A systematic genome-wide account of binding sites for the model transcription factor Gcn4

Multilevel Regulation of Membrane Proteins in Response to Metal and Metalloid Stress: A Lesson from Yeast

In the face of flourishing industrialization and global trade, heavy metal and metalloid contamination of the environment is a growing concern throughout the world. The widespread presence of highly toxic compounds of arsenic, antimony, and cadmium in nature poses a particular threat to human health. Prolonged exposure to these toxins has been associated with severe human diseases, including cancer, diabetes, and neurodegenerative disorders. These toxins are known to induce analogous cellular stresses, such as DNA damage, disturbance of redox homeostasis, and proteotoxicity. To overcome these threats and improve or devise treatment methods, it is crucial to understand the mechanisms of cellular detoxification in metal and metalloid stress. Membrane proteins are key cellular components involved in the uptake, vacuolar/lysosomal sequestration, and efflux of these compounds; thus, deciphering the multilevel regulation of these proteins is of the utmost importance. In this review, we summarize data on the mechanisms of arsenic, antimony, and cadmium detoxification in the context of membrane proteome. We used yeast Saccharomyces cerevisiae as a eukaryotic model to elucidate the complex mechanisms of the production, regulation, and degradation of selected membrane transporters under metal(loid)-induced stress conditions. Additionally, we present data on orthologues membrane proteins involved in metal(loid)-associated diseases in humans.

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO)

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.

Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode

Gene expression changes are orchestrated by transcription factors (TFs), which bind to DNA to regulate gene expression. It remains surprisingly difficult to predict basic features of the transcriptional process, including in vivo TF occupancy. Existing thermodynamic models of TF function are often not concordant with experimental measurements, suggesting undiscovered biology. Here, we analyzed one of the most well-studied TFs, the yeast zinc cluster Gal4, constructed a Shea-Ackers thermodynamic model to describe its binding, and compared the results of this model to experimentally measured Gal4p binding in vivo. We found that at many promoters, the model predicted no Gal4p binding, yet substantial binding was observed. These outlier promoters lacked canonical binding motifs, and subsequent investigation revealed Gal4p binds unexpectedly to DNA sequences with high densities of its half site (CGG). We confirmed this novel mode of binding through multiple experimental and computational paradigms; we also found most other zinc cluster TFs we tested frequently utilize this binding mode, at 27% of their targets on average. Together, these results demonstrate a novel mode of binding where zinc clusters, the largest class of TFs in yeast, bind DNA sequences with high densities of half sites.

Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors

Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. Dimerization of BASIC LEUCINE ZIPPER (bZIP) TFs are critical for their functions, but the molecular mechanism underlying the DNA binding and functional specificity of homo- versus heterodimers remains elusive. To address this gap, we present the double DNA Affinity Purification-sequencing (dDAP-seq) technique that maps heterodimer binding sites on endogenous genomic DNA. Using dDAP-seq we profile twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis and show that heterodimerization significantly expands the DNA binding preferences of these TFs. Analysis of dDAP-seq binding sites reveals the function of bZIP9 in abscisic acid response and the role of bZIP53 heterodimer-specific binding in seed maturation. The C/S1 heterodimers show distinct preferences for the ACGT elements recognized by plant bZIPs and motifs resembling the yeast GCN4 cis-elements. This study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.