Exploring transcriptomic regulation of the developing brain through integrative bioinformatics and deep learning approaches

The mammalian brain is a remarkably complex organ whose development relies on precise molecular and cellular processes. One of the most sophisticated brain structures is the cerebral cortex. Many proteins and microRNAs play distinct roles in the posttranscriptional regulation of corticogenesis. Mutations in RNA-binding proteins and miRNAs are linked to neurodevelopmental disorders, highlighting their crucial role in central nervous system development. This doctoral thesis investigates posttranscriptional mechanisms that shape brain development, focusing on alternative splicing and microRNA regulation. Premature overexpression of the RNA binding protein Rbfox2 during murine corticogenesis caused migration and differentiation defects through an altered splicing pattern, likely antagonizing PTBP2’s splicing program. In this study, it was further shown that the neuronal progenitor cell-specific miR-92a-3p repressed Rbfox2 both in vitro and in vivo, revealing a novel regulatory relationship between miRNAs and splicing factors during neural development. Furthermore, analysis of miRNAs during embryonic corticogenesis revealed clusters of coregulated miRNAs with overlapping target sets. Luciferase assays showed that such cotargeting of miRNAs enhances the repression effect on shared target mRNAs. This finding suggests a complex regulatory network where multiple miRNAs cooperatively fine-tune gene expression during brain development. To support future research, two computational tools were developed: a web portal (www.cortexa-rna.com) providing access to neocortex/hippocampus RNA-sequencing datasets, improving the accessibility of transcriptomic data to the broader scientific community. Additionally, I implemented a deep learning framework for denoising functional imaging recordings. This computational tool enables the validation of molecular changes in synaptic function at single synapse resolution, bridging the gap between molecular alterations (including splicing-induced changes) and their functional consequences in neurons.

Publication Details

Journal
Gutenberg Open Science
Published
2025-01-01
DOI
https://doi.org/10.25358/openscience-11257
Primary Topic
Machine Learning in Bioinformatics
Type
article
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0.00
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article

Exploring transcriptomic regulation of the developing brain through integrative bioinformatics and deep learning approaches

Gutenberg Open Science
Machine Learning in Bioinformatics
article

Exploring transcriptomic regulation of the developing brain through integrative bioinformatics and deep learning approaches

article en

Abstract

The mammalian brain is a remarkably complex organ whose development relies on precise molecular and cellular processes. One of the most sophisticated brain structures is the cerebral cortex. Many proteins and microRNAs play distinct roles in the posttranscriptional regulation of corticogenesis. Mutations in RNA-binding proteins and miRNAs are linked to neurodevelopmental disorders, highlighting their crucial role in central nervous system development. This doctoral thesis investigates posttranscriptional mechanisms that shape brain development, focusing on alternative splicing and microRNA regulation. Premature overexpression of the RNA binding protein Rbfox2 during murine corticogenesis caused migration and differentiation defects through an altered splicing pattern, likely antagonizing PTBP2’s splicing program. In this study, it was further shown that the neuronal progenitor cell-specific miR-92a-3p repressed Rbfox2 both in vitro and in vivo, revealing a novel regulatory relationship between miRNAs and splicing factors during neural development. Furthermore, analysis of miRNAs during embryonic corticogenesis revealed clusters of coregulated miRNAs with overlapping target sets. Luciferase assays showed that such cotargeting of miRNAs enhances the repression effect on shared target mRNAs. This finding suggests a complex regulatory network where multiple miRNAs cooperatively fine-tune gene expression during brain development. To support future research, two computational tools were developed: a web portal (www.cortexa-rna.com) providing access to neocortex/hippocampus RNA-sequencing datasets, improving the accessibility of transcriptomic data to the broader scientific community. Additionally, I implemented a deep learning framework for denoising functional imaging recordings. This computational tool enables the validation of molecular changes in synaptic function at single synapse resolution, bridging the gap between molecular alterations (including splicing-induced changes) and their functional consequences in neurons.

Gutenberg Open Science
Openalex Percentile: Top 62%
Machine Learning in Bioinformatics
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