Modeling suggests combined-drug treatments for disorders impairing synaptic plasticity via shared signaling pathways.

IF 2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2021-02-01 Epub Date: 2020-11-11 DOI:10.1007/s10827-020-00771-4
Paul Smolen, Marcelo A Wood, Douglas A Baxter, John H Byrne
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引用次数: 4

Abstract

Genetic disorders such as Rubinstein-Taybi syndrome (RTS) and Coffin-Lowry syndrome (CLS) cause lifelong cognitive disability, including deficits in learning and memory. Can pharmacological therapies be suggested that improve learning and memory in these disorders? To address this question, we simulated drug effects within a computational model describing induction of late long-term potentiation (L-LTP). Biochemical pathways impaired in these and other disorders converge on a common target, histone acetylation by acetyltransferases such as CREB binding protein (CBP), which facilitates gene induction necessary for L-LTP. We focused on four drug classes: tropomyosin receptor kinase B (TrkB) agonists, cAMP phosphodiesterase inhibitors, histone deacetylase inhibitors, and ampakines. Simulations suggested each drug type alone may rescue deficits in L-LTP. A potential disadvantage, however, was the necessity of simulating strong drug effects (high doses), which could produce adverse side effects. Thus, we investigated the effects of six drug pairs among the four classes described above. These combination treatments normalized impaired L-LTP with substantially smaller individual drug 'doses'. In addition three of these combinations, a TrkB agonist paired with an ampakine and a cAMP phosphodiesterase inhibitor paired with a TrkB agonist or an ampakine, exhibited strong synergism in L-LTP rescue. Therefore, we suggest these drug combinations are promising candidates for further empirical studies in animal models of genetic disorders that impair histone acetylation, L-LTP, and learning.

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模型表明联合药物治疗通过共享信号通路损害突触可塑性的疾病。
遗传疾病如Rubinstein-Taybi综合征(RTS)和Coffin-Lowry综合征(CLS)会导致终身认知障碍,包括学习和记忆缺陷。是否可以建议药物治疗来改善这些疾病的学习和记忆?为了解决这个问题,我们在描述后期长期增强(L-LTP)诱导的计算模型中模拟了药物效应。在这些疾病和其他疾病中受损的生化途径集中在一个共同的目标上,即通过乙酰转移酶(如CREB结合蛋白(CBP))进行组蛋白乙酰化,这促进了L-LTP所需的基因诱导。我们重点研究了四类药物:原肌球蛋白受体激酶B (TrkB)激动剂、cAMP磷酸二酯酶抑制剂、组蛋白去乙酰化酶抑制剂和氨pakines。模拟表明,每种药物都可以单独挽救L-LTP的缺陷。然而,一个潜在的缺点是必须模拟强烈的药物作用(高剂量),这可能产生不利的副作用。因此,我们研究了上述四类药物中六种药物对的作用。这些联合治疗使受损的L-LTP恢复正常,单个药物“剂量”要小得多。此外,TrkB激动剂与ampakine配对,cAMP磷酸二酯酶抑制剂与TrkB激动剂或ampakine配对,这三种组合在L-LTP抢救中表现出很强的协同作用。因此,我们认为这些药物组合是有希望在损害组蛋白乙酰化、L-LTP和学习的遗传疾病动物模型中进行进一步实证研究的候选者。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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