A MicroRNA Signature of Cognitive Decline
An enormous amount of data can be derived from analysis of the cells and molecules found in a blood sample. Researchers will be kept busy for decades yet, ever more fficiently gathering and mining this data, in search of ways to assess the progression of aging and specific age-related diseases. The work here is interesting for finding a correlation between the abundance of a small number of microRNA molecules and age-related cognitive decline. Many microRNAs are promiscuously involved in the regulation of important cellular pathways, altering the expression levels of proteins that are themselves important the regulation of cell activities. Evolution finds new uses for existing molecules, and the microRNA layer of gene expression is a good example of this principle.
The establishment of effective therapies for age-associated neurodegenerative diseases such as Alzheimer's disease (AD) is still challenging because pathology accumulates long before there are any clinical signs of disease. Thus, patients are often only diagnosed at an already advanced state of molecular pathology, when causative therapies fail. Therefore, there is an urgent need for molecular biomarkers that are (i) minimally invasive, (ii) can inform about individual disease risk, and (iii) ideally indicate the presence of multiple pathologies. Such biomarkers should eventually be applicable in the context of routine screening approaches with the aim to detect individuals at risk for developing AD that could then be subjected to further diagnostics via more invasive and time-consuming examinations.
We use a novel experimental approach combining the analysis of young and healthy humans with already diagnosed patients as well as animal and cellular disease models to eventually identify a 3-microRNA signature (miR-181a-5p, miR-146a-5p, and miR-148a-3p) that can inform about the risk of cognitive decline when measured in blood. The 3-microRNA signature also informs about relevant patho-mechanisms in the brain, and targeting this signature via RNA therapeutics can ameliorate AD disease phenotypes in animal models.
We suggest that the analysis of this microRNA signature could be used as point-of-care screening approach to detect individuals at risk for developing AD that can then undergo further diagnostics to allow for early and effective intervention. In addition, our data highlight the potential of stratified RNA therapies to treat Alzheimer's disease.