Mutational processes in cancer preferentially affect binding of particular transcription factors

Protein binding microarrays provide comprehensive information about the DNA binding specificities of transcription factors (TFs), and can be used to quantitatively predict the effects of DNA sequence variation on TF binding. There has also been substantial progress in dissecting the patterns of mutations, i.e., the "mutational signatures", generated by different mutational processes. By combining these two layers of information we can investigate whether certain mutational processes tend to preferentially affect binding of particular classes of TFs. Such preferential alterations of binding might predispose to particular oncogenic pathways. We developed and implemented a method, termed "Signature-QBiC", that integrates protein binding microarray data with the signatures of mutational processes, with the aim of predicting which TFs’ binding profiles are preferentially perturbed by particular mutational processes. We used Signature-QBiC to predict the effects of 47 signatures of mutational processes on 582 human TFs. Pathway analysis showed that binding of TFs involved in NOTCH1 signaling is strongly affected by the signatures of several mutational processes, including exposure to ultraviolet radiation. Additionally, toll-like-receptor signaling pathways are also vulnerable to disruption by this exposure. This study provides a novel overview of the effects of mutational processes on TF binding and the potential of these processes to activate oncogenic pathways through mutating TF binding sites.

Adaptive gene misregulation

Because gene expression is important for evolutionary adaptation, its misregulation is an important cause of maladaptation. A misregulated gene can be incorrectly silent (“off”) when a transcription factor (TF) that is required for its activation does not binds its regulatory region. Conversely, a misregulated gene can be incorrectly active (“on”) when a TF not normally involved in its activation binds its regulatory region, a phenomenon also known as regulatory crosstalk. DNA mutations that destroy or create TF binding sites on DNA are an important source of misregulation and crosstalk. Although misregulation reduces fitness in an environment to which an organism is well-adapted, it may become adaptive in a new environment. Here, I derive simple yet general mathematical expressions that delimit the conditions under which misregulation can be adaptive. These expressions depend on the strength of selection against misregulation, on the fraction of DNA sequence space filled with TF binding sites, and on the fraction of genes that must be expressed for optimal adaptation. I then use empirical data from RNA sequencing, protein-binding microarrays, and genome evolution, together with population genetic simulations to ask when these conditions are likely to be met. I show that they can be met under realistic circumstances, but these circumstances may vary among organisms and environments. My analysis provides a framework in which improved theory and data collection can help us demonstrate the role of misregulation in adaptation. It also shows that misregulation, like DNA mutation, is one of life’s many imperfections that can help propel Darwinian evolution.

Cytokinin induces genome-wide binding of the type-B response regulator ARR10 to regulate growth and development in Arabidopsis

The plant hormone cytokinin affects a diverse array of growth and development processes and responses to the environment. How a signaling molecule mediates such a diverse array of outputs and how these response pathways are integrated with other inputs remain fundamental questions in plant biology. To this end, we characterized the transcriptional network initiated by the type-B ARABIDOPSIS RESPONSE REGULATORs (ARRs) that mediate the cytokinin primary response, making use of chromatin immunoprecipitation sequencing (ChIP-seq), protein-binding microarrays, and transcriptomic approaches. By ectopic overexpression of ARR10, Arabidopsis lines hypersensitive to cytokinin were generated and used to clarify the role of cytokinin in regulation of various physiological responses. ChIP-seq was used to identify the cytokinin-dependent targets for ARR10, thereby defining a crucial link between the cytokinin primary-response pathway and the transcriptional changes that mediate physiological responses to this phytohormone. Binding of ARR10 was induced by cytokinin with binding sites enriched toward the transcriptional start sites for both induced and repressed genes. Three type-B ARR DNA-binding motifs, determined by use of protein-binding microarrays, were enriched at ARR10 binding sites, confirming their physiological relevance. WUSCHEL was identified as a direct target of ARR10, with its cytokinin-enhanced expression resulting in enhanced shooting in tissue culture. Results from our analyses shed light on the physiological role of the type-B ARRs in regulating the cytokinin response, mechanism of type-B ARR activation, and basis by which cytokinin regulates diverse aspects of growth and development as well as responses to biotic and abiotic factors.

MotifMark: Finding Regulatory Motifs in DNA Sequences

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein binding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.

5-Hydroxymethylcytosine in E-box motifs ACAT|GTG and ACAC|GTG increases DNA-binding of the B-HLH transcription factor TCF4

We designed a novel method to double-strand Agilent microarrays such that 5mC and 5hmC are incorporated on one DNA strand. Using protein binding microarrays we demonstrate the utility of this method in exploring how cytosine modification outside of CG dinucleotide alter the DNA binding of sequence-specific transcription factors.