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Transcription of the platelet-derived growth factor receptor α (PDGFRA/Pdgfra) gene is considered to be precisely regulated. We have previously reported that the PDGFRA/Pdgfra gene is regulated by a dual promoter system in human and mouse, in which a novel PDGFRA/Pdgfra transcript has a first exon (exon 1β) different from that of the canonical PDGFRA/Pdgfra transcript (exon 1α). To elucidate the function of each transcript, we first investigated the contribution of different PDGFRA transcripts to final protein levels. Notably, knockdown experiments suggested the existence of other PDGFRA transcripts, and we identified five additional first exons (exons 1γ, 1δ, 1ε, 1ζ, and 1η) in intron 1 in both the human and mouse genes. The first exons of the mouse Pdgfra gene showed unique expression patterns: exon 1α was broadly expressed; exon 1β was highly expressed in embryos; exon 1γ was observed at relatively high levels in the adult central nervous system (CNS); and exon 1δ was expressed at relatively high levels in the developing CNS. Furthermore, in silico analysis of common putative transcription factor binding sites in the upstream regions of the first exons of both human and mouse PDGFRA/Pdgfra genes predicted common (such as Sry, Mzf1, and Cdx) and unique (such as Sox5, Lmo2, and GATA) transcription factors. Our findings show the diversity of the transcriptional regulation of the PDGFRA/Pdgfra gene.

Motivation Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural networks (CNNs) have achieved state of the art performance. These methods were able to learn transcription factor binding sites from ChIP-seq data, resulting in accurate predictions on test data. However, CNNs typically distribute learned motifs across multiple filters, making them difficult to interpret. Furthermore, networks trained on small datasets often do not generalize well to new sequences. Results Here we present circular filters, a novel convolutional architecture, that convolves sequences with circularly permutated variants of the same filter. We motivate circular filters by the observation that CNNs frequently learn filters that correspond to shifted and truncated variants of the true motif. Circular filters enable learning of full-length motifs and allow easy interpretation of the learned filters. We show that circular filters improve motif inference performance over a wide range of hyperparameters as well as sequence length. Furthermore, we show that CNNs with circular filters in most cases outperform conventional CNNs at inferring DNA binding sites from ChIP-seq data. Availability and implementation Code is available at https://github.com/christopherblum. Supplementary information Supplementary data are available at Bioinformatics online.

Paired-like homeodomain transcription factor 1 (PITX1) is a pivotal gene in the hypothalamic–pituitary–adrenal axis, which is a well-known pathway affecting lactation performance. The aim of this study was to analyze the DNA methylation profile of the PITX1 gene and its relevance to milk performance in Xinong Saanen dairy goats; thus, potential epigenetic markers of lactation performance were identified. A total of 267 goat blood samples were divided into “low” and “high” groups according to two milk traits: the average milk yield (AMY) and the average milk density (AMD). One CpG island in the 3′-flanking region of the PITX1 gene was identified as being related to milk performance. Fisher's exact test demonstrated that the methylation rates of the overall CpG island and the 3rd and 12th CpG-dinucleotide loci in the blood were significantly associated with the AMY, and the overall methylation rate of the high AMY group was relative hypomethylation compared with the low AMY group. The overall methylation rates of this CpG island in mammary gland tissue from dry and lactation periods again exhibited a significant difference: the lactation period showed relative hypomethylation compared with the dry period. Bioinformatic transcription factor binding site prediction identified some lactation performance related transcription factors in this CpG island, such as CTCF, STAT, SMAD, CDEF, SP1, and KLFS. Briefly, overall methylation changes of the CpG island in the PITX1 gene are relevant to lactation performance, which will be valuable for future studies and epigenetic marker-assisted selection (eMAS) in the breeding of goats with respect to lactation performance.

Pseudogenes (Ψs), nonfunctional relatives of functional genes, form by duplication or retrotransposition, and loss of gene function by disabling mutations. Evolutionary analysis provides clues to Ψ origins and effects on gene regulation. However, few systematic studies of plant Ψs have been conducted, hampering comparative analyses. Here, we examined the origin, evolution, and expression patterns of Ψs and their relationships with noncoding sequences in seven angiosperm plants. We identified ∼250,000 Ψs, most of which are more lineage specific than protein-coding genes. The distribution of Ψs on the chromosome indicates that genome recombination may contribute to Ψ elimination. Most Ψs evolve rapidly in terms of sequence and expression levels, showing tissue- or stage-specific expression patterns. We found that a surprisingly large fraction of nontransposable element regulatory noncoding RNAs (microRNAs and long noncoding RNAs) originate from transcription of Ψ proximal upstream regions. We also found that transcription factor binding sites preferentially occur in putative Ψ proximal upstream regions compared with random intergenic regions, suggesting that Ψs have conditioned genome evolution by providing transcription factor binding sites that serve as promoters and enhancers. We therefore propose that rapid rewiring of Ψ transcriptional regulatory regions is a major mechanism driving the origin of novel regulatory modules.

The occurrence of homotypic clusters of transcription factor binding sites (HCTs) and their contribution to regulatory evolution is documented in animals, but their manifestation in regulating plant genome expression remained an enigma. To explore the existence and functions of HCTs in highly constrained non-coding sequences of MADS-box transcription factors—generally involved in floral organ identity and phase transition—we employed cis-regulatory cluster finding in silico tools augmented with in vitro assays and site-directed mutagenesis in the STMADS11 superclade of this family. Thousands of transcription factor binding sites (TFBSs) organized into HCTs and multiple-factor binding elements belonging to various families of TFs were identified in the promoter as well as in the intronic region of the selected members of STMADS11 subfamily. A total of 151 HCTs were detectable in the defined promoter in comparison with 144 in the intronic regions. The MADS-domain binding HCTs (overlapping CArG-boxes) constitute a major portion (18%) of these HCTs. A protrusive HCT of 11 TFBSs was identified in the 1st large intron of the AGAMOUS LIKE 24 (AGL24) pertinent to the SVP clade of the STMADS11 subfamily but no such cluster could be detected in the region proximal to the core promoter. These HCTs in the intronic region could bind the SEP4 MADS-domain protein as revealed by in vitro assay. Using HCT cluster swapping of CArG-boxes through the GUS reporter assay, it was possible to empirically validate that a change in the position of intronic MADS-box cluster of AGL24 to the promoter region adjacent to the transcriptional start site (TSS) invoke an enhancement of floral expression. This study unveils the occurrence of HCTs in both the promoter and downstream intronic regions in the selected members of the STMADS11 subfamily of plants. These HCTs play an important role in deciphering the gene regulatory code of MADS-box TFs particularly AGL24.

Stochastic gene expression has been extensively studied in natural and synthetic gene expression systems. However, the majority of those contributions, if not all, were concentrated on the haploid systems, where a single allele is inherited by asexual reproduction. Largely left unexplored is the question of how much the allelic imbalance in chromatin accessibility or divergence in transcription factor binding sites would impact the organismal fitness and thus shaped the sequence evolution. Here, we explicitly consider diploid systems with homo- and heterozygous combination of alleles in the regulatory sequences rather than the primary coding sequence and characterize the genetic noise profiles associated with the zygosity. In particular, we show that the heterozygosity may arise as an outcome of evolutionary selection, when the regulatory proteins are subject to stochastic fluctuations, acting as a source of extrinsic noise. Based on this buffering mechanism in heterozygote, we discuss the relevance of our results to the recent reports on the balancing selection enriched in the cis-regulatory sequences of immune cells and epithelial cells.

Background The p28 peptide, derived from the blue copper protein Azurin, exerts an anticancer action due to interaction with the tumor suppressor p53, likely interfering with its down-regulators. Knowledge of both the kinetics and topological details of the interaction, could greatly help to understand the peptide anticancer mechanism. Methods Fluorescence and Forster resonance energy transfer (FRET) were used to determine both the binding affinity and the distance between the lone tryptophan (FRET donor) of DNA Binding Domain (DBD) of p53 and the Iaedens dye (FRET acceptor) bound to the p28 peptide. Docking, Molecular Dynamic simulations and free energy binding calculations were used to single out the best complex model, compatible with the distance measured by FRET. Results Tryptophan fluorescence quenching provided a 105 M−1 binding affinity for the complex. Both FRET donor fluorescence quenching and acceptor enhancement are consistent with a donor-acceptor distance of about 2.6 nm. Docking and molecular dynamics simulations allowed us to select the best complex, enlightening the contact regions between p28 and DBD. Conclusions p28 binds to DBD partially engaging the L1 loop, at the same region of the p53 down-regulator COP1, leaving however the DNA binding site available for functional interactions. General significance Elucidation of the DBD-p28 complex gets insights into the functional role of p28 in regulating the p53 anticancer activity, also offering new perspectives to design new drugs able to protect the p53 anticancer function.

Intrinsic noise is inherent to many biological processes and provokes variation in gene expression in a population of isogenic cells leading to phenotypic diversity. Intrinsic noise is generated by different sources of noise such as the number of molecules, the stochastic binding and unbinding of transcription factor and/or the number and strength of transcription factor binding sites. In this work, we use numerical simulations to study the effects of the number of operators and different types of cooperativity on the Fano factor of three different molecules of the tryptophan (trp) operon of E. Coli. We analyze the Fano factor for the mRNA, anthranilate synthase and tryptophan molecules, because it represents the effects of the noise in the variation or variability of the gene expression, a larger Fano factor implies a larger variation. Our model takes into consideration the presence of intrinsic noise and all the known mechanisms of regulation. In particular, we consider hypothetical promoters in the repression mechanism with different numbers of operators and three cases of cooperativity: positive, negative, and no-cooperativity.

Quantitative PAINT (qPAINT) is a useful method for counting well-separated molecules within nanoscale assemblies. But whether cross-reactivity in densely-packed arrangements perturbs measurements is unknown. Here we establish that qPAINT measurements are robust even when target molecules are separated by as little as 3 nm, sufficiently close that single-stranded DNA binding sites can interact.

Over the past decade, there has been an active study of the interactions between the population of transposable elements (TEs) and the rest of the genome. Many regulatory sequences of TEs have been used for different genes regulation and genome organization. These regulatory sequences can be transcription factor binding sites, enhancers, and insulators. TEs may contain promoters that can be domesticated during substitution of original gene promoters or during de novo formation of long noncoding RNAs. In addition, there are many examples of domestication of TE-encoded proteins, for example, transposases, proteases, and Gag proteins. This review highlights the role of TEs in the evolution of gene regulatory networks and the principles determining it.

Quorum planted (l, d) motif search (qPMS) is a challenging computational problem in bioinformatics, mainly for the identification of regulatory elements such as transcription factor binding sites in DNA sequences. Large DNA datasets play an important role in identifying high-quality (l, d) motifs, while most existing qPMS algorithms are too time-consuming to complete the calculation of qPMS in a reasonable time. We propose an approximate qPMS algorithm called APMS to deal with large DNA datasets mainly by accelerating neighboring substring search and filtering redundant substrings. Experimental results on them show that APMS can not only identify the implanted (l, d) motifs, but also run orders of magnitude faster than the state-of-the-art qPMS algorithms. The source code of APMS and the python wrapper for the code are freely available at https://github.com/qyu071/apms.

Motif discovery problem (MDP) is one of the well-known problems in biology which tries to find the transcription factor binding site (TFBS) in DNA sequences. In one aspect, there is not enough biological knowledge on motif sites and on the other side, the problem is NP-hard. Thus, there is not an efficient procedure capable of finding motifs in every dataset. Some algorithms use exhaustive search, which is very time-consuming for large-scale datasets. On the other side, metaheuristic procedures seem to be a good selection for finding a motif quickly that at least has some acceptable biological properties. Most of the previous methods model the problem as a single objective optimization problem; however, considering multi-objectives for modeling the problem leads to improvements in the quality of obtained motifs. Some multi-objective optimization models for MDP have tried to maximize three objectives simultaneously: Motif length, support, and similarity. In this study, the multi-objective Imperialist Competition Algorithm (ICA) is adopted for this problem as an approximation algorithm. ICA is able to simulate more exploration along the solution space, so avoids trapping into local optima. So, it promises to obtain good solutions in a reasonable time. Experimental results show that our method produces good solutions compared to well-known algorithms in the literature, according to computational and biological indicators.

Background/aim Several links between DNA replication, pluripotency and development have been recently identified. The involvement of miRNA in the regulation of cell cycle events and pluripotency factors has also gained attention. Materials and methods In the present study, we used the g:Profiler platform to analyze transcription factor binding sites, miRNA networks and protein-protein interactions to identify novel links among the aforementioned processes. Results and conclusion A complex circuitry between retinoic acid signaling, SWI/SNF components, pluripotency factors including Oct4, Sox2 and Nanog and cell cycle regulators was identified. It is suggested that the DNA replication inhibitor geminin plays a central role in this circuitry.

Researchers use ChIP binding data to identify potential transcription factor binding sites. Similarly, they use gene expression data from sequencing or microarrays to quantify the effect of the transcription factor overexpression or knockdown on its targets. Therefore, the integration of the binding and expression data can be used to improve the understanding of a transcription factor function. Here, we implemented the binding and expression target analysis (BETA) in an R/Bioconductor package. This algorithm ranks the targets based on the distances of their assigned peaks from the transcription factor ChIP experiment and the signed statistics from gene expression profiling with transcription factor perturbation. We further extend BETA to integrate two sets of data from two transcription factors to predict their targets and their combined functions. In this article, we briefly describe the workings of the algorithm and provide a workflow with a real dataset for using it. The gene targets and the aggregate functions of transcription factors YY1 and YY2 in HeLa cells were identified. Using the same datasets, we identified the shared targets of the two transcription factors, which were found to be, on average, more cooperatively regulated.

Simple Summary Sheep are affected by a viral infection that causes an incurable and difficult to treat lung, joint and brain disease that decreases production efficiency. This small ruminant lentivirus is closely related to human immunodeficiency virus (HIV) that causes AIDS in humans. Differences in breed susceptibility to disease are known in sheep that indicate genetic, or hereditary, resilience factors exist. Our objective was to study the source of this hereditary advantage so that it can eventually be translated into a tool for sheep breeders to improve herd health. Previously, one such hereditary region was detected, but little was known about possible mechanisms or nearby mutations. Here, we report several mutations that may underlie this hereditary mechanism and are in regions of DNA that are known to affect genes by increasing or decreasing gene expression, akin to gene “on/off switches.” These mutations were strongly associated with a predictor of disease severity in live animals and had a greater predicted effect on the degree of disease than previously studied mutations. Statistical association (p < 0.05) of disease was demonstrated in two different groups of sheep that were reared in different environments, which indicates increased likelihood that a genetic factor is producing this effect. Abstract Small ruminant lentivirus (SRLV) causes Maedi-Visna or Ovine Progressive Pneumonia in sheep and creates insidious livestock production losses. This retrovirus is closely related to human immunodeficiency virus and currently has no vaccines or cure. Genetic marker assisted selection for sheep disease resiliency presents an attractive management solution. Previously, we identified a region containing a cluster of zinc finger genes that had association with ovine SRLV proviral concentration. Trait-association analysis validated a small insertion/deletion variant near ZNF389 (rs397514112) in multiple sheep breeds. In the current study, 543 sheep from two distinct populations were genotyped at 34 additional variants for fine mapping of the regulatory elements within this locus. Variants were selected based on ChIP-seq annotation data from sheep alveolar macrophages that defined active cis-regulatory elements predicted to influence zinc finger gene expression. We present a haplotype block of variants within regulatory elements that have improved associations and larger effect sizes (up to 4.7-fold genotypic difference in proviral concentration) than the previously validated ZNF389 deletion marker. Hypotheses for the underlying causal mutation or mutations are presented based on changes to in silico transcription factor binding sites. These variants offer alternative markers for selective breeding and are targets for future functional mutation assays.

In contrast to oligodeoxynucleotides, protein:DNA complexes crystallize from a broad range of precipitants and conditions, much as proteins by themselves. There are, however, a number of factors that should be considered, at least in the early stages of cocrystallization attempts. These include the length and construction of the oligodeoxynucleotide itself, a pH near or below neutrality, a stoichiometric excess of DNA, and di- and polyvalent cations. By far the more important of these factors are the length of the DNA fragment and the nature of the terminal nucleotides. Unfortunately, experience suggests that, in general, the length and construction of the DNA fragment for optimal crystal growth cannot be predicted in advance. A systematic search of cocrystallization conditions with different DNA fragments will normally be required. It is important to avoid sequences that provide possible subsites within the fragment or at junctions between fragments related by translations. Sample purity, especially that of the DNA, can also have important effects. Detailed protocols for the purification of chemically synthesized fragments are suggested. Finally, a set of conditions for initial cocrystallization trials with a new DNA binding protein is suggested.

Follistatin (FST) is a secretory glycoprotein and belongs to the TGF-β superfamily. Previously, we found that two single nucleotide polymorphisms (SNPs) of sheep FST gene were significantly associated with wool quality traits in Chinese Merino sheep (Junken type), indicating that FST is involved in the regulation of hair follicle development and hair trait formation. The transcription regulation of human and mouse FST genes has been widely investigated, and many transcription factors have been identified to regulate FST gene. However, to date, the transcriptional regulation of sheep FST is largely unknown. In the present study, genome walking was used to close the genomic gap upstream of the sheep genomic FST gene and to obtain the FST gene promoter sequence. Transcription factor binding site analysis showed sheep FST promoter region contained a conserved putative binding site for signal transducer and activator of transcription 3 (STAT3), located at nucleotides −423 to −416 relative to the first nucleotide (A, +1) of the initiation codon (ATG) of sheep FST gene. The dual-luciferase reporter assay demonstrated that STAT3 inhibited the FST promoter activity and that the mutation of the putative STAT3 binding site attenuated the inhibitory effect of STAT3 on the FST promoter activity. Additionally, chromatin immunoprecipitation assay (ChIP) exhibited that STAT3 is directly bound to the FST promoter. Cell proliferation assay displayed that FST and STAT3 played opposite roles in cell proliferation. Overexpression of sheep FST significantly promoted the proliferation of sheep fetal fibroblasts (SFFs) and human keratinocyte (HaCaT) cells, and overexpression of sheep STAT3 displayed opposite results, which was accompanied by a significantly reduced expression of FST gene (P < 0.05). Taken together, STAT3 directly negatively regulates sheep FST gene and depresses cell proliferation. Our findings may contribute to understanding molecular mechanisms that underlie hair follicle development and morphogenesis.

Inhibitors of transcriptional protein–protein interactions (PPIs) have high value both as tools and for therapeutic applications. The PPI network mediated by the transcriptional coactivator Med25, for example, regulates stress-response and motility pathways, and dysregulation of the PPI networks contributes to oncogenesis and metastasis. The canonical transcription factor binding sites within Med25 are large (∼900 Å2) and have little topology, and thus, they do not present an array of attractive small-molecule binding sites for inhibitor discovery. Here we demonstrate that the depsidone natural product norstictic acid functions through an alternative binding site to block Med25–transcriptional activator PPIs in vitro and in cell culture. Norstictic acid targets a binding site comprising a highly dynamic loop flanking one canonical binding surface, and in doing so, it both orthosterically and allosterically alters Med25-driven transcription in a patient-derived model of triple-negative breast cancer. These results highlight the potential of Med25 as a therapeutic target as well as the inhibitor discovery opportunities presented by structurally dynamic loops within otherwise challenging proteins. National Institutes of Health//NIH/Estados Unidos Breast Cancer Research Foundation//BCRF/Estados Unidos Rogel Cancer Center///Estados Unidos UCR::Vicerrectoría de Investigación UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Productos Naturales (CIPRONA)

Predicting functionality of noncoding variation is one of the major challenges in modern genetics. We employed massively parallel reporter assays to screen 5,706 variants from genome-wide association studies for both Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). We identified 320 functional regulatory polymorphisms (SigVars) comprising 27 of 34 unique tested loci, including multiple independent signals across the complex 17q21.31 region. We identify novel risk genes includingPLEKHM1in PSP andAPOC1in AD, and perform gene-editing to validate four distinct causal loci, confirming complement 4 (C4A) as a novel genetic risk factor for AD. Moreover, functional variants preferentially disrupt transcription factor binding sites that converge on enhancers with differential cell-type specific activity in PSP and AD, implicating a neuronalSP1-driven regulatory network in PSP pathogenesis. These analyses support a novel mechanism underlying noncoding genetic risk, whereby common genetic variants drive disease risk via their aggregate activity on specific transcriptional programs.One Sentence SummaryHigh-throughput functional analysis of GWAS loci reveals cell-type specific regulatory networks that mediate genetic risk for dementia.

B-cell lymphoma 2 (Bcl-2) and cytochrome c (Cycs) are two important proteins relevant to cellular apoptosis. In this study, we characterized the functions of the promoter regions of two apoptosis-related genes, Bcl-2 and Cycs, in yellow catfish Pelteobagrus fulvidraco. We obtained a 1989 bp Bcl-2 promoter and an 1830 bp Cycs promoter and predicted several key transcription factor binding sites (TFBSs) on the promoters, such as Kruppel-like factor 4 (KLF4), signal transducer and activator of transcription factor 3 (STAT3), forkhead box O (FOXO), metal-responsive element (MRE) and hepatocyte nuclear factor 1α (HNF-1α). Zinc (Zn) increased the activities of the Bcl-2 promoter but decreased the activities of the Cycs promoter. Metal-responsive transcription factor 1 (MTF-1) and HNF-1α directly bound with Bcl-2 and Cycs promoters, and they positively regulated the activity of the Bcl-2 promoter but negatively regulated the activity of the Cycs promoter. Zn promoted the binding ability of HNF-1α to the Bcl-2 promoter but decreased its binding ability to the Cycs promoter. However, Zn had no significant effect on the binding capability of MTF-1 to the regions of Bcl-2 and Cycs promoters. Zn upregulated the mRNA and total protein expression of Bcl-2 but downregulated the mRNA and total protein expression of Cycs. At the same time, Annexin V–FITC/PI staining showed that Zn significantly reduced the apoptosis of primary hepatocytes. For the first time, our study provides evidence for the MRE and HNF-1α response elements on the Bcl-2 and Cycs promoters, offering new insight into the mechanism by which Zn affects apoptosis in vertebrates.

Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, no TRN is fully characterized since the identity and activity of all the transcriptional regulators that comprise a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in Escherichia coli K-12 MG1655, which were computationally predicted to be transcription factors (TFs). First, we used a multiplexed ChIP-exo assay to characterize genome-wide binding sites for these candidate TFs; 34 of them were found to be DNA-binding protein. We then compared the relative location between binding sites and RNA polymerase (RNAP). We found 48% (283/588) overlap between the TFs and RNAP. Finally, we used these data to infer potential functions for 10 of the 34 TFs with validated DNA binding sites and consensus binding motifs. These TFs were found to have various roles in regulating primary cellular processes in E. coli. Taken together, this study: (1) significantly expands the number of confirmed TFs, close to the estimated total of about 280 TFs; (2) predicts the putative functions of the newly discovered TFs, and (3) confirms the functions of representative TFs through mutant phenotypes.

Mutation in the germline is the ultimate source of genetic variation, but little is known about the influence of germline chromatin structure on mutational processes. Using ATAC-seq, we profile the open chromatin landscape of human spermatogonia, the most proliferative cell-type of the germline, identifying transcription factor binding sites (TFBSs) and PRDM9-binding sites, a subset of which will initiate meiotic recombination. We observe an increase in rare structural variant (SV) breakpoints at PRDM9-bound sites, implicating meiotic recombination in the generation of structural variation. Many germline TFBSs, such as NRF, are also associated with increased rates of SV breakpoints, apparently independent of recombination. Singleton short insertions (>=5 bp) are highly enriched at TFBSs, particularly at sites bound by testis active TFs, and their rates correlate with those of structural variant breakpoints. Short insertions often duplicate the TFBS motif, leading to clustering of motif sites near regulatory regions in this male-driven evolutionary process. Increased mutation loads at germline TFBSs disproportionately affect neural enhancers with activity in spermatogonia, potentially altering neurodevelopmental regulatory architecture. Local chromatin structure in spermatogonia is thus pervasive in shaping both evolution and disease.

Choline kinase (CK) is the enzyme catalyzing the first reaction in CDP-choline pathway for the biosynthesis of phosphatidylcholine. Higher expression of the α isozyme of CK has been implicated in carcinogenesis, and inhibition or downregulation of CKα (CHKA) is a promising anticancer approach. This study aimed to investigate the regulation of CKα expression by DNA methylation of the CpG islands found on the promoter of this gene in MCF-7 cells. Four CpG islands have been predicted in the 2000 bp promoter region of ckα (chka) gene. Six CpG island deletion mutants were constructed using PCR site-directed mutagenesis method and cloned into pGL4.10 vectors for promoter activity assays. Deletion of CpG4C region located between –225 and –56 significantly increased the promoter activity by 4-fold, indicating the presence of important repressive transcription factor binding site. The promoter activity of methylated full-length promoter was significantly lower than the methylated CpG4C deletion mutant by 16-fold. The results show that DNA methylation of CpG4C promotes the binding of the transcription factor that suppresses the promoter activity. Electrophoretic mobility shift assay analysis showed that cytosine methylation at MZF1 binding site in CpG4C increased the binding of putative MZF1 in nuclear extract. In conclusion, the results suggest that DNA methylation decreased the promoter activity by promoting the binding of putative MZF1 transcription factor at CpG4C region of the ckα gene promoter.

DNA sequences that are exactly conserved over long evolutionary time scales have been observed in a variety of taxa. Such sequences are likely under strong functional constraint and they have been useful in the field of comparative genomics for identifying genome regions with regulatory function. A potential new application for these ultra-conserved elements has emerged in the development of gene drives to control mosquito populations. Many gene drives work by recognising and inserting at a specific target sequence in the genome, often imposing a reproductive load as a consequence. They can therefore select for target sequence variants that provide resistance to the drive. Focusing on highly conserved, highly constrained sequences lowers the probability that variant, gene drive-resistant alleles can be tolerated. Here we search for conserved sequences of 18bp and over in an alignment of 21 Anopheles genomes, spanning an evolutionary timescale of 100 million years, and characterise the resulting sequences according to their location and function. Over 8000 ultra-conserved elements were found across the alignment, with a maximum length of 164 bp. Length-corrected gene ontology analysis revealed that genes containing Anopheles ultra-conserved elements were over-represented in categories with structural or nucleotide binding functions. Known insect transcription factor binding sites were found in 48% of intergenic Anopheles ultra-conserved elements. When we looked at the genome sequences of 1142 wild-caught mosquitoes we found that 15% of the Anopheles ultra-conserved elements contained no polymorphisms. Our list of Anopheles ultra-conserved elements should provide a valuable starting point for the selection and testing of new targets for gene-drive modification in the mosquitoes that transmit malaria.

In recent years, many studies have been conducted to investigate the influence of terahertz (THz) radiation on the gene expression in various cell types, but the underling molecular mechanism has not yet been fully revealed. In this study, we explored the effects of 0.1 THz radiation on the gene expression in primary neuron cells through RNA-seq analysis. 111 up-regulated and 54 down-regulated genes were identified. Several biomolecule binding related categories such as “long-chain fatty acid binding”, “tropomyosin binding”, “BMP receptor binding”, as well as “GTPase binding” and “phospholipid binding” were enriched by GO analysis. Moreover, the GSEA analysis indicated that genes encoding protein biosynthetic machinery ribosome were up-regulated by 0.1 THz irradiation. In addition, we demonstrated that the binding efficiency of a transcription factor (TF) AP-1 with its transcription factor binding site (TFBS) in DNA was reduced by THz irradiation, which suggested that THz irradiation might affect the interaction between TFs with DNA and consequently regulate the gene expression. Our results provide new insights into the biological effects of terahertz irradiation.

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%–20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression., SCOPUS: ar.j, info:eu-repo/semantics/published

Motivation Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution. Results We show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings. Availability and implementation NetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897. Supplementary information Supplementary data are available at Bioinformatics online.

Predicting in vivo protein–DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein–DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size k with a specific stride is used to obtain its k-mer representation of input sequences. Next, we use word2vec to obtain a pre-trained k-mer word distributed representation model. Finally, the probability of protein–DNA binding is predicted by using the recurrent and convolutional neural network. The experiment results on 50 public ChIP-seq benchmark datasets demonstrate the superior performance and robustness of DeepD2V. Moreover, we verify that the performance of DeepD2V using word2vec-based k-mer distributed representation is better than one-hot encoding, and the integrated framework of both convolutional neural network (CNN) and bidirectional LSTM (bi-LSTM) outperforms CNN or the bi-LSTM model when used alone. The source code of DeepD2V is available at the github repository.

Kisspeptin gene (Kiss1) has a significant role in reproductive processes in mammals. However, only little information is available about the association of Kiss1 gene with litter size in Indian goat breeds. Thus, blood samples from 285 randomly selected animals were collected for DNA isolation and SNP profiling. The PCR product of 242 bp size harboring g.2540CT mutation of Kiss1 gene was digested with the restriction enzyme Sac1. Least squares analysis revealed that Barbari goats showed significantly higher average litter size (2.86±0.08) compared to Beetal, Sirohi and Sojat breeds (P0.01). SNP locus g.2540CT of Kiss1 gene also showed significant effect on litter size (P0.01). Goats with Genotype CT (2.66 ± 0.07) and TT (2.67 ± 0.26) had significantly higher (P0.01) litter size than CC (1.50 ± 0.05). From the transcription factor binding site analysis, it was predicted that due to g.2540CT SNP, both native and mutant variant forms coded for putative binding sites for different transcription Factor. Allele T had putative binding sites for the androgen receptor which plays a significant role in the signaling pathway involved in increase in ovulation rate; which consequently can have a tremendous effect on average litter size.

Transcription Factors (TFs) are one of the most important agents acting on gene expression regulation, fundamentally determining the organized functional operation of cellular machinery. At a molecular level, this effect is achieved by the sequence specific physical binding of TF proteins to particular parts of the DNA. Transcription Factors regulate gene expression in complex ways and the detection of their binding sites is an important part of many experiments. Predicting Transcription Factor Binding Sites (TFBS) from DNA sequence data has been a challenging task in the field of bioinformatics. The abundance of available DNA sequences strongly encourages the use of machine learning for this problem. Until now most of these efforts were primarily based on the traditional nucleotide-based representation of DNA. To elaborate a more detailed description of this macromolecule, we have worked out a new Physico-Chemical Descriptor (PCD) based DNA representation and used it as input for training neural networks to predict TFBSs. We show that the PCD representation is a viable format for deep learning models, and our feature selection investigation highlights the importance of proper PCD subset choices. The distinct prediction efficiencies detected upon the usage of arbitrarily selected feature subsets indicates that the different DNA features affect the DNA binding process of TFs to various extent.

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation.

Low temperature leads to abnormal ripening and poor quality of the harvested banana fruit, which is an urgent problem limiting the development of industry in China. To comprehensively understand the mechanism underlying low-temperature-affected ripening, we performed comparative RNA-Seq analysis of ethylene-induced ripening of banana fruit after 3 days of pre-storage at 7 °C and 22 °C. A total of 986 differentially expressed genes (DEGs) were identified in both RT-0 d versus RT-3 d, LT-0 d versus LT-3 d, RT-0 d versus LT-0 d and RT-3 d versus LT-3 d, and the RNA-Seq outputs of 15 randomly selective DEGs were verified using qRT-PCR. Among the 986 DEGs obtained in the four groups, 9 MYB genes (MaMYB75/281/219/4/151/156/3/37 and MaMYB3R1) and 32 genes related to carotenoid biosynthesis (MaPSY1/2a), flavor formation (MaLOX6, MaADH7, MaAAT1), sucrose transport (MaSUS2/4), ethylene production (MaSAM1, MaACO9/10/12, MaACS1/12), starch degradation (MaAMY1A/1B, MaPHS1/2, MaMEX2, MapGlcT1) and cell wall degradation (MaPG3/X1, MaPME25/41, MaXTH5/7/22/23/25, MaEXP2/20/A1/A15) were characterized. Combining transcription factor binding site (TFBS) analysis as well as cis-acting element analysis, the regulatory network of low-temperature-affected ripening mediated by MYBs were constructed. The data generated in this study may unravel the transcriptional regulatory network of MYBs associated with low-temperature-affected ripening and provide a solid foundation for future studies.

Objective LAMA3 is a widely studied methylated gene in multiple tumors, but the relationship between chemotherapy resistance in ovarian cancer is unclear. In this study, LAMA3 methylation was predicted by bioinformatics, and the ability of LAMA3 methylation to predict the chemotherapy resistance and prognosis of ovarian cancer was confirmed in experiments. Methods Multiple databases have performed the bioinformatics analysis of methylation and transcription factor binding site (TFBS) on the promoter region of LAMA3 gene. Pyrosequencing detected the methylation of LAMA3. QRT-PCR and immunohistochemistry detected the expression of LAMA3. Real Time Cell Analyzer (RTCA) detects changes in cell proliferation, migration and invasion ability. Flow cytometry was used to detect apoptosis. Results CPG islands of 176 bp, 134 bp, 125 bp and 531 bp were predicted in the promoter region of LAMA3 gene. The 4 prediction results are basically overlapped. 7 transcription factor binding sites were predicted, and the one with the highest score was on the predicted CpG island located in the proximal promoter region. LAMA3 hypermethylation and low expression are both associated with chemotherapy resistance and poor prognosis in ovarian cancer. LAMA3 methylation was negatively correlated with expression. After upregulation of LAMA3, the proliferation ability of chemoresistant ovarian cancer cell decreased, while the ability of apoptosis, invasion and migration increased. Conclusion LAMA3 hypermethylation is associated with chemotherapy resistance and poor prognosis. As a typical CpG island gene, LAMA3(cg20937934) and LAMA3(cg13270625) hypermethylation is negatively correlated with low expression. LAMA3 promotes the invasion, migration and apoptosis of SKOV3DDP. In the future, the mechanism of LAMA3 methylation in ovarian cancer will need to be further studied.

VirB is a key regulator of genes located on the large virulence plasmid (pINV) in the bacterial pathogen Shigella flexneri VirB is unusual; it is not related to other transcriptional regulators, instead, it belongs to a family of proteins that primarily function in plasmid and chromosome partitioning; exemplified by ParB. Despite this, VirB does not function to segregate DNA, but rather counters transcriptional silencing mediated by the nucleoid structuring protein, H-NS. Since ParB localizes subcellularly as discrete foci in the bacterial cytoplasm, we chose to investigate the subcellular localization of VirB to gain novel insight into how VirB functions as a transcriptional anti-silencer. To do this, a GFP-VirB fusion that retains the regulatory activity of VirB and yet, does not undergo significant protein degradation in S. flexneri, was used. Surprisingly, discrete fluorescent foci were observed in live wild-type S. flexneri cells and an isogenic virB mutant using fluorescence microscopy. In contrast, foci were rarely observed (

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

Plant NAC (NAM, ATAF1/2, and CUC2) family is involved in various development processes including Programmed Cell Death (PCD) associated development. However, the relationship between NAC family and PCD-associated cotton pigment gland development is largely unknown. In this study, we identified 150, 153 and 299 NAC genes in newly updated genome sequences of G. arboreum, G. raimondii and G. hirsutum, respectively. All NAC genes were divided into 8 groups by the phylogenetic analysis and most of them were conserved during cotton evolution. Using the vital regulator of gland formation GhMYC2-like as bait, expression correlation analysis screened out 6 NAC genes which were low-expressed in glandless cotton and high-expressed in glanded cotton. These 6 NAC genes acted downstream of GhMYC2-like and were induced by MeJA. Silencing CGF1(Cotton Gland Formation1), another MYC-coding gene, caused almost glandless phenotype and down-regulated expression of GhMYC2-like and the 6 NAC genes, indicating a MYC-NAC regulatory network in gland development. In addition, predicted regulatory mechanism showed that the 6&nbsp, NAC genes were possibly regulated by light, various phytohormones and transcription factors as well as miRNAs. The interaction network and DNA binding sites of the 6 NAC transcription factors were also predicted. These results laid the foundation for further study of gland-related genes and gland development regulatory network.

Large sets of whole cancer genomes make it possible to study mutation hotspots genome-wide. Here we detect, categorize, and characterize site-specific hotspots using 2279 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes project and provide a resource of annotated hotspots genome-wide. We investigate the excess of hotspots in both protein-coding and gene regulatory regions and develop measures of positive selection and functional impact for individual hotspots. Using cancer allele fractions, expression aberrations, mutational signatures, and a variety of genomic features, such as potential gain or loss of transcription factor binding sites, we annotate and prioritize all highly mutated hotspots. Genome-wide we find more high-frequency SNV and indel hotspots than expected given mutational background models. Protein-coding regions are generally enriched for SNV hotspots compared to other regions. Gene regulatory hotspots show enrichment of potential same-patient second-hit missense mutations, consistent with enrichment of hotspot driver mutations compared to singletons. For protein-coding regions, splice-sites, promoters, and enhancers, we see an excess of hotspots associated with cancer genes. Interestingly, missense hotspot mutations in tumor suppressors are associated with elevated expression, suggesting localized amino-acid changes with functional impact. For individual non-coding hotspots, only a small number show clear signs of positive selection, including known sites in the TERT promoter and the 5’ UTR of TP53. Most of the new candidates have few mutations and limited driver evidence. However, a hotspot in an enhancer of the oncogene POU2AF1, which may create a transcription factor binding site, presents multiple lines of driver-consistent evidence.

Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision medicine. Recently, deep learning-based models have shown exemplary performance in the prediction of TFBSs at the base-pair level. However, the previous models fail to integrate nucleotide position information and semantic information without noisy responses. Thus, there is still room for improvement. Moreover, both the inner mechanism and prediction results of these models are challenging to interpret. To this end, the Deep Attentive Encoder-Decoder Neural Network (D-AEDNet) is developed to identify the location of TFs–DNA binding sites in DNA sequences. In particular, our model adopts Skip Architecture to leverage the nucleotide position information in the encoder and removes noisy responses in the information fusion process by Attention Gate. Simultaneously, the Transcription Factor Motif Discovery based on Sliding Window (TF-MoDSW), an approach to discover TFs–DNA binding motifs by utilizing the output of neural networks, is proposed to understand the biological meaning of the predicted result. On ChIP-exo datasets, experimental results show that D-AEDNet has better performance than competing methods. Besides, we authenticate that Attention Gate can improve the interpretability of our model by ways of visualization analysis. Furthermore, we confirm that ability of D-AEDNet to learn TFs–DNA binding motifs outperform the state-of-the-art methods and availability of TF-MoDSW to discover biological sequence motifs in TFs–DNA interaction by conducting experiment on ChIP-seq datasets.

In a multicentric, observational, investigator-blinded, and longitudinal clinical study of 764 ART-naïve subjects, we identified nine different promoter-variant strains of HIV-1 subtype C (HIV-1C) emerging in the Indian population, with some of these variants being reported for the first time. Unlike several previous studies, our work here focuses on the evolving viral regulatory elements, not coding sequences. The emerging viral strains contain additional copies of the existing transcription factor binding sites (TFBS), including TCF-1α/LEF-1, RBEIII, AP-1, and NF-κB, created by sequence duplication. The additional TFBS are genetically diverse and may blur the distinction between the modulatory region of the promoter and the viral enhancer. In a follow-up analysis, we found trends, but not significant associations between any specific variant promoter and prognostic markers, probably because the emerging viral strains might not have established mono infections yet. Illumina sequencing of four clinical samples containing a co-infection indicated the domination of one strain over the other and establishing a stable ratio with the second strain at the follow-up time-points. Since a single promoter regulates viral gene expression and constitutes the master regulatory circuit with Tat, the acquisition of additional and variant copies of the TFBS may significantly impact viral latency and latent reservoir characteristics. Further studies are urgently warranted to understand how the diverse TFBS profiles of the viral promoter may modulate the characteristics of the latent reservoir, especially following the initiation of antiretroviral therapy.

The idea that information can be transmitted to subsequent generation(s) by epigenetic means has been studied for decades but remains controversial in humans. Epidemiological studies have established that grandparental exposures are associated with health outcomes in their grandchildren, often with sex-specific effects; however the mechanism of transmission is still unclear. We conducted Epigenome Wide Association Studies (EWAS) to test whether grandmaternal smoking during pregnancy is associated with altered DNA methylation (DNAm) in their adolescent grandchildren. We used data from a birth cohort, with discovery and replication datasets of 1225 and 708 individuals (respectively), aged 15-17 years, and tested replication in the same individuals at birth and 7 years. We show for the first time that DNAm at a small number of loci is associated with grandmaternal smoking in humans, and their locations in the genome suggest hypotheses of transmission. We observe and replicate sex-specific associations at two sites on the X chromosome, one located in an imprinting control region and both within transcription factor binding sites (TFBSs). In fact, we observe enrichment for TFBSs among the CpG sites with the strongest associations, suggesting that TFBSs may be a mechanism by which grandmaternal exposures influence offspring DNA methylation. There is limited evidence that these associations appear at earlier timepoints, so effects are not static throughout development. The implication of this work is that effects of smoking during pregnancy may induce DNAm changes in later generations and that these changes are often sex-specific, in line with observational associations.

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Although enzyme-encoding genes involved in the degradation of carbaryl have been reported in Pseudomonas sp. strain XWY-1, no regulator has been identified yet. In the mcbABCDEF cluster responsible for the upstream pathway of carbaryl degradation (from carbaryl to salicylate), the mcbA gene is constitutively expressed, while mcbBCDEF is induced by 1-naphthol, the hydrolysis product of carbaryl by McbA. In this study, we identified McbG, a transcriptional activator of the mcbBCDEF cluster. McbG is a 315-amino-acid protein with a molecular mass of 35.7 kDa. It belongs to the LysR family of transcriptional regulators and shows 28.48% identity to the pentachlorophenol (PCP) degradation transcriptional activation protein PcpR from Sphingobium chlorophenolicum ATCC 39723. Gene disruption and complementation studies reveal that mcbG is essential for transcription of the mcbBCDEF cluster in response to 1-naphthol in strain XWY-1. The results of the electrophoretic mobility shift assay (EMSA) and DNase I footprinting show that McbG binds to the 25-bp motif in the mcbBCDEF promoter area. The palindromic sequence TATCGATA within the motif is essential for McbG binding. The binding site is located between the –10 box and the transcription start site. In addition, McbG can repress its own transcription. The EMSA results show that a 25-bp motif in the mcbG promoter area plays an important role in McbG binding to the promoter of mcbG. This study reveals the regulatory mechanism for the upstream pathway of carbaryl degradation in strain XWY-1. The identification of McbG increases the variety of regulatory models within the LysR family of transcriptional regulators. IMPORTANCE Pseudomonas sp. strain XWY-1 is a carbaryl-degrading strain that utilizes carbaryl as the sole carbon and energy source for growth. The functional genes involved in the degradation of carbaryl have already been reported. However, the regulatory mechanism has not been investigated yet. Previous studies demonstrated that the mcbA gene, responsible for hydrolysis of carbaryl to 1-naphthol, is constitutively expressed in strain XWY-1. In this study, we identified a LysR-type transcriptional regulator, McbG, which activates the mcbBCDEF gene cluster responsible for the degradation of 1-naphthol to salicylate and represses its own transcription. The DNA binding site of McbG in the mcbBCDEF promoter area contains a palindromic sequence, which affects the binding of McbG to DNA. These findings enhance our understanding of the mechanism of microbial degradation of carbaryl.

Transcription factors (TFs) regulate gene expression by binding to cis-regulatory sequences in the promoters of target genes. Recent research is helping to decipher in part the cis-regulatory code in eukaryotes, including plants, but it is not yet fully understood how paralogous TFs select their targets. Here we addressed this question by studying several proteins of the basic helix-loop-helix (bHLH) family of plant TFs, all of which recognize the same DNA motif. We focused on the MYC-related group of bHLHs, that redundantly regulate the jasmonate (JA) signaling pathway, and we observed a high correspondence between DNA-binding profiles in vitro and MYC function in vivo. We demonstrated that A/T-rich modules flanking the MYC-binding motif, conserved from bryophytes to higher plants, are essential for TF recognition. We observed particular DNA-shape features associated with A/T modules, indicating that the DNA shape may contribute to MYC DNA binding. We extended this analysis to 20 additional bHLHs and observed correspondence between in vitro binding and protein function, but it could not be attributed to A/T modules as in MYCs. We conclude that different bHLHs may have their own codes for DNA binding and specific selection of targets that, at least in the case of MYCs, depend on the TF-DNA interplay., Transcription factors (TFs) regulate gene expression by recognizing regulatory sequences in the promoters of target genes. This study reports the investigation of the DNA-binding specificities of the bHLH MYC2-related proteins and demonstrates that specific sequence modules flanking the binding site are essential for TF recognition. It concludes that different bHLHs may have their own codes for DNA binding and specific selection of targets that, in the case of the MYCs, depend on the TF-DNA interplay.

Patterns of somatic single nucleotide variants observed in human cancers vary widely between different tumor types. They depend not only on the activity of diverse mutational processes, such as exposure to ultraviolet light and the deamination of methylated cytosines, but largely also on the sequence content of different genomic regions on which these processes act. With MutViz (http://gmql.eu/mutviz/), we have presented a user-friendly web tool for the identification of mutation enrichments that offers preloaded mutations from public datasets for a variety of cancer types, well organized within an effective database architecture. Somatic mutation patterns can be visually and statistically analyzed within arbitrary sets of small, user-provided genomic regions, such as promoters or collections of transcription factor binding sites. Here, we present MutViz 2.0, a largely extended and consolidated version of the tool: we took into account the immediate (trinucleotide) sequence context of mutations, improved the representation of clinical annotation of tumor samples and devised a method for signature refitting on limited genomic regions to infer the contribution of individual mutational processes to the mutation patterns observed in these regions. We described both the features of MutViz 2.0, concentrating on the novelties, and the substantial re-engineering of the cloud-based architecture., Graphical Abstract Graphical AbstractSimplified workflow of data analysis and visualization with MutViz: Analysis of somatic mutations within user-defined genomic regions for identification and visualization of region-specific mutation characteristics.

Transcriptional regulation is associated with complicated mechanisms including multiple molecular interactions and collaborative drive. Long noncoding RNAs (lncRNAs) have highly structured characteristics and play vital roles in the regulation of transcription in organisms. However, the specific contributions of conformation feature and underlying molecular mechanisms are still unclear. In the present paper, a hypothesis regarding molecular structure effect is presented, which proposes that lncRNAs fold into a complex spatial architecture and act as a skeleton to recruit transcription factors (TF) targeted binding, and which is involved in cooperative regulation. A candidate set of TF-lncRNA coregulation was constructed, and it was found that structural accessibility affected molecular binding force. In addition, transcription factor binding site (TFBS) regions of myopia-related lncRNA transcripts were disturbed, and it was discovered that base mutations affected the occurrence of significant molecular allosteric changes in important elements and variable splicing regions, mediating the onset and development of myopia. The results originated from structureomics and interactionomics and created conditions for systematic research on the mechanisms of structure-mediated TF-lncRNA coregulation in transcriptional regulation. Finally, these findings will help further the understanding of key regulatory roles of molecular allostery in cell physiological and pathological processes.

Background Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) measures genome-wide chromatin accessibility for the discovery of cell-type specific regulatory networks. ScATAC-seq combined with single-cell RNA sequencing (scRNA-seq) offers important avenues for ongoing research, such as novel cell-type specific activation of enhancer and transcription factor binding sites as well as chromatin changes specific to cell states. On the other hand, scATAC-seq data is known to be challenging to interpret due to its high number of zeros as well as the heterogeneity derived from different protocols. Because of the stochastic lack of marker gene activities, cell type identification by scATAC-seq remains difficult even at a cluster level. Results In this study, we exploit reference knowledge obtained from external scATAC-seq or scRNA-seq datasets to define existing cell types and uncover the genomic regions which drive cell-type specific gene regulation. To investigate the robustness of existing cell-typing methods, we collected 7 scATAC-seq datasets targeting mouse brain for a meta-analytic comparison of neuronal cell-type annotation, including a reference atlas generated by the BRAIN Initiative Cell Census Network (BICCN). By comparing the area under the receiver operating characteristics curves (AUROCs) for the three major cell types (inhibitory, excitatory, and non-neuronal cells), cell-typing performance by single markers is found to be highly variable even for known marker genes due to study-specific biases. How-ever, the signal aggregation of a large and redundant marker gene set, optimized via multiple scRNA-seq data, achieves the highest cell-typing performances among 5 existing marker gene sets, from the individual cell to cluster level. That gene set also shows a high consistency with the cluster-specific genes from inhibitory subtypes in two well-annotated datasets, suggesting applicability to rare cell types. Next, we demonstrate a comprehensive assessment of scATAC-seq cell typing using exhaustive combinations of the marker gene sets with supervised learning methods including machine learning classifiers and joint clustering methods. Our results show that the combinations using robust marker gene sets systematically ranked at the top, not only with model based prediction using a large reference data but also with a simple summation of expression strengths across markers. To demonstrate the utility of this robust cell typing approach, we trained a deep neural network to predict chromatin accessibility in each subtype using only DNA sequence. Through model interpretation methods, we identify key motifs enriched about robust gene sets for each neuronal subtype. Conclusions Through the meta-analytic evaluation of scATACseq cell-typing methods, we develop a novel method set to exploit the BICCN reference atlas. Our study strongly supports the value of robust marker gene selection as a feature selection tool and cross-dataset comparison between scATAC-seq datasets to improve alignment of scATAC-seq to known biology. With this novel, high quality epigenetic data, genomic analysis of regulatory regions can reveal sequence motifs that drive cell type-specific regulatory programs.

Mammalian cytosine DNA methylation (5mC) is associated with the integrity of the genome and the transcriptional status of nuclear DNA. Due to technical limitations, it has been less clear if mitochondrial DNA (mtDNA) is methylated and whether 5mC has a regulatory role in this context. Here, we used bisulfite-independent single-molecule sequencing of native human and mouse DNA to study mitochondrial 5mC across different biological conditions. We first validated the ability of long-read nanopore sequencing to detect 5mC in CpG (5mCpG) and non-CpG (5mCpH) context in nuclear DNA at expected genomic locations (i.e. promoters, gene bodies, enhancers, and cell type-specific transcription factor binding sites). Next, using high coverage nanopore sequencing we found low levels of mtDNA CpG and CpH methylation (with several exceptions) and little variation across biological processes: differentiation, oxidative stress, and cancer. 5mCpG and 5mCpH were overall higher in tissues compared to cell lines, with small additional variation between cell lines of different origin. Despite general low levels, global and single-base differences were found in cancer tissues compared to their adjacent counterparts, in particular for 5mCpG. In conclusion, nanopore sequencing is a useful tool for the detection of modified DNA bases on mitochondria that avoid the biases introduced by bisulfite and PCR amplification. Enhanced nanopore basecalling models will provide further resolution on the small size effects detected here, as well as rule out the presence of other DNA modifications such as oxidized forms of 5mC.

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI) become DNA hypermethylated and silenced in cancer. Here, we perform a systematic analysis of CGI genes across TCGA cancer types, finding that PRC2+-CGI genes are frequently prone to transcriptional upregulation as well. These upregulated PRC2+-CGI genes control important pathways such as Epithelial-Mesenchymal Transition (EMT) and TNFα-associated inflammatory response, and have greater cancer-type specificity than other CGI genes. Using publicly available chromatin datasets and genetic perturbations, we show that transcription factor binding sites (TFBSs) within distal enhancers underlie transcriptional activation of PRC2+-CGI genes, coinciding with loss of the PRC2-associated mark H3K27me3 at the linked promoter. In contrast, PRC2-free CGI genes are predominantly regulated by promoter TFBSs which are common to most cancer types. Surprisingly, a large subset of PRC2+-CGI genes that are upregulated in one cancer type are also hypermethylated/silenced in at least one other cancer type, underscoring the high degree of regulatory plasticity of these genes, likely derived from their complex regulatory control during normal development., A subset of CpG Island promoter genes are regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI), which become DNA hypermethylated and silenced in cancer. Here, the authors investigate the transcriptomic and epigenomic characteristics of PRC2-occupied CGI and free CGI across pan-cancer types.

Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes. Made available in DSpace on 2021-08-17T12:02:54Z (GMT). No. of bitstreams: 1 MuscleAlleleSpecific.pdf: 13183203 bytes, checksum: a634e71a998500cd0f7dd0ad1e644846 (MD5) Previous issue date: 2021 Article number: 7321.