Computational Analysis of Binding of the GBD Domain of WASP to Different Binding Partners

The GTP-ase binding domain (GBD) of the signaling protein Wiskott-Aldrich Syndrome Protein (WASP) is intrinsically disordered and mutations in it have been linked with Wiskott-Aldrich Syndrome (WAS), an X-linked disorder characterized by thrombocytopenia, eczema and recurrent infections. Here, we use molecular dynamics simulations and the semi-empirical GROMOS 45A3 force field to study interaction of the GBD domain of WASP with a fragment of the protein EspFU as well as with the VCA domain of WASP (auto-inhibited state). EspFU is secreted and used by enterohaemorrhagic Escherichia coli to hijack eukaryotic cytoskeletal machinery, and it does so by competitively disrupting the auto-inhibitory interaction between GBD and VCA domains of WASP. In addition, naturally occurring mutations in the VCA domain cause different variants of WAS. Our simulations confirm that the EspFU domain binds the GBD domain similarly to the VCA domain, which explains why these two binding partners are competitive binders of the GBD domain. Furthermore, we propose a possible mechanism to explain the higher affinity of EspFU for the GBD domain. Finally, we show that the mutations in the VCA domain responsible for Wiskott-Aldrich syndrome can cause formation of β-sheets in the VCA domain. This effect, combined with the mutation-induced rearrangement of the salt bridge network, consequently disables tight binding between GBD and VCA domains. Overall, our results provide a microscopic, dynamic picture behind the two main ways through which the interactions involving the GBD domain of WASP participate in different disease processes.(doi: 10.5562/cca1806

Osteoprotegerin in Exosome-Like Vesicles from Human Cultured Tubular Cells and Urine

Urinary exosomes have been proposed as potential diagnostic tools. TNF superfamily cytokines and receptors may be present in exosomes and are expressed by proximal tubular cells. We have now studied the expression of selected TNF superfamily proteins in exosome-like vesicles from cultured human proximal tubular cells and human urine and have identified additional proteins in these vesicles by LC-MS/MS proteomics. Human proximal tubular cells constitutively released exosome-like vesicles that did not contain the TNF superfamily cytokines TRAIL or TWEAK. However, exosome-like vesicles contained osteoprotegerin (OPG), a TNF receptor superfamily protein, as assessed by Western blot, ELISA or selected reaction monitoring by nLC-(QQQ)MS/MS. Twenty-one additional proteins were identified in tubular cell exosomelike vesicles, including one (vitamin D binding protein) that had not been previously reported in exosome-like vesicles. Twelve were extracellular matrix proteins, including the basement membrane proteins type IV collagen, nidogen-1, agrin and fibulin-1. Urine from chronic kidney disease patients contained a higher amount of exosomal protein and exosomal OPG than urine from healthy volunteers. Specifically OPG was increased in autosomal dominant polycystic kidney disease urinary exosome-like vesicles and expressed by cystic epithelium in vivo. In conclusion, OPG is present in exosome-like vesicles secreted by proximal tubular epithelial cells and isolated from Chronic Kidney Disease urine.This work was supported by grants from the Instituto de Salud Carlos III (ISCIIIRETIC REDINREN RD06/0016, RD12/0021, PI11/01854, PI10/00072 PI09/ 00641 and PS09/00447); Comunidad de Madrid (Fibroteam S2010/BMD-2321, S2010/BMD-2378); Sociedad Española de NefrologÍa; European Network (HEALTH F2-2008-200647); DIALOK European project LSHB-CT-2007-036644; Fundacion Lilly and IRSIN/FRIAT to JE; Programa Intensificación Actividad Investigadora (ISCIII/ Agencia Laín-Entralgo/CM) to AO; Instituto de Salud Carlos III (FIS PI11/01401, CP09/00229); and Fundación Conchita Rábago de Jiménez DÍaz to GAL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Mitochondrial physiology

As the knowledge base and importance of mitochondrial physiology to evolution, health and disease expands, the necessity for harmonizing the terminology concerning mitochondrial respiratory states and rates has become increasingly apparent. The chemiosmotic theory establishes the mechanism of energy transformation and coupling in oxidative phosphorylation. The unifying concept of the protonmotive force provides the framework for developing a consistent theoretical foundation of mitochondrial physiology and bioenergetics. We follow the latest SI guidelines and those of the International Union of Pure and Applied Chemistry (IUPAC) on terminology in physical chemistry, extended by considerations of open systems and thermodynamics of irreversible processes. The concept-driven constructive terminology incorporates the meaning of each quantity and aligns concepts and symbols with the nomenclature of classical bioenergetics. We endeavour to provide a balanced view of mitochondrial respiratory control and a critical discussion on reporting data of mitochondrial respiration in terms of metabolic flows and fluxes. Uniform standards for evaluation of respiratory states and rates will ultimately contribute to reproducibility between laboratories and thus support the development of data repositories of mitochondrial respiratory function in species, tissues, and cells. Clarity of concept and consistency of nomenclature facilitate effective transdisciplinary communication, education, and ultimately further discovery

Mitochondrial physiology

As the knowledge base and importance of mitochondrial physiology to evolution, health and disease expands, the necessity for harmonizing the terminology concerning mitochondrial respiratory states and rates has become increasingly apparent. The chemiosmotic theory establishes the mechanism of energy transformation and coupling in oxidative phosphorylation. The unifying concept of the protonmotive force provides the framework for developing a consistent theoretical foundation of mitochondrial physiology and bioenergetics. We follow the latest SI guidelines and those of the International Union of Pure and Applied Chemistry (IUPAC) on terminology in physical chemistry, extended by considerations of open systems and thermodynamics of irreversible processes. The concept-driven constructive terminology incorporates the meaning of each quantity and aligns concepts and symbols with the nomenclature of classical bioenergetics. We endeavour to provide a balanced view of mitochondrial respiratory control and a critical discussion on reporting data of mitochondrial respiration in terms of metabolic flows and fluxes. Uniform standards for evaluation of respiratory states and rates will ultimately contribute to reproducibility between laboratories and thus support the development of data repositories of mitochondrial respiratory function in species, tissues, and cells. Clarity of concept and consistency of nomenclature facilitate effective transdisciplinary communication, education, and ultimately further discovery

Mechanistic insight into the relationship between N-terminal acetylation of α-synuclein and fibril formation rates by NMR and fluorescence.

Aggregation of α-synuclein (αSyn), the primary protein component in Lewy body inclusions of patients with Parkinson's disease, arises when the normally soluble intrinsically disordered protein converts to amyloid fibrils. In this work, we provide a mechanistic view of the role of N-terminal acetylation on fibrillation by first establishing a quantitative relationship between monomer secondary structural propensity and fibril assembly kinetics, and secondly by demonstrating in the N-terminal acetylated form of the early onset A53T mutation, that N-terminal transient helices formed and/or inhibited by N-terminal acetylation modulate the fibril assembly rates. Using NMR chemical shifts and fluorescence experiments, we report that secondary structural propensity in residues 5-8, 14-31, and 50-57 are highly correlated to fibril growth rate. A four-way comparison of secondary structure propensity and fibril growth rates of N-terminally acetylated A53T and WT αSyn with non-acetylated A53T and WT αSyn present novel mechanistic insight into the role of N-terminal acetylation in amyloid fibril formation. We show that N-terminal acetylation inhibits the formation of the "fibrillation promoting" transient helix at residues 14-31 resulting from the A53T mutation in the non-acetylated variant and supports the formation of the "fibrillation inhibiting" transient helix in residues 1-12 thereby resulting in slower fibrillation rates relative to the previously studied non-acetylated A53T variant. Our results highlight the critical interplay of the region-specific transient secondary structure of the N-terminal region with fibrillation, and the inhibitory role of the N-terminal acetyl group in fibril formation

NMR and fibril morphology comparison of Ac-WT and Ac-A53T.

<p>A. Overlay of ¹⁵N-¹H HSQC of Ac-A53T (black) and Ac-WT(magenta) at 15 °C in PBS buffer at pH 7.4. B. SSP analysis of Ac-WT (magenta) and Ac-A53T (black) plotted for the N-terminal region. The overlay of the rest of the proteins can be seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone.0075018.s002" target="_blank">Figure S2</a>. C. Negatively stained electron micrographs of the end products of fibril formation of Ac-A53T fibril. The scale bar is 200 nm.</p

The effect of the A53T mutation upon the acetylated mutant.

<p>A. Histogram plot of the apparent rate of fibril growth, <i>k</i>_<i>app</i> of A53T (blue), Ac-A53T (red), WT (black) and Ac-WT (green) calculated by sigmoidal fitting of ThT fluorescence curves. Due to the different purification approach from the previously published one, the absolute <i>k</i>_<i>app</i> values are different, however the ratio relative to WT is the same. B. Differences of SSP shown for the N terminal residues 1 to 60 for Ac-A53T vs. WT (red) and A53T (blue) vs. WT; ΔSSP = SSP (Variant) -SSP (WT). The red and blue shading in the positive regions of the SSP curves correspond to increased transient helix observed for Ac-A53T and A53T relative the WT. The black rectangles at the top of the plot represent the regions of increased transient helix that are quantitatively correlated with accelerated growth rates from the chimera set (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone-0075018-g001" target="_blank">Figure 1B</a>). The overlay of the shaded regions and the black rectangles highlights the overlapping boundaries between these data sets. These data in combination with the fibril kinetics suggest that increased helicity in residues 1-12 is fibril inhibiting, while increased helicity in residues14-31, 50-57 are fibril accelerating, and increased transient helix at 5-8 may depend on the overall context. C. A schematic representation of the secondary structure propensity differences in regions in A53T and Ac-A53T, based on the ∆SSP of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone-0075018-g003" target="_blank">Figure 3B</a>. The blue rectangle represents the N-terminal sequence, the black scale the residue number, and the yellow blocks the increase of SSP values of the variants relative to WT. The red outline and an X in the block represents the removal, or inability to sample increased SSP in this region by N-terminal acetylation highlighting that transient helix supported by the A53T mutation in the non-acetylated protein is not supported alongside acetylation.</p

Schematic diagram of a conformational selection and population shift mechanism for A53T and acetylated proteins.

<p>The representation of the population distribution is based on a description by Ma et al. The populations of the various monomer species in the fibrillation process are represented by blue curves. Monomer conformations exist in a heterogeneous ensemble and are shown by schematic drawings with the red cylinder representing α-helix. The disordered monomers sample large heterogeneous ensembles that are differentially populated; both fibrillation prone and non-fibrillation prone monomers exist within the heterogeneous ensemble. Sequence modifications shift the population to favored conformations, which are boxed. The increase in transient helix propensity of the modified protein relative to the WT αSyn is presented. Secondary structure propensity correlations with fibril growth rates suggest that the fibrillation prone conformation consists of increased transient helix in residues 14–31 and 50–57 relative to WT, while transient helix in residues 1–12 arising from N-terminal acetylation is fibrillation inhibiting. By definition, the WT protein does not have fibrillation prone regions, and Ac-A53T which contains both aggregation promoting (residues 50–57) and fibrillation inhibiting conformations (residues 1–12) has similar fibrillation behavior to WT. Ac-WT, which contains only the fibrillation inhibiting conformation (residues 1–12) fibrillates the slowest.</p

Quantitative correlation of SSP values to fibril kinetics in the non-acetylated human-mouse chimera αSyn set.

<p>A. The N-, NAC, and C-terminal regions are shown in the schematic representation of αSyn. The black dots represent seven possible substitutions between human and mouse WT αSyn. B. Boxplot diagram representing the dispersion of SSP values for each human-mouse chimera as a function of residue, where each boxplot is a five-number summary of the SSP distribution of the eight variants. Residues are shaded based on their correlation with the growth rate (k_app), data from a previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#B39" target="_blank">39</a>]. Residues with high positive correlation are shaded in red (r > 0.7), high negative correlation in blue (r < -0.7) and no correlation are shown as grey (-0.7 < r < 0.7). C. The representation is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone-0075018-g001" target="_blank">Figure 1B</a>, however, the boxplots are shaded based on correlation to lag times. Residues with highest positive correlation are shaded in light red (r > 0.5), with highest negative correlation are shaded in light blue (r < -0.5) and no correlation with grey (-0.5 < r < 0.5). D. Correlation between the average SSP in regions that have a strong correlation with <i>k</i>_<i>app</i> determined by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone-0075018-g001" target="_blank">Figure 1B</a> (residues 5–8, 14–31, and 50–57) and <i>k</i>_<i>app</i>(variant)/<i>k</i>_<i>app</i>(HHH) for all eight variants, of which details of naming and BMRB accession numbers are indicated in Table 1. The correlation coefficient is r = 0.93. The correlation function is Y = (2.32±0.2)+ (0.57±0.9)* X.E. Correlation between the average SSP in regions that have strong correlation with lag times determined by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075018#pone-0075018-g001" target="_blank">Figure 1C</a> (residues 51–55 and 84–87) and lag times (HHH)/lag times (variant) for all eight variants. The correlation coefficient is r = 0.75. The correlation function is Y = (2.46±0.66) + (0.53±0.19)* X.</p