Recent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative regulatory elements marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuron subtypes: cortical excitatory, PV, D1, and D2. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuron subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.
We would like to thank members of the Eric Yttri lab and Aryn Gittis lab at Carnegie Mellon University for providing Drd1-cre, Adora2a-cre, and PValb-2a-Cre mice for cell type-specific ATAC-seq experiments.
Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtypes are low yield, involving intensive transgenic strain or virus screening. Here, we present Specific Nuclear-Anchored Independent Labeling (SNAIL), an improved virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and then make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV+) neurons. Nuclear isolation using SNAIL in wild-type mice is sufficient to capture characteristic open chromatin features of PV+ neurons in the cortex, striatum, and external globus pallidus. The SNAIL framework also has high utility for multispecies cell probe engineering; expression from a mouse PV+ SNAIL enhancer sequence was enriched in PV+ neurons of the macaque cortex. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.
We profile genome-wide histone 3 lysine 27 acetylation (H3K27ac) of 3 major brain cell types from hippocampus and dorsolateral prefrontal cortex (dlPFC) of subjects with and without Alzheimer's Disease (AD). We confirm that single nucleotide polymorphisms (SNPs) associated with late onset AD (LOAD) prefer to reside in the microglial histone acetylome, which varies most strongly with age. We observe acetylation differences associated with AD pathology at 3,598 peaks, predominantly in an oligodendrocyte-enriched population. Strikingly, these differences occur at the promoters of known early onset AD (EOAD) risk genes (APP, PSEN1, PSEN2, BACE1), late onset AD (LOAD) risk genes (BIN1, PICALM, CLU, ADAM10, ADAMTS4, SORL1 and FERMT2), and putative enhancers annotated to other genes associated with AD pathology (MAPT). More broadly, acetylation differences in the oligodendrocyte-enriched population occur near genes in pathways for central nervous system myelination and oxidative phosphorylation. In most cases, these promoter acetylation differences are associated with differences in transcription in oligodendrocytes. Overall, we reveal deregulation of known and novel pathways in AD and highlight genomic regions as therapeutic targets in oligodendrocytes of hippocampus and dlPFC. INTRODUCTION:Alzheimer's Disease (AD) is the most common age-related neurodegenerative disorder 1 . The hallmarks of AD pathology are numerous and include neuronal loss, synaptic dysfunction, gliosis, and the accumulation of intercellular plaques of amyloid-β (Aβ) protein and intracellular neurofibrillary tangles (NFT) of phosphorylated tau protein (MAPT) 2 .Aβ plaques are formed by differential proteolytic cleavage of the amyloid β precursor protein (APP) 3-6 by the α-secretase, β-secretase and γ-secretase enzymes 7 . Studies of individuals affected by early onset (<60 yrs.) familial AD (EOAD) have identified causal autosomal dominant mutations primarily in Aβ processing proteins presenilin-1 (PSEN1) and presenilin-2 (PSEN2), which are part of the γ-secretase complex 8-10 , but also causal mutations or duplications in APP itself [11][12][13] . However, EOAD only accounts for a small minority of AD cases. Late onset sporadic AD (LOAD) is more frequent and accounts for up to 99% or more of AD cases. While increased age is the strongest risk factor and several environmental factors also confer risk for LOAD, its heritability has been estimated to be as high as 79% 14 .In contrast to EOAD, genetic risk for LOAD is less well understood. The ε4 allele comprising mutations in two codons in Apolipoprotein E (APOE) has been identified as the strongest genetic risk factor for LOAD [15][16][17][18][19] . More recently, genome wide association studies (GWAS) 20-27 have reproduced the APOE association and also identified 28 other unique loci harboring genetic variants which increase risk for developing LOAD 26-28 . Strikingly, from the set of most significant (or "sentinel") single nucleotide polymorphisms (SNPs) derived from GWAS and SNPs in strong linkage...
We profile genome-wide histone 3 lysine 27 acetylation (H3K27ac) of 3 major brain cell types from hippocampus and dorsolateral prefrontal cortex (dlPFC) of subjects with and without Alzheimer’s Disease (AD). We confirm that single nucleotide polymorphisms (SNPs) associated with late onset AD (LOAD) show a strong tendency to reside in microglia-specific gene regulatory elements. Despite this significant colocalization, we find that microglia harbor more acetylation changes associated with age than with amyloid-β (Aβ) load. In contrast, we detect that an oligodendrocyte-enriched glial (OEG) population contains the majority of differentially acetylated peaks associated with Aβ load. These differential peaks reside near both early onset risk genes (APP, PSEN1, PSEN2) and late onset AD risk loci (including BIN1, PICALM, CLU, ADAM10, ADAMTS4, SORL1, FERMT2), Aβ processing genes (BACE1), as well as genes involved in myelinating and oligodendrocyte development processes. Interestingly, a number of LOAD risk loci associated with differentially acetylated risk genes contain H3K27ac peaks that are specifically enriched in OEG. These findings implicate oligodendrocyte gene regulation as a potential mechanism by which early onset and late onset risk genes mediate their effects, and highlight the deregulation of myelinating processes in AD. More broadly, our dataset serves as a resource for the study of functional effects of genetic variants and cell type specific gene regulation in AD.
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