Integrated Venomics and Venom Gland Transcriptome Analysis of Juvenile and Adult Mexican Rattlesnakes Crotalus simus, C. tzabcan, and C. culminatus Revealed miRNA-modulated Ontogenetic Shifts

Abstract

Adult rattlesnakes within genus Crotalus express one of two distinct venom phenotypes, type I (hemorrhagic) and type II (neurotoxic). In Costa Rican Central American rattlesnake, ontogenetic changes in the concentration of miRNAs modulate venom type II to type I transition. Venomics and venom gland transcriptome analyses showed that adult C. simus and C. tzabcan expressed intermediate patterns between type II and type I venoms, whereas C. culminatus had a canonical type I venom. Neonate/juvenile and adult Mexican rattlesnakes showed notable inter- and intraspecific variability in the number, type, abundance and ontogenetic shifts of the transcriptional and translational venom gland activities. These results support a role for miRNAs in the ontogenetic venom compositional changes in the three congeneric Mexican rattlesnakes. It is worth noting the finding of dual-action miRNAs, which silence the translation of neurotoxic heterodimeric PLA crotoxin and acidic PLA mRNAs while simultaneously up-regulating SVMP-targeting mRNAs. Dual transcriptional regulation potentially explains the existence of mutually exclusive crotoxin-rich (type-II) and SVMP-rich (type-I) venom phenotypic dichotomy among rattlesnakes. Our results support the hypothesis that alterations of the distribution of miRNAs, modulating the translational activity of venom gland toxin-encoding mRNAs in response to an external cue, may contribute to the mechanism generating adaptive venom variability.

Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

“…The latter are more frequently used when genomic data from closely related species are unavailable, but not always. It seems that some researchers tend to overestimate the prediction accuracy of such software, to a degree that experimentally non‐validated miRNA targets are speculated to control various biological processes . This is probably due to the misconception that the parameters that these software programs take into account (e.g., computation of miRNA–mRNA favorable thermodynamics and secondary structure of mRNA) enable us to efficiently eliminate most false positives.…”

“…It seems that speculations about miRNA–target regulation, based on prediction alone are less frequent in highly studied model organisms as experimental support can be obtained relatively easily. However, when it comes to non‐model organisms, recent publications continue with bold speculations regarding the putative role of miRNAs that regulate targets that were chosen from a predicted list without any experimental support . A disturbing fact is that in non‐model organisms the search is often made by assuming miRNA–target recognition rules that might not reflect the biology of the studied organisms .…”

“…Notably, faulty target prediction is not restricted only to non‐bilaterian animals and lineages where the mechanism is unknown as some studies in bilaterian non‐model organisms are suffering from the same problems . These studies searched for seed‐matching targets, which is indeed the relevant mechanism for bilaterians; however, their conclusions about the importance of miRNA‐related targets are most likely wrong because of the reasons discussed in the previous chapter.…”

“…In summary, we discussed major problems related to computational predictions of miRNA targets and their regulatory pathways. The high level of noise in lists of predicted targets (Figures and ), the failure to consider the type of the relevant miRNA–target interaction, and the pitfalls of using GO enrichment analyses on predicted targets, altogether strongly suggest that miRNA‐regulated pathways being reported without experimental support are false.…”

“…However, when it comes to non-model organisms, recent publications continue with bold speculations regarding the putative role of miRNAs that regulate targets that were chosen from a predicted list without any experimental support. [37][38][39][43][44][45][46][47][48][49][50][51] A disturbing fact is that in non-model organisms the search is often made by assuming miRNA-target recognition rules that might not reflect the biology of the studied organisms. [38,39,48] Thus, in the next section we will focus on what biological information needs to be taken into account regarding miRNA-target identification in non-model organisms.…”