of differential gene expression; A. CYBB; B. CAMK2B; C. ITPR1; D. GPX3; E. SOD2; F. CAT. Detailed Implementation

[0042] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the embodiments described below can be combined with each other as long as they do not conflict with each other.

[0043] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the experimental materials used in the following embodiments are all available through conventional commercial channels.

[0044] The “oxidative stress” described in this article refers to a state of imbalance between the body’s oxidative and antioxidant capacities, with a tendency towards oxidation, leading to neutrophil inflammatory infiltration, increased protease secretion, and the production of a large number of oxidative intermediates.

[0045] The term "transcription abundance" as used in this article refers to the amount of mature mRNA transcribed from genes, which is determined by the promoter and influenced by cell state and promoter strength.

[0046] The reactive oxygen species (ROS) detection kit was purchased from Beijing Solarbio Science & Technology Co., Ltd., and the cell / bacterial total RNA extraction kit was purchased from Nanjing Novizan Biotechnology Co., Ltd.

[0047] Example 1: Culture of macrophages and Mycobacterium tuberculosis

[0048] Experimental methods

[0049] Culture of THP-1 cells

[0050] (1) Turn on the switch of the electric thermostatic water bath and preheat it to 37°C.

[0051] (2) Take out the frozen THP-1 cells from the liquid nitrogen tank, and after the liquid nitrogen on the surface of the cell cryopreservation tube has evaporated, put it into an electric thermostatic water bath at 37°C to thaw.

[0052] (3) Wipe the surface of the biosafety cabinet clean with 75% alcohol, mix the cell suspension by pipetting, transfer it to a sterile 15 ml centrifuge tube, add an equal volume of RPMI 1640 complete culture medium, centrifuge at 800 rpm for 5 min at room temperature, and carefully discard the supernatant.

[0053] (4) Use a pipette to take an appropriate amount of RPMI 1640 complete culture medium to resuspend the cell pellet, mix by pipetting, and transfer to a cell culture flask.

[0054] (5) Carefully observe the morphology of the cells under a microscope. After confirming that the cells are in good condition, place the cell culture flask in a 37°C, 5% CO2 cell culture incubator.

[0055] (6) Remove the cell culture flask from the 5% CO2 cell culture incubator and observe the cell growth status under a microscope. When the cells are growing well, have clear outlines, and a density of about 2×10⁻⁶, the growth status is considered good. 6 When the cell count is 100 cells / ml, transfer the cell suspension to a sterile 50 ml centrifuge tube in a biosafety cabinet, centrifuge at 800 rpm for 5 min at room temperature, and carefully discard the supernatant.

[0056] (7) Use a pipette to resuspend the cell pellet in an appropriate amount of RPMI 1640 complete culture medium, mix well by pipetting, and transfer the cell suspension to a new cell culture flask at a ratio of 1:2 to 1:3 for passage. Change the medium every 2-3 days, and select cells that are growing vigorously and are of uniform size for subsequent experiments.

[0057] THP-1 cell differentiation

[0058] (1) When the number of cultured cells reaches the experimental requirement, observe the cells under a microscope to confirm that the cells are growing well. Centrifuge at 800 rpm for 5 min at room temperature, carefully discard the supernatant, and add an appropriate amount of fresh RPMI 1640 complete culture medium to resuspend the cell pellet.

[0059] (2) The cell density was adjusted to 1×10⁻⁶ by cell counting. 6 1 ml of cell suspension was added to the cell suspension to achieve a final concentration of 100 ng / ml. After the cell suspension was thoroughly mixed, it was seeded into 12-well cell culture plates with 1 ml of cell suspension per well. Three replicates were set for each group at each time point.

[0060] (3) Place the cell culture plate in a 37°C, 5% CO2 cell culture incubator to induce differentiation for 36 h for subsequent experiments.

[0061] MTB cultivation

[0062] The frozen MTB standard strain H37Rv, preserved in the laboratory, was taken out and thawed at room temperature. After mixing thoroughly by pipetting in a biosafety cabinet, 100 μl of the bacterial suspension was added to the slant of neutral Löwenkel medium. The bottle was gently rotated to ensure even coverage of the slant. The slant was then labeled, and the medium was incubated at 37°C for 3-4 weeks. This invention uses only the MTB standard strain H37Rv; clinical and drug-resistant strains were not used.

[0063] Example 2: Establishment of a cell infection model

[0064] Experimental Groups

[0065] (1) Uninfected group: Uninfected macrophages.

[0066] (2) Infection group: macrophages infected with MTB H37Rv.

[0067] Methods of infection

[0068] (1) Zero the UV-Vis spectrophotometer using RPMI 1640 complete culture medium to achieve the initial OD. 600 =0.00.

[0069] (2) After thoroughly grinding the MTB strain in the logarithmic growth phase in a grinding flask, add 1 ml of RPMI 1640 complete medium to each flask to form a bacterial suspension. Separately, take a sterile 15 ml centrifuge tube and adjust the relative content of RPMI 1640 complete medium and bacterial suspension to adjust the OD of the strain. 600 =1. At this point, the concentration of each strain is approximately 1×10⁻⁶. 8 CFU / ml.

[0070] (3) Wash the induced differentiated macrophages three times with 1×PBS buffer. Add RPMI 1640 complete medium containing MTB to the induced differentiated macrophages at an MOI ratio of 10:1. Place the 12-well cell culture plate in a 37℃, 5% CO2 cell culture incubator and continue to culture for 3h to allow MTB to infect the macrophages.

[0071] Macrophage intracellular MTB count

[0072] (1) Three hours after infecting macrophages, the cell culture supernatant was aspirated, and the cells were washed three times with 1×PBS buffer to remove extracellular bacteria. Then, according to the experimental setup, 1 ml of RPMI 1640 complete culture medium was added to the corresponding cell culture wells. The cell culture plates were placed in a 37°C, 5% CO2 cell culture incubator for further culture.

[0073] (2) At 0, 4, 24 and 48 h after adding the corresponding culture medium, the cell culture supernatant was aspirated with a pipette, and 1 ml of 0.05% SDS solution was added to each well. After standing for 5 min, the cells at the bottom of the cell culture plate were thoroughly pipetted until they ruptured, so that the MTB in the macrophages was completely released.

[0074] (3) Prepare four sterile 1.5 ml EP tubes for the cell lysate obtained from the infection and label them. Add 900 μl of 1×PBS buffer to the second, third, and fourth sterile 1.5 ml EP tubes respectively. Transfer the obtained cell lysate to the corresponding first sterile 1.5 ml EP tube.

[0075] (4) After thoroughly mixing the lysis buffer, use a pipette to add 100 μl to the corresponding sterile 1.5 ml EP tube No. 2 and dilute it 10 times. Then, after thoroughly mixing the 1.5 ml EP tube No. 2, use a pipette to add 100 μl to the 1.5 ml EP tube No. 3 and dilute it 10 times. Repeat the same method until the 1.5 ml EP tube No. 4 is used to perform a concentration gradient dilution of the two cell lysis buffers.

[0076] (5) Use a pipette to take 100 μl of cell lysis buffer diluted at different ratios from sterile 1.5 ml EP tubes No. 3 and No. 4 and drop it into 7H10 agar plates containing 10% OADC. Spread the cell lysis buffer evenly with a spreader. Then seal the 7H10 agar plates with sealing film and invert them in a 37 ℃ constant temperature incubator for 3-4 weeks. Count the colonies when they are visible to the naked eye and are uniform in size.

[0077] ROS measurement

[0078] (1) Preparation: Run Guava once for washing; preheat 1 ml of trypsin to 37°C. Discard the old culture medium and wash the adherent cells once with 1 × PBS.

[0079] (2) Digestion: Add 250 μl of trypsin to each well, incubate at 37°C for 5 min, add 750 µl of complete culture medium to stop digestion, gently pipette the cells, aspirate 500 µl into an EP tube, centrifuge at 1500 rpm for 5 min, and discard the supernatant. Resuspend the cells in 500 μl of PBS, centrifuge at 1500 rpm for 5 min, and discard the supernatant.

[0080] (3) Staining: Resuspend the cells in 500 μl of DCFH-DA solution, place the EP tubes together, cover with aluminum foil to block light, and incubate in a cell culture incubator at 37°C for 20 min, inverting and mixing every 5 min. Centrifuge at 1500 rpm for 5 min and discard the supernatant.

[0081] (4) Washing after staining: Resuspend in 500 μl of serum-free 1640 medium, centrifuge at 1500 rpm for 5 min, and discard the supernatant. Resuspend in 500 μl of 1× PBS, centrifuge at 1500 rpm for 5 min, and discard the supernatant.

[0082] (5) Detection: Resuspend in PBS, inject 200 μl into each well of a 96-well plate, and detect by flow cytometry.

[0083] Experimental results

[0084] like Figure 1 As shown in Figure A, the number of bacteria in macrophages infected with MTB gradually increases with the duration of post-infection. Figure 1 As shown in B, ROS levels in the uninfected group did not change significantly at 0, 4, 24, and 48 h. However, after macrophages were infected with MTB, ROS levels increased at 4 h and peaked at 48 h, significantly higher than in the uninfected group (p<0.05). (A) CFU (B) ROS. Uninfected group; Infected group; ROS, reactive oxygen species; CFU, colony-forming units; MFI, mean fluorescence intensity; DCFH-DA, 2,7-dichlorodihydrofluorescein diacetate. Values ​​are expressed as mean ± SEM. *** p < 0.001; *** p < 0.0001. * In (A), each group is compared to 0 h.

[0085] Example 3: Differential gene screening and validation

[0086] Total RNA extraction

[0087] The experimental cell pellet was collected by centrifugation and stored in a freezer at -80°C. Before the experiment, the sample was thawed on ice. Total RNA was extracted from the cells using a cell / bacterial total RNA extraction kit. The specific operation steps are as follows: All the following processes were carried out in an RNase-free environment. 80 ml of anhydrous ethanol was added to Buffer RW2.

[0088] (1) Transfer the lysed sample to FastPure gDNA-Filter Columns Ⅲ (FastPure gDNA-Filter Columns Ⅲ has been placed in the collection tube), and centrifuge at 12,000 rpm for 30 seconds. Discard FastPure gDNA-Filter Columns Ⅲ and collect the filtrate.

[0089] (2) Add 0.5 times the volume of anhydrous ethanol to the filtrate (for liver tissue samples, add 1 times the volume of 50% ethanol) and mix thoroughly. After adding ethanol, the solution will become turbid or develop flocculent precipitate, which is normal. After shaking and mixing, you can proceed to the next step.

[0090] (3) Transfer all the mixture from step 2 to FastPure RNA Columns III (FastPure RNA Columns III has been placed in the collection tube), centrifuge at 12,000 rpm for 30 s, and discard the filtrate.

[0091] (4) Add 700 μl of Buffer RW1 to FastPure RNA Columns Ⅲ, centrifuge at 12,000 rpm (13,400× g) for 30 s, and discard the filtrate.

[0092] (5) Add 700 μl of Buffer RW2 (with anhydrous ethanol added) to FastPure RNA Columns Ⅲ, centrifuge at 12,000 rpm for 30 s, and discard the filtrate.

[0093] (6) Add 500 μl of Buffer RW2 (with anhydrous ethanol added) to FastPure RNA Columns Ⅲ, centrifuge at 12,000 rpm (13,400 × g) for 2 min, and carefully remove the adsorption column from the collection tube to avoid contact with the filtrate and contamination.

[0094] (7) If there is liquid residue on the adsorption column or it comes into contact with the filtrate, discard the filtrate, put FastPure RNA Columns Ⅲ back into the collection tube, and air-free for 1 min at 12,000 rpm (13,400 × g) to prevent ethanol contamination.

[0095] (8) Carefully transfer the adsorption column to a new RNase-free Collection Tubes 1.5 ml centrifuge tube. Add 50-200 μl of RNase-free ddH2O dropwise to the center of the adsorption column, incubate at room temperature for 1 min, and centrifuge at 12,000 rpm (13,400 × g) for 1 min to elute RNA. To increase yield, the RNase-free ddH2O can be preheated at 65℃, added to the membrane, and incubated at room temperature for 2-5 min; or a second elution can be performed after centrifugation.

[0096] (9) The concentration and quality of RNA samples were determined. When all samples met the standards, the next step of the experiment was carried out. All RNA samples were frozen at -80℃ for later use.

[0097] Total RNA concentration determination and quality assessment

[0098] (1) The total RNA concentration and A260 / 280 ratio of each extracted sample were determined using a micro-ultraviolet spectrophotometer. All RNA samples were placed on ice during the measurement process to prevent degradation. RNA samples with an A260 / 280 ratio of around 2 were selected for subsequent experiments in this study.

[0099] (2) Weigh 1.5 g of agarose powder using an electronic balance, add it to 100 ml of 1× TAE buffer, mix thoroughly, heat in a microwave oven until the mixture is clear and transparent, and when the temperature drops to about 55°C, add 10 μl of 4S Green Plus nucleic acid dye to it using a pipette, mix thoroughly, pour it into a gel casting plate with the comb inserted, let it stand at room temperature for 30 min, and after the gel has completely solidified, pull the comb vertically upward to prepare a 1.5% agarose gel.

[0100] (3) Place the 1.5% agarose gel into an electrophoresis tank containing 1× TAE buffer. Use a pipette to take 3 μl of each RNA sample and mix it with 3 μl of RNA loading buffer. Heat at 65℃ for 10 min, cool rapidly, and then add each sample to the loading well in sequence. Adjust the voltage to 180 V and perform electrophoresis for 15 min. After electrophoresis, remove the agarose gel from the electrophoresis tank, take a picture using a gel imaging system, and select RNA samples without obvious degradation for subsequent experiments.

[0101] RNA sequencing

[0102] (1) Oligo dT enrichment of mRNA: Eukaryotic mRNA has a polyA tail at the 3' end. By using magnetic beads with Oligo (dT) to pair with polyA for AT base pairing, mRNA can be isolated from total RNA for transcriptome analysis.

[0103] (2) Fragmenting mRNA: The Illumina platform is designed for sequencing short sequence fragments. The enriched mRNA is a complete RNA sequence with an average length of several kb, so it needs to be randomly fragmented. By adding a fragmentation buffer and selecting appropriate conditions, the mRNA can be randomly fragmented into small fragments of about 300 bp.

[0104] (3) Reverse synthesis of cDNA: Under the action of reverse transcriptase, using random primers, one-stranded cDNA is synthesized by reverse synthesis with mRNA as a template, followed by two-strand synthesis to form a stable double-stranded structure.

[0105] (4) Connecting adapter: The double-stranded cDNA structure has sticky ends. Add End Repair Mix to make it into blunt ends, and then add an A base at the 3' end to connect the Y-shaped adapter.

[0106] (5) Fragment screening and library enrichment: The product after ligation with the adapter is purified and fragments are sorted. The sorted products are used for PCR amplification, and the final library is obtained after purification.

[0107] (6) Sequencing on Illumina NovaSeq6000: Quantification using QuantiFluor® dsDNA System, mixing data according to the ratio for sequencing; bridge PCR amplification on cBot to generate clusters; sequencing on Illumina.

[0108] Transcriptomics analysis

[0109] (1) Statistics of raw sequence data

[0110] Illumina sequencing is a second-generation sequencing technology that can generate billions of reads in a single run. Such a massive amount of data makes it impossible to show the quality of each read individually. By using statistical methods to analyze the base distribution and quality fluctuations of each cycle of all sequencing reads, the sequencing quality and library construction quality of the sample can be reflected in a macroscopic and intuitive way.

[0111] (2) Quality control of raw sequencing data

[0112] Because raw sequencing data often contains sequencing adapter sequences, low-quality reads, sequences with high base information rates but uncertain lengths, and excessively short sequences, the quality of subsequent analyses can be severely affected. To ensure the accuracy of subsequent bioinformatics analysis, the raw sequencing data is first filtered to obtain high-quality sequencing data, thus guaranteeing the smooth progress of subsequent analyses. The software used is FastP.

[0113] (3) Alignment with reference genome

[0114] The raw data after quality control were compared with the reference genome using HiSat2 and StringTie software.

[0115] (4) Expression level analysis

[0116] Transcript abundance reflects gene expression levels; higher transcript abundance indicates higher gene expression levels. In RNA sequencing analysis, gene expression levels are calculated by the number of sequences located in genomic regions. RSEM software is used to quantitatively analyze gene and transcript expression levels separately, enabling subsequent analysis of differential gene / transcript expression among different samples. Furthermore, by combining sequence functional information, the regulatory mechanisms of genes can be revealed.

[0117] (5) Analysis of expression differences

[0118] After obtaining the gene count, differential gene expression analysis was performed on multiple samples (≥2) to identify differentially expressed genes among samples, and then the functions of differentially expressed genes were studied. Software used included edgeR, DEGseq, DESeq2, Limma, and NOIseq; DESeq2 was used for differential expression analysis.

[0119] (6) GO annotation analysis of differentially expressed genes

[0120] GO (Gene Ontology) is a comprehensive database established by the Gene Ontology Consortium that categorizes and summarizes all gene-related research results worldwide. This database standardizes biological terminology related to genes and gene products from different databases, defining and describing gene and protein functions. Using the GO database, genes can be classified according to the biological processes they participate in (BP), cell components (CC), and molecular functions they perform (MF). GO annotation is performed on differentially expressed genes, and the results are plotted as up- and down-regulated gene GO annotation histograms.

[0121] (7) Differential gene KEGG annotation analysis

[0122] KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematically analyzing gene function and linking genomic and functional information. Using the KEGG database, genes can be classified according to the pathways they participate in or the functions they perform. KEGG annotation of differentially expressed genes displays these genes on a KEGG pathway map, showing the KEGG-annotated pathway maps of up- and down-regulated differentially expressed genes.

[0123] (8) GO enrichment analysis of differentially expressed genes

[0124] GO (Gene Ontology) is a comprehensive database established by the Gene Ontology Consortium, classifying and summarizing all gene-related research results worldwide. This database standardizes biological terminology related to genes and gene products across different databases, defining and describing gene and protein functions. Using the GO database, genes can be categorized according to the biological processes (BP) they participate in, cell components (CC), and molecular functions (MF). GO functional enrichment analysis of differentially expressed genes can illustrate their functional enrichment, clarifying differences between samples at the gene function level. This analysis used Goatools software for enrichment analysis, employing Fisher's exact test. To control for false positives, four multiple test methods (Bonferroni, Holm, BH, and BY) were used to correct the p-values. Generally, a corrected p-value (p_fdr) ≤ 0.05 is considered a significant GO functional enrichment.

[0125] (9) Differential gene KEGG enrichment analysis

[0126] The KEGG database is a public database for genome decoding. Enrichment analysis typically analyzes whether a group of genes has appeared at a specific functional node, evolving from single-gene annotation analysis to gene set annotation analysis. Enrichment analysis improves the reliability of research, enabling the identification of biological processes most relevant to biological phenomena. This analysis used KOBAS for KEGG pathway enrichment analysis, with the calculation principle the same as GO functional enrichment analysis, and Fisher's exact test was used for calculation. To control the false positive rate, the BH (FDR) method was used for multiple testing, with the calculation formula the same as in the previous section. The corrected p-value was set with a threshold of 0.05. KEGG pathways meeting this condition were defined as significantly enriched in differentially expressed genes.

[0127] Reverse transcription to synthesize cDNA

[0128] (1) The Hifair II 1st strand cDNA synthesis supermix from Yeasen Biotech was used to reverse transcribe each RNA sample that met the standard. The total system was 20 μl. The preparation method is shown in Table 1. All reaction systems were prepared on ice.

[0129] Table 1 Reverse transcription system

[0130]

[0131] (2) After the reaction system is prepared, the reaction is carried out on the PCR instrument under the conditions shown in Table 2:

[0132] Table 2 Reverse transcription conditions

[0133]

[0134] (3) After the reaction is complete, freeze the cDNA sample synthesized by reverse transcription at -20°C for later use.

[0135] Primer design

[0136] We searched for human β-actin and differentially expressed gene primer sequences on the Primer Bank website and verified them using Primer-Blast on the NCBI website. The primers were synthesized by Beijing Qingke Biotechnology Co., Ltd., and the primer sequences are shown in Table 3.

[0137] Table 3. Primer sequences for differentially expressed genes

[0138]

[0139] RT-qPCR detection of mRNA expression levels

[0140] (1) Using cDNA as a template and β-actin as an internal control gene, the expression levels of the target genes at each time point in both groups were detected using the Hifair® qPCR SYBR green master mix (Low Rox) kit from Yisheng Biotechnology Co., Ltd. The cDNA template was appropriately diluted so that the amplification cycle number (CycleThreshold, Ct) of β-actin and the target gene fell between 20 and 30 for optimal results.

[0141] (2) Add each sample to the corresponding 96-well PCR reaction plate according to the reaction system shown in Table 4. Set up 3 replicates for each time point of each group.

[0142] Table 4 RT-qPCR reaction system

[0143]

[0144] (3) After preparing the reaction system on ice, the reaction was carried out on an ABI 7500 real-time quantitative PCR instrument from Applied Biosystems according to the reaction conditions shown in Table 5:

[0145] Table 5 RT-qPCR reaction conditions

[0146]

[0147] Experimental results

[0148] like Figure 2 As shown in Figures AB, differentially expressed genes in macrophages between MTB-infected and uninfected macrophages were identified using RNA sequencing and bioinformatics analysis. The results showed a total of 1613 differentially expressed genes, of which 726 were upregulated and 887 were downregulated. Furthermore, we retrieved ROS-related human genes from the GeneCards database and created a gene set, which was then used to analyze gene expression differences between MTB-infected and uninfected macrophages using the gene set retrieved from Venn. Figure 2 C shows 458 differentially expressed genes. (A) Cluster heatmap of differentially expressed genes (B) Volcano plot of differentially expressed genes (C) Venn analysis of differentially expressed genes and ROS-related genes. Note: Inf, infected group; Uninf, uninfected group.

[0149] like Figure 3 As shown in Table A, the GO annotation analysis results indicate the influence of 458 differentially expressed genes in three aspects. For molecular function, they primarily affect binding and catalytic activity. For cellular components, they primarily affect organelle function, followed by cell membrane function. For biological processes, they primarily affect cellular processes, biological regulation, and responses to stimuli. As shown in Table 6, of the 458 differentially expressed genes, 269 are related to receiving external stimuli. From a cellular composition perspective, we identified 272 differentially expressed genes associated with organelles, confirming that mitochondria, endoplasmic reticulum, and other related organelles are involved in ROS production in MTB-infected macrophages. In terms of molecular function, some differentially expressed ROS-related enzymes, such as PTGS2, GPX3, SOD2, CAT, ALDH1A2, and CHAC1. GO enrichment analysis showed that the main functions of these genes were enriched in cytokine production, acute inflammatory responses, and cholesterol homeostasis. Figure 3 B), (A) GO annotation analysis (B) GO enrichment analysis.

[0150] Table 6. Statistical analysis of some GO annotations

[0151]

[0152] like Figure 4 As shown in Figure A, the KEGG annotation analysis results show that the differentially expressed genes are mainly related to the pathogenesis of infectious diseases, cancer, and cardiovascular diseases in humans. In terms of biological systems, they affect the immune system, endocrine system, and digestive system, as well as eukaryotic cells, cell growth, and cell death.

[0153] like Figure 4 As shown in B and Table 7, KEGG enrichment analysis revealed that it primarily affects the IL-17 signaling pathway and NOD-like receptor pathway in biological systems, involving diseases such as rheumatoid arthritis, atherosclerosis, and cancer. It also influences cytokine-receptor interactions and the PI3K-AKT, JAK-STAT, MAPK, and Rap1 signaling pathways. Furthermore, a related pathway, FoXO, was also observed, with differentially expressed genes potentially involving upstream SGK and downstream genes closely related to antioxidant activity, such as CAT and MnSOD. (A) KEGG annotation analysis (B) KEGG enrichment analysis.

[0154] Table 7. Statistical table of partial KEGG enrichment analysis

[0155]

[0156] Figure 5 PPI analysis revealed that IL6 has 21 associated proteins and exhibits the most protein interactions. Subsequent sub-analysis yielded two sub-rings, one containing proteins such as IL6, IL1A, IL1B, CXCL2, IL10, CXCL8, CCL4, CCL3, CXCL1, and CSF2, and the other containing proteins such as STAT1, ASG15, IRF7, OAS1, IFIT1, IFIT3, IRF9, IFI6, XAF1, and MX1.

[0157] like Figure 6 As shown in the figure. For genes related to ROS production, the expression level of CAMK2B gene in the infected group was significantly higher than that in the uninfected group (p < 0.05), the expression level of CYBB gene was significantly lower than that in the uninfected group, the expression level of ITPR1 was similar to that in the uninfected group (p > 0.05), the expression levels of GPX3 and SOD2 genes in the infected group were significantly higher than those in the uninfected group (p < 0.05), while the expression level of CAT gene was significantly lower than that in the uninfected group. Uninfected: Uninfected group; Infected: Infected group. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.

Claims

1. Application of biomarkers in the preparation of kits for regulating oxidative stress in tuberculosis patients, wherein the biomarkers are CYBB, ITPR1, CAMK2B, GPX3, CAT, and SOD2 genes. Among them, CYBB, ITPR1, and CAMK2B genes are mainly related to the production of reactive oxygen species (ROS) in the oxidative stress state of tuberculosis patients, while GPX3, CAT, and SOD2 genes are mainly related to the clearance of ROS in the oxidative stress state of tuberculosis patients. When the expression of CAMK2B, GPX3, and SOD2 genes is upregulated, and the expression of CYBB and CAT genes is downregulated, the ROS level of macrophages infected with Mycobacterium tuberculosis changes, thereby leading to changes in oxidative stress state.

2. The application as described in claim 1, characterized in that, Tuberculosis includes infections caused by the presence of the pathogen Mycobacterium tuberculosis.

3. The application of a primer set in the preparation of a kit for regulating oxidative stress in tuberculosis patients, wherein the primer set comprises an upstream primer of CYBB, a downstream primer of CYBB, an upstream primer of ITPR1, a downstream primer of ITPR1, an upstream primer of CAMK2B, a downstream primer of CAMK2B, an upstream primer of GPX3, a downstream primer of GPX3, an upstream primer of CAT, a downstream primer of CAT, an upstream primer of SOD2, and a downstream primer of SOD2; the primer set sequences are shown in SEQ ID NO. 1~12. CYBB upstream primer: SEQ ID NO.1 ACCGGGTTTATGATATTCCACCT; CYBB downstream primer: SEQ ID NO.2 GATTTCGACAGACTGGCAAGA; ITPR1 upstream primer: SEQ ID NO.3 ATTGCTGGGGACCGTAATCC; ITPR1 downstream primer: SEQ ID NO.4 TCCAATGTGACTCTCATGGCA; CAMK2B upstream primer: SEQ ID NO. 5 GCACACCAGGCTACCTGTC; CAMK2B downstream primer: SEQ ID NO. 6 GGACGGGAAGTCATAGGCA; GPX3 upstream primer: SEQ ID NO.7 GAGCTTGCACCATTCGGTCT; GPX3 downstream primer: SEQ ID NO. 8 GGGTAGGAAGGATCTCTGAGTTC; CAT upstream primer: SEQ ID NO.9 TGTTGCTGGAGAATCGGGTTC; CAT downstream primer: SEQ ID NO. 10 TCCCAGTTACCATCTTCTGTGTA; SOD2 upstream primer: SEQ ID NO.11 TTTCAATAAGGAACGGGGACAC; SOD2 downstream primer: SEQ ID NO.12 GTGCTCCCACACATCAATCC.

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