# Homeostatic control of the NMDA receptor co-agonist D-serine by SLC1A4

> **NIH NIH R01** · MC LAUGHLIN RESEARCH INSTITUTE · 2020 · $405,000

## Abstract

NMDA receptors (NMDARs) participate in processes ranging from neural development to
learning and memory. Disorders of NMDAR signaling are linked to several neurological
diseases. Accumulating evidence suggests that the endogenous co-agonist D-serine plays a
prominent role in activation of synaptic NMDARs in cortex. However, there are significant gaps
in our understanding of the physiological mechanisms involved in D-serine homeostasis in brain
and their potential impact on NMDAR signaling. Our preliminary data suggest that SLC1A4, a
neutral amino acid transporter paralog within the SLC1 solute carrier family that includes
glutamate transporters, unexpectedly mediates transmembrane flux of D-serine. We will test the
hypotheses that SLC1A4 is in fact the major route of sodium-dependent D-serine uptake in
brain and that selective SLC1A4 inhibitors developed from a hydroxyproline pharmacophore can
alter D-serine homeostasis and thereby modulate NMDAR function and synaptic plasticity. We
will also characterize the structure and function of a recently identified mutation in the human
gene encoding SLC1A4 that is linked to neurodevelopmental and cognitive deficits, and we will
create and study a transgenic mouse model of this human disease.

## Key facts

- **NIH application ID:** 9890859
- **Project number:** 5R01MH110646-03
- **Recipient organization:** MC LAUGHLIN RESEARCH INSTITUTE
- **Principal Investigator:** MICHAEL PATRICK KAVANAUGH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $405,000
- **Award type:** 5
- **Project period:** 2018-03-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9890859

## Citation

> US National Institutes of Health, RePORTER application 9890859, Homeostatic control of the NMDA receptor co-agonist D-serine by SLC1A4 (5R01MH110646-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9890859. Licensed CC0.

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