Biology subjects

McEwen, L. M.

Publications and source records attributed to McEwen, L. M..

3 recordsLinked to original sources

bioRxiv2020

An integrated, modular approach to data science education in the life sciences

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, post-secondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has potential to improve research and learning outcomes and better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated co-curricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants, postdocs, instructors and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences. Availability and implementationThe EDUCE teaching and learning framework is accessible at educe-ubc.github.io

scientific communication and education↗

bioRxiv2018

Exploring the Genetic Basis of Human Population Differences in DNA Methylation and their Causal Impact on Immune Gene Regulation

DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, its genetic determinants, and their respective causal impact on immune gene regulation remain elusive. We report extensive population differences in DNA methylation between individuals of African and European descent -- detected in primary monocytes that were used as a model of a major innate immunity cell type. Most of these differences (~70%) were driven by DNA sequence variants nearby CpG sites (meQTLs), which account for ~60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

genomics↗

bioRxiv2018

A novel, biologically-informed polygenic score reveals role of mesocorticolimbic insulin receptor gene network on impulsivity and addiction

ImportanceActivation of brain insulin receptors occurs on mesocorticolimbic regions, modulating reward sensitivity and inhibitory control. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related psychopathologies (attention-deficit hyperactivity disorder, addiction), an idea that agrees with the high comorbidity between insulin resistant states and psychiatric conditions. While genetic studies comprise an interesting tool to explore neurobiological mechanisms in community samples, the conventional genome-wide association studies and polygenic risk score methodologies completely ignore the fact that genes operate in networks, and code for precise biological functions in specific tissues.\n\nObjectiveWe propose a novel, biologically informed genetic score reflecting the mesocorticolimbic insulin receptor-related gene network, and investigate if it predicts dopamine-related psychopathology (impulsivity and addiction) in community samples.\n\nDesignBirth cohort (Maternal Adversity, Vulnerability and Neurodevelopment, MAVAN) and adult cohort (Study of Addiction, Genes and Environment, SAGE).\n\nSettingGeneral community.\n\nParticipants212 4-year-old children (MAVAN), and 1626 adults (SAGE).\n\nExposureThe biologically informed, mesocorticolimbic specific, insulin receptor polygenic score was created based on levels of co-expression with the insulin receptor in striatum and prefrontal cortex, and calculated in the two samples using the genotype data (Psychip/Psycharray).\n\nMain outcomechildhood impulsivity in the Information Sampling task, and risk for early addiction onset.\n\nResultsThe insulin receptor polygenic score showed improved prediction of childhood impulsivity in boys and risk for early addiction onset in males in comparison to conventional polygenic risk scores for attention-deficit hyperactivity disorder or addiction.\n\nConclusions and relevanceThis novel genomic approach reveals insulin action as a relevant biological process involved in the risk for dopamine-related psychopathology.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSConsidering the modulation of mesocorticolimbic dopaminergic pathways by insulin through the action on its receptors (IR), we investigated if a novel, region specific polygenic score on the IR-related gene network (ePRS-IR) is associated with dopamine-related behaviors (impulsivity and addiction).\n\nFindingsThe ePRS-IR showed improved prediction of childhood impulsivity and risk for early addiction onset in comparison to conventional polygenic risk scores for ADHD or addiction.\n\nMeaningThis novel genomic approach reveals insulin action as a biological process involved in the risk for dopamine-related psychopathology.

neuroscience↗