Adams, D. A. | Search bioRxiv

bioRxiv2026

STRADA Deficiency Impairs Cortical Interneuron Development in Humans and Mice

Polyhydramnios, Megalencephaly, and Symptomatic Epilepsy syndrome (PMSE/STRADA-related disorder) is a rare neurodevelopmental disorder characterized by megalencephaly (ME), early-onset drug-resistant epilepsy, neurocognitive impairment, and high early mortality, often due to status epilepticus. PMSE is caused by a multi-exon deletion in STRADA, encoding STRADA, which regulates the mechanistic target of rapamycin (mTOR) pathway. GABAergic inhibitory interneurons (INs) critically modulate the excitatory:inhibitory balance in cortical and hippocampal networks, and IN deficits contribute to epileptogenesis in several epileptic encephalopathies. However, no studies have investigated INs in PMSE. We used a multimodal approach to study INs in a Strada-/- mouse model engineered with the same causative 5-exon deletion identified in human PMSE. We demonstrate that Strada/STRADA loss causes a reduction of INs in the somatosensory cortex and a corresponding increase in the striatum, representative of remnant ganglionic eminence progenitor origin, in Strada-/- mice and a single PMSE brain tissue specimen. RNA sequencing comparing wildtype to Strada-/- cortex and striatum corroborated these findings, revealing increased IN-related gene expression (e.g., Dlx2) in the striatum and decreased IN-related gene expression (e.g., Pvalb) in the developing cortex. Cytoskeletal (e.g., Tpp3, Kank4, Map1a) and mTOR-associated genes (e.g., Rictor, Cryab) are differentially expressed in the developing cortex, mature striatum, and mature cortex of Strada-/- mice. Functional validation confirmed enlarged INs in mouse and human Strada/STRADA-deficient brain and enhanced S6 phosphorylation in Strada-/- striatum. Together, these findings suggest STRADA/Strada loss contributes to failed IN migration -- the first such report in a developmental, mTOR-associated megalencephaly syndrome -- highlighting INs as a therapeutic target for seizure prevention in PMSE. Key PointsO_LI- Reduced numbers of cortical inhibitory interneurons were observed in the cerebral cortex of Strada-/- mice, with striatal interneuron aggregation C_LIO_LI- Reduced numbers of cortical inhibitory interneurons, with an aggregation in striatum, were observed in human PMSE brain, supporting the observations in Strada-/- mouse C_LIO_LI- Transcriptomic analysis in Strada-/- mice reveals evidence of early developmental interneuron and cytoskeletal dysfunction C_LIO_LI- We introduce a loss of cortical interneurons as a salient feature of PMSE developmental pathogenesis, potentially contributing to a loss of inhibitory modulation C_LIO_LI- This is the first study proposing interneuron migration impairment in the developmental pathogenesis of an mTOR-associated megalencephaly syndrome C_LI

neuroscience↗

bioRxiv2026

A predicted cancer dependency map for paralog pairs

BackgroundGenome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identification of genetic vulnerabilities associated with specific biomarkers. Paralogs, despite being common drug targets, are often missed in these screens as their individual disruption rarely causes a significant fitness defect. Combinatorial screens have revealed that paralog pairs are often synthetic lethal but that these effects are highly context specific. To develop paralogs as therapeutic targets we must identify which paralog pairs are synthetic lethal in which cancer contexts. ResultsWe develop a machine learning classifier to predict cell-line specific synthetic lethality between paralog pairs. We demonstrate the utility of features derived from the cell-line specific expression and essentiality of the pair and their protein-protein interaction partners for this purpose. We evaluate our predictions across multiple scenarios: predicting for the same pairs in unseen cell lines, for new gene pairs in seen cell lines, and for entirely uncharacterized pairs in unseen cell lines. We show that we can make predictions across all scenarios. We validate our predictions using independent combinatorial CRISPR screens and show that the agreement between our predictions and published experiments approaches the agreement across experiments. ConclusionsOur classifier predicts cell-line-specific synthetic lethality between paralog pairs and provides insights into the underlying features driving these interactions. We make our predictions for 1,005 cell lines available as a resource to facilitate the discovery of context-specific paralog synthetic lethalities and to guide the design of more targeted combinatorial screens.

cancer biology↗