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Variants with increased negative electrostatic potential in the Cx50 gap junction pore increased unitary channel conductance and magnesium modulation.

Mary Grace TejadaSwathy SudhakarNicholas K KimHiroshi AoyamaBrian H ShiltonDonglin Bai
Published in: The Biochemical journal (2018)
Gap junction (GJ) channels are oligomers of connexins forming channels linking neighboring cells. GJs formed by different connexins show distinct unitary channel conductance (γj), transjunctional voltage-dependent gating (V j-gating) properties, and modulation by intracellular magnesium ([Mg2+]i). The underlying molecular determinants are not fully clear. Previous experimental evidence indicates that residues in the amino terminal (NT) and initial segment of the first extracellular (E1) domain influence the γj, V j-gating, and/or [Mg2+]i modulation in several GJs. Increasing negatively charged residues in Cx50 (connexin50) E1 (G46D or G46E) increased γj, while increasing positively charged residue (G46K) reduced the γj Sequence alignment of Cx50 and Cx37 in the NT and E1 domains revealed that in Cx50 G8 and V53, positions are negatively charged residues in Cx37 (E8 and E53, respectively). To evaluate these residues together, we generated a triple variant in Cx50, G8E, G46E, and V53E simultaneously to study its γj, V j-gating properties, and modulation by [Mg2+]i Our data indicate that the triple variant and individual variants G8E, G46E, and V53E significantly increased Cx50 GJ γj without a significant change in the V j gating. In addition, elevated [Mg2+]i reduced γj in Cx50 and all the variant GJs. These results and our homology structural models suggest that these NT/E1 residues are likely to be pore-lining and the variants increased the negative electrostatic potentials along the GJ pore to facilitate the γj of this cation-preferring GJ channel. Our results indicate that electrostatic properties of the Cx50 GJ pore are important for the γj and the [Mg2+]i modulation.
Keyphrases
  • copy number
  • gene expression
  • artificial intelligence
  • molecular dynamics simulations
  • machine learning
  • cell proliferation
  • single cell