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Biomedical subjects

Martha L Bulyk

Publications and source records attributed to Martha L Bulyk.

17 recordsLinked to original sources
PubMed2025

Missense variants in human forkhead transcription factors reveal determinants of forkhead DNA bispecificity.

Recognition of specific DNA sequences by transcription factors (TFs) is a key step in transcriptional control of gene expression. While most forkhead (FH) TFs bind either an FKH (RYAAAYA) or an FHL (GACGC) recognition motif, some FHs can bind both motifs. Mechanisms that control whether an FH is monospecific vs. bispecific have remained unknown. Screening a library of 12 reference FH proteins, 61 naturally occurring missense variants including clinical variants, and 22 designed mutant FHs for DNA-binding activity using universal ("all 10-mer") protein-binding microarrays revealed non-DNA-contacting residues that control mono- vs. bispecificity. Variation in non-DNA-contacting amino acid residues of TFs is associated with human traits and may play a role in the evolution of TF DNA-binding activities and gene regulatory networks.

Humans↗
PubMed2006

Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities.

Transcription factors (TFs) interact with specific DNA regulatory sequences to control gene expression throughout myriad cellular processes. However, the DNA binding specificities of only a small fraction of TFs are sufficiently characterized to predict the sequences that they can and cannot bind. We present a maximally compact, synthetic DNA sequence design for protein binding microarray (PBM) experiments that represents all possible DNA sequence variants of a given length k (that is, all 'k-mers') on a single, universal microarray. We constructed such all k-mer microarrays covering all 10-base pair (bp) binding sites by converting high-density single-stranded oligonucleotide arrays to double-stranded (ds) DNA arrays. Using these microarrays we comprehensively determined the binding specificities over a full range of affinities for five TFs of different structural classes from yeast, worm, mouse and human. The unbiased coverage of all k-mers permits high-throughput interrogation of binding site preferences, including nucleotide interdependencies, at unprecedented resolution.

Animals↗
PubMed2006

DNA microarray technologies for measuring protein-DNA interactions.

DNA-binding proteins have key roles in many cellular processes, including transcriptional regulation and replication. Microarray-based technologies permit the high-throughput identification of binding sites and enable the functional roles of these binding proteins to be elucidated. In particular, microarray readout either of chromatin immunoprecipitated DNA-bound proteins (ChIP-chip) or of DNA adenine methyltransferase fusion proteins (DamID) enables the identification of in vivo genomic target sites of proteins. A complementary approach to analyse the in vitro binding of proteins directly to double-stranded DNA microarrays (protein binding microarrays; PBMs), permits rapid characterization of their DNA binding site sequence specificities. Recent advances in DNA microarray synthesis technologies have facilitated the definition of DNA-binding sites at much higher resolution and coverage, and advances in these and emerging technologies will further increase the efficiencies of these exciting new approaches.

Animals↗
PubMed2006

Expression-guided in silico evaluation of candidate cis regulatory codes for Drosophila muscle founder cells.

While combinatorial models of transcriptional regulation can be inferred for metazoan systems from a priori biological knowledge, validation requires extensive and time-consuming experimental work. Thus, there is a need for computational methods that can evaluate hypothesized cis regulatory codes before the difficult task of experimental verification is undertaken. We have developed a novel computational framework (termed "CodeFinder") that integrates transcription factor binding site and gene expression information to evaluate whether a hypothesized transcriptional regulatory model (TRM; i.e., a set of co-regulating transcription factors) is likely to target a given set of co-expressed genes. Our basic approach is to simultaneously predict cis regulatory modules (CRMs) associated with a given gene set and quantify the enrichment for combinatorial subsets of transcription factor binding site motifs comprising the hypothesized TRM within these predicted CRMs. As a model system, we have examined a TRM experimentally demonstrated to drive the expression of two genes in a sub-population of cells in the developing Drosophila mesoderm, the somatic muscle founder cells. This TRM was previously hypothesized to be a general mode of regulation for genes expressed in this cell population. In contrast, the present analyses suggest that a modified form of this cis regulatory code applies to only a subset of founder cell genes, those whose gene expression responds to specific genetic perturbations in a similar manner to the gene on which the original model was based. We have confirmed this hypothesis by experimentally discovering six (out of 12 tested) new CRMs driving expression in the embryonic mesoderm, four of which drive expression in founder cells.

Amino Acid Motifs↗
PubMed2006

Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data.

BACKGROUND: A key step in the regulation of gene expression is the sequence-specific binding of transcription factors (TFs) to their DNA recognition sites. However, elucidating TF binding site (TFBS) motifs in higher eukaryotes has been challenging, even when employing cross-species sequence conservation. We hypothesized that for human and mouse, many orthologous genes expressed in a similarly tissue-specific manner in both human and mouse gene expression data, are likely to be co-regulated by orthologous TFs that bind to DNA sequence motifs present within noncoding sequence conserved between these genomes. RESULTS: We performed automated motif searching and merging across four different motif finding algorithms, followed by filtering of the resulting motifs for those that contain blocks of information content. Applying this motif finding strategy to conserved noncoding regions surrounding co-expressed tissue-specific human genes allowed us to discover both previously known, and many novel candidate, regulatory DNA motifs in all 18 tissue-specific expression clusters that we examined. For previously known TFBS motifs, we observed that if a TF was expressed in the specified tissue of interest, then in most cases we identified a motif that matched its TRANSFAC motif; conversely, of all those discovered motifs that matched TRANSFAC motifs, most of the corresponding TF transcripts were expressed in the tissue(s) corresponding to the expression cluster for which the motif was found. CONCLUSION: Our results indicate that the integration of the results from multiple motif finding tools identifies and ranks highly more known and novel motifs than does the use of just one of these tools. In addition, we believe that our simultaneous enrichment strategies helped to identify likely human cis regulatory elements. A number of the discovered motifs may correspond to novel binding site motifs for as yet uncharacterized tissue-specific TFs. We expect this strategy to be useful for identifying motifs in other metazoan genomes.

Algorithms↗
PubMed2006

Analysis of sequence specificities of DNA-binding proteins with protein binding microarrays.

DNA-binding proteins are important for various cellular processes, such as transcriptional regulation, recombination, replication, repair, and DNA modification. Of particular interest are transcription factors (TFs), since through interactions with their DNA binding sites, they modulate gene expression in a manner required for normal cellular growth and differentiation, and also for response to environmental stimuli. To date, the DNA-binding specificities of most DNA-binding proteins remain unknown, as earlier technologies aimed at characterizing DNA-protein interactions have been laborious and not highly scalable. New DNA microarray-based technology, termed protein binding microarrays (PBMs), has been developed that allows rapid, high-throughput characterization of in vitro DNA binding site sequence specificities of TFs or of any DNA binding protein. DNA binding site data from PBMs can be used to predict what genes are regulated by a given TF, what the functions are of a given TF and its predicted target genes, and how that TF may fit into the transcriptional regulatory networks of the cell.

Animals↗
PubMed2006

Protein binding microarrays (PBMs) for rapid, high-throughput characterization of the sequence specificities of DNA binding proteins.

DNA binding proteins play a number of key roles in cells, in processes including transcriptional regulation, recombination, genome rearrangements, and DNA replication, repair, and modification. Of particular interest are the interactions between transcription factors and their DNA binding sites, as they are an integral part of the transcriptional regulatory networks that control gene expression. Despite their importance, the DNA binding specificities of most DNA binding proteins remain unknown, as earlier technologies aimed at characterizing DNA-protein interactions have been time consuming and not highly scalable. We have developed a new DNA microarray-based technology, termed protein binding microarrays (PBMs), that allows rapid, high-throughput characterization of the in vitro DNA binding site sequence specificities of transcription factors in a single day. The resulting DNA binding site data can be used in a number of ways, including for the prediction of the genes regulated by a given transcription factor, annotation of transcription factor function, and functional annotation of the predicted target genes.

Base Sequence↗
PubMed2005

Modulefinder: a tool for computational discovery of cis regulatory modules.

Regulation of gene expression occurs largely through the binding of sequence-specific transcription factors (TFs) to genomic binding sites (BSs). We present a rigorous scoring scheme, implemented as a C program termed "ModuleFinder", that evaluates the likelihood that a given genomic region is a cis regulatory module (CRM) for an input set of TFs according to its degree of: (1) homotypic site clustering; (2) heterotypic site clustering; and (3) evolutionary conservation across multiple genomes. Importantly, ModuleFinder obtains all parameters needed to appropriately weight the relative contributions of these sequence features directly from the input sequences and TFBS motifs, and does not need to first be trained. Using two previously described collections of experimentally verified CRMs in mammals and in fly as validation datasets, we show that ModuleFinder is able to identify CRMs with great sensitivity and specificity.

Amino Acid Sequence↗
PubMed2004

Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays.

We developed a new DNA microarray-based technology, called protein binding microarrays (PBMs), that allows rapid, high-throughput characterization of the in vitro DNA binding-site sequence specificities of transcription factors in a single day. Using PBMs, we identified the DNA binding-site sequence specificities of the yeast transcription factors Abf1, Rap1 and Mig1. Comparison of these proteins' in vitro binding sites with their in vivo binding sites indicates that PBM-derived sequence specificities can accurately reflect in vivo DNA sequence specificities. In addition to previously identified targets, Abf1, Rap1 and Mig1 bound to 107, 90 and 75 putative new target intergenic regions, respectively, many of which were upstream of previously uncharacterized open reading frames. Comparative sequence analysis indicated that many of these newly identified sites are highly conserved across five sequenced sensu stricto yeast species and, therefore, are probably functional in vivo binding sites that may be used in a condition-specific manner. Similar PBM experiments should be useful in identifying new cis regulatory elements and transcriptional regulatory networks in various genomes.

Base Sequence↗
PubMed2004

A motif co-occurrence approach for genome-wide prediction of transcription-factor-binding sites in Escherichia coli.

Various computational approaches have been developed for predicting cis-regulatory DNA elements in prokaryotic genomes. We describe a novel method for predicting transcription-factor-binding sites in Escherichia coli. Our method takes advantage of the principle that transcription factors frequently coregulate gene expression, but without requiring prior knowledge of which groups of genes are coregulated. Using position weight matrices for 49 known transcription factors, we examined spacings between pairs of matrix hits. These pairs were assigned probabilities according to the overrepresentation of their separation distance. The functions of many open reading frames (ORFs) downstream from predicted binding sites are unknown, and may correspond to novel regulon members. For five predictions, knockouts with mutated replacements of the predicted binding sites were created in E. coli MG1655. Quantitative real-time PCR (RT-PCR) indicates that for each of the knockouts, at least one gene immediately downstream exhibits a statistically significant change in mRNA expression. This approach may be useful in analyzing binding sites in a variety of organisms.

Base Composition↗
PubMed2003

Computational prediction of transcription-factor binding site locations.

Identifying genomic locations of transcription-factor binding sites, particularly in higher eukaryotic genomes, has been an enormous challenge. Various experimental and computational approaches have been used to detect these sites; methods involving computational comparisons of related genomes have been particularly successful.

Animals↗
PubMed2002

Additivity in protein-DNA interactions: how good an approximation is it?

Man and Stormo and Bulyk et al. recently presented their results on the study of the DNA binding affinity of proteins. In both of these studies the main conclusion is that the additivity assumption, usually applied in methods to search for binding sites, is not true. In the first study, the analysis of binding affinity data from the Mnt repressor protein bound to all possible DNA (sub)targets at positions 16 and 17 of the binding site, showed that those positions are not independent. In the second study, the authors analysed DNA binding affinity data of the wild-type mouse EGR1 protein and four variants differing on the middle finger. The binding affinity of these proteins was measured to all 64 possible trinucleotide (sub)targets of the middle finger using microarray technology. The analysis of the measurements also showed interdependence among the positions in the DNA target. In the present report, we review the data of both studies and we re- analyse them using various statistical methods, including a comparison with a multiple regression approach. We conclude that despite the fact that the additivity assumption does not fit the data perfectly, in most cases it provides a very good approximation of the true nature of the specific protein-DNA interactions. Therefore, additive models can be very useful for the discovery and prediction of binding sites in genomic DNA.

Animals↗
PubMed2002

Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors.

We can determine the effects of many possible sequence variations in transcription factor binding sites using microarray binding experiments. Analysis of wild-type and mutant Zif268 (Egr1) zinc fingers bound to microarrays containing all possible central 3 bp triplet binding sites indicates that the nucleotides of transcription factor binding sites cannot be treated independently. This indicates that the current practice of characterizing transcription factor binding sites by mutating individual positions of binding sites one base pair at a time does not provide a true picture of the sequence specificity. Similarly, current bioinformatic practices using either just a consensus sequence, or even mononucleotide frequency weight matrices to provide more complete descriptions of transcription factor binding sites, are not accurate in depicting the true binding site specificities, since these methods rely upon the assumption that the nucleotides of binding sites exert independent effects on binding affinity. Our results stress the importance of complete reference tables of all possible binding sites for comparing protein binding preferences for various DNA sequences. We also show results suggesting that microarray binding data using particular subsets of all possible binding sites can be used to extrapolate the relative binding affinities of all possible full-length binding sites, given a known binding site for use as a starting sequence for site preference refinement.

Animals↗
PubMed2002

A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

Analysis of Variance↗