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      Transcriptomic characterization and curation of candidate neuropeptides regulating reproduction in the eyestalk ganglia of the Australian crayfish, Cherax quadricarinatus

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          Abstract

          The Australian redclaw crayfish ( Cherax quadricarinatus) has recently received attention as an emerging candidate for sustainable aquaculture production in Australia and worldwide. More importantly, C. quadricarinatus serves as a good model organism for the commercially important group of decapod crustaceans as it is distributed worldwide, easy to maintain in the laboratory and its reproductive cycle has been well documented. In order to better understand the key reproduction and development regulating mechanisms in decapod crustaceans, the molecular toolkit available for model organisms such as C. quadricarinatus must be expanded. However, there has been no study undertaken to establish the C. quadricarinatus neuropeptidome. Here we report a comprehensive study of the neuropeptide genes expressed in the eyestalk in the Australian crayfish C. quadricarinatus. We characterised 53 putative neuropeptide-encoding transcripts based on key features of neuropeptides as characterised in other species. Of those, 14 neuropeptides implicated in reproduction regulation were chosen for assessment of their tissue distribution using RT-PCR. Further insights are discussed in relation to current knowledge of neuropeptides in other species and potential follow up studies. Overall, the resulting data lays the foundation for future gene-based neuroendocrinology studies in C. quadricarinatus.

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          Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

          Background With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data. Results To reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies. Conclusions Our work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.
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            Optimizing de novo assembly of short-read RNA-seq data for phylogenomics

            Background RNA-seq has shown huge potential for phylogenomic inferences in non-model organisms. However, error, incompleteness, and redundant assembled transcripts for each gene in de novo assembly of short reads cause noise in analyses and a large amount of missing data in the aligned matrix. To address these problems, we compare de novo assemblies of paired end 90 bp RNA-seq reads using Oases, Trinity, Trans-ABySS and SOAPdenovo-Trans to transcripts from genome annotation of the model plant Ricinus communis. By doing so we evaluate strategies for optimizing total gene coverage and minimizing assembly chimeras and redundancy. Results We found that the frequency and structure of chimeras vary dramatically among different software packages. The differences were largely due to the number of trans-self chimeras that contain repeats in the opposite direction. More than half of the total chimeras in Oases and Trinity were trans-self chimeras. Within each package, we found a trade-off between maximizing reference coverage and minimizing redundancy and chimera rate. In order to reduce redundancy, we investigated three methods: 1) using cap3 and CD-HIT-EST to combine highly similar transcripts, 2) only retaining the transcript with the highest read coverage, or removing the transcript with the lowest read coverage for each subcomponent in Trinity, and 3) filtering Oases single k-mer assemblies by number of transcripts per locus and relative transcript length, and then finding the transcript with the highest read coverage. We then utilized results from blastx against model protein sequences to effectively remove trans chimeras. After optimization, seven assembly strategies among all four packages successfully assembled 42.9–47.1% of reference genes to more than 200 bp, with a chimera rate of 0.92–2.21%, and on average 1.8–3.1 transcripts per reference gene assembled. Conclusions With rapidly improving sequencing and assembly tools, our study provides a framework to benchmark and optimize performance before choosing tools or parameter combinations for analyzing short-read RNA-seq data. Our study demonstrates that choice of assembly package, k-mer sizes, post-assembly redundancy-reduction and chimera cleanup, and strand-specific RNA-seq library preparation and assembly dramatically improves gene coverage by non-redundant and non-chimeric transcripts that are optimized for downstream phylogenomic analyses.
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              A comparative review of short and long neuropeptide F signaling in invertebrates: Any similarities to vertebrate neuropeptide Y signaling?

              Neuropeptides referred to as neuropeptide F (NPF) and short neuropeptide F (sNPF) have been identified in numerous invertebrate species. Sequence information has expanded tremendously due to recent genome sequencing and EST projects. Analysis of sequences of the peptides and prepropeptides strongly suggest that NPFs and sNPFs are not closely related. However, the NPFs are likely to be ancestrally related to the vertebrate family of neuropeptide Y (NPY) peptides. Peptide diversification may have been accomplished by different mechanisms in NPFs and sNPFs; in the former by gene duplications followed by diversification and in the sNPFs by internal duplications resulting in paracopies of peptides. We discuss the distribution and functions of NPFs and their receptors in several model invertebrates. Signaling with sNPF, however, has been investigated mainly in insects, especially in Drosophila. Both in invertebrates and in mammals NPF/NPY play roles in feeding, metabolism, reproduction and stress responses. Several other NPF functions have been studied in Drosophila that may be shared with mammals. In Drosophila sNPFs are widely distributed in numerous neurons of the CNS and some gut endocrines and their functions may be truly pleiotropic. Peptide distribution and experiments suggest roles of sNPF in feeding and growth, stress responses, modulation of locomotion and olfactory inputs, hormone release, as well as learning and memory. Available data indicate that NPF and sNPF signaling systems are distinct and not likely to play redundant roles. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                07 December 2016
                2016
                : 6
                : 38658
                Affiliations
                [1 ]GeneCology Research Centre, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast , Sunshine Coast, Queensland, Australia
                Author notes
                Article
                srep38658
                10.1038/srep38658
                5141488
                27924858
                943ad677-3751-4dac-a966-a4c22d8f9833
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 05 July 2016
                : 11 November 2016
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