Biology subjects

Price, A.

Publications and source records attributed to Price, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

E3 ligases regulate key processes, but many of their roles remain unknown. Using Perturb-seq, we interrogated the function of 1,130 E3 ligases, partners and substrates in the inflammatory response in primary dendritic cells (DCs). Dozens impacted the balance of DC1, DC2, migratory DC and macrophage states and a gradient of DC maturation. Family members grouped into co-functional modules that were enriched for physical interactions and impacted specific programs through substrate transcription factors. E3s and their adaptors co-regulated the same processes, but partnered with different substrate recognition adaptors to impact distinct aspects of the DC life cycle. Genetic interactions were more prevalent within than between modules, and a deep learning model, com{beta}VAE, predicts the outcome of new combinations by leveraging modularity. The E3 regulatory network was associated with heritable variation and aberrant gene expression in immune cells in human inflammatory diseases. Our study provides a general approach to dissect gene function.

genomics↗

bioRxiv2023

Morphological changes and two Nodal paralogs drive left-right asymmetry in the squamate veiled chameleon (C. calyptratus).

The ancestral mode of left-right (L-R) patterning involves cilia in the L-R organizer. However, the mechanisms regulating L-R patterning in non-avian reptiles remains an enigma, since most squamate embryos are undergoing organogenesis at oviposition. In contrast, veiled chameleon (Chamaeleo calyptratus) embryos are pre-gastrula at oviposition, making them an excellent organism for studying L-R patterning evolution. Here we show that veiled chameleon embryos lack motile cilia in their L-R organizer, consistent with the loss of motile cilia being a synapomorphy of all reptiles. Furthermore, in contrast to avians, geckos and turtles, which have one Nodal gene, veiled chameleon exhibits expression of two paralogs of Nodal in the left lateral plate mesoderm, albeit in non-identical patterns. Using live imaging, we observed asymmetric morphological changes that precede, and likely trigger, asymmetric expression of the Nodal cascade. Thus, veiled chameleons are a new and unique model for studying the evolution of L-R patterning.

developmental biology↗

bioRxiv2022

Cellular and Fibrillar Collagen Analyses in an Animal Model of Retinal Detachment-Related Proliferative Vitreoretinopathy Reveals a Defined Transition to Chronic Fibrosis

PurposeProliferative vitreoretinopathy (PVR) is the most common cause of failure of surgically repaired rhegmatogenous retinal detachment (RRD). Chemically-induced and cell-injection PVR models do not fully simulate the clinical characteristics of PVR in the post-RRD context. There is an unmet need for translational models in which to study mechanisms and treatments specific to RRD-PVR. MethodsRRD-PVR was induced in adult Dutch Belted rabbits. Posterior segments of enucleated globes were fixed or processed for RNA-Seq at 6 hours and 2, 7, 14, and 35 days post-induction. Histochemical staining and immunolabeling for glial fibrillary acidic protein (GFAP), alpha smooth muscle actin (SMA), vascular endothelial growth factor receptor 2 (VEGFR2), CD68, and retinal pigment epithelium 65 kDa protein (RPE65) were performed, and labeling intensity was scored. Single cell RNA sequencing was performed. ResultsAcute histopathologic changes included intravitreal and intraretinal hemorrhage, leukocytic vitritis, chorioretinitis, and retinal rarefaction. Chronic lesions showed retinal atrophy, gliosis, fibrotic subretinal membranes, and epiretinal fibrovascular proliferation. Fibrillar collagen was present in the fibrocellular and fibrovascular membranes in chronic lesions. Moderate to strong labeling of glia and vasculature was detected in chronic lesions. At day 14, most cells profiled by single cell sequencing were identified as Muller glia and microglia, consistent with immunolabeling. Expression of several fibrillar collagen genes were upregulated in chronic lesions. ConclusionsHistologic and transcriptional features of this rabbit model simulate important features of human RRD-PVR, including the transition to chronic intra and periretinal fibrosis. This high-fidelity in vivo model of RRD-PVR will enable further research on targeted treatment interventions.

pathology↗

bioRxiv2022

Widespread, depth-dependent microstructural damage in the cortex of children with drug resistant focal epilepsy: A quantitative T1 and T2 mapping study

ObjectiveTissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes, treatment-related side-effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilised quantitative MRI, which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity. MethodsTo assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy [age-range=4-18 years] and 46 typically-developing children [age-range=2-18 years]. We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically-defined abnormalities (MRI-positive) and healthy controls, and tested if this could classify patients without reported radiological abnormalities (MRI-negative). ResultsWe uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 62% at 100% specificity on held-out MRI-negative patients. SignificanceThese findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically-identified abnormalities, and potentially evident pre-symptomatically. Key PointsO_LIWe assessed cortical microstructure in children with focal epilepsy C_LIO_LIQuantitative T1 and T2 relaxometry (qT1 and qT2) was measured in the neocortex C_LIO_LIPatients showed extensive qT1/qT2 increases and intracortical organization changes C_LIO_LIAlterations may appear during cerebral development, prior to disease onset C_LI

neuroscience↗

bioRxiv2022

Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain

The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion MRI from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in the fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.

neuroscience↗

bioRxiv2022

Development of neonatal brain functional centrality and alterations associated with preterm birth

Formation of the functional connectome in early life underpins future learning and behaviour. However, our understanding of how the functional organisation of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterised voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and decrease within motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive, and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

neuroscience↗

bioRxiv2022

Stem Cell Secretome Promotes Scarless Corneal Wound Healing and Rescues Corneal Sensory Nerves

Corneal scarring is a leading cause of blindness in the world. In this study, we explored the therapeutic potential of corneal stromal stem cell (CSSC)-derived secretome in a mechanical debridement mouse model of corneal scarring. CSSC secretome was able to promote scarless corneal wound healing. The mechanisms include 1) dampening inflammation with reduced CD45+, CD11b+/GR1+, and CD11b+/F4/80+ inflammatory cells in the wounded corneas; 2) reducing fibrotic extracellular matrix deposition such as collagen IV, collagen 3A1, SPARC, and -SMA; 3) rescuing sensory nerves. The proteomic analysis shows upregulated proteins related to wound healing and cell adhesion which boost scarless wound healing. It also shows upregulated neuroprotective proteins in CSSC secretome related to axon guidance, neurogenesis, neuron projection development, and neuron differentiation. Four unique complement inhibitory proteins CD59, vitronectin, SERPING1, and C1QBP found in CSSC secretome contribute to reducing a complement cascade mediating cell death and membrane attacking complex autoantibodies after corneal injury. This study provides novel insights into mechanisms of stem cell secretome induced scarless corneal wound healing and neuroprotection and identifies regenerative proteins in the CSSC secretome. Significance StatementIn this study, we report the therapeutic role of corneal stromal stem cell (CSSC) secretome for scarless corneal wound healing and corneal sensory nerve rescuing. We uncovered that CSSC secretome dampens inflammation, reduces fibrosis, induces sensory nerve regeneration, and rescues corneal cells by inhibiting the complement system in the wounded mouse corneas. This study provides pre-clinical evidence for the use of CSSC secretome as a biologic treatment for corneal scarring to prevent corneal blindness. We delineated a plethora of proteins in the CSSC secretome, which individually or in combination have the potential as future therapies for scarless corneal wound healing.

cell biology↗

bioRxiv2022

High Quality Phasing Using Linked-Read Whole Genome Sequencing of Patient Cohorts Informs Genetic Understanding of Complex Traits

Phasing of heterozygous alleles is critical for interpretation of cis-effects of disease-relevant variation. For population studies, phase is often inferred from external data but read-based phasing approaches that span long genomic distances would be more accurate because they enable both genotype and phase to be obtained from a single dataset. To demonstrate how read-based phasing can provide functional insights, we sequenced 477 individuals with Cystic Fibrosis (CF) using linked-read sequencing. We benchmark read-based phasing with different short- and long-read sequencing technologies, prioritize linked-read technology as the most informative and produce a benchmark phase call set from reference sample HG002 for the community. The 477 samples display an average phase block N50 of 4.39 Mb. We use these samples to construct a graph representation of CFTR haplotypes, which facilitates understanding of complex CF alleles. Fine-mapping and phasing of the chr7q35 trypsinogen locus associated with CF meconium ileus demonstrates a 20 kb deletion and a PRSS2 missense variant p.Thr8Ile (rs62473563) independently contribute to meconium ileus risk (p=0.0028, p=0.011, respectively) and are PRSS2 pancreas eQTLs (p=9.5e-7 and p=1.4e-4, respectively), explaining the mechanism by which these polymorphisms contribute to CF. Phase enables access to haplotypes that can be used for genome graph or reference panel construction, identification of cis-effects, and for understanding disease associated loci. The phase information from linked-reads provides a causal explanation for variation at a CF-relevant locus which also has implications for the genetic basis of non-CF pancreatitis to which this locus has been reported to contribute.

genetics↗

bioRxiv2022

The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK

The first SARS-CoV-2 variant of concern (VOC) to be designated was lineage B.1.1.7, later labelled by the World Health Organisation (WHO) as Alpha. Originating in early Autumn but discovered in December 2020, it spread rapidly and caused large waves of infections worldwide. The Alpha variant is notable for being defined by a long ancestral phylogenetic branch with an increased evolutionary rate, along which only two sequences have been sampled. Alpha genomes comprise a well-supported monophyletic clade within which the evolutionary rate is more typical of SARS-CoV-2. The Alpha epidemic continued to grow despite the continued restrictions on social mixing across the UK, and the imposition of new restrictions, in particular the English national lockdown in November 2020. While these interventions succeeded in reducing the absolute number of cases, the impact of these non-pharmaceutical interventions was predominantly to drive the decline of the SARS-CoV-2 lineages which preceded Alpha. We investigate the only two sampled sequences that fall on the branch ancestral to Alpha. We find that one is likely to be a true intermediate sequence, providing information about the order of mutational events that led to Alpha. We explore alternate hypotheses that can explain how Alpha acquired a large number of mutations yet remained largely unobserved in a region of high genomic surveillance: an under-sampled geographical location, a non-human animal population, or a chronically-infected individual. We conclude that the last hypothesis provides the best explanation of the observed behaviour and dynamics of the variant, although we find that the individual need not be immunocompromised, as persistently-infected immunocompetent hosts also display a higher within-host rate of evolution. Finally, we compare the ancestral branches and mutation profiles of other VOCs to each other, and identify that Delta appears to be an outlier both in terms of the genomic locations of its defining mutations, and its lack of rapid evolutionary rate on the ancestral branch. As new variants, such as Omicron, continue to evolve (potentially through similar mechanisms) it remains important to investigate the origins of other variants to identify ways to potentially disrupt their evolution and emergence.

evolutionary biology↗

bioRxiv2021

Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease

Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences between cell states. T cells, for example, have fluid, multifaceted functional states in vivo that cannot be modeled in eQTL studies that aggregate cells. Here, we modeled T cell states and eQTLs at single-cell resolution. Using >500,000 resting memory T cells from 259 Peruvians, we found over one-third of the 6,511 cis-eQTLs had state-dependent effects. By integrating single-cell RNA and surface protein measurements, we defined continuous cell states that explained more eQTL variation than discrete states like CD4+ or CD8+ T cells and could have opposing effects on independent eQTL variants in a locus. Autoimmune variants were enriched in cell-state-dependent eQTLs, such as a rheumatoid-arthritis variant near ORMDL3 strongest in cytotoxic CD8+ T cells. These results argue that fine-grained cell state context is crucial to understanding disease-associated eQTLs.

genetics↗

bioRxiv2021

The Forget-Me-Not dHCP study: 7 Tesla high resolution diffusion imaging in the unfixed post-mortem neonatal brain

Diffusion MRI of the neonatal brain allows investigation of the organisational structure of maturing fibres during brain development. Post-mortem imaging has the potential to achieve high resolution by using long scan times, enabling precise assessment of small structures. The Forget-Me-Not study, part of the Developing Human Connectome Project (dHCP), aims to acquire and publicly distribute high-resolution diffusion MRI data for unfixed post-mortem neonatal brain at 7T with a custom-built head coil. This paper describes how the study addressed logistical, technical and ethical challenges relating to recruitment pipeline, care pathway, tissue preservation, scan setup and protocol optimisation. Results from the first subject recruited to the study demonstrate high-quality diffusion MRI data. Preliminary voxel-wise and tractography-based analyses are presented for the cortical plate, subplate and white matter pathways, with comparison to age-matched in vivo dHCP data. These results demonstrate that high quality post-mortem data can be acquired and provide a sensitive means to explore the developing human brain, as well as altered diffusion properties consistent with post-mortem changes, at high resolution.

bioengineering↗

bioRxiv2021

Stem cell-free therapy for glaucoma to preserve vision

Glaucoma is the leading cause of irreversible blindness with trabecular meshwork (TM) dysfunction resulting in elevated intraocular pressure and retinal ganglion cell (RGC) damage leading to vision loss. In this study, we discovered that secretome, derived from human TM stem cells, via minimal invasive periocular injection, can reduce intraocular pressure, restore TM homeostasis, protect RGC, and restore RGC function in both steroid-induced and genetic myocilin mutant mouse models of glaucoma. The secretome upregulated the COX2-PGE2 axis via mitochondrial TMEM177 and led to activation of endogenous stem cells and TM regeneration. Inhibition of COX2 abolished the protective effect of secretome on TM cells. Secretome treatment also enhanced RGC survival and function. Proteomic analysis revealed that the secretome is enriched with proteins involved in extracellular matrix modulation leading to the remodeling of TM to restore homeostasis. This study highlights the feasibility of stem cell-free therapy for glaucoma with minimal invasive administration and the involvement of multiple novel pathways for a cumulative regenerative effect on the TM to protect RGC. Brief summaryThis study describes a cell-free treatment using stem cell secretome in two animal models of glaucoma and explores the potential mechanisms

cell biology↗

bioRxiv2021

Intranasal oxytocin modulates very early visual processing of emotional faces

Functional imaging and behavioural studies have shown that the neuropeptide oxytocin influences processing of emotional faces. However, it is not clear whether these effects reflect modulation at an early or late stage of affective processing. We investigated the effects of oxytocin administration on early and late visual evoked potentials (VEP) in response to faces with neutral, fearful and happy expressions. In addition, we measured multifocal VEP and its associated nonlinearities to ascertain whether any changes observed in electrophysiology were indicative of a generalised effect or of one tied strictly to emotional processing. In a randomized, double-blind, cross-over design, 27 healthy male participants self-administered a nasal spray of either oxytocin (24 IU) or placebo. At very early latencies (40-60ms), oxytocin reduced right-temporal responses to fearful faces (d = .51), and central responses to both fearful (d = .48) and neutral faces (d = .54). For left occipito-temporal electrode sites, oxytocin decreased P100 reactivity to fearful expressions (d = 0.72). Oxytocin also decreased the amplitudes of the vertex positive potential (140-180ms) and late positive potential (400-600ms), regardless of whether the faces had fearful, happy or neutral expressions. The mfVEP showed no signs of selective magno-or parvo-cellular peak modulation comparing OXT with placebo with either low or high contrast stimulation. These results suggest that at early stages of visual processing, nasal oxytocin modulates responses to facial emotions, whereas at later stages of visual processing, it appears to influence more general face processing mechanisms. In addition, the measurable effects of OXT appear to be not a result of generalized brain change, but systematically related to emotional processing.

neuroscience↗

bioRxiv2021

One Health Investigation of SARS-CoV-2 Infection and Seropositivity among Pets in Households with Confirmed Human COVID-19 Cases - Utah and Wisconsin, 2020

BackgroundApproximately 67% of U.S. households have pets. Limited data are available on SARS-CoV-2 in pets. We assessed SARS-CoV-2 infection in pet cohabitants as a sub-study of an ongoing COVID-19 household transmission investigation. MethodsMammalian pets from households with [≥]1 person with laboratory-confirmed COVID-19 were eligible for inclusion from April-May 2020. Demographic/exposure information, oropharyngeal, nasal, rectal, and fur swabs, feces, and blood were collected from enrolled pets and tested by rRT-PCR and virus neutralization assays. FindingsWe enrolled 37 dogs and 19 cats from 34 of 41 eligible households. All oropharyngeal, nasal, and rectal swabs tested negative by rRT-PCR; one dogs fur swabs (2%) tested positive by rRT-PCR at the first animal sampling. Among 47 pets with serological results from 30 households, eight (17%) pets (4 dogs, 4 cats) from 6 (20%) households had detectable SARS-CoV-2 neutralizing antibodies. In households with a seropositive pet, the proportion of people with laboratory-confirmed COVID-19 was greater (median 79%; range: 40-100%) compared to households with no seropositive pet (median 37%; range: 13-100%) (p=0.01). Thirty-three pets with serologic results had frequent daily contact ([≥]1 hour) with the human index patient before the persons COVID-19 diagnosis. Of these 33 pets, 14 (42%) had decreased contact with the human index patient after diagnosis and none (0%) were seropositive; of the 19 (58%) pets with continued contact, 4 (21%) were seropositive. InterpretationsSeropositive pets likely acquired infection from humans, which may occur more frequently than previously recognized. People with COVID-19 should restrict contact with animals. FundingCenters for Disease Control and Prevention, U.S. Department of Agriculture

microbiology↗

bioRxiv2020

Thinking ahead: prediction in context as a keystone of language in humans and machines

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.

neuroscience↗

bioRxiv2020

Negative short-range genomic autocorrelation of causal effects on human complex traits

Most models of complex trait genetic architecture assume that signed causal effect sizes of each SNP (defined with respect to the minor allele) are uncorrelated with those of nearby SNPs, but it is currently unknown whether this is the case. We develop a new method, autocorrelation LD regression (ACLR), for estimating the genome-wide autocorrelation of causal minor allele effect sizes as a function of genomic distance. Our method estimates these autocorrelations by regressing the products of summary statistics on distance-dependent LD scores. We determined that ACLR robustly assesses the presence or absence of nonzero autocorrelation, producing unbiased estimates with well-calibrated standard errors in null simulations regardless of genetic architecture; if true autocorrelation is nonzero, ACLR correctly detects its sign, although estimates of the autocorrelation magnitude are susceptible to bias in cases of certain genetic architectures. We applied ACLR to 31 diseases and complex traits from the UK Biobank (average N=331K), meta-analyzing results across traits. We determined that autocorrelations were significantly negative at distances of 1-50bp (P = 8 x 10-6, point estimate -0.35 {+/-}0.08) and 50-100bp (P = 2 x 10-3, point estimate -0.33 {+/-} 0.11). We show that the autocorrelation is primarily driven by pairs of SNPs in positive LD, which is consistent with the expectation that linked SNPs with opposite effects are less impacted by natural selection. Our findings suggest that this mechanism broadly affects complex trait genetic architectures, and we discuss implications for association mapping, heritability estimation, and genetic risk prediction.

genetics↗

bioRxiv2020

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

Deep learning models have achieved great success in predicting genome-wide regulatory effects from DNA sequence, but recent work has reported that SNP annotations derived from these predictions contribute limited unique information for human complex disease. Here, we explore three integrative approaches to improve the disease informativeness of allelic-effect annotations (predicted difference between reference and variant alleles) constructed using several previously trained deep learning models: DeepSEA, Basenji and DeepBind (and a related machine learning model, deltaSVM). First, we employ gradient boosting to learn optimal combinations of deep learning annotations, using fine-mapped SNPs and matched control SNPs (on held-out chromosomes) for training. Second, we improve the specificity of these annotations by restricting them to SNPs implicated by (proximal and distal) SNP-to-gene (S2G) linking strategies, e.g. prioritizing SNPs involved in gene regulation. Third, we predict gene expression (and derive allelic-effect annotations) from deep learning annotations at SNPs implicated by S2G linking strategies -- generalizing the previously proposed ExPecto approach, which incorporates deep learning annotations based on distance to TSS. We evaluated these approaches using stratified LD score regression, using functional data in blood and focusing on 11 autoimmune diseases and blood-related traits (average N =306K). We determined that the three approaches produced SNP annotations that were uniquely informative for these diseases/traits, despite the fact that linear combinations of the underlying DeepSEA, Basenji, DeepBind and deltaSVM blood annotations were not uniquely informative for these diseases/traits. Our results highlight the benefits of integrating SNP annotations produced by deep learning models with other types of data, including data linking SNPs to genes.

genetics↗

bioRxiv2020

Unique contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies

Gene regulation is known to play a fundamental role in human disease, but mechanisms of regulation vary greatly across genes. Here, we explore the contributions to disease of two types of genes: genes whose regulation is driven by enhancer regions as opposed to promoter regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using a comprehensive set of SNP-to-gene (S2G) strategies and apply stratified LD score regression to the resulting SNP annotations to draw three main conclusions about 11 autoimmune diseases and blood cell traits (average Ncase=13K across 6 autoimmune diseases, average N =443K across 5 blood cell traits). First, several characterizations of enhancer-related genes defined in blood using functional genomics data (e.g. ATAC-seq, RNA-seq, PC-HiC) are conditionally informative for autoimmune disease heritability, after conditioning on a broad set of regulatory annotations from the baseline-LD model. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and candidate master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2x stronger conditional signal (maximum standardized SNP annotation effect size ({tau}*) = 2.0 (s.e. 0.3) vs. 0.91 (s.e. 0.21)), and >2x stronger gene-level enrichment for approved autoimmune disease drug targets (5.3x vs. 2.1x), as compared to the recently proposed Enhancer Domain Score (EDS). In each case, using functionally informed S2G strategies to link genes to SNPs that may regulate them produced much stronger disease signals (4.1x-13x larger{tau} * values) than conventional window-based S2G strategies. We conclude that our characterizations of enhancer-related and candidate master-regulator genes identify gene sets that are important for autoimmune disease, and that combining those gene sets with functionally informed S2G strategies enables us to identify SNP annotations in which disease heritability is concentrated.

genetics↗