To date, scientists have faced challenges with understanding Late Onset Alzheimer's Disease (LOAD), the most common form of Alzheimer's disease. For the past 30 years, clinicians, epidemiologists, and geneticists have focused primarily on unusual forms of Alzheimer's that begin early (usually around age 50) and that cluster in families. Three classes of discoveries have resulted:
(a) Three genes that could have mutations that guarantee a heritable form of Alzheimer's and contribute to only about 3% of all Alzheimer's
(b) A single gene that acts as a risk factor and plays some role in at least half of all Alzheimer's
(c) 20 other genes that each contribute variably to risk.
Only (a) and (b) have any clinical utility. Genes in the (c) class each contributes a variable amount of risk and cannot be used clinically. However, genes in all three classes converge on a pathway that begins with amyloid but then proceeds to include tangles, inflammation, cell injury and death.
The great challenge arises in part from the fact that the risk factors and candidate genes identified by AD GWA studies themselves don't really tell us much about the cascade signaling pathways underlying AD. Hence, even today the mechanisms initiating the disease still remain elusive and this in turn contributes to the failure of development of effective disease modifying or preventive therapies. Therefore, there is a compelling need for more innovative approaches to identify the causal mechanisms.
At Mount Sinai, Drs. Bin Zhang, Jun Zhu and Eric Schadt have pioneered to develop a breakthrough approach known as multi-scale network analysis, enabling the integration of all the data accumulated so far from previous genetic studies with gene expression data from the brains of patients with common forms of Alzheimer's. This integration of genetics and gene expression is the essence of multi-scale network analysis, and the gene expression data helps amplify tiny genetic signals that would be otherwise overlooked.
A new paper published in Cell, from Bin Zhang with colleagues at Mount Sinai and external collaborators, reports the discovery of a network of genes involved in inflammation response as the top ranked pathway causally linked to Late Onset Alzheimer's Disease (LOAD).
These human brain gene networks revealed multiple aspects of molecular modifications in LOAD across multiple brain regions. This new understanding of key pathways and genes involved in LOAD offers valuable insights to develop potential therapies for the disease.
Zhang et al. identified an inflammatory protein, known as TYROBP, as the key driver of the discovered inflammation network/pathway. TYROBP interacts with another molecule TREM2, that was linked to Alzheimer's by several groups reporting in NEJM a few months ago. Together, these two discoveries implicate that the TREM2-TYROBP pathway may be at the center of common forms of Alzheimer's, playing a key role in amyloid clearance and amyloid related inflammation. Recently, another inflammatory pathway molecule, CR1, was recently shown to be linked to Alzheimer's but rather than increasing brain amyloid, the risky form of CR1 reduces brain amyloid.
This paper in Cell, along with the previous publications about TREM2 and CR1, will likely lead to a revisitation of the inflammatory hypothesis of Alzheimer's, which held favor for many years, fell out of favor, but has recently been revisited in a clinical trial context. We expect to see much more attention to inflammation in Alzheimer's in the next 5-10 years. This could be important in helping us understand the underpinnings of the long lag time between appearance of amyloid on brain scans and the appearance of clinical symptoms. An individual's inflammatory response could well play a role in that progression, and the appropriate anti-inflammatory drug, given after amyloid is detected but before symptoms begin, could be an important part of dementia prevention.
As a next step, the Mount Sinai team plans to validate key causal networks and evaluate drugs to impact the TREM2-TYROBP pathway, and other key pathways, as potential therapies for Alzheimer's disease. This team will also apply their gene network modeling approach to other neurodegenerative diseases.
Learn more about our latest discovery for Alzheimer's disease.