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

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs' potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease's comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods.

The Potential of ANK1 to Predict Parkinson's Disease

The main cause of Parkinson's disease (PD) remains unknown and the pathologic changes in the brain limit rapid diagnosis. Herein, differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database (GSE8397 and GSE22491) were assessed using linear models for microarray analysis (limma). Ankyrin 1 (ANK1) was the only common gene differentially down-regulated in lateral substantia nigra (LSN), medial substantia nigra (MSN) and blood. Additionally, DEGs between high ANK1 and low ANK1 in GSE99039 were picked out and then uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology (GO) functional annotation analysis. GO analysis displayed that these DEGs were mainly enriched in oxygen transport, myeloid cell development and gas transport (biological process (BP)); hemoglobin complex, haptoglobin-hemoglobin complex and cortical cytoskeleton (cellular component (CC)); and oxygen transporter activity, haptoglobin binding and oxygen binding (molecular function (MF)). Receiver operating characteristic (ROC) curve analysis showed ANK1 had good diagnostic accuracy and increased the area under the curve (AUC) value when combined with other biomarkers. Consistently, intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropy-ridi-ne (MPTP) in C57BL/6J mice reduced ANK1 mRNA expression in both substantia nigra and blood compared to the control group. Thus, ANK1 may serve as a candidate biomarker for PD diagnosis.

FGF13-Sensitive Alteration of Parkin Safeguards Mitochondrial Homeostasis in Endothelium of Diabetic Nephropathy

Studies of diabetic glomerular injury have raised the possibility of developing useful early biomarkers and therapeutic approaches for the treatment of type 2 diabetic nephropathy (T2DN). In this study, we found that FGF13 expression is induced in glomerular endothelial cells (GECs) during T2DN progression. Endothelial-specific deletion of Fgf13 potentially alleviates T2DN damage, while Fgf13 overexpression has the opposite effect. Mechanistically, Fgf13 deficiency results in improved mitochondrial homeostasis and endothelial barrier integrity in T2DN. Moreover, FGF13-sensitive alteration of Parkin safeguards mitochondrial homeostasis in endothelium of T2DN through promotion of mitophagy and inhibition of apoptosis. Additionally, it is confirmed that the beneficial effects of Fgf13 deficiency on T2DN are abolished by endothelial-specific double deletion of Fgf13 and Prkn. The effects of Fgf13 deficiency on mitophagy and apoptosis through Parkin-dependent regulation may be distinct and separable events under diabetic conditions. These data show that the bifunctional role of Fgf13 deficiency in promoting mitophagy and inhibiting apoptosis through Parkin can shape mitochondrial homeostasis regulation in GECs and T2DN progression. As a potential therapeutic target for prevention and control of T2DN, a mechanistic understanding of the biofunction of FGF13 may also be relevant to the pathogenesis of other FGF13- and Parkin-associated diseases.

Mercury Induced Tissue Damage, Redox Metabolism, Ion Transport, Apoptosis, and Intestinal Microbiota Change in Red Swamp Crayfish (Procambarus clarkii): Application of Multi-Omics Analysis in Risk Assessment of Hg

As one of the most toxic elements, mercury (Hg) is a widespread toxicant in aquatic environments. Crayfish are considered suitable for indicating the impact of heavy metals on aquatic crustaceans. Nevertheless, Hg toxicity on Procambarus clarkii is largely unknown. In this research, the acute Hg-induced alterations of biochemical responses, histopathology, hepatopancreatic transcriptome, and intestinal microbiome of Procambarus clarkii were studied. Firstly, Hg induced significant changes in reactive oxygen species (ROS) and malonaldehyde (MDA) content as well as antioxidant enzyme activity. Secondly, Hg exposure caused structural damage to the hepatopancreas (e.g., vacuolization of the epithelium and dilatation of the lumen) as well as to the intestines (e.g., dysregulation of lamina epithelialises and extension of lamina proprias). Thirdly, after treatment with three different concentrations of Hg, RNA-seq assays of the hepatopancreas revealed a large number of differentially expressed genes (DEGs) linked to a specific function. Among the DEGs, a lot of redox metabolism- (e.g., ACOX3, SMOX, GPX3, GLO1, and P4HA1), ion transport- (e.g., MICU3, MCTP, PYX, STEAP3, and SLC30A2), drug metabolism- (e.g., HSP70, HSP90A, CYP2L1, and CYP9E2), immune response- (e.g., SMAD4, HDAC1, and DUOX), and apoptosis-related genes (e.g., CTSL, CASP7, and BIRC2) were identified, which suggests that Hg exposure may perturb the redox equilibrium, disrupt the ion homeostasis, weaken immune response and ability, and cause apoptosis. Fourthly, bacterial 16S rRNA gene sequencing showed that Hg exposure decreased bacterial diversity and dysregulated intestinal microbiome composition. At the phylum level, there was a marked decrease in Proteobacteria and an increase in Firmicutes after exposure to high levels of Hg. With regards to genus, abundances of Bacteroides, Dysgonomonas, and Arcobacter were markedly dysregulated after Hg exposures. Our findings elucidate the mechanisms involved in Hg-mediated toxicity in aquatic crustaceans at the tissue, cellular, molecular as well as microbial levels.

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.

Clinical Sphingolipids Pathway in Parkinson’s Disease: From GCase to Integrated-Biomarker Discovery

Alterations in the sphingolipid metabolism of Parkinson’s Disease (PD) could be a potential diagnostic feature. Only around 10–15% of PD cases can be diagnosed through genetic alterations, while the remaining population, idiopathic PD (iPD), manifest without validated and specific biomarkers either before or after motor symptoms appear. Therefore, clinical diagnosis is reliant on the skills of the clinician, which can lead to misdiagnosis. IPD cases present with a spectrum of non-specific symptoms (e.g., constipation and loss of the sense of smell) that can occur up to 20 years before motor function loss (prodromal stage) and formal clinical diagnosis. Prodromal alterations in metabolites and proteins from the pathways underlying these symptoms could act as biomarkers if they could be differentiated from the broad values seen in a healthy age-matched control population. Additionally, these shifts in metabolites could be integrated with other emerging biomarkers/diagnostic tests to give a PD-specific signature. Here we provide an up-to-date review of the diagnostic value of the alterations in sphingolipids pathway in PD by focusing on the changes in definitive PD (postmortem confirmed brain data) and their representation in “probable PD” cerebrospinal fluid (CSF) and blood. We conclude that the trend of holistic changes in the sphingolipid pathway in the PD brain seems partly consistent in CSF and blood, and could be one of the most promising pathways in differentiating PD cases from healthy controls, with the potential to improve early-stage iPD diagnosis and distinguish iPD from other Parkinsonism when combined with other pathological markers.

Transcriptomic characterization of tissues from patients and subsequent pathway analyses reveal biological pathways that are implicated in spastic ataxia

BACKGROUND

Spastic ataxias (SAs) encompass a group of rare and severe neurodegenerative diseases, characterized by an overlap between ataxia and spastic paraplegia clinical features. They have been associated with pathogenic variants in a number of genes, including GBA2. This gene codes for the non-lysososomal β-glucosylceramidase, which is involved in sphingolipid metabolism through its catalytic role in the degradation of glucosylceramide. However, the mechanism by which GBA2 variants lead to the development of SA is still unclear.

METHODS

In this work, we perform next-generation RNA-sequencing (RNA-seq), in an attempt to discover differentially expressed genes (DEGs) in lymphoblastoid, fibroblast cell lines and induced pluripotent stem cell-derived neurons derived from patients with SA, homozygous for the GBA2 c.1780G > C missense variant. We further exploit DEGs in pathway analyses in order to elucidate candidate molecular mechanisms that are implicated in the development of the GBA2 gene-associated SA.

RESULTS

Our data reveal a total of 5217 genes with significantly altered expression between patient and control tested tissues. Furthermore, the most significant extracted pathways are presented and discussed for their possible role in the pathogenesis of the disease. Among them are the oxidative stress, neuroinflammation, sphingolipid signaling and metabolism, PI3K-Akt and MAPK signaling pathways.

CONCLUSIONS

Overall, our work examines for the first time the transcriptome profiles of GBA2-associated SA patients and suggests pathways and pathway synergies that could possibly have a role in SA pathogenesis. Lastly, it provides a list of DEGs and pathways that could be further validated towards the discovery of disease biomarkers.

Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson's Disease and Bipolar Disorder: A Pilot Study

Parkinson’s disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients’ coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M’s interaction with molecular drug candidates like N-formylmethionine, 3-indolebutyric acid, and doxycycline. Finally, a link between pathological processes of PD and BD is identified at transcriptional level. This study may help us to predict the development of PD among the people suffering from BD and gives some clue to understand significant pathological mechanisms.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.

Differentially Expressed Genes and Enriched Signaling Pathways in the Adipose Tissue of Obese People

As the prevalence of obesity increases, so does the occurrence of obesity-related complications, such as cardiovascular and cerebrovascular diseases, diabetes, and some cancers. Increased adipose tissue is the main cause of harm in obesity. To better understand obesity and its related complications, we analyzed the mRNA expression profiles of adipose tissues from 126 patients with obesity and 275 non-obese controls. Using an integrated bioinformatics method, we explored the functions of 113 differentially expressed genes (DEGs) between them. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses revealed that upregulated DEGs were enriched in immune cell chemotaxis, complement-related cascade activation, and various inflammatory signaling pathways, while downregulated DEGs enriched in nutrient metabolism. The CIBERSORT algorithm indicated that an increase in macrophages may be the main cause of adipose tissue inflammation, while decreased γδ T cells reduce sympathetic action, leading to dysregulation of adipocyte thermogenesis. A protein-protein interaction network was constructed using the STRING database, and the top 10 hub genes were identified using the cytoHubba plug-in in Cytoscape. All were confirmed to be obesity-related using a separate dataset. In addition, we identified chemicals related to these hub genes that may contribute to obesity. In conclusion, we have successfully identified several hub genes in the development of obesity, which provide insights into the possible mechanisms controlling obesity and its related complications, as well as potential biomarkers and therapeutic targets for further research.

Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.

Explorative Combined Lipid and Transcriptomic Profiling of Substantia Nigra and Putamen in Parkinson's Disease

Parkinson’s disease (PD) is characterized by the loss of dopaminergic neurons from the substantia nigra (SN) that project to the dorsal striatum (caudate-putamen). To better understand the molecular mechanisms underlying PD, we performed combined lipid profiling and RNA sequencing of SN and putamen samples from PD patients and age-matched controls. SN lipid analysis pointed to a neuroinflammatory component and included elevated levels of the endosomal lipid Bis (Monoacylglycero)Phosphate 42:8, while two of the three depleted putamen lipids were saturated sphingomyelin species. Remarkably, we observed gender-related differences in the SN and putamen lipid profiles. Transcriptome analysis revealed that the top-enriched pathways among the 354 differentially expressed genes (DEGs) in the SN were “protein folding” and “neurotransmitter transport”, and among the 261 DEGs from putamen “synapse organization”. Furthermore, we identified pathways, e.g., “glutamate signaling”, and genes, encoding, e.g., an angiotensin receptor subtype or a proprotein convertase, that have not been previously linked to PD. The identification of 33 genes that were common among the SN and putamen DEGs, which included the α-synuclein paralog β-synuclein, may contribute to the understanding of general PD mechanisms. Thus, our proof-of-concept data highlights new genes, pathways and lipids that have not been explored before in the context of PD.

GEO Data Sets Analysis Identifies COX-2 and Its Related Micro RNAs as Biomarkers for Non-Ischemic Heart Failure

Heart failure (HF) is a heterogeneous clinical syndrome with a variety of causes, risk factors, and pathology. Clinically, only brain natriuretic peptide (BNP) or its precursor N-terminus proBNP (NTproBNP) has been validated for HF diagnosis, but they are also affected by other conditions, such as female gender, renal disease, and acute coronary syndromes, and false low levels in the setting of obesity or flash pulmonary edema. In addition, there is no one biomarker which could encompass all heart failure phenotypes. Advances in bioinformatics have provided us with large databases that characterize the complex genetic and epigenetic changes associated with human diseases. The use of data mining strategies on public access databases to identify previously unknown disease markers is an innovative approach to identify potential biomarkers or even new therapeutic targets in complex diseases such as heart failure (HF). In this study, we analyzed the genomic and transcription data of HF peripheral blood mononuclear cell (PBMC) samples obtained from the Gene Expression Omnibus data sets using Omicsbean online database (http://www.omicsbean.cn/) and found that the prostaglandin-endoperoxide synthase 2 (PTGS2), also named as cyclooxygenase-2 (COX-2), as well as its related micro RNAs including miR-1297 and miR-4649-3p might be used as potential biomarkers for non-ischemic heart failure. Our result showed that plasma COX-2 and miR-4649-3p were significantly up-regulated, whereas the plasma miR-1297 was significantly decreased, and miR-4649-3p displayed high predictive power for non-ischemic heart failure.

Clinical Features of Parkinson's Disease: The Evolution of Critical Symptoms

Parkinson’s disease (PD) is a multi-attribute neurodegenerative disorder combining motor and nonmotor symptoms without well-defined diagnostic clinical markers. The presence of primary motor features (bradykinesia, rest tremor, rigidity and loss of postural reflexes) are the most characteristic signs of PD that are also utilized to identify patients in current clinical practice. The successful implementation of levodopa treatment revealed that nonmotor features are the main contributors of patient disability in PD, and their occurrence might be earlier than motor symptoms during disease progression. Targeted detection of prodromal PD symptoms can open up new possibilities in the identification of PD patients and provide potential patient populations for developing novel neuroprotective therapies. In this review, the evolution of critical features in PD diagnosis is described with special attention to nonmotor symptoms and their possible detection.