1. S. cerevisiae kültür ve Protein ifade
Not: Aβ türevleri farklı göreli toplama eğilimlerini de Aβ42 peptid (Şekil 1A) 19 (Phe19) konumunu tek bir kalıntı bir mutasyon nedeniyle sergi. Bu peptid çeşitleri bir toplama muhabir (Şekil 1)29davranan floresan yeşil protein (GFP), ile etiketlenir.
<ol><li>Maya hücreleri BY4741 ebeveyn arka plan (MATa onun3Δ1 leu2Δ0 bir araya geldi15Δ0 ura3Δ0)30ile 20 Aβ42-GFP türevleri için kodlama plazmid dönüşümü. Her plazmid GFP için GSSGSSG bağlayıcı tarafından erimiş bir Aβ42 mutant için kodlar. Plazmid pESC(-URA) ve seçilebilir işaret URA329içerir.<ol>
<li>Sentetik tam orta urasil (SC-URA) aşağıdaki bileşenleri içeren olmadan hazırlamak: 1.9 g/L Maya azot urasil, 5 g/L amonyum sülfat ve 900 mL GKD2O temel kombine ile 100 mL % 20 (w/v) glikoz.</li>
		<li>Dönüştürülmüş Maya hücreleri bir koloni % 2 glikoz içeren SC-URA orta 20 mL koymak ve kültürler gecede 30 ° C'de ajitasyon 210 devirde altında büyür.</li>
	</ol>
</li>
	<li>Ertesi gün, yeni Maya hücreleri kültürleri 5 mL taze SC-URA orta de aşı 100 µL gecede kültür tarafından büyümek.</li>
	<li>Bir OD590 0,5, 3000 x g 4 dk için de kültürler santrifüj kapasitesi, süpernatant atmak ve aynı birimin taze SC-URA Orta % 2 raffinose içeren hücrelerde resuspend.</li>
	<li>30 dk için 210 devirde ajitasyon altında 30 ° C'de kuluçkaya. Sonra 4 dk 3000 x g de hücreleri santrifüj kapasitesi ve süpernatant atın. Taze SC-URA orta Rekombinant protein ifade ikna etmek için % 2 galaktoz içeren hücrelerde resuspend.</li>
	<li>16 h SC-URA Orta % 2 galaktoz içeren protein ifadede inducing sonra Santrifüjü 3000 x g 4 dk için de tarafından 1 mL steril microcentrifuge tüpler indüklenen hücre hasat. Not: 16 h. Induced Maya hücreleri Aβ42-GFP türevleri ifade bir indüksiyon döneminde Rekombinant protein dağıtım hücrelere (Şekil 2B) belirlemek için floresans mikroskobu tarafından görüntülenmiştir sonra en alakalı farklılıklar ortaya.</li>
</ol>2. hücre boyama
Not: Taze sigara kaynaklı hücreler bir FC çözümleme sırasında bir floresan eşiği belirlemek için bir negatif kontrol olarak gereklidir.
<ol><li>16 h kaynaklı Maya hücreleri almak ve 590 optik onların yoğunluğuna belirlemek nm (OD590). Onları steril fosfat tamponlu tuz (PBS) 0.1 OD590 için sulandırmak.<ol>
<li>1 fosfat tamponlu tuz x aşağıdaki gibi hazırlamak: NaCl, KCl, Na2HPO4, 1.44 g 0.2 g 8 g ekleyin ve KH2PO4 GKD2O. 800 ml 0,24 g doldurun 1 L-GKD2O kadar çözüm pH 7.4 için ayarladıktan sonra HCl. ile arabellek 0.2 µm filtreden filtre.</li>
	</ol>
</li>
	<li>Uygun şekilde etiketlenmiş tüpler (12 × 75 mm yuvarlak alt Polistiren) ifade hücre süspansiyonlar aktarmak ve onları ışıktan korumak. Akış Sitometresi Analizi, Sigara kaynaklı ve lekeli olmayan hücreleri ile birlikte için üzerinde yüklü olması için hazır olduklarından emin olun.</li>
	<li>Her örnek 5 mikron son bir konsantrasyon, oksidatif stres sonda (Örneğin, CellROX derin kırmızı) ekleyin ve karanlıkta 30 dk 30 ° C'de hücreler kuluçkaya.</li>
	<li>Kuluçka sonra 3 kez 1 x PBS içeren hücreleri yıkama ve onları arabellek onların analiz FC tarafından daha önce aynı hacmine resuspend.</li>
</ol>3. Akış Sitometresi Analizi
Not: bir FC Kur uygun lazerler ve filtreler ile GFP ve oksidatif stres sonda floresans sinyal algılamak için kullanır. GFP tespiti için 488 nm mavi lazer ve oksidatif stres sonda floresans tespiti için 635 nm kırmızı lazer ile donatılmış bir akış sitometresi kullanılabilir. Emisyon floresans GFP ve oksidatif stres sonda edinimi yapılır 530/30 nm BP filtre ve 660/20 BP filtrenin sırasıyla (Tablo 1).
<ol><li>Yeni çalışma sayfası panelini açma tıklayın ve aşağıdaki edinme nokta araziler oluşturun.<ol>
<li>Araç çubuğundan Dağılım çizim aracı seçin ve bir komplo ile yan dağılım-alan (SSC-A) y ekseni vs üzerinde ileri dağılım alanı (FSC-A) x ekseni üzerinde doğrusal bir ölçek değişkenlerinin.</li>
		<li>Araç çubuğundan Dağılım çizim aracını tıklatın ve x ekseni vsFL1-alan (FITC) değişkenleri ile bir çizim oluşturma. Fl3-alan (APC) y ekseni Logaritmik bir ölçekte.</li>
	</ol>
</li>
	<li>Aletleri ayarları simgesini tıklatın. Tazminat sekmesini tıklatın ve tüm tazminat düzeyleri ayarlayın. Satın alma sekmesinde tıklatın ve kaydedilecek 20.000 olayların toplam sayısı seçin.</li>
	<li>Akış hızı düşükolarak geçmek için Akış hızı simgesini tıklatın.</li>
	<li>Sigara kaynaklı, günahı hücreleri yayınlanmaya başlaması için Al sekmesini tıklatın ve nüfus orta-sol çeyreğinde dağıtılır kadar ileri Araç ayarları ve yan dağılım gerilim ayarlayın.<ol>
<li>Hücre popülasyonu hücre artıkları dışında bir bölgesini R1 ayarlamak için Çokgen ikonuna tıklayın ve bu geçişli nüfus P1 kullanın R1 = tüm floresan nokta araziler ve çubuk grafik gösterimleri (Şekil 3).</li>
	</ol>
</li>
	<li>Floresans sinyalinin Devresel_ödeme gerilimi ayarlamak için hücreleri içinde sol alt çeyrekte dağıtılır kadar kazanç Araç ayarı sekmesinde ayarlama FL1-FL3 nokta Arsa günahı hücreleri çalıştırın.</li>
	<li>Örnek GFP floresans ölçmek için indüklenen hücrelere değiştirin. Nüfus sağ alt çeyreğinde dağıtıldığında Araç ayarı sekmesinde kazanç FSC-A vs FL1 (FITC) olarak ayarlayın. Bir kapı (Gate P2) ile pozitif hücre nüfus tanımlayın.</li>
	<li>Değişiklik olmayan indüklenen için örnek bir FSC-A vs FL3 (APC) nokta arsa üzerinde floresans tarafından oksidatif stres görüntülemek için hücreleri lekeli. Hücre popülasyon sol üst çeyreğinde dağıtılır kadar kazanç ayarlayın. P3 pozitif hücre kalabalıkta bir kapı.</li>
	<li>Hücre floresans temsil etmek için iki çubuk grafik bir cenaze dört nikah çubuk grafik simge olun. Floresan yoğunluğu temsil etmek için FL1 (FITC GFP floresans temsil eden) x ekseni ise FL3 (APC) Floresan temsil eden y ekseni, P2 ve P3 nüfusu sırasıyla logaritmik bir ölçekte uygulama emin olun. Not: Histogram bindirmeleri floresans profilleri farklı Aβ türevleri ifade hücre arasındaki farkları göstermek için iyi bir yoldur.</li>
</ol>4. rekombinant Protein Immunodetection
<ol><li>Protein ifade düzeylerini belirlemek için Santrifüjü 4.000 x g 6 min için de 16 h için indüklenen maya kültürleri hasat ve hücre granül-80 ° C'de depolayın<ol>
<li>16 h PBS için Rekombinant protein ifade kendine hakim hücre resuspend. 200 µL hücre süspansiyonlar, her Aβ42-GFP mutant bir OD590 20 için hazırlayın.</li>
	</ol>
</li>
	<li>Toplam protein fraksiyonu analiz, her mutant 100 µL Santrifüjü 14.000 x g 20 dk için de tarafından hasat ve Maya lizis arabellek Y aynı birim hücre granül resuspend-(50 mM Tris-HCl pH 8.0, % 1 DMSO, 200 mM NaCl, 1 mM EDTA içeren 10 mg/mL SB3-14 (en düşük ücret istyl sulfobetaine) takıma 1 mM phenylmethylsulfonyl florür (PMSF)).<ol>
<li>Örnekleri hafif ajitasyon altında oda sıcaklığında 20 dk için kuluçkaya.</li>
	</ol>
</li>
	<li>Protein özü konsantrasyon Bradford tahlil kullanarak belirleyin. % 15 akrilamid SDS-sayfa Elektroforez jel ve leke üzerine polivinilidin difluoride (PVDF) membran 100 V 60 dk için her örneğinin protein ekstresinin 5 µg yükleyin.<ol>
<li>Bir Bradford reaktifi ile Coomassie parlak mavi G-250 100 mg 50 mL % 95 etanol çözünmüş ve 100 mL % 85 (w/v) fosforik asit ekleyin hazırlayın. O zaman, boya tamamen çözüldüğünde, GKD2O 1 m'ye karışımla sulandırmak ve selüloz filtre kağıdı kullanmadan önce aracılığıyla filtre. Not: Bradford reaktifi olmak hafif kahverengi.</li>
		<li>Sığır serum albumin (BSA) 1.000 µL Bradford reaktif proteinin 5-100 µg arasında değişen bir standartta hazırlayın.</li>
		<li>1-10 µL protein Bradford reaktifi 1 mL için ayıklamak ve 5 min için oda sıcaklığında karışımı kuluçkaya ekleyin.</li>
		<li>Standartları ve protein özü örnekler, OD595absorbans ölçmek.</li>
		<li>Analiz için bir arsa için standartlar vselde absorbans değerleriyle olun. µg protein. Standart eğri dayalı, protein, miktarı üzerinden orijinal örnekleri konsantrasyonları hacmi ve seyreltme, varsa göz önünde bulundurarak belirleyin.</li>
	</ol>
</li>
	<li>Membran ile 1 x Tris arabelleğe alınmış serum fizyolojik (TBS), % 0,1 durulama ara 20 (TTBS) ve %5 (w/v) yağsız Kuru süt 1 x TTBS kullanarak engelleyiniz.<ol>
<li>10 x TBS 1 litre hazırlamak için 24 g Tris temel ve NaCl 88 g H2Odd 900 ml dağıtılması ve pH 7,6 için ayarlayın. GKD2O 1 L. son bir birime ekleme 1 x çözüm için 10 x hisse senedi, 1 kısım GKD2O. 9 parçaları ile karıştırın.</li>
	</ol>
</li>
	<li>Membran bir birincil β-amiloid antikor seyreltilmiş 6E10 1:1,000 ile 1 h için kuluçkaya ve ikincil antikor keçi ile Oda sıcaklığında 1 h için 1:10, 000 Anti-fare IgG-HRP eşlenik seyreltilmiş.</li>
	<li>1 mL Luminata floresan reaktif kullanarak membranlar geliştirmek ve grup tarafından densitometric analiz 5.2 adımda anlatıldığı gibi ölçmek.</li>
</ol>5. veri analizi
<ol><li>FC tarafından elde edilen verileri analiz etmek için Ortalama floresan yoğunluğu (MFI) görüntüleyen bir tablo oluşturmak ve onun karşılık gelen standart hata ve/veya GFP floresans ve oksidatif stres düzeyleri farkı (MF) katsayısı ile ortalama Floresans.<ol>
<li>Müfettiş sekmesini ve her kanal için gerekli olan istatistiksel verileri özelleştirmek için İstatistikleri simgesini tıklatın. Not: protein türevleri karşılaştırılırken, katlanır arka plan veya çözünürlük metrik (RD) Floresan veri değerleri dönüştürür: <img alt="Equation 1" src="/files/ftp_upload/57470/57470eq1.jpg"> Bu ölçüm, sorun örneklerinin ortalama floresans kötü örnek olduğunu, bir denetim olarak kullanılan hücre ortalama floresans ortalama denetimidir ve SD ortalama floresans standart sapmasıdır. Sadece örnekleri aynı zamanda veri yaymak için hesapları arasındaki farkı hesaplar gibi RD faktör MFI değerden daha doğru bir dizindir. Zaman içinde farklı deneyler gelen verilere karşılaştırma yürütülen bu hesaplama özellikle uygundur. RDs hesaplanan bir kez bir istatistiksel test kullanımı protein çeşitleri arasında gözlenen farklılıklar önemini onaylamak için tavsiye edilir.</li>
	</ol>
</li>
	<li>Protein immunodetection için ImageJ yazılım kullanarak Western blot tarafından Rekombinant protein düzeyleri ölçmek.<ol>
<li>Dansitometresi analiz daha önce geliştirilen membran orijinal raw görüntüleri JPEG dosya biçimi ve gri tonlama moduna dönüştürün.</li>
		<li>Çözümle menüsünde demek grideğerine Ayarla ölçümler kısmında ayarlayın.</li>
		<li>ImageJ dikdörtgen aracını tıklatın ve grup Dansitometresi analiz için tutarlı boyutu ile bir bölgesi (ROI) ilgi tanımlamak. Membran görüntü oluşturulan dikdörtgen çerçeve içindeki her band merkezi ve ölçüm Ctrl + M (ölçü komutu) kullanarak tek tek kaydedin.</li>
		<li>Aynı YG alan uygulama her grup için bitişik yerel arka plan ölçmek. Ölçüm sonra tek tek her gruptan karşılık gelen yerel arka plan çıkarma.</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>

Çok çeşitli hastalıklar misfolded proteinleri birikimi hücresel mevduat6,7,8,33içine bağlıdır. Birçok çabaları rapor protein konsantrasyonları almayın, Hesaplamalı yaklaşımları kullanarak bu hastalıkların başlangıcı tetiklemek moleküler mekanizmaları çözmeye gelmiş yapılmış veya vitro yaklaşımları, içinde protein konsantrasyonu reaksiyon sırasında s...

Proteostasis hücre fitness ve yaşlanma süreçlerinin temel bir belirleyici olduğunu. Hücrelerde, protein homeostazı sofistike protein kalite kontrol doğru misfolded protein conformers chaperones ve/veya onların hedeflenen proteolizis tarafından birkaç iyi korunmuş mekanizmalarıyla1 refolding emin olmak için ağlar amaçlayan tarafından yapılmaktadır ,2,3,4,5. Çok sayıda çalışmalar başlangıçlı ve geniş bir insan hastalıkları ve proteostasis, protein misfolding ve toplama için önde gelen başarısızlık arasındaki bağlantı için destek sağlar. Örneğin, protein mevduat varlığı çok nörodejeneratif hastalıkları, Alzheimer, Parkinson, gibi ve Huntington hastalıkları6,7,8patolojik damgasını olarak kabul edilir, prionogenic hastalıklar ve sigara dejeneratif amyloidoses9. Toplama tepki erken oligomeric ve protofibrillar derlemelerde diğer proteinler ile anormal etkileşim içinde kalabalık hücresel ortamı10kurulması ana elicitors, sitotoksisite, çoğu sürülmüştür. Buna ek olarak, protein kapanımlar (PI) onların toksik etkisi11,12yayılıyor hücreler arasında aktarılabilir. Bu nedenle, PI oluşumu gerçekten tehlikeli toplanan türlerin varlığı belirli konumlara hücresinde, nerede onlar olabilir işlenen veya önemli yan etkileri olmadan birikmiş kısıtlar detoxifying bir mekanizma teşkil olabilir 13 , 14.
Standart vitro biyokimyasal yaklaşımlar toplama reaksiyonlar ve onların özellikleri15,16doldurmak farklı türlerin önemli anlayışlar hazırladık. Ancak, bu deneyleri içinde kullanılan koşulları açıkça farklı hücre içinde meydana gelen ve bu nedenle, kendi fizyolojik alaka soru. Protein kalite kontrol, autophagy veya Düzenleme Ökaryotlar19,20,21 arasında hücresel redoks devlet17,18 , gibi hücresel yolları önemli korunması nedeniyle ,22,23, tomurcuklanma Maya Saccharomyces cerevisiae (S. cerevisiae) protein toplama moleküler belirleyicileri çalışmaya ayrıcalıklı basit bir hücresel model çıkmıştır ve ilişkili sitotoksik etkisini de biyolojik olarak uygun ortamlar24,25,26.
Protein toplama eğilimi doğal olarak birincil sırada kodlanmış bir özelliktir. Böylece, amiloid benzeri yapıların oluşumu kimlik ve toplama teşvik potens değerlendirilmesi dayalı tahmin edilebilir polipeptitler27bölgelerde. Ancak, protein sequences vitro toplama özelliklerini tahmin etmek için bioinformatic algoritmaları başarı rağmen bunlar hala bu eğilimlerini vivo içinde sitotoksik etki nasıl tercüme tahmini çok uzak. Belirli bir protein toplanan durumunu ve onun ilişkili hücresel hasarı arasındaki bağlantıyı sistematik bir şekilde ele alan çalışmalar Hesaplamalı bu sınırlamayı aşmak için yardımcı olabilir. Bu bağlantı yalnızca tek bir kalıntı farklı ama toplama eğilimlerini vivo içinde28sürekli bir dizi görüntüleme β-amiloid peptid Aβ42 türevleri büyük bir set yararlanarak bu da çalışmanın, yöneliktir. Maya hücreleri toplama eğilimli proteinler tarafından elde edildi oksidatif hasar için muhasebe konformasyon türler tanımlamak için FC dayalı bir yaklaşım özellikle de açıklanmıştır. Metodoloji basitlik, yüksek üretilen iş yetenekleri ve doğru nicel ölçüm gibi birçok avantaj sağlar. Bu yaklaşım o PI oksidatif strese karşı koruyucu bir rol oynar teyit mümkün kıldı.

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

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

Protein misfolding ve toplama amiloid biçimler içine başlangıçlı ve ilerleme çeşitli nörodejeneratif hastalıklar ile ilgili olduğu. Ancak, hala çok az bilgi var hakkında nasıl çözünmez protein toplamları onların toksik etkileri içinde vivouygulamayın. Basit prokaryotik ve ökaryotik canlılar, bakteri ve Maya, gibi önemli ölçüde mevcut anlayışımız mekanizmaları hücre içi amiloid dil oluşumu, toplamları yayma ve toksisite arkasında katkıda bulunmuştur. Bu protokol için maya kullanımı protein toplamları oluşumu ve hücresel oksidatif stres üzerindeki etkileri arasındaki ilişkiyi incelemek için bir model olarak tanımlanıyor. Yöntem bir amyloidogenic protein çözünür/toplanan hücre içi durumunu algılama akış sitometresi (FC) kullanarak kendi ifadeden kaynaklanan hücresel oksidatif hasar miktar ile birleştirir. Bu basit, hızlı ve nicel yaklaşımdır. Çalışma tekniği β-amiloid peptid türevleri ile onların anılan sıraya göre iç Toplama eğilimlerini büyük bir set neden hücresel oksidatif stres birleştiriliyor tarafından gösterilmektedir.

Bu iletişim kuralı 20 değişik-in nereye Phe19 için tüm doğal proteinogenic amino asitler28mutasyona uğrattı Aβ42 peptid topluluğu istihdam edileceğini açıklar. Bu proteinlerin teorik toplama eğilimlerini iki farklı bioinformatic algoritmaları (AGGRESCAN ve TANGO31,32) kullanılarak analiz edilebilir. Her iki durumda da, bu analiz ascribing, genel bir kural olarak, en yüksek değerleri hidr...

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

Protein toplama Maya hücresel oksidatif stres üzerinde etkisi değerlendirilmesi

Protein toplama hücresel oksidatif stres ortaya çıkarır. Bu iletişim kuralı amyloidogenic proteinlerin hücre içi Birleşik ve onlarla akış sitometresi kullanarak ilişkili oksidatif stres izlemek için bir yöntem açıklanır. Yaklaşım β-amiloid peptid çözünür ve toplama eğilimli türevleri davranışını incelemek için kullanılır.

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 Görüntüleme.  Universitat Autònoma de Barcelona. Protein toplama hücresel oksidatif stres ortaya çıkarır. Bu iletişim kuralı amyloidogenic proteinlerin hücre içi Birleşik ve onlarla akış sitometresi kullanarak ilişkili oksidatif stres izlemek için bir yöntem açıklanır. Yaklaşım β-amiloid peptid çözünür ve toplama eğilimli türevleri davranışını incelemek için kullanılır.

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.

Hsp33'ın redoks düzenlenir refakatçi etkinlik tanımlama ve Hsp33 konformasyon değişiklikleri hidrojen-döteryum Exchange kütle spektrometresi kullanarak eşleme

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

Biyokimya

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.

Proteotoksik Stresör ile Escherichia coli Tedavisinden Sonra Protein Agregalarının Ekstraksiyonu ve Görselleştirilmesi

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.

Mikroarray Analizi için 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 ...

Biyoloji

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.

Büyüme Tahliller Mayalarda poliglutamin toksisite değerlendirilmesi için

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.

Canlı Hücrelerde Protein Aggregation 4D Görüntüleme

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.

İzlenmesi protein refolding için birleştiğinde Tahliller<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.

Kuvvetlendiği Hsp104 Varyantlar kullanarak Maya Proteinopathy Modelleri Ayırma

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).

Protein Bozulması ve Siklohekzimid Chase Analizi<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.

Yöntemleri ile Caenorhabditis elegans çağda doğal Protein toplama çalışma değişikliklere

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 ...

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