Mapping Molecular Changes in Human Neuropsychiatric Disorders to Zebrafish Behavioral Profiles

Despite decades of work, understanding the etiology of human psychiatric disorders has remained elusive. Recent advances in molecular profiling have allowed researchers to probe genetic and transcriptomic differences in post-mortem samples from individuals with healthy and diseased brains, which has provided some hints into the underlying biological dysregulation in these disorders. However, linking these molecular changes to alterations in behavior and developing therapies to ameliorate the effects of disease will require animal models, including zebrafish, which are unique as vertebrate models with complex behavioral traits that also allow for high-throughput perturbations. How to bridge the gap between molecular profiling data from omics experiments in human post-mortem samples and behavioral data from high-throughput drug screens in zebrafish remains an outstanding challenge. Here, we develop a computational method for cross-species translation of these disparate kinds of data, in which we construct a shared latent space of the human and zebrafish data for downstream multivariate analysis. Applying this method to a microarray dataset profiling transcriptional changes in the prefrontal cortex in human schizophrenia, we identify gene modules with coordinated effects on zebrafish behavior that are discriminative of schizophrenia in humans. In particular, we identify zebrafish gene modules involved in amino acid, neurotransmitter, and cation transport that are also dysregulated in human schizophrenia. These results suggest that such computational models for cross-species translation are promising tools for integrating molecular data from human post-mortem samples and behavioral drug screens in zebrafish.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Stein, Daniel J.

Advisor dc:contributor.advisor

• Lauffenburger, Douglas A.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144788

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144788