scholarly journals Transcriptomic profiling of differentially expressed genes and related pathways in different brain regions of Parkinson’s disease

2019 ◽  
Author(s):  
Qinyu Ge ◽  
Erteng Jia ◽  
Min Pan ◽  
Zhiyu Liu ◽  
Ying Zhou ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Therapeutic Strategies ◽  
Differentially Expressed ◽  
Control Group ◽  
Transcriptomic Data ◽  
Potential Use

Abstract BackgroundParkinson’s disease (PD) is the second most common neurodegenerative disease and many studies have researched its complex pathophysiological processes. However, it is unclear how PD affects the structure of transcripts in different brain regions and how changes in the transcriptomes in different brain regions affect the pathogenesis of PD.ResultsWe generated a PD mouse model by injecting with MPTP solution. RNA sequencing was performed in the cerebral cortex, hippocampus, striatum, and cerebellum regions of the PD mouse. Compared with the control group, these four brain regions showed significant transcriptomic alterations, with the most differentially expressed genes (DEGs) found in the striatum region. The main DEGs were Lrrk2, Mtor, Gxylt1, C920006o11Rik, Vdac1, Ano3, Drd4, and Ncan. DEGs were enriched using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analysis methods, which identified significant GO and molecular pathways. In addition, we used network biology methods to analyze protein–protein relationships, which can accelerate the identification of new PD drugs. The results showed that LRRK2, DRD2, IGF-1, GNAI1, GNAI3, PRKACA, PPP2R5C, and PIK3R1 played a major role in protein regulation.ConclusionsOur analysis showed that these DEGs and proteins play an important role in the occurrence and development of PD. Our study also highlighted the potential use of this transcriptomic data for therapeutic strategies and treatment of PD.

The Genetics Analysis of Molecular Pathogenesis for Alzheimer's Disease

2020 ◽  
Vol 83 (5) ◽  
pp. 458-467
Author(s):  
Guanchuan Lin ◽  
Kaiyuan Ji ◽  
Shiyu Li ◽  
Wenli Ma ◽  
Xinghua Pan
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Brain Regions ◽  
Molecular Pathogenesis ◽  
Differentially Expressed ◽  
Control Group ◽  
Membrane Structures

<b><i>Introduction:</i></b> The molecular pathogenesis of Alzheimer’s disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied. <b><i>Objective:</i></b> Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship. <b><i>Methods:</i></b> In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots. <b><i>Results:</i></b> There were 20 differentially expressed genes related to AD selected from GSE36980 (<i>p</i> = 6.2e<sup>−6</sup>, <i>q</i> = 2.9422e<sup>−4</sup>) and GSE9770 (<i>p</i> = 3.3e<sup>−4</sup>, <i>q</i> = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and <i>LRRTM1</i> and <i>RASAL1</i> were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of <i>LRRTM1</i> and <i>RASAL1</i> in Alzheimer’s patients, which was much lower than that in normal people aged &#x3e;65 years. <b><i>Conclusions:</i></b> The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction; hence, a list of 20 genes, including <i>LRRTM1</i> and <i>RASAL1,</i>potentially are important for the discovery of treatment target or molecular marker of AD.

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

DNA-methylation analysis of differentially expressed genes in fibroblast cell-lines, derived from monozygotic twins discordant for Parkinson’s disease

2020 ◽  
pp. 73-74
Author(s):  
И.Н. Рыболовлев ◽  
И.Н. Власов ◽  
А.Х. Алиева ◽  
П.А. Сломинский ◽  
М.И. Шадрина
Keyword(s):  
Parkinson’S Disease ◽  
Dna Methylation ◽  
Parkinson's Disease ◽  
Cell Lines ◽  
Differentially Expressed Genes ◽  
Regulation Of Gene Expression ◽  
Monozygotic Twins ◽  
Fibroblast Cell ◽  
Differentially Expressed ◽  
Pathogenesis Related

Болезнь Паркинсона (БП) является многофакторным гетерогенным нейродегенеративным заболеванием. Поскольку этиопатогенез БП недостаточно изучен, кроме поиска и анализа изменений на уровне ДНК, необходимо распространить фокус исследований на другие уровни: транскриптом и метилом. Изменения на уровне эпигенома можно исследовать у лиц с идентичной генетической конституцией, такой «моделью» являются дискордантные по этому заболеванию монозиготные близнецы. В исследовании приняло участие 3 пары фенотипически и генотипически монозиготных близнецов русского происхождения; В исследовании приняло участие 3 пары фенотипически и генотипически монозиготных близнецов русского происхождения. БП была уточнена у одного из каждой пары близнецов; длительность течения болезни у близнеца с БП составило по меньшей мере 7 лет.; длительность течения болезни у близнеца с БП составила по меньшей мере 7 лет. Были проанализированы метиломы крови и отобраны точки варьирующегося метилирования. Нами было найдено 8 дифференциально экспрессирующихся генов, которые могут быть дифференциально метилированы. Были выявлены различия между здоровым близнецом и близнецом с БП по уровню метилирования ДНК для ряда этих генов в клеточных линиях фибробластов. Полученные нами данные могут указывать на участие процесса ДНК-метилирования в регуляции транскрипции кандидатных генов-участников патогенеза БП. In recent years it has been convincingly demonstrated that genetic factors play an important role in progression of Parkinson’s disease (PD). Since the etiology of PD has not been elucidated completely yet, it is crucial to shift focus of the research to the broader areas - to dive into investigations of methylome and transcriptome. Epigenetic regulation of gene expression may take part in pathogenesis of PD. Changes in epigenome can be conveniently investigated in case of individuals with almost identical genetic makeup, and monozygotic twins discordant for PD may be such “model”. 3 pairs phenotypically and genotypically monozygous twins of Russian ancestry were enrolled in the study. PD was diagnosed in one of each pair. The disease duration was at least 7 years. Data on blood methylomes was analyzed. Points of variable methylation in blood methylomes were selected. With this approach, 8 differentially expressed genes were found that also may be differentially methylated. Changes in methylation level for some of this genes were found in monozygotic twins discordant for PD fibroblasts cell-lines between healthy and afflicted siblings. Acquired data might suggest participation of DNA-methylation in transcription regulation of PD pathogenesis-related candidate genes.

scholarly journals Transcriptomic profiles in Parkinson’s disease

2020 ◽  
pp. 153537022096732
Author(s):  
Lille Kurvits ◽  
Freddy Lättekivi ◽  
Ene Reimann ◽  
Liis Kadastik-Eerme ◽  
Kristjan M Kasterpalu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Brain Tissue ◽  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Differentially Expressed ◽  
Peripheral Tissues

Transcriptomics in Parkinson’s disease offers insights into the pathogenesis of Parkinson’s disease but obtaining brain tissue has limitations. In order to bypass this issue, we profile and compare differentially expressed genes and enriched pathways (KEGG) in two peripheral tissues (blood and skin) of 12 Parkinson’s disease patients and 12 healthy controls using RNA-sequencing technique and validation with RT-qPCR. Furthermore, we compare our results to previous Parkinson’s disease post mortem brain tissue and blood results using the robust rank aggregation method. The results show no overlapping differentially expressed genes or enriched pathways in blood vs. skin in our sample sets (25 vs. 1068 differentially expressed genes with an FDR ≤ 0.05; 1 vs. 9 pathways in blood and skin, respectively). A meta-analysis from previous transcriptomic sample sets using either microarrays or RNA-Seq yields a robust rank aggregation list of cortical gene expression changes with 43 differentially expressed genes; a list of substantia nigra changes with 2 differentially expressed genes and a list of blood changes with 1 differentially expressed gene being statistically significant at FDR ≤ 0.05. In cortex 1, KEGG pathway was enriched, four in substantia nigra and two in blood. None of the differentially expressed genes or pathways overlap between these tissues. When comparing our previously published skin transcription analysis, two differentially expressed genes between the cortex robust rank aggregation and skin overlap. In this study, for the first time a meta-analysis is applied on transcriptomic sample sets in Parkinson’s disease. Simultaneously, it explores the notion that Parkinson’s disease is not just a neuronal tissue disease by exploring peripheral tissues. The comparison of different Parkinson’s disease tissues yields surprisingly few significant differentially expressed genes and pathways, suggesting that divergent gene expression profiles in distinct cell lineages, metabolic and possibly iatrogenic effects create too much transcriptomic noise for detecting significant signal. On the other hand, there are signs that point towards Parkinson’s disease-specific changes in non-neuronal peripheral tissues in Parkinson’s disease, indicating that Parkinson’s disease might be a multisystem disorder.

scholarly journals Genomic profiling of cancerous patients identifies FPR2 as an alternative immunotherapeutic target in glioblastoma multiforme

2020 ◽  
Author(s):  
Yuqing Yang ◽  
Ting Sun ◽  
Chuchen Qiu ◽  
Dongjing Chen ◽  
You Wu
Keyword(s):  
Glioblastoma Multiforme ◽  
Tumor Progression ◽  
Differentially Expressed Genes ◽  
Primary Tumor ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Control Group ◽  
Western Blots ◽  
Potential Biomarker

ABSTRACTBackgroundGlioblastoma multiforme (GBM) is a type of high-grade brain tumor known for its proliferative, invasive property, and low survival rate. Recently, with the advancement in therapeutics for tumors such as targeted therapy, individual cancer-specific biomarkers could be recognized as targets for curative purposes. This study identified six differentially expressed genes that have shown significant implications in clinical field, including FPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3. FPR2 was of the same protein family with FPR1, and the latter has been repeatedly reported to promote motility and invasiveness of multiple tumor forms.MethodsThe gene expression profiling of 40 GBM samples and five normal samples from the TCGA database were comprehensively analyzed. The differentially expressed genes (DEGs) were identified using R package and screened by enrichment analysis and examination of protein–protein interaction networks, in order to further explore the functions of DEGs with the highest association with clinical traits and to find hub genes. A qRT-PCR and Western blots were conducted to verify the results of this study.ResultsOur investigation showed that FPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3 were significantly up-regulated in GBM primary tumor compared to the control group. Functional enrichment analysis of the DEGs demonstrated that biological functions related to immune systems, cell division and cell cycle were significantly increased, which were closely related to tumor progression and development. Downstream construction of PPI network analysis indicated that FPR2 was a hub gene involved in high level of interaction with CR3 and VEGFA, which played a key role in inflammatory pathways and cellular dysfunction.ConclusionFPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3 were significantly over-expressed in primary tumor samples of GBM patients and were involved in cellular functions and pathways contributing to tumor progression. Out of these six pivotal genes, we intensively focused on FPR2, and our analysis and experimental data both suggested its efficacy as a potential biomarker, serving as an alternative immunotherapeutic target for glioblastoma multiforme.

scholarly journals Expression Signatures of Long Noncoding RNAs in Left Ventricular Noncompaction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qingshan Tian ◽  
Hanxiao Niu ◽  
Dingyang Liu ◽  
Na Ta ◽  
Qing Yang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Noncoding Rnas ◽  
Enrichment Analysis ◽  
Left Ventricular ◽  
Long Noncoding Rnas ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Control Group ◽  
Left Ventricular Noncompaction ◽  
Ventricular Noncompaction

Long noncoding RNAs have gained widespread attention in recent years for their crucial role in biological regulation. They have been implicated in a range of developmental processes and diseases including cancer, cardiovascular, and neuronal diseases. However, the role of long noncoding RNAs (lncRNAs) in left ventricular noncompaction (LVNC) has not been explored. In this study, we investigated the expression levels of lncRNAs in the blood of LVNC patients and healthy subjects to identify differentially expressed lncRNA that develop LVNC specific biomarkers and targets for developing therapies using biological pathways. We used Agilent Human lncRNA array that contains both updated lncRNAs and mRNAs probes. We identified 1,568 upregulated and 1,141 downregulated (log fold-change &gt; 2.0) lncRNAs that are differentially expressed between LVNC and the control group. Among them, RP11-1100L3.7 and XLOC_002730 are the most upregulated and downregulated lncRNAs. Using quantitative real-time reverse transcription polymerase chain reaction (RT-QPCR), we confirmed the differential expression of three top upregulated and downregulated lncRNAs along with two other randomly picked lncRNAs. Gene Ontology (GO) and KEGG pathways analysis with these differentially expressed lncRNAs provide insight into the cellular pathway leading to LVNC pathogenesis. We also identified 1,066 upregulated and 1,017 downregulated mRNAs. Gene set enrichment analysis (GSEA) showed that G2M, Estrogen, and inflammatory pathways are enriched in differentially expressed genes (DEG). We also identified miRNA targets for these differentially expressed genes. In this study, we first report the use of LncRNA microarray to understand the pathogenesis of LVNC and to identify several lncRNA and genes and their targets as potential biomarkers.

scholarly journals Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuya Uehara ◽  
Shin-Ichi Ueno ◽  
Haruka Amano-Takeshige ◽  
Shuji Suzuki ◽  
Yoko Imamichi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Alpha Synuclein ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Non Invasive

AbstractParkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.

scholarly journals Identification of Differentially Expressed Genes and Long Noncoding RNAs Associated with Parkinson’s Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Lu-Mei Chi ◽  
Li-Ping Wang ◽  
Dan Jiao
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Noncoding Rnas ◽  
Gene Expression Omnibus ◽  
Long Noncoding Rnas ◽  
Differentially Expressed ◽  
Similar Treatment ◽  
Blood Samples

Objectives. This study aims to determine differentially expressed genes (DEGs) and long noncoding RNAs (lncRNAs) associated with Parkinson’s disease (PD) using a microarray. Methods. We downloaded the microarray data GSE6613 from the Gene Expression Omnibus, which included 105 samples. We selected 72 samples comprising 22 healthy control blood samples and 50 PD blood samples for further analysis. Later, we used Limma to screen DEGs and differentially expressed lncRNAs (DElncRNAs) and estimated their functions by the Gene Ontology (GO). Besides, the competing endogenous RNA (ceRNA) network, including microRNAs, lncRNAs, and mRNAs, was constructed to elucidate the regulatory mechanism. Furthermore, we performed the KEGG pathway enrichment with mRNAs in the ceRNA regulatory network and constructed a final network, including pathways, mRNAs, microRNAs, and lncRNAs. Results. Overall, we obtained 394 DEGs, including 207 upregulated DEGs and 187 downregulated DEGs, and 7 DElncRNAs, including 2 upregulated DElncRNAs and 5 downregulated DElncRNAs. Insulin-like growth factor-1 receptor (IGF1R) was considerably enriched in the endocytosis pathway. In the ceRNA regulation network, IGF1R was the target of hsa-miR-133b and lncRNAs of XIST, and PART1 could also be the target of hsa-miR-133b. While the upregulated DEGs were enriched in the GO terms of the cytoskeleton, cytoskeletal part, and microtubule cytoskeleton, the downregulated DEGs were enriched in the immune response. PRKACA was markedly enriched in numerous pathways, including the MAPK and insulin signaling pathways. Conclusions. IGF1R, PRKACA, and lncRNA-XIST could be potentially involved in PD, and these diverse molecular mechanisms could support the development of the similar treatment for PD.

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