Modulation of α-synuclein in vitro aggregation kinetics by its alternative splice isoforms.

The misfolding and aggregation of α-synuclein is linked to a family of neurodegenerative disorders known as synucleinopathies, the most prominent of which is Parkinson's disease (PD). Understanding the aggregation process of α-synuclein from a mechanistic point of view is thus of key importance. SNCA, the gene encoding α-synuclein, comprises six exons and produces various isoforms through alternative splicing. The most abundant isoform is expressed as a 140-amino acid protein (αSyn-140), while three other isoforms, αSyn-126, αSyn-112, and αSyn-98, are generated by skipping exon 3, exon 5, or both exons, respectively. In this study, we performed a detailed biophysical characterization of the aggregation of these four isoforms. We found that αSyn-112 and αSyn-98 exhibit accelerated aggregation kinetics compared to αSyn-140 and form distinct aggregate morphologies, as observed by transmission electron microscopy. Moreover, we observed that the presence of relatively small amounts of αSyn-112 accelerates the aggregation of αSyn-140, significantly reducing the aggregation half-time. These results indicate a potential role of alternative splicing in the pathological aggregation of α-synuclein and provide insights into how this process could be associated with the development of synucleinopathies.

Cytosolic sequestration of spatacsin by Protein Kinase A and 14-3-3 proteins.

Mutations in SPG11, encoding spatacsin, constitute the major cause of autosomal recessive Hereditary Spastic Paraplegia (HSP) with thinning of the corpus callosum. Previous studies showed that spatacsin orchestrates cellular traffic events through the formation of a coat-like complex and its loss of function results in lysosomal and axonal transport impairments. However, the upstream mechanisms that regulate spatacsin trafficking are unknown. Here, using proteomics and CRISPR/Cas9-mediated tagging of endogenous spatacsin, we identified a subset of 14-3-3 proteins as physiological interactors of spatacsin. The interaction is modulated by Protein Kinase A (PKA)-dependent phosphorylation of spatacsin at Ser1955, which initiates spatacsin trafficking from the plasma membrane to the intracellular space. Our study provides novel insight in understanding spatacsin physio-pathological roles with mechanistic dissection of its associated pathways.

The Roc domain of LRRK2 as a hub for protein-protein interactions: a focus on PAK6 and its impact on RAB phosphorylation.

Leucine-rich repeat kinase 2 (LRRK2) has taken center stage in Parkinson's disease (PD) research as mutations cause familial PD and more common variants increase lifetime risk for disease. One unique feature in LRRK2 is the coexistence of GTPase/Roc (Ras of complex) and kinase catalytic functions, bridged by a COR (C-terminal Of Roc) platform for dimerization. Multiple PD mutations are located within the Roc/GTPase domain and concomitantly lead to defective GTPase activity and augmented kinase activity in cells, supporting a crosstalk between GTPase and kinase domains. In addition, biochemical and structural data highlight the importance of Roc as a molecular switch modulating LRRK2 monomer-to-dimer equilibrium and building the interface for interaction with binding partners. Here we review the effects of PD Roc mutations on LRRK2 function and discuss the importance of Roc as a hub for multiple molecular interactions relevant for the regulation of cytoskeletal dynamics and intracellular trafficking pathways. Among the well-characterized Roc interactors, we focused on the cytoskeletal-related kinase p21-activated kinase 6 (PAK6). We report the affinity between LRRK2-Roc and PAK6 measured by microscale thermophoresis (MST). We further show that PAK6 can modulate LRRK2-mediated phosphorylation of RAB substrates in the presence of LRRK2 wild-type (WT) or the PD G2019S kinase mutant but not when the PD Roc mutation R1441G is expressed. These findings support a mechanism whereby mutations in Roc might affect LRRK2 activity through impaired protein-protein interaction in the cell.

Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias.

The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Owing to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human data were used to create a protein-protein interaction network based on the causative genes. Network evaluation as a combination of topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes, such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, indicating that there is scope to further classify conditions currently described under the same umbrella-term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

Advances in protein-protein interaction network analysis for Parkinson's disease.

Protein-protein interactions (PPIs) are a key component of the subcellular molecular networks which enable cells to function. Due to their importance in homeostasis, alterations to the networks can be detrimental, leading to cellular dysfunction and ultimately disease states. Parkinson's disease (PD) is a progressive neurodegenerative condition with multifactorial aetiology, spanning genetic variation and environmental modifiers. At a molecular and systems level, the characterisation of PD is the focus of extensive research, largely due to an unmet need for disease modifying therapies. PPI network analysis approaches are a valuable strategy to accelerate our understanding of the molecular crosstalk and biological processes underlying PD pathogenesis, especially due to the complex nature of this disease. In this review, we describe the utility of PPI network approaches in modelling complex systems, focusing on previous work in PD research. We discuss four principal strategies for using PPI network approaches: to infer PD related cellular functions, pathways and novel genes; to support genomics studies; to study the interactome of single PD related genes; and to compare the molecular basis of PD to other neurodegenerative disorders. This is an evolving area of research which is likely to further expand as omics data generation and availability increase. These approaches complement and bridge-the-gap between genetics and functional research to inform future investigations. In this review we outline several limitations that require consideration, acknowledging that ongoing challenges in this field continue to be addressed and the refinement of these approaches will facilitate further advances using PPI network analysis for understanding complex diseases.

Genomewide Association Studies of LRRK2 Modifiers of Parkinson's Disease.

OBJECTIVE: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. METHODS: We performed the first genomewide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genomewide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. RESULTS: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; p value = 2.5E-08, beta = 1.27, SE = 0.23, risk allele: C) met genomewide significance for the penetrance model. Co-immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these 2 proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: p value = 1.1E-07; age-at-onset top variant: p value = 9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. INTERPRETATION: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. ANN NEUROL 2021;90:82-94.

PINOT: an intuitive resource for integrating protein-protein interactions.

BACKGROUND: The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming. METHODS: We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks. RESULTS: Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool. CONCLUSIONS: PINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software. Video abstract.

Secretome of adipose-derived mesenchymal stem cells promotes skeletal muscle regeneration through synergistic action of extracellular vesicle cargo and soluble proteins.

BACKGROUND: The mechanisms underpinning the regenerative capabilities of mesenchymal stem cells (MSC) were originally thought to reside in their ability to recognise damaged tissue and to differentiate into specific cell types that would replace defective cells. However, recent work has shown that molecules produced by MSCs (secretome), particularly those packaged in extracellular vesicles (EVs), rather than the cells themselves are responsible for tissue repair. METHODS: Here we have produced a secretome from adipose-derived mesenchymal stem cells (ADSC) that is free of exogenous molecules by incubation within a saline solution. Various in vitro models were used to evaluate the effects of the secretome on cellular processes that promote tissue regeneration. A cardiotoxin-induced skeletal muscle injury model was used to test the regenerative effects of the whole secretome or isolated extracellular vesicle fraction in vivo. This was followed by bioinformatic analysis of the components of the protein and miRNA content of the secretome and finally compared to a secretome generated from a secondary stem cell source. RESULTS: Here we have demonstrated that the secretome from adipose-derived mesenchymal stem cells shows robust effects on cellular processes that promote tissue regeneration. Furthermore, we show that the whole ADSC secretome is capable of enhancing the rate of skeletal muscle regeneration following acute damage. We assessed the efficacy of the total secretome compared with the extracellular vesicle fraction on a number of assays that inform on tissue regeneration and demonstrate that both fractions affect different aspects of the process in vitro and in vivo. Our in vitro, in vivo, and bioinformatic results show that factors that promote regeneration are distributed both within extracellular vesicles and the soluble fraction of the secretome. CONCLUSIONS: Taken together, our study implies that extracellular vesicles and soluble molecules within ADSC secretome act in a synergistic manner to promote muscle generation.

Measuring Lactase Enzymatic Activity in the Teaching Lab.

Understanding how enzymes work, and relating this to real life examples, is critical to a wide range of undergraduate degrees in the biological and biomedical sciences. This easy to follow protocol was developed for first year undergraduate pharmacy students and provides an entry-level introduction to enzyme reactions and analytical procedures for enzyme analysis. The enzyme of choice is lactase, as this represents an example of a commercially available enzyme relevant to human disease/pharmaceutical practice. Lactase is extracted from dietary supplement tablets, and assessed using a colorimetric assay based upon hydrolysis of an artificial substrate for lactase (ortho-nitrophenol-beta-D-galactopyranoside, ONPG). Release of ortho-nitrophenol following the hydrolytic cleavage of ONPG by lactase is measured by a change in absorbance at 420 nm, and the effect of the temperature on the enzymatic reaction is evaluated by carrying out the reaction on ice, at room temperature and at 37 °C. More advanced analysis can be implemented using this protocol by assessing the enzyme activity under different conditions and using different reagents.

Stratification of candidate genes for Parkinson's disease using weighted protein-protein interaction network analysis.

BACKGROUND: Genome wide association studies (GWAS) have helped identify large numbers of genetic loci that significantly associate with increased risk of developing diseases. However, translating genetic knowledge into understanding of the molecular mechanisms underpinning disease (i.e. disease-specific impacted biological processes) has to date proved to be a major challenge. This is primarily due to difficulties in confidently defining candidate genes at GWAS-risk loci. The goal of this study was to better characterize candidate genes within GWAS loci using a protein interactome based approach and with Parkinson's disease (PD) data as a test case. RESULTS: We applied a recently developed Weighted Protein-Protein Interaction Network Analysis (WPPINA) pipeline as a means to define impacted biological processes, risk pathways and therein key functional players. We used previously established Mendelian forms of PD to identify seed proteins, and to construct a protein network for genetic Parkinson's and carried out functional enrichment analyses. We isolated PD-specific processes indicating 'mitochondria stressors mediated cell death', 'immune response and signaling', and 'waste disposal' mediated through 'autophagy'. Merging the resulting protein network with data from Parkinson's GWAS we confirmed 10 candidate genes previously selected by pure proximity and were able to nominate 17 novel candidate genes for sporadic PD. CONCLUSIONS: With this study, we were able to better characterize the underlying genetic and functional architecture of idiopathic PD, thus validating WPPINA as a robust pipeline for the in silico genetic and functional dissection of complex disorders.

Comparative Protein Interaction Network Analysis Identifies Shared and Distinct Functions for the Human ROCO Proteins.

Signal transduction cascades governed by kinases and GTPases are a critical component of the command and control of cellular processes, with the precise outcome partly determined by direct protein-protein interactions (PPIs). Here, we use the human ROCO proteins as a model for investigating PPI signaling events-taking advantage of the unique dual kinase/GTPase activities and scaffolding properties of these multidomain proteins. PPI networks are reported that encompass the human ROCO proteins, developed using two complementary approaches. First, using the recently developed weighted PPI network analysis (WPPINA) pipeline, a confidence-weighted overview of validated ROCO protein interactors is obtained from peer-reviewed literature. Second, novel ROCO PPIs are assessed experimentally via protein microarray screens. The networks derived from these orthologous approaches are compared to identify common elements within the ROCO protein interactome; functional enrichment analysis of this common core of the network identified stress response and cell projection organization as shared functions within this protein family. Despite the presence of these commonalities, the results suggest that many unique interactors and therefore some specialized cellular roles have evolved for different members of the ROCO proteins. Overall, this multi-approach strategy to increase the resolution of protein interaction networks represents a prototype for the utility of PPI data integration in understanding signaling biology.

Additional rare variant analysis in Parkinson's disease cases with and without known pathogenic mutations: evidence for oligogenic inheritance.

Oligogenic inheritance implies a role for several genetic factors in disease etiology. We studied oligogenic inheritance in Parkinson's (PD) by assessing the potential burden of additional rare variants in established Mendelian genes and/or GBA, in individuals with and without a primary pathogenic genetic cause in two large independent cohorts totaling 7,900 PD cases and 6,166 controls. An excess (≥30%) of cases with a recognised primary genetic cause had ≥1 additional rare variants in Mendelian PD genes, as compared with no known mutation PD cases (17%) and unaffected controls (16%), supporting our hypothesis. Carriers of additional Mendelian gene variants have younger ages at onset (AAO). The effect of additional Mendelian variants in LRRK2 G2019S mutation carriers, of which ATP13A2 variation is particularly common, may account for some of the variation in penetrance. About 10% of No Known Mutation-PD cases harbour a rare GBA variant compared to known pathogenic mutation PD cases (8%) and controls (5%), with carriers having earlier AAOs. Together, the data suggest that the oligogenic inheritance of rare Mendelian variants may be important in patient with a primary pathogenic cause, whereas GBA increases risk across all forms of PD. This study highlights the potential genetic complexity of Mendelian PD. The identification of potential modifying variants provides new insights into disease mechanisms by potentially separating relevant from benign variants and by the interaction between genes in specific pathways. In the future this may be relevant to genetic testing and counselling of patients with PD and their families.