Dataset Information

A standardised hERG phenotyping pipeline to evaluate KCNH2 genetic variant pathogenicity.

ABSTRACT:

Background and aims

Mutations in KCNH2 cause long or short QT syndromes (LQTS or SQTS) predisposing to life-threatening arrhythmias. Over 1000 hERG variants have been described by clinicians, but most remain to be characterised. The objective is to standardise and accelerate the phenotyping process to contribute to clinician diagnosis and patient counselling. In silico evaluation was also included to characterise the structural impact of the variants.

Methods

We selected 11 variants from known LQTS patients and two variants for which diagnosis was problematic. Using the Gibson assembly strategy, we efficiently introduced mutations in hERG cDNA despite GC-rich sequences. A pH-sensitive fluorescent tag was fused to hERG for efficient evaluation of channel trafficking. An optimised 35-s patch-clamp protocol was developed to evaluate hERG channel activity in transfected cells. R software was used to speed up analyses.

Results

In the present work, we observed a good correlation between cell surface expression, assessed by the pH-sensitive tag, and current densities. Also, we showed that the new biophysical protocol allows a significant gain of time in recording ion channel properties and provides extensive information on WT and variant channel biophysical parameters, that can all be recapitulated in a single parameter defined herein as the repolarisation power. The impacts of the variants on channel structure were also reported where structural information was available. These three readouts (trafficking, repolarisation power and structural impact) define three pathogenicity indexes that may help clinical diagnosis.

Conclusions

Fast-track characterisation of KCNH2 genetic variants shows its relevance to discriminate mutants that affect hERG channel activity from variants with undetectable effects. It also helped the diagnosis of two new variants. This information is meant to fill a patient database, as a basis for personalised medicine. The next steps will be to further accelerate the process using an automated patch-clamp system.

SUBMITTER: Oliveira-Mendes B

PROVIDER: S-EPMC8609418 | biostudies-literature |

REPOSITORIES: biostudies-literature

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