Decoding the Inversion Symmetry Underlying Transcription Factor DNA-Binding Specificity and Functionality in the Genome.
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ABSTRACT:
SUBMITTER: Coons LA
PROVIDER: S-EPMC6542189 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
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Project description:We study the limits imposed by transcription factor specificity on the maximum number of binding motifs that can coexist in a gene regulatory network, using the SwissRegulon Fantom5 collection of 684 human transcription factor binding sites as a model. We describe transcription factor specificity using regular expressions and find that most human transcription factor binding site motifs are separated in sequence space by one to three motif-discriminating positions. We apply theorems based on the pigeonhole principle to calculate the maximum number of transcription factors that can coexist given this degree of specificity, which is in the order of ten thousand and would fully utilize the space of DNA subsequences. Taking into account an expanded DNA alphabet with modified bases can further raise this limit by several orders of magnitude, at a lower level of sequence space usage. Our results may guide the design of transcription factors at both the molecular and system scale.
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Project description:Transcription factor (TF) recognition is dictated by the underlying DNA motif sequence specific for each TF. Here, we reveal that DNA sequence repeat symmetry plays a central role in defining TF-DNA-binding preferences. In particular, we find that different TFs bind similar symmetry patterns in the context of different developmental layers. Most TFs possess dominant preferences for similar DNA repeat symmetry types. However, in some cases, preferences of specific TFs are changed during differentiation, suggesting the importance of information encoded outside of known motif regions. Histone modifications also exhibit strong preferences for similar DNA repeat symmetry patterns unique to each type of modification. Next, using an in vivo reporter assay, we show that gene expression in embryonic stem cells can be positively modulated by the presence of genomic and computationally designed DNA oligonucleotides containing identified nonconsensus-repetitive sequence elements. This supports the hypothesis that certain nonconsensus-repetitive patterns possess a functional ability to regulate gene expression. We also performed a solution NMR experiment to probe the stability of double-stranded DNA via imino proton resonances for several double-stranded DNA sequences characterized by different repetitive patterns. We suggest that such local stability might play a key role in determining TF-DNA binding preferences. Overall, our findings show that despite the enormous sequence complexity of the TF-DNA binding landscape in differentiating embryonic stem cells, this landscape can be quantitatively characterized in simple terms using the notion of DNA sequence repeat symmetry.
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Project description:In developing bilaterans, the Hox transcription factor family regulates batteries of downstream genes to diversify serially repeated units. Given Hox homeodomains bind a wider array of DNA binding sites in vitro than are regulated by the full-length protein in vivo, regions outside the homeodomain must aid DNA site selection. Indeed, we find affinity for disparate DNA sequences varies less than 3-fold for the homeodomain isolated from the Drosophila Hox protein Ultrabithorax Ia (UbxHD), whereas for the full-length protein (UbxIa) affinity differs by more than 10-fold. The rank order of preferred DNA sequences also differs, further demonstrating distinct DNA binding preferences. The increased specificity of UbxIa can be partially attributed to the I1 region, which lies adjacent to the homeodomain and directly impacts binding energetics. Each of three segments within I1-the Extradenticle-binding YPWM motif, the six amino acids immediately N-terminal to this motif, and the eight amino acids abutting the YPWM C-terminus-uniquely contribute to DNA specificity. Combination of these regions synergistically modifies DNA binding to further enhance specificity. Intriguingly, the presence of the YPWM motif in UbxIa inhibits DNA binding only to Ubx-Extradenticle heterodimer binding sites, potentially functioning in vivo to prevent Ubx monomers from binding and misregulating heterodimer target genes. However, removal of the surrounding region allows the YPWM motif to also inhibit binding to Hox-only recognition sequences. Despite a modular domain design for Hox proteins, these results suggest that multiple Hox protein regions form a network of regulatory interactions that coordinate context- and gene-specific responses. Since most nonhomeodomain regions are not conserved between Hox family members, these regulatory interactions have the potential to diversify binding by the highly homologous Hox homeodomains.
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Project description:MotivationThe DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret.ResultsWe propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein-binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max and Mad2) in their native genomic context. These high-throughput quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar position weight matrices, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step toward better sequence-based models of individual TF-DNA binding specificity.AvailabilityOur code is available at http://genome.duke.edu/labs/gordan/ISMB2013. The PBM data used in this article are available in the Gene Expression Omnibus under accession number GSE47026.
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Project description:Transcription factors are proteins that recognize specific DNA sequences and affect local transcriptional processes. They are the primary means by which all organisms control specific gene expression. Understanding which DNA sequences a particular transcription factor recognizes provides important clues into the set of genes that they regulate and, through this, their potential biological functions. Insights may be gained through homology searches and genetic means. However, these approaches can be misleading, especially when comparing distantly related organisms or in cases of complicated transcriptional regulation. In this work, we used a biochemistry-based approach to determine the spectrum of DNA sequences specifically bound by the Thermus thermophilus HB8 TetR-family transcription factor TTHB023. The consensus sequence 5'-(a/c)Y(g/t)A(A/C)YGryCR(g/t)T(c/a)R(g/t)-3' was found to have a nanomolar binding affinity with TTHB023. Analyzing the T. thermophilus HB8 genome, several TTHB023 consensus binding sites were mapped to the promoters of genes involved in fatty acid biosynthesis. Notably, some of these were not identified previously through genetic approaches, ostensibly given their potential co-regulation by the Thermus thermophilus HB8 TetR-family transcriptional repressor TTHA0167. Our investigation provides additional evidence supporting the usefulness of a biochemistry-based approach for characterizing putative transcription factors, especially in the case of cooperative regulation.
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Project description:Understanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. We observed that DNA shape features were individually weighted for each transcription factor, even if they shared the same binding sequence.
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Project description:Transcription factor-DNA interactions and their specificities have been described for many different classes of transcription factor families. However, heterodimeric transcription factor complexes still remain poorly characterised. The basic-Helix-Loop-Helix (bHLH) transcription factor family is one of the largest transcription factor families that typically bind DNA though a degenerate CANNTG elements as heterodimers or homodimers. Here we characterise the DNA binding of the bHLH - Per-Arnt-Sim (PAS) (bHLH-PAS) domain containing transcription factor family using SELEX-high-throughput sequencing coupled with quantitative computational modelling analysis. We show that most dimeric bHLH-PAS transcription factors bind to distinct core NNCGTG response elements but bind over a much larger footprint than previously characterised. Modelled DNA-protein interactions were found to correlate with structural analysis, DNA shape predictions and in vivo transcription factor occupancy.
2022-07-02 | GSE159989 | GEO
Project description:The transcription factor VP30 of the non-segmented RNA negative strand Ebola virus balances viral transcription and replication. Here, we comprehensively studied RNA binding by VP30. Using a novel VP30:RNA electrophoretic mobility shift assay, we tested truncated variants of 2 potential natural RNA substrates of VP30 - the genomic Ebola viral 3'-leader region and its complementary antigenomic counterpart (each ∼155 nt in length) - and a series of other non-viral RNAs. Based on oligonucleotide interference, the major VP30 binding region on the genomic 3'-leader substrate was assigned to the internal expanded single-stranded region (∼ nt 125-80). Best binding to VP30 was obtained with ssRNAs of optimally ∼ 40 nt and mixed base composition; underrepresentation of purines or pyrimidines was tolerated, but homopolymeric sequences impaired binding. A stem-loop structure, particularly at the 3'-end or positioned internally, supports stable binding to VP30. In contrast, dsRNA or RNAs exposing large internal loops flanked by entirely helical arms on both sides are not bound. Introduction of a 5´-Cap(0) structure impaired VP30 binding. Also, ssDNAs bind substantially weaker than isosequential ssRNAs and heparin competes with RNA for binding to VP30, indicating that ribose 2'-hydroxyls and electrostatic contacts of the phosphate groups contribute to the formation of VP30:RNA complexes. Our results indicate a rather relaxed RNA binding specificity of filoviral VP30, which largely differs from that of the functionally related transcription factor of the Paramyxoviridae which binds to ssRNAs as short as 13 nt with a preference for oligo(A) sequences.
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Project description:To elucidate how genomic sequences build transcriptional control networks, we need to understand the connection between DNA sequence and transcription factor binding and function. Binding predictions based solely on consensus predictions are limited, because a single factor can use degenerate sequence motifs and because related transcription factors often prefer identical sequences. The ETS family transcription factor, ETS1, exemplifies these challenges. Unexpected, redundant occupancy of ETS1 and other ETS proteins is observed at promoters of housekeeping genes in T cells due to common sequence preferences and the presence of strong consensus motifs. However, ETS1 exhibits a specific function in T cell activation; thus, unique transcriptional targets are predicted. To uncover the sequence motifs that mediate specific functions of ETS1, a genome-wide approach, chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq), identified both promoter and enhancer binding events in Jurkat T cells. A comparison with DNase I sensitivity both validated the dataset and also improved accuracy. Redundant occupancy of ETS1 with the ETS protein GABPA occurred primarily in promoters of housekeeping genes, whereas ETS1 specific occupancy occurred in the enhancers of T cell-specific genes. Two routes to ETS1 specificity were identified: an intrinsic preference of ETS1 for a variant of the ETS family consensus sequence and the presence of a composite sequence that can support cooperative binding with a RUNX transcription factor. Genome-wide occupancy of RUNX factors corroborated the importance of this partnership. Furthermore, genome-wide occupancy of co-activator CBP indicated tight co-localization with ETS1 at specific enhancers, but not redundant promoters. The distinct sequences associated with redundant versus specific ETS1 occupancy were predictive of promoter or enhancer location and the ontology of nearby genes. These findings demonstrate that diversity of DNA binding motifs may enable variable transcription factor function at different genomic sites.
| S-EPMC2787013 | biostudies-literature
Project description:BACKGROUND: Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? RESULTS: We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. CONCLUSIONS: Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.
| S-EPMC3574057 | biostudies-literature