Christopher L. Warren

A Library of Yeast Transcription Factor Motifs Reveals a Widespread Function for Rsc3 in Targeting Nucleosome Exclusion at Promoters

The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes. One-third of the binding specificities have not been previously reported. Several binding sequences have striking genomic distributions relative to transcription start sites, supporting their biological relevance and suggesting a role in promoter architecture. Among these are Rsc3 binding sequences, containing the core CGCG, which are found preferentially approximately 100 bp upstream of transcription start sites. Mutation of RSC3 results in a dramatic increase in nucleosome occupancy in hundreds of proximal promoters containing a Rsc3 binding element, but has little impact on promoters lacking Rsc3 binding sequences, indicating that Rsc3 plays a broad role in targeting nucleosome exclusion at yeast promoters.

Genome-Wide Distribution of Yeast RNA Polymerase II and Its Control by Sen1 Helicase

Functional engagement of RNA polymerase II (Pol II) with eukaryotic chromosomes is a fundamental and highly regulated biological process. Here we present a high-resolution map of Pol II occupancy across the entire yeast genome. We compared a wild-type strain with a strain bearing a substitution in the Sen1 helicase, which is a Pol II termination factor for noncoding RNA genes. The wild-type pattern of Pol II distribution provides unexpected insights into the mechanisms by which genes are repressed or silenced. Remarkably, a single amino acid substitution that compromises Sen1 function causes profound changes in Pol II distribution over both noncoding and protein-coding genes, establishing an important function of Sen1 in the regulation of transcription. Given the strong similarity of the yeast and human Sen1 proteins, our results suggest that progressive neurological disorders caused by substitutions in the human Sen1 homolog Senataxin may be due to misregulation of transcription.

Specificity landscapes of DNA binding molecules elucidate biological function.

Evaluating the specificity spectra of DNA binding molecules is a nontrivial challenge that hinders the ability to decipher gene regulatory networks or engineer molecules that act on genomes. Here we compare the DNA sequence specificities for different classes of proteins and engineered DNA binding molecules across the entire sequence space. These high-content data are visualized and interpreted using an interactive “specificity landscape” which simultaneously displays the affinity and specificity of a million-plus DNA sequences. Contrary to expectation, specificity landscapes reveal that synthetic DNA ligands match, and often surpass, the specificities of eukaryotic DNA binding proteins. The landscapes also identify differential specificity constraints imposed by diverse structural folds of natural and synthetic DNA binders. Importantly, the sequence context of a binding site significantly influences binding energetics, and utilizing the full contextual information permits greater accuracy in annotating regulatory elements within a given genome. Assigning such context-dependent binding values to every DNA sequence across the genome yields predictive genome-wide binding landscapes (genomescapes). A genomescape of a synthetic DNA binding molecule provided insight into its differential regulatory activity in cultured cells. The approach we describe will accelerate the creation of precision-tailored DNA therapeutics and uncover principles that govern sequence-specificity of DNA binding molecules.

The conserved bromo-adjacent homology domain of yeast Orc1 functions in the selection of DNA replication origins within chromatin

The origin recognition complex (ORC) binds to the specific positions on chromosomes that serve as DNA replication origins. Although ORC is conserved from yeast to humans, the DNA sequence elements that specify ORC binding are not. In particular, metazoan ORC shows no obvious DNA sequence specificity, whereas yeast ORC binds to a specific DNA sequence within all yeast origins. Thus, whereas chromatin must play an important role in metazoan ORCs ability to recognize origins, it is unclear whether chromatin plays a role in yeast ORCs recognition of origins. This study focused on the role of the conserved N-terminal bromo-adjacent homology domain of yeast Orc1 (Orc1BAH). Recent studies indicate that BAH domains are chromatin-binding modules. We show that the Orc1BAH domain was necessary for ORCs stable association with yeast chromosomes, and was physiologically relevant to DNA replication in vivo. This replication role was separable from the Orc1BAH domains previously defined role in transcriptional silencing. Genome-wide analyses of ORC binding in ORC1 and orc1bahDelta cells revealed that the Orc1BAH domain contributed to ORCs association with most yeast origins, including a class of origins highly dependent on the Orc1BAH domain for ORC association (orc1bahDelta-sensitive origins). Orc1bahDelta-sensitive origins required the Orc1BAH domain for normal activity on chromosomes and plasmids, and were associated with a distinct local nucleosome structure. These data provide molecular insights into how the Orc1BAH domain contributes to ORCs selection of replication origins, as well as new tools for examining conserved mechanisms governing ORCs selection of origins within eukaryotic chromosomes.

Matrix Metalloproteinase-9 Genotype as a Potential Genetic Marker for Abdominal Aortic Aneurysm

Background—Degradation of extracellular matrix support in the large abdominal arteries contribute to abnormal dilation of aorta, leading to abdominal aortic aneurysms, and matrix metalloproteinase-9 (MMP-9) is the predominant enzyme targeting elastin and collagen present in the walls of the abdominal aorta. Previous studies have suggested a potential association between MMP-9 genotype and abdominal aortic aneurysm, but these studies have been limited only to the p-1562 and (CA) dinucleotide repeat microsatellite polymorphisms in the promoter region of the MMP-9 gene. We determined the functional alterations caused by 15 MMP-9 single-nucleotide polymorphisms (SNPs) reported to be relatively abundant in the human genome through Western blots, gelatinase, and promoter–reporter assays and incorporated this information to perform a logistic-regression analysis of MMP-9 SNPs in 336 human abdominal aortic aneurysm cases and controls. Methods and Results—Significant functional alterations were observed for 6 exon SNPs and 4 promoter SNPs. Genotype analysis of frequency-matched (age, sex, history of hypertension, hypercholesterolemia, and smoking) cases and controls revealed significant genetic heterogeneity exceeding 20% observed for 6 SNPs in our population of mostly white subjects from Northern Wisconsin. A step-wise logistic-regression analysis with 6 functional SNPs, where weakly contributing confounds were eliminated using Akaike information criteria, gave a final 2 SNP (D165N and p-2502) model with an overall odds ratio of 2.45 (95% confidence interval, 1.06–5.70). Conclusions—The combined approach of direct experimental confirmation of the functional alterations of MMP-9 SNPs and logistic-regression analysis revealed significant association between MMP-9 genotype and abdominal aortic aneurysm.

The Origin Recognition Complex Interacts with a Subset of Metabolic Genes Tightly Linked to Origins of Replication

The origin recognition complex (ORC) marks chromosomal sites as replication origins and is essential for replication initiation. In yeast, ORC also binds to DNA elements called silencers, where its primary function is to recruit silent information regulator (SIR) proteins to establish transcriptional silencing. Indeed, silencers function poorly as chromosomal origins. Several genetic, molecular, and biochemical studies of HMR-E have led to a model proposing that when ORC becomes limiting in the cell (such as in the orc2-1 mutant) only sites that bind ORC tightly (such as HMR-E) remain fully occupied by ORC, while lower affinity sites, including many origins, lose ORC occupancy. Since HMR-E possessed a unique non-replication function, we reasoned that other tight sites might reveal novel functions for ORC on chromosomes. Therefore, we comprehensively determined ORC “affinity” genome-wide by performing an ORC ChIP–on–chip in ORC2 and orc2-1 strains. Here we describe a novel group of orc2-1–resistant ORC–interacting chromosomal sites (ORF–ORC sites) that did not function as replication origins or silencers. Instead, ORF–ORC sites were comprised of protein-coding regions of highly transcribed metabolic genes. In contrast to the ORC–silencer paradigm, transcriptional activation promoted ORC association with these genes. Remarkably, ORF–ORC genes were enriched in proximity to origins of replication and, in several instances, were transcriptionally regulated by these origins. Taken together, these results suggest a surprising connection among ORC, replication origins, and cellular metabolism.

Single nucleotide polymorphism-specific regulation of matrix metalloproteinase-9 by multiple miRNAs targeting the coding exon

Microribonucleic acids (miRNAs) work with exquisite specificity and are able to distinguish a target from a non-target based on a single nucleotide mismatch in the core nucleotide domain. We questioned whether miRNA regulation of gene expression could occur in a single nucleotide polymorphism (SNP)-specific manner, manifesting as a post-transcriptional control of expression of genetic polymorphisms. In our recent study of the functional consequences of matrix metalloproteinase (MMP)-9 SNPs, we discovered that expression of a coding exon SNP in the pro-domain of the protein resulted in a profound decrease in the secreted protein. This missense SNP results in the N38S amino acid change and a loss of an N-glycosylation site. A systematic study demonstrated that the loss of secreted protein was due not to the loss of an N-glycosylation site, but rather an SNP-specific targeting by miR-671-3p and miR-657. Bioinformatics analysis identified 41 SNP-specific miRNA targeting MMP-9 SNPs, mostly in the coding exon and an extension of the analysis to chromosome 20, where the MMP-9 gene is located, suggesting that SNP-specific miRNAs targeting the coding exon are prevalent. This selective post-transcriptional regulation of a target messenger RNA harboring genetic polymorphisms by miRNAs offers an SNP-dependent post-transcriptional regulatory mechanism, allowing for polymorphic-specific differential gene regulation.

Mapping of Three Genetic Determinants of Susceptibility to Estrogen-Induced Mammary Cancer within the Emca8 Locus on Rat Chromosome 5

The ACI rat model of 17β-estradiol (E2)-induced mammary cancer has gained wide use in the study of breast cancer etiology, prevention, and genetics. Emca8, a QTL that determines susceptibility to E2-induced mammary cancer, was previously mapped to rat chromosome 5 (RNO5) in an intercross between resistant Brown Norway (BN) and susceptible ACI rats. In this study, a panel of congenic rat strains, each of which carries BN alleles across a defined segment of RNO5 on the ACI genetic background, was generated and used to map more precisely the Emca8 determinants of mammary cancer susceptibility. Three distinct genetic determinants were localized within Emca8, and two of these were mapped to intervals of less than 15 megabases. Emca8.1 harbors Cdkn2a, Cdkn2b, and other genes and is orthologous to the 9p21 breast cancer locus identified in genome-wide and candidate gene association studies. Emca8.2 harbors Cdkn2c and other genes and is orthologous to the 1p32 locus in humans that is frequently deleted in breast cancers. Both Emca8.1 and Emca8.2 harbor copy number variants that are orthologous to copy number variant regions in humans. Gene expression profiles were defined for mammary tissues from E2-treated ACI and ACI.BN-Emca8 rats to define the impact of Emca8 on gene expression and identify differentially expressed genes residing within Emca8.1 and Emca8.2. This study further illustrates the relevance of the ACI rat model of E2-induced mammary cancer for identifying novel genetic determinants of breast cancer susceptibility and defining the mechanisms through which estrogens contribute to breast cancer development. Cancer Prev Res; 6(1); 59–69. ©2012 AACR.

Cognate Site Identifier analysis reveals novel binding properties of the Sex Inducer homeodomain proteins of Cryptococcus neoformans

Homeodomain proteins function in fungi to specify cell types and control sexual development. In the meningoencephalitis‐causing fungal pathogen Cryptococcus neoformans, sexual development leads to the production of spores (suspected infectious particles). Sexual development is controlled by the homeodomain transcription factors Sxi1α and Sxi2a, but the mechanism by which they act is unknown. To understand how the Sxi proteins regulate development, we characterized their binding properties in vitro, showing that Sxi2a does not require a partner to bind DNA with high affinity. We then utilized a novel approach, Cognate Site Identifier (CSI) arrays, to define a comprehensive DNA‐binding profile for Sxi2a, revealing a consensus sequence distinct from those of other fungal homeodomain proteins. Finally, we show that the homeodomains of both Sxi proteins are required for sexual development, a departure from related fungi. Our findings support a model in which Sxi1α and Sxi2a control sexual development in a homeodomain‐dependent manner by binding to DNA sequences that differ from those defined in previously established fungal paradigms.

CSI-Tree: a regression tree approach for modeling binding properties of DNA-binding molecules based on cognate site identification (CSI) data

The identification and characterization of binding sites of DNA-binding molecules, including transcription factors (TFs), is a critical problem at the interface of chemistry, biology and molecular medicine. The Cognate Site Identification (CSI) array is a high-throughput microarray platform for measuring comprehensive recognition profiles of DNA-binding molecules. This technique produces datasets that are useful not only for identifying binding sites of previously uncharacterized TFs but also for elucidating dependencies, both local and nonlocal, between the nucleotides at different positions of the recognition sites. We have developed a regression tree technique, CSI-Tree, for exploring the spectrum of binding sites of DNA-binding molecules. Our approach constructs regression trees utilizing the CSI data of unaligned sequences. The resulting model partitions the binding spectrum into homogeneous regions of position specific nucleotide effects. Each homogeneous partition is then summarized by a position weight matrix (PWM). Hence, the final outcome is a binding intensity rank-ordered collection of PWMs each of which spans a different region in the binding spectrum. Nodes of the regression tree depict the critical position/nucleotide combinations. We analyze the CSI data of the eukaryotic TF Nkx-2.5 and two engineered small molecule DNA ligands and obtain unique insights into their binding properties. The CSI tree for Nkx-2.5 reveals an interaction between two positions of the binding profile and elucidates how different nucleotide combinations at these two positions lead to different binding affinities. The CSI trees for the engineered DNA ligands exhibit a common preference for the dinucleotide AA in the first two positions, which is consistent with preference for a narrow and relatively flat minor groove. We carry out a reanalysis of these data with a mixture of PWMs approach. This approach is an advancement over the simple PWM model and accommodates position dependencies based on only sequence data. Our analysis indicates that the dependencies revealed by the CSI-Tree are challenging to discover without the actual binding intensities. Moreover, such a mixture model is highly sensitive to the number and length of the sequences analyzed. In contrast, CSI-Tree provides interpretable and concise summaries of the complete recognition profiles of DNA-binding molecules by utilizing binding affinities.