Transcription factor regulation and chromosome dynamics during pseudohyphal growth

Pseudohyphal growth is a developmental pathway seen in some strains of yeast in which cells form multicellular filaments in response to environmental stresses. We used multiplexed transposon “Calling Cards” to record the genome-wide binding patterns of 28 transcription factors (TFs) in nitrogen-starved yeast. We identified TF targets relevant for pseudohyphal growth, producing a detailed map of its regulatory network. Using tools from graph theory, we identified 14 TFs that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA-binding preferences for these key TFs were unknown. Using Calling Card data, we predicted the in vivo DNA-binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them more rapidly to execute the filamentous growth program when nitrogen starved if they had been previously exposed to this condition

Quantitative analysis of transcription factor binding and expression using Calling Cards Reporter Arrays

We report a tool, Calling Cards Reporter Arrays (CCRA), that measures transcription factor (TF) binding and the consequences on gene expression for hundreds of synthetic promoters in yeast. Using Cbf1p and MAX, we demonstrate that the CCRA method is able to detect small changes in binding free energy with a sensitivity comparable to in vitro methods, enabling the measurement of energy landscapes in vivo. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. We find that the cooperativity between Cbf1p dimers varies sinusoidally with a period of 10.65 bp and energetic cost of 1.37 KBT for sites that are positioned \u27out of phase\u27. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p and Gcr2p, that act together as a \u27TF collective\u27, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function

A systematic study of gene expression variation at single-nucleotide resolution reveals widespread regulatory roles for uAUGs

Regulatory single-nucleotide polymorphisms (rSNPs) alter gene expression. Common approaches for identifying rSNPs focus on sequence variants in conserved regions; however, it is unknown what fraction of rSNPs is undetectable using this approach. We present a systematic analysis of gene expression variation at the single-nucleotide level in the Saccharomyces cerevisiae GAL1-10 regulatory region. We exhaustively mutated nearly every base and measured the expression of each variant with a sensitive dual reporter assay. We observed an expression change for 7% (43/582) of the bases in this region, most of which (35/43, 81%) reside in conserved positions. The most dramatic changes were caused by variants that produced AUGs upstream of the translation start (uAUGs), and we sought to understand the consequences and molecular mechanisms underlying this class of mutations. A genome-wide analysis showed that genes with uAUGs display significantly lower mRNA and protein levels than genes without uAUGs. To determine the generality of this mechanism, we introduced uAUGs into S. cerevisiae genes and observed significantly reduced expression in 17/21 instances (p < 0.01), suggesting that uAUGs are functional in a wide variety of sequence contexts. Quantification of mRNA and protein levels for uAUG mutants showed that uAUGs affect both transcription and translation. Expression of uAUG mutants under the upf1Δ strain demonstrated that uAUGs stimulate the nonsense-mediated decay pathway. Our results suggest that uAUGs are potent and widespread regulators of gene expression that act by attenuating both protein and RNA levels

Transposase mapping identifies the genomic targets of BAP1 in uveal melanoma

Table summarizing the RNA-seq results. Differential gene expression results in BAP1-knockdown compared to control OCM-1A cells are shown from the RNA-seq data. Each row gives the unique Ensembl identifier, gene name, and description for each gene, as well as the log of the fold change (logFC), average expression, adjusted p-value, and linear fold change. (XLSX 1392 kb

TATA is a modular component of synthetic promoters

The expression of most genes is regulated by multiple transcription factors. The interactions between transcription factors produce complex patterns of gene expression that are not always obvious from the arrangement of cis-regulatory elements in a promoter. One critical element of promoters is the TATA box, the docking site for the RNA polymerase holoenzyme. Using a synthetic promoter system coupled to a thermodynamic model of combinatorial regulation, we analyze the effects of different strength TATA boxes on various aspects of combinatorial cis-regulation. The thermodynamic model explains 75% of the variance in gene expression in synthetic promoter libraries with different strength TATA boxes, suggesting that many of the salient aspects of cis-regulation are captured by the model. Our results demonstrate that the effect of changing the TATA box on gene expression is the same for all synthetic promoters regardless of the arrangement of cis-regulatory sites we studied. Our analysis also showed that in our synthetic system the strength of the RNA polymerase–TATA interaction does not alter the combinatorial interactions between transcription factors, or between transcription factors and RNA polymerase. Finally, we show that although stronger TATA boxes increase expression in a predictable fashion, stronger TATA boxes have very little effect on noise in our synthetic promoters, regardless of the arrangement of cis-regulatory sites. Our results support a modular model of promoter function, where cis-regulatory elements can be mixed and matched (programmed) with outcomes on expression that are predictable based on the rules of simple protein–protein and protein–DNA interactions

Mutation and expression analysis in medulloblastoma yields prognostic variants and a putative mechanism of disease for i17q tumors

Current consensus identifies four molecular subtypes of medulloblastoma (MB): WNT, sonic hedgehog (SHH), and groups “3/C” and “4/D”. Group 4 is not well characterized, but harbors the most frequently observed chromosomal abnormality in MB, i17q, whose presence may confer a worse outcome. Recent publications have identified mutations in chromatin remodeling genes that may be overrepresented in this group, suggesting a biological role for these genes in i17q. This work seeks to explore the pathology that underlies i17q in MB. Specifically, we examine the prognostic significance of the previously-identified gene mutations in an independent set of MBs as well as to examine biological relevance of these genes and related pathways by gene expression profiling. The previously-implicated p53 signaling pathway is also examined as a putative driver of i17q tumor oncogenesis. The data show gene mutations associated with i17q tumors in previous studies (KMD6A, ZMYM3, MLL3 and GPS2) were correlated with significantly worse outcomes despite not being specific to i17q in this set. Expression of these genes did not appear to underlie the biology of the molecular variants. TP53 expression was significantly reduced in i17q/group 4 tumors; this could not be accounted for by dosage effects alone. Expression of regulators and mediators of p53 signaling were significantly altered in i17q tumors. Our findings support that chromatin remodeling gene mutations are associated with significantly worse outcomes in MB but cannot explain outcomes or pathogenesis of i17q tumors. However, expression analyses of the p53 signaling pathway shows alterations in i17q tumors that cannot be explained by dosage effects and is strongly suggestive of an oncogenic role. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40478-014-0074-1) contains supplementary material, which is available to authorized users

Sequencing of idiopathic pulmonary fibrosis-related genes reveals independent single gene associations

BACKGROUND: Previous studies investigating a genetic basis for idiopathic pulmonary fibrosis (IPF) have focused on resequencing single genes in IPF kindreds or cohorts to determine the genetic contributions to IPF. None has investigated interactions among the candidate genes. OBJECTIVE: To compare the frequencies and interactions of mutations in six IPF-associated genes in a cohort of 132 individuals with IPF with those of a disease-control cohort of 192 individuals with chronic obstructive pulmonary disease (COPD) and the population represented in the Exome Variant Server. METHODS: We resequenced the genes encoding surfactant proteins A2 (SFTPA2), and C (SFTPC), the ATP binding cassette member A3 (ABCA3), telomerase (TERT), thyroid transcription factor (NKX2-1) and mucin 5B (MUC5B) and compared the collapsed frequencies of rare (minor allele frequency <1%), computationally predicted deleterious variants in each cohort. We also genotyped a common MUC5B promoter variant that is over-represented in individuals with IPF. RESULTS: We found 15 mutations in 14 individuals (11%) in the IPF cohort: (SFTPA2 (n=1), SFTPC (n=5), ABCA3 (n=4) and TERT (n=5)). No individual with IPF had two different mutations, but one individual with IPF was homozygous for p.E292V, the most common ABCA3 disease-causing variant. We did not detect an interaction between any of the mutations and the MUC5B promoter variant. CONCLUSIONS: Rare mutations in SFTPA2, SFTPC and TERT are collectively over-represented in individuals with IPF. Genetic analysis and counselling should be considered as part of the IPF evaluation

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

Measuring transcription factor binding and gene expression using barcoded self-reporting transposon calling cards and transcriptomes

Calling cards technology using self-reporting transposons enables the identification of DNA-protein interactions through RNA sequencing. Although immensely powerful, current implementations of calling cards in bulk experiments on populations of cells are technically cumbersome and require many replicates to identify independent insertions into the same genomic locus. Here, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells by introducing a DNA barcode into the calling card itself. An additional barcode incorporated during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recove

Pycallingcards: An integrated environment for visualizing, analyzing, and interpreting Calling Cards data

MOTIVATION: Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors\u27 (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. RESULTS: We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. AVAILABILITY AND IMPLEMENTATION: Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards