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      A trade-off in evolution: the adaptive landscape of spiders without venom glands

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          Abstract

          Background

          Venom glands play a key role in the predation and defense strategies of almost all spider groups. However, the spider family Uloboridae lacks venom glands and has evolved an adaptive strategy: they excessively wrap their prey directly with spider silk instead of paralyzing it first with toxins. This shift in survival strategy is very fascinating, but the genetic underpinnings behind it are poorly understood.

          Results

          Spanning multiple spider groups, we conducted multiomics analyses on Octonoba sinensis and described the adaptive evolution of the Uloboridae family at the genome level. We observed the coding genes of myosin and twitchin in muscles are under positive selection, energy metabolism functions are enhanced, and gene families related to tracheal development and tissue mechanical strength are expanded or emerged, all of which are related to the unique anatomical structure and predatory behavior of spiders in the family Uloboridae. In addition, we also scanned the elements that are absent or under relaxed purifying selection, as well as toxin gene homologs in the genomes of 2 species in this family. The results show that the absence of regions and regions under relaxed selection in these spiders’ genomes are concentrated in areas related to development and neurosystem. The search for toxin homologs reveals possible gene function shift between toxins and nontoxins and confirms that there are no reliable toxin genes in the genome of this group.

          Conclusions

          This study demonstrates the trade-off between different predation strategies in spiders, using either chemical or physical strategy, and provides insights into the possible mechanism underlying this trade-off. Venomless spiders need to mobilize multiple developmental and metabolic pathways related to motor function and limb mechanical strength to cover the decline in adaptability caused by the absence of venom glands.

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          Most cited references92

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Author and article information

                Contributors
                Role: Writing - original draft
                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                2024
                05 August 2024
                05 August 2024
                : 13
                : giae048
                Affiliations
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                Hebei Key Laboratory of Animal Diversity, College of Life Sciences, Langfang Normal University , Langfang 065000, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                Hebei Key Laboratory of Animal Diversity, College of Life Sciences, Langfang Normal University , Langfang 065000, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                University of Chinese Academy of Sciences , Beijing 101408, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China
                Author notes
                Correspondence address. Shuqiang Li, Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China, E-mail: lisq@ 123456ioz.ac.cn

                Yiming Zhang and Yunxiao Shen contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-8547-6654
                https://orcid.org/0009-0003-9186-5068
                https://orcid.org/0000-0003-1310-2711
                https://orcid.org/0000-0001-9875-3286
                https://orcid.org/0000-0002-6789-2731
                https://orcid.org/0000-0002-6148-1157
                https://orcid.org/0009-0007-3665-6254
                https://orcid.org/0009-0007-6882-1066
                https://orcid.org/0000-0002-0781-0204
                https://orcid.org/0000-0002-3290-5416
                Article
                giae048
                10.1093/gigascience/giae048
                11299198
                39101784
                404ead42-700f-480f-b3c7-3c6770f183bf
                © The Author(s) 2024. Published by Oxford University Press GigaScience.

                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 September 2023
                : 26 January 2024
                : 26 June 2024
                Page count
                Pages: 16
                Categories
                Research
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI02254

                venom gland deficient,adaptive evolution,genome,octonoba sinensis

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