Changes in Fluorescence Recovery After Photobleaching (FRAP) as an indicator of SOX9 transcription factor activity

Dissecting SOX9 dynamics reveals its differential regulation in osteoarthritis

The transcription factor SOX9 is integral to tissue homeostasis and is implicated in skeletal malformation, campomelic dysplasia, and osteoarthritis (OA). Despite extensive research, the complete regulatory landscape of SOX9 transcriptional activity, interconnected with signaling pathways (TGFβ, WNT, BMP, IHH, NFκB, and HIF), remains challenging to decipher. This study focuses on elucidating SOX9 signaling in OA pathology using Fluorescence Recovery After Photobleaching (FRAP) to assess SOX9 activity directly in live human primary chondrocytes (hPCs). Single cell FRAP data revealed two distinct subpopulations with differential SOX9 dynamics, showing varied distribution between healthy and OA hPCs. Moreover, inherently elevated SOX9-DNA binding was observed in healthy hPCs compared to preserved and OA counterparts. Anabolic factors (BMP7 and GREM1) and catabolic inhibitors (DKK1 and FRZb) were found to modulate SOX9 transcriptional activity in OA-hPCs. These findings provide valuable insights into the intricate regulation of SOX9 signaling in OA, suggesting potential therapeutic avenues for modulating SOX9 activity in diseased states.

The Drosophila Hippo pathway transcription factor Scalloped and its co-factors alter each other's chromatin binding dynamics and transcription in vivo

The Hippo pathway is an important regulator of organ growth and cell fate. The major mechanism by which Hippo is known to control transcription is by dictating the nucleo-cytoplasmic shuttling rate of Yorkie, a transcription co-activator, which promotes transcription with the DNA binding protein Scalloped. The nuclear biophysical behavior of Yorkie and Scalloped, and whether this is regulated by the Hippo pathway, remains unexplored. Using multiple live-imaging modalities on Drosophila tissues, we found that Scalloped interacts with DNA on a broad range of timescales, and enrichment of Scalloped at sites of active transcription is mediated by longer DNA dwell times. Further, Yorkie increased Scalloped's DNA dwell time, whereas the repressors Nervous fingers 1 (Nerfin-1) and Tondu-domain-containing growth inhibitor (Tgi) decreased it. Therefore, the Hippo pathway influences transcription not only by controlling nuclear abundance of Yorkie but also by modifying the DNA binding kinetics of the transcription factor Scalloped.

Osmolarity-Induced Altered Intracellular Molecular Crowding Drives Osteoarthritis Pathology

Osteoarthritis (OA) is a multifactorial degenerative joint disease of which the underlying mechanisms are yet to be fully understood. At the molecular level, multiple factors including altered signaling pathways, epigenetics, metabolic imbalance, extracellular matrix degradation, production of matrix metalloproteinases, and inflammatory cytokines, are known to play a detrimental role in OA. However, these factors do not initiate OA, but are mediators or consequences of the disease, while many other factors causing the etiology of OA are still unknown. Here, it is revealed that microenvironmental osmolarity can induce and reverse osteoarthritis-related behavior of chondrocytes via altered intracellular molecular crowding, which represents a previously unknown mechanism underlying OA pathophysiology. Decreased intracellular crowding is associated with increased sensitivity to proinflammatory triggers and decreased responsiveness to anabolic stimuli. OA-induced lowered intracellular molecular crowding could be renormalized via exposure to higher extracellular osmolarity such as those found in healthy joints, which reverse OA chondrocyte's sensitivity to catabolic stimuli as well as its glycolytic metabolism.

The Hox transcription factor Ultrabithorax binds RNA and regulates co-transcriptional splicing through an interplay with RNA polymerase II

Transcription factors (TFs) play a pivotal role in cell fate decision by coordinating gene expression programs. Although most TFs act at the DNA layer, few TFs bind RNA and modulate splicing. Yet, the mechanistic cues underlying TFs activity in splicing remain elusive. Focusing on the Drosophila Hox TF Ultrabithorax (Ubx), our work shed light on a novel layer of Ubx function at the RNA level. Transcriptome and genome-wide binding profiles in embryonic mesoderm and Drosophila cells indicate that Ubx regulates mRNA expression and splicing to promote distinct outcomes in defined cellular contexts. Our results demonstrate a new RNA-binding ability of Ubx. We find that the N51 amino acid of the DNA-binding Homeodomain is non-essential for RNA interaction in vitro, but is required for RNA interaction in vivo and Ubx splicing activity. Moreover, mutation of the N51 amino acid weakens the interaction between Ubx and active RNA Polymerase II (Pol II). Our results reveal that Ubx regulates elongation-coupled splicing, which could be coordinated by a dynamic interplay with active Pol II on chromatin. Overall, our work uncovered a novel role of the Hox TFs at the mRNA regulatory layer. This could be an essential function for other classes of TFs to control cell diversity.

An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

Endocrine disrupting chemicals differentially alter intranuclear dynamics and transcriptional activation of estrogen receptor-α

Transcription is a highly regulated sequence of stochastic processes utilizing many regulators, including nuclear receptors (NR) that respond to stimuli. Endocrine disrupting chemicals (EDCs) in the environment can compete with natural ligands for nuclear receptors to alter transcription. As nuclear dynamics can be tightly linked to transcription, it is important to determine how EDCs affect NR mobility. We use an EPA-assembled set of 45 estrogen receptor-α (ERα) ligands and EDCs in our engineered PRL-Array model to characterize their effect upon transcription using fluorescence in situ hybridization and fluorescence recovery after photobleaching (FRAP). We identified 36 compounds that target ERα-GFP to a transcriptionally active, visible locus. Using a novel method for multi-region FRAP analysis we find a strong negative correlation between ERα mobility and inverse agonists. Our findings indicate that ERα mobility is not solely tied to transcription but affected highly by the chemical class binding the receptor.

Using FRAP to Quantify Changes in Transcription Factor Dynamics After Cell Stimulation: Cell Culture, FRAP, Data Analysis, and Visualization

Here we show how to measure the mobility of transcription factors using fluorescence recovery after photobleaching (FRAP). Transcription factors are DNA-binding proteins that, upon binding to specific DNA motifs, regulate transcription of their target genes. FRAP is a simple, fast, and cost-effective method, and is a widely used quantitative method to measure the dynamics of fluorescently labeled molecules in solution, membranes, and inside living cells. Dynamics, specified by the immobile fraction, recovery half-time, diffusion constant, and ratio of molecules contributing to different phases of FRAP recovery, can be quantified by FRAP. This can be useful to understand their function in gene regulation. This tutorial is intended to familiarize the reader with the FRAP procedure to quantify transcription factor dynamics using a standard confocal microscope and analysis using MATLAB (MathWorks^®). This article will guide the reader through the preconditions of FRAP, and a detailed and step-by-step procedure of preparing cells, bleaching protocol, data analysis in MATLAB, and visualization of the FRAP data.