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      Transcriptome analysis provides genome annotation and expression profiles in the central nervous system of Lymnaea stagnalis at different ages

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          Abstract

          Background

          The pond snail, Lymnaea stagnalis ( L. stagnalis), has served as a valuable model organism for neurobiology studies due to its simple and easily accessible central nervous system (CNS). L. stagnalis has been widely used to study neuronal networks and recently gained popularity for study of aging and neurodegenerative diseases. However, previous transcriptome studies of L. stagnalis CNS have been exclusively carried out on adult L. stagnalis only. As part of our ongoing effort studying L. stagnalis neuronal growth and connectivity at various developmental stages, we provide the first age-specific transcriptome analysis and gene annotation of young (3 months), adult (6 months), and old (18 months) L. stagnalis CNS.

          Results

          Using the above three age cohorts, our study generated 55–69 millions of 150 bp paired-end RNA sequencing reads using the Illumina NovaSeq 6000 platform. Of these reads, ~ 74% were successfully mapped to the reference genome of L. stagnalis. Our reference-based transcriptome assembly predicted 42,478 gene loci, of which 37,661 genes encode coding sequences (CDS) of at least 100 codons. In addition, we provide gene annotations using Blast2GO and functional annotations using Pfam for ~ 95% of these sequences, contributing to the largest number of annotated genes in L. stagnalis CNS so far. Moreover, among 242 previously cloned L. stagnalis genes, we were able to match ~ 87% of them in our transcriptome assembly, indicating a high percentage of gene coverage. The expressional differences for innexins, FMRFamide, and molluscan insulin peptide genes were validated by real-time qPCR. Lastly, our transcriptomic analyses revealed distinct, age-specific gene clusters, differentially expressed genes, and enriched pathways in young, adult, and old CNS. More specifically, our data show significant changes in expression of critical genes involved in transcription factors, metabolisms (e.g. cytochrome P450), extracellular matrix constituent, and signaling receptor and transduction (e.g. receptors for acetylcholine, N-Methyl-D-aspartic acid, and serotonin), as well as stress- and disease-related genes in young compared to either adult or old snails.

          Conclusions

          Together, these datasets are the largest and most updated L. stagnalis CNS transcriptomes, which will serve as a resource for future molecular studies and functional annotation of transcripts and genes in L. stagnalis.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-021-07946-y.

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          Most cited references85

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          KEGG: kyoto encyclopedia of genes and genomes.

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          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            Toward understanding the origin and evolution of cellular organisms

            In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Contributors
                fenglian.xu@slu.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                3 September 2021
                3 September 2021
                2021
                : 22
                : 637
                Affiliations
                [1 ]GRID grid.262962.b, ISNI 0000 0004 1936 9342, Department of Biology, College of Arts and Sciences, , Saint Louis University, ; St. Louis, MO USA
                [2 ]GRID grid.262962.b, ISNI 0000 0004 1936 9342, Henry and Amelia Nasrallah Center for Neuroscience, , Saint Louis University, ; St. Louis, MO USA
                [3 ]GRID grid.262962.b, ISNI 0000 0004 1936 9342, Department of Pharmacology and Physiology, , Saint Louis University, School of Medicine, ; St. Louis, MO USA
                Article
                7946
                10.1186/s12864-021-07946-y
                8414863
                34479505
                7808142c-9ee2-4efb-9891-53b0d50b00bc
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 May 2021
                : 23 August 2021
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Genetics
                lymnaea stagnalis,cns,transcriptome,gene annotation,different ages,neurodevelopment,synaptic genes,disease-related genes

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