Real-time measurements of second messengers in living cells, such as cAMP, are usually performed by ratiometric fluorescence resonance energy transfer (FRET) imaging. However, correct calibration of FRET ratios, accurate calculations of absolute cAMP levels and actual permeabilities of different cAMP analogs have been challenging. Here we present a protocol that allows precise measurements of cAMP concentrations and kinetics by expressing FRET-based cAMP sensors in cells and modulating them with an inhibitor of adenylyl cyclase activity and a cell-permeable cAMP analog that fully inhibits and activates the sensors, respectively. Using this protocol, we observed different basal cAMP levels in primary mouse cardiomyocytes, thyroid cells and in 293A cells. The protocol can be generally applied for calibration of second messenger or metabolite concentrations measured by FRET, and for studying kinetics and pharmacological properties of their membrane-permeable analogs. The complete procedure, including cell preparation and FRET measurements, takes 3-6 d.

Background Atrial fibrillation is the most common serious abnormal heart rhythm, and a frequent cause of ischaemic stroke. Recent experimental studies, mainly in orchiectomised rats, report a relationship between sex hormones and atrial electrophysiology and electroanatomy. We aimed to evaluate whether low testosterone levels are predictive for atrial fibrillation and/or ischaemic stroke in men and women. Design and methods The serum total testosterone levels were measured at baseline in a population cohort of 7892 subjects (3876 male, 4016 female), aged 25-74 years, using a commercially available immunoassay. The main outcome measure was atrial fibrillation or ischaemic stroke, whichever came first. Results During a median follow-up of 13.8 years, a total of 629 subjects (8.0%) suffered from incident atrial fibrillation ( n = 426) and/or ischemic stroke ( n = 276). Cox regression analyses, adjusted for age (used as time-scale), geographical region, total cholesterol (log), high-density lipoprotein-cholesterol (log), hypertension medication, known diabetes, smoking status, waist-hip-ratio, and time of blood drawn, documented differential predictive value of low sex-specific testosterone levels for atrial fibrillation and/or ischaemic stroke, in men and in women: Increasing levels were associated with lower risk in men (hazard ratio per one nmol/l increase 0.98 (95% confidence interval 0.93-1.00); p = 0.049). On the other hand, increasing testosterone levels were associated with higher risk in women (hazard ratio per one nmol/l increase 1.17 (95% confidence interval 1.02-1.36); p = 0.031). Conclusion Our study indicates that low testosterone levels are associated with increased risk of future atrial fibrillation and/or ischaemic stroke in men, while they are protective in women.

Rationale: 3',5'-cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger which, upon β-adrenergic receptor (β-AR) stimulation, acts in microdomains to regulate cardiac excitation-contraction coupling by activating phosphorylation of calcium handling proteins. One crucial microdomain is in vicinity of the cardiac ryanodine receptor type 2 (RyR2) which is associated with arrhythmogenic diastolic calcium leak from the sarcoplasmic reticulum (SR) often occurring in heart failure. Objective: We sought to establish a real time live cell imaging approach capable of directly visualizing cAMP in the vicinity of mouse and human RyR2 and to analyze its pathological changes in failing cardiomyocytes under β-AR stimulation. Methods and Results: We generated a novel targeted fluorescent biosensor Epac1-JNC for RyR2-associated cAMP and expressed it in transgenic mouse hearts as well in human ventricular myocytes using adenoviral gene transfer. In healthy cardiomyocytes, β 1 -AR but not β 2 -AR stimulation strongly increased local RyR2-associated cAMP levels. However, already in cardiac hypertrophy induced by aortic banding, there was a marked subcellular redistribution of phosphodiesterases (PDEs) 2, 3 and 4, which included a dramatic loss of the local pool of PDE4. This was also accompanied by measurableβ2-AR/AMP signals in the vicinity of RyR2 in failing mouse and human myocytes, increased β2-AR-dependent RyR2 phosphorylation, SR calcium leak and arrhythmia susceptibility. Conclusions: Our new imaging approach could visualize cAMP levels in the direct vicinity of cardiac RyR2. Unexpectedly, in mouse and human failing myocytes, it could uncover functionally relevant local arrhythmogenic β2-AR/cAMP signals which might be an interesting antiarrhythmic target for heart failure.

Most studies reporting on the association of circulating testosterone levels with type 2 diabetes in men are of cross-sectional design. Reports on the relevance of altered testosterone levels in women are scarce. Here, we evaluate the role of low serum testosterone levels for incident diabetes in men and women in a population setting of 7706 subjects (3896 females). During a mean follow up time of 13.8 years, 7.8% developed type 2 diabetes. Significant correlations of testosterone with high density lipoprotein (HDL)-cholesterol (R = 0.21, p < 0.001), body-mass-index (R = −0.23, p < 0.001), and waist-to-hip-ratio (R = −0.21, p < 0.001) were found in men. No correlation was found with age in men; in women, the correlation was negligible (R = 0.04, p = 0.012). In men, low testosterone levels predicted high risk of type 2 diabetes, while in women this relationship was opposite. Men with low testosterone levels showed increased risk of future diabetes (hazard ratio (HR) 2.66, 95% confidence interval (CI) 1.91–3.72, p < 0.001 in basic model; HR 1.56 95%, CI 1.10–2.21, p = 0.003). In women, low testosterone levels indicated lower risk with (HR 0.53, 95% CI 0.37–0.77, p = 0.003), while the association lost significance in the fully adjusted model (HR 0.72, 95% CI 0.49–1.05, p = 0.09). Low levels of testosterone predicted future diabetes in men. A borderline opposite association was found in women.

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Background and Aims One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system. Methods We collected 1079 histopathology slides from 325 patients from three transplant centers in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross validation and by deploying it to three cohorts. Results For binary prediction (rejection yes/no) the mean Area Under the Receiver Operating Curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729 and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633 and 0.905 in the cross-validated experiment and 0.764, 0.597, 0.913, and 0.631, 0.633, 0.682, and 0.722, 0.601, 0.805 in the validation cohorts, respectively. The predictions of the AI ​​model were interpretable by human experts and highlighted plausible morphological patterns. Conclusions We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.

Rationale-Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger which, upon b-adrenergic receptor (b-AR) stimulation, acts in microdomains to regulate cardiac excitation-contraction coupling by activating the cAMP-dependent protein kinase (PKA) phosphorylation of calcium handling proteins. One crucial microdomain is in vicinity of the cardiac ryanodine receptor type 2 (RyR2) which is associated with arrhythmogenic diastolic calcium leak from the sarcoplasmic reticulum (SR) often occurring upon RyR2 hyperphosphorylation by PKA and calcium/calmodulin-dependent kinase.Objective-We sought to establish a real time approach capable of directly visualizing cAMP and its pathological changes in the vicinity of RyR2 by generating a proper targeted biosensor and transgenic mouse model to express it in adult cardiomyocytes.Methods and Results-We generated transgenic mice expressing a novel targeted fluorescent biosensor for RyR2-associated cAMP in adult mouse cardiomyocytes. In healthy cardiomyocytes, b1-AR but not b2-AR stimulation strongly increased local RyR2-associated cAMP levels. However, in cardiac hypertrophy induced by aortic banding, there was a marked subcellular redistribution of phosphodiesterases (PDEs) 2, 3 and 4, which included a dramatic loss of the local pool of PDE4. This was also accompanied by measurable b2-ARinduced cAMP signals, increased SR calcium leak and arrhythmia susceptibility. Conclusions-Our new targeted biosensor expressed in transgenic mice can visualize cAMP levels in the vicinity of cardiac RyR2 in healthy and diseased cardiomyocytes. In the future, this novel biosensor can be used to better understand alterations of RyR2-associated cAMP in cardiovascular diseases and local actions of new therapies.

One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system. We collected 1079 slides from 325 patients from three transplant centers in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross validation and by deploying it to three cohorts. For binary prediction (rejection yes/no) the mean Area Under the Receiver Operating Curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729 and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633 and 0.905 in the cross-validated experiment and 0.764, 0.597, 0.913, and 0.631, 0.633, 0.682, and 0.722, 0.601, 0.805 in the validation cohorts, respectively. The predictions of the AI model were interpretable by human experts and highlighted plausible morphological patterns. We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small patient cohorts.