Coregulation mapping based on individual phenotypic variation in response to virus infection

Area of Science:

• Immunology
• Computational Biology
• Genetics

Background:

• Human immune responses to viral infections exhibit significant inter-individual variability.
• Understanding gene coregulation is crucial for studying this variability.
• Existing methodologies require integration of various analytical techniques.

Purpose of the Study:

• To develop and present a novel methodology for investigating gene-to-gene correlations in immune response gene expression.
• To identify coregulated gene clusters in human dendritic cells (DCs) in response to Newcastle Disease Virus (NDV).
• To explore the role of transcription factor (TF) sharing in explaining observed gene co-expression patterns.

Main Methods:

• Analysis of gene expression data from 145 human donors' conventional dendritic cells (DCs) infected with NDV.
• Generation of a gene co-expression map using pairwise correlation and multidimensional scaling (MDS).
• Transcription factor (TF) enrichment analysis on gene promoter regions, incorporating Gene Ontology (GO) filtering.

Main Results:

• Identified two distinct clusters of transcriptionally co-regulated genes: (CCL5, IFNA1, IFNA2, IFNB1) and (IKBKE, IL6, IRF7, MX1).
• Demonstrated a significant positive correlation between MDS distances and TF sharing across gene pairs.
• Developed an interactive web-based tool (CorEx) and web services for applying the methodology.

Conclusions:

• Multidimensional scaling (MDS) mapping combined with TF enrichment analysis is an effective computational method.
• This approach aids in predicting gene coregulation patterns within a population.
• The developed tools facilitate broader research application of this methodology.