# Massively Parallel Characterization of Cell-type and Context Specific Regulatory Risk Elements Across Psychiatric Disorders in a Stem Cell Model of Neurodevelopment

> **NIH NIH F31** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $46,592

## Abstract

There is an urgent need to decipher the complex polygenic risk architecture of neuropsychiatric and
neurodevelopmental disorders. Most disease-associated common variants are non-coding. Candidate risk loci
in noncoding regions are often regulatory elements, such as enhancers and promoters, that may modulate
transcriptional activity of key genes contributing to one or many endophenotypes. The functional impact of
most disease-associated non-coding variants remains unknown. Enhancers are known to underlie cell-type
specific patterning of gene expression influencing cellular development and responses to environmental
conditions. Candidate regulatory sequences (CRS) associated with genetic risk are likely key drivers of
underlying endophenotypes and represent potential therapeutic targets. Over the past decade, large-scale
identification of regulatory sequences has expanded our awareness and highlighted their importance yet
functional characterization of regulatory elements on a meaningful scale remained inaccessible. Only with
contemporary advances in high-throughput sequencing and large-scale screening techniques, such as the
Massively Parallel Reporter Assay (MPRA), has characterizing the growing list of non-coding risk loci en
masse become feasible. I will apply a MPRA in hiPSC-derived brain cells to identify psychiatric risk variants
within CRS that demonstrate cell-type specific transcriptional activity. Additionally, I wi/1 lool< at the interactions
ol pre-natal stress and inflammation with genetic risl< and consequent susceptibility to negative mental health
later in life. There is sufficient evidence correlating fetal environmental factors with neurodevelopmental
trajectories. Stress or inflammation during pregnancy has been linked to mental health outcomes in the
offspring. The influence of pre-natal environmental factors on brain-related CRSs may explain correlations with
Maternal-Immune Activation (MIA) and increased susceptibility to stress and negative health trajectories of the
offspring. Biological mechanisms underlying MIA may contribute to this increased susceptibility. Using MPRAs,
I will identify risk variants within brain-related CRS that confer greater susceptibility, or resilience, to
environmental stressors. By assessing environmental interactions during development, we will look at
contributions to risk that precede later-life traumatic experiences or symptom presentations. This research will
take place under the Mount Sinai Neuroscience Training Program. Mount Sinai's Department of Neuroscience
currently ranks 2nd nationally in NIH funding. Nearly 5,000 ft2 of space are allocated to the Training Program
within the Neuroscience Department and Friedman Brain Institute. There is, additionally, ~100,000 ft2 that
house the Department's and the lnstitute's research programs and 4,500 ft2 dedicated to Institutional CORE
facilities. This fellowship would support my journey to become a multidisciplinary, translational principal
inves...

## Key facts

- **NIH application ID:** 10465773
- **Project number:** 1F31MH130122-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Kayla Retallick-Townsley
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,592
- **Award type:** 1
- **Project period:** 2022-04-11 → 2025-04-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465773

## Citation

> US National Institutes of Health, RePORTER application 10465773, Massively Parallel Characterization of Cell-type and Context Specific Regulatory Risk Elements Across Psychiatric Disorders in a Stem Cell Model of Neurodevelopment (1F31MH130122-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10465773. Licensed CC0.

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