1. cultivos de S. cerevisiae y expresión de la proteína
Nota: Variantes Aβ exhibición inclinaciones diferentes agregación relativa debido a una mutación en un solo residuo en la posición 19 (Phe19) del péptido de Aβ42 (figura 1A). Estas variantes de péptidos se han etiquetado con la proteína fluorescente verde (GFP), que actúa como una agregación de reportero (figura 1)29.
<ol><li>Transforman plásmidos codifican para 20 variantes de Aβ42-GFP en células de levadura con un BY4741 fondo parental (MATa su3Δ1 leu2Δ0 met15Δ0 ura3Δ0)30. Cada plásmido codifica para un mutante de Aβ42 fusionado a GFP por un enlazador GSSGSSG. La plásmidos son pESC(-URA) e incluyen el marcador seleccionable URA329.<ol>
<li>Preparar el medio completo sintético sin uracilo (SC-URA) con los siguientes componentes: 1,9 g/L nitrógeno levadura base uracilo, sulfato de amonio 5 g/L y 900 mL de ddH2O combinado con 100 mL de glucosa 20% (p/v).</li>
		<li>Poner una colonia de las células de levadura transformadas en 20 mL de medio SC-URA que contiene 2% de glucosa y crecer los cultivos durante la noche a 30 ° C bajo agitación en rpm 210.</li>
	</ol>
</li>
	<li>Al día siguiente, crecen nuevas culturas de células de levadura por inoculación 100 μl de la cultura durante la noche en 5 mL de medio fresco de SC-URA.</li>
	<li>Un OD590 de 0.5, centrifugar los cultivos a 3.000 x g durante 4 min, descartar el sobrenadante y resuspender las células en el mismo volumen de medio fresco de SC-URA que contienen rafinosa 2%.</li>
	<li>Incubar a 30 ° C bajo agitación a 210 rpm por 30 min. Luego, centrifugar las células a 3.000 x g durante 4 min y descarte el sobrenadante. Resuspender las células en medio fresco de SC-URA que contiene 2% galactosa para inducir la expresión de proteína recombinante.</li>
	<li>Después de 16 h de inducir la expresión de proteínas en medio SC-URA que contiene galactosa 2%, 1 mL de las células inducidas en tubos estériles de microcentrífuga la cosecha por centrifugación a 3.000 x g durante 4 minutos. Nota: Las diferencias más relevantes se producen después de un período de inducción de 16 h. inducida levadura las células que expresan variantes de Aβ42-GFP puede visualizarse por microscopía de fluorescencia para determinar la distribución de la proteína recombinante dentro de las células (figura 2B).</li>
</ol>2. células de tinción
Nota: Células no-inducida son necesarias como control negativo para establecer un umbral fluorescente durante un análisis FC.
<ol><li>Tomar las células de levadura inducida por h 16 y determinar su densidad óptica a 590 nm (OD590). Les diluido en un tampón de fosfato estéril salino (PBS) a un OD590 de 0,1.<ol>
<li>Preparar como sigue 1 x de tampón fosfato salino: Añadir 8 g de NaCl, 0,2 g de KCl, 1,44 g de Na2HPO4, y 0.24 g de KH2PO4 a 800 mL de ddH2O. llenar la solución hasta 1 L de ddH2O después de ajustar el pH a 7.4 con HCl. filtro el búfer a través de un filtro de 0.2 μm.</li>
	</ol>
</li>
	<li>Las suspensiones de células expresando la transferencia a tubos debidamente etiquetados (12 × 75 mm fondo redondo poliestireno expandido) y proteger de la luz. Asegúrese de que están listos para ser cargados en el citómetro de flujo para el análisis, junto con las células no inducidas y no manchado.</li>
	<li>Añadir la punta de prueba de estrés oxidativo (por ejemplo, rojo oscuro CellROX) a cada muestra a una concentración final de 5 μm e incubar las células en 30 ° C por 30 min en la oscuridad.</li>
	<li>Después de la incubación, lavar las células 3 veces con 1 x PBS y resuspender en el mismo volumen de tampón antes de su análisis por FC.</li>
</ol>3. Análisis de citometría de flujo de
Nota: Usar una configuración FC con láseres apropiados y filtros para detectar GFP y señal de fluorescencia de punta de prueba de estrés oxidativo. Puede utilizarse un citómetro de flujo equipado con un láser de nm azul 488 para la detección de GFP y un 635 nm rojo para la detección de la fluorescencia de punta de prueba de estrés oxidativo. Adquisición de la fluorescencia de emisión de GFP y probe el estrés oxidativo se realiza con un 530/30 nm BP filtro y un 660/20 BP, respectivamente (tabla 1).
<ol><li>Haga clic en Abra el Panel de hoja de cálculo nueva y crear los siguientes diagramas de punto de adquisición.<ol>
<li>Seleccione la Herramienta de diagrama Scatter de la barra de herramientas y crear un diagrama con las variables área de dispersión lateral (SSC-A) en el eje y frente a zona de dispersión hacia adelante (FSC-A) en una escala lineal en el eje x.</li>
		<li>Haga clic en la Herramienta de diagrama Scatter de la barra de herramientas y crear un diagrama con las variables área de FL1 (FITC) en el eje x vs. FL3-área (APC) en una escala logarítmica en el eje y.</li>
	</ol>
</li>
	<li>Haga clic en el icono Configuración de instrumentos . Haga clic en la ficha de compensación y ajuste todos los niveles de compensación. Haga clic en la ficha de adquisición y seleccione un número total de 20.000 eventos a registrar.</li>
	<li>Haga clic en el icono de Flujo para cambiar la velocidad de flujo a baja.</li>
	<li>Haga clic en la ficha de adquisición para comenzar a correr las células no inducidas, sin manchas y ajustar la tensión en los Parámetros fijados del instrumento del adelante y dispersión lateral hasta que la población se distribuye en el cuadrante de centro-izquierda.<ol>
<li>Haga clic en el icono de polígono para definir una región R1 alrededor de la población de la célula excepto restos de células y esta población cerrada P1 = R1 para todas las parcelas de punto fluorescente y representaciones de histograma (figura 3).</li>
	</ol>
</li>
	<li>Para ajustar la tensión de la PMT de la señal de fluorescencia, ejecute las células sin manchas en la trama de punto FL1-FL3, ajuste la ganancia en la ficha Configuración del instrumento , hasta que las células se distribuyen en el cuadrante izquierdo más bajo.</li>
	<li>Cambiar la muestra a las células inducidas para medir la fluorescencia de GFP. En la ficha Configuración del instrumento , establecer la ganancia de la FSC-A vs FL1 (FITC) cuando la población se distribuye en el cuadrante de abajo a la derecha. Definir la población de células positivas con una puerta (puerta P2).</li>
	<li>Cambio la muestra a la inducida mancharon la fluorescencia en una trama de puntos FSC-A vs FL3 (APC) para mostrar el estrés oxidativo de las células. Afine la ganancia hasta que la población celular se distribuye en el cuadrante superior izquierdo. Puerta de la población de células positivas en P3.</li>
	<li>Hacer dos parcelas histograma con el icono de histograma para representar a la fluorescencia de la célula. Para representar la intensidad de fluorescencia, asegúrese que es FL1 (FITC) que representa a la fluorescencia de GFP en el eje x, y FL3 (APC) que representa la fluorescencia en el eje y, aplicando las poblaciones P2 y P3 en una escala logarítmica, respectivamente. Nota: Superposiciones de histograma son una buena manera de ilustrar las diferencias entre los perfiles de fluorescencia de las células que expresan diversas variantes Aβ.</li>
</ol>4. inmunodetección de proteínas recombinantes
<ol><li>Para determinar los niveles de expresión de la proteína, las culturas de levadura inducidas durante 16 horas por centrifugación a 4.000 x g durante 6 minutos la cosecha y almacenar los pellets de células a-80 ° C.<ol>
<li>Resuspender las células colectadas expresan la proteína recombinante por 16 h en PBS. Preparación de suspensiones celulares de 200 μL de cada mutante de Aβ42-GFP a una OD590 de 20.</li>
	</ol>
</li>
	<li>Para el análisis de la fracción de proteína total, cosechar 100 μl de cada mutante por centrifugación a 14.000 x g por 20 min y resuspender el pellet celular en el mismo volumen de tampón de lisis de la levadura Y-por (50 mM Tris-HCl pH 8.0, 1% DMSO, 200 mM NaCl, 1 mM EDTA que contiene 10 mg/mL SB3-14 (myr istyl sulfobetaína) complementado con fluoruro (PMSF) del phenylmethylsulfonyl de 1 mM).<ol>
<li>Incubar las muestras durante 20 min a temperatura ambiente en agitación suave.</li>
	</ol>
</li>
	<li>Determinar la concentración de extracto de proteína mediante el ensayo de Bradford. Carga hasta 5 μg de extracto de proteína de cada muestra en un gel de electroforesis de SDS-PAGE de acrilamida 15% y el blot en una membrana de difluoruro (PVDF) de polivinilideno a 100 V por 60 min.<ol>
<li>Preparar a un Bradford reactivo con 100 mg de azul brillante de Coomassie G-250 disuelto en 50 mL de etanol al 95% y agregar 100 mL de ácido fosfórico al 85% (w/v). Entonces, cuando el colorante se haya disuelto completamente, diluir la mezcla con ddH2O a 1 L y filtrar con papel de filtro de celulosa antes de uso. Nota: El reactivo de Bradford debe ser marrón claro.</li>
		<li>Preparar un estándar de albúmina de suero bovino (BSA) que van desde 5 a 100 μg de proteína en 1000 μl del reactivo de Bradford.</li>
		<li>Añadir 1-10 μl de proteína extraer 1 ml del reactivo de Bradford e incubar la mezcla a temperatura ambiente durante 5 minutos.</li>
		<li>Medir la absorbancia de los estándares y las muestras de extracto de proteína a OD595.</li>
		<li>Para el análisis, hacer un diagrama con los valores de absorbancia obtenidos para los estándares vs. μg de proteína. Basado en la curva estándar, determinar las concentraciones de las muestras originales de la cantidad de proteína, teniendo en cuenta el volumen y la dilución, si cualquier.</li>
	</ol>
</li>
	<li>Lavar la membrana con 1 x solución salina tamponada con Tris (TBS), 0,1% Tween 20 (TTBS) y bloquear usando leche descremada en polvo 5% (w/v) con 1 x TTBS.<ol>
<li>Para preparar 1 L de 10 x TBS, disolver 24 g de Tris base y 88 g de NaCl en 900 mL de H2Odd y ajustar el pH a 7,6. Añadir ddH2O a un volumen final de 1 L. Para una solución de 1 x, mezcle 1 parte de stock de x 10 con 9 partes de ddH2O.</li>
	</ol>
</li>
	<li>Incubar la membrana durante 1 h con un anticuerpo primario de β-amiloide 6E10 diluido 1:1,000 y por 1 h a temperatura ambiente con una cabra de anticuerpo secundario conjugado de anti-mouse IgG-HRP diluido 1:10, 000.</li>
	<li>Desarrollar membranas usando 1 mL de reactivo fluorescente Luminata y cuantificar bandas por análisis densitométrico como se describe en el paso 5.2.</li>
</ol>5. Análisis de datos
<ol><li>Para analizar los datos obtenidos por FC, crear una tabla mostrando la intensidad fluorescente media (IFM) y mediana de la fluorescencia con su correspondiente error estándar o el coeficiente de variación (CV) para la fluorescencia de GFP y niveles de estrés oxidativo.<ol>
<li>Haga clic en la ficha Inspector y el icono de estadísticas para personalizar los datos estadísticos que se requieren para cada canal. Nota: Cuando se comparan las variantes de la proteína, convertir datos de valores de fluorescencia a una doblar-sobre fondo o resolución métrico (RD): <img alt="Equation 1" src="/files/ftp_upload/57470/57470eq1.jpg"> En esta métrica, la muestra media es la media fluorescencia de las muestras problema, el control media es la media fluorescencia de células utilizadas como control, y la SD es la desviación estándar de la media de la fluorescencia. Factor de RD es un índice más preciso que el valor de la IMF como no solo calcula la diferencia entre las muestras, sino también representa la difusión de los datos. Este cálculo es específicamente relevante al comparar datos de diferentes experimentos realizados en el tiempo. Una vez que se han calculado los RDs, el uso de una prueba estadística es recomendable confirmar la significación de las diferencias observadas entre las variantes de la proteína.</li>
	</ol>
</li>
	<li>De inmunodetección de proteínas, cuantificar los niveles de proteína recombinante por Western blot utilizando el software ImageJ.<ol>
<li>Antes del análisis de densitometría, convertir las imágenes raws originales de la membrana desarrollada en modo del archivo JPEG formato y escala de grises.</li>
		<li>Ajustar la sección Establecer mediciones del menú analizar valor de gris medio.</li>
		<li>Haga clic en la herramienta rectángulo de ImageJ y definir una región de interés (ROI) con un tamaño consistente para el análisis de densitometría de la banda. Centro de cada banda en la imagen de la membrana dentro del marco rectangular creado y registre la medida uno por uno usando Ctrl + M (el comando de medición).</li>
		<li>Medir el fondo local adyacente a cada banda, aplicación de la misma área de retorno de la inversión. Después de la medida, restar el fondo local correspondiente de cada banda individual.</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 amplia gama de enfermedades está ligada a la acumulación de proteínas mal plegadas en depósitos celulares6,7,8,33. Se han hecho muchos esfuerzos para desentrañar los mecanismos moleculares que desencadenan la aparición de estas enfermedades usando el enfoque computacional, que no tienen en las concentraciones de proteína de cuenta, o en la vitro se acerca, en la que la conce...

Proteostasis es un determinante fundamental de los procesos de acondicionamiento físico y el envejecimiento celular. En las células, se mantiene la homeostasis de proteínas por control de calidad de la proteína sofisticadas redes destinadas a asegurar la correcta refolding de Conformadores de proteínas mal plegadas por acompañantes o su proteólisis específica con varios mecanismos bien conservados1 ,2,3,4,5. Un gran número de estudios proporciona apoyo a la relación entre el inicio y la progresión de una amplia gama de enfermedades humanas y la falta de proteostasis, llevando a mal plegamiento de proteínas y agregación. Por ejemplo, la presencia de depósitos de proteína se considera una característica patológica de muchas enfermedades neurodegenerativas, como Alzheimer, Parkinson y Huntington enfermedades6,7,8, enfermedades prionogenic y amiloidosis no degenerativas9. Se ha sugerido que temprana asambleas oligomeric y protofibrillar en la reacción de agregación son los elicitores principal de citotoxicidad, establecer aberrantes interacciones con otras proteínas en el medio celular lleno de gente10. Además, inclusiones de proteína (PI) pueden transmitirse entre las células, propagando su efecto tóxico11,12. Por lo tanto, podría ser que la formación de PI de hecho podría constituir un mecanismo de desintoxicación que restringe la presencia de especies peligrosas agregadas a lugares específicos en la célula, donde pueden ser procesados o acumulados sin efectos secundarios importantes 13 , 14.
Estándar en vitro bioquímicos enfoques han aportado importante información sobre las diferentes especies que pueblan las reacciones de agregación y sus propiedades15,16. Sin embargo, las condiciones utilizadas en estos ensayos son claramente diferentes de los que ocurren dentro de la célula y, por lo tanto, cuestionan su importancia fisiológica. Debido a la notable conservación de vías celulares como control de calidad de la proteína, la autofagia o la regulación de redox celular estado17,18 entre eucariotas19,20,21 ,22,23, la levadura de florecimiento Saccharomyces cerevisiae (S. cerevisiae) ha surgido como un simple modelo celular privilegiado para el estudio de los determinantes moleculares de la agregación de proteínas y sus efectos citotóxicos asociados en entornos biológicamente relevantes24,25,26.
Propensión de agregación de la proteína es una característica intrínsecamente codificada en la secuencia primaria. Así, la formación de amiloide-como las estructuras se puede predecir basado en la identificación y evaluación de la potencia de promover la agregación de polipéptidos27regiones. Sin embargo, a pesar del éxito de bioinformáticas algoritmos para predecir las propiedades de agregación en vitro de secuencias de proteínas, son aún muy lejos de pronosticar cómo estas propensiones traducen en vivo impacto citotóxico. Estudios que aborden la relación entre el estado agregado de una proteína dada y su asociado daño celular de forma sistemática pueden ayudar a eludir esta limitación computacional. Esta conexión se aborda en el presente estudio, tomando ventaja de un amplio conjunto de variantes del péptido amiloide-β Aβ42 difieren sólo en un único residuo, pero mostrando un rango continuo de agregación propensiones en vivo28de. En particular, se describe un enfoque basado en FC para identificar las especies conformacionales responsables del daño oxidativo provocado por propensas a la agregación de proteínas en las células de levadura. La metodología ofrece muchas ventajas tales como simplicidad, capacidad de alto rendimiento y precisión para la medición cuantitativa. Este enfoque permitió confirmar ese juego un papel protector contra el estrés oxidativo de la PI.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Yeast cells BY4741&#160;</td>
			<td>ATCC</td>
			<td>201388</td>
			<td>Genotype: MATa his3&#916;1 leu2&#916;0 met15&#916;0 ura3&#916;0</td>
		</tr>
		<tr>
			<td>pESC(-Ura) plasmid&#160;</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&#160;</td>
			<td>Solution 10X</td>
		</tr>
		<tr>
			<td>CellROX Deep Red Reagent&#160;</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.&#160;</td>
		</tr>
		<tr>
			<td>Y-PER protein extraction reagent&#160;</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&#160;</td>
			<td>5000205</td>
			<td>Dye reagent for one-step determination of protein concentration</td>
		</tr>
		<tr>
			<td>&#946;-amyloid antibody 6E10&#160;</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&#160;</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&#160;</td>
			<td>BD Biosciences</td>
			<td>657338</td>
			<td>Equipped with a 488&#160;nm blue laser for the detection of GFP, and 635&#160;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

Mal plegamiento de proteínas y acumulación en conformaciones amiloides han sido relacionadas con la aparición y progresión de varias enfermedades neurodegenerativas. Sin embargo, todavía hay poca información sobre cómo insoluble proteína agregados ejercen sus efectos tóxicos in vivo. Organismos modelo simples de procariotas y eucariotas, tales como bacterias y levadura, han contribuido significativamente a nuestra actual comprensión de los mecanismos detrás de la formación amiloide intracelular, la propagación de agregados y la toxicidad. En el presente Protocolo, el uso de levadura se describe como un modelo para analizar la relación entre la formación de agregados proteicos y su impacto sobre el estrés oxidativo celular. El método combina la detección del estado intracelular soluble/agregados de una proteína amyloidogenic con la cuantificación del daño oxidativo celular resultantes de su expresión mediante citometría de flujo (FC). Este enfoque es simple, rápida y cuantitativa. El estudio ilustra la técnica al correlacionar el estrés oxidativo celular causado por un amplio conjunto de variantes de péptido amiloide-β con sus propensiones respectiva agregación intrínseca.

Este protocolo describe cómo utilizar una colección de 20 variantes del péptido de Aβ42 donde Phe19 ha sido mutado a todos natural proteinogenic aminoácidos28. Las propensiones de teórico de la agregación de estas proteínas pueden ser analizadas utilizando dos algoritmos bioinformáticos diferentes (AGGRESCAN y TANGO31,32). En ambos casos, este análisis representa una progresiva gradación de tende...

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

Evaluación del impacto de la agregación de proteína sobre el estrés oxidativo celular en levaduras

Agregación de la proteína provoca estrés oxidativo celular. Este protocolo describe un método de control de los Estados intracelulares de proteínas amyloidogenic y el estrés oxidativo asociado con ellos, mediante citometría de flujo. El enfoque se utiliza para estudiar el comportamiento de las variantes soluble y propensa a la agregación del péptido 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 vistas.  Universitat Autònoma de Barcelona. Agregación de la proteína provoca estrés oxidativo celular. Este protocolo describe un método de control de los Estados intracelulares de proteínas amyloidogenic y el estrés oxidativo asociado con ellos, mediante citometría de flujo. El enfoque se utiliza para estudiar el comportamiento de las variantes soluble y propensa a la agregación del péptido 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.

Definición acompañante Regulación Redox actividad de Hsp33 y mapeo de cambios conformacionales en la Hsp33 mediante espectrometría de masas de intercambio hidrógeno-deuterio

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

Investigación

Bioquímica

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.

Extracción y visualización de agregados proteicos después del tratamiento de Escherichia coli con un estresor proteotóxico

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.

El análisis de microarrays para 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 ...

Biología

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.

Los ensayos de crecimiento para evaluar la toxicidad de poliglutaminas en la levadura

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 de imágenes de la agregación de proteínas en células vivas

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.

Los ensayos acoplados para Monitoreo replegamiento de proteínas en<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.

Aislar Potenciada Hsp104 Variantes Usando levadura Modelos proteinopatía

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).

Análisis cicloheximida persecución de degradación de proteínas en<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.

Métodos de estudio cambios de agregación inherente de proteína con la edad en 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 ...