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. 2021 Sep 24;17(9):e1009941. doi: 10.1371/journal.ppat.1009941

TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis

Jeffrey M Collins 1,*, Dean P Jones 1, Ashish Sharma 2, Manoj Khadka 3, Ken H Liu 1, Russell R Kempker 1, Brendan Prideaux 4, Kristal Maner-Smith 3, Nestani Tukvadze 5, N Sarita Shah 1,6,7, James C M Brust 8, Rafick-Pierre Sékaly 2, Neel R Gandhi 1,6,7, Henry M Blumberg 1,6,7, Eric A Ortlund 3,‡, Thomas R Ziegler 1,9,‡
Editor: David M Lewinsohn10
PMCID: PMC8494353  PMID: 34559866

Abstract

The metabolic signaling pathways that drive pathologic tissue inflammation and damage in humans with pulmonary tuberculosis (TB) are not well understood. Using combined methods in plasma high-resolution metabolomics, lipidomics and cytokine profiling from a multicohort study of humans with pulmonary TB disease, we discovered that IL-1β-mediated inflammatory signaling was closely associated with TCA cycle remodeling, characterized by accumulation of the proinflammatory metabolite succinate and decreased concentrations of the anti-inflammatory metabolite itaconate. This inflammatory metabolic response was particularly active in persons with multidrug-resistant (MDR)-TB that received at least 2 months of ineffective treatment and was only reversed after 1 year of appropriate anti-TB chemotherapy. Both succinate and IL-1β were significantly associated with proinflammatory lipid signaling, including increases in the products of phospholipase A2, increased arachidonic acid formation, and metabolism of arachidonic acid to proinflammatory eicosanoids. Together, these results indicate that decreased itaconate and accumulation of succinate and other TCA cycle intermediates is associated with IL-1β-mediated proinflammatory eicosanoid signaling in pulmonary TB disease. These findings support host metabolic remodeling as a key driver of pathologic inflammation in human TB disease.

Author summary

Pulmonary tuberculosis (TB) often results in pathologic lung inflammation that causes tissue damage and does not control bacterial replication. This impairs the host response to antibiotic treatment and can result in long term deficits in lung function. Currently, the role of host metabolism in regulating the inflammatory response in TB disease is not well understood. Here, we use detailed immunometabolic phenotyping to show that metabolic remodeling of the tricarboxylic acid (TCA) cycle is closely associated with pathologic inflammatory signaling in humans with TB disease. Accumulation of TCA cycle intermediates in plasma, including the proinflammatory metabolite succinate, as well as decreased concentrations of the anti-inflammatory metabolite itaconate, were associated with increases in IL-1β and upregulation of proinflammatory lipid signaling cascades. This inflammatory network was upregulated following delays in appropriate anti-TB treatment and was associated with prolonged time to sputum culture clearance of Mycobacterium tuberculosis. Our study provides new insights into the metabolic reprograming that leads to pathologic inflammation in humans with pulmonary TB.

Introduction

Prior to the COVID-19 pandemic, tuberculosis (TB) was the leading global cause of infectious disease mortality, accounting for ~1.4 million deaths each year [1]. In persons successfully treated for pulmonary TB, rates of post-TB obstructive lung disease are high [2,3] and may be even higher in persons treated for multidrug resistant (MDR)-TB [4]. Such long-term changes in pulmonary function are likely the result of pathologic lung inflammation and tissue damage prior to and shortly after initiation of anti-TB chemotherapy [5]. Elucidating host response pathways that promote pathologic tissue inflammation in persons with pulmonary TB will be critical to detect those at greatest risk for adverse pulmonary outcomes and identify targets for host-directed therapeutics.

The cytokine IL-1 plays a complex role following infection with Mycobacterium tuberculosis (Mtb) that appears to evolve as one progresses to TB disease. In mice infected with Mtb, IL-1 initially augments production of prostaglandin E2 (PGE2) and limits production of type I interferons, thereby enhancing control of bacterial replication [6]. However, in later stages of TB disease, IL-1 signaling leads to upregulation of proinflammatory eicosanoids and an influx of neutrophils, which promote further bacterial replication and tissue destruction [7]. Similarly, IL-1 signaling in macaques with TB disease is highly correlated with pulmonary inflammation, and IL-1 receptor blockade attenuates tissue damage [8]. This suggests that as TB disease progresses, IL-1 signaling becomes pathologic and leads to tissue inflammation that is permissive to bacterial growth. The primary drivers of IL-1 signaling in humans with pulmonary TB and whether such signaling yields a primarily proinflammatory or anti-inflammatory phenotype has yet to be established.

Recent studies in macrophage biology show tricarboxylic acid (TCA) cycle remodeling may play an important role in regulating IL-1 [9–11]. Inflammatory macrophage activation leads to accumulation of TCA cycle intermediates such as succinate, which, in turn, results in upregulation of the proinflammatory IL-1β-HIF-1α axis [9–11]. The metabolite itaconate counteracts this inflammatory metabolic remodeling by inhibiting succinate dehydrogenase and stabilizing the anti-inflammatory transcription factor Nrf2 [9,12]. However, the contribution of this host metabolic response pathway to inflammation in human pulmonary TB has not been previously described.

To better characterize the contribution of host metabolism to pathologic inflammatory cascades in humans with pulmonary TB, we performed detailed immunometabolic profiling using combined approaches in targeted and untargeted high-resolution metabolomics (HRM), lipidomics and cytokine profiling in a multicohort study of persons with drug susceptible (DS) and MDR pulmonary TB. Our aim was to gain a more comprehensive understanding of the metabolic drivers of inflammation in human TB disease. Here we show TCA cycle remodeling, characterized by accumulation of TCA cycle intermediates and decreased itaconate, is closely associated with molecular response networks of pathologic inflammation in human TB disease.

Results

Metabolic Pathway Regulation in MDR-TB

We hypothesized that persons experiencing delays in the receipt of effective anti-TB chemotherapy would experience continued progression of pulmonary TB disease and propagation of proinflammatory signaling cascades. We therefore sought to determine which metabolic pathways were most regulated in a population with MDR-TB (n = 85) that was initially treated with at least 2 months of ineffective, first-line anti-TB drug therapy prior to the diagnosis of MDR-TB (Table 1; described in Methods) [13]. Persons with MDR-TB were enrolled from 2011–2013 and prior to widespread implementation of rapid molecular diagnostic testing (Xpert MTB/RIF). The diagnosis of MDR-TB was made after a positive sputum culture for Mtb demonstrated MDR-TB using phenotypic drug susceptibility testing (DST). We compared the plasma metabolome in the MDR-TB cohort to a population of persons with drug susceptible (DS)-TB (n = 89) enrolled within 1 week of pulmonary TB diagnosis as well as a group of control participants, which included asymptomatic individuals with LTBI (n = 20) enrolled from a clinic serving refugees recently arrived in the U.S. and a population of U.S.-born individuals without evidence of TB disease or Mtb infection (n = 37) [14].

Table 1. Clinical and demographic characteristics of study participants.

Participant Characteristics MDR-TB HIV positive (n = 64) MDR-TB HIV negative (n = 21) DS-TB (n = 89) LTBI (n = 20) Controls without Mtb infection (n = 37)
Female sex, n (%) 42 (66) 11 (52) 31 (35) 9 (45) 31 (84)
Age, years (median [IQR]) 35 (29–41) 28 (20–48) 31 (24–43) 39 (28–44) 51 (41–56)
CD4, cells/mm3
(median [IQR])		 229 (138–400) N/A N/A N/A N/A
HIV Viral Load, copies/mL
(median [IQR])		 122 (<40–14,850) N/A N/A N/A N/A
Receiving anti-retroviral therapy at enrollment n (%) 50 (78) N/A N/A N/A N/A
TB disease history, n (%)
No TB history
Yes, completed treatment
Yes, failed treatment
21 (33)
31 (48)
12 (19)
9 (43)
6 (29)
6 (29)
89 (100)		 N/A N/A
+AFB smear at first study visit, n (%)* 28 (56) 8 (50) 67 (75) N/A N/A
+Sputum culture at diagnosis, n (%) 64 (100) 21 (100) 89 (100) N/A N/A
+Sputum culture at first study visit, n (%)** 56 (79) 16 (80) 76 (85) N/A N/A
Time to sputum culture conversion, days (median [IQR]) § 81 (51–90) 57 (53–80) 28 (22–47) N/A N/A
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Multidrug resistant (MDR); Drug susceptible (DS)

*AFB sputum smear results were not available for 19 MDR-TB participants (22.4%)

**AFB sputum culture results were not available for 14 MDR-TB participants (16.5%)

§Data on sputum culture conversion was missing for 7 MDR-TB participants (12.5%) with a positive sputum culture at baseline

With plasma HRM, we detected 9,787 m/z features using C18 liquid chromatography in negative ionization mode, of which 5,146 were present in >90% of samples and selected for downstream analysis. We first sought to determine which metabolic pathways were significantly regulated in persons with MDR-TB versus controls and persons with DS-TB. After adjustment for age, sex and HIV status, unbiased pathway analysis [15] revealed the TCA cycle and multiple overlapping pathways in fatty acid metabolism, including de novo fatty acid biosynthesis, fatty acid activation, and fatty acid oxidation, were the most significantly regulated metabolic pathways in persons with MDR-TB versus persons without TB disease (Fig 1A) as well as persons with DS-TB (Fig 1B). We then performed a correlation analysis of metabolites with primary adducts (M-H) mapping to significant metabolic pathways. This analysis yielded a cluster of highly correlated metabolites that were upregulated in the MDR-TB cohort including arachidonic acid, homolinoleic acid and TCA cycle intermediates alpha-ketoglutarate, succinate, fumarate and malate (p<0.001 for all; Fig 1C). There was a smaller cluster of correlated metabolites mapping to the xenobiotics and tyrosine metabolic pathways that were down-regulated in MDR-TB.

Fig 1. Metabolic pathway regulation in MDR-TB.

Fig 1

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(A) Unbiased pathway analysis using plasma HRM with C18 negative chromatography to compare the metabolome of persons with multidrug resistant (MDR)-TB (n = 85) to (A) asymptomatic controls without TB disease (n = 57) and (B) persons with drug susceptible (DS)-TB (n = 89) shows TCA cycle metabolism and multiple overlapping pathways in fatty acid synthesis, activation and metabolism were the most significantly regulated pathways in MDR-TB. The y-axis shows significantly regulated metabolic pathways and the x-axis shows the -log p-value for pathway enrichment adjusted for age, sex and HIV status. (C) Correlation analysis of primary metabolite adducts (M-H) mapping to significant pathways reveals a network of highly correlated metabolites involved in the TCA cycle, fatty acid metabolism and methionine and cysteine metabolism that were significantly increased in persons with MDR-TB.

Accumulation of succinate and downstream TCA cycle intermediates has been described as a potent driver of inflammation in multiple disease states [9,10,16]. We therefore hypothesized that TCA cycle remodeling in MDR-TB represented a proinflammatory signaling cascade. To better characterize observed changes in this metabolic pathway, TCA cycle intermediates were confirmed and quantified by accurate mass, MS/MS and retention time relative to authentic standards [17]. TCA cycle remodeling in persons with MDR-TB both with and without HIV coinfection was characterized by significant increases in plasma concentrations of succinate, fumarate, and malate versus persons with DS-TB, LTBI and controls without evidence of infection with Mtb (p<0.001 for all; Fig 2). Persons with MDR-TB also exhibited significant increases in plasma concentrations of alpha-ketoglutarate and glutamate (p<0.001 for all) whereas concentrations of citrate were significantly decreased in persons with MDR-TB and concentrations of cis-aconitate were similar between groups. These findings suggest the primary source of succinate in those with MDR-TB was not flux through the TCA cycle, but rather glutamine-dependent anaplerosis to alpha-ketoglutarate as has been described in in vitro and mouse models [10].

Fig 2. Characterization of TCA cycle remodeling in MDR-TB.

Fig 2

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Plasma quantification of TCA cycle metabolites in persons with multidrug resistant (MDR)-TB with (orange; n = 64) and without (green; n = 21) HIV co-infection shows significant increases in the metabolites alpha-ketoglutarate, succinate, fumarate, and malate versus persons with drug susceptible (DS)-TB (blue; n = 89), LTBI (yellow; n = 20), and controls without LTBI or TB disease (purple; n = 37). Plasma concentrations of glutamate were also significantly increased in MDR-TB while citrate was significantly decreased and concentrations of cis-aconitate were similar between groups. Plasma concentrations of the anti-inflammatory metabolite itaconate were significantly decreased in persons with MDR-TB. Groups were compared using the Wilcox rank sum test: * p<0.05, ** p<0.01, *** p<0.001.

The metabolite itaconate is derived from TCA cycle intermediate cis-aconitate and has been shown to attenuate inflammatory metabolic signals through inhibition of succinate dehydrogenase [9], induction of Nrf2 [12], and inhibition of inflammasome activation [18]. Thus, we posited that accumulation of TCA cycle intermediates in MDR-TB may be accompanied by decreases in itaconate. Indeed, we found itaconate was significantly decreased in HIV-positive and HIV-negative persons with MDR-TB compared to persons with DS-TB, LTBI and uninfected controls (p<0.001 for all). Plasma concentrations of itaconate were significantly and negatively correlated with plasma concentrations of succinate, fumarate, and malate (p<0.001 for all; S1A–S1C Fig) consistent with a counterregulatory role. These findings suggest persons with progressive and untreated pulmonary TB experience proinflammatory metabolic remodeling characterized by accumulation of succinate via glutamine-dependent anaplerosis and decreased itaconate.

TCA cycle remodeling is associated with a proinflammatory plasma cytokine response

Accumulation of TCA cycle intermediates such as succinate drive inflammation by increasing production of IL-1β [9,10,16]. We therefore hypothesized that the primary role of TCA cycle remodeling seen in participants with MDR-TB was induction of IL-1β. To answer this question, we measured plasma cytokine concentrations in a subset of persons with MDR-TB (n = 37), persons with DS-TB (n = 29) and persons without Mtb infection (n = 20) (S1 Table). We found concentrations of IL-1β were highly correlated with plasma concentrations of TCA cycle intermediates and negatively correlated with itaconate (p<0.001 for all; Fig 3A). Using hierarchical clustering analysis (HCA) of correlations between plasma cytokines and TCA cycle intermediates, we found IL-8 and IL-4 were also highly correlated with TCA cycle intermediates and formed a cluster with IL-1β. Plasma concentrations of IL-1β were significantly elevated in HIV-positive persons with MDR-TB, HIV-negative persons with MDR-TB and persons with DS-TB versus controls without Mtb infection (p<0.001, p = 0.003 and p = 0.03 respectively; Fig 3B). The elevation was significantly greater in persons with MDR-TB with and without HIV-coinfection versus persons with DS-TB (p<0.001 and p = 0.03 respectively). Conversely, plasma concentrations of granulocyte-macrophage colony stimulating factor (GM-CSF) were significantly decreased in all groups with TB disease versus controls with significantly greater decreases seen in persons with MDR-TB with or without HIV co-infection versus persons with DS-TB (p<0.001 and p = 0.002 respectively; Fig 3C). The plasma ratio of succinate to itaconate was highly correlated with the plasma concentrations of IL-1β and negatively correlated with concentrations of GM-CSF (p<0.001 for both; Fig 3D and 3E). For those participants with available culture conversion data, the plasma succinate/itaconate ratio at study enrollment was significantly and positively correlated with subsequent time to sputum culture conversion (p<0.001; Fig 3F). Time to sputum culture conversion was also positively correlated with plasma concentrations of IL-1β and negatively correlated with GM-CSF (p<0.001 and p = 0.02 respectively; S2A and S2B Fig). Additionally, persons with DS-TB and a persistently positive AFB sputum smear at study enrollment (within 1 week of treatment start) had a significantly higher plasma succinate/itaconate ratio versus those who had already converted to a negative AFB sputum smear (p = 0.04; S2C Fig). These findings indicate that TCA cycle remodeling in pulmonary TB is associated with increased inflammation through upregulation of IL-1β and down-regulation of GM-CSF, leading to greater disease severity.

Fig 3.

Fig 3

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TCA cycle remodeling is associated with increased IL-1β and delayed response to treatment (A) In a subset of persons with MDR-TB with (n = 31) and without (n = 6) HIV co-infection, DS-TB (n = 29) and controls without TB infection or disease (n = 20), plasma concentrations of TCA cycle intermediates and arachidonic acid were positively correlated with concentrations of IL-1β, IL-4 and IL-8 and negatively correlated with GM-CSF. (B) Plasma concentrations of IL-1β were significantly higher in person with MDR-TB versus those with DS-TB and controls while (C) GM-CSF was significantly lower in the MDR-TB group. (D) The plasma succinate/itaconate ratio at enrolment was significantly and positively correlated with plasma concentrations of IL-1β and (E) negatively correlated with concentrations of GM-CSF. (F) The plasma ratio of succinate/itaconate was also significantly and positively correlated with subsequent time to sputum culture conversion. Groups were compared using the Wilcox rank sum test: * p<0.05, ** p<0.01, *** p<0.001.

TCA cycle remodeling and secretion of IL-1β are associated with proinflammatory lipid signaling

In early Mtb infection, IL-1β shifts the balance of eicosanoid production toward PGE2 and away from proinflammatory eicosanoids, thereby limiting Mtb replication [6]. However, in later stages of TB disease, IL-1β signaling is associated with eicosanoid-mediated inflammation and tissue damage [7,8,19]. Given plasma concentrations of IL-1β and TCA cycle intermediates were highly correlated with concentrations of arachidonic acid, we posited that TCA cycle remodeling and IL-1β were acting primarily to drive proinflammatory lipid signaling cascades in pulmonary TB disease in humans. To examine this question, we first sought to determine whether persons with MDR-TB exhibited increased phospholipase A2 activity, the first step in formation of arachidonic acid. We performed untargeted lipidomics on plasma samples from a subset of the study population including persons with MDR-TB and HIV co-infection (n = 29), MDR-TB alone (n = 21), DS-TB (n = 30) and LTBI (n = 20) (S2 Table). Of plasma lipids with confirmed chemical identities, persons with MDR-TB demonstrated significant upregulation of multiple species of lyso-phospholipids and significant decreases in phospholipids with two acyl chains compared to persons with LTBI (Fig 4A) and persons with DS-TB (Fig 4B), consistent with significant increases in phospholipase A2 activity.

Fig 4. Increased phospholipase activity in MDR-TB.

Fig 4

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Volcano plots of plasma lipids with confirmed chemical identities shows significant increases of lyso-phospholipids and significant decreases in phospholipids with two acyl chains in persons with MDR-TB with (n = 29) and without (n = 21) HIV co-infection versus (A) persons with latent TB infection (n = 20) and (B) persons with drug susceptible (DS)-TB (n = 30). The x-axis shows the log2 fold change in expression in MDR-TB versus controls and DS-TB for each lipid and the y-axis shows the -log p-value for each comparison after adjustment for age, sex and HIV status. Lipids upregulated in MDR-TB with >1 log2 fold change at a false discovery rate (FDR) [60] of q<0.05 are labelled in red and those downregulated are labelled in blue.

Following formation from phospholipase A2, arachidonic acid is further metabolized through one of three metabolic pathways to regulate inflammation: cyclooxygenases (COX) to form prostaglandins, lipoxygenases (LOX) to form proinflammatory eicosanoids and CYP450 enzymes to form the less biologically active dihydroxyeicosatrienoic acids (DHETs) [20]. Prior studies have shown that proinflammatory eicosanoids formed through the LOX pathway are significantly upregulated in persons with pulmonary TB disease and associated with more severe clinical disease [6,7,21] and cavity formation [22]. We therefore hypothesized that accumulation of TCA cycle intermediates, IL-1β and arachidonic acid in MDR-TB patients were part of an inflammatory cascade resulting in increased production of proinflammatory eicosanoids. To further characterize downstream changes in arachidonic acid metabolism in persons with MDR-TB versus those with DS-TB and asymptomatic controls, we performed a targeted oxylipin assay to identify and quantify relevant eicosanoids in plasma samples [23]. All persons with pulmonary TB demonstrated significant increases in 11,12-DHET and 14,15-DHET versus controls with and without LTBI (p<0.05 for all; Fig 5A and 5B), consistent with increases in arachidonic metabolism. However, while concentrations of the less biologically active DHET molecules did not differ between TB disease groups, persons with MDR-TB with and without HIV coinfection demonstrated significant increases in the proinflammatory eicosanoids 5-HETE and 12-HETE compared to persons with DS-TB, LTBI and controls without Mtb infection (p<0.001 for all; Fig 5C and 5D). Similarly, leukotriene B4 was significantly increased in persons with MDR-TB with and without HIV coinfection versus other groups (p<0.001 for all) while concentrations of PGF2a were undetectable (S3 Fig). This indicates arachidonic acid metabolism in persons with MDR-TB was both increased and more likely to produce proinflammatory eicosanoids metabolized via LOX pathways compared to persons with DS-TB. Plasma concentrations of 5-HETE and 12-HETE, but not 11,12-DHETE or 14,15-DHETE, were highly correlated with plasma concentrations of succinate, fumarate and malate as well as IL-1β, IL-8 and IL-4 while they were negatively correlated with concentrations of itaconate and GM-CSF (p<0.001 for all; Fig 5E). These findings suggest TCA cycle remodeling, increased IL-1β and decreased GM-CSF are closely associated with proinflammatory lipid signaling in pulmonary TB.

Fig 5. Immunometabolic remodeling is associated with proinflammatory eicosanoid signaling.

Fig 5

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Compared to persons with LTBI (yellow; n = 20) and persons without LTBI or TB disease (n = 19), persons with multidrug resistant (MDR)-TB with (orange; n = 29) and without (green; n = 21) HIV co-infection, as well as persons with drug susceptible (DS)-TB (blue; n = 30) demonstrated significant increases in plasma concentrations of the arachidonic acid metabolites (A) 11,12-DHET and (B) 14,15-DHET metabolized by the CYP450 system. Proinflammatory eicosanoids including (C) 5-HETE and (D) 12-HETE produced by lipoxygenase metabolism of arachidonic acid were significantly increased in persons with MDR-TB versus persons with DS-TB, LTBI and uninfected controls. (E) Shows a heatmap of metabolites and cytokines correlated with plasma concentrations of 5-HETE, 12-HETE, 11,12-DHET, and 14,15-DHET with dark red indicating a higher Pearson correlation coefficient and dark blue indicating a more negative correlation. Groups were compared using the Wilcox rank sum test: * p<0.05, ** p<0.01, *** p<0.001.

Proinflammatory metabolic remodeling is reversed with TB treatment

Because patients with MDR-TB were enrolled from a different country from those with DS-TB and controls with and without LTBI, we examined the possibility that observed differences in this population could be due to factors unrelated to TB disease. We reasoned that if TCA cycle remodeling were due to TB disease, then these changes would be reversed with appropriate anti-TB chemotherapy. We therefore examined the change in plasma concentrations of metabolites and cytokines in a subset of MDR-TB patients where plasma samples were available over the first year of treatment (n = 17). While there was minimal change in plasma concentrations of succinate, fumarate and malate at 2–4 months, there was a significant decline after 1 year of appropriate treatment for MDR-TB (p<0.05 for all; Fig 6A–6C). Plasma concentrations of these metabolites after 12–15 months of MDR-TB treatment were not significantly different than those of controls without Mtb infection. There was an increase in plasma itaconate concentrations after 2–4 months of treatment and 12–15 months of treatment that did not meet statistical significance (p = 0.06 and 0.12 respectively) and remained significantly lower than uninfected controls (p = 0.01; Fig 6D). Plasma concentrations of arachidonic acid also significantly declined relative to baseline after 1 year of MDR-TB treatment (p = 0.03) and were not significantly different from controls (Fig 6E). In the DS-TB group, plasma concentrations of these metabolites were unchanged after 2 months of treatment (S4 Fig). While IL-1β, IL-4 and IL-8 significantly declined after 1 year of MDR-TB treatment (p<0.05 for all), they remained significantly higher versus controls without Mtb infection, suggesting increases in systemic inflammation remained even after 1 year of treatment (p<0.05 for all; Fig 6F–6H). Similar to itaconate, GM-CSF did not significantly increase over the course of MDR-TB treatment and remained significantly lower than controls without Mtb infection (p<0.001; Fig 6I). Such ongoing immune activation could be due, in part, to concomitant administration of antiretroviral therapy in those with HIV co-infection [24], though trends of all metabolites and cytokines over time did not differ by HIV infection status in the MDR-TB group (S5 Fig). Together, these findings indicate that proinflammatory metabolic remodeling is reversed with appropriate TB treatment, but that the timeline of this reversal may be prolonged. Further, they suggest mechanisms other than itaconate and GM-CSF signaling are required to dampen inflammation.

Fig 6. Proinflammatory immunometabolic changes are reversed with appropriate TB treatment.

Fig 6

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In persons with multidrug resistant (MDR)-TB (n = 17) here was a significant decline in plasma concentrations of (A) succinate, (B) fumarate, (C) malate, and (D) arachidonic acid after 1 year of TB therapy with concentrations declining to similar levels as controls without Mtb infection. (E) While plasma itaconate concentrations increased after 2–4 months and 1 year of treatment, this did not meet statistical significance and remained significantly lower than controls. Plasma concentrations of (F) IL-1β, (G) IL-8, and (H) IL-4 also significantly declined after 1 year of treatment, but remained significantly higher than uninfected controls. (I) Plasma concentrations of GM-CSF did not significantly change with MDR-TB treatment and remained significantly lower than those of controls. Groups were compared using the Wilcox signed rank test: * p<0.05, ** p<0.01, *** p<0.001.

Discussion

In this multicohort study, we used unbiased plasma metabolic pathway analyses combined with targeted and untargeted lipidomics and cytokine profiling to discover that TCA cycle remodeling is strongly associated with IL-1β secretion and proinflammatory eicosanoid signaling in persons with pulmonary TB disease. We found this inflammatory cascade, characterized by increased plasma concentrations of TCA cycle intermediates succinate, fumarate and malate and decreased concentrations of itaconate, was particularly upregulated in persons with MDR-TB after at least 2 months of ineffective anti-TB chemotherapy and was reversed only after 1 year of efficacious treatment. Collectively, these results indicate that TCA cycle remodeling is an important driver of proinflammatory cytokine and eicosanoid signaling in pulmonary TB disease. Furthermore, our findings suggest that prolonged delays in effective treatment allow this proinflammatory cascade to perpetuate, potentially contributing to poor treatment outcomes in MDR-TB. These findings also indicate that further examination of host-directed therapies that reverse such immunometabolic remodeling is warranted [6–9,12].

Accumulation of succinate is known to drive inflammation in macrophages through stabilization of the transcription factor HIF-1α to increase transcription of Il1b [10,16]. This metabolic signal is the result of a shift in cellular energy metabolism toward glycolysis and away from oxidative phosphorylation, thereby causing succinate and other TCA cycle intermediates to accumulate, a process termed aerobic glycolysis or the “Warburg Effect” [25]. Prior studies have shown Mtb is able to induce aerobic glycolysis in pulmonary macrophages and that this change is necessary for production of IL-1β [26,27]. The strong association between elevated succinate and IL-1β-mediated inflammatory signaling in our study suggests that TCA cycle remodeling plays a critical role in regulating the inflammatory response in humans with pulmonary TB. Further, our results indicate the primary source of TCA cycle intermediates in TB disease is glutamine-dependent anaplerosis, consistent with inflammatory metabolic remodeling driven by aerobic glycolysis [10].

However, while prior in vitro and mouse models have shown induction of IL-1β results in control of Mtb replication [6,26], we found TCA cycle remodeling and induction of IL-1β produced a distinctly proinflammatory phenotype in humans that was associated with increased bacterial burden and prolonged time to sputum culture conversion. Our findings are supported by independent human studies, which have consistently show elevated plasma concentrations of IL-1β in persons with pulmonary TB are correlated with markers of inflammation [28] and associated with greater extent of disease and cavitation on chest radiograph [19,29]. A possible explanation for these apparently discordant findings is that immunometabolic remodeling of macrophages evolves following infection with Mtb [30]. Initially, IL-1β provides a counterbalance to type I IFN signaling by promoting metabolism of arachidonic acid to prostaglandins [6]. Early IL-1 receptor blockade leads to greater abundance of proinflammatory eicosanoids and increased bacterial burden [6,31]. In later stages of TB disease in mice, IL-1β becomes a driver of proinflammatory eicosanoid signaling leading to an influx of neutrophilic inflammation that is permissive to bacterial growth [7]. In macaques with TB disease, plasma concentrations of IL-1β are highly correlated with levels of pulmonary inflammation and IL-1 receptor blockade limits tissue damage [8]. Thus, accumulating TCA cycle intermediates and decreased itaconate during later stages of TB disease may be another example of Mtb exploiting a host metabolic adaption aimed at controlling Mtb replication during the initial stages of infection [32,33]. While aerobic glycolysis and increased IL-1β initially promotes control of bacterial replication [26], our findings suggest that as TB disease progresses in humans, TCA cycle remodeling increases arachidonic acid metabolism and conversion to proinflammatory eicosanoids, potentially worsening tissue damage and increasing disease severity. In future studies, it will be important to determine whether such metabolic remodeling is a function of uncontrolled bacterial replication or whether the host response itself creates an environment that promotes bacterial survival.

The observation that dysregulated IL-1β signaling occurs disproportionately in persons with MDR-TB has been reported previously [28], and inhaled IFN-α, which suppresses IL-1β production in pulmonary TB disease [34] has been studied as a potential treatment for refractory MDR-TB [35]. We hypothesize that the immunometabolic remodeling in the MDR-TB group in the present study was caused primarily by propagation of inflammatory signaling cascades resulting from prior, inadequate MDR-TB treatment. However, the possibility that differences in Mtb strains themselves can induce differential immunometabolic phenotypes in humans warrants further investigation. The ability of Mtb to induce IFN-β production using the ESX-1 system, which permeabilizes the mitochondrial membrane in infected macrophages [36] and inhibits aerobic glycolysis [37], differs by strain type [38]. Further, mutations in the Mtb rpoB gene that confer rifampin resistance have been associated with differential metabolic responses and secretion of IFN-β and IL-1β in macrophages [39]. Thus, it remains possible that the different host responses observed in the MDR-TB group are related to differences in infecting Mtb strains themselves.

Our findings also demonstrate the importance of understanding the role of itaconate and GM-CSF in regulating the inflammatory response in human TB disease. Itaconate has been shown to limit inflammation in a variety of ways, including inhibition of succinate dehydrogenase [9], activation of the antioxidant transcription factor NRF2 [12] and inhibition of NRLP3 inflammasome activation [18]. Irg1-/- mice, which lack the enzyme that converts cis-aconitate to itaconate, experience pathologic pulmonary inflammation following infection with Mtb driven by an influx of neutrophils that results in rapid mortality [40]. Similarly, mice with impaired GM-CSF production fail to control Mtb replication [41] and GM-CSF neutralization during sub-optimal TB treatment exacerbates lung inflammation [42]. The strong negative correlation between plasma concentrations of itaconate and GM-CSF and the inflammatory eicosanoids 5-HETE and 12-HETE, which are known to stimulate neutrophilic inflammation in pulmonary TB [7], support these signaling molecules as important immune regulators in human TB disease. However, it is important to note that plasma concentrations both itaconate and GM-CSF did not significantly increase with MDR-TB treatment, suggesting alternative mechanisms are able to reverse proinflammatory metabolic signaling cascades. Further investigation into the role of these molecules in limiting tissue damage in TB disease has potential to reveal new targets for host-directed therapies.

This study is subject to several limitations. Though we demonstrate a strong association between increased plasma concentrations of TCA cycle intermediates, increased concentrations of IL-1β and proinflammatory eicosanoids, and decreased concentrations of GM-CSF and itaconate, the observational nature of the study precludes us from definitively establishing a causal relationship. While persons with MDR-TB were enrolled from three study sites, all were from the same country, so it remains possible that metabolic differences in this group are driven by host or environmental differences, such as polymorphisms in key metabolic enzymes [43] or co-infections with other endemic pathogens, rather than MDR-TB status. Finally, all measurements of metabolites, cytokines and lipids were taken from plasma samples rather than the pulmonary compartment, which was the primary site of TB disease in these cohorts. In future studies it will be important to combine systemic measurement immunometabolic signaling cascades with measures of the host microbiome [44], as well as cell subset and intracellular molecular cascade characterization data to better understand the immune cell phenotypes and signaling pathways associated with pathologic metabolic remodeling in TB disease.

In summary, we demonstrate that TCA cycle remodeling is closely associated with IL-1β-mediated proinflammatory eicosanoid signaling in humans with pulmonary TB. This remodeling is characterized by significant increases in plasma succinate concentrations and significant decreases in concentrations of itaconate. These findings provide evidence that pathologic inflammatory signaling in pulmonary TB is driven by host metabolic remodeling. Further elucidating the role of these immunometabolic networks in driving human TB disease progression will be critical to better understand differential host outcomes and identify new targets for host-directed therapies.

Methods

Ethics statement

All studies were approved by the Institutional Review Board (IRB) of Emory University (Atlanta, GA, USA), and by the individual IRBs associated with the original cohort studies: the Ethics Committee of the National Center for Tuberculosis and Lung Diseases of Georgia (Tbilisi, Georgia), the University of KwaZulu-Natal IRB (Durban, South Africa), and the Georgia Department of Public Health IRB (Atlanta, GA, USA), respectively, depending on the site of participant enrollment. All subjects provided written informed consent.

Sample collection

For all cohorts, blood was collected in ethylenediaminetetraacetic acid (EDTA)-containing tubes and centrifuged; isolated plasma was immediately frozen and stored at -80°C. Samples collected outside of the U.S. were subsequently shipped on dry ice to Emory University, Atlanta, GA, USA. All samples remained frozen during transit and were kept at -80°C prior to metabolomics, lipidomics and cytokine analysis.

Multidrug resistant-TB cohort

Persons with pulmonary TB from KwaZulu-Natal province, South Africa were enrolled as part of a prospective, observational cohort study of MDR-TB and HIV co-infection from 2011–2013 [13]. All persons in the South African cohort had MDR-TB as demonstrated by a positive sputum culture for Mtb and phenotypic DST indicating resistance to at least both isoniazid and rifampin. Baseline plasma samples were collected within 7 days of starting conventional treatment for MDR-TB. Persons with HIV-co-infection were continued on conventional anti-retroviral therapy (ART) and those not previously on ART were started on treatment. All patients were referred to a dedicated MDR-TB treatment center and treated with a standardized drug regimen that included kanamycin (15 mg/kg, maximum 1 g daily), moxifloxacin (400 mg daily), ethionamide (15–20 mg/kg, maximum 750 mg daily), terizidone (15–20 mg/ kg, maximum 750 mg daily), ethambutol (15–20 mg/kg, maximum 1200 mg daily), and pyrazinamide (20–30 mg/kg, maximum 1600 mg daily). All persons who completed the study were treated for a period of two years and serial plasma samples were analyzed at enrollment and 2–4 months and 12–15 months after treatment initiation.

Drug susceptible-TB cohort

Persons with DS-pulmonary TB were selected from a randomized, double blind controlled trial of adjunctive high-dose cholecalciferol (vitamin D3) for TB treatment conducted in the country of Georgia (clinicaltrials.gov identifier NCT00918086) [14]. All persons included in this metabolomics sub-study were HIV-negative. Inclusion criteria for patients included age ≥ 18 years and newly diagnosed active TB disease, based on a positive AFB sputum smear and confirmed by positive sputum culture for Mtb. Baseline plasma samples for HRM were obtained from eligible subjects within 7 days of initiating therapy with conventional dosing of first-line anti-TB drugs (isoniazid, rifampicin, pyrazinamide and ethambutol) [14]. Phenotypic DST was performed on Mtb isolates recovered from all persons with pulmonary TB using the absolute concentration method [45].

Controls without active TB disease

Plasma from persons with and without LTBI was analyzed for cross-sectional comparison with pulmonary TB cases. Persons with LTBI were enrolled from the DeKalb County Board of Health in DeKalb County, GA, USA. All persons with LTBI had positive test results from at least two FDA-approved tests for LTBI (QFT, TSPOT.TB [TSPOT] and/or tuberculin skin test [TST]). All tests were interpreted according to the guidelines from the Centers for Disease Control and Prevention [46,47]. Controls without LTBI were U.S.-born adults at low risk for Mtb exposure and infection, who had at least one negative TST documented in medical records [48].

Plasma metabolomics analysis

De-identified samples were randomized by a computer-generated list into blocks of 40 samples prior to transfer to the analytical laboratory where personnel were blinded to clinical and demographic data. Thawed plasma (65 μL) was treated with 130 μl acetonitrile (2:1, v/v) containing an internal isotopic standard mixture (3.5 μL/sample), as previously described [49]. The internal standard mix for quality control consisted of 14 stable isotopic chemicals covering a broad range of small molecules [49]. Samples were mixed and placed on ice for 30 min prior to centrifugation to remove protein. The resulting supernatant was transferred to low-volume autosampler vials maintained at 4°C and analyzed in triplicate using an Orbitrap Fusion Mass Spectrometer (Thermo Scientific, San Jose, CA, USA) with c18 liquid chromatography (Higgins Analytical, Targa, Mountain View, CA, USA, 2.1 x 10 cm) with a formic acid/acetonitrile gradient. The high-resolution mass spectrometer was operated in negative electrospray ionization mode over a scan range of 85 to 1275 mass/charge (m/z) and stored as. Raw files [50]. Data were extracted and aligned using apLCMS [51] and xMSanalyzer [52] with each feature defined by specific m/z value, retention time and integrated ion intensity [50]. Three technical replicates were performed for each plasma sample and intensity values were median summarized [53].

Metabolite identification and reference standardization

Identities of metabolites of interest were confirmed using ion dissociation methods (tandem MS/MS). Fragmentation spectra were generated using a Q Exactive HF Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific, Waltham, MA) with parallel reaction monitoring mode using a targeted inclusion list. TCA cycle metabolites and arachidonic acid were confirmed and quantified by accurate mass, MS/MS and retention time relative to authentic standards [17].

Untargeted lipidomics

Lipids were extracted from each plasma sample using a high throughput methyl t butyl ether (MtBE) extraction procedure with an automated and robust liquid handling instrument (Biotage Extrahera, Uppsala, Sweden) as previously described [54]. Extracted samples were dried under nitrogen and reconstituted in 200 μl 1:1 chloroform:methanol prior to injection into the LC/MS system and lipids were resolved using a Thermo Acclaim C18 reverse phase column on a Thermo Vanquish UPLC coupled to a Thermo Fusion IDX mass spectrometer (Thermo, Waltham, MA) [55]. Data were acquired at full scan mode at a resolution of 240,000 FWHM for all the samples and iterative data dependent acquisition (DDA) mode was collected on pooled samples at a resolution of 35,000 step-wise collision energy for identification of lipids. Data was processed using LipidSearch (Thermo Fisher, San Jose, CA). Lipids that contained a signal to noise ratio of greater than 10 and had a high confidence level (MS/MS) identifications with CVs less than 30% across pooled QCs were considered for downstream analysis.

Targeted measurement of eicosanoids

For targeted analysis of oxylipins, these lipids were enriched from bulk membrane lipids by performing solid phase extraction using the Biotage Extrahera liquid handling system as previously described [23,56,57]. The resulting extracts were analyzed using a multiple reaction monitoring (MRM)-based LC/MS protocol on a QTrap 5500 (Sciex, Waltham, MA) synced to Exion LC AD system (SCIEX, Waltham, MA), whereby detected oxylipins and endocannabinoids were fragmented and quantified against external standard curves. Missing values were imputed using half of the minimum detected value for each lipid.

Plasma cytokine detection

The U-PLEX assay (Meso Scale MULTI-ARRAY Technology) commercially available by Meso Scale Discovery (MSD) was used for plasma cytokine detection. This technology allows the evaluation of multiplexed biomarkers by using custom made U-PLEX sandwich antibodies with a SULFO-TAG conjugated antibody and next generation of electrochemiluminescence (ECL) detection. The assay was performed according to the manufacturer’s instructions (https://www.mesoscale.com/en/technical_resources/technical_literature/techncal_notes_search). In summary, 25μL of plasma from each participant was combined with the biotinylated antibody plus the assigned linker and the SULFO-TAG conjugated detection antibody; in parallel a multi-analyte calibrator standard was prepared by doing 4-fold serial dilutions. Both samples and calibrators were mixed with the Read buffer and loaded in a 10-spot U-PLEX plate, which was read by the MESO QuickPlex SQ 120. The plasma cytokines values (pg/mL) were extrapolated from the standard curve of each specific analyte.

Statistics

Statistical comparisons of metabolite and lipid intensity values (abundance) and concentrations were performed in R version 3.6.1. For untargeted metabolomics and lipidomics analyses, metabolite intensity values were log2 transformed and compared between groups using linear regression, controlling for age, sex, and HIV status [58]. Metabolic pathway enrichment analysis was performed using mummichog, a Python-based informatics tool that leverages the organization of metabolic networks to predict functional changes in metabolic pathway activity [15,32,59]. Following quantification of selected metabolites and lipids, cross-sectional comparison of plasma concentrations between groups was made using the Wilcoxon Rank Sum test. Changes relative to baseline during treatment of TB disease were tested using a Wilcoxon Signed-Rank test. For correlation analyses, plasma metabolite, lipid and cytokine concentrations were normalized using log transformation. A p-value less than or equal to 0.05 was considered statistically significant. For untargeted correlation analyses, a false discovery rate (FDR) of q<0.05 was used [60].

Supporting information

S1 Fig. Correlation of plasma itaconate concentrations with TCA cycle intermediates.

Plasma concentrations of itaconate were significantly and negatively correlated with TCA cycle intermediates (A) succinate, (B) fumarate and (C) malate.

(TIF)

Click here for additional data file. (1.8MB, tif)
S2 Fig. Immunometabolic remodeling and disease severity.

(A) Time to culture conversion was significantly and positively correlated with plasma concentrations of IL-1β at study enrollment and (B) negatively correlated with concentrations of GM-CSF. (C) In persons with drug susceptible (DS)-TB, the plasma ratio of succinate to itaconate was significantly increased in those with a persistently positive sputum smear for acid-fast bacilli (AFB) at enrollment versus those who converted to a negative AFB sputum smear.

(TIF)

Click here for additional data file. (1.6MB, tif)
S3 Fig. Prostaglandin F2α and Leukotriene B4 in TB disease.

(A) Plasma concentrations of PGF2a were below the limit of detection in all TB disease groups as well as most uninfected U.S. controls while all persons with LTBI had detectable plasma concentrations. (B) LTB4 was only detected in plasma in persons with MDR-TB.

(TIF)

Click here for additional data file. (1.2MB, tif)
S4 Fig. Plasma concentrations of TCA cycle metabolites after 2 months of treatment for drug-susceptible TB.

Plasma concentrations of (A) succinate, (B) Malate, (C) fumarate, (D) itaconate and (E) arachidonic acid were not significantly different after 2 months of treatment versus baseline in persons with drug-susceptible TB from Georgia (n = 89).

(TIF)

Click here for additional data file. (2MB, tif)
S5 Fig. Reversal of immunomedabolic remodeling with MDR-TB treatment does not differ by HIV status.

Decreases in proinflammatory metabolites and cytokines with MDR-TB treatment was similar in persons with MDR-TB with (orange; n = 12) and without (green; n = 5) HIV co-infection. In both groups, plasma concentrations of itaconate and GM-CSF were unchanged over time.

(TIF)

Click here for additional data file. (2.7MB, tif)
S1 Table. Clinical and demographic characteristics of study participants included in the cytokine substudy.

(DOCX)

Click here for additional data file. (15.1KB, docx)
S2 Table. Clinical and demographic characteristics of study participants included in the lipidomics substudy.

(DOCX)

Click here for additional data file. (15.2KB, docx)
S1 Data. Untargeted plasma metabolomics feature table.

(TXT)

Click here for additional data file. (32.2MB, txt)
S2 Data. Table of plasma concentrations of targeted metabolites.

(TXT)

Click here for additional data file. (53.1KB, txt)
S3 Data. Table of plasma cytokine concentrations.

(TXT)

Click here for additional data file. (44.2KB, txt)
S4 Data. Untargeted plasma lipidomics feature table.

(TXT)

Click here for additional data file. (93.7KB, txt)
S5 Data. Table of plasma oxylipin concentrations.

(TXT)

Click here for additional data file. (300.6KB, txt)
S6 Data. Clinical and demographic characteristics of study participants.

(TXT)

Click here for additional data file. (9.5KB, txt)

Acknowledgments

We thank all the study teams and study sites from the multiple cohorts leveraged by this metabolomics study, including the Georgia National Center for Tuberculosis and Lung Disease, the University of KwaZulu-Natal and the DeKalb County Board of Health, Georgia, USA.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The study has received funding from: National Institutes of Health grant R21 AI130918 (TRZ, NRG, JCMB, DPJ, RRK, HMB) National Institutes of Health grant K23 AI144040 (JMC) National Institutes of Health grant P30 AI050409 (JMC) National Institutes of Health grant T32 AI074492 (JMC) National Institutes of Health grant P30 ES019776 (DPJ, TRZ) National Institutes of Health grant R01 AI087465 (NRG, NSS, JCMB) National Institutes of Health grant K24 AI114444 (NRG) National Institutes of Health grant D43 TW007124 (RRK, HMB) National Institutes of Health grant R01AI114304 (JCMB) National Institutes of Health grant R01AI145679 (JCMB) National Institutes of Health grant K24AI155045 (JCMB) National Institutes of Health grant P30AI124414 (JCMB) National Institutes of Health grant UL1 TR002378 (JMC, MK, HMB, EAO, TRZ) National Institutes of Health grant UL1TR001073 (JCMB) Emory University Global Health Institute (TRZ, HMB, NT, RRK) Emory Medical Care Foundation (RRK, JMC, HMB, TRZ) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

JoAnne L Flynn, David M Lewinsohn

7 Jun 2021

Dear Dr. Collins,

Thank you very much for submitting your manuscript "TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis" for consideration at PLOS Pathogens. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

David M. Lewinsohn

Associate Editor

PLOS Pathogens

JoAnne Flynn

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064

***********************

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: The manuscript by Collins et al., investigated the role of host metabolism in regulating the inflammatory response to tuberculosis. Using a combination of metabolomics, lipidomics and cytokine profiling on a multicohort study of humans with pulmonary TB, the authors demonstrated that MDR-TB disease is associated with a switch from anti-inflammatory metabolite itaconate to inflammatory metabolite succinate, leading to increased production of IL1 beta. In addition, the authors showed that this metabolic switch was associated with an increased production of inflammatory eicosanoids for the lipoxygenase pathways. Furthermore, the authors show that this metabolic switch could be reversed following 12-month anti-TB treatment. The effect of MDR Tb strains on metabolic reprogramming and subsequent immune response represents an important avenue of investigations and this study provides new evidence that MDR-TB infection has unique effect on host metabolism. The study is clearly written and logically presented, but further work and clarification are needed to fully support the findings.

Reviewer #2: In this paper the authors aim to demonstrate how pulmonary TB disease leads to dysregulation of IL-1B signalling, through disruption of the TCA cycle – leading to downstream changes in inflammatory lipid signalling. They discuss how MDR-TB patients demonstrate more disruption, inflammation and therefore tissue damage than their DS or uninfected controls. They go on to explain how these effects are reversed on effective treatment.

Methods

This is an ex vivo study, where conclusions about disease of the pulmonary compartment are drawn from analysis of plasma from peripheral blood sampling. Acellular work from a body compartment remote from the source of disease, makes it difficult to ascertain whether the two are linked. Results are therefore generally representative of correlations, and not causation (this is acknowledged by the authors in the discussion).

Reviewer #3: In this manuscript by Collins et al the authors profile the metabolome of patients with MDR-TB and compare it to uninfected controls and Drug Sensitive-TB patients. They identify strong correlations between an accumulation of TCA intermediates, IL1b and pro-inflammatory lipid mediators suggesting a metabolic reprogramming in active TB patients that are not responding to antibiotic therapy. A comparison of these changes over time show reversal of several metabolites suggesting these changes are associated with progressive disease. The authors suggest that targeting this immunometabolic reprogramming may lead to potential host-directed therapy to prevent TB. This study is a nice catalog of systemic metabolic changes that is robust and well controlled. Understanding factors associated with progressive disease is very important and will be of interest to many readers. Limitations of the study are the descriptive nature relying on correlations and thus providing limited mechanistic insight, and no truly novel discoveries (ie most of the findings were previously shown in other systems). However, the metabolic links connect several disparate pieces of data to formulate a more complete model which is significant and novel. Specific comments are below:

**********

Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: 1) The current study highlights important differences between MDR TB-cohort and DS-TB cohorts/controls. However, the high prevalence of HIV co-infection limits the enthusiasm of the reviewer. Given the known effect of viral infection on host metabolism and eicosanoid production, the co-infection with HIV could lead to under- or over-estimation of the response. The authors should consider separating the MDR-TB cohort into HIV+ and HIV- throughout the study.

2) In addition, the control groups were not clear as it appears to be a mix of LTBI to healthy individuals. I would suggest the authors to separate the LTBI from Healthy controls for the analysis

2) In Figure 3, the data shows that several cytokines are correlated with TCA cycle intermediates/succinate. Why did the authors choose to focus on IL1-beta, considering the existent literature that MDR limits IL1beta production by inducing IFN-I (Howard et al., 2018, Nat Microbiol)? For example, GM-CSF levels are negatively correlated with succinate. Given the predominant role of GM-CSF in combination with antibiotic treatment (Benmerzoug et al., 2018, Scientific Reports), the authors should perform the same experiments as in Figure 3B-D with IL1-beta.

3) In Figure 5, please show a heatmap or correlation between the different oxylipins and TCA metabolites as in Figure 3. In addition, the authors demonstrate that infection with MDR is associated with a shift towards increased production of lipoxygenase derived eicosanoids, such as 5-HETE and 12-HETE. What is the effect of MDR on prostanoids (e.g. PGE2) or lipoxins (e.g. lipoxin A4) which have been previously linked to susceptibility/resistance to TB (PMID: 20622882, PMID: 18955568, PMID: 24990750 ). Is there any correlation between these particular eicosanoids and GM-CSF?

Reviewer #2: Study groups

This study has been performed on a multi-cohort group of patients who were recruited for other studies. The cohort of MDR-TB patients were recruited from a previous study designed to assess treatment outcomes for HIV+/MDR patients vs HIV-/MDR patients. The cohort for the study under review has a 75% positivity rate for HIV infection – we are not given any information on the CD4 counts, viral load titres, treatment regimen (if any), treatment duration etc. These patients were recruited before the introduction of WHO recommendations to treat people diagnosed with HIV at diagnosis, and would therefore likely have been subject to previous guidelines where HIV treatment was based on CD4 counts. These patients have likely therefore had a period of unsuppressed HIV infection, before having a CD4 count low enough to merit therapy. Considering there are studies looking at the effects of HIV on cellular metabolism, this is a highly relevant confounding factor on their study selection. The authors sate that the reported changes are not due to HIV.

Other important features of this study group (by review of reference 13) are that the HIV positive cohort are 70% female and over 70% have been previously treated for TB, compared to 46% female and 46% previously treated for TB in the HIV- group. It’s unclear how these baseline characteristics carried over to the selected cohort for the current study.

In relation to the DS and control groups: both of these groups differ in country of origin to the MDR group. A study in PNAS looked at differences in the crystal structure of the itaconic acid pathway enzymes (https://doi.org/10.1073/pnas.1908770116) and found major differences between people of different ethnicity, specifically a higher rate of a mutation related to enzyme activity among African ethnicity compared to American, Asian and European. Comparing differences in this immune pathway across groups of different ethnicity is therefore challenging.

Rates of concurrent (infectious) illness, or of previous exposure is not discussed, and most likely varies between the groups – considering the significant differences in disease epidemiology between South Africa and the United States.

Lastly, the Control group seems to consist of people with LTBI and those without – why are these not separated? This makes it hard to disentangle differences between those with LTBI, those with exposure and no LTBI, and those without prior exposure.

Sample methods/analysis

The authors have examined plasma samples taken from patients over the length of a treatment course for MDR/DS-TB. Each of these cohorts will be on differing drug regimens, with different administration frequencies, with potentially individual effects on cellular metabolism. This extends further to the HIV+ cohort, who, based on the original cohort study information, are mostly also on ART during the study period. Any changes in glycolytic/oxidative metabolism caused by drug therapy might lead to changes in TCA cycling, and therefore metabolite production/signalling. Furthermore, sampling the MDR-TB and DS-TB groups at 12-15 months presumably means one groups has finished drug therapy and the other not. The control group is also not on any drug therapy, at any point. This has not been acknowledged.

The other issue with sample analysis is to draw conclusions about disease in the pulmonary compartment by studying plasma samples. Bronchoalveolar lavage samples have not been studied. As the authors state themselves, the microbiome contributes significantly to succinate presence in the plasma. This study seemed to be limited by samples that had been previously collected during the original MDR-TB treatment study (reference 13).

Assessment of lipidomics is performed on a “subset” of the original population – we are given no information, however, of the rate of HIV infection or other baseline characteristics in this group. This is also the case for the cytokine analysis (Fig 6).

The study is limited by lack of mechanistic analysis. It is acknowledged by the authors that the results are observational, however they draw a bold conclusion that the IL-1B/eicosanoid signalling pathway could be targeted by HDT: I would feel it is difficult to jump to this conclusion with observational data, particularly in a pathway this novel. The relationship between itaconate and Mtb in humans is somewhat unexplored in even in vitro studies – one would imagine mechanistic interrogation would be quite important to understand given the various roles of itaconate that we currently know about (inhibition of SDH, stabilisation of NRF2, inhibition of isocitrate lyase).

Reviewer #3: 1. One shortcoming of this paper is the lack of follow up studies to test the mechanisms and the model presented. While this is understandable given the human patients, it does raise questions that are important to consider. For example the authors cannot distinguish whether the increased inflammation creates an environment that drives bacterial replication or if uncontrolled bacterial replication is driving inflammation. This is not entirely addressable by the nice inclusion of drug treatment conditions but could be addressed more explicitly in the discussion.

2. Do the authors think the metabolic reprogramming is solely driven by the host response, or could factors encoded by Mtb also contribute to this process. For example, the ESX-1 secretion system is known to disrupt mitochondria would this accelerate the switch to glycolysis?

3. Did the authors examine IL1Ra which was shown in the mouse model to also drive disease and over-ride elevated IL1b and shutting down IL1R signaling?

4. The fact that Itaconate is not significantly different following 12 months of treatment should be discussed more thoroughly as it might suggest alternative mechanisms to dampen inflammation.

5. Given IL1b is a key part to the story, are the authors able to examine whether signal 1 (Pro-IL1b) and/or signal 2 (inflammasome activation) are different based on their samples in hand? This may help drive at some mechanism given the constraints of these samples. If not maybe discuss these possibilities and what could drive each (ie more Mtb more TLR activation/Signal 1 or more cell damage so more inflammasome activation).

**********

Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: 1) In general, is there any correlation between the metabolic and lipidomic reprogramming observed with MDR-TB and circulating levels of immune cells in comparison to DS-TB or controls? What is the level of protection from the macrophages derived from MDR-TB individuals compared to DS-TB?

2) Line 792: Immonometabolic should be immunometabolic

3) In Figure 6, Please show the controls on the same graphs demonstrating the return to baseline following TB treatment.

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Decision Letter 1

JoAnne L Flynn, David M Lewinsohn

3 Sep 2021

Dear Dr. Collins,

We are pleased to inform you that your manuscript 'TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis' has been provisionally accepted for publication in PLOS Pathogens.

Before your manuscript can be formally accepted you will need to complete some formatting changes as well as addressing the minor comment from one reviewer, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Pathogens.

Best regards,

David M. Lewinsohn

Associate Editor

PLOS Pathogens

JoAnne Flynn

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064

***********************************************************

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: The authors have adequately addressed my concerns.

Reviewer #2: The authors have done their best to address the issues we raised - and whilst they weren't able to modify their original patient cohort, they have gone some distance to increase the transparency of the data, specifically the baseline characteristics of the MDR/HIV cohort, and of those brought forward for further in-depth analysis within the MDR group.

This makes it much easier to put their findings into context.

The HIV+ group had - as we expected - significant viral load titres, and low CD4 counts. It's interesting to see that their findings in most of the data hold with the separation of HIV cohorts.

Rigor: Significant comment has been made about the nature of the observational study data here and how it fails to address mechanism. The analysis cannot identify important drivers, because the study is not designed to do so. The current draft of the abstract, at the moment, includes a comment in the penultimate sentence, which says

.... that metabolites and intermediates are "important drivers" of IL-1beta mediated proinflammatory signals.....

We suggest that the word "drivers" be removed, and use a word like "correlates", or "associations" please.

Reviewer #3: This is a revised submission that examines the linking TCA cycle remodeling with inflammation in humans infected with Mtb. The authors have sufficiently addressed my concerns either experimentally or with revised text. This manuscript helps further understand how host metabolism modulates Mtb disease and will be of great interest to the plos pathogen readership.

**********

Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Maziar Divangahi

Reviewer #2: Yes: Joseph Keane

Reviewer #3: No

Acceptance letter

JoAnne L Flynn, David M Lewinsohn

21 Sep 2021

Dear Dr. Collins,

We are delighted to inform you that your manuscript, "TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis," has been formally accepted for publication in PLOS Pathogens.

We have now passed your article onto the PLOS Production Department who will complete the rest of the pre-publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Pearls, Reviews, Opinions, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript, if you opted to have an early version of your article, will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Pathogens.

Best regards,

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Correlation of plasma itaconate concentrations with TCA cycle intermediates.

    Plasma concentrations of itaconate were significantly and negatively correlated with TCA cycle intermediates (A) succinate, (B) fumarate and (C) malate.

    (TIF)

    Click here for additional data file. (1.8MB, tif)
    S2 Fig. Immunometabolic remodeling and disease severity.

    (A) Time to culture conversion was significantly and positively correlated with plasma concentrations of IL-1β at study enrollment and (B) negatively correlated with concentrations of GM-CSF. (C) In persons with drug susceptible (DS)-TB, the plasma ratio of succinate to itaconate was significantly increased in those with a persistently positive sputum smear for acid-fast bacilli (AFB) at enrollment versus those who converted to a negative AFB sputum smear.

    (TIF)

    Click here for additional data file. (1.6MB, tif)
    S3 Fig. Prostaglandin F2α and Leukotriene B4 in TB disease.

    (A) Plasma concentrations of PGF2a were below the limit of detection in all TB disease groups as well as most uninfected U.S. controls while all persons with LTBI had detectable plasma concentrations. (B) LTB4 was only detected in plasma in persons with MDR-TB.

    (TIF)

    Click here for additional data file. (1.2MB, tif)
    S4 Fig. Plasma concentrations of TCA cycle metabolites after 2 months of treatment for drug-susceptible TB.

    Plasma concentrations of (A) succinate, (B) Malate, (C) fumarate, (D) itaconate and (E) arachidonic acid were not significantly different after 2 months of treatment versus baseline in persons with drug-susceptible TB from Georgia (n = 89).

    (TIF)

    Click here for additional data file. (2MB, tif)
    S5 Fig. Reversal of immunomedabolic remodeling with MDR-TB treatment does not differ by HIV status.

    Decreases in proinflammatory metabolites and cytokines with MDR-TB treatment was similar in persons with MDR-TB with (orange; n = 12) and without (green; n = 5) HIV co-infection. In both groups, plasma concentrations of itaconate and GM-CSF were unchanged over time.

    (TIF)

    Click here for additional data file. (2.7MB, tif)
    S1 Table. Clinical and demographic characteristics of study participants included in the cytokine substudy.

    (DOCX)

    Click here for additional data file. (15.1KB, docx)
    S2 Table. Clinical and demographic characteristics of study participants included in the lipidomics substudy.

    (DOCX)

    Click here for additional data file. (15.2KB, docx)
    S1 Data. Untargeted plasma metabolomics feature table.

    (TXT)

    Click here for additional data file. (32.2MB, txt)
    S2 Data. Table of plasma concentrations of targeted metabolites.

    (TXT)

    Click here for additional data file. (53.1KB, txt)
    S3 Data. Table of plasma cytokine concentrations.

    (TXT)

    Click here for additional data file. (44.2KB, txt)
    S4 Data. Untargeted plasma lipidomics feature table.

    (TXT)

    Click here for additional data file. (93.7KB, txt)
    S5 Data. Table of plasma oxylipin concentrations.

    (TXT)

    Click here for additional data file. (300.6KB, txt)
    S6 Data. Clinical and demographic characteristics of study participants.

    (TXT)

    Click here for additional data file. (9.5KB, txt)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (35KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

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