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Gain-of-function mutations in the serotonin transporter cluster to sites of conformational change

gse109499

Description

The human serotonin transporter SERT catalyzes serotonin reuptake at the synapse to terminate neurotransmission, and is targeted by therapeutics treating depression and anxiety disorders. Here, the effects of nearly all single amino acid substitutions on SERT surface expression and import of a fluorescent neurotransmitter analogue were determined by deep mutational scanning. By using a non-physiological substrate for which the SERT sequence has not been optimized by natural evolution, hundreds of gain-of-function mutations are discovered that delineate the permeation pathway. The mutations localize to sites of motion based on simulation of serotonin import, including gating residues, helix hinges, and especially the exit pathway. Together, the mutational landscape and simulations support an import mechanism in which exit pathway opening is rate-limiting, and substrate movement into the exit pathway displaces a single symported sodium, although three metal ions bind the transporter. Furthermore, energy barriers between conformational states maintain ion dependencies for coupled transport.

Overall Design

Human SERT was encoded by a synthetic codon-optimized gene. The SERT sequence was diversified by overlap-extension PCR to generate two single-site saturation mutagenesis libraries spanning the full length of the gene. The libraries were expressed in human Expi293F cells (a HEK293 derivative) and sorted by FACS for ability to express on the cell surface and the ability to transport a fluorescent neurotransmitter analog, APP+. Enrichment ratios were calculated for all single amino acid substitutions by comparing the frequencies of sequence variants in the sorted cells (from RNA transcripts) with the naive libraries (plasmid DNA). Evolution experiments were duplicated.

Histogram

Data and Resources

Raw Files [10]

Additional Info

Field Value
Source https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109499
Type of Data

Other

Technology

None

GSE Submission Date 23/01/2018
GSE Authors Erik,,Procko; Heather,J,Young; Steven,K,Szymanski
Dataset Last Updated December 1, 2020, 19:22 (UTC)
Dataset Created December 1, 2020, 17:09 (UTC)