Open Access Highly Accessed Research article

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

Adriane F Evangelista1, Cristhianna VA Collares12, Danilo J Xavier1, Claudia Macedo1, Fernanda S Manoel-Caetano1, Diane M Rassi1, Maria C Foss-Freitas2, Milton C Foss2, Elza T Sakamoto-Hojo13, Catherine Nguyen4, Denis Puthier4, Geraldo A Passos15 and Eduardo A Donadi12*

Author Affiliations

1 Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil

2 Division Clinical Immunology, Faculty of Medicine of Ribeirão Preto, (USP), 14049-900 Ribeirão Preto, SP, Brazil

3 Department of Biology, Faculty of Philosophy, Sciences and Letters, (USP), 14040-900 Ribeirão Preto, SP, Brazil

4 INSERM U1090, TAGC, Aix-Marseille Université IFR137, 13100 Marseille, France

5 Disciplines of Genetics and Molecular Biology, Department of Morphology, Physiology and Basic Pathology, School of Dentistry of Ribeirão Preto, USP, 14040-904 Ribeirão Preto, SP, Brazil

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BMC Medical Genomics 2014, 7:28  doi:10.1186/1755-8794-7-28

Published: 23 May 2014



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.


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.


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.


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.

Type 1 diabetes; Type 2 diabetes; Gestational diabetes; Module map; Immune cell signatures; Transcriptome analysis