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Asim M, Wang H, Chen X. Shedding light on cholecystokinin's role in hippocampal neuroplasticity and memory formation. Neurosci Biobehav Rev 2024; 159:105615. [PMID: 38437975 DOI: 10.1016/j.neubiorev.2024.105615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/26/2024] [Accepted: 03/01/2024] [Indexed: 03/06/2024]
Abstract
The hippocampus is a crucial brain region involved in the process of forming and consolidating memories. Memories are consolidated in the brain through synaptic plasticity, and a key mechanism underlying this process is called long-term potentiation (LTP). Recent research has shown that cholecystokinin (CCK) plays a role in facilitating the formation of LTP, as well as learning and memory consolidation. However, the specific mechanisms by which CCK is involved in hippocampal neuroplasticity and memory formation are complicated or poorly understood. This literature review aims to explore the role of LTP in memory formation, particularly in relation to hippocampal memory, and to discuss the implications of CCK and its receptors in the formation of hippocampal memories. Additionally, we will examine the circuitry of CCK in the hippocampus and propose potential CCK-dependent mechanisms of synaptic plasticity that contribute to memory formation.
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Affiliation(s)
- Muhammad Asim
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong; Department of Biomedical Science, City University of Hong Kong, Kowloon Tong, Hong Kong; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.
| | - Huajie Wang
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong
| | - Xi Chen
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong; Department of Biomedical Science, City University of Hong Kong, Kowloon Tong, Hong Kong; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong
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Raja R, Khanum S, Aboulmouna L, Maurya MR, Gupta S, Subramaniam S, Ramkrishna D. Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach. Biophys J 2024; 123:221-234. [PMID: 38102827 PMCID: PMC10808046 DOI: 10.1016/j.bpj.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/18/2023] [Accepted: 12/12/2023] [Indexed: 12/17/2023] Open
Abstract
Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.
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Affiliation(s)
- Rubesh Raja
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana
| | - Sana Khanum
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana
| | - Lina Aboulmouna
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Mano R Maurya
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Shakti Gupta
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Shankar Subramaniam
- Department of Bioengineering, University of California San Diego, La Jolla, California; Departments of Computer Science and Engineering, Cellular and Molecular Medicine, San Diego Supercomputer Center, and the Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, California.
| | - Doraiswami Ramkrishna
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana.
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Raja R, Khanum S, Aboulmouna L, Maurya MR, Gupta S, Subramaniam S, Ramkrishna D. Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533676. [PMID: 36993235 PMCID: PMC10055344 DOI: 10.1101/2023.03.21.533676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present an elegant modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding entirely novel strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), utilizing the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this state-of-the-art framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights. STATEMENT OF SIGNIFICANCE Cellular processes like cell cycle are overly complex, involving multiple players interacting at multiple levels, and explicit modeling of such systems is challenging. The availability of longitudinal RNA measurements provides an opportunity to "reverse-engineer" for novel regulatory models. We develop a novel framework, inspired using goal-oriented cybernetic model, to implicitly model transcriptional regulation by constraining the system using inferred temporal goals. A preliminary causal network based on information-theory is used as a starting point, and our framework is used to distill the network to temporally-based networks containing essential molecular players. The strength of this approach is its ability to dynamically model the RNA temporal measurements. The approach developed paves the way for inferring regulatory processes in many complex cellular processes.
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Ballaz SJ, Bourin M. Cholecystokinin-Mediated Neuromodulation of Anxiety and Schizophrenia: A "Dimmer-Switch" Hypothesis. Curr Neuropharmacol 2021; 19:925-938. [PMID: 33185164 PMCID: PMC8686311 DOI: 10.2174/1570159x18666201113145143] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/08/2020] [Accepted: 11/10/2020] [Indexed: 11/22/2022] Open
Abstract
Cholecystokinin (CCK), the most abundant brain neuropeptide, is involved in relevant behavioral functions like memory, cognition, and reward through its interactions with the opioid and dopaminergic systems in the limbic system. CCK excites neurons by binding two receptors, CCK1 and CCK2, expressed at low and high levels in the brain, respectively. Historically, CCK2 receptors have been related to the induction of panic attacks in humans. Disturbances in brain CCK expression also underlie the physiopathology of schizophrenia, which is attributed to the modulation by CCK1 receptors of the dopamine flux in the basal striatum. Despite this evidence, neither CCK2 receptor antagonists ameliorate human anxiety nor CCK agonists have consistently shown neuroleptic effects in clinical trials. A neglected aspect of the function of brain CCK is its neuromodulatory role in mental disorders. Interestingly, CCK is expressed in pivotal inhibitory interneurons that sculpt cortical dynamics and the flux of nerve impulses across corticolimbic areas and the excitatory projections to mesolimbic pathways. At the basal striatum, CCK modulates the excitability of glutamate, the release of inhibitory GABA, and the discharge of dopamine. Here we focus on how CCK may reduce rather than trigger anxiety by regulating its cognitive component. Adequate levels of CCK release in the basal striatum may control the interplay between cognition and reward circuitry, which is critical in schizophrenia. Hence, it is proposed that disturbances in the excitatory/ inhibitory interplay modulated by CCK may contribute to the imbalanced interaction between corticolimbic and mesolimbic neural activity found in anxiety and schizophrenia.
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Affiliation(s)
- Santiago J. Ballaz
- Address correspondence to this author at the School of Biological Sciences & Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí, Ecuador; Tel: 593 (06) 299 9100, ext. 2626; E-mail:
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Gu QL, Xiao Y, Li S, Zhou D. Emergence of spatially periodic diffusive waves in small-world neuronal networks. Phys Rev E 2019; 100:042401. [PMID: 31770933 DOI: 10.1103/physreve.100.042401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Indexed: 01/20/2023]
Abstract
It has been observed in experiment that the anatomical structure of neuronal networks in the brain possesses the feature of small-world networks. Yet how the small-world structure affects network dynamics remains to be fully clarified. Here we study the dynamics of a class of small-world networks consisting of pulse-coupled integrate-and-fire (I&F) neurons. Under stochastic Poisson drive, we find that the activity of the entire network resembles diffusive waves. To understand its underlying mechanism, we analyze the simplified regular-lattice network consisting of firing-rate-based neurons as an approximation to the original I&F small-world network. We demonstrate both analytically and numerically that, with strongly coupled connections, in the absence of noise, the activity of the firing-rate-based regular-lattice network spatially forms a static grating pattern that corresponds to the spatial distribution of the firing rate observed in the I&F small-world neuronal network. We further show that the spatial grating pattern with different phases comprise the continuous attractor of both the I&F small-world and firing-rate-based regular-lattice network dynamics. In the presence of input noise, the activity of both networks is perturbed along the continuous attractor, which gives rise to the diffusive waves. Our numerical simulations and theoretical analysis may potentially provide insights into the understanding of the generation of wave patterns observed in cortical networks.
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Affiliation(s)
- Qinglong L Gu
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Yanyang Xiao
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA and NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Songting Li
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Douglas Zhou
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
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Xie J, Gao J, Gao Z, Lv X, Wang R. Adaptive symbolic transfer entropy and its applications in modeling for complex industrial systems. CHAOS (WOODBURY, N.Y.) 2019; 29:093114. [PMID: 31575150 DOI: 10.1063/1.5086100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 08/23/2019] [Indexed: 06/10/2023]
Abstract
Directed coupling between variables is the foundation of studying the dynamical behavior of complex systems. We propose an adaptive symbolic transfer entropy (ASTE) method based on the principle of equal probability division. First, the adaptive kernel density method is used to obtain an accurate probability density function for an observation series. Second, the complete phase space of the system can be obtained by using the multivariable phase space reconstruction method. This provides common parameters for symbolizing a time series, including delay time and embedding dimension. Third, an optimization strategy is used to select the appropriate symbolic parameters of a time series, such as the symbol set and partition intervals, which can be used to convert the time series to a symbol sequence. Then the transfer entropy between the symbolic sequences can be carried out. Finally, the proposed method is analyzed and validated using the chaotic Lorenz system and typical complex industrial systems. The results show that the ASTE method is superior to the existing transfer entropy and symbolic transfer entropy methods in terms of measurement accuracy and noise resistance, and it can be applied to the network modeling and performance safety analysis of complex industrial systems.
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Affiliation(s)
- Juntai Xie
- Western China Institute of Quality Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jianmin Gao
- Western China Institute of Quality Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhiyong Gao
- Western China Institute of Quality Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaozhe Lv
- Western China Institute of Quality Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Rongxi Wang
- Western China Institute of Quality Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
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Li S, Xiao Y, Zhou D, Cai D. Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information. Phys Rev E 2018; 97:052216. [PMID: 29906860 DOI: 10.1103/physreve.97.052216] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Indexed: 01/17/2023]
Abstract
The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems-it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.
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Affiliation(s)
- Songting Li
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
| | - Yanyang Xiao
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA and NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Douglas Zhou
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - David Cai
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; and School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
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