Pliatsika, V. | bioRxiv Science

bioRxiv2022

Single-nucleotide Differences and Cell Type Decide the Subcellular Localization of miRNA Isoforms (isomiRs), tRNA-derived Fragments (tRFs) and rRNA-derived Fragments (rRFs)

BackgroundMicroRNAs (miRNAs) and their isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) represent ~95% of all short RNAs found in cells. All three types modulate mRNA and protein abundance and are dysregulated in diseases. Experimental studies to date assumed that the subcellular localization of these molecules is well understood and constant across cell types. ResultsWe investigated the localization of isomiRs, tRFs, and rRFs in biological replicates from three frequently-used model cell lines. In each case, we analyzed the contents of the nucleus, cytoplasm, whole mitochondrion, mitoplast, and the whole cell. We used a rigorous mathematical model to account for cross-fraction contamination and technical errors and adjusted abundances accordingly. We found that isomiRs, tRFs, and rRFs exhibit complex and unexpected patterns of subcellular localization. These patterns depend on the type of the RNA molecule, its exact sequence, and the cell type. Even for "sibling" RNAs from the same parental RNA whose sequences differ by only a few nucleotides, their subcellular localization depends on each siblings exact sequence and the cell type. ConclusionsPrevious studies of isomiRs, tRFs, and rRFs that used ectopic expression without accounting for isoforms may need to be re-evaluated. Future experiments with these molecules will need to distinguish among the multiple isoforms and account for the fact that each isoforms abundance and destination depend on its exact sequence and cell type. The findings additionally suggest the existence of an intracellular trafficking program for isomiRs, tRFs, and rRFs and, by extension, expanded roles for these molecules - both dimensions await characterization. To help design future experiments, we compiled a first-of-its-kind Atlas to catalogue the subcellular localization and abundance of 5,898 isomiRs, tRFs, and rRFs across three model cell lines. Results SummaryO_LIWe analyzed the distribution of microRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) in the{whitebullet} nucleus {whitebullet}cytoplasm {whitebullet}mitochondrion, and {whitebullet}mitoplast of biological replicates from three cell lines from the same tissue. C_LIO_LIWe corrected the measured abundances by accounting for cross-fraction contamination and technical errors through a rigorous mathematical model. C_LIO_LIOur analysis revealed complex localization patterns involving numerous isomiRs, tRFs, and rRFs. C_LIO_LIThe subcellular localization of these RNAs depends on their exact sequence and differs even for molecules that arise from the same parental miRNA, tRNA, or rRNA. C_LIO_LIFor a given RNA, its subcellular localization additionally depends on cell type. C_LIO_LIThe findings have implications for previous and future molecular studies of the function of isomiRs, tRFs, and rRFs. C_LIO_LIThe findings suggest the existence of a complex subcellular trafficking program, and hint at expanded functions for these RNA molecules that differ by compartment. C_LIO_LITo assist with the design of future experiments, we created a first-of-its-kind Atlas that catalogues the subcellular distribution and abundance of 5,898 isomiRs, tRFs, and rRFs across three cell lines. C_LI

molecular biology↗

bioRxiv2017

Systems-Level Analysis Of 32 TCGA Cancers Reveals Disease-Dependent tRNA Fragmentation Patterns And Very Selective Associations With Messenger RNAs And Repeat Elements

We mined 10,274 datasets from The Cancer Genome Atlas (TCGA) for tRNA fragments (tRFs) that overlap nuclear and mitochondrial (MT) mature tRNAs. Across 32 cancer types, we identified 20,722 distinct tRFs, a third of which arise from MT tRNAs. Most of the fragments belong to the novel category of i-tRFs, i.e. they are wholly internal to the mature tRNAs. The abundances and cleavage patterns of the identified tRFs depend strongly on cancer type. Of note, in all 32 cancer types, we find that tRNAHisGTG produces multiple and abundant 5{acute}-tRFs with a uracil at the -1 position, instead of the expected post-transcriptionally-added guanosine. Strikingly, these -1U His 5{acute}tRFs are produced in ratios that remain constant across all analyzed normal and cancer samples, a property that makes tRNAHisGTG unique among all tRNAs. We also found numerous tRFs to be negatively correlated with many messenger RNAs (mRNAs) that belong primarily to four universal biological processes: transcription, cell adhesion, chromatin organization and development/morphogenesis. However, the identities of the mRNAs that belong to these processes and are negatively correlated with tRFs differ from cancer to cancer. Notably, the protein products of these mRNAs localize to specific cellular compartments, and do so in a cancer-dependent manner. Moreover, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in multiple categories of repeat elements. Conversely, the genomic span of mRNAs that are positively correlated with tRFs are depleted in repeat elements. These findings suggest novel and far-reaching roles for tRFs and indicate their involvement in system-wide interconnections in the cell. All discovered tRFs from TCGA can be downloaded from https://cm.jefferson.edu/tcga-mintmap-profiles or studied interactively through the newly-designed version 2.0 of MINTbase at https://cm.jefferson.edu/MINTbase.\n\nNOTE: while the manuscript is under review, the content on the page https://cm.jefferson.edu/tcgamintmap-profiles is password protected and available only to Reviewers.\n\nKey PointsO_LIComplexity: tRNAs exhibit a complex fragmentation pattern into a multitude of tRFs that are conserved within the samples of a given cancer but differ across cancers.\nC_LIO_LIVery extensive mitochondrial contributions: the 22 tRNAs of the mitochondrion (MT) contribute 1/3rd of all tRFs found across cancers, a disproportionately high number compared to the tRFs from the 610 nuclear tRNAs.\nC_LIO_LIUridylated (not guanylated) 5{acute}-His tRFs: in all human tissues analyzed, tRNAHisGTG produces many abundant modified 5{acute}-tRFs with a U at their \"-1\" position (-1U 5{acute}-tRFs), instead of a G.\nC_LIO_LILikely central roles for tRNAHisGTG: the relative abundances of the -1U 5{acute}-tRFs from tRNAHisGTG remain strikingly conserved across the 32 cancers, a property that makes tRNAHisGTG unique among all tRNAs and isoacceptors.\nC_LIO_LISelective tRF-mRNA networks: tRFs are negatively correlated with mRNAs that differ characteristically from cancer to cancer.\nC_LIO_LIMitochondrion-encoded tRFs are associated with nuclear proteins: in nearly all cancers, and in a cancer-specific manner, tRFs produced by the 22 mitochondrial tRNAs are negatively correlated with mRNAs whose protein products localize to the nucleus.\nC_LIO_LItRFs are associated with membrane proteins: in all cancers, and in a cancer-specific manner, nucleus-encoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products localize to the cells membrane.\nC_LIO_LItRFs are associated with secreted proteins: in all cancers, and in a cancer-specific manner, nucleusencoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products are secreted from the cell.\nC_LIO_LItRFs are associated with numerous mRNAs through repeat elements: in all cancers, and in a cancerspecific manner, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in specific categories of repeat elements.\nC_LIO_LIintra-cancer tRF networks can depend on sex and population origin: within a cancer, positive and negative tRF-tRF correlations can be modulated by patient attributes such as sex and population origin.\nC_LIO_LIweb-enabled exploration of an \"Atlas for tRFs\": we released a new version of MINTbase to provide users with the ability to study 26,531 tRFs compiled by mining 11,719 public datasets (TCGA and other sources).\nC_LI

systems biology↗