Profiling the functionality of designed transcription activation domain sequences to identify their grammar rules and exceptions
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE277056 | GEO | 2024/09/17
REPOSITORIES: GEO
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Project description:Profiling the functionality of designed transcription activation domain sequences to identify their grammar rules and exceptions
| PRJNA1160300 | ENA
Project description:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not known. Here, we study how different noise levels are encoded by the promoter sequence using massively parallel noise measurements of thousands of synthetically designed promoters. We find that the noise levels of promoters with similar mean expression levels can vary over more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We devised a computational model that can accurately predict the mean-independent component of the noise from DNA sequence alone. Our model suggests that the effect of promoters on noise is partly mediated by the combination of non-specific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Overall, our results demonstrate that small changes in the DNA sequence of promoters can allow tuning of noise levels in a manner that is largely predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels. Expression measurements of a collection of synthetic promoters collection that was published in Sharon et al. Nature Biotechnology 2012(doi: 10.1038/nbt.2205). Two replicates of the promoter library integrated into a plasmid in yeast were measured in SC-Glu-URA medium. The promoter library was measured as described in Sharon et. al.(Sharon et al. 2012), except for the differences below. Briefly, a large collection of synthetic promoter reporter gene strains was generated by a pooled ligation of 6500 fully designed DNA oligos (obtained by synthesis on a microarray(LeProust et al. 2010) by Agilent Technologies, Santa Clara, California). The oligos were ligated upstream to a yellow fluorescent protein (YFP) gene with a short (100 bp) core promoter sequence taken from HIS3 gene promoter and into a low copy plasmid which also contains a TEF2 promoter deriving red fluorescent protein (mCherry). The resulting plasmids were then transformation into yeast (S. cerevisiae). Next, the pool of cells was grown in amino acid starvation condition (SCD without amino acid except Histidine), and sorted according to their YFP expression level into 32 expression bins (mCherry was used for gating one plasmid copy cells and for normalization). The DNA of the promoters in each bin were then amplified and sent to multiplexed parallel sequencing. Each sequencing result was mapped to a specific promoter and expression bin, resulting in a distribution of cells that contain each promoter across all expression bins. The following differences were applied relative to the description in Sharon et. al.(Sharon et al. 2012). The medium used both for growing the cells and for their sorting was SC-Glu-URA (synthetic complete media with 2% glucose and without uracil) medium without amino acids, except for Histidine. In order to achieve expression distributions with high resolution that would allow good assessment of expression noise, the library cells were sorted into 32 bins according to their ratio of YFP and mCherry expression level, thereby normalizing for extrinsic noise effects. Each of the two extreme expression bins contained 2% of the library cells and each of the remaining 30 bins contained 3.2%. We collected a total of 10,000,000 cells. As previously described, the mapping of cells to bins involves parallel sequencing of the amplified promoter regions. For this purpose, Illumina Hi-Seq 2000 was used to obtain >30,000,000 mapped reads. The two replicates were separately generated from the ssDNA oligo library and separately measured as described above.
2014-07-12 | E-GEOD-55346 | biostudies-arrayexpress
Project description:The stability of mRNAs is regulated by signals within their sequences, but a systematic and predictive understanding of the underlying sequence rules remains elusive. Here, we introduce UTR-Seq, a combination of massively parallel reporter assays and regression models, to survey the dynamics of tens-of-thousands of 3’UTR sequences during early zebrafish embryogenesis. UTR-Seq revealed two temporal degradation programs: a maternally encoded early-onset program and a late-onset program that accelerated degradation after zygotic genome activation. Three signals regulated early-onset rates: stabilizing poly-U and UUAG sequences, and destabilizing GC-rich signals. Three signals explained late-onset degradation: miR-430 seeds, AU-rich sequences and Pumilio recognition sites. Sequence based regression models translated 3’UTRs into their unique decay patterns, and predicted the in vivo impact of sequence signals on mRNA stability. Their application led to the successful design of artificial 3’UTRs that conferred specific mRNA dynamics. UTR-Seq provides a general strategy to uncover the rules of RNA cis-regulation.
2017-12-12 | GSE106677 | GEO
Project description:Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the translational efficiency of the 5’ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide- long random 5’ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on translation of Kozak sequence composition, upstream open reading frames (uORFs) and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the translational efficiency of both a held-out set of the random 5’ UTRs as well as native S. cerevisiae 5’ UTRs. The model additionally was used to computationally evolve highly translating 5’ UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model. The sequences were assayed in the context of a constant promoter and downstream coding sequence. Cell growth was used as a proxy for protein expression. Growth was measured by determining the ratio of sequence counts before and after growth selection.
2017-09-27 | GSE104252 | GEO
Project description:Sequences of 11 amino acids belonging to the KPN_00363 protein and KPN_00459 protein from Klebsiella pneumoniae MGH 78578 which was previously identified as potentially immunogenic was analyzed via alanine scanning to narrow down the significant amino acid residues within the sequence.
2014-08-22 | GSE60588 | GEO
Project description:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not known. Here, we study how different noise levels are encoded by the promoter sequence using massively parallel noise measurements of thousands of synthetically designed promoters. We find that the noise levels of promoters with similar mean expression levels can vary over more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We devised a computational model that can accurately predict the mean-independent component of the noise from DNA sequence alone. Our model suggests that the effect of promoters on noise is partly mediated by the combination of non-specific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Overall, our results demonstrate that small changes in the DNA sequence of promoters can allow tuning of noise levels in a manner that is largely predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels.
2014-07-12 | GSE55346 | GEO
Project description:Histones are among the most conserved proteins known, but organismal differences do exist. In this study we examined the contribution that divergent amino acids within histone H3 make to cell growth and chromatin structure in S. cerevisiae. We show that, while amino acids that define histone H3.3 are dispensable for yeast growth, substitution of residues within the histone H3 alpha 3 helix with the human counterparts results in a severe growth defect. Mutations within this domain also result in altered nucleosome positioning, both in vivo and in vitro, which is accompanied by increased preference for nucleosome favoring sequences. These results suggest that divergent amino acids within the histone H3 alpha 3 helix play organismal roles in defining chromatin structure.
2015-11-10 | GSE73425 | GEO
Project description:Histones are among the most conserved proteins known, but organismal differences do exist. In this study we examined the contribution that divergent amino acids within histone H3 make to cell growth and chromatin structure in S. cerevisiae. We show that, while amino acids that define histone H3.3 are dispensable for yeast growth, substitution of residues within the histone H3 alpha 3 helix with the human counterparts results in a severe growth defect. Mutations within this domain also result in altered nucleosome positioning, both in vivo and in vitro, which is accompanied by increased preference for nucleosome favoring sequences. These results suggest that divergent amino acids within the histone H3 alpha 3 helix play organismal roles in defining chromatin structure. Mnase-seq for two replicates each of wildtype and H3 c-terminal mutants.
2015-11-10 | E-GEOD-73425 | biostudies-arrayexpress
Project description:In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions.
| S-EPMC10078511 | biostudies-literature
Project description:BackgroundMany tree structures are found in nature and organisms. Such trees are believed to be constructed on the basis of certain rules. We have previously developed grammar-based compression methods for ordered and unordered single trees, based on bisection-type tree grammars. Here, these methods find construction rules for one single tree. On the other hand, specified construction rules can be utilized to generate multiple similar trees.ResultsTherefore, in this paper, we develop novel methods to discover common rules for the construction of multiple distinct trees, by improving and extending the previous methods using integer programming. We apply our proposed methods to several sets of glycans and RNA secondary structures, which play important roles in cellular systems, and can be regarded as tree structures. The results suggest that our method can be successfully applied to determining the minimum grammar and several common rules among glycans and RNAs.ConclusionsWe propose integer programming-based methods MinSEOTGMul and MinSEUTGMul for the determination of the minimum grammars constructing multiple ordered and unordered trees, respectively. The proposed methods can provide clues for the determination of hierarchical structures contained in tree-structured biological data, beyond the extraction of frequent patterns.
| S-EPMC4419412 | biostudies-literature