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# Obesity-induced emotional alterations in mice are associated with impairments of tryptophan metabolism along the kynurenine and indole pathways

Nathalie Castanon, Sylvie Vancassel, Camille Amadieu, Sofia Cussotto, Quentin Leyrolle, Céline Lucas, Antoine Lefèvre, Patrick Emond, Anne-Laure Dinel, Lucile Capuron

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## ► To cite this version:

Nathalie Castanon, Sylvie Vancassel, Camille Amadieu, Sofia Cussotto, Quentin Leyrolle, et al. Obesity-induced emotional alterations in mice are associated with impairments of tryptophan metabolism along the kynurenine and indole pathways. Brain, Behavior, and Immunity, 2025, 130, pp.106107. 10.1016/j.bbi.2025.106107. hal-05283688

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https://hal.inrae.fr/hal-05283688v1

Submitted on 26 Nov 2025

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# Obesity-induced emotional alterations in mice are associated with impairments of tryptophan metabolism along the kynurenine and indole pathways

<figure><img src="image_4.png" /></figure>

Nathalie Castanon<sup>a,∗</sup> <sup> ID </sup>, Sylvie Vancassel<sup>a</sup>, Camille Amadieu<sup>a</sup>, Sofia Cussotto<sup>a,b</sup>, Quentin Leyrolle<sup>a</sup>, Céline Lucas<sup>a,c</sup>, Antoine Lefevre<sup>d,e</sup>, Patrick Emond<sup>d,e</sup>, Anne-Laure Dinel<sup>a,c</sup>, Lucile Capuron<sup>a</sup>

ARTICLE INFO

Keywords:

Mood disorders

Neuroinflammation

Obesity

Peripheral and brain tryptophan metabolism

Kynurenine pathway

Indoles

Emotional behaviors

Metabolomics

Preclinical study

ABSTRACT

Chronic inflammatory conditions, such as obesity, are frequently associated with a high prevalence of mood disorders and a reduced response to conventional antidepressants. Therefore, identifying the biological substrates underlying these comorbidities is crucial, as it could help uncover new therapeutic strategies to improve current treatments. Growing evidence implicates inflammation-driven dysregulation of tryptophan breakdown along the kynurenine pathway (KP) as a key contributor, although the specific roles of its various metabolites remain unclear. Additionally, the involvement of the gut-derived indole pathway (IP) of tryptophan metabolism is still poorly understood, despite increasing evidence linking gut microbiota metabolites to mood regulation. To address these questions, we assessed depressive-like and anxiety-like behaviors in C57BL/6J mice chronically exposed to high-fat diet (HFD), a reliable preclinical model of inflammatory depression. We also measured plasma and brain (hippocampus, frontal cortex, striatum) levels of a broad panel of KP and IP metabolites. HFD increased emotional behaviors and altered plasma and brain levels of tryptophan metabolites. Notably, it promoted a systemic neurotoxic-neuroprotective KP imbalance favoring neurotoxicity. It also drastically reduced indole production, with significant repercussions on brain indole-3 sulfate levels. Importantly, these metabolomic changes correlated with the severity of emotional alterations, with distinct metabolite-behavior relationships depending on the specific neuropsychiatric symptom dimensions assessed. In conclusion, this study offers valuable novel insights into the role of KP- and IP-derived tryptophan metabolites as key mediators between obesity and depression, and thus potential new promising therapeutic targets to improve neuropsychiatric comorbidities of obesity.

Abbreviations: EPM, elevated plus-maze; FCx, frontal cortex; FST, forced swim test; HC, hippocampus; HFD, high-fat diet; IAA, indole-3-acetic acid; IaL, indole-3-aldehyde; IAM, indole-3-acetamide; IFN-γ, interferon-γ; ILA, indole-3-lactic acid; IL-6, interleukin-6; IL-1β, interleukin-1β; IP, indole pathway; IS, indoxyl sulfate; KA, kynurenine acid; KP, kynurenine pathway; KYN, kynurenine; LC-QqQ, liquid chromatography coupled to tandem mass spectrometry analysis; MDD, major depressive disorder; OA, open-arms; PA, picolinic acid; PLS-DA, partial least squares discriminant analysis; PL, plasma; QA, quinolinic acid; SD, standard diet; SPT, sucrose preference test; STR, striatum; TNF-α, tumor necrosis factor-α; TRP, tryptophan; VIP, variable importance in projection; XA, xanthurenic acid; 3HAA, 3-hydroxyanthranilic acid; 5-HIAA, 5-hydroxyindole acetic acid; 3HK, 3-hydroxykynurenine; 5-HT, Serotonin; 5HTP, 5-hydroxytryptophan.

* Corresponding author at: INRAE 1286, Laboratory of Nutrition and Integrative Neurobiology (NutriNeuro), University of Bordeaux, 146 rue Léo Saignat, F-33076 Bordeaux, France.

E-mail address: nathalie.castanon@inrae.fr (N. Castanon).

<footnote><sup>a</sup> Univ. Bordeaux, INRAE, Bordeaux INP, NutriNeuro, UMR 1286, F-33000 Bordeaux, France</footnote>

<footnote><sup>b</sup> Univ. Paris-Saclay, UVSQ, Centre de recherche en Épidémiologie et Santé des Populations (CESP), UMR 1018, CESP-Inserm, Team Moods, 91400 Orsay, France</footnote>

<footnote><sup>c</sup> NutriBrain Research and Technology Transfer, NutriNeuro, F-33000 Bordeaux, France</footnote>

<footnote><sup>d</sup> INSERM, Univ. Tours, Imaging Brain & Neuropsychiatry (iBraiN), UMR 1253, 37000 Tours, France</footnote>

<footnote><sup>e</sup> Plateforme de Métabolomique et d'Analyses Chimiques, US-61 ASB, Univ. Tours, CHRU Tours, INSERM, 37000 Tours, France</footnote>
# 1. Introduction

As the leading cause of disability worldwide, major depressive disorder (MDD) represents a highly challenging public health concern, especially as its prevalence continues to rise and conventional antidepressant treatments fail in up to one-third of patients (Rush et al., 2006). Worsening the picture, most of the main chronic medical conditions that are also steadily increasing in prevalence, notably obesity, are often associated with an increased vulnerability to MDD (Beurel et al., 2020; Capuron et al., 2017; Castanon et al., 2014; Gold et al., 2020; Huet et al., 2019). Additionally, obese patients are often more prone to not respond to antidepressants than non-obese ones (Jantaratnotai et al., 2017; Kloiber et al., 2007; Woo et al., 2016). This has sparked interest in identifying the biological substrates underlying these comorbid associations, with particular focus on inflammation, as a key pathophysiological process shared by both MDD and obesity (Capuron et al., 2017; Delgado et al., 2018; Huet et al., 2021; Marx et al., 2021; Milaneschi et al., 2019).

One of the most extensively studied mechanisms potentially linking inflammation and depression is inflammation-induced alteration of tryptophan (TRP) metabolism through the kynurenine pathway (KP) (Capuron and Miller, 2011; Capuron and Castanon, 2017; Dantzer et al., 2008; Pearson et al., 2024; Vancassel et al., 2018). TRP is the precursor of several bioactive molecules (Fig. 1), including serotonin (5-HT), a neurotransmitter well-known to contribute to mood regulation, and kynurenine (KYN), whose synthesis has been causally linked to inflammation-induced depression (Godbout et al., 2008; O'Connor et al., 2009a, 2009b, 2009c; Parrott et al., 2016a, Parrott et al., 2016b). In addition to competing with 5-HT synthesis, KP activation produces several neurotoxic derivatives, such as 3-hydroxykynurenine (3HK) and quinolinic acid (QA), which further impair brain function by promoting oxidative stress and/or glutamate-related neurotoxicity (Dantzer et al., 2008; Vancassel et al., 2018; Parrott et al., 2016a, Parrott et al., 2016b). Notably, compelling evidence suggests that these neurotoxic processes play a crucial role in the induction of depressive symptoms under inflammatory conditions (Capuron et al., 2017; Marx et al., 2021;

O'Connor et al., 2009a; Savitz, 2020; Vancassel et al., 2018).

Obesity is recognized as a metabolic and inflammatory disorder characterized by low-grade peripheral inflammation, originating from both the adipose tissue and the gut microbiota (Cani et al., 2008; Gregor and Hotamisligil, 2011; Leyrolle et al., 2021), together with increased activation of brain inflammatory processes (André et al., 2014; Cardinal et al., 2021; Castanon et al., 2015; Dinel et al., 2014; Fourrier et al., 2019; Guillemot-Legris and Muccioli, 2017). Inflammation-induced KP activation has also been well documented in obesity (André et al., 2014; Capuron et al., 2017; Cardinal et al., 2021; Cussotto et al., 2020; Mangge et al., 2014; Mezo-González et al., 2022), with the few studies directly measuring serum levels of KP metabolites in obese cohorts supporting a shift towards the neurotoxic branch (Arto et al., 2024; Favennec et al., 2015). Importantly, inflammation and related changes in KP activation have been shown to contribute to the depressive comorbidities of obesity (Capuron et al., 2017; Cardinal et al., 2021; Huet et al., 2019; Marx et al., 2021; Milaneschi et al., 2019), although the relative implication of each metabolite, in particular with regard to the different neuropsychiatric symptom dimensions, remains an open question.

Alongside the KP, TRP is also metabolized by the gut microbiota to produce indoles and host-generated indole derivatives (Fig. 1). Besides, mounting studies have emphasized the relationship between these metabolites and mood alterations, particularly in chronic stress conditions or inflammatory gut diseases (Agus et al., 2018; Brydges et al., 2021; Merchak and Gaultier, 2020; Mir et al., 2020; Tennoue et al., 2022). In line with strong evidence of gut microbiota alterations in obesity (Cani et al., 2008; Gregor and Hotamisligil, 2011; Leyrolle et al., 2021), reduced circulating levels of TRP-derived metabolites from the indole pathway (IP) have also been reported in obese patients (Arto et al., 2024; Cussotto et al., 2020; Su et al., 2022; Virtue et al., 2019; Wang et al., 2024). Although little is known on the potential contribution of these metabolomic changes to the depressive comorbidities of obesity, recent clinical studies from our group showed that they are both related to inflammation (Cussotto et al., 2020; Delgado et al., 2022) and relevant to concomitant depressive symptoms, as their severity negatively correlates with serum indole levels (Delgado et al., 2022). The underlying

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Overview of the tryptophan metabolic pathways. Tryptophan (TRP) is metabolized through the kynurenine pathway (KP), the serotonin pathway and the gut microbiota-derived indole pathway (IP), with the host-generated derivative indole-3 sulfate (IS) being produced in the liver.</figcaption></figure>
mechanisms remain poorly understood, but it is noteworthy that similar IP dysregulation has been described in depressed patients (Agus et al., 2018; Merchak and Gaultier, 2020; Chen et al., 2017), while antidepressant-induced reduction in symptom severity is associated with an increase in plasma levels of indoles (Bhattacharyya et al., 2019, Bhattacharyya et al., 2025). Altogether, these findings support the contribution of inflammation-related changes in TRP metabolism along both the KP and IP to the induction of depressive symptoms in obesity, pointing to their respective metabolites as promising therapeutic targets for further investigation. However, a deeper understanding of the complex equilibrium between these TRP metabolic pathways is essential, particularly with regard to their impact on the brain. Moreover, it is crucial to determine how these metabolites relate to various obesity-associated depressive symptoms and whether they hold equal significance in this context.

In this study, we addressed these questions by assessing emotional alterations and changes in TRP metabolism in a preclinical model of obesity recently shown to be reliable to investigate the pathophysiology of inflammation-related depression and antidepressant treatment resistance (Cardinal et al., 2021). A large panel of metabolites derived from both the KP and IP were simultaneously measured in plasma and several brain areas (hippocampus, frontal cortex and striatum) known to be privileged sites of brain inflammation and regulation of associated emotional alterations (Bay-Richter et al., 2011; Frenois et al., 2007). This comprehensive approach allowed to decipher the brain impact of inflammation-related systemic changes of TRP metabolism and how it relates to emotional alterations. We also provided compelling evidence suggesting a differential involvement of specific TRP metabolites in these behavioral alterations, particularly depending on the symptom dimensions to which they are related.

# 2. Materials and methods

## 2.1. Animals

Sixteen-week-old male C57BL/6J mice were obtained from Janvier Labs (Le Genest-St-Isle, France) after being fed from weaning with standard diet (SD, A04, SAFE, France; 2.9 Kcal/g, n = 30) or high-fat diet (HFD, D12492, Research Diets, New Brunswick, NJ; 5.24 Kcal/g, 60 % Kcal from fat, n = 31). Upon arrival, they were fed with their respective diets *ad libitum* and daily handled for 1 week to minimize stress reactions to manipulation. They were collectively housed under a normal 12 h/12 h light/dark cycle (lights on at 8:00 am) unless isolation was required for behavioral testing. In this case, mice were transferred to individual cages one week before behavioral tests onset to allow habituation to the new housing conditions. All experimental procedures were in accordance with European Directives (2010/63/EU) and approved by the Institutional Animal Health and Care Committee (Approval ID: A16320).

## 2.2. Behavioral characterization

Complementary behavioral tests were used to assess different MDD symptom dimensions: anhedonia (sucrose preference test), depressive-like behavior (forced swim test) and anxiety-like behavior (elevated plus-maze), following the methods described previously (André et al., 2014; Cardinal et al., 2021; Fourrier et al., 2019). All behavioral tests were conducted in the morning, under conditions of dim light and low noise. Mice were tested in a random order that was changed between each test. Testing equipment was thoroughly cleaned between each session.

### 2.2.1. Sucrose preference test (SPT)

Individually-housed mice were presented two bottles of tap water for 72 h (bottle sides reversed daily) to measure basal water consumption (by weighing the bottles) and potential place preference. Then, one of the bottles was filled with a 2 % sucrose solution and both sucrose and

water intake was measured 24 h later. Sucrose preference, whose reduction is considered as reflecting increased anhedonia, was calculated as the percentage of sucrose intake over the total fluid (water + sucrose) intake.

### 2.2.2. Forced swim test (FST)

Mice were placed individually in a cylinder (diameter: 16 cm; height: 31 cm) half-filled with 25 °C+/-1°C water for 6 min. Behavior was manually scored by a trained observer blind to group assignments using the “Observer Basic” software (Noldus, Netherlands). Latency to became immobile and increased immobility time were used as relevant indexes of despair and helplessness.

### 2.2.3. Elevated Plus-Maze (EPM)

Testing began by placing a mouse in the center of the EPM (elevated 120 cm above the floor) facing an open-arm (OA; 30 × 8 cm), which is considered as more anxiogenic than the closed-arms (30 × 8 × 15 cm). Number of OA entries and total distance traveled were then assessed for 5 min, as an index of anxiety-like behavior and general locomotor activity, respectively. Behavior was automatically recorded and analyzed (Smart Videotrack System, Bioseb, France).

## 2.3. Tissue sampling and biochemical measures

As shown in Fig. 2A, mice were euthanized with an ip injection of pentobarbital/lidocaine solution either 3 weeks after the experiment onset, to measure brain expression of inflammatory cytokines (n = 16 mice/group), or the day after completion of behavioral testing (week 6) to measure plasma and brain levels of TRP metabolites (n = 14–15 mice/group). Once asleep, blood samples were collected via cardiac puncture and centrifuged (10 min, 1000g, 4 °C) to obtain plasma aliquots. Mice were then rapidly perfused with chilled 1X PBS, to remove all traces of blood from tissues, and brains carefully dissected to collect the hippocampus (HC), frontal cortex (FCx) and striatum (STR). Brain samples were then immediately frozen and stored at -80 °C for later gene expression analysis or targeted quantitative metabolomics. As previously described (Fourrier et al., 2019), plasma leptin levels were measured on samples collected at the end of the experiment with the mouse leptin Milliplex kit (Merk-Millipore, France) following the manufacturer's instructions.

### 2.3.1. Targeted quantitative metabolomics

Levels of TRP and its metabolites were quantified from plasma aliquots and freeze-dried brain areas of interest using Liquid Chromatography coupled to Tandem Mass Spectrometry Analysis (LC-QqQ), according to the method described previously (Alarcan et al., 2021). Details are provided in the Supplementary Materials and Methods. Detected molecules were: TRP, 5-hydroxytryptophan (5HTP), 5-HT, 5-hydroxyindole acetic acid (5-HIAA), KYN, 3HK, 3-hydroxyanthranilic acid (3HAA), QA, kynurenic acid (KA), xanthurenic acid (XA), picolinic acid (PA), indole-3 sulfate (IS), indole-3-acetamide (IAM), indole-3-acetic acid (IAA), indole-3-aldehyde (IaL), tryptophol and indole-3-lactic acid (ILA).

### 2.3.2. Gene expression analysis

To assess brain inflammatory status, expression level of genes coding for interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) was measured by RT-qPCR in the HC, FCx and STR, as previously described (Monchaux De Oliveira et al., 2021). Briefly, total RNAs were extracted using Trizol reagent (Invitrogen, Life Technologies, Villebon-sur-Yvette, France) and reverse-transcribed into cDNAs with the Superscript III reverse transcriptase (Invitrogen, Waltham, MA, US). The Taqman LightCycler® 480 Probes Master mix (Roche Diagnostics, Meylan, France) and its associated FAM-labeled Taqman Primers (ThermoFisher Scientific, Waltham, MA, US; see primer references in Supplementary Table S1) were used to amplify
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** HFD mice displayed metabolic alterations and increased depressive-like and anxiety-like behavior. (A) Experimental timeline and design. Male C57BL/6J mice were fed with Standard Diet (SD; 2.9 Kcal/g, n = 30) or High-Fat Diet (HFD; 5.24 Kcal/g, n = 31) from weaning and until the end of the experiment. Euthanasia occurred either 3 weeks after the experiment onset to measure neuroinflammation (n = 16/group) or after completion of the behavioral characterization, to measure plasma and brain levels of tryptophan metabolites. (B) Body weight recorded at mice arrival (week 1) and 3 and 6 weeks later. (C) Plasma levels of leptin assessed at week 6. (D) Number of open-arms entries in the elevated plus-maze (EPM), assessing anxiety-like behavior, and (E) total distance traveled over the 5-min EPM test. (F) Time spent immobile and (G) latency to display the 1st immobility episode in the forced swim test (FST), assessing depressive-like behavior. (H) Sucrose preference displayed in the sucrose preference test (SPT), a reward-based test modeling anhedonia, and expressed as the percentage of sucrose intake over the total fluid (sucrose + water) intake. (n = 13–15 mice/group) \#p = 0.067, \*\*p < 0.01, \*\*\*p < 0.001 for Diet effect.</figcaption></figure>

targeted genes. All experiments were performed in duplicates. Data were analyzed with the comparative threshold cycle method and results were normalized with GAPDH as a house-keeping gene.

## 2.4. Statistical analysis

Data were analyzed using Graphpad 10.2.3 (Prism, San Diego CA, US) and R studio version 3.5.1 (FactoMineR package and MetaboAnalystR Package). Normality was assessed by the Shapiro-Wilk test. Depending on data distribution, Student t-test or Mann-Whitney test were applied to compare SD and HFD groups. Repeated measures ANOVA was used to analyze time-course of data assessed several times.

Partial Least Squares Discriminant Analysis (PLS-DA) was performed separately on plasma, HC, FCx, and STR metabolomic data to identify the metabolites that most effectively discriminate between SD and HFD groups. Variable Importance in Projection (VIP) plots were generated to highlight the metabolites contributing significantly to group separation, with a predefined VIP score > 1.0 used to determine importance. Data were log10-transformed and auto-scaled. Pearson correlations were then calculated between behavioral scores and selected metabolites (VIP score > 1 and p value < 0.05). Graphs were presented as mean ± SEM.

# 3. Results

## 3.1. HFD mice displayed overweight and related metabolic and inflammatory dysregulations

HFD mice were heavier than SD mice at arrival ($t_{(25)}$ = 6.83, p < 0.001; Fig. 2B) and this difference remained significant throughout the experiment (diet: $F_{1,189}$ = 60.6, p < 0.001; time: $F_{7,189}$ = 30.3, p < 0.001; diet × time: $F_{7,189}$ = 30.8, p < 0.001). Accordingly, HFD enhanced plasma levels of leptin ($t_{(27)}$ = 8.91, p < 0.001; Fig. 2C), which were tightly correlated with body weight (r = 0.86, p < 0.001), and upregulated expression of inflammatory cytokine genes in two central sites for emotional regulation, the HC and FCx (Table 1). Specifically, the expression of IL-6 was increased in the HC of HFD mice compared to SD mice ($t_{(21)}$ = 2.63, p < 0.05), with this increase being positively correlated with body weight (r = 0.50, p < 0.05). Notably, HC TNF-α expression was also significantly correlated with body weight (r = 0.44, p < 0.05), although its upregulation in HFD mice did not reach significance. IL-6 ($t_{(28)}$ = 2.61, p < 0.05) and TNF-α expression ($t_{(30)}$ = 2.09, p < 0.05) were also enhanced in the FCx of HFD mice. In contrast, both groups exhibited similar IL-1β and IFN-γ expression in the two brain areas and no significant change in the STR, whatever the cytokine. Overall, these data confirmed that HFD mice displayed the expected metabolic and inflammatory features of obesity.
<table><caption>**Table 1**
Impact of HFD on brain mRNA expression of inflammatory cytokines.</caption><thead><tr><th rowspan="2">Relative mRNA expression</th><th colspan="2">Hippocampus</th><th colspan="2">Frontal Cortex</th><th colspan="2">Striatum</th></tr><tr><th>SD</th><th>HFD</th><th>SD</th><th>HFD</th><th>SD</th><th>HFD</th></tr></thead><tbody><tr><td>IL-1β</td><td>1.01<br>± 0.15</td><td>0.96 ±<br>0.09</td><td>1.13<br>± 0.15</td><td>1.03 ±<br>0.14</td><td>1.18<br>± 0.18</td><td>1.92<br>± 0.85</td></tr><tr><td>IL-6</td><td>1.01<br>± 0.14</td><td>1.64 ±<br>0.20*</td><td>1.14<br>± 0.20</td><td>1.52 ±<br>0.22*</td><td>1.01<br>± 0.11</td><td>1.33<br>± 0.33</td></tr><tr><td>TNF-α</td><td>1.12<br>± 0.17</td><td>1.29 ±<br>0.12</td><td>1.08<br>± 0.11</td><td>1.63 ±<br>0.24*</td><td>1.28<br>± 0.18</td><td>1.25<br>± 0.42</td></tr><tr><td>IFN-γ</td><td>0.96<br>± 0.14</td><td>1.07 ±<br>0.11</td><td>1.15<br>± 0.21</td><td>1.28 ±<br>0.20</td><td>nd</td><td>nd</td></tr></tbody></table>

Relative expression levels (calculated as fold changes relative to controls) of Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α) and Interferon-γ (IFN-γ) measured in the hippocampus, frontal cortex and striatum. HFD, high-fat diet; SD, standard diet. n = 11–16 mice/group. nd: not detectable. \*p < 0.05 for Diet effect.

## 3.2. HFD mice displayed emotional alterations

Consistent with the literature reporting emotional alterations in preclinical models of obesity (Cardinal et al., 2021; Castanon et al., 2015; Dinel et al., 2014; Fourrier et al., 2019), HFD mice visited less the anxiogenic open-arms of the EPM than SD mice ($t_{(27)} = 3.43, p < 0.01$; Fig. 2D). This was likely not an unspecific consequence of potentially impaired locomotion due to being overweight, as the total distance traveled during the test was similar in both groups (Fig. 2E). HFD mice also spent more time immobile in the FST than SD mice ($t_{(25)} = 4.99, p < 0.001$; Fig. 2F) and showed a trend towards a reduced latency to immobility ($t_{(27)} = 1.90, p = 0.067$; Fig. 2G). This likely reflected increased depressive-like behavior in HFD mice rather than heightened fatigue, since the time-course analysis of immobility duration revealed an effect of diet ($F_{(1,130)} = 13.8, p < 0.01$) and time ($F_{(5,130)} = 41.1, p < 0.001$), but no significant interaction between both factors (data not shown). In agreement with enhanced depressive-like behavior in the FST, HFD mice also showed decreased sucrose preference in the SPT ($t_{(26)} = 2.80, p < 0.01$; Fig. 2H), a reward-based test modeling anhedonia.

## 3.3. TRP metabolism along the KYN and indole pathways was altered in HFD mice

We proceeded to investigate the potential pathophysiological mechanisms underlying obesity-related emotional changes by measuring the impact of HFD exposure on plasma and brain concentrations of TRP and its main derivatives. In the plasma, the PLS-DA allowed clearly dissociating HFD from SD mice, the first two components together explaining 53 % of the total variance (Fig. 3A). The metabolites with elevated VIP scores (>1), indicative of a contribution to group discrimination, were indoles (IS, tryptophol, IAA and ILA), followed by the 5-HT catabolite 5-HIAA, and KYN (Fig. 3B). HFD reduced plasma levels of 5-HIAA (145.1 ± 5.10 vs 118.1 ± 7.61 nmol/L for SD and HFD groups, respectively; U = 26, p < 0.01), but not levels of 5-HT or its precursor, 5-HTP (data not shown). Plasma TRP levels were also similar in both groups, whereas significant changes were found regarding levels of KP and IP metabolites (Table 2). Specifically, plasma KYN and 3HAA levels were respectively reduced (U = 53, p < 0.05) and increased (U = 43, p < 0.05) in HFD mice. This may suggest that HFD promoted the systemic production of KYN neurotoxic derivatives, at the expense of rather neuroprotective ones, such as KA or XA. Supporting this assumption, positive correlations were found between body weight and plasma levels of 3HAA ($r = 0.43, p < 0.05$) or QA ($r = 0.45, p < 0.05$), as well as between these metabolites and circulating leptin concentrations ($r = 0.50, p < 0.01$; $r = 0.49, p < 0.01$, respectively), consistent with the tight link between increased body fat mass and leptin release. Moreover, the 3HK/KYN, 3HK/XA and QA/KA ratios were

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Partial Least Squares Discriminant Analysis (PLS-DA) conducted on plasma and brain levels of tryptophan and its metabolites. (A) Individual plot of the PLS-DA and (B) plot of Variable Importance in Projection (VIP) scores calculated for plasma (PL) metabolomic data. PLS-DA and VIP score plots obtained for (C, D) the hippocampus (HC), (E, F) frontal cortex (FCx), and (G, H) striatum (STR). Abbreviations: HFD, high-fat diet; KA, kynurenine acid; KYN, kynurenine; IAA, indole-3-acetic acid; IaL, indole-3-aldehyde; IAM, indole-3-acetamide; ILA, indole-3-lactic acid; IS, indoxyl sulfate; PA, picolinic acid; QA, quinolinic acid; SD, standard diet; TRP, tryptophan; XA, xanthurenic acid; 3HAA, 3-hydroxyanthranilic acid; 5-HIAA, 5-hydroxyindole acetic acid; 3HK, 3-hydroxykynurenine; 5-HT, serotonin; 5HTP, 5-hydroxytryptophan. n = 14 SD and 15 HFD mice.</figcaption></figure>
<table><caption>**Table 2**

Impact of HFD on plasma and brain levels of tryptophan and its metabolites derived from the kynurenine and indole pathways.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">(nmol/L)</th><th colspan="2">PLASMA</th><th colspan="2">HIPPOCAMPUS</th><th colspan="2">CORTEX FRONTAL</th><th colspan="2">STRIATUM</th></tr><tr><th>SD</th><th>HFD</th><th>SD</th><th>HFD</th><th>SD</th><th>HFD</th><th>SD</th><th>HFD</th></tr></thead><tbody><tr><td rowspan="10">Kynurenine Pathway</td><td>TRP</td><td>49260.90<br>(2616.00)</td><td>55715.10<br>(3951.48)</td><td>2036.12<br>(133.30)</td><td>2062.57<br>(145.23)</td><td>1746.54<br>(189.02)</td><td>1235.26<br>(97.16) *</td><td>2502.82<br>(241.26)</td><td>2321.03<br>(154.50)</td></tr><tr><td>KYN</td><td>400.10 (33.49)</td><td>301.10 (33.01)*</td><td>6.22 (0.43)</td><td>4.34 (0.38)**</td><td>22.47 (2.77)</td><td>11.47 (1.47)</td><td>24.86 (3.10)</td><td>15.93 (1.41)</td></tr><tr><td>3HK</td><td>56.60 (4.34)</td><td>63.60 (6.66)</td><td>1.20 (0.08)</td><td>0.88 (0.08)**</td><td>2.01 (0.34)</td><td>1.01 (0.11)**</td><td>1.70 (0.21)</td><td>2.58 (0.93)</td></tr><tr><td>3HAA</td><td>9.54 (1.27)</td><td>14.64 (1.92)*</td><td>nd</td><td>nd</td><td>11.20 (1.85)</td><td>6.04 (0.92)*</td><td>14.77 (1.66)</td><td>11.02 (1.54)</td></tr><tr><td>QA</td><td>173.6 (21.16)</td><td>237.37 (28.89)</td><td>1.01 (0.07)</td><td>0.91 (0.08)</td><td>1.64 (0.33)</td><td>1.16 (0.20)</td><td>1.82 (0.16)</td><td>1.45 (0.10)</td></tr><tr><td>KA</td><td>71.10 (10.31)</td><td>58.41 (8.99)</td><td>nd</td><td>nd</td><td>0.15 (0.08)</td><td>0.06 (0.01)</td><td>nd</td><td>nd</td></tr><tr><td>XA</td><td>52.10 (4.65)</td><td>41.93 (6.00)</td><td>0.13 (0.01)</td><td>0.10 (0.01)*</td><td>0.26 (0.08)</td><td>0.14 (0.02)</td><td>0.14 (0.02)</td><td>0.13 (0.01)</td></tr><tr><td>PA</td><td>nd</td><td>nd</td><td>42.13 (1.82)</td><td>45.63 (1.41)</td><td>59.21 (2.45)</td><td>69.27 (3.92)*</td><td>64.15 (3.06)</td><td>60.72 (2.66)</td></tr><tr><td>3HK/KYN</td><td>0.15 (0.02)</td><td>0.23 (0.02)**</td><td>0.19 (0.01)</td><td>0.21 (0.01)</td><td>0.09 (0.01)</td><td>0.09 (0.01)</td><td>0.08 (0.01)</td><td>0.16 (0.06)</td></tr><tr><td>3HK/XA</td><td>1.24 (0.16)</td><td>1.90 (0.25)*</td><td>9.98 (0.72)</td><td>9.49 (0.92)</td><td>10.15 (1.03)</td><td>8.18 (1.02)</td><td>11.38 (1.32)</td><td>12.81 (1.39)</td></tr><tr><td rowspan="6">Indole Pathway</td><td>QA/KA</td><td>2.68 (0.31)</td><td>5.34 (0.66)**</td><td>nd</td><td>nd</td><td>32.42 (4.13)</td><td>27.06 (3.31)</td><td>nd</td><td>nd</td></tr><tr><td>Tryptophol</td><td>4.18 (0.19)</td><td>2.07 (0.33)***</td><td>0.87 (0.05)</td><td>0.82 (0.04)</td><td>1.22 (0.19)</td><td>1.15 (0.24)</td><td>3.35 (1.12)</td><td>4.45 (0.96)</td></tr><tr><td>ILA</td><td>647.10 (48.12)</td><td>455.50 (27.37)</td><td>2.20 (0.25)</td><td>1.87 (0.10)</td><td>1.83 (0.29)</td><td>1.20 (0.11)</td><td>2.06 (0.36)</td><td>1.84 (0.13)</td></tr><tr><td>IAA</td><td>152.34 (16.39)</td><td>63.46 (6.63)***</td><td>1.69 (0.15)</td><td>1.41 (0.07)</td><td>2.08 (0.31)</td><td>1.39 (0.16)</td><td>6.43 (1.17)</td><td>5.23 (0.46)</td></tr><tr><td>IAM</td><td>nd</td><td>nd</td><td>0.95 (0.04)</td><td>0.94 (0.02)</td><td>1.00 (0.06)</td><td>1.00 (0.06)</td><td>1.05 (0.03)</td><td>1.09 (0.03)</td></tr><tr><td>IAL</td><td>57.30 (7.01)</td><td>51.94 (4.56)</td><td>7.76 (0.34)</td><td>7.58 (0.36)</td><td>5.74 (0.92)</td><td>5.15 (0.36)</td><td>6.77 (0.36)</td><td>6.31 (0.30)</td></tr><tr><td rowspan="2">IS</td><td>11592.99<br>(855.60)</td><td>3791.88<br>(436.96)***</td><td>2.71 (0.53)</td><td>0.44 (0.06)</td><td>2.84 (0.57)</td><td>0.46 (0.08)</td><td>2.66 (0.82)</td><td>0.47 (0.08)</td></tr><tr><td>Sum</td><td>12454.73</td><td>4330.20</td><td>16.18 (1.01)</td><td>13.06 (0.50)</td><td>14.72 (1.80)</td><td>10.35 (0.62)*</td><td>18.97 (2.42)</td><td>14.94 (0.78)</td></tr><tr><td>Indoles</td><td>(863.80)</td><td>(433.30)***</td><td></td><td>**</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Metabolomic measures were performed using LC-QqQ in the plasma, hippocampus, frontal cortex and striatum. Values are expressed as nmoles/L and presented as mean (SEM). *Abbreviations*: HFD, high-fat diet; KA, kynurenine acid; KYN, kynurenine; IAA, indole-3-acetic acid; IaL, indole-3-aldehyde; IAM, indole-3-acetamide; ILA, indole-3-lactic acid; IS, indoxyl sulfate; LC-QqQ, liquid chromatography coupled to tandem mass spectrometry analysis; PA, picolinic acid; QA, quinolinic acid; SD, standard diet; TRP, tryptophan; XA, xanthurenic acid; 3HAA, 3-hydroxyanthranilic acid; 5-HIAA, 5-hydroxyindole acetic acid; 3HK, 3-hydroxykynurenine; 5-HT, serotonin; 5HTP, 5-hydroxytryptophan. n = 13–15 mice/group. *nd*: not detectable. *p* < 0.05, **p* < 0.01, ***p* < 0.001 for Diet effect.

significantly higher in HFD mice ($t_{(26)} = 2.96$, $p < 0.01$; $t_{(26)} = 2.14$, $p < 0.05$ and $t_{(26)} = 3.46$, $p < 0.01$, respectively), albeit HFD-induced changes in circulating levels of 3HK, QA, KA and XA did not reach significance. Concurrently, HFD mice displayed a marked reduction in plasma levels of tryptophol (U = 14, $p < 0.001$), ILA (U = 23, $p < 0.001$), IAA (U = 4, $p < 0.001$) and IS ($t_{(26)} = 8.45$, $p < 0.001$) compared to SD mice. This was indicative of an HFD-induced decrease in the systemic production of indoles, as confirmed by the drastic reduction in the sum of plasma IP metabolites ($t_{(26)} = 8.76$, $p < 0.001$) and its strong negative correlation with body weight ($r = -0.74$, $p < 0.001$) and leptin levels ($r = -0.71$, $p < 0.001$).

The PLS-DA conducted separately on HC, FCx and STR metabolomic data (Fig. 3C-H) also indicated dissociation between the two groups, with IS and KYN consistently exhibiting the highest VIP scores. However, other metabolites, including 3HK and XA in the HC or 3HK, 3HAA, TRP and PA in the FCx, also showed scores indicative of their role in this dissociation. HFD did not significantly alter 5-HT pathway metabolites, whatever the brain area (data not shown). In contrast, it reduced TRP levels in the FCx ($t_{(27)} = 2.45$, $p < 0.05$), alongside KYN levels in the HC (U = 32, $p < 0.01$), FCx (U = 39, $p < 0.01$) and STR (U = 46, $p < 0.01$), as also found in the plasma (Table 2). Nevertheless, no significant correlations were reported between KYN plasma and brain levels in HFD mice (data not shown), indicating that reduction of brain KYN abundance was not merely related to its reduced blood-brain barrier (BBB) entry. While no additional changes were found in the STR for other KP metabolites, HFD mice displayed lower FCx levels of 3HK (U = 42, $p < 0.01$) and 3HAA (U = 56.5, $p < 0.05$) than SD mice, but higher PA levels ($t_{(26)} = 2.10$, $p < 0.05$). Besides, the latter were inversely correlated with those of 3HAA in HFD mice ($r = -0.58$, $p < 0.05$). In the HC, HFD reduced levels of 3HK (U = 41, $p < 0.01$) and the neuroprotective metabolite XA (U = 47.5, $p < 0.05$), proportionally to the increase in body weight ($r = -0.48$, $p < 0.01$ and $r = -0.43$, $p < 0.05$, respectively). Moreover, HC levels of KA, also commonly associated with neuroprotection in the context of inflammation-related depression (Francis et al., 2022; Savitz, 2020), were below detection threshold. They were very low in the FCx of HFD mice and highly correlated with XA

concentrations ($r = 0.77$, $p < 0.001$). Regarding the IP, the sums of indole levels were significantly reduced in the HC ($t_{(27)} = 2.84$, $p < 0.01$) and FCx ($t_{(27)} = 2.35$, $p < 0.05$) of HFD mice, as shown systemically. Consistent with PLS-DA findings highlighting a key role of IS, this reduction was this time primarily linked to a drastic impact of HFD on IS concentrations (HC: U = 16, $p < 0.001$; FCx: U = 15, $p < 0.001$), those of other indoles being not significantly changed between the two groups, whatever the brain area. IS was also less abundant in the STR of HFD mice (U = 15, $p < 0.001$), although the reduction in the sum of STR indoles did not reach significance. Moreover, plasma IS levels were positively correlated with those measured in the HC ($r = 0.71$, $p < 0.001$), FCx ($r = 0.66$, $p < 0.001$) and STR ($r = 0.71$, $p < 0.001$), which were all significantly correlated with body weight (plasma: $r = -0.73$, $p < 0.001$; HC: $r = -0.56$, $p < 0.001$; FCx: $r = -0.54$, $p < 0.01$; STR: $r = -0.59$, $p < 0.01$).

### 3.4. HFD-induced emotional alterations were associated with impairment of TRP degradation

Given that HFD exposure induced specific systemic and brain changes in the TRP breakdown, the question arose regarding the potential contribution of these changes to the behavioral profile of HFD mice. As shown in Fig. 4, most of the significant correlations between behavioral and biological data involved plasma indoles, with their reduction being positively correlated with the severity of anhedonia, depressive-like and/or anxiety-like behaviors. In particular, reduction in circulating IS abundance correlated with increased immobility duration in the FST ($p < 0.01$), reduced sucrose preference in the SPT ($p < 0.01$) and number of open-arm entries in the EPM ($p < 0.01$). Low tryptophol levels were associated with increased immobility ($p < 0.01$) and reduced number of open-arm entries ($p < 0.01$). Similarly, plasma IAA levels correlated with immobility time ($p < 0.01$) and latency ($p < 0.05$), and number of EPM open-arm entries ($p < 0.001$), while reduced ILA levels were specifically associated with increased depressive-like behavior in the FST (immobility time and latency: $p < 0.05$) and the SPT (sucrose preference: $p < 0.05$). Beyond indoles, sucrose preference was also
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Relationship between emotional alterations and plasma and brain levels of tryptophan and its metabolites. Heatmap showing Spearman's correlation matrix between depressive-like and anxiety-like behaviors (sucrose preference in the SPT, latency to 1st immobility and immobility time in the FST, number of open-arm (OA) entries and total distance traveled in the EPM), and selected plasma and brain metabolomic data (those with VIP score > 1 and p value < 0.05), as well as plasma 3HK/KYN, 3HK/XA and QA/KA ratios. *Abbreviations:* EPM, elevated plus-maze; FST, forced swim test; HFD, high-fat diet; KA, kynurenine acid; KYN, kynurenine; IAA, indole-3-acetic acid; IaL, indole-3-aldehyde; IAM, indole-3-acetamide; ILA, indole-3-lactic acid; IS, indoxyl sulfate; PA, picolinic acid; QA, quinolinic acid; SD, standard diet; SPT, sucrose preference test; TRP, tryptophan; XA, xanthurenic acid; 3HAA, 3-hydroxyanthranilic acid; 5-HIAA, 5-hydroxyindole acetic acid; 3HK, 3-hydroxykynurenine; 5-HT, serotonin; 5HTP, 5-hydroxytryptophan. n = 14 SD and 15 HFD mice. \#p = 0.06, \textsuperscript{a}p = 0.05, \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001 for Diet effect.</figcaption></figure>

positively correlated with plasma 5-HIAA levels (p < 0.01), which were reduced in HFD mice, and negatively with QA/KA (p < 0.01), 3HK/XA (p < 0.01) and 3HK/KYN (p < 0.01) ratios, which were in contrast higher in HFD than SD mice. Further supporting a link between neurotoxic-neuroprotective KP imbalance and increased emotional alterations, plasma 3HK/XA ratio also correlated with reduced immobility latency (p < 0.05), and QA/KA with longer immobility time (p < 0.01) and fewer open-arm entries (p = 0.05).

Regarding HFD-induced brain metabolomic changes, immobility time was negatively correlated with FCx levels of IS (p < 0.05) and 3HK (p < 0.05), as well as HC KYN (p < 0.05), while immobility latency correlated with cortical 3HAA (p < 0.05). Significant correlations were also found between TRP abundance in the FCx and increased sucrose preference (p < 0.01) or reduced total distance traveled in the EPM (p < 0.01), and between HC 3HK levels and EPM open-arm entries (p < 0.05). Lastly, striatal IS levels tended to be positively associated with sucrose preference (p = 0.06). Overall, these results highlighted tight relationships between HFD-induced emotional alterations and peripheral and/or brain changes in specific TRP-related metabolites, with variations depending on symptom dimensions and areas.

## 4. Discussion

Deciphering the role of inflammation-induced changes in TRP metabolism in the pathophysiology of MDD is a critical challenge for uncovering new therapeutic strategies to improve current treatments. This study provides valuable insights into this issue through a comprehensive analysis of the relationship between depressive-like and anxiety-like behaviors and alterations in different TRP catabolic pathways in a model of inflammatory depression related to obesity. It thus highlights specific TRP metabolites, particularly from the IP, as promising therapeutic targets due to their significant contribution in shaping the emotional profile of obese mice.

In line with clinical findings emphasizing the interplay between obesity, inflammation and depression (Beurel et al., 2020; Capuron et al., 2017; Delgado et al., 2018; Huet et al., 2019; Milaneschi et al., 2019), preclinical models of obesity appear to be relevant translational models of inflammatory depression particularly useful to study the underlying pathophysiological mechanisms (André et al., 2014; Cardinal et al., 2021; Castanon et al., 2015; Fourrier et al., 2019). Metabolic dysregulation, such as hyperglycemia, insulin resistance and hyperleptinemia, has been proposed as a potential contributor (McGregor et al., 2015). However, mounting findings, including ours, suggest that these factors are unlikely to directly contribute to the depressive
symptomatology reported in this context. Although improvement of glycemic dysregulation has been associated with better behavioral outcomes (Sharma et al., 2012; Zemdegs et al., 2019) and mitigation of KP activation (Paiva et al., 2024), a reduction in inflammation has been shown to likely contribute to these effects (Paiva et al., 2024; Pomytkin et al., 2015). Moreover, obesity-related inflammation and emotional alterations can be alleviated without necessarily normalizing hyperglycemia and insulin resistance (Fourrier et al., 2019; Stranahan et al., 2009; Zhao et al., 2012), and vice-versa (de Cossío et al., 2017; Schell et al., 2023). Similarly, increased depressive-like behavior in obesity models has been associated with either normal leptin levels, heightened leptin resistance (Cardinal et al., 2021; Dinel et al., 2011, 2014; Lu et al., 2006) or elevated leptin levels, as observed in the present study where this elevation was correlated with HFD-induced alterations in TRP metabolism. Corroborating these findings, HFD mice with hyperleptinemia but without inflammation or related changes in KP activation exhibit similar depressive-like behavior as lean controls (André et al., 2014). Additional research is required to further elucidate the role of metabolic dysregulation in the neuropsychiatric comorbidities of obesity. Meanwhile, recent clinical studies indicate that systemic inflammation contributes more significantly than metabolic health to the depressive symptoms observed in obese individuals (Delgado et al., 2018), with inflammation being associated with disrupted TRP metabolism (Arto et al., 2024; Cussotto et al., 2020; Delgado et al., 2022).

Consistent with these findings, we show here that HFD tilted the systemic neurotoxic-neuroprotective balance to favor neurotoxicity and oxidative stress, as notably evidenced by positive correlations between body weight and 3HAA or QA levels, as well as increased plasma 3HAA levels or 3HK/KYN, 3HK/XA and QA/KA ratios in HFD mice. Besides, these changes were correlated with more severe depressive-like behavior in the FST and/or anhedonia-related behavior in the SPT. HFD also changed brain KP activation, primarily in the FCx and HC where KYN and 3HK levels were reduced proportionally to the increase in body weight. Similar changes have been already reported in inflammation-related depression models (Laugeray et al., 2016; Mezo-González et al., 2023), although preclinical studies directly measuring levels of KP metabolites in specific brain regions, as we did, are still limited. These results may seem counterintuitive, as the upregulation of KP enzyme expression is a well-established hallmark of inflammatory depression (André et al., 2014; Cardinal et al., 2021; Mezo-González et al., 2022; Parrott et al., 2016a). Furthermore, inhibiting these enzymes alleviates depressive-like behavior (Hu et al., 2024; O'Connor et al., 2009a, 2009b; Parrott et al., 2016b). Nevertheless, it should be kept in mind that KP metabolites influence brain function in a dynamic and context-dependent manner (Tanaka et al., 2024), suggesting that an overly binary categorization may not be the most suitable approach. Altered transport across the BBB, whose integrity is compromised in obesity (de Cossío et al., 2017), could potentially explain the current findings, as plasma KYN abundance was also reduced in HFD mice. However, this alone cannot account for reduced brain KYN levels since they were not significantly correlated with those measured in the plasma. Alternatively, as KYN and 3HK are key intermediates in the production of downstream metabolites, their reduced levels may more likely reflect heightened engagement in this process, rather than a decreased synthesis. Notably, HFD significantly increased PA levels in the FCx, at the expense of its precursor 3HAA, as suggested by the negative correlation found between both metabolites. This agrees with previous studies reporting increased PA abundance in other chronic inflammatory conditions (Clark et al., 2005; Grant et al., 2009). While PA is generally considered to be neuroprotective at physiological concentrations, its overproduction or abnormal accumulation may actually disrupt brain function by exacerbating neuroinflammation-related oxidative stress and/or neurodegenerative processes (Bohár et al., 2015; Grant et al., 2009; Pathak et al., 2024; Tanaka et al., 2024). Further research is needed to determine whether this also occurs in obesity. However, the lack of significant correlation between cortical

levels of PA and the neuroprotective metabolites XA and KA, despite a strong correlation between the latter, provides initial support for this assumption.

Alongside KP alterations, recent clinical studies have also reported reduced systemic levels of indoles in obese subjects (Arto et al., 2024; Cussotto et al., 2020; Delgado et al., 2022; Su et al., 2022), with some of these changes being linked to comorbid depressive symptoms (Delgado et al., 2022). For the first time to our knowledge, the comprehensive metabolomic approach we conducted expands these findings by highlighting the brain repercussions of these systemic changes and their involvement in shaping the behavioral phenotype of HFD mice. While the plasma abundance of almost all indoles assessed was lowered in obese mice, in line with clinical data (Arto et al., 2024; Cussotto et al., 2020; Delgado et al., 2022; Su et al., 2022), IS was the only derivative whose levels were also consistently reduced in the brain. Since its synthesis is restricted to the liver, this reduction probably reflects systemic changes, as evidenced by the positive correlations found between plasma and brain IS levels. The limited information currently available on how indoles enter the brain precludes a definitive conclusion on this issue. Nevertheless, the lack of significant correlations between plasma and brain levels of other indoles, which were reduced systemically but not centrally, suggests that this process likely involves several, yet unidentified, mechanisms.

Deleterious effects of indoles have been reported in the literature, but they appear to depend on the pathophysiological context and occur mainly when present in excess (Tennoune et al., 2022). Conversely, numerous studies highlight a wide range of beneficial effects, including protection against intestinal barrier dysfunctions, inflammation or neuronal damages (Knudsen et al., 2021; Roager and Licht, 2018; Wang et al., 2024; Zhang and Davies, 2016; Zelante et al., 2021), which are all common comorbidities of obesity and MDD. Akin with these findings, chronically increasing circulating IAA (Chen et al., 2022) or ILA levels (Qian et al., 2024) is able to alleviate stress-induced depressive-like behavior in mice. Besides, reduced urinary IS levels have been found in depressed patients (Chen et al., 2017), while antidepressant-induced improvement of neuropsychiatric symptoms is associated with an increase in plasma indole levels, including IS (Bhattacharyya et al., 2019, Bhattacharyya et al., 2025). Similarly, our group recently reported that lower serum indole levels correlate with more severe depressive symptoms in obese subjects (Delgado et al., 2022). Consistent with these findings that support a beneficial impact of indoles on brain function and mood, reduced plasma and brain indole levels were associated with more severe depressive-like and anxiety-like behaviors in HFD mice. Importantly, our study supports a key role of plasma IS in triggering these emotional alterations, as it was the only metabolite whose reduction correlated with the severity of all symptom dimensions assessed. The correlative analyses also suggest that IS shares part of this responsibility with other IP and KP metabolites, though their respective involvement appears to vary depending on the specific behavioral outcome being considered.

Overall, these findings emphasize the importance of simultaneously considering the different depressive symptom dimensions and TRP metabolic pathways in future studies. This will be crucial to uncover the pathophysiological mechanisms linking obesity-driven inflammation, alterations in TRP metabolism and depression. This question could not be addressed directly in this study, which is one of its key limitations. However, it is important to note that it was intentionally designed to provide a broad overview of TRP metabolism in obesity, with the goal of identifying potential metabolites of interest for future more mechanistic studies. By then assessing the behavioral consequences of manipulating the production and/or function of these metabolites through targeted pharmacological or genetic approaches, forthcoming experiments should help validate causal links between specific metabolic alterations and behavioral outcomes.

Additionally, upcoming studies will have to include both males and females to account for the well-documented sex disparities in depression
and obesity (Altemus et al., 2014; Koceva et al., 2024; Woodruff et al., 2024) and thus overcome another limitation of the current study, namely its restriction to males. This limitation was due to several practical and ethical constraints. This study follows previous research that validated the suitability of the current experimental design to investigate the pathophysiology of inflammation-related depression (Cardinal et al., 2021). Moreover, the mechanisms underlying these sex differences, while not yet fully understood, are certainly largely multi-factorial. This significantly complicates both the experimental design needed to control for potential confounding factors when studying males and females together, and the interpretation of the resulting data. Importantly, this approach would necessitate drastically increasing the number of experimental groups. However, this conflicts with current ethical guidelines for research in live animals, which call for reducing animal use as much as possible. Accordingly, we limited the size and number of the experimental groups by using only males, although this may have impacted the robustness and statistical power of our data, and implies caution when extrapolating these results to females. Of note, these constraints also hampered the ability to stratify mice based on their vulnerable or resilient profile to obesity and/or associated comorbidities, a strategy that could have added depth to the interpretation of the current data. Nevertheless, the biological relevance and translational potential of the present work are supported by emerging evidence from the literature. Obesity-related emotional and/or cognitive impairments and alterations in TRP metabolism have recently been reported in female models of obesity (Mezo-González et al., 2022, 2023; Schell et al., 2023; Woodruff et al., 2024). Interestingly, differential vulnerability to develop cognitive deficits between obese males and females, which appears to be influenced by sex hormones, has been associated with sex dependent changes in KP activation, in particular in the HC (Mezo-González et al., 2022, 2023). Further research is needed to test whether the link between TRP metabolism and depressive-like behavior also depends on sex-related factors. In this context, rather than measuring TRP metabolism in the entire HC, as we had to do here for technical constraints, it would be useful to measure it separately in the ventral and dorsal subregions of the HC, given their preferential roles in regulating emotional behaviors and memory processes, respectively, including in obesity (Fourrier et al., 2019; Krishna et al., 2015).

In line with the well-established role of the microbiota-gut-brain axis in the pathophysiology of mood disorders and obesity (Agus et al., 2018; Leyrolle et al., 2021; Merchak and Gaultier, 2020), restoring gut microbiota composition has been shown to ameliorate obesity-induced inflammation, KP activation, and emotional disturbances in both males and females (Paiva et al., 2024; Schell et al., 2023; Wu et al., 2020), although some sex-specific effects have been reported depending on the outcome measured (Schell et al., 2023). In addition, sex differences appear to also impact the IP, with lower levels of indoles recently reported in women compared to men (Chappell et al., 2024). These compelling findings underscore the need for deeper investigation into how alterations in gut microbiota composition and/or intestinal permeability observed in obesity contribute to its neuropsychiatric comorbidities, with particular attention to the still underexplored role of gut-derived TRP metabolism. Of note, addressing this issue gains further relevance from recent evidence that probiotic intervention alleviates stress-induced emotional disturbances by increasing gut production of indoles (Qian et al., 2024).

In conclusion, this study provides valuable new insights into the role of TRP metabolites from both the KP and IP as key mediators linking obesity and depression, paving the way for future research. It also holds promise for improving antidepressant treatments by helping identify patients most likely to benefit from novel therapeutic strategies, based on their clinical and biological profiles. Finally, this work may have broader implications beyond obesity, in other inflammatory conditions with disrupted TRP metabolism and neuropsychiatric comorbidities.

## Author contributions

NC, SC, ALD and LC designed research. SC, CL and ALD performed experiments. AL and PE conducted metabolomic assays. NC, SV, CA, SC, QL, CL and ALD contributed to data analysis and interpretation. NC, SV and CA drafted the work. QL and LC edited the manuscript. All authors provided feedback on the manuscript and approved the version submitted for publication.

## Fundings

This work was supported by funds from the French National Research Institute for Agriculture, Food and Environment (INRAE) and grants from the JPI HDHL Biomarkers for Nutrition and Health (HEALTHMARK, French National Research Agency, ANR-16-HDHL-0003-03, LC), the Fondation pour la Recherche Médicale, (FRM, DPP20151033943, LC), and the Marcel Dassault Prize for Research in Mental Disorders (Fondation FondaMental, LC).

## CRediT authorship contribution statement

**Nathalie Castanon:** Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Sylvie Vancassel:** Writing – review & editing, Writing – original draft, Formal analysis. **Camille Amadieu:** Writing – review & editing, Formal analysis. **Sofia Cussotto:** Writing – review & editing, Investigation, Formal analysis, Conceptualization. **Quentin Leyrolle:** Writing – review & editing, Formal analysis. **Céline Lucas:** Investigation, Formal analysis. **Antoine Lefevre:** Methodology, Investigation. **Patrick Emond:** Methodology, Investigation. **Anne-Laure Dinel:** Supervision, Project administration, Investigation, Formal analysis, Conceptualization. **Lucile Capuron:** Writing – review & editing, Funding acquisition, Conceptualization.

## Acknowledgements

The authors would like to thank the staff from the NutriNeuro animal housing facility for animal care.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbi.2025.106107.

## Data availability

Data will be made available on request.

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