An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases

A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease

The role of the extracellular matrix (ECM) in Parkinson's disease (PD) is not well understood, even though it is critical for neuronal structure and signaling. This systematic review identified the top deregulated ECM-related pathways in studies that used gene set enrichment analyses (GSEA) to document transcriptomic, proteomic, or genomic alterations in PD. PubMed and Google scholar were searched for transcriptomics, proteomics, or genomics studies that employed GSEA on data from PD tissues or cells and reported ECM-related pathways among the top-10 most enriched versus controls. Twenty-seven studies were included, two of which used multiple omics analyses. Transcriptomics and proteomics studies were conducted on a variety of tissue and cell types. Of the 17 transcriptomics studies (16 data sets), 13 identified one or more adhesion pathways in the top-10 deregulated gene sets or pathways, primarily related to cell adhesion and focal adhesion. Among the 8 proteomics studies, 5 identified altered overarching ECM gene sets or pathways among the top 10. Among the 4 genomics studies, 3 identified focal adhesion pathways among the top 10. The findings summarized here suggest that ECM organization/structure and cell adhesion (particularly focal adhesion) are altered in PD and should be the focus of future studies.

Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues

Advances in RNA-sequencing technologies have led to the identification of molecular biomarkers for several diseases, including neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's diseases and Amyotrophic Lateral Sclerosis. Despite the nature of glaucoma as a neurodegenerative disorder with several similarities with the other above-mentioned diseases, transcriptional data about this disease are still scarce. microRNAs are small molecules (~17-25 nucleotides) that have been found to be specifically expressed in the CNS as major components of the system regulating the development signatures of neurodegenerative diseases and the homeostasis of the brain. In this review, we sought to identify similarities between the functional mechanisms and the activated pathways of the most common neurodegenerative diseases, as well as to discuss how those mechanisms are regulated by miRNAs, using RNA-Seq as an approach to compare them. We also discuss therapeutically suitable applications for these disease hallmarks in clinical future studies.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.

Thymoma Associated Myasthenia Gravis (TAMG): Differential Expression of Functional Pathways in Relation to MG Status in Different Thymoma Histotypes

A unique feature of thymomas is their unrivaled frequency of associated myasthenia gravis (MG). Previous studies reported that MG+ thymomas contain a larger number of mature “pre-emigrant” CD4+ T cells than MG- thymomas and that most thymomas do not contain AIRE expressing cells irrespective of MG status. These findings suggest that CD4+ T cells that mature inside the abnormal microenvironment of thymomas and egress to the blood are critical to the development of thymoma-associated MG (TAMG) irrespective of thymoma histotype. However, underlying mechanisms have remained enigmatic. To get hints to mechanisms underlying TAMG, we pursue three hypotheses: (i) Functional pathways with metabolic and immunological relevance might be differentially expressed in TAMG(+) compared to TAMG(-) thymomas; (ii) differentially enriched pathways might be more evident in immature lymphocyte-poor (i.e., tumor cell/stroma-rich) thymoma subgroups; and (iii) mechanisms leading to TAMG might be different among thymoma histological subtypes. To test these hypotheses, we compared the expression of functional pathways with potential immunological relevance (N = 380) in relation to MG status separately in type AB and B2 thymomas and immature lymphocyte-rich and lymphocyte-poor subgroups of these thymoma types using the TCGA data set. We found that <10% of the investigated pathways were differentially upregulated or downregulated in MG+ compared to MG- thymomas with significant differences between AB and B2 thymomas. The differences were particularly evident, when epithelial cell/stroma-rich subsets of type AB and B2 thymomas were analyzed. Unexpectedly, some MG-associated pathways that were significantly upregulated in AB thymomas were significantly downregulated in B2 thymomas, as exemplified by the oxidative phosphorylation pathway. Conversely, the MG-associated pathway related to macrophage polarization was downregulated in MG+ AB thymoma and upregulated in MG+ B2 thymoma. We conclude that functional pathways are significantly associated with TAMG, and that some mechanisms leading to TAMG might be different among thymoma histological subtypes. Functions related to metabolisms, vascular and macrophage biology are promising new candidate mechanisms potentially involved in the pathogenesis of TAMG. More generally, the results imply that future studies addressing pathomechanisms of TAMG should take the histotype and abundance of tumor cells and non-neoplastic stromal components of thymomas into account.

Protein Deimination Signatures in Plasma and Plasma-EVs and Protein Deimination in the Brain Vasculature in a Rat Model of Pre-Motor Parkinson's Disease

The identification of biomarkers for early diagnosis of Parkinson’s disease (PD) is of pivotal importance for improving approaches for clinical intervention. The use of translatable animal models of pre-motor PD therefore offers optimal opportunities for novel biomarker discovery in vivo. Peptidylarginine deiminases (PADs) are a family of calcium-activated enzymes that contribute to protein misfolding through post-translational deimination of arginine to citrulline. Furthermore, PADs are an active regulator of extracellular vesicle (EV) release. Both protein deimination and extracellular vesicles (EVs) are gaining increased attention in relation to neurodegenerative diseases, including in PD, while roles in pre-motor PD have yet to be investigated. The current study aimed at identifying protein candidates of deimination in plasma and plasma-EVs in a rat model of pre-motor PD, to assess putative contributions of such post-translational changes in the early stages of disease. EV-cargo was further assessed for deiminated proteins as well as three key micro-RNAs known to contribute to inflammation and hypoxia (miR21, miR155, and miR210) and also associated with PD. Overall, there was a significant increase in circulating plasma EVs in the PD model compared with sham animals and inflammatory and hypoxia related microRNAs were significantly increased in plasma-EVs of the pre-motor PD model. A significantly higher number of protein candidates were deiminated in the pre-motor PD model plasma and plasma-EVs, compared with those in the sham animals. KEGG (Kyoto encyclopedia of genes and genomes) pathways identified for deiminated proteins in the pre-motor PD model were linked to “Alzheimer’s disease”, “PD”, “Huntington’s disease”, “prion diseases”, as well as for “oxidative phosphorylation”, “thermogenesis”, “metabolic pathways”, “Staphylococcus aureus infection”, gap junction, “platelet activation”, “apelin signalling”, “retrograde endocannabinoid signalling”, “systemic lupus erythematosus”, and “non-alcoholic fatty liver disease”. Furthermore, PD brains showed significantly increased staining for total deiminated proteins in the brain vasculature in cortex and hippocampus, as well as increased immunodetection of deiminated histone H3 in dentate gyrus and cortex. Our findings identify EVs and post-translational protein deimination as novel biomarkers in early pre-motor stages of PD.

Escins Isolated from Aesculus chinensis Bge. Promote the Autophagic Degradation of Mutant Huntingtin and Inhibit its Induced Apoptosis in HT22 cells

The pathogenesis of Huntington’s disease (HD), an inherited progressive neurodegenerative disease, is highly associated with the cytotoxicity-inducing mutant huntingtin (mHtt) protein. Emerging evidence indicates that autophagy plays a pivotal role in degrading aggregated proteins such as mHtt to enhance neuronal viability. In this study, by employing preparative high-performance liquid chromatography (pre-HPLC), ultra-high performance liquid chromatography diode-array-detector quadrupole time-of-flight mass spectrometry (UHPLC-DAD-Q-TOF-MS) and nuclear magnetic resonance (NMR), three escins, escin IA (EA), escin IB (EB) and isoescin IA (IEA), were isolated and identified from the seed of Aesculus chinensis Bge. (ACB). After EGFP-HTT74-overexpressing HT22 cells were treated with EA, EB and IEA at safe concentrations, the clearance of mHtt and mHtt-induced apoptosis were investigated by Western blot, immunofluorescence microscopy and flow cytometry methods. In addition, the autophagy induced by these escins in HT22 cells was monitored by detecting GFP-LC3 puncta, P62 and LC3 protein expression. The results showed that EA, EB and IEA could significantly decrease mHtt levels and inhibit its induced apoptosis in HT22 cells. In addition, these three saponins induced autophagic flux by increasing the ratio of RFP-LC3 to GFP-LC3, and by decreasing P62 expression. Among the tested escins, EB displayed the best autophagy induction, which was regulated via both the mTOR and ERK signaling pathways. Furthermore, the degradation of mHtt and the commensurate decrease in its cytotoxic effects by EA, EB and IEA were demonstrated to be closely associated with autophagy induction, which depended on ATG7. In conclusion, we are the first to report that the escins, including EA, EB and IEA are novel autophagy inducers that degrade mHtt and inhibit mHtt-induced apoptosis in vitro and in vivo. As a result of these findings, the triterpenoid saponins in ACB might be considered to be promising candidates for the treatment of HD in the future.

The "rights" of precision drug development for Alzheimer's disease

There is a high rate of failure in Alzheimer's disease (AD) drug development with 99% of trials showing no drug-placebo difference. This low rate of success delays new treatments for patients and discourages investment in AD drug development. Studies across drug development programs in multiple disorders have identified important strategies for decreasing the risk and increasing the likelihood of success in drug development programs. These experiences provide guidance for the optimization of AD drug development. The "rights" of AD drug development include the right target, right drug, right biomarker, right participant, and right trial. The right target identifies the appropriate biologic process for an AD therapeutic intervention. The right drug must have well-understood pharmacokinetic and pharmacodynamic features, ability to penetrate the blood-brain barrier, efficacy demonstrated in animals, maximum tolerated dose established in phase I, and acceptable toxicity. The right biomarkers include participant selection biomarkers, target engagement biomarkers, biomarkers supportive of disease modification, and biomarkers for side effect monitoring. The right participant hinges on the identification of the phase of AD (preclinical, prodromal, dementia). Severity of disease and drug mechanism both have a role in defining the right participant. The right trial is a well-conducted trial with appropriate clinical and biomarker outcomes collected over an appropriate period of time, powered to detect a clinically meaningful drug-placebo difference, and anticipating variability introduced by globalization. We lack understanding of some critical aspects of disease biology and drug action that may affect the success of development programs even when the "rights" are adhered to. Attention to disciplined drug development will increase the likelihood of success, decrease the risks associated with AD drug development, enhance the ability to attract investment, and make it more likely that new therapies will become available to those with or vulnerable to the emergence of AD.

Shared mechanisms among neurodegenerative diseases: from genetic factors to gene networks

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis are pressing health concerns in modern societies for which effective therapies are still lacking. Recent high-throughput genomic technologies have enabled genome-scale, multidimensional investigations to facilitate a better understanding of the underlying mechanisms and the identification of novel targets. Here we review the molecular insights gained through such studies, and compare the similarities and differences between neurodegenerative diseases revealed by systems genomics and gene network modelling approaches. We focus specifically on the shared mechanisms at multiple molecular scales ranging from genetic factors to gene expression to network-level features of gene regulation, and whenever possible also point out mechanisms that distinguish one disease from another. Our review sets the stage for similar genomewide inspection in the future on shared/distinct features of neurodegenerative diseases at the levels of cellular, proteomic or epigenomic signatures, and how these features may interact to determine the progression and treatment response of different diseases afflicting the same individual.

Whole blood transcriptome analysis in amyotrophic lateral sclerosis: A biomarker study

The biological pathways involved in amyotrophic lateral sclerosis (ALS) remain elusive and diagnostic decision-making can be challenging. Gene expression studies are valuable in overcoming such challenges since they can shed light on differentially regulated pathways and may ultimately identify valuable biomarkers. This two-stage transcriptome-wide study, including 397 ALS patients and 645 control subjects, identified 2,943 differentially expressed transcripts predominantly involved in RNA binding and intracellular transport. When batch effects between the two stages were overcome, three different models (support vector machines, nearest shrunken centroids, and LASSO) discriminated ALS patients from control subjects in the validation stage with high accuracy. The models’ accuracy reduced considerably when discriminating ALS from diseases that mimic ALS clinically (N = 75), nor could it predict survival. We here show that whole blood transcriptome profiles are able to reveal biological processes involved in ALS. Also, this study shows that using these profiles to differentiate between ALS and mimic syndromes will be challenging, even when taking batch effects in transcriptome data into account.

Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.

Preclinical Evaluation of a Lentiviral Vector for Huntingtin Silencing

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder resulting from a polyglutamine expansion in the huntingtin (HTT) protein. There is currently no cure for this disease, but recent studies suggest that RNAi to downregulate the expression of both normal and mutant HTT is a promising therapeutic approach. We previously developed a small hairpin RNA (shRNA), vectorized in an HIV-1-derived lentiviral vector (LV), that reduced pathology in an HD rodent model. Here, we modified this vector for preclinical development by using a tat-independent third-generation LV (pCCL) backbone and removing the original reporter genes. We demonstrate that this novel vector efficiently downregulated HTT expression in vitro in striatal neurons derived from induced pluripotent stem cells (iPSCs) of HD patients. It reduced two major pathological HD hallmarks while triggering a minimal inflammatory response, up to 6 weeks after injection, when administered by stereotaxic surgery in the striatum of an in vivo rodent HD model. Further assessment of this shRNA vector in vitro showed proper processing by the endogenous silencing machinery, and we analyzed gene expression changes to identify potential off-targets. These preclinical data suggest that this new shRNA vector fulfills primary biosafety and efficiency requirements for further development in the clinic as a cure for HD.

Fusing literature and full network data improves disease similarity computation

Background

Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.

Results

Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.

Conclusions

Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.