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

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

<i>Chrono</i> transcript demonstrates circadian oscillations in peripheral tissues.

<p>qPCR was used to measure transcript abundance of <i>Chrono</i>, <i>Per2</i>, and <i>Nr1d1</i> in (A) liver, (B) skeletal muscle, and (C) adipose tissue. Circadian variation is observed in each tissue with the amplitude of <i>Chrono</i> oscillations comparable to that of <i>Per2</i> and <i>Nr1d1</i>. Data shown are the average of 3–4 biological replicates.</p

CHRONO interferes with BMAL1–CBP binding.

<p>(A) BiFC was used to observe BMAL1–CBP interactions in the nuclei of HEK 293T cells. Co-expression of intact or S-tagged CHRONO reduced the complementation signal. Expression of the 212–385 CHRONO truncation mutant had no discernable effect. (B) IP confirms CHRONO-mediated interference in BMAL1/CBP complex formation. Endogenous protein was immunoprecipitated with anti-CBP antibody followed by immunoblotting as indicated. (C) ChIP qPCR analysis was used to evaluate the effect of CHRONO on the acetylation of histone H3–K9 near the <i>Per1</i> promoter E-box region. Schematic diagram of the human <i>Per1</i> promoter and primers used for ChIP assay are shown. Lysates obtained from control U2OS cells and those stably expressing CHRONO were collected 24 and 36 h after dexamethasone synchronization. ChIP DNA samples were quantified by quantitative real-time RT-PCR. Data are mean ± standard error of biological triplicates. (D) Various S-tagged, N-, and C-terminal CHRONO truncation mutants were generated. (E) Percent of cell nuclei demonstrating complementation after overexpression of various CHRONO constructs. (F) Per1:luciferase reporter signal in unsynchronized cells overexpressing BMAL1/CLOCK is enhanced by the transient overexpression of CBP. The effect of the overexpression of CHRONO constructs on reporter activity is shown.</p

CHRONO interacts with the C-terminus of BMAL1 but not BMAL2.

<p>(A) Overexpression of either BMAL1 or BMAL2, along with CLOCK, activates <i>Per1</i>:Luciferase reporter activity. Both are repressed by overexpression of CRY1. CHRONO specifically represses BMAL1-induced reporter activity. (B) BMAL1 and BMAL2 have similar structures with conserved bHLH DNA binding domains and PAS A and B interaction domains. BMAL1 contains a unique C-terminal region. Chimeric proteins were constructed by swapping corresponding domains from each protein as shown. Two-hybrid screening in HEK 293T cells demonstrates that BMAL1 truncation mutants (C) and chimeric proteins (D) that contain the 487–586 region of BMAL1 bind CHRONO and induce UAS:Luc reporter expression. This region is adjacent to but distinct from the annotated CRY1 binding site. (E) All BMAL1–BMAL2 constructs induce <i>Per1</i>-luc reporter activity in HEK 293T cells. In all constructs, reporter signal is repressed by the addition of CRY1. Functional repression by CHRONO is limited to BMAL constructs containing the implicated binding domain. (F) In cells overexpressing MYC–CHRONO along with BMAL1, BMAL2, or a chimeric BMAL2–BMAL1 construct, co-IP confirms complex formation between CHRONO and proteins containing the implicated BMAL1 C-terminal region.</p

Integration of core clock features.

<p>(A) List of exemplar core clock genes used as example models of core clock components. (B–E) Metric functions describing core clock features were generated from published data. Distributions of these metrics among nonclock genes (left panel) and exemplar clock genes (center panel) were used to construct evidence factors (right panel). (B) Cycling was evaluated using time-course microarray data from liver, pituitary, and NIH 3T3 cells. (C) Circadian disturbance metric quantifies the influence of RNAi-mediated gene knockdown on circadian dynamics in the U2OS model system. (D) The interaction metric counts the number of interactions inferred between each gene and the exemplar set of core clock genes. (E) The tissue ubiquity scores were taken from an EST database. (F) List of 20 genes most likely to have a core circadian function as determined by evidence factor integration. Genes highlighted in blue were included in the exemplar training set. Genes highlighted in purple were not in the training set but have been identified as having a role in the circadian clock. <i>Gm129</i> was selected for further characterization.</p

Supplemental Material for Higgins et al., 2018

<div>Supplemental Figures including:</div>Figure S1. The 378604X strain is highly tolerant to [C2C1im][OAc].<br>Figure S2. An approach to identify DNA sequences from the tolerant 378604X strain that confer IIL-tolerance.<br>Figure S3. Overexpression of SGE1 confers tolerance to [C2C1im][OAc] and higher concentrations of [C2C1im]Cl.<br>Figure S4. The SGE1PLL allele enhances anaerobic conversion of glucose into ethanol in the presence of [C2C1im]Cl.<br>Figure S5. The 378 strain containing homozygous sge1SLS alleles is sensitive to cationic inhibitors.<br>Figure S6. The SGE1PLL allele maintains IIL tolerance across a wide pH range.<br>Figure S7. Myc-tagged SGE1 variant strains also display a range of IL-tolerance phenotypes.<br>Figure S8. Doxycycline-inducible expression of Sge1 alleles impact cell growth and Sge1 protein levels in the presence of [C2C1im]Cl.<br>Figure S9. GFP-tagged SGE1 and ILT1 strains display similar IIL-tolerance phenotypes as untagged strains.<div><br></div><div>Table S1. Deletion of YDR090C sensitizes yeast to [C2C1im]Cl.<br>Table S2. Engineered S. cerevisiae strains and their genotypes used in this study.<br></div><div><br></div><div><div>File S1. <i>SGE1</i> genotypes, phenotypes and metadata of wild <i>S. cerevisiae</i> strains used in this study.</div><div><br></div><div>File S2. GATK SNP calls for sequenced wild <i>S. cerevisiae</i> strains.</div><div><br></div><div>File S3. <i>SGE1</i> sequences from additional <i>S. cerevisiae</i> and non-<i>S. cerevisiae</i> strains.</div></div

Directed Evolution Reveals Unexpected Epistatic Interactions That Alter Metabolic Regulation and Enable Anaerobic Xylose Use by <i>Saccharomyces cerevisiae</i>

<div><p>The inability of native <i>Saccharomyces cerevisiae</i> to convert xylose from plant biomass into biofuels remains a major challenge for the production of renewable bioenergy. Despite extensive knowledge of the regulatory networks controlling carbon metabolism in yeast, little is known about how to reprogram <i>S</i>. <i>cerevisiae</i> to ferment xylose at rates comparable to glucose. Here we combined genome sequencing, proteomic profiling, and metabolomic analyses to identify and characterize the responsible mutations in a series of evolved strains capable of metabolizing xylose aerobically or anaerobically. We report that rapid xylose conversion by engineered and evolved <i>S</i>. <i>cerevisiae</i> strains depends upon epistatic interactions among genes encoding a xylose reductase (<i>GRE3</i>), a component of MAP Kinase (MAPK) signaling (<i>HOG1</i>), a regulator of Protein Kinase A (PKA) signaling (<i>IRA2</i>), and a scaffolding protein for mitochondrial iron-sulfur (Fe-S) cluster biogenesis (<i>ISU1</i>). Interestingly, the mutation in <i>IRA2</i> only impacted anaerobic xylose consumption and required the loss of <i>ISU1</i> function, indicating a previously unknown connection between PKA signaling, Fe-S cluster biogenesis, and anaerobiosis. Proteomic and metabolomic comparisons revealed that the xylose-metabolizing mutant strains exhibit altered metabolic pathways relative to the parental strain when grown in xylose. Further analyses revealed that interacting mutations in <i>HOG1</i> and <i>ISU1</i> unexpectedly elevated mitochondrial respiratory proteins and enabled rapid aerobic respiration of xylose and other non-fermentable carbon substrates. Our findings suggest a surprising connection between Fe-S cluster biogenesis and signaling that facilitates aerobic respiration and anaerobic fermentation of xylose, underscoring how much remains unknown about the eukaryotic signaling systems that regulate carbon metabolism.</p></div

Genetic differences between parental and evolved strains.

<p>Genetic differences between parental and evolved strains.</p

Mutations in <i>ISU1</i> enhance respiration of xylose.

<p>Engineered and evolved strains were cultured in aerobic YPX media and analyzed for intracellular protein and metabolite concentrations. Average Log₂ intracellular concentrations of mitochondrial translation and respiration proteins (<b>A</b>) or hexose transporters and glucose-repressed proteins (<b>B</b>) from mutant strains relative to the Y22-3^MR parent are shown. White boxes indicate strains for which no corresponding peptides were detected. Relative protein concentrations were calculated from three independent biological replicates. Y22-3^MR <i>hog1Δ isu1Δ</i> strains were cultured in YP-Ethanol (<b>C</b>), YPD (<b>D</b>) or YPX (<b>E</b>) media and then treated with DMSO control or 0.5 μg/mL Antimycin A. Shaded areas represent the time during which DMSO or Antimycin A were present in the cultures. Average cell density, sugar and ethanol concentration with standard deviations from three independent biological replicates are reported.</p