Biology subjects

Lucas, M. C.

Publications and source records attributed to Lucas, M. C..

8 recordsLinked to original sources

bioRxiv2024

Parallel in-depth analysis of repeat expansions: an updated Clin-CATS workflow for nanopore R10 flow cells

Hereditary ataxias, caused by expansions of short tandem repeats, are difficult to diagnose using traditional PCR and Southern blot methods, which struggle to detect complex repeat expansions and cannot assess repeat interruptions or methylation. We present an updated Clinical Nanopore Cas9-Targeted Sequencing (Clin-CATS) workflow for analyzing repeat expansions, now compatible with the Oxford Nanopore Technologies R10 flow cell. The workflow incorporates the ONT wf-human-variation Epi2Me workflow, including the Straglr tool to analyze basecalled reads, ensuring compatibility with past, current, and future sequencing chemistries. It expands the number of genes analyzed from 10 to 27 within the ataxia panel and introduces new gene panels for myopathy, neurodegeneration, and ALS/motor neuron disease. Validated with Coriell reference and clinical samples, this method improves the analysis of pathogenic repeat expansions, providing deeper insights into repeat structures while addressing the limitations of traditional approaches.

genetics↗

bioRxiv2024

Epitranscriptomic rRNA fingerprinting reveals tissue-of-origin and tumor-specific signatures

Mammalian ribosomal RNA (rRNA) molecules are highly abundant RNAs, decorated with over 220 rRNA modifications. Previous works have shown that some rRNA modification types can be dynamically regulated; however, how and when the mammalian rRNA modification landscape is remodeled remains largely unexplored. Here, we employ direct RNA nanopore sequencing to chart the human and mouse rRNA epitranscriptome across tissues, developmental stages, cell types and disease. Our analyses reveal multiple rRNA sites that are differentially modified in a tissue- and/or developmental stage-specific manner, including previously unannotated modified sites. We demonstrate that rRNA modification patterns can be used for tissue and cell type identification, which we hereby term epitranscriptomic fingerprinting. We then explore rRNA modification patterns in normal-tumor matched samples from lung cancer patients, finding that epitranscriptomic fingerprinting accurately classifies clinical samples into normal and tumor groups from only 250 reads per sample, demonstrating the potential of rRNA modifications as diagnostic biomarkers.

molecular biology↗

bioRxiv2023

Enhanced detection of RNA modifications and mappability with high-accuracy nanopore RNA basecalling models

In recent years, nanopore direct RNA sequencing (DRS) has established itself as a valuable tool for studying the epitranscriptome, due to its ability to detect multiple modifications within the same full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling errors in DRS datasets, N6-methyladenosine (m6A) modifications produce relatively low errors compared to other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the error signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads -especially in shorter RNA fractions- and increased basecalling error signatures at pseudouridine ({Psi}) and N1-methylpseudouridine (m1{Psi}) modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability and basecalling accuracy in nanopore DRS datasets.

genomics↗

bioRxiv2021

RPL3L-containing ribosomes modulate mitochondrial activity in the mammalian heart

The existence of naturally occurring ribosome heterogeneity is now a well-acknowledged phenomenon. However, whether this heterogeneity leads to functionally diverse specialized ribosomes is still a controversial topic. Here, we explore the biological function of RPL3L (uL3L), a ribosomal protein (RP) paralog of RPL3 (uL3) that is exclusively expressed in muscle and heart tissues, by generating a viable homozygous Rpl3l knockout mouse strain. We identify a rescue mechanism in which, upon RPL3L depletion, RPL3 becomes upregulated, yielding RPL3-containing ribosomes instead of RPL3L-containing ribosomes that are typically found in cardiomyocytes. Using both ribosome profiling (Ribo-Seq) and a novel orthogonal approach consisting of ribosome pulldown coupled to nanopore sequencing (Nano-TRAP), we find that RPL3L neither modulates translational efficiency nor ribosome affinity towards a specific subset of transcripts. By contrast, we show that depletion of RPL3L leads to increased ribosome-mitochondria interactions in cardiomyocytes, which is accompanied by a significant increase in ATP levels, potentially as a result of mitochondrial activity fine-tuning. Our results demonstrate that the existence of tissue-specific RP paralogs does not necessarily lead to enhanced translation of specific transcripts or modulation of translational output. Instead, we reveal a complex cellular scenario in which RPL3L modulates the expression of RPL3, which in turn affects ribosomal subcellular localization and, ultimately, mitochondrial activity.

molecular biology↗

bioRxiv2020

Decoding ribosomal RNA modification dynamics at single molecule resolution

A broad diversity of modifications decorate RNA molecules. Originally conceived as static components, evidence is accumulating that some RNA modifications may be dynamic, contributing to cellular responses to external signals and environmental circumstances. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each modification type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify and quantify RNA modifications present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling error signature, which we validate using a battery of genetic strains lacking either pseudouridine (Y) or 2-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of Y modifications transcriptome-wide, confirming known Y-modified sites as well as uncovering novel Y sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent Y modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive Y-modified sites in snRNAs, snoRNAs and mRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that Y RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.

molecular biology↗

bioRxiv2019

Barcoding and demultiplexing Oxford Nanopore native RNA sequencing reads with deep residual learning

Nanopore sequencing has enabled sequencing of native RNA molecules without conversion to cDNA, thus opening the gates to a new era for the unbiased study of RNA biology. However, a formal barcoding protocol for direct sequencing of native RNA molecules is currently lacking, limiting the efficient processing of multiple samples in the same flowcell. A major limitation for the development of barcoding protocols for direct RNA sequencing is the error rate introduced during the base-calling process, especially towards the 5 and 3 ends of reads, which complicates sequence-based barcode demultiplexing. Here, we propose a novel strategy to barcode and demultiplex direct RNA sequencing nanopore data, which does not rely on base-calling or additional library preparation steps. Specifically, custom DNA oligonucleotides are ligated to RNA transcripts during library preparation. Then, raw current signal corresponding to the DNA barcode is extracted and transformed into an array of pixels, which is used to determine the underlying barcode using a deep convolutional neural network classifier. Our method, DeePlexiCon, implements a 20-layer residual neural network model that can demultiplex 93% of the reads with 95.1% specificity, or 60% of reads with 99.9% specificity. The availability of an efficient and simple barcoding strategy for native RNA sequencing will enhance the use of direct RNA sequencing by making it more cost-effective to the entire community. Moreover, it will facilitate the applicability of direct RNA sequencing to samples where the RNA amounts are limited, such as patient-derived samples.

genomics↗

bioRxiv2019

Integrative analyses of the RNA modification machinery reveal tissue- and cancer-specific signatures

BackgroundRNA modifications play central roles in cellular fate and differentiation. These features have placed the epitranscriptome in the forefront of developmental biology and cancer research. However, the machinery responsible for placing, removing and recognizing more than 170 RNA modifications remains largely uncharacterized and poorly annotated, and we currently lack integrative studies that identify which RNA modification-related proteins (RMPs) may be dysregulated in each cancer type. ResultsHere we have performed a comprehensive annotation and evolutionary analysis of human RMPs as well as an integrative analysis of their expression patterns across 32 tissues, 10 species and 13,358 paired tumor-normal human samples. Our analysis reveals an unanticipated heterogeneity of RMP expression patterns across mammalian tissues, with a vast proportion of duplicated enzymes displaying testis-specific expression, suggesting a key role for RNA modifications in sperm formation and possibly intergenerational inheritance. Moreover, through the analysis of paired tumor-normal human samples we uncover many RMPs that are dysregulated in various types of cancer, and whose expression levels are predictive of cancer progression. Surprisingly, we find that several commonly studied RNA modification enzymes such as METTL3 or FTO, are not significantly up-regulated in most cancer types, once the sample is properly scaled and normalized to the full dataset, whereas several less-characterized RMPs, such as LAGE3 and HENMT1, are dysregulated in many cancers. ConclusionsOur analyses reveal an unanticipated heterogeneity in the expression patterns of RMPs across mammalian tissues, and uncover a large proportion of dysregulated RMPs in multiple cancer types. We provide novel targets for future cancer research studies targeting the human epitranscriptome, as well as foundations to understand cell type-specific behaviours that are orchestrated by RNA modifications.

molecular biology↗

bioRxiv2019

Accurate detection of m6A RNA modifications in native RNA sequences

The field of epitranscriptomics has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using Oxford Nanopore Technologies, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide resolution, in individual RNA molecules.

molecular biology↗