1. S. cerevisiae culture e l'espressione della proteina
Nota: Varianti Aβ presentano propensioni di aggregazione relativo diversi a causa di una mutazione in un singolo residuo alla posizione 19 (Phe19) del peptide Aβ42 (Figura 1A). Queste varianti del peptide sono contrassegnate con la proteina fluorescente verde (GFP), che agisce come un'aggregazione reporter (Figura 1)29.
<ol><li>Trasformare i plasmidi codifica per 20 varianti di Aβ42-GFP in cellule di lievito con un BY4741 sfondo parentale (MATa sua3Δ1 leu2Δ0 met15Δ0 ura3Δ0)30. Ogni plasmide codifica per un mutante di Aβ42 fuso alla GFP da un linker GSSGSSG. I plasmidi sono pESC(-URA) e comprendono il marcatore selezionabile URA329.<ol>
<li>Preparare il supporto completo sintetico senza uracile (SC-URA) con i seguenti componenti: 1,9 g/L azoto lievito base senza uracile, 5 g/L di solfato di ammonio e 900 mL di ddH2O combinato con 100 mL di glucosio 20% (p/v).</li>
		<li>Mettere una colonia delle cellule del lievito trasformato in 20 mL di terreno SC-URA contenente 2% di glucosio e far crescere le colture durante la notte a 30 ° C sotto agitazione a 210 giri/min.</li>
	</ol>
</li>
	<li>Il giorno successivo, crescere nuove culture di cellule di lievito inoculando 100 µ l della coltura durante la notte in 5 mL di terreno nuovo SC-URA.</li>
	<li>A un OD590 di 0,5, centrifugare le culture a 3.000 x g per 4 minuti, eliminare il supernatante e risospendere le cellule nello stesso volume di terreno nuovo SC-URA contenente 2% raffinosio.</li>
	<li>Incubare a 30 ° C sotto agitazione a 210 giri/min per 30 min. Centrifugare le cellule a 3.000 x g per 4 minuti, quindi eliminare il supernatante. Risospendere le cellule in mezzo SC-URA fresco contenente 2% galattosio per indurre l'espressione di proteine ricombinanti.</li>
	<li>Dopo 16h di indurre l'espressione della proteina in mezzo SC-URA contenente 2% galattosio, vendemmia 1 mL di cellule indotte in microcentrifuga sterile per centrifugazione a 3.000 x g per 4 min. Nota: Le differenze più rilevanti si verificano dopo un periodo di induzione di cellule di lievito indotto 16 h. esprimendo la varianti di Aβ42-GFP possa essere visualizzato da microscopia di fluorescenza per determinare la distribuzione della proteina ricombinante all'interno delle cellule (Figura 2B).</li>
</ol>2. cellula di colorazione
Nota: Cellule fresche indotto non sono necessari come controllo negativo per stabilire una soglia fluorescente durante un'analisi FC.
<ol><li>Prendere le cellule di lievito indotta da h 16 e determinare la densità ottica a 590 nm (OD590). Diluirli in una soluzione salina tamponato fosfato sterile (PBS) a un OD590 di 0.1.<ol>
<li>Preparare 1 x tamponato fosfato salino come segue: aggiungere 8 g di NaCl, 0,2 g di KCl, 1,44 g di Na2HPO4, e 0,24 g di KH2PO4 a 800 mL di ddH2O. riempire la soluzione fino a 1 L di ddH2O dopo aver regolato il pH 7.4 con HCl. filtrare il buffer su un filtro di 0,2 µm.</li>
	</ol>
</li>
	<li>Trasferire le sospensioni delle cellule che esprimono adeguatamente etichettate provette (polistirolo mm turno-fondo di 12 × 75) e proteggerli dalla luce. Assicurarsi che sono pronti per essere caricati sul citometro a flusso per l'analisi, insieme alle cellule indotto non e non tinto.</li>
	<li>Aggiungere la sonda di stress ossidativo (ad es., CellROX profondo rosso) per ogni campione a una concentrazione finale di 5 µM e incubare le cellule a 30 ° C per 30 min al buio.</li>
	<li>Dopo l'incubazione, lavare le cellule 3 volte con 1x PBS e risospendere li nello stesso volume del buffer prima di loro analisi di FC.</li>
</ol>3. analisi di citometria a flusso di
Nota: Utilizzare un'installazione FC con laser appropriato e filtri per rilevare GFP e segnale di fluorescenza sonda di stress ossidativo. Può essere utilizzato un citometro a flusso dotato di un laser di 488 nm blu per la rilevazione di GFP e un laser 635 nm rosso per la rilevazione della fluorescenza di sonda di stress ossidativo. Acquisizione di fluorescenza emissione di GFP e stress ossidativo sonda viene eseguita con un 530/30 nm BP e un filtro di BP di 660/20, rispettivamente (tabella 1).
<ol><li>Fare clic su Nuovo foglio di lavoro aperto Pannello e creare le seguenti trame di puntino di acquisizione.<ol>
<li>Selezionare lo Scatter Plot strumento dalla barra degli strumenti e creare una trama con le variabili zona Side Scatter (SSC-A) sull'asse y vs Forward Scatter-Area (FSC-A) in una scala lineare sull'asse x.</li>
		<li>Fare clic sullo Scatter Plot strumento dalla barra degli strumenti e creare una trama con le variabili FL1-Area (FITC) sul asse x vs. FL3-Area (APC) in una scala logaritmica sull'asse y.</li>
	</ol>
</li>
	<li>Fare clic sull'icona Strumenti impostazioni . Fare clic sulla scheda compensazione e impostare tutti i livelli di compensazione. Fare clic sulla scheda di acquisizione e selezionare un numero totale di 20.000 eventi da registrare.</li>
	<li>Fare clic sull'icona di Portata per passare la portata a bassa.</li>
	<li>Fare clic sulla scheda Acquisisci per avviare l'esecuzione le cellule non-indotta, non macchiate e regolare la tensione nell' Impostazioni dello strumento di avanti e dispersione di lato fino a quando la popolazione è distribuita nel quadrante centrale a sinistra.<ol>
<li>Fare clic sull'icona del poligono per impostare una regione R1 intorno la popolazione delle cellule escluse detriti cellulari e utilizzare questa popolazione gated P1 = R1 per tutte le trame puntino fluorescente e rappresentanze di istogramma (Figura 3).</li>
	</ol>
</li>
	<li>Per regolare la tensione PMT del segnale di fluorescenza, eseguire le cellule senza macchiate nella trama FL1-FL3 dot, tuning il guadagno nella scheda di Impostazione dello strumento , fino a quando le cellule sono distribuite nel quadrante inferiore sinistro.</li>
	<li>Cambiare il campione per le cellule indotte per misurare la fluorescenza di GFP. Nella scheda Impostazione dello strumento , è necessario impostare il guadagno nella FSC-A vs FL1 (FITC) quando la popolazione è distribuita nel quadrante inferiore destro. Definire la popolazione di cellule positive con un cancello (cancello P2).</li>
	<li>Cambiamento del campione indotto non macchiato celle per visualizzare lo sforzo ossidativo dalla fluorescenza su un terreno di dot FSC-A vs FL3 (APC). Sintonizzare il guadagno fino a che la popolazione delle cellule è distribuita nel quadrante superiore sinistro. Porta la popolazione cellulare positiva nel P3.</li>
	<li>Fare due appezzamenti di istogramma con l'icona di istogramma per rappresentare la fluorescenza delle cellule. Per rappresentare l'intensità di fluorescenza, assicurarsi FL1 (FITC) che rappresenta la fluorescenza di GFP è nell'asse x e FL3 (APC) che rappresenta la fluorescenza è nell'asse y, l'applicazione di popolazioni P2 e P3 su una scala logaritmica, rispettivamente. Nota: Istogramma sovrapposizioni sono un buon modo per illustrare le differenze tra i profili di fluorescenza delle cellule che esprimono diverse varianti Aβ.</li>
</ol>4. Immunodetection recombinant della proteina
<ol><li>Per determinare i livelli di espressione della proteina, raccogliere le colture di lievito indotte per 16 h mediante centrifugazione a 4.000 x g per 6 min e conservare il pellet di cellule a-80 ° C.<ol>
<li>Risospendere le cellule raccolte che esprimono la proteina ricombinante per 16 h in PBS. Preparare sospensioni cellulari di 200 µ l di ogni mutante di Aβ42-GFP a un OD590 di 20.</li>
	</ol>
</li>
	<li>Per l'analisi di frazione della proteina totale, 100 µ l di ogni mutante della raccolta mediante centrifugazione a 14.000 x g per 20 min e risospendere il pellet cellulare nello stesso volume di tampone di lisi del lievito Y-a (50 mM Tris-HCl pH 8.0, 1% DMSO, 200 mM NaCl, 1 mM EDTA contenente 10 mg/mL SB3-14 (myr istyl solfobetaina) completati con 1 mM phenylmethylsulfonyl fluoruro (PMSF)).<ol>
<li>Incubare i campioni per 20 min a temperatura ambiente sotto lieve agitazione.</li>
	</ol>
</li>
	<li>Determinare la concentrazione di Estratto di proteine usando l'analisi di Bradford. Caricare fino a 5 µ g di estratto proteico di ogni campione su un gel di elettroforesi SDS-PAGE 15% di acrilammide e macchia su una membrana di polivinilidene difluoruro (PVDF) a 100 V per 60 min.<ol>
<li>Preparare un Bradford reagente con 100 mg di Coomassie Brilliant Blue G-250 disciolto in 50 mL di etanolo al 95% e aggiungere 100 mL di acido fosforico 85% (p/v). Poi, quando il colorante è completamente sciolto, stemperate il composto con ddH2O a 1 L e filtrare attraverso carta da filtro cellulosa appena prima dell'uso. Nota: Il reagente di Bradford dovrebbe essere marrone chiaro.</li>
		<li>Preparare un standard di albumina di siero bovino (BSA) che vanno da 5-100 µ g di proteina in 1.000 µ l del reagente Bradford.</li>
		<li>Aggiungere 1-10 µ l di proteina estrarre a 1 mL di reattivo di Bradford e incubare la miscela a temperatura ambiente per 5 min.</li>
		<li>Misurare l'assorbanza dei campioni e la proteina estrarre campioni OD595.</li>
		<li>Per l'analisi, fare una trama con i valori di assorbanza ottenuti per il standard vs. µ g di proteina. Sulla curva standard, determinare le concentrazioni dei campioni originali dalla quantità di proteine, considerando il volume e la diluizione, se presente.</li>
	</ol>
</li>
	<li>Risciacquare la membrana con 1x Tris-buffered saline (TBS), 0.1% Tween 20 (Immunotyping) e bloccarlo utilizzando latte in polvere senza grassi 5% (p/v) in 1 x TTBS.<ol>
<li>Per preparare 1 L di 10 x TBS, sciogliere 24 g di Tris base e 88 g di NaCl in 900 mL di H2Odd e regolare il pH a 7,6. Aggiungere ddH2O ad un volume finale di 1 L. Per una soluzione 1x, miscelare 1 parte di stock x 10 con 9 parti di ddH2O.</li>
	</ol>
</li>
	<li>Incubare la membrana per 1 h con un anticorpo primario di β-amiloide 6E10 diluito 1:1,000 e per 1 h a temperatura ambiente con una capra di anticorpo secondario coniugato IgG-HRP anti-topo diluito 01:10, 000.</li>
	<li>Sviluppare le membrane con 1 mL di reagente fluorescente Luminata e quantificare le bande di analisi densitometrica come descritto al punto 5.2.</li>
</ol>5. analisi dei dati
<ol><li>Per analizzare i dati ottenuti da FC, creare una tabella che mostra l'intensità di fluorescenza media (MFI) e mediana fluorescenza con relativo errore standard corrispondenti e/o il coefficiente di variazione (CV) per fluorescenza GFP e livelli di stress ossidativo.<ol>
<li>Fare clic sulla scheda "Impostazioni" e l'icona di statistiche per personalizzare i dati statistici necessari per ogni canale. Nota: Quando si confrontano le varianti della proteina, convertire dati dei valori di fluorescenza in uno sfondo di piega o metrica ad alta risoluzione (RD): <img alt="Equation 1" src="/files/ftp_upload/57470/57470eq1.jpg"> In questa metrica, il campione medio è la media di fluorescenza di campioni di problema, il controllo medio è la media di fluorescenza delle cellule usate come controllo e la SD è la deviazione standard della media di fluorescenza. Fattore di RD è un indice più accurato rispetto valore MFI non solo calcola la differenza tra i campioni, ma anche gli account per la diffusione dei dati. Questo calcolo è specificamente rilevante quando il confronto dei dati provenienti da diversi esperimenti effettuati nel corso del tempo. Una volta sono stati calcolati il RDs, l'uso di un test statistico è consigliabile confermare il significato delle differenze osservate tra varianti della proteina.</li>
	</ol>
</li>
	<li>Per proteina immunodetection, quantificare i livelli di proteina ricombinante mediante Western blot utilizzando software ImageJ.<ol>
<li>Prima dell'analisi densitometria, convertire le immagini raw originale della membrana sviluppata in modalità di scala di grigi e il formato di file JPEG.</li>
		<li>Regolare la sezione Impostare misure di menu analizza a Dire valore di grigio.</li>
		<li>Fare clic sullo strumento rettangolo da ImageJ e definire un'area di interesse (ROI) con una dimensione coerente per l'analisi di densitometria di banda. Centro di ciascuna banda sull'immagine della membrana all'interno della cornice rettangolare creata e registrare la misurazione uno utilizzando Ctrl + M (il comando di misurazione).</li>
		<li>Misurare lo sfondo locale adiacente per ogni banda, applicando la stessa area ROI. Dopo la misurazione, sottrarre lo sfondo locale corrispondente da ogni banda individuo.</li>
	</ol>
</li>
</ol>

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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>

Una vasta gamma di malattie è legata all'accumulo di proteine misfolded in depositi cellulari6,7,8,33. Molti sforzi sono stati fatti per svelare i meccanismi molecolari che determinano l'insorgenza di queste malattie utilizzando approcci computazionali, che non tengono in concentrazioni di proteina di conto, o in vitro si avvicina, in cui la concentrazione nella proteina rimane costan...

Proteostasis è un determinante fondamentale dei processi di fitness e invecchiamento delle cellule. Nelle cellule, omeostasi della proteina è mantenuto da controllo di qualità di proteina sofisticate reti intendevano ad assicurare il corretto ripiegamento di conformeri proteine misfolded da accompagnatori e/o loro proteolisi mirata con diversi meccanismi ben conservati1 ,2,3,4,5. Un gran numero di studi fornisce il supporto per il collegamento tra l'inizio e la progressione di una vasta gamma di malattie umane e il fallimento di proteostasis, che conduce al misfolding e aggregazione. Per esempio, la presenza di depositi proteici è considerata un marchio di garanzia patologico di molti disordini neurodegenerative, quali Alzheimer, Parkinson e malattie6,7,8, di Huntington prionogenic malattie e non degenerativa amiloidosi9. È stato suggerito che presto assembly oligomerici e protofibrillar nella reazione di aggregazione sono i principale elicitori di citotossicità, stabilendo aberrante interazioni con altre proteine nell' affollato ambiente cellulare10. Inoltre, inclusioni di proteina (PI) possono essere trasmessi tra le cellule, propagando il loro effetto tossico11,12. Di conseguenza, potrebbe essere che la formazione di PI potrebbe infatti costituire un meccanismo di disintossicazione che limita la presenza di specie pericolose aggregati a posizioni specifiche in cella, dove possono essere elaborati o accumulati senza effetti secondari importanti 13 , 14.
Standard in vitro approcci biochimici hanno fornito importanti intuizioni le diverse specie che popolano le reazioni di aggregazione e loro proprietà15,16. Tuttavia, le condizioni utilizzate in queste analisi sono chiaramente diverse da quelli che si verificano all'interno della cellula e, pertanto, chiedo loro rilevanza fisiologica. A causa della notevole conservazione delle vie cellulari come controllo di qualità di proteine, l'autofagia o il regolamento del17,stato redox cellulare18 tra eucarioti19,20,21 ,22,23, il lievito Saccharomyces cerevisiae (S. cerevisiae) è emerso come un modello di cellulare semplice privilegiato per studiare i determinanti molecolari dell'aggregazione proteica e il suo impatto citotossico associato in ambienti biologicamente rilevanti24,25,26.
Propensione di aggregazione della proteina è una caratteristica intrinsecamente codificata nella sequenza primaria. Così, la formazione di amiloide-come le strutture può essere preveduta basato sull'identificazione e la valutazione della potenza di aggregazione-promuovere le regioni polipeptidi27. Tuttavia, nonostante il successo di algoritmi bioinformatici per predire le proprietà di aggregazione in vitro di sequenze proteiche, sono ancora lontano da come queste propensioni si traducono in vivo effetto citotossico di previsione. Gli studi che affrontano il legame tra lo stato aggregato di una data proteina e suoi associato danno cellulare in maniera sistematica possono contribuire ad per aggirare questa limitazione computazionale. Questa connessione è indirizzata nello studio presente, approfittando di un grande insieme di varianti del peptide amiloide-β Aβ42 differiscono solo per un singolo residuo, ma la visualizzazione di un intervallo continuo di aggregazione propensioni in vivo28. In particolare, è descritto un approccio basato su FC per identificare la specie conformazionale contabilità per il danno ossidativo suscitato da aggregazione-incline proteine in cellule di lievito. La metodologia fornisce molti vantaggi come la semplicità, funzionalità di alto-rendimento e accurata misurazione quantitativa. Questo approccio ha permesso di confermare che svolgono un ruolo protettivo contro lo sforzo ossidativo di PI.

<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

Misfolding e aggregazione amiloide conformazioni hanno riguardati l'insorgenza e la progressione di parecchie malattie neurodegenerative. Tuttavia, ci sono ancora poche informazioni sulla proteina insolubile come aggregati esercitano loro effetti tossici in vivo. Organismi di modello semplice di procarioti ed eucarioti, come batteri e lieviti, hanno contribuito notevolmente alla nostra comprensione attuale dei meccanismi dietro la formazione intracellulare dell'amiloide, propagazione di aggregati e tossicità. In questo protocollo, l'uso del lievito è descritto come un modello da dissezionare il rapporto tra la formazione di aggregati proteici e del loro impatto su stress ossidativo cellulare. Il metodo combina la rilevazione dello stato solubile/aggregati intracellulare di proteina amiloidogenica con la quantificazione del danno ossidativo cellulare derivanti dalla sua espressione mediante citometria a flusso (FC). Questo approccio è semplice, veloce e quantitativa. Lo studio illustra la tecnica correlando lo stress ossidativo cellulare causato da una vasta serie di varianti del peptide amiloide-β con loro propensioni di aggregazione intrinseco rispettivi.

Questo protocollo descrive come impiegare una collezione di 20 varianti del peptide Aβ42 dove Phe19 è stato mutato a tutti naturale proteinogenici aminoacidi28. Le propensioni di aggregazione teorica di queste proteine possono essere analizzati usando due algoritmi bioinformatici diversi (AGGRESCAN e TANGO31,32). In entrambi i casi, questa analisi esegue il rendering di una gradazione progressiva delle te...

Watch this Scientific Journal Video about Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast at JoVE.com

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

Valutazione dell'impatto dell'aggregazione proteica su Stress ossidativo cellulare nel lievito

L'aggregazione della proteina induce stress ossidativo cellulare. Questo protocollo descrive un metodo per il monitoraggio degli Stati intracellulari di proteine amiloidogeniche e lo sforzo ossidativo connesso con loro, mediante citometria a flusso. L'approccio viene utilizzato per studiare il comportamento delle varianti solubile e soggetto a aggregazione del peptide amiloide-β.

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. L'aggregazione della proteina induce stress ossidativo cellulare. Questo protocollo descrive un metodo per il monitoraggio degli Stati intracellulari di proteine amiloidogeniche e lo sforzo ossidativo connesso con loro, mediante citometria a flusso. L'approccio viene utilizzato per studiare il comportamento delle varianti solubile e soggetto a aggregazione del peptide amiloide-β.

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.

Definizione attività di Chaperone Redox-regolati di Hsp33 e mappatura cambiamenti conformazionali su Hsp33 usando la spettrometria totale di scambio di idrogeno-deuterio

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

Ricerca

Biochimica

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.

Estrazione e visualizzazione di aggregati proteici dopo il trattamento di Escherichia coli con un fattore di stress proteotossico

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.

Microarray analisi per Saccharomyces cerevisiae</em

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

Biologia

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.

I saggi di crescita per valutare la tossicità poliglutamina nel lievito

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.

4D Imaging di aggregazione proteica in cellule vive

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.

Saggi accoppiati per il monitoraggio delle proteine ​​in ripiegamento<em&gt; Saccharomyces cerevisiae</em

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.

L&#39;isolamento potenziato Hsp104 Varianti Utilizzando lievito Proteinopathy modelle

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).

Cicloesimide Analisi Chase di degradazione delle proteine ​​in<em&gt; Saccharomyces cerevisiae</em

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.

Metodi di studio variazioni inerenti l'aggregazione proteica con l'età 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 ...

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 ...

Valutazione dell'impatto dell'aggregazione proteica su Stress ossidativo cellulare nel lievito

Riepilogo

Esplora Altri Video

Capitoli in questo video

Definizione attività di Chaperone Redox-regolati di Hsp33 e mappatura cambiamenti conformazionali su Hsp33 usando la spettrometria totale di scambio di idrogeno-deuterio

Estrazione e visualizzazione di aggregati proteici dopo il trattamento di Escherichia coli con un fattore di stress proteotossico

Microarray analisi per Saccharomyces cerevisiae

I saggi di crescita per valutare la tossicità poliglutamina nel lievito

4D Imaging di aggregazione proteica in cellule vive

Saggi accoppiati per il monitoraggio delle proteine in ripiegamento

L'isolamento potenziato Hsp104 Varianti Utilizzando lievito Proteinopathy modelle

Cicloesimide Analisi Chase di degradazione delle proteine in

Metodi di studio variazioni inerenti l'aggregazione proteica con l'età in Caenorhabditis elegans

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

Valutazione dell'impatto dell'aggregazione proteica su Stress ossidativo cellulare nel lievito

Riepilogo

Esplora Altri Video

Capitoli in questo video

Definizione attività di Chaperone Redox-regolati di Hsp33 e mappatura cambiamenti conformazionali su Hsp33 usando la spettrometria totale di scambio di idrogeno-deuterio

Estrazione e visualizzazione di aggregati proteici dopo il trattamento di Escherichia coli con un fattore di stress proteotossico

Microarray analisi per Saccharomyces cerevisiae

I saggi di crescita per valutare la tossicità poliglutamina nel lievito

4D Imaging di aggregazione proteica in cellule vive

Saggi accoppiati per il monitoraggio delle proteine ​​in ripiegamento

L'isolamento potenziato Hsp104 Varianti Utilizzando lievito Proteinopathy modelle

Cicloesimide Analisi Chase di degradazione delle proteine ​​in

Metodi di studio variazioni inerenti l'aggregazione proteica con l'età in Caenorhabditis elegans

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

Saggi accoppiati per il monitoraggio delle proteine in ripiegamento

Cicloesimide Analisi Chase di degradazione delle proteine in