Massively Parallel Reporter Assays Research Articles

Decoding biology with massively parallel reporter assays and machine learning.

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants.

Massively parallel reporter assay (MPRA) is an important technology for evaluating the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs, and genomic features, totaling 242818 variants tested more than 30 cell lines and 30 human diseases or traits. MPRAVarDB enables users to query MPRA variants by genomic region, disease and cell line, or any combination of these parameters. Notably, MPRAVarDB offers a suite of pretrained machine-learning models tailored to the specific disease and cell line, facilitating the prediction of regulatory variants. The user-friendly interface allows users to receive query and prediction results with just a few clicks. https://mpravardb.rc.ufl.edu.

Large-scale multi-omic analysis identifies noncoding somatic driver mutations and nominates ZFP36L2 as a driver gene for pancreatic ductal adenocarcinoma.

Identification of somatic driver mutations in the noncoding genome remains challenging. To comprehensively characterize noncoding driver mutations for pancreatic ductal adenocarcinoma (PDAC), we first created genome-scale maps of accessible chromatin regions (ACRs) and histone modification marks (HMMs) in pancreatic cell lines and purified pancreatic acinar and duct cells. Integration with whole-genome mutation calls from 506 PDACs revealed 314 ACRs/HMMs significantly enriched with 3,614 noncoding somatic mutations (NCSMs). Functional assessment using massively parallel reporter assays (MPRA) identified 178 NCSMs impacting reporter activity (19.45% of those tested). Focused luciferase validation confirmed negative effects on gene regulatory activity for NCSMs near CDKN2A and ZFP36L2 . For the latter, CRISPR interference (CRISPRi) further identified ZFP36L2 as a target gene (16.0 - 24.0% reduced expression, P = 0.023-0.0047) with disrupted KLF9 binding likely mediating the effect. Our integrative approach provides a catalog of potentially functional noncoding driver mutations and nominates ZFP36L2 as a PDAC driver gene.

Abstract C055: Unraveling pancreatic cancer susceptibility at 5p15.33: Functional characterization of a novel VNTR element

Abstract Genome-wide association studies (GWAS) have identified independent signals at 5p15.33 across numerous cancers, with protective alleles for one cancer often conferring risk for another. Many of these associations are thought to act via allele-specific alterations in the cis- regulation of target genes. Transcriptomic analyses in multiple tissue types implicate two plausible target genes: TERT and CLPTM1L. However, the mechanisms linking cancer susceptibility alleles to their presumed target genes, along with the observed antagonistic pleiotropy between different cancers remain elusive. With this in mind, we employed a comprehensive approach, integrating statistical fine-mapping of GWAS summary statistics with massively parallel reporter assays (MPRA) and a densely tiled CRISPRi screen performed across eight cell lines, representative of four distinct cancers associated with 5p15.33 alleles – pancreatic, melanoma, lung, and bladder cancer. Integrative screening identified up to 30 variants across multiple signals in the locus with putative allele-specific transcriptional activity. Furthermore, a variable number tandem repeat (VNTR) in intron 9 of CLPTM1L emerged as a potent transcriptional enhancer in CRISPRi screening. Leveraging PacBio sequencing, VNTR alleles of European ancestry samples from the 1000 Genomes Project were genotyped and found to be highly polymorphic, with longer VNTRs linked to the haplotype tagged by rs31490-A (risk increasing for pancreatic cancer). Subsequent imputation into a pancreatic cancer GWAS of 9,040 cases and 12,496 controls revealed an association between longer VNTRs and risk of pancreatic cancer (OR for VNTR in the 75th percentile = 1.17, 95% CI = 1.13-1.22, P = 2.21x10-12). The significance of the association for the lead SNP rs31490 after conditioning on the VNTR genotype dropped to P= 2.20x10-5, suggesting that VNTR alleles merit consideration as part of the credible variant set for pancreatic cancer. Targeted CRISPRi assays suggested VNTR-mediated cis-regulation of both TERT and CLPTM1L in PANC-1 and MIA PaCa-2 pancreatic cancer cell lines, while dual luciferase reporter assays in the same cell lines revealed that VNTR enhancer activity is mediated by Hippo pathway transcription factors. In ongoing experiments, we aim to expand on luciferase reporter assays and DNA-protein binding assays in relevant cell lines with the aim of not only deciphering how longer VNTRs differentially exert their transcriptional effects compared to shorter alleles but also characterizing the divergent directions of effect between different cancer types. These findings underscore the proposition that pancreatic cancer susceptibility at the 5p15.33 locus may be mediated by both SNPs and an uncharted class of variants, yet to be characterized in the post-GWAS landscape. Citation Format: Aidan O'Brien, Hyunkyung Kong, Minal Patel, Katelyn E Connelly, Mai Xu, Irene Collins, Jun Zhong, Jason Hoskins, Michelle Ho, Brenen Papenburg, Mark M Iles, Matthew H Law, Maria-Teresa Landi, Rachael Stolzenber- Solomon, Brian M Wolpin, Alison P Klein, Nick Orr, Stephen J Chanock, Sara Lindström, Marc-Henri Stern, Ludmila Prokunina-Olsson, Jieyon Choi, Kevin M Brown, Laufey T Amundadottir. Unraveling pancreatic cancer susceptibility at 5p15.33: Functional characterization of a novel VNTR element [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C055.

Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.

Alzheimer's disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, and neuroinflammatory responses. Both resident microglia and peripheral immune cells have been associated with the immune component of AD. However, the relative contribution of resident and peripheral immune cell types to AD predisposition has not been thoroughly explored due to their similarity in gene expression and function. To study the effects of AD-associated variants on cis-regulatory elements, we train convolutional neural network (CNN) regression models that link genome sequence to cell type-specific levels of open chromatin, a proxy for regulatory element activity. We then use in silico mutagenesis of regulatory sequences to predict the relative impact of candidate variants across these cell types. We develop and apply criteria for evaluating our models and refine our models using massively parallel reporter assay (MPRA) data. Our models identify multiple AD-associated variants with a greater predicted impact in peripheral cells relative to microglia or neurons. Our results support their use as models to study the effects of AD-associated variants and even suggest that peripheral immune cells themselves may mediate a component of AD predisposition. We make our library of CNN models and predictions available as a resource for the community to study immune and neurological disorders.

A systematic strategy for identifying causal single nucleotide polymorphisms and their target genes on Juvenile arthritis risk haplotypes

BackgroundAlthough genome-wide association studies (GWAS) have identified multiple regions conferring genetic risk for juvenile idiopathic arthritis (JIA), we are still faced with the task of identifying the single nucleotide polymorphisms (SNPs) on the disease haplotypes that exert the biological effects that confer risk. Until we identify the risk-driving variants, identifying the genes influenced by these variants, and therefore translating genetic information to improved clinical care, will remain an insurmountable task. We used a function-based approach for identifying causal variant candidates and the target genes on JIA risk haplotypes.MethodsWe used a massively parallel reporter assay (MPRA) in myeloid K562 cells to query the effects of 5,226 SNPs in non-coding regions on JIA risk haplotypes for their ability to alter gene expression when compared to the common allele. The assay relies on 180 bp oligonucleotide reporters (“oligos”) in which the allele of interest is flanked by its cognate genomic sequence. Barcodes were added randomly by PCR to each oligo to achieve > 20 barcodes per oligo to provide a quantitative read-out of gene expression for each allele. Assays were performed in both unstimulated K562 cells and cells stimulated overnight with interferon gamma (IFNg). As proof of concept, we then used CRISPRi to demonstrate the feasibility of identifying the genes regulated by enhancers harboring expression-altering SNPs.ResultsWe identified 553 expression-altering SNPs in unstimulated K562 cells and an additional 490 in cells stimulated with IFNg. We further filtered the SNPs to identify those plausibly situated within functional chromatin, using open chromatin and H3K27ac ChIPseq peaks in unstimulated cells and open chromatin plus H3K4me1 in stimulated cells. These procedures yielded 42 unique SNPs (total = 84) for each set. Using CRISPRi, we demonstrated that enhancers harboring MPRA-screened variants in the TRAF1 and LNPEP/ERAP2 loci regulated multiple genes, suggesting complex influences of disease-driving variants.ConclusionUsing MPRA and CRISPRi, JIA risk haplotypes can be queried to identify plausible candidates for disease-driving variants. Once these candidate variants are identified, target genes can be identified using CRISPRi informed by the 3D chromatin structures that encompass the risk haplotypes.

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

The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.

Deciphering the cis-regulatory landscape of natural yeast Transcript Leaders.

Protein synthesis is a vital process that is highly regulated at the initiation step of translation. Eukaryotic 5' transcript leaders (TLs) contain a variety of cis-regulatory features that influence translation and mRNA stability. However, the relative influences of these features in natural TLs are poorly characterized. To address this, we used massively parallel reporter assays (MPRAs) to quantify RNA levels, ribosome loading, and protein levels from 11,027 natural yeast TLs in vivo and systematically compared the relative impacts of their sequence features on gene expression. We found that yeast TLs influence gene expression over two orders of magnitude. While a leaky scanning model using Kozak contexts and uAUGs explained half of the variance in expression across transcript leaders, the addition of other features explained ~70% of gene expression variation. Our analyses detected key cis-acting sequence features, quantified their effects in vivo, and compared their roles to motifs reported from an in vitro study of ribosome recruitment. In addition, our work quantitated the effects of alternative transcription start site usage on gene expression in yeast. Thus, our study provides new quantitative insights into the roles of TL cis-acting sequences in regulating gene expression.

In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.

Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets, we demonstrate that MpraVAE significantly improves the prediction performance for MPRA regulatory variants compared to the baseline method, conventional data augmentation approaches as well as existing variant scoring methods. Taking autoimmune diseases as one example, we apply MpraVAE to perform a genome-wide prediction of regulatory variants and find that predicted regulatory variants are more enriched than background variants in enhancers, active histone marks, open chromatin regions in immune-related cell types, and chromatin states associated with promoter, enhancer activity and binding sites of cMyC and Pol II that regulate gene expression. Importantly, predicted regulatory variants are found to link immune-related genes by leveraging chromatin loop and accessible chromatin, demonstrating the importance of MpraVAE in genetic and gene discovery for complex traits.

HDI-STARR-seq: Condition-specific enhancer discovery in mouse liver in vivo.

STARR-seq and other massively-parallel reporter assays are widely used to discover functional enhancers in transfected cell models, which can be confounded by plasmid vector-induced type-I interferon immune responses and lack the multicellular environment and endogenous chromatin state of complex mammalian tissues. Here, we describe HDI-STARR-seq, which combines STARR-seq plasmid library delivery to the liver, by hydrodynamic tail vein injection (HDI), with reporter RNA transcriptional initiation driven by a minimal Albumin promoter, which we show is essential for mouse liver STARR-seq enhancer activity assayed 7 days after HDI. Importantly, little or no vector-induced innate type-I interferon responses were observed. Comparisons of HDI-STARR-seq activity between male and female mouse livers and in livers from males treated with an activating ligand of the transcription factor CAR (Nr1i3) identified many condition-dependent enhancers linked to condition-specific gene expression. Further, thousands of active liver enhancers were identified using a high complexity STARR-seq library comprised of ~ 50,000 genomic regions released by DNase-I digestion of mouse liver nuclei. When compared to stringently inactive library sequences, the active enhancer sequences identified were highly enriched for liver open chromatin regions with activating histone marks (H3K27ac, H3K4me1, H3K4me3), were significantly closer to gene transcriptional start sites, and were significantly depleted of repressive (H3K27me3, H3K9me3) and transcribed region histone marks (H3K36me3). HDI-STARR-seq offers substantial improvements over current methodologies for large scale, functional profiling of enhancers, including condition-dependent enhancers, in liver tissue in vivo, and can be adapted to characterize enhancer activities in a variety of species and tissues by selecting suitable tissue- and species-specific promoter sequences.

High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility

Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.

HDI-STARR-seq: Condition-specific enhancer discovery in mouse liver in vivo.

STARR-seq and other massively-parallel reporter assays are widely used to discover functional enhancers in transfected cell models, which can be confounded by plasmid vector-induced type-I interferon immune responses and lack the multicellular environment and endogenous chromatin state of complex mammalian tissues. Here, we describe HDI-STARR-seq, which combines STARR-seq plasmid library delivery to the liver, by hydrodynamic tail vein injection (HDI), with reporter RNA transcriptional initiation driven by a minimal Albumin promoter, which we show is essential for mouse liver STARR-seq enhancer activity assayed 7 days after HDI. Importantly, little or no vector-induced innate type-I interferon responses were observed. Comparisons of HDI-STARR-seq activity between male and female mouse livers and in livers from males treated with an activating ligand of the transcription factor CAR (Nr1i3) identified many condition-dependent enhancers linked to condition-specific gene expression. Further, thousands of active liver enhancers were identified using a high complexity STARR-seq library comprised of ~50,000 genomic regions released by DNase-I digestion of mouse liver nuclei. When compared to stringently inactive library sequences, the active enhancer sequences identified were highly enriched for liver open chromatin regions with activating histone marks (H3K27ac, H3K4me1, H3K4me3), were significantly closer to gene transcriptional start sites, and were significantly depleted of repressive (H3K27me3, H3K9me3) and transcribed region histone marks (H3K36me3). HDI-STARR-seq offers substantial improvements over current methodologies for large scale, functional profiling of enhancers, including condition-dependent enhancers, in liver tissue in vivo, and can be adapted to characterize enhancer activities in a variety of species and tissues by selecting suitable tissue- and species-specific promoter sequences.

Targeted mutagenesis of specific genomic DNA sequences in animals for the in vivo generation of variant libraries.

Understanding how the number, placement and affinity of transcription factor binding sites dictates gene regulatory programs remains a major unsolved challenge in biology, particularly in the context of multicellular organisms. To uncover these rules, it is first necessary to find the binding sites within a regulatory region with high precision, and then to systematically modulate this binding site arrangement while simultaneously measuring the effect of this modulation on output gene expression. Massively parallel reporter assays (MPRAs), where the gene expression stemming from 10,000s of in vitro-generated regulatory sequences is measured, have made this feat possible in high-throughput in single cells in culture. However, because of lack of technologies to incorporate DNA libraries, MPRAs are limited in whole organisms. To enable MPRAs in multicellular organisms, we generated tools to create a high degree of mutagenesis in specific genomic loci in vivo using base editing. Targeting GFP integrated in genome of Drosophila cell culture and whole animals as a case study, we show that the base editor AIDevoCDA1 stemming from sea lamprey fused to nCas9 is highly mutagenic. Surprisingly, longer gRNAs increase mutation efficiency and expand the mutating window, which can allow the introduction of mutations in previously untargetable sequences. Finally, we demonstrate arrays of >20 gRNAs that can efficiently introduce mutations along a 200bp sequence, making it a promising tool to test enhancer function in vivo in a high throughput manner.

Deciphering regulatory architectures from synthetic single-cell expression patterns.

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.

Abstract PR009: Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing

Abstract Cancer research has traditionally focused on protein-coding genes, but approximately 98% of the human genome is non-coding, comprising gene regulatory elements such as enhancers, silencers, and promoters. Mutations within these regions can foster tumorigenesis by modulating the expression of oncogenes. Recent whole-genome sequencing studies have revealed several recurrently mutated non-coding genomic regions, including in bladder cancer, but their role in tumorigenesis remains unknown. In this study, we delved into whole-genome sequencing data from the bladder cancer TCGA cohort and an independent set of bladder tumors, uncovering hundreds of recurrent mutations. Mutational signature analysis identified over 65% of these mutations as APOBEC3-induced. We substantiated the involvement of APOBEC3 enzymes by using conventional deamination assays and novel approach dubbed as APOBEC3-mediated mutation capture assay. Predominantly, these mutations occurred in the hairpin loop, a favored motif for APOBEC3 enzymes. To systematically assess which of the mutations are functional and prioritize them for biological characterization, we developed Massively Parallel Reporter Assays (MPRA). Two types of MPRA libraries were constructed, one for evaluating promoter activity and the other for assessing enhancer activity of the mutations. Each library construction entailed 36,000 oligonucleotides, comprehensively representing all mutation hotspots and their corresponding wild-type alleles. The application of MPRA resulted in the identification of a substantial number of functionally relevant mutations. Zooming in on the top 20 genomic regions, we employed CRISPR-based approaches to pinpoint target genes and elucidate their functional consequences. Most of these regions co-localized with enhancers, as determined by analysis of ATAC-seq, ChIP-seq, and Hi-C data from bladder cancer cell lines. Perturbation of hotspots regions resulted in dysregulation of multiple genes. The co-regulation patterns of genes were further supported by strong correlation of their mRNA expression in RNA-seq datasets (TCGA, SRA, etc.). Notable examples included twin hotspot mutations upstream of the LEPROTL1 gene, elevating the expression of LEPROTL1, DCTN6 and SARAF, resulting in aggressive tumor growth in in vitro functional experiments. These three genes emerged as novel oncogenes in cancer. Another functional hotspot influenced the expression of two known long non-coding RNAs, NEAT1 and MALAT1, previously characterized as cancer genes. Additionally, some recurrently mutated regions or their target genes were associated with worse overall survival in the TCGA cohort. In conclusion, our findings propose that non-coding mutations likely drive tumorigenesis, contributing to tumor heterogeneity. Employing a combination of functional, multi-omics, and in vivo approaches, we aim to comprehensively characterize the role of hotspot non-coding mutations in bladder cancer and assess their clinical significance. Citation Format: Rouf Banday, Bilal Lone, Kelly Butler, Dhanusha Yesudhas, Alexandra Dobbins, Arup Chakraborty. Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR009.

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants makes it difficult to develop an effective supervised learning model. Therefore, improving the accuracy of predicting non-coding causal variants has become critical. This study aims to build a transfer learning-based machine learning method for predicting regulatory variants to overcome the problem of limited sample size. This paper presents a supervised learning method transfer support vector machine (TSVM) for massively parallel reporter assays (MPRA) validated regulatory variants prediction. First, uses a convolutional neural network to extract features with transfer learning. Second, the extracted features are selected by random forest method. Third, the selected features are used to train support vector machine for classification. We performed scale sensitivity experiments on the MPRA dataset and validated the effectiveness of transfer learning. The model achieves the Mcc of 0.326 and the AUC of 0.720, which are higher than the state-of-the-art method.

Abstract 1694: Using high-throughput screening to identify DNA response elements that sense cancer dysregulated pathways

Abstract Earli is developing a highly sensitive, orthogonal approach that is designed to be delivered via IV injection and uses a genetic construct to usurp dysregulated pathways, and actively forces cancer cells to drive the expression of a detectable ‘synthetic’ biomarker. The key element of our cancer-activated construct that drives specificity and sensitivity of expression is a promoter sequence, synthetically engineered to leverage transcription factors (TFs) activated in cancer pathways. Acting as a molecular sensor for dysregulated TF levels, the DNA construct expresses a measurable biomarker in cancer cells, while remaining transcriptionally silent in normal adjacent tissues and benign lesions. Previously, we described a multiomics factor analysis (MOFA) to interrogate 200 non-small cell lung cancer (NSCLC) and matched normal adjacent tissue samples comprised of RNA-seq based transcriptional profiling, and MS-based proteomics and phospho-proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, and generated a list of top TFs dysregulated in NSCLC. In this study, we show the capability to design, engineer, and test cancer specific activity of combinations of response elements using a Massively Parallel Reporter Assay (MPRA) high-throughput pooled screening (HTS) method. More than 2,000 unique sequences of homotypic or heterotypic TF binding sites corresponding to the top 100 TFs were arrayed with diverse spacing and individually barcoded for the high-throughput screens in NSCLC cell lines. To validate our top hits, we individually tested 10 sequences from the top 25% and 8 sequences from the bottom 25%. Nine of the top sequences produced signals 5-10X higher than the background, whereas only one of the eight bottom sequences produced signal higher than the background. We have individually validated 30 different response elements and found that 28 of 30 drove the expected expression, with the majority of response elements (n=20) showing expression 5-10X higher than background. Remarkably, two of the engineered response elements were able to drive expression 20-30X higher in a specific cell line activated by WNT-signaling, suggesting that these response elements can sense specific cancer dysregulated pathways. Our lead response elements are being tested across a broad range of in vitro primary tumor lines with diverse genetic backgrounds and transcriptional profiles prior to in vivo testing using our imaging platform to distinguish malignant lung nodules. In summary, we have established a robust screening platform to identify response elements that can be activated by cancer dysregulated pathways. We are currently expanding this platform to other cancer models and indications, and leveraging this to explore more complex response elements that use combinations of TFs and natural spacing. Citation Format: Elizabeth Stroebele, Yue (Wendy) Zhang, Ishan Podar, Chloe Xia, Ajda Rojc, Dariusz Wodziak, Maggie Louie, David Suhy. Using high-throughput screening to identify DNA response elements that sense cancer dysregulated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1694.

Abstract 6154: Targeted CRISPRi screen identifies functional variants and novel target genes at multiple renal cell carcinoma (RCC) susceptibility loci

Abstract Genome-wide association studies (GWAS) have identified 13 renal cell carcinoma (RCC) risk regions, as well as 7 regions with nominal significance, but detailed investigation of how these regions function is required to reveal the underlying biological bases of disease susceptibility. While detailed study of four loci have implicated critical pathways in RCC, causal genes and pathways for most loci remain unidentified. To identify such causative variants and target genes, we evaluated a set of variants based on Massively Parallel Reporter Assays (MPRA), eQTL analysis, capture Hi-C, and an arrayed CRISPR inhibition (CRISPRi) screen. The MPRA identified 196 variants across 19 regions with significant allele-specific effects, indicating cis-regulatory activity. Of these, 39 variants across 10 RCC loci displayed chromatin loops allowing physical interaction with the promoter of 24 nearby putative gene targets, as well as a significant cis-eQTL with the same gene. To confirm the presence of a cis-regulatory relationship between these 39 candidate variants and 24 putative target genes, we performed an arrayed CRISPRi screen in ACHN (RCC) and HEK293T (embryonic renal) cells covering each of the 10 nominated regions. Cells were stably transduced with inactive Cas9 fused to the ZIM3 transcriptional repressor domain and cis-regulatory relationships were assessed by TaqMan qPCR for the target genes. The screen identified multiple novel relevant target genes of RCC predisposition variants, including UCK2 at 1q24, ABL2 and TDRD5 at 1q25, GTDC1 at 2q22, GPR37 at 7q31, AGBL3 and CALD1 at 7q33, and MAP2K1 at 15q22. At some regions, evidence indicates cis-regulatory relationships occur between a target gene and all variants predicted to be ‘causative’ at the region (e.g., UCK2 at 1q24 and ABL2 at 1q25). For other regions, cis-regulatory relationships occur between a gene and only one variant tested (e.g., GPR37 at 7q31 and TDRD5 at 1q25).Previously, target genes of only four RCC susceptibility regions had been identified and published. This study identifies eight novel target genes across six additional RCC susceptibility regions, representing a significant advance in understanding of the underlying biology of RCC susceptibility variants. Furthermore, ongoing screening using the orthologous method, CRISPR activation (CRISPRa), and integration of both normoxic and hypoxic conditions continues to provide further insight into the underlying biology at these regions. Citation Format: Timothy Winter, Timothy Myers, Leandro Colli, Lea Jessop, Jiyeon Choi, Mitchell J. Machiela, Mark P. Purdue, Kevin Brown, Stephen Chanock. Targeted CRISPRi screen identifies functional variants and novel target genes at multiple renal cell carcinoma (RCC) susceptibility loci [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6154.

The Search for and Functional Analysis of Genetic Variants in microRNA-Binding Sites using Massively Parallel Reporter Assay.

A large-scale search for the genetic variants with a bias in the representation of alleles in transcriptome data (AE SNPs) and the binding sites in microRNA 3'-UTRs was performed and their functional significance was assessed using massively parallel reporter assay (MPRA). Of the 629,559 associated "SNP-gene" pairs (eQTLs) discovered in the human liver tissue according to the GTEx Analysis V8 data, 4394 polymorphic positions in the 3'-UTRs of the genes, which represent the eQTLs for these genes were selected. The TargetScanHuman 7.0 algorithm and PolymiRTS database were searched for the potential microRNA-binding sites. Of the predicted microRNA sites affected by eQTL-SNPs, we selected 51 sites with the best evidence of functionality according to Ago2-CLIP-seq, CLEAR-CLIP, and eCLIP-seq for RNA-binding proteins. For MPRA, a library of the plasmids carrying the main and alternative alleles for each AE SNP (in total, 102 constructs) was created. Allele-specific expression for 6 SNPs was detected by transfection of the HepG2 cell line with the constructed plasmid library and sequencing of target DNA and RNA sequences using the Illumina (MiSeq) platform.

Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay.

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.