The Birth of the Modern Era of Parkinson's Disease Genetics

Genetic understanding in Parkinson’s disease (PD) has followed a path of hard won evolution occasionally punctuated by revolution. While it was suggested early on by both Leroux and Gowers that heredity had a role to play in PD, this was a view that wasn’t widely enough held to even be unpopular. The dogma was that the disease was one of environmental provenance and while the evidence for this is still rather scarce, this view pervades in the minds of patients, clinicians, and scientists. Conversely the evidence linking genetics to PD is both overwhelming and growing. Here we describe the growth of genetics in PD from backwater to driving force, and the structure and shape of its future. The localization and identification of α-synuclein mutations as a cause of PD in the mid 1990’s was perhaps the first concrete and revolutionary finding in PD genetics [1]. This came about as a result of the intuition and hard work of a clinical team from New Jersey, followed by the linkage and positional cloning efforts of a genetic team at NIH, orchestrated by the then director of NINDS, Zach Hall. This effort (described by Bob Nussbaum in another article in this issue) was an extraordinary success. The discovery of α-synuclein mutations as a rare cause of PD was an invigorating and welcome progression for myriad reasons. Most prominently, it gave us the mutation as a tool with which to attempt to understand the disease process. Perhaps more importantly, at least in the short term, it provided empirical evidence that there was a genetic basis for rare forms of the disease and because α-synuclein was a major component of all Lewy bodies, that these findings were directly relevant to all cases of PD. This in fact, prompted one of us to say, tongue in cheek “If you’re not working on synuclein, you’re not working on Parkinson’s disease”

Spotting the diffusion of New Psychoactive Substances over the Internet

Online availability and diffusion of New Psychoactive Substances (NPS) represent an emerging threat to healthcare systems. In this work, we analyse drugs forums, online shops, and Twitter. By mining the data from these sources, it is possible to understand the dynamics of drugs diffusion and their endorsement, as well as timely detecting new substances. We propose a set of visual analytics tools to support analysts in tackling NPS spreading and provide a better insight about drugs market and analysis

Abrogation of LRRK2 dependent Rab10 phosphorylation with TLR4 activation and alterations in evoked cytokine release in immune cells

LRRK2 protein is expressed prominently in immune cells, cell types whose contribution to LRRK2-associated genetic Parkinson's disease (PD) is increasingly being recognised. We investigated the effect of inflammatory stimuli using RAW264.7 murine macrophage cells as model systems. A detailed time course of TLR2 and TLR4 stimulation was investigated through measuring LRRK2 phosphorylation at its specific phospho-sites, and Rab8 and Rab10 phosphorylation together with cytokine release following treatment with LPS and zymosan. LRRK2 phosphorylation at Ser935, Ser955 and Ser973 was increased significantly over untreated conditions at 4-24h in both WT-LRRK2 and T1348N-LRRK2 cell lines to similar extents although levels of Ser910 phosphorylation were maintained at higher levels throughout. Importantly we demonstrate that LPS stimulation significantly decreased phospho-Rab10 but not phospho-Rab8 levels over 4-24h in both WT-LRRK2 and T1348N-LRRK2 cell lines. The dephosphorylation of Rab10 was not attributed to its specific phosphatase, PPM1H as the levels remained unaltered with LPS treatment. MAPK phosphorylation occurred prior to LRRK2 phosphorylation which was validated by blocking TLR4 and TLR2 receptors with TAK242 or Sparstolonin B respectively. A significant decrease in basal level of TNFα release was noted in both T1348N-LRRK2 and KO-LRRK2 cell lines at 48h compared to WT-LRRK2 cell line, however LPS and zymosan treatment did not cause any significant alteration in the TNFα and IL-6 release between the three cell lines. In contrast, LPS and zymosan caused significantly lower IL-10 release in T1348N-LRRK2 and KO-LRRK2 cell lines. A significant decrease in phospho-Rab10 levels was also confirmed in human IPS-derived macrophages with TLR4 activation. Our data demonstrates for the first time that LRRK2-dependent Rab10 phosphorylation is modulated by LPS stimulation, and that cytokine release may be influenced by the status of LRRK2. These data provide further insights into the function of LRRK2 in immune response, and has relevance for understanding cellular dysfunctions when developing LRRK2-based inhibitors for clinical treatment

Garden varieties: how attractive are recommended garden plants to butterflies?

One way the public can engage in insect conservation is through wildlife gardening, including the growing of insect-friendly flowers as sources of nectar. However, plant varieties differ in the types of insects they attract. To determine which garden plants attracted which butterflies, we counted butterflies nectaring on 11 varieties of summer-flowering garden plants in a rural garden in East Sussex, UK. These plants were all from a list of 100 varieties considered attractive to British butterflies, and included the five varieties specifically listed by the UK charity Butterfly Conservation as best for summer nectar. A total of 2659 flower visits from 14 butterfly and one moth species were observed. We performed a principal components analysis which showed contrasting patterns between the species attracted to Origanum vulgare and Buddleia davidii. The “butterfly bush” Buddleia attracted many nymphalines, such as the peacock, Inachis io, but very few satyrines such as the gatekeeper, Pyronia tithonus, which mostly visited Origanum. Eupatorium cannibinum had the highest Simpson’s Diversity score of 0.75, while Buddleia and Origanum were lower, scoring 0.66 and 0.50 respectively. No one plant was good at attracting all observed butterfly species, as each attracted only a subset of the butterfly community. We conclude that to create a butterfly-friendly garden, a variety of plant species are required as nectar sources for butterflies. Furthermore, garden plant recommendations can probably benefit from being more precise as to the species of butterfly they attract

Hip fracture fixation in a patient with below-knee amputation presents a surgical dilemma: a case report

<p>Abstract</p> <p>Introduction</p> <p>Hip fracture fixation surgery in patients with below-knee amputations poses a challenging problem to the surgeon in terms of obtaining traction for reduction of the fracture. The absence of the foot and part of the leg in these patients makes positioning on the fracture table difficult. We highlight this difficult problem and suggest techniques to overcome it.</p> <p>Case presentation</p> <p>A 73-year-old man with bilateral below-knee amputations presented with a history of fall. Radiographs revealed an inter-trochanteric fracture of the femur. A dynamic hip screw fixation was planned for the fracture but the dilemma was on how to position the patient on the fracture table for the surgery. Special attention was needed in positioning the patient and in surgical fixation of the fracture.</p> <p>Conclusion</p> <p>Hip fracture fixation in patients with below-knee amputations poses a special problem in positioning for fracture reduction and fixation. In this case report, we share our experience and suggest techniques to use when encountering this difficult problem.</p

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn). Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis

BACKGROUND: In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. RESULTS: Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. CONCLUSIONS: This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work

Gene co-expression networks shed light into diseases of brain iron accumulation

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention