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Disease Models

Modeling Genetic Diversity in Alzheimer’s Disease

By June 4, 2021No Comments

Introduction

With the increase in life expectancy, there’s been a growing concern about the prevalence and incidence of Alzheimer’s disease (AD), which is the major cause of dementia. The prevalence of AD is estimated at 1 in 10,000 people over 60 years old and 1 in 3 people over 85 years old.[1] However, by the end of 2025, it’s expected that there will be more than 10 million cases of Alzheimer’s disease in the United States alone.[2] This is going to increase the burden of society, considerably, if it stays the largest unmet medical need in neurology.

Current therapeutic agents of AD improve symptoms, but they don’t affect the course of the disease. Because of this, there have been many efforts toward obtaining a better understanding of how AD develops.

Recent studies provided a better picture of how AD is inherited. Instead of looking at AD in general, researchers make a distinction between early-onset AD and sporadic AD. Early-onset AD, or familial AD, is a rarer form of Alzheimer’s disease that affects persons under 65 (usually around 45 and 55). Sporadic AD, on the other hand, affects people over the age of 65 and accounts for the majority of AD cases. What researchers found was that genetics explained 90% of the variance in familiar AD (early-onset) and 58-79% of the variance in sporadic AD.[3]

Considering the key role genetics play in both forms of AD, genetic modeling still remains the best tool for understanding the key mechanisms underlying this disease.

Since genetics play a crucial role in Alzheimer’s disease, with late-onset (sporadic) AD being 58-79% inherited and early-onset AD over 90% [3], genetic modeling is still the best tool in understanding the key mechanisms underlying it.

Overview of Alzheimer’s Disease

Alzheimer’s Disease is characterized by irreversible and progressive cognitive dysfunction that affects memory and executive functions. It usually starts with mild cognitive impairment and slowly progresses until most of the higher-end cognitive functions are affected.

Unfortunately, the pathogenesis of AD is multifaceted and very complex, which is why pinpointing the cause has proven to be an incredibly difficult task. Yet, studies in genetics and cell biology have made remarkable findings that led to the amyloid cascade hypothesis.[4]

The amyloid cascade hypothesis was first developed by early findings of disease mutations in the amyloid precursor protein gene (APP) and presenilin genes. It’s still believed that mutations in these genes are pivotal to the development of AD, even though researchers now know that over 50 loci are implicated [5], which might explain why our current understanding of this disease is very limited.

Genetic Modeling of Alzheimer’s Disease

For years, researchers designed animal models of the disease in hopes of turning the genetic information of the above-mentioned mutations into effective drugs, but to no avail.

By studying genetically modified animals, scientists can see how specific mutations in a homogeneous strain express phenotypically and whether they lead to the observed behavioral consequences of AD in humans.

This means that a genetic model is helpful only if it leads to creating a treatment or if it helps researchers to better understand the neurological processes behind the behavioral consequences of AD. For the latter, several models have been successful. The traditional and most dominant models are transgenic mice models, although double and triple transgenic models are also developed.

It’s worth noting that none of the existing models so far has been able to fully replicate all aspects of AD.

Recently, researchers tried to improve the genetic diversity of traditional mouse models by developing new models with inbred strains.

In the following paragraphs, we’ll make a brief overview of the most important attempts in modeling AD and more closely discuss the recent advances in modeling a greater genetic diversity in Alzheimer’s Disease.

Non-Rodent Models of Alzheimer’s Disease

The idea behind non-rodent models of Alzheimer’s disease comes from evolutionary findings, where a majority of genes involved in AD were found to be preserved in simpler organisms such as the Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio (zebrafish).

The reason why some researchers turn to simpler organisms is that they’re very inexpensive, easy-to-work, and have short life spans. Considering that AD is an age-related disease the short life span makes these models very practical.

The studies in non-rodent models of AD are useful in confirming the crucial roles of APP and presenilin coding genes in the development of the disease, but they still fall short of the traditional and new rodent models of AD, which we’ll discuss in greater detail.

Rodent Models of Alzheimer’s Disease

The creation of transgenic mice that carry a single gene mutation associated with the early-onset AD (FAD) in 1995 opened the doors for many invaluable studies that followed since.

Rodent models were expected to exhibit progressive symptoms with similar neuropathology and cognitive deficits just like human AD progresses, although only certain aspects of Alzheimer’s have been successfully replicated so far.

Transgenic Alzheimer’s Disease Models

Transgenic rodent models are created by adding human genetic information to the nucleus of embryonic cells of the mouse.

The PDAPP mice model

The most notable example of a transgenic model of AD is the so-called PDAPP mice which represents the first successful attempt to develop an AD phenotype in mouse models. Created back in 1995, the PDAPP mice line contained the human beta-amyloid precursor protein V717F, which led to a 10-fold increase of APP and was sufficient to cause amyloid plugs in the extracellular space in the brain.

Dodart et al. tested the PDAPP mice in a battery of memory tasks to see whether they exhibit the expected AD-like symptoms.[6] The researchers used radial mazes for testing spatial discrimination, an object-recognition task, and a barpress learning task. What they found was that mice showed severe deficits in the radial maze before the amyloid plaque deposition, while the object recognition performance decreased with age.

What we can learn from this and similar studies is that the histological and biochemical changes of the PDAPP mice model closely resemble the changes found in human brains of AD patients. However, the models don’t develop tauopathy and neuronal cell death.

The Tg2576 mice model

The second line of genetically modified mice models was the so-called Tg2576 model created by Hsiao et al. in 1996.[7] Aside from the Aβ plugs deposits, this line of mice also showed age-related cognitive dysfunction.

The mice line was tested for spatial reference learning and memory with a Morris water maze multiple times across their 2-year lifespan.[8] The conclusion that the researchers came to was that although the model is successful in replicating amyloid depositions it doesn’t exhibit any neuronal loss – a crucial aspect of human AD.

Double Transgenic Alzheimer’s Disease Models

Double transgenic AD mice models are generated from mating two different transgenic mice lines with AD mutations. This model represents an attempt for obtaining a slight genetic diversity and including different gene mutations, so we can see the effects of one mutation in correlation with another.
One such attempt was made by crossing the Tg2576 mice line with a mice line carrying a mutation of the human presenilin gene. Several partly successful transgenic lines with APP and presenilin mutations were created, including the PSAPP, TASTPM, and the APPswe/PSEN1dE9.

The last transgenic line, the APPswe/PSEN1dE9 showed episodic-like memory deficits which are commonly seen in humans patients with AD. Researchers assessed the cognition of 6 and 18-month old double transgenic mice, by using a standard Morris Water maze task followed by Repeated Reversal and Radial Water maze tasks for assessing episodic-like memory.[9] It was found that episodic memory seems more sensitive to the toxicity of some Aβ plugs deposits.

The general conclusion is that the double transgenic mice model offers new information about the disease, such as the above-discussed specific consequences to episodic memory, but they still fail to capture all aspects of AD as observed in humans.

Tau Pathology and Triple Transgenic Alzheimer’s Disease Models

Some researchers decided to go even further with the transgenic mice model and generate triple transgenic models. The reasoning behind the belief that triple transgenic models would be more effective in replicating AD symptoms lies in the limitations of the transgenic models described above. More specifically, they don’t have tau protein mutations.  So, triple transgenic models were created.

A more notable example in this triple transgenic technique is the 3xTg mice model that carried PS1M146V, APPSwe, and tauP301L mutations. The researchers found age-related deficits in long-term synaptic plasticity which was promising, however, the deficits appeared before the plaque and tangle pathology, which is not the case in humans.[10]

In recent years, new triple transgenic mice models were generated, but they also fail to capture the full specter of AD.

Genetically Diverse Models for Alzheimer’s Disease

In a search for better mice models, some researchers tried to significantly improve the genetic diversity of traditional mice models in hopes of faithfully replicating human AD and developing effective therapeutics.

The idea behind this innovative model is that since sporadic AD has a heritability of 50-80%, there must be some unidentified genes that offer resilience against AD. So, to match the individual genetic variability found in human AD patients, Neuner et al. decided to design the AD-BXD model by crossing the 5XFAD APP/PS1 transgenic mice model of AD with previously developed BXD recombinant inbred lines – a genetically diverse panel.[11,12]

This results in F1 mouse hybrids that have all the same mutations as the traditional transgenic mice models, but the rest of their genome is different.

The most valuable information from this study is that genetic variation in the rest of the mice’s genome had a great impact on the mice’s phenotype.

The authors used three different behavioral assessment tools and measured cognitive performance at 14 months. With the help of a Y-maze, researchers measured the mice’s spatial and hippocampal-dependent memory, while an Elevated Plus-Maze helped them measure anxiety-like behavior. They also used Contextual Fear Conditioning as a learning test.

The observed cognitive deficits varied across individual genetic backgrounds, which is identical to those in human patients.

This new approach in modeling genetic diversity in AD is exciting and promising, as it can help researchers understand the etiology of AD, how background genetic variability influences the phenotype, and how different subgroups react to different therapies.

But, modeling genetic diversity also has limitations. One limitation is that very few labs across the world have the resources to develop and maintain such a large number of strains. Another limitation is that this approach doesn’t take into consideration the tau protein effects in the development and progression of AD, which is characteristic in human patients.

Hopefully, future studies in modeling genetic diversity can help us fully understand AD, by also taking into account the tau protein mutations.

Future Perspectives

There’s still a long road ahead before we can say that we fully understand the neuropathology of Alzheimer’s Disease. But, as we’ve seen, researchers are relentlessly working on generating new, more effective models that fully replicate all neural and behavioral mechanisms of the diseases as observed in humans. Hopefully, the new approaches in modeling genetic diversity in AD can offer unique insights that will lead to developing an effective therapy.

Indeed, creating a genetic model that helps researchers understand the complex interplay of genetic mutations underlying human AD will have profound implications in our society. They’ll be able to develop a drug therapy powerful enough to reverse or stop the progression of Alzheimer’s disease which will affect the lives of millions of people who suffer from this disease. Even more so, one day, we might be able to prevent AD with screening tests further improving the quality of life and increasing life expectancy.

For now, we can only support the advances in the genetic modeling of AD by providing high-quality scientific tools that can make a difference.

References:

  1. Evans DA, Funkenstein HH, Albert MS, et al. Prevalence of Alzheimer’s Disease in a Community Population of Older Persons: Higher Than Previously Reported. JAMA. 1989;262(18):2551–2556. doi:10.1001/JAMA.1989.03430180093036.
  2. Katzman, R., & Kawas, C. (1994). The epidemiology of dementia and Alzheimer’s disease. In R. D. Terry, R. Katzman, & K. L. Bick (Eds.), Alzheimer disease (p. 105–122). Raven Press.
  3. Sims, R., Hill, M. & Williams, J. The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci 23, 311–322 (2020). https://doi.org/10.1038/s41593-020-0599-5.
  4. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science. 2002 Jul 19;297(5580):353-6. doi: 10.1126/science.1072994. Erratum in: Science 2002 Sep 27;297(5590):2209. PMID: 12130773.
  5. Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., … Posthuma, D. (2019). Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature genetics, 51(3), 404–413. https://doi.org/10.1038/s41588-018-0311-9.
  6. Dodart, J. C., Meziane, H., Mathis, C., Bales, K. R., Paul, S. M., & Ungerer, A. (1999). Behavioral disturbances in transgenic mice overexpressing the V717F beta-amyloid precursor protein. Behavioral neuroscience, 113(5), 982–990. https://doi.org/10.1037//0735-7044.113.5.982.
  7. Hsiao, K., Chapman, P., Nilsen, S., Eckman, C., Harigaya, Y., Younkin, S., … & Cole, G. (1996). Correlative memory deficits, Aβ elevation, and amyloid plaques in transgenic mice. Science, 274(5284), 99-103.
  8. Westerman, M. A., Cooper-Blacketer, D., Mariash, A., Kotilinek, L., Kawarabayashi, T., Younkin, L. H., Carlson, G. A., Younkin, S. G., & Ashe, K. H. (2002). The relationship between Abeta and memory in the Tg2576 mouse model of Alzheimer’s disease. The Journal of neuroscience: the official journal of the Society for Neuroscience, 22(5), 1858–1867. https://doi.org/10.1523/JNEUROSCI.22-05-01858.2002.
  9. Savonenko, A., Xu, G. M., Melnikova, T., Morton, J. L., Gonzales, V., Wong, M. P. F., … Borchelt, D. R. (2005). Episodic-like memory deficits in the APPswe/PS1dE9 mouse model of Alzheimer’s disease: Relationships to β-amyloid deposition and neurotransmitter abnormalities. Neurobiology of Disease, 18(3), 602–617. doi:10.1016/j.nbd.2004.10.022.
  10. Oddo, S., Caccamo, A., Shepherd, J. D., Murphy, M. P., Golde, T. E., Kayed, R., … LaFerla, F. M. (2003). Triple-Transgenic Model of Alzheimer’s Disease with Plaques and Tangles. Neuron, 39(3), 409–421. doi:10.1016/s0896-6273(03)00434-3.
  11. Sadleir, K.R., Vassar, R. Modeling genetic diversity in Alzheimer’s disease. Lab Anim 48, 87–88 (2019). https://doi.org/10.1038/s41684-019-0248-3.
  12. Peirce, J.L., Lu, L., Gu, J. et al. A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genet 5, 7 (2004). https://doi.org/10.1186/1471-2156-5-7.
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MazeEngineers makes behavioral mazes for all species with high precision and accuracy. Each maze is hand made for exacting specifications, with automation, AI integration and open software integration. We’re here to build the world’s best behavioral library, we’d love to help you with your experiments. Send us questions and we’ll answer!
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