The Effect of Amyloidogenic Peptides on Bacterial Aging Correlates with Their Intrinsic Aggregation Propensity

Villar‐Piqué, Anna; Groot, Natalia Sánchez de; Sabaté, Raimon; Acebrón, Sergio P.; Celaya, Garbiñe; Fernàndez‐Busquets, Xavier; Muga, Arturo; Ventura, Salvador

doi:10.1016/j.jmb.2011.12.014

Cited by 27 publications

(28 citation statements)

References 54 publications

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“…This trend results from the fact that higher organisms contain fewer sequences with high aggregation propensity, but specially because their proteomes contain a higher proportion of IUPs. In addition, the force of natural selection against the aggregation of a given protein depends on the selective contribution of this protein to the organism fitness (Chen and Dokholyan, 2008; Villar-Pique et al, 2012). Therefore, despite the analysis of complete proteomes has contributed significantly to our present understanding on how aggregation modulates protein sequences and structures, the datasets used in these studies comprise proteins displaying divergent structures, from globular to unfolded, and functions, from essential to spurious, and thus under different evolutionary pressures.…”

Section: Discussionmentioning

confidence: 99%

Protein aggregation profile of the human kinome

Graña-Montes¹,

Oliveira²,

Ventura³

2012

Front. Physio.

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Protein aggregation into amyloid fibrils is associated with the onset of an increasing number of human disorders, including Alzheimer's disease, diabetes, and some types of cancer. The ability to form toxic amyloids appears to be a property of most polypeptides. Accordingly, it has been proposed that reducing aggregation and its effect in cell fitness is a driving force in the evolution of proteins sequences. This control of protein solubility should be especially important for regulatory hubs in biological networks, like protein kinases. These enzymes are implicated in practically all processes in normal and abnormal cell physiology, and phosphorylation is one of the most frequent protein modifications used to control protein activity. Here, we use the AGGRESCAN algorithm to study the aggregation propensity of kinase sequences. We compared them with the rest of globular proteins to decipher whether they display differential aggregation properties. In addition, we compared the human kinase complement with the kinomes of other organisms to see if we can identify any evolutionary trend in the aggregational properties of this protein superfamily. Our analysis indicates that kinase domains display significant aggregation propensity, a property that decreases with increasing organism complexity.

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Section: Discussionmentioning

confidence: 99%

Protein aggregation profile of the human kinome

Graña-Montes¹,

Oliveira²,

Ventura³

2012

Front. Physio.

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“…This second property was measured using the AGGRESCAN algorithm (Conchillo‐Sole et al , ; de Groot et al , ). The cellular behavior of Aβ42 has been extensively studied in bacteria and yeast, both by others and by us (de Groot & Ventura, ; de Groot et al , ; Morell et al , ; Villar‐Pique et al , ; Sanchez de Groot et al , ). In yeast, the formation of deposits/granules appears to protect cells against the toxic species (e.g., soluble oligomers; de Groot & Ventura, ; Villar‐Pique & Ventura, ; Sanchez de Groot et al , ).…”

Section: Methodsmentioning

confidence: 99%

“…In vitro , Aβ42 assembles into amyloid fibrils driven by its hydrophobic residues. When overexpressed in unicellular models, this peptide accumulates into inclusion bodies (de Groot & Ventura, ; de Groot et al , ; Plata et al , ; Morell et al , ; Villar‐Pique et al , ; Escusa‐Toret et al , ; Villar‐Pique & Ventura, ; Sanchez de Groot et al , ; Pak et al , ). Aβ42 has no intrinsic function in S. cerevisiae and, according to our previous studies, its expression has almost no effect on yeast growth, at least under optimal grow conditions (Villar‐Pique & Ventura, ; Sanchez de Groot et al , ).…”

Section: Methodsmentioning

confidence: 99%

The fitness cost and benefit of phase‐separated protein deposits

Groot

Torrent

Ravarani

et al. 2019

Molecular Systems Biology

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Phase separation of soluble proteins into insoluble deposits is associated with numerous diseases. However, protein deposits can also function as membrane‐less compartments for many cellular processes. What are the fitness costs and benefits of forming such deposits in different conditions? Using a model protein that phase‐separates into deposits, we distinguish and quantify the fitness contribution due to the loss or gain of protein function and deposit formation in yeast. The environmental condition and the cellular demand for the protein function emerge as key determinants of fitness. Protein deposit formation can influence cell‐to‐cell variation in free protein abundance between individuals of a cell population (i.e., gene expression noise). This results in variable manifestation of protein function and a continuous range of phenotypes in a cell population, favoring survival of some individuals in certain environments. Thus, protein deposit formation by phase separation might be a mechanism to sense protein concentration in cells and to generate phenotypic variability. The selectable phenotypic variability, previously described for prions, could be a general property of proteins that can form phase‐separated assemblies and may influence cell fitness.

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“…In this context, prokaryotic cells have recently emerged as suitable model systems to study the mechanisms of amyloid self‐assembly [13–16]. The structures adopted by recombinant amyloid proteins in bacterial inclusion bodies (IBs) resemble at the molecular level those present in neurotoxic fibrils [17, 18], they are highly toxic to both bacterial and neuronal cells [17, 19], impair bacterial metabolic activity [20] and cellular division [21], promote aging [22], determine cell fitness [20, 21] and, in the case of prion proteins, are infective and transmissible [23, 24]. These properties should permit the study of aspects of amyloid formation that are otherwise technically challenging to address.…”

Section: Introductionmentioning

confidence: 99%

Observation of a Quadrupole Surface Plasmon Mode for Au Nanorods: Effects of Surface Roughness and Crystal Facets

Hong

Shuford

Park

2013

Chemistry An Asian Journal

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This paper describes how the surface roughness and synthetic methods of Au nanorods affect the optical properties that are often associated with localized surface plasmon resonances. We synthesized Au nanorods with different aspect ratios and surface roughness by using two different synthetic strategies to observe surface plasmon resonance bands. One set of nanorods was prepared in high yield by using a seed-mediated dropwise-addition method with a growth-directing surfactant in aqueous solution (Au nanorods in aqueous phase, GNRA). The other set of Au nanorods were synthesized by the electrochemical deposition of Au onto an anodized aluminum oxide (AAO) template (Au nanorods by AAO template, GNRT). The length of the nanorods was controlled by changing the total charge that was passed through the cell and their diameter was monitored by changing the diameter of the template channel. The as-prepared Au nanorods were optimized to observe a quadrupole mode, which is one of the higher-order surface plasmon bands. Our results showed differences between the optical properties of GNRA and GNRT. The roughness and crystal structure of the Au nanorods affected their optical properties. Smooth and single-crystal surface on GNRA had larger and sharper peaks than GNRT. The discrete dipole approximation (DDA) method was used to calculate the optical properties of the Au nanorods and these results were in good agreement with our experimental results.

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The Effect of Amyloidogenic Peptides on Bacterial Aging Correlates with Their Intrinsic Aggregation Propensity

Cited by 27 publications

References 54 publications

Protein aggregation profile of the human kinome

Protein aggregation profile of the human kinome

The fitness cost and benefit of phase‐separated protein deposits

Observation of a Quadrupole Surface Plasmon Mode for Au Nanorods: Effects of Surface Roughness and Crystal Facets

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