Small compound-based direct cell conversion with combinatorial optimization of pathway regulations.

Area of Science:

• Computational biology
• Regenerative medicine
• Cellular reprogramming

Background:

• Direct cell reprogramming (DR) converts cells without stem cells, offering an alternative to gene transfection.
• Small compounds can induce DR, but identifying effective combinations is challenging due to combinatorial complexity.
• Existing methods struggle with the vast number of potential compound combinations for DR.

Purpose of the Study:

• To develop a computational method, COMPRENDRE, for understanding small compound-based DR mechanisms.
• To predict novel combinations of small compounds for efficient direct cell conversion.
• To identify new DR-related pathways for applications in regenerative medicine.

Main Methods:

• Estimated target proteins of DR-inducing small compounds.
• Identified DR-related pathways, including novel ones for neuron and cardiomyocyte generation.
• Employed a simulated annealing algorithm to overcome combinatorial explosion and find optimal compound sets.

Main Results:

• COMPRENDRE successfully predicted new DR-inducing candidate combinations using fewer compounds.
• The method reproduced experimentally verified compounds for fibroblast-to-neuron and fibroblast-to-cardiomyocyte conversion.
• Identified previously unreported DR-related pathways.

Conclusions:

• COMPRENDRE provides a computational approach to elucidate small compound-driven DR mechanisms.
• The method efficiently predicts effective compound combinations, accelerating the discovery of regenerative medicine applications.
• COMPRENDRE is a valuable tool for identifying novel pathways and compounds for direct cell conversion.