Biology subjects

Pugh, B. F.

Publications and source records attributed to Pugh, B. F..

13 recordsLinked to original sources

bioRxiv2026

Multi-modal in situ profiling of human cells using the AVITI24 platform characterizes single-cell responses to acute stress

Cells begin to respond to stress within minutes, but how this early response depends on cell type, neighboring cells, or cell cycle phase has been difficult to determine, because bulk assays average across cells and synchronization can induce stress. We measured 338 transcripts, 48 proteins and phosphoproteins, and six cell paint stains in situ in 1.18 million single cells from four human cell lines exposed to H2O2, alone and in co-culture, and in A549 cells exposed to TNF-, cadmium, dithiothreitol or lipopolysaccharide. In A549 cells, five distinct stressors changed the same targets in the same direction within 15 min, differing mainly in magnitude, and modestly increased JNK phosphorylation. By 30 min of H2O2 treatment, immediate-early genes (FOS, JUN, DDIT3, DUSP1, NR4A1) were induced most strongly in HCT116 and A549 cells. These changes occurred across the whole cell population rather than in a responsive subset. Cell line-specific differences persisted in co-culture, and the response of MCF7 cells did not increase with the number of adjacent, strongly-responding A549 cells. Ordering cells along the cell cycle from the same measurements identified 106 cycle-associated transcripts, and the response varied more between cell lines than among cell cycle phases. Overall, the measured regulators respond to stress within 30 minutes through a largely shared program whose magnitude depends more on cell identity than on cell cycle state or neighboring cell type.

genomics↗

bioRxiv2024

High-Resolution Genome-Wide Mapping of Chromatin Accessibility and Torsional Stress

Torsional stress in chromatin plays a fundamental role in cellular functions, influencing key processes such as transcription, replication, and chromatin organization. Transcription and other processes may generate and be regulated by torsional stress. In the genome, the interplay of these processes creates complicated patterns of both positive (+) and negative (-) torsion. However, a challenge in generating an accurate torsion map is determining the zero-torsion baseline signal, which is conflated with chromatin accessibility. Here, we introduce a high-resolution method based on the intercalator trimethylpsoralen (TMP) to address this challenge. We describe a method to establish the zero-torsion baseline while preserving the chromatin state of the genome of S. cerevisiae. This approach enables both high-resolution mapping of accessibility and torsional stress in chromatin in the cell. Our analysis shows transcription-generated torsional domains consistent with the twin-supercoiled-domain model of transcription and suggests a role for torsional stress in recruiting topoisomerases and in regulating 3D genome architecture via cohesin. Significantly, we reveal that insulator sequence-specific transcription factors decouple torsion between divergent promoters, whereas torsion spreads between divergent promoters lacking these factors, suggesting that torsion serves as a regulatory mechanism in these regions. Although insulators are known to decouple gene expression, our finding provides a physical explanation of how such decoupling may occur. This new method provides a potential path forward for using TMP to measure torsional stress in the genome without the confounding contribution of accessibility in chromatin.

genomics↗

bioRxiv2023

Joint sequence & chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin

The DNA-binding activities of transcription factors (TFs) are influenced by both intrinsic sequence preferences and extrinsic interactions with cell-specific chromatin landscapes and other regulatory proteins. Disentangling the roles of these binding determinants remains challenging. For example, the FoxA subfamily of Forkhead domain (Fox) TFs are known pioneer factors that can bind to relatively inaccessible sites during development. Yet FoxA TF binding also varies across cell types, pointing to a combination of intrinsic and extrinsic forces guiding their binding. While other Forkhead domain TFs are often assumed to have pioneering abilities, how sequence and chromatin features influence the binding of related Fox TFs has not been systematically characterized. Here, we present a principled approach to compare the relative contributions of intrinsic DNA sequence preference and cell-specific chromatin environments to a TFs DNA-binding activities. We apply our approach to investigate how a selection of Fox TFs (FoxA1, FoxC1, FoxG1, FoxL2, and FoxP3) vary in their binding specificity. We over-express the selected Fox TFs in mouse embryonic stem cells, which offer a platform to contrast each TFs binding activity within the same preexisting chromatin background. By applying a convolutional neural network to interpret the Fox TF binding patterns, we evaluate how sequence and preexisting chromatin features jointly contribute to induced TF binding. We demonstrate that Fox TFs bind different DNA targets, and drive differential gene expression patterns, even when induced in identical chromatin settings. Despite the association between Forkhead domains and pioneering activities, the selected Fox TFs display a wide range of affinities for preexiting chromatin states. Using sequence and chromatin feature attribution techniques to interpret the neural network predictions, we show that differential sequence preferences combined with differential abilities to engage relatively inaccessible chromatin together explain Fox TF binding patterns at individual sites and genome-wide.

genomics↗

bioRxiv2023

GenoPipe: identifying the genotype of origin within (epi)genomic datasets

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e., cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g., indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organisms genome (i.e., epitope insertions, gene deletions, and SNPs).

bioinformatics↗

bioRxiv2021

PEGR: a flexible management platform for reproducible epigenomic and genomic research

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this challenge as high-throughput sequencing data is generated at an unprecedented pace. Here we report on our development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the wet bench, while continuing to fully support reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

bioinformatics↗

bioRxiv2021

STENCIL: A web templating engine for visualizing and sharing life science datasets

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil

bioinformatics↗

bioRxiv2021

Ssl2/TFIIH Function in Transcription Start Site Scanning by RNA Polymerase II in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, RNA Polymerase II (Pol II) selects transcription start sites (TSS) by a unidirectional scanning process. During scanning, a preinitiation complex (PIC) assembled at an upstream core promoter initiates at select positions within a window [~]40-120 basepairs downstream. Several lines of evidence indicate that Ssl2, the yeast homolog of XPB and an essential and conserved subunit of the general transcription factor (GTF) TFIIH, drives scanning through its DNA-dependent ATPase activity, therefore potentially controlling both scanning rate and scanning extent (processivity). To address questions of how Ssl2 functions in promoter scanning and interacts with other initiation activities, we leveraged distinct initiation-sensitive reporters to identify novel ssl2 alleles. These ssl2 alleles, many of which alter residues conserved from yeast to human, confer either upstream or downstream TSS shifts at the model promoter ADH1 and genome-wide. Specifically, tested ssl2 alleles alter TSS selection by increasing or narrowing the distribution of TSSs used at individual promoters. Genetic interactions of ssl2 alleles with other initiation factors are consistent with ssl2 allele classes functioning through increasing or decreasing scanning processivity but not necessarily scanning rate. These alleles underpin a residue interaction network that likely modulates Ssl2 activity and TFIIH function in promoter scanning. We propose that the outcome of promoter scanning is determined by two functional networks, the first being Pol II activity and factors that modulate it to determine initiation efficiency within a scanning window, and the second being Ssl2/TFIIH and factors that modulate scanning processivity to determine the width of the scanning widow.

molecular biology↗

bioRxiv2021

High similarity among ChEC-seq datasets

ChEC-seq is a method used to identify protein-DNA interactions across a genome. It involves fusing micrococcal nuclease (MNase) to a protein of interest. In principle, specific genome-wide interactions of the fusion protein with chromatin result in local DNA cleavages that can be mapped by DNA sequencing. ChEC-seq has been used to draw conclusions about broad gene-specificities of certain protein-DNA interactions. In particular, the transcriptional regulators SAGA, TFIID, and Mediator are reported to generally occupy the promoter/UAS of genes transcribed by RNA polymerase II in yeast. Here we compare published yeast ChEC-seq data performed with a variety of protein fusions across essentially all genes, and find high similarities with negative controls. We conclude that ChEC-seq patterning for SAGA, TFIID, and Mediator differ little from background at most promoter regions, and thus cannot be used to draw conclusions about broad gene specificity of these factors.

genomics↗

bioRxiv2020

Screening of PCRP transcription factor antibodies in biochemical assays

Antibodies offer a powerful means to interrogate specific proteins in a complex milieu. However, antibody availability and reliability are problematic and epitope tagging can be impractical in many cases. In an effort to improve this situation, the Protein Capture Reagents Program (PCRP) generated over a thousand renewable monoclonal antibodies (mAbs) against human-presumptive chromatin proteins. However, these reagents have not been widely field-tested. We therefore performed a screen to test their ability to enrich genomic regions via chromatin immunoprecipitation (ChIP) and a variety of orthogonal assays. 887 unique antibodies against 681 unique human transcription factors (TFs), were assayed by ultra-high resolution ChIP-exo/seq, primarily in a single pass in one cell type (K562). Deep systematic analyses of the resulting [~]1,200 ChIP-exo datasets can be found at www.PCRPvalidation.org. Subsets of PCRP mAbs were further tested in ChIP-seq, CUT&RUN, STORM super-resolution microscopy, immunoblots, and protein binding microarray (PBM) experiments. About 5% of the tested antibodies displayed target (i.e., cognate antigen) enrichment across at least one assay and are strong candidates for additional validation. An additional 34% produced ChIP-exo data that was distinct from background and thus warrant further testing. The remaining 61% were not substantially different from background, and likely require consideration of a much broader survey of cell types and/or assay optimizations. We demonstrate and discuss the metrics and challenges to antibody validation in chromatin-based assays.

biochemistry↗

bioRxiv2020

The INO80 remodeler couples premature termination of mRNA synthesis with transcription elongation

Co-transcriptional RNA quality control is essential for gene expression. However, its regulation remains poorly understood. Here, we report that the evolutionarily conserved ATP-dependent chromatin remodelling INO80 complex promotes transcription termination by the non-coding RNA quality control pathway in S. cerevisiae. Loss of INO80 leads to accumulation of stalled RNA Polymerase II preferentially at promoter-proximal pausing sites, compromising Pol II processivity and hindering transcription elongation. We reveal that binding of RNA surveillance and non-coding transcription termination factors to promoter-proximal mRNA regions is associated with increased promoter-proximal pausing. INO80 counteracts promoter-proximal stalling of genes attenuated by the Nrd1-Nab3-Sen1 (NNS) non-coding transcription termination complex, promoting their expression. We show that INO80 interacts with Nrd1 and the Nab2 RNA surveillance factor in vivo. Absence of INO80 leads to defective transcription termination by the Nrd1-Nab3-Sen1 (NNS) complex. We demonstrate that INO80 facilitates the recruitment of Nab2 at non-coding transcription termination sites and its association with promoter-proximally terminated mRNA transcripts. Finally, we provide evidence that INO80 promotes the release of stalled RNA Polymerase II from a non-coding transcription termination site. Collectively, our work suggests that the INO80 complex regulates transcription by removal of stalled Polymerase, implicating a chromatin-based mechanism for non-coding and premature transcription termination in gene expression.

molecular biology↗

bioRxiv2019

Alignment and quantification of ChIP-exo crosslinking patterns reveal the spatial organization of protein-DNA complexes

The ChIP-exo assay precisely delineates protein-DNA crosslinking patterns by combining chromatin immunoprecipitation with 5' to 3' exonuclease digestion. Within a regulatory complex, the physical distance of a regulatory protein to DNA affects crosslinking efficiencies. Therefore, the spatial organization of a protein-DNA complex could potentially be inferred by analyzing how crosslinking signatures vary between the subunits of a regulatory complex. Here, we present a computational framework that aligns ChIP-exo crosslinking patterns from multiple proteins across a set of coordinately bound regulatory regions, and which detects and quantifies protein-DNA crosslinking events within the aligned profiles. By producing consistent measurements of protein-DNA crosslinking strengths across multiple proteins, our approach enables characterization of relative spatial organization within a regulatory complex. We demonstrate that our approach can recover aspects of regulatory complex spatial organization when applied to collections of ChIP-exo data that profile regulatory machinery at yeast ribosomal protein genes and yeast tRNA genes. We also demonstrate the ability to quantify changes in protein-DNA complex organization across conditions by applying our approach to data profiling Drosophila Pol II transcriptional components. Our results suggest that principled analyses of ChIP-exo crosslinking patterns enable inference of spatial organization within protein-DNA complexes.

bioinformatics↗

bioRxiv2019

Promoter scanning during transcription initiation in Saccharomyces cerevisiae: Pol II in the \"shooting gallery\"

BackgroundThe majority of eukaryotic promoters utilize multiple transcription start sites (TSSs). How multiple TSSs are specified at individual promoters across eukaryotes is not understood for most species. In S. cerevisiae, a preinitiation complex comprised of Pol II and conserved general transcription factors (GTFs) assembles and opens DNA upstream of TSSs. Evidence from model promoters indicates that the preinitiation complex (PIC) scans from upstream to downstream to identify TSSs. Prior results suggest that TSS distributions at promoters where scanning occurs shift in a polar fashion upon alteration in Pol II catalytic activity or GTF function. ResultsTo determine extent of promoter scanning across promoter classes in S. cerevisiae, we perturbed Pol II catalytic activity and GTF function and analyzed their effects on TSS usage genome-wide. We find that alterations to Pol II, TFIIB, or TFIIF function widely alter the initiation landscape consistent with promoter scanning operating at all yeast promoters, regardless of promoter class. Promoter architecture, however, can determine extent of promoter sensitivity to altered Pol II activity in ways that are predicted by a scanning model. ConclusionsOur observations coupled with previous data validate key predictions of the scanning model for Pol II initiation in yeast - which we term the "shooting gallery". In this model, Pol II catalytic activity, and the rate and processivity of Pol II scanning together with promoter sequence determine the distribution of TSSs and their usage.

molecular biology↗

bioRxiv2018

Characterizing protein-DNA binding event subtypes in ChIP-exo data

MotivationRegulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5 [->] 3 exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes.\n\nResultsTo systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix identifies possible recruitment mechanisms of FoxA1 and ER, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.\n\nAvailabilityChExMix is available from https://github.com/seqcode/chexmix\n\nContactmahony@psu.edu

bioinformatics↗