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. Electronic supplementary material The online version of this article (10.1186/s13287-019-1213-1) contains supplementary material, which is available to authorized users.

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.

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.

BackgroundGenome 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.ResultsWe 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.ConclusionsWith 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.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4804-9) contains supplementary material, which is available to authorized users.

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.