Summary Transposable Elements (TEs) play key roles in crucial biological pathways. Therefore, several tools enabling the quantification of their expression were recently developed. However, many of the existing tools lack the capability to distinguish between the transcription of autonomously expressed TEs and TE fragments embedded in canonical coding/non-coding non-TE transcripts. Consequently, an apparent change in the expression of a given TE may simply reflect the variation in the expression of the transcripts containing TE-derived sequences. To overcome this issue, we have developed TEspeX, a pipeline for the quantification of TE expression at the consensus level. TEspeX uses Illumina RNA-seq short reads to quantify TE expression avoiding counting reads deriving from inactive TE fragments embedded in canonical transcripts. Availability and Implementation The tool is implemented in python3, distributed under the GNU General Public License (GPL) and available on Github at https://github.com/fansalon/TEspeX (Zenodo URL: https://doi.org/10.5281/zenodo.6800331). Supplementary information Supplementary data are available at Bioinformatics online.
Transposable elements (TEs), also known as “jumping genes”, are repetitive sequences with the capability of changing their location within the genome. They are key players in many different biological processes in health and disease. Therefore, a reliable quantification of their expression as transcriptional units is crucial to distinguish between their independent expression and the transcription of their sequences as part of canonical transcripts. TEs quantification faces difficulties of different types, the most important one being low reads mappability due to their repetitive nature preventing an unambiguous mapping of reads originating from their sequences. A large fraction of TEs fragments localizes within introns, which led to the hypothesis that intron retention (IR) can be an additional source of bias, potentially affecting accurate TEs quantification. IR occurs when introns, normally removed from the mature transcript by the splicing machinery, are maintained in mature transcripts. IR is a widespread mechanism affecting many different genes with cell type-specific patterns. We hypothesized that, in an RNA-seq experiment, reads derived from retained introns can introduce a bias in the detection of overlapping, independent TEs RNA expression. In this study we performed meta-analysis using public RNA-seq data from lymphoblastoid cell lines and show that IR can impact TEs quantification using established tools with default parameters. Reads mapped on intronic TEs were indeed associated to the expression of TEs and influence their correct quantification as independent transcriptional units. We confirmed these results using additional independent datasets, demonstrating that this bias does not appear in samples where IR is not present and that differential TEs expression does not impact on IR quantification. We concluded that IR causes the over-quantification of intronic TEs and differential IR might be confused with differential TEs expression. Our results should be taken into account for a correct quantification of TEs expression from RNA-seq data, especially in samples in which IR is abundant.
LINE L1 are transposable elements that can replicate within the genome by passing through RNA intermediates. The vast majority of these element copies in the human genome are inactive and just between 100 and 150 copies are still able to mobilize. During evolution, they could have been positively selected for beneficial cellular functions. Nonetheless, L1 deregulation can be detrimental to the cell, causing diseases such as cancer. The activity of miRNAs represents a fundamental mechanism for controlling transcript levels in somatic cells. These are a class of small non-coding RNAs that cause degradation or translational inhibition of their target transcripts. Beyond this, competitive endogenous RNAs (ceRNAs), mostly made by circular and non-coding RNAs, have been seen to compete for the binding of the same set of miRNAs targeting protein coding genes. In this study, we have investigated whether autonomously transcribed L1s may act as ceRNAs by analyzing public dataset in-silico. We observed that genes sharing miRNA target sites with L1 have a tendency to be upregulated when L1 are overexpressed, suggesting the possibility that L1 might act as ceRNAs. This finding will help in the interpretation of transcriptomic responses in contexts characterized by the specific activation of transposons.
Mainly known as a transcription factor patterning the rostral brain and governing its histogenesis, Foxg1 has been also detected outside the nucleus, however biological meaning of that has been only partially clarified. Here, moving from Foxg1 expression in cytoplasm of neocortical neurons, we investigated its implication in translational control. We documented an impact of Foxg1 on ribosomal recruitment of Grin1-mRNA, encoding for the main subunit of NMDA receptor. Next, we showed that Foxg1 increases Grin1 protein level by enhancing translation of its mRNA, while not increasing its stability. Such enhancement was associated to augmented translational initiation and, possibly, polypeptide elongation. Molecular mechanisms at the basis of this activity included Foxg1 interaction with Eif4e and Eef1d as well as with Grin1-mRNA. Besides, we found that, within murine neocortical cultures, Grin1 de novo synthesis undergoes a prominent and reversible, homeostatic regulation and Foxg1 is instrumental to that. Finally, trough TRAP-seq, we discovered that Foxg1 is implicated in the translation of hundreds of neuronal at the level of ribosome engagement and progression. All that points to Foxg1 as a key effector, crucial to multi-scale temporal tuning of neocortical pyramid activity, an issue with profound physiological and neuropathological implications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.