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      Differential expression analysis of mRNAs, lncRNAs, and miRNAs expression profiles and construction of ceRNA networks in PEDV infection

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          Abstract

          Background

          Porcine Epidemic Diarrhea Virus (PEDV) is a coronavirus that seriously affects the swine industry. MicroRNAs and long noncoding RNAs are two relevant non-coding RNAs (ncRNAs) class and play crucial roles in a variety of physiological processes. Increased evidence indicates a complex interaction between mRNA and ncRNA. However, our understanding of the function of ncRNA involved in host-PEDV interaction is limited.

          Results

          A total of 1,197 mRNA transcripts, 539 lncRNA transcripts, and 208 miRNA transcripts were differentially regulated at 24 h and 48 h post-infection. Gene ontology (GO) and KEGG pathway enrichment analysis showed that DE mRNAs and DE lncRNAs were mainly involved in biosynthesis, innate immunity, and lipid metabolism. Moreover, we constructed a miRNA-mRNA-pathway network using bioinformatics, including 12 DE mRNAs, 120 DE miRNAs, and 11 pathways. Finally, the target genes of DE miRNAs were screened by bioinformatics, and we constructed immune-related lncRNA-miRNA-mRNA ceRNA networks. Then, the selected DE genes were validated by qRT-PCR, which were consistent with the results from RNA-Seq data.

          Conclusions

          This study provides the comprehensive analysis of the expression profiles of mRNAs, lncRNAs, and miRNAs during PEDV infection. We characterize the ceRNA networks which can provide new insights into the pathogenesis of PEDV.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-022-08805-0.

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          Most cited references45

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            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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              HISAT: a fast spliced aligner with low memory requirements.

              Daehwan Kim, Ben Langmead, Steven L Salzberg (2019)
              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                Xingang Xu: tiger2003@nwsuaf.edu.cn
                Journal
                Journal ID (nlm-ta): BMC Genomics
                Journal ID (iso-abbrev): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2164
                Publication date (Electronic): 13 August 2022
                Publication date PMC-release: 13 August 2022
                Publication date Collection: 2022
                Volume: 23
                Electronic Location Identifier: 586
                Affiliations
                GRID grid.144022.1, ISNI 0000 0004 1760 4150, College of Veterinary Medicine, , Northwest A&F University, ; Yangling, Xianyang, 712100 Shaanxi China
                Article
                Publisher ID: 8805
                DOI: 10.1186/s12864-022-08805-0
                PMC ID: 9375197
                PubMed ID: 35964002
                SO-VID: f017634b-2a4a-49cf-8fd6-12c4938e148a
                Copyright © © The Author(s) 2022
                License:

                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
                Date received : 16 November 2021
                Date accepted : 28 July 2022
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                ScienceOpen disciplines: Genetics
                Keywords: pedv,lncrna,functional enrichment,signaling pathway,cerna network
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: pedv, lncrna, functional enrichment, signaling pathway, cerna network

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