bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/135517

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

Rigoutsos, I.·Telonis, A. G.·Loher, P.·Magee, R.·Kirino, Y.·Pliatsika, V.
View original source ↗Export metadata ↓

Abstract

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

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rigoutsos, I., Telonis, A. G., Loher, P., Magee, R., Kirino, Y., Pliatsika, V.. 2017-05-09. Systems-Level Analysis Of 32 TCGA Cancers Reveals Disease-Dependent tRNA Fragmentation Patterns And Very Selective Associations With Messenger RNAs And Repeat Elements. https://doi.org/10.1101/135517

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗
bioRxiv2026

The Global Phosphorylation Landscape of Acute Heat Stress in Reef-Building Corals

Warming oceans disrupt the coral-algal symbiosis that sustains reefs, yet the signaling mechanisms initiating bleaching remain poorly understood. Integrating global phosphoproteomics across three reef-building corals with protein structure modeling revealed conservation between coral and human kinases, enabling inference of coral kinase activities and uncovering a shared signaling program induced by acute heat stress. Heat rapidly activated ERK, JNK, and p38 MAP kinases, suppressed cell cycle signaling, and remodeled pathways controlling chromatin, RNA processing, and metabolism. Most strikingly, thermal stress rewired immune receptor signaling networks associated with non-self recognition, suggesting that heat shifts the coral host from tolerance to immune recognition of its algal symbionts. These findings define conserved signaling mechanisms that precede bleaching, revealing actionable molecular targets for enhancing coral resilience.

systems biology↗
bioRxiv2026

Probing bacterial fitness in virtual chemostats

The growth rate of a bacterial population is a fundamental quantity in microbiology, but hard to predict from mechanistic single-cell models. Simulating exponentially growing cell populations is computationally infeasible, and single-lineage estimates are biased as they do not account for phenotypic selection between cells. We present computational algorithms to efficiently estimate growth rates with arbitrary accuracy by combining ideas from branching processes and particle filtering. These can be used with general agent-based cell models at a cost quadratic in the desired accuracy, bypassing the exponential complexity of naive approaches. Applying these methods to single-cell models of Escherichia coli, we study how single-cell heterogeneity in antibiotic responses shapes population dynamics, and how bacterial locomotion and chemotaxis in fluctuating environments affect their long-term growth.

systems biology↗