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Creators/Authors contains: "Junqueira-de-Azevedo, Inácio L. M."

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  1. Abstract Background

    The rapid development of sequencing technologies resulted in a wide expansion of genomics studies using venomous lineages. This facilitated research focusing on understanding the evolution of adaptive traits and the search for novel compounds that can be applied in agriculture and medicine. However, the toxin annotation of genomes is a laborious and time-consuming task, and no consensus pipeline is currently available. No computational tool currently exists to address the challenges specific to toxin annotation and to ensure the reproducibility of the process.

    Results

    Here, we present ToxCodAn-Genome, the first software designed to perform automated toxin annotation in genomes of venomous lineages. This pipeline was designed to retrieve the full-length coding sequences of toxins and to allow the detection of novel truncated paralogs and pseudogenes. We tested ToxCodAn-Genome using 12 genomes of venomous lineages and achieved high performance on recovering their current toxin annotations. This tool can be easily customized to allow improvements in the final toxin annotation set and can be expanded to virtually any venomous lineage. ToxCodAn-Genome is fast, allowing it to run on any personal computer, but it can also be executed in multicore mode, taking advantage of large high-performance servers. In addition, we provide a guide to direct future research in the venomics field to ensure a confident toxin annotation in the genome being studied. As a case study, we sequenced and annotated the toxin repertoire of Bothrops alternatus, which may facilitate future evolutionary and biomedical studies using vipers as models.

    Conclusions

    ToxCodAn-Genome is suitable to perform toxin annotation in the genome of venomous species and may help to improve the reproducibility of further studies. ToxCodAn-Genome and the guide are freely available at https://github.com/pedronachtigall/ToxCodAn-Genome.

     
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  2. Venom is a key adaptive innovation in snakes, and how nonvenom genes were co-opted to become part of the toxin arsenal is a significant evolutionary question. While this process has been investigated through the phylogenetic reconstruction of toxin sequences, evidence provided by the genomic context of toxin genes remains less explored. To investigate the process of toxin recruitment, we sequenced the genome ofBothrops jararaca, a clinically relevant pitviper. In addition to producing a road map with canonical structures of genes encoding 12 toxin families, we inferred most of the ancestral genes for their loci. We found evidence that 1) snake venom metalloproteinases (SVMPs) and phospholipases A2(PLA2) have expanded in genomic proximity to their nonvenomous ancestors; 2) serine proteinases arose by co-opting a local gene that also gave rise to lizard gilatoxins and then expanded; 3) the bradykinin-potentiating peptides originated from a C-type natriuretic peptide gene backbone; and 4) VEGF-F was co-opted from a PGF-like gene and not from VEGF-A. We evaluated two scenarios for the original recruitment of nontoxin genes for snake venom: 1) in locus ancestral gene duplication and 2) in locus ancestral gene direct co-option. The first explains the origins of two important toxins (SVMP and PLA2), while the second explains the emergence of a greater number of venom components. Overall, our results support the idea of a locally assembled venom arsenal in which the most clinically relevant toxin families expanded through posterior gene duplications, regardless of whether they originated by duplication or gene co-option.

     
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  3. The role of natural selection in the evolution of trait complexity can be characterized by testing hypothesized links between complex forms and their functions across species. Predatory venoms are composed of multiple proteins that collectively function to incapacitate prey. Venom complexity fluctuates over evolutionary timescales, with apparent increases and decreases in complexity, and yet the causes of this variation are unclear. We tested alternative hypotheses linking venom complexity and ecological sources of selection from diet in the largest clade of front-fanged venomous snakes in North America: the rattlesnakes, copperheads, cantils, and cottonmouths. We generated independent transcriptomic and proteomic measures of venom complexity and collated several natural history studies to quantify dietary variation. We then constructed genome-scale phylogenies for these snakes for comparative analyses. Strikingly, prey phylogenetic diversity was more strongly correlated to venom complexity than was overall prey species diversity, specifically implicating prey species’ divergence, rather than the number of lineages alone, in the evolution of complexity. Prey phylogenetic diversity further predicted transcriptomic complexity of three of the four largest gene families in viper venom, showing that complexity evolution is a concerted response among many independent gene families. We suggest that the phylogenetic diversity of prey measures functionally relevant divergence in the targets of venom, a claim supported by sequence diversity in the coagulation cascade targets of venom. Our results support the general concept that the diversity of species in an ecological community is more important than their overall number in determining evolutionary patterns in predator trait complexity.

     
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  4. Abstract Motivation

    Next-generation sequencing has become exceedingly common and has transformed our ability to explore nonmodel systems. In particular, transcriptomics has facilitated the study of venom and evolution of toxins in venomous lineages; however, many challenges remain. Primarily, annotation of toxins in the transcriptome is a laborious and time-consuming task. Current annotation software often fails to predict the correct coding sequence and overestimates the number of toxins present in the transcriptome. Here, we present ToxCodAn, a python script designed to perform precise annotation of snake venom gland transcriptomes. We test ToxCodAn with a set of previously curated transcriptomes and compare the results to other annotators. In addition, we provide a guide for venom gland transcriptomics to facilitate future research and use Bothrops alternatus as a case study for ToxCodAn and our guide.

    Results

    Our analysis reveals that ToxCodAn provides precise annotation of toxins present in the transcriptome of venom glands of snakes. Comparison with other annotators demonstrates that ToxCodAn has better performance with regard to run time ($>20x$ faster), coding sequence prediction ($>3x$ more accurate) and the number of toxins predicted (generating $>4x$ less false positives). In this sense, ToxCodAn is a valuable resource for toxin annotation. The ToxCodAn framework can be expanded in the future to work with other venomous lineages and detect novel toxins.

     
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