# Transcriptional control of neuronal plasticity by daf-16/FoxO

> **NIH NIH R21** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2020 · $445,500

## Abstract

Many neurons display the remarkable ability to alter specific phenotypic properties in response to
changes in the internal or external environment. Such “neuronal plasticity” phenomena must
intersect with genetically hardwired regulatory programs that define the fully differentiated state of a
neuron. We propose here to investigate how such hardwired gene regulatory programs are modified
to enable the nervous system to change specific phenotypic aspects of a neuron under specific
conditions. We study this problem with single neuron resolution in the context of the nematode C.
elegans whose nervous system remodels a number of features in response to the phylogenetically
conserved insulin/IGF1-like hormonal signaling system. We propose and test in this grant proposal
that the insulin/IGF-1-controlled DAF-16/FoxO transcription factors acts cell autonomously in many
different neuron types to control the expression of specific target genes that are up- or downregulated
in response to specific environmental conditions. We propose and test that DAF-16/FoxO cooperates
with neuron-type specific terminal selector transcription to either promote or antagonize the ability of
these transcription factors to control their effector genes.

## Key facts

- **NIH application ID:** 9914697
- **Project number:** 1R21NS115442-01
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Oliver Hobert
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $445,500
- **Award type:** 1
- **Project period:** 2019-12-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9914697

## Citation

> US National Institutes of Health, RePORTER application 9914697, Transcriptional control of neuronal plasticity by daf-16/FoxO (1R21NS115442-01). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9914697. Licensed CC0.

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