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Elsadany, M.

Publications and source records attributed to Elsadany, M..

3 recordsLinked to original sources

Common Dysregulated Pathways and Genes in Alzheimer's, Parkinson's, and Huntington's Diseases: A Computational Analysis to Nominate Candidate Gene Targets for Drug Repurposing

Neurodegenerative diseases are a major threat to older adults and represent a growing global health burden as the elderly population continues to expand. The complexity of neurodegenerative diseases and the incomplete understanding of their pathophysiology limit the development of effective therapeutics. To study common neurodegenerative mechanisms and accordingly potential drug targets across Alzheimers, Parkinsons, and Huntingtons diseases, transcriptomic profiles of patients with each disease were analyzed to detect common differentially expressed genes and common enriched pathways. Common differentially expressed genes involved in the shared pathways were identified as key drug targets and validated in silico to study the impact of their dysregulation. Three pathways were enriched and upregulated across the three diseases, along with 274 common differentially expressed genes. Two of the shared pathways were involved in activation of the transcription factor nuclear factor kappa B (NF-{kappa}B), indicating inflammatory signaling. The third pathway involved regulation of BCL2L11 transcription by RUNX3, which contributes to the protective effect of neurodegenerative diseases against cancer. Five key drug targets were identified: NFKBIA, NFKB1, RELA, TRIM4, and SMAD4. They were significantly upregulated across all three diseases and involved in the shared pathways. Drugs that target the expression of these genes and previously approved by Food and Drug Administration were reported for treatment repurposing for the three neurodegenerative diseases. The resultant drug list included the conventional and commonly safe antihyperlipidemics, antihypertensives, antidiabetics, analgesics, diuretics, antiparkinsons, and antipsychotics.

bioinformatics↗

Migration genetics link excitatory neurons to ancient selection and economic growth

From ancient nomadic movements to modern urbanization, migration has driven human history through mechanisms that remain poorly understood. We conducted a genome-wide association study of migration distance in 250,000 UK individuals, identifying 20 loci in neurodevelopmental genes with 5% heritability. Migration-related variants are associated with excitatory neuron gene expression and correlate with cognition, risk-tolerance, and reduced interpersonal attachment. Within-family analyses demonstrate genetic effects remain significant after controlling for shared environmental factors between siblings. Our polygenic score predicts inferred mobility in >1,000 ancient individuals spanning 10,000 years, revealing positive selection on migration alleles that increased substantially over millennia. At the population level, each standard deviation increase in county-level migration polygenic score predicts >$4,000 greater income growth per person in the US. These findings establish migration as a heritable trait and suggest biological pathways connecting individual neurodevelopment with regional prosperity across evolutionary and contemporary timescales.

genomics↗

NEUROeSTIMator: Using Deep Learning to Quantify Neuronal Activation from Single-Cell and Spatial Transcriptomic Data

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this methods ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

bioinformatics↗