Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.: Support obtained from Financiadora de Estudos e Projetos (http://www.finep.gov.br/) Grant # 01.06.1092.00 to SR; Conselho Nacional de Desenvolvimento Científico e Tecnológico (http:// www.cnpq.br/): Grants 481506/2007-1, 481351/2011- 6 and 306604/2012-4 to SR, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (http://www.capes.gov.br/) and Ciencias sem Fronteiras (http://www.cienciasemfronteiras.gov.br/ web/csf/home) to AT and CRC; Fundação de Amparo à Pesquisa do Rio Grande do Norte (http://wwwfapern.rn.gov.br/): Grant Pronem 003/2011 to SR; Fundação de Amparo à Pesquisa do Estado de São Paulo (http://www.fapesp.br/): Grant #2013/ 07699-0 - Center for Neuromathematics to SR; CMP and VRC supported by post-doctoral fellowships from Fundação de Amparo à Pesquisa do Rio Grande do Norte /CNPq. Additional support obtained from the Federal University of Rio Grande do Norte (www.ufrn. br); Ministry of Science, Technology and Innovation (http://www.mcti.gov.br/); Associação Alberto Santos Dumont de Apoio à Pesquisa (http://natalneuro.com/ associacao/index.asp); Pew Latin American Fellows Program (http://www.pewtrusts.org/en/projects/pewlatin-american-fellows/) to SR; Informatics Department of the Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (http:// portal.ifrn.edu.br/) to WB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Computational prediction of protein subdomain stability in MYBPC3 enables clinical risk stratification in hypertrophic cardiomyopathy and enhances variant interpretation

PURPOSE: Variants in MYBPC3 causing loss of function are the most common cause of hypertrophic cardiomyopathy (HCM). However, a substantial number of patients carry missense variants of uncertain significance (VUS) in MYBPC3. We hypothesize that a structural-based algorithm, STRUM, which estimates the effect of missense variants on protein folding, will identify a subgroup of HCM patients with a MYBPC3 VUS associated with increased clinical risk. METHODS: Among 7,963 patients in the multicenter Sarcomeric Human Cardiomyopathy Registry (SHaRe), 120 unique missense VUS in MYBPC3 were identified. Variants were evaluated for their effect on subdomain folding and a stratified time-to-event analysis for an overall composite endpoint (first occurrence of ventricular arrhythmia, heart failure, all-cause mortality, atrial fibrillation, and stroke) was performed for patients with HCM and a MYBPC3 missense VUS. RESULTS: We demonstrated that patients carrying a MYBPC3 VUS predicted to cause subdomain misfolding (STRUM+, ΔΔG ≤ −1.2 kcal/mol) exhibited a higher rate of adverse events compared with those with a STRUM- VUS (hazard ratio = 2.29, P = 0.0282). In silico saturation mutagenesis of MYBPC3 identified 4,943/23,427 (21%) missense variants that were predicted to cause subdomain misfolding. CONCLUSION: STRUM identifies patients with HCM and a MYBPC3 VUS who may be at higher clinical risk and provides supportive evidence for pathogenicity

Spatial and Functional Distribution of MYBPC3 Pathogenic Variants and Clinical Outcomes in Patients with Hypertrophic Cardiomyopathy

Background - Pathogenic variants in MYBPC3, encoding cardiac MyBP-C, are the most common cause of familial hypertrophic cardiomyopathy. A large number of unique MYBPC3 variants and relatively small genotyped HCM cohorts have precluded detailed genotype-phenotype correlations. Methods - Patients with HCM and MYBPC3 variants were identified from the Sarcomeric Human Cardiomyopathy Registry (SHaRe). Variant types and locations were analyzed, morphologic severity was assessed, and time-event analysis was performed (composite clinical outcome of sudden death, class III/IV heart failure, LVAD/transplant, atrial fibrillation). For selected missense variants falling in enriched domains, myofilament localization and degradation rates were measured in vitro. Results - Among 4,756 genotyped HCM patients in SHaRe, 1,316 patients were identified with adjudicated pathogenic truncating (N=234 unique variants, 1047 patients) or non-truncating (N=22 unique variants, 191 patients) variants in MYBPC3. Truncating variants were evenly dispersed throughout the gene, and hypertrophy severity and outcomes were not associated with variant location (grouped by 5' - 3' quartiles or by founder variant subgroup). Non-truncating pathogenic variants clustered in the C3, C6, and C10 domains (18 of 22, 82%, p<0.001 vs. gnomAD common variants) and were associated with similar hypertrophy severity and adverse event rates as observed with truncating variants. MyBP-C with variants in the C3, C6, and C10 domains was expressed in rat ventricular myocytes. C10 mutant MyBP-C failed to incorporate into myofilaments and degradation rates were accelerated by ~90%, while C3 and C6 mutant MyBP-C incorporated normally with degradation rate similar to wild-type. Conclusions - Truncating variants account for 91% of MYBPC3 pathogenic variants and cause similar clinical severity and outcomes regardless of location, consistent with locus-independent loss-of-function. Non-truncating MYBPC3 pathogenic variants are regionally clustered, and a subset also cause loss-of-function through failure of myofilament incorporation and rapid degradation. Cardiac morphology and clinical outcomes are similar in patients with truncating vs. non-truncating variants

Hypertrophic Cardiomyopathy with Left Ventricular Systolic Dysfunction: Insights from the SHaRe Registry

Background: The term "end stage" has been used to describe hypertrophic cardiomyopathy (HCM) with left ventricular systolic dysfunction (LVSD), defined as occurring when left ventricular ejection fraction is <50%. The prognosis of HCM-LVSD has reportedly been poor, but because of its relative rarity, the natural history remains incompletely characterized. Methods: Data from 11 high-volume HCM specialty centers making up the international SHaRe Registry (Sarcomeric Human Cardiomyopathy Registry) were used to describe the natural history of patients with HCM-LVSD. Cox proportional hazards models were used to identify predictors of prognosis and incident development. Results: From a cohort of 6793 patients with HCM, 553 (8%) met the criteria for HCM-LVSD. Overall, 75% of patients with HCM-LVSD experienced clinically relevant events, and 35% met the composite outcome (all-cause death [n=128], cardiac transplantation [n=55], or left ventricular assist device implantation [n=9]). After recognition of HCM-LVSD, the median time to composite outcome was 8.4 years. However, there was substantial individual variation in natural history. Significant predictors of the composite outcome included the presence of multiple pathogenic/likely pathogenic sarcomeric variants (hazard ratio [HR], 5.6 [95% CI, 2.3-13.5]), atrial fibrillation (HR, 2.6 [95% CI, 1.7-3.5]), and left ventricular ejection fraction <35% (HR, 2.0 [95% CI, 1.3-2.8]). The incidence of new HCM-LVSD was ≈7.5% over 15 years. Significant predictors of developing incident HCM-LVSD included greater left ventricular cavity size (HR, 1.1 [95% CI, 1.0-1.3] and wall thickness (HR, 1.3 [95% CI, 1.1-1.4]), left ventricular ejection fraction of 50% to 60% (HR, 1.8 [95% CI, 1.2, 2.8]-2.8 [95% CI, 1.8-4.2]) at baseline evaluation, the presence of late gadolinium enhancement on cardiac magnetic resonance imaging (HR, 2.3 [95% CI, 1.0-4.9]), and the presence of a pathogenic/likely pathogenic sarcomeric variant, particularly in thin filament genes (HR, 1.5 [95% CI, 1.0-2.1] and 2.5 [95% CI, 1.2-5.1], respectively). Conclusions: HCM-LVSD affects ≈8% of patients with HCM. Although the natural history of HCM-LVSD was variable, 75% of patients experienced adverse events, including 35% experiencing a death equivalent an estimated median time of 8.4 years after developing systolic dysfunction. In addition to clinical features, genetic substrate appears to play a role in both prognosis (multiple sarcomeric variants) and the risk for incident development of HCM-LVSD (thin filament variants)

Myosin Sequestration Regulates Sarcomere Function, Cardiomyocyte Energetics, and Metabolism, Informing the Pathogenesis of Hypertrophic Cardiomyopathy

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by pathogenic variants in sarcomere protein genes that evoke hypercontractility, poor relaxation, and increased energy consumption by the heart and increased patient risks for arrhythmias and heart failure. Recent studies show that pathogenic missense variants in myosin, the molecular motor of the sarcomere, are clustered in residues that participate in dynamic conformational states of sarcomere proteins. We hypothesized that these conformations are essential to adapt contractile output for energy conservation and that pathophysiology of HCM results from destabilization of these conformations. METHODS: We assayed myosin ATP binding to define the proportion of myosins in the super relaxed state (SRX) conformation or the disordered relaxed state (DRX) conformation in healthy rodent and human hearts, at baseline and in response to reduced hemodynamic demands of hibernation or pathogenic HCM variants. To determine the relationships between myosin conformations, sarcomere function, and cell biology, we assessed contractility, relaxation, and cardiomyocyte morphology and metabolism, with and without an allosteric modulator of myosin ATPase activity. We then tested whether the positions of myosin variants of unknown clinical significance that were identified in patients with HCM, predicted functional consequences and associations with heart failure and arrhythmias. RESULTS: Myosins undergo physiological shifts between the SRX conformation that maximizes energy conservation and the DRX conformation that enables cross-bridge formation with greater ATP consumption. Systemic hemodynamic requirements, pharmacological modulators of myosin, and pathogenic myosin missense mutations influenced the proportions of these conformations. Hibernation increased the proportion of myosins in the SRX conformation, whereas pathogenic variants destabilized these and increased the proportion of myosins in the DRX conformation, which enhanced cardiomyocyte contractility, but impaired relaxation and evoked hypertrophic remodeling with increased energetic stress. Using structural locations to stratify variants of unknown clinical significance, we showed that the variants that destabilized myosin conformations were associated with higher rates of heart failure and arrhythmias in patients with HCM. CONCLUSIONS: Myosin conformations establish work-energy equipoise that is essential for life-long cellular homeostasis and heart function. Destabilization of myosin energy-conserving states promotes contractile abnormalities, morphological and metabolic remodeling, and adverse clinical outcomes in patients with HCM. Therapeutic restabilization corrects cellular contractile and metabolic phenotypes and may limit these adverse clinical outcomes in patients with HCM

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

Macroinvertebrates inhabiting the tank leaf terrestrial and epiphyte bromeliads at Reserva Adolpho Ducke, Manaus, Amazonas

The aim of this work was to investigate the diversity of macroinvertebrates and also verify if the abundance and diversity of Diptera were influenced by the abiotic factors. The samples were collected from the epiphytic and terrestrial bromeliads G. brasiliensis (1 and 3m) in wet and dry seasons at Reserva Adolpho Ducke analyzed total of 144 samples were analyzed from a total of 15,238 individuals collected. These conatined 14,097 insects and, among these, 8,258 were immature Diptera, represented by eight most abundant families: Chironomidae, Ceratopogonidae and Culicidae. The relationship of Diptera diversity was influenced by the seasons and stratifications (p= 0.01); the abundance was influenced by the volume of water (p= 0.02) and the relationship between the season and volume of water in the terrestrial bromeliads was significant (p= 0.01). This study represented the first contribution to knowledge of community of macroinvertebrates associated to bromeliads G. brasiliensis in Central Amazon

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

LTP model variations show that evaluation around the SWS/REM transition leads to pattern restructuring.

<p>(A) Initial and final synaptic weight values across the time interval of LTP simulation (11,000–14,000s) for variations of the LTP 1 and LTP 2 models. Red, green and blue lines represent positive, near-zero and negative slopes, with corresponding percentages indicated by the color bar. 1^st panel for LTP 1 model evaluated over an entire SWS episode; 2^nd panel for LTP 1 model evaluated only during the last 30s of SWS immediately before REM; 3^rd panel for LTP 1 model evaluated using the last 30s SWS period plus the following 30s of REM; 4^th panel for LTP 2 model using all positive REM slopes (permissive); 5^th panel for LTP 2 model using only positive REM slopes that were also larger than the SWS slope (restrictive). For details of restrictive and permissive LTP 2 models, see <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004241#pcbi.1004241.s016" target="_blank">S10 Fig</a></b>. (B) Initial synaptic weight values (x axis) versus difference between initial and last time-points of LTP simulation (ΔWij, y axis). The curves are quadratic fits. Left and right panels for the variations of LTP 1 and LTP 2 models, respectively. (C) Spearman´s correlations over time for variations of LTP 1 and LTP 2 models, comparing the distribution of w_<i>ij</i>(t) values at the time <i>t</i> with the initial distribution of w_<i>ij</i>(0). Dashed lines indicate the time range [10,500s to 14,100s] when the LTP Gaussian was applied (boundaries of LTP simulation).</p

Statistics of real spikes from the hippocampal CA1 field during WK, SWS or REM.

<p>(A) Probability distributions of spike rates across states, with mean population values represented by dashed lines (left panel). Mean and variance of spike rates recorded during each state (right panel). (B) Square matrix of Pearson's linear correlation coefficient for spiking of 45 neurons during WK, SWS or REM (left panel), and the corresponding mean and variance (left panel). Significant differences of the Pearson's coefficient distribution were found between WK and SWS (Kolmogorov-Smirnov (KS) p = 6.9607e-023), WK and REM (KS, p = 1.0890e-023), and SWS and REM (p = 4.0259e-017). (C) Distribution of durations for intervals separating consecutive REM episodes (left panel, n = 6 rats; 28.8 hours of recordings). Cumulative plot of the REM-to-REM interval durations (right panel). Note that 91.6% of the intervals are shorter than 30 min (dashed line). More examples in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004241#pcbi.1004241.s010" target="_blank">S4 Fig</a></b>; see also <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004241#pcbi.1004241.s017" target="_blank">S1 Table</a></b>.</p