Biology subjects

Jacobson, M. P.

Publications and source records attributed to Jacobson, M. P..

2 recordsLinked to original sources

bioRxiv2018

Covalent Modification and Regulation of the Nuclear Receptor Nurr1 by a Dopamine Metabolite

Nurr1, a nuclear receptor essential for the development, maintenance, and survival of midbrain dopaminergic neurons, is a potential therapeutic target for Parkinsons disease, a neurological disorder characterized by the degeneration of these same neurons. Efforts to identify Nurr1 agonists have been hampered by the recognition that it lacks several classic regulatory elements of nuclear receptor function, including the canonical ligand-binding pocket. Here we report that the dopamine metabolite 5,6-dihydroxyindole (DHI) binds directly to and modulates the activity of Nurr1. Using biophysical assays and x-ray crystallography we show that DHI binds to the ligand binding domain within a non-canonical pocket, forming a covalent adduct with Cys566. In cultured cells and zebrafish, DHI stimulates Nurr1 activity, including the transcription of target genes underlying dopamine homeostasis. These findings suggest avenues for developing synthetic Nurr1 ligands to ameliorate the symptoms and progression of Parkinsons disease.

biophysics↗

bioRxiv2017

Prominent Features Of The Amino Acid Mutation Landscape In Cancer

Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.

bioinformatics↗