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Integrative analysis reveals novel pathways mediating the interaction between adipose tissue and pancreatic islets in obesity in rats

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Abstract

Aims/hypothesis

Comprehensive characterisation of the interrelation between the peripancreatic adipose tissue and the pancreatic islets promises novel insights into the mechanisms that regulate beta cell adaptation to obesity. Here, we sought to determine the main pathways and key molecules mediating the crosstalk between these two tissues during adaptation to obesity by the way of an integrated inter-tissue, multi-platform analysis.

Methods

Wistar rats were fed a standard or cafeteria diet for 30 days. Transcriptomic variations by diet in islets and peripancreatic adipose tissue were examined through microarray analysis. The secretome from peripancreatic adipose tissue was subjected to a non-targeted metabolomic and proteomic analysis. Gene expression variations in islets were integrated with changes in peripancreatic adipose tissue gene expression and protein and metabolite secretion using an integrated inter-tissue pathway and network analysis.

Results

The highest level of data integration, linking genes differentially expressed in both tissues with secretome variations, allowed the identification of significantly enriched canonical pathways, such as the activation of liver/retinoid X receptors, triacylglycerol degradation, and regulation of inflammatory and immune responses, and underscored interaction network hubs, such as cholesterol and the fatty acid binding protein 4, which were unpredicted through single-tissue analysis and have not been previously implicated in the peripancreatic adipose tissue crosstalk with beta cells.

Conclusions/interpretation

The integrated analysis reported here allowed the identification of novel mechanisms and key molecules involved in peripancreatic adipose tissue interrelation with beta cells during the development of obesity; this might help the development of novel strategies to prevent type 2 diabetes.

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Abbreviations

CM:

Non-supplemented culture medium

DIGE:

Differential gel electrophoresis

E-WAT:

Epididymal white adipose tissue

FABP4:

Fatty acid binding protein 4

IPA:

Ingenuity pathway analysis

LXR:

Liver X receptor

NAD:

Nicotinamide adenine dinucleotide

OB:

Wistar rats fed a high-energy cafeteria diet for 30 days

PM-WAT:

Peripancreatic white adipose tissue

q-PCR:

Quantitative real-time PCR

RXR:

Retinoid X receptor

STD:

Wistar rats fed a standard chow diet for 30 days

TCA:

Tricarboxylic acid

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Acknowledgements

This work was developed at the Centro Esther Koplowitz, Barcelona, Spain. We acknowledge the Unit of Proteomics from the Centres Científics i Tecnològics-Universitat de Barcelona (CCiT-UB, Barcelona, Spain), a member of ProteoRed network, for technical help. We acknowledge the Genomics Unit of IDIBAPS (Barcelona, Spain), where part of the transcriptomic analysis was carried out, for technical help. We acknowledge the Bioinformatics Unit of IDIBAPS (Barcelona, Spain), where the statistical transcriptomics and IPA analyses were carried out, for bioinformatics resources support.

Funding

This work was supported by grants from the Ministerio de Ciencia y Innovación (Spain), SAF2010-19527, and from Generalitat de Catalunya (Spain), 2009SGR1426. CIBERDEM is an initiative of Instituto de Salud Carlos III (Madrid, Spain). RM was a recipient of a grant from the European Commission for financial support (Marie Curie Grant - FP7 PEOPLE-2007-3-1-IAPP-218130).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

RM, SR and RGomis designed the study. All authors contributed to acquisition or interpretation of data, drafted the article or revised it critically for important intellectual content of the paper. RM is responsible for the integrity of the work as a whole. All authors approved the final version of the manuscript to be published.

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Authors and Affiliations

  1. Diabetes and Obesity Laboratory, Institut d’Investigations Biomediques August Pi I Sunyer (IDIBAPS), Carrer Rosselló 153, 08036, Barcelona, Spain

    Rita Malpique, Hugo Figueiredo, Yaiza Esteban, Sandra A. Rebuffat, Felicia A. Hanzu, Rosa Gasa & Ramon Gomis

  2. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain http://www.ciberdem.org

    Rita Malpique, Hugo Figueiredo, Yaiza Esteban, Sandra A. Rebuffat, Maria Vinaixa, Oscar Yanes, Xavier Correig, Rosa Gasa & Ramon Gomis

  3. Endocrinology and Diabetes Unit, Hospital Clínic, Barcelona, Spain

    Felicia A. Hanzu & Ramon Gomis

  4. Center for Omics Sciences (COS) – Universitat Rovira I Virgili, Reus, Spain

    Maria Vinaixa, Oscar Yanes & Xavier Correig

  5. Proteomics Unit, Fundació Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain

    Sílvia Barceló-Batllori

  6. Bioinformatics Core Facility, IDIBAPS – CEK, Hospital Clínic/Universitat de Barcelona, Barcelona, Spain

    Susana G. Kalko

  7. Barcelona University, Barcelona, Spain

    Ramon Gomis

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  1. Rita Malpique
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Correspondence to Ramon Gomis.

Electronic supplementary material

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ESM Table 1

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ESM Table 2

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ESM Table 3

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ESM Table 4

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ESM Fig. 1

2D-DIGE differential analysis of adipose tissue secretomes from OB vs STD rats. Representative image gel evidencing the 36 spot locations selected for excision after applying the statistical analysis (Two-way ANOVA and the two-tailed Student’s T-test for paired samples, p-value<0.05). (PDF 2939 kb)

ESM Fig. 2

Single-tissue, single-platform pathway mapping. PM-WAT (a) and pancreatic islets (b) transcriptomics integrative pathway mapping and PM-WAT secretome proteomics (c) and metabolomics (d) integrative pathway mapping (highest score network), considering only direct interactions. (PDF 546 kb)

ESM Fig. 3

Inter-tissue, multi-platform integrative pathway mapping, considering only direct interactions: (a) Pancreatic transcriptomics data superimposed on the secretome metabolite network (b) Pancreatic islets transcriptomics data and PM-WAT secretome proteomics data superimposed on the PM-WAT secretome metabolite network. (PDF 386 kb)

ESM Fig. 4

Gene expression changes in islet immune and inflammatory response signalling due to 30 day (a) and 6 month (b) cafeteria diet-induced obesity. Real-time PCR results for statistically significant genes: black bars = STD group; white bars = OB group. The expression of each gene was normalized with the constitutive expression of the gene Gadph. All values are presented as mean ± SEM from 6 to 10 animals per group. *P < 0.05, **P < 0.01 and ***P < 0.005. (PDF 369 kb)

ESM Fig. 5

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Malpique, R., Figueiredo, H., Esteban, Y. et al. Integrative analysis reveals novel pathways mediating the interaction between adipose tissue and pancreatic islets in obesity in rats. Diabetologia 57, 1219–1231 (2014). https://doi.org/10.1007/s00125-014-3205-0

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