Modulation of dendritic spines by protein phosphatase-1

A spatial model of autophosphorylation of CaMKII predicts that the lifetime of phospho-CaMKII after induction of synaptic plasticity is greatly prolonged by CaM-trapping

Long-term potentiation (LTP) is a biochemical process that underlies learning in excitatory glutamatergic synapses in the Central Nervous System (CNS). A critical early driver of LTP is autophosphorylation of the abundant postsynaptic enzyme, Ca2+/calmodulin-dependent protein kinase II (CaMKII). Autophosphorylation is initiated by Ca2+ flowing through NMDA receptors activated by strong synaptic activity. Its lifetime is ultimately determined by the balance of the rates of autophosphorylation and of dephosphorylation by protein phosphatase 1 (PP1). Here we have modeled the autophosphorylation and dephosphorylation of CaMKII during synaptic activity in a spine synapse using MCell4, an open source computer program for creating particle-based stochastic, and spatially realistic models of cellular microchemistry. The model integrates four earlier detailed models of separate aspects of regulation of spine Ca2+ and CaMKII activity, each of which incorporate experimentally measured biochemical parameters and have been validated against experimental data. We validate the composite model by showing that it accurately predicts previous experimental measurements of effects of NMDA receptor activation, including high sensitivity of induction of LTP to phosphatase activity in vivo, and persistence of autophosphorylation for a period of minutes after the end of synaptic stimulation. We then use the model to probe aspects of the mechanism of regulation of autophosphorylation of CaMKII that are difficult to measure in vivo. We examine the effects of "CaM-trapping," a process in which the affinity for Ca2+/CaM increases several hundred-fold after autophosphorylation. We find that CaM-trapping does not increase the proportion of autophosphorylated subunits in holoenzymes after a complex stimulus, as previously hypothesized. Instead, CaM-trapping may dramatically prolong the lifetime of autophosphorylated CaMKII through steric hindrance of dephosphorylation by protein phosphatase 1. The results provide motivation for experimental measurement of the extent of suppression of dephosphorylation of CaMKII by bound Ca2+/CaM. The composite MCell4 model of biochemical effects of complex stimuli in synaptic spines is a powerful new tool for realistic, detailed dissection of mechanisms of synaptic plasticity.

A spatial model of autophosphorylation of Ca2+/calmodulin-dependent protein kinase II (CaMKII) predicts that the lifetime of phospho-CaMKII after induction of synaptic plasticity is greatly prolonged by CaM-trapping

Long-term potentiation (LTP) is a biochemical process that underlies learning in excitatory glutamatergic synapses in the Central Nervous System (CNS). The critical early driver of LTP is autophosphorylation of the abundant postsynaptic enzyme, Ca2+/calmodulin-dependent protein kinase II (CaMKII). Autophosphorylation is initiated by Ca2+ flowing through NMDA receptors activated by strong synaptic activity. Its lifetime is ultimately determined by the balance of the rates of autophosphorylation and of dephosphorylation by protein phosphatase 1 (PP1). Here we have modeled the autophosphorylation and dephosphorylation of CaMKII during synaptic activity in a spine synapse using MCell4, an open source computer program for creating particle-based stochastic, and spatially realistic models of cellular microchemistry. The model integrates four earlier detailed models of separate aspects of regulation of spine Ca2+ and CaMKII activity, each of which incorporate experimentally measured biochemical parameters and have been validated against experimental data. We validate the composite model by showing that it accurately predicts previous experimental measurements of effects of NMDA receptor activation, including high sensitivity of induction of LTP to phosphatase activity in vivo, and persistence of autophosphorylation for a period of minutes after the end of synaptic stimulation. We then use the model to probe aspects of the mechanism of regulation of autophosphorylation of CaMKII that are difficult to measure in vivo. We examine the effects of "CaM-trapping," a process in which the affinity for Ca2+/CaM increases several hundred-fold after autophosphorylation. We find that CaM-trapping does not increase the proportion of autophosphorylated subunits in holoenzymes after a complex stimulus, as previously hypothesized. Instead, CaM-trapping may dramatically prolong the lifetime of autophosphorylated CaMKII through steric hindrance of dephosphorylation by protein phosphatase 1. The results provide motivation for experimental measurement of the extent of suppression of dephosphorylation of CaMKII by bound Ca2+/CaM. The composite MCell4 model of biochemical effects of complex stimuli in synaptic spines is a powerful new tool for realistic, detailed dissection of mechanisms of synaptic plasticity.

The epigenetically regulated PP1α expression by KDM1A may contribute to oxycodone conditioned place preference in mice

The lysine-specific demethylase 1 (KDM1A) is reported to be a regulator in learning and memory. However, the effect of KDM1A in oxycodone rewarding memory has yet to be studied. In our study, rewarding memory was assessed by using conditioned place preference (CPP) in male mice. Next generation sequencing and chromatin immunoprecipitation-PCR were used to explore the molecular mechanisms. Oxycodone significantly decreased PP1α mRNA and protein levels in hippocampal neurons. Oxycodone significantly increased KDM1A and H3K4me1 levels, while significantly decreased H3K4me2 levels in a time- and dose-dependent manner. Behavioral data demonstrated that intraperitoneal injection of ORY-1001 (KDM1A inhibitor) or intra-hippocampal injection of KDM1A siRNA/shRNA blocked the acquisition and expression of oxycodone CPP and facilitated the extinction of oxycodone CPP. The decrease of PP1α was markedly blocked by the injection of ORY-1001 or KDM1A siRNA/shRNA. Oxycodone-induced enhanced binding of CoRest with KDM1A and binding of CoRest with the PP1α promoter was blocked by ORY-1001. The level of H3K4me2 demethylation was also decreased by the treatment. The results suggest that oxycodone-induced upregulation of KDM1A via demethylation of H3K4me2 promotes the binding of CoRest with the PP1α promoter, and the subsequent decrease in PP1α expression in hippocampal neurons may contribute to oxycodone reward.

RNA velocity prediction via neural ordinary differential equation

RNA velocity is a crucial tool for unraveling the trajectory of cellular responses. Several approaches, including ordinary differential equations and machine learning models, have been proposed to interpret velocity. However, the practicality of these methods is constrained by underlying assumptions. In this study, we introduce SymVelo, a dual-path framework that effectively integrates high- and low-dimensional information. Rigorous benchmarking and extensive studies demonstrate that SymVelo is capable of inferring differentiation trajectories in developing organs, analyzing gene responses to stimulation, and uncovering transcription dynamics. Moreover, the adaptable architecture of SymVelo enables customization to accommodate intricate data and diverse modalities in forthcoming research, thereby providing a promising avenue for advancing our understanding of cellular behavior.

Mammal comparative tendon biology: advances in regulatory mechanisms through a computational modeling

There is high clinical demand for the resolution of tendinopathies, which affect mainly adult individuals and animals. Tendon damage resolution during the adult lifetime is not as effective as in earlier stages where complete restoration of tendon structure and property occurs. However, the molecular mechanisms underlying tendon regeneration remain unknown, limiting the development of targeted therapies. The research aim was to draw a comparative map of molecules that control tenogenesis and to exploit systems biology to model their signaling cascades and physiological paths. Using current literature data on molecular interactions in early tendon development, species-specific data collections were created. Then, computational analysis was used to construct Tendon NETworks in which information flow and molecular links were traced, prioritized, and enriched. Species-specific Tendon NETworks generated a data-driven computational framework based on three operative levels and a stage-dependent set of molecules and interactions (embryo-fetal or prepubertal) responsible, respectively, for signaling differentiation and morphogenesis, shaping tendon transcriptional program and downstream modeling of its fibrillogenesis toward a mature tissue. The computational network enrichment unveiled a more complex hierarchical organization of molecule interactions assigning a central role to neuro and endocrine axes which are novel and only partially explored systems for tenogenesis. Overall, this study emphasizes the value of system biology in linking the currently available disjointed molecular data, by establishing the direction and priority of signaling flows. Simultaneously, computational enrichment was critical in revealing new nodes and pathways to watch out for in promoting biomedical advances in tendon healing and developing targeted therapeutic strategies to improve current clinical interventions.