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Effects of sequence variation on differential allelic transcription factor occupancy and gene expression.

ABSTRACT: A complex interplay between transcription factors (TFs) and the genome regulates transcription. However, connecting variation in genome sequence with variation in TF binding and gene expression is challenging due to environmental differences between individuals and cell types. To address this problem, we measured genome-wide differential allelic occupancy of 24 TFs and EP300 in a human lymphoblastoid cell line GM12878. Overall, 5% of human TF binding sites have an allelic imbalance in occupancy. At many sites, TFs clustered in TF-binding hubs on the same homolog in especially open chromatin. While genetic variation in core TF binding motifs generally resulted in large allelic differences in TF occupancy, most allelic differences in occupancy were subtle and associated with disruption of weak or noncanonical motifs. We also measured genome-wide differential allelic expression of genes with and without heterozygous exonic variants in the same cells. We found that genes with differential allelic expression were overall less expressed both in GM12878 cells and in unrelated human cell lines. Comparing TF occupancy with expression, we found strong association between allelic occupancy and expression within 100 bp of transcription start sites (TSSs), and weak association up to 100 kb from TSSs. Sites of differential allelic occupancy were significantly enriched for variants associated with disease, particularly autoimmune disease, suggesting that allelic differences in TF occupancy give functional insights into intergenic variants associated with disease. Our results have the potential to increase the power and interpretability of association studies by targeting functional intergenic variants in addition to protein coding sequences.

SUBMITTER: Reddy TE

PROVIDER: S-EPMC3337432 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Effects of sequence variation on differential allelic transcription factor occupancy and gene expression.

Reddy Timothy E TE Gertz Jason J Pauli Florencia F Kucera Katerina S KS Varley Katherine E KE Newberry Kimberly M KM Marinov Georgi K GK Mortazavi Ali A Williams Brian A BA Song Lingyun L Crawford Gregory E GE Wold Barbara B Willard Huntington F HF Myers Richard M RM

Genome research 20120202 5

A complex interplay between transcription factors (TFs) and the genome regulates transcription. However, connecting variation in genome sequence with variation in TF binding and gene expression is challenging due to environmental differences between individuals and cell types. To address this problem, we measured genome-wide differential allelic occupancy of 24 TFs and EP300 in a human lymphoblastoid cell line GM12878. Overall, 5% of human TF binding sites have an allelic imbalance in occupancy. ...[more]

PMID: 22300769

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Project description:BackgroundFlooding during seasonal monsoons affects millions of hectares of rice-cultivated areas across Asia. Submerged rice plants die within a week due to lack of oxygen, light and excessive elongation growth to escape the water. Submergence tolerance was first reported in an aus-type rice landrace, FR13A, and the ethylene-responsive transcription factor (TF) gene SUB1A-1 was identified as the major tolerance gene. Intolerant rice varieties generally lack the SUB1A gene but some intermediate tolerant varieties, such as IR64, carry the allelic variant SUB1A-2. Differential effects of the two alleles have so far not been addressed. As a first step, we have therefore quantified and compared the expression of nearly 2500 rice TF genes between IR64 and its derived tolerant near isogenic line IR64-Sub1, which carries the SUB1A-1 allele. Gene expression was studied in internodes, where the main difference in expression between the two alleles was previously shown.ResultsNineteen and twenty-six TF genes were identified that responded to submergence in IR64 and IR64-Sub1, respectively. Only one gene was found to be submergence-responsive in both, suggesting different regulatory pathways under submergence in the two genotypes. These differentially expressed genes (DEGs) mainly included MYB, NAC, TIFY and Zn-finger TFs, and most genes were downregulated upon submergence. In IR64, but not in IR64-Sub1, SUB1B and SUB1C, which are also present in the Sub1 locus, were identified as submergence responsive. Four TFs were not submergence responsive but exhibited constitutive, genotype-specific differential expression. Most of the identified submergence responsive DEGs are associated with regulatory hormonal pathways, i.e. gibberellins (GA), abscisic acid (ABA), and jasmonic acid (JA), apart from ethylene. An in-silico promoter analysis of the two genotypes revealed the presence of allele-specific single nucleotide polymorphisms, giving rise to ABRE, DRE/CRT, CARE and Site II cis-elements, which can partly explain the observed differential TF gene expression.ConclusionThis study identified new gene targets with the potential to further enhance submergence tolerance in rice and provides insights into novel aspects of SUB1A-mediated tolerance.

Project description:Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

Dataset Information

Effects of sequence variation on differential allelic transcription factor occupancy and gene expression.

Publications

Effects of sequence variation on differential allelic transcription factor occupancy and gene expression.

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