Network-based visualisation reveals new insights into transposable element diversity

The curious case of the disappearing piRNAs

Small non-coding RNAs are key regulators of gene expression across eukaryotes. Piwi-interacting small RNAs (piRNAs) are a specific type of small non-coding RNAs, conserved across animals, which are best known as regulators of genome stability through their ability to target transposable elements for silencing. Despite the near ubiquitous presence of piRNAs in animal lineages, there are some examples where the piRNA pathway has been lost completely, most dramatically in nematodes where loss has occurred in at least four independent lineages. In this perspective I will provide an evaluation of the presence of piRNAs across animals, explaining how it is known that piRNAs are missing from certain organisms. I will then consider possible explanations for why the piRNA pathway might have been lost and evaluate the evidence in favor of each possible mechanism. While it is still impossible to provide definitive answers, these theories will prompt further investigations into why such a highly conserved pathway can nevertheless become dispensable in certain lineages. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

Evolutionary dynamics of the LTR-retrotransposon crapaud in the Podospora anserina species complex and the interaction with repeat-induced point mutations

BACKGROUND

The genome of the filamentous ascomycete Podospora anserina shows a relatively high abundance of retrotransposons compared to other interspersed repeats. The LTR-retrotransposon family crapaud is particularly abundant in the genome, and consists of multiple diverged sequence variations specifically localized in the 5' half of both long terminal repeats (LTRs). P. anserina is part of a recently diverged species-complex, which makes the system ideal to classify the crapaud family based on the observed LTR variation and to study the evolutionary dynamics, such as the diversification and bursts of the elements over recent evolutionary time.

RESULTS

We developed a sequence similarity network approach to classify the crapaud repeats of seven genomes representing the P. anserina species complex into 14 subfamilies. This method does not utilize a consensus sequence, but instead it connects any copies that share enough sequence similarity over a set sequence coverage. Based on phylogenetic analyses, we found that the crapaud repeats likely diversified in the ancestor of the complex and have had activity at different time points for different subfamilies. Furthermore, while we hypothesized that the evolution into multiple subfamilies could have been a direct effect of escaping the genome defense system of repeat induced point mutations, we found this not to be the case.

CONCLUSIONS

Our study contributes to the development of methods to classify transposable elements in fungi, and also highlights the intricate patterns of retrotransposon evolution over short timescales and under high mutational load caused by nucleotide-altering genome defense.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.