Integrated method of combining target gene set and kegg weight for downstream focusing analysis of differential proteins

Abstract

The application provides a proteomics data mining-function annotation analysis method combining a target gene set and KEGG weight, the method focuses on the differential proteins related to the target gene set, and most of the interference information is excluded through the target gene set. The method can reduce the noise of irrelevant proteins, more efficiently screen key target proteins and perform preliminary function annotation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological information analysis, and relates to a process method for key protein analysis of differential proteins or genes. BACKGROUND

[0002] Proteomics is the science of studying the composition, structure, function and interaction of all proteins in cells. With the development of high-throughput sequencing technology, proteomics research has been able to reveal the differences in protein expression of cells under different physiological and pathological conditions. The discovery of these differential proteins is of great significance for understanding disease mechanisms, discovering biomarkers and developing new treatment methods.

[0003] There are many downstream analysis methods for differential proteins, including functional enrichment analysis, protein-protein interaction network analysis, etc. Each analysis type contains different data sets and methods, which greatly facilitate the key protein research of biological information analysis. Although these methods can provide a large amount of data and information, it is often difficult to focus on the key pathways and key proteins most relevant to specific biological problems or diseases; in addition, KEGG enrichment of pathways and protein PPI interaction are often used as parallel information mining methods and are not well integrated.

[0004] In order to solve the above problems, the present application proposes a new research process which focuses on differential proteins related to the target gene set. The core of this process is to exclude most of the interference information through the target gene set, and then to filter out the proteins that play a key role in a specific biological process by converting the network relationship of genes in KEGG into kegg weight, combining the degree centrality and betweenness centrality scores of protein interaction. Through this method, the role of proteins in the development of diseases can be more effectively revealed, and more accurate molecular targets can be provided for clinical diagnosis and treatment. SUMMARY

[0005] The present application first provides a proteomics data mining-function annotation analysis method combining target gene set and KEGG weight, which comprises the following steps:

[0006] 1) providing a target gene set of interest for research;

[0007] 2) providing differential protein information, including gene name (symbol) and fold change information;

[0008] 3) screening differential proteins based on the target gene set of 1), and counting the number of pathways involved in the differential proteins, while drawing a network diagram related to the target gene set, visualizing the gene set relationship and up-regulation and down-regulation of the differential proteins;

[0009] 4) Based on the number of pathways kc (KEGG count) of the differential proteins in 3), the KEGG weight (kegg_weights) of the proteins is assigned by the min-max method, and the weight value range is 0 to 1. The KEGG weight calculation formula of the i-th differential protein is as follows:

[0010] weights(i) = kc(i) - min(kc) / max(kc) - min(kc);

[0011] wherein weights(i) represents the KEGG weight of the i-th protein, kc(i) represents the number of pathways of the i-th protein, max(kc) and min(kc) represent the maximum number of pathways and the minimum number of pathways in the differential proteins, respectively.

[0012] 5) The protein interaction network diagram is drawn for the differential proteins in 3), and the degree centrality (degree_centrality) and the betweenness centrality (betweenness_centrality) of each node in the network are calculated. The degree centrality refers to the number of edges connected in the network, and the betweenness centrality measures the "intermediary" role of a node in the network, that is, the frequency of the node as a bridge or intermediary point of the shortest path between other nodes in the network. Preferably, the degree centrality (degree_centrality) and the betweenness centrality (betweenness_centrality) are calculated by using the degree and betweenness commands of the R package igraph;

[0013] 6) The comprehensive importance score of each protein node is calculated according to the following formula, wherein CS (combine_score) represents the final importance score, DC (degree_centrality) represents the degree centrality value, BC (betweenness_centrality) represents the betweenness centrality value, and KW (kegg_weights) represents the KEGG pathway weight calculated in step 4);

[0014] CS = (DC + BC) KW;

[0015] 7) Based on CS, the first few node proteins are output, preferably 2-10;

[0016] 8) The subsequent functional annotation and mining are carried out according to the output node proteins.

[0017] In the specific embodiments of the present application, the differential protein information is the differential protein information generated after the intervention of stem cells on healthy subjects or patients.

[0018] In the specific embodiments of the present application, the differential protein information is obtained by the SomaScan protein sequencing platform.

[0019] In the specific embodiments of the present application, the seven classification gene sets according to the SomaScan protein sequencing platform are taken as the target gene set, which includes proteins related to cardiovascular diseases, inflammation and immune response, metabolic diseases, cancer, nerves, cytokines and respiration.

[0020] In the specific embodiments of the present application, the differential proteins are screened according to the gene set, protein interaction network analysis and KEGG weight assignment are performed, key candidate proteins are screened, and the candidate proteins are preliminarily functionally annotated by combining the KEGG enrichment information of all differential proteins to determine the key proteins.

[0021] In the specific embodiments of the present application, the pathway enrichment analysis of the differential proteins is performed according to the KEGG database.

[0022] In the specific embodiments of the present application, the significant pathways are determined according to the p.adjust (corrected p value) of KEGG enrichment; and preferably, the first 20 pathways with the smallest p.adjust value are focused on.

[0023] In the specific embodiments of the present application, the mapping and display of the key proteins on the target pathway are performed by the Kegg Mapper method to preliminarily determine the biological significance of the target proteins.

[0024] The present application also provides a computer element, which stores a computer program for implementing the above method.

[0025] Beneficial technical effects

[0026] The method can reduce the noise of irrelevant proteins, more efficiently screen key target proteins and perform preliminary functional annotation. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 Protein changes ranked first after adding KEGG weight

[0028] Figure 2 Routine downstream analysis of differential proteins

[0029] Figure 3 Network relationship diagram between seven classification function sets and differential proteins

[0030] Figure 4 Protein interaction network diagram between differential proteins related to seven classifications

[0031] Figure 5Interaction network diagram of all differential proteins

[0032] Figure 6 Top 20 pathway enrichment hierarchy diagram of all differential proteins

[0033] Figure 7 PRKACA is mapped in the pathway, the red box is the differential protein in the pathway, and the pink box is PRKACA

[0034] Figure 8 CASP3 is mapped in the pathway, the red box is the differential protein in the pathway, and the pink box is CASP3

[0035] Figure 9 Focused analysis flowchart DETAILED DESCRIPTION

[0036] The analysis process extracts and organizes differential protein information based on functional gene sets, constructs a gene set-differential protein network diagram, and displays the network relationship between the gene set and the differential protein. The extracted differential proteins are used to construct a protein interaction network, and the KEGG weight of the differential protein is combined to screen candidate key proteins. At the same time, all differential proteins are subjected to pathway enrichment analysis, and the top 20 enriched pathways are visualized to observe the pathway of the key protein and map it, thereby quickly annotating the potential differential proteins.

[0037] 1) Organize target proteins. Generally, target proteins are organized according to the research focus. In this experiment, seven classification gene sets provided by the SomaScan protein sequencing platform are used as research objects;

[0038] 2) The seven classification gene sets include proteins related to cardiovascular diseases, inflammation and immune response, metabolic diseases, cancer, nerves, cytokines, and respiration;

[0039] 3) The differential proteins are subjected to pathway enrichment analysis and the top 20 enrichment results are visualized, including the hierarchical information of the pathway;

[0040] 4) The differential proteins are labeled according to the gene set, and a gene set-differential protein network diagram is constructed to preliminarily observe the relationship between the differential proteins and the gene set;

[0041] 5) The differential proteins labeled in the gene set are plotted for protein interaction, and the KEGG weight of the differential protein is calculated. According to the CS calculation formula result, the key protein is screened;

[0042] 6) The screened key protein is mapped to the target pathway to complete the preliminary screening-function annotation analysis.

[0043] The analysis process of the present application can be seen in detailFigure 8 The whole process can be implemented under Linux by programming.

[0044] Technical terms

[0045] SomaScan protein sequencing platform

[0046] SomaScan protein sequencing platform discovers new disease biomarkers through high-throughput protein analysis technology. More than 11000 proteins can be detected in a single sample at ultra-low volume. The detection technology covers sample types such as blood, urine, body fluids, etc. The sample source includes humans and other model animals, and only 55 microliters of sample are required to detect proteins. The dynamic detection range is 10log, and it can detect very low abundance proteins.

[0047] The seven categories described in this paper come from the seven panels designed by SomaScan platform for different directions according to 1178 papers. The specific literature can be viewed at https: / / somalogic.com / publications / .

[0048] Protein interaction network diagram

[0049] Protein interaction network, namely Protein-protein interaction (PPI) network, is an analysis method to describe the interaction relationship between a group of proteins. Because the interaction relationship between proteins is complex, each protein is a node, and the interaction between multiple proteins is connected into a line to form a network structure, so it is called protein interaction network.

[0050] In this paper, the protein interaction diagram is realized by stringDB, and the igraph package is used to calculate the degree centrality value and betweenness centrality value of the interaction network.

[0051] Gene set-differential protein network diagram

[0052] In the gene set and differential protein network diagram, the gene set classification label and the gene are nodes, the classification to which the gene belongs is the connected edge, and different classifications are connected through common genes. Based on this, the relationship network is drawn to show the most involved classification of differential proteins and the up-regulation and down-regulation.

[0053] The determination of key differential proteins includes three steps: 1) screening based on the specified gene set; 2) PPI network construction and importance score calculation; 3) final scoring combined with KEGG weight. The final most suitable score CS is the final basis for judgment, and according to the CS score, the top-ranked ones are selected for further research.

[0054] Pathway enrichment analysis

[0055] Pathway enrichment analysis: Focus on checking whether genes / metabolites in a gene set are enriched in a specific pathway. Databases such as KEGG, Reactome, etc. provide information on known biological pathways, which can be used for enrichment analysis. Specific calculation methods are, for example:

[0056] 1) Provide the number of differentially expressed genes n;

[0057] 2) Calculate the number of differentially expressed genes k from pathway A;

[0058] 3) Read the number of genes M from the KEGG background file;

[0059] 4) Read the number of genes N in pathway A;

[0060] 5) Calculate the enrichment fold (Fold Enrichment) or enrichment score, the formula is: EnrichmentScore = (k / n) / (N / M);

[0061] 6) Calculate the P value to evaluate the significance of enrichment, such as hypergeometric distribution test and Fisher's exact test.

[0062] In this paper, KEGG enrichment analysis is performed by R package clusterProfiler.

[0063] In this paper, significant pathways are usually selected according to the adjusted p value (p.adjust) of the enrichment result, and generally p.adjust < 0.05 can be considered as significant enrichment, and the enrichment results of different studies are different.

[0064] Pathway mapping

[0065] In this paper, pathway mapping refers to color labeling of differential proteins on related pathways, the main purpose of which is to view the functional distribution of differential proteins in the pathway and the functional position of candidate key proteins in the pathway of interest, as well as the upstream and downstream regulation relationship.

[0066] Examples

[0067] For a study on the effect of stem cells on healthy people, the differential proteins obtained are screened and analyzed using the method of the present application, as follows:

[0068] Example 1 Screening protein interaction network mapping and KEGG weight calculation

[0069] The seven major classification provided by SomaScan protein sequencing platform were used as the research object in this experiment. The seven classification gene sets include cardiovascular disease related, inflammation and immune response related, metabolic disease related, cancer related, nerve related, cytokine related, and respiratory related proteins. Unlike traditional differential protein routine downstream analysis (see Figure 2 ), first draw the network relationship diagram between the seven classification function sets and the differential proteins (see Figure 3 ). The gene set and differential protein network diagram shows that the functions involved in the most differential proteins are inflammation and immune response, and nerve related proteins. These two functions are the fields where stem cells are concentrated. The cardiovascular disease related gene set is also significant, which can be observed in combination with the follow-up.

[0070] Based on the network relationship diagram between the seven classification function sets and the differential proteins, further draw the protein interaction network diagram between the differential proteins related to the seven classifications (see Figure 4 ). At the same time, calculate the KEGG weight of the differential proteins. Combined with the degree centrality value, betweenness centrality value and KEGG weight calculated based on the interaction network, the differential proteins of interest are obtained. By adding KEGG weight, 'PRKACA' and 'CASP3' become the two most important candidate proteins (see Figure 1 ).

[0071] Example 2 Differential protein pathway enrichment analysis

[0072] Next, the pathway enrichment analysis of all differential proteins was performed, and the top 20 pathways were visualized, as shown in Figure 4 . From Figure 4 , the function level information and the most numerous levels involved in the main enriched pathways are immune related, signal transduction related, cancer related, and cardiovascular disease related levels.

[0073] Among the top 20 pathways, the proteins of interest PRKACA and CASP3 were mapped and displayed in the pathways of interest, as shown in Figures 5-7 .

[0074] Example 3 Result analysis

[0075] For the top-ranked PRKACA and CASP3 after adding KEGG, combined with the functional annotation of the interested pathways, PRKACA (protein kinase A catalytic subunit alpha) and CASP3 (caspase 3) are two important proteins that play a key role in cell signaling, immune response and apoptosis, and have an important role in maintaining health, especially in inflammation and cardiovascular disease-related pathways. PRKACA is the catalytic subunit of protein kinase A (PKA), which is activated when cyclic adenosine monophosphate (cAMP) levels rise, leading to the phosphorylation of multiple target proteins. PRKACA plays a role in many biological processes, including metabolism, gene expression, cell division and apoptosis. In the chemokine signaling pathway, PRKACA is involved in downstream signaling, affecting the movement and response of immune cells. Activation of PKA in immune cells can change their response to chemokines, which is crucial for guiding immune cells to migrate to sites of infection or injury and promoting the regulation of immune response. CASP3 is a key execution protease in the apoptosis pathway, which initiates and completes programmed cell death by cleaving various substrates. It plays an important role in removing damaged or unnecessary cells and maintaining cellular homeostasis. CASP3-mediated apoptosis of endothelial cells and vascular smooth muscle cells contributes to the progression of atherosclerosis. Apoptosis in atherosclerotic plaques can lead to plaque instability, which is crucial in the pathogenesis of cardiovascular disease.

[0076] This analysis provides some explanation for the biological functional impact of stem cell therapy, especially mesenchymal stem cell (MSC) therapy, which affects the chemokine and lipid pathways by secreting various factors, regulating the function of PRKACA and CASP3: stem cells can secrete factors that enhance the expression or activity of PKA-related signaling molecules. This regulatory effect can reduce inflammation, improve immune function, and reduce the risk of plaque formation by promoting vascular health and reducing lipid accumulation. In addition, stem cells reduce CASP3 activation in key tissues by secreting growth factors, cytokines and anti-inflammatory molecules. This helps to protect important tissues from unnecessary cell death, thereby inhibiting the progression of conditions such as atherosclerosis.

[0077] It is shown that by targeting the regulation of inflammatory response, enhancing tissue repair and reducing apoptosis, stem cells can help to rejuvenate organs and maintain tissue function. The comprehensive impact of stem cell therapy on signaling pathways and protein activity demonstrates its potential in delaying aging-related diseases and maintaining the health of multiple organs. Through the key mediation of PRKACA and CASP3, stem cell therapy can have a positive regulatory effect on systemic health in humans by reducing inflammation and supporting tissue repair. This pathway regulation shows therapeutic prospects for chronic diseases, helping to maintain the body's homeostasis and the health of tissue function.

[0078] According to the preliminary investigation of the differential proteins FN1 and MMP9 screened from the protein interaction network diagram, Matrix Metalloproteinase-9 (MMP-9) is an enzyme that plays multiple roles in the human body. It is mainly involved in the degradation and remodeling of the extracellular matrix (ECM), affecting processes such as tissue repair, angiogenesis, and embryonic development. MMP-9 is also involved in immune regulation of leukocytes, angiogenesis, neuronal genesis and synaptic remodeling, as well as tumor growth and metastasis. Fibronectin 1 (FN1) is a key extracellular matrix protein that plays an important role in regulating cell adhesion, migration, proliferation, and is involved in various physiological and pathological processes such as wound healing, tumor progression, tissue fibrosis, and immune regulation. FN1 affects cell signaling by binding to integrin receptors on the cell surface, particularly in the TGF-β / PI3K / Akt signaling pathway, and its abnormal expression is closely related to the occurrence and development of various diseases, making it an important target for drug development and disease diagnosis. Compared with PRKACA and CASP3, the impact on stem cells is not as closely related.

Claims

1. A method for proteomics data mining and functional annotation analysis combining target gene sets and KEGG weights, the method comprising the following steps: 1) Provide a set of target genes for research purposes; 2) Provide information on differentially expressed proteins, including gene names (symbols) and fold change information; 3) Based on the target gene set in 1), differentially expressed proteins are screened, and the number of pathways involved in the differentially expressed proteins is counted. At the same time, a network diagram related to the target gene set is drawn to visualize the gene set relationship and up- and down-regulation of differentially expressed proteins. 4) Based on the pathway count kc of differentially expressed proteins in 3), the Kegg weights kegg_weights of the proteins are assigned using the min-max method. The weight values ​​range from 0 to 1. The formula for calculating the Kegg weight of the i-th differentially expressed protein is as follows: kegg_ ; 5) Draw a protein interaction network graph for the differentially expressed proteins in 3), and calculate the degree centrality and betweenness centrality of each node in the network. Degree centrality refers to the number of edges a node connects to in the network, and betweenness centrality is the frequency with which the node acts as a bridge or intermediary point on the shortest path between other node pairs in the network. 6) Calculate the overall importance score for each protein node according to the following formula, where CS represents the final importance score, DC represents the degree centrality value, BC represents the betweenness centrality value, and KW represents the Kegg weight calculated in step 4). ； 7) Based on the top 2-10 key proteins output by CS; 8) Perform subsequent functional annotation and mining based on the key proteins output.

2. The method as described in claim 1, wherein the differential protein information is differential protein information generated after stem cell intervention on healthy subjects or patients.

3. The method as described in claim 1 or 2, wherein the differential protein information is obtained through the SomaScan protein sequencing platform.

4. The method as described in claim 3, wherein the target gene set is determined based on the seven major gene sets of the SomaScan protein sequencing platform, wherein the seven major gene sets include proteins related to cardiovascular diseases, inflammation and immune responses, metabolic diseases, cancer, nerves, cytokines, and respiration.

5. The method of claim 4, wherein differentially expressed proteins are screened according to the seven major gene sets, protein interaction network analysis and Kegg weight assignment are performed, key proteins are screened, and the KEGG enrichment information of all differentially expressed proteins is combined to perform preliminary functional annotation of key proteins.

6. The method of claim 5, wherein pathway enrichment analysis of differentially expressed proteins is performed based on the KEGG database.

7. The method of claim 6, wherein significant pathways are determined based on the adjusted p-value p.adjust of KEGG enrichment.

8. The method of claim 7, wherein the top 20 pathways with the smallest p.adjust values ​​are selected as significant pathways.

9. The method of claim 8, wherein key proteins are mapped and visualized on target pathways using Kegg Mapper to preliminarily determine the biological significance of key proteins.

10. In the method of claim 1, step 5) calculates the degree centrality and betweenness centrality values ​​using the degree and betweenness commands of the R package igraph.

11. A computer device storing a computer program for implementing the method of any one of claims 1-10.

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