🔴 Integrating Gene and Protein Networks for Disease Biomarker Discovery

AIVIA Project Engine AI in HealthCare & Medicine
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Integrating Gene and Protein Networks for Biomarker Discovery

Objective

The primary 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, you will construct and analyze these networks, apply network-based algorithms to identify key nodes, and explore their functional and pathway associations. The project culminates in the identification of potential disease biomarkers and the interpretation of their biological significance in the context of the disease of interest.


Learning Outcomes

By completing this project, you will:

  1. Gain domain knowledge in genomics and proteomics:

    • Understand the principles and applications of gene expression profiling and protein-protein interactions in disease research.
    • Appreciate the importance of integrating multi-omics data for comprehensive biological insights.

  2. Develop skills in data integration:

    • Learn how to integrate data from multiple sources, such as gene expression profiles and protein-protein interaction databases.
    • Handle different types of biological identifiers and map them across datasets.

  3. Acquire proficiency in network construction:

    • Construct gene and protein interaction networks using appropriate computational tools.
    • Understand network topologies and their biological implications.

  4. Enhance network analysis capabilities:

    • Apply network analysis techniques to identify key nodes (genes/proteins) in the network.
    • Utilize centrality measures and module detection methods to prioritize potential biomarkers.

  5. Identify potential disease biomarkers:

    • Use network-based algorithms to prioritize genes/proteins that may serve as biomarkers.
    • Integrate statistical analysis with network properties for robust biomarker selection.

  6. Interpret biological significance:

    • Conduct functional and pathway enrichment analyses.
    • Interpret the results in the context of disease mechanisms and pathways.

Prerequisites and Theoretical Foundations

1. Basic Knowledge of R Programming

  • Data Structures: Vectors, data frames, lists.
  • Control Flow: If-else statements, loops, functions.
  • Data Manipulation: Reading and writing data, subsetting, merging, applying functions.
Click to view R code examples 
# Basic data structures
vector <- c(1, 2, 3)
data_frame <- data.frame(sample = c('A', 'B', 'C'), value = c(10, 20, 30))
list <- list(name = 'Sample1', values = vector)

# Control flow
for (i in 1:5) {
  print(i)
}

# Functions
add_numbers <- function(x, y) {
  return(x + y)
}
result <- add_numbers(5, 3)

2. Understanding of Genomics and Proteomics

  • Gene Expression Profiling:
    • Microarray and RNA-Seq technologies.
    • Differential gene expression analysis.
  • Protein-Protein Interactions (PPIs):
    • Biological significance of PPIs.
    • Sources of PPI data (e.g., STRING, BioGRID).
  • Biomarkers:
    • Definition and importance in disease diagnosis and prognosis.
    • Criteria for biomarker selection.
Click to view genomics and proteomics concepts
  • Differential Expression: Identifying genes whose expression levels differ significantly between conditions.
  • Co-expression Networks: Networks where nodes are genes and edges represent co-expression relationships.
  • Centrality Measures: Metrics to identify important nodes within a network (e.g., degree, betweenness).

3. Fundamentals of Network Biology

  • Network Construction:
    • Nodes and edges representation.
    • Weighted vs. unweighted networks.
  • Network Analysis Techniques:
    • Centrality measures (degree, betweenness, closeness).
    • Community/module detection (e.g., WGCNA).
  • Functional Enrichment Analysis:
    • Gene Ontology (GO) terms.
    • Pathway databases (KEGG, Reactome).
Click to view network biology concepts
  • Degree Centrality: Number of connections a node has.
  • Betweenness Centrality: Measure of a node’s centrality in a network, equal to the number of shortest paths that pass through the node.
  • Module/Community: A group of nodes that are more connected to each other than to the rest of the network.

Skills Gained

  • Data Acquisition and Preprocessing:

    • Downloading gene expression and PPI data from public databases.
    • Preprocessing gene expression data (normalization, filtering).
    • Handling and mapping gene/protein identifiers.

  • Network Construction and Integration:

    • Building gene co-expression networks.
    • Constructing protein-protein interaction networks.
    • Integrating gene and protein networks.

  • Network Analysis:

    • Applying centrality measures to identify key nodes.
    • Detecting modules or communities within networks.
    • Prioritizing potential biomarkers based on network properties.

  • Functional and Pathway Enrichment Analysis:

    • Performing GO and pathway enrichment analyses.
    • Interpreting biological significance in the context of disease.

  • Data Visualization:

    • Visualizing networks using various layouts.
    • Creating plots for network measures and enrichment results.

Tools Required

  • Programming Language: R (version 4.0 or higher recommended)

  • Integrated Development Environment (IDE):

    • RStudio: Provides a user-friendly interface for R programming.

  • R Packages:

    • Bioconductor Packages:
      • limma: Differential expression analysis (BiocManager::install("limma"))
      • WGCNA: Weighted gene co-expression network analysis (BiocManager::install("WGCNA"))
      • STRINGdb: Access to STRING database for PPI data (BiocManager::install("STRINGdb"))
      • clusterProfiler: Functional enrichment analysis (BiocManager::install("clusterProfiler"))
      • org.Hs.eg.db: Human genome annotation (BiocManager::install("org.Hs.eg.db"))
    • CRAN Packages:
      • igraph: Network analysis and visualization (install.packages("igraph"))
      • ggplot2: Data visualization (install.packages("ggplot2"))
      • reshape2: Data reshaping (install.packages("reshape2"))

  • Datasets:

    • Gene Expression Data: Download from GEO (e.g., GSEXXXXX).
    • Protein-Protein Interaction Data: Access via STRING database.

Steps and Tasks

Step 1: Data Acquisition and Preprocessing

Tasks:

  • Download Gene Expression Data:

    • Access the Gene Expression Omnibus (GEO) and download a suitable dataset (e.g., GSEXXXXX).
    • Choose a dataset relevant to the disease of interest.

  • Obtain Protein-Protein Interaction Data:

    • Use the STRING database to retrieve PPI data.
    • Ensure that the data corresponds to the same species as the gene expression data.

  • Preprocess Gene Expression Data:

    • Load the data into R.
    • Perform background correction and normalization.
    • Filter out lowly expressed genes.
    • Conduct differential expression analysis to identify significant genes.

Implementation:

# Install and load required packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("limma", "GEOquery"))
install.packages("ggplot2")

library(limma)
library(GEOquery)
library(ggplot2)

# Download gene expression data from GEO
gse <- getGEO("GSEXXXXX", GSEMatrix = TRUE)  # Replace GSEXXXXX with actual GEO accession number
expr_data <- exprs(gse[[1]])

# View sample information
pheno_data <- pData(gse[[1]])

# Normalize the data (if not already normalized)
expr_data <- normalizeBetweenArrays(expr_data, method = "quantile")

# Filter lowly expressed genes
keep <- rowSums(expr_data > 5) >= (ncol(expr_data) * 0.5)
expr_data_filtered <- expr_data[keep, ]

# Create design matrix for differential expression
group <- factor(pheno_data$condition)  # Adjust 'condition' based on actual column name
design <- model.matrix(~0 + group)
colnames(design) <- levels(group)

# Fit linear model
fit <- lmFit(expr_data_filtered, design)

# Create contrast matrix (e.g., Disease vs. Control)
contrast_matrix <- makeContrasts(Disease - Control, levels = design)

# Fit contrasts
fit2 <- contrasts.fit(fit, contrast_matrix)
fit2 <- eBayes(fit2)

# Get differentially expressed genes
deg <- topTable(fit2, adjust.method = "BH", number = Inf)
deg_filtered <- deg[deg$adj.P.Val < 0.05 & abs(deg$logFC) > 1, ]
Explanation
  • Normalization: Ensures comparability across samples.
  • Filtering: Removes genes with low expression to reduce noise.
  • Differential Expression Analysis: Identifies genes significantly upregulated or downregulated in disease vs. control.

Step 2: Gene Interaction Network Construction

Tasks:

  • Construct a Gene Co-expression Network:

    • Use the WGCNA package to build the network based on gene expression data.
    • Identify modules (clusters) of co-expressed genes.

  • Identify Modules of Interest:

    • Relate modules to clinical traits or conditions.
    • Select modules significantly associated with the disease.

Implementation:

# Install and load WGCNA
BiocManager::install("WGCNA")
library(WGCNA)

# Prepare data for WGCNA
datExpr <- t(expr_data_filtered)
datExpr <- as.data.frame(datExpr)

# Check for good samples and genes
gsg <- goodSamplesGenes(datExpr, verbose = 3)
if (!gsg$allOK) {
  # Remove bad samples/genes
  datExpr <- datExpr[gsg$goodSamples, gsg$goodGenes]
}

# Choose soft-thresholding power
powers <- c(1:20)
sft <- pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)

# Plot the results to choose power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     xlab="Soft Threshold (power)", ylab="Scale Free Topology Model Fit,signed R^2",
     type="n")
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     labels=powers, cex=0.9, col="red")

# Select power based on scale-free topology criterion
softPower <- 6  # Example value; choose based on plot

# Construct network and identify modules
net <- blockwiseModules(datExpr, power = softPower,
                        TOMType = "unsigned", minModuleSize = 30,
                        reassignThreshold = 0, mergeCutHeight = 0.25,
                        numericLabels = TRUE, pamRespectsDendro = FALSE,
                        saveTOMs = TRUE, verbose = 3)

# Relate modules to traits
moduleColors <- labels2colors(net$colors)
MEs <- net$MEs
moduleTraitCor <- cor(MEs, pheno_data$condition, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples = nrow(datExpr))

# Plot module-trait relationships
library("reshape2")
moduleTraitCor_melt <- melt(moduleTraitCor)
ggplot(moduleTraitCor_melt, aes(x=Var2, y=Var1, fill=value)) +
  geom_tile() +
  scale_fill_gradient2(low="blue", high="red", mid="white",
                       midpoint=0, limit=c(-1,1), space="Lab",
                       name="Correlation") +
  theme_minimal() +
  labs(x = "Trait", y = "Module")
Explanation
  • WGCNA: Constructs a network based on co-expression.
  • Soft-thresholding Power: Parameter that affects network adjacency calculation.
  • Module Identification: Genes grouped into modules based on expression patterns.
  • Module-Trait Relationship: Correlates module eigengenes with traits.

Step 3: Protein Interaction Network Construction

Tasks:

  • Retrieve PPI Data from STRING:

    • Use the STRINGdb package to obtain PPI data for the genes of interest.

  • Construct the Protein Interaction Network:

    • Build a network where nodes are proteins and edges represent interactions.

Implementation:

# Install and load STRINGdb
BiocManager::install("STRINGdb")
library(STRINGdb)

# Initialize STRINGdb
string_db <- STRINGdb$new(version = "11", species = 9606, score_threshold = 400, input_directory = "")

# Map gene symbols to STRING IDs
gene_list <- rownames(expr_data_filtered)
mapped_genes <- string_db$map(data.frame(gene=gene_list), "gene", removeUnmappedRows = TRUE)
string_ids <- mapped_genes$STRING_id

# Retrieve interactions
ppi <- string_db$get_interactions(string_ids)

# Build PPI network
library(igraph)
ppi_network <- graph_from_data_frame(d = ppi[, c("from", "to")], directed = FALSE)

# Simplify network
ppi_network <- simplify(ppi_network)
Explanation
  • STRINGdb: Provides access to STRING database within R.
  • Score Threshold: Filters interactions based on confidence scores.
  • Mapping Genes: Ensures correct identifiers are used for PPI retrieval.

Step 4: Network Integration

Tasks:

  • Integrate Gene and Protein Networks:

    • Map genes from the co-expression network to the protein network.
    • Combine networks to form an integrated network.

  • Assign Weights and Attributes:

    • Assign edge weights based on interaction confidence or co-expression strength.
    • Annotate nodes with relevant attributes (e.g., differential expression status).

Implementation:

# Map gene modules to PPI network
module_genes <- names(net$colors[net$colors == module_of_interest])  # Replace 'module_of_interest'

# Filter PPI network to include only module genes
ppi_subnetwork <- induced_subgraph(ppi_network, vids = V(ppi_network)$name %in% module_genes)

# Combine with co-expression network (optional)
# This step depends on data availability and specific analysis goals

# Assign edge weights (if available)
E(ppi_subnetwork)$weight <- ppi[stringr::str_c(ppi$from, ppi$to) %in% E(ppi_subnetwork)$name, "combined_score"]

# Annotate nodes with differential expression
V(ppi_subnetwork)$logFC <- deg_filtered[V(ppi_subnetwork)$name, "logFC"]
V(ppi_subnetwork)$pvalue <- deg_filtered[V(ppi_subnetwork)$name, "adj.P.Val"]
Explanation
  • Subnetwork Extraction: Focuses analysis on genes of interest.
  • Edge Weights: Reflects interaction confidence from STRING.
  • Node Attributes: Adds biological context to the network.

Step 5: Network Analysis and Biomarker Identification

Tasks:

  • Calculate Network Centrality Measures:

    • Compute degree, betweenness, closeness centrality for nodes.

  • Identify Key Nodes (Potential Biomarkers):

    • Select nodes with high centrality measures.
    • Consider differential expression and module membership.

  • Apply Thresholds for Biomarker Selection:

    • Set criteria based on centrality scores and statistical significance.

Implementation:

# Calculate centrality measures
V(ppi_subnetwork)$degree <- degree(ppi_subnetwork)
V(ppi_subnetwork)$betweenness <- betweenness(ppi_subnetwork, normalized = TRUE)
V(ppi_subnetwork)$closeness <- closeness(ppi_subnetwork, normalized = TRUE)

# Create a data frame with node attributes
node_attributes <- data.frame(
  gene = V(ppi_subnetwork)$name,
  degree = V(ppi_subnetwork)$degree,
  betweenness = V(ppi_subnetwork)$betweenness,
  closeness = V(ppi_subnetwork)$closeness,
  logFC = V(ppi_subnetwork)$logFC,
  pvalue = V(ppi_subnetwork)$pvalue
)

# Filter potential biomarkers
threshold_degree <- quantile(node_attributes$degree, 0.9)
threshold_betweenness <- quantile(node_attributes$betweenness, 0.9)
threshold_logFC <- 1
threshold_pvalue <- 0.05

potential_biomarkers <- subset(node_attributes,
                               degree >= threshold_degree &
                               betweenness >= threshold_betweenness &
                               abs(logFC) >= threshold_logFC &
                               pvalue <= threshold_pvalue)
Explanation
  • Centrality Measures: Identifies influential nodes in the network.
  • Thresholds: Applied to select top candidates based on multiple criteria.

Step 6: Functional and Pathway Enrichment Analysis

Tasks:

  • Perform Functional Enrichment Analysis:

    • Use clusterProfiler to conduct GO enrichment.

  • Conduct Pathway Analysis:

    • Identify enriched pathways using KEGG or Reactome.

  • Interpret Results in Disease Context:

    • Relate enriched functions and pathways to disease mechanisms.

Implementation:

# Install and load clusterProfiler and org.Hs.eg.db
BiocManager::install(c("clusterProfiler", "org.Hs.eg.db"))
library(clusterProfiler)
library(org.Hs.eg.db)

# Convert gene symbols to Entrez IDs
gene_symbols <- potential_biomarkers$gene
entrez_ids <- mapIds(org.Hs.eg.db, keys = gene_symbols, column = "ENTREZID", keytype = "SYMBOL", multiVals = "first")

# Remove NAs
entrez_ids <- na.omit(entrez_ids)

# GO Enrichment Analysis
ego <- enrichGO(gene = entrez_ids,
                OrgDb = org.Hs.eg.db,
                keyType = "ENTREZID",
                ont = "BP",
                pAdjustMethod = "BH",
                pvalueCutoff = 0.05,
                qvalueCutoff = 0.2,
                readable = TRUE)

# KEGG Pathway Analysis
ekegg <- enrichKEGG(gene = entrez_ids,
                    organism = 'hsa',
                    keyType = 'kegg',
                    pvalueCutoff = 0.05)

# Visualize top GO terms
barplot(ego, showCategory = 10, title = "Top GO Biological Processes")

# Visualize KEGG pathways
dotplot(ekegg, showCategory = 10, title = "KEGG Pathway Enrichment")
Explanation
  • clusterProfiler: Facilitates enrichment analysis.
  • Entrez IDs: Required for many databases.
  • Visualization: Helps interpret results and identify key biological themes.

Step 7: Results Interpretation and Conclusion

Tasks:

  • Interpret Enrichment Results:

    • Identify key biological processes and pathways associated with potential biomarkers.

  • Discuss Implications:

    • Explain how identified biomarkers and pathways relate to the disease.

  • Formulate Conclusions and Future Directions:

    • Summarize findings.
    • Suggest validation experiments or further analysis.

Implementation:

  • Write a Report:
    • Include introduction, methods, results, discussion, and conclusion sections.
    • Use figures and tables to present data.
Example Interpretation

  • Biomarker Candidates:

    • Genes X, Y, and Z are highly connected nodes with significant differential expression.

  • Enriched GO Terms:

    • Terms related to immune response, inflammation, or cell proliferation.

  • KEGG Pathways:

    • Pathways such as cytokine-cytokine receptor interaction, apoptosis, etc.

  • Conclusion:

    • The identified biomarkers are involved in critical pathways related to the disease.
    • These candidates warrant further investigation for potential diagnostic or therapeutic applications.

Code Snippets

Click to view code snippets

All code snippets are provided within each step under the Implementation sections.


Conclusion

In this project, you have:

  • Integrated gene expression and protein interaction data to build comprehensive networks.
  • Constructed gene co-expression networks using WGCNA to identify modules associated with the disease.
  • Built protein-protein interaction networks using data from the STRING database.
  • Performed network analysis to identify key nodes and potential biomarkers based on centrality measures and differential expression.
  • Conducted functional and pathway enrichment analyses to interpret the biological significance of identified biomarkers.
  • Enhanced your understanding of bioinformatics techniques in the context of disease biomarker discovery.

This project equips you with valuable skills in data integration, network biology, and bioinformatics analysis, preparing you for advanced research in genomics, proteomics, and systems biology.

Next Steps:

  • Experimental Validation:

    • Validate potential biomarkers using laboratory techniques (e.g., qPCR, Western blot).

  • Data Integration:

    • Incorporate additional omics data (e.g., metabolomics) for a multi-layered analysis.

  • Machine Learning Approaches:

    • Use predictive modeling to classify disease states based on biomarker expression.

  • Clinical Translation:

    • Explore the potential of biomarkers in clinical diagnostics or therapeutics.