Name: evaluation of the impact of protein aggregation on cellular oxidative stress in yeast
Uploaded: 2018-06-23T13:00:00.000+00:00
Duration: 11 min 4 s
Description: Watch this Scientific Journal Video about Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast at JoVE.com

1酵母文化や蛋白発現。
注: a β 亜種展示位置 19 (Phe19) Aβ42 ペプチド (図 1 a) の 1 つ残基変異による異なる相対集計性癖。これらのペプチドの亜種は緑色蛍光タンパク質 (GFP) 集計記者 (図 1)29として機能するとタグ付けされます。
<ol><li>BY4741 親の背景 (マタ彼3Δ1ルー2Δ0に会った15Δ0 ura3Δ0)30酵母に 20 Aβ42 GFP 亜種のプラスミドを変換します。GSSGSSG リンカーによって GFP の融合 Aβ42 変異体の各プラスミドをエンコードします。プラスミッドは pESC(-URA) であり、選択可能なマーカー URA329を含んでいます。<ol>
<li>ウラシル (SC 浦) で以下のコンポーネントなし合成の完全なメディアの準備: 1.9 グラム/L の 20% (w/v) ブドウ糖の 100 mL と組み合わせて酵母窒素ウラシル、硫酸アンモニウム 5 g/L、900 mL ddH2O のも基本。</li>
		<li>変換後の酵母細胞のコロニーの SC 裏培 2% ブドウ糖 20 mL を入れ、210 rpm で攪拌下 30 ° C で一晩の文化を育てます。</li>
	</ol>
</li>
	<li>次の日、一晩かけて培養の 100 μ L 接種培 SC 裏の 5 mL に新しい酵母細胞文化を成長します。</li>
	<li>0.5 の590を OD、4 分間 3,000 x g で文化を遠心、上澄みを廃棄し、2% ラフィノース培 SC 裏の同じ容積の細胞を再懸濁します。</li>
	<li>210 rpm で 30 分間攪拌下 30 ° C で孵化させなさい。4 分間 3,000 x g で細胞を遠心分離し、上澄みを廃棄します。組換えタンパク質の発現を誘導するために 2% ガラクトースを含む新鮮な SC 裏中細胞を再懸濁します。</li>
	<li>SC 裏中 2% ガラクトースを含むタンパク質の発現を誘導する 16 時間後 4 分 3,000 × g で遠心分離によって滅菌遠心管の細胞の 1 つの mL を収穫します。 注: 最も関連性の違いは、(図 2 b) 細胞内遺伝子組換え蛋白質の分布を決定するための蛍光顕微鏡で目視できる Aβ42 GFP のバリエーションを表現する 16 h. による酵母細胞の誘導期後に発生します。</li>
</ol>2. 細胞染色
Note: 新鮮な非誘導細胞は FC 解析中に蛍光のしきい値を確立するネガティブ コントロールとして必要です。
<ol><li>16 h による酵母細胞を取るし、590 で彼らの光学密度を決定する nm (外径590).0.1 の外径590に滅菌リン酸緩衝生理食塩水 (PBS) でそれらを希釈します。<ol>
<li>リン酸緩衝生理食塩水 x 1 を次のように準備: NaCl、KCl、Na2HPO4、1.44 g 0.2 g の 8 g を追加し、KH2PO4 ddH2o. の 800 mL の 0.24 g pH 7.4 に調整後最大 1 L ddH2O のソリューションを入力塩酸フィルター 0.2 μ m のフィルターを通してバッファー。</li>
	</ol>
</li>
	<li>適切にラベル付けされた管 (12 × 75 mm 丸底ポリスチレン) を表現する細胞懸濁液を転送し、光から身を守る。彼らは非誘導し、非染め色細胞と一緒に分析の流れの cytometer でロードする準備ができていることを確認します。</li>
	<li>最終濃度 5 μ M で各サンプルに酸化ストレスのプローブ (例えば、 CellROX 深い赤) を追加し、暗闇の中で 30 分間 30 ° C で細胞をインキュベートします。</li>
	<li>インキュベーション後、1x PBS で 3 回細胞を洗浄し、FC で分析する前にバッファーの同じボリュームにそれらを再懸濁します。</li>
</ol>3. フローのフローサイトメトリー解析
注: は、GFP や酸化ストレス プローブ蛍光シグナルを検出するのに適切なレーザーとフィルターを FC セットアップを使用します。流れの cytometer GFP の検出のための 488 nm の青色レーザーと酸化ストレス プローブの蛍光性の検出の 635 nm の赤色レーザーを使用ことができます。GFP および酸化ストレス プローブの発光蛍光の買収がそれぞれ 530/30 nm BP フィルターと 660/20 BP フィルターで実行されます (表 1)。
<ol><li>新しいワークシート パネルを開くをクリックし、次の取得ドット プロットを作成します。<ol>
<li>散布プロット ツールをツールバーから選択し、作成プロット変数側散布領域 (SSC-A)、y 軸対前方の散布面積の x 軸上線形スケールで (FSC A) します。</li>
		<li>ツールバーから散布プロット ツールをクリックし、変数 FL1 エリア (FITC) x 軸対上にプロットを作成します。FL3 エリア (APC) y 軸に対数スケールで。</li>
	</ol>
</li>
	<li>楽器の設定アイコンをクリックします。[補正] タブをクリックし、すべての補正レベルを設定します。[取得] タブをクリックし、記録される 20,000 イベントの合計数を選択します。</li>
	<li>低流量を切り替える流量アイコンをクリックします。</li>
	<li>非誘導、無染色の細胞を実行を開始する [取得] タブをクリックし、人口は中央左の象限に分布するまでの前方の機器の設定および側方散乱で電圧を調整します。<ol>
<li>細胞の残骸を除くセル人口のまわりの領域 R1 を設定するポリゴンアイコンをクリックし、使用するこのゲート人口 P1 = R1 すべて蛍光ドット プロット、ヒストグラム表現 (図 3)。</li>
	</ol>
</li>
	<li>蛍光信号の PMT 電圧を調整するには、左下腹部に分布しているセルまで計測器の設定タブで、利得をチューニング、FL1 FL3 ドット プロットで無染色の細胞を実行します。</li>
	<li>GFP の蛍光を測定する誘導された細胞にサンプルを変更します。計測器の設定タブで、人口は右下の象限に分布するとき、FSC A対FL1 (FITC) のゲインを設定します。ゲート (ゲート P2) 肯定的な細胞集団を定義します。</li>
	<li>変更非誘導サンプル染色 FSC A対FL3 (APC) ドット プロット蛍光による酸化ストレスを表示するセルです。左上腹部にセル人口を分散までゲインを調整します。ゲート P3 で肯定的な細胞の集団。</li>
	<li>細胞蛍光を表すヒストグラムアイコンと 2 つのヒストグラム プロットを作る。蛍光強度を表す、FL1 (FITC) GFP 蛍光を表す x 軸にあり FL3 (APC) 蛍光性を表すは、y 軸に対数目盛で P2 と P3 の人口をそれぞれ適用を確認します。 メモ: ヒストグラムのオーバーレイは、バリエーション a β を発現する細胞の蛍光プロファイルの違いを説明するために良い方法です。</li>
</ol>4. 組換え蛋白質バイオアセッテイ
<ol><li>バイオマーカ発現量を調べるには、16 時間 6 分、4,000 x g で遠心分離によるイースト文化を収穫し、-80 ° C で細胞ペレットを格納<ol>
<li>PBS の 16 h の組換え蛋白を発現する収集した細胞を再懸濁します。20 の外径590に各 Aβ42 GFP 変異体の 200 μ L の細胞懸濁液を準備します。</li>
	</ol>
</li>
	<li>総蛋白分画分析のため 20 分間 14,000 × g で遠心分離によって各変異体の 100 μ L を収穫し、酵母の換散バッファー Y の同じボリュームで細胞ペレットを再懸濁します-(50 mM トリス塩酸 pH 8.0、1 %dmso、200 mM の NaCl、1 mM EDTA を含む 10 mg/mL SB3 14 (myr あたりistyl スルホベタイン) を添加した 1 mM 特異フッ化物 (PMSF))。<ol>
<li>軽度の動揺の下で室温で 20 分間サンプルをインキュベートします。</li>
	</ol>
</li>
	<li>ブラッドフォードの試金を使用して蛋白質の抽出濃度を決定します。15% アクリルアミド SDS-PAGE の電気泳動のゲルや、100 V 60 分のポリフッ化ビニリデン (PVDF) 二フッ化膜にしみの各サンプルのタンパク質抽出物の最大 5 μ g をロードします。<ol>
<li>100 mg Coomassie ブリリアント ブルー G-250 の試薬 95% エタノール 50 mL に溶解し、85% (w/v) リン酸の 100 mL を追加ブラッドフォードを準備します。その後、染料が完全に解散したとき ddH21 L O との混合物を希釈し、使用直前のセルロース フィルター ペーパーを通してフィルターします。 注: ブラッドフォード試薬は茶色光する必要があります。</li>
		<li>ウシ血清アルブミン (BSA) ブラッドフォード試薬の 1,000 μ L 中のタンパク質の 5-100 μ g に至る標準を準備します。</li>
		<li>1-10 μ L タンパク質のブラッドフォード試薬 1 ml を抽出し、5 分間室温で混合物をインキュベートを追加します。</li>
		<li>基準及び外径595タンパク質抽出試料の吸光度を測定します。</li>
		<li>分析に標準対で得られた吸光度値でプロットを作る。蛋白質の μ g。標準曲線に基づいて、あればボリュームと、希釈を考慮したタンパク質の量から元のサンプルの濃度を決定します。</li>
	</ol>
</li>
	<li>0.1% 含有トリス緩衝生理食塩水 (TBS) x 1 と膜をすすぎトゥイーン 20 (TTBS) とブロック 1 x TTBS で 5% (w/v) 脱脂乾燥したミルクを使用しては。<ol>
<li>10 x TBS の 1 L を準備、基本トリスの 24 g と H2Oddの 900 mL の塩化ナトリウムの 88 g を溶解、7.6 に pH を調整します。DdH2O 1 L の最終巻を追加します。1 x ソリューションの ddH2o. の 9 の部分と 10 x の在庫の 1 部分をミックスします。</li>
	</ol>
</li>
	<li>一次 β-アミロイド抗体希釈 6E10 縮尺で 1 h の膜をインキュベートし、二次抗体のヤギと室温で 1 時間抗マウス IgG HRP 共役希釈 1:10, 000。</li>
	<li>Luminata 蛍光試薬 1 mL を使用して膜を開発して手順 5.2 デンシトメトリー分析によってバンドを定量化します。</li>
</ol>5. データの解析
<ol><li>FC によって得られたデータを解析する (MFI) 平均蛍光強度を表示するテーブルを作成して、対応する標準のエラーおよび/または GFP 蛍光および酸化ストレス レベルの差異 (CV) の係数と平均蛍光。<ol>
<li>インスペクターのタブと各チャネルに必要な統計データをカスタマイズするのには [統計] アイコンをクリックします。 注: 蛋白質の変形を比較すると、折り畳み式の背景または解像度指標 (RD) に蛍光値データに変換します。 <img alt="Equation 1" src="/files/ftp_upload/57470/57470eq1.jpg"> この指標で平均サンプル問題サンプルの平均蛍光、平均のコントロールは、コントロールとして使用するセルの平均蛍光あり SD は平均蛍光の標準偏差。RD 要因は MFI 値よりもより正確な指標だけでなくサンプルもデータの広がりのためのアカウントの違いを計算します。時間をかけて行ったさまざまな実験からのデータを比較する場合、この計算は具体的に関連します。RDs が計算されると、統計的検定の使用は蛋白質の変形の観察の違いの重要性を確認することをお勧め。</li>
	</ol>
</li>
	<li>タンパク質バイオアセッテイ用ウエスタンブロット ImageJ ソフトウェアを使用して組換え蛋白質のレベルを定量化します。<ol>
<li>デンシトメトリーの分析の前に開発された膜のオリジナルの raw 画像を JPEG ファイル形式とグレースケール モードに変換します。</li>
		<li>平均グレー値を [分析] メニューの設定測定セクションを調整します。</li>
		<li>ImageJ から長方形ツールをクリックし、バンドのデンシトメトリーの分析のための一貫性のあるサイズの利益 (率 ROI) の領域を定義します。作成された長方形フレーム内膜イメージの各バンドのセンターし、Ctrl + M (測定コマンド) を使用して 1 つずつ測定値を記録します。</li>
		<li>同じ ROI 領域を適用すること、各バンドに隣接したローカルのバック グラウンドを測定します。測定後、各バンドは個別から対応するローカル背景を減算します。</li>
	</ol>
</li>
</ol>

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J., B&#252;ttner, S., Ring, J., Kroemer, G., Madeo, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nervous+yeast:+modeling+neurotoxic+cell+death.">Nervous yeast: modeling neurotoxic cell death.</a> Trends in Biochemical Sciences. 35, 135-144 (2010).</li><li>Figley, M. D., Gitler, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast+genetic+screen+reveals+novel+therapeutic+strategy+for+ALS.">Yeast genetic screen reveals novel therapeutic strategy for ALS.</a> Rare Diseases. 1, e24420 (2013).</li><li>Cooper, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alpha-synuclein+blocks+ER-Golgi+traffic+and+Rab1+rescues+neuron+loss+in+Parkinson's+models.">Alpha-synuclein blocks ER-Golgi traffic and Rab1 rescues neuron loss in Parkinson's models.</a> Science. 313, 324-328 (2006).</li><li>Johnson, B. S., McCaffery, J. M., Lindquist, S., Gitler, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+yeast+TDP-43+proteinopathy+model:+exploring+the+molecular+determinants+of+TDP-43+aggregation+and+cellular+toxicity.">A yeast TDP-43 proteinopathy model: exploring the molecular determinants of TDP-43 aggregation and cellular toxicity.</a> Proceedings of the National Academy of Sciences of the United States of America. 105, 6439-6444 (2008).</li><li>Bharathi, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+ade1+and+ade2+mutations+for+development+of+a+versatile+red/white+color+assay+of+amyloid-induced+oxidative+stress+in+saccharomyces+cerevisiae.">Use of ade1 and ade2 mutations for development of a versatile red/white color assay of amyloid-induced oxidative stress in saccharomyces cerevisiae.</a> Yeast. 33, 607-620 (2016).</li><li>Pallar&#232;s, I., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+the+prediction+of+protein+aggregation+propensity.">Advances in the prediction of protein aggregation propensity.</a> Current Medicinal Chemistry. , (2017).</li><li>Villar-Piqu&#233;, A., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+propensity+is+a+crucial+determinant+of+intracellular+inclusion+formation+and+quality+control+degradation.">Protein aggregation propensity is a crucial determinant of intracellular inclusion formation and quality control degradation.</a> Biochimica et Biophysica Acta. 1833, 2714-2724 (2013).</li><li>Navarro, S., Villar-Piqu&#233;, A., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selection+against+toxic+aggregation-prone+protein+sequences+in+bacteria.">Selection against toxic aggregation-prone protein sequences in bacteria.</a> Biochimica et Biophysica Acta. 1843, 866-874 (2014).</li><li>Morell, M., de Groot, N. S., Vendrell, J., Avil&#233;s, F. X., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linking+amyloid+protein+aggregation+and+yeast+survival.">Linking amyloid protein aggregation and yeast survival.</a> Molecular BioSystems. 7, 1121-1128 (2011).</li><li>Conchillo-Sol&#233;, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AGGRESCAN:+a+server+for+the+prediction+and+evaluation+of+&amp;#34;hot+spots&amp;#34;+of+aggregation+in+polypeptides.">AGGRESCAN: a server for the prediction and evaluation of &amp;#34;hot spots&amp;#34; of aggregation in polypeptides.</a> BMC Bioinformatics. 8, 65 (2007).</li><li>Fernandez-Escamilla, A. M., Rousseau, F., Schymkowitz, J., Serrano, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+sequence-dependent+and+mutational+effects+on+the+aggregation+of+peptides+and+proteins.">Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.</a> Nature Biotechnology. 22, 1302-1306 (2004).</li><li>Renner, M., Melki, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+and+prionopathies.">Protein aggregation and prionopathies.</a> Pathologie Biologie.(Paris). 62, 162-168 (2014).</li><li>Carija, A., Navarro, S., de Groot, N. S., Ventura, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+aggregation+into+insoluble+deposits+protects+from+oxidative+stress.">Protein aggregation into insoluble deposits protects from oxidative stress.</a> Redox Biology. 12, 699-711 (2017).</li></ol>

病気の広い範囲は、携帯電話預金6,7,8,33への誤って折りたたまれたタンパク質の蓄積にリンクされます。アカウント タンパク質濃度を考慮していない、計算のアプローチを使用してこれらの病気の発症を引き起こす分子メカニズムを解明するため多くの努力がなされたまたは体外に近づく?...

プロテオステイシスはセルのフィットネスと老化のプロセスの基本的な決定要因です。ネットワーク、正しいリフォールディング conformers の誤って折りたたまれたタンパク質のシャペロンおよびその対象となるタンパク質がいくつか保存されているメカニズム1 を確保することを目的と高度な品質管理によって細胞の蛋白質の恒常性を維持します。 ,2,3,4,5。多数の調査では、発症および人間の病気の広い範囲とのプロテオステイシス、タンパクにつながる障害の進行の間のリンクをサポートします。例えば、タンパク質の堆積物の存在の多くの神経変性疾患、アルツハイマー病、パーキンソン病などとハンチントン病6,7、8、病理学的特徴であります。prionogenic 病と非変性アミロイドーシス9。それは、凝集反応の初期のオリゴマーと protofibrillar のアセンブリが混雑した細胞環境の10の他の蛋白質と異常な相互作用を確立する細胞毒性の主な成分エリシターであることが示唆されています。また、その毒性効果11,12を伝播する、細胞間タンパク質介在物 (PI) を送信できます。したがって、それは、PI の形成可能性があります確かに危険な集約された種の存在を処理または主要な副作用のない蓄積があるなどこのセルに特定の場所に制限する解毒機構を構成するかもしれない13,14。
標準的な体外生化学的アプローチは、凝集反応およびそのプロパティ15,16種に重要な洞察を提供しています。しかし、これらの試金で使用される条件は、細胞内で発生する明確に異なると、したがって、その生理学的な妥当性を質問します。タンパク質品質管理、オートファジーは、細胞の酸化還元状態の17,18真核生物19,20,21 の規制など携帯電話経路の注目すべき保全のため ,22,23, 出芽酵母酵母(出芽酵母) 蛋白質の集合の分子決定要因を研究する特権を持つ単純な細胞モデルとして浮上しています。関連する生物学的環境24,25,26で関連付けられた細胞毒性影響。
タンパク質凝集傾向は、本質的に主なシーケンスでエンコード機能です。識別と集計推進の力の評価に基づくアミロイド様構造の形成を予測できるため、ポリペプチド27の地域。しかし、タンパク質の体外集計プロパティを予測するバイオインフォマティクス アルゴリズムの成功にもかかわらずこれらの性癖が生体内で細胞毒性影響に変換する方法を予測には程遠いです。体系的に特定の蛋白質の集合の状態とその関連付けられている細胞の損傷間のリンクに対応する研究はこの計算の制限を回避するために役立つことがあります。この接続は、単一の残渣でのみが異なるが、凝集体内性癖28の連続した範囲を表示するアミロイド β ペプチド Aβ42 の亜種の大規模なセットを利用して、本研究で扱われます。特に、酵母細胞の凝集を起こしやすい蛋白質によって誘発される酸化的損傷の会計構造の種を識別するために FC ベースのアプローチを説明します。方法論は、シンプルさ、高処理能力と正確な定量的測定など多くの利点を提供します。このアプローチでは、PI プレイ酸化ストレスに対する保護の役割を確認可能。

<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

タンパク質のフォールディングとアミロイドの立体構造に集約は、発症といくつかの神経変性疾患の進行に関連しています。しかし、少し情報はまだどのように不溶性タンパク質について集計はその毒性で生体を発揮します。細菌や酵母などの単純な原核生物と真核生物のモデル生物は、細胞内アミロイド線維形成、集計の伝達、および毒性の背後にあるメカニズムの私達の現在の理解に大きく貢献しています。このプロトコルでは、酵母の使用は、タンパク質凝集体の形成と細胞の酸化ストレスに与える影響との関係を分析するモデルとして記述されます。メソッドは、フローサイトメトリー (FC) を使用して、式から生じる細胞の酸化損傷の定量化とアミロイド蛋白質の細胞内の可溶性/集計状態の検出を兼ね備えています。この方法は、簡単、迅速、かつ定量的です。研究は、彼らのそれぞれの組み込み集計性癖とアミロイド β ペプチド亜種の大規模なセットによって引き起こされる細胞の酸化ストレスを関連付けることによってテクニックを示しています。

このプロトコルでは、Phe19 がすべて天然蛋白質構成アミノ酸28変異されてどこ Aβ42 ペプチドの 20 個の商品のコレクションを使用する方法について説明します。これらの蛋白質の集計の理論的な性癖は、2 つの異なるバイオ情報アルゴリズム (AGGRESCAN とタンゴ31,32) を使用して分析できます。両方のケースで?...

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

酵母細胞の酸化ストレスに対するタンパク質凝集の影響の評価

蛋白質の集合は、細胞の酸化ストレスを引き出します。このプロトコルでは、アミロイド蛋白質の細胞内の状態およびフローサイトメトリーを使用して、それらに関連付けられている酸化ストレスを監視する方法について説明します。アミロイド β ペプチドの水溶性で集計しやすい亜種の挙動を調べるためのアプローチが使用されます。

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 ビュー.  Universitat Autònoma de Barcelona. 蛋白質の集合は、細胞の酸化ストレスを引き出します。このプロトコルでは、アミロイド蛋白質の細胞内の状態およびフローサイトメトリーを使用して、それらに関連付けられている酸化ストレスを監視する方法について説明します。アミロイド β ペプチドの水溶性で集計しやすい亜種の挙動を調べるためのアプローチが使用されます。

ビデオ: 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 の酸化還元規制シャペロン活性を定義および水素重水素交換質量分析法による Hsp33 の構造変化をマッピング

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

生化学

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.

タンパク質毒性ストストレスマによる 大腸菌 治療後のタンパク質凝集体の抽出と可視化

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.

マイクロアレイ解析のためのサッカロマイセスセレビシエ</em

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

生物学

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.

成長アッセイ酵母におけるポリグルタミン毒性を評価する

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.

生細胞中のタンパク質凝集の4Dイメージング

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.

のリフォールディング監視タンパク質を結合アッセイ<em&gt;サッカロミセス·セレビシエ（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.

酵母Proteinopathyモデルを用いた増強Hsp104のバリアントを分離

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).

タンパク質分解中のシクロヘキシミドチェイス分析<em&gt;サッカロマイセス・セレビシエ</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.

線虫の加齢とともに固有の蛋白質の集合の変化を研究するためのメソッド

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 ...

酵母細胞の酸化ストレスに対するタンパク質凝集の影響の評価

要約

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この動画の章

Hsp33 の酸化還元規制シャペロン活性を定義および水素重水素交換質量分析法による Hsp33 の構造変化をマッピング

タンパク質毒性ストストレスマによる大腸菌治療後のタンパク質凝集体の抽出と可視化

マイクロアレイ解析のためのサッカロマイセスセレビシエ

成長アッセイ酵母におけるポリグルタミン毒性を評価する

生細胞中のタンパク質凝集の4Dイメージング

のリフォールディング監視タンパク質を結合アッセイ

酵母Proteinopathyモデルを用いた増強Hsp104のバリアントを分離

タンパク質分解中のシクロヘキシミドチェイス分析

線虫の加齢とともに固有の蛋白質の集合の変化を研究するためのメソッド

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

酵母細胞の酸化ストレスに対するタンパク質凝集の影響の評価

要約

さらに動画を探す

この動画の章

Hsp33 の酸化還元規制シャペロン活性を定義および水素重水素交換質量分析法による Hsp33 の構造変化をマッピング

タンパク質毒性ストストレスマによる 大腸菌 治療後のタンパク質凝集体の抽出と可視化

マイクロアレイ解析のためのサッカロマイセスセレビシエ

成長アッセイ酵母におけるポリグルタミン毒性を評価する

生細胞中のタンパク質凝集の4Dイメージング

のリフォールディング監視タンパク質を結合アッセイ

酵母Proteinopathyモデルを用いた増強Hsp104のバリアントを分離

タンパク質分解中のシクロヘキシミドチェイス分析

線虫の加齢とともに固有の蛋白質の集合の変化を研究するためのメソッド

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

タンパク質毒性ストストレスマによる大腸菌治療後のタンパク質凝集体の抽出と可視化