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      Characterization of Biological Pathways Regulating Acute Cold Resistance of Zebrafish

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          Abstract

          Low temperature stress represents a major threat to the lives of both farmed and wild fish species. However, biological pathways determining the development of cold resistance in fish remain largely unknown. Zebrafish larvae at 96 hpf were exposed to lethal cold stress (10 °C) for different time periods to evaluate the adverse effects at organism, tissue and cell levels. Time series RNA sequencing (RNA-seq) experiments were performed to delineate the transcriptomic landscape of zebrafish larvae under cold stress and during the subsequent rewarming phase. The genes regulated by cold stress were characterized by progressively enhanced or decreased expression, whereas the genes associated with rewarming were characterized by rapid upregulation upon return to normal temperature (28 °C). Genes such as trib3, dusp5 and otud1 were identified as the representative molecular markers of cold-induced damages through network analysis. Biological pathways involved in cold stress responses were mined from the transcriptomic data and their functions in regulating cold resistance were validated using specific inhibitors. The autophagy, FoxO and MAPK (mitogen-activated protein kinase) signaling pathways were revealed to be survival pathways for enhancing cold resistance, while apoptosis and necroptosis were the death pathways responsible for cold-induced mortality. Functional mechanisms of the survival-enhancing factors Foxo1, ERK (extracellular signal-regulated kinase) and p38 MAPK were further characterized by inhibiting their activities upon cold stress and analyzing gene expression though RNA-seq. These factors were demonstrated to determine the cold resistance of zebrafish through regulating apoptosis and p53 signaling pathway. These findings have provided novel insights into the stress responses elicited by lethal cold and shed new light on the molecular mechanisms underlying cold resistance of fish.

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          Most cited references69

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                16 March 2021
                March 2021
                : 22
                : 6
                : 3028
                Affiliations
                [1 ]State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; renjing@ 123456ihb.ac.cn (J.R.); liuran@ 123456ihb.ac.cn (R.L.); guilisong@ 123456ihb.ac.cn (G.S.); qli@ 123456ihb.ac.cn (Q.L.)
                [2 ]College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
                [3 ]Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
                [4 ]College of Fisheries and Life Science, Dalian Ocean University, Dalian 116023, China
                [5 ]The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China
                Author notes
                [* ]Correspondence: longyong@ 123456ihb.ac.cn (Y.L.); zbcui@ 123456ihb.ac.cn (Z.C.); Tel.: +86-27-68780100 (Y.L.); +86-27-68780090 (Z.C.)
                Author information
                https://orcid.org/0000-0001-6439-0181
                https://orcid.org/0000-0002-2113-9111
                https://orcid.org/0000-0002-3443-3168
                Article
                ijms-22-03028
                10.3390/ijms22063028
                8001686
                33809683
                545a8bdd-79f1-4464-b98b-43655c333ed5
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 08 February 2021
                : 13 March 2021
                Categories
                Article

                Molecular biology
                cold resistance,zebrafish,stress response,biological pathways,transcriptional regulation,cell death

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