Biology subjects

Barbosa, P.

Publications and source records attributed to Barbosa, P..

2 recordsLinked to original sources

bioRxiv2024

Erosion of X-Chromosome Inactivation in female hiPSCs is heterogeneous and persists during differentiation

During culture, female human pluripotent stem cells (hPSCs), including human induced PSCs (hiPSCs) exhibit a propensity for erosion of X-chromosome inactivation (XCI). This phenomenon is characterized by the loss of XIST RNA expression and reactivation of a subset of X-linked genes from the inactive X chromosome (Xi). XCI erosion, despite its common occurrence, is often overlooked by the stem cell community, hindering a complete understanding of its impact on both fundamental and translational applications of hiPSCs. Investigating erosion dynamics in female hiPSCs, our study reveals that XCI erosion is a frequent yet heterogeneous phenomenon, resulting in the reactivation of several X-linked genes. The likelihood of a gene to erode increases for those located on the short arm of the X chromosome and within H3K27me3-enriched domains. Paradoxically, genes that typically escape XCI are hypersensitive to loss of XIST RNA and XCI erosion. This implies that XIST RNA normally restrains expression levels of these genes on the Xi. Importantly, increased X-linked gene expression upon erosion does not globally impact (hydroxy)methylation levels in hiPSCs or at imprinted regions. By exploring diverse differentiation paradigms, such as trilineage commitment and cardiac differentiation, our study reveals the persistence of abnormal XCI patterns throughout differentiation. This finding has significant implications for fundamental research, translational applications, and clinical use of stem cells. We underscore the importance of raising awareness within the stem cell community regarding XCI erosion and advocate for its inclusion in comprehensive hiPSC quality control.

molecular biology↗

bioRxiv2023

Computational prediction of human deep intronic variation

The adoption of whole genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to differentiate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce. In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that affect splicing regulatory elements or the branchpoint region. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground truth information, but the use of these tools results in decreased predictive power when compared to black box methods. Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.

bioinformatics↗