Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE104903 | GEO | 2019/01/01
REPOSITORIES: GEO
Similar Datasets
Project description:In order to generate data suitable to decipher cis-regulatory logic, we generated ~100 million synthetic promoters (in yeast) comprised of random DNA and measured their expression by FACS (sorting into 18 bins).
2019-01-01 | GSE104878 | GEO
Project description:Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)
| PRJNA414115 | ENA
Project description:Deciphering cis-regulatory logic with 100 million synthetic promoters
| PRJNA414104 | ENA
Project description:How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF's specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.
| S-EPMC6954276 | biostudies-literature
Project description:When a cell's environment changes, a large transcriptional response often takes place. The exquisite sensitivity and specificity of these responses are controlled in large part by the combinations of cis-regulatory elements that reside in gene promoters and adjacent control regions. Here, we present a study aimed at accurately modeling the relationship between combinations of cis-regulatory elements and the expression levels they drive in different environments. We constructed four libraries of synthetic promoters in yeast, consisting of combinations of transcription factor binding sites and assayed their expression in four different environments. Thermodynamic models relating promoter sequences to their corresponding four expression levels explained at least 56% of the variation in expression in each library through the different conditions. Analyses of these models suggested that a large fraction of regulated gene expression is explained by changes in the effective concentration of sequence-specific transcription factors, and we show that in most cases, the corresponding transcription factors are expressed in a pattern that is predicted by the thermodynamic models. Our analysis uncovered two binding sites that switch from activators to repressors in different environmental conditions. In both the cases, the switch was not the result of a single transcription factor changing regulatory modes, but most likely due to competition between multiple factors binding to the same site. Our analysis suggests that this mode of regulation allows for large and steep changes in expression in response to changing transcription factor concentrations. Our results demonstrate that many complex changes in gene expression are accurately explained by simple changes in the effective concentrations of transcription factors.
| S-EPMC2657533 | biostudies-literature
Project description:Transcription factor binding sites are being discovered at a rapid pace. It is now necessary to turn attention towards understanding how these sites work in combination to influence gene expression. Quantitative models that accurately predict gene expression from promoter sequence will be a crucial part of solving this problem. Here we present such a model, based on the analysis of synthetic promoter libraries in yeast (Saccharomyces cerevisiae). Thermodynamic models based only on the equilibrium binding of transcription factors to DNA and to each other captured a large fraction of the variation in expression in every library. Thermodynamic analysis of these libraries uncovered several phenomena in our system, including cooperativity and the effects of weak binding sites. When applied to the S. cerevisiae genome, a model of repression by Mig1 (which was trained on synthetic promoters) predicts a number of Mig1-regulated genes that lack significant Mig1-binding sites in their promoters. The success of the thermodynamic approach suggests that the information encoded by combinations of cis-regulatory sites is interpreted primarily through simple protein-DNA and protein-protein interactions, with complicated biochemical reactions-such as nucleosome modifications-being downstream events. Quantitative analyses of synthetic promoter libraries will be an important tool in unravelling the rules underlying combinatorial cis-regulation.
| S-EPMC2677908 | biostudies-literature
Project description:The classic view of nucleosome organization at active promoters is that two well-positioned nucleosomes flank a nucleosome-depleted region (NDR). However, this view has been recently disputed by contradictory reports as to whether wider (≳150 bp) NDRs instead contain unstable, micrococcal nuclease-sensitive ("fragile") nucleosomal particles. To determine the composition of fragile particles, we introduce CUT&RUN.ChIP, in which targeted nuclease cleavage and release is followed by chromatin immunoprecipitation. We find that fragile particles represent the occupancy of the RSC (remodeling the structure of chromatin) nucleosome remodeling complex and RSC-bound, partially unwrapped nucleosomal intermediates. We also find that general regulatory factors (GRFs) bind to partially unwrapped nucleosomes at these promoters. We propose that RSC binding and its action cause nucleosomes to unravel, facilitate subsequent binding of GRFs, and constitute a dynamic cycle of nucleosome deposition and clearance at the subset of wide Pol II promoter NDRs.
| S-EPMC6475595 | biostudies-literature
Project description:Following an in-depth transcriptomics-based approach, we first screened out and analyzed (in silico) cis motifs in a group of 63 drought-inducible genes (in soybean). Six novel synthetic promoters (SynP14-SynP19) were designed by concatenating 11 cis motifs, ABF, ABRE, ABRE-Like, CBF, E2F-VARIANT, G-box, GCC-Box, MYB1, MYB4, RAV1-A, and RAV1-B (in multiple copies and various combination) with a minimal 35s core promoter and a 222 bp synthetic intron sequence. In order to validate their drought-inducibility and root-specificity, the designed synthetic assemblies were transformed in soybean hairy roots to drive GUS gene using pCAMBIA3301. Through GUS histochemical assay (after a 72 h 6% PEG6000 treatment), we noticed higher glucuronidase activity in transgenic hairy roots harboring SynP15, SynP16, and SynP18. Further screening through GUS fluorometric assay flaunted SynP16 as the most appropriate combination of efficient drought-responsive cis motifs. Afterwards, we stably transformed SynP15, SynP16, and SynP18 in Arabidopsis and carried out GUS staining as well as fluorometric assays of the transgenic plants treated with simulated drought stress. Consistently, SynP16 retained higher transcriptional activity in Arabidopsis roots in response to drought. Thus the root-specific drought-inducible synthetic promoters designed using stimulus-specific cis motifs in a definite fashion could be exploited in developing drought tolerance in soybean and other crops as well. Moreover, the rationale of design extends our knowledge of trial-and-error based cis engineering to construct synthetic promoters for transcriptional upgradation against other stresses.
| S-EPMC7072871 | biostudies-literature
Project description:As an important part of synthetic biology, synthetic promoter has gradually become a hotspot in current biology. The purposes of the present study were to synthesize green tissue-specific promoters and to discover green tissue-specific cis-elements. We first assembled several regulatory sequences related to tissue-specific expression in different combinations, aiming to obtain novel green tissue-specific synthetic promoters. GUS assays of the transgenic plants indicated 5 synthetic promoters showed green tissue-specific expression patterns and different expression efficiencies in various tissues. Subsequently, we scanned and counted the cis-elements in different tissue-specific promoters based on the plant cis-elements database PLACE and the rice cDNA microarray database CREP for green tissue-specific cis-element discovery, resulting in 10 potential cis-elements. The flanking sequence of one potential core element (GEAT) was predicted by bioinformatics. Then, the combination of GEAT and its flanking sequence was functionally identified with synthetic promoter. GUS assays of the transgenic plants proved its green tissue-specificity. Furthermore, the function of GEAT flanking sequence was analyzed in detail with site-directed mutagenesis. Our study provides an example for the synthesis of rice tissue-specific promoters and develops a feasible method for screening and functional identification of tissue-specific cis-elements with their flanking sequences at the genome-wide level in rice.
| S-EPMC4676006 | biostudies-other
Project description:BackgroundA major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence.ResultsWe developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence.ConclusionWe developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.
| S-EPMC2375358 | biostudies-literature