Application of a protein detection reagent in the preparation of a product for diagnosing aggressive behavior in schizophrenia

Abstract

The present invention provides a reagent for detecting a protein for use in preparing a product for diagnosing aggressive behavior in schizophrenia. The protein is serum polio receptor protein, whose amino acid sequence is shown in SEQ NO. 1. The present invention utilizes high-throughput combined analysis of transcriptomics, proteomics, and metabolomics to screen for differentially expressed molecules between aggressive and non-aggressive schizophrenia groups. Combined with bioinformatics analysis of the interactions among the three, the present invention screens for differentially expressed proteins between the aggressive and non-aggressive schizophrenia groups. Further validation with expanded sample sizes reveals that the differentially expressed protein, serum polio receptor protein, exhibits a difference between the two groups (P < 0.05), and can serve as a potential biomarker for determining aggressive behavior in schizophrenia.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, in particular to the application of a reagent for detecting protein in the preparation of a product for diagnosing aggressive behavior in schizophrenia. Background Art

[0002] People with schizophrenia are 4 to 7 times more likely to commit violent crimes (such as assault and homicide) and 4 to 6 times more likely to exhibit general aggressive behavior (such as verbal and physical threats) than the general population.

[0003] Schizophrenia is a complex psychiatric disorder. A study used multi-omics analysis to identify changes in gene expression in postmortem brain tissue from patients with schizophrenia. The study integrated transcriptomics, proteomics, and metabolomics data to identify altered pathways and potential therapeutic targets for schizophrenia. The study identified several dysregulated pathways, including oxidative phosphorylation, mitochondrial function, and energy metabolism. Another study used a multi-omics approach, including genomics, transcriptomics, and proteomics, to analyze blood samples from patients with schizophrenia and healthy controls to identify potential biomarkers for schizophrenia, discovering abnormalities in the immune system, oxidative stress, and neurotransmitter signaling. The transcriptomics, proteomics, and metabolomics methods in the multi-omics approach can all provide supporting evidence for the development of new biomarkers and therapeutic targets, thereby providing a more comprehensive and integrated understanding of schizophrenia and its subtypes. However, there has been no report on the use of multi-omics approaches (including transcriptomics, proteomics, and metabolomics) to study aggressive behavior in schizophrenia, and there has been no report on the use of multi-omics approaches to screen potential protein biomarkers for aggressive behavior in schizophrenia.

[0004] Aggressive behavior is a complex, multifactorial phenomenon in schizophrenia. Genetic, environmental, and neurobiological factors all play a role in the development of aggressive behavior in this population. Schizophrenia patients with aggressive behavior require both safety precautions and treatment. If the risk of aggressive behavior is too high, protective measures (including appropriate restraint, protection, and isolation) may be necessary. Furthermore, active medication is necessary to control impulsive, violent, and aggressive behavior. However, due to the complex etiology and unclear pathological mechanisms of schizophrenia, there is a lack of specific biomarkers for aggressive behavior, resulting in a lack of objective indicators for the clinical diagnosis of aggressive behavior in schizophrenia.

[0005] Therefore, exploring specific protein biomarkers for the diagnosis and treatment of aggressive behavior in schizophrenia is of great significance and value for the diagnosis and treatment of aggressive behavior in schizophrenia. Summary of the Invention

[0006] One of the purposes of the present invention is to provide a reagent for detecting protein for use in the preparation of a product for diagnosing aggressive behavior in schizophrenia. The protein is a protein biomarker for aggressive behavior in schizophrenia, which provides a potential biomarker for determining aggressive behavior in schizophrenia and has important significance and value for the diagnosis and treatment of aggressive behavior in schizophrenia.

[0007] A reagent for detecting a protein is used in the preparation of a product for diagnosing aggressive behavior in schizophrenia. The protein is a protein biomarker for aggressive behavior in schizophrenia, specifically: serum poliovirus receptor protein (PVR), whose amino acid sequence is shown in SEQ NO.1.

[0008] The method for screening protein biomarkers of schizophrenia aggressive behavior of the present invention comprises the following steps:

[0009] (1) Selection of research subjects. Each of the research subjects must meet the following inclusion criteria: ① meet the diagnostic criteria for schizophrenia according to DSM-IV (Diagnostic and Statistical Manual of Mental Disorders IV); ② be diagnosed with schizophrenia by two doctors with psychiatric attending physicians or above; and each of the research subjects must not meet any of the following exclusion criteria: ① suffer from other mental illnesses; ② have neurological diseases such as brain trauma, AD, Parkinson's disease, and intellectual disability; ③ have severe physical diseases such as liver and kidney dysfunction and heart failure; ④ abuse psychoactive substances; ⑤ have received electroconvulsive therapy within 90 days; ⑥ have received intravenous blood products within 30 days;

[0010] (2) All subjects screened in step (1) were evaluated using the existing Modified Overt Aggression Scale (MOAS). All subjects were then divided into two groups: an aggressive behavior group and a non-aggressive behavior group, according to the grouping criteria of "MOAS total score ≥ 5 points was considered to have aggressive behavior, and MOAS total score < 5 points was considered to have no aggressive behavior."

[0011] (3) Blood samples are collected from patients in each aggressive behavior group and non-aggressive behavior group in step (2), and transcriptomics, proteomics, and metabolomics are used to analyze and screen out differential circRNAs, differential proteins, and differential metabolites in the blood samples between the aggressive behavior group and the non-aggressive behavior group in step (2). The interaction between the differential circRNAs, differential proteins, and differential metabolites is analyzed using bioinformatics analysis methods to screen out differential proteins with interaction relationships among the three.

[0012] (4) The expanded sample was verified according to the inclusion and exclusion criteria for the selection of research subjects in step (1); and the expanded sample was divided into two groups of patients, the aggressive behavior group and the non-aggressive behavior group, according to the grouping criteria of the aggressive behavior group and the non-aggressive behavior group in step (2); and the blood samples of the patients in each aggressive behavior group and each non-aggressive behavior group of the expanded sample were further subjected to the expression level detection of the differential proteins with the interactive correlation relationship among the three as described in step (3), and it was finally determined that the differential protein - serum poliomyelitis receptor protein was different between the two groups, P < 0.05, which is the protein biomarker of aggressive behavior in schizophrenia described in the present invention.

[0013] This study employed proteomics to screen for 21 differentially expressed proteins (i.e., differentially expressed proteins) between the aggressive and non-aggressive schizophrenia groups, using a criterion of a 1.5-fold differential expression change (P<0.05). Nine of these proteins were upregulated and 12 were downregulated. Furthermore, high-throughput transcriptomics and metabolomics analyses were combined to identify differentially expressed circRNAs (i.e., differential circRNAs) and metabolites between the aggressive and non-aggressive schizophrenia groups. Bioinformatics analysis was then used to analyze the interactions among these differentially expressed circRNAs, proteins, and metabolites, identifying potential signaling pathways underlying aggressive behavior in schizophrenia. Furthermore, an expanded sample of patients with aggressive and non-aggressive schizophrenia that met the criteria was selected for validation. The number of patients in the expanded sample, both in the non-aggressive and aggressive schizophrenia groups, was generally required to be at least 1-fold greater than the baseline. The expanded sample was then tested for differentially expressed proteins, proteins, and metabolites using methods such as qRT-PCR, ELISA, and LC-MS. Finally, combining the results of expanded sample validation with the potential signaling pathways underlying aggressive behavior in schizophrenia identified through bioinformatics analysis, the authors proposed that serum poliovirus receptor (PVR) protein, a differentially expressed protein, was significantly different between the schizophrenia aggressive behavior group and the non-aggressive behavior group (P<0.05), potentially serving as a biomarker for identifying aggressive behavior in schizophrenia. The relative expression (normalized peak area) of the PVR biomarker in the expanded blood samples was measured, and receiver operating characteristic (ROC) curves were plotted. The area under the curve (AUC) for serum PVR levels in diagnosing aggressive behavior in schizophrenia was 0.673 (95% CI: 0.563, 0.784). AUC values ​​closer to 1 indicate better predictive performance for identifying aggressive behavior in schizophrenia. When the AUC is 0.5, the diagnostic model's predictions are indistinguishable from random chance, indicating a lack of predictive power. Therefore, the protein marker described in this invention has a high predictive value and can serve as a specific biomarker for aggressive behavior in schizophrenia. The research and discovery of specific biomarkers of aggressive behavior in schizophrenia is expected to bring about revolutionary changes in the prediction, diagnosis and treatment of aggressive behavior in schizophrenia.

[0014] The beneficial effects of the present invention are:

[0015] (1) The prediction and diagnosis of aggressive behavior in schizophrenia will not rely on subjective experience, but can be predicted through objective indicators such as PVR expression levels;

[0016] (2) It is expected that altering the expression level of specific proteins (PVR) through protein mimetics and antagonists can normalize abnormal gene regulatory networks and signaling pathways, thereby regulating aggressive behavior in schizophrenia;

[0017] (3) It is expected that the efficacy of drugs can be predicted by detecting the expression level of a specific protein (PVR). BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a statistical graph of filtered components of the original data (i.e., raw data) obtained during sequencing of schizophrenia aggressive behavior-related circRNAs of the present invention;

[0019] Figure 2 is a base quality distribution diagram of cleanreads obtained when sequencing circRNA related to schizophrenia aggressive behavior of the present invention;

[0020] Figure 3 It is the overall situation of peptides and proteins identified by mass spectrometry after proteomics data filtering during protein identification and data quality assessment of the present invention, where "Tatal Spectra" is: the total number of spectra, that is, the number of secondary spectra generated by mass spectrometry detection; "Matched Spectra" is: the number of valid spectra, that is, the number of spectra matching the theoretical secondary spectra; "Peptides" is: the number of identified peptides, that is, the number of peptide sequences resolved by the matching results; "Unique Peptides" is: the number of identified unique peptides, that is, the number of unique peptide sequences resolved by the matching results; "Identified Proteins" is: the number of identified proteins, that is, the number of proteins resolved by specific peptides; "Quantifiable Proteins" is: the number of quantified proteins, that is, the number of proteins quantified by specific peptides;

[0021] Figure 4 This is a volcano plot of differential protein expression between patients in the attack group and the non-attack group during the differentially expressed protein screening of the present invention;

[0022] Figure 5 It is a differential protein interaction network diagram during the protein interaction network analysis of the present invention;

[0023] Figure 6 is a total ion current chromatogram during the chromatographic analysis of the metabolomics of the present invention;

[0024] Figure 7 : is the ROC curve of the serum PVR level during the validation of the biomarker of the present invention. DETAILED DESCRIPTION

[0025] The protein biomarker for aggressive behavior in schizophrenia of the present invention includes serum poliovirus receptor protein (PVR), the amino acid sequence of which is shown in SEQ NO.1.

[0026] The screening process of the protein biomarkers of the present invention is specifically as follows:

[0027] 1. Research Subjects

[0028] Ten inpatients admitted to Fuzhou Neuropsychiatric Hospital from 2019 to 2022 were selected as research subjects.

[0029] Inclusion criteria: ① Meet the diagnostic criteria for schizophrenia according to DSM-IV (Diagnostic and Statistical Manual of Mental Disorders IV); ② Be diagnosed with schizophrenia by two physicians with attending psychiatric qualifications or above; ③ The patient and / or family member (guardian) sign the informed consent.

[0030] Exclusion criteria: ① Other psychiatric illnesses; ② Neurological diseases such as brain trauma, Alzheimer's disease, Parkinson's disease, and intellectual disability; ③ Severe physical illnesses such as liver and kidney dysfunction or heart failure; ④ Psychoactive substance abuse; ⑤ Electroconvulsive therapy within 90 days; ⑥ Intravenous blood product transfusion within 30 days. Subjects meeting any of the above criteria were excluded.

[0031] Patients were assessed using the Modified Overt Aggression Scale (MOAS). A MOAS total score of 5 or higher was considered to indicate aggressive behavior, while a MOAS total score of <5 was considered to indicate the absence of aggressive behavior. Based on the MOAS total score, the subjects were divided into two groups: an aggressive behavior group and a non-aggressive behavior group. There were five patients in the aggressive behavior group (abbreviated as the "aggressive group," also known as the "experimental group") and five patients in the non-aggressive behavior group (abbreviated as the "non-aggressive group," also known as the "control group").

[0032] 2. Research Methods

[0033] 1. Sample collection

[0034] 3-5 mL of fasting cubital venous blood was collected from each subject and placed at room temperature for 30 minutes. The peripheral blood was then centrifuged at 2400 r / min for 15 minutes and aliquoted into microcentrifuge tubes and stored at -80 °C.

[0035] 2. Transcriptomic Analysis

[0036] (1) RNA extraction

[0037] Take a 15mL RNase-free centrifuge tube (not provided) and add 2mL of lysis buffer, followed by 2mL of thawed or fresh blood (refrigerated blood should be gently shaken before addition). Gently invert and mix eight times. Then add 200μL of digestion buffer and 100μL of solution A. Gently shake thoroughly and mix thoroughly. In a 56°C water bath for 10 minutes. Remove the centrifuge tube and let it stand at room temperature for 2 minutes. Add 3mL of anhydrous ethanol (not provided) and gently invert and mix eight times. If translucent suspended matter is present, it will not affect RNA extraction and subsequent experiments. Place the adsorption column in the collection tube, transfer 3625μL of the above solution into the adsorption column, let it stand for 2 minutes, centrifuge at 5000 rpm for 2 minutes, and discard the waste liquid in the collection tube. Place the adsorption column in the collection tube, transfer the remaining 3625μL of the above solution into the adsorption column, let it stand for 2 minutes, centrifuge at 5000 rpm for 2 minutes, and discard the waste liquid in the collection tube. Return the adsorption column to the collection tube, add 3 mL of wash buffer, let stand for 2 minutes, centrifuge at 5000 rpm for 1 minute, and discard the waste liquid in the collection tube. Return the adsorption column to the collection tube and centrifuge at 5000 rpm for 2 minutes to remove the remaining wash buffer. Remove the adsorption column and place it in a new 15 mL RNase-free collection tube. Add 300 μL of elution buffer, let stand for 3 minutes, and centrifuge at 5000 rpm for 3 minutes at 4°C to collect the RNA solution. The extracted RNA can be used for the next step or stored at -20°C.

[0038] (2) Total RNA concentration determination and integrity test The RNA concentration and the ratios of OD260 / OD230 and OD260 / OD280 were determined using NanoDrop. To further test the integrity of total RNA, 10 μg of total RNA was electrophoresed on a 1.2% denaturing agarose gel. The integrity of the total RNA was determined based on the electrophoresis results.

[0039] (3) circRNA expression profile sequencing

[0040] DNA fragments present in the total RNA sample are digested with DNase I and then purified and recovered. Ribosomal RNA is removed from the total RNA using the Ribo-off method. An RNase R reaction system is prepared to digest the linear RNA fraction, and the reaction product is purified and recovered. RNA is fragmented under a specific temperature and ionic conditions. Single-strand cDNA is synthesized using the fragmented RNA as a template. Second-strand cDNA is synthesized using dUTP instead of dTTP. The double-stranded cDNA is end-repaired, an "A" base is added to the 3' end, and an adapter is ligated. The second-stranded cDNA containing the U strand is digested with UDG enzyme and amplified simultaneously by PCR. The constructed library is quality-tested, and library products that pass the quality test are circularized. The circular DNA molecules undergo rolling circle replication to form DNA nanoballs (DNBs). Sequencing is then performed on the DNBSEQ sequencing platform.

[0041] (4) Data analysis process

[0042] ①Original sequence data

[0043] The original image data generated by sequencing is converted into sequence data through Base Calling #, which is called raw data and stored in Fasto file format.

[0044] ②Remove impurity data

[0045] Since raw data can contain low-quality sequences and adapter sequences, it cannot be used directly for information analysis and must be processed and converted into clean data before it can be used for subsequent data analysis. Therefore, the raw sequences are stripped of adapter sequences, sequences with an N ratio greater than 10%, and low-quality sequences (bases with a quality value Q≤5 accounting for more than 50% of the entire base sequence) to obtain clean reads.

[0046] ③Clean reads data

[0047] Clean data is stored in FASTQ format. The clean data file is the original file obtained by the user (can be directly used for publication, public database submission, etc.). The file contains the sequence reads and their corresponding sequencing quality value sequence. Each character in the quality value sequence corresponds one-to-one with each base in the read sequence, reflecting the sequencing quality of the base. The inventors also used the short sequence alignment software SOAPaligner / SOAP2 to align the clean reads to the genomic sequence (allowing two base mismatches).

[0048] ④ Prediction and annotation of circRNA

[0049] The inventors used CIRI and find_circ software to predict circRNAs. The preceding and following parts of a read must be cross-aligned to the genome to confirm that they are junction reads connecting the two ends of a circRNA. Therefore, during the first scan, CIRI selects junction reads with paired chiastic clipping (PCC) signals. Junction reads are then further filtered using paired-end mapping (PEM) range restrictions and GT-AG clipping signals. During the second scan of the SAM file, CIRI detects new junction reads while eliminating false positive circRNAs caused by misalignment to homologous genes or repetitive sequences, thereby improving the software's sensitivity and accuracy. find_circ is a genome alignment software based on Bowtie2. First, find_circ discards reads that can be continuously and fully aligned to the genome. Then, 20 base pairs are extracted from each end of the remaining reads and aligned to the genome. Sequences that can cross-align to exons confirm the presence of the circRNA. This portion of the alignment is extended to further collect information about junction sites. Finally, candidate circRNAs are identified through a series of filtering strategies. The results from these two software programs are combined based on the start and end positions of the circRNA (circRNAs with start and end positions within 10 bases of the circRNA are combined into one category).

[0050] ⑤Analysis of circRNA expression

[0051] The inventors calculated circRNA expression levels based on the number of junction reads mapped to both ends of the circRNA. Because they used two software programs, CIRI and find_circ, for prediction, the final junction read count was the average of the two results. The inventors normalized each sample using RPB (junction reads per billion mapped reads, which is the number of junction reads that span back-spliced ​​sites after normalizing all reads in the genome to one billion).

[0052] ⑥ Screening of differential circRNAs

[0053] The present inventors used the DEGseq algorithm to detect differentially expressed circRNAs (i.e., differential circRNAs), and screened for significantly differential circRNAs using a fold difference of more than two times and a P value of ≤ 0.05.

[0054] ⑦ Target gene prediction of cicrRNA

[0055] The target genes of significantly differentially expressed circRNAs were obtained using the Circular RNA Interactome database.

[0056] ⑧Gene Ontology functional significant enrichment analysis

[0057] The Gene Ontology (GO) is an internationally standardized classification system for gene function. It provides a dynamically updated controlled vocabulary to comprehensively describe the properties of genes and gene products in organisms. GO has three ontologies, describing a gene's molecular function, cellular component, and biological process.

[0058] GO functional significance enrichment analysis identifies GO functional terms that are significantly enriched in differentially expressed genes (i.e., differentially expressed genes) compared to reference genes and identifies biological functions significantly associated with these differentially expressed genes. This analysis first maps all differentially expressed genes to terms in the Gene Ontology database, calculates the number of genes per term, and then applies a hypergeometric test to identify GO terms that are significantly enriched in differentially expressed genes compared to the entire genomic background. The calculated p-value is then subjected to Bonferroni analysis, and GO terms that meet this threshold, with a corrected p-value of 0.05, are defined as significantly enriched in differentially expressed genes. GO functional significance enrichment analysis can identify the primary biological functions of differentially expressed genes. GO functional analysis also integrates expression pattern clustering analysis.

[0059] Based on the nr annotation information, we used Blast2GO to obtain GO annotation information for all differentially expressed genes. Blast2GO has been cited over 150 times in other publications and is widely recognized by peers as a GO annotation software. After obtaining the GO annotation for each differentially expressed gene, we used WEGO to perform GO functional classification statistics for the differentially expressed genes, providing a macroscopic understanding of their functional distribution.

[0060] ⑨KEGG Pathway significant enrichment analysis

[0061] In organisms, different genes coordinate their biological functions. Pathway analysis can help further understand the biological functions of genes. The KEGG database, a major public database of pathways, uses pathway units in pathway analysis, applying a hypergeometric test to identify pathways that are significantly enriched among differentially expressed genes compared to the entire genome. The calculation formula for this analysis is the same as that for Go functional significant enrichment analysis, where N is the number of genes with pathway annotations among all genes, n is the number of differentially expressed genes in N, M is the number of genes annotated to a specific pathway among all genes, and m is the number of differentially expressed genes annotated to a specific pathway. Pathways with a P value of ≤ 0.05 are defined as those significantly enriched among differentially expressed genes. Pathway significant enrichment can identify the most important biochemical metabolic pathways and signal transduction pathways in which differentially expressed genes participate. Pathway significant enrichment analysis of differentially expressed genes not only generates a meaningful pathway table but also provides detailed information about the pathways in the KEGG database.

[0062] ⑩Detection of differential circRNA relative expression levels

[0063] Real-time fluorescence quantitative PCR technology was used to detect differential circRNA in peripheral blood using the same reaction system and parameters with GADPH as the internal reference. ‐ΔΔCt The relative expression levels of differentially expressed circRNAs were calculated using the PCR method. The experiment was repeated three times and the average value was taken.

[0064] 3. Proteomic analysis

[0065] Through the organic combination of a series of technologies such as protein extraction, enzyme digestion, liquid chromatography-mass spectrometry tandem analysis, and bioinformatics analysis, quantitative proteomics research of samples is carried out.

[0066] (1) Protein extraction

[0067] Blood samples were removed from -80°C and centrifuged at 12,000 g for 10 minutes at 4°C to remove cell debris. The supernatant was transferred to a new centrifuge tube and high-abundance proteins were removed using the Pierce™ Top 12 Abundant Protein Depletion Spin Columns Kit (Thermo Scientific) / Proteominer™ Protein Enrichment Small-Capacity Kit (Bio-rad) / Seppro ® Rat Spin Columns (Sigma) according to the manufacturer's instructions.

[0068] (2) Protein quantification

[0069] Pipette 20 μL of the protein sample into a new centrifuge tube, add 1 mL of dye, and vortex. Let the solution stand at room temperature for 5 minutes, then measure the absorbance of the solution at 595 nm using a spectrophotometer. Use the absorbance values ​​of the standard solution to construct a standard curve, and calculate the protein sample concentration based on the standard curve.

[0070] (3) Enzymatic hydrolysis with pancreatic enzymes

[0071] Equal amounts of protein from each sample were enzymatically digested. The volumes were adjusted to a uniform volume with lysis buffer, followed by the addition of dithiothreitol (DTT) to a final concentration of 5 mM and reduction at 56°C for 30 minutes. Iodoacetamide (IAM) was then added to a final concentration of 11 mM and incubated at room temperature in the dark for 15 minutes. The alkylated sample was transferred to an ultrafiltration tube and centrifuged at 12,000 g for 20 minutes at room temperature. The urea was then replaced three times with 8 M urea and then three times with replacement buffer. Trypsin was then added at a 1:50 ratio (protease:protein, m / m) and digested overnight. Peptides were recovered by centrifugation at 12,000 g for 10 minutes at room temperature and then recovered once with ultrapure water. The two peptide solutions were combined.

[0072] (4) Biological mass spectrometry analysis

[0073] Peptides were dissolved in liquid chromatography mobile phase A and separated using an EASY-nLC 1200 UPLC system. Mobile phase A consisted of 0.1% formic acid and 2% acetonitrile in water; mobile phase B consisted of 0.1% formic acid and 90% acetonitrile in water. The gradient was as follows: 0% to 68% B (0–68 min); 68% to 82% B (68–82 min); 82% to 86% B (82–86 min); and 86% to 90% B (86–90 min). The flow rate was maintained at 500 nL / min. Following UPLC separation, the peptides were injected into the NSI ion source for ionization and then analyzed on an Orbitrap Exploris™ 480 mass spectrometer. The ion source voltage was set at 2.3 kV, and the FAIMS compensation voltage (CV) was set at -70 V and -45 V. Peptide precursor ions and their secondary fragments were detected and analyzed using a high-resolution Orbitrap. The primary mass spectrometer scan range was set to 400-1200 m / z with a scan resolution of 60,000. The secondary mass spectrometer scan range was fixed at 110 m / z with a secondary scan resolution of 30,000. TurboTMT was set to Off. Data acquisition used a data-dependent scanning (DDA) program. After the primary scan, the top 15 peptide precursor ions with the highest signal intensity were sequentially introduced into the HCD collision cell for fragmentation at 27% fragmentation energy, and then analyzed sequentially by secondary mass spectrometry. To maximize mass spectrometry efficiency, the automatic gain control (AGC) was set to 7.5E4, the signal threshold to 1E4 ions / s, the maximum injection time to 100 ms, and the dynamic exclusion time for the tandem mass spectrometer scan to 30 s to avoid duplicate scanning of precursor ions. Professional mass spectrometry data analysis software was used for data analysis. The raw data from the mass spectrometer was imported into the search software, and the corresponding analysis parameters were set according to the experimental plan. The secondary mass spectrometry data of the present invention were searched using Proteome Discoverer (v2.4.1.15). Search parameter settings: the database was Homo_sapiens_9606_PR_20210721.fasta (78120 sequences), and the reverse library was added to calculate the false positive rate (FDR) caused by random matching; the enzyme digestion method was set to Trypsin (Full); the number of missed cleavage sites was set to 2; the minimum length of the peptide was set to 6 amino acid residues; the maximum number of peptide modifications was set to 3; the primary parent ion mass error tolerance was set to 10 ppm, and the secondary fragment ion mass error tolerance was 0.02 Da. Carbamidomethyl (C) was set as a fixed modification, and Oxidation (M), Acetyl (N-ter minutes), Met-loss (M), and Met-loss+acetyl (M) were set as variable modifications.The FDR for protein, peptide, and PSM identification was set to 1%.

[0074] (5) Protein quantitative analysis

[0075] The search results provide the LFQ intensity of each protein in different samples (the original protein intensity value after correction for sample differences). The relative quantification value (R) of a protein in different samples is calculated by centering the LFQ intensity (I) of the protein in different samples. The calculation formula is as follows: [R_

[50] =I_

[50] / Mean(I_j)\], where I represents the sample and j represents the protein.

[0076] (6) Differential protein screening

[0077] First, select the samples to be compared and use the ratio of the relative quantitative values ​​of each protein in the samples as the fold change (FC). For example, to calculate the fold change of a protein between sample A and sample B, use the following formula: where R represents the protein relative quantitative value and k represents the protein. \[FC_{A / B, k}=R_{Ak} / R_ \] Differentially expressed proteins were screened based on a criterion of a 1.5-fold difference in expression and a P < 0.05.

[0078] (7) Functional classification of differentially expressed proteins

[0079] We used Wolf Psort software to annotate proteins for subcellular structure. Unlike eukaryotic cells, prokaryotic cells generally lack intracellular membranes, a fully formed nucleus enclosed by a nuclear membrane, and no chromosomes. DNA strands are not coiled and exist in a free form in the cytoplasm, which also lacks any membrane-bound organelles (such as mitochondria or chloroplasts). Based on this, we used PSORTb (v3.0) software to annotate proteins for subcellular structure.

[0080] (8) Differential protein functional enrichment analysis

[0081] Differentially expressed proteins (i.e., differentially expressed proteins) were analyzed for significant enrichment at three levels: GO function, KEGG pathway, and protein domain. Fisher's exact test was used to calculate the significant P value and analyze the significant enrichment trend of differentially expressed proteins.

[0082] (9) Protein interaction network analysis

[0083] The differential protein database numbers or protein sequences obtained by screening with a difference of more than 1.5 times were compared with the STRING (v.11.0) protein interaction network database, and the differential protein interaction relationships were extracted according to the confidence score > 0.7 (high confidence).

[0084] (10) Detection of differential protein levels by ELISA

[0085] ELISA kits were purchased from Shanghai Enzyme-Linked Biochemical Reagent Co., Ltd. Double-antibody sandwich assays were used, and the kit instructions were strictly followed. Blood samples were centrifuged at 3000 rpm for 10 minutes, and serum was collected for later use. 0.05 mL of carbonate coating buffer (pH 9.6) was added to the serum for dilution. The diluted blood samples were placed in the reaction wells of a polystyrene plate and incubated. 0.1 mL of sample was placed in each well, sealed with plastic wrap, and incubated at -4°C. After incubation, the polystyrene plate was removed, the solution removed, and the plate was rinsed three times with buffer. The specimens to be tested were placed in the reaction wells and incubated in a 37°C incubator for 60 minutes. After incubation, the plates were rinsed three times. Positive and negative control wells were prepared. 0.1 mL of enzyme-labeled antibody was placed in the reaction wells and incubated for 60 minutes. After incubation, the wells were washed and incubated with 0.1 mL of LTMB solution for 30 minutes. After the incubation, 0.05 mL of 2M sulfuric acid was added to the reaction wells for observation. The PVR content in peripheral blood was finally determined.

[0086] 4. Metabolomics analysis

[0087] Through the organic combination of a series of technologies such as metabolite extraction, liquid chromatography-mass spectrometry tandem analysis, data preprocessing, and bioinformatics analysis, the quantitative metabolome of the samples was studied.

[0088] (1) Metabolite extraction

[0089] Remove the sample from -80°C, slowly thaw, and add 4 volumes of extraction buffer (MeOH / ACN, 1:1, v / v). Vortex thoroughly and sonicate. Precipitate at -20°C for 1 hour. Centrifuge at 18,000 g for 15 minutes at 4°C to remove the protein precipitate. Transfer the supernatant to a fresh centrifuge tube, drain using a concentrator, and add an equal volume of ACN:H2O (1:1, v / v) for reconstitution by sonication. Centrifuge at 18,000 g for 15 minutes at 4°C. Transfer the supernatant to a fresh centrifuge tube and store at -80°C or analyze on a liquid chromatography-mass spectrometry (LC / MS).

[0090] (2) LC / MS analysis

[0091] Metabolites were separated using a Waters ACQUITY UPLC system coupled with a Waters ACQUITY UPLCBEH C18 column (1.7 μm, 2.1 mm × 100 mm). The injection volume was 10 μL, and elution was performed at a flow rate of 400 μL / min at a column temperature of 40°C. Mobile phase A consisted of an aqueous solution containing 0.1% formic acid, and mobile phase B consisted of an acetonitrile-water solution containing 0.1% formic acid. The liquid phase gradient was as follows: 0–11 min, 5%–90% B; 11.0–12.0 min, 90% B; 12.0–12.1 min, 90%–5% B.

[0092] 12.1-15.0 minutes, 5% B. Metabolites were separated by ultra-high performance liquid chromatography (UPLC) and injected into the ESI ion source for ionization and analysis on a timsTOF Pro mass spectrometer. The ion source voltage was set to 1.6 kV, and both peptide precursor ions and their secondary fragments were detected and analyzed using a high-resolution TOF. The mass spectrometer scan range was set from 20 to 1300 m / z. Data acquisition was performed in parallel accumulation serial fragmentation (PASEF) mode. Following the primary mass spectrum, two secondary spectra were acquired in PASEF mode with precursor ion charge values ​​in the 0-1 range. The dynamic exclusion time for the tandem mass spectrometer scan was set to 6 s to avoid duplicate scanning of the precursor ion.

[0093] (3) Database search

[0094] MetaboScape 2022 was used to perform peak extraction, alignment, and retention time correction on the raw mass spectrometry data. The primary and secondary mass errors were controlled within 20 ppm to ensure the accuracy of the identification results. The structure and annotation information of the metabolites were obtained through spectral comparison with NIST, HMDB, proprietary databases, and integrated public databases.

[0095] (4) Metabolite data analysis

[0096] Based on the quantitative metabolite information obtained from database matching, data screening and statistical algorithms are used to fill and correct missing values. For multiple replicate samples, the corrected expression levels are used to calculate the metabolite fold difference (FC) between the two groups. The VIP value is calculated by combining the P value from the univariate T-test analysis and the multivariate statistical analysis Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) analysis. Significantly differential metabolites are further screened for P < 0.05 and VIP ≥ 1. These significantly differential metabolites are then subjected to multi-level bioinformatics and functional analyses. When there are more than three sample groups, the Anova analysis P value for all groups is also provided to screen for differentially differential metabolites. Expression clustering and KEGG enrichment analysis are then performed on these differentially ...

[0097] 5. Collection of clinical samples and validation of biomarkers

[0098] According to the inclusion and exclusion criteria described in "I. Research subjects" above, 90 patients from Fuzhou Neuropsychiatric Hospital from 2019 to 2022 were selected, including 30 patients in the aggressive behavior group (i.e., 6 times the base number) and 60 patients in the non-aggressive behavior group (i.e., 12 times the base number). The clinical data of the patients were collected and peripheral blood was collected for biomarker determination. Differential circRNA and protein were detected by the methods under "⑩ Detection of relative expression levels of differential circRNA" in "2. Transcriptomics analysis" and "(10) ELISA method for detection of differential protein levels" in "3. Proteomics" above. Differential metabolites were detected by LC-MS, and the relative content values ​​of the above eight metabolites in peripheral blood were obtained (normalized peak area). SPSS 21.0 statistical software was used to analyze the data. The comparison between the two groups was performed using non-parametric tests. The results were expressed as follows: The difference was statistically significant when P < 0.05.

[0099] 1. Sequencing of circRNAs associated with aggressive behavior in schizophrenia

[0100] (1) Differentially expressed circRNAs

[0101] Statistics of filtered components of raw data, such as Figure 1 As shown in Table 1, the clean reads are over 99%. The quality indicators of the filtered reads are shown in Table 1. The base quality distribution is shown in Table 1. Figure 2 , the proportion of low-quality (Quality<20) bases is low, indicating that the sequencing quality is good.

[0102] Table 1

[0103]

[0104] A total of 37,910 circRNAs were detected, of which 4,234 were expressed in both the control and experimental groups, 84 of which were differentially expressed, with 55 upregulated and 29 downregulated. Table 2 lists the basic information of the ten genes with the most significant upregulated and downregulated folds.

[0105] Table 2

[0106] (2) Prediction of target genes of differentially expressed circRNAs

[0107] Based on differentially expressed circRNAs, a total of 530 miRNAs were predicted. Among them, hsa_circ_0003170 had the most miRNA binding sites, followed by hsa_circ_0001826, hsa_circ_0009099, and hsa_circ_0001014. The top 11 degree-ranked circRNAs (the 10th and 11th degrees were the same, so the top 11 were used) corresponded to 364 miRNAs.

[0108] (3) Enrichment analysis of differentially expressed circRNA genes

[0109] GO enrichment results showed that the top 10 pathways significantly enriched in differentially circRNA-derived genes included RNA binding, post-transcriptional regulation, RNA catabolism, translational regulation, cellular amide metabolism regulation, negative regulation of translation, and negative regulation of cellular amide metabolism. KEGG enrichment results showed that differentially circRNA-derived genes were significantly enriched in cancer transcriptional dysregulation, spliceosomes, basal transcription factors, mRNA surveillance pathways, RNA transport, and cancer-related miRNAs.

[0110] 2. Proteomics related to aggressive behavior in schizophrenia

[0111] (1) Protein identification and data quality assessment

[0112] A total of 1376 proteins and 8844 peptides were identified by mass spectrometry (e.g. Figure 3 (as shown). Most peptides range from 7 to 20 amino acids, consistent with general patterns based on enzymatic digestion and mass spectrometry fragmentation. The distribution of peptide lengths identified by mass spectrometry meets quality control requirements. Most proteins correspond to two or more peptides, demonstrating good quantification accuracy and reliability.

[0113] (2) Sample cluster analysis

[0114] There was good correlation between the samples. The results of principal component analysis showed that there was little overlap between the attack group and the non-attack group, and the two groups were not very similar, with a clear separation.

[0115] (3) Screening of differentially expressed proteins (i.e., differential proteins)

[0116] Volcano plot of differential proteins between patients in the attack group and non-attack group, e.g. Figure 4 A total of 21 differentially expressed proteins were screened using the criteria of differential expression of more than 1.5-fold and P < 0.05, of which 9 were upregulated and 12 were downregulated.

[0117] (4) Functional enrichment of differentially expressed proteins

[0118] GO enrichment results showed that these differentially expressed proteins were mainly involved in BP, including response to fungi, dephosphorylation, fungal defense response, and negative regulation of catabolic processes; those involved in MF were phosphatase activity; and those involved in CC included proteasome core complex, proteasome core complex, proteasome complex, endopeptidase complex, peptidase complex, etc. (all P values ​​< 0.05). KEGG enrichment results showed that these differentially expressed proteins were mainly enriched in pathways such as proteasome and spinocerebellar ataxia (all P values ​​< 0.05).

[0119] (5) Protein interaction network analysis (PPI)

[0120] The results of interaction analysis of differentially expressed proteins showed that there were 21 differentially expressed proteins, among which PSMA5 and PSMA7 were in the same protein interaction network with a high degree of connectivity (e.g. Figure 5 These differentially expressed proteins may be associated with the pathophysiological changes of aggressive behavior in schizophrenia.

[0121] 3. Metabolomics related to aggressive behavior in schizophrenia

[0122] (1) Chromatographic analysis of metabolomics

[0123] From the total ion current chromatogram (such as Figure 6 As shown in the figure, the sample chromatographic peak retention time and signal intensity overlap well, indicating that the instrument is very stable. The variation caused by instrument error is small during the entire test process, and some systematic errors can be ruled out.

[0124] (2) Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of metabolites. The principal component analysis showed that all samples were within the 95% confidence interval, and the data points of the attack group and the non-attack group were significantly distinguished in the PCA scatter plot. Through OPLS-DA analysis, the influence of orthogonal variables in the metabolites that are not related to the classification variables can be eliminated, and then the non-orthogonal and orthogonal variables can be analyzed separately to obtain more reliable information on the differences between metabolite groups and the degree of correlation with the experimental group. The results showed that the metabolites of the non-attack group samples and the metabolites of the attack group samples were clearly distinguished. The results of OPLS-DA further confirmed the results of PCA. In addition, according to the permutation test results of the OPLS-DA model, the R2Y of the original model was close to 1, which indicated that the established model could accurately reflect the actual situation of the sample data; the Q of the original model was very close to 1, indicating that if new samples were added to the model, an approximate distribution would be obtained. Overall, the original model can well explain the differences between the two groups of samples; and the original model has good stability and does not suffer from overfitting.

[0125] (3) Screening of differential metabolites

[0126] A combination of multidimensional and unidimensional analysis was used to analyze the experimental data. Metabolites that differed significantly between groups were screened based on the variable importance in the projection (VIP) values ​​generated by OPLS-DA. The screening criteria were P < 0.05 and VIP ≥ 1. A higher VIP value indicates a stronger influence and explanatory power of the differentially expressed metabolite on the classification and discrimination between samples. The results showed that 20 metabolites were significantly differentially expressed in the challenge group, of which 10 were upregulated and 10 were downregulated. Most of the differentially expressed metabolites were lipids, such as 11a-hydroxyprogesterone, 1-stearoyl-2-arachidoyl, and 2-oleoyl-1-palmitoyl-sn-glycero-3-phosphocholine.

[0127] (4) Enrichment of differential metabolites

[0128] After importing different metabolite data into the KEGG database for analysis, 12 metabolic pathways related to differential metabolites were successfully obtained, including endocrine and other factors regulating calcium reabsorption, synthesis / secretion / action of parathyroid hormone, steroid biosynthesis, tuberculosis, mineral absorption, retrograde endocannabinoid signaling, choline metabolism in cancer, glycerophospholipid metabolism, arachidonic acid metabolism, steroid hormone biosynthesis, α-linolenic acid metabolism and linoleic acid metabolism.

[0129] 4. Association analysis of differential circRNAs, proteins, and metabolites

[0130] Targetscan predicted 2,448 miRNAs associated with the differentially expressed proteins, and the intersection of these miRNAs with the differentially expressed circRNAs yielded 132 miRNAs. Furthermore, correlation analysis between differentially expressed circRNAs and proteins and differentially expressed metabolites between the two groups of patients with and without aggressive behavior revealed numerous significant relationships. Potential signaling pathways underlying aggressive behavior in schizophrenia are those that both meet database predictions and exhibit significant correlations among circRNAs, proteins, and metabolites in patients.

[0131] 5. Biomarker Validation

[0132] Highly potential differentially expressed proteins were selected and validated in 90 schizophrenia patients. Testing of PVR in blood samples from these 90 schizophrenia patients revealed that the relative expression of PVR in the attack group was significantly lower than that in the non-attack group (P < 0.01).

[0133] SPSS 21.0 statistical software was used to analyze the differences between the aggressive behavior group and the non-aggressive behavior group, and the ROC curve corresponding to the biomarker PVR was drawn (e.g. Figure 7 The receiver operating characteristic (ROC) curve is a line graph plotted with the true positive rate (TPR) (sensitivity) as the vertical axis and the false positive rate (FPR) (1-specificity) as the horizontal axis. The area under the curve (AUC) (generally ranging from 0.5 to 1) is used as an evaluation metric. A higher AUC value indicates a better predictive effect of the biomarker on the disease. The results showed that the AUC for PVR levels in diagnosing aggressive behavior was 0.673 (95% CI: 0.563, 0.784). This biomarker, PVR, has a good predictive effect on aggressive behavior in schizophrenia and can be used as a marker for aggressive behavior in schizophrenia.

[0134] The differentially expressed protein PVR obtained by the present invention can be used together with the differentially expressed circRNA and differentially expressed metabolites as a marker of aggressive behavior in schizophrenia to comprehensively judge the aggressive behavior in schizophrenia, thereby improving the accuracy of prediction.

[0135] For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. Use of a reagent for detecting protein in the preparation of a product for diagnosing aggressive behavior in schizophrenia, characterized in that: The protein is serum poliomyelitis receptor protein, and its amino acid sequence is shown in SEQ NO.1.

Images