CN118298911A - Multi-part chronic pain potential treatment target point determining system - Google Patents
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Abstract
The invention discloses a multi-part chronic pain potential treatment target point determining system, which comprises a data acquisition module, an intervention gene target point determining module, an intervention protein target point determining module and an MCP potential treatment target point determining module; after the data acquisition module acquires the first GWAS data, the gene expression data of whole blood and the gene expression data of brain tissue, the blood protein expression data and the brain protein expression data, the intervention gene target point confirmation module and the intervention protein target point confirmation module respectively determine an intervention gene target point and an intervention protein target point related to MCP according to the acquired data, and generate an intervention gene target point set and an intervention protein target point set; finally, each target point in the intersection of the interventional gene target point set and the protein target point set is taken as the MCP potential treatment target point through the MCP potential treatment target point confirmation module. By implementing the invention, the treatment target related to MCP can be identified more accurately.
Description
Technical Field
The invention relates to the field of biomedical science and technology, in particular to a multi-part potential treatment target point determining system for chronic pain.
Background
According to ICD-11 diagnostic criteria and global disease burden analysis, multi-site chronic pain (MCP) is a disease afflicting more than 30% of the world population, and at present, the traditional drug development process generally includes the following major steps: (1) target spot discovery and verification: at this stage, researchers have searched and validated biomolecules, such as proteins, genes or cellular pathways, associated with disease; (2) drug screening and optimization: once the potential targets are identified, researchers will conduct high throughput screens to find compounds with therapeutic potential, which will then be optimized to improve their efficacy, selectivity and bioavailability; (3) preclinical studies: prior to entering clinical trials, drug candidates need extensive testing in vitro and in vivo models to assess their pharmacokinetics, toxicity and efficacy; (4) clinical trial: drug candidates that have undergone preclinical studies will enter the clinical trial stage, and are divided into three stages: phase I (safety), phase II (availability) and phase III (large scale availability and safety);
However, in the prior art, the discovery and verification stage of the drug target is generally limited to a single data type and analysis method, but the judgment of the drug target by using only one method may cause false positive and the like, so that the treatment target related to the MCP cannot be accurately identified, and therefore, how to accurately identify the treatment target related to the MCP is an urgent problem to be solved.
Disclosure of Invention
The invention provides a multi-site chronic pain potential treatment target determination system, which can achieve the aim of more accurately identifying a treatment target related to MCP by determining a target which simultaneously influences gene expression and protein expression of MCP and taking the target as the MCP potential treatment target.
An embodiment of the present invention provides a multi-site chronic pain potential therapeutic target determination system, comprising: the system comprises a data acquisition module, an intervention gene target point confirmation module, an intervention protein target point confirmation module and an MCP potential treatment target point confirmation module;
The data acquisition module is used for acquiring the gene expression data of whole blood and the gene expression data of brain tissues in the first GWAS data and GTEx V data sets, the blood protein expression data in the code data sets and the brain protein expression data in the NIAGADS data sets; wherein the first GWAS data is GWAS data related to MCP;
The system comprises a first GWAS data, a second GWAS data, a gene expression data of whole blood and a gene expression data of brain tissue, wherein the first GWAS data is used for acquiring first GWAS data, the second GWAS data is used for acquiring second GWAS data, the first GWAS data, the second GWAS data and the gene expression data of brain tissue;
The protein target spot confirmation module is used for confirming a plurality of protein target spots related to MCP according to the first GWAS data, the blood protein expression data and the brain protein expression data, and generating a protein target spot set capable of being interfered;
The MCP potential therapeutic target confirming module is used for determining the intersection of the interventional gene target set and the interventional protein target set, and taking all targets in the determined intersection as MCP potential therapeutic targets.
Further, the determining a number of interventional gene targets associated with MCP based on the first GWAS data, the gene expression data of whole blood, and the gene expression data of brain tissue, includes:
Performing whole transcriptome association analysis according to the gene expression data of the whole blood, the gene expression data of the brain tissue and the corresponding genetic variation data in the first GWAS data to obtain P values and a first effect value direction of the gene expression data of the whole blood and the gene expression data of the brain tissue;
Performing FDR correction on the P value of the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain first candidate gene expression data with corrected P value smaller than a preset FDR correction threshold;
Performing SMR & HEIDI analysis on the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain the gene expression data of each whole blood and p-SMR value, p-HEIDI value and second effect value direction corresponding to the gene expression data of each brain tissue;
Performing FDR correction on the p-SMR value of each gene expression data to obtain candidate gene expression data with a corrected p-SMR value smaller than a preset FDR correction threshold value, and screening out second candidate gene expression data with a p-HEIDI value larger than a preset value and the direction of the first effect value being consistent with the direction of the second effect value from the candidate gene expression data with the corrected p-SMR value smaller than the preset FDR correction threshold value;
taking an intersection part of the first candidate gene expression data and the second candidate gene expression data, and taking the gene expression data of the intersection part as an intervened gene target point related to MCP.
Further, the determining a number of tamper-able protein targets associated with MCP based on the first GWAS data, the gene expression data, and the brain protein expression data, comprises:
Taking the blood protein expression data in the decoding data set and the brain protein expression data in the NIAGADS data set as two-sample Mendelian randomized exposure data, taking the first GWAS data as two-sample Mendelian randomized result data, and carrying out two-sample Mendelian randomization to obtain third effect value directions and P values corresponding to the blood protein expression data and the brain protein expression data;
Performing FDR correction on the P value of the blood protein expression data and the brain protein expression data to obtain MR-positive protein with the corrected P value smaller than an FDR correction threshold;
And performing Bayesian co-localization analysis on the MR positive proteins, and taking the MR positive proteins with posterior result probability larger than a preset value as the protein targets related to MCP.
Further, the multi-site chronic pain potential therapeutic target determination system further comprises: a cell analysis module;
The cell analysis module comprises a sequencing data acquisition unit, a key cell type analysis unit and a cell process analysis unit;
The sequencing data acquisition unit is used for acquiring single-cell RNA sequencing data;
the key cell type unit is used for analyzing and obtaining the key cell type related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data;
The cell process analysis unit is used for analyzing and obtaining the cell process related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data.
Further, the multi-site chronic pain potential therapeutic target determination system further comprises: an MCP potential therapeutic target effect analysis module;
The MCP potential therapeutic target analysis module is used for acquiring second GWAS data and obtaining a plurality of related diseases except MCP of each protein target capable of being interfered and a corresponding fourth effect value direction according to the protein targets capable of being interfered and the second GWAS data;
Judging whether side effects of each protein target capable of being interfered on the plurality of related diseases exist according to the third effect value direction and the fourth effect value direction of each protein target capable of being interfered;
Judging whether the protein targets can interfere with the related diseases is an additional indication according to the third effect value direction and the fourth effect value direction of each protein targets which can interfere with the protein targets.
Further, the multi-site chronic pain potential therapeutic target determination system further comprises: a protein network construction and analysis module;
The protein network construction and analysis module comprises a protein network construction unit and a protein network analysis unit;
the protein network construction unit is used for inputting the protein target capable of being intervened into a tool for constructing a protein interaction network, so that the tool constructs a network according to the input data;
The protein network analysis unit is used for analyzing and obtaining a path and a biological process participated by the protein target capable of being interfered and a protein interacted with the protein target capable of being interfered according to the constructed network; wherein the participating pathways and biological processes include: synaptic protein localization, cell-linked protein localization, axonal transport, presynaptic protein localization, axonal-dendritic transport, and cytoskeletal dependent intracellular trafficking.
The invention has the following beneficial effects:
The invention provides a multi-part chronic pain potential treatment target determination system, which comprises a data acquisition module, an intervention gene target determination module, an intervention protein target determination module and an MCP potential treatment target determination module; the data acquisition module acquires gene expression data of whole blood and gene expression data of brain tissue in a GWAS data set and GTEx V data set, and then the intervention gene target point confirmation module determines a plurality of intervention gene targets related to MCP and generates an intervention gene target point set according to the acquired gene expression data of whole blood and the gene expression data of brain tissue; an interfered protein target point confirming module determines a plurality of interfered protein target points related to MCP according to the GWAS data and generates an interfered protein target point set; finally, the MCP potential therapeutic target confirming module determines the intersection of the interventional gene target set and the interventional protein target set, and takes each target in the determined intersection as the MCP potential therapeutic target; therefore, the system can analyze the protein targets related to the MCP and a plurality of gene targets related to the MCP, and can take the targets which simultaneously affect the gene expression and the protein expression of the MCP as the potential therapeutic targets of the MCP through the consistency analysis of the genes and the protein levels, so that the therapeutic targets related to the MCP can be more accurately identified, and the effectiveness and the pertinence of the MCP therapy can be improved.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a multi-site system for determining potential therapeutic targets for chronic pain according to an embodiment of the invention;
FIG. 2 is a graph of whole blood results from TWAS analysis according to one embodiment of the present invention;
FIG. 3 is a graph of brain tissue results from TWAS analysis according to one embodiment of the present invention;
FIG. 4 is a graph of whole blood results from SMR & HEIDI analysis according to one embodiment of the present invention;
FIG. 5 is a graph of brain tissue results from SMR & HEIDI analysis according to one embodiment of the present invention;
FIG. 6 is a graph of cross results of whole blood obtained by two methods, TWAS and SMR & HEIDI, according to one embodiment of the present invention;
FIG. 7 is a graph of brain tissue intersection results obtained by two methods, TWAS and SMR & HEIDI, according to one embodiment of the present invention;
FIG. 8 is a graph showing the results of analysis of blood proteins by an interventional protein target validation module according to one embodiment of the present invention;
FIG. 9 is a graph showing the results of brain proteins analyzed by the protein target validation module of the present invention;
FIG. 10 is a schematic diagram of another multi-site chronic pain potential therapeutic target determination system according to an embodiment of the present invention;
FIG. 11 is a graph showing the results of cell types obtained by the sc-linker assay according to one embodiment of the present invention;
FIG. 12 is a graph showing the results of the relevant cell processes obtained by the sc-linker assay according to one embodiment of the invention;
FIG. 13 is a schematic diagram of another multi-site chronic pain potential therapeutic target determination system according to an embodiment of the present invention;
FIG. 14 is a schematic diagram of another multi-site chronic pain potential therapeutic target determination system according to an embodiment of the present invention;
FIG. 15 is a schematic diagram showing the results of analysis by the protein network construction and analysis module according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In the description of the embodiments of the present application, the term "plurality" means two or more (including two), and similarly, "plural sets" means two or more (including two), and "plural sheets" means two or more (including two).
In the description of the embodiments of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like should be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; or may be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to specific circumstances.
Referring to fig. 1, a schematic structural diagram of a multi-site potential therapeutic target determining system for chronic pain according to the present application includes: the system comprises a data acquisition module, an intervention gene target point confirmation module, an intervention protein target point confirmation module and an MCP potential treatment target point confirmation module;
The data acquisition module is used for acquiring the gene expression data of whole blood and the gene expression data of brain tissues in the first GWAS data and GTEx V data sets, the blood protein expression data in the code data sets and the brain protein expression data in the NIAGADS data sets; wherein the first GWAS data is GWAS data related to MCP;
The system comprises a first GWAS data, a second GWAS data, a gene expression data of whole blood and a gene expression data of brain tissue, wherein the first GWAS data is used for acquiring first GWAS data, the second GWAS data is used for acquiring second GWAS data, the first GWAS data, the second GWAS data and the gene expression data of brain tissue;
The protein target spot confirmation module is used for confirming a plurality of protein target spots related to MCP according to the first GWAS data, the blood protein expression data and the brain protein expression data, and generating a protein target spot set capable of being interfered;
The MCP potential therapeutic target confirming module is used for determining an intersection of the interventional gene target set and the interventional protein target set, and taking all targets in the determined intersection as MCP potential therapeutic targets;
In a preferred embodiment, said determining a number of interventional gene targets associated with MCP based on said first GWAS data, said gene expression data of whole blood, and said gene expression data of brain tissue, comprises:
Performing whole transcriptome association analysis according to the gene expression data of the whole blood, the gene expression data of the brain tissue and the corresponding genetic variation data in the first GWAS data to obtain P values and a first effect value direction of the gene expression data of the whole blood and the gene expression data of the brain tissue;
Performing FDR correction on the P value of the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain first candidate gene expression data with corrected P value smaller than a preset FDR correction threshold;
Performing SMR & HEIDI analysis on the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain the gene expression data of each whole blood and p-SMR value, p-HEIDI value and second effect value direction corresponding to the gene expression data of each brain tissue;
Performing FDR correction on the p-SMR value of each gene expression data to obtain candidate gene expression data with a corrected p-SMR value smaller than a preset FDR correction threshold value, and screening out second candidate gene expression data with a p-HEIDI value larger than a preset value and the direction of the first effect value being consistent with the direction of the second effect value from the candidate gene expression data with the corrected p-SMR value smaller than the preset FDR correction threshold value;
Taking an intersection part of the first candidate gene expression data and the second candidate gene expression data, and taking the gene expression data of the intersection part as an intervened gene target point related to MCP;
Illustratively, the first GWAS data, i.e., the GWAS data for MCPs, is derived from UK Biobank databases;
Specifically, through integration GTEx V of a data set, firstly, correlating the gene expression data of the whole blood and the gene expression data of brain tissue with corresponding genetic variation data in the GWAS data through a FUSION software tool, then analyzing the gene expression data of the whole blood and the brain tissue by using a whole transcriptome correlation analysis (TWAS) method, calculating to obtain P values and Z-score values of the gene expression data of the whole blood and the gene expression data of the brain tissue, and then performing FDR correction on the P values of the gene expression data of the whole blood and the gene expression data of the brain tissue (false discovery rate correction, using a Benjamini-Hochberg method), and confirming that the corrected P value is less than 0.05 as a final significant result, namely, first candidate gene expression data related to MCP;
the smaller the P value, the higher the association degree between the gene and MCP;
It should be noted that, the present application needs to use the first effect value (Z-score value) direction, and does not need the specific magnitude of the Z-score value, where the first effect value direction refers to: when the Z-score value is positive, the corresponding first effect value direction is positive, and when the Z-score value is negative, the corresponding first effect value direction is negative;
Referring to fig. 2-3, where fig. 2 is a whole blood result graph of TWAS, fig. 3 is a brain tissue result graph of TWAS, the dashed line is a threshold value of FDR correction value, and only if the correction P value of candidate gene data is less than the FDR correction threshold value is considered statistically significant; the upper graph in FIG. 2 and the upper graph in FIG. 3 show gene expression data in the case where the direction of the effect value of the Z-score is positive, the lower graph in FIG. 2 and the lower graph in FIG. 3 show gene expression data in the case where the direction of the effect value of the Z-score is negative, and the ordinate in FIG. 2 and FIG. 3 shows the conversion value of the P value and the abscissa shows the position of each chromosome;
Referring to fig. 4 to 7, specifically, since false positives or the like may occur when only one method is used, then verifying gene expression data of whole blood and gene expression data of the brain tissue by SMR software using a sum-data-based Mendelian Randomization (SMR) and Heterogeneity IN DEPENDENT Instruments (HEIDI) test, obtaining p-SMR values, p-HEIDI values and beta-SMR values of the gene expression data of the whole blood and the brain tissue, performing FDR correction on the p-SMR value of each gene expression data by FDR correction, screening second candidate gene expression data with the p-HEIDI value being greater than 0.01 and the first effect value direction (i.e., the effect value direction of the Z-score value) being identical to the second effect value direction (i.e., the effect value direction of the beta-SMR value) after candidate gene expression data with the p-SMR value being less than 0.05 is obtained by FDR correction; that is, the direction of the first effector value (Z-score value) and the direction of the second effector value (beta-SMR value) of the second candidate gene expression data are the same positive or the same negative;
wherein, fig. 4 is a SMR & HEIDI result graph of whole blood, fig. 5 is a SMR & HEIDI result graph of brain tissue, and the dashed line is a threshold value of FDR correction value;
The present application needs to use the second effect value (beta-SMR value) direction, which refers to the value of the second effect value (beta-SMR value): when the beta-SMR value is positive, the corresponding second effect value direction is positive, and when the beta-SMR value is negative, the corresponding second effect value direction is negative;
it should be noted that, the p-SMR value represents the statistically significant degree of the relationship between the gene and MCP, and then the HEIDI method is used to determine whether the effect of the genetic variation site on MCP is mediated by gene expression to exclude the level pleiotropic of genetic variation, and if the p-HEIDI value is greater than 0.01, the probability of the level pleiotropic effect is considered to be less, indicating that the genetic variation affects MCP through the gene expression, i.e., the gene expression has a causal relationship with MCP;
Finally, the results obtained by the TWAS method and the SMR method and the HEIDI method, namely the first candidate gene expression data and the second candidate gene expression data are crossed to obtain an intersection part, and the gene expression data of the intersection part are used as an interveneable gene target point related to MCP to generate an interveneable gene target point set, wherein the cross result of whole blood is shown in FIG. 6, and the cross result of brain tissue is shown in FIG. 7.
In a preferred embodiment, said determining a number of tamper-evident protein targets associated with MCP based on said first GWAS data, said gene expression data and said brain protein expression data comprises:
Taking the blood protein expression data in the decoding data set and the brain protein expression data in the NIAGADS data set as two-sample Mendelian randomized exposure data, taking the first GWAS data as two-sample Mendelian randomized result data, and carrying out two-sample Mendelian randomization to obtain third effect value directions and P values corresponding to the blood protein expression data and the brain protein expression data;
Performing FDR correction on the P value of the blood protein expression data and the brain protein expression data to obtain MR-positive protein with the corrected P value smaller than an FDR correction threshold;
Performing Bayes co-localization analysis on the MR positive proteins, and taking the MR positive proteins with posterior result probability larger than a preset value as the protein targets which are related to MCP and can be intervened;
Specifically, taking blood protein expression data in the decoding data set and brain protein expression data in the NIAGADS data set as Two-sample Mendelian randomized exposure data, taking first GWAS data as Two-sample Mendelian randomized result data, performing Two-sample Mendelian randomization (Two-SAMPLE MENDELIAN Randomization, MR) analysis to obtain P values and beta-MR values of the blood protein expression data and the brain protein expression data, and then performing FDR correction on the P values of the blood protein expression data and the brain protein expression data to obtain MR positive proteins with corrected P values smaller than 0.05;
It should be noted that, the mendelian randomization analysis of two samples is a fixed analysis method name, "two samples" means that Exposure data and ending Outcome data are derived from different populations, specifically, in the present invention, "Exposure data" means that data such as genes, proteins and the like are derived from one population, and "ending data" means that data of multiple chronic pains are derived from different populations of the same population;
specifically, since MR positive proteins have been derived, it is necessary to further analyze the MR positive proteins by bayesian co-localization of these positive proteins to identify whether there is a mutation site shared by a potential protein target and MCP, i.e., whether the presence of a certain genetic mutation site results in the generation of MCP phenotype by altering the expression of a certain protein; thus, the co-location results are partitioned by using Coloc software tools;
Note that Coloc a posterior probability has the following assumption: (1) H0: indicating that no genetic variation sites were found to be associated with MCP or detected protein expression; (2) H1: indicating that a certain genetic variation site is found to be associated with protein expression but not with MCP; (3) H2: indicating that a certain genetic variation site is found to be associated with MCP, but not with protein expression; (4) H3: indicating that two genetic variation sites are found, which are respectively related to protein expression and MCP; (5) H4: indicating that a certain genetic variation site is found to be associated with both protein and MCP;
specifically, referring to fig. 9-10, pp.h4>60% is set according to a common threshold of Coloc kit as a basis for distinguishing whether co-localization exists between a protein and MCP, pp.h4>60% is the existence of a common genetic variation site between the protein and MCP, thereby confirming that an MR positive protein with a posterior probability of greater than 0.6 is co-localization positive, namely, an MR positive protein with a posterior probability of greater than 0.6 is used as an interventionable protein target protein related to MCP, and the interventionable protein target protein and MCP are considered to have a shared pathogenic site, and generating an interventionable protein target set;
It should be noted that, the shared pathogenic site, that is SHARED GENETIC VARIANT, also called a single nucleotide polymorphism, is a genetic variation site, and affects the level of the protein and the incidence of MCP, so that the shared pathogenic site is often used for genotyping, so that the incidence of a genotype is high, and the protein target determined through the process is less affected by level pleiotropic and linkage disequilibrium and is a reliable potential protein target capable of being interfered;
Illustratively, determining an intersection of the set of interventional gene targets and the set of interventional protein targets, and taking each target in the determined intersection as a potential therapeutic target for MCP;
Specifically, after the set of the gene targets capable of intervention and the set of the protein targets capable of intervention are obtained, taking an intersection of the set of the gene targets capable of intervention and the set of the protein targets capable of intervention, and taking each target in the determined intersection as a potential therapeutic target of MCP.
Referring to fig. 8, in a preferred embodiment, the multi-site chronic pain potential therapeutic target determination system further comprises: a cell analysis module;
The cell analysis module comprises a sequencing data acquisition unit, a key cell type analysis unit and a cell process analysis unit;
The sequencing data acquisition unit is used for acquiring single-cell RNA sequencing data;
the key cell type unit is used for analyzing and obtaining the key cell type related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data;
The cell process analysis unit is used for analyzing and obtaining a cell process related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data;
illustratively, single cell RNA sequencing data is obtained, and a sc-linker analysis method is used to identify key cell types associated with the MCP potential therapeutic targets;
Specifically, analyzing to obtain blood LANCL and KLC1 as potential therapeutic targets, and further analyzing cell types of the two potential targets, wherein the LANCL1 target is mainly related to B lymphocytes in blood, GABAergic neurons in brain tissues, LAMP5+ neurons, VIP+ neurons, L5/6NP neurons, L5 ET neurons and astrocytes; the KLC1 target is mainly related to megakaryocytes in blood, GABAergic neurons, LAMP5+ neurons, PVABL + neurons, glutamatergic neurons, IT neurons, L4IT neurons, L5/6NP neurons, L5 ET neurons and L6 CT neurons in brain tissues;
Specifically, referring to fig. 11, wherein fig. 11 is a cell type result graph, fig. 11 (a) is a whole blood result, and fig. 11 (B) is a brain tissue result;
illustratively, single cell RNA sequencing data is obtained, and a sc-linker analysis method is used to identify cellular processes associated with the MCP potential therapeutic targets;
Specifically, through sc-linker analysis, LANCL1 was obtained to be mainly related to ribosomal processes in blood and to transfer processes of electrons in brain tissue; KLC1 was found to be associated with ribosomal processes in blood, mainly with axonal guidance processes in brain tissue;
Specifically, referring to fig. 12, a schematic diagram of the relevant cell process is shown, fig. 12 (a) is a whole blood result, and fig. 12 (B) is a brain tissue result.
Referring to fig. 13, in a preferred embodiment, the multi-site chronic pain potential therapeutic target determination system further comprises: an MCP potential therapeutic target effect analysis module;
the MCP potential treatment target analysis module is used for acquiring second GWAS data; obtaining a plurality of related diseases except MCP of each protein target capable of being interfered and a corresponding fourth effect value direction according to the protein targets capable of being interfered and the second GWAS data;
Judging whether side effects of each protein target capable of being interfered on the plurality of related diseases exist according to the third effect value direction and the fourth effect value direction of each protein target capable of being interfered;
Judging whether the protein targets can interfere with the related diseases is an additional indication according to the third effect value direction and the fourth effect value direction of each protein target;
Illustratively, the second GWAS data is the MCP-independent GWAS data in FinnGen database (R8);
Illustratively, to understand the role of the MCP potential therapeutic target in other diseases, the safety and versatility of the MCP potential therapeutic target is assessed, using the GWAS data in FinnGen database (R8) that is not related to MCP, by using a two-sample mendelian randomization assay, i.e., MR-PheWAS assay, to analyze and evaluate several associated diseases of each of the tamper-able protein targets other than MCP, and the corresponding fourth effect values;
Specifically, for a certain related disease of each protein target capable of being interfered, judging whether the direction of a fourth effect value of the related disease is consistent with the direction of a beta-MR value of the protein target capable of being interfered, when the direction of the fourth effect value obtained according to the MR result is inconsistent with the direction of the third effect value, that is, the direction of the fourth effect value is in negative correlation with the direction of the third effect value, judging that the protein target capable of being interfered has side effects on the related disease corresponding to the fourth effect value, and when the direction of the fourth effect value obtained according to the MR result is consistent with the direction of the third effect value, indicating that the direction of the fourth effect value is in positive correlation with the third effect value, judging that the related disease corresponding to the fourth effect value at present is an additional indication of the protein target capable of being interfered;
it should be noted that, after the third effect value (beta-MR effect value) is obtained, the direction of the beta-MR effect value needs to be used, and the specific value of the beta-MR value is not needed, where the third effect value direction refers to: when the beta-MR value is positive, the corresponding third effect value direction is positive, and when the beta-MR value is negative, the corresponding third effect value direction is negative;
Referring to fig. 14, in a preferred embodiment, the multi-site chronic pain potential therapeutic target determination system further comprises: a protein network construction and analysis module;
The protein network construction and analysis module comprises a protein network construction unit and a protein network analysis unit;
the protein network construction unit is used for inputting the protein target capable of being intervened into a tool for constructing a protein interaction network, so that the tool constructs a network according to the input data;
The protein network analysis unit is used for analyzing and obtaining a path and a biological process participated by the protein target capable of being interfered and a protein interacted with the protein target capable of being interfered according to the constructed network;
Illustratively, inputting said protein target to GENEMANIA, such that GENEMANIA is analyzed to obtain pathways and biological processes involved in said protein target and proteins interacting with said protein target according to the construction of a protein interaction network;
Specifically, referring to fig. 15, pathways and biological processes involved in the target of the protein can be analyzed to include synaptoprotein localization, cell-linked protein localization, axon transport, presynaptic protein localization, axon-dendrite transport, cytoskeletal dependent intracellular trafficking, and the interacting proteins can be analyzed to see the outer lane protein of fig. 13.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (6)
1. A multi-site chronic pain potential therapeutic target determination system, comprising: the system comprises a data acquisition module, an intervention gene target point confirmation module, an intervention protein target point confirmation module and an MCP potential treatment target point confirmation module;
The data acquisition module is used for acquiring the gene expression data of whole blood and the gene expression data of brain tissues in the first GWAS data and GTEx V data sets, the blood protein expression data in the code data sets and the brain protein expression data in the NIAGADS data sets; wherein the first GWAS data is GWAS data related to MCP;
The system comprises a first GWAS data, a second GWAS data, a gene expression data of whole blood and a gene expression data of brain tissue, wherein the first GWAS data is used for acquiring first GWAS data, the second GWAS data is used for acquiring second GWAS data, the first GWAS data, the second GWAS data and the gene expression data of brain tissue;
The protein target spot confirmation module is used for confirming a plurality of protein target spots related to MCP according to the first GWAS data, the blood protein expression data and the brain protein expression data, and generating a protein target spot set capable of being interfered;
The MCP potential therapeutic target confirming module is used for determining the intersection of the interventional gene target set and the interventional protein target set, and taking all targets in the determined intersection as MCP potential therapeutic targets.
2. The multi-site chronic pain potential therapeutic target determination system of claim 1, wherein the determining a number of interventional gene targets associated with MCP based on the first GWAS data, the gene expression data of whole blood, and the gene expression data of brain tissue comprises:
Performing whole transcriptome association analysis according to the gene expression data of the whole blood, the gene expression data of the brain tissue and the corresponding genetic variation data in the first GWAS data to obtain P values and a first effect value direction of the gene expression data of the whole blood and the gene expression data of the brain tissue;
Performing FDR correction on the P value of the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain first candidate gene expression data with corrected P value smaller than a preset FDR correction threshold;
Performing SMR & HEIDI analysis on the gene expression data of the whole blood and the gene expression data of the brain tissue to obtain the gene expression data of each whole blood and p-SMR value, p-HEIDI value and second effect value direction corresponding to the gene expression data of each brain tissue;
Performing FDR correction on the p-SMR value of each gene expression data to obtain candidate gene expression data with a corrected p-SMR value smaller than a preset FDR correction threshold value, and screening out second candidate gene expression data with a p-HEIDI value larger than a preset value and the direction of the first effect value being consistent with the direction of the second effect value from the candidate gene expression data with the corrected p-SMR value smaller than the preset FDR correction threshold value;
taking an intersection part of the first candidate gene expression data and the second candidate gene expression data, and taking the gene expression data of the intersection part as an intervened gene target point related to MCP.
3. The multi-site chronic pain potential therapeutic target determination system according to claim 1, wherein the determining a number of tamper-evident protein targets associated with MCP based on the first GWAS data, the gene expression data, and the brain protein expression data comprises:
Taking the blood protein expression data in the decoding data set and the brain protein expression data in the NIAGADS data set as two-sample Mendelian randomized exposure data, taking the first GWAS data as two-sample Mendelian randomized result data, and carrying out two-sample Mendelian randomization to obtain third effect value directions and P values corresponding to the blood protein expression data and the brain protein expression data;
Performing FDR correction on the P value of the blood protein expression data and the brain protein expression data to obtain MR-positive protein with the corrected P value smaller than an FDR correction threshold;
And performing Bayesian co-localization analysis on the MR positive proteins, and taking the MR positive proteins with posterior result probability larger than a preset value as the protein targets related to MCP.
4. The multi-site chronic pain potential therapeutic target determination system according to claim 1, further comprising: a cell analysis module;
The cell analysis module comprises a sequencing data acquisition unit, a key cell type analysis unit and a cell process analysis unit;
The sequencing data acquisition unit is used for acquiring single-cell RNA sequencing data;
The key cell type analysis unit is used for analyzing and obtaining key cell types related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data;
The cell process analysis unit is used for analyzing and obtaining the cell process related to the MCP potential therapeutic target according to a sc-linker analysis method, the MCP potential therapeutic target and the single cell RNA sequencing data.
5. The multi-site chronic pain potential therapeutic target determination system according to claim 3, further comprising: an MCP potential therapeutic target effect analysis module;
The MCP potential therapeutic target analysis module is used for acquiring second GWAS data and obtaining a plurality of related diseases except MCP of each protein target capable of being interfered and a corresponding fourth effect value direction according to the protein targets capable of being interfered and the second GWAS data;
Judging whether side effects of each protein target capable of being interfered on the plurality of related diseases exist according to the third effect value direction and the fourth effect value direction of each protein target capable of being interfered;
Judging whether the protein targets can interfere with the related diseases is an additional indication according to the third effect value direction and the fourth effect value direction of each protein targets which can interfere with the protein targets.
6. The multi-site chronic pain potential therapeutic target determination system according to claim 1, further comprising: a protein network construction and analysis module;
The protein network construction and analysis module comprises a protein network construction unit and a protein network analysis unit;
the protein network construction unit is used for inputting the protein target capable of being intervened into a tool for constructing a protein interaction network, so that the tool constructs a network according to the input data;
The protein network analysis unit is used for analyzing and obtaining a path and a biological process participated by the protein target capable of being interfered and a protein interacted with the protein target capable of being interfered according to the constructed network; wherein the participating pathways and biological processes include: synaptic protein localization, cell-linked protein localization, axonal transport, presynaptic protein localization, axonal-dendritic transport, and cytoskeletal dependent intracellular trafficking.
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| US20030219771A1 (en) * | 2001-11-09 | 2003-11-27 | Michael Bevilacqua | Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles |
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