Zinc Signaling in Neuronal Tolerance

Sub-lethal preconditioning stimuli can confer neuronal tolerance by triggering the activation of endogenous survival pathways that limit or resist subsequent injury. Recent evidence has demonstrated that neuroprotection is paradoxically dependent on the sub-lethal activation of cell death mediators. As intracellular Zn2+ accumulation has been closely associated with neuronal cell death pathways, I tested the hypothesis that neuronal tolerance is also dependent on sub-lethal Zn2+ signals. I found that preconditioning triggered an immediate transient rise in neuronal free Zn2+, while lethal excitotoxicity led to a delayed accumulation of the metal. The sub-lethal rise in Zn2+ was necessary and sufficient in attenuating subsequent Zn2+-dependent toxicity in preconditioned neurons. Chelating Zn2+ during the preconditioning stimulus restored the lethal excitotoxic accumulation in neuronal Zn2+ and abolished neuronal tolerance. These data suggested that preconditioning-induced Zn2+ could trigger mechanisms for preventing subsequent Zn2+-dependent cell death. Indeed, preconditioning triggered protein kinase C (PKC)-dependent Zn2+-regulated gene expression in neurons. Examination of the mechanism involved in modulating Zn2+-regulated gene expression revealed a surprisingly early role for PKC in directly modifying the intracellular source of Zn2+. A conserved PKC phosphorylation site was identified at serine 32 of the metal binding protein metallothionein, which was important in modulating Zn2+ regulated gene expression and ultimately conferring neuronal tolerance. In addition to modulating gene expression, Zn2+ signals may also be important in mediating the acute cellular response to stress. Here, I found a critical role for the transient Zn2+ rise in modulating changes in voltage-gated potassium channel activity and localization following ischemia. Together, these data strongly suggest that a transient rise in neuronal free Zn2+ is an important early signal in conferring neuronal tolerance and in mediating acute cellular adaptive responses to stress. Thus, Zn2+ is a previously unrecognized, highly regulated signaling component in the initiation of survival pathways in neurons

Cardiopulmonary exercise testing to evaluate post-acute sequelae of COVID-19 ("Long COVID"): a systematic review and meta-analysis.

IMPORTANCE: Reduced exercise capacity is commonly reported among individuals with Long COVID (LC). Cardiopulmonary exercise testing (CPET) is the gold-standard to measure exercise capacity to identify causes of exertional intolerance. OBJECTIVES: To estimate the effect of SARS-CoV-2 infection on exercise capacity including those with and without LC symptoms and to characterize physiologic patterns of limitations to elucidate possible mechanisms of LC. DATA SOURCES: We searched PubMed, EMBASE, and Web of Science, preprint severs, conference abstracts, and cited references in December 2021 and again in May 2022. STUDY SELECTION: We included studies of adults with SARS-CoV-2 infection at least three months prior that included CPET measured peak VO 2 . 3,523 studies were screened independently by two blinded reviewers; 72 (2.2%) were selected for full-text review and 36 (1.2%) met the inclusion criteria; we identified 3 additional studies from preprint servers. DATA EXTRACTION AND SYNTHESIS: Data extraction was done by two independent reviewers according to PRISMA guidelines. Data were pooled with random-effects models. MAIN OUTCOMES AND MEASURES: A priori primary outcomes were differences in peak VO 2 (in ml/kg/min) among those with and without SARS-CoV-2 infection and LC. RESULTS: We identified 39 studies that performed CPET on 2,209 individuals 3-18 months after SARS-CoV-2 infection, including 944 individuals with LC symptoms and 246 SARS-CoV-2 uninfected controls. Most were case-series of individuals with LC or post-hospitalization cohorts. By meta-analysis of 9 studies including 404 infected individuals, peak VO 2 was 7.4 ml/kg/min (95%CI 3.7 to 11.0) lower among infected versus uninfected individuals. A high degree of heterogeneity was attributable to patient and control selection, and these studies mostly included previously hospitalized, persistently symptomatic individuals. Based on meta-analysis of 9 studies with 464 individuals with LC, peak VO 2 was 4.9 ml/kg/min (95%CI 3.4 to 6.4) lower compared to those without symptoms. Deconditioning was common, but dysfunctional breathing, chronotropic incompetence, and abnormal oxygen extraction were also described. CONCLUSIONS AND RELEVANCE: These studies suggest that exercise capacity is reduced after SARS-CoV-2 infection especially among those hospitalized for acute COVID-19 and individuals with LC. Mechanisms for exertional intolerance besides deconditioning may be multifactorial or related to underlying autonomic dysfunction

Association of SARS‐CoV‐2 Infection and Cardiopulmonary Long COVID With Exercise Capacity and Chronotropic Incompetence Among People With HIV

Background Postacute sequelae of COVID‐19 (PASC) and HIV are both associated with reduced exercise capacity, but whether SARS‐CoV‐2 or PASC are associated with exercise capacity among people with HIV (PWH) is unknown. We hypothesized that PWH with PASC would have reduced exercise capacity from chronotropic incompetence. Methods and Results We conducted cross‐sectional cardiopulmonary exercise testing within a COVID recovery cohort that included PWH with and without prior SARS‐CoV‐2 infection and people without HIV with prior SARS‐CoV‐2 infection (controls). We evaluated associations of HIV, SARS‐CoV‐2, and PASC with exercise capacity (peak oxygen consumption) and chronotropy (adjusted heart rate reserve). We included 83 participants (median age, 54 years; 35% women; 37 PWH): 23 out of 37 (62%) PWH and all 46 controls had prior SARS‐CoV‐2 infection, and 11 out of 23 (48%) PWH and 28 out of 46 (61%) without HIV had PASC. Peak oxygen consumption was reduced among PWH versus controls (80% predicted versus 99%, P=0.005), a difference of 5.5 mL/kg per minute (95% CI, 2.7–8.2; P<0.001). Chronotropic incompetence was more prevalent among PWH (38% versus 11%, P=0.002), with lower adjusted heart rate reserve (60% versus 83%, P<0.0001) versus controls. Among PWH, SARS‐CoV‐2 coinfection and PASC were not associated with exercise capacity. Chronotropic incompetence was more common among PWH with PASC: 7 out of 11 (64%) with PASC versus 7 out of 26 (27%) without PASC (P=0.04). Conclusions Exercise capacity and chronotropy are lower among PWH compared with individuals with SARS‐CoV‐2 infection without HIV. Among PWH, SARS‐CoV‐2 infection and PASC were not strongly associated with reduced exercise capacity. Chronotropic incompetence may be a common underrecognized mechanism of exercise intolerance among PWH, especially those with cardiopulmonary PASC

Association of SARS‐CoV‐2 Infection and Cardiopulmonary Long COVID With Exercise Capacity and Chronotropic Incompetence Among People With HIV

Background Postacute sequelae of COVID-19 (PASC) and HIV are both associated with reduced exercise capacity, but whether SARS-CoV-2 or PASC are associated with exercise capacity among people with HIV (PWH) is unknown. We hypothesized that PWH with PASC would have reduced exercise capacity from chronotropic incompetence. Methods and Results We conducted cross-sectional cardiopulmonary exercise testing within a COVID recovery cohort that included PWH with and without prior SARS-CoV-2 infection and people without HIV with prior SARS-CoV-2 infection (controls). We evaluated associations of HIV, SARS-CoV-2, and PASC with exercise capacity (peak oxygen consumption) and chronotropy (adjusted heart rate reserve). We included 83 participants (median age, 54 years; 35% women; 37 PWH): 23 out of 37 (62%) PWH and all 46 controls had prior SARS-CoV-2 infection, and 11 out of 23 (48%) PWH and 28 out of 46 (61%) without HIV had PASC. Peak oxygen consumption was reduced among PWH versus controls (80% predicted versus 99%, P=0.005), a difference of 5.5 mL/kg per minute (95% CI, 2.7-8.2; P&lt;0.001). Chronotropic incompetence was more prevalent among PWH (38% versus 11%, P=0.002), with lower adjusted heart rate reserve (60% versus 83%, P&lt;0.0001) versus controls. Among PWH, SARS-CoV-2 coinfection and PASC were not associated with exercise capacity. Chronotropic incompetence was more common among PWH with PASC: 7 out of 11 (64%) with PASC versus 7 out of 26 (27%) without PASC (P=0.04). Conclusions Exercise capacity and chronotropy are lower among PWH compared with individuals with SARS-CoV-2 infection without HIV. Among PWH, SARS-CoV-2 infection and PASC were not strongly associated with reduced exercise capacity. Chronotropic incompetence may be a common underrecognized mechanism of exercise intolerance among PWH, especially those with cardiopulmonary PASC

Fully Automated Echocardiogram Interpretation in Clinical Practice.

BackgroundAutomated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in primary care and rural settings. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram interpretation, including (1) view identification, (2) image segmentation, (3) quantification of structure and function, and (4) disease detection.MethodsUsing 14 035 echocardiograms spanning a 10-year period, we trained and evaluated convolutional neural network models for multiple tasks, including automated identification of 23 viewpoints and segmentation of cardiac chambers across 5 common views. The segmentation output was used to quantify chamber volumes and left ventricular mass, determine ejection fraction, and facilitate automated determination of longitudinal strain through speckle tracking. Results were evaluated through comparison to manual segmentation and measurements from 8666 echocardiograms obtained during the routine clinical workflow. Finally, we developed models to detect 3 diseases: hypertrophic cardiomyopathy, cardiac amyloid, and pulmonary arterial hypertension.ResultsConvolutional neural networks accurately identified views (eg, 96% for parasternal long axis), including flagging partially obscured cardiac chambers, and enabled the segmentation of individual cardiac chambers. The resulting cardiac structure measurements agreed with study report values (eg, median absolute deviations of 15% to 17% of observed values for left ventricular mass, left ventricular diastolic volume, and left atrial volume). In terms of function, we computed automated ejection fraction and longitudinal strain measurements (within 2 cohorts), which agreed with commercial software-derived values (for ejection fraction, median absolute deviation=9.7% of observed, N=6407 studies; for strain, median absolute deviation=7.5%, n=419, and 9.0%, n=110) and demonstrated applicability to serial monitoring of patients with breast cancer for trastuzumab cardiotoxicity. Overall, we found automated measurements to be comparable or superior to manual measurements across 11 internal consistency metrics (eg, the correlation of left atrial and ventricular volumes). Finally, we trained convolutional neural networks to detect hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with C statistics of 0.93, 0.87, and 0.85, respectively.ConclusionsOur pipeline lays the groundwork for using automated interpretation to support serial patient tracking and scalable analysis of millions of echocardiograms archived within healthcare systems

Fully Automated Echocardiogram Interpretation in Clinical Practice.

BackgroundAutomated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in primary care and rural settings. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram interpretation, including (1) view identification, (2) image segmentation, (3) quantification of structure and function, and (4) disease detection.MethodsUsing 14 035 echocardiograms spanning a 10-year period, we trained and evaluated convolutional neural network models for multiple tasks, including automated identification of 23 viewpoints and segmentation of cardiac chambers across 5 common views. The segmentation output was used to quantify chamber volumes and left ventricular mass, determine ejection fraction, and facilitate automated determination of longitudinal strain through speckle tracking. Results were evaluated through comparison to manual segmentation and measurements from 8666 echocardiograms obtained during the routine clinical workflow. Finally, we developed models to detect 3 diseases: hypertrophic cardiomyopathy, cardiac amyloid, and pulmonary arterial hypertension.ResultsConvolutional neural networks accurately identified views (eg, 96% for parasternal long axis), including flagging partially obscured cardiac chambers, and enabled the segmentation of individual cardiac chambers. The resulting cardiac structure measurements agreed with study report values (eg, median absolute deviations of 15% to 17% of observed values for left ventricular mass, left ventricular diastolic volume, and left atrial volume). In terms of function, we computed automated ejection fraction and longitudinal strain measurements (within 2 cohorts), which agreed with commercial software-derived values (for ejection fraction, median absolute deviation=9.7% of observed, N=6407 studies; for strain, median absolute deviation=7.5%, n=419, and 9.0%, n=110) and demonstrated applicability to serial monitoring of patients with breast cancer for trastuzumab cardiotoxicity. Overall, we found automated measurements to be comparable or superior to manual measurements across 11 internal consistency metrics (eg, the correlation of left atrial and ventricular volumes). Finally, we trained convolutional neural networks to detect hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with C statistics of 0.93, 0.87, and 0.85, respectively.ConclusionsOur pipeline lays the groundwork for using automated interpretation to support serial patient tracking and scalable analysis of millions of echocardiograms archived within healthcare systems

Regulation of apoptotic potassium currents by coordinated zinc-dependent signalling

Oxidant-liberated intracellular Zn2+ regulates neuronal apoptosis via an exocytotic membrane insertion of Kv2.1-encoded ion channels, resulting in an enhancement of voltage-gated K+ currents and a loss of intracellular K+ that is necessary for caspase-mediated proteolysis. In the present study we show that an N-terminal tyrosine of Kv2.1 (Y124), which is a known target of Src kinase, is critical for the apoptotic current surge. Moreover, we demonstrate that Y124 works in concert with a C-terminal serine (S800) target of p38 mitogen-activated protein kinase (MAPK) to regulate Kv2.1-mediated current enhancement. While Zn2+ was previously shown to activate p38, we show here that this metal inhibits cytoplasmic protein tyrosine phosphatase ɛ (Cyt-PTPɛ), which specifically targets Y124. Importantly, a point mutation of Y124 to a non-phosphorylatable residue or over-expression of Cyt-PTPɛ protects cells from injury. Kv2.1-encoded channels thus regulate neuronal survival by providing a converging input for two Zn2+-dependent signal transduction cascades