Multicenter integrated analysis of noncoding CRISPRi screens.

The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

Convergence of coronary artery disease genes onto endothelial cell programs.

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

High Shear Stress Reduces ERG Causing Endothelial-Mesenchymal Transition and Pulmonary Arterial Hypertension.

Pathological high shear stress (HSS, 100 dyn/cm 2 ) is generated in distal pulmonary arteries (PA) (100-500 µm) in congenital heart defects and in progressive PA hypertension (PAH) with inward remodeling and luminal narrowing. Human PA endothelial cells (PAEC) were subjected to HSS versus physiologic laminar shear stress (LSS, 15 dyn/cm 2 ). Endothelial-mesenchymal transition (EndMT), a feature of PAH not previously attributed to HSS, was observed. H3K27ac peaks containing motifs for an ETS-family transcription factor (ERG) were reduced, as was ERG-Krüppel-like factors (KLF)2/4 interaction and ERG expression. Reducing ERG by siRNA in PAEC during LSS caused EndMT; transfection of ERG in PAEC under HSS prevented EndMT. An aorto-caval shunt was preformed in mice to induce HSS and progressive PAH. Elevated PA pressure, EndMT and vascular remodeling were reduced by an adeno-associated vector that selectively replenished ERG in PAEC. Agents maintaining ERG in PAEC should overcome the adverse effect of HSS on progressive PAH.

Rewriting regulatory DNA to dissect and reprogram gene expression.

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Luteolin inhibits melanoma growth in vitro and in vivo via regulating ECM and oncogenic pathways but not ROS.

Luteolin inhibited growth of several cancer cells in vitro in previous studies, with limited in vivo studies, and no comprehensive understanding of molecular mechanisms at genomics level. This study identified luteolin as an effective agent to inhibit melanoma cell growth in vitro and in vivo. Molecular studies and genomic profiling were used to identify the mechanism of action of luteolin in melanoma cells. As a ROS (reactive oxygen species) scavenger, luteolin unexpectedly induced ROS; but co-treatment with antioxidants NAC or mito-TEMPO did not rescue cell growth inhibition, although the levels of ROS levels were reduced. Next, we profiled luteolin-induced differentially expressed genes (DEGs) in 4 melanoma cell lines using RNA-Seq, and performed pathway analysis using a combination of bioinformatics software including PharmetRx which was especially effective in discovering pharmacological pathways for potential drugs. Our results show that luteolin induces changes in three main aspects: the cell-cell interacting pathway (extracellular matrix, ECM), the oncogenic pathway and the immune response signaling pathway. Based on these results, we further validated that luteolin was especially effective in inhibiting cell proliferation when cells were seeded at low density, concomitantly with down-regulation of fibronectin accumulation. In conclusion, through extensive DEG profiling in a total of 4 melanoma cell lines, we found that luteolin-mediated growth inhibition in melanoma cells was perhaps not through ROS induction, but likely through simultaneously acting on multiple pathways including the ECM (extracellular matrix) pathway, the oncogenic signaling and the immune response pathways. Further investigations on the mechanisms of this promising compound are warranted and likely result in application to cancer patients as its safety pharmacology has been validated in autism patients.

A Possible Link of Genetic Variations in ER/IGF1R Pathway and Risk of Melanoma.

The mechanism of gender disparity in cutaneous melanoma incidence remains unclear. Steroid hormones including estrogens have long been implicated in the course of melanoma, but the conclusion is controversial. Estrogen receptors (ERs) and insulin-like growth factor 1 receptor (IGF1R) show extensive crosstalk in cancer development, but how the ER/IGF1R network impacts melanoma is currently unclear. Here we studied the melanoma associations of selected SNPs from the ER/IGF1R network. Part of the International Genes, Environment, and Melanoma (GEM) cohort was used as a discovery set, and the Gene Environment Association Studies Initiative (GENEVA) dataset served as a validation set. Based on the associations with other malignant disease conditions, thirteen single nucleotide polymorphism (SNP) variants in ESR1, ESR2, IGF1, and IGF1R were selected for candidate gene association analyses. The rs1520220 in IGF1 and rs2229765 in IGF1R variants were significantly associated with melanoma risk in the GEM dataset after Benjamini-Hochberg multiple comparison correction, although they were not validated in the GENEVA set. The discrepancy may be caused by the multiple melanoma characteristics in the GEM patients. Further analysis of gender disparity was carried out for IGF1 and IGF1R SNPs in the GEM dataset. The GG phenotype in IGF1 rs1520220 (recessive model) presented an increased risk of melanoma (OR = 8.11, 95% CI: 2.20, 52.5, p = 0.006) in men but a significant opposite effect in women (OR = 0.15, 95% CI: 0.018, 0.86, p = 0.045). The AA genotype in IGF1R rs2229765 (recessive model) showed a significant protective effect in men (OR = 0.24, 95% CI: 0.07, 0.64, p = 0.008) and no effect in women. Results from the current study are warranted for further validation.

Sensitive Detection and Analysis of Neoantigen-Specific T Cell Populations from Tumors and Blood.

Neoantigen-specific T cells are increasingly viewed as important immunotherapy effectors, but physically isolating these rare cell populations is challenging. Here, we describe a sensitive method for the enumeration and isolation of neoantigen-specific CD8+ T cells from small samples of patient tumor or blood. The method relies on magnetic nanoparticles that present neoantigen-loaded major histocompatibility complex (MHC) tetramers at high avidity by barcoded DNA linkers. The magnetic particles provide a convenient handle to isolate the desired cell populations, and the barcoded DNA enables multiplexed analysis. The method exhibits superior recovery of antigen-specific T cell populations relative to literature approaches. We applied the method to profile neoantigen-specific T cell populations in the tumor and blood of patients with metastatic melanoma over the course of anti-PD1 checkpoint inhibitor therapy. We show that the method has value for monitoring clinical responses to cancer immunotherapy and might help guide the development of personalized mutational neoantigen-specific T cell therapies and cancer vaccines.

MATE-Seq: microfluidic antigen-TCR engagement sequencing.

Adaptive immunity is based on peptide antigen recognition. Our ability to harness the immune system for therapeutic gain relies on the discovery of the T cell receptor (TCR) genes that selectively target antigens from infections, mutated proteins, and foreign agents. Here we present a method that selectively labels peptide antigen-specific CD8+ T cells using magnetic nanoparticles functionalized with peptide-MHC tetramers, isolates these specific cells within an integrated microfluidic device, and directly amplifies the TCR genes for sequencing. Critically, the identity of the peptide recognized by the TCR is preserved, providing the link between peptide and gene. The platform requires inputs on the order of just 100 000 CD8+ T cells, can be multiplexed for simultaneous analysis of multiple peptides, and performs sorting and isolation on chip. We demonstrate 1000-fold sensitivity enhancement of detecting antigen-specific TCRs relative to bulk analysis and simultaneous capture of two virus antigen-specific TCRs from a population of T cells.