Moving beyond amyloid and tau to capture the biological heterogeneity of Alzheimer's disease

Abstract

Alzheimer's disease (AD) manifests along a spectrum of cognitive deficits and levels of neuropathology. Genetic studies support a heterogeneous disease mechanism, with around 70 associated loci to date, implicating several biological processes that mediate risk for AD. Despite this heterogeneity, most experimental systems for testing new therapeutics are not designed to capture the genetically complex drivers of AD risk. In this review, we first provide an overview of those aspects of AD that are largely stereotyped and those that are heterogeneous, and we review the evidence supporting the concept that different subtypes of AD are important to consider in the design of agents for the prevention and treatment of the disease. We then dive into the multifaceted biological domains implicated to date in AD risk, highlighting studies of the diverse genetic drivers of disease. Finally, we explore recent efforts to identify biological subtypes of AD, with an emphasis on the experimental systems and data sets available to support progress in this area.

Keywords: GWAS; LOAD; RNA splicing; amyloid beta; endosome; genetics; iPSCs; lysosome; mitochondria.

Figure 1.. Heterogeneity in cognitive trajectory and neuropathological burden in the aging human population.

Data collected in the ROS and MAP cohorts for deceased individuals are shown to visualize the heterogeneity within the clinal diagnostic categories of not cognitively impaired (NCI) vs. Alzheimer’s dementia (AD) for each of the traits shown. Each dot represents data from one ROSMAP participant. The panels display age at death (A), as well as a range of quantitative measures: amyloid plaque deposition (B), neurofibrillary tangles (C), and cognitive trajectory with age (slope of changes in cognitive scores over age), (D).. E) Mixed pathologies are common within the population of individuals with AD. Shown is a Venn diagram depicting the percent of individuals with each of the listed brain pathologies . F) Percentages of the individuals in each of the groups, color coded as in E. To be included in the analysis, individuals had to have both a clinical diagnosis of Alzheimer’s dementia and a postmortem pathological diagnosis of Alzheimer’s disease, as well as determinations for each of the pathologies listed. Created with nVenn [178].

Figure 2.. Diverse combinations of genetic factors converge on interrelated but varied biological domains to impact risk and resilience to AD.

(A) Over 70 different loci have been identified through GWAS of LOAD, and there are likely more rare genetic mutations and variants that have not yet been discovered. Across the human population, different combinations of risk and resilience factors converge on different biological domains that have been implicated in AD pathogenesis. Alterations of different combinations of these biological domains in turn impact the neuropathological and cognitive trajectory of individuals. (B) Experimental studies have revealed that many of these biological domains are highly interrelated (for example, endolysosomal dysfunction impacts APP metabolism). Therefore, rather than a single linear road to AD, we conceptualize several different but interrelated molecular pathways that lead to AD in different individuals. The diversity of “roads to AD” manifests in part in the heterogeneity in neuropathological and cognitive outcomes with age. Figure created with BioRender.com.

Figure 3.. LOAD risk genes are expressed strongly in a variety of cell types in the brain.

Risk-conferring and protective genetic factors for AD are expressed in a variety of cell types, underscoring the idea that there are multiple molecular roads to AD. Shown are examples of iPSC-derived brain cell types (images courtesy of the Young-Pearse lab, for illustration purposes). LOAD GWAS hits expressed in each cell type are noted within the boxes. Expression was determined by probing a large database of single cell RNAseq data obtained from over 400 samples of ROSMAP human brain tissue [179] (see also ). Data from all participants were aggregated and pseudobulk scores calculated to determine if the listed gene was in the upper (green) or lower (black) quartile of expression relative (abundance) to all genes detected in that cell type. If a LOAD GWAS gene is not listed under a given cell type, then it was not detected in that cell type in the snRNAseq dataset.

Figure 4.. A variety of experimental systems are necessary to capture different aspects of disease pathogenesis.

Each experimental system has its advantages and disadvantages. Shown are three examples of genetic risk/protective factors for AD, and the direct and indirect molecular consequences of these variants on biological domains. For the identification of the proximal, cis effects of specific genetic variants on RNA and protein levels, having homogenous, robust cultures of a single cell type can be of the highest utility for two reasons: 1) reductionist systems are generally more robust and reproducible due to the simplified nature of the system and 2) the cis-effects observed are less likely be confounded by the expression of other, downstream consequences of the variant due to the presence of other cell types. For the same reasons, these same monocultures are useful for capturing the direct, cell autonomous consequences of genetic variants on a particular biological domain. However, more complex systems such as co-cultures of multiple human cell types or organoids may be needed for capturing the downstream, indirect effects on secondary biological domains if interactions between multiple cell types are involved. Animal models are necessary for capturing system-level dysfunction that impacts upon cognition and behavior. However, animal models are less useful for studying the direct effects of genetic variants associated with risk for human diseases, as it is important to interrogate these effects in the context of the human genome. Finally, no experimental system is able to precisely model Alzheimer’s dementia, and ultimately the determination of causality for dementia can only come from human clinical trials that target that biological domain. Three examples are provided to outline models that were proposed based upon data from human cellular and animal experimental systems. Example #1 summarizes findings from a number of studies using human iPSC models and animal models, which have shown that SORL1 coding variants (including truncation variants) can lead to reduced SORL1 protein levels and impaired retromer transport, which in turn affects the accumulation of Aβ and phosphorylated tau (reviewed in [180]). The second example summarizes studies from human cells that revealed that a LOAD-associated variant in CD33 affects CD33 splicing [181], reduced cell surface CD33 levels [182, 183] and induced an impairment of Aβ clearance [182, 184]. Finally, the third example highlights the results of a recent study that showed that high polygenic risk score across a large cohort of human iPSC derived neurons was associated with elevation of longer Aβ peptides, which in turn induced a reduction in protein phosphatase 1 (PP1) level and altered tau proteostasis [34].