Population Genomic Analysis of a Pitviper Reveals Microevolutionary Forces Underlying Venom Chemistry
AbstractVenoms are among the most biologically active secretions known, and are commonly believed to evolve under extreme positive selection. Many venom gene families, however, have undergone duplication, and are often deployed in doses vastly exceeding the LD50 for most prey species, which should reduce the strength of positive selection. Here, we contrast these selective regimes using snake venoms, which consist of rapidly evolving protein formulations. Though decades of extensive studies have found that snake venom proteins are subject to strong positive selection, the greater action of drift has been hypothesized, but never tested. Using a combination of de novo genome sequencing, population genomics, transcriptomics, and proteomics, we compare the two modes of evolution in the pitviper, Protobothrops mucrosquamatus. By partitioning selective constraints and adaptive evolution in a McDonald–Kreitman-type framework, we find support for both hypotheses: venom proteins indeed experience both stronger positive selection, and lower selective constraint than other genes in the genome. Furthermore, the strength of selection may be modulated by expression level, with more abundant proteins experiencing weaker selective constraint, leading to the accumulation of more deleterious mutations. These findings show that snake venoms evolve by a combination of adaptive and neutral mechanisms, both of which explain their extraordinarily high rates of molecular evolution. In addition to positive selection, which optimizes efficacy of the venom in the short term, relaxed selective constraints for deleterious mutations can lead to more rapid turnover of individual proteins, and potentially to exploration of a larger venom phenotypic space.
[…] Venom gland RNA seq libraries were prepared as described previously (), except that ERCC92 synthetic spike-ins were added to the RNA extracts as described by for quality control. In addition, pooled libraries were normalized using the Evrogen Trimmer-2 cDNA normalization kit and sequenced to improve genome annotation for nonvenom transcripts. Reads were mapped to predicted coding sequences using Bowtie2 () within the RSEM package (). Though expression data doesn’t accurately always reflect protein levels in the venom (), it does in Protobothrops (, ). Therefore, we used the number of fragments per kilobase mapped (FPKM) as a measure of protein abundance. Mapped read counts for each sample, as estimated by RSEM, can be found in , online. Proteomic analysis was conducted as described previously (, ). […]
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