Biology subjects

Guertin, M. J.

Publications and source records attributed to Guertin, M. J..

17 recordsLinked to original sources

bioRxiv2025

Genome-wide dynamic nascent transcript profiles reveal that most paused RNA polymerases terminate

We present a simple model for analyzing and interpreting data from kinetic experiments that measure engaged RNA polymerase occupancy. The framework represents the densities of nascent transcripts within the pause region and the gene body as steady-state values determined by four key transcriptional processes: initiation, pause release, premature termination, and elongation. We validate the models predictions using data from experiments that rapidly inhibit initiation and pause release. The model successfully classified factors based on the steps in early transcription that they regulate, confirming TBP and ZNF143 as initiation factors and HSF and GR as pause release factors. We found that most paused polymerases terminate and paused polymerases are short-lived with half lives less than a minute. We make this model available as software to serve as a quantitative tool for determining the kinetic mechanisms of transcriptional regulation.

genomics↗

bioRxiv2024

ZNF143 binds DNA and stimulates transcription initiation to activate and repress direct target genes

Transcription factors bind to sequence motifs and act as activators or repressors. Transcription factors interface with a constellation of accessory cofactors to regulate distinct mechanistic steps to regulate transcription. We rapidly degraded the essential and ubiquitously expressed transcription factor ZNF143 to determine its function in the transcription cycle. ZNF143 facilitates RNA Polymerase initiation and activates gene expression. ZNF143 binds the promoter of nearly all its activated target genes. ZNF143 also binds near the site of genic transcription initiation to directly repress a subset of genes. Although ZNF143 stimulates initiation at ZNF143-repressed genes (i.e. those that increase expression upon ZNF143 depletion), the molecular context of binding leads to cis repression. ZNF143 competes with other more efficient activators for promoter access, physically occludes transcription initiation sites and promoter-proximal sequence elements, and acts as a molecular roadblock to RNA Polymerases during early elongation. The term context specific is often invoked to describe transcription factors that have both activation and repression functions. We define the context and molecular mechanisms of ZNF143-mediated cis activation and repression.

genomics↗

bioRxiv2023

TRPS1 modulates chromatin accessibility to regulate estrogen receptor (ER) binding and ER target gene expression in luminal breast cancer cells

Breast cancer is the most frequently diagnosed cancer in women. The most common subtype is luminal breast cancer, which is typically driven by the estrogen receptor (ER), a transcription factor (TF) that activates many genes required for proliferation. Multiple effective therapies target this path-way, but individuals often develop resistance. Thus, there is a need to identify additional targets that regulate ER activity and contribute to breast tumor progression. TRPS1 is a repressive GATA-family TF that is overexpressed in breast tumors. Common genetic variants in the TRPS1 locus are associated with breast cancer risk, and luminal breast cancer cell lines are particularly sensitive to TRPS1 knockout. However, we do not know how TRPS1 regulates target genes to mediate these breast cancer patient and cellular outcomes. We introduced an inducible degron tag into the native TRPS1 locus within a luminal breast cancer cell line to identify the direct targets of TRPS1 and determine how TRPS1 mechanistically regulates gene expression. We acutely deplete over eighty percent of TRPS1 from chromatin within 30 minutes of inducing degradation. We find that TRPS1 regulates transcription of hundreds of genes, including those related to estrogen signaling. TRPS1 directly regulates chromatin structure, which causes ER to redistribute in the genome. ER redistribution leads to both repression and activation of dozens of ER target genes. Downstream from these primary effects, TRPS1 depletion represses cell cycle-related gene sets and reduces cell doubling rate. Finally, we show that high TRPS1 activity, calculated using a gene expression signature defined by primary TRPS1-regulated genes, is associated with worse breast cancer patient prognosis. Taken together, these data suggest a model in which TRPS1 modulates the activity of other TFs, both activating and repressing transcription of genes related to cancer cell fitness.

genomics↗

bioRxiv2023

The androgen receptor does not directly regulate the transcription of DNA damage response genes

The clinical success of combined androgen deprivation therapy (ADT) and radiation therapy (RT) in prostate cancer (PCa) created interest in understanding the mechanistic links between androgen receptor (AR) signaling and the DNA damage response (DDR). Convergent data have led to a model where AR both regulates, and is regulated by, the DDR. Integral to this model is that the AR regulates the transcription of DDR genes both at steady state and in response to ionizing radiation (IR). In this study, we sought to determine which immediate transcriptional changes are induced by IR in an AR-dependent manner. Using PRO-seq to quantify changes in nascent RNA transcription in response to IR, the AR antagonist enzalutamide, or the combination of the two, we find that enzalutamide treatment significantly decreased expression of canonical AR target genes but had no effect on DDR gene sets in PCa cells. Surprisingly, we also found that the AR is not a primary regulator of DDR genes either in response to IR or at steady state in asynchronously growing PCa cells. Our data indicate that the clinical benefit of ADT and RT is not due to the direct regulation of DDR gene transcription by AR.

cancer biology↗

bioRxiv2022

Processing and evaluating the quality of genome-wide nascent transcription profiling libraries

Precision genomic run-on assays (PRO-seq) quantify nascent RNA at single nucleotide resolution with strand specificity. Here we deconstruct a recently published genomic nascent RNA processing pipeline (PEPPRO) into its components and link the analyses to the underlying molecular biology. PRO-seq experiments are evolving and variations can be found throughout the literature. The analyses are presented as individual code chunks with comprehensive details so that users can modify the framework to accommodate different protocols. We present the framework to quantify the following quality control metrics: library complexity, nascent RNA purity, nuclear run-on efficiency, alignment rate, sequencing depth, and RNA degradation.

bioinformatics↗

bioRxiv2022

Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling

Chromatin accessibility assays have revolutionized the field of transcription regulation by providing single-nucleotide resolution measurements of regulatory features such as promoters and transcription factor binding sites. ATAC-seq directly measures how well the Tn5 transpose accesses chromatinized DNA. Tn5 has a complex sequence bias that is not effectively scaled with traditional bias-correction methods. We model this complex bias using a rule ensemble machine learning approach that integrates information from many input k-mers proximal to the ATAC sequence reads. We effectively characterize and correct single-nucleotide sequence biases and regional sequence biases of the Tn5 enzyme. Correction of enzymatic sequence bias is an important step in interpreting chromatin accessibility assays that aim to infer transcription factor binding and regulatory activity of elements in the genome.

bioinformatics↗

bioRxiv2021

Kinetic networks identify key regulatory nodes and transcription factor functions in early adipogenesis

Adipocytes contribute to metabolic disorders such as obesity, diabetes, and atherosclerosis. Prior characterizations of the transcriptional network driving adipogenesis overlook transiently acting transcription factors (TFs), genes, and regulatory elements that are essential for proper differentiation. Moreover, traditional gene regulatory networks provide neither mechanistic details about individual RE-gene relationships nor temporal information needed to define a regulatory hierarchy that prioritizes key regulatory factors. To address these shortcomings, we integrate kinetic chromatin accessibility (ATAC-seq) and nascent transcription (PRO-seq) data to generate temporally resolved networks that describe TF binding events and resultant effects on target gene expression. Our data indicate which TF families cooperate with and antagonize each other to regulate adipogenesis. Compartment modeling of RNA polymerase density quantifies how individual TFs mechanistically contribute to distinct steps in transcription. Glucocorticoid receptor activates transcription by inducing RNA polymerase pause release while SP and AP1 factors affect RNA polymerase initiation. We identify Twist2 as a previously unappreciated effector of adipocyte differentiation. We find that TWIST2 acts as a negative regulator of 3T3-L1 and primary preadipocyte differentiation. We confirm that Twist2 knockout mice have compromised lipid storage within subcutaneous and brown adipose tissue. Previous phenotyping of Twist2 knockout mice and Setleis syndrome (Twist2-/-) patients noted deficiencies in subcutaneous adipose tissue. This network inference framework is a powerful and general approach for interpreting complex biological phenomena and can be applied to a wide range of cellular processes.

genomics↗

bioRxiv2021

ANKLE1 cleaves mitochondrial DNA and contributes to cancer risk by driving the Warburg effect and apoptosis resistance

Alleles within the chr19p13.1 locus are associated with increased risk of both ovarian and breast cancer and increased expression of the ANKLE1 gene. ANKLE1 is molecularly characterized as an endonuclease that efficiently cuts branched DNA and shuttles between the nucleus and cytoplasm. However, the role of ANKLE1 in mammalian development and homeostasis remains unknown. In normal development ANKLE1 expression is limited to the erythroblast lineage and we found that ANKLE1s role is to cleave the mitochondrial genome during erythropoiesis. We show that ectopic expression of ANKLE1 in breast epithelial-derived cells leads to genome instability and mitochondrial DNA (mtDNA) cleavage. mtDNA degradation then leads to mitophagy and causes a shift from oxidative phosphorylation to glycolysis (Warburg effect). Moreover, mtDNA degradation activates STAT1 and expression of epithelial-mesenchymal transition (EMT) genes. Reduction in mitochondrial content contributes to apoptosis resistance, which may allow precancerous cells to avoid apoptotic checkpoints and proliferate. These findings provide evidence that ANKLE1 is the causal cancer susceptibility gene in the chr19p13.1 locus and describe mechanisms by which higher ANKLE1 expression promotes cancer risk.

cancer biology↗

bioRxiv2021

Accurate estimation of intrinsic biases for improved analysis of chromatin accessibility sequencing data using SELMA

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

bioinformatics↗

bioRxiv2021

Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors

Many lncRNAs have been discovered using transcriptomic data, however, it is broadly unclear what fraction of lncRNAs are functional and what structural properties affect their phenotype. MUNC lncRNA, also known as DRReRNA, stimulates skeletal muscle differentiation. The prevailing hypothesis is that MUNC stimulates the Myod1 gene in cis as an enhancer RNA and stimulates expression of several other promyogenic genes in trans by recruiting the cohesin complex to their promoters. Experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by RNA structure prediction algorithms in the absence of experimental information. We discovered that these specific and structurally distinct domains are required for induction of different promyogenic genes, for binding at different genomic sites to regulate the expression of adjacent genes, and for binding the cohesin complex. Moreover, we found that induction of Myod1 or interaction with cohesin comprise only a subset of the broad regulatory impact of this lncRNA. Our study thus reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.

molecular biology↗

bioRxiv2020

Quality control and processing of nascent RNA profiling data

Nascent RNA profiling is growing in popularity; however, there is no standard analysis pipeline to uniformly process the data and assess quality. Here, we introduce PEPPRO, a comprehensive, scalable work-flow for GRO-seq, PRO-seq, and ChRO-seq data. PEPPRO produces uniformly processed output files for downstream analysis and assesses adapter abundance, RNA integrity, library complexity, nascent RNA purity, and run-on efficiency. PEPPRO is restartable and fault-tolerant, records copious logs, and provides a web-based project report. PEPPRO can be run locally or using cluster, providing a portable first step for genomic nascent RNA analysis. AvailabilityBSD2-licensed code and documentation: https://peppro.databio.org.

bioinformatics↗

bioRxiv2019

Defining data-driven primary transcript annotations with primaryTranscriptAnnotation in R

Nascent transcript measurements derived from run-on sequencing experiments are critical for the investigation of transcriptional mechanisms and regulatory networks. However, conventional gene annotations specify the boundaries of mRNAs, which significantly differ from the boundaries of primary transcripts. Moreover, transcript isoforms with distinct transcription start and end coordinates can vary between cell types. Therefore, new primary transcript annotations are needed to accurately interpret run-on data. We developed the primaryTranscriptAnnotation R package to infer the transcriptional start and termination sites of annotated genes from genomic run-on data. We then used these inferred co-ordinates to annotate transcriptional units identified de novo. Hence, this package provides the novel utility to integrate data-driven primary transcript annotations with transcriptional unit coordinates identified in an unbiased manner. Our analyses demonstrated that this new methodology increases the sensitivity for detecting differentially expressed transcripts and provides more accurate quantification of RNA polymerase pause indices, consistent with the importance of using accurate primary transcript coordinates for interpreting genomic nascent transcription data.\n\nAvailabilityhttps://github.com/WarrenDavidAnderson/genomicsRpackage/tree/master/primaryTranscriptAnnotation

genomics↗

bioRxiv2019

An improved auxin-inducible degron system preserves native protein levels and enables rapid and specific protein depletion

Rapid perturbation of protein function permits the ability to define primary molecular responses while avoiding down-stream cumulative effects of protein dysregulation. The auxin-inducible degron (AID) system was developed as a tool to achieve rapid and inducible protein degradation in non-plant systems. However, tagging proteins at their endogenous loci results in chronic, auxin-independent degradation by the proteasome. To correct this deficiency, we expressed the Auxin Response Transcription Factor (ARF) in an improved inducible degron system. ARF is absent from previously engineered AID systems, but ARF is a critical component of native auxin signaling. In plants, ARF directly interacts with AID in the absence of auxin and we found that expression of the ARF Phox and Bem1 (PB1) domain suppresses constitutive degradation of AID-tagged proteins. Moreover, the rate of auxin-induced AID degradation is substantially faster in the ARF-AID system. To test the ARF-AID system in a quantitative and sensitive manner, we measured genome-wide changes in nascent transcription after rapidly depleting the ZNF143 transcription factor. Transciptional profiling indicates that ZNF143 activates transcription in cis and ZNF143 regulates promoter-proximal paused RNA Polymerase density. Rapidly inducible degradation systems that preserve the target proteins native expression levels and patterns will revolutionize the study of biological systems by enabling specific and temporally defined protein dysregulation.

molecular biology↗

bioRxiv2018

Genetic and Epigenetic Determinants Establish a Continuum of Hsf1 Occupancy and Activity Across the Yeast Genome

Heat Shock Factor 1 (Hsf1) is the master transcriptional regulator of molecular chaperones and binds to the same cis-acting element - Heat Shock Element (HSE) - across the eukaryotic lineage. In budding yeast, Hsf1 drives transcription of ~20 genes essential to maintain proteostasis under basal conditions, yet its specific targets and extent of inducible binding during heat shock remain unclear. Here we combine Hsf1 ChIP-seq, nascent RNA-seq and Hsf1 nuclear depletion to quantify Hsf1 binding and transcription across the yeast genome. Hsf1 binds 74 loci during acute heat shock, 46 of which are linked to genes with strong Hsf1-dependent transcription. Most of these targets show detectable Hsf1 binding under basal conditions, but basal occupancy and heat shock-inducible binding both vary over two orders of magnitude. Notably, Hsf1s induced DNA binding leads to a disproportionate (up to 50-fold) increase in nascent transcription. While variation in basal Hsf1 occupancy poorly correlates with the strength of the HSE, promoters with high basal Hsf1 occupancy have nucleosome-depleted regions due to the presence of pioneer factors. Such accessible chromatin may be critical for Hsf1 occupancy of its genomic sites as the activator is incapable of binding HSEs embedded within a stable nucleosome in vitro. In response to heat shock, however, Hsf1 is able to gain access to nucleosomal sites and promotes chromatin remodeling with the RSC complex playing a key role. We propose that the interplay between nucleosome occupancy, HSE strength and active Hsf1 levels allows cells to precisely tune expression of the proteostasis network.

genomics↗

bioRxiv2018

BART: a transcription factor prediction tool with query gene sets or epigenomic profiles

SummaryIdentification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive to detect factors binding at distal enhancers. Here we present Binding Analysis for Regulation of Transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6,000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.\n\nAvailabilityBART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bart\n\nContact: zang@virginia.edu

bioinformatics↗

bioRxiv2017

Novel Quantitative ChIP-seq Methods Measure Absolute Fold-Change in ER Binding Upon Fulvestrant Treatment

A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery.\n\nWe present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome-wide and generate metrics for differential binding at individual sites.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/182261_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@177285borg.highwire.dtl.DTLVardef@1fa7d79org.highwire.dtl.DTLVardef@43d17forg.highwire.dtl.DTLVardef@6eff8d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

genomics↗

bioRxiv2017

Universal correction of enzymatic sequence bias

Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

genomics↗