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      Dynamic profiles of lncRNAs reveal a functional natural antisense RNA that regulates the development of Schistosoma japonicum

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          Abstract

          Schistosomes are flatworm parasites that undergo a complex life cycle involving two hosts. The regulation of the parasite’s developmental processes relies on both coding RNAs and non-coding RNAs. However, the roles of non-coding RNAs, including long non-coding RNAs (lncRNAs) in schistosomes remain largely unexplored. Here we conduct advanced RNA sequencing on male and female S. japonicum during their pairing and reproductive development, resulting in the identification of nearly 8,000 lncRNAs. This extensive dataset enables us to construct a comprehensive co-expression network of lncRNAs and mRNAs, shedding light on their interactions during the crucial reproductive stages within the mammalian host. Importantly, we have also revealed a specific lncRNA, LNC3385, which appears to play a critical role in the survival and reproduction of the parasite. These findings not only enhance our understanding of the dynamic nature of lncRNAs during the reproductive phase of schistosomes but also highlight LNC3385 as a potential therapeutic target for combating schistosomiasis.

          Author summary

          Schistosomiasis, a disease that affects millions, has only one drug available treatment, making it essential for us to uncover other potential therapeutic avenues. In our quest to understand the disease better, we delved into the role of long non-coding RNAs (lncRNAs) during the parasite’s reproductive phase, a crucial period for the disease’s spread. Using advanced RNA sequencing, we identified nearly 8,000 lncRNAs during the parasite’s reproductive development. Most intriguingly, we identified one specific lncRNA, named LNC3385, that appears vital for the parasite’s survival and reproduction. Our study, therefore, not only provides a deeper understanding of the molecular dynamics during the reproductive phase of the parasite but also highlights a potential new target, LNC3385, for therapeutic strategies against schistosomiasis.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Data curation
                Role: Data curation
                Role: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                PLOS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                29 January 2024
                January 2024
                : 20
                : 1
                : e1011949
                Affiliations
                [1 ] State Key Laboratory of Genetic Engineering, Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
                [2 ] National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, China
                [3 ] College of Life Sciences, Inner Mongolia University, Hohhot, Inner Mongolia Autonomous Region, China
                [4 ] Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
                Rush University Medical Center, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-0107-424X
                https://orcid.org/0000-0001-6070-5529
                Article
                PPATHOGENS-D-23-01367
                10.1371/journal.ppat.1011949
                10878521
                38285715
                e6ad15eb-155f-404b-82fa-affb206b528c
                © 2024 Cheng et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 August 2023
                : 6 January 2024
                Page count
                Figures: 6, Tables: 0, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100012166, National Key Research and Development Program of China;
                Award ID: No. 2021YFC2300800, 2021YFC2300803
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31972699, 31725025
                Award Recipient :
                Funded by: Science and Technology Leading Talent Team in Inner Mongolia Autonomous Region
                Award ID: 2022LJRC0009
                Award Recipient :
                This research was supported by the National Key Research and Development Program of China (No. 2021YFC2300800, 2021YFC2300803 to JW and WH), National Natural Science Foundation of China (31972699, 31725025 to WH), Science and Technology Leading Talent Team in Inner Mongolia Autonomous Region (2022LJRC0009 to WH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Long non-coding RNA
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Helminths
                Schistosoma
                Schistosoma Japonicum
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Helminths
                Schistosoma
                Schistosoma Japonicum
                Biology and life sciences
                Genetics
                Epigenetics
                RNA interference
                Biology and life sciences
                Genetics
                Gene expression
                RNA interference
                Biology and life sciences
                Genetics
                Genetic interference
                RNA interference
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                RNA interference
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Helminths
                Schistosoma
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Helminths
                Schistosoma
                Biology and Life Sciences
                Physiology
                Reproductive Physiology
                Eggs
                Biology and Life Sciences
                Developmental Biology
                Morphogenesis
                Sexual Differentiation
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Custom metadata
                vor-update-to-uncorrected-proof
                2024-02-20
                The raw sequencing data reported in this paper have been deposited in the NCBI Sequence Read Archive (SRA) database under the accession number PRJNA992996 and can be accessed via link https://www.ncbi.nlm.nih.gov/bioproject/PRJNA992996. The S.japonicum version 3 genome sequences utilized in our study, along with the annotations for the identified long non-coding RNAs and protein-coding gene annotations, are available for download at the following link: https://doi.org/10.5061/dryad.x95x69prn.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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