Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate...

Area of Science:

• Systems Biology
• Genetics
• Metabolomics

Background:

• Cellular processes are regulated by transcriptional and translational mechanisms.
• Small-molecule metabolites are key cellular intermediates reflecting physiological states.
• Integrating metabolite data with other molecular profiles offers insights into cellular regulation.

Purpose of the Study:

• To develop and apply a network reconstruction approach integrating diverse data types.
• To construct probabilistic causal networks elucidating cell regulation complexity in yeast.
• To identify causal regulators influencing gene expression and metabolite concentrations.

Main Methods:

• Simultaneous integration of six data types: metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction.
• Network reconstruction to build probabilistic causal networks.
• Causal regulator detection algorithm applied to identify genetic influences.

Main Results:

• Developed a novel network reconstruction approach integrating six distinct data types.
• Constructed probabilistic causal networks for a segregating yeast population.
• Identified causal regulators demonstrating how sequence variations impact gene expression and metabolite levels.
• Examined eQTL hotspots with colocalized metabolite QTLs, uncovering known and novel biological mechanisms.

Conclusions:

• The integrated network approach successfully elucidates complex cell regulation.
• Genetic control over metabolites is significant, enabling identification of causal regulators.
• The study reveals novel biological mechanisms associated with eQTL hotspots and metabolite regulation.