Biology subjects

Fordyce, P.

Publications and source records attributed to Fordyce, P..

7 recordsLinked to original sources

bioRxiv2024

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 Kds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

biophysics↗

bioRxiv2022

Large-scale mapping and systematic mutagenesis of human transcriptional effector domains

Human gene expression is regulated by over two thousand transcription factors and chromatin regulators1,2. Effector domains within these proteins can activate or repress transcription. However, for many of these regulators we do not know what type of transcriptional effector domains they contain, their location in the protein, their activation and repression strengths, and the amino acids that are necessary for their functions. Here, we systematically measure the transcriptional effector activity of >100,000 protein fragments (each 80 amino acids long) tiling across most chromatin regulators and transcription factors in human cells (2,047 proteins). By testing the effect they have when recruited at reporter genes, we annotate 307 new activation domains and 592 new repression domains, a [~]5-fold increase over the number of previously annotated effectors3,4. Complementary rational mutagenesis and deletion scans across all the effector domains reveal aromatic and/or leucine residues interspersed with acidic, proline, serine, and/or glutamine residues are necessary for activation domain activity. Additionally, the majority of repression domain sequences contain either sites for SUMOylation, short interaction motifs for recruiting co-repressors, or are structured binding domains for recruiting other repressive proteins. Surprisingly, we discover bifunctional domains that can both activate and repress and can dynamically split a cell population into high- and low-expression subpopulations. Our systematic annotation and characterization of transcriptional effector domains provides a rich resource for understanding the function of human transcription factors and chromatin regulators, engineering compact tools for controlling gene expression, and refining predictive computational models of effector domain function.

systems biology↗

bioRxiv2019

Why Do Long Zinc Finger Proteins have Short Motifs? A case study of ZFY and CTCF reveals non-independent recognition of tandem zinc finger proteins.

The human genome contains around 800 C2H2 Zinc Finger Proteins (ZFPs), and many of them are composed of long tandem arrays of zinc fingers. Current motif prediction models assume longer finger arrays correspond to longer DNA-binding motifs and higher specificity. However, recent experimental efforts to identify ZFP binding sites in vivo contradict this assumption, with many having short motifs. Here, we systematically test how multiple zinc fingers contribute to binding for three model ZFPs: Zinc Finger Y (ZFY), CTCF, and ZNF343. Using ZFY, which contains 13 fingers, we quantitatively characterize its binding specificity with several methods, including Affinity-seq, HT-SELEX, Spec-seq and fluorescence anisotropy, and find evidence for dependent recognition where downstream fingers can recognize some extended motifs only in the presence of an intact core site. For the genomic insulator CTCF, additional high-throughput affinity measurements reveal that its upstream specificity profile depends on the strength of the core, violating presumed additivity and positionindependence. Moreover, the effect of different epigenetic modifications within the core site depends on the strength of flanking upstream site, providing new insight into how the previously identified intellectual disability-causing and cancer-related mutant R567W disrupts upstream recognition and deregulates CTCFs methylation sensitivity. Lastly, we used ZNF343 as example to show that a simple iterative motif analysis strategy based on a small set of prefixed cores can reveal the dependent relationship between cores and upstream motifs. These results establish that the current underestimation of ZFPs motif lengths is due to our lack of understanding of intrinsic properties of tandem zinc finger recognition, including irregular motif structure, variable spacing, and dependent recognition between sub-motifs. These results also motivate a need for better recognition models beyond additive, position-weight matrix to predict ZFP specificities, occupancies, and the molecular mechanisms of disease mutations.

genetics↗

bioRxiv2018

An open-source software analysis package for Microspheres with Ratiometric Barcode Lanthanide Encoding (MRBLEs)

Multiplexed bioassays, in which multiple analytes of interest are probed in parallel within a single small volume, have greatly accelerated the pace of biological discovery. Bead-based multiplexed bioassays have many technical advantages, including near solution-phase kinetics, small sample volume requirements, many within-assay replicates to reduce measurement error, and, for some bead materials, the ability to synthesize analytes directly on beads via solid-phase synthesis. To allow bead-based multiplexing, analytes can be synthesized on spectrally encoded beads with a 1:1 linkage between analyte identity and embedded codes. Bead-bound analyte libraries can then be pooled and incubated with a fluorescently-labeled macromolecule of interest, allowing downstream quantification of interactions between the macromolecule and all analytes simultaneously via imaging alone. Extracting quantitative binding data from these images poses several computational image processing challenges, requiring the ability to identify all beads in each image, quantify bound fluorescent material associated with each bead, and determine their embedded spectral code to reveal analyte identities. Here, we present a novel open-source Python software package (the mrbles analysis package) that provides the necessary tools to: (1) find encoded beads in a bright-field microscopy image; (2) quantify bound fluorescent material associated with bead perimeters; (3) identify embedded ratiometric spectral codes within beads; and (4) return data aggregated by embedded code and for each individual bead. We demonstrate the utility of this package by applying it towards analyzing data generated via multiplexed measurement of calcineurin protein binding to MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding) containing known and mutant binding peptide motifs. We anticipate that this flexible package should be applicable to a wide variety of assays, including simple bead or droplet finding analysis, quantification of binding to non-encoded beads, and analysis of multiplexed assays that use ratiometric, spectrally encoded beads.

bioengineering↗

bioRxiv2018

High-throughput chromatin accessibility profiling at single-cell resolution

We have developed a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4-5 hour on-chip processing time) and cost (~98{cents} per cell) compared to prior microfluidic implementations. We applied this method to measure regulatory variation in Peripheral Blood Mononuclear Cells (PBMCs) and show robust, de-novo clustering of single cells by hematopoietic cell type.

genomics↗

bioRxiv2018

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

Transcription factors bind complex regulatory DNA sequence patterns in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn these cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating the predictive importance of individual features (nucleotides or motifs) in any input DNA sequence to its associated output prediction from a DNN model. However, these methods do not reveal higher-order, epistatic feature interactions encoded by the models. We present a new method called Deep Feature Interaction Maps (DFIM) to efficiently estimate interactions between all pairs of features in any input DNA sequence. DFIM accurately identifies ground truth motif interactions embedded in simulated regulatory DNA sequences. DFIM identifies synergistic interactions between GATA1 and TAL1 motifs from in vivo TF binding models. DFIM reveals epistatic interactions involving nucleotides flanking the core motif of the Cbf1 TF in yeast from in vitro TF binding models. We also apply DFIM to regulatory sequence models of in vivo chromatin accessibility to reveal interactions between regulatory genetic variants and proximal motifs of target TFs as validated by TF binding quantitative trait loci. Our approach makes significant strides in improving the interpretability of deep learning models for genomics.

bioinformatics↗

bioRxiv2017

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and further shed light on how transcriptional networks are rewired throughout evolution. Here, we present a novel sequencing-based TF binding assay and analysis pipeline capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target for two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NN) and biophysical models trained on these data, we establish that dinucleotide specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more epistasis than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest-affinity sites.

biophysics↗