1. S. cerevisiae Cultures and Protein Expression
Note: A&#946; variants exhibit different relative aggregation propensities due to a mutation in a single residue at position 19 (Phe19) of the A&#946;42 peptide (Figure 1A). These peptide variants are tagged with green fluorescent protein (GFP), which acts as an aggregation reporter (Figure 1)29.
<ol>
	<li>Transform plasmids encoding for 20 A&#946;42-GFP variants into yeast cells with a BY4741 parental background (MATa his3&#916;1 leu2&#916;0 met15&#916;0 ura3&#916;0)30. Each plasmid encodes for an A&#946;42 mutant fused to GFP by a GSSGSSG linker. The plasmids are pESC(-URA) and include the selectable marker URA329.
	<ol>
		<li>Prepare the synthetic complete medium without uracil (SC-URA) with the following components: 1.9 g/L yeast nitrogen base without uracil, 5 g/L ammonium sulfate, and 900 mL of ddH2O combined with 100 mL of 20% (w/v) glucose.</li>
		<li>Put one colony of the transformed yeast cells into 20 mL of the SC-URA medium containing 2% glucose and grow the cultures overnight at 30 &#176;C under agitation at 210 rpm.</li>
	</ol>
	</li>
	<li>The next day, grow new yeast cells cultures by inoculating 100 &#181;L of the overnight culture into 5 mL of fresh SC-URA medium.</li>
	<li>At an OD590 of 0.5, centrifuge the cultures at 3,000 x g for 4 min, discard the supernatant, and resuspend the cells in the same volume of fresh SC-URA medium containing 2% raffinose.</li>
	<li>Incubate at 30 &#176;C under agitation at 210 rpm for 30 min. Then, centrifuge the cells at 3,000 x g for 4 min and discard the supernatant. Resuspend the cells in fresh SC-URA medium containing 2% galactose to induce the recombinant protein expression.</li>
	<li>After 16 h of inducing protein expression in SC-URA medium containing 2% galactose, harvest 1 mL of the induced cells in sterile microcentrifuge tubes by centrifugation at 3,000 x g for 4 min. 
	NOTE: The most relevant differences occur after an induction period of 16 h. Induced yeast cells expressing A&#946;42-GFP variants can be visualized by fluorescence microscopy to determine the recombinant protein distribution inside the cells (Figure 2B).</li>
</ol>
2. Cell Staining
Note: Fresh non-induced cells are required as a negative control to establish a fluorescent threshold during an FC analysis.
<ol>
	<li>Take the 16 h-induced yeast cells and determine their optical density at 590 nm (OD590). Dilute them in a sterile phosphate buffered saline (PBS) to an OD590 of 0.1.
	<ol>
		<li>Prepare 1x phosphate buffered saline as follows: add 8 g of NaCl, 0.2 g of KCl, 1.44 g of Na2HPO4, and 0.24 g of KH2PO4 to 800 mL of ddH2O. Fill the solution up to 1 L of ddH2O after adjusting the pH to 7.4 with HCl. Filter the buffer through a 0.2 &#181;m filter.</li>
	</ol>
	</li>
	<li>Transfer the expressing cell suspensions to appropriately labeled tubes (12 &#215; 75 mm round-bottom polystyrene) and protect them from light. Make sure they are ready to be loaded on the flow cytometer for analysis, together with non-induced and non-stained cells.</li>
	<li>Add the oxidative stress probe (e.g., CellROX Deep Red) to each sample at a final concentration of 5 &#181;M and incubate the cells at 30 &#176;C for 30 min in the dark.</li>
	<li>After incubation, wash the cells 3 times with 1x PBS and resuspend them in the same volume of buffer before their analysis by FC.</li>
</ol>
3. Flow Cytometry Analysis
Note: Use an FC setup with appropriate lasers and filters to detect GFP and oxidative stress probe fluorescence signal. A flow cytometer equipped with a 488 nm blue laser for the detection of GFP and a 635 nm red laser for the detection of oxidative stress probe fluorescence can be used. Acquisition of emission fluorescence of GFP and oxidative stress probe is performed with a 530/30 nm BP filter and a 660/20 BP filter, respectively (Table 1).
<ol>
	<li>Click on Open New Worksheet Panel and create the following acquisition dot plots.
	<ol>
		<li>Select the Scatter Plot Tool from the toolbar and create a plot with the variables Side Scatter-Area (SSC-A) on the y-axis vs. Forward Scatter-Area (FSC-A) in a linear scale on the x-axis.</li>
		<li>Click the Scatter Plot Tool from the toolbar and create a plot with the variables FL1-Area (FITC) on the x-axis vs. FL3-Area (APC) in a logarithmic scale on the y-axis.</li>
	</ol>
	</li>
	<li>Click the Instruments Settings icon. Click the Compensation tab and set all compensation levels. Click on the Acquisition tab and select a total number of 20,000 events to be recorded.</li>
	<li>Click the Flow Rate icon to switch the flow rate to Low.</li>
	<li>Click the Acquire tab to start running the non-induced, unstained cells and adjust the voltage in the Instrument Settings of the forward and side scatter until the population is distributed in the middle-left quadrant.
	<ol>
		<li>Click on the Polygon icon to set a region R1 around the cell population excluding cell debris, and use this gated population P1 = R1 for all fluorescent dot plots and histogram representations (Figure 3).</li>
	</ol>
	</li>
	<li>To adjust the PMT voltage of the fluorescence signal, run the unstained cells in the FL1-FL3 dot plot, tuning the gain in the Instrument Setting tab, until the cells are distributed in the lower left quadrant.</li>
	<li>Change the sample to the induced cells to measure GFP fluorescence. In the Instrument Setting tab, set the gain in the FSC-A vs. FL1 (FITC) when the population is distributed in the lower-right quadrant. Define the positive cell population with a gate (Gate P2).</li>
	<li>Change the sample to the non-induced stained cells to display oxidative stress by the fluorescence on an FSC-A vs. FL3 (APC) dot plot. Tune the gain until the cell population is distributed in the left-upper quadrant. Gate the positive cell population in P3.</li>
	<li>Make two histogram plots with the histogram icon to represent the cell fluorescence. To represent the fluorescence intensity, make sure FL1 (FITC) representing the GFP fluorescence is in the x-axis, and FL3 (APC) representing the fluorescence is in the y-axis, applying P2 and P3 populations on a logarithmic scale, respectively. 
	NOTE: Histogram overlays are a good way to illustrate differences between the fluorescence profiles of cells expressing different A&#946; variants.</li>
</ol>
4. Recombinant Protein Immunodetection
<ol>
	<li>To determine protein expression levels, harvest the yeast cultures induced for 16 h by centrifugation at 4,000 x g for 6 min, and store the cell pellets at -80 &#176;C.
	<ol>
		<li>Resuspend the collected cells expressing recombinant protein for 16 h in PBS. Prepare 200 &#181;L cell suspensions of each A&#946;42-GFP mutant to an OD590 of 20.</li>
	</ol>
	</li>
	<li>For total protein fraction analysis, harvest 100 &#181;L of each mutant by centrifugation at 14,000 x g for 20 min and resuspend the cell pellets in the same volume of yeast lysis buffer Y-PER (50 mM Tris-HCl pH 8.0, 1% DMSO, 200 mM NaCl, 1 mM EDTA containing 10 mg/mL SB3-14 (myristyl sulfobetaine) supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF)).
	<ol>
		<li>Incubate the samples for 20 min at room temperature under mild agitation.</li>
	</ol>
	</li>
	<li>Determine the protein extract concentration by using the Bradford assay. Load up to 5 &#181;g of protein extract of each sample on a 15% acrylamide SDS-PAGE electrophoresis gel and blot onto a polyvinylidene difluoride (PVDF) membrane at 100 V for 60 min.
	<ol>
		<li>Prepare a Bradford reagent with 100 mg of Coomassie Brilliant Blue G-250 dissolved in 50 mL of 95% ethanol and add 100 mL of 85% (w/v) phosphoric acid. Then, when the dye is completely dissolved, dilute the mixture with ddH2O to 1 L and filter it through cellulose filter paper just before use. 
		NOTE: The Bradford reagent should be light brown.</li>
		<li>Prepare a standard of bovine serum albumin (BSA) ranging from 5 - 100 &#181;g of protein in 1,000 &#181;L of the Bradford reagent.</li>
		<li>Add 1 -&#160;10 &#181;L of protein extract to 1 mL of the Bradford reagent and incubate the mixture at room temperature for 5 min.</li>
		<li>Measure the absorbance of the standards and the protein extract samples at OD595.</li>
		<li>For the analysis, make a plot with the absorbance values obtained for the standards vs. &#181;g of protein. Based on the standard curve, determine the concentrations of the original samples from the amount of protein, considering the volume and dilution, if any.</li>
	</ol>
	</li>
	<li>Rinse the membrane with 1x Tris-buffered saline (TBS), 0.1% Tween 20 (TTBS), and block it using 5% (w/v) nonfat dry milk in 1x TTBS.
	<ol>
		<li>To prepare 1 L of 10x TBS, dissolve 24 g of Tris base and 88 g of NaCl in 900 mL of H2Odd and adjust the pH to 7.6. Add ddH2O to a final volume of 1 L. For a 1x solution, mix 1 part of 10x stock with 9 parts of ddH2O.</li>
	</ol>
	</li>
	<li>Incubate the membrane for 1 h with a primary &#946;-amyloid antibody 6E10 diluted 1:1,000 and for 1 h at room temperature with a secondary antibody goat anti-mouse IgG-HRP conjugate diluted 1:10,000.</li>
	<li>Develop membranes using 1 mL of Luminata fluorescent reagent and quantify bands by densitometric analysis as described in step 5.2.</li>
</ol>
5. Data Analysis
<ol>
	<li>To analyze the data obtained by FC, create a table displaying the mean fluorescent intensity (MFI) and median fluorescence with its corresponding standard error and/or the coefficient of variance (CV) for GFP fluorescence and oxidative stress levels.
	<ol>
		<li>Click the Inspector tab and the Statistics icon to customize the statistical data that are required for each channel. 
		NOTE: When comparing protein variants, convert fluorescence values data to a fold-over background or resolution metric (RD): 
		<img alt="Equation 1" src="/files/ftp_upload/57470/57470eq1.jpg" /> 
		In this metric, the mean sample is the mean fluorescence of problem samples, the mean control is the mean fluorescence of cells used as a control, and the SD is the standard deviation of the mean fluorescence. RD factor is a more accurate index than MFI value as it not only calculates the difference between samples but also accounts for the spreading of the data. This calculation is specifically relevant when comparing data coming from different experiments carried out over time. Once the RDs have been calculated, the use of a statistical test is advisable to confirm the significance of the observed differences between protein variants.</li>
	</ol>
	</li>
	<li>For protein immunodetection, quantify the recombinant protein levels by Western blot using ImageJ software.
	<ol>
		<li>Before the densitometry analysis, convert the original raw images of the developed membrane into JPEG file format and greyscale mode.</li>
		<li>Adjust the Set Measurements section of the Analyze menu to Mean Grey Value.</li>
		<li>Click on the Rectangle tool from ImageJ and define a region of interest (ROI) with a consistent size for the band densitometry analysis. Center each band on the membrane image inside the created rectangular frame and record the measurement one by one using Ctrl+M (the measurement command).</li>
		<li>Measure the local background adjacent to each band, applying the same ROI area. After measurement, subtract the corresponding local background from each individual band.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Frydman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Folding+of+newly+translated+proteins+in+vivo:+the+role+of+molecular+chaperones.">Folding of newly translated proteins in vivo: the role of molecular chaperones.</a> Annual Review of Biochemistry. 70, 603-647 (2001).</li><li>Hartl, F. U., Bracher, A., Hayer-Hartl, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+chaperones+in+protein+folding+and+proteostasis.">Molecular chaperones in protein folding and proteostasis.</a> Nature. 475, 324-332 (2011).</li><li>Wong, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+determinants+of+selective+clearance+of+protein+inclusions+by+autophagy.">Molecular determinants of selective clearance of protein inclusions by autophagy.</a> Nature Communications. 3, (2012).</li><li>Winkler, J., Tyedmers, J., Bukau, B., Mogk, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chaperone+networks+in+protein+disaggregation+and+prion+propagation.">Chaperone networks in protein disaggregation and prion propagation.</a> Journal of Structural Biology. 179, 152-160 (2012).</li><li>Glickman, M. H., Ciechanover, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ubiquitin-proteasome+proteolytic+pathway:+destruction+for+the+sake+of+construction.">The ubiquitin-proteasome proteolytic pathway: destruction for the sake of construction.</a> Physiological Reviews. 82, 373-428 (2002).</li><li>Dobson, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+out+of+shape.">Getting out of shape.</a> Nature. 418, 729-730 (2002).</li><li>Chiti, F., Dobson, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+misfolding,+functional+amyloid,+and+human+disease.">Protein misfolding, functional amyloid, and human disease.</a> Annual Review of Biochemistry. 75, 333-366 (2006).</li><li>Aguzzi, A., O'Connor, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+diseases:+pathogenicity+and+therapeutic+perspectives.">Protein aggregation diseases: pathogenicity and therapeutic perspectives.</a> Nature Reviews Drug Discovery. 9, 237-248 (2010).</li><li>Rapezzi, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transthyretin-related+amyloidoses+and+the+heart:+a+clinical+overview.">Transthyretin-related amyloidoses and the heart: a clinical overview.</a> Nature Reviews Cardiology. 7, 398-408 (2010).</li><li>Deas, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alpha-synuclein+oligomers+interact+with+metal+ions+to+induce+oxidative+stress+and+neuronal+death+in+Parkinson's+disease.">Alpha-synuclein oligomers interact with metal ions to induce oxidative stress and neuronal death in Parkinson's disease.</a> Antioxidants &amp; Redox Signaling. 24, 376-391 (2016).</li><li>Brundin, P., Melki, R., Kopito, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prion-like+transmission+of+protein+aggregates+in+neurodegenerative+diseases.">Prion-like transmission of protein aggregates in neurodegenerative diseases.</a> Nature Reviews Molecular Cell Biology. 11, 301-307 (2010).</li><li>Frost, B., Diamond, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prion-like+mechanisms+in+neurodegenerative+diseases.">Prion-like mechanisms in neurodegenerative diseases.</a> Nature Reviews Neuroscience. 11, 155-159 (2009).</li><li>Arrasate, M., Mitra, S., Schweitzer, E. S., Segal, M. R., Finkbeiner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inclusion+body+formation+reduces+levels+of+mutant+huntingtin+and+the+risk+of+neuronal+death.">Inclusion body formation reduces levels of mutant huntingtin and the risk of neuronal death.</a> Nature. 431, 805-810 (2004).</li><li>Ross, C. A., Poirier, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opinion:+what+is+the+role+of+protein+aggregation+in+neurodegeneration?.">Opinion: what is the role of protein aggregation in neurodegeneration?.</a> Nature Reviews Molecular Cell Biology. 6, 891-898 (2005).</li><li>Mishra, R., Sj&#246;lander, D., Hammarstr&#246;m, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spectroscopic+characterization+of+diverse+amyloid+fibrils+in+vitro+by+the+fluorescent+dye+Nile+red.">Spectroscopic characterization of diverse amyloid fibrils in vitro by the fluorescent dye Nile red.</a> Molecular BioSystems. 7, 1232-1240 (2011).</li><li>Klunk, W. E., Jacob, R. F., Mason, R. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+amyloid+by+congo+red+spectral+shift+assay.">Quantifying amyloid by congo red spectral shift assay.</a> Methods in Enzymology. 309, 285-305 (1999).</li><li>Khurana, V., Lindquist, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+neurodegeneration+in+Saccharomyces+cerevisiae:+why+cook+with+baker's+yeast?.">Modelling neurodegeneration in Saccharomyces cerevisiae: why cook with baker's yeast?.</a> Nature Reviews Neuroscience. 11, 436-449 (2010).</li><li>Tenreiro, S., Outeiro, T. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simple+is+good:+yeast+models+of+neurodegeneration.">Simple is good: yeast models of neurodegeneration.</a> FEMS Yeast Research. 10, 970-979 (2010).</li><li>Moosavi, B., Mousavi, B., Macreadie, I. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast+model+of+amyloid-&#946;+and+Tau+aggregation+in+Alzheimer's+disease.">Yeast model of amyloid-&#946; and Tau aggregation in Alzheimer's disease.</a> Journal of Alzheimer's Disease. 47, 9-16 (2015).</li><li>Tenreiro, S., Munder, M. C., Alberti, S., Outeiro, T. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+the+power+of+yeast+to+unravel+the+molecular+basis+of+neurodegeneration.">Harnessing the power of yeast to unravel the molecular basis of neurodegeneration.</a> Journal of Neurochemistry. 127, 438-452 (2013).</li><li>Yang, J., Hao, X., Cao, X., Liu, B., Nystr&#246;m, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+sequestration+and+detoxification+of+huntingtin+by+the+ribosome+quality+control+complex.">Spatial sequestration and detoxification of huntingtin by the ribosome quality control complex.</a> eLife. 5, (2016).</li><li>Braun, R. J., B&#252;ttner, S., Ring, J., Kroemer, G., Madeo, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nervous+yeast:+modeling+neurotoxic+cell+death.">Nervous yeast: modeling neurotoxic cell death.</a> Trends in Biochemical Sciences. 35, 135-144 (2010).</li><li>Figley, M. D., Gitler, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast+genetic+screen+reveals+novel+therapeutic+strategy+for+ALS.">Yeast genetic screen reveals novel therapeutic strategy for ALS.</a> Rare Diseases. 1, e24420 (2013).</li><li>Cooper, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alpha-synuclein+blocks+ER-Golgi+traffic+and+Rab1+rescues+neuron+loss+in+Parkinson's+models.">Alpha-synuclein blocks ER-Golgi traffic and Rab1 rescues neuron loss in Parkinson's models.</a> Science. 313, 324-328 (2006).</li><li>Johnson, B. S., McCaffery, J. M., Lindquist, S., Gitler, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+yeast+TDP-43+proteinopathy+model:+exploring+the+molecular+determinants+of+TDP-43+aggregation+and+cellular+toxicity.">A yeast TDP-43 proteinopathy model: exploring the molecular determinants of TDP-43 aggregation and cellular toxicity.</a> Proceedings of the National Academy of Sciences of the United States of America. 105, 6439-6444 (2008).</li><li>Bharathi, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+ade1+and+ade2+mutations+for+development+of+a+versatile+red/white+color+assay+of+amyloid-induced+oxidative+stress+in+saccharomyces+cerevisiae.">Use of ade1 and ade2 mutations for development of a versatile red/white color assay of amyloid-induced oxidative stress in saccharomyces cerevisiae.</a> Yeast. 33, 607-620 (2016).</li><li>Pallar&#232;s, I., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+the+prediction+of+protein+aggregation+propensity.">Advances in the prediction of protein aggregation propensity.</a> Current Medicinal Chemistry. , (2017).</li><li>Villar-Piqu&#233;, A., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+propensity+is+a+crucial+determinant+of+intracellular+inclusion+formation+and+quality+control+degradation.">Protein aggregation propensity is a crucial determinant of intracellular inclusion formation and quality control degradation.</a> Biochimica et Biophysica Acta. 1833, 2714-2724 (2013).</li><li>Navarro, S., Villar-Piqu&#233;, A., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selection+against+toxic+aggregation-prone+protein+sequences+in+bacteria.">Selection against toxic aggregation-prone protein sequences in bacteria.</a> Biochimica et Biophysica Acta. 1843, 866-874 (2014).</li><li>Morell, M., de Groot, N. S., Vendrell, J., Avil&#233;s, F. X., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linking+amyloid+protein+aggregation+and+yeast+survival.">Linking amyloid protein aggregation and yeast survival.</a> Molecular BioSystems. 7, 1121-1128 (2011).</li><li>Conchillo-Sol&#233;, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AGGRESCAN:+a+server+for+the+prediction+and+evaluation+of+&amp;#34;hot+spots&amp;#34;+of+aggregation+in+polypeptides.">AGGRESCAN: a server for the prediction and evaluation of &amp;#34;hot spots&amp;#34; of aggregation in polypeptides.</a> BMC Bioinformatics. 8, 65 (2007).</li><li>Fernandez-Escamilla, A. M., Rousseau, F., Schymkowitz, J., Serrano, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+sequence-dependent+and+mutational+effects+on+the+aggregation+of+peptides+and+proteins.">Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.</a> Nature Biotechnology. 22, 1302-1306 (2004).</li><li>Renner, M., Melki, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+and+prionopathies.">Protein aggregation and prionopathies.</a> Pathologie Biologie.(Paris). 62, 162-168 (2014).</li><li>Carija, A., Navarro, S., de Groot, N. S., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+into+insoluble+deposits+protects+from+oxidative+stress.">Protein aggregation into insoluble deposits protects from oxidative stress.</a> Redox Biology. 12, 699-711 (2017).</li></ol>

A wide range of diseases is linked to the accumulation of misfolded proteins into cellular deposits6,7,8,33. Many efforts have been made to unravel the molecular mechanisms that trigger the onset of these diseases using computational approaches, which do not take into account protein concentrations, or in vitro approaches, in which the protein concentration remains constant during the ...

Proteostasis is a fundamental determinant of cell fitness and aging processes. In cells, protein homeostasis is maintained by sophisticated protein quality control networks aimed to ensure the correct refolding of misfolded protein conformers by chaperones and/or their targeted proteolysis with several well-conserved mechanisms1,2,3,4,5. A large number of studies provide support to the link between the onset and progression of a broad range of human diseases and the failure of proteostasis, leading to protein misfolding and aggregation. For instance, the presence of protein deposits is considered a pathological hallmark of many neurodegenerative disorders, such as Alzheimer&#39;s, Parkinson&#39;s, and Huntington&#39;s diseases6,7,8, prionogenic diseases, and non-degenerative amyloidoses9. It has been suggested that early oligomeric and protofibrillar assemblies in the aggregation reaction are the main elicitors of cytotoxicity, establishing aberrant interactions with other proteins in the crowded cellular milieu10. In addition, protein inclusions (PI) can be transmitted between cells, propagating their toxic effect11,12. Therefore, it could be that the formation of PI might indeed constitute a detoxifying mechanism that restricts the presence of dangerous aggregated species to specific locations in the cell, where they can be processed or accumulated without major side effects13,14.
Standard in vitro biochemical approaches have provided important insights into the different species that populate aggregation reactions and their properties15,16. However, the conditions used in these assays are clearly different from those occurring within the cell and, therefore, question their physiological relevance. Because of the notable conservation of cellular pathways such as protein quality control, autophagy, or the regulation of the cellular redox state17,18 among eukaryotes19,20,21,22,23, the budding yeast Saccharomyces cerevisiae (S. cerevisiae) has emerged as a privileged simple cellular model to study the molecular determinants of protein aggregation and its associated cytotoxic impact in biologically relevant environments24,25,26.
Protein aggregation propensity is a feature inherently encoded in the primary sequence. Thus, the formation of amyloid-like structures can be predicted based on the identification and evaluation of the potency of aggregation-promoting regions in polypeptides27. However, despite the success of bioinformatic algorithms to predict the in vitro aggregation properties of protein sequences, they are still far from forecasting how these propensities translate into in vivo cytotoxic impact. Studies that address the link between the aggregated state of a given protein and its associated cellular damage in a systematic manner may help to circumvent this computational limitation. This connection is addressed in the present study, taking advantage of a large set of variants of the amyloid-&#946; peptide A&#946;42 differing only in a single residue, but displaying a continuous range of aggregation propensities in vivo28. In particular, an FC-based approach to identify the conformational species accounting for the oxidative damage elicited by aggregation-prone proteins in yeast cells is described. The methodology provides many advantages such as simplicity, high-throughput capability, and accurate quantitative measurement. This approach made it possible to confirm that PI play a protective role against oxidative stress.

<table><tbody><tr><td>Yeast cells BY4741&amp;nbsp;</td><td>ATCC</td><td>201388</td><td>Genotype: MATa his3&amp;Delta;1 leu2&amp;Delta;0 met15&amp;Delta;0 ura3&amp;Delta;0</td></tr><tr><td>pESC(-Ura) plasmid&amp;nbsp;</td><td>Agilent Genomics</td><td>217454</td><td>Yeast expression plasmid with a Gal promotor. Selectable marker URA3</td></tr><tr><td>Yeast Synthetic Drop-out Medium Supplements</td><td>Sigma</td><td>Y1501</td><td>Powder</td></tr><tr><td>Yeast Nitrogen Base Without Amino Acids</td><td>Sigma</td><td>Y0626</td><td>Powder</td></tr><tr><td>Raffinose</td><td>Sigma</td><td>R7630</td><td>Powder</td></tr><tr><td>Glucose</td><td>Sigma</td><td>G7021</td><td>Powder</td></tr><tr><td>Galactose</td><td>Sigma</td><td>G0750</td><td>Powder</td></tr><tr><td>Phosphate Buffered Saline (PBS)</td><td>Fisher Scientific</td><td>BP3991&amp;nbsp;</td><td>Solution 10X</td></tr><tr><td>CellROX Deep Red Reagent&amp;nbsp;</td><td>Life Technologies</td><td>C10422</td><td>Free radical cell-permeant fluorescent sensor, non-fluorescent while in a reduced state, and exhibits bright fluorescence upon oxidation by reactive oxygen species (ROS), with absorption/emission maxima at 644/665 nm.&amp;nbsp;</td></tr><tr><td>Y-PER protein extraction reagent&amp;nbsp;</td><td>Thermo Scientific</td><td>78990</td><td>Liquid cell lysis buffer</td></tr><tr><td>Acrylamide/Bis-acrylamide</td><td>Sigma</td><td>A6050</td><td>Solution</td></tr><tr><td>Bradford dye reagent</td><td>Bio-Rad&amp;nbsp;</td><td>5000205</td><td>Dye reagent for one-step determination of protein concentration</td></tr><tr><td>&amp;beta;-amyloid antibody 6E10&amp;nbsp;</td><td>BioLegend</td><td>803001</td><td>Mouse IgG1. The epitope lies within amino acids 3-8 of beta amyloid (EFRHDS).</td></tr><tr><td>Goat anti-mouse IgG-HRP conjugate&amp;nbsp;</td><td>Bio-Rad</td><td>1721011</td><td></td></tr><tr><td>Membrane Immobilon-P, PVDF</td><td>Millipore</td><td>IPVH00010</td><td></td></tr><tr><td>Luminata forte</td><td>Merk</td><td>WBLUF0100</td><td>Premixed, ready to use chemiluminescent HRP detection reagent</td></tr><tr><td>Phenylmethanesulfonyl fluoride solution (PMSF)</td><td>Sigma</td><td>93482</td><td>Protease inhibitor. Dissolved at 0.1 M in ethanol</td></tr><tr><td>FACSCanto flow cytometer&amp;nbsp;</td><td>BD Biosciences</td><td>657338</td><td>Equipped with a 488&amp;nbsp;nm blue laser for the detection of GFP, and 635&amp;nbsp;nm red laser / 530/30 nm BP filter and 660/20 BP filter</td></tr><tr><td>Mini Trans-Blot Electrophoresis Transfer cell</td><td>Bio-Rad</td><td>1703930</td><td>Protein transference system</td></tr><tr><td>Mini-PROTEAN Tetra Handcast Systems</td><td>Bio-Rad</td><td>1658000FC</td><td>Electrophoresis system</td></tr></tbody></table>

evaluation of the impact of protein aggregation on cellular oxidative stress in yeast

Protein misfolding and aggregation into amyloid conformations have been related to the onset and progression of several neurodegenerative diseases. However, there is still little information about how insoluble protein aggregates exert their toxic effects in vivo. Simple prokaryotic and eukaryotic model organisms, such as bacteria and yeast, have contributed significantly to our present understanding of the mechanisms behind the intracellular amyloid formation, aggregates propagation, and toxicity. In this protocol, the use of yeast is described as a model to dissect the relationship between the formation of protein aggregates and their impact on cellular oxidative stress. The method combines the detection of the intracellular soluble/aggregated state of an amyloidogenic protein with the quantification of the cellular oxidative damage resulting from its expression using flow cytometry (FC). This approach is simple, fast, and quantitative. The study illustrates the technique by correlating the cellular oxidative stress caused by a large set of amyloid-&#946; peptide variants with their respective intrinsic aggregation propensities.

This protocol describes how to employ a collection of 20 variants of the A&#946;42 peptide where Phe19 has been mutated to all natural proteinogenic amino acids28. The theoretical aggregation propensities of these proteins can be analyzed using two different bioinformatic algorithms (AGGRESCAN and TANGO31,32). In both cases, this analysis renders a progressive gradation of aggregation tendencies, ascribing,...

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Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

Protein aggregation elicits cellular oxidative stress. This protocol describes a method for monitoring the intracellular states of amyloidogenic proteins and the oxidative stress associated with them, using flow cytometry. The approach is used to study the behavior of soluble and aggregation-prone variants of the amyloid-&#946; peptide.

Protein misfolding and aggregation into amyloid conformations have been related to the onset and progression of several neurodegenerative diseases. ...

evaluation-impact-protein-aggregation-on-cellular-oxidative-stress

Research

JoVE Journal

Biochemistry

7.2K Views.  Universitat Autònoma de Barcelona. Eiwit aggregatie lokt cellulaire oxidatieve stress. Dit protocol wordt een methode voor de controle van de intracellulaire toestanden van eiwitten van de amyloidogenic en de oxidatieve stress geassocieerd met hen, met behulp van stroom cytometry beschreven. De aanpak wordt gebruikt voor het bestuderen van het gedrag van oplosbare en aggregatie-naar voren gebogen varianten van de amyloid-β-peptide.

Video: Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

Defining Hsp33's Redox-regulated Chaperone Activity and Mapping Conformational Changes on Hsp33 Using Hydrogen-deuterium Exchange Mass Spectrometry

Living organisms regularly need to cope with fluctuating environments during their life cycle, including changes in temperature, pH, the accumulation of reactive oxygen species, and more. These fluctuations can lead to a widespread protein unfolding, aggregation, and cell death. Therefore, cells have evolved a dynamic and stress-specific network of molecular chaperones, which maintain a "healthy" proteome during stress conditions. ATP-independent chaperones constitute one major class of molecular chaperones, which serve as first-line defense molecules, protecting against protein aggregation in a stress-dependent manner. One feature these chaperones have in common is their ability to utilize structural plasticity for their stress-specific activation, recognition, and release of the misfolded client. 
In this paper, we focus on the functional and structural analysis of one such intrinsically disordered chaperone, the bacterial redox-regulated Hsp33, which protects proteins against aggregation during oxidative stress. Here, we present a toolbox of diverse techniques for studying redox-regulated chaperone activity, as well as for mapping conformational changes of the chaperone, underlying its activity. Specifically, we describe a workflow which includes the preparation of fully reduced and fully oxidized proteins, followed by an analysis of the chaperone anti-aggregation activity in vitro using light-scattering, focusing on the degree of the anti-aggregation activity and its kinetics. To overcome frequent outliers accumulated during aggregation assays, we describe the usage of Kfits, a novel graphical tool which allows easy processing of kinetic measurements. This tool can be easily applied to other types of kinetic measurements for removing outliers and fitting kinetic parameters. To correlate the function with the protein structure, we describe the setup and workflow of a structural mass spectrometry technique, hydrogen-deuterium exchange mass spectrometry, that allows the mapping of conformational changes on the chaperone and substrate during different stages of Hsp33 activity. The same methodology can be applied to other protein-protein and protein-ligand interactions.

One of the most challenging stress conditions that organisms encounter during their lifetime involves the accumulation of oxidants. During oxidative stress, cells heavily rely on molecular chaperones. Here, we present methods used to investigate the redox-regulated anti-aggregation activity, as well as to monitor structural changes governing the chaperone function using HDX-MS.

Living organisms regularly need to cope with fluctuating environments during their life cycle, including changes in temperature, pH, the accumulation of ...

Extraction and Visualization of Protein Aggregates after Treatment of Escherichia coli with a Proteotoxic Stressor

The exposure of living organisms to environmental and cellular stresses often causes disruptions in protein homeostasis and can result in protein aggregation. The accumulation of protein aggregates in bacterial cells can lead to significant alterations in the cellular phenotypic behavior, including a reduction in growth rates, stress resistance, and virulence. Several experimental procedures exist for the examination of these stressor-mediated phenotypes. This paper describes an optimized assay for the extraction and visualization of aggregated and soluble proteins from different Escherichia coli strains after treatment with a silver-ruthenium-containing antimicrobial. This compound is known to generate reactive oxygen species and causes widespread protein aggregation. 
The method combines a centrifugation-based separation of protein aggregates and soluble proteins from treated and untreated cells with subsequent separation and visualization by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Coomassie staining. This approach is simple, fast, and allows a qualitative comparison of protein aggregate formation in different E. coli strains. The methodology has a wide range of applications, including the possibility to investigate the impact of other proteotoxic antimicrobials on in vivo protein aggregation in a wide range of bacteria. Moreover, the protocol can be used to identify genes that contribute to increased resistance to proteotoxic substances. Gel bands can be used for the subsequent identification of proteins that are particularly prone to aggregation.

Extraction and Visualization of Protein Aggregates after Treatment of Escherichia coli with a Proteotoxic Stressor

This protocol describes the extraction and visualization of aggregated and soluble proteins from Escherichia coli after treatment with a proteotoxic antimicrobial. Following this procedure allows a qualitative comparison of protein aggregate formation in vivo in different bacterial strains and/or between treatments.

The exposure of living organisms to environmental and cellular stresses often causes disruptions in protein homeostasis and can result in protein ...

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent. In the experiment, yeast is grown for 48 hours in 1/2X YPD broth containing 3X glucose. The culture is split into a control and treated group. The experiment culture is treated with 0.5 mM H2O2 in Hanks Buffered Saline (HBSS) for 1 hour. The control culture is treated with HBSS only. Total RNA is extracted from both cultures and is converted to a biotin-labeled cRNA product through a multistep process. The final synthesis product is taken back to the UVM Microarray Core Facility and hybridized to the Affymetrix yeast GeneChips. The resulting gene expression data are uploaded into bioinformatics data analysis software.

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent.

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen ...

Biology

Growth Assays to Assess Polyglutamine Toxicity in Yeast

Protein misfolding is associated with many human diseases, particularly neurodegenerative diseases, such as Alzheimer&#x2019;s disease, Parkinson's disease, and Huntington's disease 1. Huntington's disease (HD) is caused by the abnormal expansion of a polyglutamine (polyQ) region within the protein huntingtin. The polyQ-expanded huntingtin protein attains an aberrant conformation (i.e. it misfolds) and causes cellular toxicity 2. At least eight further neurodegenerative diseases are caused by polyQ-expansions, including the Spinocerebellar Ataxias and Kennedy&#x2019;s disease 3.
The model organism yeast has facilitated significant insights into the cellular and molecular basis of polyQ-toxicity, including the impact of intra- and inter-molecular factors of polyQ-toxicity, and the identification of cellular pathways that are impaired in cells expressing polyQ-expansion proteins 3-8. Importantly, many aspects of polyQ-toxicity that were found in yeast were reproduced in other experimental systems and to some extent in samples from HD patients, thus demonstrating the significance of the yeast model for the discovery of basic mechanisms underpinning polyQ-toxicity. 
A direct and relatively simple way to determine polyQ-toxicity in yeast is to measure growth defects of yeast cells expressing polyQ-expansion proteins. This manuscript describes three complementary experimental approaches to determine polyQ-toxicity in yeast by measuring the growth of yeast cells expressing polyQ-expansion proteins. The first two experimental approaches monitor yeast growth on plates, the third approach monitors the growth of liquid yeast cultures using the BioscreenC instrument. 
Furthermore, this manuscript describes experimental difficulties that can occur when handling yeast polyQ models and outlines strategies that will help to avoid or minimize these difficulties. The protocols described here can be used to identify and to characterize genetic pathways and small molecules that modulate polyQ-toxicity. Moreover, the described assays may serve as templates for accurate analyses of the toxicity caused by other disease-associated misfolded proteins in yeast models.

This manuscript describes three complementary protocols for assessing the toxicity of polyglutamine (polyQ)-expansion proteins in the yeast Saccharomyces cerevisiae. These protocols can easily be modified to monitor the toxicity of other misfolded proteins in yeast.

Protein misfolding is associated with many human diseases, particularly neurodegenerative diseases, such as Alzheimer&#x2019;s disease, Parkinson's ...

4D Imaging of Protein Aggregation in Live Cells

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental conditions and an intracellular environment that is tightly packed, sticky, and hazardous to protein stability1. The ability to dynamically balance protein production, folding and degradation demands highly-specialized quality control machinery, whose absolute necessity is observed best when it malfunctions. Diseases such as ALS, Alzheimer's, Parkinson's, and certain forms of Cystic Fibrosis have a direct link to protein folding quality control components2, and therefore future therapeutic development requires a basic understanding of underlying processes. Our experimental challenge is to understand how cells integrate damage signals and mount responses that are tailored to diverse circumstances.
The primary reason why protein misfolding represents an existential threat to the cell is the propensity of incorrectly folded proteins to aggregate, thus causing a global perturbation of the crowded and delicate intracellular folding environment1. The folding health, or "proteostasis," of the cellular proteome is maintained, even under the duress of aging, stress and oxidative damage, by the coordinated action of different mechanistic units in an elaborate quality control system3,4. A specialized machinery of molecular chaperones can bind non-native polypeptides and promote their folding into the native state1, target them for degradation by the ubiquitin-proteasome system5, or direct them to protective aggregation inclusions6-9.
In eukaryotes, the cytosolic aggregation quality control load is partitioned between two compartments8-10: the juxtanuclear quality control compartment (JUNQ) and the insoluble protein deposit (IPOD) (Figure 1 - model). Proteins that are ubiquitinated by the protein folding quality control machinery are delivered to the JUNQ, where they are processed for degradation by the proteasome. Misfolded proteins that are not ubiquitinated are diverted to the IPOD, where they are actively aggregated in a protective compartment.
Up until this point, the methodological paradigm of live-cell fluorescence microscopy has largely been to label proteins and track their locations in the cell at specific time-points and usually in two dimensions. As new technologies have begun to grant experimenters unprecedented access to the submicron scale in living cells, the dynamic architecture of the cytosol has come into view as a challenging new frontier for experimental characterization. We present a method for rapidly monitoring the 3D spatial distributions of multiple fluorescently labeled proteins in the yeast cytosol over time. 3D timelapse (4D imaging) is not merely a technical challenge; rather, it also facilitates a dramatic shift in the conceptual framework used to analyze cellular structure.
We utilize a cytosolic folding sensor protein in live yeast to visualize distinct fates for misfolded proteins in cellular aggregation quality control, using rapid 4D fluorescent imaging. The temperature sensitive mutant of the Ubc9 protein10-12 (Ubc9ts) is extremely effective both as a sensor of cellular proteostasis, and a physiological model for tracking aggregation quality control. As with most ts proteins, Ubc9ts is fully folded and functional at permissive temperatures due to active cellular chaperones. Above 30 &deg;C, or when the cell faces misfolding stress, Ubc9ts misfolds and follows the fate of a native globular protein that has been misfolded due to mutation, heat denaturation, or oxidative damage. By fusing it to GFP or other fluorophores, it can be tracked in 3D as it forms Stress Foci, or is directed to JUNQ or IPOD.

Cellular viability depends on timely and efficient management of protein misfolding. Here we describe a method for visualizing the different potential fates of a misfolded protein: refolding, degradation, or sequestration in inclusions. We demonstrate the use of a folding sensor, Ubc9ts, for monitoring proteostasis and aggregation quality control in live cells using 4D microscopy.

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental ...

Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must be maintained to avoid deleterious consequences 1. External or internal factors that disrupt this balance can lead to protein aggregation, toxicity and cell death. In humans this is a major contributing factor to the symptoms associated with neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases 10. It is therefore essential that the proteins involved in maintenance of proteostasis be identified in order to develop treatments for these debilitating diseases. This article describes techniques for monitoring in vivo protein folding at near-real time resolution using the model protein firefly luciferase fused to green fluorescent protein (FFL-GFP). FFL-GFP is a unique model chimeric protein as the FFL moiety is extremely sensitive to stress-induced misfolding and aggregation, which inactivates the enzyme 12. Luciferase activity is monitored using an enzymatic assay, and the GFP moiety provides a method of visualizing soluble or aggregated FFL using automated microscopy. These coupled methods incorporate two parallel and technically independent approaches to analyze both refolding and functional reactivation of an enzyme after stress. Activity recovery can be directly correlated with kinetics of disaggregation and re-solubilization to better understand how protein quality control factors such as protein chaperones collaborate to perform these functions. In addition, gene deletions or mutations can be used to test contributions of specific proteins or protein subunits to this process. In this article we examine the contributions of the protein disaggregase Hsp104 13, known to partner with the Hsp40/70/nucleotide exchange factor (NEF) refolding system 5, to protein refolding to validate this approach.

Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

This article describes the use of a firefly luciferase-GFP fusion protein to investigate in vivo protein folding in Saccharomyces cerevisiae. Using this reagent, refolding of a model heat-denatured protein can be monitored simultaneously by fluorescence microscopy and an enzymatic assay to probe the roles of proteostasis network components in protein quality control.

Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must ...

Isolating Potentiated Hsp104 Variants Using Yeast Proteinopathy Models

Many protein-misfolding disorders can be modeled in the budding yeast Saccharomyces cerevisiae. Proteins such as TDP-43 and FUS, implicated in amyotrophic lateral sclerosis, and &#945;-synuclein, implicated in Parkinson&#8217;s disease, are toxic and form cytoplasmic aggregates in yeast. These features recapitulate protein pathologies observed in patients with these disorders. Thus, yeast are an ideal platform for isolating toxicity suppressors from libraries of protein variants. We are interested in applying protein disaggregases to eliminate misfolded toxic protein conformers. Specifically, we are engineering Hsp104, a hexameric AAA+ protein from yeast that is uniquely capable of solubilizing both disordered aggregates and amyloid and returning the proteins to their native conformations. While Hsp104 is highly conserved in eukaryotes and eubacteria, it has no known metazoan homologue. Hsp104 has only limited ability to eliminate disordered aggregates and amyloid fibers implicated in human disease. Thus, we aim to engineer Hsp104 variants to reverse the protein misfolding implicated in neurodegenerative disorders. We have developed methods to screen large libraries of Hsp104 variants for suppression of proteotoxicity in yeast. As yeast are prone to spontaneous nonspecific suppression of toxicity, a two-step screening process has been developed to eliminate false positives. Using these methods, we have identified a series of potentiated Hsp104 variants that potently suppress the toxicity and aggregation of TDP-43, FUS, and &#945;-synuclein. Here, we describe this optimized protocol, which could be adapted to screen libraries constructed using any protein backbone for suppression of toxicity of any protein that is toxic in yeast.

Yeast proteinopathy models are valuable tools to assess the toxicity and aggregation of proteins implicated in disease. Here, we present methods for screening Hsp104 variant libraries for toxicity suppressors. This protocol could be adapted to screen any protein library for toxicity suppressors of any protein that is toxic in yeast.

Many protein-misfolding disorders can be modeled in the budding yeast Saccharomyces cerevisiae. Proteins such as TDP-43 and FUS, implicated in amyotrophic ...

Cycloheximide Chase Analysis of Protein Degradation in Saccharomyces cerevisiae

Regulation of protein abundance is crucial to virtually every cellular process. Protein abundance reflects the integration of the rates of protein synthesis and protein degradation. Many assays reporting on protein abundance (e.g., single-time point western blotting, flow cytometry, fluorescence microscopy, or growth-based reporter assays) do not allow discrimination of the relative effects of translation and proteolysis on protein levels. This article describes the use of cycloheximide chase followed by western blotting to specifically analyze protein degradation in the model unicellular eukaryote, Saccharomyces cerevisiae (budding yeast). In this procedure, yeast cells are incubated in the presence of the translational inhibitor cycloheximide. Aliquots of cells are collected immediately after and at specific time points following addition of cycloheximide. Cells are lysed, and the lysates are separated by polyacrylamide gel electrophoresis for western blot analysis of protein abundance at each time point. The cycloheximide chase procedure permits visualization of the degradation kinetics of the steady state population of a variety of cellular proteins. The procedure may be used to investigate the genetic requirements for and environmental influences on protein degradation.

Cycloheximide Chase Analysis of Protein Degradation in Saccharomyces cerevisiae

Protein abundance reflects the rates of both protein synthesis and protein degradation. This article describes the use of cycloheximide chase followed by western blotting to analyze protein degradation in the model unicellular eukaryote, Saccharomyces cerevisiae (budding yeast).

Regulation of protein abundance is crucial to virtually every cellular process. Protein abundance reflects the integration of the rates of protein ...

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

In the last decades, the prevalence of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD), has grown. These age-associated disorders are characterized by the appearance of protein aggregates with fibrillary structure in the brains of these patients. Exactly why normally soluble proteins undergo an aggregation process remains poorly understood. The discovery that protein aggregation is not limited to disease processes and instead part of the normal aging process enables the study of the molecular and cellular mechanisms that regulate protein aggregation, without using ectopically expressed human disease-associated proteins. Here we describe methodologies to examine inherent protein aggregation in Caenorhabditis elegans through complementary approaches. First, we examine how to grow large numbers of age-synchronized C. elegans to obtain aged animals and we present the biochemical procedures to isolate highly-insoluble-large aggregates. In combination with a targeted genetic knockdown, it is possible to dissect the role of a gene of interest in promoting or preventing age-dependent protein aggregation by using either a comprehensive analysis with quantitative mass spectrometry or a candidate-based analysis with antibodies. These findings are then confirmed by in vivo analysis with transgenic animals expressing fluorescent-tagged aggregation-prone proteins. These methods should help clarify why certain proteins are prone to aggregate with age and ultimately how to keep these proteins fully functional.

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

The goal of the method presented here is to explore protein aggregation during normal aging in the model organism C. elegans. The protocol represents a powerful tool to study the highly insoluble large aggregates that form with age and to determine how changes in proteostasis impact protein aggregation.

In the last decades, the prevalence of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD), has grown. These ...

Quantification of Insoluble Protein Aggregation in Caenorhabditis elegans during Aging with a Novel Data-Independent Acquisition Workflow

We and others have shown that the aging process results in a proteome-wide accumulation of insoluble proteins. Knocking down genes encoding the insoluble proteins over 40% of the time results in an extension of the lifespan in C. elegans, suggesting that many of these proteins are key determinants of the aging process. Isolation and quantitative identification of these insoluble proteins are crucial to understand key biological processes that occur during aging. Here, we present a modified and improved protocol that details how to extract and isolate the SDS-insoluble proteins (insolublome) from C. elegans more efficiently to streamline mass spectrometric workflows via a novel label-free quantitative proteomics analysis. This improved protocol utilizes a highly efficient sonicator for worm lysis that greatly increases efficiency for protein extraction and allows us to use significantly less starting material (approximately 3,000 worms) than in previous protocols (typically using at least 40,000 worms). Subsequent quantitative proteomic analysis of the insolublome was performed using data-dependent acquisition (DDA) for protein discovery and identification and data-independent acquisition (DIA) for comprehensive and more accurate protein quantification. Bioinformatic analysis of quantified proteins provides potential candidates that can be easily followed up with other molecular methods in C. elegans. With this workflow, we routinely identify more than 1000 proteins and quantify more than 500 proteins. This new protocol enables efficient compound screening with C. elegans. Here, we validated and applied this improved protocol to wild-type C. elegans N2-Bristol strain and confirmed that aged day-10 N2 worms showed greater accumulation of the insolublome than day-2 young worms.

Quantification of Insoluble Protein Aggregation in Caenorhabditis elegans during Aging with a Novel Data-Independent Acquisition Workflow

This novel workflow efficiently extracts and isolates SDS-insoluble proteins (insolublome) from Caenorhabditis elegans with minimal starting material for quantitative differential proteomic analysis. The protocol uses a comprehensive data-independent acquisition mass spectrometry analysis to quantify the insolublome and bioinformatic analysis to gain biological insights into aging mechanisms and pathologies.

We and others have shown that the aging process results in a proteome-wide accumulation of insoluble proteins. Knocking down genes encoding the insoluble ...

Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

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