# Subtyping the autisms using individualized protein network analysis

> **NIH NIH R01** · SEATTLE CHILDREN'S HOSPITAL · 2020 · $717,506

## Abstract

PROJECT SUMMARY
Autism is a behaviorally-defined diagnosis that affects approximately 1 in 59 children in the US. Recent genetic
studies have revealed that autism is an umbrella term for a large family of individually rare, collectively
common genetic disorders, with each individual gene accounting for only a small portion of total cases. This
presents a problem for researchers attempting to develop biologically-based drug or treatment strategies: the
autism diagnostic entity is too broad to be biologically meaningful, but most individual genetic mutations are too
rare to allow for sufficient patient recruitment or for commercially viable drug development. There is an urgent
unmet need to develop a subtyping strategy that can assign patients into one of a small number of biologically
meaningful subtypes that might be amenable to targeted treatment strategies. We have recently developed a
novel proteomic strategy that makes high-dimensional measurements of protein-protein interaction networks
(PINs). These measures reflect several relevant features of autism pathogenesis- synaptic content, recent
activity, and developmental stage of the neuron. We postulate that different genetic autisms converge on two
specific PINs and produce patterns of network disruption that, while individually unique, share common
features that will allow clustering of PIN matrices into subtypes. Importantly, our clustering methods allow
identification of specific signal transduction nodes that define each sub-type, linking biologically-relevant
information with our proposed clusters. In published proof-of-concept work, we were able to cluster seven
different mouse models and make predictions about previously unknown molecular pathologies. Here, we
propose to extend this work to human neurons, using primary patient cells taken from genetically sequenced
autistic research subjects with identified likely causative genetic mutations, or `idiopathic' autism patients who
were sequenced but no mutation was identified, or age-and-sex-matched typically developing controls. This
work will reveal new, biologically relevant relationships between autisms of different known and unknown
genetic etiologies, and offers the opportunity to simultaneously identify sub-groups of patients and potential
drug targets that may effectively treat each identified sub-group.

## Key facts

- **NIH application ID:** 10049531
- **Project number:** 1R01MH121487-01A1
- **Recipient organization:** SEATTLE CHILDREN'S HOSPITAL
- **Principal Investigator:** Stephen Edward Paucha Smith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $717,506
- **Award type:** 1
- **Project period:** 2020-07-09 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10049531

## Citation

> US National Institutes of Health, RePORTER application 10049531, Subtyping the autisms using individualized protein network analysis (1R01MH121487-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10049531. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*