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Molecular characterization of depression trait and state

Abstract

Major depressive disorder (MDD) is a brain disorder often characterized by recurrent episode and remission phases. The molecular correlates of MDD have been investigated in case-control comparisons, but the biological alterations associated with illness trait (regardless of clinical phase) or current state (symptomatic and remitted phases) remain largely unknown, limiting targeted drug discovery. To characterize MDD trait- and state-dependent changes, in single or recurrent depressive episode or remission, we generated transcriptomic profiles of subgenual anterior cingulate cortex of postmortem subjects in first MDD episode (n = 20), in remission after a single episode (n = 15), in recurrent episode (n = 20), in remission after recurring episodes (n = 15) and control subject (n = 20). We analyzed the data at the gene, biological pathway, and cell-specific molecular levels, investigated putative causal events and therapeutic leads. MDD-trait was associated with genes involved in inflammation, immune activation, and reduced bioenergetics (q < 0.05) whereas MDD-states were associated with altered neuronal structure and reduced neurotransmission (q < 0.05). Cell-level deconvolution of transcriptomic data showed significant change in density of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin, or vasoactive-intestinal peptide (p < 3 × 10−3). A probabilistic Bayesian-network approach showed causal roles of immune-system-activation (q < 8.67 × 10−3), cytokine-response (q < 4.79 × 10−27) and oxidative-stress (q < 2.05 × 10−3) across MDD-phases. Gene-sets associated with these putative causal changes show inverse associations with the transcriptomic effects of dopaminergic and monoaminergic ligands. The study provides first insights into distinct cellular and molecular pathologies associated with trait- and state-MDD, on plasticity mechanisms linking the two pathologies, and on a method of drug discovery focused on putative disease-causing pathways.

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Fig. 1: RNAseq-based identification of trait and state-dependent phasic molecular changes in MDD.
Fig. 2: Profiles of biological pathway affected in the various MDD group contrasts.
Fig. 3: Cell-type deconvolution of gray matter RNAseq reveals gene expression changes in synchrony with MDD phases for CRH-, SST- and VIP-expressing GABAergic interneurons.
Fig. 4: Prioritizing putative causal gene modules in MDD using Bayesian network.
Fig. 5: Molecules antagonizing or mimicking the MDD-related expression profile.

Data availability

All datasets analyzed during the current study are available as supplementary tables. Raw data (count matrix, fastq.gz, or.bam) are available from the corresponding author on reasonable request.

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Acknowledgements

The study was supported by a project grant from the Canadian Institute of Health Research (CIHR) PJT-153175.

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RS and ES conceptualized the study and together wrote the manuscript. RS performed all the sequencing library preparation, quality check and bioinformatics analysis. DFN participated in in-silico validations. TT and AS performed the QPCR validation. HZ participated in Bayesian network analysis. RM participated in cmap analysis and DAL provided the resources.

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Correspondence to Rammohan Shukla or Etienne Sibille.

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ES is founder and Acting Chief Scientific Officer of Damona Pharmaceuticals, a drug development company with small molecules in the pipeline for treatment of cognitive deficits across brain disorders and aging. All other authors declare no competing interests.

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Shukla, R., Newton, D.F., Sumitomo, A. et al. Molecular characterization of depression trait and state. Mol Psychiatry (2021). https://doi.org/10.1038/s41380-021-01347-z

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