Identifying KIF1B as a Potential Disease Driver for Charcot Marie Tooth Disease through Phenomics-Driven Prioritization


Pre-Clinical Research

Poster Number: 283


Mikalai Malinouski, PhD, Biosymetrics, Victoria Catterson, PhD, Biosymetrics, Shahrzad Hosseini, PhD, Biosymetrics, David Kokel, PhD, Biosymetrics, Gabriel Musso, PhD, Biosymetrics

Charcot Marie Tooth (CMT) disease is a group of inherited neuropathies that affect the peripheral nervous system, resulting in muscle weakness and sensory loss. In this study, we utilized a phenomics-driven approach to further phenogroup subclusters of CMT types in order to identify novel targets for precision medicines. By prioritizing genes based on genetic evidence and high phenotypic probability scores for peripheral neuropathy, Phenograph AI allowed the identification and prioritization of KIF1B as a disease driver for the peripheral neuropathy phenotype and thus may work for multiple types of CMT. These data suggest that KIF1b, a motor protein, may be useful as a therapeutic target for CMT. Zebrafish genes were mapped to phenotypic terms using over 300 individually trained machine learning models. Phenograph prediction scores were calculated for the “peripheral neuropathy” term and each subtype of CMT2 and CMT4. Aggregate scores were calculated using 12 phenotypic terms – neuron, CNS, PNS, efferent neuron, motor neuron, sensory neuron, muscle, brain, skeletal muscle, muscle cells, spinal cord, and nerve. 11 genes were nominated to receive domain expert scores (tractability, pathway analysis, inheritance, preclinical evidence). The Phenograph allowed the mapping of human genes and phenotypes to pre-clinical model systems, virtual phenotypic screening, target selection, and validation.