ENHANCING BIOLOGICAL RELEVANCE OF A WEIGHTED GENE CO-EXPRESSION NETWORK FOR FUNCTIONAL MODULE IDENTIFICATION

2011 ◽  
Vol 09 (01) ◽  
pp. 111-129 ◽  
Author(s):  
SANTITHAM PROM-ON ◽  
ATTHAWUT CHANTHAPHAN ◽  
JONATHAN HOYIN CHAN ◽  
ASAWIN MEECHAI
Keyword(s):  
Gene Expression ◽  
Minimum Spanning Tree ◽  
Structural Information ◽  
Functional Module ◽  
Autism Spectrum ◽  
Functional Modules ◽  
Expression Levels ◽  
Module Identification ◽  
Backbone Network ◽  
Biological Relevance

Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.

scholarly journals A PRELIMINARY INVESTIGATION OF HTR1A GENE EXPRESSION LEVELS IN AUTISM SPECTRUM DISORDERS

2019 ◽  
pp. 1-3
Author(s):  
SHAYMAA M. YAHYA ◽  
OLA GEBRIL ◽  
EHAB R. ABDEL RAOUF ◽  
MOHAMED E. ELHADIDY
Keyword(s):  
Gene Expression ◽  
Preliminary Investigation ◽  
Autism Spectrum ◽  
Quantitative Real Time Pcr ◽  
Gene Expressions ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Beta Actin ◽  
Spectrum Disorders ◽  
Gene Expression Levels

Objective: This study was conducted to explore the expression levels of HTR1A gene in a sample of Egyptian autistic children. Methods: Thirty autistic patients (18 boys, 12 girls) and 20 controls were enrolled in the study. From each child, we isolated RNA samples from whole blood. Quantitative Real-Time PCR (qRT-PCR) was used to measure the gene expressions of HTR1A and normalized to the house keeping gene, beta-actin. Results: The HTR1A gene expression of healthy controls and ASD subjects were varied significantly (p =0.0062). As compared to control healthy subjects, the HTR1A expressions were greatly reduced in samples of ASD. Conclusion: HTR1A gene expression level is a candidate gene for further studies to explore its potential roles in ASD related pathways.

scholarly journals Protein functional module identification method combining topological features and gene expression data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zihao Zhao ◽  
Wenjun Xu ◽  
Aiwen Chen ◽  
Yueyue Han ◽  
Shengrong Xia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Functional Module ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Structure Characteristic ◽  
Functional Modules ◽  
Expression Data ◽  
Ppi Network ◽  
Topological Features

Abstract Background The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. Results This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. Conclusions The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.

Gene expression analysis of metabolic markers during bone regeneration in a rat model

2014 ◽  
Vol 23 (03) ◽  
pp. 207-211
Author(s):  
C. Kasch ◽  
A. Osterberg ◽  
Thordis Granitzka ◽  
T. Lindner ◽  
M. Haenle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analysis ◽  
Saline Solution ◽  
Female Rats ◽  
Synthesis Rate ◽  
Metabolic Markers ◽  
Expression Levels ◽  
Fibrotic Tissue ◽  
Intermittent Pth ◽  
Lower Expression

SummaryThe RANK/RANKL/OPG system plays an important role in the regulation of bone metabolism and bony integration around implants. The aim of this study was to analyse gene expression of OPG, RANK, and RANKL in regenerating bone during implant integration. Additionally, the effect of intermittent para - thyroid hormone (PTH) treatment was analysed. A titanium chamber was implanted in the proximal tibiae of 48 female rats. The animals received either human PTH or saline solution (NaCl). After 21 and 42 days, RNA was isolated from tissue adjacent to the implant and expression of RANK, RANKL, and OPG was analysed. After 21 days, very low expression levels of all genes were shown. In contrast, increased gene expression after 42 days was determined. Expression of RANK and RANKL was lower than that for OPG. The lower expression levels after 21 days might be due to still ossifying, fibrotic tissue around the titanium chamber. An increased OPG synthesis rate associated with decreased RANKL expression after 42 days revealed bone-forming processes. Despite significant differences in gene expression between the time points, only slight differences were observed between application of intermittent PTH and NaCl after a period of 42 days.

Neuroserpin in Bipolar Disorder

2020 ◽  
Vol 20 (7) ◽  
pp. 518-523
Author(s):  
Rugül Köse Çinar
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Polymerase Chain Reaction Method ◽  
Type I ◽  
Healthy Controls ◽  
Neuroprotective Effects ◽  
Expression Levels ◽  
Disease States ◽  
Cord Injury ◽  
System Functioning

Objective: Neuroserpin is a serine protease inhibitor predominantly expressed in the nervous system functioning mainly in neuronal migration and axonal growth. Neuroprotective effects of neuroserpin were shown in animal models of stroke, brain, and spinal cord injury. Postmortem studies confirmed the involvement of neuroserpin in Alzheimer’s disease. Since altered adult neurogenesis was postulated as an aetiological mechanism for bipolar disorder, the possible effect of neuroserpin gene expression in the disorder was evaluated. Methods: Neuroserpin mRNA expression levels were examined in the peripheral blood of bipolar disorder type I manic and euthymic patients and healthy controls using the polymerase chain reaction method. The sample comprised of 60 physically healthy, middle-aged men as participants who had no substance use disorder. Results: The gene expression levels of neuroserpin were found lower in the bipolar disorder patients than the healthy controls (p=0.000). The neuroserpin levels did not differ between mania and euthymia (both 96% down-regulated compared to the controls). Conclusion: Since we detected differences between the patients and the controls, not the disease states, the dysregulation in the neuroserpin gene could be interpreted as a result of the disease itself.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals The Potential of 2-aza-8-Oxohypoxanthine as a Cosmetic Ingredient

Cosmetics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 60
Author(s):  
Hisae Aoshima ◽  
Masayuki Ito ◽  
Rinta Ibuki ◽  
Hirokazu Kawagishi
Keyword(s):  
Gene Expression ◽  
Cell Proliferation ◽  
Microarray Analysis ◽  
Dna Microarray ◽  
Cell Activation ◽  
Skin Barrier ◽  
Efficacy Evaluation ◽  
Activation Effect ◽  
Expression Levels ◽  
Dna Microarray Analysis

In this study, we verified the effects of 2-aza-8-oxohypoxanthine (AOH) on human epidermal cell proliferation by performing DNA microarray analysis. Cell proliferation was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, which measures mitochondrial respiration in normal human epidermal keratinocyte (NHEK) cells. Gene expression levels were determined by DNA microarray analysis of 177 genes involved in skin aging and disease. AOH showed a significant increase in cell viability at concentrations between 7.8 and 31.3 μg/mL and a significant decrease at concentrations above 250 μg/mL. DNA microarray analysis showed that AOH significantly increased the gene expression of CLDN1, DSC1, DSG1, and CDH1 (E-cadherin), which are involved in intercellular adhesion and skin barrier functioning. AOH also up-regulated the expression of KLK5, KLK7, and SPIMK5, which are proteases involved in stratum corneum detachment. Furthermore, AOH significantly stimulated the expression of KRT1, KRT10, TGM1, and IVL, which are considered general differentiation indicators, and that of SPRR1B, a cornified envelope component protein. AOH exerted a cell activation effect on human epidermal cells. Since AOH did not cause cytotoxicity, it was considered that the compound had no adverse effects on the skin. In addition, it was found that AOH stimulated the expression levels of genes involved in skin barrier functioning by DNA microarray analysis. Therefore, AOH has the potential for practical use as a cosmetic ingredient. This is the first report of efficacy evaluation tests performed for AOH.

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