Biology subjects

Yano, Y.

Publications and source records attributed to Yano, Y..

3 recordsLinked to original sources

bioRxiv2020

The tRNA pseudouridine synthase TruB1 regulates the maturation and function of let-7 miRNA

Let-7 is an evolutionary conserved microRNA that mediates post-transcriptional gene silencing to regulate a wide range of biological processes, including development, differentiation, and tumor suppression. Let-7 biogenesis is tightly regulated by several RNA-binding proteins, including Lin28A/B, which represses let-7 maturation. To identify new regulators of let-7, we devised a cell-based functional screen of RNA-binding proteins using a let-7 sensor luciferase reporter, and identified the tRNA pseudouridine synthase, TruB1. TruB1 enhanced maturation specifically of let-7 family members. Rather than inducing pseudouridylation of the miRNAs, HITS-CLIP (High throughput sequencing crosslinking immunoprecipitation) and biochemical analyses revealed direct binding between endogenous TruB1 and the stem-loop structure of pri-let-7, which also binds Lin28A/B. TruB1 selectively enhanced the interaction between pri-let-7 and the microprocessor DGCR-8, which mediates miRNA maturation. Finally, TruB1 suppressed cell proliferation, which was mediated in part by let-7. Altogether, we reveal an unexpected function for TruB1 in promoting let-7 maturation and function.

molecular biology↗

bioRxiv2018

adductomicsR: A package for detection and quantification of protein adducts from mass spectra of tryptic digests

SummaryLiquid chromatography-high resolution mass spectrometry (LC-HRMS) has been used to establish a method, referred to as adductomics, for characterisation of putative protein adducts at selected loci in human serum albumin (HSA). Applications of this method have been limited by the lack of software for untargeted analysis of modified peptides in protein digests. Here we present adductomicsR, an open-source R package for processing LC-HRMS data from analysis of adducted HSA peptides. The software interrogates mass spectra to correct for retention-time drift, and to discover and quantify putative adducts along with those for a housekeeping peptide and internal standard.\n\nAvailability and implementationadductomicsR is written in R and publicly available at https://github.com/JosieLHayes/adductomicsR, which includes a vignette with example data.\n\nSupplementary informationmzXML files for the vignette and test dataset are available in an associated data package adductData (https://github.com/JosieLHayes/adductData).\n\nContactjosie.hayes@berkeley.edu\n\nIssue SectionAPPLICATIONS NOTE

bioinformatics↗

bioRxiv2018

Data-adaptive pipeline for filtering and normalizing metabolomics data.

IntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena.\n\nObjectivesHere, we propose and evaluate a data-adaptive pipeline for metabolomics data that are generated by liquid chromatography-mass spectrometry platforms.\n\nMethodsOur data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. It also incorporates a variant of k-nearest-neighbor imputation of missing values. Finally, we adapted an RNA-Seq approach and R package, scone, to select an appropriate normalization scheme for removing unwanted variation from metabolomics datasets.\n\nResultsUsing two metabolomics datasets that were generated in our laboratory from samples of human blood serum and neonatal blood spots, we compared our data-adaptive pipeline with a traditional filtering and normalization scheme. The data-adaptive approach outperformed the traditional pipeline in almost all metrics related to removal of unwanted variation and maintenance of biologically relevant signatures. The R code for running the data-adaptive pipeline is provided with an example dataset at https://github.com/courtneyschiffman/Data-adaptive-metabolomics.\n\nConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

bioinformatics↗