Integrating pathways of Parkinson's disease in a molecular interaction map

Converging peripheral blood microRNA profiles in Parkinson's disease and progressive supranuclear palsy

MicroRNAs act via targeted suppression of messenger RNA translation in the DNA-RNA-protein axis. The dysregulation of microRNA(s) reflects the epigenetic changes affecting the cellular processes in multiple disorders. To understand the complex effect of dysregulated microRNAs linked to neurodegeneration, we performed a cross-sectional microRNA expression analysis in idiopathic Parkinson's disease (n = 367), progressive supranuclear palsy (n = 35) and healthy controls (n = 416) from the Luxembourg Parkinson's Study, followed by prediction modelling, enriched pathway analysis and target simulation of dysregulated microRNAs using probabilistic Boolean modelling. Forty-six microRNAs were identified to be dysregulated in Parkinson's disease versus controls and 16 in progressive supranuclear palsy versus controls with 4 overlapping significantly dysregulated microRNAs between the comparisons. Predictive power of microRNA subsets (including up to 100 microRNAs) was modest for differentiating Parkinson's disease or progressive supranuclear palsy from controls (maximal cross-validated area under the receiver operating characteristic curve 0.76 and 0.86, respectively) and low for progressive supranuclear palsy versus Parkinson's disease (maximal cross-validated area under the receiver operating characteristic curve 0.63). The enriched pathway analysis revealed natural killer cell pathway to be dysregulated in both, Parkinson's disease and progressive supranuclear palsy versus controls, indicating that the immune system might play an important role in both diseases. Probabilistic Boolean modelling of pathway dynamics affected by dysregulated microRNAs in Parkinson's disease and progressive supranuclear palsy revealed partially overlapping dysregulation in activity of the transcription factor EB, endoplasmic reticulum stress signalling, calcium signalling pathway, dopaminergic transcription and peroxisome proliferator-activated receptor gamma coactivator-1α activity, though involving different mechanisms. These findings indicated a partially convergent (sub)cellular end-point dysfunction at multiple levels in Parkinson's disease and progressive supranuclear palsy, but with distinctive underlying molecular mechanisms.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease

Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

Protective effect of increased O-GlcNAc cycling against 6-OHDA induced Parkinson's disease pathology

This study aimed to elucidate the role of O-GlcNAc cycling in 6-hydroxydopamine (6-OHDA)-induced Parkinson's disease (PD)-like neurodegeneration and the underlying mechanisms. We observed dose-dependent downregulation of O-GlcNAcylation, accompanied by an increase in O-GlcNAcase following 6-OHDA treatment in both mouse brain and Neuro2a cells. Interestingly, elevating O-GlcNAcylation through glucosamine (GlcN) injection provided protection against PD pathogenesis induced by 6-OHDA. At the behavioral level, GlcN mitigated motor deficits induced by 6-OHDA, as determined using the pole, cylinder, and apomorphine rotation tests. Furthermore, GlcN attenuated 6-OHDA-induced neuroinflammation and mitochondrial dysfunction. Notably, augmented O-GlcNAcylation, achieved through O-GlcNAc transferase (OGT) overexpression in mouse brain, conferred protection against 6-OHDA-induced PD pathology, encompassing neuronal cell death, motor deficits, neuroinflammation, and mitochondrial dysfunction. These collective findings suggest that O-GlcNAcylation plays a crucial role in the normal functioning of dopamine neurons. Moreover, enhancing O-GlcNAcylation through genetic and pharmacological means could effectively ameliorate neurodegeneration and motor impairment in an animal model of PD. These results propose a potential strategy for safeguarding against the deterioration of dopamine neurons implicated in PD pathogenesis.

Genotype-phenotype correlation in PRKN-associated Parkinson's disease

Bi-allelic pathogenic variants in PRKN are the most common cause of autosomal recessive Parkinson's disease (PD). 647 patients with PRKN-PD were included in this international study. The pathogenic variants present were characterised and investigated for their effect on phenotype. Clinical features and progression of PRKN-PD was also assessed. Among 133 variants in index cases (n = 582), there were 58 (43.6%) structural variants, 34 (25.6%) missense, 20 (15%) frameshift, 10 splice site (7.5%%), 9 (6.8%) nonsense and 2 (1.5%) indels. The most frequent variant overall was an exon 3 deletion (n = 145, 12.3%), followed by the p.R275W substitution (n = 117, 10%). Exon3, RING0 protein domain and the ubiquitin-like protein domain were mutational hotspots with 31%, 35.4% and 31.7% of index cases presenting mutations in these regions respectively. The presence of a frameshift or structural variant was associated with a 3.4 ± 1.6 years or a 4.7 ± 1.6 years earlier age at onset of PRKN-PD respectively (p < 0.05). Furthermore, variants located in the N-terminus of the protein, a region enriched with frameshift variants, were associated with an earlier age at onset. The phenotype of PRKN-PD was characterised by slow motor progression, preserved cognition, an excellent motor response to levodopa therapy and later development of motor complications compared to early-onset PD. Non-motor symptoms were however common in PRKN-PD. Our findings on the relationship between the type of variant in PRKN and the phenotype of the disease may have implications for both genetic counselling and the design of precision clinical trials.

Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview

Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.

Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape .

Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis

Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

Towards improved screening of toxins for Parkinson's risk

Parkinson's disease (PD) is a chronic, progressive and disabling neurodegenerative disorder. The prevalence of PD has risen considerably over the past decades. A growing body of evidence suggest that exposure to environmental toxins, including pesticides, solvents and heavy metals (collectively called toxins), is at least in part responsible for this rapid growth. It is worrying that the current screening procedures being applied internationally to test for possible neurotoxicity of specific compounds offer inadequate insights into the risk of developing PD in humans. Improved screening procedures are therefore urgently needed. Our review first substantiates current evidence on the relation between exposure to environmental toxins and the risk of developing PD. We subsequently propose to replace the current standard toxin screening by a well-controlled multi-tier toxin screening involving the following steps: in silico studies (tier 1) followed by in vitro tests (tier 2), aiming to prioritize agents with human relevant routes of exposure. More in depth studies can be undertaken in tier 3, with whole-organism (in)vertebrate models. Tier 4 has a dedicated focus on cell loss in the substantia nigra and on the presumed mechanisms of neurotoxicity in rodent models, which are required to confirm or refute the possible neurotoxicity of any individual compound. This improved screening procedure should not only evaluate new pesticides that seek access to the market, but also critically assess all pesticides that are being used today, acknowledging that none of these has ever been proven to be safe from a perspective of PD. Importantly, the improved screening procedures should not just assess the neurotoxic risk of isolated compounds, but should also specifically look at the cumulative risk conveyed by exposure to commonly used combinations of pesticides (cocktails). The worldwide implementation of such an improved screening procedure, would be an essential step for policy makers and governments to recognize PD-related environmental risk factors.

Gene Signals and SNPs Associated with Parkinson's Disease: A Nutrigenomics and Computational Prospective Insights

Parkinson's disease is the most prevalent chronic neurodegenerative disease. Neurological conditions for PD were influenced by a variety of epigenetic factors and SNPs in some of the coexisting genes that were expressed. This article focused on nutrigenomics of PD and the prospective highlighting of how these genes are regulated in terms of nutritive factors and the genetic basis of PD risk, onset, and progression. Multigenetic associations of the following genetic alterations in the genes of SNCA, LRRK2, UCHL1, PARK2,PINK1, DJ-1, and ATP13A2 have been reported with the familial and de novo genetic origins of PD. Over the past two decades, significant attempts have been made to understand the biological mechanisms that are potential causes for this disease, as well as to identify therapeutic substances for the prevention and management of PD. Nutrigenomics has sparked considerable interest due to its nutritional, safe, and therapeutic effects on a variety of chronic diseases. In this study, we summarise some of the nutritive supplements that have an impact on PD.

Non-steroidal anti-inflammatory drug use and markers of Parkinson's disease progression: A retrospective cohort study

BACKGROUND

Previous studies demonstrated reduced incidence of Parkinson's disease (PD) with regular non-steroidal anti-inflammatory drug (NSAID) exposure, particularly ibuprofen. No studies have investigated the impact of NSAID exposure on markers of disease progression for established PD.

METHODS

This is a retrospective observational study using two cohorts. The Deprenyl and Tocopheral Anti-Oxidative Therapy of Parkinsonism (DATATOP) study enrolled 800 drug naïve people with PD with a median follow-up duration of 6.5 years. The DATATOP primary outcome measures were mortality at last study visit. The Parkinson's Progression Markers Initiative (PPMI) cohort was limited to drug naïve PD participants (423 at time of analysis). The PPMI primary outcome measure was annual rate of change in ipsilateral putamen 123I-ioflupane binding ratio at four years study duration. Regular NSAID exposure was defined as any scheduled NSAID use (as needed use was excluded). Analysis was performed separately for recent exposure and cumulative exposure time (CET).

RESULTS

Total CET median and interquartile range (years) for ibuprofen, non-aspirin NSAID, and aspirin were respectively 0.9 (0.3-2.9), 1.1 (0.3-2.6), and 1.5 (0.4-2.8) for DATATOP and 0.4 (0.01-2.2), 1.4 (0.3-4.4), and 5.5 (2.6-7.1) for PPMI. Exposure was usually discontinuous. Exposure to ibuprofen was low in both cohorts. There was no significant association between NSAID recent exposure or CET and primary outcome measures in either cohort.

CONCLUSIONS

NSAID exposure in established PD does not appear to provide protective effect although exposure may not have occurred continuously enough in these two cohorts to provide benefit. Statistical power for ibuprofen exposure analyses was limited.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.

Integrative Proteome Analysis Revels 3-Hydroxybutyrate Exerts Neuroprotective Effect by Influencing Chromatin Bivalency

3-hydroxybutyrate (3OHB) has been proved to act as a neuroprotective molecule in multiple neurodegenerative diseases. Here, we employed a quantitative proteomics approach to assess the changes of the global protein expression pattern of neural cells upon 3OHB administration. In combination with a disease-related, protein-protein interaction network we pinpointed a hub marker, histone lysine 27 trimethylation, which is one of the key epigenetic markers in multiple neurodegenerative diseases. Integrative analysis of transcriptomic and epigenomic datasets highlighted the involvement of bivalent transcription factors in 3OHB-mediated disease protection and its alteration of neuronal development processes. Transcriptomic profiling revealed that 3OHB impaired the fate decision process of neural precursor cells by repressing differentiation and promoting proliferation. Our study provides a new mechanism of 3OHB's neuroprotective effect, in which chromatin bivalency is sensitive to 3OHB alteration and drives its neuroprotective function both in neurodegenerative diseases and in neural development processes.

Acute Effects of a Haptic Anchor System on Postural Sway of Individuals with Parkinson's Disease: A Preliminary Study

Some investigators have demonstrated that an anchor system can improve postural control in elderly persons during balance tasks, but none have reported on the use of this approach in individuals with Parkinson's disease (PD). Therefore, we aimed to evaluate the effect of an anchor system on postural sway in elderly individuals with (n = 13) and without (n = 14) PD. In this cross-sectional study, we measured postural sway with a force platform based on the Clinical Test of Sensory Interaction of Balance (CTSIB). We calculated center of pressure (COP) parameters, as a function of time, based on the ellipse sway area (cm²) and evaluated self-efficacy for postural control based on the degree of difficulty in each task. With the anchor system (i.e., handheld ropes attached to weights on the floor), we observed a significant reduction in the ellipse sway area in the semi-tandem position among individuals with PD (p = .04). For participants without PD, there was no significant difference in sway with or without the anchor system in all positions. Also, for participants with PD, there was an improvement in self-efficacy for postural control associated with the anchor system in several positions while there was only a self-efficacy improvement with the anchor system in the semi-tandem position for those without PD. Acute use of a haptic anchor system reduced postural sway in the semi-tandem position in individuals with PD, and the anchor system generally improved postural control self-efficacy for body sway in individuals with PD.

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia

Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.

Clinical applications of exercise in Parkinson's disease: what we need to know?

INTRODUCTION

Exploring the potential of exercise in the rehabilitation process of patients with Parkinson's (PD) may be an interesting treatment perspective. Exercise-induced responses derived from neurotrophic elements appear to ameliorate the decline in neurodegeneration. Despite this understanding, the literature needs to be updated.

AREAS COVERED

Our review focuses on: a) the key mechanisms of exercise on PD, highlighting mainly the responses related to neuroplasticity; b) the effects induced by different traditional types of exercise, also highlighting the effects of complementary therapies related to movement; c) the volume of exercise required to support efficient results are explored in the context of PD. Additionally, the proposition of new clinical application strategies in the context of PD will also be determined.

EXPERT OPINION

It is suggested that different intensities of aerobic exercise be explored for the treatment of PD. The results associated with high intensity seem promising for performance, physiological and clinical parameters, such as BDNF production and cognition. On the other hand, the diversification of tasks and repetition of motor gestures appear as consistent arguments to exercise prescription. Finally, for future investigations, the neuromodulation strategy in association with aerobic exercise appears as a potential inducer of benefits on gait and cognitive function.

Post-COVID-19 Parkinsonism and Parkinson’s Disease Pathogenesis: The Exosomal Cargo Hypothesis

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease after Alzheimer’s disease, globally. Dopaminergic neuron degeneration in substantia nigra pars compacta and aggregation of misfolded alpha-synuclein are the PD hallmarks, accompanied by motor and non-motor symptoms. Several viruses have been linked to the appearance of a post-infection parkinsonian phenotype. Coronavirus disease 2019 (COVID-19), caused by emerging severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, has evolved from a novel pneumonia to a multifaceted syndrome with multiple clinical manifestations, among which neurological sequalae appear insidious and potentially long-lasting. Exosomes are extracellular nanovesicles bearing a complex cargo of active biomolecules and playing crucial roles in intercellular communication under pathophysiological conditions. Exosomes constitute a reliable route for misfolded protein transmission, contributing to PD pathogenesis and diagnosis. Herein, we summarize recent evidence suggesting that SARS-CoV-2 infection shares numerous clinical manifestations and inflammatory and molecular pathways with PD. We carry on hypothesizing that these similarities may be reflected in exosomal cargo modulated by the virus in correlation with disease severity. Travelling from the periphery to the brain, SARS-CoV-2-related exosomal cargo contains SARS-CoV-2 RNA, viral proteins, inflammatory mediators, and modified host proteins that could operate as promoters of neurodegenerative and neuroinflammatory cascades, potentially leading to a future parkinsonism and PD development.

Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps

Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Exploring the evolution of biochemical models at the network level

The evolution of biochemical models is difficult to track. At present, it is not possible to inspect the differences between model versions at the network level. Biochemical models are often constructed in a distributed, non-linear process: collaborators create model versions on different branches from novel information, model extensions, during curation and adaption. To discuss and align the versions, it is helpful to abstract the changes to the network level. The differences between two model versions can be detected by the software tool BiVeS. However, it cannot show the structural changes resulting from the differences. Here, we present a method to visualise the differences between model versions effectively. We developed a JSON schema to communicate the differences at the network level and extended BiVeS accordingly. Additionally, we developed DiVil, a web-based tool to represent the model and the differences as a standardised network using D3. It combines an automatic layout with an interactive user interface to improve the visualisation and to inspect the model. The network can be exported in standardised formats as images or markup language. Our method communicates the structural differences between model versions. It facilitates the discussion of changes and thus supports the collaborative and non-linear nature of model development.

Availability and implementation: DiVil prototype: https://divil.bio.informatik.uni-rostock.de, Code on GitHub: https://github.com/Gebbi8/DiVil, licensed under Apache License 2.0.

Contact:url="tom.gebhardt@uni-rostock.de.

Metabolomics analysis highlights Yashtimadhu (Glycyrrhiza glabra L.)-mediated neuroprotection in a rotenone-induced cellular model of Parkinson's disease by restoring the mTORC1-AMPK1 axis in autophagic regulation

Parkinson's disease (PD) is an age-associated progressive neurodegenerative movement disorder, and its management strategies are known to cause complications with prolonged usage. We aimed to explore the neuroprotective mechanism of the Indian traditional medicine Yashtimadhu, prepared from the dried roots of Glycyrrhiza glabra L. (licorice) in the rotenone-induced cellular model of PD. Retinoic acid-differentiated IMR-32 cells were treated with rotenone (PD model) and Yashtimadhu extract. Mass spectrometry-based untargeted and targeted metabolomic profiling was carried out to discover altered metabolites. The untargeted metabolomics analysis highlighted the rotenone-induced dysregulation and Yashtimadhu-mediated restoration of metabolites involved in the metabolism of nucleic acids, amino acids, lipids, and citric acid cycle. Targeted validation of citric acid cycle metabolites showed decreased α-ketoglutarate and succinate with rotenone treatment and rescued by Yashtimadhu co-treatment. The dysregulation of the citric acid cycle by rotenone-induced energetic stress via dysregulation of the mTORC1-AMPK1 axis was prevented by Yashtimadhu. Yashtimadhu co-treatment restored rotenone-induced ATG7-dependent autophagy and eventually caspases-mediated cell death. Our analysis links the metabolic alterations modulating energy stress and autophagy, which underlies the Yashtimadhu-mediated neuroprotection in the rotenone-induced cellular model of PD.

A Hybrid Machine Learning and Network Analysis Approach Reveals Two Parkinson's Disease Subtypes from 115 RNA-Seq Post-Mortem Brain Samples

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson’s disease (PD) heterogeneity among individuals proves the existence of disease subtypes, so subgrouping patients is vital for better understanding disease mechanisms and designing precise treatment. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. Two hundred and ten post mortem brain RNA-Seq samples from PD (n = 115) and normal controls (NCs, n = 95) were obtained with systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p < 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

In situ proximity labeling identifies Lewy pathology molecular interactions in the human brain

The intracellular misfolding and accumulation of alpha-synuclein into structures collectively called Lewy pathology (LP) is a central phenomenon for the pathogenesis of synucleinopathies, including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Understanding the molecular architecture of LP is crucial for understanding synucleinopathy disease origins and progression. Here we used a technique called biotinylation by antibody recognition (BAR) to label total (BAR-SYN1) and pathological alpha-synuclein (BAR-PSER129) in situ for subsequent mass spectrometry analysis. Results showed superior immunohistochemical detection of LP following the BAR-PSER129 protocol, particularly for fibers and punctate pathology within the striatum and cortex. Mass spectrometry analysis of BAR-PSER129-labeled LP identified 261 significantly enriched proteins in the synucleinopathy brain when compared to nonsynucleinopathy brains. In contrast, BAR-SYN1 did not differentiate between disease and nonsynucleinopathy brains. Pathway analysis of BAR-PSER129-enriched proteins revealed enrichment for 718 pathways; notably, the most significant KEGG pathway was PD, and Gene Ontology (GO) cellular compartments were the vesicle, extracellular vesicle, extracellular exosome, and extracellular organelle. Pathway clustering revealed several superpathways, including metabolism, mitochondria, lysosome, and intracellular vesicle transport. Validation of the BAR-PSER129-identified protein hemoglobin beta (HBB) by immunohistochemistry confirmed the interaction of HBB with PSER129 Lewy neurites and Lewy bodies. In summary, BAR can be used to enrich for LP from formalin-fixed human primary tissues, which allowed the determination of molecular signatures of LP. This technique has broad potential to help understand the phenomenon of LP in primary human tissue and animal models.

Neurodegeneration and neuroinflammation are linked, but independent of alpha‐synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease

A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α‐synuclein. Alpha‐synuclein (α‐syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α‐syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α‐syn induced by striatal injection of α‐syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α‐syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α‐syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α‐syn inclusion formation is not the major driver in the early phases of PD‐like neurodegeneration, but that microglia, activated by diffusible, oligomeric α‐syn, may play a key role in this process. Our findings uncover new features of α‐syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α‐syn spreading.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

CyFi-MAP: an interactive pathway-based resource for cystic fibrosis

Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.

Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.

Effect of Melatonin Administration on Mitochondrial Activity and Oxidative Stress Markers in Patients with Parkinson's Disease

Mitochondrial dysfunction and oxidative stress are extensively linked to Parkinson's disease (PD) pathogenesis. Melatonin is a pleiotropic molecule with antioxidant and neuroprotective effects. The aim of this study was to evaluate the effect of melatonin on oxidative stress markers, mitochondrial complex 1 activity, and mitochondrial respiratory control ratio in patients with PD. A double-blind, cross-over, placebo-controlled randomized clinical trial study was conducted in 26 patients who received either 25 mg of melatonin or placebo at noon and 30 min before bedtime for three months. At the end of the trial, in patients who received melatonin, we detected a significant diminution of lipoperoxides, nitric oxide metabolites, and carbonyl groups in plasma samples from PD patients compared with the placebo group. Conversely, catalase activity was increased significantly in comparison with the placebo group. Compared with the placebo group, the melatonin group showed significant increases of mitochondrial complex 1 activity and respiratory control ratio. The fluidity of the membranes was similar in the melatonin group and the placebo group at baseline and after three months of treatment. In conclusion, melatonin administration was effective in reducing the levels of oxidative stress markers and restoring the rate of complex I activity and respiratory control ratio without modifying membrane fluidity. This suggests that melatonin could play a role in the treatment of PD.

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

New therapeutic approaches to Parkinson's disease targeting GBA, LRRK2 and Parkin

Parkinson's disease (PD) is defined as a complex disorder with multifactorial pathogenesis, yet a more accurate definition could be that PD is not a single entity, but rather a mixture of different diseases with similar phenotypes. Attempts to classify subtypes of PD have been made based on clinical phenotypes or biomarkers. However, the most practical approach, at least for a portion of the patients, could be to classify patients based on genes involved in PD. GBA and LRRK2 mutations are the most common genetic causes or risk factors of PD, and PRKN is the most common cause of autosomal recessive form of PD. Patients carrying variants in GBA, LRRK2 or PRKN differ in some of their clinical characteristics, pathology and biochemical parameters. Thus, these three PD-associated genes are of special interest for drug development. Existing therapeutic approaches in PD are strictly symptomatic, as numerous clinical trials aimed at modifying PD progression or providing neuroprotection have failed over the last few decades. The lack of precision medicine approach in most of these trials could be one of the reasons why they were not successful. In the current review we discuss novel therapeutic approaches targeting GBA, LRRK2 and PRKN and discuss different aspects related to these genes and clinical trials.

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation

Background

Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task.

Results

We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance.

Conclusions

Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00279-2.

Chemical analysis of the human brain by imaging mass spectrometry

Analysis of the chemical makeup of the brain enables a deeper understanding of several neurological processes. Molecular imaging that deciphers the spatial distribution of neurochemicals with high specificity and sensitivity is an exciting avenue in this aspect. The past two decades have witnessed a significant surge of mass spectrometry imaging (MSI) that can simultaneously map the distribution of hundreds to thousands of biomolecules in the tissue specimen at a fairly high resolution, which is otherwise beyond the scope of other molecular imaging techniques. In this review, we have documented the evolution of MSI technologies in imaging the anatomical distribution of neurochemicals in the human brain in the context of several neuro diseases. This review also addresses the potential of MSI to be a next-generation molecular imaging technique with its promising applications in neuropathology.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Placental mitochondrial function as a driver of angiogenesis and placental dysfunction

The placenta is a highly vascularized and complex foetal organ that performs various tasks, crucial to a healthy pregnancy. Its dysfunction leads to complications such as stillbirth, preeclampsia, and intrauterine growth restriction. The specific cause of placental dysfunction remains unknown. Recently, the role of mitochondrial function and mitochondrial adaptations in the context of angiogenesis and placental dysfunction is getting more attention. The required energy for placental remodelling, nutrient transport, hormone synthesis, and the reactive oxygen species leads to oxidative stress, stemming from mitochondria. Mitochondria adapt to environmental changes and have been shown to adjust their oxygen and nutrient use to best support placental angiogenesis and foetal development. Angiogenesis is the process by which blood vessels form and is essential for the delivery of nutrients to the body. This process is regulated by different factors, pro-angiogenic factors and anti-angiogenic factors, such as sFlt-1. Increased circulating sFlt-1 levels have been linked to different preeclamptic phenotypes. One of many effects of increased sFlt-1 levels, is the dysregulation of mitochondrial function. This review covers mitochondrial adaptations during placentation, the importance of the anti-angiogenic factor sFlt-1in placental dysfunction and its role in the dysregulation of mitochondrial function.

The Olfactory System as Marker of Neurodegeneration in Aging, Neurological and Neuropsychiatric Disorders

Research studies that focus on understanding the onset of neurodegenerative pathology and therapeutic interventions to inhibit its causative factors, have shown a crucial role of olfactory bulb neurons as they transmit and propagate nerve impulses to higher cortical and limbic structures. In rodent models, removal of the olfactory bulb results in pathology of the frontal cortex that shows striking similarity with frontal cortex features of patients diagnosed with neurodegenerative disorders. Widely different approaches involving behavioral symptom analysis, histopathological and molecular alterations, genetic and environmental influences, along with age-related alterations in cellular pathways, indicate a strong correlation of olfactory dysfunction and neurodegeneration. Indeed, declining olfactory acuity and olfactory deficits emerge either as the very first symptoms or as prodromal symptoms of progressing neurodegeneration of classical conditions. Olfactory dysfunction has been associated with most neurodegenerative, neuropsychiatric, and communication disorders. Evidence revealing the dual molecular function of the olfactory receptor neurons at dendritic and axonal ends indicates the significance of olfactory processing pathways that come under environmental pressure right from the onset. Here, we review findings that olfactory bulb neuronal processing serves as a marker of neuropsychiatric and neurodegenerative disorders.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Machine Learning to Support the Presentation of Complex Pathway Graphs

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.

MicroRNA Dysregulation in Parkinson's Disease: A Narrative Review

Parkinson’s disease (PD) is a severely debilitating neurodegenerative disease, affecting the motor system, leading to resting tremor, cogwheel rigidity, bradykinesia, walking and gait difficulties, and postural instability. The severe loss of dopaminergic neurons in the substantia nigra pars compacta causes striatal dopamine deficiency and the presence of Lewy bodies indicates a pathological hallmark of PD. Although the current treatment of PD aims to preserve dopaminergic neurons or to replace dopamine depletion in the brain, it is notable that complete recovery from the disease is yet to be achieved. Given the complexity and multisystem effects of PD, the underlying mechanisms of PD pathogenesis are yet to be elucidated. The advancement of medical technologies has given some insights in understanding the mechanism and potential treatment of PD with a special interest in the role of microRNAs (miRNAs) to unravel the pathophysiology of PD. In PD patients, it was found that striatal brain tissue and dopaminergic neurons from the substantia nigra demonstrated dysregulated miRNAs expression profiles. Hence, dysregulation of miRNAs may contribute to the pathogenesis of PD through modulation of PD-associated gene and protein expression. This review will discuss recent findings on PD-associated miRNAs dysregulation, from the regulation of PD-associated genes, dopaminergic neuron survival, α-synuclein-induced inflammation and circulating miRNAs. The next section of this review also provides an update on the potential uses of miRNAs as diagnostic biomarkers and therapeutic tools for PD.

Gene4PD: A Comprehensive Genetic Database of Parkinson's Disease

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component. A growing number of variants and genes have been reported to be associated with PD; however, there is no database that integrate different type of genetic data, and support analyzing of PD-associated genes (PAGs). By systematic review and curation of multiple lines of public studies, we integrate multiple layers of genetic data (rare variants and copy-number variants identified from patients with PD, associated variants identified from genome-wide association studies, differentially expressed genes, and differential DNA methylation genes) and age at onset in PD. We integrated five layers of genetic data (8302 terms) with different levels of evidences from more than 3,000 studies and prioritized 124 PAGs with strong or suggestive evidences. These PAGs were identified to be significantly interacted with each other and formed an interconnected functional network enriched in several functional pathways involved in PD, suggesting these genes may contribute to the pathogenesis of PD. Furthermore, we identified 10 genes were associated with a juvenile-onset (age ≤ 30 years), 11 genes were associated with an early-onset (age of 30–50 years), whereas another 10 genes were associated with a late-onset (age > 50 years). Notably, the AAOs of patients with loss of function variants in five genes were significantly lower than that of patients with deleterious missense variants, while patients with VPS13C (P = 0.01) was opposite. Finally, we developed an online database named Gene4PD (http://genemed.tech/gene4pd) which integrated published genetic data in PD, the PAGs, and 63 popular genomic data sources, as well as an online pipeline for prioritize risk variants in PD. In conclusion, Gene4PD provides researchers and clinicians comprehensive genetic knowledge and analytic platform for PD, and would also improve the understanding of pathogenesis in PD.

Genotype-driven therapeutic developments in Parkinson's disease

BACKGROUND

Remarkable advances have been reached in the understanding of the genetic basis of Parkinson's disease (PD), with the identification of monogenic causes (mPD) and a plethora of gene loci leading to an increased risk for idiopathic PD. The expanding knowledge and subsequent identification of genetic contributions fosters the understanding of molecular mechanisms leading to disease development and progression. Distinct pathways involved in mitochondrial dysfunction, oxidative stress, and lysosomal function have been identified and open a unique window of opportunity for individualized treatment approaches. These genetic findings have led to an imminent progress towards pathophysiology-targeted clinical trials and potentially disease-modifying treatments in the future.

MAIN BODY OF THE MANUSCRIPT

In this review article we will summarize known genetic contributors to the pathophysiology of Parkinson's disease, the molecular mechanisms leading to disease development, and discuss challenges and opportunities in clinical trial designs.

CONCLUSIONS

The future success of clinical trials in PD is mainly dependent on reliable biomarker development and extensive genetic testing to identify genetic cases. Whether genotype-dependent stratification of study participants will extend the potential application of new drugs will be one major challenge in conceptualizing clinical trials. However, the latest developments in genotype-driven treatments will pave the road to individualized pathophysiology-based therapies in the future.

Automated inference of Boolean models from molecular interaction maps using CaSQ

Motivation

Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner.

Results

We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability.

Availability and implementation

The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq.

Supplementary information

Supplementary data are available at Bioinformatics online.

A New Synuclein-Transgenic Mouse Model for Early Parkinson's Reveals Molecular Features of Preclinical Disease

Understanding Parkinson's disease (PD), in particular in its earliest phases, is important for diagnosis and treatment. However, human brain samples are collected post-mortem, reflecting mainly end-stage disease. Because brain samples of mouse models can be collected at any stage of the disease process, they are useful in investigating PD progression. Here, we compare ventral midbrain transcriptomics profiles from α-synuclein transgenic mice with a progressive, early PD-like striatal neurodegeneration across different ages using pathway, gene set, and network analysis methods. Our study uncovers statistically significant altered genes across ages and between genotypes with known, suspected, or unknown function in PD pathogenesis and key pathways associated with disease progression. Among those are genotype-dependent alterations associated with synaptic plasticity and neurotransmission, as well as mitochondria-related genes and dysregulation of lipid metabolism. Age-dependent changes were among others observed in neuronal and synaptic activity, calcium homeostasis, and membrane receptor signaling pathways, many of which linked to G-protein coupled receptors. Most importantly, most changes occurred before neurodegeneration was detected in this model, which points to a sequence of gene expression events that may be relevant for disease initiation and progression. It is tempting to speculate that molecular changes similar to those changes observed in our model happen in midbrain dopaminergic neurons before they start to degenerate. In other words, we believe we have uncovered molecular changes that accompany the progression from preclinical to early PD.

LimeMap: a comprehensive map of lipid mediator metabolic pathways

Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.

Large-scale regulatory and signaling network assembly through linked open data

Huge efforts are currently underway to address the organization of biological knowledge through linked open databases. These databases can be automatically queried to reconstruct regulatory and signaling networks. However, assembling networks implies manual operations due to source-specific identification of biological entities and relationships, multiple life-science databases with redundant information and the difficulty of recovering logical flows in biological pathways. We propose a framework based on Semantic Web technologies to automate the reconstruction of large-scale regulatory and signaling networks in the context of tumor cells modeling and drug screening. The proposed tool is pyBRAvo (python Biological netwoRk Assembly), and here we have applied it to a dataset of 910 gene expression measurements issued from liver cancer patients. The tool is publicly available at https://github.com/pyBRAvo/pyBRAvo.