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      Relationship between circadian genes and memory impairment caused by sleep deprivation

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          Abstract

          Background

          Sleep deprivation (SD)-induced cognitive impairment is highly prevalent worldwide and has attracted widespread attention. The temporal and spatial oscillations of circadian genes are severely disturbed after SD, leading to a progressive loss of their physiological rhythms, which in turn affects memory function. However, there is a lack of research on the role of circadian genes and memory function after SD. Therefore, the present study aims to investigate the relationship between circadian genes and memory function and provide potential therapeutic insights into the mechanism of SD-induced memory impairment.

          Methods

          Gene expression profiles of GSE33302 and GSE9442 from the Gene Expression Omnibus (GEO) were applied to identify differentially expressed genes (DEGs). Subsequently, both datasets were subjected to Gene Set Enrichment Analysis (GSEA) to determine the overall gene changes in the hippocampus and brain after SD. A Gene Oncology (GO) analysis and Protein-Protein Interaction (PPI) analysis were employed to explore the genes related to circadian rhythm, with their relationship and importance determined through a correlation analysis and a receiver operating characteristic curve (ROC), respectively. The water maze experiments detected behavioral changes related to memory function in SD rats. The expression of circadian genes in several critical organs such as the brain, heart, liver, and lungs and their correlation with memory function was investigated using several microarrays. Finally, changes in the hippocampal immune environment after SD were analyzed using the CIBERSORT in R software.

          Results

          The quality of the two datasets was very good. After SD, changes were seen primarily in genes related to memory impairment and immune function. Genes related to circadian rhythm were highly correlated with engagement in muscle structure development and circadian rhythm. Seven circadian genes showed their potential therapeutic value in SD. Water maze experiments confirmed that SD exacerbates memory impairment-related behaviors, including prolonged escape latencies and reduced numbers of rats crossing the platform. The expression of circadian genes was verified, while some genes were also significant in the heart, liver, and lungs. All seven circadian genes were also associated with memory markers in SD. The contents of four immune cells in the hippocampal immune environment changed after SD. Seven circadian genes were related to multiple immune cells.

          Conclusions

          In the present study, we found that SD leads to memory impairment accompanied by changes in circadian rhythm-related genes. Seven circadian genes play crucial roles in memory impairment after SD. Naïve B cells and follicular helper T cells are closely related to SD. These findings provide new insights into the treatment of memory impairment caused by SD.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
          Article 0 comments Cited 12454 times – based on 0 reviews     Review now
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

              Yingyao Zhou, Bin Zhou, Lars Pache … (2019)
              A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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                Author and article information

                Contributors
                Xiaodan Wu
                Kai Zeng
                Journal
                Journal ID (nlm-ta): PeerJ
                Journal ID (iso-abbrev): PeerJ
                Journal ID (publisher-id): peerj
                Title: PeerJ
                Publisher: PeerJ Inc. (San Diego, USA )
                ISSN (Electronic): 2167-8359
                Publication date (Electronic): 21 March 2022
                Publication date Collection: 2022
                Volume: 10
                Electronic Location Identifier: e13165
                Affiliations
                [1 ]Department of Anesthesiology, Anesthesiology Research Institute, the First Affiliated Hospital of Fujian Medical University , Fuzhou, Fujian, China
                [2 ]Department of Anesthesiology, Shengli Clinical Medical College, Fujian Medical University , Fuzhou, Fujian, China
                [3 ]Institute of Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang, China
                Article
                Publisher ID: 13165
                DOI: 10.7717/peerj.13165
                PMC ID: 8944342
                SO-VID: 3537ac02-a530-4b54-89e2-abfa417e2223
                Copyright © ©2022 Ke et al.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                Date received : 27 December 2021
                Date accepted : 4 March 2022
                Funding
                Funded by: The Natural Science Foundation of Fujian Province, China
                Award ID: 2019Y9013
                Award ID: 2020J011080
                This work was supported by the Natural Science Foundation of Fujian Province, China (Grant No. 2019Y9013 and No. 2020J011080). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Bioinformatics
                Subject: Cognitive Disorders
                Subject: Global Health
                Subject: Public Health

                Keywords: memory,sleep deprivation,immune environment,hippocampus,circadian gene
                Data availability:
                Keywords: memory, sleep deprivation, immune environment, hippocampus, circadian gene

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