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Understanding the neurochemical composition of the enteric nervous system (ENS) is critical for elucidating neurological function in the gastrointestinal (GI) tract in health and disease. Despite their status as the closest models of human neurological systems, relatively little is known about enteric neurochemistry in nonhuman primates. We describe neurochemical coding of the enteric nervous system, specifically the myenteric plexus, of the rhesus monkey (Macaca mulatta) by immunohistochemistry and directly compare it to human tissues. There are considerable differences in the myenteric plexus along different segments of the monkey GI tract. While acetylcholine neurons make up the majority of myenteric neurons in the stomach (70%), they are a minority in the rectum (47%). Conversely, only 22% of gastric myenteric neurons express nitric oxide synthase (NOS) compared to 52% in the rectum. Vasoactive intestinal peptide (VIP) is more prominent in the stomach (37%) versus the rest of the GI tract (≈10%), and catecholamine neurons are rare (≈1%). There is significant coexpression of NOS and VIP in myenteric neurons that is more prominent in the proximal GI tract. Taken as a whole, these data provide insight into the neurochemical anatomy underlying GI motility. While overall similarity to other mammalian species is clear, there are some notable differences between the ENS of rhesus monkeys, humans, and other species that will be important to take into account when evaluating models of human diseases in animals.
Pubmed ID: 21618236 RIS Download
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View all literature mentionsNEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
View all literature mentionsThis monoclonal targets HuC/HuD
View all literature mentionsThis polyclonal targets VIP (H-16)
View all literature mentionsThis monoclonal targets nNOS / NOS Type I
View all literature mentionsThis polyclonal targets Nitric Oxide Sythase I (NOS-I, bNOS, nNOS)
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