Integrated microarray analysis provided a new insight of the pathogenesis of Parkinson’s disease

Mitochondrial proteome analysis reveals that an augmented cytochrome c oxidase assembly and activity potentiates respiratory capacity in sarcoma

Mitochondrial oxidative phosphorylation (OXPHOS) is an obligatory process in sarcoma. Despite that, the metabolic programming of sarcoma mitochondria is still unknown. To obtain a comprehensive metabolic insight of mitochondria, we developed a mouse fibrosarcoma model by injecting 3-methylcholanthrene and compared mitochondrial proteomes between sarcoma and its contralateral normal muscle using mass spectrometry. Our study identified ∼449 proteins listed in the SwissProt databases, and all the data sets are available via ProteomeXchange with the identifier PXD044903. In sarcoma, 49 mitochondrial proteins were found differentially expressed, including 36 proteins up-regulated and 13 proteins down-regulated, with the significance of p-value <0.05 and the log2[fold change] > 1 and < -1 as compared to normal muscle. Our data revealed that various anaplerotic reactions actively replenish the TCA cycle in sarcoma. The comparative expression profile and Western blotting analysis of OXPHOS subunits showed that complex-IV subunits, MT-CO3 and COX6A1, were significantly up-regulated in sarcoma vs. normal muscle. Further, biochemical and physiological assays confirmed enhanced complex-IV specific enzymatic and supercomplex activities with a concomitant increase of oxygen consumption rate in sarcoma mitochondria compared to normal muscle. Validation with human post-operative sarcoma tissues also confirms an increased MT-CO3 expression compared to normal tissue counterparts. Thus, our data comprehensively analyses the mitochondrial proteome and identifies augmented complex-IV assembly and activity in sarcoma.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

LncRNA MEG3 activates CDH2 expression by recruitment of EP300 in valproic acid-induced autism spectrum disorder

LncRNAs partake in the biological processes contributing to development of autism spectrum disorder (ASD). The aim of the present study is to investigate the effects of lncRNA maternally expressed gene 3 (MEG3) on viability and apoptosis of hippocampal neurons from ASD rats. Rats with ASD were induced using valproic acid (VPA) with normal saline (NS) as control. We performed microarray analysis on hippocampal tissues of NS rats and ASD rats to screen the differentially expressed lncRNAs. MEG3 loss in rats alleviated the impairment of learning and memory abilities induced by VPA, and promoted neuronal viability and inhibited apoptosis. MEG3 could recruit the transcription factor E1A binding protein p300 (EP300) in the nucleus and promote the cadherin 2 (CDH2) expression. CDH2 depletion in rats ameliorated the impairment of learning and memory capacities in ASD rats. After upregulation of CDH2 in neurons with sh-MEG3, we found diminished viability and increased apoptosis in hippocampal neurons of ASD rats. Taken together, MEG3 supports activation of CDH2 via EP300, thus repressing the viability of hippocampal neurons. Therefore, MEG3 upregulation may be partially responsible for the pathogenesis of ASD.

Ethnic-Specific Type 2 Diabetes Risk Factor PAX4 R192H Is Associated with Attention-Specific Cognitive Impairment in Chinese with Type 2 Diabetes

Background: Type 2 diabetes mellitus (T2DM) has been shown to increase the risks of cognitive decline and dementia. Paired box gene 4 (PAX4), a transcription factor for beta cell development and function, has recently been implicated in pathways intersecting Alzheimer’s disease and T2DM. Objective: In this report, we evaluated the association of the ethnic-specific PAX4 R192H variant, a T2DM risk factor for East Asians which contributes to earlier diabetes onset, and cognitive function of Chinese T2DM patients. Methods: 590 Chinese patients aged 45–86 from the SMART2D study were genotyped for PAX4 R192H variation using Illumina OmniExpress-24 Array. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) which had been validated in the Singapore population was administered to assess five cognitive domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. Multiple linear regression was used to assess the association of the R192H risk allele and cognitive domains. Results: Patients with two PAX4 R192H risk alleles showed significantly lower attention index score (β= –8.46, 95% CI [–13.71, –3.21], p = 0.002) than patients with wild-type alleles after adjusting for age, gender, diabetes onset age, HbA1c, body-mass index, renal function, lipid profiles, systolic blood pressure, metformin usage, smoking history, education level, Geriatric Depression Scale score, and presence of APOE ɛ4 allele. Conclusion: Ethnic-specific R192H variation in PAX4 is associated with attention-specific cognitive impairment in Chinese with T2DM. Pending further validation studies, determining PAX4 R192H genotype may be helpful for early risk assessment of early-onset T2DM and cognitive impairment to improve diabetes care.

Somatic Mutations Detected in Parkinson Disease Could Affect Genes With a Role in Synaptic and Neuronal Processes

The role of somatic mutations in complex diseases, including neurodevelopmental and neurodegenerative disorders, is becoming increasingly clear. However, to date, no study has shown their relation to Parkinson disease’s phenotype. To explore the relevance of embryonic somatic mutations in sporadic Parkinson disease, we performed whole-exome sequencing in blood and four brain regions of ten patients. We identified 59 candidate somatic single nucleotide variants (sSNVs) through sensitive calling and a careful filtering strategy (COSMOS). We validated 27 of them with amplicon-based ultra-deep sequencing, with a 70% validation rate for the highest-confidence variants. The identified sSNVs are in genes with synaptic functions that are co-expressed with genes previously associated with Parkinson disease. Most of the sSNVs were only called in blood but were also found in the brain tissues with ultra-deep amplicon sequencing, demonstrating the strength of multi-tissue sampling designs.

A co-expression network based molecular characterization of genes responsive for Braak stages in Parkinson's disease

The progression of Parkinson's disease (PD) is defined by six Braak stages. We used transcriptome data from PD patients with Braak stage information to understand underlying molecular mechanisms for the progress of the disease. We created networks of genes with decreased/increased co-expression from control group to Braak 5-6 stages. These networks are significantly associated with PD related mechanisms such as mitochondrial dysfunction and synaptic signaling among others. Applying Weighted Gene Correlation Network Analysis (WGCNA) algorithm to the co-expression networks led to more specific modules enriched with neurodegeneration related disease pathways, seizure, abnormality of coordination, and hypotonia. Furthermore, we showed that one of the co-expression networks is clustered into three major communities with dedicated molecular functions: (i) tubulin folding pathway, gap junction related mechanisms, neuronal system (ii) synaptic vesicle, intracellular vesicle, proteasome complex, PD genes (iii) energy metabolism, mitochondrial mechanisms, oxidative phosphorylation, TCA cycle, PD genes. The co-expression relations we identified in this study as crucial players in the disease progression cover several known PD-associated genes and genes whose products are known to physically interact with alpha-synuclein protein.

Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.

A nexus of miR-1271, PAX4 and ALK/RYK influences the cytoskeletal architectures in Alzheimer's Disease and Type 2 Diabetes

Alzheimer's Disease (AD) and Type 2 Diabetes (T2D) share a common hallmark of insulin resistance. Reportedly, two non-canonical Receptor Tyrosine Kinases (RTKs), ALK and RYK, both targets of the same micro RNA miR-1271, exhibit significant and consistent functional down-regulation in post-mortem AD and T2D tissues. Incidentally, both have Grb2 as a common downstream adapter and NOX4 as a common ROS producing factor. Here we show that Grb2 and NOX4 play critical roles in reducing the severity of both the diseases. The study demonstrates that the abundance of Grb2 in degenerative conditions, in conjunction with NOX4, reverse cytoskeletal degradation by counterbalancing the network of small GTPases. PAX4, a transcription factor for both Grb2 and NOX4, emerges as the key link between the common pathways of AD and T2D. Down-regulation of both ALK and RYK through miR-1271, elevates the PAX4 level by reducing its suppressor ARX via Wnt/β-Catenin signaling. For the first time, this study brings together RTKs beyond Insulin Receptor (IR) family, transcription factor PAX4 and both AD and T2D pathologies on a common regulatory platform.

DNA Methylation and Expression Profiles of Whole Blood in Parkinson's Disease

Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease. It is presently only accurately diagnosed at an advanced stage by a series of motor deficits, which are predated by a litany of non-motor symptoms manifesting over years or decades. Aberrant epigenetic modifications exist across a range of diseases and are non-invasively detectable in blood as potential markers of disease. We performed comparative analyses of the methylome and transcriptome in blood from PD patients and matched controls. Our aim was to characterize DNA methylation and gene expression patterns in whole blood from PD patients as a foundational step toward the future goal of identifying molecular markers that could predict, accurately diagnose, or track the progression of PD. We found that differentially expressed genes (DEGs) were involved in the processes of transcription and mitochondrial function and that PD methylation profiles were readily distinguishable from healthy controls, even in whole-blood DNA samples. Differentially methylated regions (DMRs) were functionally varied, including near transcription factor nuclear transcription factor Y subunit alpha (NFYA), receptor tyrosine kinase DDR1, RING finger ubiquitin ligase (RNF5), acetyltransferase AGPAT1, and vault RNA VTRNA2-1. Expression quantitative trait methylation sites were found at long non-coding RNA PAX8-AS1 and transcription regulator ZFP57 among others. Functional epigenetic modules were highlighted by IL18R1, PTPRC, and ITGB2. We identified patterns of altered disease-specific DNA methylation and associated gene expression in whole blood. Our combined analyses extended what we learned from the DEG or DMR results alone. These studies provide a foundation to support the characterization of larger sample cohorts, with the goal of building a thorough, accurate, and non-invasive molecular PD biomarker.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Proteomic Workflows for High-Quality Quantitative Proteome and Post-Translational Modification Analysis of Clinically Relevant Samples from Formalin-Fixed Paraffin-Embedded Archives

Well-characterized archival formalin-fixed paraffin-embedded (FFPE) tissues are of much value for prospective biomarker discovery studies, and protocols that offer high throughput and good reproducibility are essential in proteomics. Therefore, we implemented efficient paraffin removal and protein extraction from FFPE tissues followed by an optimized two-enzyme digestion using suspension trapping (S-Trap). The protocol was then combined with TMTpro 16plex labeling and applied to lung adenocarcinoma patient samples. In total, 9585 proteins were identified, and proteins related to the clinical outcome were detected. Because acetylation is known to play a major role in cancer development, a fast on-trap acetylation protocol was developed for studying endogenous lysine acetylation, which allows identification and localization of the lysine acetylation together with quantitative comparison between samples. We demonstrated that FFPE tissues are equivalent to frozen tissues to study the degree of acetylation between patients. In summary, we present a reproducible sample preparation workflow optimized for FFPE tissues that resolves known proteomic-related challenges. We demonstrate compatibility of the S-Trap with isobaric labeling and for the first time, we prove that it is feasible to study endogenous lysine acetylation stoichiometry in FFPE tissues, contributing to better utility of the existing global tissue archives. The MS proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifiers PXD020157, PXD021986, and PXD021964.

C/EBPβ mediates NQO1 and GSTP1 anti-oxidative reductases expression in glioblastoma, promoting brain tumor proliferation

Glioblastoma (GBM) is the most common and most aggressive brain tumor, associated with high levels of reactive oxidative species (ROS) due to metabolic and signaling aberrations. High ROS levels are detrimental to cells, but it remains incompletely understood how cancer cells cope with the adverse effects. Here we show that C/EBPβ, a ROS responsive transcription factor, regulates the transcription of NQO1 and GSTP1, two antioxidative reductases, which neutralize ROS in the GBM and mediates their proliferation. C/EBPβ is upregulated in EGFR overexpressed GBM cells, inversely correlated with the survival rates of brain tumor patients. Interestingly, C/EBPβ binds the promoters of NQO1 and GSTP1 and escalates their expression. Overexpression of C/EBPβ selectively decreases the ROS in EGFR-overexpressed U87MG cells and promotes cell proliferation via upregulating NQO1 and GSTP1; whereas knocking down C/EBPβ elevates the ROS and reduces proliferation by repressing the reductases. Accordingly, C/EBPβ mediates the brain tumor growth in vivo, coupling with NQO1 and GSTP1 expression and ROS levels. Hence, C/EBPβ regulates the expression of antioxidative reductases and balances the ROS, promoting brain tumor proliferation.

Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and classify the primary differentially expressed genes (DEGs).

METHODS

The present study searched for and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differences in mRNA expression in the substantia nigra (SN) and blood of patients with PD and healthy controls. In addition, in order to investigate the biological functions of the classified dysregulated genes, the present study utilized Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), reverse transcription-quantitative PCR (RT-qPCR), gene co-expression network analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A receiver operating characteristic (ROC) curve was applied to assay TMEM243 as a diagnostic marker.

RESULTS

Between PD and controls in GSE20292, the present study identified 1862 DEGs. Using the weighted gene co-expression network analysis, the present study identified 15 modules in PD. The module preservation analysis revealed that the tan, blue and green-yellow modules were the most stable. KEGG pathway analysis revealed that five DEGs in the black module were significantly enriched in the ubiquitin-mediated proteolysis pathway, nucleotide excision repair pathway, mismatch repair pathway. The present study selected 303 genes with high connectivity in blue, green-yellow and tan modules as hub genes, where 58 were differentially expressed in both the GSE20292 and GSE54536 datasets. In the SN and blood, 11 genes exhibited the same trend of expression. Furthermore, in the blood samples of patients with PD, the results displayed a significant upregulation of TMEM243. The expression levels of CCR4, CAMK1D, ACTR1B and SPSB3 increased, while both the levels of INA and PSMD4 decreased. These findings are consistent with the bioinformatics analysis results but are not statistically significant. TMEM243 can be considered as a diagnostic biomarker (area under the curve = 0.694; sensitivity, 80 %; specificity, 56 %; P < 0.018).

CONCLUSION

TMEM243 was distinctly upregulated in the blood samples of patients with PD, as validated via RT-qPCR, and was highly sensitive, revealing its potential as a biomarker for the future diagnosis of PD.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

Identification of key genes and upstream regulators in ischemic stroke

INTRODUCTION

Ischemic stroke (IS) causes severe neurological impairments and physical disabilities and has a high economic burden. Our study aims to identify the key genes and upstream regulators in IS by integrated microarray analysis.

METHODS

An integrated analysis of microarray studies of IS was performed to identify the differentially expressed genes (DEGs) in IS compared to normal control. Based on these DEGs, we performed the functional annotation and transcriptional regulatory network constructions. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the expression of DEGs.

RESULTS

From two Gene Expression Omnibus datasets obtained, we obtained 1526 DEGs (534 up-regulated and 992 down-regulated genes) between IS and normal control. The results of functional annotation showed that Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in IS. Top four transcription factors (TFs) with the most downstream genes including PAX4, POU2F1, ELK1, and NKX2-5. The expression of six genes (ID3, ICAM2, DCTPP1, ANTXR2, DUSP1, and RGS2) was detected by qRT-PCR. Except for DUSP1 and RGS2, the other four genes in qRT-PCR played the same pattern with that in our integrated analysis.

CONCLUSIONS

The dysregulation of these six genes may involve with the process of ischemic stroke (IS). Four TFs (PAX4, POU2F1, ELK1 and NKX2-5) were concluded to play a role in IS. Our finding provided clues for exploring mechanism and developing novel diagnostic and therapeutic strategies for IS.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

Background

Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.

Results

We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.

Conclusions

Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users.

Mitochondrial proteostasis in the context of cellular and organismal health and aging

As a central hub of cellular metabolism and signaling, the mitochondrion is a crucial organelle whose dysfunction can cause disease and whose activity is intimately connected to aging. We review how the mitochondrial network maintains proteomic integrity, how mitochondrial proteotoxic stress is communicated and resolved in the context of the entire cell, and how mitochondrial systems function in the context of organismal health and aging. A deeper understanding of how mitochondrial protein quality control mechanisms are coordinated across these distinct biological levels should help explain why these mechanisms fail with age and, ultimately, how routes to intervention might be attained.