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Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation.

Evangelista AF, Collares CV, Xavier DJ, Macedo C, Manoel-Caetano FS, Rassi DM, Foss-Freitas MC, Foss MC, Sakamoto-Hojo ET, Nguyen C, Puthier D, Passos GA, Donadi EA - BMC Med Genomics (2014)

Bottom Line: Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis.Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil. eadonadi@fmrp.usp.br.

ABSTRACT

Background: Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.

Methods: Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.

Results: The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.

Conclusion: This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.

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Related in: MedlinePlus

Heatmap representative of type of diabetes, demographic, clinical, laboratory and treatment features of the patients. Qualitative variables were assigned by the absence or presence of the characteristic, and quantitative variables were assigned by values below or above the mean values. This information was used as array (experimental) set for the module map construction.
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Figure 1: Heatmap representative of type of diabetes, demographic, clinical, laboratory and treatment features of the patients. Qualitative variables were assigned by the absence or presence of the characteristic, and quantitative variables were assigned by values below or above the mean values. This information was used as array (experimental) set for the module map construction.

Mentions: Overall, a schematic heatmap with all demographic, clinical and laboratory patient features is shown in Figure 1. The global partitioning analysis of the three types of diabetes disclosed 8,469 transcripts considered as informative, which are available at http://www.rge.fmrp.usp.br/passos/DBF-MCL, the principal component analysis (PCA) of these genes are shown in Figure 2 and the summarized DAVID functional categories (Kegg pathways) of the main clusters are shown in Figure 3.


Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation.

Evangelista AF, Collares CV, Xavier DJ, Macedo C, Manoel-Caetano FS, Rassi DM, Foss-Freitas MC, Foss MC, Sakamoto-Hojo ET, Nguyen C, Puthier D, Passos GA, Donadi EA - BMC Med Genomics (2014)

Heatmap representative of type of diabetes, demographic, clinical, laboratory and treatment features of the patients. Qualitative variables were assigned by the absence or presence of the characteristic, and quantitative variables were assigned by values below or above the mean values. This information was used as array (experimental) set for the module map construction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4066312&req=5

Figure 1: Heatmap representative of type of diabetes, demographic, clinical, laboratory and treatment features of the patients. Qualitative variables were assigned by the absence or presence of the characteristic, and quantitative variables were assigned by values below or above the mean values. This information was used as array (experimental) set for the module map construction.
Mentions: Overall, a schematic heatmap with all demographic, clinical and laboratory patient features is shown in Figure 1. The global partitioning analysis of the three types of diabetes disclosed 8,469 transcripts considered as informative, which are available at http://www.rge.fmrp.usp.br/passos/DBF-MCL, the principal component analysis (PCA) of these genes are shown in Figure 2 and the summarized DAVID functional categories (Kegg pathways) of the main clusters are shown in Figure 3.

Bottom Line: Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis.Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil. eadonadi@fmrp.usp.br.

ABSTRACT

Background: Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.

Methods: Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.

Results: The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.

Conclusion: This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.

Show MeSH
Related in: MedlinePlus