Biology subjects

Mina, E.

Publications and source records attributed to Mina, E..

3 recordsLinked to original sources

bioRxiv2025

Determining off-target effects of splice-switching antisense oligonucleotides using short read RNAseq in neuronally differentiated human induced pluripotent stem cells

Antisense oligonucleotides (AONs) are small pieces of chemically modified DNA or RNA that bind to RNA in a sequence-specific manner based on Watson-Crick base-pairing. Splice-switching AONs are designed to modulate pre-mRNA splicing, thereby for instance restoring protein expression or modifying the eventual protein to restore its function or reduce its toxicity. Given the current lack of in silico methods that adequately predict off-target splicing events, assessment of off-target effects of AONs in human cells using RNAseq could be a promising approach. The identification and prioritization of potential off-target effects for validation and further investigation into the biological relevance would contribute to the development of safe and effective AONs. In this study, we used three different splice-switching AONs targeting three different human transcripts to study their transcriptome-wide, hybridization-dependent off-target effects with short read RNAseq. Using the computational tools rMATS and Whippet, we identified differential splicing events of which only a minority could be explained by hybridization, illustrating the difficulty of predicting off-target effects based on sequence homology. The main splicing events could all be validated with RT-PCR. Furthermore, from the three AONs studied, one AON induced considerably more changes in gene expression and splicing compared to the two other AONs assessed, which was confirmed in a validation experiment. Our study demonstrates that the interpretation of short read RNAseq data to determine off-target effects is challenging. Nonetheless, we showed that valuable results can be obtained as it allows the comparison of toxicity between different AONs within an experiment and identification of AON-specific off-target profiles.

genetics↗

bioRxiv2024

The FAIR Data Point Populator: collaborative FAIRification and population of FAIR Data Points

BackgroundUse of the FAIR principles (Findable, Accessible, Interoperable and Reusable) allows the rapidly growing number of biomedical datasets to be optimally (re)used. An important aspect of the FAIR principles is metadata. The FAIR Data Point specifications and reference implementation have been designed as an example on how to publish metadata according to the FAIR principles. Various tools to create metadata have been created, but many of these have limitations, such as interfaces that are not intuitive, metadata that does not adhere to a common metadata schema, limited scalability, and inefficient collaboration. We aim to address these limitations in the FAIR Data Point Populator. ResultsThe FAIR Data Point Populator consists of a GitHub workflow together with Excel templates that have tooltips, validation and documentation. The Excel templates are targeted towards non-technical users, and can be used collaboratively in online spreadsheet software. A more technical user then uses the GitHub workflow to read multiple entries in the Excel sheets, and transform it into machine readable metadata. This metadata is then automatically uploaded to a connected FAIR Data Point. We applied the FAIR Data Point Populator on the metadata of two datasets, and a patient registry. We were then able to run a query on the FAIR Data Point Index, in order to retrieve one of the datasets. ConclusionThe FAIR Data Point Populator addresses several limitations of other tools. It makes creating metadata easier, ensures adherence to a common metadata schema, allows bulk creation of metadata entries and increases collaboration. As a result of this, the barrier of entry for FAIRification is lower, which enables the creation of FAIR data by more people.

bioinformatics↗

bioRxiv2023

Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington's disease

BackgroundRare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. ResultsWe analyzed a Huntingtons disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. ConclusionsIn summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

bioinformatics↗