SAM Domain-Based Protein Oligomerization Observed by Live-Cell Fluorescence Fluctuation Spectroscopy

Sterile-alpha-motif (SAM) domains are common protein interaction motifs observed in organisms as diverse as yeast and human. They play a role in protein homo- and hetero-interactions in processes ranging from signal transduction to RNA binding. In addition, mutations in SAM domain and SAM-mediated oligomers have been linked to several diseases. To date, the observation of heterogeneous SAM-mediated oligomers in vivo has been elusive, which represents a common challenge in dissecting cellular biochemistry in live-cell systems. In this study, we report the oligomerization and binding stoichiometry of high-order, multi-component complexes of (SAM) domain proteins Ste11 and Ste50 in live yeast cells using fluorescence fluctuation methods. Fluorescence cross-correlation spectroscopy (FCCS) and 1-dimensional photon counting histogram (1dPCH) confirm the SAM-mediated interaction and oligomerization of Ste11 and Ste50. Two-dimensional PCH (2dPCH), with endogenously expressed proteins tagged with GFP or mCherry, uniquely indicates that Ste11 and Ste50 form a heterogeneous complex in the yeast cytosol comprised of a dimer of Ste11 and a monomer of Ste50. In addition, Ste50 also exists as a high order oligomer that does not interact with Ste11, and the size of this oligomer decreases in response to signals that activate the MAP kinase cascade. Surprisingly, a SAM domain mutant of Ste50 disrupted not only the Ste50 oligomers but also Ste11 dimerization. These results establish an in vivo model of Ste50 and Ste11 homo- and hetero-oligomerization and highlight the usefulness of 2dPCH for quantitative dissection of complex molecular interactions in genetic model organisms such as yeast

Nuclear cGMP-Dependent Kinase Regulates Gene Expression via Activity-Dependent Recruitment of a Conserved Histone Deacetylase Complex

Elevation of the second messenger cGMP by nitric oxide (NO) activates the cGMP-dependent protein kinase PKG, which is key in regulating cardiovascular, intestinal, and neuronal functions in mammals. The NO-cGMP-PKG signaling pathway is also a major therapeutic target for cardiovascular and male reproductive diseases. Despite widespread effects of PKG activation, few molecular targets of PKG are known. We study how EGL-4, the Caenorhabditis elegans PKG ortholog, modulates foraging behavior and egg-laying and seeks the downstream effectors of EGL-4 activity. Using a combination of unbiased forward genetic screen and proteomic analysis, we have identified a conserved SAEG-1/SAEG-2/HDA-2 histone deacetylase complex that is specifically recruited by activated nuclear EGL-4. Gene expression profiling by microarrays revealed >40 genes that are sensitive to EGL-4 activity in a SAEG-1–dependent manner. We present evidence that EGL-4 controls egg laying via one of these genes, Y45F10C.2, which encodes a novel protein that is expressed exclusively in the uterine epithelium. Our results indicate that, in addition to cytoplasmic functions, active EGL-4/PKG acts in the nucleus via a conserved Class I histone deacetylase complex to regulate gene expression pertinent to behavioral and physiological responses to cGMP. We also identify transcriptional targets of EGL-4 that carry out discrete components of the physiological response

Constrained fits of the 1dPCH data to two-species allows for the examination of monomer and oligomer populations.

<p>A. A model was assumed where Ste50 could exist as either a monomer with fixed brightness, or oligomer with unconstrained brightness and number (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001931#s4" target="_blank">Materials and Methods</a>, and main text). Results are displayed to show the percentage of each component. Error bars are the standard error of the mean. B. Average brightness values for autofluorescence, GFP, GFP-GFP, and GFP-GFP-GFP from the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001931#pone-0001931-g003" target="_blank">Figure 3</a>, fit to a line. Error bars are the standard deviation. The line represents the best fit of the data to a linear model with a slope of 1959 and intercept 1193, which was then extrapolated toward higher brightness. Average brightness observed for the Ste50 oligomer from the analysis described above are marked on the extrapolated part of the plot.</p

2dPCH analysis of Ste50-mCherry and Ste11-GFP detects binding stoichiometry.

<p>50 µs bins were used. Data were fit to a one-component model or two-component model, as explained in the text. Symbols and bars represent the averages and standard deviations, respectively. Schematic representations of average stoichiometry observed; possible geometries of the interactions (see main text) are displayed next to the corresponding regions of the graph.</p

Fluctuation data can probe protein-protein interactions.

<p>A. Example traces of fluctuation data for dual-color experiments. B. Data can be analyzed by correlation analysis to examine concentration, diffusion, and co-diffusion of red and green particles. C. 1dPCH examines the distribution of photon events per time interval, and reports concentration and ‘brightness’, or oligomeric status. D. 2dPCH reports simultaneously concentration, interaction, oligomerization, and binding stoichiometry of heterogeneous complexes. An example two-dimensional PCH histogram is shown, with frequency versus number of green photons and number of red photons per time bin. E. Example, two-dimensional plot of a fit of modeled 2dPCH data. If a monomer red or green probe has a brightness of 3000 CPSM, for example, the plot demonstrates points one would expect to find values for with non-interacting monomeric species, or interacting monomeric species, or interacting dimeric species, as labeled.</p

1dPCH analysis of Ste50-GFP and Ste11-GFP probes homo-oligomerization.

<p>A. Example curves for GFP and mCherry (mCH.) controls in live yeast. B. Notched box plots of PCH fits, ranging from 18 to 30 individual, 7 second data traces from 5 to 10 cells. For auto-fluorescence measurements, data represents 7 measurements for mCherry and 15 measurements for GFP. 50 µs bins were used to generate the PCH distributions. C. Notched box plots of 1dPCH fits of GFP tagged species, with lines (same color scheme as in B) representing average brightness values of monomer, dimer, and trimer controls for a basis of comparison.</p

Yeast strains used in this study.

<p>all strains are S288C background, his3Δ1;leu2Δ0;met15Δ0;ura3Δ0.</p

CellProfiler 3.0: Next-generation image processing for biology

<div><p>CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.</p></div