RNA-Sequencing Reveals Similarities and Differences in Gene Expression in Vulnerable Brain Tissues of Alzheimer’s and Parkinson’s Diseases

Bennett, James P.; Keeney, Paula M.

doi:10.3233/adr-180072

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…“Heatmap” is a graphical representation of data where the individual count values contained in a matrix are represented as pseudo-colors. Hierarchical clustering is usually performed on heatmap, enabling users to interpret the overall picture of expression patterns with ease [ 21 ]. “Expression Level” can be used to visualize expression patterns for genes of interest.…”

Section: Main Textmentioning

confidence: 99%

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

et al. 2019

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ObjectiveDifferential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users.ResultsHere, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use (https://infinityloop.shinyapps.io/TCC-GUI/). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.Electronic supplementary materialThe online version of this article (10.1186/s13104-019-4179-2) contains supplementary material, which is available to authorized users.

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Section: Main Textmentioning

confidence: 99%

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

et al. 2019

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“…The average expression value for each gene across the arrays was used to normalize the mRNA intensities. The CEL files were transformed into intensity information using the RMA normalization of GeneSifter (http://www.genesifter.net/web/qs_analysis.html) and Qlucore 3.0 (New York, NY), and fold change was used to select differentially expressed genes [23–25]. Differentially expressed gene identifiers were uploaded into a heat map.…”

Section: Methodsmentioning

confidence: 99%

Transcriptional activation of CBFβ by CDK11p110 is necessary to promote osteosarcoma cell proliferation

et al. 2019

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Background Aberrant expression of cyclin-dependent protein kinases (CDK) is a hallmark of cancer. CDK11 plays a crucial role in cancer cell growth and proliferation. However, the molecular mechanisms of CDK11 and CDK11 transcriptionally regulated genes are largely unknown. Methods In this study, we performed a global transcriptional analysis using gene array technology to investigate the transcriptional role of CDK11 in osteosarcoma. The promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay were used to identify direct transcriptional targets of CDK11. Clinical relevance and function of core-binding factor subunit beta (CBFβ) were further accessed in osteosarcoma. Results We identified a transcriptional role of protein-DNA interaction for CDK11p110, but not CDK11p58, in the regulation of CBFβ expression in osteosarcoma cells. The CBFβ promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay confirmed that CBFβ is a direct transcriptional target of CDK11. High expression of CBFβ is associated with poor outcome in osteosarcoma patients. Expression of CBFβ contributes to the proliferation and metastatic behavior of osteosarcoma cells. Conclusions These data establish CBFβ as a mediator of CDK11p110 dependent oncogenesis and suggest that targeting the CDK11- CBFβ pathway may be a promising therapeutic strategy for osteosarcoma treatment. Graphical Abstract

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“…Pbx1 , which encodes a nuclear protein that belongs to the PBX homeobox family of transcriptional factors, is involved in neurogenesis during development and adulthood (Grebbin et al, 2016). Of note, a general downregulation of let-7 family members, as well as an increase in Pbx1 gene expression, has been previously described in AD patients (Maes et al, 2009; Bennett and Keeney, 2018).…”

Section: Discussionmentioning

confidence: 65%

Temporal Integrative Analysis of mRNA and microRNAs Expression Profiles and Epigenetic Alterations in Female SAMP8, a Model of Age-Related Cognitive Decline

et al. 2018

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A growing body of research shows that epigenetic mechanisms are critically involved in normal and pathological aging. The Senescence-Accelerated Mouse Prone 8 (SAMP8) can be considered a useful tool to better understand the dynamics of the global epigenetic landscape during the aging process since its phenotype is not fully explained by genetic factors. Here we investigated dysfunctional age-related transcriptional profiles and epigenetic programming enzymes in the hippocampus of 2- and 9-month-old SAMP8 female mice using the Senescent-Accelerated Resistant 1 (SAMR1) mouse strain as control. SAMP8 mice presented 1,062 genes dysregulated at 2 months of age, and 1,033 genes at 9 months, with 92 genes concurrently dysregulated at both ages compared to age-matched SAMR1. SAMP8 mice showed a significant decrease in global DNA methylation (5-mC) at 2 months while hydroxymethylation (5-hmC) levels were increased in SAMP8 mice at 2 and 9 months of age compared to SAMR1. These changes were accompanied by changes in the expression of several enzymes that regulate 5-mC and methylcytosine oxidation. Acetylated H3 and H4 histone levels were significantly diminished in SAMP8 mice at 2-month-old compared to SAMR1 and altered Histone DeACetylase (HDACs) profiles were detected in both young and old SAMP8 mice. We analyzed 84 different mouse miRNAs known to be altered in neurological diseases or involved in neuronal development. Compared with SAMR1, SAMP8 mice showed 28 and 17 miRNAs differentially expressed at 2 and 9 months of age, respectively; 6 of these miRNAs overlapped at both ages. We used several bioinformatic approaches to integrate our data in mRNA:miRNA regulatory networks and functional predictions for young and aged animals. In sum, our study reveals interplay between epigenetic mechanisms and gene networks that seems to be relevant for the progression toward a pathological aging and provides several potential markers and therapeutic candidates for Alzheimer's Disease (AD) and age-related cognitive impairment.

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RNA-Sequencing Reveals Similarities and Differences in Gene Expression in Vulnerable Brain Tissues of Alzheimer’s and Parkinson’s Diseases

Cited by 15 publications

References 29 publications

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

Transcriptional activation of CBFβ by CDK11p110 is necessary to promote osteosarcoma cell proliferation

Temporal Integrative Analysis of mRNA and microRNAs Expression Profiles and Epigenetic Alterations in Female SAMP8, a Model of Age-Related Cognitive Decline

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