Figures & data
Figure 1. Features characterizing protein aggregates. Physiological and pathological protein aggregates are very diverse and multifaceted, and may be categorized based on features like structure [Citation5,Citation8,Citation9,Citation13,Citation15,Citation21,Citation22,Citation38,Citation44], function [Citation1,Citation2,Citation5,Citation8,Citation9,Citation12,Citation13,Citation23–Citation26,Citation44–Citation46], reversibility [Citation5–Citation9], infectivity [Citation27–Citation31], localization [Citation5,Citation8,Citation15,Citation32–Citation34,Citation47] and composition [Citation5,Citation35,Citation36]
![Figure 1. Features characterizing protein aggregates. Physiological and pathological protein aggregates are very diverse and multifaceted, and may be categorized based on features like structure [Citation5,Citation8,Citation9,Citation13,Citation15,Citation21,Citation22,Citation38,Citation44], function [Citation1,Citation2,Citation5,Citation8,Citation9,Citation12,Citation13,Citation23–Citation26,Citation44–Citation46], reversibility [Citation5–Citation9], infectivity [Citation27–Citation31], localization [Citation5,Citation8,Citation15,Citation32–Citation34,Citation47] and composition [Citation5,Citation35,Citation36]](/cms/asset/01c9086c-0413-479a-bb60-882a4449f491/kccy_a_1480220_f0001_c.jpg)
Figure 2. The yeast pyruvate kinase Cdc19 forms reversible, amyloid-like aggregates upon stress. (a) Cdc19 aggregates reversibly upon stress. Cells expressing Cdc19-GFP were grown in SD-full (synthetic complete medium containing glucose) and followed during shifts to glucose starvation media and subsequent nutrient repletion in microfluidic chips, as described previously [Citation5,Citation60]. Time-lapse fluorescence microscopy was performed as described previously [Citation60] and images were taken every 10 min. To monitor successful switch of media, glucose starvation media was supplemented with a fluorescent dye (Alexa Fluor 680-Dextran, 3,000 MW, Invitrogen). Representative images of three independent experiments showing cells during exponential phase in glucose-rich medium (control), glucose starvation (every 30 min for 4 h) and 90 min after re-addition of glucose are shown. Scale bar, 3 μm. (b) Cdc19 contains several stretches of amino acids that are computationally predicted to form amyloid-like fibrils. The region with highest fibrillation propensity correlates with the experimentally validated Cdc19 LCR (highlighted in the red box) [Citation5]. The amino acid sequence of Cdc19 is plotted along the x-axis. On the y-axis, the fibrillation propensities of Cdc19 segments are shown, as computed using the consensus tool AmylPred2.0 (upper part, (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]), and the structure-based ZipperDB prediction method (lower part, (http://services.mbi.ucla.edu/zipperdb/) [Citation56]). AmylPred2.0 combines 11 different prediction tools. A peptide is considered amyloidogenic if the consensus of at least 5 out of 11 methods is reached. With ZipperDB, fibrillation propensities for every possible six-residue peptide not containing a proline from the protein sequence of interest are computed using a structure-based algorithm. Proline-containing hexapeptides are not included in the profile, as prolines are β-strand breakers and thus incompatible with the structure required for amyloid fibrils formation. Based on experimental data, a predicted energy of −23 kcal/mol was chosen as threshold and thus segments with energies below this value are considered to have high fibrillation propensity. (c) Predicted fibrillar structure of the hexamer with highest fibrillation propensity in Cdc19 compared to the fibrillar structure of a classical amyloidogenic peptide of Aβ42. (d) Cdc19 aggregates are stained by the amyloid-binding dye Congo Red. Cdc19 was purified as described previously [Citation5]. A 1 mM stock solution of Congo Red (Sigma-Aldrich) was prepared in protein lysis buffer and filtered (0.2 μm, Millipore). Before each assay aliquots of purified wild-type Cdc19 (Cdc19-WT) or of the non-aggregating Cdc19-R49A mutant (Cdc19-R49A) were thawed on ice and cleared by centrifugation (at 4°C for 10 min at 21,000 g). The proteins (0.3 mg/ml) were then incubated 20 min at 42°C to induce aggregation or kept on ice. Heat shocked and control samples were incubated with Congo Red solution (final concentration of 100 μM) and fluorescence intensity was measured in 96-well half-area non-binding polystyrene plates (Corning). The Congo Red fluorescence signal was recorded at 30°C in a plate reader (Clariostar, BMG Labtech) by monitoring the emission signal at 614 ± 15 nm after excitation at 560 ± 30 nm. Within an individual experiment the fluorescence measurements were carried out in triplicate. Three independent repetitions were performed using three different protein aliquots for Cdc19-WT and two different aliquots for the non-aggregating control Cdc19-R49A. Data are represented as mean ± s.e.m. of three independent experiments
![Figure 2. The yeast pyruvate kinase Cdc19 forms reversible, amyloid-like aggregates upon stress. (a) Cdc19 aggregates reversibly upon stress. Cells expressing Cdc19-GFP were grown in SD-full (synthetic complete medium containing glucose) and followed during shifts to glucose starvation media and subsequent nutrient repletion in microfluidic chips, as described previously [Citation5,Citation60]. Time-lapse fluorescence microscopy was performed as described previously [Citation60] and images were taken every 10 min. To monitor successful switch of media, glucose starvation media was supplemented with a fluorescent dye (Alexa Fluor 680-Dextran, 3,000 MW, Invitrogen). Representative images of three independent experiments showing cells during exponential phase in glucose-rich medium (control), glucose starvation (every 30 min for 4 h) and 90 min after re-addition of glucose are shown. Scale bar, 3 μm. (b) Cdc19 contains several stretches of amino acids that are computationally predicted to form amyloid-like fibrils. The region with highest fibrillation propensity correlates with the experimentally validated Cdc19 LCR (highlighted in the red box) [Citation5]. The amino acid sequence of Cdc19 is plotted along the x-axis. On the y-axis, the fibrillation propensities of Cdc19 segments are shown, as computed using the consensus tool AmylPred2.0 (upper part, (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]), and the structure-based ZipperDB prediction method (lower part, (http://services.mbi.ucla.edu/zipperdb/) [Citation56]). AmylPred2.0 combines 11 different prediction tools. A peptide is considered amyloidogenic if the consensus of at least 5 out of 11 methods is reached. With ZipperDB, fibrillation propensities for every possible six-residue peptide not containing a proline from the protein sequence of interest are computed using a structure-based algorithm. Proline-containing hexapeptides are not included in the profile, as prolines are β-strand breakers and thus incompatible with the structure required for amyloid fibrils formation. Based on experimental data, a predicted energy of −23 kcal/mol was chosen as threshold and thus segments with energies below this value are considered to have high fibrillation propensity. (c) Predicted fibrillar structure of the hexamer with highest fibrillation propensity in Cdc19 compared to the fibrillar structure of a classical amyloidogenic peptide of Aβ42. (d) Cdc19 aggregates are stained by the amyloid-binding dye Congo Red. Cdc19 was purified as described previously [Citation5]. A 1 mM stock solution of Congo Red (Sigma-Aldrich) was prepared in protein lysis buffer and filtered (0.2 μm, Millipore). Before each assay aliquots of purified wild-type Cdc19 (Cdc19-WT) or of the non-aggregating Cdc19-R49A mutant (Cdc19-R49A) were thawed on ice and cleared by centrifugation (at 4°C for 10 min at 21,000 g). The proteins (0.3 mg/ml) were then incubated 20 min at 42°C to induce aggregation or kept on ice. Heat shocked and control samples were incubated with Congo Red solution (final concentration of 100 μM) and fluorescence intensity was measured in 96-well half-area non-binding polystyrene plates (Corning). The Congo Red fluorescence signal was recorded at 30°C in a plate reader (Clariostar, BMG Labtech) by monitoring the emission signal at 614 ± 15 nm after excitation at 560 ± 30 nm. Within an individual experiment the fluorescence measurements were carried out in triplicate. Three independent repetitions were performed using three different protein aliquots for Cdc19-WT and two different aliquots for the non-aggregating control Cdc19-R49A. Data are represented as mean ± s.e.m. of three independent experiments](/cms/asset/e0cd3150-4813-4844-a063-25e66d300e2f/kccy_a_1480220_f0002_c.jpg)
Figure 3. LCR-mediated reversible protein aggregation can be regulated by several mechanisms, including LCR phosphorylation, oligomerization and/or interaction with a binding partner. (a) Model showing possible mechanisms to prevent LCR-mediated aggregation. Unscheduled aggregation can be avoided by protecting aggregation-prone LCRs by means of post-translational modifications (e.g. phosphorylation), oligomerization and/or interaction with a binding partner. Upon stress, these protections are removed and the LCR becomes exposed, thus allowing functional, reversible aggregation. (b) In vivo, Cdc19 is in an equilibrium between a monomeric form (left) and a tetrameric form (right). The red region corresponds to the predicted LCR identified by the SEG program (http://mendel.imp.ac.at/METHODS/seg.server.html) [Citation67], using a trigger window length [W] = 25, trigger complexity [K(1)] = 3.0 and extension complexity [K(2)]) = 3.3. Blue regions correspond to amyloidogenic peptides as predicted by AmylPred2.0 (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]. (c) Schematic representation of Cdc19 showing that empirically measured phosphosites taken from our previously published data [Citation5]) often map within Cdc19 LCR (in red) or in predicted amyloidogenic regions (in blue)
![Figure 3. LCR-mediated reversible protein aggregation can be regulated by several mechanisms, including LCR phosphorylation, oligomerization and/or interaction with a binding partner. (a) Model showing possible mechanisms to prevent LCR-mediated aggregation. Unscheduled aggregation can be avoided by protecting aggregation-prone LCRs by means of post-translational modifications (e.g. phosphorylation), oligomerization and/or interaction with a binding partner. Upon stress, these protections are removed and the LCR becomes exposed, thus allowing functional, reversible aggregation. (b) In vivo, Cdc19 is in an equilibrium between a monomeric form (left) and a tetrameric form (right). The red region corresponds to the predicted LCR identified by the SEG program (http://mendel.imp.ac.at/METHODS/seg.server.html) [Citation67], using a trigger window length [W] = 25, trigger complexity [K(1)] = 3.0 and extension complexity [K(2)]) = 3.3. Blue regions correspond to amyloidogenic peptides as predicted by AmylPred2.0 (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]. (c) Schematic representation of Cdc19 showing that empirically measured phosphosites taken from our previously published data [Citation5]) often map within Cdc19 LCR (in red) or in predicted amyloidogenic regions (in blue)](/cms/asset/8dbce5b6-c82f-46d9-9c4e-72c3c0c92688/kccy_a_1480220_f0003_c.jpg)
Figure 4. The mammalian pyruvate kinase PKM2 might also form amyloid-like aggregates, which could be regulated similar to Cdc19. A-B) Both PKM1 and PKM2 contain several stretches of amino acids that are predicted to form amyloids. However, only PKM2 presents a predicted LCR, which overlaps with the region with highest fibrillation propensity (highlighted in the red box). In addition, known PKM2 phosphorylation sites show a remarkable overlap with predicted amyloidogenic regions, suggesting that PKM2 might aggregate in a regulated way similar to Cdc19. The fibrillation profiles of PKM2 (upper part) and PKM1 (lower part) were computed using AmylPred2.0 (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]. Predicted amyloidogenic regions are shown in in blue. PKM2 phosphorylation sites were found in the Phospho.ELM database (http://phospho.elm.eu.org) [Citation79]. PKM2’s predicted low complexity region was identified by the SEG program (http://mendel.imp.ac.at/METHODS/seg.server.html) [Citation67], using a trigger window length [W] = 25, trigger complexity [K(1)] = 3.0 and extension complexity [K(2)] = 3.3., PKM2 LCR is highlighted in red
![Figure 4. The mammalian pyruvate kinase PKM2 might also form amyloid-like aggregates, which could be regulated similar to Cdc19. A-B) Both PKM1 and PKM2 contain several stretches of amino acids that are predicted to form amyloids. However, only PKM2 presents a predicted LCR, which overlaps with the region with highest fibrillation propensity (highlighted in the red box). In addition, known PKM2 phosphorylation sites show a remarkable overlap with predicted amyloidogenic regions, suggesting that PKM2 might aggregate in a regulated way similar to Cdc19. The fibrillation profiles of PKM2 (upper part) and PKM1 (lower part) were computed using AmylPred2.0 (http://aias.biol.uoa.gr/AMYLPRED2/) [Citation57]. Predicted amyloidogenic regions are shown in in blue. PKM2 phosphorylation sites were found in the Phospho.ELM database (http://phospho.elm.eu.org) [Citation79]. PKM2’s predicted low complexity region was identified by the SEG program (http://mendel.imp.ac.at/METHODS/seg.server.html) [Citation67], using a trigger window length [W] = 25, trigger complexity [K(1)] = 3.0 and extension complexity [K(2)] = 3.3., PKM2 LCR is highlighted in red](/cms/asset/711a26c9-65e2-41af-8144-255409fbdd93/kccy_a_1480220_f0004_c.jpg)
