A cell based screening approach for identifying protein degradation regulators

<p>Cellular transitions are achieved by the concerted actions of regulated degradation pathways. In the case of the cell cycle, ubiquitin mediated degradation ensures unidirectional transition from one phase to another. For instance, turnover of the cell cycle regulator cyclin B1 occurs after metaphase to induce mitotic exit. To better understand pathways controlling cyclin B1 turnover, the N-terminal domain of cyclin B1 was fused to luciferase to generate an N-cyclin B1-luciferase protein that can be used as a reporter for protein turnover. Prior studies demonstrated that cell-based screens using this reporter identified small molecules inhibiting the ubiquitin ligase controlling cyclin B1-turnover. Our group adapted this approach for the G2-M regulator Wee1 where a Wee1-luciferase construct was used to identify selective small molecules inhibiting an upstream kinase that controls Wee1 turnover. In the present study we present a screening approach where cell cycle regulators are fused to luciferase and overexpressed with cDNAs to identify specific regulators of protein turnover. We overexpressed approximately 14,000 cDNAs with the N-cyclin B1-luciferase fusion protein and determined its steady-state level relative to other luciferase fusion proteins. We identified the known APC/C regulator Cdh1 and the F-box protein Fbxl15 as specific modulators of N-cyclin B1-luciferase steady-state levels and turnover. Collectively, our studies suggest that analyzing the steady-state levels of luciferase fusion proteins in parallel facilitates identification of specific regulators of protein turnover.</p

Integrative model incorporating transcript, phospho-protein, and metabolite changes across the strain panel.

Map of central carbon metabolism. Each step is annotated with boxes indicating mRNA difference (left) or phosphorylation difference (middle) in Y128 versus Y22-3, or phosphorylation difference (right) in Y184 bcy1Δ versus Y184 grown anaerobically on xylose, according to the key. Gray indicates no significant change, white represents missing data, and multi-colored blue/yellow boxes indicate multiple phospho-sites with different changes. Metabolites measured previously [29] are colored to indicate an increase (orange) or decrease (magenta) in abundance in Y128 versus Y22-3 grown anaerobically on xylose. Reactions predicted to be active (orange) or suppressed (magenta) in xylose fermenting strains based on mRNA, protein, and/or metabolite abundances are highlighted. Hexose transporters marked with a star have been implicated in xylose transport.</p

Response to anaerobiosis in xylose-grown cells.

A) Log2(fold change) in mRNA and protein for cells grown ±O2, on glucose (black) or xylose (colored), with linear fit (R2) listed. B) Expression of 21 classically defined hypoxic genes (S3 Table). Asterisks indicate significant differences in mRNA change relative to Y22-3 (paired T-test). C) Identified promoter element (top) and known Azf1 site [134] (bottom). D) Average (n = 3) and standard deviation of xylose utilization rates in marker-rescued Y128 (Y133) wild-type (‘WT’) or strains lacking AZF1 (azf1Δ), over-expressing (OE) AZF1, or harboring an empty vector (‘Control’) during exponential growth. Xylose utilization rates in marker-rescued Y127 (Y132) empty vector (‘Control’) and OE of AZF1 are included as indicated. Different growth conditions in the two experiments prevent direct comparison. Asterisks indicate significant differences as indicated (paired T-test).</p

Mutation of <i>BCY1</i> decouples growth from anaerobic xylose metabolism.

A) OD600 (circles), xylose concentration (squares), and ethanol concentration (triangles) for Y184 (Y22-3 gre3Δisu1Δ) (black) and Y184 bcy1Δ (green) during anaerobic growth on xylose. Note that the culture was inoculated at low OD to show the effect; cells do not use all the xylose because there are very few cells in the experiment. B-D) Phospho-peptide changes in Y184 bcy1Δ relative to references, for phospho-peptides (rows) specific to Y184 bcy1Δ (B) or similar to Y184 (C) or Y184 ira2Δ (D). Functional enrichments for each denoted cluster are listed below each heat map. (E-F) Growth of strains in glucose +O2 (E) and xylose -O2 (F) as indicated in the key. (G-H) Average (n = 3) specific xylose consumption rate (G) or ethanol production rate (H). Asterisks indicate significant differences relative to Y128 (paired T-test).</p

Azf1 and Mga2 regulate anaerobic xylose responses.

A) Average log2(fold change) in mRNA abundance of denoted genes as listed in the key. B-D) Distributions of log2(fold change) in mRNA abundances for Hap4 (B), Msn2/Msn4 (C), and Mga2 (D) targets that are affected by AZF1 overexpression and show a corresponding change in Y128 versus controls (see text). Asterisks indicate significant difference compared to azf1Δ versus WT comparison (paired T-test). E-F) OD600 (circles), xylose concentration (squares), and ethanol concentration (triangles) for strain (orange) Y133 (marker-rescued Y128) lacking (mga2Δ, orange plot on the left) or over-expressing (‘OE’, green plot on the right) MGA2, and Y133 wild type (‘WT’) or empty-vector control (black) during anaerobic growth on xylose. Different growth conditions in the two experiments prevent direct comparison.</p

Strains used in this study.

Strains used in this study.</p

Inferred network regulating phosphorylation changes during anaerobic xylose growth.

A) Modules of peptides are shaped and colored according to class (Class A, diamond; Class B, square) and increase (yellow) or decrease (blue) of phosphorylation change across the strain panel, as described in the text. Each module is labeled with the phospho-motif sequence, with small case letter representing the phosphorylated site and “‥” indicating non-specific residues. Implicated kinase regulators are shown as purple circles; proteins whose peptides belong to each module are shown as smaller circles, color-coded by protein function as listed in the key. Note that proteins with multiple phospho-sites can belong to multiple modules. B) Average (n = 3) and standard deviation of the relative in vitro phosphorylation of a PKA substrate (ABCAM kit, see Methods) for lysates from cells that can (Y128, Y184 bcy1Δ, Y184 Bcy1-AiD–described in text) or cannot (Y22-3, Y127) use xylose anaerobically. Orange bars represent phosphorylation in the presence of PKA inhibitor H-89. C) Average (n = 3) and standard deviation of sugar utilization rates for Y133 tpk1Δtpk3Δtpk2as or Y133 tpk1Δtpk3ΔTPK2 during anaerobic growth, in the presence (green) or absence (black) of 1-NM-PP1. D) OD600 (circles), xylose concentration (squares), and ethanol concentration (triangles) for WT (black) or snf1Δ (orange) Y133 (marker-rescued Y128) grown in xylose -O2. E) Average (n = 3) and standard deviation of xylose utilization rates for strains in the presence (+) or absence (-) of SNF1. Asterisks indicate significant differences according to the key (paired T-tests).</p

Physical and functional interactions of CHRONO.

<p>(A) Results from a matrix of mammalian two-hybrid assays between known circadian clock components fused to Gal4 DNA binding domain (Gal4 DBD) or VP16 activation domain (VP16 AD). Black and gold indicate bait–prey interactions that resulted in less or greater than 5-fold activation of the 4XUAS reporter, respectively. Co-IP with tagged CHRONO confirms complex formation with (B) BMAL1 and (C) PER2. (D) C- and N-terminal regions of Venus, an enhanced florescent protein, were fused with identified constructs. A yellow bi-molecular fluorescence signal identifies interactions. (E) HEK 293T cells were transiently transfected with a Per1:luc reporter, wild-type, or mutant <i>Bmal1/Clock</i>, and increasing amounts of <i>Cry1</i> or <i>Chrono</i>. BMAL1/CLOCK point mutants are resistant to CRY1-mediated repression but sensitive to CHRONO. (F) The ability of native CHRONO to repress BMAL1/CLOCK activity was determined by transient transfection with two distinct shRNA constructs directed against <i>Chrono</i>. The indicated plasmids were co-transfected with the Per1-luc reporter into HEK 293T cells. Average activities and standard deviations from reporter assays were determined from independent biological triplicates.</p

Machine Learning Helps Identify CHRONO as a Circadian Clock Component

<div><p>Over the last decades, researchers have characterized a set of “clock genes” that drive daily rhythms in physiology and behavior. This arduous work has yielded results with far-reaching consequences in metabolic, psychiatric, and neoplastic disorders. Recent attempts to expand our understanding of circadian regulation have moved beyond the mutagenesis screens that identified the first clock components, employing higher throughput genomic and proteomic techniques. In order to further accelerate clock gene discovery, we utilized a computer-assisted approach to identify and prioritize candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experimental screen to characterize the top candidates. We found that several physically interact with known clock components in a mammalian two-hybrid screen and modulate <i>in vitro</i> cellular rhythms in an immortalized mouse fibroblast line (NIH 3T3). One candidate, Gene Model 129, interacts with BMAL1 and functionally represses the key driver of molecular rhythms, the BMAL1/CLOCK transcriptional complex. Given these results, we have renamed the gene CHRONO (computationally highlighted repressor of the network oscillator). Bi-molecular fluorescence complementation and co-immunoprecipitation demonstrate that CHRONO represses by abrogating the binding of BMAL1 to its transcriptional co-activator CBP. Most importantly, CHRONO knockout mice display a prolonged free-running circadian period similar to, or more drastic than, six other clock components. We conclude that CHRONO is a functional clock component providing a new layer of control on circadian molecular dynamics.</p></div

Influence of CHRONO on <i>in vitro</i> and <i>in vivo</i> rhythms.

<p>(A–D) Raw bioluminescence data from NIH 3T3 fibroblasts expressing BMAL:dLUC reporter are plotted after transfection with four shRNA constructs targeted against <i>Chrono</i>. Control and <i>Chrono</i> knockdown tracings are depicted in blue and red, respectively. Two replicates are shown. The period (E) and amplitude (F) of the observed rhythms are plotted. Representative wheel-running activity records for (G) wild-type control and (H) <i>Chrono^flx/flx</i> knockout mice. Blue shading indicates light exposure during the initial 12∶12 h, L∶D cycle. Arrows indicate transition to constant darkness. Regression lines fit to activity onset and computed period are shown. (I) Periodogram estimates of observed periods from wild-type (<i>n</i> = 5), <i>Chrono^flx/+</i> (<i>n</i> = 8), and <i>Chrono^flx/flx</i> mice (<i>n</i> = 6). Error bars indicate standard error of the mean.</p