Massively Parallel Reporter Assays Research Articles

LentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements.

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode. This technology has been used to test thousands of sequences and their variants for regulatory activity, to decipher the regulatory code and its evolution, and to develop genetic switches. Lentivirus-based MPRA (lentiMPRA) produces 'in-genome' readouts and enables the use of this technique in hard-to-transfect cells. Here, we provide a detailed protocol for lentiMPRA, along with a user-friendly Nextflow-based computational pipeline-MPRAflow-for quantifying CRS activity from different MPRA designs. The lentiMPRA protocol takes ~2 months, which includes sequencing turnaround time and data processing with MPRAflow.

Dissection of c-AMP Response Element Architecture by Using Genomic and Episomal Massively Parallel Reporter Assays.

In eukaryotes, transcription factors (TFs) orchestrate gene expression by binding to TF-binding sites (TFBSs) and localizing transcriptional co-regulators and RNA polymerase II to cis-regulatory elements. However, we lack a basic understanding of the relationship between TFBS composition and their quantitative transcriptional responses. Here, we measured expression driven by 17,406 synthetic cis-regulatory elements with varied compositions of a model TFBS, the c-AMP response element (CRE) by using massively parallel reporter assays (MPRAs). We find CRE number, affinity, and promoter proximity largely determines expression. In addition, we observe expression modulation based on the spacing between CREs and CRE distance to the promoter, where expression follows a helical periodicity. Finally, we compare library expression between an episomal MPRA and a genomically integrated MPRA, where a single cis-regulatory element is assayed per cell at a defined locus. These assays largely recapitulate each other, although weaker, non-canonical CREs exhibit greater activity in a genomic context.

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

SUMMARYEngineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity. Here, we develop deep exploration networks (DENs), a class of activation-maximizing generative models, which minimize the cost of a neural network fitness predictor by gradient descent. By penalizing any two generated patterns on the basis of a similarity metric, DENs explicitly maximize sequence diversity. To avoid drifting into low-confidence regions of the predictor, we incorporate variational autoencoders to maintain the likelihood ratio of generated sequences. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns, identified splice regulatory sequences predicted to result in highly differential splicing between cell lines, and improved on state-of-the-art results for protein design tasks.

Massively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting melanoma

Genome-wide association studies (GWAS) have identified ~20 melanoma susceptibility loci, most of which are not functionally characterized. Here we report an approach integrating massively-parallel reporter assays (MPRA) with cell-type-specific epigenome and expression quantitative trait loci (eQTL) to identify susceptibility genes/variants from multiple GWAS loci. From 832 high-LD variants, we identify 39 candidate functional variants from 14 loci displaying allelic transcriptional activity, a subset of which corroborates four colocalizing melanocyte cis-eQTL genes. Among these, we further characterize the locus encompassing the HIV-1 restriction gene, MX2 (Chr21q22.3), and validate a functional intronic variant, rs398206. rs398206 mediates the binding of the transcription factor, YY1, to increase MX2 levels, consistent with the cis-eQTL of MX2 in primary human melanocytes. Melanocyte-specific expression of human MX2 in a zebrafish model demonstrates accelerated melanoma formation in a BRAFV600E background. Our integrative approach streamlines GWAS follow-up studies and highlights a pleiotropic function of MX2 in melanoma susceptibility.

Locally acting transcription factors regulate p53-dependent cis-regulatory element activity.

The master tumor suppressor p53 controls transcription of a wide-ranging gene network involved in apoptosis, cell cycle arrest, DNA damage repair, and senescence. Recent studies revealed pervasive binding of p53 to cis-regulatory elements (CREs), which are non-coding segments of DNA that spatially and temporally control transcription through the combinatorial binding of local transcription factors. Although the role of p53 as a strong trans-activator of gene expression is well known, the co-regulatory factors and local sequences acting at p53-bound CREs are comparatively understudied. We designed and executed a massively parallel reporter assay (MPRA) to investigate the effect of transcription factor binding motifs and local sequence context on p53-bound CRE activity. Our data indicate that p53-bound CREs are both positively and negatively affected by alterations in local sequence context and changes to co-regulatory TF motifs. Our data suggest p53 has the flexibility to cooperate with a variety of transcription factors in order to regulate CRE activity. By utilizing different sets of co-factors across CREs, we hypothesize that global p53 activity is guarded against loss of any one regulatory partner, allowing for dynamic and redundant control of p53-mediated transcription.

Candidate silencer elements for the human and mouse genomes

The study of gene regulation is dominated by a focus on the control of gene activation or increase in the level of expression. Just as critical is the process of gene repression or silencing. Chromatin signatures have identified enhancers, however, genome-wide identification of silencers by computational or experimental approaches are lacking. Here, we first define uncharacterized cis-regulatory elements likely containing silencers and find that 41.5% of ~7500 tested elements show silencer activity using massively parallel reporter assay (MPRA). We trained a support vector machine classifier based on MPRA data to predict candidate silencers in over 100 human and mouse cell or tissue types. The predicted candidate silencers exhibit characteristics expected of silencers. Leveraging promoter-capture HiC data, we find that over 50% of silencers are interacting with gene promoters having very low to no expression. Our results suggest a general strategy for genome-wide identification and characterization of silencer elements.

FAM13A Represses AMPK Activity and Regulates Hepatic Glucose and Lipid Metabolism.

SummaryObesity commonly co-exists with fatty liver disease with increasing health burden worldwide. Family with Sequence Similarity 13, Member A (FAM13A) has been associated with lipid levels and fat mass by genome-wide association studies (GWAS). However, the function of FAM13A in maintaining metabolic homeostasis in vivo remains unclear. Here, we demonstrated that rs2276936 in this locus has allelic-enhancer activity in massively parallel reporter assays (MPRA) and reporter assay. The DNA region containing rs2276936 regulates expression of endogenous FAM13A in HepG2 cells. In vivo, Fam13a−/− mice are protected from high-fat diet (HFD)-induced fatty liver accompanied by increased insulin sensitivity and reduced glucose production in liver. Mechanistically, loss of Fam13a led to the activation of AMP-activated protein kinase (AMPK) and increased mitochondrial respiration in primary hepatocytes. These findings demonstrate that FAM13A mediates obesity-related dysregulation of lipid and glucose homeostasis. Targeting FAM13A might be a promising treatment of obesity and fatty liver disease.

Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in mouse embryonic stem cells.

In embryonic stem cells (ESCs), a core transcription factor (TF) network establishes the gene expression program necessary for pluripotency. To address how interactions between four key TFs contribute to cis-regulation in mouse ESCs, we assayed two massively parallel reporter assay (MPRA) libraries composed of binding sites for SOX2, POU5F1 (OCT4), KLF4, and ESRRB. Comparisons between synthetic cis-regulatory elements and genomic sequences with comparable binding site configurations revealed some aspects of a regulatory grammar. The expression of synthetic elements is influenced by both the number and arrangement of binding sites. This grammar plays only a small role for genomic sequences, as the relative activities of genomic sequences are best explained by the predicted occupancy of binding sites, regardless of binding site identity and positioning. Our results suggest that the effects of transcription factor binding sites (TFBS) are influenced by the order and orientation of sites, but that in the genome the overall occupancy of TFs is the primary determinant of activity.

Massively Parallel Reporter Assays (MPRAs) Identify Allelic Transcriptional Dysregulation in Atopic Dermatitis

Atopic dermatitis (AD) is an allergic disease with an estimated heritability of 75%. Genome-wide association studies have identified 29 independent genetic risk loci (“tagged” by 122 reported variants with r2>0.8). Over 95% of AD risk variants are in non-coding regions of the genome, which may affect the transcriptional regulation. Massively Parallel Reporter Assays (MPRAs) enable the identification of AD risk variants that have enhancer activity and act in a genotype-dependent manner. We therefore designed our MPRA “library” to include all of the AD genetic risk variants and all variants that are in genetic linkage disequilibrium with the reported AD risk variants. 341/3691 of the variants in our AD MPRA library demonstrate enhancer activity (pFDR<0.05 and greater than a 50% change in expression) in epithelial cells. Thirty-three of these variants in were IL-13-dependent, a cytokine known to drive allergic inflammation. The sequences around the oligos with IL13 dependence were most strongly enriched for STAT and IRF transcription factor binding motifs (p= 10-5-10-18). 108 variants had genotype-dependent enhancer activty (pFDR<0.05 and greater than a 20% change in expression between the two alleles). Of these variants with allelic activity in the MPRA, 62/108 are expressed allelicly in GTEx skin datasets (enriched 31-fold), and 13/108 are expressed allelically a DICE T cells subset (enriched 29-fold). Our results are consistent with a model in which many causal AD genetic risk variants regulate gene expression in a genotype-dependent manner, resulting in increased disease risk through genotype-dependent dysregulation of gene expression.

Deciphering Gene Regulation Using Massively Parallel Reporter Assays

Massively parallel reporter assays (MPRAs) serve to measure the transcriptional activity of up to millions of DNA elements. These elements are cloned en masse in a reporter construct, with each element typically linked to a unique DNA barcode located in the transcription unit (A). The activity of the elements is inferred from the abundance of mRNA after in vivo expression, either as episomes [1] or integrated in the genome [2] (B). In some designs, the query elements are part of the transcription unit and serve as themselves [3]. Alternatively, the reporter does not carry barcodes but encodes a fluorescent protein; cells are then sorted by fluorescence levels and the activity of the query elements is inferred from their abundance in the sorted cell pools [4] (C). Input DNA sequences for MPRAs can be generated by synthesis [1], PCR [2] or DNA capture methods [5]. Alternatively, entire genomes can be queried as random fragments [3].

Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction.

Epigenomic regulation and lineage-specific gene expression act in concert to drive cellular differentiation, but the temporal interplay between these processes is largely unknown. Using neural induction from human pluripotent stem cells (hPSCs) as a paradigm, we interrogated these dynamics by performing RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and assay for transposase accessible chromatin using sequencing (ATAC-seq) at seven time points during early neural differentiation. We found that changes in DNA accessibility precede H3K27ac, which is followed by gene expression changes. Using massively parallel reporter assays (MPRAs) to test the activity of 2,464 candidate regulatory sequences at all seven time points, we show that many of these sequences have temporal activity patterns that correlate with their respective cell-endogenous gene expression and chromatin changes. A prioritization method incorporating all genomic and MPRA data further identified key transcription factors involved in driving neural fate. These results provide a comprehensive resource of genes and regulatory elements that orchestrate neural induction and illuminate temporal frameworks during differentiation.

MPRAnalyze: statistical framework for massively parallel reporter assays

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences’ activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.

Resolving Molecular Mechanisms of Autoimmune Disease&amp;nbsp;In Primary CD4 T Cells

Numerous genomic loci have been implicated in autoimmune disorders, but attempts to identify causal variants, and thereby disease mechanisms, have been hampered by strong linkage disequilibrium; leaving most loci unresolved and the potential of GWAS largely unfulfilled. To overcome this, we developed a massively-parallel reporter assay for use in primary CD4 T-cells, a key effector of many autoimmune diseases, and sought to resolve potential causal variants via their functional effects. This provided testable hypotheses into disease mechanisms, which we illustrate using a multi-disease-associated locus on chromosome 6. By investigating the lead expression-modulating SNP, we uncover an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates expression of TNFAIP3 (A20), a key NF-κB inhibitor. In activated T-cells, this circuit is disrupted – and super-enhancer formation prevented – by the risk variant at the lead SNP, thus revealing a molecular mechanism that predisposes to multiple autoimmune diseases.

Beyond the Exome: The Non-coding Genome and Enhancers in Neurodevelopmental Disorders and Malformations of Cortical Development.

The development of the human cerebral cortex is a complex and dynamic process, in which neural stem cell proliferation, neuronal migration, and post-migratory neuronal organization need to occur in a well-organized fashion. Alterations at any of these crucial stages can result in malformations of cortical development (MCDs), a group of genetically heterogeneous neurodevelopmental disorders that present with developmental delay, intellectual disability and epilepsy. Recent progress in genetic technologies, such as next generation sequencing, most often focusing on all protein-coding exons (e.g., whole exome sequencing), allowed the discovery of more than a 100 genes associated with various types of MCDs. Although this has considerably increased the diagnostic yield, most MCD cases remain unexplained. As Whole Exome Sequencing investigates only a minor part of the human genome (1–2%), it is likely that patients, in which no disease-causing mutation has been identified, could harbor mutations in genomic regions beyond the exome. Even though functional annotation of non-coding regions is still lagging behind that of protein-coding genes, tremendous progress has been made in the field of gene regulation. One group of non-coding regulatory regions are enhancers, which can be distantly located upstream or downstream of genes and which can mediate temporal and tissue-specific transcriptional control via long-distance interactions with promoter regions. Although some examples exist in literature that link alterations of enhancers to genetic disorders, a widespread appreciation of the putative roles of these sequences in MCDs is still lacking. Here, we summarize the current state of knowledge on cis-regulatory regions and discuss novel technologies such as massively-parallel reporter assay systems, CRISPR-Cas9-based screens and computational approaches that help to further elucidate the emerging role of the non-coding genome in disease. Moreover, we discuss existing literature on mutations or copy number alterations of regulatory regions involved in brain development. We foresee that the future implementation of the knowledge obtained through ongoing gene regulation studies will benefit patients and will provide an explanation to part of the missing heritability of MCDs and other genetic disorders.

Functionalization of CD36 cardiovascular disease and expression associated variants by interdisciplinary high throughput analysis.

CD36 is a platelet membrane glycoprotein whose engagement with oxidized low-density lipoprotein (oxLDL) results in platelet activation. The CD36 gene has been associated with platelet count, platelet volume, as well as lipid levels and CVD risk by genome-wide association studies. Platelet CD36 expression levels have been shown to be associated with both the platelet oxLDL response and an elevated risk of thrombo-embolism. Several genomic variants have been identified as associated with platelet CD36 levels, however none have been conclusively demonstrated to be causative. We screened 81 expression quantitative trait loci (eQTL) single nucleotide polymorphisms (SNPs) associated with platelet CD36 expression by a Massively Parallel Reporter Assay (MPRA) and analyzed the results with a novel Bayesian statistical method. Ten eQTLs located 13kb to 55kb upstream of the CD36 transcriptional start site of transcript ENST00000309881 and 49kb to 92kb upstream of transcript ENST00000447544, demonstrated significant transcription shifts between their minor and major allele in the MPRA assay. Of these, rs2366739 and rs1194196, separated by only 20bp, were confirmed by luciferase assay to alter transcriptional regulation. In addition, electromobility shift assays demonstrated differential DNA:protein complex formation between the two alleles of this locus. Furthermore, deletion of the genomic locus by CRISPR/Cas9 in K562 and Meg-01 cells results in upregulation of CD36 transcription. These data indicate that we have identified a variant that regulates expression of CD36, which in turn affects platelet function. To assess the clinical relevance of our findings we used the PhenoScanner tool, which aggregates large scale GWAS findings; the results reinforce the clinical relevance of our variants and the utility of the MPRA assay. The study demonstrates a generalizable paradigm for functional testing of genetic variants to inform mechanistic studies, support patient management and develop precision therapies.

Abstract SY25-01: Identification of enhancer elements at kidney cancer susceptibility loci using genome-wide approaches in which post-GWAS functional studies implicate the SWI/SNF DPF3 gene for the 14q24 risk locus

Abstract We investigated the set of common single nucleotide polymorphisms (SNPs) that are highly correlated with 20 renal cancer (RCC) susceptibility regions identified by Genome-wide Association Study (GWAS). Together extensive fine-mapping and in silico functional analyses, based on publicly available resources, we used an integrated approach to pursue regulatory elements in which one or more variant allele could confer differential functional activity. To this end, we conducted an initial screening Massively Parallel Reporter Assay (MPRA), followed by three additional approaches: 1. Assay for Transposase-Accessible Chromatin (ATAC-seq); 2. RCC eQTL analysis;3. Capture-HiC. We confirmed the effects reported for three regions (8q24, 11q13 and 12p12). We selected 784 SNPs with an r2&amp;gt;0.4 or D’&amp;gt;0.5 and MAF&amp;lt;0.05 that had evidence for enhancer activity based on available ENCODE transcription factor or histone ChIP-seq, FAIRE, and DNase I hypersensitive data. The MPRA library included 47,461 oligonucleotides of which, 145 bps contained the SNP in either forward or reverse orientation followed by an identification barcode. The set of 784 SNPs tested were tagged 10 separate times for both orientations, while controls were constructed by randomizing the 10 nucleotides centered on the SNP. The MPRA library was the cloned into a pMPRA reporter vector that was transfected into HEK293T and ACHN cell lines, under normoxia and hypoxia conditions; next generation sequence was performed on 5 replicates per condition and analyzed using regression for allele, direction, cell line and condition effects. For 18 of 20 RCC regions, we identified one or more functional variants while Cis-eQTL identified 16 RCC regions that mapped to alterations in expression of 198 genes. Our integrated approach underscores the spectrum of effects of genetic variation on regulation of genes critical for RCC, each notable for determining functional allelic effects on nearby (but not always the closest gene). Our work illustrates the importance of regulatory variants and RCC risk. We further pursued the region on 14q24 and confirmed the strongest signal on MPRA, the GWAS tag SNP, rs4903064, by luciferase assay. In a series of electromobility shift assays (EMSA), we showed higher binding to the rs4903064 T-allele, associated with lower risk (compared to the C-risk allele). Interestingly, the T-allele creates a binding site for IRX2/IRX5, which can act as a transcriptional repressor, whereas the C-allele generates a HIF1a binding site. By eQTL analysis, we observed higher expression of the nearby gene DPF3 (on 14q24) associated with the C-allele in both the TCGA RCC and a normal kidney eQTL data set. In four stable renal cell lines that included a DOX-dependency for overexpression of DPF3 isoforms (DPF3a or DPF3b), we performed RNA-seq analysis and observed over 500 genes deregulated when either isoform is overexpressed. To investigate chromatin accessibility changes due to DPF3 overexpression, we used ATAC-seq and found 86 regions with altered accessibility and 15% of the RNA-seq deregulated genes are located within 2 MB of one of the regions, including the promoter of the top deregulated gene (CEMIP). In growth curve assays, DPF3 increased the growth rate of renal cell lines. Moreover, knockdown of the top two DPF3-deregulated genes (CEMIP and IL23R) by siRNA, reduced the effect of DPF3, suggesting that CEMIP and IL23R could be important cofactors for growth along with DPF3. Since CEMIP is important for apoptosis and in our dox-inducible-DPF3 cell lines, DPF3 overexpression decreased the percentage of apoptotic cells, measured by flow cytometry, and reduced Caspase-3 and PARP cleavage, as determined by Western blot (WB). The second deregulated gene, IL23R, interacts with STAT3, an important RCC oncogene, which has increased phosphorylation following DPF3 overexpression and leads to increased STAT3 phosphorylation. While the SWI/SNF complex genes have been shown to influence response to immunotherapy, we extended our studies to look at co-cultured of tumor cells with stimulated fresh peripheral blood mononuclear cells (PBMCs); DPF3 overexpression in RRC cells leads to increased PBMC-cytotoxicity, mainly due to an increased proportion of T CD8+ cells. In summary, we integrated RCC post-GWAS functional studies to pinpoint notable variants on 14q24 and suggest that altering DPF3 regulation can perturb the SWI/SNF complex by reduction of apoptosis and STAT3 pathway activation. Further confirmation and follow-up studies are required to explain the functional underpinnings of the additional promising regions identified by GWAS and pursued by large-scale screening approaches and fine-mapping. Citation Format: Leandro M. Colli, Lea Jessop J. Chanock, Timothy Myers, Mitchell Machiela, Jiyeon Choi, Mark Purdue, Kevin Brown, Stephen J. Chanock. Identification of enhancer elements at kidney cancer susceptibility loci using genome-wide approaches in which post-GWAS functional studies implicate the SWI/SNF DPF3 gene for the 14q24 risk locus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY25-01.

Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.

Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data‐driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta‐analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell‐type‐specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation “Regulation Saturation” Challenge for predicting effects of single‐nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.

Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays.

The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

397 Uncovering causative, noncoding genetic variants in cutaneous diseases

Noncoding regions of the human genome are frequently and strongly linked with genetically complex human diseases by genome wide association studies (GWAS). These regions are thought to harbor single nucleotide polymorphisms (SNPs) with gene regulatory (enhancer or repressor) activity that modulate expression of disease mediating genes. Given the large number of SNPs in a linked genomic region, identification of causative variants has been hampered by a lack of methods to rapidly measure the gene regulatory function of numerous SNPs at once. Here we apply a combination of epigenomic filtering and a massively parallel reporter assay (MPRA) to identify causative, noncoding SNPs underlying several cutaneous diseases. Integrating published GWAS data from 8 polygenic skin diseases, including cutaneous cancers and inflammatory diseases with epigenetic profiling of human primary keratinocytes (ATAC-Seq and ChIP-Seq) identified numerous candidate disease-linked SNPs in regions of transcriptionally active chromatin. To functionally characterize the filtered SNPs, we measured allele-specific gene regulatory activity using MPRA – a multiplexed, next-gen sequencing method based on the canonical luciferase-based reporter assay. We performed MPRA with 516 SNP pairs using primary human keratinocytes in distinct states of differentiation and identified disease-linked SNPs with allele- and differentiation state-specific gene regulatory activity. We further linked active SNPs to putative disease-mediating target genes using the GTEx eQTL database and chromatin looping analysis (Hi-ChIP) in primary human keratinocytes. Our results not only help further mechanistic studies for understanding the basis of cutaneous disorders, but also provide a framework for identifying causative, noncoding SNPs in complex genetic diseases.

High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity.

Transcription initiates at both coding and noncoding genomic elements, including mRNA and long noncoding RNA (lncRNA) core promoters and enhancer RNAs (eRNAs). However, each class has a different expression profile with lncRNAs and eRNAs being the most tissue specific. How these complex differences in expression profiles and tissue specificities are encoded in a single DNA sequence remains unresolved. Here, we address this question using computational approaches and massively parallel reporter assays (MPRA) surveying hundreds of promoters and enhancers. We find that both divergent lncRNA and mRNA core promoters have higher capacities to drive transcription than nondivergent lncRNA and mRNA core promoters, respectively. Conversely, intergenic lncRNAs (lincRNAs) and eRNAs have lower capacities to drive transcription and are more tissue specific than divergent genes. This higher tissue specificity is strongly associated with having less complex transcription factor (TF) motif profiles at the core promoter. We experimentally validated these findings by testing both engineered single-nucleotide deletions and human single-nucleotide polymorphisms (SNPs) in MPRA. In both cases, we observe that single nucleotides associated with many motifs are important drivers of promoter activity. Thus, we suggest that high TF motif density serves as a robust mechanism to increase promoter activity at the expense of tissue specificity. Moreover, we find that 22% of common SNPs in core promoter regions have significant regulatory effects. Collectively, our findings show that high TF motif density provides redundancy and increases promoter activity at the expense of tissue specificity, suggesting that specificity of expression may be regulated by simplicity of motif usage.