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      Egg Yolk Protein Homologs Identified in Live-Bearing Sharks: Co-Opted in the Lecithotrophy-to-Matrotrophy Shift?

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          Abstract

          Reproductive modes of vertebrates are classified into two major embryonic nutritional types: yolk deposits (i.e., lecithotrophy) and maternal investment (i.e., matrotrophy). Vitellogenin (VTG), a major egg yolk protein synthesized in the female liver, is one of the molecules relevant to the lecithotrophy-to-matrotrophy shift in bony vertebrates. In mammals, all VTG genes are lost following the lecithotrophy-to-matrotrophy shift, and it remains to be elucidated whether the lecithotrophy-to-matrotrophy shift in nonmammalians is also associated with VTG repertoire modification. In this study, we focused on chondrichthyans (cartilaginous fishes)—a vertebrate clade that underwent multiple lecithotrophy-to-matrotrophy shifts. For an exhaustive search of homologs, we performed tissue-by-tissue transcriptome sequencing for two viviparous chondrichthyans, the frilled shark Chlamydoselachus anguineus and the spotless smooth-hound Mustelus griseus, and inferred the molecular phylogeny of VTG and its receptor very low-density lipoprotein receptor (VLDLR), across diverse vertebrates. As a result, we identified either three or four VTG orthologs in chondrichthyans including viviparous species. We also showed that chondrichthyans had two additional VLDLR orthologs previously unrecognized in their unique lineage (designated as VLDLRc2 and VLDLRc3). Notably, VTG gene expression patterns differed in the species studied depending on their reproductive mode; VTGs are broadly expressed in multiple tissues, including the uterus, in the two viviparous sharks, and in addition to the liver. This finding suggests that the chondrichthyans VTGs do not only function as the yolk nutrient but also as the matrotrophic factor. Altogether, our study indicates that the lecithotrophy-to-matrotrophy shift in chondrichthyans was achieved through a distinct evolutionary process from mammals.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

            We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                Genome Biology and Evolution
                Oxford University Press (US )
                1759-6653
                March 2023
                20 February 2023
                20 February 2023
                : 15
                : 3
                : evad028
                Affiliations
                Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research (BDR) , Kobe, Japan
                Department of Biology, Graduate School of Science, Kobe University , Kobe, Japan
                Laboratory of Physiology, Atmosphere and Ocean Research Institute, University of Tokyo , Kashiwa, Japan
                Laboratory of Physiology, Atmosphere and Ocean Research Institute, University of Tokyo , Kashiwa, Japan
                Marine Science Museum, Tokai University , Shimizu, Japan
                Husbandry Department, Environmental Aquarium Aquamarine Fukushima , Iwaki, Japan
                Department of Marine Biology, School of Marine Science and Technology, Tokai University , Shimizu, Shizuoka, Japan
                Laboratory of Physiology, Atmosphere and Ocean Research Institute, University of Tokyo , Kashiwa, Japan
                Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research (BDR) , Kobe, Japan
                Molecular Life History Laboratory, National Institute of Genetics , Mishima, Japan
                Depertment of Genetics, Sokendai (Graduate University for Advanced Studies) , Mishima, Japan
                Author notes
                Corresponding author: E-mail: skuraku@ 123456nig.ac.jp .
                Author information
                https://orcid.org/0000-0002-0995-9619
                https://orcid.org/0000-0001-7931-6363
                https://orcid.org/0000-0002-5354-8892
                https://orcid.org/0000-0001-8146-2386
                https://orcid.org/0000-0003-1464-8388
                Article
                evad028
                10.1093/gbe/evad028
                10015161
                36808237
                3f21b95f-f7a7-469c-bae8-be789d27f10a
                © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 February 2023
                : 15 March 2023
                Page count
                Pages: 13
                Categories
                Article
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01140

                Genetics
                vitellogenin,very low-density lipoprotein receptor (vldlr),chondrichthyes,viviparity,yolk,frilled shark,spotless smooth-hound

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