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Review
. 2018 Jan:84:272-288.
doi: 10.1016/j.neubiorev.2017.08.019. Epub 2017 Aug 30.

Treatment resistant depression: A multi-scale, systems biology approach

Huda Akil  1 , Joshua Gordon  2 , Rene Hen  2 , Jonathan Javitch  2 , Helen Mayberg  3 , Bruce McEwen  4 , Michael J Meaney  5 , Eric J Nestler  6
Affiliations
Review

Treatment resistant depression: A multi-scale, systems biology approach

Huda Akil et al. Neurosci Biobehav Rev. 2018 Jan.

Abstract

An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. _targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.

Keywords: Amygdala; ChIP-sequencing; Epigenetics; GWAS; Gene expression; Hippocampus; Major depressive disorder; Neural circuits; Nucleus accumbens; Prefrontal cortex; RNA-sequencing.

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Figures

Figure 1
Figure 1
Cortico-limbic circuitry implicated in mood regulation and depression. (A) Simplified schematic diagram of the cortico-limbic circuitry and the many interactions across the various brain regions. Not all known connections are depicted. Likewise, not all outputs of each region are depicted. mPFC, medial prefrontal cortex; HIP, hippocampus; NAc, nucleus accumbens. (B) Midline sagittal view of the human brain illustrating the location of major PFC regions, with the anterior cingulate cortex highlighted: blue, MCC24, mid-cingulate cortex; yellow, pACC24, pre-genual anterior cingulate cortex; red, SCC24/25, subcallosal cingulate cortex. Other brain regions noted include: dMF9, dorsomedial frontal cortex; vMF10, ventromedial frontal; OF11, orbitofrontal; A-Hc, amygdala-hippocampus in the temporal lobe; BS, brainstem; PCC23, posterior cingulate cortex; c. callosum, corpus callosum.
Figure 2
Figure 2
Three-dimensional rendering of all significant volume changes after chronic social defeat stress in mice. Red clusters indicate positive correlations with social avoidance, and blue clusters indicate negative correlations with social avoidance. (A) Top view. (B) Bottom view. (C) Side view. From Anacker et al. (2016).
Figure 3
Figure 3
Projections from the ventral HIP modulate distinct emotional behaviors. The ventral HIP sends direct projections to numerous structures that directly influence emotional behaviors (green structures indicate ventral HIP projection _targets and dotted lines describe their putative behavioral consequences upon activation). These projections contribute to distinct aspects of behavior, such as spatial working memory through the mPFC, fear, anxiety and stress responses through the mPFC, amygdala, hypothalamus, septum and BNST, and reward-seeking behaviors through the NAc and septum. Moreover, there is evidence that many of these projections arise from largely non-overlapping cell populations within the CA1 pyramidal layer, including projections to the amygdala, mPFC, NAc, septum and lateral hypothalamus (Jin and Maren, 2015; Okuyama et al., 2016; Parfitt et al., 2017; Xu et al., 2016; Jimenez JC and Hen R, personal communication). Abbreviations: HIP, hippocampus; mPFC, medial prefrontal cortex; BNST, bed nucleus of stria terminalis; NAc, nucleus accumbens.
Figure 4
Figure 4
Example of transcriptomic analysis of treatment response vs. non-response in a mouse model. Mice were subjected to chronic social defeat stress. Susceptible mice were treated with repeated imipramine or single dose ketamine and responders and non-responders were identified. Four brain regions were then subjected to RNA-seq. (A) Heatmaps show the union of ketamine response (SUS-KET-RESP vs. SUS-SAL) and imipramine response (SUS-IMI-RESP vs. SUS-SAL) differentially expressed genes (DEGs) rank ordered by log2 fold change of ketamine response and scaled by relative number of DEGs. (B) Table of p value (text) and odds ratio (warmer colors indicating increasing odds ratio) for Fisher’s exact test for enrichment of ketamine response DEGs in imipramine response DEGs. (C) Heatmaps show the union of ketamine nonresponse (SUS-KET-NON vs. SUS-SAL) and imipramine nonresponse (SUS-IMI-NON vs. SUS-SAL) DEGs rank ordered by log2 fold change of ketamine nonresponse and scaled by relative number of DEGs. (D) Table of p value (text) and odds ratio (warmer colors indicating increasing odds ratio) for Fisher’s exact test for enrichment of ketamine nonresponse DEGs in imipramine nonresponse DEGs. *p<0.05. AMY, amygdala; HIP, hippocampus; NAC, nucleus accumbens; ns, nonsignificant; PFC, prefrontal cortex. From Bagot et al., 2016b.
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