Abstract
Uterine smooth muscle cells remain quiescent throughout most of gestation, only generating spontaneous action potentials immediately prior to, and during, labor. This study presents a method that combines transcriptomics with biophysical recordings to characterise the conductance repertoire of these cells, the ‘conductance repertoire’ being the total complement of ion channels and transporters expressed by an electrically active cell. Transcriptomic analysis provides a set of potential electrogenic entities, of which the conductance repertoire is a subset. Each entity within the conductance repertoire was modeled independently and its gating parameter values were fixed using the available biophysical data. The only remaining free parameters were the surface densities for each entity. We characterise the space of combinations of surface densities (density vectors) consistent with experimentally observed membrane potential and calcium waveforms. This yields insights on the functional redundancy of the system as well as its behavioral versatility. Our approach couples high-throughput transcriptomic data with physiological behaviors in health and disease, and provides a formal method to link genotype to phenotype in excitable systems. We accurately predict current densities and chart functional redundancy. For example, we find that to evoke the observed voltage waveform, the BK channel is functionally redundant whereas hERG is essential. Furthermore, our analysis suggests that activation of calcium-activated chloride conductances by intracellular calcium release is the key factor underlying spontaneous depolarisations.
Highlights
The human genome contains a large number of distinct ion channels, each of which may be assembled into multimeric complexes that modulate the electrical activity of the cell in a subtly different way [1]
A well-known problem in electrophysiologal modeling is that the parameters of the gating kinetics of the ion channels cannot be uniquely determined from observed behavior at the cellular level
We propose an approach, which we apply to uterine smooth muscle cells, whereby we constrain the list of possible entities by means of transcriptomics and chart the indeterminacy of the problem in terms of the kernel of the corresponding linear transformation
Summary
The human genome contains a large number of distinct ion channels, each of which may be assembled into multimeric complexes that modulate the electrical activity of the cell in a subtly different way [1]. Determination of the total ion channel repertoire of a given cell (its ‘conductance repertoire’) is cumbersome via conventional biophysical techniques The latter rely on the specificity and availability of suitable pharmalogical agents or protocols, which may or may not be able to differentiate between combinations of conductances that can give rise to similar behaviors at the electrophysiological level. The classic approach in electrophysiology has been to fit a suitable mathematical model to the data, where the parameters to be estimated represent the densities of the electrogenic entities and their biophysical properties [2,3,4] This approach is hampered by the large number of distinct electrogenic species. Such models may not be sufficiently detailed for pharmacological purposes and accurate assessment of currents within native cells can be technically challenging
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