Biology subjects

Logsdon, B.

Publications and source records attributed to Logsdon, B..

4 recordsLinked to original sources

bioRxiv2019

Brain microRNAs associated with late-life depressive symptoms are also associated with cognitive trajectory and dementia

ObjectiveLate-life depression is associated with an increased risk for dementia, but our knowledge of the molecular mechanisms underlying this association is limited. Hence, the authors investigated whether microRNAs, important post-transcriptional regulators of gene expression, contribute to this association.\n\nMethodLate-life depressive symptoms were assessed annually in 300 non-demented participants of the Religious Orders Study and Rush Memory and Aging Project for a mean of seven years using the Center for Epidemiological Studies Depression scale. Participants underwent annual cognitive testing, clinical assessment of cognitive status, and uniform neuropathologic examination after death. microRNAs were profiled from the prefrontal cortex using Nanostring platform. A global microRNA association study of late-life depressive symptoms was performed using linear mixed model adjusting for sex, age, Alzheimers dementia pathological burden, proportions of brain cell types, post-mortem interval, and RNA integrity.\n\nResultsFour brain microRNAs were associated with late-life depressive symptoms at adjusted p<0.05 (miR-484, miR-26b, miR-30d, and miR-197). Lower expressions of these miRNAs were associated with greater depressive symptoms. Furthermore, lower expressions of miR-484 and miR-197 were associated with faster decline of cognitive performance over time. Additionally, lower miR-484 level was associated with higher probability of having Alzheimers dementia. Lastly, the predicted targets of miR-484 were enriched in a brain protein co-expression module involving synaptic transmission and regulation of long-term neuronal synaptic plasticity.\n\nConclusionsThis is the first study to identify brain microRNAs associated with late-life depressive symptoms assessed longitudinally. Additionally, the authors found a link between late-life depressive symptoms and dementia through miR-484 and miR-197.

genomics↗

bioRxiv2019

Identifying and ranking potential driver genes of Alzheimer's Disease using multi-view evidence aggregation

MotivationLate onset Alzheimers disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Mueller et al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets 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 paper 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 (GWAS) 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. ResultsWe demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease. AvailabilitySource code and link to all feature sets is availabile at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking. Contactben.logsdon@sagebionetworks.org

bioinformatics↗

bioRxiv2019

Functional dissection of Alzheimer’s disease brain gene expression signatures in humans and mouse models

Human brain transcriptomes can highlight biological pathways associated with Alzheimers disease (AD); however, challenges remain to link expression changes with causal triggers. We have examined 30 AD-associated, gene coexpression modules from human brains for overlap with 251 differentially-expressed gene sets from mouse brain RNA-sequencing experiments, including from models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus neurofibrillary tangle pathology and further reveal age- and sex-dependent expression signatures for AD progression. Human coexpression modules enriched for neuronal and/or microglial genes overlap broadly with signatures from mouse models of AD, Huntingtons disease, Amyotrophic Lateral Sclerosis, and also aging. Several human AD coexpression modules, including those implicated in the unfolded protein response and oxidative phosphorylation, were not activated in AD models, but instead were detected following other, unexpected mouse genetic manipulations. Our results comprise a powerful, cross-species resource and pinpoint experimental models for diverse features of AD pathophysiology from human brain transcriptomes.

systems biology↗

bioRxiv2019

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Alzheimers disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

systems biology↗