Integrating Gene and Protein Networks for Disease Biomarker Discovery

Objective: The objective of this project is to develop a comprehensive understanding of how gene and protein networks can be integrated to identify potential disease biomarkers. By leveraging data from genomics and proteomics, this project aims to construct and analyze these networks, apply network-based algorithms to identify key nodes, and explore their functional and pathway associations. The project will culminate in the identification of potential disease biomarkers and the interpretation of their biological significance.

Learning Outcomes:

  1. Domain Knowledge: Gain a deep understanding of genomics, proteomics, and their applications in disease research.
  2. Data Integration: Learn how to integrate data from multiple sources, such as gene expression profiles and protein-protein interaction databases.
  3. Network Construction: Acquire skills in constructing gene and protein interaction networks.
  4. Network Analysis: Develop proficiency in analyzing networks, including identifying key nodes and exploring their functional and pathway associations.
  5. Biomarker Identification: Learn how to apply network-based algorithms to prioritize potential disease biomarkers.
  6. Biological Interpretation: Gain the ability to interpret the biological significance of identified biomarkers in the context of disease pathways.

Steps and Tasks:

  1. Data Acquisition and Preprocessing

    • Download gene expression data (e.g., microarray or RNA-seq data) from a public repository such as Gene Expression Omnibus (GEO).
    • Obtain protein-protein interaction (PPI) data from a reliable database, such as the Human Protein Reference Database (HPRD) or the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).
    • Ensure that the gene and protein identifiers are compatible and can be mapped to each other.
    • Preprocess the gene expression data by removing noise, normalizing the expression values, and selecting relevant samples or conditions.
  2. Gene and Protein Interaction Network Construction

    • Construct a gene interaction network using the preprocessed gene expression data. You can use methods like co-expression network analysis or differential expression analysis followed by gene-gene interaction prediction.
    • Build a protein interaction network using the PPI data.
    • Map the gene nodes in the gene interaction network to their corresponding proteins in the protein interaction network.

    Need a little extra help? Please see the code snippets below:

    For gene expression data preprocessing, you can use the limma package in R. Here’s an example:

    # Assuming you have already loaded and preprocessed the gene expression data
    library(limma)
    
    # Perform differential expression analysis
    design <- model.matrix(~ condition)  # Replace 'condition' with your actual variable of interest
    fit <- lmFit(data, design)
    contrast <- makeContrasts(conditionA - conditionB, levels = design)
    fit2 <- contrasts.fit(fit, contrast)
    fit2 <- eBayes(fit2)
    
    # Select differentially expressed genes
    DE_genes <- topTable(fit2, coef = 1, adjust.method = "fdr", sort.by = "p.value", number = Inf)
    DE_genes <- DE_genes[DE_genes$adj.P.Val < 0.05, ]  # Adjust p-value threshold as needed
    
    # Create a list of selected gene identifiers
    selected_genes <- DE_genes$GeneSymbol  # Replace 'GeneSymbol' with the column name of your gene identifiers
    

    For constructing the gene interaction network, you can use the WGCNA package in R. Here’s an example:

    library(WGCNA)
    
    # Assuming you have a matrix of gene expression data (rows = genes, columns = samples)
    # and a vector of sample conditions
    gene_expr <- data  # Replace 'data' with your actual gene expression data
    sample_conditions <- condition  # Replace 'condition' with your actual sample conditions
    
    # Construct a gene co-expression network using WGCNA
    gene_expr <- as.data.frame(gene_expr)
    gene_network <- blockwiseModules(gene_expr, power = 6, TOMType = "unsigned",
                                     minModuleSize = 30, reassignThreshold = 0,
                                     mergeCutHeight = 0.25, numericLabels = TRUE,
                                     pamRespectsDendro = FALSE, saveTOMs = TRUE,
                                     saveTOMFileBase = "TOM", verbose = 0)
    
    # Identify the module(s) of interest based on their correlation with sample conditions
    module_trait_cor <- cor(as.data.frame(gene_network$colors), sample_conditions)
    module_of_interest <- names(module_trait_cor)[which.max(module_trait_cor)]
    
    # Extract the gene nodes and their module assignments from the network
    gene_nodes <- gene_network$colors == module_of_interest
    gene_module_assignments <- gene_network$colors
    
    # You can also calculate other network measures, such as node degree, to identify key nodes
    gene_node_degree <- intramodularConnectivity(gene_network$TOM, gene_module_assignments,
                                                 type = "unsigned")$degree
    
    # Visualize the network using a heatmap or a network plot
    plotDendroAndColors(gene_network$dendrograms[[1]], gene_network$colors)
    

    For constructing the protein interaction network, you can use the STRINGdb package in R. Here’s an example:

    library(STRINGdb)
    
    # Assuming you have a vector of gene symbols as your input
    gene_symbols <- selected_genes  # Replace 'selected_genes' with your actual gene symbols
    
    # Convert gene symbols to Entrez IDs
    ensembl_ids <- mapIds(org.Hs.eg.db, gene_symbols, "SYMBOL", "ENSEMBL")
    entrez_ids <- mapIds(org.Hs.eg.db, ensembl_ids, "ENSEMBL", "ENTREZID")
    
    # Query STRING for protein-protein interactions
    string_db <- STRINGdb$new(version = "11", species = 9606, score_threshold = 0.7)
    ppi <- string_db$networkFromGene(gene = entrez_ids)
    
    # Extract the protein nodes and their interaction scores from the network
    protein_nodes <- unique(c(ppi$protein1, ppi$protein2))
    interaction_scores <- ppi$score
    
    # You can also calculate other network measures, such as node degree, to identify key nodes
    protein_node_degree <- table(c(ppi$protein1, ppi$protein2))
    
    # Visualize the network using a network plot
    plot(ppi, layout = "fruchterman.reingold", edge.color = "gray", edge.width = 2,
         vertex.size = 3, vertex.label = NA)
    
  3. Network Integration

    • Integrate the gene and protein interaction networks by connecting the gene nodes to their corresponding proteins.
    • Assign appropriate weights or confidence scores to the edges connecting the gene and protein nodes.

    Need a little extra help? Please see the code snippets below:

    To integrate the gene and protein interaction networks, you can use the igraph package in R. Here’s an example:

    library(igraph)
    
    # Assuming you have already constructed the gene and protein interaction networks
    gene_network <- ...  # Replace '...' with your actual gene interaction network
    protein_network <- ...  # Replace '...' with your actual protein interaction network
    
    # Convert the networks to igraph objects
    gene_network <- graph_from_adjacency_matrix(as.matrix(gene_network))
    protein_network <- graph_from_adjacency_matrix(as.matrix(protein_network))
    
    # Map the gene nodes to their corresponding proteins in the protein network
    gene_nodes <- V(gene_network)$name
    protein_nodes <- V(protein_network)$name
    mapped_nodes <- match(gene_nodes, protein_nodes, nomatch = 0)
    
    # Combine the networks by connecting the gene nodes to their corresponding proteins
    integrated_network <- gene_network + protein_network
    integrated_network <- add_edges(integrated_network, cbind(gene_nodes, mapped_nodes))
    
    # Assign appropriate weights or confidence scores to the edges
    edge_weights <- ...  # Replace '...' with your method of assigning weights
    E(integrated_network)$weight <- edge_weights
    
    # Visualize the integrated network using a network plot
    plot(integrated_network, layout = layout_with_fr, vertex.size = 3,
         edge.width = E(integrated_network)$weight * 2)
    
  4. Network Analysis and Biomarker Identification

    • Apply network analysis techniques to the integrated gene and protein network to identify key nodes.
    • Use network-based algorithms, such as centrality measures (e.g., degree centrality, betweenness centrality) or module detection methods (e.g., weighted gene co-expression network analysis), to prioritize potential disease biomarkers.
    • Set appropriate thresholds or criteria to select the most promising biomarker candidates.

    Need a little extra help? Please see the code snippets below:

    For network analysis and biomarker identification, you can use the igraph and WGCNA packages in R. Here’s an example:

    library(igraph)
    library(WGCNA)
    
    # Assuming you have already integrated the gene and protein interaction networks
    integrated_network <- ...  # Replace '...' with your actual integrated network
    
    # Convert the network to an igraph object
    integrated_network <- graph_from_adjacency_matrix(as.matrix(integrated_network))
    
    # Calculate node degree and betweenness centrality as network measures
    node_degree <- degree(integrated_network, mode = "all")
    node_betweenness <- betweenness(integrated_network, normalized = TRUE)
    
    # Perform module detection using weighted gene co-expression network analysis (WGCNA)
    gene_expr <- ...  # Replace '...' with your actual gene expression data
    gene_expr <- as.data.frame(gene_expr)
    gene_network <- blockwiseModules(gene_expr, power = 6, TOMType = "unsigned",
                                     minModuleSize = 30, reassignThreshold = 0,
                                     mergeCutHeight = 0.25, numericLabels = TRUE,
                                     pamRespectsDendro = FALSE, saveTOMs = TRUE,
                                     saveTOMFileBase = "TOM", verbose = 0)
    gene_module_assignments <- gene_network$colors
    
    # Combine the network measures and module assignments into a single data frame
    biomarker_candidates <- data.frame(node_degree, node_betweenness, gene_module_assignments)
    
    # Select the most promising biomarker candidates based on the network measures and module assignments
    selected_biomarkers <- subset(biomarker_candidates, node_degree > threshold1 &
                                  node_betweenness > threshold2 &
                                  gene_module_assignments == module_of_interest)
    
    # Visualize the network measures and module assignments of the selected biomarkers
    plot(selected_biomarkers)
    
    # You can also perform functional enrichment analysis to further validate the selected biomarkers
    
  5. Functional and Pathway Analysis

    • Conduct functional and pathway analysis to interpret the biological significance of the identified biomarkers.
    • Utilize bioinformatics resources, such as the Gene Ontology (GO) database and pathway databases (e.g., Kyoto Encyclopedia of Genes and Genomes, Reactome), to annotate the functions and pathways associated with the biomarkers.
    • Prioritize the enriched functions and pathways based on their relevance to the disease of interest.

    Need a little extra help? Please see the code snippets below:

    For functional and pathway analysis, you can use the clusterProfiler and org.Hs.eg.db packages in R. Here’s an example:

    library(clusterProfiler)
    library(org.Hs.eg.db)
    
    # Assuming you have a list of selected biomarkers
    selected_biomarkers <- ...  # Replace '...' with your actual list of selected biomarkers
    
    # Convert the biomarker list to Entrez IDs
    ensembl_ids <- mapIds(org.Hs.eg.db, selected_biomarkers, "SYMBOL", "ENSEMBL")
    entrez_ids <- mapIds(org.Hs.eg.db, ensembl_ids, "ENSEMBL", "ENTREZID")
    
    # Perform Gene Ontology (GO) enrichment analysis
    go_results <- enrichGO(entrez_ids, OrgDb = org.Hs.eg.db, keyType = "ENTREZID",
                           ont = "BP", pvalueCutoff = 0.05, qvalueCutoff = 0.1,
                           readable = TRUE)
    
    # Perform pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database
    kegg_results <- enrichKEGG(entrez_ids, OrgDb = org.Hs.eg.db, keyType = "ENTREZID",
                              pvalueCutoff = 0.05, qvalueCutoff = 0.1, readable = TRUE)
    
    # Visualize the enriched GO terms and KEGG pathways
    dotplot(go_results, showCategory = 10)
    dotplot(kegg_results, showCategory = 10)
    
    # You can also perform network-based pathway analysis using tools like Gene Set Network Analysis (GSNA)
    
  6. Results Interpretation and Conclusion

    • Interpret the results of the functional and pathway analysis in the context of the disease of interest.
    • Discuss the potential implications of the identified biomarkers and their associated functions and pathways.
    • Formulate a conclusion summarizing the findings and suggesting future directions for the research.

Evaluation: To evaluate the success of this project, you can consider the following criteria:

  • The ability to correctly acquire, preprocess, and integrate the gene expression and protein-protein interaction data.
  • The quality of the constructed gene and protein interaction networks, including the appropriateness of the network integration approach.
  • The application of effective network analysis techniques and biomarker identification methods.
  • The biological relevance and interpretability of the identified biomarkers, as demonstrated by the functional and pathway analysis.
  • The clarity and insightfulness of the results interpretation and the formulation of a meaningful conclusion.

Resources and Learning Materials:

  1. R for statistical computing: https://www.r-project.org/
  2. Bioconductor for genomics and proteomics data analysis in R: https://www.bioconductor.org/
  3. Gene Expression Omnibus (GEO): https://www.ncbi.nlm.nih.gov/geo/
  4. Human Protein Reference Database (HPRD): http://www.hprd.org/
  5. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING): https://string-db.org/
  6. Limma package for gene expression data analysis in R: https://bioconductor.org/packages/release/bioc/html/limma.html
  7. WGCNA package for weighted gene co-expression network analysis in R: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/
  8. igraph package for network analysis in R: https://igraph.org/r/
  9. clusterProfiler package for functional enrichment analysis in R: https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
  10. org.Hs.eg.db package for annotation of gene identifiers in R: https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html

Need a little extra help? Here are some additional code snippets to assist you in the initial steps of the project:

  1. Data Acquisition and Preprocessing

    • Download gene expression data (e.g., microarray or RNA-seq data) from a public repository such as Gene Expression Omnibus (GEO).
    • Obtain protein-protein interaction (PPI) data from a reliable database, such as the Human Protein Reference Database (HPRD) or the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).
    • Ensure that the gene and protein identifiers are compatible and can be mapped to each other.
    • Preprocess the gene expression data by removing noise, normalizing the expression values, and selecting relevant samples or conditions.
    library(Biobase)
    library(GEOquery)
    
    # Download gene expression data from GEO
    gse <- getGEO("GSE12345", GSEMatrix = TRUE)  # Replace "GSE12345" with the actual GEO accession number
    data <- exprs(gse[[1]])  # Assuming you only have one matrix of gene expression data
    
    # Preprocess the gene expression data
    # Remove probes with low expression
    data <- data[rowSums(data) > threshold, ]
    # Normalize the expression values
    data <- normalize(data, method = "quantile")
    # Select relevant samples or conditions
    data <- data[, conditions_of_interest]
    
    # Download PPI data from HPRD or STRING
    # ...
    
  2. Gene and Protein Interaction Network Construction

    • Construct a gene interaction network using the preprocessed gene expression data. You can use methods like co-expression network analysis or differential expression analysis followed by gene-gene interaction prediction.
    • Build a protein interaction network using the PPI data.
    • Map the gene nodes in the gene interaction network to their corresponding proteins in the protein interaction network.
    library(WGCNA)
    
    # Construct a gene co-expression network using WGCNA
    gene_expr <- as.data.frame(data)
    gene_network <- blockwiseModules(gene_expr, power = 6, TOMType = "unsigned",
                                     minModuleSize = 30, reassignThreshold = 0,
                                     mergeCutHeight = 0.25, numericLabels = TRUE,
                                     pamRespectsDendro = FALSE, saveT

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