Exploring celiac disease candidate pathways by global gene expression profiling and gene network cluster analysis

Abstract Celiac disease (CeD) is a gastrointestinal autoimmune disorder, whose specific molecular basis is not yet fully interpreted. Therefore, in this study, we compared the global gene expression profile of duodenum tissues from CeD patients, both at the time of disease diagnosis and after two years of the gluten-free diet. A series of advanced systems biology approaches like differential gene expression, protein–protein interactions, gene network-cluster analysis were deployed to annotate the candidate pathways relevant to CeD pathogenesis. The duodenum tissues from CeD patients revealed the differential expression of 106 up- and 193 down-regulated genes. The pathway enrichment of differentially expressed genes (DEGs) highlights the involvement of biological pathways related to loss of cell division regulation (cell cycle, p53 signalling pathway), immune system processes (NOD-like receptor signalling pathway, Th1, and Th2 cell differentiation, IL-17 signalling pathway) and impaired metabolism and absorption (mineral and vitamin absorptions and drug metabolism) in celiac disease. The molecular dysfunctions of these 3 biological events tend to increase the number of intraepithelial lymphocytes (IELs) and villous atrophy of the duodenal mucosa promoting the development of CeD. For the first time, this study highlights the involvement of aberrant cell division, immune system, absorption, and metabolism pathways in CeD pathophysiology and presents potential novel therapeutic opportunities.

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Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

Egyptian Journal of Medical Human Genetics ◽

10.1186/s43042-021-00161-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rowan AlEjielat ◽

Anas Khaleel ◽

Amneh H. Tarkhan

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Differentially Expressed Genes ◽

Gene Network ◽

Disease Diagnosis ◽

Receptor Activation ◽

Functional Enrichment ◽

Differentially Expressed ◽

Inflammatory Disorder ◽

Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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Effects of curcumin on global gene expression profiles in the highly invasive human breast carcinoma cell line MDA‑MB 231: A gene network-based microarray analysis

Experimental and Therapeutic Medicine ◽

10.3892/etm.2012.754 ◽

2012 ◽

Cited By ~ 2

Author(s):

Pornngarm Limtrakul

Keyword(s):

Gene Expression ◽

Breast Carcinoma ◽

Cell Line ◽

Gene Network ◽

Carcinoma Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Global Gene Expression ◽

Breast Carcinoma Cell Line ◽

Human Breast

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Su1171 - Gene Expression Signatures Differentiate Non-Celiac Gluten Sensitivity from Celiac Disease Patients and Controls and may Potentially Contribute to Disease Diagnosis

Gastroenterology ◽

10.1016/s0016-5085(18)31871-7 ◽

2018 ◽

Vol 154 (6) ◽

pp. S-493

Author(s):

Konstantinos Efthymakis ◽

Emanuela Clemente ◽

Michele Sallese ◽

Matteo Neri

Keyword(s):

Gene Expression ◽

Celiac Disease ◽

Disease Diagnosis ◽

Gluten Sensitivity ◽

Gene Expression Signatures

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Gene-Set Enrichment Analysis of Differentially Expressed Genes of Amniotic-Fluid RNA Providing Insights into the Phenotype of Turner’s Syndrome

10.20944/preprints201810.0268.v1 ◽

2018 ◽

Author(s):

Sarbojoy Saha ◽

Shampa Barmon

Keyword(s):

Gene Expression ◽

Immune System ◽

Amniotic Fluid ◽

Genetic Disorders ◽

Enrichment Analysis ◽

Global Gene Expression ◽

Gene Set Enrichment Analysis ◽

Organ Systems ◽

Expression Atlas ◽

Biological Interpretation

Genetic disorders are quite a major topic of discussion and debate in the recent world of biological sciences. Turner’s syndrome is one such disorder caused by a chromosome aneuploidy and it has characteristic symptoms in the patient or the affected individual.  The amniotic fluid is a complex biological material found in the amniotic sac of pregnant women and they can provide valuable knowledge and understanding of the pathogenesis of this particular chromosomal abnormality. In this study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was used to detect genes/organ systems which may be significant in the pathophysiology of Turner’s syndrome. The cell-free RNA from the amniotic fluid of five mid-trimester Turner’s syndrome fetuses and five euploid female fetuses matched for age of gestation were extracted, amplified and hybridized onto Affymetrix U33 Plus 2.0. array. The paired t-test was used to identify the significantly differentially regulated genes. Biological interpretation was conducted using ingenuity pathway analysis and BioGPS gene expression atlas. Of the genes, XIST was especially downregulated and SHOX was not expressed differentially. One of the most highly represented organ systems was the hematologic/immune system, differentiating the transcriptome of Turner’s syndrome from other chromosomal aneuploidies that are discussed in this area of science. The differences in the transcriptome of the Turner’s syndrome are due to genome-wide dysregulation. The hematologic/immune system differences are significant in early-onset autoimmune dysfunction. There are other genes which have been identified that are associated with the cardiovascular and the skeletal system, as these are often seen to be affected in the female patients with turner’s syndrome. Hopefully, such knowledge gained from this study will help us to understand the deeper mechanisms of this disorder and the possible treatments of this disease.

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Changes in Gene Expression Linked to Adverse Health Effects of Herbicide Atrazine

International Journal of Pharmacology Phytochemistry and Ethnomedicine ◽

10.18052/www.scipress.com/ijppe.6.17 ◽

2017 ◽

Vol 6 ◽

pp. 17-20 ◽

Cited By ~ 2

Author(s):

Anthony Concilla ◽

He Liu

Keyword(s):

Gene Expression ◽

Immune System ◽

Cell Division ◽

Health Effects ◽

Physiological Functions ◽

Adverse Health Effects ◽

Adverse Health ◽

Multiple Genes ◽

Herbicide Atrazine

Atrazine is widely-used as an agricultural herbicide and has contaminated some watersupply. Here we review recent studies showing atrazine affects the expression of multiple genes, whichin turn disrupts physiological functions in metabolism, reproduction, immune system, and cell division.

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Global gene expression profiling and cluster analysis in Xenopus laevis

Mechanisms of Development ◽

10.1016/j.mod.2004.11.007 ◽

2005 ◽

Vol 122 (3) ◽

pp. 441-475 ◽

Cited By ~ 38

Author(s):

Danila Baldessari ◽

Yongchol Shin ◽

Olga Krebs ◽

Rainer König ◽

Tetsuya Koide ◽

...

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Xenopus Laevis ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Global Gene Expression ◽

Global Gene Expression Profiling ◽

And Cluster Analysis

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DYNAMIC MODEL-BASED CLUSTERING FOR TIME–COURSE GENE EXPRESSION DATA

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001314 ◽

2005 ◽

Vol 03 (04) ◽

pp. 821-836 ◽

Cited By ~ 22

Author(s):

FANG-XIANG WU ◽

W. J. ZHANG ◽

ANTHONY J. KUSALIK

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Dynamic Model ◽

Gene Expression Data ◽

Time Course ◽

Disease Diagnosis ◽

Model Parameters ◽

Expression Data ◽

Model Based Clustering ◽

Model Based

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

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Fusion Analysis of Economic Data of the Medical and Health Industry Based on Blockchain Technology and Two-Way Spectral Cluster Analysis

Mobile Information Systems ◽

10.1155/2021/7731387 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Runhua Liu ◽

Chengcheng Zhang ◽

Tenglong Feng

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Clustering Algorithm ◽

Service Industry ◽

Science Research ◽

Development Trend ◽

Disease Diagnosis ◽

Spectral Cluster ◽

Economic Data ◽

Blockchain Technology

Due to the huge potential in gene expression analysis, which is helpful for disease diagnosis, new drug development, and life science research, the two-way clustering algorithm was proposed and it was widely used in gene expression data research. In order to understand the economic data of medical and health industry, this paper analyzes the economic data of the medical and health industry in different regions of China based on blockchain technology and two-way spectral cluster analysis and makes statistics on the economic data of the medical and health industry in eastern, central, and western regions of China. This paper studies the development status of China’s medical and health industry and the factors affecting the agglomeration of medical and health service industry and analyzes them under the blockchain technology and two-way spectral cluster analysis method. The results show that the overall development trend of China’s medicine and health is from government-led to government, society, and individual sharing. After the transformation of blockchain technology and two-way spectral cluster analysis, the output value of the pharmaceutical industry increased by about 10%.

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