↓ Skip to main content

Gene expression profiles and protein-protein interaction networks in amyotrophic lateral sclerosis patients with C9orf72 mutation

Overview of attention for article published in Orphanet Journal of Rare Diseases, November 2016
Altmetric Badge

Mentioned by

twitter
3 X users

Readers on

mendeley
90 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Gene expression profiles and protein-protein interaction networks in amyotrophic lateral sclerosis patients with C9orf72 mutation
Published in
Orphanet Journal of Rare Diseases, November 2016
DOI 10.1186/s13023-016-0531-y
Pubmed ID
Authors

Meena Kumari Kotni, Mingzhu Zhao, Dong-Qing Wei

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that involves the death of neurons. ALS is associated with many gene mutations as previously studied. In order to explore the molecular mechanisms underlying ALS with C9orf72 mutation, gene expression profiles of ALS fibroblasts and control fibroblasts were subjected to bioinformatics analysis. Genes with critical functional roles can be detected by a measure of node centrality in biological networks. In gene co-expression networks, highly connected genes called as candidate hubs have been associated with key disease-related pathways. Herein, this method was applied to find the hub genes related to ALS disease. Illumina HiSeq microarray gene expression dataset GSE51684 was retrieved from Gene Expression Omnibus (GEO) database which included four Sporadic ALS, twelve Familial ALS and eight control samples. Differentially Expressed Genes (DEGs) were identified using the Student's t test statistical method and gene co-expression networking. Gene ontology (GO) function and KEGG pathway enrichment analysis of DEGs were performed using the DAVID online tool. Protein-protein interaction (PPI) networks were constructed by mapping the DEGs onto protein-protein interaction data from publicly available databases to identify the pathways where DEGs are involved in. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. The results revealed that the expression of DEGs was mainly involved in cell adhesion, cell-cell signaling, Extra cellular matrix region GO processes and focal adhesion, neuroactive ligand receptor interaction, Extracellular matrix receptor interaction. Tumor necrosis factor (TNF), Endothelin 1 (EDN1), Angiotensin (AGT) and many cell adhesion molecules (CAM) were detected as hub genes that can be targeted as novel therapeutic targets for ALS disease. These analyses and findings enhance the understanding of ALS pathogenesis and provide references for ALS therapy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 1%
Unknown 89 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 20%
Other 8 9%
Researcher 8 9%
Student > Bachelor 8 9%
Student > Master 8 9%
Other 14 16%
Unknown 26 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 22%
Agricultural and Biological Sciences 11 12%
Neuroscience 10 11%
Computer Science 5 6%
Medicine and Dentistry 4 4%
Other 12 13%
Unknown 28 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2017.
All research outputs
#15,392,529
of 22,899,952 outputs
Outputs from Orphanet Journal of Rare Diseases
#1,803
of 2,629 outputs
Outputs of similar age
#195,698
of 311,515 outputs
Outputs of similar age from Orphanet Journal of Rare Diseases
#33
of 40 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,629 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,515 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.