Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Cell death and survival pathways in Alzheimer's disease: an integrative hypothesis testing approach utilizing -omic data sets

Whether a cell lives or dies is controlled by an array of intercepting and dynamic molecular pathways. Although there is evidence of neuronal loss in Alzheimer's disease (AD) and multiple programmed cell death (PCD) pathways have been implicated in this process, there has been no comprehensive evaluation of the dominant pathway responsible for cell death in AD. Likewise, the relative dominance of survival and PCD pathways in AD remains unclear. Here, we present the results of hypothesis-driven bioinformatic analysis of PCD and survival pathway activation in paired methylation and expression data from the middle temporal gyrus (MTG) as well as expression from laser-captured cells from the MTG and hippocampus. The results not only indicate activation of cell death pathways in AD-of which apoptosis is responsible for the largest fraction of upregulated genes-but also of cell survival pathways. These results are indicative of a complex balance between survival and death pathways in AD that future studies should work to delineate at a single cell level.