Identifying and ranking potential driver genes of Alzheimer\u27s disease using multiview evidence aggregation.

MOTIVATION: Late onset Alzheimer\u27s disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. RESULTS: We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer\u27s. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer\u27s and are enriched in pathways that have been previously associated with the disease. AVAILABILITY AND IMPLEMENTATION: Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking

Prodromal Variability in Huntington\u27s Disease Progression and Resistance

Huntington’s disease (HD) is a neurodegenerative movement disorder caused by abnormal cytosine-adenine-guanine (CAG) expansion on the HTT gene. As both a proteinopathy and the most common PolyQ disease, HD shares key features with several disorders that disproportionately affect the growing elderly population in the United States, including delayed-onset, selective neuronal death, and protein misfolding. Across these conditions, there are few treatments and no known cures; however, their shared features suggest common underlying mechanisms, and delayed-onset hints at possible prevention or reversal. CAG-expansion-number and age are related to diagnosis and can be used to estimate age-of-onset for prodromal (pre-diagnosis) individuals, who possess the causal mutation but have not manifested diagnosis-associated motor symptoms. Over a decade before diagnosis, prodromal individuals differ from controls in brain structure and connectivity, cognition, and motor functioning. Although age and CAG-number account for most observed variability in HD-onset, persons with identical CAG-numbers often develop symptoms at different ages, indicating that additional genetic and environmental factors also mediate decline. Little is known about detrimental and protective genetic factors in HD. Studying prodromal progression can inform interventions by highlighting early prevention targets. This research leverages advanced multivariate techniques applied to legacy PREDICT-HD data to characterize brain structure, cognition, and motor functioning across prodromal HD and investigate genetic factors accounting for variability in these domains. Regarding brain structure, these experiments provide evidence for: regional co-occurrence in prodromal decline, early fronto-striatal degradation, dorso-ventral reduction gradients, and delayed atrophy in certain movement-related and subcortical regions. The genetic findings suggest a protective role of NTRK2 and identify NCOR1 and ADORA2B variants with early, CAG-independent detrimental effects on gray matter. Previously identified onset-delaying variants are also confirmed as CAG-independent modulators of brain structure and clinical functioning. Clinical findings highlight motor functioning as the best indicator of brain-structural integrity until the late prodrome and demonstrate that distinct regions coincide with cognitive compared to motor functioning; furthermore, regions that most align with clinical functioning vary at different prodromal stages

Understanding the molecular, cellular, and circuit defects characterizing the early stages of Alzheimer’s disease

One of the most foundational and personal philosophical questions one can ask is what makes you, you? In large part, you are made up of your relationships, experiences, and memories. The hippocampus, a brain region which is critical for the formation of memories, has been the focus of neuroscience research for decades due partially to this function, which is foundational to our individuality. In Alzheimer’s disease (AD), one of the most common and well-researched neurodegenerative diseases in the world, the hippocampus is one of the earliest targets. Despite extensive work on AD, we still lack a coherent understanding of what is causing the disease, the mechanisms by which it is causing neuronal dysfunction and death within the hippocampus and other brain regions, and how it ultimately causes deficits in cognition and behavior, leading to an erosion of our selves. In this thesis, I explore three independent but related questions: 1) what molecular mechanisms are causing early synaptic loss in AD, specifically within the hippocampus, 2) what molecular effectors are responsible for establishing and maintaining intracellular architecture in hippocampal neurons, which are exploited in early AD, and 3) how and when does the hippocampal circuit dysfunction in AD progression? Using a variety of experimental techniques, ranging from in utero and ex utero electroporation, primary murine and human neuronal cell culture, longitudinal confocal microscopy, immunohistochemistry, biochemistry, cell and molecular biology, in vivo two-photon calcium imaging, and behavioral assays, I have found that, within CA1 of the hippocampus, synapse loss requires degradation of the dendritic mitochondrial network, activity and input specificity are driving mitochondrial compartmentalization within CA1 neurons through the same pathway that is aberrantly overactivated in AD, and the hippocampal circuit is overly rigid in encoding the environment as the disease progresses