Permissive and nonpermissive channel closings in CFTR revealed by a factor graph inference algorithm

Area of Science:

• Biophysics
• Ion Channel Physiology
• Computational Biology

Background:

• The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel.
• CFTR channel closing events can be permissive or nonpermissive to reopening, influenced by nucleotide binding (ADP or ATP).
• Distinguishing these closing types is vital for understanding CFTR function and mutations but is challenging due to their electrical silence.

Purpose of the Study:

• To develop and validate a method for accurately classifying CFTR channel closing events.
• To differentiate between permissive and nonpermissive CFTR closings using electrophysiological data.
• To investigate the impact of nucleotide interactions on CFTR gating.

Main Methods:

• Utilized single-channel patch-clamp recordings.
• Developed an inference algorithm implemented on a factor graph.
• Applied the algorithm to both simulated and experimentally obtained patch-clamp traces.

Main Results:

• The inference algorithm accurately determined the type of CFTR channel closing.
• Successful differentiation between permissive and nonpermissive closing events was achieved.
• The method proved effective on both simulated and real-world patch-clamp data.

Conclusions:

• The developed factor graph-based inference algorithm provides a robust method for classifying CFTR closing events.
• This technique overcomes the challenge of differentiating electrically silent, nucleotide-dependent gating states.
• The findings offer a valuable tool for studying CFTR function in various conditions, including mutants and agonist treatments.