Biology subjects

Pastore, S.

Publications and source records attributed to Pastore, S..

8 recordsLinked to original sources

bioRxiv2025

Redefining human pre-rRNA processing at single nucleotide resolution using long read Nanopore sequencing

Ribosome biogenesis requires the synthesis and processing of precursor rRNAs (pre-rRNAs) into mature rRNAs. Traditional methods like northern blotting and metabolic labeling offer limited resolution. We present NanoRibolyzer, a nanopore-based, long-read sequencing approach that enables ab initio identification and quantification of rRNA precursors. Using both supervised and unsupervised mapping, it detects known and novel pre-rRNA species and defines cleavage sites at single-nucleotide resolution. A simple cell fractionation protocol provides spatial separation of nuclear and cytoplasmic pre-rRNAs. Targeted knockdowns quantify intermediate accumulations, revealing condition-specific processing fingerprints with biomarker potential. Pseudouridine mapping shows that the primary 47S transcript is extensively modified, while aberrant products are not. With its high resolution and unique mapping strategy, NanoRibolyzer offers new insights into rRNA processing and modification, enhancing our understanding of ribosome biogenesis.

cell biology↗

bioRxiv2025

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of epitranscriptomes and RNA modification.

bioinformatics↗

bioRxiv2024

Yeast elongation factor homolog New1 protects a subset of mRNAs from degradation by no-go decay

New1 is a homologue of the essential yeast translation elongation factor eEF3. Lack of New1 has previously been shown to induce queueing of ribosomes upstream of the stop codon on mRNAs encoding specific C-terminal amino acids, primarily lysine and arginine. Here, we used UV crosslinking and analysis of cDNA, long-read nanopore sequencing and proteomics to address the open question of what consequences such queues have for the yeast cell. We show that these queues are ribosomal collisions, which are recognized by the collision sensor and E3 ubiquitin ligase Hel2, marking these collided ribosome complexes for mRNA degradation via canonical no-go decay. Decay is initiated by Cue2-mediated cleavage upstream of the stop codon. Ultimately, this leads to downregulation of encoded proteins, including highly abundant and important metabolic enzymes Pgk1 and Gpm1, as well as translation elongation factors eEF1-alpha and eEF1-beta. Collisions and resulting downstream effects are codon-, rather than amino acid dependent. E.g., for C-terminal lysine and arginine, only specific codons induce collisions upon lack of New1. Our study shows that New1 protects highly abundant and essential genes from degradation by no-go decay thatwould otherwise occur in the absence of translation inhibitors or other direct perturbations of translation.

molecular biology↗

bioRxiv2024

ClearFinder: a Python GUI for annotating cells in cleared mouse brain

BackgroundTissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. ResultsWe developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole-cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we report significant differences in total cell counts between CellFinder and ClearMap when cross-analyzing the same samples, underscoring the need for optimizing reproducibility within the field. ConclusionsOur easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.

bioinformatics↗

bioRxiv2024

Cortexa - a comprehensive resource for studying gene expression and alternative splicing in the murine brain.

MotivationGene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate devel-oping harmonized data handling and processing pipelines which in turn requires considerable computational resources and in-depth bioinformatics expertise. ResultsHere, we present Cortexa - a comprehensive web-portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform also provides SplicePCA - a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All gene expression and alternative splicing data have been processed in a standardized manner and they can also be downloaded for further in-depth down-stream analysis. AvailabilityThe data portal is available at https://cortexa-rna.com/ Contacthristo.todorov@uni-mainz.de.

genomics↗

bioRxiv2024

Real-time transcriptomic profiling in distinct experimental conditions

Nanopore technology offers real-time sequencing opportunities, providing rapid access to sequenced data and allowing researchers to manage the sequencing process efficiently, resulting in cost-effective strategies. Here, we present focused case studies demonstrating the versatility of real-time transcriptomics analysis in rapid quality control for long-read RNA-seq. We illustrate its utility through four experimental setups: 1) transcriptome profiling of distinct human cellular populations, 2) identification of experimentally enriched transcripts, 3) transcriptional analysis of cells under heat shock conditions and 4) identification of experimentally manipulated genes (knockout and overexpression) in several yeast strains. We show how to perform multiple layers of quality control as soon as sequencing has started, addressing both the quality of the experimental and sequencing traits. Real-time quality control measures assess sample/condition variability and determine the number of identified genes per sample/condition. Furthermore, real-time differential gene/transcript expression analysis can be conducted at various time points post-sequencing initiation (PSI), revealing dynamic changes in gene/transcript expression between two conditions. Using real-time analysis, which occurs in parallel to the sequencing run, we identified differentially expressed genes/transcripts as early as 1hr PSI. These changes were consistently observed throughout the entire sequencing process. We discuss the new possibilities offered by real-time data analysis, which have the potential to serve as a valuable tool for rapid and cost-effective quality checks in specific experimental settings and can be potentially integrated into clinical applications in the future.

bioinformatics↗

bioRxiv2022

NanopoReaTA: a user-friendly tool for nanopore-seq real-time transcriptional analysis

SummaryOxford Nanopore Technologies (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface (GUI) with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision-making that could also be applied to clinical cases. Availabilityhttps://github.com/AnWiercze/NanopoReaTA Contactbuttamer@uni-mainz.de and sugerber@uni-mainz.de

bioinformatics↗

bioRxiv2022

Nonsynonymous Mutations in Intellectual Disability and Autism Spectrum Disorder Gene PTCHD1 Disrupt N-Glycosylation and Reduce Protein Stability

PTCHD1 has been implicated in Autism Spectrum Disorders (ASD) and/or intellectual disability, where copy number variant losses or loss-of-function coding mutations segregate with disease in an X-linked recessive fashion. Missense variants of PTCHD1 have also been reported in patients. However, the significance of these mutations remains undetermined since the activities, subcellular localization and regulation of the PTCHD1 protein are currently unknown. This paucity of data concerning PTCHD1 prevents the effective evaluation of sequence variants identified during diagnostic screening. Here, we characterize PTCHD1 protein binding partners, extending previously reported interactions with postsynaptic scaffolding protein, SAP102. Six rare missense variants of PTCHD1 were also identified from patients with neurodevelopmental disorders. After modelling these variants on a hypothetical three-dimensional structure of PTCHD1, based on the solved structure of NPC1, PTCHD1 variants harboring these mutations were assessed for protein stability, post-translational processing and protein trafficking. We show here that wild-type PTCHD1 post-translational modification includes complex N-glycosylation and that specific mutant proteins disrupt normal N-link glycosylation processing. However, regardless of their processing, these mutants still localized to PSD95-containing dendritic processes and remained competent for complexing SAP102.

molecular biology↗