A systems biology-led insight into the role of the proteome in neurodegenerative diseases

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.

Looking at COVID-19 from a Systems Biology Perspective

The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus-human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus-host interactions, and in the development of disease classifiers and tools for drug repurposing.

Characterisation of the Mouse Cerebellar Proteome in the GFAP-IL6 Model of Chronic Neuroinflammation

GFAP-IL6 transgenic mice are characterised by astroglial and microglial activation predominantly in the cerebellum, hallmarks of many neuroinflammatory conditions. However, information available regarding the proteome profile associated with IL-6 overexpression in the mouse brain is limited. This study investigated the cerebellum proteome using a top-down proteomics approach using 2-dimensional gel electrophoresis followed by liquid chromatography-coupled tandem mass spectrometry and correlated these data with motor deficits using the elevated beam walking and accelerod tests. In a detailed proteomic analysis, a total of 67 differentially expressed proteoforms including 47 cytosolic and 20 membrane-bound proteoforms were identified. Bioinformatics and literature mining analyses revealed that these proteins were associated with three distinct classes: metabolic and neurodegenerative processes as well as protein aggregation. The GFAP-IL6 mice exhibited impaired motor skills in the elevated beam walking test measured by their average scores of 'number of footslips' and 'time to traverse' values. Correlation of the proteoforms' expression levels with the motor test scores showed a significant positive correlation to peroxiredoxin-6 and negative correlation to alpha-internexin and mitochondrial cristae subunit Mic19. These findings suggest that the observed changes in the proteoform levels caused by IL-6 overexpression might contribute to the motor function deficits.

Retinal Genomic Fabric Remodeling after Optic Nerve Injury

Glaucoma is a multifactorial neurodegenerative disease, characterized by degeneration of the retinal ganglion cells (RGCs). There has been little progress in developing efficient strategies for neuroprotection in glaucoma. We profiled the retina transcriptome of Lister Hooded rats at 2 weeks after optic nerve crush (ONC) and analyzed the data from the genomic fabric paradigm (GFP) to bring additional insights into the molecular mechanisms of the retinal remodeling after induction of RGC degeneration. GFP considers three independent characteristics for the expression of each gene: level, variability, and correlation with each other gene. Thus, the 17,657 quantified genes in our study generated a total of 155,911,310 values to analyze. This represents 8830x more data per condition than a traditional transcriptomic analysis. ONC led to a 57% reduction in RGC numbers as detected by retrograde labeling with 1,1'-dioctadecyl-3,3,3,3'-tetramethylindocarbocyanine perchlorate (DiI). We observed a higher relative expression variability after ONC. Gene expression stability was used as a measure of transcription control and disclosed a robust reduction in the number of very stably expressed genes. Predicted protein-protein interaction (PPI) analysis with STRING revealed axon and neuron projection as mostly decreased processes, consistent with RGC degeneration. Conversely, immune response PPIs were found among upregulated genes. Enrichment analysis showed that complement cascade and Notch signaling pathway, as well as oxidative stress and kit receptor pathway were affected after ONC. To expand our studies of altered molecular pathways, we examined the pairwise coordination of gene expressions within each pathway and within the entire transcriptome using Pearson correlations. ONC increased the number of synergistically coordinated pairs of genes and the number of similar profiles mainly in complement cascade and Notch signaling pathway. This deep bioinformatic study provided novel insights beyond the regulation of individual gene expression and disclosed changes in the control of expression of complement cascade and Notch signaling functional pathways that may be relevant for both RGC degeneration and remodeling of the retinal tissue after ONC.

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.

Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks

MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.

Exploring the Impact of PARK2 Mutations on the Total and Mitochondrial Proteome of Human Skin Fibroblasts

Mutations in PARK2 gene are the most frequent cause of familial forms of Parkinson’s disease (PD). This gene encodes Parkin, an E3 ubiquitin ligase involved in several cellular mechanisms, including mitophagy. Parkin loss-of-function is responsible for the cellular accumulation of damaged mitochondria, which in turn determines an increment of reactive oxygen species (ROS) levels, lower ATP production, and apoptosis activation. Given the importance of mitochondrial dysfunction and mitophagy impairment in PD pathogenesis, the aim of the present study was to investigate both total and mitochondrial proteome alterations in human skin fibroblasts of PARK2-mutated patients. To this end, both total and mitochondria-enriched protein fractions from fibroblasts of five PARK2-mutated patients and five control subjects were analyzed by quantitative shotgun proteomics to identify proteins specifically altered by Parkin mutations (mass spectrometry proteomics data have been submitted to ProteomeXchange with the identifier PXD015880). Both the network-based and gene set enrichment analyses pointed out pathways in which Rab GTPase proteins are involved. To have a more comprehensive view of the mitochondrial alterations due to PARK2 mutations, we investigated the impact of Parkin loss on mitochondrial function and network morphology. We unveiled that the mitochondrial membrane potential was reduced in PARK2-mutated patients, without inducing PINK1 accumulation, even when triggered with the ionophore carbonyl cyanide m-chlorophenylhydrazone (CCCP). Lastly, the analysis of the mitochondrial network morphology did not reveal any significant alterations in PARK2-mutated patients compared to control subjects. Thus, our results suggested that the network morphology was not influenced by the mitochondrial depolarization and by the lack of Parkin, revealing a possible impairment of fission and, more in general, of mitochondrial dynamics. In conclusion, the present work highlighted new molecular factors and pathways altered by PARK2 mutations, which will unravel possible biochemical pathways altered in the sporadic form of PD.

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".

Decreased Tissue COX5B Expression and Mitochondrial Dysfunction during Sepsis-Induced Kidney Injury in Rats

Background. Sepsis is defined as a life-threatening organ dysfunction due to a dysregulated host response to infection. Sepsis is the dominant cause of acute kidney injury (AKI), accounting for nearly 50% of episodes of acute renal failure. Signaling cascades and pathways within the kidney are largely unknown and analysis of these molecular mechanisms may enhance knowledge on pathophysiology and possible therapeutic options. Material and Methods. 26 male Wistar rats were assigned to either a sham group (control, N = 6) or sepsis group (N = 20; cecal ligature and puncture model, 24 and 48 hours after CLP). Surviving rats (n = 12) were decapitated at 24 hours (early phase; n = 6) or 48 hours (late phase; n = 6) after CLP and kidneys removed for proteomic analysis. 2D-DIGE and DeCyder 2D software (t-test, P < 0.01) were used for analysis of significantly regulated protein spots. MALDI-TOF in combination with peptide mass fingerprinting (PMF) as well as Western Blot analysis was used for protein identification. Bioinformatic network analyses (STRING, GeneMania, and PCViz) were used to describe protein-protein interactions. Results. 12 spots were identified with significantly altered proteins (P < 0.01) in the three analyzed groups. Two spots could not be identified. Four different proteins were found significantly changed among the groups: major urinary protein (MUP5), cytochrome c oxidase subunit B (COX5b), myosin-6 (MYH6), and myosin-7 (MYH7). A significant correlation with the proteins was found for mitochondrial energy production and electron transport. Conclusions. COX5B could be a promising biomarker candidate since a significant association was found during experimental sepsis in the present study. For future research, COX5B should be evaluated as a biomarker in both human urine and serum to identify sepsis.

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

Neurodegenerative diseases are characterized by the progressive loss of specific neurons in selected regions of the central nervous system. The main clinical manifestation (movement disorders, cognitive impairment, and/or psychiatric disturbances) depends on the neuron population being primarily affected. Parkinson's disease is a common movement disorder, whose etiology remains mostly unknown. Progressive loss of dopaminergic neurons in the substantia nigra causes an impairment of the motor control. Some of the pathogenetic mechanisms causing the progressive deterioration of these neurons are not specific for Parkinson's disease but are shared by other neurodegenerative diseases, like Alzheimer's disease and amyotrophic lateral sclerosis. Here, we performed a meta-analysis of the literature of all the quantitative proteomic investigations of neuronal alterations in different models of Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis to distinguish between general and Parkinson's disease-specific pattern of neurodegeneration. Then, we merged proteomics data with genetics information from the DisGeNET database. The comparison of gene and protein information allowed us to identify 25 proteins involved uniquely in Parkinson's disease and we verified the alteration of one of them, i.e., transaldolase 1 (TALDO1), in the substantia nigra of 5 patients. By using open-source bioinformatics tools, we identified the biological processes specifically affected in Parkinson's disease, i.e., proteolysis, mitochondrion organization, and mitophagy. Eventually, we highlighted four cellular component complexes mostly involved in the pathogenesis: the proteasome complex, the protein phosphatase 2A, the chaperonins CCT complex, and the complex III of the respiratory chain.