Gene Discovery

Hunting for New Therapies

There is a treasure trove of valuable information in both the mapped and unmapped reads of the genome.

Gene Discovery involves identifying novel genes implicated in causing rare disease, developing methods to identify patient’s predisposition to a rare disease and building the knowledge base to improve clinical management of novel genetic disease.

At RCIGM, this work is led by Matthew Bainbridge, PhD, Assistant Director of Translational Research. His team develops novel analysis techniques to squeeze every last bit of information from WGS and to attempt to identify uncommon disease mechanisms (such as ALU insertions and deep intronic mutations) in the pediatric patient population. 

Bioinformatic analysis of Whole Genome Sequencing (WGS) data is used to gain a better understanding of the mechanisms by which pathogenic genomic variants contribute to the development of rare diseases.

Traditional wet-lab modeling of novel diseases is used to functionalize variants of uncertain significance.

Research Projects

Several grant funded research projects are currently under Dr. Bainbridge’s direction:

  • Oligogenic Models of Cardiomyopathy
    The goal is to identify synergistic and modifier mutations that impact structural cardiomyopathies.  Bioinformatically identified variants are prioritized and then functionally tested by Dr. Neil Chi at UC San Diego. Learn More

Matthew Bainbridge, PhD

RCIGM Assistant Director of Translational Research

Publications

Brain. 2022 Dec 8:awac461. doi: 10.1093/brain/awac461. Online ahead of print.

NO ABSTRACT

PMID:36477332 DOI:10.1093/brain/awac461

Am J Med Genet A. 2021 Nov 26. doi: 10.1002/ajmg.a.62574. Online ahead of print.

ABSTRACT

Pegvaliase is approved to reduce phenylalanine (Phe) levels for people with phenylketonuria (PKU). PRISM-1 (NCT01819727) and PRISM-2 (NCT01889862) data were analyzed to evaluate the relationship between Phe and inattention in adult participants with PKU. In the modified-intent-to-treat population (N = 156), baseline mean (SE) plasma Phe was 1263 (29) μmol/L and the Attention Deficit Hyperactivity Disorder Rating Scale-IV Inattentive (IA) symptoms score was 9.8 (0.5). Mean (SE) IA scores fell 9.0 (1.1) in Quartile 1 (Phe reduction between 1166 and 2229 μmol/L) versus 4.3 (0.7) in Quartile 4 (Phe reduction of 139 μmol/L to increase of 934 μmol/L), p = 0.004. Least squares mean (SE) change from baseline IA score was -7.9 (0.7) for participants with final Phe ≤ 360 μmol/L and -4.5 (0.7) for final Phe > 360 μmol/L, p < 0.001. In the inattention subgroup, IA scores fell 13.3 (1.5) in Quartile 1 (Phe reduction between 1288 and 2229 μmol/L) versus 6.2 (1.3) in Quartile 4 (Phe reduction of 247 to increase of 934 μmol/L), p = 0.009. Inattention symptoms improved among those whose Phe levels decreased, particularly those with high baseline IA scores. IA improvements were larger among participants with the greatest plasma Phe reductions, supporting this value as a therapeutic goal.

PMID:34826353 | DOI:10.1002/ajmg.a.62574

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