Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in Aspergillus oryzae.

DNA-binding transcription factors (TFs) play a central role in transcriptional regulation mechanisms, mainly through their specific binding to target sites on the genome and regulation of the expression of downstream genes. Therefore, a comprehensive analysis of the function of these TFs will lead to the understanding of various biological mechanisms. However, the functions of TFs in vivo are diverse and complicated, and the identified binding sites on the genome are not necessarily involved in the regulation of downstream gene expression. In this study, we investigated whether DNA structural information around the binding site of TFs can be used to predict the involvement of the binding site in the regulation of the expression of genes located downstream of the binding site. Specifically, we calculated the structural parameters based on the DNA shape around the DNA binding motif located upstream of the gene whose expression is directly regulated by one TF AoXlnR from Aspergillus oryzae, and showed that the presence or absence of expression regulation can be predicted from the sequence information with high accuracy ([Formula: see text]-1.0) by machine learning incorporating these parameters.

Gene representation bias in spatial transcriptomics.

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.

Position-dependent function of human sequence-specific transcription factors.

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.

Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understanding their regulatory mechanisms and advancing cancer therapy. Previous studies often suffered from insufficient research data, underutilization of motif information, and lack of interpretability. In this work, we designed a novel motif-based interpretable method for m7G modification site prediction, called Moss-m7G. This approach enables the analysis of RNA sequences from a motif-centric perspective. Our proposed word-detection module and motif-embedding module within Moss-m7G extract motif information from sequences, transforming the raw sequences from base-level into motif-level and generating embeddings for these motif sequences. Compared with base sequences, motif sequences contain richer contextual information, which is further analyzed and integrated through the Transformer model. We constructed a comprehensive m7G data set to implement the training and testing process to address the data insufficiency noted in prior research. Our experimental results affirm the effectiveness and superiority of Moss-m7G in predicting m7G modification sites. Moreover, the introduction of the word-detection module enhances the interpretability of the model, providing insights into the predictive mechanisms.

Nanopore-Based Single-Molecule Investigation of Cation Effect on the i-Motif Structure.

The i-motif, a secondary structure of a four-helix formed by cytosine-rich DNA (i-DNA) through C-C+ base pairing, is prevalent in human telomeres and promoters. This structure creates steric hindrance, thereby inhibiting both gene expression and protein coding. The conformation of i-DNA is intricately linked to the intracellular ionic environment. Hence, investigating its conformation under various ion conditions holds significant importance. In this study, we explored the impact of cations on the i-motif structure at the single-molecule level using the α-hemolysin (α-HL) nanochannel. Our findings reveal that the ability of i-DNA to fold into the i-motif structure follows the order Cs+ > Na+ > K+ > Li+ for monovalent cations. Furthermore, we observed the interconversion of single-stranded DNA (ss-DNA) and the i-motif structure at high and low concentrations of Mg2+ and Ba2+ electrolyte solutions. This study not only has the potential to extend the application of i-motif-based sensors in complex solution environments but also provides a new idea for the detection of metal ions.

Modular RNA motifs for orthogonal phase separated compartments.

Recent discoveries in biology have highlighted the importance of protein and RNA-based condensates as an alternative to classical membrane-bound organelles. Here, we demonstrate the design of pure RNA condensates from nanostructured, star-shaped RNA motifs. We generate condensates using two different RNA nanostar architectures: multi-stranded nanostars whose binding interactions are programmed via linear overhangs, and single-stranded nanostars whose interactions are programmed via kissing loops. Through systematic sequence design, we demonstrate that both architectures can produce orthogonal (distinct and immiscible) condensates, which can be individually tracked via fluorogenic aptamers. We also show that aptamers make it possible to recruit peptides and proteins to the condensates with high specificity. Successful co-transcriptional formation of condensates from single-stranded nanostars suggests that they may be genetically encoded and produced in living cells. We provide a library of orthogonal RNA condensates that can be modularly customized and offer a route toward creating systems of functional artificial organelles for the task of compartmentalizing molecules and biochemical reactions.

Targeting pericentric non-consecutive motifs for heterochromatin initiation.

Pericentric heterochromatin is a critical component of chromosomes marked by histone H3 K9 (H3K9) methylation1-3. However, what recruits H3K9-specific histone methyltransferases to pericentric regions in vertebrates remains unclear4, as does why pericentric regions in different species share the same H3K9 methylation mark despite lacking highly conserved DNA sequences2,5. Here we show that zinc-finger proteins ZNF512 and ZNF512B specifically localize at pericentric regions through direct DNA binding. Notably, both ZNF512 and ZNF512B are sufficient to initiate de novo heterochromatin formation at ectopically targeted repetitive regions and pericentric regions, as they directly recruit SUV39H1 and SUV39H2 (SUV39H) to catalyse H3K9 methylation. SUV39H2 makes a greater contribution to H3K9 trimethylation, whereas SUV39H1 seems to contribute more to silencing, probably owing to its preferential association with HP1 proteins. ZNF512 and ZNF512B from different species can specifically target pericentric regions of other vertebrates, because the atypical long linker residues between the zinc-fingers of ZNF512 and ZNF512B offer flexibility in recognition of non-consecutively organized three-nucleotide triplets targeted by each zinc-finger. This study addresses two long-standing questions: how constitutive heterochromatin is initiated and how seemingly variable pericentric sequences are targeted by the same set of conserved machinery in vertebrates.

TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.

DNA Transposons Favor De Novo Transcript Emergence Through Enrichment of Transcription Factor Binding Motifs.

De novo genes emerge from noncoding regions of genomes via succession of mutations. Among others, such mutations activate transcription and create a new open reading frame (ORF). Although the mechanisms underlying ORF emergence are well documented, relatively little is known about the mechanisms enabling new transcription events. Yet, in many species a continuum between absent and very prominent transcription has been reported for essentially all regions of the genome. In this study, we searched for de novo transcripts by using newly assembled genomes and transcriptomes of seven inbred lines of Drosophila melanogaster, originating from six European and one African population. This setup allowed us to detect sample specific de novo transcripts, and compare them to their homologous nontranscribed regions in other samples, as well as genic and intergenic control sequences. We studied the association with transposable elements (TEs) and the enrichment of transcription factor motifs upstream of de novo emerged transcripts and compared them with regulatory elements. We found that de novo transcripts overlap with TEs more often than expected by chance. The emergence of new transcripts correlates with regions of high guanine-cytosine content and TE expression. Moreover, upstream regions of de novo transcripts are highly enriched with regulatory motifs. Such motifs are more enriched in new transcripts overlapping with TEs, particularly DNA TEs, and are more conserved upstream de novo transcripts than upstream their 'nontranscribed homologs'. Overall, our study demonstrates that TE insertion is important for transcript emergence, partly by introducing new regulatory motifs from DNA TE families.

iMab antibody binds single-stranded cytosine-rich sequences and unfolds DNA i-motifs.

i-Motifs (iMs) are non-canonical, four-stranded secondary structures formed by stacking of hemi-protonated CH+·C base pairs in cytosine-rich DNA sequences, predominantly at pH < 7. The presence of iM structures in cells was a matter of debate until the recent development of iM-specific antibody, iMab, which was instrumental for several studies that suggested the existence of iMs in live cells and their putative biological roles. We assessed the interaction of iMab with cytosine-rich oligonucleotides by biolayer interferometry (BLI), pull-down assay and bulk-FRET experiments. Our results suggest that binding of iMab to DNA oligonucleotides is governed by the presence of runs of at least two consecutive cytosines and is generally increased in acidic conditions, irrespectively of the capacity of the sequence to adopt, or not, an iM structure. Moreover, the results of the bulk-FRET assay indicate that interaction with iMab results in unfolding of iM structures even in acidic conditions, similarly to what has been observed with hnRNP K, well-studied single-stranded DNA binding protein. Taken together, our results strongly suggest that iMab actually binds to blocks of 2-3 cytosines in single-stranded DNA, and call for more careful interpretation of results obtained with this antibody.

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translation process, which supports the development of mRNA-based medicine or vaccines. Deep neural networks (DNN) can learn complex cis-regulatory codes from RNA sequences. However, extracting these cis-regulatory codes efficiently from DNN remains a significant challenge. Here, we propose a method based on our toolkit NeuronMotif and motif mutagenesis, which not only enables the discovery of diverse and high-quality motifs but also efficiently reveals motif interactions. By interpreting deep-learning models, we have discovered several crucial motifs that impact mRNA translation efficiency and stability, as well as some unknown motifs or motif syntax, offering novel insights for biologists. Furthermore, we note that it is challenging to enrich motif syntax in datasets composed of randomly generated sequences, and they may not contain sufficient biological signals. AVAILABILITY AND IMPLEMENTATION: The source code and data used to produce the results and analyses presented in this manuscript are available from GitHub (https://github.com/WangLabTHU/combmotif).

Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

Introduction: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially within WRCH motifs (where W=A or T, R=A or G and H=A, C or T). However, it has been consistently observed that the mutability of WRCH motifs varies substantially, with large variations in mutation frequency even between multiple occurrences of the same motif within a single V region. This has led to the notion that the immediate sequence context of WRCH motifs contributes to mutability. Recent studies have highlighted the potential role of local DNA sequence features in promoting mutagenesis of AGCT, a commonly mutated WRCH motif. Intriguingly, AGCT motifs closer to 5' ends of V regions, within the framework 1 (FW1) sub-region1, mutate less frequently, suggesting an SHM-suppressing sequence context. Methods: Here, we systematically examined the basis of AGCT positional biases in human SHM datasets with DeepSHM, a machine-learning model designed to predict SHM patterns. This was combined with integrated gradients, an interpretability method, to interrogate the basis of DeepSHM predictions. Results: DeepSHM predicted the observed positional differences in mutation frequencies at AGCT motifs with high accuracy. For the conserved, lowly mutating AGCT motifs in FW1, integrated gradients predicted a large negative contribution of 5'C and 3'G flanking residues, suggesting that a CAGCTG context in this location was suppressive for SHM. CAGCTG is the recognition motif for E-box transcription factors, including E2A, which has been implicated in SHM. Indeed, we found a strong, inverse relationship between E-box motif fidelity and mutation frequency. Moreover, E2A was found to associate with the V region locale in two human B cell lines. Finally, analysis of human SHM datasets revealed that naturally occurring mutations in the 3'G flanking residues, which effectively ablate the E-box motif, were associated with a significantly increased rate of AGCT mutation. Discussion: Our results suggest an antagonistic relationship between mutation frequency and the binding of E-box factors like E2A at specific AGCT motif contexts and, therefore, highlight a new, suppressive mechanism regulating local SHM patterns in human V regions.

Structure of an internal loop motif with three consecutive U•U mismatches from stem-loop 1 in the 3'-UTR of the SARS-CoV-2 genomic RNA.

The single-stranded RNA genome of SARS-CoV-2 is highly structured. Numerous helical stem-loop structures interrupted by mismatch motifs are present in the functionally important 5'- and 3'-UTRs. These mismatches modulate local helical geometries and feature unusual arrays of hydrogen bonding donor and acceptor groups. However, their conformational and dynamical properties cannot be directly inferred from chemical probing and are difficult to predict theoretically. A mismatch motif (SL1-motif) consisting of three consecutive U•U base pairs is located in stem-loop 1 of the 3'-UTR. We combined NMR-spectroscopy and MD-simulations to investigate its structure and dynamics. All three U•U base pairs feature two direct hydrogen bonds and are as stable as Watson-Crick A:U base pairs. Plasmodium falciparum 25S rRNA contains a triple U•U mismatch motif (Pf-motif) differing from SL1-motif only with respect to the orientation of the two closing base pairs. Interestingly, while the geometry of the outer two U•U mismatches was identical in both motifs the preferred orientation of the central U•U mismatch was different. MD simulations and potassium ion titrations revealed that the potassium ion-binding mode to the major groove is connected to the different preferred geometries of the central base pair in the two motifs.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

Quercetin Exhibits Preferential Binding Interaction by Selectively Targeting HRAS1 I-Motif DNA-Forming Promoter Sequences.

I-Motif (iM) DNA structures represent among the most significant noncanonical nucleic acid configurations. iM-forming DNA sequences are found in an array of vital genomic locations and are particularly frequent in the promoter islands of various oncogenes. Thus, iM DNA is a crucial candidate for anticancer medicines; therefore, binding interactions between iM DNA and small molecular ligands, such as flavonoids, are critically important. Extensive sets of spectroscopic strategies and thermodynamic analysis were utilized in the present investigation to find out the favorable interaction of quercetin (Que), a dietary flavonoid that has various health-promoting characteristics, including anticancer properties, with noncanonical iM DNA structure. Spectroscopic studies and thermal analysis revealed that Que interacts preferentially with HRAS1 iM DNA compared with VEGF, BCL2 iM, and duplex DNA. Que, therefore, emerged as a suitable natural-product-oriented antagonist for targeting HRAS1 iM DNA. The innovative spectroscopic as well as mechanical features of Que and its specific affinity for HRAS1 iM may be useful for therapeutic applications and provide crucial insights for the design of compounds with remarkable medicinal properties.

MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer.

Maturation of eukaryotic pre-mRNAs via splicing and polyadenylation is modulated across cell types and conditions by a variety of RNA-binding proteins (RBPs). Although there exist over 1,500 RBPs in human cells, their binding motifs and functions still remain to be elucidated, especially in the complex environment of tissues and in the context of diseases. To overcome the lack of methods for the systematic and automated detection of sequence motif-guided pre-mRNA processing regulation from RNA sequencing (RNA-Seq) data we have developed MAPP (Motif Activity on Pre-mRNA Processing). Applying MAPP to RBP knock-down experiments reveals that many RBPs regulate both splicing and polyadenylation of nascent transcripts by acting on similar sequence motifs. MAPP not only infers these sequence motifs, but also unravels the position-dependent impact of the RBPs on pre-mRNA processing. Interestingly, all investigated RBPs that act on both splicing and 3' end processing exhibit a consistently repressive or activating effect on both processes, providing a first glimpse on the underlying mechanism. Applying MAPP to normal and malignant brain tissue samples unveils that the motifs bound by the PTBP1 and RBFOX RBPs coordinately drive the oncogenic splicing program active in glioblastomas demonstrating that MAPP paves the way for characterizing pre-mRNA processing regulators under physiological and pathological conditions.

G-quadruplex forming motifs in the promoter region of the B-MYB proto-oncogene.

To combat cancer, a comprehensive understanding of the molecular mechanisms and behaviors involved in carcinogenesis is crucial, as tumorigenesis is a complex process influenced by various genetic events and disease hallmarks. The B-MYB gene encodes a transcription factor involved in cell cycle regulation, survival, and differentiation in normal cells. B-MYB can be transformed into an oncogene through mutations, and abnormal expression of B-MYB has been identified in various cancers, including lung cancer, and is associated with poor prognosis. Targeting this oncogene is a promising approach for anti-cancer drug design. B-MYB has been deemed undruggable in previous reports, necessitating the search for novel therapeutic options. In this study, we found that the B-MYB gene promoter contains several G/C rich motifs compatible with G-quadruplex (G4) formation. We investigated and validated the existence of G4 structures in the promoter region of B-MYB, first in vitro using a combination of bioinformatics, biophysical, and biochemical methods, then in cell with the recently developed G4access method.

AlPaCas: allele-specific CRISPR gene editing through a protospacer-adjacent-motif (PAM) approach.

Gene therapy of dominantly inherited genetic diseases requires either the selective disruption of the mutant allele or the editing of the specific mutation. The CRISPR-Cas system holds great potential for the genetic correction of single nucleotide variants (SNVs), including dominant mutations. However, distinguishing between single-nucleotide variations in a pathogenic genomic context remains challenging. The presence of a PAM in the disease-causing allele can guide its precise targeting, preserving the functionality of the wild-type allele. The AlPaCas (Aligning Patients to Cas) webserver is an automated pipeline for sequence-based identification and structural analysis of SNV-derived PAMs that satisfy this demand. When provided with a gene/SNV input, AlPaCas can: (i) identify SNV-derived PAMs; (ii) provide a list of available Cas enzymes recognizing the SNV (s); (iii) propose mutational Cas-engineering to enhance the selectivity towards the SNV-derived PAM. With its ability to identify allele-specific genetic variants that can be targeted using already available or engineered Cas enzymes, AlPaCas is at the forefront of advancements in genome editing. AlPaCas is open to all users without a login requirement and is freely available at https://schubert.bio.uniroma1.it/alpacas.

RUNX1 regulates promoter activity in the absence of cognate DNA binding motifs.

Runt-related transcription factor 1 (RUNX1) plays an important role in normal haematopoietic cell development and function, and its function is frequently disrupted in leukaemia. RUNX1 is widely recognised as a sequence-specific DNA binding factor that recognises the motif 5'-TG(T/C)GGT-3' in promoter and enhancer regions of its target genes. Moreover, RUNX1 fusion proteins, such as RUNX1-ETO formed by the t(8;21) translocation, retain the ability to recognise and bind to this sequence to elicit atypical gene regulatory effects on bona fide RUNX1 targets. However, our analysis of publicly available RUNX1 chromatin immunoprecipitation sequencing (ChIP-Seq) data has provided evidence challenging this dogma, revealing that this motif-specific model of RUNX1 recruitment and function is incomplete. Our analyses revealed that the majority of RUNX1 genomic localisation occurs outside of promoters, that 20% of RUNX1 binding sites lack consensus RUNX motifs, and that binding in the absence of a cognate binding site is more common in promoter regions compared to distal sites. Reporter assays demonstrate that RUNX1 can drive promoter activity in the absence of a recognised DNA binding motif, in contrast to RUNX1-ETO. RUNX1-ETO supresses activity when it is recruited to promoters containing a sequence specific motif, while interestingly, it binds but does not repress promoters devoid of a RUNX1 recognition site. These data suggest that RUNX1 regulation of target genes occurs through multiple mechanisms depending on genomic location, the type of regulatory element and mode of recruitment.