Determining cellular CTCF and cohesin abundances to constrain 3D genome models.

Achieving a quantitative and predictive understanding of 3D genome architecture remains a major challenge, as it requires quantitative measurements of the key proteins involved. Here, we report the quantification of CTCF and cohesin, two causal regulators of topologically associating domains (TADs) in mammalian cells. Extending our previous imaging studies (Hansen et al., 2017), we estimate bounds on the density of putatively DNA loop-extruding cohesin complexes and CTCF binding site occupancy. Furthermore, co-immunoprecipitation studies of an endogenously tagged subunit (Rad21) suggest the presence of cohesin dimers and/or oligomers. Finally, based on our cell lines with accurately measured protein abundances, we report a method to conveniently determine the number of molecules of any Halo-tagged protein in the cell. We anticipate that our results and the established tool for measuring cellular protein abundances will advance a more quantitative understanding of 3D genome organization, and facilitate protein quantification, key to comprehend diverse biological processes

Imaging dynamic and selective low-complexity domain interactions that control gene transcription

INTRODUCTION. DNA binding transcription factors (TFs) are quintessential regulators of eukaryotic gene expression. Early studies of TFs revealed their well-structured DNA binding domains (DBDs) and identified functionally critical activation domains (ADs) required for transcription. It later became evident that many ADs contain intrinsically disordered low-complexity sequence domains (LCDs), but how LCDs activate transcription has remained unclear. Although it is known that transcriptional activation by LCDs requires selective interaction with binding partners, it has been challenging to directly measure selective LCD-LCD recognition in vivo and unravel its mechanism of action. RATIONALE. Traditional biochemical reconstitution and genetics studies have identified most of the molecular players central to transcription regulation. However, the mechanism by which weak, dynamic protein-protein interactions drive gene activation in living cells has remained unknown. Advances in live-cell single-molecule imaging have opened a new frontier for studying transcription in vivo. In this study, we used synthetic LacO (Lac operator) arrays as well as endogenous GGAA microsatellite loci to study LCD-LCD interactions of TFs such as EWS/FLI1, TAF15, and Sp1 in live cells. To probe the dynamic behavior of TF LCDs at target genomic loci, we have combined CRISPR-Cas9 genome editing, mutagenesis, gene activation, cell transformation assays, and various high-resolution imaging approaches including fluorescence correlation spectroscopy, fluorescence recovery after photobleaching, lattice light-sheet microscopy, three-dimensional DNA fluorescence in situ hybridization, and live-cell single-particle tracking. RESULTS. Live-cell single-molecule imaging revealed that TF LCDs interact to form local high-concentration hubs at both synthetic DNA arrays and endogenous genomic loci. TF LCD hubs stabilize DNA binding, recruit RNA polymerase II (RNA Pol II), and activate transcription. LCD-LCD interactions within hubs are highly dynamic (seconds to minutes), selective for binding partners, and differentially sensitive to disruption by hexanediols. These findings suggest that under physiological conditions, rapid, reversible, and selective multivalent LCD-LCD interactions occur between TFs and the RNA Pol II machinery to activate transcription. We observed formation of functional TF LCD hubs at a wide range of intranuclear TF concentrations. Although we detected apparent liquid-liquid phase separation with gross overexpression of LCDs, transcriptionally competent TF LCD hubs were observed at physiological TF levels at endogenous chromosomal loci in the absence of detectable phase separation. In addition, mutagenesis, gene expression, and cell transformation assays in Ewing’s sarcoma cells revealed a functional link between LCD-LCD interactions, transactivation capacity, and oncogenic potential. CONCLUSION. The use of various imaging methods in live cells powerfully complements in vitro studies and provides new insights into the nature of LCD interactions and their role in gene regulation. We propose that transactivation domains function by forming local high-concentration hubs of TFs via dynamic, multivalent, and specific LCD-LCD interactions. It also seems likely that weak, dynamic, and transient contacts between TFs play a role in disease-causing dysregulation of gene expression (i.e., EWS/FLI1 in Ewing’s sarcoma), suggesting that LCD-LCD interactions may represent a new class of viable drug targets. Although we examined a small subset of TF LCDs, the principles uncovered regarding the dynamics and mechanisms driving LCD-LCD interactions may be applicable to other classes of proteins and biomolecular interactions occurring in many cell types

Simple, Inexpensive RNA Isolation and One-Step RT-qPCR Methods for SARS-CoV-2 Detection and General Use.

The most common method for RNA detection involves reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) analysis. Commercial one-step master mixes-which include both a reverse transcriptase and a thermostable polymerase and thus allow performing both the RT and qPCR steps consecutively in a sealed well-are key reagents for SARS-CoV-2 diagnostic testing; yet, these are typically expensive and have been affected by supply shortages in periods of high demand. As an alternative, we describe here how to express and purify Taq polymerase and M-MLV reverse transcriptase and assemble a homemade one-step RT-qPCR master mix. This mix can be easily assembled from scratch in any laboratory equipped for protein purification. We also describe two simple alternative methods to prepare clinical swab samples for SARS-CoV-2 RNA detection by RT-qPCR: heat-inactivation for direct addition, and concentration of RNA by isopropanol precipitation. Finally, we describe how to perform RT-qPCR using the homemade master mix, how to prepare in vitro-transcribed RNA standards, and how to use a fluorescence imager for endpoint detection of RT-PCR amplification in the absence of a qPCR machine In addition to being useful for diagnostics, these versatile protocols may be adapted for nucleic acid quantification in basic research. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation of a one-step RT-qPCR master mix using homemade enzymes Basic Protocol 2: Preparation of swab samples for direct RT-PCR Alternate Protocol 1: Concentration of RNA from swab samples by isopropanol precipitation Basic Protocol 3: One-step RT-qPCR of RNA samples using a real-time thermocycler Support Protocol: Preparation of RNA concentration standards by in vitro transcription Alternate Protocol 2: One-step RT-PCR using endpoint fluorescence detection

Detecting molecular interactions in live-cell single-molecule imaging with proximity-assisted photoactivation (PAPA).

Single-molecule imaging provides a powerful way to study biochemical processes in live cells, yet it remains challenging to track single molecules while simultaneously detecting their interactions. Here, we describe a novel property of rhodamine dyes, proximity-assisted photoactivation (PAPA), in which one fluorophore (the 'sender') can reactivate a second fluorophore (the 'receiver') from a dark state. PAPA requires proximity between the two fluorophores, yet it operates at a longer average intermolecular distance than Förster resonance energy transfer (FRET). We show that PAPA can be used in live cells both to detect protein-protein interactions and to highlight a subpopulation of labeled protein complexes in which two different labels are in proximity. In proof-of-concept experiments, PAPA detected the expected correlation between androgen receptor self-association and chromatin binding at the single-cell level. These results establish a new way in which a photophysical property of fluorophores can be harnessed to study molecular interactions in single-molecule imaging of live cells

Mechanisms governing target search and binding dynamics of hypoxia-inducible factors

Transcription factors (TFs) are classically attributed a modular construction, containing well-structured sequence-specific DNA-binding domains (DBDs) paired with disordered activation domains (ADs) responsible for protein-protein interactions targeting co-factors or the core transcription initiation machinery. However, this simple division of labor model struggles to explain why TFs with identical DNA-binding sequence specificity determined in vitro exhibit distinct binding profiles in vivo. The family of hypoxia-inducible factors (HIFs) offer a stark example: aberrantly expressed in several cancer types, HIF-1α and HIF-2α subunit isoforms recognize the same DNA motif in vitro - the hypoxia response element (HRE) - but only share a subset of their target genes in vivo, while eliciting contrasting effects on cancer development and progression under certain circumstances. To probe the mechanisms mediating isoform-specific gene regulation, we used live-cell single particle tracking (SPT) to investigate HIF nuclear dynamics and how they change upon genetic perturbation or drug treatment. We found that HIF-α subunits and their dimerization partner HIF-1β exhibit distinct diffusion and binding characteristics that are exquisitely sensitive to concentration and subunit stoichiometry. Using domain-swap variants, mutations, and a HIF-2α specific inhibitor, we found that although the DBD and dimerization domains are important, another main determinant of chromatin binding and diffusion behavior is the AD-containing intrinsically disordered region (IDR). Using Cut&Run and RNA-seq as orthogonal genomic approaches, we also confirmed IDR-dependent binding and activation of a specific subset of HIF target genes. These findings reveal a previously unappreciated role of IDRs in regulating the TF search and binding process that contribute to functional target site selectivity on chromatin