Biology subjects

Huckins, L. M.

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

18 recordsLinked to original sources

bioRxiv2026

Estradiol modulates neuronal network hyperexcitability in select NDD risk genes

Sex hormone signaling during neurodevelopment may act as a modulator of risk for neurodevelopmental disorders (NDDs), including autism spectrum disorders (ASDs). By conducting a systematic and multi-modal evaluation of estradiol effects across loss-of-function deletions in 36 functionally diverse, large-effect ASD/NDD genes in human induced pluripotent stem cell-derived neurons and larval zebrafish, we uncover convergent and divergent gene-by-estradiol interactions at the transcriptomic, circuit, and behavioral levels. Whereas estradiol partially ameliorates dysregulated expression signatures across all ASD/NDD gene knockouts examined, it selectively and rapidly dampens network hyperexcitability phenotypes in only a subset of ASD/NDD gene knockouts. Two genes, ASH1L and SCN2A, are particularly responsive to estradiol treatment, with dramatic rescue of molecular and cellular effects in human knockout neurons, coupled with behavioral phenotypes in larval zebrafish mutants. The seven most estradiol responsive genes show enhanced protein-protein interaction network connectivity, strong co-expression in specific postnatal time periods, and enrichment in upper-layer mature excitatory neurons. Altogether, we describe distinct estradiol rescue in different modalities, elucidating shared transcriptomic effects as well as gene-specific cellular and behavioral rescue in the context of loss of function mutations of select ASD/NDD genes.

genomics↗

bioRxiv2025

Mechanical stretch disrupts calcium dynamics and redistributes Piezo1 in human astrocytes

Astrocytes regulate the activity of nearby neurons so disruption of astrocyte calcium dynamics by traumatic brain injury (TBI) could have profound consequences for neural network activity in the brain. In this study, human induced pluripotent stem cell (hiPSC)-derived astrocytes were used in a two-dimensional (2D) in vitro stretch injury model to evaluate the effect of trauma on calcium dynamics, mitochondrial function, and the mechanosensitive ion channel Piezo1. Outcomes were assessed using live imaging, immunostaining, and RNA sequencing. Cell viability, mitochondrial membrane potential, and spontaneous calcium transients declined as injury severity increased. At moderate injury severity, the decreases in mitochondrial membrane potential and calcium dynamics were temporary. The spatial distribution of Piezo1 also changed temporarily after injury. RNA sequencing identified 196 genes that changed expression after injury, including downregulation of mitochondrial and oxidative metabolic processes and upregulation of cortical thinning pathways. These findings establish this model as a platform for investigating the cellular mechanisms of TBI and its influence on neurodegeneration.

neuroscience↗

bioRxiv2025

Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states

The accumulation of abnormal tau protein selectively affects distinct brain regions and specific populations of neurons and glial cells in tau-related dementias, such as Alzheimers disease (AD), Picks disease (PiD), and progressive supranuclear palsy (PSP). Although the three disorders share the feature of tau protein pathology, the regulatory circuitry of non-coding genetic variants underlying risk-associated cell states remains to be elucidated. Using paired single-nucleus profiling of chromatin accessibility and gene expression across AD, PiD, and PSP, we define cell-type-specific cis-regulatory elements (CREs) across six cell types and fifty subclasses. Comparing disease-dynamic CREs across three disorders, we find that glia overrepresent disorder-specific gene regulation related to dynamic cellular response to stress. We show that human genetic variants affecting microglial gene regulation converge into distinct and co-regulated modules affecting specific cellular functions. Moreover, polygenic risk modifiers are maximally co-accessible in disorder-specific glial states, modifying distinct pathways such as sphingomyelin regulation in PiD. Our study informs glial regulators linked to polygenic modifiers of primary tauopathy, introducing modifiable pathways governing resilience for therapeutic consideration.

genomics↗

bioRxiv2024

Decoding the transcriptomic signatures of psychological trauma in human cortex and amygdala

Psychological trauma has profound effects on brain function and precipitates psychiatric disorders in vulnerable individuals, however, the molecular mechanisms linking trauma with psychiatric risk remain incompletely understood. Using RNA-seq data postmortem brain tissue of a cohort of 304 donors (N=136 with trauma exposure), we investigated transcriptional signatures of trauma exposures in two cortical regions (dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex) and two amygdala regions (medial amygdala and basolateral amygdala) associated with stress processing and regulation. We focused on dissecting heterogeneity of traumatic experiences in these transcriptional signatures by investigating exposure to several trauma types (childhood, adulthood, complex, single acute, combat, and interpersonal traumas) and interactions with sex. Overall, amygdala regions were more vulnerable to childhood traumas, whereas cortical regions were more vulnerable to adulthood trauma (regardless of childhood experience). Using cell-type-specific expression imputation, we identified a strong transcriptional response of medial amygdala excitatory neurons to childhood trauma, which coincided with dysregulation observed in a human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons exposed to hydrocortisone. We resolved multiscale coexpression networks for each brain region and identified modules enriched in trauma signatures and whose connectivity was altered with trauma. Trauma-associated coexpression modules provide insight into coordinated functional dysregulation with different traumas and point to potential gene targets for further dissection. Together, these data provide a characterization of the long-lasting human encoding of traumatic experiences in corticolimbic regions of human brain.

neuroscience↗

bioRxiv2024

Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons

The immune environment influences neurodevelopment and subsequent clinical trajectories for psychiatric outcomes in childhood and adolescence. Yet it remains unclear if the impact of maternal and fetal immune activation varies with distinct polygenic risk profiles. Therefore, here we catalogue genotype and environment (GxE) interactions, contrasting allele-specific regulatory activity between inflammatory cues. We report a cue-specific neuronal massively parallel reporter assay (MPRA) of 152 loci from genome-wide association study (GWAS) linked to ten brain traits/disorders, empirically dissecting the impact of interleukin-6 (IL-6) and interferon-alpha (IFN) on transcriptional activity. In human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons, 1,156 active candidate regulatory risk sequences (MPRA-active CRSs) are resolved, including 267 with variant-specific effects (MPRA-emVars) and 61 with variant-by-cytokine interaction effects (interaction MPRA-emVars). Broadly, neuronal immune-mediated regulatory activity is associated with differences in transcription factor binding and chromatin accessibility, the gene targets of which show pleiotropic enrichments for brain, metabolic, and immune disorders. Dynamic genetic regulation mediates neuroimmune effects, informing our understanding of the genomics of psychiatric and neurological traits, mechanisms governing pleiotropy across disorders, and how immune mechanisms mediate genetic risk.

genetics↗

bioRxiv2023

Title: Leveraging Base Pair Mammalian Constraint to Understand Genetic Variation and Human Disease

Although thousands of genomic regions have been associated with heritable human diseases, attempts to elucidate biological mechanisms are impeded by a general inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function that is agnostic to cell type or disease mechanism. Here, single base phyloP scores from the whole genome alignment of 240 placental mammals identified 3.5% of the human genome as significantly constrained, and likely functional. We compared these scores to large-scale genome annotation, genome-wide association studies (GWAS), copy number variation, clinical genetics findings, and cancer data sets. Evolutionarily constrained positions are enriched for variants explaining common disease heritability (more than any other functional annotation). Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

genomics↗

bioRxiv2023

Transcriptional signatures of heroin intake and seeking throughout the brain reward circuit

Opioid use disorder (OUD) looms as one of the most severe medical crises currently facing society. More effective therapeutics for OUD requires in-depth understanding of molecular changes supporting drug-taking and relapse. Recent efforts have helped advance these aims, but studies have been limited in number and scope. Here, we develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNAseq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following prolonged abstinence, and heroin-primed drug-seeking (i.e., "relapse"). Bioinformatics analysis of this rich dataset identified numerous patterns of molecular changes, transcriptional regulation, brain-region-specific involvement in various aspects of OUD, and both region-specific and pan-circuit biological domains affected by heroin. Integrating RNAseq data with behavioral outcomes using factor analysis to generate an "addiction index" uncovered novel roles for particular brain regions in promoting addiction-relevant behavior, and implicated multi-regional changes in affected genes and biological processes. Comparisons with RNAseq and genome-wide association studies from humans with OUD reveal convergent molecular regulation that are implicated in drug-taking and relapse, and point to novel gene candidates with high therapeutic potential for OUD. These results outline broad molecular reprogramming that may directly promote the development and maintenance of OUD, and provide a foundational resource to the field for future research into OUD mechanisms and treatment strategies.

neuroscience↗

bioRxiv2022

Convergent impact of schizophrenia risk genes

Genetic studies of schizophrenia reveal a complex polygenic risk architecture comprised of hundreds of risk variants; most are common in the population at-large, non-coding, and act by genetically regulating the expression of one or more gene targets ("eGenes"). It remains unclear how genetic variants predicted to confer individually small effects combine to yield substantial clinical impacts in aggregate. Here, we demonstrate that eGenes have shared downstream transcriptomic effects ("convergence") that may underlie unexpected interactions ("non-additive effects") observed when eGenes are manipulated in combination. We apply a pooled CRISPR approach to perturb schizophrenia eGenes in human induced pluripotent stem cell-derived glutamatergic neurons. The strength and specificity of convergence increased between functionally similar eGenes. Predicting that convergence might impact additive relationships between risk loci when inherited together, we use an arrayed approach to explore bidirectional combinatorial perturbations of a partially overlapping set of fifteen schizophrenia eGenes. When specifically considering groups of synaptic or epigenetic eGenes, combinatorial eGene perturbations yield changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Moreover, convergent and non-additive downstream transcriptomic effects overlap, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Combinatorial perturbations result in outcomes that are not yet well-predicted by single eGene perturbations alone, indicating that the effects of polygenic risk cannot necessarily be extrapolated from experiments testing one risk gene at a time.

neuroscience↗

bioRxiv2021

Comparison of confound adjustment methods in the construction of gene co-expression networks

Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.

bioinformatics↗

bioRxiv2021

Induction of Dopaminergic Neurons for Neuronal Subtype-Specific Modeling of Psychiatric Disease Risk

Dopaminergic neurons are critical to movement, mood, addiction, and stress. Current techniques for generating dopaminergic neurons from human induced pluripotent stem cells (hiPSCs) yield heterogenous cell populations with variable purity and inconsistent reproducibility between donors, hiPSC clones, and experiments. Here, we report the rapid (5 weeks) and efficient (~90%) induction of induced dopaminergic neurons (iDANs) through transient overexpression of lineage-promoting transcription factors combined with stringent selection across five donors. We observe maturation-dependent increase in dopamine synthesis, together with electrophysiological properties consistent with midbrain dopaminergic neuron identity, such as slow-rising after hyperpolarization potentials, an action potential duration of ~3ms, tonic sub-threshold oscillatory activity, and spontaneous burst firing at frequency of ~1.0-1.75 Hz. Transcriptome analysis reveals robust expression of genes involved in fetal midbrain dopaminergic neuron identity. Specifically expressed genes in iDANs, relative to their isogenic glutamatergic and GABAergic counterparts, were linked to the genetic risk architecture of a broad range of psychiatric traits, with iDANs showing particularly strong enrichment in loci conferring heritability for cannabis use disorder, schizophrenia, and bipolar disorder. Therefore, iDANs provide a critical tool for modeling midbrain dopaminergic neuron development and dysfunction in psychiatric disease.

neuroscience↗

bioRxiv2019

Analysis of Genetically Regulated Gene Expression identifies a trauma type specific PTSD gene, SNRNP35

PTSD has significant genetic heritability; however, it is unclear how genetic risk influences tissue-specific gene expression. We used brain and non-brain transcriptomic imputation models to impute genetically regulated gene expression (GReX) in 9,087 PTSD-cases and 23,811 controls and identified thirteen significant GReX-PTSD associations. The results suggest substantial genetic heterogeneity between civilian and military PTSD cohorts. The top study-wide significant PTSD-association was with predicted downregulation of the Small Nuclear Ribonucleoprotein U11/U12 Subunit 35 (SNRNP35) in the BA9 region of the prefrontal cortex (PFC) in military cohorts. In peripheral leukocytes from 175 U.S. Marines, the observed PTSD differential gene expression correlated with the predicted blood GReX differences for these individuals, and deployment stress downregulated SNRNP35 expression, primarily in Marines with post-deployment PTSD. SNRNP35 is a subunit of the minor spliceosome complex and SNRNP35 knockdown in cells validated its functional importance in U12-intron splicing. Finally, mimicking acute activation of the endogenous stress axis in mice downregulated PFC Snrnp35 expression.

genetics↗

bioRxiv2018

Prioritizing risk genes for neurodevelopmental disorders using pathway information

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.

genomics↗

bioRxiv2018

Identifying tissues implicated in Anorexia Nervosa using Transcriptomic Imputation

Anorexia nervosa (AN) is a complex and serious eating disorder, occurring in ~1% of individuals. Despite having the highest mortality rate of any psychiatric disorder, little is known about the aetiology of AN, and few effective treatments exist.\n\nGlobal efforts to collect large sample sizes of individuals with AN have been highly successful, and a recent study consequently identified the first genome-wide significant locus involved in AN. This result, coupled with other recent studies and epidemiological evidence, suggest that previous characterizations of AN as a purely psychiatric disorder are over-simplified. Rather, both neurological and metabolic pathways may also be involved.\n\nIn order to elucidate more of the system-specific aetiology of AN, we applied transcriptomic imputation methods to 3,495 cases and 10,982 controls, collected by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Transcriptomic Imputation (TI) methods approaches use machine-learning methods to impute tissue-specific gene expression from large genotype data using curated eQTL reference panels. These offer an exciting opportunity to compare gene associations across neurological and metabolic tissues. Here, we applied CommonMind Consortium (CMC) and GTEx-derived gene expression prediction models for 13 brain tissues and 12 tissues with potential metabolic involvement (adipose, adrenal gland, 2 colon, 3 esophagus, liver, pancreas, small intestine, spleen, stomach).\n\nWe identified 35 significant gene-tissue associations within the large chromosome 12 region described in the recent PGC-ED GWAS. We applied forward stepwise conditional analyses and FINEMAP to associations within this locus to identify putatively causal signals. We identified four independently associated genes; RPS26, C12orf49, SUOX, and RDH16. We also identified two further genome-wide significant gene-tissue associations, both in brain tissues; REEP5, in the dorso-lateral pre-frontal cortex (DLPFC; p=8.52x10-07), and CUL3, in the caudate basal ganglia (p=1.8x10-06). These genes are significantly enriched for associations with anthropometric phenotypes in the UK BioBank, as well as multiple psychiatric, addiction, and appetite/satiety pathways. Our results support a model of AN risk influenced by both metabolic and psychiatric factors.

genetics↗

bioRxiv2017

Genome-wide association study implicates CHRNA2 in cannabis use disorder

Introductory paragraphCannabis is the most frequently used illicit psychoactive substance worldwide1. Life time use has been reported among 35-40% of adults in Denmark2 and the United States3. Cannabis use is increasing in the population4-6 and among users around 9% become dependent7. The genetic risk component is high with heritability estimates of 518-70%9. Here we report the first genome-wide significant risk locus for cannabis use disorder (CUD, P=9.31x10-12) that replicates in an independent population (Preplication=3.27x10-3, Pmetaanalysis=9.09x10-12). The finding is based on a genome-wide association study (GWAS) of 2,387 cases and 48,985 controls followed by replication in 5,501 cases and 301,041 controls. The index SNP (rs56372821) is a strong eQTL for CHRNA2 and analyses of the genetic regulated gene expressions identified significant association of CHRNA2 expression in cerebellum with CUD. This indicates a potential therapeutic use in CUD of compounds with agonistic effect on the neuronal acetylcholine receptor alpha-2 subunit encoded by CHRNA2. At the polygenic level analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance.

genomics↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗

bioRxiv2017

Genetic Diversity Turns a New PAGE in Our Understanding of Complex Traits

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

genetics↗

bioRxiv2017

Bayesian Integrated Analysis Of Multiple Types Of Rare Variants To Infer Risk Genes For Schizophrenia And Other Neurodevelopmental Disorders

BackgroundIntegrating rare variation from trio family and case/control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DD), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through study of rare variation, only two risk genes have been identified.\n\nMethodsWe used hierarchical Bayesian modeling of rare variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence (WES) data for schizophrenia (1,077 trios, 6,699 cases and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).\n\nResultsFor SCZ, we estimate 1,551 risk genes, more risk genes and weaker effects than for NDDs. We provide power analyses to predict the number of risk gene discoveries as more data become available, demonstrating greater value of case-control over trio samples. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs ({rho} > 0.55), but low between SCZ and the NDDs ({rho} < 0.3). In depth analysis of 288 NDD genes shows highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichments, single-cell RNA-seq (scRNAseq) cell types and multi-region developmental brain RNA-seq.\n\nConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs. We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

genomics↗

bioRxiv2017

Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissecting this signal into multiple independent eQTL for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (N=467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context specific (i.e. tissue, cell type, or developmental time point specific) regulation of gene expression. Integrating the Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC conditional eQTL data reveals forty loci with strong evidence for co-localization (posterior probability >0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes and identify novel genes for schizophrenia risk, and provide specific hypotheses for their functional follow-up.

genetics↗