Haploinsufficiency and Alzheimer’s Disease: The Possible Pathogenic and Protective Genetic Factors

Abstract

Alzheimer’s disease (AD) is a complex neurodegenerative disorder influenced by various genetic factors. In addition to the well-established amyloid precursor protein (APP), Presenilin-1 (PSEN1), Presenilin-2 (PSEN2), and apolipoprotein E (APOE), several other genes such as Sortilin-related receptor 1 (SORL1), Phospholipid-transporting ATPase ABCA7 (ABCA7), Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), Phosphatidylinositol-binding clathrin assembly protein (PICALM), and clusterin (CLU) were implicated. These genes contribute to neurodegeneration through both gain-of-function and loss-of-function mechanisms. While it was traditionally thought that heterozygosity in autosomal recessive mutations does not lead to disease, haploinsufficiency was linked to several conditions, including cancer, autism, and intellectual disabilities, indicating that a single functional gene copy may be insufficient for normal cellular functions. In AD, the haploinsufficiency of genes such as ABCA7 and SORL1 may play significant yet under-explored roles. Paradoxically, heterozygous knockouts of PSEN1 or PSEN2 can impair synaptic plasticity and alter the expression of genes involved in oxidative phosphorylation and cell adhesion. Animal studies examining haploinsufficient AD risk genes, such as vacuolar protein sorting-associated protein 35 (VPS35), sirtuin-3 (SIRT3), and PICALM, have shown that their knockout can exacerbate neurodegenerative processes by promoting amyloid production, accumulation, and inflammation. Conversely, haploinsufficiency in APOE, beta-secretase 1 (BACE1), and transmembrane protein 59 (TMEM59) was reported to confer neuroprotection by potentially slowing amyloid deposition and reducing microglial activation. Given its implications for other neurodegenerative diseases, the role of haploinsufficiency in AD requires further exploration. Modeling the mechanisms of gene knockout and monitoring their expression patterns is a promising approach to uncover AD-related pathways. However, challenges such as identifying susceptible genes, gene–environment interactions, phenotypic variability, and biomarker analysis must be addressed. Enhancing model systems through humanized animal or cell models, utilizing advanced research technologies, and integrating multi-omics data will be crucial for understanding disease pathways and developing new therapeutic strategies.