Biology subjects

Askarova, A.

Publications and source records attributed to Askarova, A..

3 recordsLinked to original sources

bioRxiv2026

Cell type-specific histone acetylation landscape in Alzheimer's disease reveals a putative role of MITF in microglia

Alzheimer's disease (AD) is characterised by aberrant amyloid beta and tau aggregation, neuroinflammation, demyelination and neurodegeneration, which have been linked to changes in cell-specific gene expression signatures. Among the mechanisms driving cell-type-specific transcriptional changes, histone acetylation plays a central role in regulating gene activity. While global alterations in histone acetylation have been implicated in AD, the contribution of individual cell types to these epigenetic changes remains poorly understood. To decode cell-type-specific changes in gene regulation in AD pathogenesis, we profiled histone H3 lysine 27 acetylation (H3K27ac) in microglia, oligodendrocytes and neurons from the prefrontal cortex of individuals with late-stage AD and non-dementia controls. Oligodendrocytes had the highest number of differential H3K27ac regions in AD, followed by microglia. Genes nearest to differential H3K27ac in purified microglia were enriched for phagocytosis, lipid processing, inflammatory and disease-associated cell state signature genes. Gene network analysis revealed downregulation of homeostatic genes in AD microglia and upregulation of immune activation, including signatures of lipid-handling and monocyte-derived macrophages. Oligodendrocyte co-regulated regions were indicative of increased MHC class I antigen presentation and altered neuron-oligodendrocyte interactions in AD. We identified H3K27ac allele-specific variants (ASVs) enriched near endolysosomal and ubiquitin-proteasome-associated genes in microglia and neurons. ASVs coincided with AD genome-wide association study (GWAS) risk loci, including CLU in oligodendrocytes and HLA-DRB1 in microglia. DNA motif analysis identified putative transcription factor drivers of AD glial dysregulation, including the lysosomal-associated MITF, Cap'n'collar (CNC) family (BACH1 and NFE2) and AP-1 activation in microglia. DNA binding of the MITF protein in human microglia was localised to lysosomal-associated genes and enriched in H3K27ac regions upregulated in AD and near disease-associated microglia (DAM) genes. Collectively, these findings implicate lysosomal dysfunction and upstream transcriptional regulation via MITF as key processes in AD microglia.

neuroscience↗

bioRxiv2024

Genetic risk for neurodegenerative conditions is linked to disease-specific microglial pathways

Genome-wide association studies have identified thousands of common variants associated with an increased risk of neurodegenerative disorders. However, the noncoding localization of these variants has made the assignment of target genes for brain cell types challenging. Genomic approaches that infer chromosomal 3D architecture can link noncoding risk variants and distal gene regulatory elements such as enhancers to gene promoters. By using enhancer-to-promoter interactome maps for microglia, neurons, and oligodendrocytes, we identified cell-type-specific enrichment of genetic heritability for brain disorders through stratified linkage disequilibrium score regression. Our analysis suggests that genetic heritability for multiple neurodegenerative disorders is enriched at microglial chromatin contact sites. Through Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA) we identified disease risk genes for Alzheimers disease, Parkinsons disease, multiple sclerosis and amyotrophic lateral sclerosis. We found that disease-risk genes were overrepresented in microglia compared to other brain cell types across neurodegenerative conditions. Notably, the microglial risk genes and pathways identified were largely specific to each disease. Our findings reinforce microglia as an important, genetically informed cell type for therapeutic interventions in neurodegenerative conditions and highlight potentially targetable disease-relevant pathways.

genomics↗

bioRxiv2024

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors like cell state, histone mark function or distal effects, that impact the relationship, limiting their findings. Moreover, downstream use of these models for new biological insight is lacking. Here, we present the most comprehensive study of this relationship to date - investigating seven histone marks, in eleven cell types, across a diverse range of cell states. We used convolutional and attention-based models to predict transcription from histone mark activity at promoters and distal regulatory elements. Our work shows that histone mark function, genomic distance and cellular states collectively influence a histone marks relationship with transcription. We found that no individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/587323v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@18dfcbcorg.highwire.dtl.DTLVardef@cba6b8org.highwire.dtl.DTLVardef@2eb0ceorg.highwire.dtl.DTLVardef@159d5b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗