# Determining the cell type specific roles of Foxp1 in cortical development.

> **NIH NIH F31** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $12,966

## Abstract

Project Summary / Abstract:
Approximately, 1 in 59 children in the United States are affected with autism spectrum disorder (ASD). Numerous
studies have identified mutations in FOXP1, a gene encoding a transcription factor enriched in the developing
and mature neocortex, as causal for ASD. Our group has previously shown that loss of Foxp1 in the mouse
cortex leads to reduced cortex size, alterations in cortical lamination, and changes in relative thickness of cortical
layers. However, the mechanisms underlying these changes remain unclear. I propose to characterize the
developmental requirement of neocortical Foxp1 using time point specific conditional knock-outs of Foxp1 and
by measuring transcriptional targets using single nuclei RNA sequencing. I hypothesize that during early
embryonic development, Foxp1 facilitates the development of deep layer neurons by repressing factors that
specify upper layer neuronal fate. In contrast, I hypothesize that Foxp1 is critical for neuronal identity during late
cortical development. The outcome of this work will allow for a better understanding of how Foxp1 governs early
cortical development and potential therapeutic windows and molecular pathways for ameliorating loss of function
Foxp1 mutations.

## Key facts

- **NIH application ID:** 9991580
- **Project number:** 1F31MH123140-01
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Ana Karen Ortiz
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $12,966
- **Award type:** 1
- **Project period:** 2020-06-01 → 2020-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991580

## Citation

> US National Institutes of Health, RePORTER application 9991580, Determining the cell type specific roles of Foxp1 in cortical development. (1F31MH123140-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9991580. Licensed CC0.

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