WO2019117400A1 - Appareil et procédé de construction de réseau de gènes - Google Patents

Appareil et procédé de construction de réseau de gènes Download PDF

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WO2019117400A1
WO2019117400A1 PCT/KR2018/002915 KR2018002915W WO2019117400A1 WO 2019117400 A1 WO2019117400 A1 WO 2019117400A1 KR 2018002915 W KR2018002915 W KR 2018002915W WO 2019117400 A1 WO2019117400 A1 WO 2019117400A1
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gene
disease
network
pairs
mutual information
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PCT/KR2018/002915
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Korean (ko)
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박상현
박치현
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연세대학교 산학협력단
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics

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  • the present invention relates to an apparatus and a method for constructing a gene network. More particularly, the present invention relates to an apparatus and a method for constructing a gene network related to Alzheimer's disease (AD).
  • AD Alzheimer's disease
  • the present invention corresponds to the results of a research project funded by the Ministry of Creation Science (government) in 2017, supported by a mature researcher (research on disease module search and disease network construction using data mining analysis technique) Assignment number: NRF-2015R1A2A1A05001845).
  • AD Alzheimer's Disease
  • the mechanism of Alzheimer's disease (AD) reveals how molecular entities such as genes interact at the pathway level, how some pathways on the pathway affect disease outbreaks, can do.
  • the pathway and related mechanisms that explain life phenomena are so complex that they need systems and methods to support expert analysis.
  • the present invention has been conceived to solve the problems described above. It is an object of the present invention to select gene pairs from gene samples based on Mutual Information (MI) And to propose a gene network construction apparatus and method for extracting a network to construct a gene network specific to Alzheimer's disease.
  • MI Mutual Information
  • the present invention has been conceived in order to achieve the above-mentioned object, and it is an object of the present invention to provide a method for detecting a mutual information of genes associated with persons without AD (Alzheimer's Disease)
  • a gene pair selector for selecting a first gene pair associated with the AD disease among predetermined gene samples based on the gene pair;
  • a gene pair integrating unit that integrates the first gene pairs using a node property based on information on the path of each gene pair;
  • a sub-network extracting unit for extracting sub-networks related to the AD disease based on a seed based search when the first gene pairs are integrated;
  • a gene network constructing unit for constructing a gene network specific to the AD disease on the basis of sub-networks related to the AD disease.
  • the present invention provides a method for diagnosing an AD disorder, comprising the steps of: determining a first mutual information of genes associated with persons without AD (Alzheimer's Disease) disease and a second mutual information amount of genes associated with AD disease Selecting a first gene pair associated with AD disease; Integrating the first gene pairs using a node property based on information on the path of each gene pair; Extracting sub-networks related to the AD disease based on a seed based search if the first gene pairs are integrated; And constructing a gene network specific to the AD disease based on sub-networks related to the AD disease.
  • the present invention can achieve the following effects through the configurations for achieving the above object.
  • AD Alzheimer's disease
  • the performance of genetic networks can be improved by effectively detecting and eliminating false positive interactions more effectively than conventional methods.
  • FIG. 1 is a flow chart schematically showing a method for constructing a gene network specific to Alzheimer's disease.
  • FIGS. 2 to 10 are reference diagrams for explaining each step in constructing a gene network specific to Alzheimer's disease.
  • FIG. 11 is a conceptual diagram schematically showing an internal configuration of a gene network construction apparatus according to a preferred embodiment of the present invention.
  • FIG. 12 is a flowchart schematically illustrating a method of constructing a gene network according to a preferred embodiment of the present invention.
  • AD Alzheimer's Disease
  • the present invention provides a new method for constructing a gene network optimized for Alzheimer's disease (AD) by integrating protein gene (gene) interaction data and gene expression data with high reliability. Further, in the present invention, genes differentially methylated in Alzheimer ' s disease (AD) are identified in order to consider the epigenetic factor, and the results are included in the network.
  • AD Alzheimer's disease
  • AD Alzheimer's disease
  • FETs Functional Enhancement Tests
  • the proposed method is applicable not only to Alzheimer's disease (AD) but also to various disease studies based on gene networks.
  • the method proposed in the present invention uses biophysically interacting PPI data with HumanNet, which is known to be a bit more accurate than the existing PPI, and provides the most optimal interaction ) Were confirmed through experiments, and genetic networks were constructed with these interactions. In other words, we were able to establish a disease-specific and accurate gene network.
  • Gene Expression profiles, Interactome databases, Pathway databases, and DNA methylation profiles are used to build networks. Among them, Gene Expression and Interactome are used to identify informative interactions that provide useful information, and Pathway and Methylation are used to support disease analysis.
  • the present invention utilizes two independent array-based expression profiles based on gene expression profiles to construct a network .
  • DEG is the most prominent part of PFC (Postmortem Prefrontal Cortex samples, posterior frontal cortex).
  • GEO Gene Expression Omnibus accession numbers of the two array-based Expression Profiles are GSE33000 and GSE44770.
  • GSE33000 and GSE44770 consist of 467 samples including 157 normal genes and 310 AD genes, and 229 samples including 100 normal genes and 129 AD genes, respectively. As shown in FIG. 2, the two expression profiles according to respective states such as normal, AD, and the like are similar to each other. With the exception of these two expression profiles, there are very few large gene expression data available.
  • the Interactome database is used to identify the connectivity between the two genes.
  • a genetic interaction dataset such as humanNet and a human protein interaction dataset are used.
  • the protein interaction data set used in the present invention is composed of 23,233 interactions with high reliability.
  • the present invention utilizes systems based on the yeast two hybrid method to compile a set of protein interaction data with systematic screening with high throughput, assays are used to validate protein interaction data sets.
  • this protein interaction data set is defined as bPPI (biophysical protein-protein interaction).
  • HumanNet is well suited for detecting disease-associated genes through the Guilt-By-Association approach, which is one of the types of association fallacy.
  • the data set consists of over 400,000 genetic interactions, including scores.
  • the present invention combines bPPI with high scoring interactions in humanNet as shown in Figure 3 to obtain more accurate and meaningful interactions.
  • DNA methylation profiles corresponding to the prefrontal cortex region are used to investigate how differentially methylated genes (DMGs) affect AD (Alzheimer's Disease) .
  • the GEO accession number of DNA methylation profiles is GSE80970, which consists of 142 samples, including 68 common genes and 74 AD genes.
  • FIG. 1 is a flowchart schematically showing a method proposed by the present invention, that is, a method for constructing a gene network specific to Alzheimer's disease.
  • the method proposed by the present invention can be roughly divided into two steps.
  • the first step is to extract differentially expressed gene pairs.
  • the first step as shown in FIG. 4, when connectivity information between genes is generated through the Interactome database (S120), a degree of different expression of each gene pair is measured based on the connectivity information (S130). Then, statistical tests such as T-test and Fisher's exact test are used to determine optimal parameters reflecting AD specificity (S140).
  • the pathway information is integrated in the established network as shown in FIG. 5 (S150).
  • AD related information is also integrated according to the node properties.
  • topological analysis and functional enrichment analysis are used to clarify AD relativeness (S160).
  • a scoring scheme is often used to identify interactions that provide useful information in relation to cancer.
  • this scoring schedule is utilized in order to measure differentially expressed patterns for distinguishing each pair of genes into a normal tissue and an AD tissue (S130).
  • MI mutual information
  • PCC Pearson's Correlation Coefficient
  • Linear similarity measures such as Pearson's correlation coefficient (PCC), dot product, and the like are suitable for generating an associated pattern from gene expression data. Particularly, Pearson's correlation coefficient (PCC) is most suitable because the score values are bounded in a specified range.
  • the mutual information amount is applied instead of the Pearson correlation coefficient (PCC) to the scoring schedule in consideration of this point.
  • the mutual information (MI) can be used to counter heterogeneity of expression intensities and is therefore suitable for use in the present invention.
  • the scoring scheme applied to the present invention is defined by the following equation (1).
  • G iNorm denotes vectors associated with expression values of the i-th gene i among normal samples
  • g iAD denotes vectors associated with the i-th gene among AD samples (AD samples) Means vectors associated with expression values
  • gjNorm denotes vectors associated with the expression values of the jth gene (gene j) among the general samples
  • gjAD denotes vectors associated with the expression values of the jth gene among the AD samples.
  • the score value is the threshold weight; the (threshold weight value threshold weight) equal to or greater gene pairs (gene pairs) discrimination expressed interaction than the threshold weight (differentially expressed interactions.
  • the interactions associated with bPPI are not as large as those with high confidence. Therefore, the present invention utilizes all the interactions related to bPPI.
  • humanNet there is a score that evaluates each interaction according to the correlation of gene pairs. Therefore, humanNet will only use interactions with scores higher than the reference value (see FIG. 3).
  • MI Mutual information
  • the random variable X is a set of possible states ⁇ x 1 , x 2 , ...
  • the Shannon entropy is defined as the following equation (2).
  • the joint entropy H (X, Y) of the two random variables X and Y can be defined by the following equation (3).
  • the mutual information MI (X, Y) of the two random variables X and Y can be defined by the following Equation (4).
  • MI mutual information amount
  • a method of predicting mutual information (MI) using a B-spline function in a binning method can be used.
  • MI mutual information
  • each data point is represented by one bin.
  • data points can be simultaneously allocated to a plurality of bins through a B-spline.
  • B-spline binning can greatly improve the discrimination of correlations obtained from the hypothesis of statistical independence.
  • a data point is assigned to n bins, where the data point has a probability n (where n> 1).
  • the mutual information (MI) is calculated based on the binning method for measuring the correlation of gene pairs.
  • the size of the network can be determined by parameters such as a threshold weight value, a usage ratio N of humanNet, and the like. And the values of the parameters can be determined by considering how many AD related gene pairs can be excluded in which the network excludes false positive interactions with AD .
  • T-test is performed based on the comparison between two different groups to determine the utilization ratio N of humanNet.
  • null hypothesis is set as follows.
  • the null hypothesis applied to the present invention is that the interactions belonging to the first group do not have randomly selected interactions in the Interactome database (i.e., the first group is a gene- gene interaction), and among the interactions belonging to the second group, when there is a randomly selected interaction in the Interactome database (i.e., a gene-gene interaction ), The difference value between the average score of the first group and the average score of the second group is zero. The difference between the two groups is only whether the selected interactions are in the Interactome dataset.
  • the first group and the second group are not large and fixed groups, they sample some of the first and second groups to test for differences between the first group and the second group.
  • the t-test can determine the significance level for rejecting the null hypothesis in the hypothesis test (p-value ⁇ 0.05) a t-test can be repeated at least 100 times for size boosting.
  • the optimal usage ratio of humanNet can be selected through such a t-test (t-test), and the pair of genes linked through the interactome database is associated with a differentially expressed pattern And the probability of that.
  • AD-associated network that can be detected from IntAct, that is, an Interactome database, which is one of the correct answers to the AD network, can be used as a result of the reference value, and the AD- It is also possible to use AD-related genes (AD-related genes) identified through an approach (see FIG. 6) as an answer set.
  • genes to be applied to the present invention are extracted from ReliefF based on the number of genes included in the established network. For example, in the present invention, the number of genes equal to or greater than the number of genes included in the established network is extracted from the results of ReliefF.
  • a Fisher's exact test using a contingency table as shown in FIG. 7 can be applied to measure the significance associated with the overlap.
  • A represents the AD-related interaction shown in IntAct
  • B represents the interaction found by reflecting the weight threshold.
  • the minimum value satisfying the significance level (p-value ⁇ 0.01) for each comparison between the reference value results can be determined as the threshold weight value.
  • the subnetworks sub-networks can be extracted (S160). 8 to 10 are examples of subnetworks obtained through seed based biclustering. From these results, it can be confirmed that DNA methylation is related to AD.
  • the node property is performed using DMG (Differentially Methylated Gene) information obtained by comparing the normal status of the general gene and the AD status of the AD gene.
  • DMG Differently Methylated Gene
  • DNA methylation associated with AD increases with time and tends to occur more frequently than before.
  • DMG information can be identified using the Limma R package, and DMG information can be identified by setting a p-value cutoff and a fold change cutoff to 0.01 and 1.5, respectively. can do.
  • probes located in TFBS Transcription Factor Binding Sites
  • the probes detected in the above can be recorded in the genes as DMG information.
  • DMG information DMG set 54 differentially methylated genes obtained from the prefrontal cortex of Alzheimer's disease patients.
  • the present invention performs some simple statistical testing can do.
  • some statistical tests can be performed to determine the optimum value of the threshold weight and the usage ratio N of humanNet. Once the critical weights and the utilization ratio of humanNet are determined, we then construct an AD specific differential gene network and integrate informative properties that provide useful information about AD through this network. . Finally, the network is analyzed using various methods to demonstrate whether the network constructed according to the method proposed in the present invention is useful for identifying Alzheimer's patients.
  • omics layer data that can affect AD such as DNA methylation data
  • DNA methylation data is used for network construction. It is necessary to integrate data of other omics layer such as methylation to find functional module in network.
  • the method proposed in the present invention models a disease-specific network from molecular layer data such as gene expression data and DNA methylation data for disease patients, It can be used as core algorithm of intelligent medical information analysis system.
  • FIG. 11 is a conceptual diagram schematically showing an internal configuration of a gene network construction apparatus according to a preferred embodiment of the present invention.
  • the gene network construction apparatus 200 is for constructing a gene network specific to Alzheimer's disease (AD), and includes a gene pair selection unit 210, a gene pair integration unit 220, An extraction unit 230, a gene network construction unit 240, a power supply unit 250, and a main control unit 260.
  • AD Alzheimer's disease
  • the power supply unit 250 functions to supply power to each of the components constituting the gene network construction apparatus 200.
  • the main control unit 260 performs a function of controlling the overall operation of each component constituting the gene network construction apparatus 200.
  • the gene pair selection unit 210 selects a first gene pair related to AD (Alzheimer's Disease) disease among predetermined gene samples based on the first mutual information (MI) and the second mutual information amount Function.
  • the first mutual information amount refers to mutual information amount of genes related to those without AD disease and the second mutual information amount refers to mutual information amount of genes related to those with AD disease.
  • the gene pair selector 210 can select the first pair of genes based on the result obtained by comparing the difference value between the first mutual information amount and the second mutual information amount with the threshold value.
  • the gene pair selection unit 210 may calculate the first mutual information amount and the second mutual information amount based on the expression value of each gene.
  • the gene pair selection unit 210 calculates the first mutual information amount and the second mutual information amount using a binning method for measuring a correlation between gene pairs using a B-spline function .
  • the gene pair integrating unit 220 integrates the first gene pairs using the node property based on the information on the path of each gene pair.
  • the sub-network extracting unit 230 extracts sub-networks related to the AD disease based on a seed based search.
  • the sub-network extracting unit 230 may further extract a sub-network related to the AD disease by applying FEA (Functional Enrichment Analysis).
  • the sub-network extracting unit 230 may extract the sub-network related to the AD disease based on the DNA methylation information associated with each gene included in the first gene pairs when the node property is used.
  • the sub-network extractor 230 extracts a fold change cutoff associated with the first gene pairs, a p-value cutoff associated with the first gene pairs, and a TFBS (Transcription Factor Binding) associated with the first gene pairs. Sites may be further applied to extract subnetworks related to AD disease.
  • TFBS Transcription Factor Binding
  • the gene network construction unit 240 functions to construct a gene network specific to AD disease based on sub-networks related to AD disease.
  • the gene network construction apparatus 200 may further include a gene sample generation unit 270.
  • the gene sample generator 270 functions to generate gene samples by combining genes based on genetic interaction datasets and protein interaction datasets stored in a database.
  • the gene sample generator 270 may use compiled and verified data sets based on a yeast two hybrid method and a biological assay as protein interaction data sets.
  • the gene sample generator 270 may generate gene samples based on genes associated with the prefrontal cortex.
  • the gene network construction apparatus 200 may further include at least one of a first gene pair detection unit 280a and a second gene pair detection unit 280b.
  • the first gene pair detecting unit 280a determines the scale of the gene network based on the T-test using the first group and the second group, and based on the determined gene network size, And detects the second pair of genes.
  • the first group means a group containing gene interaction data sets stored in the database and the second group means a group not including the gene interaction data sets stored in the database.
  • FIG. 12 is a flowchart schematically illustrating a method of constructing a gene network according to a preferred embodiment of the present invention.
  • the gene pair selection unit 210 selects a gene related to the AD disease among the predetermined gene samples based on the first mutual information amount of the genes related to the AD disease-free persons and the second mutual information amount of the genes related to the AD disease persons 1 gene pairs are selected (S310).
  • the gene pair integrating unit 220 integrates the first gene pairs using the node property based on the information on the path of each gene pair (S320).
  • the sub-network extracting unit 230 extracts the sub-networks related to the AD disease based on the seed-based search method when the first gene pairs are integrated (S330).
  • the gene network construction unit 240 constructs a gene network specific to the AD disease based on the sub-networks related to the AD disease (S340).
  • the gene sample generator 270 may generate the gene samples by combining the genes based on the gene interaction data sets stored in the database and the protein interaction data sets.
  • the first gene pair detection unit 280a uses a first group including sets of gene interaction data stored in the database and a second group that does not include the gene interaction data sets stored in the database T assay to determine the size of the gene network and to detect the second pair of genes from the first gene pairs based on the size of the gene network.
  • the second gene pair detecting unit 280b detects the gene mutation data sets related to the AD disease, the gene interaction data sets obtained through the GWAS method, and the gene mutually obtained through the feature selection algorithm Determining the scale of the gene network based on Fisher's exact test using at least one of the gene interaction data sets from among the sets of action data and detecting the second pair of genes among the first gene pairs based on the scale of the gene network .
  • the present invention is not limited to these embodiments, and all elements constituting the embodiment of the present invention described above are described as being combined or operated in one operation. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. In addition, such a computer program may be stored in a computer readable medium such as a USB memory, a CD disk, a flash memory, etc., and read and executed by a computer to implement an embodiment of the present invention. As the recording medium of the computer program, a magnetic recording medium, an optical recording medium, or the like can be included.

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Abstract

La présente invention concerne un appareil et un procédé de construction de réseau de gènes permettant de construire un réseau de gènes spécifique à la maladie d'Alzheimer (MA) par sélection de paires de gènes parmi des échantillons de gènes sur la base de quantités d'informations mutuelles de gènes et par extraction de sous-réseaux associés à la MA à partir des paires de gènes. L'appareil selon la présente invention comprend : une unité de sélection de paires de gènes pour sélectionner des premières paires de gènes associées à la MA parmi des échantillons de gènes prédéterminés sur la base d'une première quantité d'informations mutuelles de gènes associés à des personnes ne souffrant pas de la MA et une seconde quantité d'informations mutuelles de gènes associés à des personnes souffrant de la MA; une unité d'intégration de paires de gènes pour intégrer les premières paires de gènes à l'aide d'une propriété de nœud sur la base d'informations sur une voie de chaque paire de gènes; une unité d'extraction de sous-réseau pour extraire des sous-réseaux associés à la MA sur la base d'un procédé de recherche basé sur des semences lorsque les premières paires de gènes sont intégrées; et une unité de construction de réseau de gènes pour construire le réseau de gènes spécifique à la MA sur la base des sous-réseaux associés à la MA.
PCT/KR2018/002915 2017-12-11 2018-03-13 Appareil et procédé de construction de réseau de gènes WO2019117400A1 (fr)

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CN115019884B (zh) * 2022-05-13 2023-11-03 华东交通大学 一种融合多组学数据的网络标志物识别方法

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