Biology subjects

Michaelson, J.

Publications and source records attributed to Michaelson, J..

8 recordsLinked to original sources

bioRxiv2026

Migration genetics link excitatory neurons to ancient selection and economic growth

From ancient nomadic movements to modern urbanization, migration has driven human history through mechanisms that remain poorly understood. We conducted a genome-wide association study of migration distance in 250,000 UK individuals, identifying 20 loci in neurodevelopmental genes with 5% heritability. Migration-related variants are associated with excitatory neuron gene expression and correlate with cognition, risk-tolerance, and reduced interpersonal attachment. Within-family analyses demonstrate genetic effects remain significant after controlling for shared environmental factors between siblings. Our polygenic score predicts inferred mobility in >1,000 ancient individuals spanning 10,000 years, revealing positive selection on migration alleles that increased substantially over millennia. At the population level, each standard deviation increase in county-level migration polygenic score predicts >$4,000 greater income growth per person in the US. These findings establish migration as a heritable trait and suggest biological pathways connecting individual neurodevelopment with regional prosperity across evolutionary and contemporary timescales.

genomics↗

bioRxiv2022

NEUROeSTIMator: Using Deep Learning to Quantify Neuronal Activation from Single-Cell and Spatial Transcriptomic Data

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this methods ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

bioinformatics↗

bioRxiv2021

Endoplasmic Reticulum Chaperone Genes Encode Effectors of Long-Term Memory

The mechanisms underlying memory loss associated with Alzheimers disease and related dementias (ADRD) remain unclear, and no effective treatments exist. Fundamental studies have shown that a set of transcriptional regulatory proteins of the nuclear receptor 4a (Nr4a) family serve as molecular switches for long-term memory. Here, we show that Nr4a proteins regulate the transcription of a group of genes encoding chaperones that localize to the endoplasmic reticulum (ER), which function to traffic plasticity-related proteins to the cell surface during long lasting forms of synaptic plasticity and memory. Nr4a transcription factors and ER chaperones are linked to ADRD in human samples as well as mouse models, and overexpressing Nr4a1 or the ER chaperone Hspa5 ameliorates the long-term memory deficits in a tau-based mouse model of ADRD, pointing towards novel therapeutic approaches for treating memory loss. Thus, our findings establish protein folding in the ER as a novel molecular concept underlying long-term memory, providing new insights into the mechanistic basis of cognitive deficits in dementia. One-Sentence SummaryMolecular approaches establish protein folding in the endoplasmic reticulum as a novel molecular concept underlying synaptic plasticity and memory, serving as a switch to regulate protein folding and trafficking, and driving cognitive deficits in neurodegenerative disorders.

neuroscience↗

bioRxiv2021

p53-mediated neurodegeneration in the absence of the nuclear protein Akirin2

Proper gene regulation is critical for both neuronal development and maintenance as the brain matures. We previously demonstrated that Akirin2, an essential nuclear protein that interacts with transcription factors and chromatin remodeling complexes, is required for the embryonic formation of the cerebral cortex. Here we show that Akirin2 plays a mechanistically distinct role in maintaining healthy neurons during cortical maturation. Restricting Akirin2 loss to excitatory cortical neurons resulted in progressive neurodegeneration via necroptosis and severe cortical atrophy with age. Comparing transcriptomes from Akirin2-null postnatal neurons and cortical progenitors revealed that targets of the tumor suppressor p53, a regulator of both proliferation and cell death encoded by Trp53, were consistently upregulated. Heterozygous deletion of Trp53 rescued neurodegeneration in Akirin2-null neurons. These data: 1) implicate Akirin2 as a critical neuronal maintenance protein; 2) identify p53 pathways as mediators of Akirin2 functions; and 3) suggest Akirin2 dysfunction may be relevant to neurodegenerative diseases.

neuroscience↗

bioRxiv2020

VCFdbR: A method for expressing biobank-scale Variant Call Format data in a SQLite database using R

As exome and whole-genome sequencing cohorts grow in size, the data they produce strains the limits of current tools and data structures. The Variant Call Format (VCF) was originally created as part of the 1,000 Genomes project. Flexible and concise enough to describe the genetic variations of thousands of samples in a single flat file, the VCF has become the standard for communicating the results of large-scale sequencing experiments. Because of its static and text-based structure, VCFs remain cumbersome to parse and filter in an interactive way, even with the aid of indexing. Iterating on previous concepts, we propose here a pipeline for converting VCFs to simple SQLite databases, which allow for rapid searching and filtering of genetic variants while minimizing memory overhead. Code can be found at https://github.com/tkoomar/VCFdbR

bioinformatics↗

bioRxiv2019

Exome sequencing of 457 autism families recruited online provides evidence for novel ASD genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. Additionally, we identified variants that are possibly associated with autism in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.2 provides statistical support for 34 ASD risk genes with at least one damaging variant identified in SPARK. Nine of these genes (BRSK2, DPP6, EGR3, FEZF2, ITSN1, KDM1B, NR4A2, PAX5 and RALGAPB) are newly emerging genes in autism, of which BRSK2 has the strongest statistical support as a risk gene for autism (TADA q-value = 0.0015). Future studies leveraging the thousands of individuals with ASD that have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate autism research that incorporates genetic etiology.

genetics↗

bioRxiv2018

Impact of genetic variations on the human brain: when and where?

Genetic studies have found candidates in most of neurological and psychiatric conditions. However, it remains challenging to assess, in which brain region, and when in the brain development, a mutation is more likely to have an impact, especially for intergenic loci. Here we present, BRAVA, a publicly available resource for estimating genetic mutations spatio-temporal context on the human brain. Different contexts were observed for schizophrenia associated loci, suggesting both neurodevelopmental and late onset components.

bioinformatics↗

bioRxiv2018

Forecasting autism gene discovery with machine learning and genome-scale data

BackgroundGenes are one of the most powerful windows into the biology of autism, and it has been estimated that perhaps a thousand or more genes may confer risk. However, less than 100 genes are currently viewed as having robust enough evidence to be considered true \"autism genes\". Massive genetic studies are underway to produce data to implicate additional genes, but this approach, although necessary, is costly and slow-moving.\n\nMethodsWe approach autism gene discovery as a machine learning problem, rather than a genetic association problem, and use genome-scale data as predictors for identifying further genes that have similar properties in the feature space compared to established autism risk genes. This approach, which we call forecASD, integrates spatiotemporal gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score that indexes each genes evidence for being involved in the etiology of autism.\n\nResultsWe demonstrate that forecASD has substantially increased sensitivity and specificity compared to previous gene-level predictors of autism association, including genetic measures such as TADA. On an independent test set, consisting of newly-released pilot data from the SPARK Genomics Consortium, we show that forecASD best predicts which genes will have an excess of likely gene disrupting (LGD) de novo mutations. We further use independent data from a recent post mortem study of case/control gene expression to show that forecASD is also a significant predictor of genes implicated in ASD through differential expression. Using forecASD results, we show which molecular pathways are currently under-represented in the autism literature and likely represent under-appreciated biological mechanisms of autism. Finally, forecASD correctly predicted 12 of 16 genes implicated at FDR=0.2 by the latest ASD gene discovery study, while also identifying the most likely false positives among the candidate genes.\n\nConclusionsThese results demonstrate that forecASD bridges the gap between genetic- and expression-based ASD gene discovery, and provides a data-driven replacement to much of the manual filtering and curation that is a critical step in ensuring the robustness of gene discovery studies.

bioinformatics↗