Architecture of the rat nephron-arterial network: analysis with micro computed tomography

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Among solid organs the kidney's vascular network stands out because each nephron has 2 distinct capillary structures in series, and because tubuloglomerular feedback (TGF), one of the mechanisms responsible for blood flow autoregulation, is specific to renal tubules. TGF and the myogenic mechanism, acting jointly, autoregulate single nephron blood flow. Each generates a self-sustained periodic oscillation and an oscillating electrical signal that propagates upstream along arterioles. Similar electrical signals from other nephrons interact, allowing nephron synchronization. Experimental measurements show synchronization over fields of a few nephrons; simulations based on a simplified network structure that could obscure complex interactions predict more widespread synchronization. To permit more realistic simulations we made a cast of blood vessels in a rat kidney, performed micro-computed tomography at 2.5 μm resolution, and recorded 3-dimensional coordinates of arteries, afferent arterioles, and glomeruli. Non-terminal branches of arcuate arteries form tree-like structures requiring from 2 to 6 bifurcations to reach terminal branches at the tree tops. Terminal arterial structures were either paired branches at tops of arterial trees, from which 52.6 % of all afferent arterioles originated, or unpaired arteries not at tree tops yielding the other 22.9 % . The other 24.5 % originated directly from non-terminal arteries. Afferent arterioles near the cortico-medullary boundary were longer than those farther away, suggesting juxtamedullary nephrons have longer afferent arterioles. The distance separating origins of pairs of afferent arterioles varied randomly. The results suggest an irregular network tree structure with vascular nodes where arteriolar activity and local blood pressure interact.

TidsskriftAmerican Journal of Physiology: Renal Physiology
Udgave nummer2
Sider (fra-til)F351-F360
Antal sider10
StatusUdgivet - aug. 2017

ID: 176771282