Circadian rhythms are ubiquitous, and most of the organs in the body contain circadian oscillators. The intestine comprises diverse cell types with distinct developmental origins and physiological functions. However, which intestinal cells contain cell-autonomous circadian oscillators and how circadian oscillators in distinct intestinal cell types synchronize with one another and with environmental daily cycles remain poorly understood. To address these questions, we generated a Cre-dependent PER2::LUCIFERASE reporter knock-in mouse that enables ex vivo measurement of circadian oscillations in defined cell populations. Ex vivo gut explants from mice expressing the reporter in one of five major cell types of the muscularis externa—enteric neurons (ENs), enteric glial cells (EGCs), interstitial cells of Cajal (ICCs), smooth muscle cells (SMCs), and muscularis macrophages (MMs)—exhibited robust, self-sustained circadian bioluminescence rhythms, demonstrating that each of these cell types harbors a cell-autonomous circadian oscillator. Importantly, circadian oscillators in ENs, EGCs, SMCs, and MMs entrained to feeding-fasting cycles, whereas circadian oscillators in ICCs remained resistant to food entrainment. These findings reveal that distinct intestinal cell types possess unique entrainment properties, and feeding during inactive time induces heterogeneous phase shifts across gut circadian oscillators, leading to temporal circadian misalignment within the intestine. As circadian disruptions, such as those associated with shift work, contribute to intestinal disorders, including inflammatory bowel disease and disorders of the gut–brain interaction, studying how intercellular circadian desynchrony influences intestinal homeostasis should provide chronobiology-based therapeutic strategies to combat these diseases.
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Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.
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- Award ID(s):
- 1763070
- PAR ID:
- 10107543
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 115
- Issue:
- 37
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- 9300 to 9305
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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