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Large-Scale Computational Discovery of Binding Motifs in tRNA Fragments.

ABSTRACT: Accumulating evidence has suggested that tRNA-derived fragments (tRFs) could be loaded to Argonaute proteins and function as regulatory small RNAs. However, their mode of action remains largely unknown, and investigations of their binding mechanisms have been limited, revealing little more than microRNA-like seed regions in a handful of tRFs and a few targets. Here, we identified such regions of potential interaction on a larger scale, using in vivo formed hybrids of guides and targets in crosslinked chimeric reads in two orientations. We considered "forward pairs" (with guides located on the 5' ends and targets on the 3' ends of hybrids) and "reverse pairs" (opposite orientation) and compared them as independent sets of biological constructs. We observed intriguing differences between the two chimera orientations, including the paucity of tRNA halves and abundance of polyT-containing targets in forward pairs. We found a total of 197 quality-ranked motifs supported by ∼120,000 tRF-mRNA chimeras, with 103 interacting motifs common in forward and reverse pairs. By analyzing T→C conversions in human and mouse PAR-CLIP datasets, we detected Argonaute crosslinking sites in tRFs, conserved across species. We proposed a novel model connecting the formation of asymmetric pairs in two sets to the potential binding mechanisms of tRFs, involving the identified interaction motifs and crosslinking sites to Argonaute proteins. Our results suggest the way forward for further experimental elucidation of tRF-binding mechanisms.

SUBMITTER: Guan L

PROVIDER: S-EPMC8258673 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

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Large-Scale Computational Discovery of Binding Motifs in tRNA Fragments.

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