Age is the greatest risk factor for Alzheimer’s disease (AD). During normal aging, several cellular processes gradually lose efficiency, including those that regulate the production and clearance of proteins from cells. The central dogma of molecular biology says that genetic information flows from DNA to RNA to proteins. But not all RNAs serve as templates for proteins; many perform other important functions within the cell. However, under certain conditions, these noncoding RNAs may get modified in a way that signals the cell’s machinery to translate them into proteins anyway. In well-functioning cells, these noncoding translation products are detected and appropriately degraded. But with age, those detection systems begin to falter, and more noncoding translation events occur. Extensive research has shown that age and disease lead to proteins misfolding and aggregating into hallmark pathologies, such as plaques and tangles. Work from Dr. Wu’s lab suggests that noncoding translation events may contribute to this toxic buildup.
Dr. Wu hypothesizes that aging increases the chances that cells mistakenly produce proteins from RNA that are not meant to be translated. At the same time, the cellular systems that usually catch and correct these errors become less efficient. He also suggests that amyloid and tau exacerbate the problem by further impairing these quality control systems. He describes a negative feedback loop comprising proteostasis imbalance, defective mRNA processing, and noncoding translation, in which the disruption of any one of these drives disruption in all of them, potentially accelerating the buildup of abnormal proteins in the brain and consequently driving disease progression.
The Wu lab proposed two experimental aims to test these hypotheses. In the first aim, the team is investigating whether noncoding translation is altered during normal brain aging and whether this alteration becomes further disrupted in AD. They are examining post-mortem human brain samples from early- and late-stage AD patients and age-matched controls to identify changes in RNA and protein processing steps. They are measuring key characteristics of RNAs using advanced sequencing methods (RNA-seq, Ribo-seq) and assessing protein aggregation through mass spectrometry-based proteomics. In the second aim, they are exploring the role of noncoding translation in driving AD progression in a mouse model engineered to express human amyloid beta and tau. They are determining whether disrupting any one of RNA processing, noncoding translation, or protein aggregation leads to an increase in the progression of disease-related pathology, and whether therapeutic intervention to restore any one of the elements of the feedback loop can restore healthy processing of all of them. Specifically, they are testing several pharmacological tools that both enhance noncoding translation and promote degradation of the translation products. These experiments are laying the groundwork for future therapeutic exploration, as the team is assessing whether targeting individual components of RNA processing can alter the progression of Alzheimer’s-related brain pathology.
Over the course of the first year, the Wu lab has focused on establishing the workflows necessary to achieve their aims. For Aim 1, they developed and tested a computer-based tool to measure noncoding translation activity—the production of proteins from RNAs that typically should not be used to make proteins. They successfully validated their tool on known datasets which showed increased translation in noncoding regions and then used it to analyze public datasets for age-related changes in noncoding translation. In parallel, they began the technically challenging development of a lab-based method to study protein production using frozen human brain tissue and mouse brains. Initial results revealed high variability across individual samples, and current efforts are focused on determining whether this variability represents actual biological differences or technical noise before scaling up to larger studies of human tissue. Progress in Aim 2 included determining therapeutic agents that optimally inhibit each of the three components of the feedback loop for use in AD mouse models. In cell cultures, they tested pharmacological inhibitors for their impact on the products of the feedback loop, including the spliceosome, proteasome, and nonsense-mediated decay pathways. They confirmed that certain nonsense suppression drugs caused cells to ignore stop signals in RNA. Over the next year, they plan to extend these studies to mouse models to test whether adjusting the production of faulty or improperly processed RNA can restore the translation feedback loop to healthy processing and thus influence the course of AD.
