☆ 4.7 Article

Comparative gene expression analysis in mouse models for multiple sclerosis, Alzheimer's disease and stroke for identifying commonly regulated and disease-specific gene changes

GENOMICS (2010)

获取全文
添加到收藏夹

期刊

GENOMICS
卷 96, 期 2, 页码 82-91

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2010.04.004

关键词

cDNA microarrays; Brain inflammation; Cerebral stroke; Alzheimer's disease; Systems biology; Gene expression profiling

类别

Biotechnology & Applied Microbiology Genetics & Heredity

资金

  1. Greek Secretariat of Research and Technology, EPAN Greece-USA
  2. European Union
  3. NeuroproMiSe [LSHM-CT-2005-018637]

向作者/读者索取更多资源

Protocol

社区支持

Reagent

社区支持

The brain responds to injury and infection by activating innate defense and tissue repair mechanisms. Working upon the hypothesis that the brain defense response involves common genes and pathways across diverse pathologies, we analysed global gene expression in brain from mouse models representing three major central nervous system disorders, cerebral stroke, multiple sclerosis and Alzheimer's disease compared to normal brain using DNA microarray expression profiling. A comparison of dysregulated genes across disease models revealed common genes and pathways including key components of estrogen and TGF-beta signaling pathways that have been associated with neuroprotection as well as a neurodegeneration mediator, TRPM7. Further, for each disease model, we discovered collections of differentially expressed genes that provide novel insight into the individual pathology and its associated mechanisms. Our data provide a resource for exploring the complex molecular mechanisms that underlie brain neurodegeneration and a new approach for identifying generic and disease-specific targets for therapy. (C) 2010 Elsevier Inc. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Food Science & Technology

Stability of melamine-formaldehyde (MF) resin under different pH and ionic strength values

Lambros Farmakis, Athanasia Koliadima, Sotirios Vamvakas

JOURNAL OF FOOD PROCESSING AND PRESERVATION (2020)

添加到收藏夹
Review Biotechnology & Applied Microbiology

Factors affecting yeast ethanol tolerance and fermentation efficiency

Sotirios-Spyridon Vamvakas, John Kapolos

WORLD JOURNAL OF MICROBIOLOGY & BIOTECHNOLOGY (2020)

添加到收藏夹
Article Cell Biology

Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

Camila M. Lopes-Ramos, Cho-Yi Chen, Marieke L. Kuijjer, Joseph N. Paulson, Abhijeet R. Sonawane, Maud Fagny, John Platig, Kimberly Glass, John Quackenbush, Dawn L. DeMeo

CELL REPORTS (2020)

添加到收藏夹
Article Multidisciplinary Sciences

BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52

Elodie Hatchi, Liana Goehring, Serena Landini, Konstantina Skourti-Stathaki, Derrick K. DeConti, Fieda O. Abderazzaq, Priyankana Banerjee, Timothy M. Demers, Yaoyu E. Wang, John Quackenbush, David M. Livingston

Summary: Strong connections are found between R-loops, genome instability, and human disease. R-loops play a role in maintaining homeostasis by regulating certain physiological processes, such as the synthesis of antisense transcripts through transcription termination pause sites. A species of single-stranded, DNA-damage-associated small RNA (sdRNA) generated by a BRCA1-RNAi complex promotes DNA repair at transcriptional termination pause sites forming R-loops.

NATURE (2021)

添加到收藏夹
Article Mathematical & Computational Biology

Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

Albert T. Young, Xavier Carette, Michaela Helmel, Hanno Steen, Robert N. Husson, John Quackenbush, John Platig

Summary: The inhibition of Mtb serine/threonine protein kinases PknA and PknB leads to changes in the TF regulatory network, affecting regulatory programs involved in cell wall integrity, stress response, and energy production. This highlights the importance of incorporating signal-driven TF modifications in regulatory network approaches.

NPJ SYSTEMS BIOLOGY AND APPLICATIONS (2021)

添加到收藏夹
Article Oncology

Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme

Camila M. Lopes-Ramos, Tatiana Belova, Tess H. Brunner, Marouen Ben Guebila, Daniel Osorio, John Quackenbush, Marieke L. Kuijjer

Summary: Analysis identified seven pathways associated with survival in glioblastoma patients, with dysregulation of PD1 signaling correlating with poor prognosis. This suggests a new approach to predict patient survival and highlights potential therapeutic interventions based on gene regulatory network analysis.

CANCER RESEARCH (2021)

添加到收藏夹
Article Biochemistry & Molecular Biology

GRAND: a database of gene regulatory network models across human conditions

Marouen Ben Guebila, Camila M. Lopes-Ramos, Deborah Weighill, Abhijeet Rajendra Sonawane, Rebekka Burkholz, Behrouz Shamsaei, John Platig, Kimberly Glass, Marieke L. Kuijjer, John Quackenbush

Summary: Gene regulation networks play a crucial role in tissue identity, disease development, and therapeutic response. The GRAND database provides computationally-inferred gene regulatory network models and targeting scores for predicting drug effects on network structures and matching potential therapeutic drugs to disease states.

NUCLEIC ACIDS RESEARCH (2022)

添加到收藏夹
Article Cell Biology

Using graph convolutional neural networks to learn a representation for glycans

Rebekka Burkholz, John Quackenbush, Daniel Bojar

Summary: Glycans pose substantial challenges for computational biology due to their nonlinear and diverse nature. Sweet-Net, a graph convolutional neural network, outperforms other computational methods in predicting glycan properties and can learn representations that are predictive of organismal phenotypic and environmental properties. Additionally, glycan-focused machine learning can be used to predict viral glycan binding and discover viral receptors.

CELL REPORTS (2021)

添加到收藏夹
Article Biochemistry & Molecular Biology

Predicting genotype-specific gene regulatory networks

Deborah Weighill, Marouen Ben Guebila, Kimberly Glass, John Quackenbush, John Platig

Summary: Understanding how individual genotypes influence gene regulation can improve our understanding of human health and development. EGRET is a method that infers genotype-specific gene regulatory networks to reveal the genetic associations driving complex phenotypes.

GENOME RESEARCH (2022)

添加到收藏夹
Article Mathematical & Computational Biology

Constructing gene regulatory networks using epigenetic data

Abhijeet Rajendra Sonawane, Dawn L. DeMeo, John Quackenbush, Kimberly Glass

Summary: The study focuses on the application of epigenetic data in gene regulatory networks, developing the SPIDER method for network reconstruction, which shows high accuracy and cell-line-specific interactions, and has the potential to identify novel hypotheses for better characterizing cell-type and phenotype-specific regulatory mechanisms.

NPJ SYSTEMS BIOLOGY AND APPLICATIONS (2021)

添加到收藏夹
Article Biochemistry & Molecular Biology

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

Katherine H. Shutta, Deborah Weighill, Rebekka Burkholz, Marouen Ben Guebila, Dawn L. DeMeo, Helena U. Zacharias, John Quackenbush, Michael Altenbuchinger

Summary: In this study, we propose a network approach based on Gaussian Graphical Models (GGMs) called DRAGON, which allows for the joint analysis of multi-omic data. DRAGON adapts to the differences between omics layers, improving model inference and edge recovery, and can identify key molecular mechanisms.

NUCLEIC ACIDS RESEARCH (2023)

添加到收藏夹
Article Biochemical Research Methods

Connectivity in eQTL networks dictates reproducibility and genomic properties

Sheila M. Gaynor, Maud Fagny, Xihong Lin, John Platig, John Quackenbush

Summary: This study constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated different network specifications. The researchers found that using a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balanced metric reproducibility and computational efficiency. The eQTL networks were complementary to gene regulatory networks in understanding regulation, and highly connected nodes were enriched for tissue-relevant traits.

CELL REPORTS METHODS (2022)

添加到收藏夹
Article Genetics & Heredity

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Marouen Ben Guebila, Daniel C. Morgan, Kimberly Glass, Marieke L. Kuijjer, Dawn L. DeMeo, John Quackenbush

Summary: Gene regulatory network inference allows for modeling genome-scale regulatory processes. Researchers have developed a collection of tools to model various regulatory processes and improve their performance through GPU-accelerated calculations.

NAR GENOMICS AND BIOINFORMATICS (2022)

添加到收藏夹
Article Urology & Nephrology

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Kinsuk Chauhan, Girish N. Nadkarni, Fergus Fleming, James McCullough, Cijiang J. He, John Quackenbush, Barbara Murphy, Michael J. Donovan, Steven G. Coca, Joseph Bonventre

KIDNEY360 (2020)

添加到收藏夹
Article Computer Science, Information Systems

Clustering Sparse Data With Feature Correlation With Application to Discover Subtypes in Cancer

Jipeng Qiang, Wei Ding, Marieke Kuijjer, John Quackenbush, Ping Chen

IEEE ACCESS (2020)

添加到收藏夹
Article Biotechnology & Applied Microbiology

MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks

Zutan Li, Bingbing Jin, Jingya Fang

Summary: In this study, we propose MetaAc4C, an advanced deep learning model for accurate identification of N4-acetylcytidine (ac4C) sites using pre-trained BERT and various optimization techniques. By adapting generative adversarial networks to address data imbalance and augmenting training RNA samples, our model outperforms existing methods in terms of ACC, MCC, and AUROC.

GENOMICS (2024)

添加到收藏夹
研讨会 海报 问题 讨论圈 基金 期刊 论文 科研社交 资讯集成 审稿人 关于我们 常见问题 移动App 隐私条款 使用条款
© Peeref 2019-2024. All rights reserved.