TIANA: transcription factors cooperativity inference analysis with neural attention
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ABSTRACT:
SUBMITTER: Li R
PROVIDER: S-EPMC11342676 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Transcription regulation in eukaryotes is known to occur through the coordinated action of multiple transcription factors (TFs). Recently, a few genome-wide transcription studies have begun to explore the combinatorial nature of TF interactions. We propose a novel approach that reveals how multiple TFs cooperate to regulate transcription in the yeast cell cycle. Our method integrates genome-wide gene expression data and chromatin immunoprecipitation (ChIP-chip) data to discover more biologically relevant synergistic interactions between different TFs and their target genes than previous studies. Given any pair of TFs A and B, we define a novel measure of cooperativity between the two TFs based on the expression patterns of sets of target genes of only A, only B, and both A and B. If the cooperativity measure is significant then there is reason to postulate that the presence of both TFs is needed to influence gene expression. Our results indicate that many cooperative TFs that were previously characterized experimentally indeed have high values of cooperativity measures in our analysis. In addition, we propose several novel, experimentally testable predictions of cooperative TFs that play a role in the cell cycle and other biological processes. Many of them hold interesting clues for cross talk between the cell cycle and other processes including metabolism, stress response and pseudohyphal differentiation. Finally, we have created a web tool where researchers can explore the exhaustive list of cooperative TFs and survey the graphical representation of the target genes' expression profiles. The interface includes a tool to dynamically draw a TF cooperativity network of 113 TFs with user-defined significance levels. This study is an example of how systematic combination of diverse data types along with new functional genomic approaches can provide a rigorous platform to map TF interactions more efficiently.
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Project description:To mechanistically characterize the microevolutionary processes active in altering transcription factor (TF) binding among closely related mammals, we compared the genome-wide binding of three tissue-specific TFs that control liver gene expression in six rodents. Despite an overall fast turnover of TF binding locations between species, we identified thousands of TF regions of highly constrained TF binding intensity. Although individual mutations in bound sequence motifs can influence TF binding, most binding differences occur in the absence of nearby sequence variations. Instead, combinatorial binding was found to be significant for genetic and evolutionary stability; cobound TFs tend to disappear in concert and were sensitive to genetic knockout of partner TFs. The large, qualitative differences in genomic regions bound between closely related mammals, when contrasted with the smaller, quantitative TF binding differences among Drosophila species, illustrate how genome structure and population genetics together shape regulatory evolution.
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Project description:Cooperative binding of transcription factors (TFs) to chromatin orchestrates gene expression programming and cell fate specification. However, the biophysical principles of TF cooperativity remain incompletely understood. Here we use single-molecule fluorescence microscopy to study the partnership between Sox2 and Oct4, two core members of the pluripotency gene regulatory network. We find that the ability of Sox2 to target DNA inside nucleosomes is strongly affected by the translational and rotational positioning of its binding motif. In contrast, Oct4 can access nucleosomal sites with equal capacities. Furthermore, the Sox2-Oct4 pair displays nonreciprocal cooperativity, with Oct4 modulating interaction of Sox2 with the nucleosome but not vice versa. Such cooperativity is conditional upon the composite motif's residing at specific nucleosomal locations. These results reveal that pioneer factors possess distinct chromatin-binding properties and suggest that the same set of TFs can differentially regulate gene activities on the basis of their motif positions in the nucleosomal context.
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Project description:The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Despite recent characterization of genome-wide HIF DNA binding locations and hypoxia-regulated transcripts in different cell types, the molecular bases of HIF target selection remain unresolved. Herein, we combined multi-level experimental data and computational predictions to identify sequence motifs that may contribute to HIF target selectivity. We obtained a core set of bona fide HIF binding regions by integrating multiple HIF1 DNA binding and hypoxia expression profiling datasets. This core set exhibits evolutionarily conserved binding regions and is enriched in functional responses to hypoxia. Computational prediction of enriched transcription factor binding sites identified sequence motifs corresponding to several stress-responsive transcription factors, such as activator protein 1 (AP1), cAMP response element-binding (CREB), or CCAAT-enhancer binding protein (CEBP). Experimental validations on HIF-regulated promoters suggest a functional role of the identified motifs in modulating HIF-mediated transcription. Accordingly, transcriptional targets of these factors are over-represented in a sorted list of hypoxia-regulated genes. Altogether, our results implicate cooperativity among stress-responsive transcription factors in fine-tuning the HIF transcriptional response.
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Project description:BackgroundThe aberrant transcription in cancer of genes normally associated with embryonic tissue differentiation at various organ sites may be a hallmark of tumour progression. For example, neuroendocrine differentiation is found more commonly in cancers destined to progress, including prostate and lung. We sought to identify proteins which are involved in neuroendocrine differentiation and differentially expressed in aggressive/metastatic tumours.ResultsExpression arrays were used to identify up-regulated transcripts in a neuroendocrine (NE) transgenic mouse model of prostate cancer. Amongst these were several genes normally expressed in neural tissues, including the pro-neural transcription factors Ascl1 and Hes6. Using quantitative RT-PCR and immuno-histochemistry we showed that these same genes were highly expressed in castrate resistant, metastatic LNCaP cell-lines. Finally we performed a meta-analysis on expression array datasets from human clinical material. The expression of these pro-neural transcripts effectively segregates metastatic from localised prostate cancer and benign tissue as well as sub-clustering a variety of other human cancers.ConclusionBy focussing on transcription factors known to drive normal tissue development and comparing expression signatures for normal and malignant mouse tissues we have identified two transcription factors, Ascl1 and Hes6, which appear effective markers for an aggressive phenotype in all prostate models and tissues examined. We suggest that the aberrant initiation of differentiation programs may confer a selective advantage on cells in all contexts and this approach to identify biomarkers therefore has the potential to uncover proteins equally applicable to pre-clinical and clinical cancer biology.
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Project description:Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
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Project description:Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.
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Project description:Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.
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Project description:Pioneer transcription factors have the ability to access DNA in compacted chromatin1. Multiple transcription factors can bind together to a regulatory element in a cooperative way, and cooperation between the pioneer transcription factors OCT4 (also known as POU5F1) and SOX2 is important for pluripotency and reprogramming2-4. However, the molecular mechanisms by which pioneer transcription factors function and cooperate on chromatin remain unclear. Here we present cryo-electron microscopy structures of human OCT4 bound to a nucleosome containing human LIN28B or nMATN1 DNA sequences, both of which bear multiple binding sites for OCT4. Our structural and biochemistry data reveal that binding of OCT4 induces changes to the nucleosome structure, repositions the nucleosomal DNA and facilitates cooperative binding of additional OCT4 and of SOX2 to their internal binding sites. The flexible activation domain of OCT4 contacts the N-terminal tail of histone H4, altering its conformation and thus promoting chromatin decompaction. Moreover, the DNA-binding domain of OCT4 engages with the N-terminal tail of histone H3, and post-translational modifications at H3K27 modulate DNA positioning and affect transcription factor cooperativity. Thus, our findings suggest that the epigenetic landscape could regulate OCT4 activity to ensure proper cell programming.
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