A Robust Method for Detection of Linear and Nonlinear Interactions: Application to Renal Blood Flow Dynamics

The nephron-arterial network and its interactions

Tubuloglomerular feedback and the myogenic mechanism form an ensemble in renal afferent arterioles that regulate single-nephron blood flow and glomerular filtration. Each mechanism generates a self-sustained oscillation, the mechanisms interact, and the oscillations synchronize. The synchronization generates a bimodal electrical signal in the arteriolar wall that propagates retrograde to a vascular node, where it meets similar electrical signals from other nephrons. Each signal carries information about the time-dependent behavior of the regulatory ensemble. The converging signals support synchronization of the nephrons participating in the information exchange, and the synchronization can lead to formation of nephron clusters. We review the experimental evidence and the theoretical implications of these interactions and consider additional interactions that can limit the size of nephron clusters. The architecture of the arterial tree figures prominently in these interactions.

Renal autoregulation in health and disease

Intrarenal autoregulatory mechanisms maintain renal blood flow (RBF) and glomerular filtration rate (GFR) independent of renal perfusion pressure (RPP) over a defined range (80-180 mmHg). Such autoregulation is mediated largely by the myogenic and the macula densa-tubuloglomerular feedback (MD-TGF) responses that regulate preglomerular vasomotor tone primarily of the afferent arteriole. Differences in response times allow separation of these mechanisms in the time and frequency domains. Mechanotransduction initiating the myogenic response requires a sensing mechanism activated by stretch of vascular smooth muscle cells (VSMCs) and coupled to intracellular signaling pathways eliciting plasma membrane depolarization and a rise in cytosolic free calcium concentration ([Ca(2+)]i). Proposed mechanosensors include epithelial sodium channels (ENaC), integrins, and/or transient receptor potential (TRP) channels. Increased [Ca(2+)]i occurs predominantly by Ca(2+) influx through L-type voltage-operated Ca(2+) channels (VOCC). Increased [Ca(2+)]i activates inositol trisphosphate receptors (IP3R) and ryanodine receptors (RyR) to mobilize Ca(2+) from sarcoplasmic reticular stores. Myogenic vasoconstriction is sustained by increased Ca(2+) sensitivity, mediated by protein kinase C and Rho/Rho-kinase that favors a positive balance between myosin light-chain kinase and phosphatase. Increased RPP activates MD-TGF by transducing a signal of epithelial MD salt reabsorption to adjust afferent arteriolar vasoconstriction. A combination of vascular and tubular mechanisms, novel to the kidney, provides for high autoregulatory efficiency that maintains RBF and GFR, stabilizes sodium excretion, and buffers transmission of RPP to sensitive glomerular capillaries, thereby protecting against hypertensive barotrauma. A unique aspect of the myogenic response in the renal vasculature is modulation of its strength and speed by the MD-TGF and by a connecting tubule glomerular feedback (CT-GF) mechanism. Reactive oxygen species and nitric oxide are modulators of myogenic and MD-TGF mechanisms. Attenuated renal autoregulation contributes to renal damage in many, but not all, models of renal, diabetic, and hypertensive diseases. This review provides a summary of our current knowledge regarding underlying mechanisms enabling renal autoregulation in health and disease and methods used for its study.

Nephron blood flow dynamics measured by laser speckle contrast imaging

Tubuloglomerular feedback (TGF) has an important role in autoregulation of renal blood flow and glomerular filtration rate (GFR). Because of the characteristics of signal transmission in the feedback loop, the TGF undergoes self-sustained oscillations in single-nephron blood flow, GFR, and tubular pressure and flow. Nephrons interact by exchanging electrical signals conducted electrotonically through cells of the vascular wall, leading to synchronization of the TGF-mediated oscillations. Experimental studies of these interactions have been limited to observations on two or at most three nephrons simultaneously. The interacting nephron fields are likely to be more extensive. We have turned to laser speckle contrast imaging to measure the blood flow dynamics of 50-100 nephrons simultaneously on the renal surface of anesthetized rats. We report the application of this method and describe analytic techniques for extracting the desired data and for examining them for evidence of nephron synchronization. Synchronized TGF oscillations were detected in pairs or triplets of nephrons. The amplitude and the frequency of the oscillations changed with time, as did the patterns of synchronization. Synchronization may take place among nephrons not immediately adjacent on the surface of the kidney.

A statistical model and simulator for cardiovascular pressure signals

Physiological signal simulators are often used to conduct validation studies of commercially available devices such as oscillometric non-invasive blood pressure (NIBP) monitors. Numerous assessment studies have been conducted using simulators to validate commercial NIBP monitors. While there are several simulators commercially available to evaluate oscillometric NIBP devices, currently there are no simulators designed to validate invasive pressure signal devices. A statistical model and simulator for invasive cardiovascular pressure signals such as arterial blood pressure and intracranial pressure are described. The model incorporates the effects of respiration on pressure signals and can be used to generate synthetic signals with time and frequency domain characteristics matching any desired subject population. Additionally, the way that noise and artefacts typically present in real pressure signals should be modelled is described. The proposed statistical model is a useful tool for validation of algorithms designed to process or analyse biomedical pressure signals to estimate parameters of clinical interest such as the cardiac frequency, heart rate variability, respiratory frequency, and pulse pressure variation in the presence of noise. The model can be used to simulate signals in order to validate commercial devices that process and analyse invasive pressure signals.

Renal blood flow and dynamic autoregulation in conscious mice

Autoregulation of renal blood flow (RBF) occurs via myogenic and tubuloglomerular feedback (TGF) mechanisms that are engaged by pressure changes within preglomerular arteries and by tubular flow and content, respectively. Our understanding of autoregulatory function in the kidney largely stems from experiments in anesthetized animals where renal perfusion pressure is precisely controlled. However, normally occurring variations in blood pressure are sufficient to engage both myogenic and TGF mechanisms, making the assessment of autoregulatory function in conscious animals of significant value. To our knowledge, no studies have evaluated the dynamics of RBF in conscious mice. Therefore, we used spectral analysis of blood pressure and RBF and identified dynamic operational characteristics of the myogenic and TGF mechanisms in conscious, freely moving mice instrumented with ultrasound flow probes and arterial catheters. The myogenic response generates a distinct resonance peak in transfer gain at 0.31 +/- 0.01 Hz. Myogenic-dependent attenuation of RBF oscillations, indicative of active autoregulation, is apparent as a trough in gain below 0.3 Hz (-6.5 +/- 1.3 dB) and a strong positive phase peak (93 +/- 9 deg), which are abolished by amlodipine infusion. Operation of TGF produces a local maximum in gain at 0.05 +/- 0.01 Hz and a positive phase peak (62.3 +/- 12.3 deg), both of which are eliminated by infusion of furosemide. Administration of amlodipine eliminated both myogenic and TGF signature peaks, whereas furosemide shifted the myogenic phase peak to a slower operational frequency. These data indicate that myogenic and TGF dynamics may be used to investigate the effectiveness of renal autoregulatory mechanisms in conscious mice.

Mechanisms of renal blood flow autoregulation: dynamics and contributions

Autoregulation of renal blood flow (RBF) is caused by the myogenic response (MR), tubuloglomerular feedback (TGF), and a third regulatory mechanism that is independent of TGF but slower than MR. The underlying cause of the third regulatory mechanism remains unclear; possibilities include ATP, ANG II, or a slow component of MR. Other mechanisms, which, however, exert their action through modulation of MR and TGF are pressure-dependent change of proximal tubular reabsorption, resetting of RBF and TGF, as well as modulating influences of ANG II and nitric oxide (NO). MR requires < 10 s for completion in the kidney and normally follows first-order kinetics without rate-sensitive components. TGF takes 30-60 s and shows spontaneous oscillations at 0.025-0.033 Hz. The third regulatory component requires 30-60 s; changes in proximal tubular reabsorption develop over 5 min and more slowly for up to 30 min, while RBF and TGF resetting stretch out over 20-60 min. Due to these kinetic differences, the relative contribution of the autoregulatory mechanisms determines the amount and spectrum of pressure fluctuations reaching glomerular and postglomerular capillaries and thereby potentially impinge on filtration, reabsorption, medullary perfusion, and hypertensive renal damage. Under resting conditions, MR contributes approximately 50% to overall RBF autoregulation, TGF 35-50%, and the third mechanism < 15%. NO attenuates the strength, speed, and contribution of MR, whereas ANG II does not modify the balance of the autoregulatory mechanisms.