scholarly journals Structure and Evolution of Constitutive Bacterial Promoters

2020 ◽  
Author(s):  
Mato Lagator ◽  
Srdjan Sarikas ◽  
Magdalena Steinrück ◽  
David Toledo-Aparicio ◽  
Jonathan P. Bollback ◽  
...  
Keyword(s):  
Gene Expression ◽  
Binding Sites ◽  
Structural Features ◽  
Biophysical Model ◽  
Regulatory Evolution ◽  
Random Sequences ◽  
Expression Levels ◽  
Constitutive Gene Expression ◽  
Polymerase Binding ◽  
Gene Expression Levels

AbstractPredicting gene expression levels from any DNA sequence is a major challenge in biology. Using libraries with >25,000 random mutants, we developed a biophysical model that accounts for major features of σ70-binding bacterial promoters to accurately predict constitutive gene expression levels of any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and 82% of non-expressing sequences are one point mutation away from a functional promoter. Generating expression from random sequences is pervasive, such that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. The pervasiveness of σ70– binding sites, which arises from the structural features of promoters captured by our biophysical model, implies that their emergence is unlikely the limiting step in gene regulatory evolution.

scholarly journals Sequence-specific dynamics of DNA response elements and their flanking sites regulate the recognition by AP-1 transcription factors

2020 ◽  
Author(s):  
Johanna Hörberg ◽  
Kevin Moreau ◽  
Anna Reymer
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulation Of Gene Expression ◽  
Dna Complexes ◽  
Response Element ◽  
Response Elements ◽  
Expression Levels ◽  
The Stability ◽  
Gene Expression Levels

AbstractActivator proteins 1 (AP-1) comprise one of the largest families of eukaryotic basic leucine zipper transcription factors. Despite advances in the characterization of AP-1 DNA-binding sites, our ability to predict new binding sites and explain how the proteins achieve different gene expression levels remains limited. Here we address the role of sequence-specific DNA dynamics for stability and specific binding of AP-1 factors, using microseconds long molecular dynamics simulations. As a model system, we employ yeast AP-1 factor Yap1 binding to three different response elements from two genetic environments. Our data show that Yap1 actively exploits the sequence-specific plasticity of DNA within the response element to form stable protein-DNA complexes. The stability also depends on the four to six flanking nucleotides, adjacent to the response elements. The flanking sequences modulate the conformational adaptability of the response element, making it more shape-efficient to form specific contacts with the protein. Bioinformatics analysis of differential expression of the studied genes supports our conclusions: the stability of Yap1-DNA complexes, modulated by the flanking environment, influences the gene expression levels. Our results provide new insights into mechanisms of protein-DNA recognition and the biological regulation of gene expression levels in eukaryotes.

scholarly journals Deciphering eukaryotic cis-regulatory logic with 100 million random promoters

2017 ◽  
Author(s):  
Carl G. de Boer ◽  
Eeshit Dhaval Vaishnav ◽  
Ronen Sadeh ◽  
Esteban Luis Abeyta ◽  
Nir Friedman ◽  
...  
Keyword(s):  
Binding Sites ◽  
Control Gene ◽  
Random Sequences ◽  
Expression Levels ◽  
Regulatory Motifs ◽  
Control Gene Expression ◽  
Independent Test ◽  
Complex Models ◽  
Regulatory Dna ◽  
Gene Expression Levels

AbstractDeciphering cis-regulation, the code by which transcription factors (TFs) interpret regulatory DNA sequence to control gene expression levels, is a long-standing challenge. Previous studies of native or engineered sequences have remained limited in scale. Here, we use random sequences as an alternative, allowing us to measure the expression output of over 100 million synthetic yeast promoters. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From these data we learn models of transcriptional regulation that predict over 94% of the expression driven from independent test data and nearly 89% from sequences from yeast promoters. These models allow us to characterize the activity of TFs and their interactions with chromatin, and help refine cis-regulatory motifs. We find that strand, position, and helical face preferences of TFs are widespread and depend on interactions with neighboring chromatin. Such massive-throughput regulatory assays of random DNA provide the diverse examples necessary to learn complex models of cis-regulatory logic.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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