Transfer RNA-Derived Fragments and isomiRs Are Novel Components of Chronic TBI-Induced Neuropathology

Neuroinflammation is a secondary injury mechanism that evolves in the brain for months after traumatic brain injury (TBI). We hypothesized that an altered small non-coding RNA (sncRNA) signature plays a key role in modulating post-TBI secondary injury and neuroinflammation. At 3threemonths post-TBI, messenger RNA sequencing (seq) and small RNAseq were performed on samples from the ipsilateral thalamus and perilesional cortex of selected rats with a chronic inflammatory endophenotype, and sham-operated controls. The small RNAseq identified dysregulation of 2 and 19 miRNAs in the thalamus and cortex, respectively. The two candidates from the thalamus and the top ten from the cortex were selected for validation. In the thalamus, miR-146a-5p and miR-155-5p levels were upregulated, and in the cortex, miR-375-3p and miR-211-5p levels were upregulated. Analysis of isomiRs of differentially expressed miRNAs identified 3′nucleotide additions that were increased after TBI. Surprisingly, we found fragments originating from 16 and 13 tRNAs in the thalamus and cortex, respectively. We further analyzed two upregulated fragments, 3′tRF-IleAAT and 3′tRF-LysTTT. Increased expression of the full miR-146a profile, and 3′tRF-IleAAT and 3′tRF-LysTTT was associated with a worse behavioral outcome in animals with chronic neuroinflammation. Our results highlight the importance of understanding the regulatory roles of as-yet unknown sncRNAs for developing better strategies to treat TBI and neuroinflammation.

Unveiling a Ghost Proteome in the Glioblastoma Non-Coding RNAs

Glioblastoma is the most common brain cancer in adults. Nevertheless, the median survival time is 15 months, if treated with at least a near total resection and followed by radiotherapy in association with temozolomide. In glioblastoma (GBM), variations of non-coding ribonucleic acid (ncRNA) expression have been demonstrated in tumor processes, especially in the regulation of major signaling pathways. Moreover, many ncRNAs present in their sequences an Open Reading Frame (ORF) allowing their translations into proteins, so-called alternative proteins (AltProt) and constituting the “ghost proteome.” This neglected world in GBM has been shown to be implicated in protein–protein interaction (PPI) with reference proteins (RefProt) reflecting involvement in signaling pathways linked to cellular mobility and transfer RNA regulation. More recently, clinical studies have revealed that AltProt is also involved in the patient’s survival and bad prognosis. We thus propose to review the ncRNAs involved in GBM and highlight their function in the disease.

Deciphering the tRNA-derived small RNAs: origin, development, and future

Transfer RNA (tRNA)-derived small RNAs (tsRNAs), a novel category of small noncoding RNAs, are enzymatically cleaved from tRNAs. Previous reports have shed some light on the roles of tsRNAs in the development of human diseases. However, our knowledge about tsRNAs is still relatively lacking. In this paper, we review the biogenesis, classification, subcellular localization as well as action mechanism of tsRNAs, and discuss the association between chemical modifications of tRNAs and the production and functions of tsRNAs. Furthermore, using immunity, metabolism, and malignancy as examples, we summarize the molecular mechanisms of tsRNAs in diseases and evaluate the potential of tsRNAs as new biomarkers and therapeutic targets. At the same time, we compile and introduce several resource databases that are currently publicly available for analyzing tsRNAs. Finally, we discuss the challenges associated with research in this field and future directions.

PlantRNA 2.0 : an updated database dedicated to tRNAs of photosynthetic eukaryotes

PlantRNA (http://plantrna.ibmp.cnrs.fr/) is a comprehensive database of transfer RNA (tRNA) gene sequences retrieved from fully annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms. In the first release (PlantRNA 1.0), tRNA genes from 11 organisms were annotated. In this second version, the annotation was implemented to 48 photosynthetic species covering the whole phylogenetic tree of photosynthetic organisms, from the most basal group of Archeplastida, the glaucophyte Cyanophora paradoxa, to various land plants. Transfer RNA genes from lower photosynthetic organisms such as streptophyte algae or lycophytes as well as extremophile photosynthetic species such as Eutrema parvulum were incorporated in the database. As a whole, circa 35 000 tRNA genes were accurately annotated. In the frame of the tRNA genes annotation from the genome of the Rhodophyte Chondrus crispus, putative unconventional splicing sites in the D- or T- regions of tRNA molecules were experimentally determined to strengthen the quality of the database. As for PlantRNA 1.0, comprehensive biological information including flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences and tRNA mitochondrial import are included.

MitoVisualize: A resource for analysis of variants in human mitochondrial RNAs and DNA

We present MitoVisualize, a new tool for analysis of the human mitochondrial DNA (mtDNA). MitoVisualize enables visualization of: (1) the position and effect of variants in mitochondrial transfer RNA (tRNA) and ribosomal RNA (rRNA) secondary structures alongside curated variant annotations, (2) data across RNA structures, such as to show all positions with disease-associated variants or with post-transcriptional modifications, and (3) the position of a base, gene or region in the circular mtDNA map, such as to show the location of a large deletion. All visualizations can be easily downloaded as figures for reuse. MitoVisualize can be useful for anyone interested in exploring mtDNA variation, though is designed to facilitate mtDNA variant interpretation in particular. MitoVisualize can be accessed via https://www.mitovisualize.org/. The source code is available at https://github.com/leklab/mito_visualize/.

Altered Expression of Transfer-RNA-Derived Small RNAs in Human With Rheumatic Heart Disease

Rheumatic heart disease (RHD) remains a severe public health problem in developing countries. Atrial fibrillation (AF) is a medical complication of RHD. Although the understanding of disease pathogenesis has advanced in recent years, the key questions need to be addressed. Transfer RNA–derived small RNAs (tsRNAs) are a novel type of short non-coding RNAs with potential regulatory functions in various physiological and pathological processes. The present study used tsRNAs sequencing to investigate the relationship between RHD and atrial fibrillation (AF). Three paired cardiac papillary muscles were taken from six rheumatic RHD patients with AF (3 cases) or without AF (3 cases) from January 2016 to January 2017 in Xiangya Hospital, Central South University. A total of 219 precisely matched tsRNAs were identified, and 77 tsRNAs (fold change > 2.0 and P < 0.05) were differently changed. Three tsRNAs (AS-tDR-001269, AS-tDR-001363, AS-tDR-006049) were randomly selected and confirmed by qRT-PCR. The results of qRT-PCR were consistent with tsRNAs sequencing, suggesting the tsRNAs sequencing was reliable. Subsequently, we predicted the target mRNAs of the three tsRNAs. Moreover, we verified the functions of tsRNAs targeting mRNAs in vitro. Finally, bioinformatics analysis indicated that the target genes were abundant in regulation of transcription, DNA binding, intracellular. Most of the genes were predicted to interplay with cytokine-cytokine receptor by KEGG analysis. Our findings uncover the pathological process of AF in RHD through tsRNAs sequencing. This research provides a new perspective for future research on elucidating the mechanism of AF in RHD and offers potential new candidates for the treatment and diagnosis.

Potential Biomarkers for Therapeutic Monitoring and Clinical Outcome in Breast Cancer

Non-coding RNAs are a species of RNA that are not translated to proteins. These include transfer RNAs and ribosomal RNAs, microRNAs, transfer RNA-derived fragments, and long non-coding RNA. It is known that expression levels of some non-coding RNAs included microRNAs are altered in cancer cells or tumor tissues. Moreover, expression profiles of such non-coding RNAs correlate between tissues and body fluids. Therefore, several non-coding RNAs are being used as diagnostic/prognosis biomarkers or therapeutic targets in cancer. In this chapter, we review about representative non-coding RNAs and introduce especially microRNA as diagnosis/prognosis biomarkers and therapeutic targets.

The Effect of tRNA[Ser]Sec Isopentenylation on Selenoprotein Expression

Transfer RNA[Ser]Sec carries multiple post-transcriptional modifications. The A37G mutation in tRNA[Ser]Sec abrogates isopentenylation of base 37 and has a profound effect on selenoprotein expression in mice. Patients with a homozygous pathogenic p.R323Q variant in tRNA-isopentenyl-transferase (TRIT1) show a severe neurological disorder, and hence we wondered whether selenoprotein expression was impaired. Patient fibroblasts with the homozygous p.R323Q variant did not show a general decrease in selenoprotein expression. However, recombinant human TRIT1R323Q had significantly diminished activities towards several tRNA substrates in vitro. We thus engineered mice conditionally deficient in Trit1 in hepatocytes and neurons. Mass-spectrometry revealed that hypermodification of U34 to mcm5Um occurs independently of isopentenylation of A37 in tRNA[Ser]Sec. Western blotting and 75Se metabolic labeling showed only moderate effects on selenoprotein levels and 75Se incorporation. A detailed analysis of Trit1-deficient liver using ribosomal profiling demonstrated that UGA/Sec re-coding was moderately affected in Selenop, Txnrd1, and Sephs2, but not in Gpx1. 2′O-methylation of U34 in tRNA[Ser]Sec depends on FTSJ1, but does not affect UGA/Sec re-coding in selenoprotein translation. Taken together, our results show that a lack of isopentenylation of tRNA[Ser]Sec affects UGA/Sec read-through but differs from a A37G mutation.