cAMP signaling affects age-associated deterioration of pacemaker beating interval dynamics.

Sinoatrial node (SAN) beating interval variability (BIV) and the average beating interval (BI) are regulated by a coupled-clock system, driven by Ca2+-calmodulin activated adenylyl cyclase, cAMP, and downstream PKA signaling. Reduced responsiveness of the BI and BIV to submaximal, [X]50, ß-adrenergic receptor (ß-AR) stimulation, and phosphodiesterase inhibition (PDEI) have been documented in aged SAN tissue, whereas the maximal responses, [X]max, do not differ by age. To determine whether age-associated dysfunction in cAMP signaling leads to altered responsiveness of BI and BIV, we measured cAMP levels and BI in adult (2-4 months n = 27) and aged (22-26 months n = 25) C57/BL6 mouse SAN tissue in control and in response to ß-AR or PDEI at X50 and [X]max. Both cAMP and average BI in adult SAN were reduced at X50, whereas cAMP and BI at Xmax did not differ by age. cAMP levels and average BI were correlated both within and between adult and aged SAN. BIV parameters in long- and short-range terms were correlated with cAMP levels for adult SAN. However, due to reduced cAMP within aged tissues at [X]50, these correlations were diminished in advanced age. Thus, cAMP level generated by the coupled clock mechanisms is tightly linked to average BI. Reduced cAMP level at X50 in aged SAN explains the reduced responsiveness of the BI and BIV to ß-AR stimulation and PDEI.

Changes in cAMP signaling are associated with age-related downregulation of spontaneously beating atrial tissue energetic indices.

The prevalence of atria-related diseases increases exponentially with age and is associated with ATP supply-to-demand imbalances. Because evidence suggests that cAMP regulates ATP supply-to-demand, we explored aged-associated alterations in atrial ATP supply-to-demand balance and its correlation with cAMP levels. Right atrial tissues driven by spontaneous sinoatrial node impulses were isolated from aged (22-26 months) and adult (3-4 months) C57/BL6 mice. ATP demand increased by addition of isoproterenol or 3-Isobutyl-1-methylxanthine (IBMX) and decreased by application of carbachol. Each drug was administrated at the dose that led to a maximal change in beating rate (Xmax) and to 50% of that maximal change in adult tissue (X50). cAMP, NADH, NAD + NADH, and ATP levels were measured in the same tissue. The tight correlation between cAMP levels and the beating rate (i.e., the ATP demand) demonstrated in adult atria was altered in aged atria. cAMP levels were lower in aged compared to adult atrial tissue exposed to X50 of ISO or IBMX, but this difference narrowed at Xmax. Neither ATP nor NADH levels correlated with ATP demand in either adult or aged atria. Baseline NADH levels were lower in aged as compared to adult atria, but were restored by drug perturbations that increased cAMP levels. Reduction in Ca2+-activated adenylyl cyclase-induced decreased cAMP and prolongation of the spontaneous beat interval of adult atrial tissue to their baseline levels in aged tissue, brought energetics indices to baseline levels in aged tissue. Thus, cAMP regulates right atrial ATP supply-to-demand matching and can restore age-associated ATP supply-to-demand imbalance.

Beating Rate Variability of Isolated Mammal Sinoatrial Node Tissue: Insight Into Its Contribution to Heart Rate Variability.

BACKGROUND: Because of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. The in situ approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart's primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings. METHODS: We used EGM recordings obtained from control SAN tissues of rabbits (n = 9) and mice (n = 30) and from mouse SAN tissues (n = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse. RESULTS: The beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of in vivo recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for in vivo analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under in situ conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/). CONCLUSION: Our approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under in vivo conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV in vivo.

PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data.

Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access. Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo. Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F 1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species. Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo.com.

A Universal Scaling Relation for Defining Power Spectral Bands in Mammalian Heart Rate Variability Analysis.

Background: Power spectral density (PSD) analysis of the heartbeat intervals in the three main frequency bands [very low frequency (VLF), low frequency (LF), and high frequency (HF)] provides a quantitative non-invasive tool for assessing the function of the cardiovascular control system. In humans, these frequency bands were standardized following years of empirical evidence. However, no quantitative approach has justified the frequency cutoffs of these bands and how they might be adapted to other mammals. Defining mammal-specific frequency bands is necessary if the PSD analysis of the HR is to be used as a proxy for measuring the autonomic nervous system activity in animal models. Methods: We first describe the distribution of prominent frequency peaks found in the normalized PSD of mammalian data using a Gaussian mixture model while assuming three components corresponding to the traditional VLF, LF and HF bands. We trained the algorithm on a database of human electrocardiogram recordings (n = 18) and validated it on databases of dogs (n = 17) and mice (n = 8). Finally, we tested it to predict the bands for rabbits (n = 4) for the first time. Results: Double-logarithmic analysis demonstrates a scaling law between the GMM-identified cutoff frequencies and the typical heart rate (HRm): fVLF-LF = 0.0037⋅ HRm0.58 , fLF-HF = 0.0017⋅ HRm1.01 and fHFup = 0.0128⋅ HRm0.86 . We found that the band cutoff frequencies and Gaussian mean scale with a power law of 1/4 or 1/8 of the typical body mass (BMm), thus revealing allometric power laws. Conclusion: Our automated data-driven approach allowed us to define the frequency bands in PSD analysis of beat-to-beat time series from different mammals. The scaling law between the band frequency cutoffs and the HRm can be used to approximate the PSD bands in other mammals.