Abstract

An enhanced understanding of the pathophysiology of depression would facilitate the discovery of new efficacious medications. To this end, we examined hippocampal transcriptional changes in rat models of disease and in humans to identify common disease signatures by using a new algorithm for signature-based clustering of expression profiles. The tool identified a transcriptomic signature comprising 70 probesets able to discriminate depression models from controls in both Flinders Sensitive Line and Learned Helplessness animals. To identify disease-relevant pathways, we constructed an expanded protein network based on signature gene products and performed functional annotation analysis. We applied the same workflow to transcriptomic profiles of depressed patients. Remarkably, a 171-probesets transcriptional signature which discriminated depressed from healthy subjects was identified. Rat and human signatures shared the SCARA5 gene, while the respective networks derived from protein-based significant interactions with signature genes contained 25 overlapping genes. The comparison between the most enriched pathways in the rat and human signature networks identified a highly significant overlap (p-value: 3.85 × 10–6) of 67 terms including ErbB, neurotrophin, FGF, IGF, and VEGF signaling, immune responses and insulin and leptin signaling. In conclusion, this study allowed the identification of a hippocampal transcriptional signature of resilient or susceptible responses in rat MDD models which overlapped with gene expression alterations observed in depressed patients. These findings are consistent with a loss of hippocampal neural plasticity mediated by altered levels of growth factors and increased inflammatory responses causing metabolic impairments as crucial factors in the pathophysiology of MDD.

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Acknowledgements

We thank Dr. Stewart A. Bates for help with gene expression experiments and for critical reading of the manuscript and Dr. Roberto Arban for help with the SD experiments and helpful discussions.

Funding

This work was part of a project funded by the European Commission that combined large-scale clinical pharmacogenomic studies on depressed patients with preclinical investigations on animal models of disease, focusing on treatment with pro-serotonergic and pro-noradrenergic antidepressants, called ‘Genome-based therapeutic drugs for depression (GENDEP)’, contract number LSHB-CT-2003-503428. The work was also supported by the University of Bologna (RFO 2014) to L. Carboni and the Swedish Medical Research Council to AAM (10414). The funding bodies had no role in the design of the study, collection and analysis of data and decision to publish. L. Carboni, MR, ED, and L. Caberlotto were GlaxoSmithKline employees when this work was started. During the past year, Dr Malki received income from and has been an employee and stockholder of Eli Lilly and UCB Celltech. The authors declare no conflicts of interests.

Author information

Author notes

  1. These authors contributed equally: Laura Caberlotto, Aleksander A. Mathé.

Affiliations

  1. Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, Bologna, Italy

    • Lucia Carboni
  2. The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Trento, Italy

    • Luca Marchetti
    • , Mario Lauria
    • , Enrico Domenici
    •  & Laura Caberlotto
  3. Department of Mathematics, University of Trento, Povo, Trento, Italy

    • Mario Lauria
  4. RG Animal Models in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany

    • Peter Gass
    •  & Barbara Vollmayr
  5. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

    • Amanda Redfern
    •  & Lesley Jones
  6. Department of Integrative Biology and Physiology University of Minnesota, 2231 6th Street SE, Minneapolis, USA

    • Maria Razzoli
  7. King’s College London, at the Institute of Psychiatry, Psychology and Neuroscience (IOPPN), London, UK

    • Karim Malki
  8. Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, Italy

    • Veronica Begni
    •  & Marco A. Riva
  9. Laboratory of Neurogenomic Biomarkers, Centre for Integrative Biology (CIBIO), University of Trento, Povo, Trento, Italy

    • Enrico Domenici
  10. The Aptuit Center for Drug Discovery & Development, Via Fleming, 4, 37135, Verona, Italy

    • Laura Caberlotto
  11. Karolinska Institutet, Department of Clinical Neuroscience, Stockholm, Sweden

    • Aleksander A. Mathé

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Correspondence to Lucia Carboni.

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DOI

https://doi.org/10.1038/s41386-018-0117-6