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Bennett, D. F.

Publications and source records attributed to Bennett, D. F..

2 recordsLinked to original sources

Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders

The hypothalamus, composed of several nuclei, is essential for maintaining our bodys homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.

developmental biology↗

Rilmenidine mimics caloric restriction via the nischarin I1-imidazoline receptor to extend lifespan in C. elegans

Caloric restriction increases lifespan across species and has health benefits in humans. Because complying with a low-calorie diet is challenging, here we investigated pharmacological interventions mimicking the benefits of caloric restriction. Searching for compounds that elicit a similar gene expression signature to caloric restriction, we identified rilmenidine, an I1-imidazoline receptor agonist and prescription medication for the treatment of hypertension. We then show that treating C. elegans with rilmenidine at young and older ages increases lifespan. We also demonstrate that the stress-resilience, healthspan, and lifespan benefits upon rilmenidine treatment in worms are mediated by the I1-imidazoline receptor nish-1, implicating this receptor as a potential longevity target. Furthermore, we show that rilmenidine treatment increased ERK phosphorylation via NISH-1. Consistent with the shared caloric-restriction-mimicking gene signature, supplementing rilmenidine to caloric restricted C. elegans, genetic reduction of TORC1 function, or rapamycin treatment did not further increase lifespan. The rilmenidine-induced longevity required the transcription factors FOXO/DAF-16 and NRF1,2,3/SKN-1, both important for caloric restriction-mediated longevity. Furthermore, we find that autophagy, but not AMPK signaling, was needed for rilmenidine-induced longevity. Lastly, we find that treating mice with rilmenidine showed transcriptional changes in liver and kidney similar to caloric restriction. Overall, our findings reveal rilmenidine as a caloric restriction mimetic and as a novel geroprotective compound.

pharmacology and toxicology↗