bioRxivCancer BiologyPreprint
Positive selection and structural disruption of KCNA5 by mutation in colorectal cancer
Abstract
Ion channels regulate cell proliferation, survival and migration, but the functional consequences of many ion channel mutations in cancer are unknown, and electrophysiological characterisation at scale is impractical. Here we use the mutational landscape of colorectal adenocarcinoma as a functional filter to identify potassium channel mutations under selection. A dN/dS screen of 16 potassium channels expressed in the large intestine identified KCNA5 (Kv1.5) as the strongest candidate under positive mutational selection. These mutations cluster in the T1 tetramerisation, S1-S4 voltage-sensor and pore domains, yet avoid the selectivity filter, indicating that selection does not favour loss of conduction. Within the pore region, the observed mutations occupy a biophysical space distinct from known C-type inactivation, activation-gate and toxin-binding mutations. In the voltage sensor, mutations affect gating-charge arginines. In the T1 domain, mutations fall on inter-subunit interfaces conserved across the KCNA family, and saturation mutagenesis energy calculations predict that they destabilise subunit contacts or the folding of the domain. Molecular dynamics simulations of D166N and G128R show loss of coordinated interface dynamics and distortion of the local backbone, respectively. Transcriptomic analysis of KCNA5-mutant tumours shows reduced expression of cell-cycle, KRAS-related and inflammatory pathways, and we observe differences between transcriptional effects induced by T1 and S1-S4 domain mutations. These changes are not reproduced by low KCNA5 expression alone, consistent with an effect specific to the mutant protein. Together, these computational results highlight that KCNA5 is under mutational selection in colorectal adenocarcinoma, and that these mutations are likely functional and induce phenotypic effects on tumors.
- organism: human
- assay: DNA methylation
- assay: whole-exome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- approach: simulation
- Citations
- 0
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