Comparative Analysis of Glycogene Expression in Different Mouse Tissues Using RNA-Seq Data

Firoz, Ahmad; Malik, Adeel; Singh, Sanjay Kumar; Jha, Vivekanand; Ali, Amjad

doi:10.1155/2014/837365

Cited by 7 publications

(5 citation statements)

References 69 publications

(78 reference statements)

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“…This study picked multiple microarray datasets of MS, preprocessed and analyzed via R. Then the consensus data were utilized for different networks using Cytoscape and various other sources [ 21 - 23 ]. Gene-gene interactions have been discussed in one of the other studies [ 79 ] via GeneMANIA where comparative analysis of Glycogene expression was focused in different tissues. This study also discussed the identification of Hub node based on degree centrality via Cytoscape plugin.…”

Section: Discussionmentioning

confidence: 99%

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

Habib

Noureen

Nadeem

2018

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Background: Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer’s Disease, (AD), Parkinson’s Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas.Objective: In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs.Methods: Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these dis-orders at various stages.Results: Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene in-teraction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA’s has been scrutinized.Conclusion: The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

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Section: Discussionmentioning

confidence: 99%

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

Habib

Noureen

Nadeem

2018

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“…Identifying the structure and function of biological networks is crucial for the exploration of biological phenomena. The interest in exploiting the use of network based studies to address key biological issues is ever increasing [ 52 – 54 ]. In this work, four functional modules or clusters were identified in Network 3 using the GLay plugin for Cytoscape.…”

Section: Discussionmentioning

confidence: 99%

Network Analysis for the Identification of Differentially Expressed Hub Genes Using Myogenin Knock-down Muscle Satellite Cells

Malik¹,

Lee²,

Jan³

et al. 2015

PLoS ONE

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Muscle, a multinucleate syncytium formed by the fusion of mononuclear myoblasts, arises from quiescent progenitors (satellite cells) via activation of muscle-specific transcription factors (MyoD, Myf5, myogenin: MYOG, and MRF4). Subsequent to a decline in Pax7, induction in the expression of MYOG is a hallmark of myoblasts that have entered the differentiation phase following cell cycle withdrawal. It is evident that MYOG function cannot be compensated by any other myogenic regulatory factors (MRFs). Despite a plethora of information available regarding MYOG, the mechanism by which MYOG regulates muscle cell differentiation has not yet been identified. Using an RNA-Seq approach, analysis of MYOG knock-down muscle satellite cells (MSCs) have shown that genes associated with cell cycle and division, DNA replication, and phosphate metabolism are differentially expressed. By constructing an interaction network of differentially expressed genes (DEGs) using GeneMANIA, cadherin-associated protein (CTNNA2) was identified as the main hub gene in the network with highest node degree. Four functional clusters (modules or communities) were identified in the network and the functional enrichment analysis revealed that genes included in these clusters significantly contribute to skeletal muscle development. To confirm this finding, in vitro studies revealed increased expression of CTNNA2 in MSCs on day 12 compared to day 10. Expression of CTNNA2 was decreased in MYOG knock-down cells. However, knocking down CTNNA2, which leads to increased expression of extracellular matrix (ECM) genes (type I collagen α1 and type I collagen α2) along with myostatin (MSTN), was not found significantly affecting the expression of MYOG in C2C12 cells. We therefore propose that MYOG exerts its regulatory effects by acting upstream of CTNNA2, which in turn regulates the differentiation of C2C12 cells via interaction with ECM genes. Taken together, these findings highlight a new mechanism by which MYOG interacts with CTNNA2 in order to promote myoblast differentiation.

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“…Meta-analysis of brain genes notes this limitation and discuses the need to extend the gene pool that is evaluated in a brain-wise approach [66,67]. Some bottom-up studies of GRNs across different tissues and diseases have shown how distributed modulation across a wide range of genes plays essential roles in function and pathophysiology [68][69][70][71]. Here we present a bottom-up model [34] that explores the molecular assemblies (in terms of gene expression) and the gene communities as the dynamic clusters that constitute functional regulatory units, as a first step to understand gene regulatory networks as the precursors of a brain archetype.…”

Section: Gene-wise Analysismentioning

confidence: 99%

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

Paredes

López

Covantes-Osuna

et al. 2021

Entropy

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Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network

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Comparative Analysis of Glycogene Expression in Different Mouse Tissues Using RNA-Seq Data

Cited by 7 publications

References 69 publications

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

Network Analysis for the Identification of Differentially Expressed Hub Genes Using Myogenin Knock-down Muscle Satellite Cells

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

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