Dalakishvili, L. | bioRxiv Science

bioRxiv2025

Single-Molecule DNA Footprinting and Transcription Imaging Reveal the Molecular Mechanisms of Promoter Dynamics

Live cell RNA imaging revealed that transcription levels are encoded by the intrinsic dynamics of promoters. However, capturing both kinetic and molecular aspects of promoter fluctuations has been challenging. Here, we resolve this key issue by combining Single Molecule DNA footprinting (SMF) with live transcription imaging. Using HIV-1 as a model, SMF reveals that the promoter functions in two modes depending on the viral transactivator Tat. Without Tat, a nucleosome occupies the core promoter and prevents assembly of the pre-initiation complex. With Tat, this nucleosome is absent while TBP and initiating polymerases are frequently detected. Combining live imaging with SMF provides a mechanistic model of promoter dynamics, which estimates the rates of deposition and removal of promoter nucleosomes (0.7 h-1), TBP binding (0.04 min-1) and polymerase loading (seconds). The data further reveal a kinetic proofreading mechanism of initiating polymerases, which enables Tat to indirectly control promoter nucleosomes by promoting elongation.

molecular biology↗

bioRxiv2025

Biophysical Modeling Uncovers Transcription Factor and Nucleosome Binding on Single DNA Molecules

Gene regulation in eukaryotes emerges from a dynamic interplay between transcription factors (TFs), nucleosomes, and RNA Polymerase II (Pol II), whose competitive and cooperative binding shapes DNA accessibility and transcriptional output. Single-molecule footprinting (SMF) and long-read chromatin accessibility assays such as Fiber-seq now capture these interactions at nucleotide resolution on individual DNA molecules. However, existing computational tools remain insufficient to decode complex binding events from sparse methylation data. Here, we introduce HiddenFoot, a probabilistic modeling framework based on statistical mechanics that quantitatively infers TF, nucleosome, and Pol II occupancy profiles on single DNA molecules by systematically evaluating all thermodynamically plausible binding configurations. Applying HiddenFoot to SMF and Fiber-seq data from mouse, Drosophila, and human cells, we recovered known TF footprints, precisely resolved Pol II pausing, and identified extensive heterogeneity in nucleosome positioning driven by TF binding. HiddenFoot further distinguishes direct TF-TF cooperativity from nucleosome-mediated co-dependency by estimating pairwise interaction energies and comparing to null models under equilibrium. By integrating biophysical modeling with high-resolution single-molecule data, HiddenFoot offers a general, interpretable framework for dissecting regulatory logic in native chromatin with base-pair precision. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/653852v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@46f3b5org.highwire.dtl.DTLVardef@2a2cbdorg.highwire.dtl.DTLVardef@df42f2org.highwire.dtl.DTLVardef@1a4205d_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗