From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

Transforming neurodegenerative disorder care with machine learning: Strategies and applications

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement disorders, pose significant challenges due to their complex molecular mechanisms and heterogeneous patient presentations. Diagnosis often relies heavily on clinical assessments and neuroimaging, with definitive confirmation frequently requiring post-mortem autopsy. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative potential. These technologies can enable the development of non-invasive tools for early diagnosis, biomarker identification, personalized treatment strategies, patient subtyping and stratification, and disease risk prediction. This review aims to provide a starting point for researchers, both with and without clinical backgrounds, who are interested in applying ML to NDs. We will discuss available data resources for key diseases like Alzheimer's and Parkinson's, explore how ML can revolutionize neurodegenerative care, and emphasize the importance of integrating multiple high-dimensional data sources to gain deeper insights and inform effective therapeutic strategies.

Evaluation of Salivary Diagnostics: Applications, Benefits, Challenges, and Future Prospects in Dental and Systemic Disease Detection

Saliva is a multifaceted biological fluid that plays a pivotal role in oral health and overall well-being. It is primarily produced by major salivary glands, with additional contributions from minor glands. Saliva is essential for various physiological functions, including oral lubrication, digestion, and defense against pathogens. Its intricate composition comprises proteins, electrolytes, enzymes, hormones, and microbial DNA, enabling it to act as a dynamic indicator of both local and systemic health. A literature search was conducted using PubMed, Web of Science, and Google Scholar to identify relevant studies published up to June 2024. The included studies involved human participants and provided original data or comprehensive reviews on salivary biomarkers. The findings indicate that salivary diagnostics show promise in diagnosing and monitoring systemic conditions such as diabetes and cardiovascular diseases, with salivary glucose levels correlating well with blood glucose levels. Biomarkers such as C-reactive protein (CRP) have been linked to cardiovascular risk, while saliva has been explored for cancer detection, including pancreatic and prostate cancers. Advances in techniques such as enzyme-linked immunosorbent assay (ELISA), saliva omics, and single-cell sequencing have furthered salivary diagnostics, providing insights into disease mechanisms. Additionally, quantitative mass spectrometry (qMS) and Raman spectroscopy (RS) contribute to non-invasive diagnostics for various conditions, including cancer. Collecting saliva samples from healthy individuals is crucial for early disease detection and evaluating treatment efficacy. This review underscores the growing importance of salivary tests in dental practice and their potential for diagnosing various health conditions. Further research is essential to address challenges related to variability and standardization. Dentists and healthcare professionals should consider incorporating salivary tests into clinical decision-making to enhance patient outcomes.

Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes

Objective

Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles and underlying mechanisms remain poorly understood. This study aims to identify differentially expressed genes (DEGs) that could serve as biomarkers for diagnosis and prognosis.

Methods

Gene expression datasets (GSE122063, GSE13353, GSE161870) were analyzed using machine learning algorithms and logistic regression to identify DEGs associated with both SAH and tumorigenesis. Lasso regression and receiver operating characteristic (ROC) curve analysis were employed to evaluate the classification accuracy of these genes. Validation of critical DEGs was performed through pan-cancer analysis and experimental studies, focusing on the role of DOK3 in modulating inflammation and oxidative stress in U251MG glioblastoma and BV2 microglia cells.

Results

Fifteen common DEGs were identified, with DOK3 and PAPOLA highlighted as crucial genes implicated in SAH and neurodegenerative processes. Experimental validation demonstrated that DOK3 overexpression significantly reduced pro-inflammatory cytokine levels and oxidative stress markers while enhancing antioxidant enzyme activity. Additionally, DOK3 influenced tumorigenic processes such as apoptosis, cell cycle regulation, and proliferation, effectively mitigating LPS-induced cytotoxicity and inflammation in BV2 microglial cells.

Conclusions

DOK3 and PAPOLA play critical roles in both SAH and related neurodegeneration, presenting themselves as potential prognostic biomarkers and therapeutic targets. Notably, DOK3 exhibits potential as an antitumor agent with anti-inflammatory and antioxidative properties, offering therapeutic benefits for both cancer and neuroinflammatory conditions.

The role of inflammation in neurological disorders: a brief overview of multiple sclerosis, Alzheimer's, and Parkinson's disease'

Neuroinflammation is a central feature in the pathophysiology of several neurodegenerative diseases, including MS, AD, and PD. This review aims to synthesize current research on the role of inflammation in these conditions, emphasizing the potential of inflammatory biomarkers for diagnosis and treatment. We highlight recent findings on the mechanisms of neuroinflammation, the utility of biomarkers in disease differentiation, and the implications for therapeutic strategies. Advances in understanding inflammatory pathways offer promising avenues for developing targeted interventions to improve patient outcomes. Future research should focus on validating these biomarkers in larger cohorts and integrating them into clinical practice to enhance diagnostic accuracy and therapeutic efficacy.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

Refining the clinical diagnosis of Parkinson's disease

Our ability to define, understand, and classify Parkinson's disease (PD) has undergone significant changes since the disorder was first described in 1817. Clinical features and neuropathologic signatures can now be supplemented by in-vivo interrogation of genetic and biological substrates of disease, offering great opportunity for further refining the diagnosis of PD. In this mini-review, we discuss the historical perspectives which shaped our thinking surrounding the definition and diagnosis of PD. We highlight the clinical, genetic, pathologic and biologic diversity which underpins the condition, and proceed to discuss how recent developments in our ability to define biologic and pathologic substrates of disease might impact PD definition, diagnosis, individualised prognostication, and personalised clinical care. We argue that Parkinson's 'disease', as currently diagnosed in the clinic, is actually a syndrome. It is the outward manifestation of any array of potential dysfunctional biologic processes, neuropathological changes, and disease aetiologies, which culminate in common outward clinical features which we term PD; each person has their own unique disease, which we can now define with increasing precision. This is an exciting time in PD research and clinical care. Our ability to refine the clinical diagnosis of PD, incorporating in-vivo assessments of disease biology, neuropathology, and neurogenetics may well herald the era of biologically-based, precision medicine approaches PD management. With this however comes a number of challenges, including how to integrate these technologies into clinical practice in a way which is acceptable to patients, promotes meaningful changes to care, and minimises health economic impact.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Gene Expression Profiling of Post Mortem Midbrain of Parkinson's Disease Patients and Healthy Controls

Parkinson's disease (PD) stands as the most prevalent degenerative movement disorder, marked by the degeneration of dopaminergic neurons in the substantia nigra of the midbrain. In this study, we conducted a transcriptome analysis utilizing post mortem mRNA extracted from the substantia nigra of both PD patients and healthy control (CTRL) individuals. Specifically, we acquired eight samples from individuals with PD and six samples from CTRL individuals, with no discernible pathology detected in the latter group. RNA sequencing was conducted using the TapeStation 4200 system from Agilent Technologies. A total of 16,148 transcripts were identified, with 92 mRNAs displaying differential expression between the PD and control groups. Specifically, 33 mRNAs were significantly up-regulated, while 59 mRNAs were down-regulated in PD compared to the controls. The identification of statistically significant signaling pathways, with an adjusted p-value threshold of 0.05, unveiled noteworthy insights. Specifically, the enriched categories included cardiac muscle contraction (involving genes such as ATPase Na+/K+ transporting subunit beta 2 (ATP1B2), solute carrier family 8 member A1 (SLC8A1), and cytochrome c oxidase subunit II (COX2)), GABAergic synapse (involving GABA type A receptor-associated protein-like 1 (GABARAPL1), G protein subunit beta 5 (GNB5), and solute carrier family 38 member 2 (SLC38A2), autophagy (involving GABARAPL1 and tumor protein p53-inducible nuclear protein 2 (TP53INP2)), and Fc gamma receptor (FcγR) mediated phagocytosis (involving amphiphysin (AMPH)). These findings uncover new pathophysiological dimensions underlying PD, implicating genes associated with heart muscle contraction. This knowledge enhances diagnostic accuracy and contributes to the advancement of targeted therapies.

From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

Towards a Multi-Omics of Male Infertility

Infertility is a common problem affecting one in six couples and in 30% of infertile couples, the male factor is a major cause. A large number of genes are involved in spermatogenesis and a significant proportion of male infertility phenotypes are of genetic origin. Studies on infertility have so far primarily focused on chromosomal abnormalities and sequence variants in protein-coding genes and have identified a large number of disease-associated genes. However, it has been shown that a multitude of factors across various omics levels also contribute to infertility phenotypes. The complexity of male infertility has led to the understanding that an integrated, multi-omics analysis may be optimal for unravelling this disease. While there is a vast array of different factors across omics levels associated with infertility, the present review focuses on known factors from the genomics, epigenomics, transcriptomics, proteomics, metabolomics, glycomics, lipidomics, miRNomics, and integrated omics levels. These include: repeat expansions in AR, POLG, ATXN1, DMPK, and SHBG, multiple SNPs, copy number variants in the AZF region, disregulated miRNAs, altered H3K9 methylation, differential MTHFR, MEG3, PEG1, and LIT1 methylation, altered protamine ratios and protein hypo/hyperphosphorylation. This integrative review presents a step towards a multi-omics approach to understanding the complex etiology of male infertility. Currently only a few genetic factors, namely chromosomal abnormalities and Y chromosome microdeletions, are routinely tested in infertile men undergoing intracytoplasmic sperm injection. A multi-omics approach to understanding infertility phenotypes may yield a more holistic view of the disease and contribute to the development of improved screening methods and treatment options. Therefore, beside discovering as of yet unknown genetic causes of infertility, integrating multiple fields of study could yield valuable contributions to the understanding of disease development. Future multi-omics studies will enable to synthesise fragmented information and facilitate biomarker discovery and treatments in male infertility.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens

BACKGROUND

Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis.

OBJECTIVE

To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD.

METHODS

Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression.

RESULTS

Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative.

CONCLUSIONS

UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.

[Latest Research Findings on Health Management Based on Saliva Testing]

Being one of the most important exocrine fluids of the human body, saliva can reflect the health status of the body. Soliva collection has various advantages--it is simple, painless, fast, and safe, and soliva can be collected multiple times a day. Therefore, it has been gradually applied in the exploration for biomarkers for disease detection, providing a basis for the monitoring of the course of diseases, medication monitoring, and efficacy evaluation. We should implement health management based on saliva testing, collect the medical data of the healthy and diseased individuals and monitor their whole life cycle health, perform clinical cohort study, aggregate the data on platforms for big data on health and medicine, manage and provide guidance for the health status of populations, pinpoint the high-risk factors for pathogenesis, and provide effective intervention, early warning, and assessment of the vital signs of individuals, thereby reinforcing health management of the whole life cycle and safeguarding people's health in an all-round way. In addition, it drives the development of the health industry and bears strategic significance for the promotion of national economic development. It is becoming a hot research topic promising great potential and impressive applicational prospects. Herein, we reviewed new techniques for clinical saliva testing and health management based on saliva testing.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

Plasma Metabolite Signature Classifies Male LRRK2 Parkinson’s Disease Patients

Parkinson’s disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients’ dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options.

Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms

Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.

Multiomic Analyses of Dopaminergic Neurons Isolated from Human Substantia Nigra in Parkinson's Disease: A Descriptive and Exploratory Study

Dopaminergic neurons (DA) of the substantia nigra pars compacta (SNpc) selectively and progressively degenerate in Parkinson’s disease (PD). Until now, molecular analyses of DA in PD have been limited to genomic or transcriptomic approaches, whereas, to the best of our knowledge, no proteomic or combined multiomic study examining the protein profile of these neurons is currently available. In this exploratory study, we used laser capture microdissection to extract regions from DA in 10 human SNpc obtained at autopsy in PD patients and control subjects. Extracted RNA and proteins were identified by RNA sequencing and nanoliquid chromatography–mass spectrometry, respectively, and the differential expression between PD and control group was assessed. Qualitative analyses confirmed that the microdissection protocol preserves the integrity of our samples and offers access to specific molecular pathways. This multiomic analysis highlighted differential expression of 52 genes and 33 proteins, including molecules of interest already known to be dysregulated in PD, such as LRP2, PNMT, CXCR4, MAOA and CBLN1 genes, or the Aldehyde dehydrogenase 1 protein. On the other hand, despite the same samples were used for both analyses, correlation between RNA and protein expression was low, as exemplified by the CST3 gene encoding for the cystatin C protein. This is the first exploratory study analyzing both gene and protein expression of laser-dissected neuronal parts from SNpc in PD. Data are available via ProteomeXchange with identifier PXD024748 and via GEO with identifier GSE 169755.

Atremorine in Parkinson's disease: From dopaminergic neuroprotection to pharmacogenomics

Atremorine is a novel bioproduct obtained by nondenaturing biotechnological processes from a genetic species of Vicia faba. Atremorine is a potent dopamine (DA) enhancer with powerful effects on the neuronal dopaminergic system, acting as a neuroprotective agent in Parkinson's disease (PD). Over 97% of PD patients respond to a single dose of Atremorine (5 g, p.o.) 1 h after administration. This response is gender-, time-, dose-, and genotype-dependent, with optimal doses ranging from 5 to 20 g/day, depending upon disease severity and concomitant medication. Drug-free patients show an increase in DA levels from 12.14 ± 0.34 pg/ml to 6463.21 ± 1306.90 pg/ml; and patients chronically treated with anti-PD drugs show an increase in DA levels from 1321.53 ± 389.94 pg/ml to 16,028.54 ± 4783.98 pg/ml, indicating that Atremorine potentiates the dopaminergic effects of conventional anti-PD drugs. Atremorine also influences the levels of other neurotransmitters (adrenaline, noradrenaline) and hormones which are regulated by DA (e.g., prolactin, PRL), with no effect on serotonin or histamine. The variability in Atremorine-induced DA response is highly attributable to pharmacogenetic factors. Polymorphic variants in pathogenic (SNCA, NUCKS1, ITGA8, GPNMB, GCH1, BCKDK, APOE, LRRK2, ACMSD), mechanistic (DRD2), metabolic (CYP2D6, CYP2C9, CYP2C19, CYP3A4/5, NAT2), transporter (ABCB1, SLC6A2, SLC6A3, SLC6A4) and pleiotropic genes (APOE) influence the DA response to Atremorine and its psychomotor and brain effects. Atremorine enhances DNA methylation and displays epigenetic activity via modulation of the pharmacoepigenetic network. Atremorine is a novel neuroprotective agent for dopaminergic neurons with potential prophylactic and therapeutic activity in PD.

A geroscience approach for Parkinson's disease: Conceptual framework and design of PROPAG-AGEING project

Advanced age is the major risk factor for idiopathic Parkinson's disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project "PROPAG-AGEING", whose aim is to characterize the contribution of the ageing process to PD development. We summarize current evidences that support the existence of a continuum between ageing and PD and justify the use of a Geroscience approach to study PD. We focus in particular on the role of inflammaging, the chronic, low-grade inflammation characteristic of elderly physiology, which can propagate and transmit both locally and systemically. We then describe PROPAG-AGEING design, which is based on the multi-omic characterization of peripheral samples from clinically characterized drug-naïve and advanced PD, PD discordant twins, healthy controls and "super-controls", i.e. centenarians, who never showed clinical signs of motor disability, and their offspring. Omic results are then validated in a large number of samples, including in vitro models of dopaminergic neurons and healthy siblings of PD patients, who are at higher risk of developing PD, with the final aim of identifying the molecular perturbations that can deviate the trajectories of healthy ageing towards PD development.

Status and future directions of clinical trials in Parkinson's disease

Novel therapies are needed to treat Parkinson's disease (PD) in which the clinical unmet need is pressing. Currently, no clinically available therapeutic strategy can either retard or reverse PD or repair its pathological consequences. l-DOPA (levodopa) is still the gold standard therapy for motor symptoms yet symptomatic therapies for both motor and non-motor symptoms are improving. Many on-going, intervention trials cover a broad range of targets, including cell replacement and gene therapy approaches, quality of life improving technologies, and disease-modifying strategies (e.g., controlling aberrant α-synuclein accumulation and regulating cellular/neuronal bioenergetics). Notably, the repurposing of glucagon-like peptide-1 analogues with potential disease-modifying effects based on metabolic pathology associated with PD has been promising. Nevertheless, there is a clear need for improved therapeutic and diagnostic options, disease progression tracking and patient stratification capabilities to deliver personalized treatment and optimize trial design. This review discusses some of the risk factors and consequent pathology associated with PD and particularly the metabolic aspects of PD, novel therapies targeting these pathologies (e.g., mitochondrial and lysosomal dysfunction, oxidative stress, and inflammation/neuroinflammation), including the repurposing of metabolic therapies, and unmet needs as potential drivers for future clinical trials and research in PD.

Development of early diagnosis of Parkinson's disease: Illusion or reality?

The fight against neurodegenerative diseases, Alzheimer disease and Parkinson's disease (PD), is a challenge of the 21st century. The low efficacy of treating patients is due to the late diagnosis and start of therapy, after the degeneration of most specific neurons and depletion of neuroplasticity. It is believed that the development of early diagnosis (ED) and preventive treatment will delay the onset of specific symptoms. This review evaluates methodologies for developing ED of PD. Since PD is a systemic disease, and the degeneration of certain neurons precedes that of nigrostriatal dopaminergic neurons that control motor function, the current methodology is based on searching biomarkers, such as premotor symptoms and changes in body fluids (BF) in patients. However, all attempts to develop ED were unsuccessful. Therefore, it is proposed to enhance the current methodology by (i) selecting among biomarkers found in BF in patients at the clinical stage those that are characteristics of animal models of the preclinical stage, (ii) searching biomarkers in BF in subjects at the prodromal stage, selected by detecting premotor symptoms and failure of the nigrostriatal dopaminergic system. Moreover, a new methodology was proposed for the development of ED of PD using a provocative test, which is successfully used in internal medicine.

An Integrated Transcriptomic and Proteomic Analysis Identifies Significant Novel Pathways for Henoch-Schönlein Purpura Nephritis Progression

Background

Although Henoch-Schönlein purpura nephritis (HSPN) is characterized by glomerular deposition of aberrantly glycosylated immunoglobulin A1 (IgA1), the underlying mechanism of HSPN progression has not yet been completely elucidated. In this study, we integrated transcriptomic and proteomic analyses to explore the underlying mechanism of HSPN progression.

Methods

RNA sequencing and tandem mass tag- (TMT-) based quantitative proteomics were used to gain serum transcriptomic and proteomic profiles of patients with different types of HSPN (3 × type 1, 3 × type 2, and 3 × type 3). Student's t-tests were performed to obtain the significance of the differential gene expression. The clusterProfiler package was used to conduct the functional annotation of the DEGs for both Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways.

Results

A total of 2315 mRNAs and 30 proteins were differentially expressed between the different types of HSPN. 58 mRNAs and one protein changed continuously during HSPN development and are potential biomarkers for HSPN progression. The validation cohort (another 9 patients) confirmed the high-throughput results of the transcriptomic and proteomic analyses. A total of 385 significant pathways were related to HSPN progression, and four of them were closely related to clinical biochemical indicators and may play an important role in the progression of HSPN. Those pathways reveal that HSPN progression may be related to the inhibition of inflammation, promotion of apoptosis, and repair of renal injury.

Conclusions

Four pathways were found to be closely related to HSPN progression, and it seems that HSPN progression is mainly due to the inhibition of inflammation, promotion of apoptosis, and repair of renal injury.

[Emerging concepts for precision medicine in Parkinson's disease with focus on genetics]

The diverse and highly individual presentations of Parkinson's disease (PD) as a complex combination of motor and non-motor symptoms are being increasingly well characterised not least through large patient cohorts applying deep phenotyping. However, in terms of treatment of PD, the approach is uniform and purely symptomatic. Better stratification strategies with better precision medicine approaches offer opportunities to improve symptomatic treatment, define first causative therapies and provide more patient-centred care. Insight from targeted therapies for monogenic forms of PD aiming at neuroprotection may pave the way for new mechanism-based interventions also for the more common idiopathic PD. Improved stratification of patients may support symptomatic treatments by predicting treatment efficacy and long-term benefit of current pharmacological or neuromodulatory therapies, e.g. in the context of emerging pharmacogenomic knowledge. Based on asymptomatic carriers with monogenic PD or patients with REM sleep behaviour disorder (RBD), first options for applying preventive treatments emerge. The implications of these treatment strategies in relation to disease progression, and the prospects of their implementation in clinical practice need to be addressed.

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".

Rise of Systems Glycobiology and Personalized Glycomedicine: Why and How to Integrate Glycomics with Multiomics Science?

Glycomics is a rapidly emerging subspecialty of system sciences that evaluates the structures and functions of glycans in biological systems. Moreover, glycomics informs allied scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between-population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. For glycomics to make greater, systems-scale, contributions to biology and medical research, it is facing a new developmental challenge: transition from single omics to multiomics integrative technology platforms. A comprehensive map of all possible connections between glycomics and other omics types has not yet been developed. Glycomics aims to discover a complex interplay of molecular interactions; however, little is known about the regulatory networks controlling these complex processes. In addition, the glycomics knowledgebase is presently scattered across various publications and databases, and therefore does not enable a holistic or systems view of this study field. Therefore, researchers are not always aware, for example, that a given analyzed genetic locus is linked with glycans, and that there are also glycomics determinants of complex phenotypes in health and biology. This review presents several published examples of glycomics science in association with other omics levels, such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, ncRNomics, lipidomics, and interactomics. I also highlight the salient knowledge gaps and suggest future research directions. Understanding the interconnections of glycomics with other omics technologies will facilitate multiomics science and knowledge integration, enhance development of systems glycobiology and personalized glycomedicine.

A systems biology approach towards the identification of candidate therapeutic genes and potential biomarkers for Parkinson's disease

Parkinson's disease (PD) is an irreversible and incurable multigenic neurodegenerative disorder. It involves progressive loss of mid brain dopaminergic neurons in the substantia nigra pars compacta (SN). We compared brain gene expression profiles with those from the peripheral blood cells of a separate sample of PD patients to identify disease-associated genes. Here, we demonstrate the use of gene expression profiling of brain and blood for detecting valid targets and identifying early PD biomarkers. Implementing this systematic approach, we discovered putative PD risk genes in brain, delineated biological processes and molecular functions that may be particularly disrupted in PD and also identified several putative PD biomarkers in blood. 20 of the differentially expressed genes in SN were also found to be differentially expressed in the blood. Further application of this methodology to other brain regions and neurological disorders should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for PD. The identification of valid peripheral biomarkers for PD may ultimately facilitate early identification, intervention, and prevention efforts as well.

Recent advances in our understanding of central and peripheral nervous system progenitors

Several decades of intense research provided us with a grand framework describing the emergence of neurons in central (CNS) and peripheral (PNS) nervous systems. However, the specifics of molecular events and lineage control leading to a plethora of neuronal subtypes stayed largely unclear. Today, the advances in single cell omics, sample clearing and 3D-microscopy techniques, brain organoids, and synaptic connectivity tracing enabled systematic and unbiased understanding of neuronal diversity, development, circuitry and cell identity control. Novel technological advancements stimulated the transition from conceptual scheme of neuronal differentiation into precise maps of molecular events leading to the diversity of specific neuronal subtypes in relation to their locations and microenvironment. These high-resolution data opened a set of new questions including how transcriptional and epigenetics states control the proportionality of neuronal subpopulations or what are the evolutionary mechanisms of origin of different neuronal subtypes. In this review, we outline the most recent advancements in our understanding of how the neuronal diversity is generated in CNS and PNS and briefly address the challenges and questions arising in the field of neurogenesis.

Integrative transcriptomics, proteomics, and metabolomics data analysis exploring the injury mechanism of ricin on human lung epithelial cells

Ricin (RT) is a plant toxin belonging to the family of type II ribosome-inactivating protein with high bioterrorism potential. Aerosol RT exposure is the most lethal route, but its mechanism of injury needs further investigation. In the present study, we performed a comprehensive transcriptomics, proteomics and metabolomics analysis on the potential mechanism of injury caused by RT on human lung epithelial cells. In total, 5872 genes, 187 proteins, and 143 metabolites were shown to be significantly changed in human lung epithelial cells after RT treatment. Molecular function, pathway, and network analyses, the genes and proteins regulated in RT-treated cells were mainly attributed to fatty acid metabolism, arginine and proline metabolism and ubiquitin-mediated proteolysis pathway. Furthermore, a comprehensive analysis of transcripts, proteins, and metabolites was performed. The results revealed the correlated genes, proteins, and metabolic pathways regulated in metabolic pathways, amino acid metabolism, transcription and energy metabolism. These genes, proteins, and metabolites involved in these dis-regulated pathways may provide a more targeted and credible direction to study the mechanism of RT injury on human lung epithelial cells. This study provides large-scale omics data that can be used to develop a new strategy for the prevention, rapid diagnosis, and treatment of RT poisoning, especially of RT aerosol.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

Genetic variability of inflammation and oxidative stress genes does not play a major role in the occurrence of adverse events of dopaminergic treatment in Parkinson's disease

BACKGROUND

Inflammation and oxidative stress are recognized as important contributors to Parkinson's disease pathogenesis. As such, genetic variability in these pathways could have a role in susceptibility for the disease as well as in the treatment outcome. Dopaminergic treatment is effective in management of motor symptoms, but poses a risk for motor and non-motor adverse events. Our aim was to evaluate the impact of selected single-nucleotide polymorphisms in genes involved in inflammation and oxidative stress on Parkinson's disease susceptibility and the occurrence of adverse events of dopaminergic treatment.

METHODS

In total, 224 patients were enrolled, and their demographic and clinical data on the disease course were collected. Furthermore, a control group of 146 healthy Slovenian blood donors were included for Parkinson's disease' risk evaluation. Peripheral blood was obtained for DNA isolation. Genotyping was performed for NLRP3 rs35829419, CARD8 rs2043211, IL1β rs16944, IL1β rs1143623, IL6 rs1800795, CAT rs1001179, CAT rs10836235, SOD2 rs4880, NOS1 rs2293054, NOS1 rs2682826, TNF-α rs1800629, and GPX1 rs1050450. Logistic regression was used for analysis of possible associations.

RESULTS

We observed a nominally significant association of the IL1β rs1143623 C allele with the risk for Parkinson's disease (OR = 0.59; 95%CI = 0.38-0.92, p = 0.021). CAT rs1001179 A allele was significantly associated with peripheral edema (OR = 0.32; 95%CI = 0.15-0.68; p = 0.003). Other associations observed were only nominally significant after adjustments: NOS1 rs2682826 A allele and excessive daytime sleepiness and sleep attacks (OR = 1.75; 95%CI = 1.00-3.06, p = 0.048), SOD2 rs4880 T allele and nausea/vomiting (OR = 0.49, 95%CI = 0.25-0.94; p = 0.031), IL1β rs1143623 C allele and orthostatic hypotension (OR = 0.57, 95%CI = 0.32-1.00, p = 0.050), and NOS1 rs2682826 A allele and impulse control disorders (OR = 2.59; 95%CI = 1.09-6.19; p = 0.032). We did not find any associations between selected polymorphisms and motor adverse events.

CONCLUSIONS

Apart from some nominally significant associations, one significant association between CAT genetic variability and peripheral edema was observed as well. Therefore, the results of our study suggest some links between genetic variability in inflammation- and oxidative stress-related pathways and non-motor adverse events of dopaminergic treatment. However, the investigated polymorphisms do not play a major role in the occurrence of the disease and the adverse events of dopaminergic treatment.

Diagnostic biomarkers for Parkinson's disease at a glance: where are we?

Parkinson's disease (PD) is a neurodegenerative disorder whose aetiology remains unclear: degeneration involves several neurotransmission systems, resulting in a heterogeneous disease characterized by motor and non-motor symptoms. PD causes progressive disability that responds only to symptomatic therapies. Future advances include neuroprotective strategies for use in at-risk populations before the clinical onset of disease, hence the continuing need to identify reliable biomarkers that can facilitate the clinical diagnosis of PD. In this evaluative review, we summarize information on potential diagnostic biomarkers for use in the clinical and preclinical stages of PD.

New Targets for Parkinson's Disease: Adhesion G Protein-Coupled Receptor B1 is Downregulated by AMP-Activated Protein Kinase Activation

While progressive dopaminergic neurodegeneration is responsible for the cardinal motor defects in Parkinson's disease (PD), new diagnostics and therapeutic targets are necessary to effectively address this major global health burden. We evaluated whether the adhesion G protein-coupled receptor B1 (ADGRB1, formerly BAI1, brain-specific angiogenesis inhibitor 1) might contribute to dopaminergic neuronal loss. We used bioinformatic analyses, as well as in vitro and in vivo PD models. We report in this study that ADGRB1 is decreased in PD and that the ADGRB1 level is specifically decreased in dopaminergic neurons in the substantia nigra of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-intoxicated mice. In primary mouse mesencephalic neurons and human neuroblastoma cell lines, 1-methyl-4-phenylpyridinium (MPP⁺), a toxic metabolite of MPTP, suppressed the expression of ADGRB1. Moreover, we applied a network generation tool, Ingenuity Pathway Analysis^®, with the transcriptomics dataset to extend the upstream regulatory pathway of ADGRB1 expression. AMP-activated protein kinase (AMPK) was predicted as a regulator, and consequently, 5-aminoimidazole-4-carboxamide ribonucleotide, a specific activator of AMPK, reduced the ADGRB1 protein level. Finally, ADGRB1 overexpression decreased nuclear condensation induced by MPP⁺ treatment. Taken together, we observed that decreased ADGRB1 by activation of AMPK induced neuronal cell death in MPTP/MPP⁺-mediated PD models, suggesting that ADGRB1 might potentially play a survival role in the neurodegenerative pathway of PD. These data offer new insights into dopaminergic cell death with therapeutic implications for neurodegenerative disorders.