Flexible promoter architecture requirements for coactivator recruitment

Biochemical Identification of a Nuclear Coactivator Protein Required for AtrR-Dependent Gene Regulation in Aspergillus fumigatus

Azole drugs represent the primary means of treating infections associated with the filamentous fungal pathogen Aspergillus fumigatus. A central player in azole resistance is the Zn₂Cys₆ zinc cluster-containing transcription factor AtrR. This factor stimulates expression of both the cyp51A gene, which encodes the azole drug target enzyme, as well as an ATP-binding cassette transporter-encoding gene called abcG1 (cdr1B). We used a fusion protein between AtrR and the tandem affinity purification (TAP) moiety to purify proteins that associated with AtrR from A. fumigatus. Protein fractions associated with AtrR-TAP were subjected to multidimensional protein identification technology mass spectrometry, and one of the proteins identified was encoded by the AFUA_6g08010 gene. We have designated this protein NcaA (for nuclear coactivator of AtrR). Loss of ncaA caused a reduction in voriconazole resistance and drug-induced abcG1 expression, although it did not impact induction of cyp51A transcription. We confirmed the association of AtrR and NcaA by coimmunoprecipitation from otherwise-wild-type cells. Expression of fusion proteins between AtrR and NcaA with green fluorescent protein allowed determination that these two proteins were localized in the A. fumigatus nucleus. Together, these data support the view that NcaA is required for nuclear gene transcription controlled by AtrR. IMPORTANCE Aspergillus fumigatus is a major filamentous fungal pathogen in humans and is susceptible to the azole antifungal class of drugs. However, loss of azole susceptibility has been detected with increasing frequency in the clinic, and infections associated with these azole-resistant isolates have been linked to treatment failure and worse outcomes. Many of these azole-resistant strains contain mutant alleles of the cyp51A gene, which encodes the azole drug target. A transcription factor essential for cyp51A gene transcription has been identified and designated AtrR. AtrR is required for azole-inducible cyp51A transcription, but we know little of the regulation of this transcription factor. Using a biochemical approach, we identified a new protein called NcaA that is involved in regulation of AtrR at certain target gene promoters. Understanding the mechanisms controlling AtrR function is an important goal in preventing or reversing azole resistance in this pathogen.

The grammar of transcriptional regulation

Eukaryotes employ combinatorial strategies to generate a variety of expression patterns from a relatively small set of regulatory DNA elements. As in any other language, deciphering the mapping between DNA and expression requires an understanding of the set of rules that govern basic principles in transcriptional regulation, the functional elements involved, and the ways in which they combine to orchestrate a transcriptional output. Here, we review the current understanding of various grammatical rules, including the effect on expression of the number of transcription factor binding sites, their location, orientation, affinity and activity; co-association with different factors; and intrinsic nucleosome organization. We review different methods that are used to study the grammar of transcription regulation, highlight gaps in current understanding, and discuss how recent technological advances may be utilized to bridge them.

Mapping the fine structure of a eukaryotic promoter input-output function

The precise tuning of gene expression levels is essential for the optimal performance of transcriptional regulatory networks. We created 209 variants of the Saccharomyces cerevisiae PHO5 promoter to quantify how different binding sites for the transcription factor Pho4 affect its output. We found that transcription-factor binding affinities determined in vitro could quantitatively predict the output of a complex yeast promoter. Promoter output was precisely tunable by subtle changes in binding-site affinity of less than 3 kcal mol(-1), which are accessible by modifying 1-2 bases. Our results provide insights into how transcription-factor binding sites regulate gene expression, their possible evolution and how they can be used to precisely tune gene expression. More generally, we show that in vitro binding-energy landscapes of transcription factors can precisely predict the output of a native yeast promoter, indicating that quantitative models of transcriptional regulatory networks are feasible.

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Despite much research, our understanding of the rules by which cis-regulatory sequences are translated into expression levels is still lacking. We devised a method for obtaining parallel and highly accurate expression measurements of thousands of fully designed promoters, and applied it to measure the effect of systematic changes to location, number, orientation, affinity and organization of transcription factor (TF) binding sites and of nucleosome disfavoring sequences. Our analyses reveal a clear relationship between expression and binding site number, and TF-specific dependencies of expression on the distance between sites and gene starts including a striking ~10bp periodic relationship. We also demonstrate the utility of our approach for measuring TF sequence specificities and sensitivity of TF sites to surrounding sequence context, and for profiling the activity of most yeast transcription factors. Our method is readily applicable for studying both the cis and trans effects of genotype on transcriptional, post-transcriptional, and translational control.

Non-DNA-binding cofactors enhance DNA-binding specificity of a transcriptional regulatory complex

Recruitment of cofactors to specific DNA sites is integral for specificity in gene regulation. As a model system, we examined how targeting and transcriptional control of the sulfur metabolism genes in Saccharomyces cerevisiae is governed by recruitment of the transcriptional co-activator Met4. We developed genome-scale approaches to measure transcription factor (TF) DNA-binding affinities and cofactor recruitment to >1300 genomic binding site sequences. We report that genes responding to the TF Cbf1 and cofactor Met28 contain a novel 'recruitment motif' (RYAAT), adjacent to Cbf1 binding sites, which enhances the binding of a Met4-Met28-Cbf1 regulatory complex, and that abrogation of this motif significantly reduces gene induction under low-sulfur conditions. Furthermore, we show that correct recognition of this composite motif requires both non-DNA-binding cofactors Met4 and Met28. Finally, we demonstrate that the presence of an RYAAT motif next to a Cbf1 site, rather than Cbf1 binding affinity, specifies Cbf1-dependent sulfur metabolism genes. Our results highlight the need to examine TF/cofactor complexes, as novel specificity can result from cofactors that lack intrinsic DNA-binding specificity.

Genome-wide analysis of the cis-regulatory modules of divergent gene pairs in yeast

In budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated.

CRX ChIP-seq reveals the cis-regulatory architecture of mouse photoreceptors

Approximately 98% of mammalian DNA is noncoding, yet we understand relatively little about the function of this enigmatic portion of the genome. The cis-regulatory elements that control gene expression reside in noncoding regions and can be identified by mapping the binding sites of tissue-specific transcription factors. Cone-rod homeobox (CRX) is a key transcription factor in photoreceptor differentiation and survival, but its in vivo targets are largely unknown. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) on CRX to identify thousands of cis-regulatory regions around photoreceptor genes in adult mouse retina. CRX directly regulates downstream photoreceptor transcription factors and their target genes via a network of spatially distributed regulatory elements around each locus. CRX-bound regions act in a synergistic fashion to activate transcription and contain multiple CRX binding sites which interact in a spacing- and orientation-dependent manner to fine-tune transcript levels. CRX ChIP-seq was also performed on Nrl(-/-) retinas, which represent an enriched source of cone photoreceptors. Comparison with the wild-type ChIP-seq data set identified numerous rod- and cone-specific CRX-bound regions as well as many shared elements. Thus, CRX combinatorially orchestrates the transcriptional networks of both rods and cones by coordinating the expression of photoreceptor genes including most retinal disease genes. In addition, this study pinpoints thousands of noncoding regions of relevance to both Mendelian and complex retinal disease.

Dissection of combinatorial control by the Met4 transcriptional complex

Met4 is the transcriptional activator of the sulfur metabolic network in Saccharomyces cerevisiae. Lacking DNA-binding ability, Met4 must interact with proteins called Met4 cofactors to target promoters for transcription. Two types of DNA-binding cofactors (Cbf1 and Met31/Met32) recruit Met4 to promoters and one cofactor (Met28) stabilizes the DNA-bound Met4 complexes. To dissect this combinatorial system, we systematically deleted each category of cofactor(s) and analyzed Met4-activated transcription on a genome-wide scale. We defined a core regulon for Met4, consisting of 45 target genes. Deletion of both Met31 and Met32 eliminated activation of the core regulon, whereas loss of Met28 or Cbf1 interfered with only a subset of targets that map to distinct sectors of the sulfur metabolic network. These transcriptional dependencies roughly correlated with the presence of Cbf1 promoter motifs. Quantitative analysis of in vivo promoter binding properties indicated varying levels of cooperativity and interdependency exists between members of this combinatorial system. Cbf1 was the only cofactor to remain fully bound to target promoters under all conditions, whereas other factors exhibited different degrees of regulated binding in a promoter-specific fashion. Taken together, Met4 cofactors use a variety of mechanisms to allow differential transcription of target genes in response to various cues.

Repression of sulfate assimilation is an adaptive response of yeast to the oxidative stress of zinc deficiency

The Zap1 transcription factor is a central player in the response of yeast to changes in zinc status. Previous studies identified over 80 genes activated by Zap1 in zinc-limited cells. In this report, we identified 36 genes repressed in a zinc- and Zap1-responsive manner. As a result, we have identified a new mechanism of Zap1-mediated gene repression whereby transcription of the MET3, MET14, and MET16 genes is repressed in zinc-limited cells. These genes encode the first three enzymes of the sulfate assimilation pathway. We found that MET30, encoding a component of the SCF(Met30) ubiquitin ligase, is a direct Zap1 target gene. MET30 expression is increased in zinc-limited cells, and this leads to degradation of Met4, a transcription factor responsible for MET3, MET14, and MET16 expression. Thus, Zap1 is responsible for a decrease in sulfate assimilation in zinc-limited cells. We further show that cells that are unable to down-regulate sulfate assimilation under zinc deficiency experience increased oxidative stress. This increased oxidative stress is associated with an increase in the NADP(+)/NADPH ratio and may result from a decrease in NADPH-dependent antioxidant activities. These studies have led to new insights into how cells adapt to nutrient-limiting growth conditions.

A motif within a complex promoter from the oomycete Phytophthora infestans determines transcription during an intermediate stage of sporulation

Sporulation in Phytophthora infestans is associated with a major remodeling of the transcriptome. To better understand promoter structure and how sporulation-specific expression is determined in this organism, the Pks1 gene was analyzed. Pks1 encodes a protein kinase that is induced at an intermediate stage of sporulation, prior to sporangium maturation. Major and minor transcription start sites mapped throughout the promoter, which contains many T-rich stretches and Inr-like elements. Within the T-rich region are several motifs which bound nuclear proteins in EMSA. Tests of modified promoters in transformants implicated a CCGTTG located 110-nt upstream of the transcription start point as a major regulator of sporulation-specific transcription. The motif also bound a sporulation-specific nuclear protein complex. A bioinformatics analysis indicated that the motif is highly over-represented within co-expressed promoters, in which it predominantly resides 100-300-nt upstream of transcription start sites. Other sequences, such as a CATTTGTT motif, also bound nuclear proteins but did not play an essential role in spore-specific expression.

Transcriptional regulation of Saccharomyces cerevisiae CYS3 encoding cystathionine gamma-lyase

In studying the regulation of GSH11, the structural gene of the high-affinity glutathione transporter (GSH-P1) in Saccharomyces cerevisiae, a cis-acting cysteine responsive element, CCGCCACAC (CCG motif), was detected. Like GSH-P1, the cystathionine gamma-lyase encoded by CYS3 is induced by sulfur starvation and repressed by addition of cysteine to the growth medium. We detected a CCG motif (-311 to -303) and a CGC motif (CGCCACAC; -193 to -186), which is one base shorter than the CCG motif, in the 5'-upstream region of CYS3. One copy of the centromere determining element 1, CDE1 (TCACGTGA; -217 to -210), being responsible for regulation of the sulfate assimilation pathway genes, was also detected. We tested the roles of these three elements in the regulation of CYS3. Using a lacZ-reporter assay system, we found that the CCG/CGC motif is required for activation of CYS3, as well as for its repression by cysteine. In contrast, the CDE1 motif was responsible for only activation of CYS3. We also found that two transcription factors, Met4 and VDE, are responsible for activation of CYS3 through the CCG/CGC and CDE1 motifs. These observations suggest a dual regulation of CYS3 by factors that interact with the CDE1 motif and the CCG/CGC motifs.

Determining physical constraints in transcriptional initiation complexes using DNA sequence analysis

Eukaryotic gene expression is often under the control of cooperatively acting transcription factors whose binding is limited by structural constraints. By determining these structural constraints, we can understand the “rules” that define functional cooperativity. Conversely, by understanding the rules of binding, we can infer structural characteristics. We have developed an information theory based method for approximating the physical limitations of cooperative interactions by comparing sequence analysis to microarray expression data. When applied to the coordinated binding of the sulfur amino acid regulatory protein Met4 by Cbf1 and Met31, we were able to create a combinatorial model that can correctly identify Met4 regulated genes. Interestingly, we found that the major determinant of Met4 regulation was the sum of the strength of the Cbf1 and Met31 binding sites and that the energetic costs associated with spacing appeared to be minimal.

Genome clashes in hybrids: insights from gene expression

In interspecific hybrids, novel phenotypes often emerge from the interaction of two divergent genomes. Interactions between the two transcriptional networks are assumed to contribute to these unpredicted new phenotypes by inducing novel patterns of gene expression. Here we provide a review of the recent literature on the accumulation of regulatory incompatibilities. We review specific examples of regulatory incompatibilities reported at particular loci as well as genome-scale surveys of gene expression in interspecific hybrids. Finally, we consider and preview novel technologies that could help decipher how divergent transcriptional networks interact in hybrids between species.

Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites

Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP–chip experiments. Results from these experiments are publicly available at http://cagt10.bu.edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of transcription factor binding specificities.

A historically difficult problem in computational biology is the identification of transcription factor binding sites (TFBS) in the promoters of co-regulated genes. With increasing emphasis on research in transcriptional regulation, this problem is also uniquely relevant to emerging results from recent experiments in high-throughput and systems biology. Despite extensive research in the area, recent evaluations of previously published techniques show much room for improvement. In this paper, we introduce a fundamentally new approach to the identification of TFBS. First, we start by representing nucleotides in promoters as an undirected, weighted graph. Given this representation of a binding site graph (BSG), we employ relatively simple graph clustering techniques to identify functional TFBS. We show that BSG predictions significantly outperform all previously evaluated methods in nearly every performance measure using a standardized assessment benchmark. We also find that this approach is more robust than traditional Gibbs sampling to selection of input promoters, and thus more likely to perform well under noisy experimental conditions. Finally, BSGs are very good at predicting specificity determining nucleotides. Using BSG predictions, we were able to confirm recent experimental results on binding specificity of E-box TFs CBF1 and PHO4 and predict novel specificity determining nucleotides for TYE7.