Person:

Bulyk, Martha

Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein–DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the (in) (vitro) DNA binding specificities of 19 (Caenorhabditis) (elegans) basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH–DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans.

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible (\kappa)-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families.

The UniPROBE (Universal PBM Resource for Oligonucleotide Binding Evaluation) database hosts data generated by universal protein binding microarray (PBM) technology on the in vitro DNA-binding specificities of proteins. This initial release of the UniPROBE database provides a centralized resource for accessing comprehensive PBM data on the preferences of proteins for all possible sequence variants (‘words’) of length k (‘k-mers’), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. In total, the database hosts DNA-binding data for over 175 nonredundant proteins from a diverse collection of organisms, including the prokaryote Vibrio harveyi, the eukaryotic malarial parasite Plasmodium falciparum, the parasitic Apicomplexan Cryptosporidium parvum, the yeast Saccharomyces cerevisiae, the worm Caenorhabditis elegans, mouse and human. Current web tools include a text-based search, a function for assessing motif similarity between user-entered data and database PWMs, and a function for locating putative binding sites along user-entered nucleotide sequences. The UniPROBE database is available at http://thebrain.bwh.harvard.edu/uniprobe/.

Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein’s function is not trivial. Protein design tools are commonly used to explain mutations that affect protein stability, or protein–protein interaction, but not for mutations that could affect protein–DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several diseases such as aniridia. The validity of FoldX to deal with protein–DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos. We conclude that 88% of the Pax6 mutations can be linked to changes in intrinsic stability (77%) and/or to its capabilities to bind DNA (30%). Our study emphasizes the importance of structure-based analysis to understand the molecular basis of diseases and shows that protein–DNA interactions can be analyzed to the same level of accuracy as protein stability, or protein–protein interactions.

Background: Calorie restriction (CR) is the only intervention known to extend lifespan in a wide range of organisms, including mammals. However, the mechanisms by which it regulates mammalian aging remain largely unknown, and the involvement of the TOR and sirtuin pathways (which regulate aging in simpler organisms) remain controversial. Additionally, females of most mammals appear to live longer than males within species; and, although it remains unclear whether this holds true for mice, the relationship between sex-biased and CR-induced gene expression remains largely unexplored. Methodology/Principal Findings: We generated microarray gene expression data from livers of male mice fed high calorie or CR diets, and we find that CR significantly changes the expression of over 3,000 genes, many between 10- and 50-fold. We compare our data to the GenAge database of known aging-related genes and to prior microarray expression data of genes expressed differently between male and female mice. CR generally feminizes gene expression and many of the most significantly changed individual genes are involved in aging, hormone signaling, and p53-associated regulation of the cell cycle and apoptosis. Among the genes showing the largest and most statistically significant CR-induced expression differences are Ddit4, a key regulator of the TOR pathway, and Nnmt, a regulator of lifespan linked to the sirtuin pathway. Using western analysis we confirmed post-translational inhibition of the TOR pathway. Conclusions: Our data show that CR induces widespread gene expression changes and acts through highly evolutionarily conserved pathways, from microorganisms to mammals, and that its life-extension effects might arise partly from a shift toward a gene expression profile more typical of females.

To elucidate the transcriptional regulation of Bmp4 expression during organogenesis, we used phylogenetic footprinting and transgenic reporter analyses to identify Bmp4 cis-regulatory modules (CRMs). These analyses identified a regulatory region located (\sim)46 kb upstream of the mouse Bmp4 transcription start site that had previously been shown to direct expression in lateral plate mesoderm. We refined this regulatory region to a 396-bp minimal enhancer, and show that it recapitulates features of endogenous Bmp4 expression in developing mandibular arch ectoderm and incisor epithelium during the initiation-stage of tooth development. In addition, this enhancer directs expression in the apical ectodermal ridge (AER) of the developing limb and in anterior and posterior limb mesenchyme. Transcript profiling of E11.5 mouse incisor dental lamina, together with protein binding microarray (PBM) analyses, allowed identification of a conserved DNA binding motif in the Bmp4 enhancer for Pitx homeoproteins, which are also expressed in the developing mandibular and incisor epithelium. In vitro electrophoretic mobility shift assays (EMSA) and in vivo transgenic reporter mutational analyses revealed that this site supports Pitx binding and that the site is necessary to recapitulate aspects of endogenous Bmp4 expression in developing craniofacial and limb tissues. Finally, Pitx2 chromatin immunoprecipitation (ChIP) demonstrated direct binding of Pitx2 to this Bmp4 enhancer site in a dental epithelial cell line. These results establish a direct molecular regulatory link between Pitx family members and Bmp4 gene expression in developing incisor epithelium.

Background: Skin aging is associated with intrinsic processes that compromise the structure of the extracellular matrix while promoting loss of functional and regenerative capacity. These processes are accompanied by a large-scale shift in gene expression, but underlying mechanisms are not understood and conservation of these mechanisms between humans and mice is uncertain. Results: We used genome-wide expression profiling to investigate the aging skin transcriptome. In humans, age-related shifts in gene expression were sex-specific. In females, aging increased expression of transcripts associated with T-cells, B-cells and dendritic cells, and decreased expression of genes in regions with elevated Zeb1, AP-2 and YY1 motif density. In males, however, these effects were contrasting or absent. When age-associated gene expression patterns in human skin were compared to those in tail skin from CB6F1 mice, overall human-mouse correspondence was weak. Moreover, inflammatory gene expression patterns were not induced with aging of mouse tail skin, and well-known aging biomarkers were in fact decreased (e.g., Clec7a, Lyz1 and Lyz2). These unexpected patterns and weak human-mouse correspondence may be due to decreased abundance of antigen presenting cells in mouse tail skin with age. Conclusions: Aging is generally associated with a pro-inflammatory state, but we have identified an exception to this pattern with aging of CB6F1 mouse tail skin. Aging therefore does not uniformly heighten inflammatory status across all mouse tissues. Furthermore, we identified both intercellular and intracellular mechanisms of transcriptome aging, including those that are sex- and species-specific.

Members of the large ETS family of transcription factors (TFs) have highly similar DNA-binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA-binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA-binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high-throughput microwell-based TF DNA-binding specificity assay, and protein-binding microarrays (PBMs). Both approaches reveal that the ETS-binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino-acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

The Universal PBM Resource for Oligonucleotide-Binding Evaluation (UniPROBE) database is a centralized repository of information on the DNA-binding preferences of proteins as determined by universal protein-binding microarray (PBM) technology. Each entry for a protein (or protein complex) in UniPROBE provides the quantitative preferences for all possible nucleotide sequence variants (‘words’) of length k (‘k-mers’), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. In this update, we describe >130% expansion of the database content, incorporation of a protein BLAST (blastp) tool for finding protein sequence matches in UniPROBE, the introduction of UniPROBE accession numbers and additional database enhancements. The UniPROBE database is available at http://uniprobe.org.