Network Analysis Identifies Disease-Specific Pathways for Parkinson's Disease

Shared and Unique Disease Pathways in Amyotrophic Lateral Sclerosis and Parkinson's Disease Unveiled in Peripheral Blood Mononuclear Cells

Recent evidence supports an association between amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD). Indeed, prospective population-based studies demonstrated that about one-third of ALS patients develop parkinsonian (PK) signs, even though different neuronal circuitries are involved. In this context, proteomics represents a valuable tool to identify unique and shared pathological pathways. Here, we used two-dimensional electrophoresis to obtain the proteomic profile of peripheral blood mononuclear cells (PBMCs) from PD and ALS patients including a small cohort of ALS patients with parkinsonian signs (ALS-PK). After the removal of protein spots correlating with confounding factors, we applied a sparse partial least square discriminant analysis followed by recursive feature elimination to obtain two protein classifiers able to discriminate (i) PD and ALS patients (30 spots) and (ii) ALS-PK patients among all ALS subjects (20 spots). Functionally, the glycolysis pathway was significantly overrepresented in the first signature, while extracellular interactions and intracellular signaling were enriched in the second signature. These results represent molecular evidence at the periphery for the classification of ALS-PK as ALS patients that manifest parkinsonian signs, rather than comorbid patients suffering from both ALS and PD. Moreover, we confirmed that low levels of fibrinogen in PBMCs is a characteristic feature of PD, also when compared with another movement disorder. Collectively, we provide evidence that peripheral protein signatures are a tool to differentially investigate neurodegenerative diseases and highlight altered biochemical pathways.

The impact of aging and oxidative stress in metabolic and nervous system disorders: programmed cell death and molecular signal transduction crosstalk

Life expectancy is increasing throughout the world and coincides with a rise in non-communicable diseases (NCDs), especially for metabolic disease that includes diabetes mellitus (DM) and neurodegenerative disorders. The debilitating effects of metabolic disorders influence the entire body and significantly affect the nervous system impacting greater than one billion people with disability in the peripheral nervous system as well as with cognitive loss, now the seventh leading cause of death worldwide. Metabolic disorders, such as DM, and neurologic disease remain a significant challenge for the treatment and care of individuals since present therapies may limit symptoms but do not halt overall disease progression. These clinical challenges to address the interplay between metabolic and neurodegenerative disorders warrant innovative strategies that can focus upon the underlying mechanisms of aging-related disorders, oxidative stress, cell senescence, and cell death. Programmed cell death pathways that involve autophagy, apoptosis, ferroptosis, and pyroptosis can play a critical role in metabolic and neurodegenerative disorders and oversee processes that include insulin resistance, β-cell function, mitochondrial integrity, reactive oxygen species release, and inflammatory cell activation. The silent mating type information regulation 2 homolog 1 (Saccharomyces cerevisiae) (SIRT1), AMP activated protein kinase (AMPK), and Wnt1 inducible signaling pathway protein 1 (WISP1) are novel targets that can oversee programmed cell death pathways tied to β-nicotinamide adenine dinucleotide (NAD+), nicotinamide, apolipoprotein E (APOE), severe acute respiratory syndrome (SARS-CoV-2) exposure with coronavirus disease 2019 (COVID-19), and trophic factors, such as erythropoietin (EPO). The pathways of programmed cell death, SIRT1, AMPK, and WISP1 offer exciting prospects for maintaining metabolic homeostasis and nervous system function that can be compromised during aging-related disorders and lead to cognitive impairment, but these pathways have dual roles in determining the ultimate fate of cells and organ systems that warrant thoughtful insight into complex autofeedback mechanisms.

Tripartite Motif Protein Family in Central Nervous System Diseases

Tripartite motif (TRIM) protein superfamily is a group of E3 ubiquitin ligases characterized by the conserved RING domain, the B-box domain, and the coiled-coil domain (RBCC). It is widely involved in various physiological and pathological processes, such as intracellular signal transduction, cell cycle regulation, oncogenesis, and innate immune response. Central nervous system (CNS) diseases are composed of encephalopathy and spinal cord diseases, which have a high disability and mortality rate. Patients are often unable to take care of themselves and their life quality can be seriously declined. Initially, the function research of TRIM proteins mainly focused on cancer. However, in recent years, accumulating attention is paid to the roles they play in CNS diseases. In this review, we integrate the reported roles of TRIM proteins in the pathological process of CNS diseases and related signaling pathways, hoping to provide theoretical bases for further research in treating CNS diseases targeting TRIM proteins. TRIM proteins participated in CNS diseases. TRIM protein family is characterized by a highly conserved RBCC domain, referring to the RING domain, the B-box domain, and the coiled-coil domain. Recent research has discovered the relations between TRIM proteins and various CNS diseases, especially Alzheimer's disease, Parkinson's disease, and ischemic stroke.

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.

Current Insights on Neurodegeneration by the Italian Proteomics Community

The growing number of patients affected by neurodegenerative disorders represents a huge problem for healthcare systems, human society, and economics. In this context, omics strategies are crucial for the identification of molecular factors involved in disease pathobiology, and for the discovery of biomarkers that allow early diagnosis, patients’ stratification, and treatment response prediction. The integration of different omics data is a required step towards the goal of personalized medicine. The Italian proteomics community is actively developing and applying proteomics approaches to the study of neurodegenerative disorders; moreover, it is leading the mitochondria-focused initiative of the Human Proteome Project, which is particularly important given the central role of mitochondrial impairment in neurodegeneration. Here, we describe how Italian research groups in proteomics have contributed to the knowledge of many neurodegenerative diseases, through the elucidation of the pathobiology of these disorders, and through the discovery of disease biomarkers. In particular, we focus on the central role of post-translational modifications analysis, the implementation of network-based approaches in functional proteomics, the integration of different omics in a systems biology view, and the development of novel platforms for biomarker discovery for the high-throughput quantification of thousands of proteins at a time.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Common X-Chromosome Variants Are Associated with Parkinson Disease Risk

OBJECTIVE

The objective of this study was to identify genetic variants on the X-chromosome associated with Parkinson disease (PD) risk.

METHODS

We performed an X-chromosome-wide association study (XWAS) of PD risk by meta-analyzing results from sex-stratified analyses. To avoid spurious associations, we designed a specific harmonization pipeline for the X-chromosome and focused on a European ancestry sample. We included 11,142 cases, 280,164 controls, and 5,379 proxy cases, based on parental history of PD. Additionally, we tested the association of significant variants with (1) PD risk in an independent replication with 1,561 cases and 2,465 controls and (2) putamen volume in 33,360 individuals from the UK Biobank.

RESULTS

In the discovery meta-analysis, we identified rs7066890 (odds ratio [OR] = 1.10, 95% confidence interval [CI] = 1.06-1.14, p = 2.2 × 10-9 ), intron of GPM6B, and rs28602900 (OR = 1.10, 95% CI = 1.07-1.14, p = 1.6 × 10-8 ) in a high gene density region including RPL10, ATP6A1, FAM50A, and PLXNA3. The rs28602900 association with PD was replicated (OR = 1.16, 95% CI = 1.03-1.30, p = 0.016) and shown to colocalize with a significant expression quantitative locus (eQTL) regulating RPL10 expression in the putamen and other brain tissues in the Genotype-Tissue Expression Project. Additionally, the rs28602900 locus was found to be associated with reduced brain putamen volume. No results reached genome-wide significance in the sex-stratified analyses.

INTERPRETATION

We report the first XWAS of PD and identify 2 genome-wide significant loci. The rs28602900 association was replicated in an independent PD dataset and showed concordant effects in its association with putamen volume. Critically, rs26802900 is a significant eQTL of RPL10. These results support a role for ribosomal proteins in PD pathogenesis and show that the X-chromosome contributes to PD genetic risk. ANN NEUROL 2021;90:22-34.

Protein Aggregation Patterns Inform about Breast Cancer Response to Antiestrogens and Reveal the RNA Ligase RTCB as Mediator of Acquired Tamoxifen Resistance

Simple Summary

Acquired resistance to antiestrogenic therapy remains the major obstacle to curing luminal subtype breast cancer. While current treatment in acquired endocrine-resistant settings includes combined therapy with receptor tyrosine kinase or cyclin-dependent kinase inhibitors, progression to incurable disease remains possible. In recent years, the antioxidant system and the protein quality control network have been associated with the enhanced resistance of breast cancer cells to hormonal therapy. In this work, we raise the hypothesis that antiestrogen treatment induces the accumulation of protein aggregates in sensitive cells, which in turn could hinder the activation of survival pathways. We present evidence concerning a novel way to identify antiestrogen response and disclose a novel protein, RTBC, that controls acquired antiestrogen resistance. This work opens a new avenue for research towards finding breast cancer prognostic markers and therapeutic targets, where the identification of proteins prone to aggregate could help to identify antiestrogen response and understand mechanisms of disease.

Abstract

The protein quality control network, including autophagy, the proteasome and the unfolded protein response (UPR), is triggered by stress and is overactive in acquired antiestrogen therapy resistance. We show for the first time that the aggresome load correlates with apoptosis and is increased in antiestrogen-sensitive cells compared to endocrine-resistant variants. LC-MS/MS analysis of the aggregated proteins obtained after 4OH-tamoxifen and Fulvestrant treatment identified proteins with essential function in protein quality control in antiestrogen-sensitive cells, but not in resistant variants. These include the UPR modulators RTCB and PDIA6, as well as many proteasome proteins such as PSMC2 and PSMD11. RTCB is a tRNA and XBP1 ligase and its aggregation induced by antiestrogens correlated with impaired XBP1s expression in sensitive cells. Knock down of RTCB was sufficient to restore sensitivity to tamoxifen in endocrine-resistant cells and increased the formation of aggresomes, leading to apoptotic cell death. Analysis of primary human breast cancer samples and their metastases appearing after endocrine treatment showed that RTCB is only localized to aggresomes in the primary tumors, while total aggresomes, including aggregated RTCB, were significantly reduced in the metastases. Therefore, different protein aggregation patterns may indicate loss of function of essential proteins resulting in enhanced protein aggregation that can be used to identify antiestrogen-resistant breast cancer cells and improve the response to antiestrogenic therapy.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein–protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of “cell cycle” among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson’s disease. The selection of these proteins improved the specificity of GSEA, with “Metabolism of amino acids and derivatives” and “Cellular response to stress or external stimuli” as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

Integrated Metabolomics and Proteomics Analysis Reveals Plasma Lipid Metabolic Disturbance in Patients With Parkinson's Disease

Parkinson’s disease (PD) is a common neurodegenerative disease in the elderly with a pathogenesis that remains unclear. We aimed to explore its pathogenesis through plasma integrated metabolomics and proteomics analysis. The clinical data of consecutively recruited PD patients and healthy controls were assessed. Fasting plasma samples were obtained and analyzed using metabolomics and proteomics methods. After that, differentially expressed metabolites and proteins were identified for further bioinformatics analysis. No significant difference was found in the clinical data between these two groups. Eighty-three metabolites were differentially expressed in PD patients identified by metabolomics analysis. These metabolites were predominately lipid and lipid-like molecules (63%), among which 25% were sphingolipids. The sphingolipid metabolism pathway was enriched and tended to be activated in the following KEGG pathway analysis. According to the proteomics analysis, forty proteins were identified to be differentially expressed, seven of which were apolipoproteins. Furthermore, five of the six top ranking Gene Ontology terms from cellular components and eleven of the other fourteen Gene Ontology terms from biological processes were directly associated with lipid metabolism. In KEGG pathway analysis, the five enriched pathways were also significantly related with lipid metabolism (p < 0.05). Overall, Parkinson’s disease is associated with plasma lipid metabolic disturbance, including an activated sphingolipid metabolism and decreased apolipoproteins.

Proteomics of neurodegenerative diseases: analysis of human post-mortem brain

Dementias are prevalent brain disorders in the aged population. Dementias pose major socio-medical burden, but currently there is no cure available. Novel proteomics approaches hold promise to identify alterations of the brain proteome that could provide clues on disease etiology, and identify candidate proteins to develop further as a biomarker. In this review, we focus on recent proteomics findings from brains affected with Alzheimer's Disease, Parkinson Disease Dementia, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. These studies confirmed known cellular changes, and in addition identified novel proteins that may underlie distinct aspects of the diseases. This article is part of the special issue "Proteomics".

Proteomics and bioinformatics approaches for the identification of plasma biomarkers to detect Parkinson's disease

The aim of the present study was to screen for biomarkers of Parkinson's disease (PD) using proteomics and bioinformatics approaches. PD patients were divided into three groups: Those without surgery (PD1 group); those who had undergone deep brain stimulation (DBS) surgery without electrode stimulation (PD2 group); and those who had undergone DBS surgery with 1 month of electrode stimulation (PD3 group). The non-Parkinson control group (CK group) was also involved. Quantitative proteomic analysis of human sera was performed through the use of tandem mass tag markers and liquid chromatography-mass spectrometry (LC-MS)-based techniques. For the proteins with quantitative information, a systematic bioinformatics analysis was then performed, including protein annotation, functional classification, functional enrichment and cluster analysis based on functional enrichment. Of the 739 proteins identified, quantitative information was available for 644. With regard to differential expression, 18 upregulated and 21 downregulated proteins were screened in the PD1/CK comparison group; 12 upregulated and 12 downregulated proteins in the PD2/PD1 comparison group; and 16 upregulated and 19 downregulated proteins in the PD3/PD2 comparison group. Coiled-coil domain-containing protein 154 (CCDC154) and tripartite motif-containing protein 3 (TRIM3) were key proteins involved in the molecular mechanisms of PD, participating in intracellular vesicle, ubiquitin protein ligase and transition metal ion-binding activities. After DBS surgery, desert hedgehog protein (DHH) was downregulated, whereas neuropilin-2 (NRP2) was upregulated; these participated in the ensheathment of neurons and the semaphorin receptor complex, respectively. The expression level of chloride intracellular channel protein 1 (CLIC1) was increased after 1 month of electrode stimulation following DBS. By combining proteomic approaches and LC-MS methods, significant proteins including CCDC154, TRIM3, DHH, NRP2 and CLIC1 were detected with high specificity and sensitivity. These may be used as novel biomarkers for early diagnosis of PD and the future development of treatments.

MENDA: a comprehensive curated resource of metabolic characterization in depression

Depression is a seriously disabling psychiatric disorder with a significant burden of disease. Metabolic abnormalities have been widely reported in depressed patients and animal models. However, there are few systematic efforts that integrate meaningful biological insights from these studies. Herein, available metabolic knowledge in the context of depression was integrated to provide a systematic and panoramic view of metabolic characterization. After screening more than 10 000 citations from five electronic literature databases and five metabolomics databases, we manually curated 5675 metabolite entries from 464 studies, including human, rat, mouse and non-human primate, to develop a new metabolite-disease association database, called MENDA (http://menda.cqmu.edu.cn:8080/index.php). The standardized data extraction process was used for data collection, a multi-faceted annotation scheme was developed, and a user-friendly search engine and web interface were integrated for database access. To facilitate data analysis and interpretation based on MENDA, we also proposed a systematic analytical framework, including data integration and biological function analysis. Case studies were provided that identified the consistently altered metabolites using the vote-counting method, and that captured the underlying molecular mechanism using pathway and network analyses. Collectively, we provided a comprehensive curation of metabolic characterization in depression. Our model of a specific psychiatry disorder may be replicated to study other complex diseases.

Network approach identifies Pacer as an autophagy protein involved in ALS pathogenesis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a multifactorial fatal motoneuron disease without a cure. Ten percent of ALS cases can be pointed to a clear genetic cause, while the remaining 90% is classified as sporadic. Our study was aimed to uncover new connections within the ALS network through a bioinformatic approach, by which we identified C13orf18, recently named Pacer, as a new component of the autophagic machinery and potentially involved in ALS pathogenesis.

METHODS

Initially, we identified Pacer using a network-based bioinformatic analysis. Expression of Pacer was then investigated in vivo using spinal cord tissue from two ALS mouse models (SOD1^G93A and TDP43^A315T) and sporadic ALS patients. Mechanistic studies were performed in cell culture using the mouse motoneuron cell line NSC34. Loss of function of Pacer was achieved by knockdown using short-hairpin constructs. The effect of Pacer repression was investigated in the context of autophagy, SOD1 aggregation, and neuronal death.

RESULTS

Using an unbiased network-based approach, we integrated all available ALS data to identify new functional interactions involved in ALS pathogenesis. We found that Pacer associates to an ALS-specific subnetwork composed of components of the autophagy pathway, one of the main cellular processes affected in the disease. Interestingly, we found that Pacer levels are significantly reduced in spinal cord tissue from sporadic ALS patients and in tissues from two ALS mouse models. In vitro, Pacer deficiency lead to impaired autophagy and accumulation of ALS-associated protein aggregates, which correlated with the induction of cell death.

CONCLUSIONS

This study, therefore, identifies Pacer as a new regulator of proteostasis associated with ALS pathology.

Update of the Functional Mitochondrial Human Proteome Network

Because of the pivotal role of mitochondrial alterations in several diseases, the Human Proteome Organization (HUPO) has promoted in recent years an initiative to characterize the mitochondrial human proteome, the mitochondrial human proteome project (mt-HPP). Here we generated an updated version of the functional mitochondrial human proteome network, made by nodes (mitochondrial proteins) and edges (gold binary interactions), using data retrieved from neXtProt, the reference database for HPP metrics. The principal new concept suggested was the consideration of mitochondria-associated proteins (first interactors), which may influence mitochondrial functions. All of the proteins described as mitochondrial in the sublocation or the GO Cellular Component sections of neXtProt were considered. Their other subcellular and submitochondrial localizations have been analyzed. The network represents the effort to collect all of the high-quality binary interactions described so far for mitochondrial proteins and the possibility for the community to reuse the information collected. As a proof of principle, we mapped proteins with no function, to speculate on their role by the background knowledge of their interactors, and proteins described to be involved in Parkinson's Disease, a neurodegenerative disorder, where it is known that mitochondria play a central role.