Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson's disease

A new perspective on the autophagic and non-autophagic functions of the GABARAP protein family: a potential therapeutic target for human diseases

Mammalian autophagy-related protein Atg8, including the LC3 subfamily and GABARAP subfamily. Atg8 proteins play a vital role in autophagy initiation, autophagosome formation and transport, and autophagy-lysosome fusion. GABARAP subfamily proteins (GABARAPs) share a high degree of homology with LC3 family proteins, and their unique roles are often overlooked. GABARAPs are as indispensable as LC3 in autophagy. Deletion of GABARAPs fails autophagy flux induction and autophagy lysosomal fusion, which leads to the failure of autophagy. GABARAPs are also involved in the transport of selective autophagy receptors. They are engaged in various particular autophagy processes, including mitochondrial autophagy, endoplasmic reticulum autophagy, Golgi autophagy, centrosome autophagy, and dorphagy. Furthermore, GABARAPs are closely related to the transport and delivery of the inhibitory neurotransmitter γ-GABA_A and the angiotensin II AT1 receptor (AT1R), tumor growth, metastasis, and prognosis. GABARAPs also have been confirmed to be involved in various diseases, such as cancer, cardiovascular disease, and neurodegenerative diseases. In order to better understand the role and therapeutic potential of GABARAPs, this article comprehensively reviews the autophagic and non-autophagic functions of GABARAPs, as well as the research progress of the role and mechanism of GABARAPs in cancer, cardiovascular diseases and neurodegenerative diseases. It emphasizes the significance of GABARAPs in the clinical prevention and treatment of diseases, and may provide new therapeutic ideas and targets for human diseases. GABARAP and GABARAPL1 in the serum of cancer patients are positively correlated with the prognosis of patients, which can be used as a clinical biomarker, predictor and potential therapeutic target. GABARAP family proteins: autophagy and non-autophagy related functions in diseases. By Figdraw ( https://www.figdraw.com ).

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Drug repurposing: Iron in the fire for older drugs

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

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

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

The neuroprotective effect of Rho-kinase Inhibition in 1-methyl-4-phenylpyridinium (MPP+)-induced cellular model of neurodegeneration

Introduction: The 1-methyl 4-phenyl 1,2,3,6-tetrahydropiridium (MPTP) induced model of neurodegeneration in Parkinson's disease (PD) is one of the most commonly used experimental models. This neurotoxic agent , or rather its metabolite MPP+, leads to inhibition of mitochondrial complex I, an increase in free radicals' production and ATP depletion, all resulting in cellular demise and death. Rho-kinase is an enzyme involved with numerous cellregulatory mechanisms, such as cytoskeleton organization, axonogenesis, vesicular transport regulation and apoptosis regulation, which are all important for cell survival. Aim: Our aim was to investigate the effects of Rho-kinase inhibition on the MPP+ induced model of neurodegeneration and the role of Akt and adenosine monophosphate-activated protein kinase (AMPK) signaling pathways in this process. Material and methods: The experiments were performed on the human neuroblastoma SHSY5Y cell line. The MTT test was used to measure the viability of the cells after the MPP+ and/ or Rho-kinase inhibitor, fasudil, treatments. Changes in activation levels, or expression of pAMPK, pAkt, AMPK and Akt, were measured using the immunoblotting method, and the protein levels were quantified by densitometry. Results: The MPP+ caused a dose-dependent decrease in cellular viability, compared to the control group (untreated cells), while fasudil treatment, prior to MPP+ exposure, improved cell viability in a dose dependant manner, compared to MPP+ treatment. Analysis of activation status of target proteins showed an increase in Akt activation after the fasudil treatment, while the AMPK activation was not significantly changed. Conclusion: Inhibition of Rho-kinase using fasudil causes a decrease in MPP+ induced cell death, which is possibly mediated by an activation of the Akt/PI3K signaling pathway.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

A review of network-based approaches to drug repositioning

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

The Effect of Fucoidan on Cellular Oxidative Stress and the CatD-Bax Signaling Axis in MN9D Cells Damaged by 1-Methyl-4-Phenypyridinium

Background: The purpose of this study was to investigate the impact of fucoidan (FUC) on the oxidative stress response and lysosomal apoptotic pathways in the Parkinson disease (PD) cell model.

Methods: The Dopaminergic nerve precursor cell line(MN9D) cells that served as a PD model in this study underwent damage induced by 100 μM 1-methyl-4-phenyl pyridine (MPP⁺). Cell viability was assessed after FUC treatment and intracellular SOD GSH was measured via immunofluorescence assay. Cellular changes in cathepsin D, Autophagy marker Light Chain 3-II (LC3-II), and apoptotic protein Bax were assessed by Western blot. The expression of Cat D, LC3-II, and B cell lymphoma-2-associated x protein (Bax) was also measured after addition of the cathepsin inhibitor, pepstatin A.

Results: The results indicated that MN9D cell viability decreased by 50% within 24 h after 100 μM MPP⁺ induced toxicity. Pretreatment with 100 μM Fucoidan reduced cellular expression of LC3-II and CatD in 3 h and suppressed the induction of Bax protein. After pepstatin A treatment, Bax expression was significantly downregulated.FUC reversed the reduction of superoxide dismutase (SOD) L-Glutathione(GSH), decreased cell viability, and apoptosis induced by MPP⁺ in 6 h, suggesting that Fucoidan can attenuate damage to MN9D cells induced by MPP⁺.

Conclusions: Fucoidan protected lysosomes, reduced the expression of LC3-II, inhibited the expression of CatD-Bax and the oxidative stress response, suppressed apoptosis, and thus conferred protective effects for dopaminergic neural cells. FUC may have neuroprotective effects on PD and further research is needed.

A nonparametric significance test for sampled networks

Motivation

Our work is motivated by an interest in constructing a protein–protein interaction network that captures key features associated with Parkinson’s disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation.

Results

We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein–protein interaction network.

Availability and implementation

The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library.

Supplementary information

Supplementary data are available at Bioinformatics online.

Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.

Biological and Clinical Implications of Comorbidities in Parkinson's Disease

A wide spectrum of comorbidities has been associated with Parkinson's disease (PD), a progressive neurodegenerative disease that affects more than seven million people worldwide. Emerging evidence indicates that chronic diseases including diabetes, depression, anemia and cancer may be implicated in the pathogenesis and progression of PD. Recent epidemiological studies suggest that some of these comorbidities may increase the risk of PD and precede the onset of motor symptoms. Further, drugs to treat diabetes and cancer have elicited neuroprotective effects in PD models. Nonetheless, the mechanisms underlying the occurrence of these comorbidities remain elusive. Herein, we discuss the biological and clinical implications of comorbidities in the pathogenesis, progression, and clinical management, with an emphasis on personalized medicine applications for PD.

Protein-protein interaction networks and different clustering analysis in Burkitt's lymphoma

OBJECTIVE

Burkitt's lymphoma (BL) is a highly aggressive malignant lymphoma, its molecular biological mechanism has not been fully investigated. The construction of protein-protein interaction (PPI) networks and the identification of complexes through a cluster analysis are important research directions in the post-genome era. However, different cluster analysis algorithms have their own characteristics, and a single analysis has some limitations. In this study, we obtained the target and pathway information of BL using different clustering analyses.

MATERIAL AND METHODS

First, we obtained 50 BL genes by screening the Online Mendelian Inheritance in Man (OMIM) database; their related genes were further extracted from the literature. The PPI network was constructed with the Search Tool for Retrieval of Interacting Genes/Proteins (STRING). Afterward, the interaction data were input in Cytoscape3.4.0 software and related plug-ins were used to implement topology analysis and clustering analysis. Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to characterize the biological importance of the clusters.

RESULTS

We constructed a PPI network consisting of 459 nodes (proteins) and 1399 sides (interactions), 12 genes and 8 signaling pathways were found to be closely related to BL.

CONCLUSION

In this study, the use of combined algorithms to analyse gene interactions provides a new perspective for network-based analysis. The results of this study reveal new insights into the molecular mechanisms underlying BL, which may be novel therapeutic targets for disease management and may provide a bioinformatic basis for the further understanding of BL.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

Overexpression of p58ipk protects neuroblastoma against paraquat-induced toxicity

BACKGROUND

Paraquat (PQ) is a powerful pathologic pesticide that contribute to the neurotoxicity, however, the pathogenic mechanism between them was unclear. The aims of this study were to explore the underlying mechanism of PQ-induced toxicity and then make potential contribute to such neuronal diseases therapy.

METHODS

Human cell line SH-SY5Y was pretreated with a set concentrations of PQ to detect the cell apoptosis and the expression of related genes and proteins. Next, pcDNA 3.1-p58ipk or si-p58ipk was transfected the PQ-induced cells to detect the cytotoxicity.

RESULTS

PQ significantly increased the cell apoptosis as well as the expression of p58ipk and CHOP, but decreased the expression of pAKT. p58ipk suppression resulted in an increase of cell apoptosis and CHOP expression, but the expression of pAKT was significantly decreased in PQ-induced SH-SY5Y cells. However, overexpressed p58ipk led to an opposite result.

CONCLUSION

The results indicated that the expression of p58ipk was related to the toxicity level of PQ-induced cells and the mechanism between them was that p58ipk regulated the toxicity might through regulating the endoplasmic reticulum stress (ER-stress) and then regulating cell apoptosis. Further studies take emphasize on the effect of ER-stress on neuron system and explore ER-stress-related therapy are important on the treatment of neurodegenerative disease.

Astragaloside IV rescues MPP+-induced mitochondrial dysfunction through upregulation of methionine sulfoxide reductase A

Methionine sulfoxide reductase (Msr) repairs oxidatively damaged proteins through acting as an antioxidant. Oxidative stress has been postulated to cause the mitochondrial dysfunction that is associated with aging and certain diseases, including Parkinson's disease (PD). The present study investigated the protective effects of astragaloside IV (AS-IV) on 1-methyl-4-phenylpyridinium (MPP⁺)-induced mitochondrial dysfunction through MsrA in PC12 cells. This revealed that oxidative stress reduced the expression of MsrA following MPP⁺ treatment. AS-IV was demonstrated to protect PC12 cells from MPP⁺-induced oxidative damage through upregulating MsrA. MsrA expression was dependent on the Sirt1-FOXO3a signaling pathway. In addition, knockdown of MsrA reduced the protective effects of AS-IV, indicating that the antioxidant effects of AS-UV occurred through MsrA. These results suggest that AS-IV exerts antioxidant effects and regulates mitochondrial function. Thus, AS-IV may serve as an effective therapeutic agent for aging and PD.

Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology

Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD), Parkinson’s (PD) and Huntington’s diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.

Increased copper toxicity in Saccharomyces cerevisiae lacking VPS35, a component of the retromer and monogenic Parkinson disease gene in humans

The Saccharomyces cerevisiae gene VPS35 encodes a component of the retromer complex which is involved in vesicle transport from endosomes to the trans-Golgi network. Yeast and human VPS35 orthologs are highly conserved and mutations in human VPS35 cause an autosomal dominant form of late-onset Parkinson disease (PD). We now show that deletion of VPS35 in yeast (vps35Δ) leads to a dose-dependent growth defect towards copper. This increased sensitivity could be rescued by transformation with yeast wild-type VPS35 but not by the expression of a construct harboring the yeast equivalent (i.e. D686N) of the most commonly identified VPS35-associated PD mutation, p.D620N. In addition, we show that expression of one copy of α-synuclein, which is known to directly interact with copper, leads to a pronounced aggravation of copper toxicity in vps35Δ cells, thereby linking the regulation of copper homeostasis by Vps35p in yeast to one of the key molecules in PD pathophysiology.