1. Immunology and Inflammation

Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis

  1. Edwin Ardiansyah
  2. Julian Avila-Pacheco
  3. Le Thanh Hoang Nhat
  4. Sofiati Dian
  5. Dao Nguyen Vinh
  6. Hoang Thanh Hai
  7. Kevin Bullock
  8. Bachti Alisjahbana
  9. Mihai G Netea
  10. Riwanti Estiasari
  11. Trinh Thi Bich Tram
  12. Joseph Donovan
  13. Dorothee Heemskerk
  14. Tran Thi Hong Chau
  15. Nguyen Duc Bang
  16. Ahmad Rizal Ganiem
  17. Rovina Ruslami
  18. Valerie ACM Koeken
  19. Raph L Hamers
  20. Darma Imran
  21. Kartika Maharani
  22. Vinod Kumar
  23. Clary B Clish
  24. Reinout van Crevel
  25. Guy Thwaites
  26. Arjan van Laarhoven  Is a corresponding author
  27. Nguyen Thuy Thuong Thuong  Is a corresponding author
  1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Indonesia
  2. Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Netherlands
  3. The Broad Institute of MIT and Harvard, United States
  4. Oxford University Clinical Research Unit, Viet Nam
  5. Department of Neurology, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Indonesia
  6. Department of Internal Medicine, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Indonesia
  7. Department of Neurology, Cipto Mangunkusumo Hospital, Faculty of Medicine Universitas Indonesia, Indonesia
  8. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom
  9. London School of Hygiene and Tropical Medicine, United Kingdom
  10. Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centre, Netherlands
  11. Hospital for Tropical Diseases, Viet Nam
  12. Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Viet Nam
  13. Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Indonesia
  14. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Germany
  15. Oxford University Clinical Research Unit Indonesia, Faculty of Medicine Universitas Indonesia, Indonesia
https://doi.org/10.7554/eLife.85307
Share this article
Cite this article
  1. Edwin Ardiansyah
  2. Julian Avila-Pacheco
  3. Le Thanh Hoang Nhat
  4. Sofiati Dian
  5. Dao Nguyen Vinh
  6. Hoang Thanh Hai
  7. Kevin Bullock
  8. Bachti Alisjahbana
  9. Mihai G Netea
  10. Riwanti Estiasari
  11. Trinh Thi Bich Tram
  12. Joseph Donovan
  13. Dorothee Heemskerk
  14. Tran Thi Hong Chau
  15. Nguyen Duc Bang
  16. Ahmad Rizal Ganiem
  17. Rovina Ruslami
  18. Valerie ACM Koeken
  19. Raph L Hamers
  20. Darma Imran
  21. Kartika Maharani
  22. Vinod Kumar
  23. Clary B Clish
  24. Reinout van Crevel
  25. Guy Thwaites
  26. Arjan van Laarhoven
  27. Nguyen Thuy Thuong Thuong
(2023)
Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis
eLife 12:e85307.
https://doi.org/10.7554/eLife.85307
  2. Download BibTeX
  3. Download .RIS

Abstract

Background:

Cellular metabolism is critical for the host immune function against pathogens, and metabolomic analysis may help understand the characteristic immunopathology of tuberculosis. We performed targeted metabolomic analyses in a large cohort of patients with tuberculous meningitis (TBM), the most severe manifestation of tuberculosis, focusing on tryptophan metabolism.

Methods:

We studied 1069 Indonesian and Vietnamese adults with TBM (26.6% HIV-positive), 54 non-infectious controls, 50 with bacterial meningitis, and 60 with cryptococcal meningitis. Tryptophan and downstream metabolites were measured in cerebrospinal fluid (CSF) and plasma using targeted liquid chromatography–mass spectrometry. Individual metabolite levels were associated with survival, clinical parameters, CSF bacterial load and 92 CSF inflammatory proteins.

Results:

CSF tryptophan was associated with 60-day mortality from TBM (hazard ratio [HR] = 1.16, 95% confidence interval [CI] = 1.10–1.24, for each doubling in CSF tryptophan) both in HIV-negative and -positive patients. CSF tryptophan concentrations did not correlate with CSF bacterial load nor CSF inflammation but were negatively correlated with CSF interferon-gamma concentrations. Unlike tryptophan, CSF concentrations of an intercorrelating cluster of downstream kynurenine metabolites did not predict mortality. These CSF kynurenine metabolites did however correlate with CSF inflammation and markers of blood–CSF leakage, and plasma kynurenine predicted death (HR 1.54, 95% CI = 1.22–1.93). These findings were mostly specific for TBM, although high CSF tryptophan was also associated with mortality from cryptococcal meningitis.

Conclusions:

TBM patients with a high baseline CSF tryptophan or high systemic (plasma) kynurenine are at increased risk of death. These findings may reveal new targets for host-directed therapy.

Funding:

This study was supported by National Institutes of Health (R01AI145781) and the Wellcome Trust (110179/Z/15/Z and 206724/Z/17/Z).

Editor's evaluation

This important study by Ardiansyah and colleagues reports the association of tryptophan levels in cerebrospinal fluid with 60-day mortality in patients with tuberculosis meningitis. Good evidence is presented that cerebrospinal fluid tryptophan levels are associated with mortality. The findings continue to remain an association, without clarity of whether tryptophan is a key mediator of mortality or another inflammatory biomarker. The work will be of interest to tuberculosis researchers.

https://doi.org/10.7554/eLife.85307.sa0

Introduction

Tuberculous meningitis (TBM) is the most severe manifestation of tuberculosis affecting approximately 160,000 adults each year (Dodd et al., 2021). Patients suffer from varying degrees of intracerebral inflammation, commonly manifest as leptomeningitis, vasculitis, and space-occupying brain lesions (tuberculomas). Hydrocephalus, stroke, seizures, focal neurological deficits, and loss of consciousness are common complications and lead to death in around 30% of patients, even when treated with anti-tuberculosis drugs and adjuvant corticosteroid therapy (Dodd et al., 2021). Development of more effective host-directed therapy is hampered by a lack of knowledge on the biological pathways involved in the immunopathology of TBM (Wilkinson et al., 2017).

Metabolism is critical for the function of immune cells, and analysis of cerebrospinal fluid (CSF) metabolites could help unravel underlying biological mechanisms in TBM. Previously, using a large-scale metabolomics analysis, we found that lower CSF tryptophan concentrations were associated with survival of TBM patients in Indonesia (van Laarhoven et al., 2018). This study did not include HIV-infected patients and the association was not validated in other populations. Moreover, there is a need to investigate the downstream metabolites in the kynurenine pathway (Figure 1), through which 95% of tryptophan is initially catabolized via indoleamine 2,3-dioxygenase (IDO) or tryptophan 2,3-dioxygenase (TDO) and which includes metabolites with putative neuroprotective (e.g. kynurenic acid) or neurodamaging (e.g. quinolinic acid) properties (Lovelace et al., 2017). Lastly, there is a need to compare these findings in other neuro-infectious diseases to distinguish disease specific from broader mechanisms.

Figure 1
Download asset Open asset
Tryptophan metabolism pathway.

Tryptophan is metabolized mainly through the kynurenine pathway through indoleamine 2,3-dioxygenase 1 (IDO1), generating kynurenine and its downstream metabolites (blue box). IDO1 is partly stimulated by M. tuberculosis-induced interferon gamma (IFN-γ) production by T helper 1cells.

We therefore sought to define and validate the relationship between tryptophan and its metabolites and survival from TBM in large, independent populations, including HIV-positive individuals. We aimed to confirm that a higher CSF tryptophan would predict higher mortality across different populations and we hypothesized that high tryptophan would be associated with a higher CSF bacterial load, more inflammation, and lower downstream kynurenine metabolites. We lastly sought to investigate how systemic (plasma) metabolite concentrations linked to outcome.

Materials and methods

Setting and patients

Request a detailed protocol

Patients with subacute meningitis were included from the Hospital for Tropical Diseases and Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease in Ho Chi Minh City, Vietnam between 2011 and 2014 (Thuong et al., 2017; Heemskerk et al., 2016), and Hasan Sadikin Hospital in Indonesia between 2007 and 2019 (van Laarhoven et al., 2018; van Laarhoven et al., 2017). TBM patients were defined as having ‘definite TBM’ if they had either microbial confirmation by Ziehl–Neelsen staining, positive CSF culture, or GeneXpert. Based on previous studies (van Laarhoven et al., 2017), probable TBM was defined as clinically suspected TBM fulfilling at least two out of the following three criteria: CSF leukocytes ≥5 cells/μl, CSF/blood glucose ratio <0.5, and CSF protein >0.45 g/l. Patients were treated with antibiotics according to national guidelines for 180 days minimally, and received adjunctive dexamethasone starting at 0·3 mg/kg for grade I and 0·4 mg/kg for grade II or III TBM and tapered thereafter (Thwaites et al., 2004). Patients were followed-up clinically or by phone up until day 180 from admission. Primary outcome was 60-day survival, when most deaths attributable to TBM occur. As a secondary endpoint, earlier and later mortality were explored separately. We ensured equal power for both time windows by separating them by the median time to death for those patients who died during the total follow-up of 180 days. The median time to death was 14 days and was used as the cut-off to differentiate early (days 0–14) from late (days 14–180) mortality. Patients without an infection (non-infectious controls) were included from the same sites. In Indonesia, patients in this group had undergone a lumbar puncture for suspected central nervous system infection or subarachnoid bleeding, but infection was excluded by negative microscopy, GeneXpert and bacterial culture, and CSF leucocytes <5 cells/μl and CSF/blood glucose ratio ≥0.5. In Vietnam, patients were included as controls if they had undergone a lumbar puncture, but an alternative, non-infectious, diagnosis was confirmed. In both sites, none of the non-infectious controls received anti-tuberculosis treatment. HIV-negative patients with microbiologically confirmed bacterial meningitis and HIV-positive patients with cryptococcal meningitis patients were included from the same sites.

Ethical approval was obtained from the Ethical Committee of Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia (No. 114/FKUP-RSHS/KEPK/Kep./EC/2007 and No. 330/UN6.C1.3.2/KEPK/PN/2016) and from the Oxford Tropical Research Ethics Committee in the United Kingdom (OXTREC reference number: 33-09), the Institutional Review Boards of the Hospital for Tropical Diseases and Pham Ngoc Thach Hospital in Vietnam. Written (Vietnam) or oral (Indonesia) consent to be included in the study, for storage of surplus sample, and to obtain follow-up data, was obtained from patients or close relatives of patients who were unconscious. The paper adheres to the STROBE methodology.

Metabolite measurements

Request a detailed protocol

CSF and blood samples were centrifuged for 15 min according to local protocols (865–3000 × g) and supernatants were stored at −80°C (Rohlwink et al., 2019). CSF and plasma metabolites were measured using targeted a liquid chromatography–tandem mass spectrometry (LC–MS) method with a system comprised of a 1290 Infinity II U-HPLC coupled to an Agilent 6495 Triple Quadrupole mass spectrometer (Agilent Tech., Santa Clara, CA). Metabolites were extracted from plasma or CSF (10 µl) using 90 µl of acetonitrile/methanol/formic acid (74.9:24.9:0.2, vol/vol/vol) containing stable isotope-labeled internal standards (valine-d8, Sigma-Aldrich, St. Louis, MO; and phenylalanine-d8, Cambridge Isotope Laboratories, Andover, MA). The samples were centrifuged (10 min, 9000 × g, 4°C), and the supernatants were injected directly onto a 150 × 2 mm, 3 µm Atlantis HILIC column (Waters, Milford, MA). The column was eluted isocratically at a flow rate of 250 µl/min with 5% mobile phase A (10 mM ammonium formate and 0.1% formic acid in water) for 0.5 min followed by a linear gradient to 40% mobile phase B (acetonitrile with 0.1% formic acid) over 10 min. Pairs of pooled samples generated using aliquots of all samples in the study were included every 20 samples correct for MS sensitivity drift and for quality control analyses. Sample stability over the 7-year study inclusion and 4-year storage time was checked by plotting metabolite levels of definite TBM patients against storage time. Tryptophan metabolites were measured using the following multiple reaction monitoring transitions: 3-hydroxyanthranilic acid (154.1 to >136.0), 3-indoleacetic acid (176.1 to >130.1), 3-methoxyanthranilate (168.1 to >150.0), 5-hydroxyindoleacetic acid (192.1 to >146.0), 5-methoxytryptophol (192.1 to >130.0), kynurenic acid (190.1 to >144.1), kynurenine (209.1 to >94.0), tryptophan (205.1 to >187.9), N-acetylserotonin (219.1 to >160.0), niacinamide (123.1 to >80.1), quinolinic acid (168.0 to >149.9), and xanthurenic acid (206.1 to >132.0). Absolute concentrations were determined using external calibration curves created via serial dilution of stable isotope-labeled compounds in CSF and plasma. These compounds were sourced from Cambridge Isotope Labs: 3-indoleacetic acid-d7 (DLM-8040), anthranilic acid-13C6 (CLM-701), 5-HIAA-13C6 (CLM-9936), kynurenic acid-d5 (DLM-7374), L-kynurenine-d6 (DLM-7842), L-tryptophan-13C11 (CLM-4290), and niacinamide-13C6 (CLM-9925). Peak abundances were manually integrated using the MassHunter software provided by the LC–MS manufacturer.

CSF mycobacterial load and inflammatory proteins

Request a detailed protocol

The CSF mycobacterial load was inferred qualitatively by comparing patients with negative versus positive CSF culture, and semiquantitively from the GeneXpert Ct-values as described previously (Thuong et al., 2019), and inferred from CSF M. tuberculosis culture. CSF inflammatory cytokines in 178 Indonesian HIV-negative TBM patients were measured using a multiplex proximity extension assay (Olink) in two batches. Olink uses a multiplex assay that simultaneously recognize 96 target proteins through specific paired-antibodies which are coupled with unique oligonucleotides, for quantitative PCR measurement (Assarsson et al., 2014). For each protein, overlapping samples from two batches were fitted in a linear regression model, where the linear components were subsequently extracted, and used as correction factors for batches normalization. In 304 Vietnamese HIV-negative patients, 10 human cytokines were measured in CSF with Luminex multiplex bead array technology (Bio-Rad Laboratories, Hercules, CA) (Thuong et al., 2017). CSF total protein was used as proxy for blood–CSF barrier disruption as it showed a near-perfect correlation with the established marker CSF–serum albumin (r2 = 0.98) (Svensson et al., 2020).

Quality control and statistical analysis

Request a detailed protocol

Only metabolites and proteins with a coefficient of variation of the pooled samples <30% and<25% missing values, respectively, among TBM patients were further included in the analysis. Remaining missing metabolite values after quality control were replaced with half of the minimum measured value of the corresponding metabolite, and log2-transformed subsequently. Statistical analyses were performed in R 4.0.4 (R Development Core Team, 2022), using the R packages survival, tableone, dplyr, openxlsx, pheatmap, grid, and ggplot2. Correlation analyses between metabolites levels, and between metabolites levels and clinical and inflammatory parameters, were calculated using Spearman-rank correlation. The impact of baseline CSF and plasma metabolite levels on 60-day survival was tested in a Cox-regression model, adjusted for sex, age, and HIV status as covariates. The model stratified by study site as mortality is known to be higher in the Indonesian75 than in the Vietnamese (Heemskerk et al., 2016) cohort. An analysis plan was made before the study; however, it was modified because of similar metabolite levels between HIV-infected and -uninfected metabolites, we decided to incorporate HIV status as covariate, rather than a stratum, to improve power. Correction for multiple testing using the Benjamini–Hochberg method was done if multiple comparisons were done in primary analysis.

Results

Baseline characteristics of TBM patients and controls

We studied 1069 adults with TBM, 390 from Indonesia and 679 from Vietnam (Table 1). Patients were young (median age 34 years), 26.6% were HIV positive, and the majority presented with a moderately severe (55.6% grade II) to severe (17.0% grade III) disease according to the international classification (Thwaites et al., 2003). The rate of mycobacterial confirmation was 64.1%. Sixty-day mortality, the primary endpoint in the analysis, was 21.6%. Patients who died within 180 days from admission did so after a median of 14 days. A 14-day cut-off was therefore used to distinguish early from late mortality as a secondary endpoint.

Table 1
Patient baseline characteristics.
Tuberculous meningitisNon-infectious controlBacterial meningitis*Cryptococcal meningitis*
(n = 1069)(n = 54)(n = 50)(n = 60)
Clinical features
Age, years34 (27–44)35 (25–44)46 (34–57)33 (27–37)
Sex, % male700 (65.5%)30 (55.6%)12 (60.0%)26 (78.8%)
Glasgow Coma Scale14 (12–15)15 (12–15)13 (9–14)15 (13–15)
HIV, % positive284 (26.6%)11 (20.4%)0 (0%)60 (100%)
Tuberculous meningitis grade (%)
Grade I287 (27.3%)---
Grade II584 (55.6%)---
Grade III179 (17.0%)---
Cerebrospinal fluid features
Leukocytes, cells/μl150 (49–336)2 (1–3)1900 (739–5460)86 (24–192)
Neutrophils, cells/μl22 (3–99)1 (0–1)1527 (538–4986)17 (6–109)
Mononuclear cells, cells/μl98 (38–207)2 (1–3)307 (134–646)31 (6–89)
Protein, g/l1.46 (0.90–2.40)0.40 (0.26–0.59)1.90 (1.10–3.80)0.76 (0.58–1.60)
CSF to blood glucose ratio0.28 (0.17–0.40)0.60 (0.56–0.70)0.46 (0.17–1.00)0.50 (0.30–1.00)
M. tuberculosis culture or ZN staining or GeneXpert positive686 (64.17%)---
Outcomes
Outcome at day 60
Alive825 (77.2%)---
Deceased231 (21.6%)---
Lost to follow-up13 (1.2%)---
Outcome at day 180
Alive731 (68.4%)---
Deceased304 (28.4%)---
Lost to follow-up34 (3.2%)---

  1. Categorical variables are presented in N (%); continuous variables are presented in median (IQR). Abbreviation: CSF = cerebrospinal fluid.

  2. *

    Clinical metadata available for 40% of bacterial meningitis and 56% of cryptococcal meningitis patients.

There were some differences between the populations. Indonesian patients presented with more severe diseases (91.9% grade with grade II or III) than Vietnamese patients (62.2%). Also, CSF total protein, a proxy for blood–CSF barrier leakage (Svensson et al., 2020), was higher in Indonesian (median = 1.6 g/l, IQR = 0.8–3.1) than Vietnamese (1.3 g/l, IQR = 0.8–2.0) patients. CSF polymorphonuclear cell counts were higher in the Indonesian than in the Vietnamese patients where it showed a bimodal distribution associated to study site (Figure 2—figure supplement 1) and stratified analyses were performed taking this into account. Compared to the TBM patients, non-infectious controls (n = 54), bacterial meningitis patients (n = 50), and cryptococcal meningitis patients (n = 60) had a similar age range and gender distribution.

Ten metabolites showed detectable levels in >75% of patients and passed quality control, while two metabolites, 3-methoxyanthranilate and 5-hydroxyindoleacetic acid, were detected in less than 75% of patients and excluded from further analysis. Metabolite measurements showed stable concentrations over and were not affected by year of patient inclusion and duration of sample (Figure 2—figure supplement 2). The clinical metadata and LC–MS data before pre-processing can be found in Source data 1.

Increased CSF tryptophan levels were associated with mortality of TBM patients independent of HIV status

Confirming our previous findings (van Laarhoven et al., 2018), higher baseline CSF tryptophan levels predicted 60-day survival in patients with TBM (hazard ratio [HR] = 1.16 for each doubling in CSF tryptophan, 95% confidence interval [CI] = 1.10–1.24), all analyses corrected for age, sex, and HIV status, and stratified for cohort (Figure 2 and Table 2). This was both true for HIV-negative (HR = 1.13, 95% CI = 1.05–1.21) and HIV-positive patients (HR = 1.19, 95% CI = 1.07–1.33), who showed a much higher mortality (Figure 2—figure supplement 3), as reported previously (Thuong et al., 2017; van Laarhoven et al., 2017). Because of weak negative correlation between tryptophan and GCS (r = −0.08) and to exclude the possibility that tryptophan is a marker of patients with more severe disease, we performed a post hoc analysis with two additional models. These models included the basic pre-defined variables (age, sex, HIV as covariates, study site as stratum). Adding GCS did not substantially change the effect size of CSF tryptophan (HR 1.15, 95% CI 1.08–1.23), and neither did adding GCS and CSF cell counts, CSF protein and glucose ratio (HR for CSF tryptophan 1.14, 95% CI 1.07–1.22) confirming that tryptophan was associated with mortality independent of these parameters. Baseline CSF tryptophan was associated with both early (HR = 1.14, 95% CI = 1.06–1.23) and late (HR = 1.17, 95% CI = 1.08–1.26) mortality (Table 3). Compared to non-infectious controls, CSF tryptophan was lower. This was also observed in patients with cryptococcal, but not in those with bacterial meningitis (Figure 3). Interestingly, among 17 cryptococcal meningitis patients with available in-hospital mortality data in Indonesia, baseline CSF tryptophan was significantly higher in those who died in hospital compared to those discharged alive (Figure 2—figure supplement 4), similar as in TBM.

Figure 2 with 5 supplements see all
Download asset Open asset
Six-month survival curve of tuberculous meningitis (TBM) patients.

Patients were stratified by cerebrospinal fluid (CSF) tryptophan concentrations tertiles.

Table 2
Univariate Cox regression for influence of cerebrospinal fluid (CSF) metabolites on 60-day mortality.
MetabolitesHR*95% CI*p-valueFDR†
Tryptophan1.161.10, 1.24<0.001<0.001
Kynurenine1.000.93, 1.07>0.9>0.9
Kynurenic acid1.000.93, 1.070.9>0.9
3-Hydroxyanthranilic acid1.010.97, 1.050.60.9
Xanthurenic acid0.950.90, 1.000.050.2
Quinolinic acid0.920.85, 1.000.0380.2
Niacinamide1.030.95, 1.110.50.8
3-Indoleacetic acid1.110.96, 1.290.20.4
N-Acetylserotonin1.010.94, 1.090.70.9
5-Methoxytryptophol1.110.93, 1.320.30.5

  1. Baseline cerebrospinal fluid (CSF) metabolites were measured in 1069 TBM patients. Cox regression models were stratified by cohort and adjusted by sex, age, and HIV status. Hazard ratio (HR) was calculated per twofold increase in metabolite concentration. Bold: False Discovery Rate (FDR) < 0.05.

  2. *

    HR = hazard ratio, CI = confidence interval.

  3. †

    FDR = false discovery rate; Benjamini and Hochberg correction for multiple testing.

Table 3
Univariate Cox regression for influencen of baseline cerebrospinal fluid (CSF) metabolites on early and late mortality.
MetaboliteEarly mortality (days 0–14)Late mortality (days 14–180)
HR*95% CI*p-valueFDR†HR*95% CI*p-valueFDR†
Tryptophan1.141.06, 1.23<0.0010.0051.171.08, 1.26<0.001<0.001
Kynurenine1.030.95, 1.130.40.610.91, 1.10>0.9>0.9
Kynurenic acid1.050.96, 1.140.30.50.950.86, 1.040.30.5
3-Hydroxyanthranilic acid1.020.97, 1.060.50.61.010.96, 1.060.60.7
Xanthurenic acid0.960.90, 1.030.20.50.960.89, 1.040.30.5
Quinolinic acid0.890.81, 0.980.020.10.90.81, 1.000.0520.2
Niacinamide1.020.92, 1.120.70.71.050.95, 1.160.30.5
3-Indoleacetic acid1.10.92, 1.320.30.51.180.97, 1.430.0930.2
N-Acetylserotonin1.050.96, 1.140.30.50.960.88, 1.060.40.5
5-Methoxytryptophol1.090.87, 1.350.50.61.291.04, 1.590.020.1

  1. Baseline cerebrospinal fluid (CSF) metabolites were measured in 1069 TBM patients. Cox regression models were stratified by sites and adjusted by age, sex, and GCS. Bold: False Discovery Rate (FDR) < 0.05.

  2. *

    HR = hazard ratio, CI = confidence interval.

  3. †

    Benjamini and Hochberg correction for multiple testing.

Figure 3 with 1 supplement see all
Download asset Open asset
Cerebrospinal fluid and plasma metabolites concentrations in tuberculous meningitis (TBM) and all controls for the tryptophan metabolites associated with outcome: tryptophan and kynurenine.

(A) Relative concentrations based on peak ion intensity and (B) absolute concentrations in μM. Boxplots are shown with Holm-adjusted Wilcoxon rank-sum test p-values are presented: (*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). No measurements were excluded for these graphs. Note: plasma measurements were available for a subset of 300 TBM patients and all controls.

CSF tryptophan levels do not reflect mycobacterial burden

We next examined if CSF tryptophan was associated with CSF mycobacterial load. We hypothesized that a low baseline tryptophan might either reflect a lower bacterial load, as M. tuberculosis can produce tryptophan, or might cause a lower bacterial load as tryptophan depletion impairs mycobacterial growth (Zhang et al., 2013). Instead, we found a reverse, albeit weak relationship: tryptophan was higher in CSF culture-negative (median = 0.31 µM) than culture-positive (median = 0.14 µM, p < 0.001) TBM patients. Similarly, among patients with CSF GeneXpert-confirmed TBM patients, we did not find a correlation between CSF tryptophan and quantitative PCR results (Spearman’s rho = 0.084, p = 0.105, Figure 2—figure supplement 5). Interestingly, within patients with microbiologically confirmed TBM the effect of tryptophan was stronger (HR = 1.28, 95% CI = 1.17–1.40) than in patients with probable TBM (HR = 1.07, 95% CI = 0.98–1.18).

Relationship between cerebral and systemic metabolism and its impact on survival

Ninety-five percent of tryptophan is converted to kynurenine (Lovelace et al., 2017) and we therefore hypothesized that lower CSF tryptophan levels in TBM are caused by higher conversion to kynurenine, and that the higher CSF tryptophan associated with death could reflect reduced activity of IDO1 and other downstream enzymes. CSF kynurenine (Figure 3) and its downstream metabolite kynurenic acid (Figure 3—figure supplement 1) were higher in TBM patients, bacterial meningitis and cryptococcal meningitis patients compared to non-infectious controls, but not significantly different between surviving and non-surviving TBM patients (Table 2).

Then, to examine the relation between cerebral and systemic tryptophan metabolism, we compared concentrations of CSF metabolites with those in plasma, measured in a subset of 300 TBM patients. In contrast to our findings in CSF, plasma tryptophan levels were higher and kynurenine levels were lower in TBM patients compared to controls. As the CSF kynurenine metabolites positively correlated with CSF protein (Figure 4), a proxy for barrier leakage (Svensson et al., 2020), we hypothesized that systemic leakage might be an additional source of kynurenine. For a subset of metabolites, absolute quantification of metabolite levels was achieved. This showed that the increase in CSF kynurenine in TBM patients (Δ = 3.52 µM) was much more marked than the decrease in CSF tryptophan (Δ = 0.39 µM, Figure 3B). Corroborating our leakage hypothesis, the CSF–plasma gradient of the kynurenine metabolites correlated positively with total CSF protein (Figure 4—figure supplement 1). Plasma tryptophan did not predict mortality, but plasma levels of its downstream metabolites kynurenine strongly predicted mortality (Table 4).

Figure 4 with 1 supplement see all
Download asset Open asset
Correlation between tryptophan metabolites and with clinical and cerebrospinal fluid (CSF) parameters.

Significant Spearman’s correlation coefficients are presented in the correlation matrix, while the ones with not significant correlations were blank. Red indicates positive correlations, and blue indicates negative ones. The color gradient shows the strength of the associations.

Table 4
Univariate Cox regression for influence of baseline plasma metabolites on 60-day mortality.
MetaboliteHR*95% CI*p-valueFDR†
Tryptophan0.80.56, 1.160.20.4
Kynurenine1.541.22, 1.93<0.0010.002
Kynurenic acid1.21.01, 1.430.0360.2
3-Hydroxyanthranilic acid1.131.00, 1.280.0450.2
Xanthurenic acid1.110.98, 1.250.10.3
Quinolinic acid0.990.88, 1.120.90.9
Niacinamide0.920.79, 1.070.30.4
3-Indoleacetic acid1.040.97, 1.120.30.4
N-Acetylserotonin1.050.91, 1.220.50.6
5-Methoxytryptophol1.040.85, 1.280.70.8

  1. Baseline plasma tryptophan metabolites were measured in a subset 300 patients. Cox regression models were stratified by sites and adjusted by age, sex, and GCS. Bold: False Discovery Rate (FDR) < 0.05.

  2. *

    HR = hazard ratio, CI = confidence interval.

  3. †

    Benjamini and Hochberg correction for multiple testing.

CSF tryptophan is inversely correlated with interferon gamma

We next looked at correlations of tryptophan metabolites and inflammation, as inflammation is a determinant of outcome from TBM (Wilkinson et al., 2017). Tryptophan is transported into the brain by the large neutral amino acid transporters (Boado et al., 1999). Out of 92 inflammatory proteins measured in CSF from 176 TBM patients from Indonesia, 80 proteins were detectable in >75% of patients. Tryptophan correlated inversely to a small cluster of 13 cytokines, including interferon gamma (IFN-γ, r = −0.48, Figure 5—figure supplement 1A). In line with this finding, the 10 measured CSF cytokines in the Vietnamese patients showed a very similar pattern (Figure 5—figure supplement 1B) and a higher CSF IFN-γ has previously been shown to predict survival of Vietnamese TBM patients (Thuong et al., 2017). IFN-γ is known to induce IDO1 (Zhang et al., 2013), which converts tryptophan to kynurenine. We indeed confirmed the inverse correlation between CSF tryptophan and IFN-γ in our Vietnamese patients (Spearman’s rho = −0.45, p < 0.0001, Figure 5A), irrespective of HIV status. Different from tryptophan, the kynurenine metabolites (kynurenine, kynurenic acid, 3-hydroxyanthranilic acid, and quinolinic acid) correlated positively with a large cluster of inflammatory proteins including the hallmark inflammatory protein tumor necrosis factor alpha (TNF-α; r = 0.30 for kynurenine), which we could again confirm in the Vietnamese patients (Spearman’s rho = 0.30, p < 0.0001, Figure 5B).

Figure 5 with 1 supplement see all
Download asset Open asset
Associations of cerebrospinal fluid (CSF) tryptophan with IFN-γ (A) and with TNF-α (B) in 176 Indonesian (left) and 304 (Vietnamese) tuberculous meningitis (TBM) patients.

The boxplots on the left of each plot show the association of metabolites with cytokines below the detection limit. Abbreviations: IFN-γ: interferon gamma, TNF-α: tumor necrosis factor alpha, LOD: lower limit of detection.

Discussion

We previously found that CSF concentrations of tryptophan were lower in HIV-negative Indonesian adults with TBM compared to non-infectious controls, and that TBM patients with lower tryptophan levels had lower mortality (van Laarhoven et al., 2018). In the current study, we confirm these observations in a much larger cohort of HIV-negative and -positive patients from both Vietnam and Indonesia. Aiming to understand how tryptophan metabolism is altered in TBM and how it might exert its effect on patient outcome, we correlated its concentrations with bacterial load and CSF inflammatory markers and measured downstream metabolites in both CSF and plasma. Our findings show that CSF concentrations of downstream kynurenine metabolites did not predict mortality, and that higher tryptophan levels were not associated with a higher bacterial load. Also, while kynurenine metabolites strongly correlated with CSF inflammatory markers and CSF protein, a marker of blood–CSF leakage, there was no association with CSF tryptophan. Tryptophan did however show a negative correlation with IFN-γ, important for immunity against mycobacteria. Collectively, these findings suggest that a process involving tryptophan metabolism affects outcome from TBM. Given the lack of plasma tryptophan with outcome, this process may take place in the brain rather than systemically. It is potentially driven by IFN-γ but not associated with nonspecific inflammation, and independent from downstream tryptophan metabolism or bacterial replication. In contrast, kynurenine may affect outcome systemically by leakage across the blood–brain barrier.

CSF tryptophan increases with age in individuals without central nervous system infections (Hestad et al., 2017). Age is known to negatively impact outcome of TBM and in this study, higher age was associated with higher CSF tryptophan concentrations. All mortality analyses were therefore corrected for age, as well as sex and HIV status, and analysis was stratified for country because of the overall higher mortality in Indonesian compared to Vietnamese TBM patients (Thuong et al., 2017; van Laarhoven et al., 2017). We further tested whether higher CSF tryptophan reflected a higher mycobacterial burden and refuted this hypothesis. For cryptococcal meningitis, no previous data on cerebral tryptophan metabolism were known. These patients follow a pattern similar to TBM, with low tryptophan and high kynurenine, and in a small number of cryptococcal meningitis patients, a high baseline CSF tryptophan predicted mortality, similar as for TBM.

Systemic tryptophan and kynurenine are transported into the brain over the large amino acid transporter LAT1 (Boado et al., 1999). In a healthy brain, systemic and CSF kynurenine positively correlate, as do CSF concentrations of tryptophan and kynurenine (Hestad et al., 2017). In patients with cerebral inflammation, the correlation between CSF kynurenine and tryptophan can be lost, probably through increased catabolism through IDO upregulation, which also has been demonstrated in the brain parenchyma of deceased TBM patients (Kumar et al., 2012). Although we found low CSF tryptophan and high CSF kynurenine in TBM compared to healthy controls, the two did not intercorrelate and moreover, the increase in CSF kynurenine was much larger than the decrease in CSF tryptophan and it is therefore unlikely that upregulation of IDO1 solely explains this which precludes catabolism as the sole explanation. This suggests that increased blood to central nervous system kynurenine transport as an additional mechanism to IDO1 upregulation. Endothelial cells and pericytes of the blood–brain barrier can upregulate tryptophan catabolism into kynurenine metabolites upon IFN-γ stimulation (Owe-Young et al., 2008). Our findings corroborate this hypothesis because we find a negative correlation between CSF IFN-γ and CSF tryptophan in our patients.

We examined whether higher CSF tryptophan concentrations reflected higher concentrations of downstream kynurenine metabolites that may have neurotoxic (quinolinic acid) or lower levels of the metabolites that may have neuroprotective (kynurenic acid) properties (Lovelace et al., 2017) and refuted these hypotheses. Interestingly however, CSF kynurenine metabolites correlated with CSF cell counts and pro-inflammatory proteins, including TNF-α. Kynurenine is sensed by the aryl hydrocarbon receptor (AhR), which is important for the upregulation of TNF among other pro-inflammatory cytokines in a mouse model (Moura-Alves et al., 2014), in line with our CSF findings. The increased CSF kynurenine levels we found in TBM have been reported before in bacterial meningitis (Coutinho et al., 2014; Sühs et al., 2019) and in cerebral malaria (Medana et al., 2003) and in plasma from pulmonary TB patients (Weiner et al., 2018). Of interest, nicotinamide can inhibit M. tuberculosis growth, and can compete with isoniazid for antimycobacterial effects (Murray, 2003). We did however not find a detrimental effect of a higher nicotinamide, possibly because of its complex biology, that is it can also be produced by M. tuberculosis when human dietary niacin intake is limited (Adu-Gyamfi et al., 2019).

Strengths of our study include the large numbers of clinically well-phenotyped patients from multiple independent study sites in Indonesia and Vietnam, including a significant proportion of HIV-positive patients. We moreover used a sensitive triple quadrupole (QQQ) mass spectrometry method specifically designed to accurately target the tryptophan metabolites. Absolute quantification of a subset of metabolites further facilitated interpretation. Due to differences in polarity of the downstream tryptophan metabolites, we could not measure the complete tryptophan pathway. The availability of CSF at baseline only, limits our ability to understand how changes in tryptophan metabolism influence mortality. And we infer our observations from lumbar CSF, which reflects biological processes from both the blood and the brain. The use of ventricular CSF could help establishing what processes in the brain parenchyma take place. Moreover, although the lower CSF tryptophan values in TBM patients compared to non-infectious controls, implies active tryptophan metabolism in TBM, definite claims can only be made in an interventional study. Therefore animal studies, preferably combined with live imaging (Mota et al., 2022), are warranted to see whether pharmacological induction of IDO1 (for instance with recombinant IFN-γ), or inhibitory tryptophan analogues (Wang et al., 2021) should be priorities as adjuvant therapeutic candidates for future personalized trials.

In summary, we confirm the importance of CSF tryptophan to outcome from HIV-negative and -positive adults with TBM, independent from downstream kynurenine metabolism, bacterial load, and inflammation. We additionally show the potential importance of systemic kynurenine as a predictor of mortality. Better understanding of the metabolic pathways associated with TBM may lead to more targeted therapies, as adjuvant immunotherapy may modulate the aberrant metabolic pathways and thus improve outcome.

Materials availability

The clinical metadata and LC–MS data before pre-processing are available in Source data 1.

Data availability

The data generated or analyzed during this study are included in the supporting file.

References

  15. Software
    1. R Development Core Team
    (2022) R: a language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.
    https://www.R-project.org

Decision letter

  1. Bavesh D Kana
    Senior and Reviewing Editor; University of the Witwatersrand, South Africa
  2. Diederik van de Beek
    Reviewer; Amsterdam UMC, Netherlands

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Bavesh Kana as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Diederik van de Beek (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Modelling. The impact of baseline CSF and plasma metabolite levels on 60-day survival was tested in a Cox-regression model, adjusted for sex, age, and HIV. One of the research questions could be whether tryptophan concentrations and outcome in TBM is a defining factor for outcome or just simply a bystander effect of damage. To study this in a cohort with >1000 TBM patient and 231 deaths, the authors might want to perform multiple regression analyses for 30-day survival, including baseline and clinical features presented in the baseline table. They could include tryptophan concentrations in categories depending on the distribution of values (i.e., quartiles) and perform multiple imputation for missing values. While the association of CSF tryptophan levels with mortality (high tryptophan levels associated with higher 60-day mortality) are an interesting finding, it is confusing to note that uninfected control subjects had much higher CSF tryptophan levels (Figure 3). Similar, trend to TB meningitis (i.e. higher mortality in those with higher tryptophan levels) were noted in patients with cryptococcal meningitis. Finally, patients with bacterial meningitis also had much higher tryptophan levels but information on mortality is lacking. Therefore, it is likely that CSF tryptophan levels may not be the driver of mortality and more likely is an inflammatory biomarker in chronic forms of meningitis. This needs more detailed analysis and discussion.

2. Study power. Based on the previous findings and the number of patients in the cohorts, the authors could say something about the statistical power, i.e., what are changes that they are able to detect.

3. Early vs. late mortality. The association between tryptophan concentrations and outcome in TBM seems to be not only true for the early period (e.g., the first 14 days), but also for the late period. As suggested in the results (HIV+ vs HIV- patients; suppl table 1). Could the authors explain in more detail how these analyses were performed? It is difficult to understand how base-line metabolites have a strong effect on late outcome (i.e., at day 180). This might even point towards the hypothesis that found metabolites are not the causing factor of death but more just a bystander effect.

4. CSF tryptophan is inversely correlated with interferon-γ. 92 inflammatory proteins measured in CSF from 176 TBM patients. 13 were inversely associated with tryptophan concentrations, only interferon-γ was validated in Vietnamese samples. What about the other inflammatory proteins?

5. Defining factor in outcome or bystander. In the first paragraph of the discussion the authors overstate the impact of their results. This is a pity because it is not needed. They state that "Collectively, these finding suggest that tryptophan affects outcome from TBM within the brain rather than systemically. Results do not support that tryptophan affects outcome; they support the statement that tryptophan is associated with outcome.

6. Negative correlation between tryptophan and IFN. The authors state that they find a strong negative correlation between tryptophan and IFN. The correlation found between tryptophan and IFN (which indeed is stronger than the correlation in Indonesia) has a Spearman's rho of 0.30. Correlation coefficients between 0.3 and 0.5 indicate variables which have a low correlation, not a strong correlation. So, it would be more correct to state that "a higher tryptophan showed a statistically significant but low correlation with IFN-γ". Please correct throughout results and discussion. Better not to overstate your results.

7. There is a large variability in CSF tryptophan levels. If a cutoff were defined to predict the mortality risk, what would be PPV and NPV of this test?

8. There was some discordance between the plasma and CSF levels of tryptophan and its downstream metabolites. What is known about the passage of tryptophan and its metabolites across the blood-brain barrier and inflammation? What would a multi-variate analysis utilizing markers of CSF inflammation (or IFN-g as noted by the authors) show? Would tryptophan CSF level still remain as an independent predictor of mortality?

9. It has been shown previously that ventricular and spinal CSF have different profiles (and acknowledged by the authors in the current manuscript). Since spinal CSF was utilized by the current studies, it would be interesting to know how many of the patients in this cohort also had spinal TB and if this was correlated with tryptophan/metabolite levels.

10. The authors suggest the use of animal models, but do not quote any for TB meningitis, but which could be quoted (PMID: 29777209, 32501258, 30518610, 35085105, 36581633).

Reviewer #1 (Recommendations for the authors):

Previously, lower cerebrospinal fluid tryptophan concentrations were associated with survival of TBM patients in Indonesia. This study confirms this previous finding. In addition, downstream metabolites were measured in cerebrospinal fluid and blood samples of TBM patients and controls included in multiple cohorts. These findings confirm the association between cerebrospinal fluid tryptophan concentrations and outcome in TBM.

The strength of this manuscript is the validation aspect in several cohorts. the analyses have been carefully performed. I do have some suggestions for additional analyses and would like to advise the authors to not overstate their findings.

The weakness is that it remains unclear whether the association between cerebrospinal fluid tryptophan concentrations and outcome in TBM is a defining factor in outcome or just simply a bystander effect of damage.

Suggestions for additional analyses:

One of the research questions could be whether tryptophan concentrations and outcome in TBM is a defining factor in outcome or just simply a bystander effect of damage. To study this in a cohort with >1000 TBM patient and 231 deaths, you might want to perform multiple regression analyses for 30-day survival, including baseline and clinical features presented in the baseline table.

Presentation

I am not so convinced about the "strong correlation" between tryptophan and IFN. The correlation found between tryptophan and IFN (which indeed is stronger than the correlation in Indonesia) has a Spearman's rho of 0.30 (with I consider as a weak correlation).

This is a good and solid confirmatory study. No need to overstate your findings. I have some suggestions and recommendations for improvement:

Modelling. The impact of baseline CSF and plasma metabolite levels on 60-day survival was tested in a Cox-regression model, adjusted for sex, age, and HIV. One of the research questions could be whether tryptophan concentrations and outcome in TBM is a defining factor in outcome or just simply a bystander effect of damage. To study this in a cohort with >1000 TBM patient and 231 deaths, you might want to perform multiple regression analyses for 30-day survival, including baseline and clinical features presented in the baseline table. You could include tryptophan concentrations in categories depending on the distribution of values (i.e., quartiles) and perform multiple imputation for missing values.

Study power. Based on your previous findings and the number of patients in the cohorts you could say something about the statistical power, i.e., what are changes that you are able to detect.

Early vs. late mortality. The association between tryptophan concentrations and outcome in TBM seems to be not only true for the early period (e.g., the first 14 days), but also for the late period. As suggested in the results (HIV+ vs HIV- patients; suppl table 1). Could you explain in more detail how these analyses were performed? It is difficult to understand how base-line metabolites have a strong effect on late outcome (i.e., at day 180). This might even point towards the hypothesis that found metabolites are not the causing factor of death but more just a bystander effect.

CSF tryptophan is inversely correlated with interferon-γ. 92 inflammatory proteins measured in CSF from 176 TBM patients. 13 were inversely associated with tryptophan concentrations, only interferon-γ was validated in Vietnamese samples. What about the other inflammatory proteins?

Defining factor in outcome or bystander. In the first paragraph of the discussion the authors overstate the impact of their results. This is a pity because it is not needed. They state that "Collectively, these finding suggest that tryptophan affects outcome from TBM within the brain rather than systemically. Results do not support that tryptophan affects outcome; they support the statement that tryptophan is associated with outcome. I suggest not to overstate findings.

Negative correlation between tryptophan and IFN. The authors state that they find a strong negative correlation between tryptophan and IFN. The correlation found between tryptophan and IFN (which indeed is stronger than the correlation in Indonesia) has a Spearman's rho of 0.30. Correlation coefficients between 0.3 and 0.5 indicate variables which have a low correlation, not a strong correlation. So, it would be more correct to state that "a higher tryptophan showed a statistically significant but low correlation with IFN-γ". Please correct throughout results and discussion. Better not to overstate your results.

Reviewer #2 (Recommendations for the authors):

This manuscript by Ardiansyah et al. reports the association of CSF tryptophan levels with 60-day mortality in patients with TB meningitis. This work is a follow up of a much smaller study in patients with TB meningitis in Indonesia, reporting the same findings and performed by some authors from this study. The current study is larger, includes patients with HIV co-infection as well as has control groups with individuals with other etiologies of meningitis. Suggestions for the manuscript are below.

1. While the association of CSF tryptophan levels with mortality (high tryptophan levels associated with higher 60-day mortality) are an interesting finding, it is confusing to note that uninfected control subjects had much higher CSF tryptophan levels (Figure 3). Similar, trend to TB meningitis (i.e. higher mortality in those with higher tryptophan levels) were noted in patients with cryptococcal meningitis. Finally, patients with bacterial meningitis also had much higher tryptophan levels but information on mortality is lacking. Therefore, it is likely that CSF tryptophan levels may not be the driver of mortality and more likely is an inflammatory biomarker in chronic forms of meningitis. This needs more detail in the discussion.

2. There is a large variability in CSF tryptophan levels. If a cutoff were defined to predict the mortality risk, what would be PPV and NPV of this test?

3. There was some discordance between the plasma and CSF levels of tryptophan and its downstream metabolites. What is known about the passage of tryptophan and its metabolites across the blood-brain barrier and inflammation? What would a multi-variate analysis utilizing markers of CSF inflammation (or IFN-g as noted by the authors) show? Would tryptophan CSF level still remain as an independent predictor of mortality?

4. It has been shown previously that ventricular and spinal CSF have different profiles (and acknowledged by the authors in the current manuscript). Since spinal CSF was utilized by the current studies, it would be interesting to know how many of the patients in this cohort also had spinal TB and if this was correlated with tryptophan/metabolite levels.

5. The authors suggest the use of animal models, but do not quote any for TB meningitis, but which could be quoted (PMID: 29777209, 32501258, 30518610, 35085105, 36581633).

https://doi.org/10.7554/eLife.85307.sa1

Author response

Essential revisions:

1. Modelling. The impact of baseline CSF and plasma metabolite levels on 60-day survival was tested in a Cox-regression model, adjusted for sex, age, and HIV. One of the research questions could be whether tryptophan concentrations and outcome in TBM is a defining factor for outcome or just simply a bystander effect of damage. To study this in a cohort with >1000 TBM patient and 231 deaths, the authors might want to perform multiple regression analyses for 30-day survival, including baseline and clinical features presented in the baseline table. They could include tryptophan concentrations in categories depending on the distribution of values (i.e., quartiles) and perform multiple imputation for missing values. While the association of CSF tryptophan levels with mortality (high tryptophan levels associated with higher 60-day mortality) are an interesting finding, it is confusing to note that uninfected control subjects had much higher CSF tryptophan levels (Figure 3). Similar, trend to TB meningitis (i.e. higher mortality in those with higher tryptophan levels) were noted in patients with cryptococcal meningitis. Finally, patients with bacterial meningitis also had much higher tryptophan levels but information on mortality is lacking. Therefore, it is likely that CSF tryptophan levels may not be the driver of mortality and more likely is an inflammatory biomarker in chronic forms of meningitis. This needs more detailed analysis and discussion.

We thank the reviewer for commenting on the comparison with control patients, and on causality with mortality within the group of TBM patients.

First, on the comparison with controls patients. We think that the fact that TBM and cryptococcal patients had lower CSF tryptophan than non-infectious controls, points to active tryptophan metabolism (i.e. catabolism) in TBM cryptococcal meningitis, which apparently does not occur in bacterial meningitis patients, who had similar CSF tryptophan levels compared to non-infectious controls.

Then, regarding the multivariable analysis. All the current analyses are multivariable, including age, sex and HIV as covariates, and stratified for study site as the overall mortality is higher in Indonesia than Vietnam. We deliberated the inclusion of disease severity as another covariate in our analysis plan. We think that Glasgow Coma Scale is the most appropriate proxy for disease severity because it predicts outcome in both our cohorts and because of its more granular ordinal scale (3-15) compared to TBM grade (1-3). Tryptophan did not correlate to GCS in the previous paper (van Laarhoven et al., Lancet Infectious Diseases 2018). For the analysis plan for the current study, we considered the following options for disease severity (i.e. GCS):

GCS as an intermediate between tryptophan and outcome.

GCS being an independent predictor for outcome.

GCS driving mortality, with tryptophan as an intermediate.

We argued that:

In case 1, adding it to a multivariable model would be problematic because of multicollinearity.

In case 2. adding GCS would not change the result.

Case 3 we thought unlikely, because of the lack of correlation in our previous analysis.

That said, in the current study, GCS had a very week negative correlation with tryptophan (r = -.08), i.e. patients with severe disease (lower GCS, worse outcome) had a slightly higher tryptophan (associated to worse outcome). We can therefore understand the reviewers concern, although the correlation is only weak. And because the correlation is only weak, multicollinearity is not much of a problem. We performed two additional multivariable models:

  1. including the pre-defined variables, and adding GCS.

  2. including the pre-defined variables, and adding GCS and the other baseline variables.

We have added to the Results section (lines 211-217) “Because of weak negative correlation between tryptophan and GCS (r = -0.08) and to exclude the possibility that tryptophan is a marker of patients with more severe disease, we performed a post-hoc analysis with two additional models. These models included the basic pre-defined variables (age, sex, HIV as covariates, study site as stratum). Adding GCS did not substantially change the effect size (HR 1.15, 95% CI 1.08-1.23), and adding GCS and CSF cell counts, CSF protein and glucose ratio did neither (1.14, 95% CI 1.07-1.22) confirming that tryptophan was associated to mortality independent of the aforementioned parameters.”

We have also added a sentence at the end of the discussion (lines 355-357) referring to the above and acknowledging that with the current data we cannot infer a definite causation.

“The lower CSF tryptophan values in TBM patients compared to non-infectious controls, implies active tryptophan metabolism in TBM. Combined with an improved survival in TBM patients with the lowest CSF tryptophan, this could imply that in TBM an active tryptophan metabolism is beneficial. Interventional studies will be needed to confirm this hypothesis.”

2. Study power. Based on the previous findings and the number of patients in the cohorts, the authors could say something about the statistical power, i.e., what are changes that they are able to detect.

We performed a power calculation before the study (as part of the grant application): “We based the sample size calculation on the variance for tryptophan metabolites with neuromodulating effect (kynurenic acid, 3-hydroxyanthranilic acid and quinolinic acid) in the original discovery cohort (n = 32). Applying strict Bonferroni-correction for the total number of tryptophan metabolites measured, 96-213 events (non-survivors) are needed in each group for adequate power. For HIV-uninfected patients (estimated 33% mortality), this equals to maximum 800 patients and for HIV-infected patients (estimated 50% mortality) to 600 patients for the least discriminating metabolite.“ (ULTIMATE grant proposal).”

In the final analysis plan, because of the similar metabolite levels between HIV-infected and HIV-uninfected metabolites, we decided to incorporate HIV-status as covariate, rather than a stratum, to ensure enough power for this confirmatory study.

We have added the change in data analysis (lines 173-175) to improve power by incorporating HIV-status in our analyses.

3. Early vs. late mortality. The association between tryptophan concentrations and outcome in TBM seems to be not only true for the early period (e.g., the first 14 days), but also for the late period. As suggested in the results (HIV = vs HIV- patients; suppl table 1). Could the authors explain in more detail how these analyses were performed? It is difficult to understand how base-line metabolites have a strong effect on late outcome (i.e., at day 180). This might even point towards the hypothesis that found metabolites are not the causing factor of death but more just a bystander effect.

We agree with the reviewers that the finding that baseline CSF tryptophan levels predict later mortality is intriguing. However, we know that events occurring early in treatment (e.g. new infarcts or tuberculomas) have a significant effect on long term outcomes, and that intra-cerebral inflammation lasts for many months (as detected by persistent CSF abnormalities). TBM is slow to resolve, despite optimal therapy, which makes these observations less surprising.

First, with regard to the methods. For patients who died during the specified follow-up period (180 days), the median time to death was 14 days. This was used as the cut-off to differentiate early (day 0 – day 14) from late (day 14 – day 180) mortality. In the analysis for early mortality, 1069 patients entered the analysis of which 155 died. In the analysis for late mortality, the remaining patients entered the analysis, of whom 149 died. As the power of Cox regression is dependent on the number of events (deaths), this is equal for both time periods. The results in this analysis, with similar HRs of 1.14 and 1.17 for both time periods, indicate an ongoing increased hazard for mortality for patients with a high baseline CSF tryptophan. This is illustrated by the Kaplan Meier plots (best seen in Supplementary Figure 3) showing that the lines keep separating beyond the first 14 days. We can hypothesize that an intrinsic difference in tryptophan metabolism, that is shown at baseline, is important also for later mortality, but cannot point to the exact mechanism.

We have added more detail in the methods, lines 112-113.

4. CSF tryptophan is inversely correlated with interferon-γ. 92 inflammatory proteins measured in CSF from 176 TBM patients. 13 were inversely associated with tryptophan concentrations, only interferon-γ was validated in Vietnamese samples. What about the other inflammatory proteins?

We choose to attempt to validate the inverse correlation of CSF interferon-γ with CSF tryptophan in the Vietnamese samples because (1) this correlation was the strongest and (2) biologically the most interesting.

We have added the correlation matrix for the 10 measured cytokines in the Vietnamese patients as Supplementary figure 8B, and described the results in lines 279-280.

5. Defining factor in outcome or bystander. In the first paragraph of the discussion the authors overstate the impact of their results. This is a pity because it is not needed. They state that "Collectively, these finding suggest that tryptophan affects outcome from TBM within the brain rather than systemically. Results do not support that tryptophan affects outcome; they support the statement that tryptophan is associated with outcome.

Thank you for pointing this out. We did not want to claim that tryptophan (like a toxin) increased mortality, and modified our wording accordingly based on your suggestion. We think that a mechanism affecting tryptophan metabolism (this is now added in lines 307-308), which takes place localised in the central nervous system (rather than systemically), and potentially driven by interferon-γ influences outcome.

Our previous study in Indonesia provides an extra argument for causality showing that patient genetic correlates of CSF tryptophan concentrations in tuberculous meningitis strongly predicted mortality in a separate group of patients (in which no tryptophan was measured).

6. Negative correlation between tryptophan and IFN. The authors state that they find a strong negative correlation between tryptophan and IFN. The correlation found between tryptophan and IFN (which indeed is stronger than the correlation in Indonesia) has a Spearman's rho of 0.30. Correlation coefficients between 0.3 and 0.5 indicate variables which have a low correlation, not a strong correlation. So, it would be more correct to state that "a higher tryptophan showed a statistically significant but low correlation with IFN-γ". Please correct throughout results and discussion. Better not to overstate your results.

We changed this accordingly (line 265, 285, 307). Of note, the Spearman correlation between CSF tryptophan and IFN-γ was moderate, -0.48 in the Indonesian population and -0.45 in the Vietnamese population (line 283).

The correlation between CSF kynurenine and TNF was 0.58 in the Indonesian population and 0.30 in the Vietnamese population. Of note, as shown in Supplementary Figure 8 and as expected, TNF was part of a larger cluster of pro-inflammatory cytokines. These all correlating positively with CSF kynurenine.

7. There is a large variability in CSF tryptophan levels. If a cutoff were defined to predict the mortality risk, what would be PPV and NPV of this test?

We performed this study to improve our understanding of pathophysiology. Like the clinical variables, CSF tryptophan indeed shows large variation in this cohort.

The study aim was not to identify a prognostic marker and we think our results best support a gradual additive negative effect for higher CSF tryptophan, we therefore did not calculate specific cut-offs.

8. There was some discordance between the plasma and CSF levels of tryptophan and its downstream metabolites. What is known about the passage of tryptophan and its metabolites across the blood-brain barrier and inflammation? What would a multi-variate analysis utilizing markers of CSF inflammation (or IFN-g as noted by the authors) show? Would tryptophan CSF level still remain as an independent predictor of mortality?

We thank the reviewer for these comments. For the first question, to make it clearer, we have added in line 276 “Tryptophan is transported into the brain by the large neutral amino acid transporters” (Boado RJ, Li JY, Nagaya M, Zhang C, Pardridge WM. Selective expression of the large neutral amino acid transporter at the blood–brain barrier. Proc National Acad Sci 1999; 96: 12079–84).

As suggested by reviewers, we performed some analyses as below:

1. Including CSF leukocytes, as a proxy for cerebral inflammation, and CSF protein, as a proxy for CSF barrier disfunction, did not change the results (shown under 1.)

2. The cytokine data was incomplete and determined using different methods as indicated. Cytokine data was available for a subset of 178 Indonesian patients. In the multivariable model (including sex, age and HIV status, the HR for tryptophan was 1.13 but with a confidence touching 1 (1.00-1.27) because of a lower number in individuals. Adding IFN-γ to the model did not substantially change the effect of tryptophan, with a HR of 1.13 (0.98-1.30) (Model C). Cytokine data were available for 281 Vietnamese patients. In the multivariable model (including sex, age and HIV status) in this patient subset, the HR for tryptophan was 1.13 (0.99-1.29). Adding IFN-γ to the model slightly decreased the effect of tryptophan to 1.05 (HR 0.91-1.22), Model D).

9. It has been shown previously that ventricular and spinal CSF have different profiles (and acknowledged by the authors in the current manuscript). Since spinal CSF was utilized by the current studies, it would be interesting to know how many of the patients in this cohort also had spinal TB and if this was correlated with tryptophan/metabolite levels.

Spinal MRIs were not usually performed in both cohorts, therefore we cannot identify the proportion with radiologically proven spinal disease. We agree with the reviewer that this would have been of additional value.

10. The authors suggest the use of animal models, but do not quote any for TB meningitis, but which could be quoted (PMID: 29777209, 32501258, 30518610, 35085105, 36581633).

We now completed this statement and included a reference to the animal model for TB meningitis which includes life imaging and could be valuable to study pharmacological interventions aimed at tryptophan metabolism.

https://doi.org/10.7554/eLife.85307.sa2

Article and author information

Author details

  1. Edwin Ardiansyah

    1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
    2. Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Data curation, Formal analysis, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2702-5497

  2. Julian Avila-Pacheco

    The Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Data curation, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Le Thanh Hoang Nhat

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Formal analysis, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3756-7217

  4. Sofiati Dian

    1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
    2. Department of Neurology, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9909-4171

  5. Dao Nguyen Vinh

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared

  6. Hoang Thanh Hai

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1809-5541

  7. Kevin Bullock

    The Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  8. Bachti Alisjahbana

    1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
    2. Department of Internal Medicine, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
    Contribution
    Conceptualization, Supervision
    Competing interests
    No competing interests declared

  9. Mihai G Netea

    Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Supervision, Funding acquisition
    Competing interests
    has received consulting fees from Scientific Board TTxD and is a scientific founder of TTxD, Lemba and BioTRIP. The author has no other competing interests to declare
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2421-6052

  10. Riwanti Estiasari

    Department of Neurology, Cipto Mangunkusumo Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  11. Trinh Thi Bich Tram

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  12. Joseph Donovan

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    3. London School of Hygiene and Tropical Medicine, London, United Kingdom
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  13. Dorothee Heemskerk

    Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centre, Amsterdam, Netherlands
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  14. Tran Thi Hong Chau

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  15. Nguyen Duc Bang

    Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Ho Chi Minh City, Viet Nam
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  16. Ahmad Rizal Ganiem

    1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
    2. Department of Neurology, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
    Contribution
    Supervision
    Competing interests
    No competing interests declared

  17. Rovina Ruslami

    1. Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
    2. Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
    Contribution
    Supervision
    Competing interests
    No competing interests declared

  18. Valerie ACM Koeken

    1. Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hanover, Germany
    Contribution
    Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5783-9013

  19. Raph L Hamers

    Oxford University Clinical Research Unit Indonesia, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
    Contribution
    Supervision
    Competing interests
    No competing interests declared

  20. Darma Imran

    Department of Neurology, Cipto Mangunkusumo Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared

  21. Kartika Maharani

    Department of Neurology, Cipto Mangunkusumo Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared

  22. Vinod Kumar

    Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared

  23. Clary B Clish

    The Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared

  24. Reinout van Crevel

    Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared

  25. Guy Thwaites

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2858-2087

  26. Arjan van Laarhoven

    Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Data curation, Supervision, Visualization, Writing – review and editing
    Contributed equally with
    Nguyen Thuy Thuong Thuong
    For correspondence
    Arjan.vanLaarhoven@radboudumc.nl
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6607-4075

  27. Nguyen Thuy Thuong Thuong

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Contributed equally with
    Arjan van Laarhoven
    For correspondence
    thuongntt@oucru.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8733-692X

Funding

National Institutes of Health (R01AI145781)

  • Edwin Ardiansyah
  • Julian Avila-Pacheco
  • Le Thanh Hoang Nhat
  • Sofiati Dian
  • Dao Nguyen Vinh
  • Hoang Thanh Hai
  • Kevin Bullock
  • Bachti Alisjahbana
  • Mihai G Netea
  • Riwanti Estiasari
  • Trinh Thi Bich Tram
  • Joseph Donovan
  • Dorothee Heemskerk
  • Tran Thi Hong Chau
  • Nguyen Duc Bang
  • Ahmad Rizal Ganiem
  • Valerie ACM Koeken
  • Raph L Hamers
  • Darma Imran
  • Kartika Maharani
  • Vinod Kumar
  • Clary B Clish
  • Reinout van Crevel
  • Guy Thwaites
  • Arjan van Laarhoven
  • Nguyen Thuy Thuong Thuong

Wellcome Trust (Africa and Asia Programme)

  • Le Thanh Hoang Nhat
  • Dao Nguyen Vinh
  • Hoang Thanh Hai
  • Trinh Thi Bich Tram
  • Joseph Donovan
  • Dorothee Heemskerk
  • Tran Thi Hong Chau
  • Nguyen Duc Bang
  • Guy Thwaites
  • Nguyen Thuy Thuong Thuong

Wellcome Trust (Intermediate Fellowship 110179/Z/15/Z)

  • Nguyen Thuy Thuong Thuong

Direktorat Jenderal Pendidikan Tinggi (BPPLN)

  • Sofiati Dian

Ministry of Research, Technology and Higher Education of the Republic of Indonesia (PKSLN)

  • Sofiati Dian
  • Rovina Ruslami

United States Agency for International Development (PEER Health)

  • Rovina Ruslami

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank the neurology residents and Tiara Pramaesya, Sofia Immaculata, Putri Andini, Sri Margi, Rani Trisnawati, and Shehika Shulda of the tuberculous meningitis study team for monitoring patients and data management; Lidya Chaidir and Jessi Annisa for mycobacterial diagnostics; the director of the Hasan Sadikin General Hospital, Bandung, Indonesia, for accommodating the research. We also express our gratitude to our funders: This study was supported by National Institutes of Health (R01AI145781) and the Wellcome Trust (110179/Z/15/Z and 206724/Z/17/Z). Previous establishment of the cohorts in Indonesia was supported by the Direktorat Jendral Pendidikan Tinggi (BPPLN fellowship to SD) and the Ministry of Research, Technology, and Higher Education, Indonesia (PKSLN grant to RR and SD), and United States Agency for International Development (PEER Health grant to RR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethics

Ethical approval was obtained from the Ethical Committee of Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia and from the Oxford Tropical Research Ethics Committee in the United Kingdom, the Institutional Review Boards of the Hospital for Tropical Diseases and Pham Ngoc Thach Hospital in Vietnam. Written (Vietnam) or oral (Indonesia) consent to be included in the study, for storage of surplus sample, and to obtain follow-up data was obtained from patients or close relatives of patients who were unconscious.

Senior and Reviewing Editor

  1. Bavesh D Kana, University of the Witwatersrand, South Africa

Reviewer

  1. Diederik van de Beek, Amsterdam UMC, Netherlands

Version history

  1. Received: December 1, 2022
  2. Preprint posted: January 9, 2023 (view preprint)
  3. Accepted: May 1, 2023
  4. Accepted Manuscript published: May 9, 2023 (version 1)
  5. Version of Record published: May 12, 2023 (version 2)
  6. Version of Record updated: May 16, 2023 (version 3)
  7. Version of Record updated: July 7, 2023 (version 4)

Copyright

© 2023, Ardiansyah et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 562
    Page views
  • 93
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Edwin Ardiansyah
  2. Julian Avila-Pacheco
  3. Le Thanh Hoang Nhat
  4. Sofiati Dian
  5. Dao Nguyen Vinh
  6. Hoang Thanh Hai
  7. Kevin Bullock
  8. Bachti Alisjahbana
  9. Mihai G Netea
  10. Riwanti Estiasari
  11. Trinh Thi Bich Tram
  12. Joseph Donovan
  13. Dorothee Heemskerk
  14. Tran Thi Hong Chau
  15. Nguyen Duc Bang
  16. Ahmad Rizal Ganiem
  17. Rovina Ruslami
  18. Valerie ACM Koeken
  19. Raph L Hamers
  20. Darma Imran
  21. Kartika Maharani
  22. Vinod Kumar
  23. Clary B Clish
  24. Reinout van Crevel
  25. Guy Thwaites
  26. Arjan van Laarhoven
  27. Nguyen Thuy Thuong Thuong
(2023)
Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis
eLife 12:e85307.
https://doi.org/10.7554/eLife.85307

Categories and tags

Research organism

Further reading

    1. Immunology and Inflammation

    Pulsed ultrasound promotes secretion of anti-inflammatory extracellular vesicles from skeletal myotubes via elevation of intracellular calcium level

    Atomu Yamaguchi, Noriaki Maeshige ... Hidemi Fujino
    Research Article

    The regulation of inflammatory responses is an important intervention in biological function and macrophages play an essential role during inflammation. Skeletal muscle is the largest organ in the human body and releases various factors which mediate anti-inflammatory/immune modulatory effects. Recently, the roles of extracellular vesicles (EVs) from a large variety of cells are reported. In particular, EVs released from skeletal muscle are attracting attention due to their therapeutic effects on dysfunctional organs and tissues. Also, ultrasound (US) promotes release of EVs from skeletal muscle. In this study, we investigated the output parameters and mechanisms of US-induced EV release enhancement and the potential of US-treated skeletal muscle-derived EVs in the regulation of inflammatory responses in macrophages. High-intensity US (3.0 W/cm2) irradiation increased EV secretion from C2C12 murine muscle cells via elevating intracellular Ca2+ level without negative effects. Moreover, US-induced EVs suppressed expression levels of pro-inflammatory factors in macrophages. miRNA sequencing analysis revealed that miR-206-3p and miR-378a-3p were especially abundant in skeletal myotube-derived EVs. In this study we demonstrated that high-intensity US promotes the release of anti-inflammatory EVs from skeletal myotubes and exert anti-inflammatory effects on macrophages.

    1. Genetics and Genomics
    2. Immunology and Inflammation

    Sustained store-operated calcium entry utilizing activated chromatin state leads to instability in iTregs

    Huiyun Lyu, Guohua Yuan ... Yan Shi
    Research Article

    Thymus-originated tTregs and in vitro induced iTregs are subsets of regulatory T cells. While they share the capacity of immune suppression, their stabilities are different, with iTregs losing their phenotype upon stimulation or under inflammatory milieu. Epigenetic differences, particularly methylation state of Foxp3 CNS2 region, provide an explanation for this shift. Whether additional regulations, including cellular signaling, could directly lead phenotypical instability requires further analysis. Here, we show that upon TCR (T cell receptor) triggering, SOCE (store-operated calcium entry) and NFAT (nuclear factor of activated T cells) nuclear translocation are blunted in tTregs, yet fully operational in iTregs, similar to Tconvs. On the other hand, tTregs show minimal changes in their chromatin accessibility upon activation, in contrast to iTregs that demonstrate an activated chromatin state with highly accessible T cell activation and inflammation related genes. Assisted by several cofactors, NFAT driven by strong SOCE signaling in iTregs preferentially binds to primed-opened T helper (TH) genes, resulting in their activation normally observed only in Tconv activation, ultimately leads to instability. Conversely, suppression of SOCE in iTregs can partially rescue their phenotype. Thus, our study adds two new layers, cellular signaling and chromatin accessibility, of understanding in Treg stability, and may provide a path for better clinical applications of Treg cell therapy.

    1. Immunology and Inflammation
    2. Microbiology and Infectious Disease

    Apoptotic signaling clears engineered Salmonella in an organ-specific manner

    Taylor J Abele, Zachary P Billman ... Edward A Miao
    Research Article

    Pyroptosis and apoptosis are two forms of regulated cell death that can defend against intracellular infection. When a cell fails to complete pyroptosis, backup pathways will initiate apoptosis. Here, we investigated the utility of apoptosis compared to pyroptosis in defense against an intracellular bacterial infection. We previously engineered Salmonella enterica serovar Typhimurium to persistently express flagellin, and thereby activate NLRC4 during systemic infection in mice. The resulting pyroptosis clears this flagellin-engineered strain. We now show that infection of caspase-1 or gasdermin D deficient macrophages by this flagellin-engineered S. Typhimurium induces apoptosis in vitro. Additionally, we engineered S. Typhimurium to translocate the pro-apoptotic BH3 domain of BID, which also triggers apoptosis in macrophages in vitro. During mouse infection, the apoptotic pathway successfully cleared these engineered S. Typhimurium from the intestinal niche but failed to clear the bacteria from the myeloid niche in the spleen or lymph nodes. In contrast, the pyroptotic pathway was beneficial in defense of both niches. To clear an infection, cells may have specific tasks that they must complete before they die; different modes of cell death could initiate these ‘bucket lists’ in either convergent or divergent ways.