Mar 21, 2024

Public workspaceBioinformatics Protocol for Investigating Novel Biomarkers, Conducting Statistical Analysis, and Exploring Molecular Pathways: A Unified Approach for Alzheimer's Disease

DOI
dx.doi.org/10.17504/protocols.io.5qpvokxyxl4o/v1
Bioinformatics Protocol for Investigating Novel Biomarkers, Conducting Statistical Analysis, and Exploring Molecular Pathways: A Unified Approach for Alzheimer's Disease
  • 1R V College of Engineering;
  • 2RV College of Engineering, Bangalore
Vidya Niranjan
Open access
DOI: dx.doi.org/10.17504/protocols.io.5qpvokxyxl4o/v1
Protocol Citation: Shreya Satyanarayan Bhat, Adarsh V, Spoorthi Anil Bandikatte, Vidya Niranjan 2024. Bioinformatics Protocol for Investigating Novel Biomarkers, Conducting Statistical Analysis, and Exploring Molecular Pathways: A Unified Approach for Alzheimer's Disease. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvokxyxl4o/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 18, 2024
Last Modified: March 21, 2024
Protocol Integer ID: 96840
Keywords: Bioinformatic approach, Biomarkers, statistical analysis, Alzheimer's disease, pathways
Abstract
This protocol paper presents a comprehensive bioinformatics approach aimed at identifying novel biomarkers associated with Alzheimer's disease (AD) using RNA sequencing (RNA-seq) datasets obtained from the Gene Expression Omnibus (GEO) database. Alzheimer's disease is a complex neurodegenerative disorder characterized by progressive cognitive decline, and identifying biomarkers is crucial for early diagnosis and therapeutic intervention. Leveraging bioinformatics tools and methods, this protocol outlines a step-by-step procedure for analyzing RNA-seq data to uncover potential biomarkers. The protocol includes data collection from the GEO database, quality control assessment, differential expression analysis, functional annotation, pathway enrichment analysis, and molecular pathway identification. Statistical analysis is applied throughout the protocol to ensure the robustness and reliability of the results. By following this protocol, researchers can systematically identify and validate novel biomarkers associated with Alzheimer's disease, ultimately contributing to a better understanding of its underlying molecular mechanisms and facilitating the development of targeted therapeutic interventions.
Identification of RNA-Seq Dataset
Identification of RNA-Seq Dataset
The RNA-Seq dataset utilized in this study was sourced from the European Nucleotide Archive (ENA) browser, specifically accession number GSE138853. This dataset was selected due to its relevance to Alzheimer's disease research and its availability through the ENA browser.
Dataset
ALZHEIMERS DISEASE DATASET
NAME
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138852
LINK

Retrieval of Samples
Retrieval of Samples
Raw sequencing samples were downloaded in FASTQ format, containing nucleotide sequences and quality scores for each read. This standardized format enables comprehensive bioinformatics analysis, including quality control and differential expression analysis.
Upon selecting the desired dataset, proceed to download the raw sequencing samples associated with it. The samples were retrieved directly from the ENA browser interface, providing access to the original sequencing data generated for the study. Each sample corresponds to a specific biological sample (e.g., tissue sample) that underwent RNA sequencing to generate transcriptomic data. The raw sequencing samples were downloaded in their original format, typically stored as FASTQ files. FASTQ is a standard file format used to represent raw sequencing data, containing both nucleotide sequences and quality scores for each sequenced read.
Dataset
samples retrieval
NAME
https://www.ebi.ac.uk/ena/browser/view/PRJNA577618
LINK

Command
Download samples (linux 20.4)
wget ftp://ftp.sra.ebi.ac.uk/vol1/srr/SRR102/012/SRR10278812

Quality Check using FASTQC
Quality Check using FASTQC
The quality assessment of the 32 samples using the FASTQC tool. FASTQC is a widely used tool for the initial assessment of raw sequencing data quality.
Command
The command fastqc initiates the quality checking process using FASTQC. reads.fastq.gz represents the input raw sequencing data file for each sample. The file is in compressed FASTQ format (.fastq.gz), commonly encountered in sequencing data analysis. (LINUX 20.4)
fastqc reads.fastq.gz

Conversion of BAM Files to SAM Files Using Samtools
Conversion of BAM Files to SAM Files Using Samtools
This section outlines the comprehensive conversion of BAM files to SAM files utilizing Samtools. SAM files provide a human-readable representation of sequence alignment data, facilitating downstream analysis and interpretation.
Command
This section outlines the comprehensive conversion of BAM files to SAM files utilizing Samtools. SAM files provide a human-readable representation of sequence alignment data, facilitating downstream analysis and interpretation. (linux 20.4)
samtools view -h -o output.sam input.bam

Command
This command sorts the alignments in the BAM file by their genomic coordinates. (linux 20.4)
samtools sort -@ 4 24.bam -o 24_sorted.bam

Alignment
Alignment
All 32 RNA-Seq samples were aligned to the human reference genome obtained from the National Center for Biotechnology Information (NCBI). The alignment was performed using HISAT2, a widely used alignment tool known for its efficiency and accuracy in mapping sequencing reads to a reference genome.
Command
The alignment code aligns RNA-Seq samples to the human reference genome using HISAT2. It specifies the index of the reference genome, the input file containing sequencing reads, and the output file for alignment results in SAM format. This process enables mapping of reads to the genome, facilitating downstream analyses of gene expression and regulatory mechanisms. (Linux 20.4)
hisat2 -x human_reference_genome_index -U sample.fastq.gz -S sample_aligned.sam

Generation of feature Count
Generation of feature Count
The generation of feature counts using the FeatureCounts tool. FeatureCounts is a widely used tool for counting the number of reads mapped to genomic features, such as genes, transcripts, or exons, based on the alignment results obtained from aligning RNA-Seq reads to a reference genome. The annotation file used for feature counting was obtained from the National Center for Biotechnology Information (NCBI) Genome database. This file provides genomic annotations, including the locations and characteristics of genes, transcripts, and other genomic features, necessary for accurately assigning reads to features during the counting process.
Command
featureCounts: Initiates the feature counting process using the FeatureCounts tool. -a human_annotation_file.gtf: Specifies the annotation file (in GTF format) obtained from NCBI, containing genomic annotations for the human reference genome. This file guides the assignment of reads to genomic features during counting. -o counts.txt: Specifies the output file name (counts.txt) for storing the feature counts. This file contains the counts of reads assigned to each genomic feature. sample_aligned.sam: Indicates the input SAM file containing alignment results obtained from aligning RNA-Seq reads to the human reference genome.
featureCounts -a human_annotation_file.gtf -o counts.txt sample_aligned.sam

Differential gene expression analysis
Differential gene expression analysis
We performed differential gene expression (DGE) analysis for all 32 samples using the DESeq2 package. DESeq2 is a widely used tool in bioinformatics for identifying genes that are differentially expressed between experimental conditions or sample groups based on RNA-Seq data. It employs a negative binomial distribution model to accommodate variability in sequencing depth and biological variability across samples. DESeq2 conducts normalization, estimation of dispersion, and statistical testing to identify genes that exhibit significant differences in expression levels between conditions. This approach enables researchers to gain insights into the molecular mechanisms underlying biological processes and diseases by identifying genes that are actively involved in specific conditions or experimental treatments.
Command
Dseq2 Commands (windows 11)
# Load count data
count_data <- read.csv('count.csv', header = TRUE, row.names = 1)

# Filter low-count genes
count_data <- count_data[rowSums(count_data[, c('SRR15039666', 'SRR15039668', 'SRR22702939','SRR22702940','SRR22702946','SRR22702961','SRR22801652','SRR22801654')]) > 50, ]

# Define experimental conditions
condition <- factor(c("AD", "AD", ..., "H"))

# Create a data frame with sample IDs and conditions
coldata <- data.frame(row.names = colnames(count_data), condition)

# Remove rows with missing values
count_data_clean <- count_data[complete.cases(count_data), ]

# Create DESeq2 object
dds <- DESeqDataSetFromMatrix(countData = count_data_clean, colData = coldata, design = ~condition)

# Perform differential expression analysis
dds <- DESeq(dds)

# Transform count data
vsdata <- vst(dds, blind = FALSE)

# Generate PCA plot
plotPCA(vsdata, intgroup = "condition")

# Plot dispersion estimates
plotDispEsts(dds)

# Extract differential expression results
res <- results(dds, contrast = c("condition", "C", "H"))

# Filter significant results
sigs <- na.omit(res)
sigs <- sigs[sigs$padj < 0.05, ]

# Print significant results
sigs

# Extract expression matrix of significant genes
sig_expr_matrix <- assay(dds)[rownames(sigs), ]

# Log2 transform expression matrix
log_sig_expr_matrix <- log2(sig_expr_matrix + 1)

# Generate heatmap
heatmap <- pheatmap(log_sig_expr_matrix, cluster_rows = TRUE, cluster_cols = TRUE, color = colorRampPalette(c("blue", "white", "red"))(100))

# Save heatmap as TIFF file
tiff("heatmap.tiff", width = 10, height = 10, units = "in", res = 600)
print(heatmap)
dev.off()

# Plot MA plot
plotMA(res)

# Generate volcano plot
volcano_plot <- ggplot(res, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(color = ifelse(padj < 0.05, "Significant", "Not significant")), size = 2, alpha = 0.8) +
    scale_color_manual(values = c("Significant" = "red", "Not significant" = "gray")) +
    labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "log10(p-value)") +
    theme_minimal() +
    theme(
        plot.margin = margin(20, 20, 20, 20),
        axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 10),
        legend.position = "bottom",
        legend.direction = "vertical",
        legend.box = "vertical",
        legend.margin = margin(t = 20, r = 20, b = 0.5, l = 20, unit = "pt")
    )

# Display volcano plot
print(volcano_plot)


Gene ontology
Gene ontology
In this section, we utilized the Gene Ontology (GO) database to perform a functional analysis of the differentially expressed genes identified in the RNA-Seq analysis. GO is a widely used bioinformatics resource that categorizes genes into defined terms based on their molecular functions, biological processes, and cellular components, providing valuable insights into the biological roles of genes. Gene Ontology (GO) helps us understand gene functions. It covers Biological Processes: Genes’ roles in cell division, metabolism, development, and immune response. Molecular Functions: Specific activities at the molecular level (e.g., enzyme catalysis, binding). Cellular Components, Where gene products operate within cells (nucleus, mitochondria, etc.). The image shows the Gene Ontology website, where researchers input gene IDs to explore these aspects.
image.png

gene ontology website

The image presents the outcomes of a Gene Ontology (GO) biological process analysis, highlighting processes such as response to stimulus and cell differentiation. Each process is linked to statistical values that signify its significance. This image provides valuable insights into the biological functions influenced by a specific set of query genes, laying the groundwork for understanding their roles within an organism.
The image presents the outcomes of a Gene Ontology (GO) biological process analysis, highlighting processes such as response to stimulus and cell differentiation. Each process is linked to statistical values that signify its significance. This image provides valuable insights into the biological functions influenced by a specific set of query genes, laying the groundwork for understanding their roles within an organism.

The image reveals the outcomes of a Gene Ontology (GO) molecular function analysis based on query genes. It highlights specific molecular activities associated with statistical significance, providing insights into the functional roles of these genes at the molecular.
The image reveals the outcomes of a Gene Ontology (GO) molecular function analysis based on query genes. It highlights specific molecular activities associated with statistical significance, providing insights into the functional roles of these genes at the molecular.

The image reveals the outcomes of a Gene Ontology (GO) cellular component analysis based on query genes. It highlights specific cellular locations associated with statistical significance, providing insights into where these genes operate within cells.
The image reveals the outcomes of a Gene Ontology (GO) cellular component analysis based on query genes. It highlights specific cellular locations associated with statistical significance, providing insights into where these genes operate within cells.


Pathway Analysis using KEGG MAPPER
Pathway Analysis using KEGG MAPPER
Utilization of the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and its pathway analysis tool to investigate the functional significance of the differentially expressed genes identified in our study. KEGG is a comprehensive database that provides information on biological pathways, networks, diseases, and drugs, allowing researchers to elucidate the molecular mechanisms underlying complex biological processes. the list of differentially expressed genes obtained from our analysis as input for the KEGG pathway analysis. KEGG Mapper enables the visualization and interpretation of biological pathways enriched with differentially expressed genes, providing insights into the biological processes and signaling pathways that are dysregulated under specific experimental conditions.
KEGG MAPPER

The screenshot depicts the KEGG Mapper interface utilized for pathway analysis. The interface provides options for inputting gene IDs, selecting analysis parameters, and visualizing enriched pathways. in the entry query, input a list of gene IDs obtained from differential gene expression analysis. These gene IDs represent the differentially expressed genes identified in the study. select mode hsa (Human Species)
The screenshot depicts the KEGG Mapper interface utilized for pathway analysis. The interface provides options for inputting gene IDs, selecting analysis parameters, and visualizing enriched pathways. in the entry query, input a list of gene IDs obtained from differential gene expression analysis. These gene IDs represent the differentially expressed genes identified in the study. select mode hsa (Human Species)


The screenshot illustrates the results obtained from KEGG Mapper analysis, displaying numerous enriched pathways associated with the input gene IDs.The list of enriched pathways identified through KEGG Mapper analysis is presented, showing the pathway names and their corresponding KEGG identifiers (IDs). Each pathway represents a specific biological process, cellular signaling pathway, or disease mechanism that is enriched with the input gene IDs. Key pathways that are significantly enriched with input gene IDs are identified, highlighting their importance in driving the observed changes in gene expression. These key pathways serve as focal points for further investigation into the molecular mechanisms underlying specific biological processes or disease pathways.
The screenshot illustrates the results obtained from KEGG Mapper analysis, displaying numerous enriched pathways associated with the input gene IDs.The list of enriched pathways identified through KEGG Mapper analysis is presented, showing the pathway names and their corresponding KEGG identifiers (IDs). Each pathway represents a specific biological process, cellular signaling pathway, or disease mechanism that is enriched with the input gene IDs. Key pathways that are significantly enriched with input gene IDs are identified, highlighting their importance in driving the observed changes in gene expression. These key pathways serve as focal points for further investigation into the molecular mechanisms underlying specific biological processes or disease pathways.



Enrichment and Statistical analysis of overexpressing genes
Enrichment and Statistical analysis of overexpressing genes
We utilized ShinyGO 0.8.0 to perform enrichment analysis of the input gene list obtained from our study. ShinyGO employs statistical algorithms to identify GO terms that are significantly enriched with the input genes compared to what would be expected by chance. This analysis enables the functional annotation of gene sets and helps uncover the biological themes and pathways represented by the input genes. considering factors such as gene count, background gene set, and false discovery rate (FDR) correction.

In the ShinyGO interface, users are provided with a text box or file upload option to input their list of gene identifiers. These gene identifiers can be in various formats, such as Entrez Gene IDs, Ensembl IDs, or gene symbols. Users can either manually enter the gene IDs or upload a file containing the list of gene IDs obtained from their study.
image.png



Upon performing enrichment analysis using ShinyGO, users receive a comprehensive summary of enriched Gene Ontology (GO) terms associated with the input gene list. The enrichment results provide valuable insights into the functional significance of the input genes by identifying overrepresented biological processes, molecular functions, and cellular components.
ShinyGO presents a list of enriched GO terms, categorized into biological processes, molecular functions, and cellular components. Each GO term is accompanied by statistical metrics indicating its significance level, such as p-values or false discovery rates (FDR).


The figure depicts a pathway network visualization generated from the enrichment analysis results obtained using ShinyGO. The network diagram illustrates the relationships between enriched pathways based on shared genes or functional associations. Pathway Nodes: Each node in the pathway network represents an enriched pathway identified through the analysis. Nodes are labeled with pathway names or identifiers, and their size may correspond to the significance level of the pathway enrichment. Edge Connections: Edges connecting pathway nodes indicate shared genes or functional relationships between pathways. Thicker edges represent stronger connections, suggesting a higher degree of gene overlap or functional similarity between pathways. Node Attributes: Nodes in the pathway network may be annotated with additional information, such as statistical significance scores or enrichment metrics, providing insights into the biological relevance of each pathway. Cluster Analysis: The pathway network may undergo cluster analysis to group related pathways into cohesive functional modules. Clusters of pathways with similar biological functions or regulatory mechanisms are visually highlighted within the network. Interactive Exploration: The pathway network visualization may offer interactive features that allow users to explore the network dynamically. Users can interact with nodes and edges to access detailed pathway information, visualize gene overlaps, and navigate between interconnected pathways. Biological Interpretation: Pathway network analysis facilitates the biological interpretation of enrichment results by contextualizing enriched pathways within broader functional contexts. Users can identify key pathway modules, hub pathways, or cross-talk interactions between pathways, providing insights into the underlying biological processes and regulatory mechanisms represented by the input gene list.
The figure depicts a pathway network visualization generated from the enrichment analysis results obtained using ShinyGO. The network diagram illustrates the relationships between enriched pathways based on shared genes or functional associations. Pathway Nodes: Each node in the pathway network represents an enriched pathway identified through the analysis. Nodes are labeled with pathway names or identifiers, and their size may correspond to the significance level of the pathway enrichment. Edge Connections: Edges connecting pathway nodes indicate shared genes or functional relationships between pathways. Thicker edges represent stronger connections, suggesting a higher degree of gene overlap or functional similarity between pathways. Node Attributes: Nodes in the pathway network may be annotated with additional information, such as statistical significance scores or enrichment metrics, providing insights into the biological relevance of each pathway. Cluster Analysis: The pathway network may undergo cluster analysis to group related pathways into cohesive functional modules. Clusters of pathways with similar biological functions or regulatory mechanisms are visually highlighted within the network. Interactive Exploration: The pathway network visualization may offer interactive features that allow users to explore the network dynamically. Users can interact with nodes and edges to access detailed pathway information, visualize gene overlaps, and navigate between interconnected pathways. Biological Interpretation: Pathway network analysis facilitates the biological interpretation of enrichment results by contextualizing enriched pathways within broader functional contexts. Users can identify key pathway modules, hub pathways, or cross-talk interactions between pathways, providing insights into the underlying biological processes and regulatory mechanisms represented by the input gene list.


The chi-squared test assesses the association between categorical variables, such as gene annotations or GO terms, comparing the observed distribution of these characteristics among your genes with the expected distribution based on the entire genome. If the chi-squared test yields a low p-value, typically below 0.05, it indicates a significant difference in the distribution of gene characteristics between your genes and the rest of the genome. Similarly, Student's t-test compares the means of quantitative variables, such as gene expression levels or numerical gene features, between your genes and the broader genome. A low p-value from the t-test suggests a significant difference in the mean values of gene characteristics between your genes and the rest of the genome. Overall, if both tests yield low p-values, it suggests that your genes possess special characteristics, such as enrichment for specific annotations or unique expression patterns, distinguishing them from the general population of genes in the genome and potentially indicating their distinct biological roles.

image.png


PPI Network Construction
PPI Network Construction
The PPI network construction and hub genes analysis using ShinyGO provide valuable insights into the molecular mechanisms underlying the observed upregulation of genes, helping to elucidate key regulatory pathways and potential therapeutic targets in the studied biological system.The top-upregulated genes are inputted into the platform to construct a PPI network. ShinyGO utilizes existing databases and algorithms to predict protein-protein interactions based on known protein interactions, protein domains, or functional annotations. The resulting PPI network represents the interconnectedness between the proteins encoded by the top-upregulated genes, highlighting potential functional relationships and pathways involved.
The protein-protein interaction (PPI) network visualized using ShinyGO depicts the interactions among proteins encoded by the top upregulated genes identified from differential expression analysis. Each node represents a protein, with node size indicating its degree of connectivity. Edges between nodes represent predicted or known interactions, with thicker lines indicating stronger interactions. Hub proteins, characterized by high connectivity, are highlighted within the network. Functional annotations or enriched pathways associated with the proteins are overlaid onto the network, providing insights into the biological processes or molecular functions represented.
The protein-protein interaction (PPI) network visualized using ShinyGO depicts the interactions among proteins encoded by the top upregulated genes identified from differential expression analysis. Each node represents a protein, with node size indicating its degree of connectivity. Edges between nodes represent predicted or known interactions, with thicker lines indicating stronger interactions. Hub proteins, characterized by high connectivity, are highlighted within the network. Functional annotations or enriched pathways associated with the proteins are overlaid onto the network, providing insights into the biological processes or molecular functions represented.

Chromosomal enrichment
Chromosomal enrichment
The chromosomal positions of genes are visualized using ShinyGO. Genes are represented as red dots on the chromosomes, providing a spatial overview of their distribution across the genome. Purple lines highlight regions where these genes exhibit statistically significant enrichment compared to the background gene density.
The genome is scanned using a sliding window approach, with each window subdivided into equal-sized steps for sliding. Within each window, a hypergeometric test is employed to assess whether the genes are significantly overrepresented. Essentially, the genes within each window define a gene set or pathway, and enrichment analysis is conducted to identify statistically enriched regions.
Please note that the chromosomes may only be partially shown, as the last gene's location is utilized to draw the line. This analysis offers insights into the genomic localization and enrichment patterns of the genes, shedding light on potential chromosomal hotspots or regions associated with specific biological processes or pathways.

The figure shows genomic position enrichment analysis results using ShinyGO. Red dots represent genes plotted along chromosomes, while purple lines indicate regions of statistical enrichment compared to background gene density.
The figure shows genomic position enrichment analysis results using ShinyGO. Red dots represent genes plotted along chromosomes, while purple lines indicate regions of statistical enrichment compared to background gene density.


EXPECTED OUTCOME OF THE PIPELINE
EXPECTED OUTCOME OF THE PIPELINE
The expected outcome of the study is the comprehensive characterization of top-upregulated genes in Alzheimer's disease, elucidating their roles through gene ontology (GO) analysis, pathway enrichment, and disease intervention pathways. Through rigorous statistical analysis utilizing the tools employed above, the study aims to identify novel genes associated with Alzheimer's disease pathology. The resulting document will provide detailed functional information on these novel biomarkers, shedding light on their involvement in disease mechanisms and offering insights for potential therapeutic interventions.


Expected result
1. WNT9A - Protein Wnt-9a; It’s a Ligand for members of the frizzled family of seven transmembrane receptors. Functions in the canonical Wnt/beta-catenin signaling pathway. It is required for normal timing of IHH expression during embryonic bone development, normal chondrocyte maturation, and for normal bone mineralization during embryonic bone development. It plays a redundant role in maintaining joint integrity.
2. FZD10 - Frizzled-10; Receptor for Wnt proteins. canonical Wnt Functions in the canonical Wnt/beta-catenin signaling pathway. The canonical Wnt/beta-catenin signaling pathway leads to the activation of disheveled proteins, inhibition of GSK-3 kinase, nuclear accumulation of beta-catenin, and activation of Wnt target genes.
3. Probable developmental protein.
It may be a signaling molecule that affects the development of discrete regions of tissues. Is likely to signal over only a few cell diameters.

4. DKK4 - Dickkopf-related protein 4; DKKs play an important role in vertebrate development, where they locally inhibit Wnt-regulated processes such as anteroposterior axial patterning, limb development, somitogenesis, and eye formation. In adults, Dkks are implicated in bone formation and bone disease, cancer, and Alzheimer's disease.
5. SEM1 - Putative protein SEM1, isoform 2; SEM1 26S proteasome complex subunit.