Modeling 0.6 million genes for the rational design of functional cis-regulatory variants and de novo design of cis-regulatory sequences.

Rational design of plant cis-regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis-eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis-regulatory variants, and de novo creation of species-specific cis-regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.

A proximal enhancer regulates RORA expression during early human Th17 cell differentiation.

Gene regulatory elements, such as enhancers, greatly influence cell identity by tuning the transcriptional activity of specific cell types. Dynamics of enhancer landscape during early human Th17 cell differentiation remains incompletely understood. Leveraging ATAC-seq-based profiling of chromatin accessibility and comprehensive analysis of key histone marks, we identified a repertoire of enhancers that potentially exert control over the fate specification of Th17 cells. We found 23 SNPs associated with autoimmune diseases within Th17-enhancers that precisely overlapped with the binding sites of transcription factors actively engaged in T-cell functions. Among the Th17-specific enhancers, we identified an enhancer in the intron of RORA and demonstrated that this enhancer positively regulates RORA transcription. Moreover, CRISPR-Cas9-mediated deletion of a transcription factor binding site-rich region within the identified RORA enhancer confirmed its role in regulating RORA transcription. These findings provide insights into the potential mechanism by which the RORA enhancer orchestrates Th17 differentiation.

The 3'UTR Polymorphisms in the NLRP3 Gene Associated with the Risk of COPD and Their Putative Effects on the microRNA Mechanism.

Aims: Evaluating the association between a single nucleotide polymorphism in the 3' untranslated region (3'UTR) of the miRNA binding site of the NLRP3 gene and the occurrence and development of chronic obstructive pulmonary disease (COPD) and providing information to aid in the early detection and treatment of COPD. Materials and Methods: The regulatory single nuclear polymorphisms (SNPs) located in NLRP3 3'UTR were searched by using the dbSNP database and miRNA binding site prediction database. Meanwhile, samples from COPD patients and healthy controls in the same period were used for verification. The clinical baseline information of all subjects was collected, and the transcription level and protein expression level of NLRP3 and the expression level of inflammatory factors downstream of NLRP3 were detected. The effects of SNPs' single nucleotide changes on the transcription and expression of inflammatory factors were analyzed. Results: The study included 418 participants (249 in the COPD group and 169 in the control group). NLRP3 SNPs with miRNA binding sites include rs10754558 (G > C), rs1664774076 (ATAT > del), and rs1664775106 (C > G). Furthermore, two genotypes, GCG and GCA, were discovered to have a linkage mutation at 3'UTR 459-461. COPD susceptibility is tightly associated with the expression of the rs1664774076 del/del genotype, and the risk of COPD increased by 2.770 times (p = 0.003). Type 459-461 GCA was substantially related to the likelihood of developing COPD at various stages (p < 0.05). Except for rs10754558, all homozygous mutants increased NLRP3 mRNA and protein levels. NLRP3 had the greatest area under the receiver operating characteristic (ROC) curve for predicting the development and diagnosis of COPD when compared with its downstream inflammatory variables (AUC = 0.9291). Conclusions: The NLRP3 rs1664774076 del/del genotype is a COPD susceptibility gene, and the GCA genotype at 459-461 can be used as an early predictor of COPD exacerbation. The NLRP3 3'UTR polymorphism may alter the loss of miRNA binding sites, leading to an increase in NLRP3 expression. In the development of COPD, NLRP3 has a better diagnostic value than traditional inflammatory factors. The Clinical Trials Registration number Z: protocol KY01-2020-11-06.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics.

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

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.

Disruption of a DNA G-quadruplex causes a gain-of-function SCL45A1 variant relevant to developmental disorders.

SLC45A1 encodes a glucose transporter protein highly expressed in the brain. Mutations in SLC45A1 may lead to neurological diseases and developmental disorders, but its exact role is poorly understood. DNA G-quadruplexes (DNA G4s) are stable structures formed by four guanine bases and play a role in gene regulation and genomic stability. Changes in DNA G4s may affect brain development and function. The mechanism linking alterations in DNA G-quadruplex structures to SLC45A1 pathogenicity remains unknown. In this study, we identify a functional DNA G-quadruplex and its key binding site on SLC45A1 (NM_001080397.3: exon 2: c.449 G>A: p.R150K). This variant results in the upregulation of mRNA and protein expression, which may lead to intellectual developmental disorder with neuropsychiatric features. Mechanistically, the mutation is found to disrupt DNA G-quadruplex structures on SLC45A1, leading to transcriptional enhancement and a gain-of-function mutation, which further causes increased expression and function of the SLC45A1 protein. The identification of the functional DNA G-quadruplex and its effects on DNA G4s may provide new insights into the genetic basis of SLC45A1 pathogenicity and highlight the importance of DNA G4s of SLC45A1 in regulating gene expression and brain development.

Splicing analysis of STAT3 tandem donor suggests non-canonical binding registers for U1 and U6 snRNAs.

Tandem donor splice sites (5'ss) are unique regions with at least two GU dinucleotides serving as splicing cleavage sites. The Δ3 tandem 5'ss are a specific subclass of 5'ss separated by 3 nucleotides which can affect protein function by inserting/deleting a single amino acid. One 5'ss is typically preferred, yet factors governing particular 5'ss choice are not fully understood. A highly conserved exon 21 of the STAT3 gene was chosen as a model to study Δ3 tandem 5'ss splicing mechanisms. Based on multiple lines of experimental evidence, endogenous U1 snRNA most likely binds only to the upstream 5'ss. However, the downstream 5'ss is used preferentially, and the splice site choice is not dependent on the exact U1 snRNA binding position. Downstream 5'ss usage was sensitive to exact nucleotide composition and dependent on the presence of downstream regulatory region. The downstream 5'ss usage could be best explained by two novel interactions with endogenous U6 snRNA. U6 snRNA enables the downstream 5'ss usage in STAT3 exon 21 by two mechanisms: (i) binding in a novel non-canonical register and (ii) establishing extended Watson-Crick base pairing with the downstream regulatory region. This study suggests that U6:5'ss interaction is more flexible than previously thought.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

Two redundant transcription factor binding sites in a single enhancer are essential for mammalian sex determination.

Male development in mammals depends on the activity of the two SOX gene: Sry and Sox9, in the embryonic testis. As deletion of Enhancer 13 (Enh13) of the Sox9 gene results in XY male-to-female sex reversal, we explored the critical elements necessary for its function and hence, for testis and male development. Here, we demonstrate that while microdeletions of individual transcription factor binding sites (TFBS) in Enh13 lead to normal testicular development, combined microdeletions of just two SRY/SOX binding motifs can alone fully abolish Enh13 activity leading to XY male-to-female sex reversal. This suggests that for proper male development to occur, these few nucleotides of non-coding DNA must be intact. Interestingly, we show that depending on the nature of these TFBS mutations, dramatically different phenotypic outcomes can occur, providing a molecular explanation for the distinct clinical outcomes observed in patients harboring different variants in the same enhancer.

Structural insights into the interaction between adenovirus C5 hexon and human lactoferrin.

Adenovirus (AdV) infection of the respiratory epithelium is common but poorly understood. Human AdV species C types, such as HAdV-C5, utilize the Coxsackie-adenovirus receptor (CAR) for attachment and subsequently integrins for entry. CAR and integrins are however located deep within the tight junctions in the mucosa where they would not be easily accessible. Recently, a model for CAR-independent AdV entry was proposed. In this model, human lactoferrin (hLF), an innate immune protein, aids the viral uptake into epithelial cells by mediating interactions between the major capsid protein, hexon, and yet unknown host cellular receptor(s). However, a detailed understanding of the molecular interactions driving this mechanism is lacking. Here, we present a new cryo-EM structure of HAdV-5C hexon at high resolution alongside a hybrid structure of HAdV-5C hexon complexed with human lactoferrin (hLF). These structures reveal the molecular determinants of the interaction between hLF and HAdV-C5 hexon. hLF engages hexon primarily via its N-terminal lactoferricin (Lfcin) region, interacting with hexon's hypervariable region 1 (HVR-1). Mutational analyses pinpoint critical Lfcin contacts and also identify additional regions within hLF that critically contribute to hexon binding. Our study sheds more light on the intricate mechanism by which HAdV-C5 utilizes soluble hLF/Lfcin for cellular entry. These findings hold promise for advancing gene therapy applications and inform vaccine development. IMPORTANCE: Our study delves into the structural aspects of adenovirus (AdV) infections, specifically HAdV-C5 in the respiratory epithelium. It uncovers the molecular details of a novel pathway where human lactoferrin (hLF) interacts with the major capsid protein, hexon, facilitating viral entry, and bypassing traditional receptors such as CAR and integrins. The study's cryo-EM structures reveal how hLF engages hexon, primarily through its N-terminal lactoferricin (Lfcin) region and hexon's hypervariable region 1 (HVR-1). Mutational analyses identify critical Lfcin contacts and other regions within hLF vital for hexon binding. This structural insight sheds light on HAdV-C5's mechanism of utilizing soluble hLF/Lfcin for cellular entry, holding promise for gene therapy and vaccine development advancements in adenovirus research.

SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation.

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.

Human 2'-Deoxynucleoside 5'-Phosphate N-Hydrolase 1: The Catalytic Roles of Tyr24 and Asp80.

The human enzyme 2'-deoxynucleoside 5'-phosphate N-hydrolase 1 (HsDNPH1) catalyses the hydrolysis of 5-hydroxymethyl-2'-deoxyuridine 5'-phosphate to generate 5-hydroxymethyluracil and 2-deoxyribose-5-phosphate via a covalent 5-phospho-2-deoxyribosylated enzyme intermediate. HsDNPH1 is a promising target for inhibitor development towards anticancer drugs. Here, site-directed mutagenesis of conserved active-site residues, followed by HPLC analysis of the reaction and steady-state kinetics are employed to reveal the importance of each of these residues in catalysis, and the reaction pH-dependence is perturbed by each mutation. Solvent deuterium isotope effects indicate no rate-limiting proton transfers. Crystal structures of D80N-HsDNPH1 in unliganded and substrate-bound states, and of unliganded D80A- and Y24F-HsDNPH1 offer atomic level insights into substrate binding and catalysis. The results reveal a network of hydrogen bonds involving the substrate and the E104-Y24-D80 catalytic triad and are consistent with a proposed mechanism whereby D80 is important for substrate positioning, for helping modulate E104 nucleophilicity, and as the general acid in the first half-reaction. Y24 positions E104 for catalysis and prevents a catalytically disruptive close contact between E104 and D80.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk.

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Inferred regulons are consistent with regulator binding sequences in E. coli.

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery.

Structural and functional insights into tRNA recognition by human tRNA guanine transglycosylase.

Eukaryotic tRNA guanine transglycosylase (TGT) is an RNA-modifying enzyme which catalyzes the base exchange of the genetically encoded guanine 34 of tRNAsAsp,Asn,His,Tyr for queuine, a hypermodified 7-deazaguanine derivative. Eukaryotic TGT is a heterodimer comprised of a catalytic and a non-catalytic subunit. While binding of the tRNA anticodon loop to the active site is structurally well understood, the contribution of the non-catalytic subunit to tRNA binding remained enigmatic, as no complex structure with a complete tRNA was available. Here, we report a cryo-EM structure of eukaryotic TGT in complex with a complete tRNA, revealing the crucial role of the non-catalytic subunit in tRNA binding. We decipher the functional significance of these additional tRNA-binding sites, analyze solution state conformation, flexibility, and disorder of apo TGT, and examine conformational transitions upon tRNA binding.

Expanded palette of RNA base editors for comprehensive RBP-RNA interactome studies.

RNA binding proteins (RBPs) are key regulators of RNA processing and cellular function. Technologies to discover RNA targets of RBPs such as TRIBE (targets of RNA binding proteins identified by editing) and STAMP (surveying targets by APOBEC1 mediated profiling) utilize fusions of RNA base-editors (rBEs) to RBPs to circumvent the limitations of immunoprecipitation (CLIP)-based methods that require enzymatic digestion and large amounts of input material. To broaden the repertoire of rBEs suitable for editing-based RBP-RNA interaction studies, we have devised experimental and computational assays in a framework called PRINTER (protein-RNA interaction-based triaging of enzymes that edit RNA) to assess over thirty A-to-I and C-to-U rBEs, allowing us to identify rBEs that expand the characterization of binding patterns for both sequence-specific and broad-binding RBPs. We also propose specific rBEs suitable for dual-RBP applications. We show that the choice between single or multiple rBEs to fuse with a given RBP or pair of RBPs hinges on the editing biases of the rBEs and the binding preferences of the RBPs themselves. We believe our study streamlines and enhances the selection of rBEs for the next generation of RBP-RNA target discovery.

Altered Protein Dynamics and a More Reactive Catalytic Cysteine in a Neurodegeneration-associated UCHL1 Mutant.

A mutant of ubiquitin C-terminal hydrolase L1 (UCHL1) detected in early-onset neurodegenerative patients, UCHL1R178Q, showed higher catalytic activity than wild-type UCHL1 (UCHL1WT). Lying within the active-site pocket, the arginine is part of an interaction network that holds the catalytic histidine in an inactive arrangement. However, the structural basis and mechanism of enzymatic activation upon glutamine substitution was not understood. We combined X-ray crystallography, protein nuclear magnetic resonance (NMR) analysis, enzyme kinetics, covalent inhibition analysis, and biophysical measurements to delineate activating factors in the mutant. While the crystal structure of UCHL1R178Q showed nearly the same arrangement of the catalytic residues and active-site pocket, the mutation caused extensive alteration in the chemical environment and dynamics of more than 30 residues, some as far as 15 Å away from the site of mutation. Significant broadening of backbone amide resonances in the HSQC spectra indicates considerable backbone dynamics changes in several residues, in agreement with solution small-angle X-ray scattering (SAXS) analyses which indicate an overall increase in protein flexibility. Enzyme kinetics show the activation is due to a kcat effect despite a slightly weakened substrate affinity. In line with this, the mutant shows a higher second-order rate constant (kinact/Ki) in a reaction with a substrate-derived irreversible inhibitor, Ub-VME, compared to the wild-type enzyme, an observation indicative of a more reactive catalytic cysteine in the mutant. Together, the observations underscore structural plasticity as a factor contributing to enzyme kinetic behavior which can be modulated through mutational effects.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins.

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Profiling of RNA-binding protein binding sites by in situ reverse transcription-based sequencing.

RNA-binding proteins (RBPs) regulate diverse cellular processes by dynamically interacting with RNA targets. However, effective methods to capture both stable and transient interactions between RBPs and their RNA targets are still lacking, especially when the interaction is dynamic or samples are limited. Here we present an assay of reverse transcription-based RBP binding site sequencing (ARTR-seq), which relies on in situ reverse transcription of RBP-bound RNAs guided by antibodies to identify RBP binding sites. ARTR-seq avoids ultraviolet crosslinking and immunoprecipitation, allowing for efficient and specific identification of RBP binding sites from as few as 20 cells or a tissue section. Taking advantage of rapid formaldehyde fixation, ARTR-seq enables capturing the dynamic RNA binding by RBPs over a short period of time, as demonstrated by the profiling of dynamic RNA binding of G3BP1 during stress granule assembly on a timescale as short as 10 minutes.

Transcription factor interactions explain the context-dependent activity of CRX binding sites.

The effects of transcription factor binding sites (TFBSs) on the activity of a cis-regulatory element (CRE) depend on the local sequence context. In rod photoreceptors, binding sites for the transcription factor (TF) Cone-rod homeobox (CRX) occur in both enhancers and silencers, but the sequence context that determines whether CRX binding sites contribute to activation or repression of transcription is not understood. To investigate the context-dependent activity of CRX sites, we fit neural network-based models to the activities of synthetic CREs composed of photoreceptor TFBSs. The models revealed that CRX binding sites consistently make positive, independent contributions to CRE activity, while negative homotypic interactions between sites cause CREs composed of multiple CRX sites to function as silencers. The effects of negative homotypic interactions can be overcome by the presence of other TFBSs that either interact cooperatively with CRX sites or make independent positive contributions to activity. The context-dependent activity of CRX sites is thus determined by the balance between positive heterotypic interactions, independent contributions of TFBSs, and negative homotypic interactions. Our findings explain observed patterns of activity among genomic CRX-bound enhancers and silencers, and suggest that enhancers may require diverse TFBSs to overcome negative homotypic interactions between TFBSs.