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Zalocusky, K.

Publications and source records attributed to Zalocusky, K..

2 recordsLinked to original sources

Early and lifelong effects of APOE4 on neuronal gene expression networks relevant to Alzheimer's disease

Apolipoprotein E4 (APOE4) genotype and aging are critical risk factors for Alzheimers disease (AD). Aged APOE4 knock-in (APOE4-KI) mice have phenotypes reflecting features of AD. We conducted a large-scale single nucleus RNA-sequencing study to identify cell-type-specific effects of APOE4 on hippocampal gene expression during aging. APOE4-KI mice showed prominent alterations, relative to APOE3-KI mice, in neuronal transcriptome related to synaptic function, calcium signaling, and MAPK/Rap1/Pld signal transduction, starting by 5 months and persisting during aging. Mice with the APOE4 gene removed specifically from neurons failed to show most of these neuronal transcriptomic changes, suggesting a specific effect of neuron-derived APOE4 on the transcriptome. APOE4 affects similar cellular pathways in induced pluripotent stem cell-derived human neurons transplanted into APOE4-KI mouse hippocampus and in cortical neurons from aged human brains. Thus, neuronal APOE4 has early and persistent effects on neuronal transcriptomes, suggesting the requirement of early interventions for successfully treating APOE4-related AD.

neuroscience↗

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer's Disease

In spite of evidence of females having a greater lifetime risk of developing Alzheimers Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls. Gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features revealed an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

neuroscience↗