Magnetique

Magnetique profiles transcriptome signatures associated with heart failure using the Myocardial Applied Genomics Network left ventricular RNA-seq dataset to identify differential gene expression, differential transcript isoform usage, transcription factor signaling, and RNA-binding protein interactions across DCM, HCM, and NFD samples.

Key Features:

• Differential Gene Expression Analysis: Identifies genes with significant expression changes between dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and non-failing donor (NFD) left ventricular samples.
• Pathway Signatures and Network Reconstruction: Reconstructs signaling networks and infers transcription factor activities using integer linear programming to capture pathway-level alterations in heart failure.
• Differential Transcript Isoform Usage (DTU): Detects changes in transcript isoform usage across HF subtypes to reveal post-transcriptional regulation differences.
• RNA-Binding Protein (RBP) Interactions: Predicts RBP–target transcript interactions using a Global test approach and reconstructs RBP–target networks, highlighting RBPs such as CPEB1 in the DCM versus HCM contrast.
• Transcription Factor Signaling Insights: Reports inferred transcription factor and kinase activity patterns, including opposite inferred activities for ERK1 and ERK2 between DCM and HCM samples.

Scientific Applications:

• Molecular mechanism discovery: Elucidates gene- and isoform-level alterations and signaling changes underlying heart failure subtypes (DCM, HCM, NFD).
• Post-transcriptional regulation analysis: Identifies RBP-mediated regulation and isoform-specific expression changes relevant to HF biology.
• Biomarker and target identification: Supports the nomination of candidate biomarkers and therapeutic targets based on differential expression, DTU, and network-inferred regulator activities.
• Confounding factor modeling: Enables analyses that account for patient-characteristic confounders when comparing HF groups.

Methodology:

Reanalysis of existing left ventricular RNA-seq data including differential gene expression and DTU analyses, transcription factor activity inference via integer linear programming, and RBP–target interaction prediction using a Global test approach.