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Liang Q, Liu D, Zhu B, Wang F. NMDAR-CaMKII Pathway as a Central Regulator of Aggressiveness: Evidence from Transcriptomic and Metabolomic Analysis in Swimming Crabs Portunus trituberculatus. Int J Mol Sci 2024; 25:12560. [PMID: 39684272 DOI: 10.3390/ijms252312560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Revised: 11/19/2024] [Accepted: 11/21/2024] [Indexed: 12/18/2024] Open
Abstract
Aggressiveness is one of the personality traits of crustaceans, playing a crucial role in their growth, life history, and adaptability by influencing resource acquisition. However, the neuroregulatory mechanisms of aggressiveness in crustaceans remain poorly understood. The thoracic ganglion offers valuable insights into complementary aspects of aggression control. This study identified the aggressiveness of swimming crabs Portunus trituberculatus, conducted transcriptomic and metabolomic analyses of the thoracic ganglia, and confirmed the neural regulatory effects on aggressiveness. Behavioral analyses showed that highly aggressive individuals exhibited increased frequency and duration of chela extension, more frequent attacks, approaches and retreats, as well as extended movement distances. Omics analysis revealed 11 key candidate genes and three metabolites associated with aggressiveness, which were primarily enriched in pathways related to energy metabolism and neurodegeneration. Injection of an NMDAR activator significantly decreased aggressiveness in highly aggressive crabs, accompanied by a significant increase in NMDAR protein fluorescence intensity and downregulation of NR2B, CaMKII, and CREB genes. Conversely, when lowly aggressive crabs were injected with an NMDAR inhibitor, they showed increased aggressiveness alongside significantly decreased NMDAR protein fluorescence intensity, upregulated NR2B expression, and downregulated CaMKII and CREB genes. These results suggest that NMDAR within the thoracic ganglia serves as a key receptor in modulating aggressiveness in P. trituberculatus, potentially by influencing neural energy state via the NMDAR-CaMKII pathway, which in turn affects oxidative phosphorylation, cAMP, and FoxO pathways.
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Affiliation(s)
- Qihang Liang
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Dapeng Liu
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Boshan Zhu
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Fang Wang
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
- Function Laboratory for Marine Fisheries Science and Food Production Processes, Laoshan Laboratory, Qingdao 266237, China
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2
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Zhong Z, Mu X, Lang H, Wang Y, Jiang Y, Liu Y, Zeng Q, Xia S, Zhang B, Wang Z, Wang X, Zheng H. Gut symbiont-derived anandamide promotes reward learning in honeybees by activating the endocannabinoid pathway. Cell Host Microbe 2024; 32:1944-1958.e7. [PMID: 39419026 DOI: 10.1016/j.chom.2024.09.013] [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: 03/10/2024] [Revised: 07/28/2024] [Accepted: 09/20/2024] [Indexed: 10/19/2024]
Abstract
Polyunsaturated fatty acids (PUFAs) are dietary components participating in neurotransmission and cell signaling. Pollen is a source of PUFAs for honeybees, and disruptions in dietary PUFAs reduce the cognitive performance of honeybees. We reveal that gut bacteria of honeybees contribute to fatty acid metabolism, impacting reward learning. Gut bacteria possess Δ-6 desaturases that mediate fatty acid elongation and compensate for the absence of honeybee factors required for fatty acid metabolism. Colonization with Gilliamella apicola, but not a mutant lacking the Δ-6 desaturase FADS2, increases the production of anandamide (AEA), a ligand of the endocannabinoid system, and alters learning and memory. AEA activates the Hymenoptera-specific transient receptor AmHsTRPA in astrocytes, which induces Ca2+ influx and regulates glutamate re-uptake of glial cells to enhance reward learning. These findings illuminate the roles of gut symbionts in host fatty acid metabolism and the impacts of endocannabinoid signaling on the reward system of social insects.
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Affiliation(s)
- Zhaopeng Zhong
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Xiaohuan Mu
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Haoyu Lang
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Yueyi Wang
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Yanling Jiang
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Yuwen Liu
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China
| | - Qian Zeng
- Department of Medical Neuroscience, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Siyuan Xia
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Baotong Zhang
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Zilong Wang
- Department of Medical Neuroscience, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xiaofei Wang
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China.
| | - Hao Zheng
- College of Food Science and Nutritional Engineering, China Agricultural University, 100083 Beijing, China.
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Maleszka R. Reminiscences on the honeybee genome project and the rise of epigenetic concepts in insect science. INSECT MOLECULAR BIOLOGY 2024; 33:444-456. [PMID: 38196200 DOI: 10.1111/imb.12888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
The sequencing of the honeybee genome in 2006 was an important technological and logistic achievement experience. But what benefits have flown from the honeybee genome project? What does the annotated genomic assembly mean for the study of behavioural complexity and organismal function in honeybees? Here, I discuss several lines of research that have arisen from this project and highlight the rapidly expanding studies on insect epigenomics, emergent properties of royal jelly, the mechanism of nutritional control of development and the contribution of epigenomic regulation to the evolution of sociality. I also argue that the term 'insect epigenetics' needs to be carefully redefined to reflect the diversity of epigenomic toolkits in insects and the impact of lineage-specific innovations on organismal outcomes. The honeybee genome project helped pioneer advances in social insect molecular biology, and fuelled breakthrough research into the role of flexible epigenomic control systems in linking genotype to phenotype.
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Affiliation(s)
- Ryszard Maleszka
- Research School of Biology, Australian National University, Canberra, ACT, Australia
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Liu W, Li Q. Single-cell transcriptomics dissecting the development and evolution of nervous system in insects. CURRENT OPINION IN INSECT SCIENCE 2024; 63:101201. [PMID: 38608931 DOI: 10.1016/j.cois.2024.101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
Insects can display a vast repertoire of complex and adaptive behaviors crucial for survival and reproduction. Yet, how the neural circuits underlying insect behaviors are assembled throughout development and remodeled during evolution remains largely obscure. The advent of single-cell transcriptomics has opened new paths to illuminate these historically intractable questions. Insect behavior is governed by its brain, whose functional complexity is realized through operations across multiple levels, from the molecular and cellular to the circuit and organ. Single-cell transcriptomics enables dissecting brain functions across all these levels and allows tracking regulatory dynamics throughout development and under perturbation. In this review, we mainly focus on the achievements of single-cell transcriptomics in dissecting the molecular and cellular architectures of nervous systems in representative insects, then discuss its applications in tracking the developmental trajectory and functional evolution of insect brains.
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Affiliation(s)
- Weiwei Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Yunnan Key Laboratory of Biodiversity Information, Kunming, China.
| | - Qiye Li
- BGI Research, Shenzhen 518083, China; BGI Research, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
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Pyenson BC, Rehan SM. Gene regulation supporting sociality shared across lineages and variation in complexity. Genome 2024; 67:99-108. [PMID: 38096504 DOI: 10.1139/gen-2023-0054] [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] [Indexed: 01/25/2024]
Abstract
Across evolutionary lineages, insects vary in social complexity, from those that exhibit extended parental care to those with elaborate divisions of labor. Here, we synthesize the sociogenomic resources from hundreds of species to describe common gene regulatory mechanisms in insects that regulate social organization across phylogeny and levels of social complexity. Different social phenotypes expressed by insects can be linked to the organization of co-expressing gene networks and features of the epigenetic landscape. Insect sociality also stems from processes like the emergence of parental care and the decoupling of ancestral genetic programs. One underexplored avenue is how variation in a group's social environment affects the gene expression of individuals. Additionally, an experimental reduction of gene expression would demonstrate how the activity of specific genes contributes to insect social phenotypes. While tissue specificity provides greater localization of the gene expression underlying social complexity, emerging transcriptomic analysis of insect brains at the cellular level provides even greater resolution to understand the molecular basis of social insect evolution.
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Affiliation(s)
| | - Sandra M Rehan
- Department of Biology, York University, Toronto, ON M3J 1P3, Canada
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Wang X, Zhai Y, Zheng H. Deciphering the cellular heterogeneity of the insect brain with single-cell RNA sequencing. INSECT SCIENCE 2024; 31:314-327. [PMID: 37702319 DOI: 10.1111/1744-7917.13270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 09/14/2023]
Abstract
Insects show highly complicated adaptive and sophisticated behaviors, including spatial orientation skills, learning ability, and social interaction. These behaviors are controlled by the insect brain, the central part of the nervous system. The tiny insect brain consists of millions of highly differentiated and interconnected cells forming a complex network. Decades of research has gone into an understanding of which parts of the insect brain possess particular behaviors, but exactly how they modulate these functional consequences needs to be clarified. Detailed description of the brain and behavior is required to decipher the complexity of cell types, as well as their connectivity and function. Single-cell RNA-sequencing (scRNA-seq) has emerged recently as a breakthrough technology to understand the transcriptome at cellular resolution. With scRNA-seq, it is possible to uncover the cellular heterogeneity of brain cells and elucidate their specific functions and state. In this review, we first review the basic structure of insect brains and the links to insect behaviors mainly focusing on learning and memory. Then the scRNA applications on insect brains are introduced by representative studies. Single-cell RNA-seq has allowed researchers to classify cell subpopulations within different insect brain regions, pinpoint single-cell developmental trajectories, and identify gene regulatory networks. These developments empower the advances in neuroscience and shed light on the intricate problems in understanding insect brain functions and behaviors.
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Affiliation(s)
- Xiaofei Wang
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Yifan Zhai
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
- Shandong Provincial Engineering Technology Research Center on Biocontrol of Crops Diseases and In-sect Pests, Jinan, China
| | - Hao Zheng
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
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Wu Z, Sinha S. SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae011. [PMID: 38444538 PMCID: PMC10913396 DOI: 10.1093/bioadv/vbae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/08/2023] [Accepted: 01/18/2024] [Indexed: 03/07/2024]
Abstract
Summary Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Availability and implementation Data and code are available from https://github.com/iiiime/SPREd.
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Affiliation(s)
- Zijun Wu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States
| | - Saurabh Sinha
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States
- H. Milton Steward School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States
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Sun C, Shao Y, Iqbal J. Insect Insights at the Single-Cell Level: Technologies and Applications. Cells 2023; 13:91. [PMID: 38201295 PMCID: PMC10777908 DOI: 10.3390/cells13010091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.
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Affiliation(s)
- Chao Sun
- Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou 310058, China;
| | - Yongqi Shao
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Junaid Iqbal
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
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Wu Z, Sinha S. SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.09.566399. [PMID: 38014297 PMCID: PMC10680606 DOI: 10.1101/2023.11.09.566399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.
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Affiliation(s)
- Zijun Wu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Saurabh Sinha
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- H. Milton Steward School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30318, USA
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