Introduction

Major Depressive Disorder (MDD), a complex, heterogeneous syndrome, is the leading cause of disability worldwide. The symptoms of MDD range from emotional and cognitive impairments as well as systemic dysfunctions. These diverse symptoms suggest the dysregulation of multiple brain regions and peripheral tissues, and there is evidence for brain region-specific disruptions in MDD1. Tricyclic antidepressants (TCAs) initially inhibit serotonin and/or norepinephrine reuptake, but the brain signaling networks affected upon their chronic administration—required for therapeutic efficacy—remain insufficiently understood.

Here we utilize an ethologically-validated mouse model for the study of depression called chronic social defeat stress (CSDS), where mice are exposed chronically to a social stress, which induces a range of MDD-like behavioral and molecular changes in a subset (~ 50%) of animals, referred to as susceptible2,3. These defects are ameliorated by chronic antidepressant treatment2,4,5. The remainder of the stress-exposed population does not display most of these behavioral abnormalities and are referred to as resilient3,4. This divergence in vulnerability to stress is observed within human populations6.

In the present study, we employ metabolomic, lipidomic, and proteomic analyses of the ventral hippocampus (vHipp), nucleus accumbens (NAc), and medial prefrontal cortex (mPFC)—all implicated in MDD—and serum samples from susceptible, resilient, and control (stress naïve) mice in order to comprehensively quantify the changes that occur within these tissues in response to CSDS. We interrogated these brain regions specifically, since each is spatially distinct and contributes uniquely to the limbic system, a circuit of inter-connected brain regions that has been implicated across multiple levels of analysis in the context of depression in both human and animal studies4,7,8. We hypothesized that molecular profiles within these limbic regions would be distinct between resilient and susceptible animals, and that identifying which molecules and pathways are most different would shed light onto the factors responsible for behavioral stress-responses.

Using this approach, we discover that many of the molecules affected by CSDS are involved in the molecular pathways of nucleotide metabolism, fatty acid beta oxidation, and antioxidant function. These pathways are differentially associated with susceptibility vs resilience depending on the brain region involved. We also analyzed the effect of chronic administration of imipramine, a standard tricyclic antidepressant, on these multi-OMIC endpoints. We observe that many of the same pathways are affected by imipramine treatment, further evidence that activity of these pathways contributes to stress responses. Together, this work provides a rich dataset to explore the tissue-specific, molecular mechanisms that differentiate stress resilient and stress susceptible animals, and outlines strongly-affected protein, lipid, and metabolite pathways that present promising targets for antidepressant drug discovery or biomarker efforts.

Methods and materials

Animals

Adult male 7–8 week old C57BL/6 J mice and 6-month old CD1 retired male breeders (CD1 aggressors) were housed at 22–25 °C in a 12-h light/dark cycle and provided food and water ad libitum. All methods were conducted in accordance with the IACUC regulations at Mount Sinai (LA12-00051) and Virginia Commonwealth University (AD10002174). All experiments were approved by the IACUC at these institutions and were performed in accordance with relevant guidelines and regulations.

Chronic social defeat stress and behavioral assays

We utilized an established CSDS protocol as described previously2,3,9. C57BL/6 J mice were exposed for ten consecutive days to a novel aggressive CD1 retired breeder for 10 min and were then separated from the aggressor by a perforated divider to maintain 24 h sensory contact. Each day the test mouse encountered a novel CD1 aggressor mouse. Mice were tested for social interaction (SI) 24 h after the last social defeat by first allowing 2.5 min for the test mouse to explore an arena containing a plexiglass wire mesh cage centered against one wall of the arena (target absent). In the second 2.5 min test, the same test mouse was returned to the arena with a novel CD1 mouse contained in the plexiglass and wire cage (target present). Across all SI tests for a given experiment, the same unfamiliar CD1 (i.e. not used in defeats) target mouse was used to provide consistent social interaction for our test mice. Based on the social interaction ratio, defined as time spent in the ‘interaction zone’ with target present divided by the time spent with target absent, mice were characterized as susceptible (SI ratio < 1) or resilient (SI ratio > 1). The SI ratio of 1 is a commonly used cut-off to discriminate resilient and susceptible animals10,11,12. Control mice were housed identically, yet never came in physical or sensory contact with a CD1 aggressor.

For antidepressant experiments, control, resilient, and susceptible populations were single housed and treated twice-daily with intraperitoneal (IP) injections of saline or imipramine (10 mg/kg) for 14 consecutive days after the SI test5. Treatment-induced changes in MDD-like behaviors were quantified by re-analyzing social interaction behaviors and performing elevated plus maze analysis. For the latter, mice were tested in a standard maze for 10 min, monitored by Ethovision XT as described previously13. Time in the open arms of the plus maze was quantified and expressed as a percent of total time.

Tissue preparation

Twenty-four hours after behavioral testing, vHipp, NAc, and mPFC tissue and serum were collected from consciously decapitated animals, immediately frozen, and stored at − 80 °C. Brains were sectioned in the coronal plane to 1 mm thickness in a brain matrix. Two 16-gauge punches (internal diameter 1.19 mm) were used to microdissect bilateral vHipp, two 14-gauge punches (internal diameter 1.6 mm) were used to isolate NAc bilaterally, and a single 12-gauge tissue punch (internal diameter 2.16 mm) was used to microdissect the mPFC. See Fig. 1A for size and targeting of the tissue punches. Tissue punches were pooled from between 7 and 11 animals depending on the mass of the brain region in order to create a single ~ 20 mg sample—a mass required to run metabolomics, lipidomics, and proteomics in parallel from the same sample. The bilateral vHipp punches of 11 animals were combined to create a single vHipp sample, bilateral NAc punches of eight animals were pooled to create a single NAc sample, and mPFC punches of seven animals were pooled to create a single mPFC sample. The exact number of samples per group is shown in Fig. 1C. The samples were combined in Omni homogenization bead tubes. The sera from two animals were pooled to create 400 µL samples. The aggregation of tissue and serum was performed to equalize SI ratios for samples in each group (resilient SI ratio: ~ 1.4; susceptible SI ratio: ~ 0.8; control SI ratio: ~ 1.35). From these pooled samples, all analyses were performed in parallel. Pooled samples were homogenized in water at 4 °C in an Omni Bead Ruptor 24 (Omni International, Tulsa, OK) and the protein content of each homogenate were determined via a bicinchoninic assay. Aliquots of 100 µg protein, 10 mg tissue weight, and 0.5 mg protein were separated for proteomics, metabolomics, and structural lipidomics analysis, respectively. Aliquots of pooled serum samples were likewise taken for proteomics, metabolomics, structural lipidomics, and mediator lipidomics analysis.

Figure 1
figure 1

Study overview and metabolomic, lipidomic, and proteomic analysis of serum from resilient and susceptible populations of chronically stressed mice. (A) Graphical illustration of workflow for chronic social defeat stress (CSDS) to differentiate mice into susceptible and resilient populations. All tissues harvested for analysis are displayed. Coronal brain images credit: Allen Institute. (B) Social interaction (SI) data from all mice, tested 24 h after the last CSDS bout. SI ratio is quantified for each mouse, with resilience as an SI ratio > 1 and susceptibility as an SI ratio < 1. Susceptible (n = 37), Resilient (n = 50), and Control (n = 33) (F2,117 = 32.82; ***p < 0.0001; one-way ANOVA followed by Bonferroni post-test). (C) To generate sufficient material for parallel analyses, tissues were pooled by SI ratio to achieve 20 mg/sample for brain tissues and 400 μL/sample for serum. Reported values are the sample numbers for each condition. A single mPFC sample consists of the pooled tissue of seven animals, a single NAc sample consists of the bilateral punches of eight animals, a single vHipp sample consists of the bilateral punches of 11 animals, and a single serum sample is pooled from two animals. (D) From this pooled sample, all processing and analysis occurred in parallel. (E) Heatmap of top 25 affected serum metabolites shows differences in metabolite levels, localized to experimental groups. (F) Pathway analysis of changed metabolites in the serum reveals purine and pyrimidine metabolism, the tricarboxylic acid cycle (TCA cycle), and antioxidant function, among other functions, as significantly affected in the serum of these chronically-stressed mice. Metabolites comprising “Pyrimidine metabolism” network are: Glutamine; Carbamoyl phosphate; Orotidine 5′-phosphate; Uridine; CMP; Cytidine; Deoxycytidine; Deoxyuridine; Thymidine; Thymine; N-Carbamoyl-L-aspartate; Orotate; Uracil; 3-Aminoisobutyrate. “Purine metabolism” network: Xanthine; D-Ribose 5-phosphate; L-Glutamine; 1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide; AMP; IMP; Adenosine; dAMP; Deoxyadenosine; Deoxyinosine; Xanthosine; Hypoxanthine; Inosine; Guanines; Allantoate; Guanosine; Adenine; Urate; Aminoimidazole ribotide; Urea; Allantoin. “TCA cycle” network: 2-Oxoglutarate; Succinate; Isocitrate; Malate; cis-Aconitate; Citrate; Pyruvate; Fumarate. “Glutathione metabolism” network: Glutathione; NADP + ; Glutathione disulfide; Glycine; L-Glutamate; L-Cysteine; 5-Oxoproline; L-Ornithine; Spermidine. (G) Lipidomic serum analysis reveals total circulating levels of phosphatidic acid (PA) increased in animals resilient to CSDS relative to susceptible animals (F2,58 = 3.80; *p < 0.05; one-way ANOVA followed by Fisher’s LSD comparing resilient and susceptible). (H) Proteomic analysis of serum: a total of 450 proteins were detected, with 17 proteins identified as significantly different from undefeated control animals. Of these 17, three proteins were significantly decreased solely in the susceptible cohort: kallikrein B1 (Klkb1), murinoglobulin-1 (Mug1), and thyroid receptor-interacting protein 11 (Trip11). Images in E, F, and G were generated with MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/).

Targeted and untargeted metabolomic analysis

Metabolomic analyses were performed using gas chromatography–mass spectrometry (GC/MS), reversed-phase liquid chromatography–mass spectrometry (RP-LC/MS), and hydrophilic interaction chromatography–liquid chromatography–tandem mass spectrometry (HILIC-LC/MS/MS)14,15,16,17. Metabolite extraction was achieved using a mixture of isopropanol:acetonitrile:water (3:3:2 v/v/v). Tissue samples were homogenized in an extraction mixture using Fisherbrand Model 120 Sonic Dismembrator. Extract analysis was performed using GC/MS, RP-LC/MS, and HILIC-LC/MS/MS protocols as described14. Quality control was performed using metabolite standards mixtures and pooled samples. The pooled QC sample was obtained by taking an aliquot of the same volume of all samples from the study. Supernatants of tissue and serum extracts were divided in three parts: 75 µL for GC-TOF–MS analysis, 75 µL for RP-LC/MS analysis, and 100 µL for HILIC-LC/MS/MS analysis. Collected raw data were manually inspected, merged, and imputed.

Specifically, metabolomics data were acquired using GC/MS, RP-LC/MS, and HILIC-LC–MS/MS. Recorded mass chromatograms were used for chromatography peak integration. Manual inspection of each mass chromatogram was performed to make sure no failure of separation unit and/or mass spectrometer occurred, and to ensure the acquired data were within the linear range of measurements. Peak areas of the identified metabolites were obtained using vendor’s integration software: ChromaTOF (LECO) and MultiQuant (Sciex). Three datasets containing integrated data for actual batch and all the QCs and pooled samples were merged in a single dataset (i.e. excel sheet) after normalization using external QCs (pooled control human plasma obtained from the UK Biobank, and injected after each 10 samples in a batch) and pooled batch samples was accomplished. Prior normalization missing values imputation was performed with the removal entities having more than 50% of missing or not detected data (N/D or below LOD). Duplicates removal followed with the hierarchy LC–MS/MS > LC–MS > GC–MS based on previously calculated CV for each duplicate detected with using different coupling. Further generalized log (glog) transformation and autoscaling were applied to stabilize the variability of the data. Negative values resulted for peak areas having raw values below 1 (arbitrary unit). Waaijenborg et al. provides additional information on our approaches for fusing metabolomics data from different platforms18.

Statistical analysis was performed with MetaboAnalyst 4.0https://www.metaboanalyst.ca/).