Transcriptional landscapes of Axolotl (Ambystoma mexicanum)

Abstract

The axolotl (Ambystoma mexicanum) is the vertebrate model system with the highest regeneration capacity. Experimental tools established over the past 100 years have been fundamental to start unraveling the cellular and molecular basis of tissue and limb regeneration. In the absence of a reference genome for the Axolotl, transcriptomic analysis become fundamental to understand the genetic basis of regeneration. Here we present one of the most diverse transcriptomic data sets for Axolotl by profiling coding and non-coding RNAs from diverse tissues. We reconstructed a population of 115,906 putative protein coding mRNAs as full ORFs (including isoforms). We also identified 352 conserved miRNAs and 297 novel putative mature miRNAs. Systematic enrichment analysis of gene expression allowed us to identify tissue-specific protein-coding transcripts. We also found putative novel and conserved microRNAs which potentially target mRNAs which are reported as important disease candidates in heart and liver.

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.…”

A Chromosome-Scale Assembly of the En ormous (32 Gb) Axolotl Genome

“…After construction of the framework map, an additional 17,819 markers were placed on the meiotic map based on similarity of genotypes to the framework map. Additional scaffolding and orientation information from previously-published RNA-Seq studies (Evans et al 2014;Bryant et al 2017;Caballero-Perez et al 2018) and bulked-segregant analyses were used to tune internal ordering of scaffolds and add another 1,097 previously unplaced scaffolds (grand total of 27,674). In total, 27.5 Gb of sequence was scaffolded onto 14 linkage groups (chromosomes), with the total length of scaffolded sequence per chromosome ranging from 3.14 to 0.66 Gb (Figure 1, Supplementary Tables 2 and 3).…”