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Gene expression of kynurenine pathway enzymes in depression and following electroconvulsive therapy

Published online by Cambridge University Press:  17 October 2024

Karen M. Ryan
Affiliation:
Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland Department of Psychiatry, St. Patrick’s University Hospital, Trinity College Dublin, Dublin, Ireland
Myles Corrigan
Affiliation:
Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences & Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
Therese M. Murphy
Affiliation:
School of Biological, Sports and Health Sciences, Technological University Dublin, Dublin, Ireland
Declan M. McLoughlin
Affiliation:
Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland Department of Psychiatry, St. Patrick’s University Hospital, Trinity College Dublin, Dublin, Ireland
Andrew Harkin*
Affiliation:
Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences & Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
*
Corresponding author: Andrew Harkin; Email: [email protected]
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Abstract

Objective:

This study aimed to investigate changes in mRNA expression of the kynurenine pathway (KP) enzymes tryptophan 2, 3-dioxygenase (TDO), indoleamine 2, 3-dioxygenase 1 and 2 (IDO1, IDO2), kynurenine aminotransferase 1 and 2 (KAT1, KAT2), kynurenine monooxygenase (KMO) and kynureninase (KYNU) in medicated patients with depression (n = 74) compared to age- and sex-matched healthy controls (n = 55) and in patients with depression after electroconvulsive therapy (ECT). Associations with mood score (24-item Hamilton Depression Rating Scale, HAM-D24), plasma KP metabolites and selected glucocorticoid and inflammatory immune markers known to regulate KP enzyme expression were also explored.

Methods:

HAM-D24 was used to evaluate depression severity. Whole blood mRNA expression was assessed using quantitative real-time polymerase chain reaction.

Results:

KAT1, KYNU and IDO2 were significantly reduced in patient samples compared to control samples, though results did not survive statistical adjustment for covariates or multiple comparisons. ECT did not alter KP enzyme mRNA expression. Changes in IDO1 and KMO and change in HAM-D24 score post-ECT were negatively correlated in subgroups of patients with unipolar depression (IDO1 only), psychotic depression and ECT responders and remitters. Further exploratory correlative analyses revealed altered association patterns between KP enzyme expression, KP metabolites, NR3C1 and IL-6 in depressed patients pre- and post-ECT.

Conclusion:

Further studies are warranted to determine if KP measures have sufficient sensitivity, specificity and predictive value to be integrated into stress and immune associated biomarker panels to aid patient stratification at diagnosis and in predicting treatment response to antidepressant therapy.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

Introduction

The kynurenine pathway (KP) is a major route through which the essential amino acid tryptophan is metabolised and activated in times of stress and immune activation.

Initially the conversion of tryptophan to kynurenine requires induction of either of three rate limiting enzymes, tryptophan 2, 3-dioxygenase (TDO) or indoleamine 2, 3-dioxygenase 1 and 2 (IDO1 and IDO2). TDO/IDO 1 and 2 metabolise tryptophan to kynurenine, which is subsequently converted to either kynurenic acid (KYNA) by kynurenine aminotransferase (KAT of which there are four subtypes) or 3-hydroxykynurenine by kynurenine monooxygenase (KMO). 3-Hydroxykynurenine (3-HK) is further metabolised to either anthranillic acid (AA) or 3-hydroxyanthranilic acid (3-HAA) by kynureninase (KYNU), giving rise to acetyl-CoA, or to the unstable intermediate, 2-amino-3-carboxymuconate by 3-hydroxyanthranilic acid 3, 4-dioxygenase (3-HAO). This metabolite is further enzymatically converted to picolinic acid, or non-enzymatically transformed to quinolinic acid (QUIN), the precursor for nicotinamide adenine dinucleotide (NAD+) (Lugo-Huitrón et al., Reference Lugo-Huitrón, Ugalde Muñiz, Pineda, Pedraza-Chaverrí, Ríos and Pérez-De La Cruz2013; Vécsei et al., Reference Vécsei, Szalárdy, Fülöp and Toldi2013). 3-HAA can also be metabolised to picolinic acid by aminocarboxymuconate-semialdehyde decarboxylase (ACMSD). KAT can also convert 3-HK to xanthurenic acid [for a schematic of the pathway see O’Farrell and Harkin (Reference O.’Farrell and Harkin2017)].

KP activation has been proposed to play a role in depression (Réus et al., Reference Réus, Jansen, Titus, Carvalho, Gabbay and Quevedo2015; Myint and Halaris, Reference Myint and Halaris2022), with reports of reduced circulating concentrations of tryptophan, an increased tryptophan breakdown index (kynurenine/tryptophan) and decreased KYNA concentrations in depressed patients in comparison to healthy controls (Schwieler et al., Reference Schwieler, Samuelsson, Frye, Bhat, Schuppe-Koistinen, Jungholm, Johansson, Landén, Sellgren and Erhardt2016; Allen et al., Reference Allen, Naughton, Dowling, Walsh, O’shea, Shorten, Scott, Mcloughlin, Cryan, Clarke and Dinan2018; Arnone et al., Reference Arnone, Saraykar, Salem, Teixeira, Dantzer and Selvaraj2018; Doolin et al., Reference Doolin, Allers, Pleiner, Liesener, Farrell, Tozzi, O’hanlon, Roddy, Frodl, Harkin and O’keane2018; Correia and Vale, Reference Correia and Vale2022). Activation of the pathway, coupled with reduced tryptophan, has been observed in depression occurring secondary to exogenous administration of cytokines such as IFN-α and IL-2 (Raison et al., Reference Raison, Dantzer, Kelley, Lawson, Woolwine, Vogt, Spivey, Saito and Miller2010). The KP is generally divided into neurotoxic and neuroprotective branches, with greater activation of the neurotoxic branch typically reported in depression (Savitz, Reference Savitz2020). The neuroprotective arm of the KP is driven by the KAT enzymes, which catalyse the conversion of kynurenine to KYNA. The neurotoxic arm is driven by KMO and KYNU, which catalyse the conversion of kynurenine to the downstream metabolites 3-HK, 3-HAA and QUIN (Stone et al., Reference Stone, Stoy and Darlington2013). Studies have reported that both IDO and TDO are overactive in depression, suggesting that inhibiting these enzymes might be useful for treating depression (Qin et al., Reference Qin, Wang, Zhang, Han, Zhai and Lu2018). However, increased IDO is also suggested to have a protective effect through a compensatory anti-inflammatory reflex system (CIRS), which acts to attenuate immunoinflammatory responses (Maes et al., Reference Maes, Berk, Goehler, Song, Anderson, Gałecki and Leonard2012).

We have previously reported that tryptophan and kynurenine metabolite concentrations (AA, 3-HAA, picolinic acid, KYNA and xanthurenic acid)] and KYNA/kynurenine and KYNA/QUIN ratios are reduced in medicated depressed patients in comparison to healthy controls (Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020). Improvements in mood score following a course of electroconvulsive therapy (ECT) and treatment response were correlated with increased kynurenine, 3-HK, 3-HAA, QUIN and kynurenine/tryptophan in unipolar depressed patients. These results suggest that ECT may mobilise the KP since a moderate association between selected metabolites and treatment response was evident in unipolar depressed patients.

Assessments of KP activity to date have largely relied on measuring tryptophan and kynurenine metabolite concentrations. Few have examined and reported changes in the mRNA expression of the KP enzymes (Hughes et al., Reference Hughes, Carballedo, Mcloughlin, Amico, Harkin, Frodl and Connor2012; Doolin et al., Reference Doolin, Allers, Pleiner, Liesener, Farrell, Tozzi, O’hanlon, Roddy, Frodl, Harkin and O’keane2018; Brown et al., Reference Brown, Brown, Purves-Tyson, Huang, Shannon Weickert and Newell2022). Assessment of KP enzyme expression is important considering induction of TDO or IDO enzymatic activity by glucocorticoids/cytokines respectively are reliant on de novo synthesis of the enzymes. In individuals with depression, increased KAT1 and KAT2 mRNA expression was reported in the anterior cingulate cortex (ACC), including in those patients with psychotic and non-psychotic depression compared to matched controls (Brown et al., Reference Brown, Brown, Purves-Tyson, Huang, Shannon Weickert and Newell2022). The results from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 1953) suggested that a common single nucleotide polymorphism in IDO2 is linked to the response to treatment with the antidepressant citalopram (Cutler et al., Reference Cutler, Rush, Mcmahon and Laje2012).

Glucocorticoid release from the adrenal glands, associated with stress-induced activation of the hypothalamic pituitary adrenal (HPA) axis, can induce TDO because the promoter region of TDO harbours glucocorticoid response elements. Moreover, IDO may be induced in response to stress through sympathoadrenal medullary (SAM) system-associated β-adrenergic receptor activation of immune cells, resulting in the release of pro-inflammatory cytokines, including interleukin-1 beta (IL-1β), IL-6 and interferon (IFN)-γ (Elenkov et al., Reference Elenkov, Wilder, Chrousos and Vizi2000). Moreover, Doolin and co-workers reported a positive correlation between plasma QUIN concentrations, with concentrations of the inflammatory marker C-reactive protein and whole blood IDO1 mRNA expression in patients with major depressive disorder (MDD) (Doolin et al., Reference Doolin, Allers, Pleiner, Liesener, Farrell, Tozzi, O’hanlon, Roddy, Frodl, Harkin and O’keane2018). Thus, measurement of KP enzyme expression is important considering that induction of enzymatic activity by glucocorticoids/cytokines are reliant on de novo synthesis of the enzymes.

In this study, we build on previous reported findings (Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020) to investigate mRNA expression of KP enzyme targets in samples from medicated patients with depression versus age- and sex-matched healthy controls and in patients with depression after ECT, taking account of co-variables including heterogenous psychopathology. We hypothesised that there would be a reduction in mRNA expression of KP enzymes in patient blood compared to that from controls, in line with our previous study report. In addition, we also examined associations between KP enzyme mRNA expression and mood score. We also performed exploratory analyses to assess associations between KP enzyme mRNA expression with stress and immunological markers. We hypothesised that stress and immunological markers commonly linked to the pathophysiology of depression would correlate with KP enzyme mRNA levels and be subject to change in patients with depression after ECT. Further exploratory correlation analyses were undertaken with plasma KP metabolites and the mRNA expression of selected glucocorticoid markers [glucocorticoid receptor (NR3C1), the GR co-chaperone protein FK506 binding protein (FKBP5) and glucocorticoid-induced leucine zipper (GILZ), a key molecule in glucocorticoid biology that mediates the downstream anti-inflammatory effects of the glucocorticoid receptor] and inflammatory immune markers [tumour necrosis factor alpha (TNF-α) and IL-6)].

Material and methods

Participants

This study was approved by St Patrick’s University Hospital Research Ethics Committee and carried out in line with the Declaration of Helsinki (World Medical Association, 2013). Written informed consent was provided by all participants. Medicated depressed patients were recruited in St Patrick’s Mental Health Services (http://www.stpatricks.ie/) as part of the EFFECT-Dep (Enhancing the Effectiveness of ECT in Severe Depression; ISRCTN23577151) Trial, a real-world, pragmatic, patient- and rater-blinded, non-inferiority trial of patients with major depression carried out to assess the effects of twice-weekly moderate-dose bitemporal (1.5× seizure threshold) and high-dose unilateral (6× seizure threshold) ECT (Semkovska et al., Reference Semkovska, Landau, Dunne, Kolshus, Kavanagh, Jelovac, Noone, Carton, Lambe, Mchugh and Mcloughlin2016). ECT was administered twice weekly with patients maintained on their usual pharmacotherapy during the course of treatment. Methohexital (0.75 mg/kg–1.0 mg/kg) was used for anesthesia and succinylcholine (0.5 mg/kg–1.0 mg/kg) was used as muscle relaxant. Inclusion criteria for this study were as follows: >18 years old; referred for ECT for treatment of a major depressive episode (unipolar and bipolar), as diagnosed by the Structured Clinical Interview for DSM-IV Axis I Disorders (First et al., Reference First, Spitzer, Gibbon and Williams1996); pre-treatment Hamilton Depression Rating Scale 24-item version (HAM-D24) (Beckham and Leber, Reference Beckham and Leber1985) score≥21. Exclusion criteria were as follows: ECT or substance misuse in the previous six months; medically unfit for general anaesthesia; dementia or other axis I diagnosis. Healthy controls were recruited through local newspaper and social media advertisements.

We recorded demographic data and medical/treatment history for all participants. Depression severity and ECT response were assessed using the HAM-D24. Response was defined as a ≥60% reduction in HAM-D24 and a score ≤ 16 at end-of-treatment, while remission was defined as a ≥60% reduction in HAM-D24 and a score ≤ 10 maintained for two weeks.

Fasting peripheral blood samples (2.5 mL) were collected into PAXgene© Blood RNA tubes (PreAnalytix, Qiagen Ltd., Ireland) at 07:30–09:30 on the baseline (i.e., before the first ECT treatment) day and 1–3 days after the final ECT treatment and from controls on assessment days between 07:30 and 09:30, per manufacturer’s guidelines. Tubes were stored at room temperature for 24 h, -20°C for 24 h, followed by long-term storage at –80 °C. We excluded any participants with a chronic immune disorder or neurological disorder from molecular analyses. In total, 74 patients with depression and 55 controls were included in these analyses.

Quantitative real-time polymerase chain reaction (qPCR)

qPCR was carried out as previously described (Ryan et al., Reference Ryan, Poelz and Mcloughlin2019), with slight modifications. Briefly, a PAXgene Blood RNA kit (PreAnalytix) was used to extract whole blood mRNA and a high capacity cDNA archive kit (Applied Biosystems, UK) was used to carry out reverse transcription of samples. qPCR was performed using a StepOnePlusTM instrument (Applied Biosystems) with TaqMan® Gene Expression Assays (IDO1, Hs00984148_m1; IDO2, Hs01589373_m1; KMO, Hs00175738_m1; KYNU, Hs011114099_m1; KATI, Hs00187858_m1; KATII, Hs00212039_m1; TDO, Hs00194611_gH; GAPDH, Hs02758991_g1) and TaqMan® Fast Advanced Master Mix (Applied Biosystems). The cycling conditions consisted of an initial polymerase activation step of 95 °C for 20 s followed by 50 cycles of 95 °C for 1 s and 60 °C for 20 s. We calculated relative quantification (RQ) levels using the comparative CT method (Schmittgen and Livak, Reference Schmittgen and Livak2008) against a calibrator sample from a healthy volunteer, not included in the study, after normalisation to GAPDH.

The method and assay IDs for NR3C1, FKBP5, GILZ, IL-6 and TNF-α were reported previously (Ryan and McLoughlin, Reference Ryan and Mcloughlin2019; Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020).

Tryptophan and KP metabolite concentrations

Tryptophan and KP metabolite concentration measurements were reported previously (Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020).

DNA methylation analysis

DNA methylation levels, at CpG sites annotated (based on closest transcriptional start site) to KAT1, KMO, KYNU, IDO1 and IDO2, were obtained from a previously published methylome-wide association study of a self-reported history of depression (Crawford et al., Reference Crawford, Craig, Mansell, White, Smith, Spaull, Imm, Hannon, Wood, Yaghootkar, Ji, Mullins, Lewis, Mill and Murphy2018). Briefly, DNA methylation levels were quantified using the Illumina Infinium HumanMethylation450 BeadChip (“Illumina 450K array”) array. A full description of data quality control and normalisation methods were reported previously (Crawford et al., Reference Crawford, Craig, Mansell, White, Smith, Spaull, Imm, Hannon, Wood, Yaghootkar, Ji, Mullins, Lewis, Mill and Murphy2018). DNA methylation scores for the following CpG sites (cg13263723 (KYNU); cg21542308 (IDO2); cg08465774 (IDO1); cg00606312 (KMO) were available in the dataset.

Statistical analysis

All statistical analyses (except for the DNA methylation analysis) were performed using SPSS, version 26 (IBM Corporation, NY, USA). Data were tested for normality using Q-Q plots and a Shapiro-Wilk test and subsequently log-transformed where indicated. Baseline clinical and demographic characteristics are shown as means with SD or number (%) per group where appropriate and categorical data were analysed using Chi-square (χ 2) tests.

RQ data were analysed using general linear models. We adjusted for potential variance owing to age, sex, body mass index (BMI; kg/m2) and smoking status as these have previously been associated with changes in the KP (Theofylaktopoulou et al., Reference Theofylaktopoulou, Midttun, Ulvik, Ueland, Tell, Vollset, Nygard and Eussen2013; Mangge et al., Reference Mangge, Summers, Meinitzer, Zelzer, Almer, Prassl, Schnedl, Reininghaus, Paulmichl, Weghuber and Fuchs2014; Favennec et al., Reference Favennec, Hennart, Caiazzo, Leloire, Yengo, Verbanck, Arredouani, Marre, Pigeyre, Bessede, Guillemin, Chinetti, Staels, Pattou, Balkau, Allorge, Froguel and Poulain-Godefroy2015; Raheja et al., Reference Raheja, Fuchs, Giegling, Brenner, Rovner, Mohyuddin, Weghuber, Mangge, Rujescu and Postolache2015; de Bie et al., Reference De Bie, Guest, Guillemin and Grant2016). Smoking status was dichotomised into current versus non-smoker. Adjustment was also made for educational attainment as this was significantly different between groups. For pre-/post-ECT analyses, depression polarity, depression severity at baseline and presence of psychosis were included as covariates where appropriate. Data not normally distributed after log10 transformation were analysed using non-parametric methods (Mann-Whitney U or Wilcoxon Signed Rank test). Correlation analyses were carried out using either Pearson’s product-moment correlation coefficient (Pearson’s r) or Spearman’s rank correlation coefficient rho (Spearman’s ρ). Representations of associations between mRNA expression of KP enzymes and tryptophan metabolite concentrations, IL-6 and NR3C1 in whole blood in healthy controls, depressed patients pre-ECT and depressed patients post-ECT are shown in Figure 1. All significant correlations are included. The size of each node is proportional to the number of correlations at that node, while the width of each line is proportional to the strength of the correlation (quantified by the statistical rho value).

Figure 1. Correlation-based representations of associations between tryptophan metabolite concentrations and mRNA expression of kynurenine pathway enzymes, IL-6 and the glucocorticoid receptor in whole blood in (A) a healthy control cohort, (B) a depressed patient cohort pre-ECT and (C) the same depressed patient cohort post-ECT. While a wider panel of inflammatory and stress markers were investigated, IL-6 and NR3C1 were selected as representative markers for clarity of the schematic. These additional correlations are available in Supplementary Table 8. Top panel: all significant correlations are included. The size of each node is proportional to the number of correlations at that node. The width of each line is proportional to the strength of the correlation (quantified by the statistical rho value). Red lines correspond to positive correlations and blue lines correspond to negative correlations. Bottom panel: Significant correlations common between all groups are removed in order to highlight differences between the groups. Node sizes are adjusted accordingly. Abbreviations: IDO, indolamine 2, 3-dioxygenase; KMO, kynurenine 3-monooxygenase; KYNU, kynureninase; KAT, kynurenine aminotransferase; TRP, tryptophan; KYN, kynurenine; 3-HK, 3-hydroxykynurenine; XA, xanthurenic acid; 3-HAA, 3-hydroxyanthranillic acid; KYNA, kynurenic acid; PIC, picolinic acid; QUIN, quinolinic acid; GR, glucocorticoid receptor.

Statistical analyses for the DNA methylation analysis were performed using R (version 3.2.1). Linear regression was used to examine differences in DNA methylation scores [reported as change in beta value] between individuals with a self-reported history of depression and individuals without a history of depression at CpG sites (cg13263723 (KYNU); cg21542308 (IDO2); cg08465774 (IDO1); cg00606312 (KMO)), controlling for potential confounders (history of an inflammatory disorder, age, gender, anti-depressant use, chip and estimated blood cell composition).

Data are expressed as means with standard deviation (SD). Differences with a p-value<0.01, consistent with analysis of five targets, namely KAT1, KMO, KYNU, IDO1 and IDO2, were deemed statistically significant to account for multiple comparisons (Bonferroni correction).

Results

Demographics and clinical characteristics

Demographic and clinical characteristics of patients with depression and healthy controls are shown in Table 1. Groups differed with respect to BMI, smoking and education, with the patient group having a higher BMI, a higher percentage of smokers and a lower level of educational attainment compared to controls.

Table 1. Demographic and clinical characteristics of participants

Data are presented as means with standard deviations (SD) or number (%) per group where appropriate, unless otherwise stated.

BMI, body mass index; ECT, electroconvulsive therapy; HAM-D24, Hamilton depression rating scale, 24-item version; MAOI, monoamine oxidase inhibitor; SNRI, serotonin-norepinephrine reuptake inhibitor; SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant.

KP enzyme mRNA levels in patients with depression and healthy controls

The mRNA expression of KP enzymes KAT1, KMO, KYNU, IDO1 and IDO2 in whole blood samples from medicated patients with depression compared to controls are shown in Table 2. For this study, KAT1 was chosen as representative of the KAT subtypes. The mRNA expression of KAT2 and TDO was also measured but was below the limits of detection. The unadjusted statistical analysis shows that KAT1, KYNU and IDO2 levels were significantly lower in the patient group compared to controls (p < 0.01). There was no statistically significant difference between groups following adjustment for covariates.

Table 2. KP enzyme mRNA levels in healthy controls compared to patients with depression

Data are expressed as means ± SD.

KAT1: depressed n = 74, controls n = 55; KMO: depressed n = 73, controls n = 54; KYNU: depressed n = 71, controls n = 52; IDO1: depressed n = 70, controls n = 54; IDO2: depressed n = 70, controls n = 53.

KAT1, KMO and IDO1 data were log10 transformed prior to statistical analysis. †Adjusted for age, sex, BMI, smoking, educational attainment.

We also compared KP enzyme expression in males and females (Supplemental Table 1). KAT1 significantly differed between female controls and female patients with depression, KYNU significantly differed between male controls and male patients with depression and IDO2 significantly differed between male controls and male patients with depression and female controls and female patients with depression. No significant difference was noted between males and females within the control or depressed groups.

KP enzyme mRNA levels in patients with depression pre- and post-ECT

Table 3 shows the mRNA levels of KP enzymes in blood samples from medicated patients with depression before (pre-) and after (post-) ECT. No significant changes were noted at p < 0.01 in either the unadjusted or adjusted statistical analyses.

Table 3. Depressed pre- and post-ECT

Data are expressed as means ± SD.

KAT1: n = 74; KMO: n = 73; KYNU: n = 71; IDO1: n = 70; IDO2: n = 70.

KAT1, KMO, IDO1 were log10 transformed prior to statistical analysis.

†Adjusted for age, sex, BMI, smoking, education, baseline HAM-D24, electrode placement, polarity, psychosis.

Subgroup analyses showed no difference between patients with unipolar vs. bipolar depression, psychotic vs. non-psychotic depression, ECT-responders vs. non-responders, ECT remitters versus non-remitters, or male versus female patients (Supplemental Tables 26, respectively).

Correlations between KP enzymes and mood score

Moderate negative correlations were noted between the change in KMO and change in HAM-D24 score following ECT in patients with psychotic depression and ECT remitters and responders (Table 4), indicating that an increase in KMO was associated with improvement in mood in these patient subgroups. Moderate negative correlations were also noted between the change in IDO1 and change in HAM-D24 score following ECT in patients with unipolar depression, psychotic depression and ECT remitters and responders (Table 5), indicating that an increase in IDO1 was associated with improved mood in these patient subgroups. Baseline IDO1 levels and baseline HAM-D24 score were negatively correlated in non-responders and non-remitters following ECT, indicating that lower IDO1 at baseline was associated with a lack of therapeutic response to ECT. There was a moderate negative correlation noted between baseline IDO2 levels and baseline HAM-D24 score in patients with psychotic depression (Supplemental Table 7), indicating that lower IDO2 at baseline was associated with more severe depressive symptoms in this patient subgroup, though caution is warranted owing to the small sample size included in this analysis (n = 18). There were no other significant correlations noted in patient subgroups with respect to KMO, IDO1, or IDO2 and mood score at baseline or following ECT, or between KAT1 or KYNU and baseline or post-ECT mood score (Supplemental Table 8 & 9, respectively).

Table 4. Correlations between KMO and HAM-D24 scores

HAM-D24, Hamilton depression rating scale, 24-item version; ΔHAM-D24, change in Hamilton depression rating scale, 24-item version score.

Table 5. Correlations between IDO1 and HAM-D24 scores

HAM-D24, Hamilton depression rating scale, 24-item version; ΔHAM-D24, change in Hamilton depression rating scale, 24-item version score.

Correlations between KP enzyme mRNA and KP metabolites

Correlation analyses of mRNA levels of KP enzyme targets with KP metabolite concentrations and with mRNA expression of NR3C1, FKBP5, GILZ, TNF-alpha and IL-6 revealed altered association patterns between the expression of KP enzymes, NR3C1, IL-6 and KP metabolites in depressed patients pre- and post-ECT (Supplemental Table 10). Correlations between KP metabolite concentrations themselves were also assessed. These patterns are summarised in Figure 1.

Associations between the expression of KP enzymes in depressed patients are represented by six significant correlations between mRNA expression of KP enzymes – ranging from a weak positive correlation between IDO1 and KAT1 levels to a moderate-strong positive correlation between IDO1 and KMO levels. This is compared with two such correlations in healthy controls and four in the post-ECT depressed cohort. In healthy controls, IDO1 level positively correlated with the KYN/TRP ratio, which is expected as IDO1 catalyses the conversion of TRP to KYN. However, this was absent in patients with depression. In addition, IDO1 levels and the KYNA/QUIN ratio were found to be negatively correlated in depressed patients both pre- and post-ECT. This correlation is not evident in healthy controls. IDO1 levels also negatively correlated with the KYNA/KYN ratio in the depressed patient cohort; this correlation was absent in the healthy control group and persisted post-ECT in the depressed group. Further to this, the negative correlations between (1) KAT1 mRNA and KYNA metabolite levels and (2) KAT1 mRNA and the KYNA/KYN ratio were unique to the depressed patient group indicating an interesting disparity between whole blood KAT1 expression and plasma KYNA concentrations. Correlations of NR3C1 with IDO2 and KAT1 were common to healthy controls and depressed patients pre- and post-ECT; however, correlations between NR3C1 with IDO1 and KMO were unique to depressed patients pre-ECT. In the depressed patient group, IL-6 mRNA levels correlated with IDO1, IDO2, KMO and KAT1 mRNA levels, while in healthy controls correlations between IL-6, KMO and KAT1 mRNA levels were identified. The correlation between IL-6 and KMO persisted in the depressed patient cohort post-ECT. Differential association patterns between healthy controls compared to depressed patients both pre- and post-ECT are not apparent with KP metabolites when correlated to NR3C1 or IL-6 mRNA levels (Figure 1).

KP enzyme DNA methylation levels in individuals with a self-reported history of depression compared to controls

Considering the significantly reduced mRNA levels of KAT1, KYNU and IDO2 observed in our clinically depressed cohort, we also assessed DNA methylation levels of four out of the five the KP enzyme genetic loci (KMO, KYNU, IDO1 and IDO2) to examine their association with depression. Analysis of DNA methylation levels in whole blood samples from individuals with self-reported depression history compared to controls revealed significant hypermethylation at cg21542308 (IDO2) in the depression group (p < 0.01, Supplemental Table 11).

Discussion

The results indicate significantly reduced KAT1, KYNU and IDO2 mRNA levels in whole blood from patients with depression in comparison to healthy controls, though none of these results survived statistical adjustment for potential covariates and multiple comparisons. Results also show that ECT had no effect on KP enzyme mRNA expression, either in the depressed group as a whole or in subgroup analyses. There were, however, moderate negative correlations observed between the change in KMO and change in HAM-D24 score in subgroups of patients with psychotic depression and ECT remitters and responders, indicating that, at an individual level, as KMO expression increased mood score improved. Moreover, there were weak to moderate negative correlations between the change in IDO1 and change in HAM-D24 score post-ECT in subgroups of patients with unipolar depression, psychotic depression and ECT responders and remitters, indicating that, at an individual level, as IDO1 increased following ECT mood score improved in these patient subgroups. Baseline IDO1 was negatively correlated with baseline HAM-D24 score in non-responders and non-remitters, indicating that lower IDO1 levels were related to non-response and non-remission following ECT. Baseline IDO2 also moderately negatively correlated with baseline HAM-D24 score in patients with psychotic depression and in non-remitters, indicating that lower IDO2 levels were related to more severe depressive symptoms in this patient subgroup and non-remission following ECT. Overall, these results are in line with those previously reported for this cohort with regard to the KP (Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020), where reduced activation is evident in depressed patients and pathway mobilisation is associated with improvement in mood.

Previous research has reported no change in whole blood IDO1/2, KAT1, KMO or KYNU mRNA levels in patients with MDD compared to controls (Hughes et al., Reference Hughes, Carballedo, Mcloughlin, Amico, Harkin, Frodl and Connor2012). However, in comparison to the cohort included in our study, the sample comprised a younger cohort of patients with MDD (average age = 42 years) and patients were receiving either monotherapy with selective serotonin reuptake inhibitors (SSRIs) or being treated with a dual acting antidepressant, while ∼ 30% were medication-free, with those receiving antipsychotics or mood stabilisers excluded. Further analysis of a subsequent cohort of depressed patients by Doolin et al. (Reference Doolin, Allers, Pleiner, Liesener, Farrell, Tozzi, O’hanlon, Roddy, Frodl, Harkin and O’keane2018) also reported no difference in KAT1 or IDO1 expression between controls and individuals with MDD, but as before this depressed cohort was young (average 33 years), with approximately one third of study participants medication free. Positive correlations between IDO1 mRNA expression, QUIN and the kynurenine/tryptophan ratio were observed, while KAT1 mRNA expression and depression scores were reported to be negatively correlated (Doolin et al., Reference Doolin, Allers, Pleiner, Liesener, Farrell, Tozzi, O’hanlon, Roddy, Frodl, Harkin and O’keane2018). In contrast, increased KAT1 and KAT2 mRNA expression has been reported in the ACC of individuals with depression, including in those patients with psychotic and non-psychotic depression compared to matched controls (Brown et al., Reference Brown, Brown, Purves-Tyson, Huang, Shannon Weickert and Newell2022). However, in comparison to the cohort included in the present study, the sample size was small and subjects were young (average age ∼ 42). Moreover, a subsequent study from the same group showed a significant difference in KAT2 in the ACC between controls and patients with depression and in KMO between male and female controls, though no difference in KMO was noted between males and females with depression or between the control and depressed groups as a whole (Brown et al., Reference Brown, Christofides, Weissleder, Huang, Shannon Weickert, Lim and Newell2024). In this study, we found no difference in any of the KP target enzyme mRNA levels between males and females in either depressed or healthy control groups.

As IDO is a rate-limiting enzyme of the KP, pathway activation is gauged by IDO activity. The fact that our correlation analyses showed a unique association between IDO1 and KMO levels in the depressed patient cohort suggests that in depression induction of the KP is correlated to KMO level, and a branch of the KP associated with oxidative stress – an association which appears to be negated by ECT. To our knowledge, no study has compared peripheral and central levels of KP enzyme expression in depressed patients.

Further interesting patterns emerge when assessing associations between KP enzyme mRNA levels and circulating KP metabolite concentrations. Intriguingly, the correlation between IDO1 and the KYN/TRP ratio is absent in the depressed patient group, which suggests KP metabolism is out of step with IDO1 mRNA levels. It is possible that in depression TRP is metabolised to other KP metabolites at a faster rate, which may influence the association between IDO1 levels and the KYN/TRP ratio. Peripheral blood TRP concentration is commonly reported as being lower in individuals with depression (Ryan et al., Reference Ryan, Allers, Mcloughlin and Harkin2020). It is possible that a more rapid conversion of tryptophan to QUIN or KYNA takes place in patients with depression influencing the QUIN/KYN and KYNA/KYN ratios and their associations with KP enzyme expression. Moreover, the negative correlations between IDO1 and the KYNA/QUIN and KYNA/KYN ratios suggest that IDO1 levels in depression associate with lower activation of the neuroprotective branch of the pathway. Further to this, the negative correlations between (1) KAT1 mRNA and KYNA metabolite levels and (2) KAT1 mRNA and the KYNA/KYN ratio are unique to the depressed patient group. This may seem counterintuitive as KAT enzymes are responsible for the conversion of KYN to KYNA, and KAT1 expression might be expected to positively correlate with KYNA concentrations. This raises a possibility that in depressed patients KAT1 expression may be adaptive to compensate for reduced KYNA concentrations, although further research is needed to investigate this possibility. However, KAT enzymes also convert 3-HK to XA, a reaction which may take precedence in depression. Further insights will be gained by quantifying enzyme activity.

Multiple transcript variants that encode different isoforms have been identified for KAT. pH is important for the functioning of these enzymes. KAT2 has an optimum pH at 7.4, KAT1 at pH 10, KAT3 at pH 9 and KAT4 at pH 8, where KAT2 is considered to preferentially control the pool of KYNA that can be rapidly mobilised in the brain (Wonodi and Schwarcz, Reference Wonodi and Schwarcz2010; González Esquivel et al., Reference González Esquivel, Ramírez-Ortega, Pineda, Castro, Ríos and de la Cruz2017). mRNA expression of KAT2 was also measured in this study, but expression levels were below the limits of detection. It is not possible to assume that KAT1 is a proxy for all KAT enzymes. KAT1, a cytosolic enzyme, is ubiquitously expressed in brain and many other tissues (Fagerberg et al., Reference Fagerberg, Hallström, Oksvold, Kampf, Djureinovic, Odeberg, Habuka, Tahmasebpoor, Danielsson, Edlund, Asplund, Sjöstedt, Lundberg, Szigyarto, Skogs, Takanen, Berling, Tegel, Mulder, Nilsson, Schwenk, Lindskog, Danielsson, Mardinoglu, Sivertsson, von Feilitzen, Forsberg, Zwahlen, Olsson, Navani, Huss, Nielsen, Ponten and Uhlén2014) but with a prominence in the periphery, its expression in blood and tissue compartments outside of the brain are likely to be more influential in regulating the conversion of KYN to KYNA possibly regulating the extent to which KYN may access the brain. The extent of alignment between peripheral and central alterations is however uncertain. A systematic review on brain versus blood kynurenine pathway measures in psychiatric disorders has reported concordance between peripheral and central concentrations of KYN and 3-HK (Skorobogatov et al., Reference Skorobogatov, De Picker, Verkerk, Coppens, Leboyer, Müller and Morrens2021). Nevertheless, additional research is needed to ascertain the validity of other peripheral pathway measures as proxy for activity within the central nervous system. This does not discount for the possibility that changes in expression of KAT1 in the brain may also be functionally relevant. Increased activity of KAT1 has been reported in the cerebellum of patients with schizophrenia supporting an NMDA hypofunction hypothesis (Kapoor et al., Reference Kapoor, Lim, Cheng, Garrick and Kapoor2006). Clark et al. (Reference Clark, Pocivavsek, Nicholson, Notarangelo, Langenberg, McMahon, Kleinman, Hyde, Stiller, Postolache, Schwarcz and Tonelli2016) reported a significant decrease in kynurenine pathway activation in the ventrolateral prefrontal cortex (VLPFC), with decreased levels of QUIN and decreased gene expression of IDO1, IDO2 and TDO2. Future studies to measure the expression of all transcripts of KAT (in addition to other kynurenine pathway targets) and correlation of these with enzyme activities in brain from healthy and depressed individuals will enable a more precise identification of the transcripts most relevant to depression.

The altered correlation patterns between KP enzyme, NR3C1 and IL-6 mRNA levels observed in depressed patients pre- and post-ECT support stress- and inflammatory-related mechanisms underlying changes to the KP. In depressed patients, NR3C1 is associated with induction of the KP, favouring the production of metabolites related to oxidative and neurotoxic properties. As neither of the NR3C1 correlations remained in depressed patients post-ECT, ECT appears to alter associations between NR3C1 and KP enzyme levels. Peripheral TDO expression is restricted to the liver (Stone, Reference Stone1993), making it impossible to measure mRNA expression of this enzyme in whole blood. As TDO induction is largely mediated by the glucocorticoid receptor (as discussed in a review by O’Farrell and Harkin (Reference O.’Farrell and Harkin2017)), a determination of association between expression of TDO with NR3C1 mRNA levels would be of further interest.

Our analysis of DNA methylation offers additional evidence supporting the involvement of altered gene expression of KP enzymes in depression, since hypermethylation is frequently associated with gene silencing. However, DNA methylation analysis of individuals with a self-reported history of depression is a limitation as self-reported history is not necessarily representative of a clinical depressive episode. Therefore, further investigations should explore the potential impact of epigenetic mechanisms on the gene expression of KP enzymes in depression, requiring concurrent analysis of matched DNA and RNA samples from the same individuals.

A further limitation of the present study is that full blood cell counts were unavailable at the time at which blood was collected for KP analysis. It is possible that alterations in blood cell numbers may be responsible for the reduced expression of KP enzymes observed in this study. The most abundant cell type in blood is the neutrophil, varying from 30 to 70% of the white blood cell count in healthy adults (Palmer et al., Reference Palmer, Diehn, Alizadeh and Brown2006). Neutrophil count, leucocyte count and neutrophil–lymphocyte ratio are raised in unmedicated depressed patients (Demir et al., Reference Demir, Atli, Bulut, İbiloğlu, Güneş, Kaya, Demirpençe and Sır2015, Foley et al., Reference Foley É., Parkinson, Mitchell, Turner and Khandaker2023). As whole blood was used in the present study, detection of mRNA from less abundant cell types (e.g., lymphocytes, macrophages) may be poor. Human neutrophils contain functional IDO, which may play a role in their cytotoxic activity (Ishio et al., Reference Ishio, Goto, Tahara, Tone, Kawano and Kitano2004; Kai et al., Reference Kai, Goto, Tahara, Sasaki, Tone and Kitano2004; Souza-Fonseca-Guimaraes et al., Reference Souza-Fonseca-Guimaraes, Adib-Conquy and Cavaillon2012). Information regarding KAT1 expression in neutrophils is lacking. IDO is predominantly expressed in monocytes, though it is also present in T lymphocytes, B-lymphocytes and natural killer cells (Kai et al., Reference Kai, Goto, Tahara, Sasaki, Tone and Kitano2004; De Ravin et al., Reference De Ravin, Zarember, Long-Priel, Chan, Fox, Gallin, Kuhns and Malech2010). Flow cytometric analyses have shown that while there is a basal level of expression of IDO1 and KMO in lymphocytes, upregulation of these enzymes does not occur in lymphocytes following IFN-γ stimulation. By contrast, IDO1 and KMO protein show high basal expression in monocytes and are upregulated following IFN-γ treatment (Jones et al., Reference Jones, Franco, Varney, Sundaram, Brown, De Bie, Lim, Guillemin and Brew2015). Since blood is a complex tissue that is composed of several different types of cells that all have their own unique functions and gene expression profiles, there is a need for additional studies examining cell type specific gene expression in human peripheral blood.

An additional limitation of this study is that patients with depression were receiving treatment with a range of medications throughout their course of treatment with ECT. Therefore, it was not possible to analyse the effects of individual medications on KP enzyme expression. While there is some evidence that lithium can suppress IDO1 mRNA transcription, protein expression and activity in primary and immortalised human microglial cells in vitro (Göttert et al., Reference Göttert, Fidzinkski, Kraus, Schneider, Holtkamp, Endres, Gertz and Kronenberg2021), data on the effects of antidepressant medication on KP enzyme expression in peripheral cells in vitro or in vivo are sparse. Indeed, additional large-scale studies are also required to determine if mRNA expression of KP enzymes are altered in blood samples from unmedicated patients with depression compared to healthy controls.

In summary, mRNA levels of KP enzyme targets are prominently associated with plasma KP metabolites and NR3C1 and IL-6 mRNA levels in healthy controls and in depressed patients pre- and post-ECT. Associations differ in depressed patients compared to healthy controls and to a lesser extent post-ECT. Further studies are warranted as KP measures could potentially be integrated into stress and immune associated biomarker panels to aid patient stratification at diagnosis and in predicting treatment response to antidepressant therapy.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/neu.2024.34.

Acknowledgement

The authors thank the patients and controls for participating in this study.

Author contributions

AH and KR made contributions to conception and design of, acquisition of data, analysis and interpretation of data, drafting and revising the article for publication.DM contributed to study conception, design, interpretation and revising the article critically for intellectual content. MC contributed to data analysis and interpretation, drafting and revising article drafts. TM performed the DNA methylation analysis, drafting and revising article drafts. All authors contributed to the final approval of the version to be published.

Funding statement

This work was supported by the Health Research Board (HRB), Ireland (TRA/2007/5 & HPF/2010/17). MC was supported by a postgraduate scholarship from the Irish Research Council.

Competing interests

DMM has received speaker’s honoraria from MECTA, Otsuka and Janssen and an honorarium from Janssen for participating in an esketamine advisory board meeting. KR, MC, TM and AH have no interests to declare.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Ethical approval for this study was granted by the Research Ethics Committee of St James’s and Tallaght Hospitals (ref: 2014-08-19). All participants provided written informed consent.

References

Allen, AP, Naughton, M, Dowling, J, Walsh, A, O’shea, R, Shorten, G, Scott, L, Mcloughlin, DM, Cryan, JF, Clarke, G and Dinan, TG (2018) Kynurenine pathway metabolism and the neurobiology of treatment-resistant depression: comparison of multiple ketamine infusions and electroconvulsive therapy. Journal of Psychiatric Research 100, 2432.CrossRefGoogle Scholar
Arnone, D, Saraykar, S, Salem, H, Teixeira, AL, Dantzer, R and Selvaraj, S (2018) Role of kynurenine pathway and its metabolites in mood disorders: a systematic review and meta-analysis of clinical studies. Neurosci Biobehav Rev 92, 477485.CrossRefGoogle Scholar
Beckham, EE and Leber, WR (1985) Hamilton rating scale for depression, ECDEU version used in the treatment of depression collaborative research program. Homewood: The Dorsey Press.Google Scholar
Brown, SJ, Brown, AM, Purves-Tyson, TD, Huang, XF, Shannon Weickert, C and Newell, KA (2022) Alterations in the kynurenine pathway and excitatory amino acid transporter-2 in depression with and without psychosis: evidence of a potential astrocyte pathology. Journal of Psychiatric Research 147, 203211.CrossRefGoogle Scholar
Brown, SJ, Christofides, K, Weissleder, C, Huang, XF, Shannon Weickert, C, Lim, CK and Newell, KA (2024) Sex- and suicide-specific alterations in the kynurenine pathway in the anterior cingulate cortex in major depression. Neuropsychopharmacology 49, 584592.CrossRefGoogle Scholar
Clark, SM, Pocivavsek, A, Nicholson, JD, Notarangelo, FM, Langenberg, P, McMahon, RP, Kleinman, JE, Hyde, TM, Stiller, J, Postolache, TT, Schwarcz, R and Tonelli, LH (2016) Reduced kynurenine pathway metabolism and cytokine expression in the prefrontal cortex of depressed individuals. Journal of Psychiatry & Neuroscience 41, 386394. doi: 10.1503/jpn.150226.CrossRefGoogle Scholar
Correia, AS and Vale, N (2022) Tryptophan metabolism in depression: a narrative review with a focus on serotonin and kynurenine pathways. International Journal of Molecular Sciences 23, 8493.CrossRefGoogle Scholar
Crawford, B, Craig, Z, Mansell, G, White, I, Smith, A, Spaull, S, Imm, J, Hannon, E, Wood, A, Yaghootkar, H, Ji, Y, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mullins, N, Lewis, CM, Mill, J and Murphy, TM (2018) DNA methylation and inflammation marker profiles associated with a history of depression. Human Molecular Genetics 27(16), 28402850.CrossRefGoogle Scholar
Cutler, JA, Rush, AJ, Mcmahon, FJ and Laje, G (2012) Common genetic variation in the indoleamine-2,3-dioxygenase genes and antidepressant treatment outcome in major depressive disorder. Journal of Psychopharmacology 26, 360367.CrossRefGoogle Scholar
De Bie, J, Guest, J, Guillemin, GJ and Grant, R (2016) Central kynurenine pathway shift with age in women. Journal of Neurochemistry 136, 9951003.CrossRefGoogle Scholar
De Ravin, SS, Zarember, KA, Long-Priel, D, Chan, KC, Fox, SD, Gallin, JI, Kuhns, DB and Malech, HL (2010) Tryptophan/kynurenine metabolism in human leukocytes is independent of superoxide and is fully maintained in chronic granulomatous disease. Blood 116, 17551760.CrossRefGoogle Scholar
Demir, S, Atli, A, Bulut, M, İbiloğlu, AO, Güneş, M, Kaya, MC, Demirpençe, Ö and Sır, A (2015) Neutrophil-lymphocyte ratio in patients with major depressive disorder undergoing no pharmacological therapy. Neuropsychiatric disease and treatment 11, 22532258.Google Scholar
Doolin, K, Allers, KA, Pleiner, S, Liesener, A, Farrell, C, Tozzi, L, O’hanlon, E, Roddy, D, Frodl, T, Harkin, A and O’keane, V (2018) Altered tryptophan catabolite concentrations in major depressive disorder and associated changes in hippocampal subfield volumes. Psychoneuroendocrinology 95, 817.CrossRefGoogle Scholar
Elenkov, IJ, Wilder, RL, Chrousos, GP and Vizi, ES (2000) The sympathetic nerve--an integrative interface between two supersystems: the brain and the immune system. Pharmacological Reviews 52, 595638.Google Scholar
Fagerberg, L, Hallström, BM, Oksvold, P, Kampf, C, Djureinovic, D, Odeberg, J, Habuka, M, Tahmasebpoor, S, Danielsson, A, Edlund, K, Asplund, A, Sjöstedt, E, Lundberg, E, Szigyarto, CA, Skogs, M, Takanen, JO, Berling, H, Tegel, H, Mulder, J, Nilsson, P, Schwenk, JM, Lindskog, C, Danielsson, F, Mardinoglu, A, Sivertsson, A, von Feilitzen, K, Forsberg, M, Zwahlen, M, Olsson, I, Navani, S, Huss, M, Nielsen, J, Ponten, F and Uhlén, M (2014) Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Molecular & Cellular Proteomics 13, 397406. doi: 10.1074/mcp.M113.035600.CrossRefGoogle Scholar
Favennec, M, Hennart, B, Caiazzo, R, Leloire, A, Yengo, L, Verbanck, M, Arredouani, A, Marre, M, Pigeyre, M, Bessede, A, Guillemin, GJ, Chinetti, G, Staels, B, Pattou, F, Balkau, B, Allorge, D, Froguel, P and Poulain-Godefroy, O (2015) The kynurenine pathway is activated in human obesity and shifted toward kynurenine monooxygenase activation. Obesity (Silver Spring) 23, 20662074.CrossRefGoogle Scholar
First, M, Spitzer, R, Gibbon, M and Williams, J (1996) Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV). Washington DC: American Psychiatric Press.Google Scholar
Foley É., M, Parkinson, JT, Mitchell, RE, Turner, L and Khandaker, GM (2023) Peripheral blood cellular immunophenotype in depression: a systematic review and meta-analysis. Molecular Psychiatry 28, 10041019.CrossRefGoogle Scholar
González Esquivel, D, Ramírez-Ortega, D, Pineda, B, Castro, N, Ríos, C and de la Cruz, VPérez (2017) Kynurenine pathway metabolites and enzymes involved in redox reactions. Neuropharmacology 112(Pt B), 331345.CrossRefGoogle Scholar
Göttert, R, Fidzinkski, P, Kraus, L, Schneider, UC, Holtkamp, M, Endres, M, Gertz, K and Kronenberg, G (2021) Lithium inhibits tryptophan catabolism via the inflammation-induced kynurenine pathway in human microglia. Glia 70, 558571.CrossRefGoogle Scholar
Hughes, MM, Carballedo, A, Mcloughlin, DM, Amico, F, Harkin, A, Frodl, T and Connor, TJ (2012) Tryptophan depletion in depressed patients occurs independent of kynurenine pathway activation. Brain Behavior and Immunity 26, 979987.CrossRefGoogle Scholar
Ishio, T, Goto, S, Tahara, K, Tone, S, Kawano, K and Kitano, S (2004) Immunoactivative role of indoleamine 2,3-dioxygenase in human hepatocellular carcinoma. Journal of Gastroenterology and Hepatology 19, 319326.CrossRefGoogle Scholar
Jones, SP, Franco, NF, Varney, B, Sundaram, G, Brown, DA, De Bie, J, Lim, CK, Guillemin, GJ and Brew, BJ (2015) Expression of the kynurenine pathway in human peripheral blood mononuclear cells: implications for inflammatory and neurodegenerative disease. PLoS One 10, e0131389.CrossRefGoogle Scholar
Kai, S, Goto, S, Tahara, K, Sasaki, A, Tone, S and Kitano, S (2004) Indoleamine 2,3-dioxygenase is necessary for cytolytic activity of natural killer cells. Scandinavian Journal of Immunology 59, 177182.CrossRefGoogle Scholar
Kapoor, R, Lim, KS, Cheng, A, Garrick, T and Kapoor, V (2006) Preliminary evidence for a link between schizophrenia and NMDA-glycine site receptor ligand metabolic enzymes, d-amino acid oxidase (DAAO) and kynurenine aminotransferase-1 (KAT-1) brain res , 1106:205210.CrossRefGoogle Scholar
Lugo-Huitrón, R, Ugalde Muñiz, P, Pineda, B, Pedraza-Chaverrí, J, Ríos, C and Pérez-De La Cruz, V (2013) Quinolinic acid: an endogenous neurotoxin with multiple targets. Oxidative Medicine and Cellular Longevity 2013, 104024.CrossRefGoogle Scholar
Maes, M, Berk, M, Goehler, L, Song, C, Anderson, G, Gałecki, P and Leonard, B (2012) Depression and sickness behavior are janus-faced responses to shared inflammatory pathways. Bmc Medicine 10, 66.CrossRefGoogle Scholar
Mangge, H, Summers, KL, Meinitzer, A, Zelzer, S, Almer, G, Prassl, R, Schnedl, WJ, Reininghaus, E, Paulmichl, K, Weghuber, D and Fuchs, D (2014) Obesity-related dysregulation of the tryptophan-kynurenine metabolism: role of age and parameters of the metabolic syndrome. Obesity (Silver Spring) 22, 195201.CrossRefGoogle Scholar
Myint, AM and Halaris, A (2022) Imbalances in Kynurenines as potential biomarkers in the diagnosis and treatment of psychiatric disorders. Frontiers in Psychiatry 13, 913303.CrossRefGoogle Scholar
O.’Farrell, K and Harkin, A (2017) Stress-related regulation of the kynurenine pathway: relevance to neuropsychiatric and degenerative disorders. Neuropharmacology 112, 307323.CrossRefGoogle Scholar
Palmer, C, Diehn, M, Alizadeh, AA and Brown, PO (2006) Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 7, 115.CrossRefGoogle Scholar
Qin, Y, Wang, N, Zhang, X, Han, X, Zhai, X and Lu, Y (2018) IDO and TDO as a potential therapeutic target in different types of depression. Metabolic Brain Disease 33, 17871800.CrossRefGoogle Scholar
Raheja, UK, Fuchs, D, Giegling, I, Brenner, LA, Rovner, SF, Mohyuddin, I, Weghuber, D, Mangge, H, Rujescu, D and Postolache, TT (2015) In psychiatrically healthy individuals, overweight women but not men have lower tryptophan levels. Pteridines 26, 7984.CrossRefGoogle Scholar
Raison, CL, Dantzer, R, Kelley, KW, Lawson, MA, Woolwine, BJ, Vogt, G, Spivey, JR, Saito, K and Miller, AH (2010) CSF concentrations of brain tryptophan and kynurenines during immune stimulation with IFN-alpha: relationship to CNS immune responses and depression. Molecular Psychiatry 15, 393403.CrossRefGoogle Scholar
Réus, GZ, Jansen, K, Titus, S, Carvalho, AF, Gabbay, V and Quevedo, J (2015) Kynurenine pathway dysfunction in the pathophysiology and treatment of depression: evidences from animal and human studies. Journal of Psychiatric Research 68, 316328.CrossRefGoogle Scholar
Ryan, KM, Allers, KA, Mcloughlin, DM and Harkin, A (2020) Tryptophan metabolite concentrations in depressed patients before and after electroconvulsive therapy. Brain Behavior and Immunity 83, 153162.CrossRefGoogle Scholar
Ryan, KM and Mcloughlin, DM (2019) Peripheral blood GILZ mRNA levels in depression and following electroconvulsive therapy. Psychoneuroendocrinology 101, 304310.CrossRefGoogle Scholar
Ryan, KM, Poelz, L and Mcloughlin, DM (2019) Low Circulating Levels of GR, FKBP5, and SGK1 in Medicated Patients With Depression Are Not Altered by Electroconvulsive Therapy. Journal of Electroconvulsive Therapy 36, 137143,Google Scholar
Savitz, J (2020) The kynurenine pathway: a finger in every pie. Molecular Psychiatry 25, 131147.CrossRefGoogle Scholar
Schmittgen, TD and Livak, KJ (2008) Analyzing real-time PCR data by the comparative C(T) method. Nature Protocols 3, 11011108.CrossRefGoogle Scholar
Schwieler, L, Samuelsson, M, Frye, MA, Bhat, M, Schuppe-Koistinen, I, Jungholm, O, Johansson, AG, Landén, M, Sellgren, CM and Erhardt, S (2016) Electroconvulsive therapy suppresses the neurotoxic branch of the kynurenine pathway in treatment-resistant depressed patients. Journal of Neuroinflammation 13, 51.CrossRefGoogle Scholar
Semkovska, M, Landau, S, Dunne, R, Kolshus, E, Kavanagh, A, Jelovac, A, Noone, M, Carton, M, Lambe, S, Mchugh, C and Mcloughlin, DM (2016) Bitemporal versus high-dose unilateral twice-weekly electroconvulsive therapy for depression (EFFECT-dep): a pragmatic, randomized, non-inferiority trial. American Journal of Psychiatry 173, 408417.CrossRefGoogle Scholar
Skorobogatov, K, De Picker, L, Verkerk, R, Coppens, V, Leboyer, M, Müller, N and Morrens, M (2021) Brain versus blood: a systematic review on the concordance between peripheral and central kynurenine pathway measures in psychiatric disorders. Frontiers in Immunology 12, 716980.CrossRefGoogle Scholar
Souza-Fonseca-Guimaraes, F, Adib-Conquy, M and Cavaillon, JM (2012) Natural killer (NK) cells in antibacterial innate immunity: angels or devils? Molecular Medicine 18, 270285.CrossRefGoogle Scholar
Stone, TW (1993) Neuropharmacology of quinolinic and kynurenic acids. Pharmacological Reviews 45, 309379.Google Scholar
Stone, TW, Stoy, N and Darlington, LG (2013) An expanding range of targets for kynurenine metabolites of tryptophan. Trends in Pharmacological Sciences 34, 136143.CrossRefGoogle Scholar
Theofylaktopoulou, D, Midttun, O, Ulvik, A, Ueland, PM, Tell, GS, Vollset, SE, Nygard, O and Eussen, SJ (2013) A community-based study on determinants of circulating markers of cellular immune activation and kynurenines: the hordaland health study. Clinical and Experimental Immunology 173, 121130.CrossRefGoogle Scholar
Vécsei, L, Szalárdy, L, Fülöp, F and Toldi, J (2013) Kynurenines in the CNS: recent advances and new questions. Nature Reviews Drug Discovery 12, 6482.CrossRefGoogle Scholar
Wonodi, I and Schwarcz, R (2010) Cortical kynurenine pathway metabolism: a novel target for cognitive enhancement in schizophrenia. Schizophrenia Bulletin 36, 211218.CrossRefGoogle Scholar
World Medical Association (2013) World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310, 21912194.CrossRefGoogle Scholar
Figure 0

Figure 1. Correlation-based representations of associations between tryptophan metabolite concentrations and mRNA expression of kynurenine pathway enzymes, IL-6 and the glucocorticoid receptor in whole blood in (A) a healthy control cohort, (B) a depressed patient cohort pre-ECT and (C) the same depressed patient cohort post-ECT. While a wider panel of inflammatory and stress markers were investigated, IL-6 and NR3C1 were selected as representative markers for clarity of the schematic. These additional correlations are available in Supplementary Table 8. Top panel: all significant correlations are included. The size of each node is proportional to the number of correlations at that node. The width of each line is proportional to the strength of the correlation (quantified by the statistical rho value). Red lines correspond to positive correlations and blue lines correspond to negative correlations. Bottom panel: Significant correlations common between all groups are removed in order to highlight differences between the groups. Node sizes are adjusted accordingly. Abbreviations: IDO, indolamine 2, 3-dioxygenase; KMO, kynurenine 3-monooxygenase; KYNU, kynureninase; KAT, kynurenine aminotransferase; TRP, tryptophan; KYN, kynurenine; 3-HK, 3-hydroxykynurenine; XA, xanthurenic acid; 3-HAA, 3-hydroxyanthranillic acid; KYNA, kynurenic acid; PIC, picolinic acid; QUIN, quinolinic acid; GR, glucocorticoid receptor.

Figure 1

Table 1. Demographic and clinical characteristics of participants

Figure 2

Table 2. KP enzyme mRNA levels in healthy controls compared to patients with depression

Figure 3

Table 3. Depressed pre- and post-ECT

Figure 4

Table 4. Correlations between KMO and HAM-D24 scores

Figure 5

Table 5. Correlations between IDO1 and HAM-D24 scores

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