Browsing by Subject "transcription factors"

Now showing 1 - 7 of 7

A Bacteriophage Tailspike Domain Promotes Self-Cleavage of a Human Membrane-Bound Transcription Factor, the Myelin Regulatory Factor MYRF
(PLOS Biology, 2013-08-13) Li, Zhihua; Park, Yungki; Marcotte, Edward M.
Myelination of the central nervous system (CNS) is critical to vertebrate nervous systems for efficient neural signaling. CNS myelination occurs as oligodendrocytes terminally differentiate, a process regulated in part by the myelin regulatory factor, MYRF. Using bioinformatics and extensive biochemical and functional assays, we find that MYRF is generated as an integral membrane protein that must be processed to release its transcription factor domain from the membrane. In contrast to most membrane-bound transcription factors, MYRF proteolysis seems constitutive and independent of cell- and tissue-type, as we demonstrate by reconstitution in E. coli and yeast. The apparent absence of physiological cues raises the question as to how and why MYRF is processed. By using computational methods capable of recognizing extremely divergent sequence homology, we identified a MYRF protein domain distantly related to bacteriophage tailspike proteins. Although occurring in otherwise unrelated proteins, the phage domains are known to chaperone the tailspike proteins' trimerization and auto-cleavage, raising the hypothesis that the MYRF domain might contribute to a novel activation method for a membrane-bound transcription factor. We find that the MYRF domain indeed serves as an intramolecular chaperone that facilitates MYRF trimerization and proteolysis. Functional assays confirm that the chaperone domain-mediated auto-proteolysis is essential both for MYRF's transcriptional activity and its ability to promote oligodendrocyte maturation. This work thus reveals a previously unknown key step in CNS myelination. These data also reconcile conflicting observations of this protein family, different members of which have been identified as transmembrane or nuclear proteins. Finally, our data illustrate a remarkable evolutionary repurposing between bacteriophages and eukaryotes, with a chaperone domain capable of catalyzing trimerization-dependent auto-proteolysis in two entirely distinct protein and cellular contexts, in one case participating in bacteriophage tailspike maturation and in the other activating a key transcription factor for CNS myelination.
Epigenetic Control of Gonadal Aromatase (cyp19a1) in Temperature-Dependent Sex Determination of Red-Eared Slider Turtles
(PLOS One, 2013-06-07) Matsumoto, Yuiko; Buemio, Alvin; Chu, Randy; Vafaee, Mozhgon; Crews, David
In the red-eared slider turtle (Trachemys scripta), a species with temperature-dependent sex determination (TSD), the expression of the aromatase gene during gonad development is strictly limited to the female-producing temperature. The underlying mechanism remains unknown. In this study, we identified the upstream 5′-flanking region of the aromatase gene, gonad-specific promoter, and the temperature-dependent DNA methylation signatures during gonad development in the red-eared slider turtle. The 5′-flanking region of the slider aromatase exhibited sequence similarities to the aromatase genes of the American alligator, chicken, quail, and zebra finch. A putative TATA box was located 31 bp upstream of the gonad-specific transcription start site. DNA methylation at the CpG sites between the putative binding sites of the fork head domain factor (FOX) and vertebrate steroidogenic factor 1 (SF1) and adjacent TATA box in the promoter region were significantly lower in embryonic gonads at the female-producing temperature compared the male-producing temperature. A shift from male- to female-, but not from female- to male-, producing temperature changed the level of DNA methylation in gonads. Taken together these results indicate that the temperature, particularly female-producing temperature, allows demethylation at the specific CpG sites of the promoter region which leads the temperature-specific expression of aromatase during gonad development.
Identification of Novel sRNAs in Mycobacterial Species
(PLOS One, 2013-11-14) Tsai, Chen-Hsun; Baranowski, Catherine; Livny, Jonathan; McDonough, Kathleen A.; Wade, Joseph T.; Contreras, Lydia M.
Bacterial small RNAs (sRNAs) are short transcripts that typically do not encode proteins and often act as regulators of gene expression through a variety of mechanisms. Regulatory sRNAs have been identified in many species, including Mycobacterium tuberculosis, the causative agent of tuberculosis. Here, we use a computational algorithm to predict sRNA candidates in the mycobacterial species M. smegmatis and M. bovis BCG and confirmed the expression of many sRNAs using Northern blotting. Thus, we have identified 17 and 23 novel sRNAs in M. smegmatis and M. bovis BCG, respectively. We have also applied a high-throughput technique (Deep-RACE) to map the 5′ and 3′ ends of many of these sRNAs and identified potential regulators of sRNAs by analysis of existing ChIP-seq datasets. The sRNAs identified in this work likely contribute to the unique biology of mycobacteria.
Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining
(BMC Bioinformatics, 2007-11-15) Morgan, Xochitl; Ni, Shulin; Miranker, Daniel P.; Iyer, Vishwanath R.
Background: Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cis-regulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment. -- Results: Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature. -- Conclusion: Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription factor.
Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data
(BMC Genetics, 2012-09-05) Ni, Yunyun; Weber Hall, Amelia; Battenhouse, Anna; Iyer, Vishwanath R.
Background: Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available. Results: In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes. Conclusions: Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples.
Simultaneous SNP Identification and Assessment of Allele-Specific Bias From ChiP-Seq Data
(2012-09) Ni, Yunyun; Hall, Amelia Weber; Battenhouse, Anna; Iyer, Vishwanath. R.; Ni, Yunyun; Hall, Amelia Weber; Battenhouse, Anna; Iyer, Vishwanath. R.
Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available. Results: In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes. Conclusions: Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples.
Widespread Misinterpretable ChIP-seq Bias in Yeast
(PLOS One, 2013-12-09) Park, Daechan; Lee, Yaelim; Bhupindersingh, Gurvani; Lyer, Vishwanath R.
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors appear to consistently bind to the gene bodies of highly transcribed genes in yeast. Strikingly, several types of negative control experiments, including a protein that is not expected to bind chromatin, also showed similar patterns of strong binding within gene bodies. These false positive signals were evident across sequencing platforms and immunoprecipitation protocols, as well as in previously published datasets from other labs. We show that these false positive signals derive from high rates of transcription, and are inherent to the ChIP procedure, although they are exacerbated by sequencing library construction procedures. This expression bias is strong enough that a known transcriptional repressor like Tup1 can erroneously appear to be an activator. Another type of background bias stems from the inherent nucleosomal structure of chromatin, and can potentially make it seem like certain factors bind nucleosomes even when they don't. Our analysis suggests that a mock ChIP sample offers a better normalization control for the expression bias, whereas the ChIP input is more appropriate for the nucleosomal periodicity bias. While these controls alleviate the effect of the biases to some extent, they are unable to eliminate it completely. Caution is therefore warranted regarding the interpretation of data that seemingly show the association of various transcription and chromatin factors with highly transcribed genes in yeast.