Method for screening key genes, metabolites and pathways of spinal cord injury based on multi-omics joint analysis

Abstract

The method for screening key genes, metabolites and pathways of spinal cord injury based on multi-omics joint analysis comprises the following steps: (1) establishing an experimental animal model of spinal cord injury, setting a spinal cord injury group and a sham operation group, and observing the motor function of the injury side of the experimental animal by motor function score; (2) extracting spinal cord tissue samples of each group, performing histological detection by HE staining and Nissl staining, performing transcriptome sequencing by RNA-seq technology, and performing metabolome sequencing by LC-MS / MS technology; (3) applying bioinformatics to analyze the biological significance of a single omics; (4) integrating transcriptome data and metabolome data for joint analysis, exploring key genes, metabolites and pathways affecting the pathophysiological mechanism of spinal cord injury, and constructing a gene-metabolite network interaction diagram. The molecular mechanism affecting the pathophysiological process of spinal cord injury and the relationship between injury microenvironment genes and metabolites are disclosed, which provides a research basis for multi-omics joint analysis and action of spinal cord injury.

Description

Technical Field

[0001] This invention belongs to the field of multi-omics joint analysis in medical application technology, specifically involving a method for screening key genes, metabolites and pathways of spinal cord injury through multi-omics joint analysis. Background Technology

[0002] Spinal cord injury, due to its devastating nature, causes substantial damage to the central nervous system, usually directly caused by factors such as trauma. Patients with spinal cord injuries experience varying degrees of motor and sensory function loss due to the disruption of neural circuits; in severe cases, they may even suffer complete loss of neurological function, severely impacting the quality of life of patients and their families and increasing the economic burden on society. Although numerous researchers have strived to explore neuroprotective and neuroregenerative therapies to completely improve patients' neurological function, due to the complex pathophysiological mechanisms of spinal cord injury, there is currently no cure. Based on the pathophysiological process, spinal cord injury can be divided into primary and secondary injuries. The mechanical damage of the former triggers a cascade of interacting molecular pathological reactions, disrupting the microenvironment of the affected area, thereby promoting the development of secondary spinal cord injury. During secondary spinal cord injury, the balance of the spinal cord microenvironment is disrupted at the molecular, cellular, and tissue levels: at the molecular level, this mainly involves the production of lipid mediators and free radicals, and the expression of neurotrophic factors and various cytokines and chemokines; at the cellular level, it involves the activation of microglia and astrocytes, the differentiation of endogenous neural stem cells and oligodendrocyte progenitor cells, and the infiltration of macrophages and immune cells; hemorrhage and ischemia, demyelination and remyelination, vascular remodeling, and glial scar formation mainly occur at the tissue level. This disruption of balance, accompanied by severe metabolic dysregulation, significantly hinders nerve regeneration and functional recovery after spinal cord injury. Therefore, understanding the underlying molecular mechanisms influencing the disruption of the injured spinal cord microenvironment is crucial for developing more effective neuroprotective measures.

[0003] Advances in high-throughput technologies have led to the development of a wide range of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. Through genomics, transcriptomics, and proteomics, researchers have gained a deeper understanding of the molecular mechanisms underlying disease. Furthermore, with the increasing focus on metabolites, the applications of metabolomics are also growing. Metabolites, as end products of cellular activity, are more closely related to phenotypes, and metabolomics amplifies subtle changes in genes and proteins—a unique advantage of metabolomics. Currently, metabolomics is increasingly being used as an analytical tool for the early diagnosis, prognostic assessment, and monitoring of treatment response in cancer and metabolic diseases. Simultaneously, the role of metabolomics in spinal cord injury is receiving increasing attention, particularly in studies related to specific biomarkers for assessing the severity and prognosis of spinal cord injury. Moreover, metabolomics helps to gain a deeper understanding of the pathophysiological processes of disease and alterations in metabolic pathways in vivo, enabling the study of the body's overall stress response to disease or environmental stimuli. In spinal cord injury, identifying changes in post-injury-related metabolites using metabolomics methods helps to understand and further explore the pathophysiological mechanisms of secondary spinal cord injury. However, a single "omics" approach is insufficient to fully reveal the progression of a disease. Metabolomics studies the endpoints of metabolic processes in the body and needs to be combined with other "omics" approaches (such as transcriptomics) to further elucidate the pathophysiological processes of diseases systematically.

[0004] Therefore, an increasing number of studies are shifting from single-omics approaches to using multi-omics to describe disease progression. This can effectively compensate for the shortcomings of single-omics approaches and may reveal more biological content than the sum of individual analyses. In the field of spinal cord injury, current research is mainly based on single omics, and there is an urgent need to provide a method that combines transcriptomics and metabolomics to analyze the pathological mechanisms of the spinal cord injury microenvironment. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for screening key genes, metabolites, and pathways of spinal cord injury based on multi-omics joint analysis. By combining transcriptomics and metabolomics analysis, differentially expressed genes related to spinal cord injury detected by transcriptomics and differentially expressed metabolites related to spinal cord injury detected by metabolomics are jointly analyzed to construct a gene-metabolite network interaction map. This can reveal the molecular mechanisms affecting the pathophysiological process of spinal cord injury and the relationship between genes and metabolites in the injury microenvironment at different molecular levels.

[0006] The technical solution of this invention is: a method for screening key genes, metabolites, and pathways of spinal cord injury based on multi-omics joint analysis, which is achieved through the following steps:

[0007] (1) Establish an experimental animal model of spinal cord injury, and set up a spinal cord injury group and a sham surgery group. The motor function of the experimental animals on the injured side was observed by scoring the motor function every day:

[0008] The experimental animals were divided into a sham surgery group and a spinal cord injury group. The motor function of the experimental animals in each group was scored daily after the operation.

[0009] (2) Tissue samples of the injured spinal cord were extracted from each group, and histological examination was performed using HE staining and Nissl staining. Transcriptome sequencing was performed using RNA-seq technology, and metabolome sequencing was performed using LC-MS / MS technology.

[0010] On the end of the experiment, T10 spinal cord tissue samples were collected from each group of experimental animals for histological examination to determine the pathological changes in the injury microenvironment, RNA-seq technology was used to perform transcriptome sequencing on the injured spinal cord tissue samples, and LC-MS / MS technology was used to perform metabolome sequencing on the injured spinal cord tissue samples.

[0011] (3) Applying bioinformatics to analyze the biological significance revealed by individual omics studies:

[0012] CPC2 was used to identify unigenes with coding potential. BLAST software was used to align the discovered unigenes with the Nr, Swiss-Prot, GO, eggNOG / COG, KOG, KEGG, and Pfam databases to obtain annotation information for new genes. Pathway enrichment analysis was performed on DEG using the GO and KEGG databases, and on DEM using KEGG. DEG for spinal cord injury was calculated based on FPKM values, with P < 0.05 used as the screening criterion. The DEM for spinal cord injury was analyzed using OPLS-DA, with VIP > 1 and P < 0.05 used as the screening criterion.

[0013] (4) Integrating transcriptomic and metabolomic data for joint analysis:

[0014] O2PLS analysis was used to identify the top 15 DEGs and DEMs with significant interactions between the transcriptome and metabolome. Correlation analysis was then performed on these DEGs and DEMs using R language to construct network diagrams. The Joint-Pathway Analysis tool in MetaboAnalyst was used to screen pathways co-enriched by DEGs and DEMs, with FDR < 0.01 and both enriched DEGs and DEMs having a value greater than 3 as the screening criteria.

[0015] The experimental animal mentioned in step (1) above is an SD rat.

[0016] The treatment methods for the sham surgery group and the spinal cord injury group mentioned in step (1) above are as follows: The experimental animals in the sham surgery group only underwent T9-T10 laminectomy without damaging the spinal cord; the spinal cord injury group underwent T9-T10 laminectomy to fully expose the T10 segment of the spinal cord, and a lateral hemisection was performed. A 1ml syringe needle was inserted vertically along the middle spinal artery, and then the left spinal cord was cut out three times to ensure that the hemisection was completed.

[0017] The metabolomics sequencing described in step (1) above is non-targeted metabolomics sequencing.

[0018] The motor function score mentioned in step (1) above is the BBB score.

[0019] The time points mentioned in step (1) above are the first day, the second day, and the third day after the injury.

[0020] In step (2) above, both transcriptome sequencing and metabolome sequencing are performed after the detection of total RNA and total metabolites.

[0021] Based on the above approach, primers for DEG randomly selected from transcriptome sequencing were designed to amplify spinal cord injury gene sequences using RT-PCR, thereby identifying key genes involved in spinal cord injury.

[0022] The Tmsb4x primer for spinal cord injury is:

[0023] F CCTTCTATCCTTCCCTGCCTCTCC;

[0024] R TTCCCTTCCCGTTACTCAGATACCC;

[0025] The Spp1 primer for spinal cord injury is:

[0026] F GACGATGATGACGACGACGATGAC;

[0027] R GTGTGCTGGCAGTGAAGGACTC;

[0028] The Rplp0 primer for spinal cord injury is:

[0029] F GCGGTAGGACGGATGAGAGGAG;

[0030] R GGTGTTCTGAGCAGGTACTGTGAC;

[0031] The Ftl1 primer for spinal cord injury is:

[0032] F AACCACCTGACCAACCTCCGTAG;

[0033] RCAAAGAGATACTCGCCCAGAGATGC;

[0034] The Plp1 primer for spinal cord injury is:

[0035] F GGTCTGCCTCCACTCACTCCTC;

[0036] R TCTGCCCAGTCATTAGCCCTCTAC;

[0037] The primers for GAPDH in spinal cord injury are:

[0038] F AGACAGCCGCATCTTCTTGT;

[0039] R CTTGCCGTGGGTAGAGTCAT;

[0040] Beneficial effects

[0041] This invention is the first to combine transcriptomic and metabolomic analyses in spinal cord injury, taking the intersection of the three to focus on key genes, metabolites, and pathways in spinal cord injury. This saves a lot of manpower and resources, helps to better understand the pathological mechanisms of acute spinal cord injury, and also helps to shift the treatment of spinal cord injury from a single factor to a multi-factor, complex network repair approach, providing a basis for future treatment of spinal cord injury. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the method steps of the present invention (this diagram was drawn by Figdraw).

[0043] Figure 2 This is a schematic diagram of the histological results of the acute phase of spinal cord injury as described in this invention.

[0044] In the figure: sham: sham surgery group; ASCI: acute spinal cord injury; HE: hematoxylin-eosin staining; Nissl: Nissl staining.

[0045] Figure 3 This is a schematic diagram of the motor function scoring results during the acute phase of spinal cord injury as described in this invention.

[0046] In the figure: sham: sham-operated group; ASCI: acute spinal cord injury; BBB score: motor function score of rat spinal cord injury; Days post-SCI: time point after spinal cord injury.

[0047] Figure 4 This is a schematic diagram of the pathway for the co-enrichment of genes and metabolites by the DEM described in this invention.

[0048] In the bubble diagram: Each bubble represents a path. The horizontal axis and bubble size indicate the magnitude of the influencing factors of that path in the analysis. The vertical axis and bubble color represent the p-value of the enrichment analysis (taking the negative natural logarithm, i.e., -ln(p)). Purine metabolism; Glycerophospholipid metabolism; Glycerolipid metabolism; Nitrogen metabolism; Glutathione metabolism; Phosphatidylinositol signaling system; Ascorbate and aldarate metabolism; Arginine biosynthesis; Inositol phosphate metabolism; Histidine metabolism; Arginine and proline metabolism; Drug metabolism - other enzymes; Mucin type O-glycan biosynthesis; Pyrimidine metabolism: pyrimidine metabolism; Linoleic acid metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; -ln P-value: P-value with negative natural logarithm; Impact: influencing factors. Figure 5 This is a schematic diagram of the top 15 key genes and key metabolites that affect the two omics data in the O2PLS analysis of this invention.

[0049] In the diagram, light colors represent key metabolites, and dark colors represent key genes; Apoe: apolipoprotein E; Ftl1: ferritin light chain 1; Rn18s: 18S preribosomal RNA; Rn28s: 28S preribosomal RNA; Plp1: protein lipoprotein 1; Rn45s: 45S preribosomal RNA; Tmsb4x: thymosin β4; Spp1: secretory phosphatase 1; Rplp0: ribosomal protein stalk subunit P0; Tmsb10: thymosin β10; Rplp1: ribosomal protein stalk subunit P1; Lgals1: galactolectin 1; 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine: 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine; N-Acetyl-L-aspartic acid acid: N-acetyl-L-aspartic acid; Hypoxanthine: hypoxanthine; 4-Dodecylbenzenesulfonic Acid: 4-dodecylbenzenesulfonic acid; 2-Methylbutyroylcarnitine: 2-methylbutyroylcarnitine; Uridine: uridine; L-Palmitoylcarnitine: L-palmitoylcarnitine; Elaidic carnitine: elaidine; Linoleic acid: linoleic acid; Stearoylethanolamide: hydroxyethyl stearamide; Ethyltetradecanoate: ethyl tetradecanoate; L-Carnitine: L-carnitine; PC: unnamed metabolite; LOC: unnamed gene.

[0050] Figure 6 This is a schematic diagram illustrating the correlation analysis between key genes and key metabolites in this invention.

[0051] In the figure: the darker the color, the more statistically significant the difference; Apoe: apolipoprotein E; Ftl1: ferritin light chain 1; Rn18s: 18S preribosomal RNA; Rn28s: 28S preribosomal RNA; Plp1: protein lipoprotein 1; Rn45s: 45S preribosomal RNA; Tmsb4x: thymosin β4; Spp1: secretory phosphatase 1; Rplp0: ribosomal protein stalk subunit P0; Tmsb10: thymosin β10; Rplp1: ribosomal protein stalk subunit P1; Lgals1: galactolectin 1; 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine: 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine; N-Acetyl-L-aspartic acid acid: N-acetyl-L-aspartic acid; Hypoxanthine: hypoxanthine; 4-Dodecylbenzenesulfonic Acid: 4-dodecylbenzenesulfonic acid; 2-Methylbutyroylcarnitine: 2-methylbutyroylcarnitine; Uridine: uridine; L-Palmitoylcarnitine: L-palmitoylcarnitine; Elaidic carnitine: elaidine; Linoleic acid: linoleic acid; Stearoylethanolamide: hydroxyethyl stearamide; Ethyltetradecanoate: ethyl tetradecanoate; L-Carnitine: L-carnitine; PC: unnamed metabolite; LOC: unnamed gene.

[0052] Figure 7 This is a schematic diagram of the correlation analysis network between key genes and key metabolites in this invention.

[0053] In the figure, Apoe: apolipoprotein E; Ftl1: ferritin light chain 1; Rn18s: 18S preribosomal RNA; Rn28s: 28S preribosomal RNA; Plp1: protein lipoprotein 1; Rn45s: 45S preribosomal RNA; Tmsb4x: thymosin β4; Spp1: secretory phosphatase 1; Rplp0: ribosomal protein stalk subunit P0; Tmsb10: thymosin β10; Rplp1: ribosomal protein stalk subunit P1; Lgals1: galactolectin 1; 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine: 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine; N-Acetyl-L-aspartic acid acid: N-acetyl-L-aspartic acid; Hypoxanthine: hypoxanthine; 4-Dodecylbenzenesulfonic Acid: 4-dodecylbenzenesulfonic acid; 2-Methylbutyroylcarnitine: 2-methylbutyroylcarnitine; Uridine: uridine; L-Palmitoylcarnitine: L-palmitoylcarnitine; Elaidic carnitine: elaidine; Linoleic acid: linoleic acid; Stearoylethanolamide: hydroxyethyl stearamide; Ethyl tetradecanoate: ethyl tetradecanoate; L-Carnitine: L-carnitine; PC: unnamed metabolite; LOC: unnamed gene. Detailed Implementation

[0054] The specific implementation of the technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0055] Example 1

[0056] (1) Establishment of a rat model of spinal cord injury

[0057] SD rats were anesthetized with an intraperitoneal injection of 1% sodium pentobarbital (5 mg / 100 g), and their body temperature was maintained using a heating pad. After locating the vertebral bodies of the 9th thoracic vertebra (T9) to T10, the surgical area was prepared and thoroughly disinfected with povidone-iodine and 75% ethanol. The skin, subcutaneous tissue, fascia, and muscles were incised layer by layer along the dorsal midline. Then, the paravertebral muscles were dissected laterally along the spinous processes to expose the spinous processes and laminae of T8-T11. The fully exposed T10 was then removed and a lateral hemisection was performed. A 1 ml syringe needle was inserted vertically along the middle vertebral artery, and the left spinal cord was incised three times laterally to ensure complete hemisection. In the sham-operated group, only laminectomy was performed without damaging the spinal cord. Postoperatively, the spinal cord injury group underwent bladder massage three times daily to aid urination. Motor function of the rats was evaluated using the double-blind Basso, Beattie, and Bresnahan (BBB) ​​motor scoring method on postoperative days 0, 1, 2, and 3.

[0058] (2) Histopathological staining of spinal cord tissue samples: Rats in the sham-operated group and the spinal cord injury group were anesthetized by intraperitoneal injection of 1% sodium pentobarbital (5mg / 100g) and then perfused with 0.9% physiological saline (room temperature, about 300mL), followed by injection of 4% paraformaldehyde tissue fixative. A spinal cord segment (about 2cm) was taken with the injury site as the midpoint, fixed with 4% paraformaldehyde for 24h, dehydrated with ethanol gradient, and embedded in paraffin. The spinal cord cross section was taken for HE staining and Nissl staining, and histopathological examination was performed under a light microscope. (3) Screening of differentially expressed genes by tissue transcriptome sequencing of spinal cord tissue samples.

[0059] Total RNA was extracted from the T10 spinal cord segment of rats in the spinal cord injury and sham-operated groups using TRIzol reagent (Life Technologies). RNA concentration was determined using a NanoDrop 2000 (Thermo Fisher Scientific, USA). RNA integrity was assessed using the RNA Nano 6000 assay kit from the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). 1 μg of total RNA was used as input for RNA sample preparation from each sample. Following the manufacturer's recommendations, the RNA was extracted using... of Ultra TMThe RNA Library Preparation Kit (NEB, USA) generates sequencing libraries and adds index codes to the attribute sequences of each sample. In short, mRNA is purified from total RNA using poly-T oligomer beads. Fragmentation is performed using divalent cations in NEBNext First-Strand Synthesis Reaction Buffer (5X) at elevated temperatures. First-strand cDNA is synthesized using random hexamer primers and M-MuLV reverse transcriptase. Second-strand cDNA synthesis is then performed using DNA polymerase I and RNAigenase H. Remaining dangling ends are converted to blunt ends by exonuclease / polymerase activity. After adenylation of the 3′ ends of the DNA fragments, they are ligated with a NEBNext Adapter featuring a hairpin loop structure, ready for hybridization. Library fragments are purified using the AMPureXP system, and cDNA fragments of 240 bp in length are selected. The cDNA is then ligated using 3 μl of USER Enzyme (NEB, USA) with a size-selective adapter, incubated at 37°C for 15 min, then at 95°C for 5 min, followed by PCR. PCR was then performed using Phusion High-Fidelity DNA polymerase, universal PCR primers, and index (X) primers. The PCR products were purified using the AMPure XP system, and library quality was assessed on an Agilent Bioanalyzer 2100 system. Following the manufacturer's instructions, the indexed samples were clustered on a cBot clustering system using the TruSeq PE Cluster Kit v3-cBot-HS (Illumia). After clustering, the library preparations were sequenced on the Illumina Hiseq platform to generate paired-end reads. The raw data in fastq format was first processed using an internal Perl script. In this step, clean reads were obtained by removing reads containing adapters, reads containing poly-N sequences, and low-quality reads from the raw data. Q20, Q30, GC content, and sequence repeat levels were calculated for the clean reads. All downstream analyses were based on high-quality clean data. Adapter sequences and low-quality reads were removed from the dataset. The raw sequences were transformed into clean sequences after data processing. These clean reads were then mapped to a reference genome sequence. Reads with only one exact match or one non-match were further analyzed and annotated based on the reference genome. Genome mapping analysis was performed using Tophat2 software. Gene function was annotated based on the following databases: Nr, Swiss-Prot, GO, eggNOG / COG, KOG, KEGG, and Pfam. Differential expression analysis between the two groups was performed using the DESeq R package (1.10.1).The obtained P-values ​​were corrected using the method of Benjamini and Hochberg to control the false discovery rate, and the corrected P < 0.05 was used as the screening criterion for differentially expressed genes.

[0060] By designing primers for DEG randomly selected from transcriptome sequencing, RT-PCR amplification of spinal cord injury gene sequences was performed, thereby identifying key genes involved in spinal cord injury.

[0061] The Tmsb4x primer for spinal cord injury is:

[0062] F CCTTCTATCCTTCCCTGCCTCTCC;

[0063] R TTCCCTTCCCGTTACTCAGATACCC;

[0064] The Spp1 primer for spinal cord injury is:

[0065] F GACGATGATGACGACGACGATGAC;

[0066] R GTGTGCTGGCAGTGAAGGACTC;

[0067] The Rplp0 primer for spinal cord injury is:

[0068] F GCGGTAGGACGGATGAGAGGAG;

[0069] R GGTGTTCTGAGCAGGTACTGTGAC;

[0070] The Ftl1 primer for spinal cord injury is:

[0071] F AACCACCTGACCAACCTCCGTAG;

[0072] RCAAAGAGATACTCGCCCAGAGATGC;

[0073] The Plp1 primer for spinal cord injury is:

[0074] F GGTCTGCCTCCACTCACTCCTC;

[0075] R TCTGCCCAGTCATTAGCCCTCTAC;

[0076] The primers for GAPDH in spinal cord injury are:

[0077] F AGACAGCCGCATCTTCTTGT;

[0078] R CTTGCCGTGGGTAGAGTCAT;

[0079] (4) Screening differential metabolites by tissue metabolomics sequencing of injured spinal cord tissue samples

[0080] 10 mg of the injured spinal cord sample was weighed into an Eppendorf tube, and 1000 μL of extraction buffer (methanol:water = 3:1, isotope-labeled internal standard mixture) was added. The sample was homogenized at 35 Hz for 4 minutes, vortexed for 30 seconds, and then sonicated in an ice-water bath for 5 minutes. The homogenization and sonication cycles were repeated 3 times. The sample was then incubated at -40 °C for 1 hour and centrifuged at 12,000 rpm for 15 minutes at 4 °C. The resulting supernatant was transferred to a fresh glass vial for analysis. Equal volumes of the supernatant from all samples were mixed to prepare a quality control (QC) sample. Detection was performed using a UHPLC system (Vanquish, Thermo Fisher Scientific) coupled with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) and a Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo Fisher Scientific). The mobile phase consisted of 5 mmol / L ammonium acetate and 5 mmol / L aqueous acetic acid (A) and acetonitrile (B). The autosampler temperature was 4 °C, and the injection volume was 3 μL. Under the control of the acquisition software (Xcalibur, Thermo Fisher Scientific), the QE HFX mass spectrometer acquired mass spectra in information-dependent acquisition (IDA) mode. Detailed parameters were as follows: Sheath gas flow rate: 50 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 320 °C, Full ms resolution: 60000, MS / MS resolution: 15000, Collision energy: 10 / 30 / 60 in NCE mode, Spray voltage: 3.8 kV (positive) or -3.4 kV (negative). The raw data were converted to mzXML format using ProteoWizard and processed using an internal R program based on XCMS for peak detection, extraction, alignment, and integration. Metabolite annotation was then performed using the internal MS2 database. The annotation cutoff was set to 0.3.

[0081] (5) Bioinformatics analysis of the biological significance revealed by single omics

[0082] Gene function enrichment analysis was performed on differentially expressed genes screened by transcriptome sequencing, including GO enrichment analysis and KEGG enrichment analysis. Specifically, the GOseq R package based on the Wallenius noncentrocentric hypergeometric distribution was used for GO enrichment analysis of differentially expressed genes; and KOBAS software was used for statistical enrichment analysis of differentially expressed genes in the KEGG pathway. Differentially expressed metabolites screened by metabolome sequencing were subjected to KEGG pathway enrichment analysis.

[0083] (6) Integrate transcriptome and metabolome data for joint analysis

[0084] This study utilizes O2PLS association analysis to deeply explore differentially expressed genes and metabolites, including gene-metabolite expression correlation analysis, pathway analysis for co-enrichment of differentially expressed genes and metabolites, and construction of correlation networks between differentially expressed genes and metabolites. By deeply exploring differentially expressed genes and metabolites through O2PLS association analysis, we can identify whether two datasets share common trends in determining model differences and pinpoint variables that are associated with these trends. These variables may be key variables that truly play a mechanistic role. To visually represent the correlation between differentially expressed genes obtained from transcriptomics and differentially expressed metabolites from metabolomics, we use the Spearman algorithm to perform correlation heatmap analysis on differentially expressed genes and metabolites. Subsequently, we simultaneously map differentially expressed genes and metabolites to the KEGG pathway database using the Pathview package in R to obtain their common pathway information. Using Cytoscape_v3.3.0 software, we screen for differentially expressed genes and metabolites located at key points in the network with a correlation coefficient (r) > 0.85 and p < 0.01 for correlation network analysis.

Claims

1. A method for screening key genes, metabolites and pathways of spinal cord injury based on multi-omics joint analysis, characterized in that, Includes the following steps: (1) Model establishment and phenotypic verification: SD rats were used to establish a T10 segment spinal cord lateral hemisection injury model, and a sham surgery group was set up as a control. On the 1st, 2nd and 3rd day after the operation, the BBB motor function scores of the rats were performed, and T10 spinal cord tissue was taken at the end of the experiment for HE staining and Nissl staining histological examination to confirm the success of the model and assess the degree of injury. (2) Multi-omics data collection: Total RNA and total metabolites were extracted from the spinal cord tissue samples obtained in step (1). After passing the quality test, transcriptome sequencing was performed using RNA-seq technology and non-targeted metabolome sequencing was performed using LC-MS / MS technology. (3) Single-group bioinformatics analysis: For transcriptome data, CPC2 and BLAST software were used to predict the coding potential of unigenes and to annotate the Nr, Swiss-Prot, GO, and KEGG databases. Differentially expressed genes (DEGs) were screened based on FPKM values ​​with a threshold of P<0.05, and GO and KEGG pathway enrichment analysis was performed. For metabolome data, OPLS-DA analysis was used to screen differentially expressed metabolites (DEMs) with a threshold of VIP>1 and P<0.05, and KEGG pathway enrichment analysis was performed. (4) Multi-omics joint analysis and network construction: Using the O2PLS analysis method, the top 15 DEGs and top 15 DEMs with the greatest interaction between transcriptome and metabolome were screened from the DEGs and DEMs obtained in step (3); the correlation analysis of the above key DEGs and DEMs was performed using R language and correlation heatmaps and network interaction diagrams were constructed; at the same time, the Joint-Pathway Analysis tool of the MetaboAnalyst platform was used to screen the KEGG pathways that were enriched by DEGs and DEMs. The screening criteria were FDR < 0.01 and the number of DEGs and DEMs enriched in the pathway was greater than 3; thus, the key genes, metabolites, pathways and their interactions in the acute phase of spinal cord injury were systematically revealed.

2. The method of claim 1, wherein, Key differentially expressed genes screened by transcriptome sequencing were experimentally validated using RT-PCR. The specific primer sequences used are as follows: The Tmsb4x primer for spinal cord injury is: F: CCTTCTATCCTTCCCTGCCTCTCC; R: TTCCCTTCCCGTTACTCAGATACCC; The Spp1 primer for spinal cord injury is: F: GACGATGATGACGACGACGATGAC; R:GTGTGCTGGCAGTGAAGGACTC; The Rplp0 primer for spinal cord injury is: F: GCGGTAGGACGGATGAGAGGAG; R: GGTGTTCTGAGCAGGTACTGTGAC; The Ftl1 primer for spinal cord injury is: F:AACCACCTGACCAACCTCCGTAG; R: CAAAGAGATACTCGCCCAGAGATGC; The Plp1 primer for spinal cord injury is: F: GGTCTGCCTCCACTCACTCCTC; R:TCTGCCCAGTCATTAGCCCTCTAC; The primers for the internal reference gene GAPDH are: F: AGACAAGCCGCATCTTCTTGT; R:CTTGCCGTGGGTAGAGTCAT.

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