Diversity and complexity in DNA recognition by transcription factors

Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, a novel Nucleotide-Level Deep Neural Network (NLDNN) is first proposed to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task, which takes DNA sequences as input and directly predicts experimental coverage values. Beyond predictive performance, it also assesses model performance by locating potential TF binding regions, discriminating TF-specific single-nucleotide polymorphisms (SNPs), and identifying causal disease-associated SNPs. The experimental results show that NLDNN outperforms the competing methods in these tasks. Then, a dual-path framework is designed for adversarial training of NLDNN to further improve the cross-species prediction performance by pulling the domain space of human and mouse species closer. Through comparison and analysis, it finds that adversarial training not only can improve the cross-species prediction performance between humans and mice but also enhance the ability to locate TF binding regions and discriminate TF-specific SNPs. By visualizing the predictions, it is figured out that the framework corrects some mispredictions by amplifying the coverage values of incorrectly predicted peaks.

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.

Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters

The inability to scalably and precisely measure the activity of developmental cis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.

Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.

Genome-wide identification of bZIP transcription factors and their expression analysis in Platycodon grandiflorus under abiotic stress

The Basic Leucine Zipper (bZIP) transcription factors (TFs) family is among of the largest and most diverse gene families found in plant species, and members of the bZIP TFs family perform important functions in plant developmental processes and stress response. To date, bZIP genes in Platycodon grandiflorus have not been characterized. In this work, a number of 47 PgbZIP genes were identified from the genome of P. grandiflorus, divided into 11 subfamilies. The distribution of these PgbZIP genes on the chromosome and gene replication events were analyzed. The motif, gene structure, cis-elements, and collinearity relationships of the PgbZIP genes were simultaneously analyzed. In addition, gene expression pattern analysis identified ten candidate genes involved in the developmental process of different tissue parts of P. grandiflorus. Among them, Four genes (PgbZIP5, PgbZIP21, PgbZIP25 and PgbZIP28) responded to drought and salt stress, which may have potential biological roles in P. grandiflorus development under salt and drought stress. Four hub genes (PgbZIP13, PgbZIP30, PgbZIP32 and PgbZIP45) mined in correlation network analysis, suggesting that these PgbZIP genes may form a regulatory network with other transcription factors to participate in regulating the growth and development of P. grandiflorus. This study provides new insights regarding the understanding of the comprehensive characterization of the PgbZIP TFs for further exploration of the functions of growth and developmental regulation in P. grandiflorus and the mechanisms for coping with abiotic stress response.

Stratifying TAD boundaries pinpoints focal genomic regions of regulation, damage, and repair

Advances in chromatin mapping have exposed the complex chromatin hierarchical organization in mammals, including topologically associating domains (TADs) and their substructures, yet the functional implications of this hierarchy in gene regulation and disease progression are not fully elucidated. Our study delves into the phenomenon of shared TAD boundaries, which are pivotal in maintaining the hierarchical chromatin structure and regulating gene activity. By integrating high-resolution Hi-C data, chromatin accessibility, and DNA double-strand breaks (DSBs) data from various cell lines, we systematically explore the complex regulatory landscape at high-level TAD boundaries. Our findings indicate that these boundaries are not only key architectural elements but also vibrant hubs, enriched with functionally crucial genes and complex transcription factor binding site-clustered regions. Moreover, they exhibit a pronounced enrichment of DSBs, suggesting a nuanced interplay between transcriptional regulation and genomic stability. Our research provides novel insights into the intricate relationship between the 3D genome structure, gene regulation, and DNA repair mechanisms, highlighting the role of shared TAD boundaries in maintaining genomic integrity and resilience against perturbations. The implications of our findings extend to understanding the complexities of genomic diseases and open new avenues for therapeutic interventions targeting the structural and functional integrity of TAD boundaries.

Definition of the binding specificity of the T7 bacteriophage primase by analysis of a protein binding microarray using a thermodynamic model

Protein binding microarrays (PBM), SELEX, RNAcompete and chromatin-immunoprecipitation have been intensively used to determine the specificity of nucleic acid binding proteins. While the specificity of proteins with pronounced sequence specificity is straightforward, the determination of the sequence specificity of proteins of modest sequence specificity is more difficult. In this work, an explorative data analysis workflow for nucleic acid binding data was developed that can be used by scientists that want to analyse their binding data. The workflow is based on a regressor realized in scikit-learn, the major machine learning module for the scripting language Python. The regressor is built on a thermodynamic model of nucleic acid binding and describes the sequence specificity with base- and position-specific energies. The regressor was used to determine the binding specificity of the T7 primase. For this, we reanalysed the binding data of the T7 primase obtained with a custom PBM. The binding specificity of the T7 primase agrees with the priming specificity (5'-GTC) and the template (5'-GGGTC) for the preferentially synthesized tetraribonucleotide primer (5'-pppACCC) but is more relaxed. The dominant contribution of two positions in the motif can be explained by the involvement of the initiating and elongating nucleotides for template binding.

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Functional characterization of plant specific Indeterminate Domain (IDD) transcription factors in tomato (Solanum lycopersicum L.)

Plant-specific transcription factors (TFs) are responsible for regulating the genes involved in the development of plant-specific organs and response systems for adaptation to terrestrial environments. This includes the development of efficient water transport systems, efficient reproductive organs, and the ability to withstand the effects of terrestrial factors, such as UV radiation, temperature fluctuations, and soil-related stress factors, and evolutionary advantages over land predators. In rice and Arabidopsis, INDETERMINATE DOMAIN (IDD) TFs are plant-specific TFs with crucial functions, such as development, reproduction, and stress response. However, in tomatoes, IDD TFs remain uncharacterized. Here, we examined the presence, distribution, structure, characteristics, and expression patterns of SlIDDs. Database searches, multiple alignments, and motif alignments suggested that 24 TFs were related to Arabidopsis IDDs. 18 IDDs had two characteristic C2H2 domains and two C2HC domains in their coding regions. Expression analyses suggest that some IDDs exhibit multi-stress responsive properties and can respond to specific stress conditions, while others can respond to multiple stress conditions in shoots and roots, either in a tissue-specific or universal manner. Moreover, co-expression database analyses suggested potential interaction partners within IDD family and other proteins. This study functionally characterized SlIDDs, which can be studied using molecular and bioinformatics methods for crop improvement.

Structural and dynamical aspect of DNA motif sequence specific binding of AP-1 transcription factor

Activator protein-1 (AP-1) comprises one of the largest and most evolutionary conserved families of ubiquitous eukaryotic transcription factors that act as a pioneer factor. Diversity in DNA binding interaction of AP-1 through a conserved basic-zipper (bZIP) domain directs in-depth understanding of how AP-1 achieves its DNA binding selectivity and consequently gene regulation specificity. Here, we address the structural and dynamical aspects of the DNA target recognition process of AP-1 using microsecond-long atomistic simulations based on the structure of the human AP-1 FosB/JunD bZIP-DNA complex. Our results show the unique role of DNA shape features in selective base specific interactions, characteristic ion population, and solvation properties of DNA grooves to form the motif sequence specific AP-1-DNA complex. The TpG step at the two terminals of the AP-1 site plays an important role in the structural adjustment of DNA by modifying the helical twist in the AP-1 bound state. We addressed the role of intrinsic motion of the bZIP domain in terms of opening and closing gripper motions of DNA binding helices, in target site recognition and binding of AP-1 factors. Our observations suggest that binding to the cognate motif in DNA is mainly accompanied with the precise adjustment of closing gripper motion of DNA binding helices of the bZIP domain.

Overlapping binding sites underlie TF genomic occupancy

Sequence-specific DNA binding by transcription factors (TFs) is a crucial step in gene regulation. However, current high-throughput in vitro approaches cannot reliably detect lower affinity TF-DNA interactions, which play key roles in gene regulation. Here, we developed PADIT-seq ( p rotein a ffinity to D NA by in vitro transcription and RNA seq uencing) to assay TF binding preferences to all 10-bp DNA sequences at far greater sensitivity than prior approaches. The expanded catalogs of low affinity DNA binding sites for the human TFs HOXD13 and EGR1 revealed that nucleotides flanking high affinity DNA binding sites create overlapping lower affinity sites that together modulate TF genomic occupancy in vivo . Formation of such extended recognition sequences stems from an inherent property of TF binding sites to interweave each other and expands the genomic sequence space for identifying noncoding variants that directly alter TF binding.

One-Sentence Summary

Overlapping DNA binding sites underlie TF genomic occupancy through their inherent propensity to interweave each other.

CHD7 and SOX2 act in a common gene regulatory network during mammalian semicircular canal and cochlear development

Inner ear morphogenesis requires tightly regulated epigenetic and transcriptional control of gene expression. CHD7, an ATP-dependent chromodomain helicase DNA-binding protein, and SOX2, an SRY-related HMG box pioneer transcription factor, are known to contribute to vestibular and auditory system development, but their genetic interactions in the ear have not been explored. Here, we analyzed inner ear development and the transcriptional regulatory landscapes in mice with variable dosages of Chd7 and/or Sox2. We show that combined haploinsufficiency for Chd7 and Sox2 results in reduced otic cell proliferation, severe malformations of semicircular canals, and shortened cochleae with ectopic hair cells. Examination of mice with conditional, inducible Chd7 loss by Sox2CreER reveals a critical period (~E9.5) of susceptibility in the inner ear to combined Chd7 and Sox2 loss. Data from genome-wide RNA-sequencing and CUT&Tag studies in the otocyst show that CHD7 regulates Sox2 expression and acts early in a gene regulatory network to control expression of key otic patterning genes, including Pax2 and Otx2. CHD7 and SOX2 directly bind independently and cooperatively at transcription start sites and enhancers to regulate otic progenitor cell gene expression. Together, our findings reveal essential roles for Chd7 and Sox2 in early inner ear development and may be applicable for syndromic and other forms of hearing or balance disorders.

ITGAV Promotes the Progression of Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) refers to the malignancy of squamous cells in the head and neck region. Ranked as the seventh most common cancer worldwide, HNSCC has a very low survival rate, highlighting the importance of finding therapeutic targets for the disease. Integrins are cell surface receptors that play a crucial role in mediating cellular interactions with the extracellular matrix (ECM). Within this protein family, Integrin αV (ITGAV) has received attention for its important functional role in cancer progression. In this study, we first demonstrated the upregulation of ITGAV expression in HNSCC, with higher ITGAV expression levels correlating with significantly lower overall survival, based on TCGA (the Cancer Genome Atlas) and GEO datasets. Subsequent in vitro analyses revealed an overexpression of ITGAV in highly invasive HNSCC cell lines UM1 and UMSCC-5 in comparison to low invasive HNSCC cell lines UM2 and UMSCC-6. In addition, knockdown of ITGAV significantly inhibited the migration, invasion, viability, and colony formation of HNSCC cells. In addition, chromatin immunoprecipitation (ChIP) assays indicated that SOX11 bound to the promoter of ITGAV gene, and SOX11 knockdown resulted in decreased ITGAV expression in HNSCC cells. In conclusion, our studies suggest that ITGAV promotes the progression of HNSCC cells and may be regulated by SOX11 in HNSCC cells.

Affinity-optimizing enhancer variants disrupt development

Enhancers control the location and timing of gene expression and contain the majority of variants associated with disease1-3. The ZRS is arguably the most well-studied vertebrate enhancer and mediates the expression of Shh in the developing limb4. Thirty-one human single-nucleotide variants (SNVs) within the ZRS are associated with polydactyly4-6. However, how this enhancer encodes tissue-specific activity, and the mechanisms by which SNVs alter the number of digits, are poorly understood. Here we show that the ETS sites within the ZRS are low affinity, and identify a functional ETS site, ETS-A, with extremely low affinity. Two human SNVs and a synthetic variant optimize the binding affinity of ETS-A subtly from 15% to around 25% relative to the strongest ETS binding sequence, and cause polydactyly with the same penetrance and severity. A greater increase in affinity results in phenotypes that are more penetrant and more severe. Affinity-optimizing SNVs in other ETS sites in the ZRS, as well as in ETS, interferon regulatory factor (IRF), HOX and activator protein 1 (AP-1) sites within a wide variety of enhancers, cause gain-of-function gene expression. The prevalence of binding sites with suboptimal affinity in enhancers creates a vulnerability in genomes whereby SNVs that optimize affinity, even slightly, can be pathogenic. Searching for affinity-optimizing SNVs in genomes could provide a mechanistic approach to identify causal variants that underlie enhanceropathies.

Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites

Prediction of transcription factor binding sites (TFBS) is an example of application of Bioinformatics where DNA molecules are represented as sequences of A, C, G and T symbols. The most used model in this problem is Position Weight Matrix (PWM). Notwithstanding the advantage of being simple, PWMs cannot capture dependency between nucleotide positions, which may affect prediction performance. Acyclic Probabilistic Finite Automata (APFA) is an alternative model able to accommodate position dependencies. However, APFA is a more complex model, which means more parameters have to be learned. In this paper, we propose an innovative method to identify when position dependencies influence preference for PWMs or APFAs. This implied using position dependency features extracted from 1106 sets of TFBS to infer a decision tree able to predict which is the best model - PWM or APFA - for a given set of TFBSs. According to our results, as few as three pinpointed features are able to choose the best model, providing a balance of performance (average precision) and model simplicity.

Increased iron uptake by splenic hematopoietic stem cells promotes TET2-dependent erythroid regeneration

Hematopoietic stem cells (HSCs) are capable of regenerating the blood system, but the instructive cues that direct HSCs to regenerate particular lineages lost to the injury remain elusive. Here, we show that iron is increasingly taken up by HSCs during anemia and induces erythroid gene expression and regeneration in a Tet2-dependent manner. Lineage tracing of HSCs reveals that HSCs respond to hemolytic anemia by increasing erythroid output. The number of HSCs in the spleen, but not bone marrow, increases upon anemia and these HSCs exhibit enhanced proliferation, erythroid differentiation, iron uptake, and TET2 protein expression. Increased iron in HSCs promotes DNA demethylation and expression of erythroid genes. Suppressing iron uptake or TET2 expression impairs erythroid genes expression and erythroid differentiation of HSCs; iron supplementation, however, augments these processes. These results establish that the physiological level of iron taken up by HSCs has an instructive role in promoting erythroid-biased differentiation of HSCs.

Role of bZIP Transcription Factors in Response to NaCl Stress in Tamarix ramosissima under Exogenous Potassium (K+)

Salt stress is a significant environmental factor affecting plant growth and development, with NaCl stress being one of the most common types of salt stress. The halophyte, Tamarix ramosissima Ledeb (T. ramosissima), is frequently utilized for the afforestation of saline-alkali soils. Indeed, there has been limited research and reports by experts and scholars on the regulatory mechanisms of basic leucine zipper (bZIP) genes in T. ramosissima when treated with exogenous potassium (K+) to alleviate the effects of NaCl stress. This study focused on the bZIP genes in T. ramosissima roots under NaCl stress with additional KCl applied. We identified key candidate genes and metabolic pathways related to bZIP and validated them through quantitative real-time PCR (qRT-PCR). The results revealed that under NaCl stress with additional KCl applied treatments at 0 h, 48 h, and 168 h, based on Pfam protein domain prediction and physicochemical property analysis, we identified 20 related bZIP genes. Notably, four bZIP genes (bZIP_2, bZIP_6, bZIP_16, and bZIP_18) were labeled with the plant hormone signal transduction pathway, showing a predominant up-regulation in expression levels. The results suggest that these genes may mediate multiple physiological pathways under NaCl stress with additional KCl applied at 48 h and 168 h, enhancing signal transduction, reducing the accumulation of ROS, and decreasing oxidative damage, thereby enhancing the tolerance of T. ramosissima to NaCl stress. This study provides gene resources and a theoretical basis for further breeding of salt-tolerant Tamarix species and the involvement of bZIP transcription factors in mitigating NaCl toxicity.

The forkhead DNA-binding domain binds specific G2-rich RNA sequences

Transcription factors contain a DNA-binding domain ensuring specific recognition of DNA target sequences. The family of forkhead (FOX) transcription factors is composed of dozens of paralogs in mammals. The forkhead domain (FHD) is a segment of about 100 amino acids that binds an A-rich DNA sequence. Using DNA and RNA PCR-SELEX, we show that recombinant FOXL2 proteins, either wild-type or carrying the oncogenic variant C134W, recognize similar DNA-binding sites. This suggests that the oncogenic variant does not alter the intrinsic sequence-specificity of FOXL2. Most importantly, we show that FOXL2 binds G2-rich RNA sequences whereas it virtually fails to bind similar sequences in DNA chemistry. Interestingly, a statistically significant subset of genes responding to the knock-down of FOXL2/Foxl2 harbor such G2-rich sequences and are involved in crucial signaling pathways and cellular processes. In addition, we show that FOXA1, FOXO3a and chimeric FOXL2 proteins containing the FHD of the former are also able to interact with some of the preferred FOXL2-binding sequences. Our results point to an unexpected and novel characteristic of the forkhead domain, the biological relevance of which remains to be explored.

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

BACKGROUND

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation.

RESULTS

In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach.

CONCLUSIONS

Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

FOXP3 recognizes microsatellites and bridges DNA through multimerization

FOXP3 is a transcription factor that is essential for the development of regulatory T cells, a branch of T cells that suppress excessive inflammation and autoimmunity1-5. However, the molecular mechanisms of FOXP3 remain unclear. Here we here show that FOXP3 uses the forkhead domain-a DNA-binding domain that is commonly thought to function as a monomer or dimer-to form a higher-order multimer after binding to TnG repeat microsatellites. The cryo-electron microscopy structure of FOXP3 in a complex with T3G repeats reveals a ladder-like architecture, whereby two double-stranded DNA molecules form the two 'side rails' bridged by five pairs of FOXP3 molecules, with each pair forming a 'rung'. Each FOXP3 subunit occupies TGTTTGT within the repeats in a manner that is indistinguishable from that of FOXP3 bound to the forkhead consensus motif (TGTTTAC). Mutations in the intra-rung interface impair TnG repeat recognition, DNA bridging and the cellular functions of FOXP3, all without affecting binding to the forkhead consensus motif. FOXP3 can tolerate variable inter-rung spacings, explaining its broad specificity for TnG-repeat-like sequences in vivo and in vitro. Both FOXP3 orthologues and paralogues show similar TnG repeat recognition and DNA bridging. These findings therefore reveal a mode of DNA recognition that involves transcription factor homomultimerization and DNA bridging, and further implicates microsatellites in transcriptional regulation and diseases.

Deciphering the Role of the Ser-Phosphorylation Pattern on the DNA-Binding Activity of Max Transcription Factor Using Chemical Protein Synthesis

The chemical synthesis of site-specifically modified transcription factors (TFs) is a powerful method to investigate how post-translational modifications (PTMs) influence TF-DNA interactions and impact gene expression. Among these TFs, Max plays a pivotal role in controlling the expression of 15 % of the genome. The activity of Max is regulated by PTMs; Ser-phosphorylation at the N-terminus is considered one of the key regulatory mechanisms. In this study, we developed a practical synthetic strategy to prepare homogeneous full-length Max for the first time, to explore the impact of Max phosphorylation. We prepared a focused library of eight Max variants, with distinct modification patterns, including mono-phosphorylated, and doubly phosphorylated analogues at Ser2/Ser11 as well as fluorescently labeled variants through native chemical ligation. Through comprehensive DNA binding analyses, we discovered that the phosphorylation position plays a crucial role in the DNA-binding activity of Max. Furthermore, in vitro high-throughput analysis using DNA microarrays revealed that the N-terminus phosphorylation pattern does not interfere with the DNA sequence specificity of Max. Our work provides insights into the regulatory role of Max's phosphorylation on the DNA interactions and sequence specificity, shedding light on how PTMs influence TF function.

FoxP3 recognizes microsatellites and bridges DNA through multimerization

FoxP3 is a transcription factor (TF) essential for development of regulatory T cells (Tregs), a branch of T cells that suppress excessive inflammation and autoimmunity 1-5 . Molecular mechanisms of FoxP3, however, remain elusive. We here show that FoxP3 utilizes the Forkhead domain--a DNA binding domain (DBD) that is commonly thought to function as a monomer or dimer--to form a higher-order multimer upon binding to T n G repeat microsatellites. A cryo-electron microscopy structure of FoxP3 in complex with T 3 G repeats reveals a ladder-like architecture, where two double-stranded DNA molecules form the two "side rails" bridged by five pairs of FoxP3 molecules, with each pair forming a "rung". Each FoxP3 subunit occupies TGTTTGT within the repeats in the manner indistinguishable from that of FoxP3 bound to the Forkhead consensus motif (FKHM; TGTTTAC). Mutations in the "intra-rung" interface impair T n G repeat recognition, DNA bridging and cellular functions of FoxP3, all without affecting FKHM binding. FoxP3 can tolerate variable "inter-rung" spacings, explaining its broad specificity for T n G repeat-like sequences in vivo and in vitro . Both FoxP3 orthologs and paralogs show similar T n G repeat recognition and DNA bridging. These findings thus reveal a new mode of DNA recognition that involves TF homo-multimerization and DNA bridging, and further implicates microsatellites in transcriptional regulation and diseases.

Altered binding affinity of SIX1-Q177R correlates with enhanced WNT5A and WNT pathway effector expression in Wilms tumor

Wilms tumors present as an amalgam of varying proportions of tissues located within the developing kidney, one being the nephrogenic blastema comprising multipotent nephron progenitor cells (NPCs). The recurring missense mutation Q177R in NPC transcription factors SIX1 and SIX2 is most correlated with tumors of blastemal histology and is significantly associated with relapse. Yet, the transcriptional regulatory consequences of SIX1/2-Q177R that might promote tumor progression and recurrence have not been investigated extensively. Utilizing multiple Wilms tumor transcriptomic datasets, we identified upregulation of the gene encoding non-canonical WNT ligand WNT5A in addition to other WNT pathway effectors in SIX1/2-Q177R mutant tumors. SIX1 ChIP-seq datasets from Wilms tumors revealed shared binding sites for SIX1/SIX1-Q177R within a promoter of WNT5A and at putative distal cis-regulatory elements (CREs). We demonstrate colocalization of SIX1 and WNT5A in Wilms tumor tissue and utilize in vitro assays that support SIX1 and SIX1-Q177R activation of expression from the WNT5A CREs, as well as enhanced binding affinity within the WNT5A promoter that may promote the differential expression of WNT5A and other WNT pathway effectors associated with SIX1-Q177R tumors.

High-risk histological subtype-related FAM83A hijacked FOXM1 transcriptional regulation to promote malignant progression in lung adenocarcinoma

Background

According to the histopathology, lung adenocarcinoma (LUAD) could be divided into five distinct pathological subtypes, categorized as high-risk (micropapillary and solid) group, intermediate-risk (acinar and papillary) group, and low-risk (lepidic) group. Despite this classification, there is limited knowledge regarding the role of transcription factors (TFs) in the molecular regulation of LUAD histology patterns.

Methods

Publish data was mined to explore the candidate TFs associated with high-risk histopathology in LUAD, which was validated in tissue samples. Colony formation, CCK8, EdU, transwell, and matrigel assays were performed to determine the biological function of FAM83A in vitro. Subcutaneous tumor-bearing in BALB/c nude mice and xenograft perivitelline injection in zebrafish were utilized to unreal the function of FAM83A in vivo. We also performed chromatin immunoprecipitation (ChIP), dual-luciferase reporter, and rescue assays to uncover the underline mechanism of FAM83A. Immunohistochemistry (IHC) was performed to confirm the oncogenic role of FAM83A in clinical LUAD tissues.

Results

Screening the transcriptional expression data from TCGA-LUAD, we focus on the differentially expressed TFs across the divergent pathological subtypes, and identified that the expression of FAM83A is higher in patients with high-risk groups compared with those with intermediate or low-risk groups. The FAM83A expression is positively correlated with worse overall survival, progression-free survival, and advanced stages. Gain- and loss-of-function assays revealed that FAM83A promoted cell proliferation, invasion, and migration of tumor cell lines both in vivo and in vitro. Pathway enrichment analysis shows that FAM83A expression is significantly enriched in cell cycle-related pathways. The ChIP and luciferase reporter assays revealed that FAM83A hijacks the promoter of FOXM1 to progress the malignant LUAD, and the rescue assay uncovered that the function of FAM83A is partly dependent on FOXM1 regulation. Additionally, patients with high FAM83A expression positively correlated with higher IHC scores of Ki-67 and FOXM1, and patients with active FAM83A/FOXM1 axis had poor prognoses in LUAD.

Conclusions

Taken together, our study revealed that the high-risk histological subtype-related FAM83A hijacks FOXM1 transcriptional regulation to promote malignant progression in lung adenocarcinoma, which implies targeting FAM83A/FOXM1 is the therapeutic vulnerability.

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation. Overall, we find that the presence of CpG methylation decreases the likelihood of binding for the majority of methylation-associated TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding. We illustrate that the novel methylation-aware TF binding models allow to predict differential ChIP-seq peaks and improve the genome-wide analysis of TF binding. Our work indicates that simplistic models that neglect the effect of DNA methylation on DNA binding may lead to systematic underperformance for methylation-associated TFs.

Chromatin accessibility in the Drosophila embryo is determined by transcription factor pioneering and enhancer activation

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding and chromatin accessibility data with interpretable deep learning and performed genetic validation experiments. We identify a hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, whereas patterning TFs augment chromatin accessibility in sequence contexts where they mediate enhancer activation. We conclude that chromatin accessibility occurs in two tiers: one through pioneering, which makes enhancers accessible but not necessarily active, and the second when the correct combination of TFs leads to enhancer activation.

Transcription-coupled global genomic repair in E. coli

The nucleotide excision repair (NER) pathway removes helix-distorting lesions from DNA in all organisms. Escherichia coli has long been a model for understanding NER, which is traditionally divided into major and minor subpathways known as global genome repair (GGR) and transcription-coupled repair (TCR), respectively. TCR has been assumed to be mediated exclusively by Mfd, a DNA translocase of minimal NER phenotype. This review summarizes the evidence that shaped the traditional view of NER in bacteria, and reviews data supporting a new model in which GGR and TCR are inseparable. In this new model, RNA polymerase serves both as the essential primary sensor of bulky DNA lesions genome-wide and as the delivery platform for the assembly of functional NER complexes in living cells.

Repetitive DNA regulates gene expression

Short tandem repeats affect gene expression by binding regulatory proteins.

Short tandem repeats bind transcription factors to tune eukaryotic gene expression

Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites.

The architecture of binding cooperativity between densely bound transcription factors

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

Noncanonical binding of transcription factors: time to revisit specificity?

Transcription factors (TFs) are one of the most studied classes of DNA-binding proteins that have a direct functional impact on gene transcription and thus, on human physiology and disease. The mechanisms that TFs use for recognizing target DNA binding sites have been studied for nearly five decades, yet they remain poorly understood. It is classically assumed that a TF recognizes a specific sequence pattern, or motif, as its binding sites. However, recent studies are consistently finding examples of noncanonical binding, that is, TFs binding at sites that do not resemble their sequence motifs. Here we review the current literature on four major types of noncanonical TF binding, namely binding based on DNA shape readout, at Guanine-quadruplex structures, at repeat sequences, and bispecific binding. These examples point to a critical need for studies to unify our current observations, many of which are at odds with the "one TF, one motif" view, into a more comprehensive definition of the DNA-binding specificity of TFs.

A genome-wide screen reveals new regulators of the 2-cell-like cell state

In mammals, only the zygote and blastomeres of the early embryo are totipotent. This totipotency is mirrored in vitro by mouse '2-cell-like cells' (2CLCs), which appear at low frequency in cultures of embryonic stem cells (ESCs). Because totipotency is not completely understood, we carried out a genome-wide CRISPR knockout screen in mouse ESCs, searching for mutants that reactivate the expression of Dazl, a gene expressed in 2CLCs. Here we report the identification of four mutants that reactivate Dazl and a broader 2-cell-like signature: the E3 ubiquitin ligase adaptor SPOP, the Zinc-Finger transcription factor ZBTB14, MCM3AP, a component of the RNA processing complex TREX-2, and the lysine demethylase KDM5C. All four factors function upstream of DPPA2 and DUX, but not via p53. In addition, SPOP binds DPPA2, and KDM5C interacts with ncPRC1.6 and inhibits 2CLC gene expression in a catalytic-independent manner. These results extend our knowledge of totipotency, a key phase of organismal life.

Analysis of the interaction of a non-canonical twin half-site of Cyclic AMP-Response Element (CRE) with CRE-binding protein

Differential regulation of a gene having either canonical or non-canonical cyclic AMP response element (CRE) in its promoter is primarily accomplished by its interactions with CREB (cAMP-response element binding protein). The present study aims to delineate the mechanism of the CREB-CRE interactions at the Oncostatin-M (osm) promoter by in vitro and in silico approaches. The non-canonical CREosm consists of two half-CREs separated by a short intervening sequence of 9 base pairs. In this study, in vitro binding assays revealed that out of the two CRE half-sites, the right half-CRE was indispensable for binding of CREB, while the left sequence showed weaker binding ability and specificity. Genome-wide modeling and high throughput free energy calculations for the energy-minimized models containing CREB-CREosm revealed that there was no difference in the binding of CREB to the right half of CREosm site when compared to the entire CREosm. These results were in accordance with the in vitro studies, confirming the indispensable role of the right half-CREosm site in stable complex formation with the CREB protein. Additionally, conversion of the right half-CREosm site to a canonical CRE palindrome showed stronger CREB binding, irrespective of the presence or absence of the left CRE sequence. Thus, the present study establishes an interesting insight into the interaction of CREB with a CRE variant located at the far end of a TATA-less promoter of a cytokine-encoding gene, which in turn could be involved in the regulation of transcription under specific conditions.

GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction

Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods.

The molecular grammar of protein disorder guiding genome-binding locations

Intrinsically disordered regions (IDRs) direct transcription factors (TFs) towards selected genomic occurrences of their binding motif, as exemplified by budding yeast's Msn2. However, the sequence basis of IDR-directed TF binding selectivity remains unknown. To reveal this sequence grammar, we analyze the genomic localizations of >100 designed IDR mutants, each carrying up to 122 mutations within this 567-AA region. Our data points at multivalent interactions, carried by hydrophobic-mostly aliphatic-residues dispersed within a disordered environment and independent of linear sequence motifs, as the key determinants of Msn2 genomic localization. The implications of our results for the mechanistic basis of IDR-based TF binding preferences are discussed.

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

Complementary strategies for directing in vivo transcription factor binding through DNA binding domains and intrinsically disordered regions

DNA binding domains (DBDs) of transcription factors (TFs) recognize DNA sequence motifs that are highly abundant in genomes. Within cells, TFs bind a subset of motif-containing sites as directed by either their DBDs or DBD-external (nonDBD) sequences. To define the relative roles of DBDs and nonDBDs in directing binding preferences, we compared the genome-wide binding of 48 (∼30%) budding yeast TFs with their DBD-only, nonDBD-truncated, and nonDBD-only mutants. With a few exceptions, binding locations differed between DBDs and TFs, resulting from the cumulative action of multiple determinants mapped mostly to disordered nonDBD regions. Furthermore, TFs' preferences for promoters of the fuzzy nucleosome architecture were lost in DBD-only mutants, whose binding spread across promoters, implicating nonDBDs' preferences in this hallmark of budding yeast regulatory design. We conclude that DBDs and nonDBDs employ complementary DNA-targeting strategies, whose balance defines TF binding specificity along genomes.

SOX2-associated signaling pathways regulate biological phenotypes of cancers

SOX2 is a transcription factor involved in multiple stages of embryonic development. In related reports, SOX2 was found to be abnormally expressed in tumor tissues and correlated with clinical features such as TNM staging, tumor grade, and prognosis in patients with various cancer types. In most cancer types, SOX2 is a tumor-promoting factor that regulates tumor progression and metastasis primarily by maintaining the stemness of cancer cells. In addition, SOX2 also regulates the proliferation, apoptosis, invasion, migration, ferroptosis and drug resistance of cancer cells. However, SOX2 acts as a tumor suppressor in some cases in certain cancer types, such as gastric and lung cancer. These key regulatory functions of SOX2 involve complex regulatory networks, including protein-protein and protein-nucleic acid interactions through signaling pathways and noncoding RNA interactions, modulating SOX2 expression may be a potential therapeutic strategy for clinical cancer patients. Therefore, we sorted out the phenotypes related to SOX2 in cancer, hoping to provide a basis for further clinical translation.

Genome-wide identification and expression analysis of DREB family genes in cotton

BACKGROUND

Dehydration responsive element-binding (DREB) transcription factors are widely present in plants, and involve in signalling transduction, plant growth and development, and stress response. DREB genes have been characterized in multiple species. However, only a few DREB genes have been studied in cotton, one of the most important fibre crops. Herein, the genome‑wide identification, phylogeny, and expression analysis of DREB family genes are performed in diploid and tetraploid cotton species.

RESULTS

In total, 193, 183, 80, and 79 putative genes containing the AP2 domain were identified using bioinformatics approaches in G. barbadense, G. hirsutum, G. arboretum, and G. raimondii, respectively. Phylogenetic analysis showed that based on the categorization of Arabidopsis DREB genes, 535 DREB genes were divided into six subgroups (A1-A6) by using MEGA 7.0. The identified DREB genes were distributed unevenly across 13/26 chromosomes of A and/or D genomes. Synteny and collinearity analysis confirmed that during the evolution, the whole genome duplications, segmental duplications, and/or tandem duplications occurred in cotton DREB genes, and then DREB gene family was further expanded. Further, the evolutionary trees with conserved motifs, cis-acting elements, and gene structure of cotton DREB gene family were predicted, and these results suggested that DREB genes might be involved in the hormone and abiotic stresses responses. The subcellular localization showed that in four cotton species, DREB proteins were predominantly located in the nucleus. Further, the analysis of DREB gene expression was carried out by real-time quantitative PCR, confirming that the identified DREB genes of cotton were involved in response to early salinity and osmotic stress.

CONCLUSIONS

Collectively, our results presented a comprehensive and systematic understanding in the evolution of cotton DREB genes, and demonstrated the potential roles of DREB family genes in stress and hormone response.

Intra-Domain Residue Coevolution in Transcription Factors Contributes to DNA Binding Specificity

Understanding the basis of the DNA-binding specificity of transcription factors (TFs) has been of long-standing interest. Despite extensive efforts to map millions of putative TF binding sequences, identifying the critical determinants for DNA binding specificity remains a major challenge. The coevolution of residues in proteins occurs due to a shared evolutionary history. However, it is unclear how coevolving residues in TFs contribute to DNA binding specificity. Here, we systematically collected publicly available data sets from multiple large-scale high-throughput TF-DNA interaction screening experiments for the major TF families with large numbers of TF members. These families included the Homeobox, HLH, bZIP_1, Ets, HMG_box, ZF-C4, and Zn_clus TFs. We detected TF subclass-determining sites (TSDSs) and showed that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs, particularly for the Homeobox, HLH, Ets, bZIP_1, and HMG_box TF families. By in silico modeling, we showed that mutation of the highly coevolving residues could significantly reduce the stability of the TF-DNA complex. The distant residues from the DNA interface also contributed to TF-DNA binding activity. Overall, our study gave evidence that coevolved residues relate to transcriptional regulation and provided insights into the potential application of engineered DNA-binding domains and proteins. IMPORTANCE While unraveling DNA-binding specificity of TFs is the key to understanding the basis and molecular mechanism of gene expression regulation, identifying the critical determinants that contribute to DNA binding specificity remains a major challenge. In this study, we provided evidence showing that coevolving residues in TF domains contributed to DNA binding specificity. We demonstrated that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs. Mutation of the coevolving residue pairs (CRPs) could significantly reduce the stability of THE TF-DNA complex, and even the distant residues from the DNA interface contribute to TF-DNA binding activity. Collectively, our study expands our knowledge of the interactions among coevolved residues in TFs, tertiary contacting, and functional importance in refined transcriptional regulation. Understanding the impact of coevolving residues in TFs will help understand the details of transcription of gene regulation and advance the application of engineered DNA-binding domains and protein.

UV irradiation remodels the specificity landscape of transcription factors

Somatic mutations are highly enriched at transcription factor (TF) binding sites, with the strongest trend being observed for ultraviolet light (UV)-induced mutations in melanomas. One of the main mechanisms proposed for this hypermutation pattern is the inefficient repair of UV lesions within TF-binding sites, caused by competition between TFs bound to these lesions and the DNA repair proteins that must recognize the lesions to initiate repair. However, TF binding to UV-irradiated DNA is poorly characterized, and it is unclear whether TFs maintain specificity for their DNA sites after UV exposure. We developed UV-Bind, a high-throughput approach to investigate the impact of UV irradiation on protein-DNA binding specificity. We applied UV-Bind to ten TFs from eight structural families, and found that UV lesions significantly altered the DNA-binding preferences of all the TFs tested. The main effect was a decrease in binding specificity, but the precise effects and their magnitude differ across factors. Importantly, we found that despite the overall reduction in DNA-binding specificity in the presence of UV lesions, TFs can still compete with repair proteins for lesion recognition, in a manner consistent with their specificity for UV-irradiated DNA. In addition, for a subset of TFs, we identified a surprising but reproducible effect at certain nonconsensus DNA sequences, where UV irradiation leads to a high increase in the level of TF binding. These changes in DNA-binding specificity after UV irradiation, at both consensus and nonconsensus sites, have important implications for the regulatory and mutagenic roles of TFs in the cell.

Transcriptomic changes in single yeast cells under various stress conditions

BACKGROUND

The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method.

RESULTS

We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.

CONCLUSION

Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.

Mechanisms of robustness in gene regulatory networks involved in neural development

The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

ChromDL: A Next-Generation Regulatory DNA Classifier

MOTIVATION

Predicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine learning libraries, hybrid convolutional and recurrent neural network architectures can be constructed and applied to extract crucial information from non-coding DNA.

RESULTS

Using a comparative analysis of the performance of thousands of Deep Learning (DL) architectures, we developed ChromDL, a neural network architecture combining bidirectional gated recurrent units (BiGRU), convolutional neural networks (CNNs), and bidirectional long short-term memory units (BiLSTM), which significantly improves upon a range of prediction metrics compared to its predecessors in transcription factor binding site (TFBS), histone modification (HM), and DNase-I hypersensitive site (DHS) detection. Combined with a secondary model, it can be utilized for accurate classification of gene regulatory elements. The model can also detect weak transcription factor (TF) binding with higher accuracy as compared to previously developed methods and has the potential to accurately delineate TF binding motif specificities.

AVAILABILITY

The ChromDL source code can be found at https://github.com/chrishil1/ChromDL .

A Survey of Transcription Factors in Cell Fate Control

Transcription factors (TFs) play a cardinal role in the development and maintenance of human physiology by acting as mediators of gene expression and cell state control. Recent advancements have broadened our knowledge on the potency of TFs in governing cell physiology and have deepened our understanding of the mechanisms through which they exert this control. The ability of TFs to program cell fates has gathered significant interest in recent decades, and high-throughput technologies now allow for the systematic discovery of forward programming factors to convert pluripotent stem cells into numerous differentiated cell types. The next generation of these technologies has the potential to improve our understanding and control of cell fates and states and provide advanced therapeutic modalities to address many medical conditions.

Multi-scale gene regulation mechanism: Spatiotemporal transmission of genetic information

Gene expression is regulated by many factors, including transcription factors, chromatin three-dimensional topology, modifications of DNA and histone proteins, and non-coding RNAs. The execution of these complex mechanisms requires an effectively coordinated regulation system. In this review, we emphasize that the multi-scale heterogeneous DNA sequence plays a fundamental and important role for gene expression activity and usage of different means of epigenetic regulation. We illustrate here that the chromatin structure organization provides a stage for spatiotemporal regulation between different genes or gene modules and to realize their downstream functional cooperation. Such a perspective expands our understanding of the central dogma: In addition to one-dimensional sequence information, inter-gene interactions can also be transferred from DNA and RNA to protein levels.

PlantBind: an attention-based multi-label neural network for predicting plant transcription factor binding sites

Identification of transcription factor binding sites (TFBSs) is essential to understanding of gene regulation. Designing computational models for accurate prediction of TFBSs is crucial because it is not feasible to experimentally assay all transcription factors (TFs) in all sequenced eukaryotic genomes. Although many methods have been proposed for the identification of TFBSs in humans, methods designed for plants are comparatively underdeveloped. Here, we present PlantBind, a method for integrated prediction and interpretation of TFBSs based on DNA sequences and DNA shape profiles. Built on an attention-based multi-label deep learning framework, PlantBind not only simultaneously predicts the potential binding sites of 315 TFs, but also identifies the motifs bound by transcription factors. During the training process, this model revealed a strong similarity among TF family members with respect to target binding sequences. Trans-species prediction performance using four Zea mays TFs demonstrated the suitability of this model for transfer learning. Overall, this study provides an effective solution for identifying plant TFBSs, which will promote greater understanding of transcriptional regulatory mechanisms in plants.

Sequence-Specific Structural Features and Solvation Properties of Transcription Factor Binding DNA Motifs: Insights from Molecular Dynamics Simulation

Sequence-specific recognition of transcription factor (TF) binding motifs in the target site of DNA over the vast amount of non-target DNA is of primary importance for the transcriptional regulation of gene expression by the TFs. Binding of TFs to the target site of DNA relies not only on the direct contact formation but also on the structural and conformational features of DNA. Recognition of DNA structural features or shape readout by proteins is an important factor in the context of TF-DNA interaction. Based on the atomistic molecular simulation, here we report the sequence-dependent unique structural features, solvation, and ion-binding properties of biologically relevant AT- and GC-rich human TF binding motifs in DNA. Counterion and water distribution around the motif is found to be sensitive to the motif sequence, which is accompanied with the DNA shape features. The motif sequence affects the electrostatic potential along the grooves, and cytosine methylation alters the DNA shape features. Characteristic solvation properties of TF binding motif DNA fragments infer that an ionic environment and hydration influences are essential to describe TF-DNA interactions.

Transposable elements orchestrate subgenome-convergent and -divergent transcription in common wheat

The success of common wheat as a global staple crop was largely attributed to its genomic diversity and redundancy due to the merge of different genomes, giving rise to the major question how subgenome-divergent and -convergent transcription is mediated and harmonized in a single cell. Here, we create a catalog of genome-wide transcription factor-binding sites (TFBSs) to assemble a common wheat regulatory network on an unprecedented scale. A significant proportion of subgenome-divergent TFBSs are derived from differential expansions of particular transposable elements (TEs) in diploid progenitors, which contribute to subgenome-divergent transcription. Whereas subgenome-convergent transcription is associated with balanced TF binding at loci derived from TE expansions before diploid divergence. These TFBSs have retained in parallel during evolution of each diploid, despite extensive unbalanced turnover of the flanking TEs. Thus, the differential evolutionary selection of paleo- and neo-TEs contribute to subgenome-convergent and -divergent regulation in common wheat, highlighting the influence of TE repertory plasticity on transcriptional plasticity in polyploid.