RESUMEN
Small non-coding RNAs (sncRNAs), including microRNAs (miRNAs) are important post-transcriptional gene expression regulators relevant in physiological and pathological processes. Here, we combined a high-throughput functional screening (HTFS) platform with a library of antisense oligonucleotides (ASOs) to systematically identify sncRNAs that affect neuronal cell survival in basal conditions and in response to oxidative stress (OS), a major hallmark in neurodegenerative diseases. We considered hits commonly detected by two statistical methods in three biological replicates. Forty-seven ASOs targeting miRNAs (miRNA-ASOs) consistently decreased cell viability under basal conditions. A total of 60 miRNA-ASOs worsened cell viability impairment mediated by OS, with 36.6% commonly affecting cell viability under basal conditions. In addition, 40 miRNA-ASOs significantly protected neuronal cells from OS. In agreement with cell viability impairment, damaging miRNA-ASOs specifically induced increased free radical biogenesis. miRNAs targeted by the detrimental ASOs are enriched in the fraction of miRNAs downregulated by OS, suggesting that the miRNA expression pattern after OS contributes to neuronal damage. The present HTFS highlighted potentially druggable sncRNAs. However, future studies are needed to define the pathways by which the identified ASOs regulate cell survival and OS response and to explore the potential of translating the current findings into clinical applications.
RESUMEN
Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.
Asunto(s)
Algoritmos , MicroARNs/genética , ARN Largo no Codificante/genética , ARN Nucleolar Pequeño/genética , ARN Pequeño no Traducido/genética , ARN de Transferencia/genética , Secuencia de Bases , Análisis por Conglomerados , Perfil Genético , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Internet , MicroARNs/clasificación , Anotación de Secuencia Molecular , ARN Largo no Codificante/clasificación , ARN Nucleolar Pequeño/clasificación , ARN Pequeño no Traducido/clasificación , ARN de Transferencia/clasificación , Reproducibilidad de los Resultados , Programas InformáticosRESUMEN
MOTIVATION: Most computational tools for small non-coding RNAs (sRNA) sequencing data analysis focus in microRNAs (miRNAs), overlooking other types of sRNAs that show multi-mapping hits. Here, we have developed a pipeline to non-redundantly quantify all types of sRNAs, and extract patterns of expression in biologically defined groups. We have used our tool to characterize and profile sRNAs in post-mortem brain samples of control individuals and Parkinson's disease (PD) cases at early-premotor and late-symptomatic stages. RESULTS: Clusters of co-expressed sRNAs mapping onto tRNAs significantly separated premotor and motor cases from controls. A similar result was obtained using a matrix of miRNAs slightly varying in sequence (isomiRs). The present framework revealed sRNA alterations at premotor stages of PD, which might reflect initial pathogenic perturbations. This tool may be useful to discover sRNA expression patterns linked to different biological conditions. AVAILABILITY AND IMPLEMENTATION: The full code is available at http://github.com/lpantano/seqbuster CONTACT: lpantano@hsph.harvard.edu or eulalia.marti@crg.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
