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Hafler, B. P.

Publications and source records attributed to Hafler, B. P..

2 recordsLinked to original sources

A painless nerve growth factor variant uncouples nociceptive and neurotrophic TrkA signaling

Nerve growth factor (NGF) binding to the receptor tyrosine kinase, TrkA, drives neurotrophic signaling essential for neuronal development and survival. This interaction simultaneously drives peripheral pain, making this pathway an attractive but complicated therapeutic target for chronic pain. By integrating single-molecule microscopy, structural and electrophysiology analyses, with a human NGF variant, NGFpainless, which retains neurotrophic effects but abolishes pain, we delineate the molecular mechanisms that bias TrkA signaling towards neurotrophic functions without triggering nociception. We show that, unlike wild-type NGF, NGFpainless fails to sensitize TRPV1 channels to capsaicin, thus disengaging TrkA from the nociceptive pathway. We further show that this selective loss of nociceptive TrkA signaling by NGFpainless results from its reduced ability to activate PLC{gamma}1 and trigger calcium release compared to NGF, while still preserving the ERK and AKT signaling essential for neurotrophic functions. This biased signaling arises from reduced electrostatic complementarity at the TrkA:NGFpainless complex interface, which shortens the lifetime of this functional complex on native membranes. Mutations in TrkA that restore the electrostatic complementarity at the TrkA:NGFpainless interface eliminate biased signaling. This mechanistic understanding of TrkA binding by NGFpainless, and how it differs from NGF, will spur the development of two therapeutic classes of molecules - one that selectively suppresses nociceptive signaling while preserving neurotrophic functions in chronic pain, and another that enhances neurotrophic activity without evoking peripheral pain in neurodegenerative conditions.

neuroscience↗

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

1One Sentence SummaryA novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimers disease and multiple sclerosis. 2 AbstractNeurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimers disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called diffusion condensation. By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

neuroscience↗