Solving Medical Mysteries
Through Team Science

Model Organisms


The Model Organisms Screening Center (MOSC) for the Undiagnosed Diseases Network (UDN) is a collaborative center with investigators from Baylor College of Medicine (BCM) and University of Oregon (UO). The MOSC allows for world-renowned experts in Drosophila (fruit fly) and Zebrafish genetics and biology to tackle undiagnosed diseases. By combining state-of-the-art genetic and genomic technologies, the MOSC investigates whether a rare variant identified in the genomes of UDN participants may contribute to disease pathogenesis. The MOSC is led by the Principal Investigator (PI) Hugo J. Bellen, DVM, PhD (BCM). The Drosophila Core is led by Co-PIs Michael F. Wangler, MD, and Shinya Yamamoto, DVM, PhD (BCM). The Zebrafish Core is led by Co-PIs Monte Westerfield, PhD, and John Postlethwait, PhD (UO).

Why Drosophila and Zebrafish?

Over the past century, genetic model organisms have taught us so much about human biology and disease mechanisms. Although these organisms (e.g. yeast, nematode worm, fly, zebrafish, mouse) may look very different from us, fundamental biological mechanisms and genes are well conserved throughout evolution. To investigate hundreds of rare variants found through sequencing UDN participants’ and their family members’ genomes, the MOSC uses two model organisms, fruit fly (Drosophila melanogaster) and Zebrafish (Danio rerio). These animals are cost efficient, have short life-cycles and amenable to sophisticated genetic manipulations to “model” a human disease condition. Flies and Zebrafish are complementary to one another, providing a synergism. Candidate genes and variants that are shown to be functional can be further pursued in mammalian model systems such as the mouse for further translational studies.

MOSC Workflow

When a diagnosis is not reached after performing a thorough clinical, genetic and/or metabolomic workup, the UDN Clinical Sites submit candidate gene(s)/variant(s) to the MOSC together with a brief description of the participant’s condition. The MOSC then performs a database search using the MARRVEL tool (marrvel.org) to aggregate existing information on the human gene/variant and its model organism orthologs. The MOSC also tries to identify other individuals with similar genotype and phenotype in other cohorts, a practice known as “matchmaking”. Once a variant is considered to be a high priority candidate, experiments to assess gene and variant function are designed by MOSC investigators and pursued in the fly core or the Zebrafish Core.


In collaboration with Drs. Zhandong Liu’s (BCM) and Norbert Perrimon’s (Harvard Medical School), the MOSC developed an online tool that allow anyone to quickly gather gene and variant function information in humans as well as seven key genetic model organisms. MARRVEL is a novel tool that integrates human and model organism databases to facilitate molecular diagnosis. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration), is publicly available for researchers worldwide at marrvel.org.

MOSC Publications

Research Articles

Functional variants in TBX2 are associated with a syndromic cardiovascular and skeletal developmental disorder

Biallelic Mutations in ATP5F1D, which Encodes a Subunit of ATP Synthase, Cause a Metabolic Disorder

Clinically severe CACNA1A alleles affect synaptic function and neurodegeneration differentially

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

A Syndromic Neurodevelopmental Disorder Caused by De Novo Variants in EBF3

A Recurrent De Novo Variant in NACC1 Causes a Syndrome Characterized by Infantile Epilepsy, Cataracts, and Profound Developmental Delay

Review Articles

Fruit flies in biomedical research

Morgan’s legacy: fruit flies and the functional annotation of conserved genes 

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research

Model Organisms Facilitate Rare Disease Diagnosis and Therapeutic Research


RFA: RFA-RM-14-016

Grant #: 1U54NS093793-01