Biology subjects

Swirski, M.

Publications and source records attributed to Swirski, M..

6 recordsLinked to original sources

bioRxiv2026

Decoding the Sequence Requirements for Translation Initiation

Accurate selection of start codons by ribosomes is a fundamental determinant of proteome composition. Although the Kozak sequence--an 8-nucleotide sequence flanking the start codon--has long been viewed as the primary determinant of initiation in eukaryotes, it fails to explain the large diversity of start codon usage across transcripts. Here we combine massively parallel reporter assays, bioinformatics, machine learning, single-molecule imaging and cryo-electron microscopy to define the extended translation initiation sequence (eTIS), an [~]80-nucleotide sequence surrounding the start codon that governs initiation efficiency. A deep-learning model trained on eTIS features accurately predicts translation initiation across transcripts. Unexpectedly, we find that the Kozak sequence is not optimal for initiation as is widely presumed, and we identify the origin of this discrepancy. eTIS nucleotides that promote efficient initiation are enriched in the human transcriptome and are evolutionarily conserved, underscoring their functional importance. Biophysical and structural analyses reveal that specific eTIS residues--including the key +6 position and residues in the mRNA entry and exit channel--engage ribosomal proteins, rRNA and initiation factors to promote start codon recognition by stabilizing the ribosome at the start codon and facilitating the structural transitions required for initiation. Finally, optimization of the eTIS markedly enhances translational fidelity and protein output from therapeutic mRNAs, highlighting its practical utility. Together, these findings redefine the sequence logic of translation initiation and establish a framework for precise control of protein expression.

molecular biology↗

bioRxiv2025

CHOPOFF: symbolic alignments enable fast and sensitive CRISPR off-target detection

CRISPR/Cas systems offer powerful tools for genome editing, but their therapeutic application is hampered by the risk of unintended off-target mutations. Many molecular methods have been established to detect off-target editing, however, their sensitivity depends on first identifying potential sites using in silico methods. However, these in silico prediction methods are challenged by a trade-off between speed and sensitivity, and can fail to comprehensively detect all edited off-target sites. Here, we demonstrate that ignoring bulges has led to missing editing at off-target sites in previous studies and that continuing this practice can lead to inflated claims of fidelity. As a solution, we introduce the concept of symbolic alignments to efficiently identify off-targets without sacrificing sensitivity. We further present specialized data structures that enable rapid, alignment-free probabilistic ranking of guide RNAs based on their predicted off-target burden. Implemented in the tool CHOPOFF, these innovations accommodate mismatches, bulges (insertions/deletions), and incorporate genomic sequence variants for personalized off-target assessment. Benchmarking demonstrates that CHOPOFF significantly outperforms state-of-the-art tools in both prediction accuracy and computational speed. AvailabilityCHOPOFF command line available at https://github.com/JokingHero/CHOPOFF.jl CHOPOFF web server available at https://crisprtools.org/chopoff

bioinformatics↗

bioRxiv2023

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later translons. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

genomics↗

bioRxiv2022

Integrated data-driven reannotation of the Kluyveromyces marxianus genome reveals an expanded protein coding repertoire

The coding potential of the eukaryotic genome can be greatly expanded by the regulated use of mechanisms that generate more than one protein product from a gene. We combined techniques for mapping 5 and 3 ends of RNA transcripts with ribosome profiling to study the organisation of protein coding gene expression in the yeast Kluyveromyces marxianus. We uncovered over 1000 cases of novel proteoforms due to use of alternative transcription or translation start sites, identified 800 translated upstream open reading frames, observed surprising translation of antisense RNAs, and discovered a novel case of programmed ribosomal frameshifting. In some cases, features are conserved across yeast species, whereas others are species-specific. This offers new possibilities to explore the evolution of genomes and gene regulation in budding yeasts. Our analysis also enabled us to improve the genome annotation of K. marxianus by adding or correcting annotations of over 300 protein coding genes. The processed data has been made available on the GWIPS-viz and Trips-Viz browsers, thus providing an accurate data-driven annotation of transcripts and their protein coding regions along with quantitative information on their transcription and translation.

genomics↗

bioRxiv2021

ORFik: a comprehensive R toolkit for the analysis of translation

* BackgroundWith the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. * ResultsHere, we introduce ORFik, a user-friendly R/Bioconductor toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames. As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5 UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. * Availabilityhttp://bioconductor.org/packages/ORFik

bioinformatics↗

bioRxiv2019

Measuring the relationship between sleep, physical activity and cognition

Biochemical and neuropsychological changes due to poor sleep may contribute to the development of neurodegenerative disorders, such as dementia. Physical activity is widely thought to improve sleep; however, the optimal intensity/duration of physical activity required is unknown. This 14-week, single-blind study (n=23) investigated the feasibility of a self-directed physical activity intervention in healthy adults using actigraphy and cognitive function measures as primary outcomes. Participants were randomised to a control group (no change in routine) or the intervention group (increased physical activity) and were provided with an actigraphy device to monitor activity. Participants completed daily sleep/activity diaries and three cognitive assessment sessions. Vigorous physical activity increased between baseline and week 3 for the intervention group only, with no identifiable impact on sleep. This change was not sustained at week 12. Performance on an executive function task and delayed visuospatial recall improved from baseline to week 12 for the intervention group only. Contrary to our expectations, increasing light-moderate physical activity was associated with more impaired sleep across all participants. It is clear that the relationships between physical activity, sleep and cognition are complex and require further investigation. We discuss optimal methodologies for clinical trials investigating physical activity and/or sleep interventions targeting cognition.

neuroscience↗