Toward more predictive genetic mouse models of Alzheimer's disease

The first step toward such needed medical progress is to research Alzheimer's disease in ways that are more relevant to the human disease. Much of the previous work has focused on the telltale signs of Alzheimer's: the accumulation of "plaques" of beta amyloid, a protein fragment, in the brain, as well as the prevalence of "tangles" of a protein known as tau. But while we know beta amyloid and tau are there in Alzheimer's patients' brains and likely contribute to brain cell death, their exact roles and mechanisms remain unclear, especially in the early stages. Indeed, many of the unsuccessful trial therapeutics have focused on blocking beta-amyloid accumulation, so it's apparent that there are important pieces of the Alzheimer's puzzle still missing.

The good news is that there are powerful new tools and methods available to researchers that will help them investigate the disease with far more precision and speed than previously possible. The use of these tools to study the genes and processes implicated in Alzheimer's but not yet investigated is the subject of a new review paper "Toward more predictive genetic mouse models of Alzheimer's disease," published online in Brain Research in December 2015. Authored by JAX Research Scientist Michael Sasner, Ph.D., JAX Assistant Professor Gareth Howell, Ph.D., and colleagues, the paper discusses ways animal models have been used in the past, and how both they and the experiments using them can be improved.

Scientific paper abstracts are often difficult to unpack and full of scientific vocabulary. Here, however, the abstract provides a very accessible overview. To quote: "Genetic mouse models for Alzheimer's disease (AD) have been widely used to understand aspects of the biology of the disease, but have had limited success in translating these findings to the clinic. In this review, we discuss the benefits and limitations of existing genetic models and recent advances in technologies (including high throughput sequencing and genome editing) that promise more predictive models. We summarize widely used biomarkers and behavioral tests for mouse models of AD and highlight best practices that will maximize translatability of preclinical findings."

Specifically, the authors delve into new human patient data to go beyond beta-amyloid and tau to discuss early-onset versus late-onset Alzheimer's (late onset is far more common in humans but also more difficult to model in mice), the role of inflammation, the importance of the blood-brain barrier, the complex interplay of the various systems and more in the context of expanding and improving research into the disease.

Alzheimer's disease is complex and very difficult to accurately recreate in animal models such as the mice discussed in the paper. But fast and accurate sequencing, which can find the exact genetic variations in human patients' brain cells, and efficient genomic engineering, which can introduce those variants precisely into the genomes of the mouse models, offer tremendous potential for the field. By assessing the biological consequences of each variant and each disrupted system and learning how they might contribute to brain cell death, researchers can now get to work on assembling all the pieces of the Alzheimer's puzzle. The task remains formidable and the challenges significant, but the technology is now in hand to better understand -- and hopefully prevent or cure -- this terrible disease.