A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson's disease

Unraveling Dysregulated Cell Signaling Pathways, Genetic and Epigenetic Mysteries of Parkinson's Disease

Parkinson's disease (PD) is a prevalent and burdensome neurodegenerative disorder that has been extensively researched to understand its complex etiology, diagnosis, and treatment. The interplay between genetic and environmental factors in PD makes its pathophysiology difficult to comprehend, emphasizing the need for further investigation into genetic and epigenetic markers involved in the disease. Early diagnosis is crucial for optimal management of the disease, and the development of novel diagnostic biomarkers is ongoing. Although many efforts have been made in the field of recognition and interpretation of the mechanisms involved in the pathophysiology of the disease, the current knowledge about PD is just the tip of the iceberg. By scrutinizing genetic and epigenetic patterns underlying PD, new avenues can be opened for dissecting the pathology of the disorder, leading to more precise and efficient diagnostic and therapeutic approaches. This review emphasizes the importance of studying dysregulated cell signaling pathways and molecular processes associated with genes and epigenetic alterations in understanding PD, paving the way for the development of novel therapeutic strategies to combat this devastating disease.

The identification of biomarkers for Alzheimer's disease using a systems biology approach based on lncRNA-circRNA-miRNA-mRNA ceRNA networks

In addition to being the most prevalent form of neurodegeneration among the elderly, AD is a devastating multifactorial disease. Currently, treatments address only its symptoms. Several clinical studies have shown that the disease begins to manifest decades before the first symptoms appear, indicating that studying early changes is crucial to improving early diagnosis and discovering novel treatments. Our study used bioinformatics and systems biology to identify biomarkers in AD that could be used for diagnosis and prognosis. The procedure was performed on data from the GEO database, and GO and KEGG enrichment analysis were performed. Then, we set up a network of interactions between proteins. Several miRNA prediction tools including miRDB, miRWalk, and TargetScan were used. The ceRNA network led to the identification of eight mRNAs, four circRNAs, seven miRNAs, and seven lncRNAs. Multiple mechanisms, including the cell cycle and DNA replication, have been linked to the promotion of AD development by the ceRNA network. By using the ceRNA network, it should be possible to extract prospective biomarkers and therapeutic targets for the treatment of AD. It is possible that the processes involved in DNA cell cycle and the replication of DNA contribute to the development of Alzheimer's disease.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study

INTRODUCTION

This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection.

METHODS

We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation.

RESULTS

The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa.

CONCLUSIONS

The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

Whole Transcriptome Sequencing of Peripheral Blood Identifies the Alzheimer's Disease-Related circRNA-miRNA-lncRNA Pathway

BACKGROUND

Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in Alzheimer's disease. However, despite recent investigations revealing various aspects of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) networks in Alzheimer's Disease (AD) pathogenesis, few genome-wide studies have explored circRNA-associated profiles in AD patients exhibiting varying degrees of cognitive loss.

OBJECTIVE

To investigate the potential pathogenesis-related molecular biological changes in the various stages of AD progression.

METHODS

Whole transcriptome sequencing was performed on the peripheral blood of 7 normal cognition (NC) subjects, 8 patients with mild cognitive impairment, 8 AD patients with mild dementia (miD), and 7 AD patients with moderate dementia (moD). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to predict the potential functions of the maternal genes of microRNAs (miRNAs), circRNAs and long non-coding RNAs (lncRNAs). The construction of ceRNA network was performed between the NC group and each diseased group based on the differently expressed RNAs.

RESULTS

In total, 3568 messenger RNAs (mRNAs), 142 miRNAs, 990 lncRNAs, and 183 circRNAs were identified as significantly differentially expressed across the four groups. GO and KEGG enrichment analysis revealed the significant roles of GTPase activity and the MAPK signaling pathway in AD pathogenesis. A circRNA-miRNA-lncRNA ceRNA pathway, characterized by the downregulated hsa-miR-7-5p and upregulated hsa_circ_0001170, was identified based on the differentially expressed RNAs between the NC group and the moD group.

CONCLUSION

The study suggests that circRNAs may be independent of mRNAs in AD pathogenesis and holds promise as potential biomarkers for AD clinical manifestations and pathological changes.

LncRNA RP11-10E18.7 cooperates with lncRNA RP11-481C4.2 to affect the overall survival of breast cancer patients: a TCGA-based retrospective study

Background

As either oncogenes or tumor suppressor genes, long non-coding RNAs (lncRNAs) have a major role in both tumorigenesis and progression of human cancers, including breast cancer (BC). However, the statistical correlation between the lncRNA-lncRNA interaction and prognosis of BC remains unclear.

Methods

We analyzed the fragments per kilobase per million (FPKM) lncRNA expression data in tumor tissue samples from 890 female patients with BC in The Cancer Genome Atlas (TCGA) between May 2021 and October 2022. The Cox proportional hazards model adjusted for age, race, clinical stage, neoadjuvant therapy, estrogen receptor (ER), and progesterone receptor (PR) was adopted to evaluate the lncRNA-lncRNA interaction regarding overall survival (OS) of BC. The multiple comparison was corrected by Bonferroni method.

Results

RP11-10E18.7×RP11-481C4.2 was significantly associated with OS of BC patients [hazard ratio (HR)interaction =1.04, 95% confidence interval (CI): 1.03-1.06, P=3.35×10-9]. Then, gene-gene interaction analysis was performed for genes co-expressed with lncRNAs. FOXA1×U2SURP (HRinteraction =1.49, 95% CI: 1.28-1.73, P=2.16×10-7) was found to have a similar interactive pattern to RP11-10E18.7×RP11-481C4.2. after classifying the patients by intersection (3.47), we observed that the effect of FOXA1 opposite in patients with different U2SURP expression level (HRhigh vs. low =0.58, 95% CI: 0.34-0.99, P=0.046 in low expression of U2SURP; HRhigh vs. low =1.56, 95% CI: 1.18-2.87, P=0.029 in high expression of U2SURP).

Conclusions

Our comprehensive study identified RP11-10E18.7×RP11-481C4.2 as a potential biomarker of BC prognosis. The results play an essential role in the impact of lncRNA-lncRNA interaction on BC survival. Our findings elucidated potential molecular mechanisms of BC progression under complex association patterns and provided potential dynamic and reversible therapeutic targets for BC patients.

Circulating plasma miR-23b-3p as a biomarker target for idiopathic Parkinson's disease: comparison with small extracellular vesicle miRNA

Background

Parkinson's disease (PD) is an increasingly common neurodegenerative condition, which causes movement dysfunction and a broad range of non-motor symptoms. There is no molecular or biochemical diagnosis test for PD. The miRNAs are a class of small non-coding RNAs and are extensively studied owing to their altered expression in pathological states and facile harvesting and analysis techniques.

Methods

A total of 48 samples (16 each of PD, aged-matched, and young controls) were recruited. The small extracellular vesicles (sEVs) were isolated and validated using Western blot, transmission electron microscope, and nanoparticle tracking analysis. Small RNA isolation, library preparation, and small RNA sequencing followed by differential expression and targeted prediction of miRNA were performed. The real-time PCR was performed with the targeted miRNA on PD, age-matched, and young healthy control of plasma and plasma-derived sEVs to demonstrate their potential as a diagnostic biomarker.

Results

In RNA sequencing, we identified 14.89% upregulated (fold change 1.11 to 11.04, p < 0.05) and 16.54% downregulated (fold change -1.04 to -7.28, p < 0.05) miRNAs in PD and controls. Four differentially expressed miRNAs (miR-23b-3p, miR-29a-3p, miR-19b-3p, and miR-150-3p) were selected. The expression of miR-23b-3p was "upregulated" (p = 0.002) in plasma, whereas "downregulated" (p = 0.0284) in plasma-derived sEVs in PD than age-matched controls. The ROC analysis of miR-23b-3p revealed better AUC values in plasma (AUC = 0.8086, p = 0.0029) and plasma-derived sEVs (AUC = 0.7278, p = 0.0483) of PD and age-matched controls.

Conclusion

We observed an opposite expression profile of miR-23b-3p in PD and age-matched healthy control in plasma and plasma-derived sEV fractions, where the expression of miR-23b-3p is increased in PD plasma while decreased in plasma-derived sEV fractions. We further observed the different miR-23b-3p expression profiles in young and age-matched healthy control.

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer's disease

Background

Ferroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer's disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).

Methods

The microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R "limma" package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.

Results

We identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.

Conclusion

These results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

Identification and Experimental Validation of Parkinson's Disease with Major Depressive Disorder Common Genes

Parkinson's disease (PD) is the second most common neurodegenerative disease that affects about 10 million people worldwide. Non-motor and motor symptoms usually accompany PD. Major depressive disorder (MDD) is one of the non-motor manifestations of PD it remains unrecognized and undertreated effectively. MDD in PD has complicated pathophysiologies and remains unclear. The study aimed to explore the candidate genes and molecular mechanisms of PD with MDD. PD (GSE6613) and MDD (GSE98793) gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Above all, the data of the two datasets were standardized separately, and differentially expressed genes (DEGs) were obtained by using the Limma package of R. Take the intersection of the two differential genes and remove the genes with inconsistent expression trends. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were investigated to explore the function of the common DEGs. Additionally, the construction of the protein-protein interaction (PPI) network was to search the hub genes, and then the least absolute shrinkage and selection operator (LASSO) regression was used to further identify the key genes. GSE99039 for PD and GSE201332 for MDD were performed to validate the hub genes by the violin plot and receiver operating characteristic (ROC) curve. Last but not least, immune cell dysregulation in PD was investigated by immune cell infiltration. As a result, a total of 45 common genes with the same trend. Functional analysis revealed that they were enriched in neutrophil degranulation, secretory granule membrane, and leukocyte activation. LASSO was performed on 8 candidate hub genes after CytoHubba filtered 14 node genes. Finally, AQP9, SPI1, and RPH3A were validated by GSE99039 and GSE201332. Additionally, the three genes were also detected by the qPCR in vivo model and all increased compared to the control. The co-occurrence of PD and MDD can be attributed to AQP9, SPI1, and RPH3A genes. Neutrophils and monocyte infiltration play important roles in the development of PD and MDD. Novel insights may be gained from the findings for the study of mechanisms.

Systems level analysis of sex-dependent gene expression changes in Parkinson's disease

Parkinson's disease (PD) is a heterogeneous disorder, and among the factors which influence the symptom profile, biological sex has been reported to play a significant role. While males have a higher age-adjusted disease incidence and are more frequently affected by muscle rigidity, females present more often with disabling tremors. The molecular mechanisms involved in these differences are still largely unknown, and an improved understanding of the relevant factors may open new avenues for pharmacological disease modification. To help address this challenge, we conducted a meta-analysis of disease-associated molecular sex differences in brain transcriptomics data from case/control studies. Both sex-specific (alteration in only one sex) and sex-dimorphic changes (changes in both sexes, but with opposite direction) were identified. Using further systems level pathway and network analyses, coordinated sex-related alterations were studied. These analyses revealed significant disease-associated sex differences in mitochondrial pathways and highlight specific regulatory factors whose activity changes can explain downstream network alterations, propagated through gene regulatory cascades. Single-cell expression data analyses confirmed the main pathway-level changes observed in bulk transcriptomics data. Overall, our analyses revealed significant sex disparities in PD-associated transcriptomic changes, resulting in coordinated modulations of molecular processes. Among the regulatory factors involved, NR4A2 has already been reported to harbor rare mutations in familial PD and its pharmacological activation confers neuroprotective effects in toxin-induced models of Parkinsonism. Our observations suggest that NR4A2 may warrant further research as a potential adjuvant therapeutic target to address a subset of pathological molecular features of PD that display sex-associated profiles.

Searching for Biomarkers in the Blood of Patients at Risk of Developing Parkinson's Disease at the Prodromal Stage

Parkinson's disease (PD) is diagnosed many years after its onset, under a significant degradation of the nigrostriatal dopaminergic system, responsible for the regulation of motor function. This explains the low effectiveness of the treatment of patients. Therefore, one of the highest priorities in neurology is the development of the early (preclinical) diagnosis of PD. The aim of this study was to search for changes in the blood of patients at risk of developing PD, which are considered potential diagnostic biomarkers. Out of 1835 patients, 26 patients were included in the risk group and 20 patients in the control group. The primary criteria for inclusion in a risk group were the impairment of sleep behavior disorder and sense of smell, and the secondary criteria were neurological and mental disorders. In patients at risk and in controls, the composition of plasma and the expression of genes of interest in lymphocytes were assessed by 27 indicators. The main changes that we found in plasma include a decrease in the concentrations of l-3,4-dihydroxyphenylalanine (L-DOPA) and urates, as well as the expressions of some types of microRNA, and an increase in the total oxidative status. In turn, in the lymphocytes of patients at risk, an increase in the expression of the DA D3 receptor gene and the lymphocyte activation gene 3 (LAG3), as well as a decrease in the expression of the Protein deglycase DJ-1 gene (PARK7), were observed. The blood changes we found in patients at risk are considered candidates for diagnostic biomarkers at the prodromal stage of PD.

Identification of Hub Genes and Potential Molecular Pathogenesis in Substantia Nigra in Parkinson's Disease via Bioinformatics Analysis

Parkinson's disease (PD) is the second most common neurodegenerative disease, with significant socioeconomic burdens. One of the crucial pathological features of PD is the loss of dopaminergic neurons in the substantia nigra (SN). However, the exact pathogenesis remains unknown. Moreover, therapies to prevent neurodegenerative progress are still being explored. We performed bioinformatics analysis to identify candidate genes and molecular pathogenesis in the SN of patients with PD. We analyzed the expression profiles, GSE49036 and GSE7621, which included 31 SN tissues in PD samples and 17 SN tissues in healthy control samples, and identified 86 common differentially expressed genes (DEGs). Then, GO and KEGG pathway analyses of the identified DEGs were performed to understand the biological processes and significant pathways of PD. Subsequently, a protein-protein interaction network was established, with 15 hub genes and four key modules which were screened in this network. The expression profiles, GSE8397 and GSE42966, were used to verify these hub genes. We demonstrated a decrease in the expression levels of 14 hub genes in the SN tissues of PD samples. Our results indicated that, among the 14 hub genes, DRD2, SLC18A2, and SLC6A3 may participate in the pathogenesis of PD by influencing the function of the dopaminergic synapse. CACNA1E, KCNJ6, and KCNB1 may affect the function of the dopaminergic synapse by regulating ion transmembrane transport. Moreover, we identified eight microRNAs (miRNAs) that can regulate the hub genes and 339 transcription factors (TFs) targeting these hub genes and miRNAs. Subsequently, we established an mTF-miRNA-gene-gTF regulatory network. Together, the identification of DEGs, hub genes, miRNAs, and TFs could provide better insights into the pathogenesis of PD and contribute to the diagnosis and therapies.

Genotype by environment interactions for chronic wasting disease in farmed US white-tailed deer

Despite implementation of enhanced management practices, chronic wasting disease in US white-tailed deer (Odocoileus virginianus) continues to expand geographically. Herein, we perform the largest genome-wide association analysis to date for chronic wasting disease (n = 412 chronic wasting disease-positive; n = 758 chronic wasting disease-nondetect) using a custom Affymetrix Axiom single-nucleotide polymorphism array (n = 121,010 single-nucleotide polymorphisms), and confirm that differential susceptibility to chronic wasting disease is a highly heritable (h2= 0.611 ± 0.056) polygenic trait in farmed US white-tailed deer, but with greater trait complexity than previously appreciated. We also confirm PRNP codon 96 (G96S) as having the largest-effects on risk (P ≤ 3.19E-08; phenotypic variance explained ≥ 0.025) across 3 US regions (Northeast, Midwest, South). However, 20 chronic wasting disease-positive white-tailed deer possessing codon 96SS genotypes were also observed, including one that was lymph node and obex positive. Beyond PRNP, we also detected 23 significant single-nucleotide polymorphisms (P-value ≤ 5E-05) implicating ≥24 positional candidate genes; many of which have been directly implicated in Parkinson's, Alzheimer's and prion diseases. Genotype-by-environment interaction genome-wide association analysis revealed a single-nucleotide polymorphism in the lysosomal enzyme gene ARSB as having the most significant regional heterogeneity of effects on chronic wasting disease (P ≤ 3.20E-06); with increasing copy number of the minor allele increasing susceptibility to chronic wasting disease in the Northeast and Midwest; but with opposite effects in the South. In addition to ARSB, 38 significant genotype-by-environment single-nucleotide polymorphisms (P-value ≤ 5E-05) were also detected, thereby implicating ≥ 36 positional candidate genes; the majority of which have also been associated with aspects of Parkinson's, Alzheimer's, and prion diseases.

Differentially Expressed miRNAs in Age-Related Neurodegenerative Diseases: A Meta-Analysis

To date, no neurodegenerative diseases (NDDs) have cures, and the underlying mechanism of their pathogenesis is undetermined. As miRNAs extensively regulate all biological processes and are crucial regulators of healthy brain function, miRNAs differentially expressed in NDDs may provide insight into the factors that contribute to the emergence of protein inclusions and the propagation of deleterious cellular environments. A meta-analysis of miRNAs dysregulated in Alzheimer’s disease, Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies and frontotemporal lobar degeneration (TDP43 variant) was performed to determine if diseases within a proteinopathy have distinct or shared mechanisms of action leading to neuronal death, and if proteinopathies can be classified on the basis of their miRNA profiles. Our results identified both miRNAs distinct to the anatomy, disease type and pathology, and miRNAs consistently dysregulated within single proteinopathies and across neurodegeneration in general. Our results also highlight the necessity to minimize the variability between studies. These findings showcase the need for more transcriptomic research on infrequently occurring NDDs, and the need for the standardization of research techniques and platforms utilized across labs and diseases.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson's disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

Neurodegeneration by α-synuclein-specific T cells in AAV-A53T-α-synuclein Parkinson's disease mice

BACKGROUND

Antigen-specific neuroinflammation and neurodegeneration are characteristic for neuroimmunological diseases. In Parkinson's disease (PD) pathogenesis, α-synuclein is a known culprit. Evidence for α-synuclein-specific T cell responses was recently obtained in PD. Still, a causative link between these α-synuclein responses and dopaminergic neurodegeneration had been lacking. We thus addressed the functional relevance of α-synuclein-specific immune responses in PD in a mouse model.

METHODS

We utilized a mouse model of PD in which an Adeno-associated Vector 1/2 serotype (AAV1/2) expressing human mutated A53T-α-Synuclein was stereotactically injected into the substantia nigra (SN) of either wildtype C57BL/6 or Recombination-activating gene 1 (RAG1)^-/- mice. Brain, spleen, and lymph node tissues from different time points following injection were then analyzed via FACS, cytokine bead assay, immunohistochemistry and RNA-sequencing to determine the role of T cells and inflammation in this model. Bone marrow transfer from either CD4⁺/CD8^-, CD4^-/CD8⁺, or CD4⁺/CD8⁺ (JHD^-/-) mice into the RAG-1^-/- mice was also employed. In addition to the in vivo studies, a newly developed A53T-α-synuclein-expressing neuronal cell culture/immune cell assay was utilized.

RESULTS

AAV-based overexpression of pathogenic human A53T-α-synuclein in dopaminergic neurons of the SN stimulated T cell infiltration. RNA-sequencing of immune cells from PD mouse brains confirmed a pro-inflammatory gene profile. T cell responses were directed against A53T-α-synuclein-peptides in the vicinity of position 53 (68-78) and surrounding the pathogenically relevant S129 (120-134). T cells were required for α-synuclein-induced neurodegeneration in vivo and in vitro, while B cell deficiency did not protect from dopaminergic neurodegeneration.

CONCLUSIONS

Using T cell and/or B cell deficient mice and a newly developed A53T-α-synuclein-expressing neuronal cell culture/immune cell assay, we confirmed in vivo and in vitro that pathogenic α-synuclein peptide-specific T cell responses can cause dopaminergic neurodegeneration and thereby contribute to PD-like pathology.

Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

Genetic and Epigenetic Alterations Induced by Pesticide Exposure: Integrated Analysis of Gene Expression, microRNA Expression, and DNA Methylation Datasets

Environmental or occupational exposure to pesticides is considered one of the main risk factors for the development of various diseases. Behind the development of pesticide-associated pathologies, there are both genetic and epigenetic alterations, where these latter are mainly represented by the alteration in the expression levels of microRNAs and by the change in the methylation status of the DNA. At present, no studies have comprehensively evaluated the genetic and epigenetic alterations induced by pesticides; therefore, the aim of the present study was to identify modifications in gene miRNA expression and DNA methylation useful for the prediction of pesticide exposure. For this purpose, an integrated analysis of gene expression, microRNA expression, and DNA methylation datasets obtained from the GEO DataSets database was performed to identify putative genes, microRNAs, and DNA methylation hotspots associated with pesticide exposure and responsible for the development of different diseases. In addition, DIANA-miRPath, STRING, and GO Panther prediction tools were used to establish the functional role of the putative biomarkers identified. The results obtained demonstrated that pesticides can modulate the expression levels of different genes and induce different epigenetic alterations in the expression levels of miRNAs and in the modulation of DNA methylation status.

From Junk to Function: LncRNAs in CNS Health and Disease

Recent advances in RNA sequencing technologies helped to uncover the existence of tens of thousands of long non-coding RNAs (lncRNAs) that arise from the dark matter of the genome. These lncRNAs were originally thought to be transcriptional noise but an increasing number of studies demonstrate that these transcripts can modulate protein-coding gene expression by a wide variety of transcriptional and post-transcriptional mechanisms. The spatiotemporal regulation of lncRNA expression is particularly evident in the central nervous system, suggesting that they may directly contribute to specific brain processes, including neurogenesis and cellular homeostasis. Not surprisingly, lncRNAs are therefore gaining attention as putative novel therapeutic targets for disorders of the brain. In this review, we summarize the recent insights into the functions of lncRNAs in the brain, their role in neuronal maintenance, and their potential contribution to disease. We conclude this review by postulating how these RNA molecules can be targeted for the treatment of yet incurable neurological disorders.

MicroRNAs, Parkinson's Disease, and Diabetes Mellitus

Parkinson’s disease (PD) is a neurodegenerative disorder that affects 1% of the population over the age of 60. Diabetes Mellitus (DM) is a metabolic disorder that affects approximately 25% of adults over the age of 60. Recent studies showed that DM increases the risk of developing PD. The link between DM and PD has been discussed in the literature in relation to different mechanisms including mitochondrial dysfunction, oxidative stress, and protein aggregation. In this paper, we review the common microRNA (miRNA) biomarkers of both diseases. miRNAs play an important role in cell differentiation, development, the regulation of the cell cycle, and apoptosis. They are also involved in the pathology of many diseases. miRNAs can mediate the insulin pathway and glucose absorption. miRNAs can also regulate PD-related genes. Therefore, exploring the common miRNA biomarkers of both PD and DM can shed a light on how these two diseases are correlated, and targeting miRNAs is a potential therapeutic opportunity for both diseases.

Meta-Analysis of Differentially Expressed Genes in the Substantia Nigra in Parkinson's Disease Supports Phenotype-Specific Transcriptome Changes

Background

Studies regarding differentially expressed genes (DEGs) in Parkinson’s disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies.

Purpose

Meta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner.

Methods

Six microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer’s disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures.

Results

Our meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes.

Conclusion

Downregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.

Long Noncoding RNA LOC400043 (LINC02381) Inhibits Gastric Cancer Progression Through Regulating Wnt Signaling Pathway

Gastric cancer is one of the common causes of cancer mortality worldwide, with a low survival rate for the affected people. Recent studies have revealed the key role of long non-coding RNAs (lncRNAs) in the development and progression of many cancers, including gastric cancer. Looking for the potential molecular regulators of gastric cancer incidence and progression, LINC02381 was identified as a downregulated lncRNA in gastric cancer tissues by analysis of available microarray and RNA-seq data and RT-qPCR confirmed this differential expression. MiR-21, miR-590, and miR-27a miRNAs were predicted to be sponged by LINC02381, and dual luciferase assay verified LINC02381 as a competitive endogenous RNA (CeRNA), which binds to them. Furthermore, we found that increased expression of LINC02381 attenuates Wnt pathway activity. Also, functional analysis indicates that LINC02381 arrests cell cycle, increases apoptosis and caspase activity, and reduces cell survival and proliferation rate of the human gastric cancer cell lines AGS and MKN45. Moreover, EMT analysis showed that LINC02381 is involved in gastric cancer progression and inhibits metastasis. Overall, this work for the first time introduces LINC02381 as a CeRNA involved in gastric cancer and provides novel insight into the molecular pathogenesis of gastric cancer.

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.

Comparative transcriptome analysis of Parkinson's disease and Hutchinson-Gilford progeria syndrome reveals shared susceptible cellular network processes

BACKGROUND

Parkinson's Disease (PD) and Hutchinson-Gilford Progeria Syndrome (HGPS) are two heterogeneous disorders, which both display molecular and clinical alterations associated with the aging process. However, similarities and differences between molecular changes in these two disorders have not yet been investigated systematically at the level of individual biomolecules and shared molecular network alterations.

METHODS

Here, we perform a comparative meta-analysis and network analysis of human transcriptomics data from case-control studies for both diseases to investigate common susceptibility genes and sub-networks in PD and HGPS. Alzheimer's disease (AD) and primary melanoma (PM) were included as controls to confirm that the identified overlapping susceptibility genes for PD and HGPS are non-generic.

RESULTS

We find statistically significant, overlapping genes and cellular processes with significant alterations in both diseases. Interestingly, the majority of these shared affected genes display changes with opposite directionality, indicating that shared susceptible cellular processes undergo different mechanistic changes in PD and HGPS. A complementary regulatory network analysis also reveals that the altered genes in PD and HGPS both contain targets controlled by the upstream regulator CDC5L.

CONCLUSIONS

Overall, our analyses reveal a significant overlap of affected cellular processes and molecular sub-networks in PD and HGPS, including changes in aging-related processes that may reflect key susceptibility factors associated with age-related risk for PD.

Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

α-synuclein accumulation in SH-SY5Y cell impairs autophagy in microglia by exosomes overloading miR-19a-3p

Aims: To reveal whether miRNAs in exosomes from α-synuclein transgenic SH-SY5Y cells are able to regulate autophagy in recipient microglia. Materials & methods: Microarray analysis and experimental verification were adopted to assess the significance of autophagy-associated miRNAs in exosomes from neuronal model of α-synucleinopathies. Results: We found that miR-19a-3p increased remarkably in the exosomes from α-synuclein gene transgenic SH-SY5Y cells. Further study inferred that α-synuclein gene transgenic SH-SY5Y cell-derived exosomes and miR-19a-3p mimic consistently inhibited the expression of phosphatase and tensin homolog and increased the phosphorylation of AKT and mTOR, both of which ultimately lead to the dysfunction of autophagy in recipient microglia. Conclusion: The data suggested that enhanced expression of miR-19a-3p in exosomes suppress autophagy in recipient microglia by targeting the phosphatase and tensin homolog/AKT/mTOR signaling pathway.

Elevated Plasma microRNA-105-5p Level in Patients With Idiopathic Parkinson's Disease: A Potential Disease Biomarker

Parkinson's disease (PD) is the second most common neurodegenerative disease, which still lacks a biomarker to aid in diagnosis and to differentiate diagnosis at the early stage of the disease. microRNAs (miRNAs) are small and evolutionary conserved non-coding RNAs that are involved in post-transcriptional gene regulation. Several miRNAs have been proposed as potential biomarkers in several diseases. In the present study, we screened miRNAs using a network vulnerability analysis, to evaluate their potential as PD biomarkers. We first extracted miRNAs that were differentially expressed between PD and healthy controls (HC) samples. Then we constructed the PD-specific miRNA-mRNA network and screened miRNA biomarkers using a new bioinformatics model. With this model, we identified miR-105-5p as a putative biomarker for PD. Moreover, we measured miR-105-5p levels in the plasma of patients with idiopathic PD (IPD) (n = 319), neurological disease controls (NDC, n = 305) and HC (n = 273) using reverse transcription real-time quantitative PCR (RT-qPCR). Our data clearly demonstrated that the plasma miR-105-5p level in IPD patients was significantly higher than those of HC (251%, p < 0.001) and NDC (347%, p < 0.001). There was no significant difference in miR-105-5p expression between IPD patients with or without anti-PD medications. Interestingly, we found that the plasma miR-105-5p expression level may be able to differentiate IPD from parkinsonian syndrome, essential tremor and other neurodegenerative diseases. We believe that a change in the plasma miR-105-5p level is a potential biomarker for IPD.

Expression and potential molecular mechanisms of miR‑204‑5p in breast cancer, based on bioinformatics and a meta‑analysis of 2,306 cases

Breast cancer (BC) is the most common cancer among women worldwide. However, there is insufficient research that focuses on the expression and molecular mechanisms of microRNA (miR)‑204‑5p in BC. In the current study, data were downloaded from the Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and the University of California Santa Cruz (UCSC) Xena databases. They were then used to undertake a meta‑analysis that leveraged the standard mean difference (SMD) and summarized receiver operating characteristic (sROC) to evaluate the expression of the precursor miR‑204 and mature miR‑204‑5p in BC. Additionally, an intersection of predicted genes, differentially expressed genes (DEGs) from the TCGA database and the GEO database were plotted to acquire desirable putative genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein‑protein interaction (PPI) network analyses were performed to assess the potential pathways and hub genes of miR‑204‑5p in BC. A decreased trend in precursor miR‑204 expression was detected in 1,077 BC tissue samples in comparison to 104 para‑carcinoma tissue samples in the TCGA database. Further, the expression of mature miR‑204‑5p was markedly downregulated in 756 BC tissue samples in comparison to 76 para‑carcinoma tissue samples in the UCSC Xena database. The outcome of the SMD from meta‑analysis also indicated that the expression of miR‑204‑5p was markedly reduced in 2,306 BC tissue samples in comparison to 367 para‑carcinoma tissue samples. Additionally, the ROC and sROC values indicated that miR‑204‑5p had a great discriminatory capacity for BC. In GO analysis, 'cell development', 'cell surface activity', and 'receptor agonist activity' were the most enriched terms; in KEGG analysis, 'endocytosis' was significantly enriched. Rac GTPase activating protein 1 (RACGAP1) was considered the hub gene in the PPI network. In conclusion, miR‑204‑5p may serve a suppressor role in the oncogenesis and advancement of BC, and miR‑204‑5p may have crucial functions in BC by targeting RACGAP1.