Existence of long-lasting experience-dependent plasticity in endocrine cell networks

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Abstract

Experience-dependent plasticity of cell and tissue function is critical for survival by allowing organisms to dynamically adjust physiological processes in response to changing or harsh environmental conditions. Despite the conferred evolutionary advantage, it remains unknown whether emergent experience-dependent properties are present in cell populations organized as networks within endocrine tissues involved in regulating body-wide homeostasis. Here we show, using lactation to repeatedly activate a specific endocrine cell network in situ in the mammalian pituitary, that templates of prior demand are permanently stored through stimulus-evoked alterations to the extent and strength of cell–cell connectivity. Strikingly, following repeat stimulation, evolved population behaviour leads to improved tissue output. As such, long-lasting experience-dependent plasticity is an important feature of endocrine cell networks and underlies functional adaptation of hormone release.

Abstract

Experience-dependent plasticity and functional adaptation are thought to be restricted to the central nervous and immune systems. This study shows that long-lasting experience-dependent plasticity is a key feature of endocrine cell networks, allowing improved tissue function and hormone output following repeat demand.

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Scale-free networks: a decade and beyond.

Albert-László Barabási (2009)

For decades, we tacitly assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm. The decade-old discovery of scale-free networks was one of those events that had helped catalyze the emergence of network science, a new research field with its distinct set of challenges and accomplishments.

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Genetic enhancement of learning and memory in mice.

Ya-Ping Tang, Eiji Shimizu, Gilles Dubé … (1999)

Hebb's rule (1949) states that learning and memory are based on modifications of synaptic strength among neurons that are simultaneously active. This implies that enhanced synaptic coincidence detection would lead to better learning and memory. If the NMDA (N-methyl-D-aspartate) receptor, a synaptic coincidence detector, acts as a graded switch for memory formation, enhanced signal detection by NMDA receptors should enhance learning and memory. Here we show that overexpression of NMDA receptor 2B (NR2B) in the forebrains of transgenic mice leads to enhanced activation of NMDA receptors, facilitating synaptic potentiation in response to stimulation at 10-100 Hz. These mice exhibit superior ability in learning and memory in various behavioural tasks, showing that NR2B is critical in gating the age-dependent threshold for plasticity and memory formation. NMDA-receptor-dependent modifications of synaptic efficacy, therefore, represent a unifying mechanism for associative learning and memory. Our results suggest that genetic enhancement of mental and cognitive attributes such as intelligence and memory in mammals is feasible.

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Spontaneous evolution of modularity and network motifs.

Nadav Kashtan, Uri Alon (2005)

Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such "modularly varying goals" lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems.

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Author and article information

Journal

Journal ID (nlm-ta): Nat Commun

Title: Nature Communications

Publisher: Nature Pub. Group

ISSN (Electronic): 2041-1723

Publication date (Electronic): 03 January 2012

Volume: 3

Page: 605

Affiliations

[1 ]simpleCNRS, UMR-5203, Institut de Génomique Fonctionnelle , Montpellier F-34000, France.

[2 ]simpleINSERM , U661, Montpellier F-34000, France.

[3 ]UMR-5203, simpleUniversités de Montpellier 1 & 2 , Montpellier F-34000, France.

[4 ] simpleRoyal College of Surgeons in Ireland , Dublin 17, Ireland.

[5 ] simpleDepartment of Physiology, Anatomy and Genetics, University of Oxford , Oxford OX1 3QX, simpleUK .

[6 ] simpleDivision of Molecular Neuroendocrinology, MRC National Institute for Medical Research , Mill Hill, London NW7 1AA, UK.

Author notes

[a ] david.hodson@ 123456igf.cnrs.fr

[b ] patrice.mollard@ 123456igf.cnrs.fr

Article

Publisher Item ID: ncomms1612

DOI: 10.1038/ncomms1612

PMC ID: 3272579

PubMed ID: 22215080

SO-VID: e1829b73-79b1-4e8d-8244-20da1ca4c8ed

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This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

Existence of long-lasting experience-dependent plasticity in endocrine cell networks

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Animal Welfare

Most cited references 50

Scale-free networks: a decade and beyond.

Genetic enhancement of learning and memory in mice.

Spontaneous evolution of modularity and network motifs.

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