Transcription factor expression levels and environmental signals constrain transcription factor innovation
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ABSTRACT:
PROVIDER: PRJEB64150 | ENA |
REPOSITORIES: ENA
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Project description: Not available
| S-EPMC10482368 | biostudies-literature
Project description:The survival of a population during environmental shifts depends on whether the rate of phenotypic adaptation keeps up with the rate of changing conditions. A common way to achieve this is via change to gene regulatory network (GRN) connections – known as rewiring – that facilitate novel interactions and innovation of transcription factors. To understand the success of rapidly adapting organisms, we therefore need to determine the rules that create and constrain opportunities for GRN rewiring. Here, using an experimental microbial model system based on the soil bacterium Pseudomonas fluorescens, we reveal a hierarchy among transcription factors that are rewired to rescue lost function, with alternative rewiring pathways only unmasked after the preferred pathway is eliminated. We identify three key properties - high activation, high expression, and pre-existing low-level affinity for novel target genes – that facilitate transcription factor innovation. Ease of acquiring these properties is constrained by pre-existing GRN architecture, which was overcome in our experimental system by both targeted and global network alterations. This work reveals the key properties that determine transcription factor evolvability, and as such, the evolution of GRNs.
2023-09-27 | GSE228016 | GEO
Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.
2017-03-30 | MSV000080810 | MassIVE
Project description:Genotype network intersections promote evolutionary innovation
| PRJEB30802 | ENA
Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.
2017-02-23 | PXD004435 | Pride
Project description:The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
| EGAS00001002073 | EGA
Project description:Abstract: Bacteria adapt to the constantly changing environments largely by transcriptional regulation through the activities of various transcription factors (TFs). However, techniques that monitor the in situ TF-promoter interactions in living bacteria are lacking. Herein, we developed a whole-cell TF-promoter binding assay based on the intermolecular Förster resonance energy transfer (FRET) between a fluorescent unnatural amino acid CouA which is genetically encoded into defined sites in TFs and the live cell fluorescent nucleic acid stain SYTO 9. We show that this new FRET pair monitors the intricate TF-promoter interactions elicited by various types of signal transduction systems with specificity and sensitivity. Furthermore, the assay is applicable to identify novel modulators of the regulatory systems of interest and monitor TF activities in bacteria colonized in C. elegans. In conclusion, we established a tractable and sensitive TF-promoter binding assay in living bacteria which not only complements currently available approaches for DNA-protein interactions but also provides novel opportunities for functional annotation of bacterial signal transduction systems and studies of the bacteria-host interface
2022-10-13 | PXD030973 | Pride
Project description:Enzyme promiscuity shapes evolutionary innovation and optimization
| PRJNA471071 | ENA
Project description:Evolutionary innovation through transcription factor rewiring in microbes is shaped by levels of transcription factor activity, expression, and existing connectivity
| PRJNA947669 | ENA
Project description:Site-specific transcription factors (TFs) play an essential role in mammalian development and function as they are vital for the majority of cellular processes. Despite their biological importance, TF proteomic data is scarce in the literature, likely due to difficulties in detecting peptides as the abundance of TFs in cells tends to be low. In recent years, significant improvements in mass spectrometry (MS)-based technologies in terms of sensitivity and specificity have increased the interest in developing quantitative methodologies specifically targeting relatively lowly abundant proteins such as TFs in mammalian models. Such efforts would be greatly aided by the availability of TF peptide-specific information as such data would not only enable improvements in speed and accuracy of protein identifications, but also ameliorate cross-comparisons of quantitative proteomics data and allow for a more efficient development of targeted proteomics assays. However, to date, no comprehensive TF proteotypic peptide database has been developed. To address this evident lack of TF peptide data in public repositories, we are generating a comprehensive, experimentally derived TF proteotypic peptide spectral library dataset based on in vitro protein expression. Our library currently contains peptide information for 89 TFs, and this number is set to increase in the near future.
2015-01-23 | PXD001212 | Pride