Dichloroacetate-induced metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.

Sepsis is the leading cause of death in hospitalized patients and beyond the hospital stay and these long-term sequelae are due in part to unresolved inflammation. Metabolic shift from oxidative phosphorylation to aerobic glycolysis links metabolism to inflammation and such a shift is commonly observed in sepsis under normoxic conditions. By shifting the metabolic state from aerobic glycolysis to oxidative phosphorylation, we hypothesized it would reverse unresolved inflammation and subsequently improve outcome. We propose a shift from aerobic glycolysis to oxidative phosphorylation as a sepsis therapy by targeting the pathways involved in the conversion of pyruvate into acetyl-CoA via pyruvate dehydrogenase (PDH). Chemical manipulation of PDH using dichloroacetic acid (DCA) will promote oxidative phosphorylation over glycolysis and decrease inflammation. We tested our hypothesis in a Drosophila melanogaster model of surviving sepsis infected with Staphylococcus aureus. Drosophila were divided into 3 groups: unmanipulated, sham and sepsis survivors, all treated with linezolid; each group was either treated or not with DCA for one week following sepsis. We followed lifespan, measured gene expression of Toll, defensin, cecropin A, and drosomycin, and levels of lactate, pyruvate, acetyl-CoA as well as TCA metabolites. In our model, metabolic effects of sepsis are modified by DCA with normalized lactate, TCA metabolites, and was associated with improved lifespan of sepsis survivors, yet had no lifespan effects on unmanipulated and sham flies. While Drosomycin and cecropin A expression increased in sepsis survivors, DCA treatment decreased both and selectively increased defensin.

Introduction

Advances in diagnostic modalities, prevention of complications, and care bundles improve sepsis-associated short-term mortality; however, sepsis remains a leading cause of death in hospitalized patients and beyond [1]. In addition to high mortality rates, sepsis survivors experience long-term complications, such as accelerated cardiovascular and neuro-cognitive decline, new infections, cancer, and metabolic disturbances [2, 3]. Importantly, late deaths among sepsis survivors are not solely explained by health status before sepsis, implying some feature of the sepsis itself contributes to these late sequelae [4-7].

During sepsis, immune cells shift their metabolic balance towards aerobic glycolysis over oxidative phosphorylation producing excessive amounts of lactate, a marker for sepsis severity, even under normoxic conditions [8-11]. The metabolic changes occurring during sepsis in turn lead to further immune cell activation and unresolved inflammation [12, 13]. Clinical [14] and laboratory [13, 15–17] work suggests a link between unresolved inflammation and long-term sequelae making the aerobic glycolysis a major component of this complex regulatory network. Lactate, the signature molecule of aerobic glycolysis, is a pro-inflammatory metabolite regulating cytokines and macrophage polarization [13, 18]. Lactate is generated from pyruvate by the enzyme lactate dehydrogenase (LDH). The availability of pyruvate for lactate production is regulated by pyruvate dehydrogenase (PDH), a key enzyme in the tricarboxylic acid cycle (TCA) transforming pyruvate into acetyl-CoA and subsequent mitochondrial respiration [10, 18, 19]. Interestingly, PDH quantity and activity are decreased during sepsis with resultant accumulation of pyruvate [9]. Because lactate dehydrogenase (LDH) is an equilibrium enzyme, increased lactate production during sepsis would be due to a mass-action effect exerted by an increased availability of pyruvate [20]. Consequently, a decrease in PDH activity during sepsis will result in increased lactate production and decreased mitochondrial oxidative phosphorylation [13, 21].

The PDH complex is at a key control point of energy metabolism and subject to regulation by multiple mechanisms, including succinylation of PKM2, posttranslational phosphorylation and subsequent inactivation by pyruvate dehydrogenase kinase (PDK) [22]. Dichloroacetate (DCA), a classic PDK inhibitor, has been successfully used to decrease levels of lactate and shifts metabolism towards oxidative phosphorylation in patients with congenital hyperlactatemia by directly decreasing the PDK activity with subsequent increase in the downstream enzyme, PDH, activity [23].

In the current study, we hypothesized that increased aerobic glycolysis leading to unresolved inflammation contributes to long-term complications of sepsis, including shortened lifespan [5]. We tested our hypothesis in a D. melanogaster model of surviving sepsis by manipulating PDH activity with DCA to modify the increased glycolysis, to normalize antimicrobial peptide expression, and improve lifespan [24, 25].

Materials and methods

Drosophila melanogaster strain and maintenance

The flies were raised on standard cornmeal-molasses agar medium at 22–25°C, 60% humidity, and on a 12-h light/dark cycle. The vials were changed every 3 days. We selected male flies 2–3 days after eclosion for experiments. Wild-type (WT) Canton S flies were obtained from Bloomington stock (Bloomington Drosophila Stock Center, Indiana University).

Experimental design: Fly infection and treatment

We used our previously established model of percutaneous infection in D. melanogaster with subsequent antibiotic treatment to mimic the clinical course and treatment of human sepsis [26]. The flies were divided into 3 groups: unmanipulated, sham, and sepsis survivors. To study the impact of DCA in diet, each experimental group was further divided into 2 groups depending on DCA treatment.

We prepared Staphylococcus aureus suspension in Luria-Bertani (LB) broth and grew the bacteria to an optical density (OD) of 1.0 at 600 nm. At OD of 1.0, there were ~1.67 × 106 CFU of S. aureus. We anesthetized flies with CO2 and pricked with a tungsten needle (0.01 mm at the tip and 0.25 mm across the needle body) into their thorax. The sham group was pricked with a sterile needle while the sepsis survivor group had the needle dipped into the bacterial solution. We pricked the sham flies first to coat the tungsten needle with hemolymph to achieve consistent bacterial coating with the infection group.

We transferred unmanipulated, sham, and sepsis survivors to vials containing antibiotic (linezolid) with DCA [DCA(+)] or without DCA [DCA(-)] after recovery from anesthesia and kept flies in these vials for 18 h before transferring the flies back to antibiotic-free vials. All flies were treated with linezolid (0.5 mg/mL) in the diet as described. DCA treatment continued for one week after the initial infection and it was incorporated into the diet (DCA, 0.5 mg/mL, Sigma Aldrich, St. Louis, USA) [27]. Immune and metabolic outcomes and lifespan were followed over a 7-day course.

Fly survival and lifespan

After infection and treatment with antibiotics, fly survival was assessed by visual inspection of living flies. The flies that died within 6 h after the initial inoculation were excluded from the survival analysis, because death within the first 6 hours was considered to be secondary to trauma from inoculation rather than infection. During lifespan observations, the fly media was changed and survival assessed every 3 days. The infections were performed around the same time of the day (Fig 1).

Fig 1

Experimental design.

Conceptual depiction of the experimental design. The flies underwent either sterile needle injury (“sham”) or injury with a bacteria-laden needle (“sepsis”). Following a 6-hour recovery period in vials with regular diet to assure that the needle injury did not lead to death, flies were transferred into vials containing linezolid +/- dichloroacetate (DCA). We followed the lifespan of the flies and in a subset, we measured the gene expression, geotaxis, and metabolomic changes.

Rapid Iterative Negative Geotaxis (RING)

We performed geotaxis assays to evaluate the fitness-related locomotion traits following sepsis. We joined two empty polystyrene vials by tape vertically facing each other forming an 18.5-cm-long tube. We transferred groups of 20 flies into the vials and allowed to acclimatize to the new setting for 5 min before conducting the assay. Flies were gently tapped down to the bottom of the vial for 10 s with the same interval and strength by the same operator throughout the whole experiment. Pictures of the flies were taken with a digital camera at 5 s. We repeated each geotaxis experiment six times, allowing for 1-min rest periods between each trial and pictures were analyzed by counting the number of flies that climb above the 10-cm mark in 5 s after the tap. We calculated the average of the number of flies crossing the 10-cm threshold and expressed the results as percentage of the total number of flies in the tube (% climbing index). Each geotaxis experiment was performed 1 h before the needle pricking (0 h baseline), 1 day, and 7 days after needle pricking. The data are presented as percent of the baseline at time 0 h.

Patterns of host response gene expression

We determined pathogen recognition receptor Toll and antimicrobial peptide (AMP) expression with quantitative real-time polymerase chain reaction (qRT-PCR) at 24 h and 1 week after inoculation with bacteria. AMP expression levels provide an indication of the degree to which the immune system is activated, a surrogate for inflammation. Gene expression of Toll (FBgn0262473), defensin (FBgn0010385), drosomycin (FBgn0283461), and cecropin A (FBgn0000276) were normalized to actin5C (FBgn0000042); all data were normalized to the unmanipulated group. We used 20 flies per group in triplicate. TaqMan® Gene Expression Assay primers included the following: toll, Dm02151201_g1; defensin, Dm01818074_s1; drosomycin, Dm01822006_s1; cecropin A, Dm02609400_sH; and actin5C, Dm02361909_s1. Assay details are provided as (S1 Table).

Metabolomic analysis

LC-MS sample preparation

Each sample containing 30 flies was homogenized in MeOH:ACN:H2O (2:2:1), snap frozen and sonicated. To precipitate the proteins, samples were centrifuged at 1500 g for 10 min at 4˚C and the supernatant was transferred to a new vial and dried under N2. The sample was re-suspended in 500 μL CHCl3:MeOH:H2O (2:1:1) and centrifuged for 5 min at 4°C at 1500 g to separate the upper polar phase from the lower organic phase. A similar extraction was also performed for 5 mg 13C algal cells, whose supernatant was used as an internal standard.

Targeted analysis of organic acids

Organic acids were analyzed by derivatizing them to their corresponding 3-nitrophenylhydrazones [28]. Briefly 100 μL of the supernatant along with 10 μL of internal standard was heated at 50°C with 50 μL of 200 mM 3-nitrophenylhydrazine in 50% aqueous acetonitrile and 50 μL of 120 mM N-(3-dimethylaminopropyl)-N-ethylcarbodiimide HCl in a 6% pyridine solution in the same solvent for 20 min. From this reaction, 100 μL was diluted to 500 μL using 50% aqueous acetonitrile (ACN). The reconstituted sample, 10 μL was injected into the Ultimate 3000 RSLC system (Thermo Fisher Scientific, Waltham, MA) connected to a Thermo Fisher Scientific Q Exactive mass spectrometer. Chromatographic separation was conducted using a reversed phase Phenomenex (Torrence, CA) Luna C18 column (2.1 mm × 150 mm, 5 μm) column using gradient elution with a mobile phase consisting of H2O + 0.1% formic acid (A) and acetonitrile + 0.1% formic acid (B), delivered at a flow rate of 0.2 mL/min. The gradient program was 3% B (0–3 min), from 3% B to 95% B (3–50 min), 95% B (50–55 min), from 95% B to 3% B (55–57 min) and 3% B (57–60 min). The mass spectrometer was equipped with an ESI source and was operated in negative ion mode using a full scan range of 150 m/z to 900 m/z. Analyte identification was confirmed by high resolution accurate mass and compared to the 13C internal standard spectra and retention time [29] (S2 Table).

Untargeted analysis of primary metabolites

The primary metabolites in the polar phase were separated using ion-pairing reversed phase chromatography on the LC-MS system described above [30]. The mobile phase consisted of 5mM hexylamine in H2O (A) acetonitrile (B), delivered at a flow rate of 0.15 mL/min with a post column addition of 0.1 mL/min acetonitrile before entering the mass spectrometer. The gradient was held at 3% B for the first 3 min and increased to 30% B (3–30 min) following an additional increase to 95% B (30–55 min), a 5 min wash at 95% B (55–60 min), 95% B-100% B (60–61 min), 100% B (61–66 min), and return to initial conditions for a 3 min equilibration. The mass spectrometer was operated in negative ion mode using a full scan range of 100 m/z to 900 m/z. Analyte identification was confirmed by high resolution accurate mass and compared to the 13C internal standard. Principal component analysis (PCA) was applied to visualize grouping patterns using unsupervised multivariate data analysis.

Statistical analysis

Kaplan-Meyer survival analysis was performed using Stata 15 (StataCorp. LLC, College Station, TX). Log-rank test was performed with the Kaplan-Meier survival curves for the groups adjusting for clusters, where each vial was treated as a cluster. Statistical analysis between different groups was accomplished with ANOVA using Graph-Pad Prism software version 8.0 (GraphPad Software Inc., La Jolla, CA).

SIMCA 14.1 (SIMCA, Umetrics, Umeå, Sweden) was used for LC-MS and Principal component analysis (PCA). Data obtained from LC-MS analysis were imported into SIMCA for multivariate analysis. PCA were carried out to discriminate the metabolic patterns between groups according to common variability within groups (unmanipulated vs. sham vs. sepsis survivors as well as diet effects among sepsis survivors) after mean centering and unit variance scaling. In our PCA analyses, R2X represents percentage variability of the X data (metabolites assessment by LC-MS raw values) explained by each principal component. The results are given as score plots of the first two principal components with their R2X values.

Results

DCA improves lifespan after surviving sepsis

The lifespan of flies was followed every day until death occurred in all the experimental groups of flies. The DCA treatment did not affect lifespan in unmanipulated and sham groups (S1 Fig). Interestingly, one week of DCA treatment in the sepsis survivor group led to significantly longer lifespan when compared to flies surviving sepsis not treated with DCA (p<0.001) (Fig 2). In sepsis survivors on regular diet, median lifespan was 12 days (IQR: 7–35 days) compared to sepsis survivors on DCA diet with a median survival of 20 days (IQR: 11–58 days). The sepsis survivors on DCA diet showed better survival throughout of the study period, especially after day 12.

Fig 2

Drosophila lifespan after surviving sepsis is prolonged after a 1-week exposure to DCA.

The flies were divided into 3 experimental groups: unmanipulated, sham, and sepsis survivors. To study the impact of DCA in diet, each experimental group was further divided into 2 groups depending on DCA treatment [DCA (-) and DCA (+)]. Survival of Drosophila melanogaster after septic injury with Staphylococcus aureus was assessed after the initial 4–6 hours to exclude trauma-associated mortality. All flies received oral linezolid (0.5 mg/mL) for 18 h. Lifespan analysis was performed using the Kaplan-Meyer survival analysis. In the DCA (+) groups, flies were fed DCA (0.5 mg/mL) only for 1 week following sepsis and then switched back to regular diet. The DCA (+) sepsis survivors had improved lifespan compared to flies that were fed regular diet (*p < 0.001).

Geotaxis among sepsis survivors is not improved by DCA

The geotaxis was assessed 24-hour and 7-days after surviving sepsis. While we observed a significant lifespan benefit with DCA, there was no early separation of lifespan by day 7. There was also no improvement in geotaxis in sepsis survivors exposed to DCA compared to regular diet (Fig 3).

Fig 3

Geotaxis among sepsis survivors is not improved by DCA.

Despite lifespan advantage provided by the 1-week exposure to DCA, geotaxis–a composite index of locomotor performance- did not improve negative geotaxis among survivors of sepsis.

Flies treated with DCA had decreased levels of antimicrobial peptides

Gram (+) bacterial Lys-type peptidoglycan, a characteristics of Gr (+) bacteria, strongly stimulate the Toll system with downstream activation of pathways leading to AMP expression. AMP expression levels provide a surrogate marker of immune system activation [26].

We assessed the Toll receptor (FBgn0262473) along with three effector AMPs; defensin (FBgn0010385), drosomycin (FBgn0283461), and cecropin A (FBgn0000276), all are most strongly regulated by the Toll pathway. In a recent model of Drosophila surviving sepsis, AMPs increased and remained elevated following clearance of bacterial pathogen [26]. The values reported in Fig 4 are relative values to the one measured in the unmanipulated groups [DCA (-) and DCA (+)] at 24 hr and 1-week post sepsis (Fig 4).

Fig 4

DCA selectively affects early and late AMP expression patterns in Drosophila surviving sepsis.

Flies were infected with Staphylococcus aureus and then treated with orally available linezolid (0.5 mg/mL) in the diet for 18 hours. DCA treatment continued for one week after the initial infection and it was also incorporated into the diet (DCA, 0.5 mg/mL). AMP transcription was assessed after 24 h and 1 week surviving sepsis by quantitative PCR and normalized to unmanipulated flies in each respective diet group. Significant different expression values across treatment within each antimicrobial peptide transcript were determined by one-way ANOVA and Tukey’s multiple comparison tests (*<0.05 and **<0.01). After 24 h surviving sepsis, cecropin A expression was significantly elevated regardless of the diet exposure. However, only the DCA exposed flies had significantly elevated defensin expression levels. After 1 week surviving sepsis, addition of DCA significantly decreased the expression of cecropin A and drosomycin while increasing defensin expression. The number of biological repeats for qPCR was n = 6.

In the early post-sepsis phase (24 h), the cecropin A (FBgn0000276) expression was significantly higher among flies surviving sepsis on both regular (10.2-fold) and DCA (22.6-fold) diet. The expression of defensin (FBgn0010385) was significantly increased among sepsis survivors exposed to DCA 24 h after septic injury compared to sham flies fed DCA (17.9-fold). Defensin (FBgn0010385) was also significantly elevated in the DCA (+) sepsis survivors compared to DCA (-) sepsis survivors (11.4-fold). The transmembrane protein Toll (FBgn0262473) expression was not affected by the DCA treatment.

When the same set of genes were examined 1 week after the injury, expression of drosomycin (FBgn0283461) (29-fold in DCA (-) and 25.4-fold in DCA (+)) and cecropin A (FBgn0000276) (3.11-fold in DCA (-) and 4.8-fold in DCA (+)) were significantly increased in sepsis groups when compared to sham groups regardless of the diet. However, DCA treatment significantly decreased the expression of drosomycin (FBgn0283461) (0.43-fold) and cecropin A (FBgn0000276) (0.75-fold) in flies surviving sepsis.

While DCA treatment significantly diminished drosomycin (FBgn0283461) and cecropin A (FBgn0000276) expression, the expression of defensin (FBgn0010385) was significantly higher in DCA-treated flies after sepsis compared to sepsis survivors on regular diet (2.4-fold). In summary, DCA treatment for 1 week facilitated a focused and sustained expression of defensin (FBgn0010385), the AMP against gram-positive bacteria, whereas DCA treatment reduced expression of drosomycin (FBgn0283461) and cecropin A (FBgn0000276).

Treatment of Drosophila surviving sepsis with DCA demonstrates metabolomic reprogramming

We then studied changes in metabolite levels in sham and sepsis survivor groups with or without DCA treatment. We used multivariate analysis with an unsupervised PCA model to study metabolites. PCA results were calculated by reducing the number of dimension while preserving the initial information (metabolites variability) (S2 Table) [31]. The two principal components of the score plot are considered as a combination of the initial variables, each of them having a different “weight” in the calculation of the principal component (PC). The weights of each variable on the PC1 and PC2 describe the metabolites variation responsible for the discrimination of the clusters in the PCA score-plots. The multivariate analysis with the unsupervised PCA model showed that flies on regular diet could not be separated according to their group (Fig 5A). On the contrary, when flies were treated with DCA, the PCA score plot shows that the 2 first components could discriminate sham from unmanipulated and from sepsis survivors (Fig 5B). When comparing the DCA effect among the sepsis survivors, the PCA model could clearly discriminate two clusters along the first component (R2X = 0.885) (Fig 5C). The metabolites having the highest weight on the first component are α-ketoglutarate, fumarate, and pyruvate. These PCA models confirm the metabolic consequences of the DCA treatment. For the further comparison of metabolic assessment, we used the unmanipulated flies to normalize metabolite concentrations in sham and sepsis survivor groups.

Fig 5

Multivariate analysis of metabolomic LC-MS data show DCA-dependent changes among sepsis survivors.

We studied changes in metabolite levels in sham and sepsis survivors with or without DCA treatment. We used the unmanipulated flies to normalize metabolite concentrations in sham and sepsis survivor groups. (A) Multivariate analysis with unsupervised PCA model showed that flies on regular diet could not be separated according to their group (r2x = 0.678) (Fig 5A). (B) However, when flies were treated with DCA, PCA score plot shows that the 2 first components could discriminate sham from unmanipulated and sepsis survivors (r2x = 0.532) (Fig 5B). (C) Finally, when we compared the effects of DCA among the sepsis survivors, the PCA model could clearly discriminate two clusters along the first component (r2x = 0.885) (Fig 5C). The metabolites having the highest weight on the first component were α-ketoglutarate, fumarate, and pyruvate. The number of biological repeats for LC-MS was n = 5.

From the results shown in Fig 6, two metabolomic effects became obvious. The first observed difference was the effect of infection. We observed aerobic glycolysis with acetate, lactate, and TCA metabolite accumulation such as pyruvate, citrate/isocitrate, α-ketoglutarate, succinate, and fumarate among sepsis survivors on regular diet compared to sham groups on regular diet. The second observation was the diet effect. DCA treatment has no effect on sham flies as none of the metabolites measured were significantly modified with the introduction of DCA. Once we introduced DCA into the diet, the infection effect was partially reversed. DCA (+) resulted in a decrease in lactate levels among sepsis survivors, with a return to levels similar to that in the sham group. Only malate and fumarate were significantly lower in sepsis survivors compared to sham group while on DCA. In addition, DCA diet led to decreased fumarate, α-ketoglutarate and pyruvate levels in comparison to sepsis surviving flies on regular diet. Among sepsis survivors, levels of pyruvate, citrate, α-ketoglutarate, acetate, and succinate were normalized to the levels of sham in the DCA group (Fig 6).

Fig 6

DCA promotes a shift from aerobic glycolysis to oxidative phosphorylation in flies surviving sepsis.

We studied changes in metabolite levels in sham and sepsis survivors with or without DCA treatment. We used the unmanipulated flies to normalize metabolite concentrations in sham and sepsis survivor groups. Sepsis led to acetate, lactate, and TCA metabolite accumulation such as pyruvate, citrate/isocitrate, α-ketoglutarate, succinate, and fumarate among sepsis survivors on regular diet compared to sham groups on regular diet. When flies were fed DCA, infection effect was partially reversed with a decrease in lactate among sepsis survivors returning to levels similar to that in the sham group. Only malate and fumarate were significantly lower in sepsis survivors compared to sham group while on DCA. In addition, DCA diet led to decreased fumarate, α-ketoglutarate and pyruvate levels in comparison to sepsis surviving flies on regular diet. Once exposed to DCA, the TCA metabolites were either brought back to baseline–like succinate- or decreased lower than sham in sepsis survivors. In summary, DCA reversed the systemic metabolic signatures of aerobic glycolysis among survivors of sepsis. The number of biological repeats for TCA analysis was n = 5.

Levels of acetyl-CoA in DCA (+) flies were significantly higher when compared to flies on regular diet for each corresponding group (p<0.05). However, neither in regular diet, nor in the DCA diet groups, there were no differences between the sham and sepsis survivor groups within each respective group (Fig 6).

Discussion

Sepsis was recently defined by the SEPSIS 3 consortium as a ‘life-threatening organ dysfunction caused by a dysregulated host response to infection’ [24]. Acute inflammation during early phases of sepsis is necessary to survive infection, yet sustained inflammation is deleterious [26, 32, 33]. Activated immune cells during early phases of sepsis undergo rapid activation to generate ATP and biosynthetic intermediates utilizing aerobic glycolysis and lactate accumulation, which in turn propagates further antibacterial defenses and inflammation [13, 18, 34]. In sepsis survivors, such an excessive acute immune response could transition to dysfunctional immunity with long-term poor outcomes [18, 19]. As such, the new paradigm in the management of sepsis is towards improving potentially modifiable variables of late morbidity and mortality [35-37]. However, causality between sepsis and “post-acute mortality” in sepsis survivors is not well established [38, 39]. Metabolic profiling of rodents and humans show a shift from oxidative phosphorylation to aerobic glycolysis with increased lactate production during early phases of sepsis [10, 40, 41]. While metabolic changes during an acute immune response are not novel, recent work conceptualized that acute inflammatory responses in immune cells are both supported and regulated by metabolic shifts leading to “trained immunity” in innate immune cells [18, 42]. In the current work, we used the Drosophila sepsis survival model to understand the association between innate immunity and metabolomic changes during long-term sepsis sequelae. Drosophila provides the advantage that it only has the innate immunity, yet survives infections and maintains an immune memory (trained immunity) as a whole organism [43, 44]. Trained immune memory confers a long-term protection against secondary infections, yet may consume resources for the organism to live and survive on the long run, a pragmatic trade-off [7, 45–50]. This becomes especially relevant in organisms that lack adaptive immune systems, such as the Drosophila. Key metabolic enzymes of glucose oxidation and citric acid cycle are studied within the context of trained immunity [19, 51]. One of the upstream enzymes in glucose oxidation, pyruvate dehydrogenase (PDH), is a key regulatory enzyme determining the fate of pyruvate into the TCA cycle and its activity is decreased in sepsis [9, 52].

While acknowledging the limitations of pre-clinical models, others and we defined “sepsis” in Drosophila, where the flies were infected with Staphylococcus and then treated with orally available antibiotics [25]. The antibiotic exposure eliminated the bacterial burden, however inflammation (“dysregulated host response”) persisted. In our original work, we showed decrease and subsequent elimination of bacterial burden with antibiotic treatment.

We tested our hypothesis that manipulating PDH activity with a small molecular PDH activator, DCA, will reverse the increased glycolysis in sepsis, normalize AMP expression, and improve lifespan. Pyruvate dehydrogenase kinase 1 (PDK1) is upstream of PDH, phosphorylates and inhibits PDH preventing conversion of pyruvate to acetyl-CoA and shifting cell metabolism towards aerobic glycolysis highlighted by increased lactate production. DCA, by inhibiting PDK1, activates PDH and promotes the entry of pyruvate into the TCA cycle [53]. We here showed that 1-week exposure to DCA improves lifespan of Drosophila surviving sepsis over a course of almost 12 weeks, regulates inflammatory AMP, and promotes conversion of pyruvate into acetyl-CoA facilitating oxidative phosphorylation over aerobic glycolysis. DCA is reported to promote lifespan extension in C. elegans and D. melanogaster upon continuous exposure, yet we did not observe such an effect in the control flies exposed to DCA as we exposed flies for only 1 week and subsequently reared them on regular diet [54, 55]. The relatively brief exposure to DCA could explain the lack of survival advantage in non-infected control flies. Nevertheless, DCA effect on lifespan among flies surviving sepsis was profound and did last beyond the 1-week exposure. DCA has ben used to manipulate metabolic and inflammatory changes in other infection models. Work by Yamane et al did show in a murine model of influenza that both DCA as well as a DCA analog (diisopropylamine dichloroacetate) improved survival associated with inhibition of pyruvate dehydrogenase kinase 4 [56]. While we observed increased lifespan, we did not observe a functional recovery among sepsis survivors as evaluated with geotaxis [56, 57].

Fungi or gram-positive bacteria activate the Toll pathway with subsequent activation of the NF-κΒ factor Dif, Relish, or Dorsal and production of AMPs such as drosomycin, cecropin A, and defensin [58-61]. We studied AMP’s as a surrogate for inflammation based on our earlier research [26]. In our prior work using the same model, we explored the components of the canonical transcription factor NF-κB (dorsal, Dif, Relish) and a wider panel of receptors, signaling molecules, and antimicrobial peptides (PGRP-SD, Toll, Metchnikowin, Cecropin A, JNK, Drosomycin, Defensin, InR, IRS, PTEN, Akt1, Foxo, mTORC1, and ratio of p-Akt/Akt) as surrogates for sustained inflammation among flies surviving sepsis. We observed persistent elevation of NF-κB gene expression as well as activation and AMP’s in the absence of any obvious infection as shown by culturing flies surviving sepsis. This enabled us to establish the basis for the “sepsis survivor” phenotype with the goal of mimicking patients surviving sepsis, yet having ongoing inflammation even at hospital discharge.

In our experiments, among flies not treated with DCA, the drosomycin and cecropin A expression was elevated 1 week after surviving sepsis despite clearance of bacterial burden suggesting a sustained expression and activation of AMPs. Activation of AMPs, markers of inflammation, could contribute to lifespan reduction. Support for this idea comes from the observations by the Ganetzky laboratory that overexpression of AMPs, such as drosocin, attacin, defensin and drosomycin, in young flies induces neurodegeneration in mature flies and shortens lifespan [32]. Similarly, AMP overexpression in flies deficient in Methuselah-like receptor-10 (Mthl10) did limit lifespan in Drosophila [50].

The metabolite patterns of flies surviving sepsis reared on regular diet showed characteristics of the aerobic glycolysis with lactate and TCA cycle metabolites accumulating. Lactate has been used as a marker for poor clinical outcome and, in our experience, high lactate level in Drosophila is associated with a shortened lifespan [19, 26, 62]. In addition to lactate accumulation among sepsis survivors, pyruvate, citrate/isocitrate, α-ketoglutarate, acetate, succinate, and fumarate were significantly elevated compared to sham group. However, following DCA treatment, lactate and pyruvate levels came to baseline, suggesting a shift of pyruvate into the TCA cycle away from the lactate production. Among TCA cycle metabolites, citrate/isocitrate, α-ketoglutarate, acetate and succinate also came to baseline levels with DCA treatment. Fumarate and malate levels decreased significantly in the DCA group. The decrease in fumarate and malate may also indicate a redirection of flux from pyruvate carboxylase to PDH entry of pyruvate, yet this requires flux studies [52]. We used unsupervised PCA method to analyze the metabolomic changes. When we studied the metabolic impact of DCA treatment in the sham group, DCA diet has no metabolic effects compared to regular diet other than an increase in acetyl-CoA. However, in the sepsis survivor group, DCA diet effects on metabolite contents are in good agreement with the DCA mechanism of action and the expected reversal of aerobic glycolysis. Interestingly, when compared to the sham group on regular diet, the DCA-treated sepsis-surviving group, none of the metabolites differed between these two groups. This result may support our hypothesis that metabolomic effects of sepsis have been reversed by DCA. When regular diet- and DCA- survivors were compared, we observed a clear clustering in the PCA loading plots.

How the relatively short course of DCA affects the sustained levels of anti-Gr(+) defensin expression is of interest as it could link metabolomic changes to inflammation. Pyruvate can be converted to acetyl-CoA in the nucleus by the nuclear PDH, providing a source of acetyl for histone acetylation and DCA could act in a similar fashion promoting histone acetylation and thus linking metabolic changes with epigenetic control of AMP expression [63-66].

We studied geotaxis up to day 7 to avoid any survivor bias effect on later time points and found no difference between the regular and DCA diet groups on day 1 and day 7 after sepsis. Our goal of using a functional outcome was to mimic the human experience surviving sepsis [57].

As for the limitations of our current work, the DCA effect on the life span is not mechanistically shown and requires cellular level genetically modified flies to test our hypothesis further along with 13C-based metabolomic flux studies.

In summary, our results suggest an association between DCA-induced metabolic changes in Drosophila surviving sepsis and their lifespan. This sepsis survival model in an organism with only innate immunity lends itself for further mechanistic exploration of various spatial and temporal interventions towards lifespan and healthspan outcomes.

Drosophila lifespan after sterile needle injury (“sham”) is unchanged following a 1-week exposure to regular diet or DCA.

To study the impact of DCA in diet, sterile needle injured (“sham”) flies were divided to either receive regular or DCA diet. Survival of Drosophila melanogaster after sterile injury was assessed following the initial 4–6 hours to exclude trauma-associated mortality. All flies received oral linezolid (0.5 mg/mL) for 18 h. Lifespan analysis was performed using the Kaplan-Meyer survival analysis. In the DCA diet group, sham flies were fed DCA (0.5 mg/mL) only for 1 week following needle injury and then switched back to regular diet. There was no lifespan difference between regular and DCA diet receiving sham flies (p > 0.05).

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25 Mar 2020

PONE-D-20-05383

Reversal of Warburg effect via metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.

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PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: No

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The manuscript investigates the protective effect of inhibition of glycolysis and lactate production on lifespan reduction that is observed after sepsis. Experiments are conducted with the chemical compound DCA which is known to stimulate oxidative phosphorylation over glycolysis. One week after sepsis DCA results in clear changes in the expression of antimicrobial peptide (AMP) genes (as an indication of an activated immune system) such as a reduction in Cecropin A and Drosomycin but an increase in Defensin. Targeted metabolite analysis shows some of the expected changes following DCA treatment such as the absence of increase in lactate after sepsis in DCA-treated animals while also differences are observed in the abundance of TCA cycle intermediates.

The manuscript presents interesting data but there is a need for a better explanation and expansion of the data as well as a better description of the experiments.

Better explanation and expansion of the data:

1) The manuscript would benefit from a better outline of the experiments, such as a schematic of the timing of the treatment of the flies and at which time point samples are taken for analysis.

2) The concept of “sepsis” needs to be better introduced. Are there any data that show the extent of infection of the flies after pricking them with bacterial solution?

3) What is the specificity of DCA? Has it been used in other experiments that investigate the Warburg effect?

3) As an output of activation of the immune response (as an approximation of the concept of inflammation? – needs to be explained), only a few AMP genes and Toll are selected. This seems too limited in order to have a good idea about the status of “sepsis survivor” in the treated flies.

4) The purpose of the geotaxis experiments is not explained and should be elaborated.

5) The purpose of the experiments of untargeted metabolomics is not explained and should be elaborated. Particular metabolites have the highest weight in principal component analysis: explain and provide evidence.

6) In the re-infection experiments, unmanipulated flies are compared with sepsis survivors. But sham-treated animals should be used for comparison?

Better description of experiments:

1) The number of biological repeats should be clearly mentioned in text and figure legends. The data in the supplementary figures correspond to technical or biological repeats (e.g. qPCR)?

2) The criteria for the identification of the targeted metabolites (pyruvate, lactate, TCA intermediates) needs to be described (m/z values, retention times in chromatography…), perhaps as supplementary data.

3) Primers and conditions for qPCR need to be provided.

4) Supplementary Figure 6 needs to be explained. There are no legends for the supplementary data in general.

Reviewer #2: Review on manuscript Reversal of Warburg effect via metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis

1. Summary of the research and my overall impression

The general topic of this research article is highly entertaining. Metabolic change of activated immune cells, accompanied by the shift in their role and its impact on overall survival of acute and chronic infection, has gained a lot of attention recently and represents a potential target for medical treatment.

Authors of the manuscript focused on long-term changes induced in response to bacterial infection and claim that these changes, resembling the Warburg effect, can be reversed by feeding the flies with dichloroacetic acid, which improves the outcome of the sepsis on lifespan.

Even though I like the ideas behind the design of the experiment and building of the story, the data do not entirely adhere to their interpretation and do not support the general conclusions.

Therefore, I have to declare here that data seems to me to be over-interpreted and does not support the presented interpretations. Misinterpretation of some observations, together with a low number of individuals used in survival and lifespan assays, further supports my feeling that this manuscript has to improve a lot to reach common standards of PLOS ONE journal.

2. Discussion of specific areas for improvement

Major points:

1) It is known that bacterial infection causes polarization of macrophages towards aerobic glycolysis in drosophila. Nevertheless, all the detected changes (gene expression and metabolites) have been measured on the whole fly lysates. Additionally, the treatment affecting the metabolic rearrangement of cells towards oxidative phosphorylation is systemic. The presented data, in my opinion, do not support the metabolic shift towards aerobic glycolysis. The mechanism of the DCA effect on the life span is thus elusive and cannot be interpreted as a Warburg effect reversal. Therefore the mechanism of action of this drug is entirely unknown may influence many different signaling pathways in various tissues and organs. Moreover, Drosophila - as a model organism, enables to solve particularly the question of tissue-specific effects of such treatment due to versatile genetic tools that were omitted entirely.

For me, the whole model of survivors of sepsis and the long-term persisting Warburg effect in flies is a little bit confusing. Authors claim that flies surviving the infection still show increased activation of the immune system and metabolic reprogramming. Nevertheless, in this manuscript, there are completely missing data about the level of bacteria and how long they are persisting in the flies. While trying to find the answer to this question, I found the answer to this question in supplementary data of previously published research publication establishing this experimental model (citation 23). According to the data, there are still bacteria present in the flies, even after curing the sepsis by antibiotics. Therefore, we can expect that persisting changes are caused by this chronic bacterial infection rather than cured acute infection as it is interpreted (Link to the previously published data from the corresponding author https://static-content.springer.com/esm/art%3A10.1186%2Fs40635-016-0075-4/MediaObjects/40635_2016_75_MOESM1_ESM.pdf).

2) Many data that are presented in the manuscript do not support the conclusion because of the following reasons:

Fig.1 – the number of individuals is not sufficient to claim anything about survivors of infection; it is not clear to me whether more than one biological replicate has been done.

Fig.2 – I would say that there are not enough individuals used in this assay, and the number of individuals in compared groups is strikingly different.

Fig.3 – Several significant differences do not have displayed the significances in the plots even though they are discussed in the text. Moreover, the data show a striking difference even in sham control groups in comparison to untreated ones. How can the authors explain that the expression level of cecropin-A and drosomycin is increased more than 50-times just in response to sham treatment?

Fig.4 – I like the PCA analysis (although it would be nice to see data also in RDA), but have some problems with some interpretations. How is figure 4A, showing no difference between all three groups, support the notion that infection induces the Warburg effect? Why these groups diversify because of DCA feeding (because one would instead expect that DCA treatment would diminish previously observed changes)? Further, the variance is explained mainly by metabolites of the TCA cycle metabolites (as it is mentioned in the results), but the typical changes for the Warburg effect are instead increased glycolysis and pentose phosphate pathway while the TCA cycle undergoes complicated rewiring. Are these metabolites changing as well. How are these data in adherence with previously published observation, where for instance, lactate has not been altered in the same treatment? (https://www.mdpi.com/2218-1989/6/4?view=abstract&listby=date&page_no=1)

Fig.5 – According to my opinion, the data are not interpreted correctly since the authors claim that DCA leads to increased TCA cycle rather than the conversion of pyruvate to lactate. Still, there is almost no difference in most of the TCA metabolites in the sham group of flies in response to DCA feeding.

Fig.6 – The analysis has performed to show whether gain of primed immunity to S. aureus reinfection rely in metabolic rearrangement and can be thus disturbed by DCA feeding. Unfortunately, I have to admit that this experiment failed to show that there is the primed immunity in analyzed flies. The number of individuals in this assay is insufficient. By adding the sham manipulated group that was excluded from the figure but can be found in data files, it is clear the flies did not gain the primed immunity at all. The rest of the experiment is thus irrelevant.

Minor points:

Line4 – I do not entirely understand the last sentence of the abstract, but it may be due to my English.

Introduction – I lack in the introduction the information about what is known about the Warburg effect and polarization of macrophages in Drosophila. Moreover, many of the references are not very well supporting the notions (for example – 17, 19, etc.).

Material and Methods

The paragraphs - Fly reinfection and Fly survival and lifespan - should be described more into the detail.

Even though there is mentioned the source of primers, it would be valuable to have the sequences of primers and probes used in the appendix. Further, the genes, included in the publication, should be identified by their FLY base FBGN identifier.

Discussion – the references should be revisited. The notion on line 331-334 seems to me excessive and directly connected with the published data.

Based on the justification mentioned above, I have to say that I do not recommend the manuscript to be accepted.

In Ceske Budejovice (Czech Republic) 21-3-2020 Adam Bajgar

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Reviewer #1: No

Reviewer #2: Yes: Adam Bajgar, Ph.D.

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28 Sep 2020

September 10th, 2020

Fanis Missirlis, Ph.D.

Academic Editor

PLOS ONE

Departamento de Fisiología, Biofísica y Neurociencias,

CINVESTAV-IPN, Zacatenco, 07360, México

RE: PONE-D-20-05383 “Reversal of Warburg effect via metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.”

Dear Prof. Missirlis:

During these tumultuous times with COVID-19 pandemic, we would like to thank the editorial board and reviewers for their constructive comments and time for our manuscript entitled “Reversal of Warburg effect via metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.” We here are submitting a revised version of our work and agree with all the reviewers that overinterpretation of data should be avoided. Accordingly, we revised our discussion significantly and wrote it based on the available data.

The repeat experiments for “trained immunity” are valuable suggestions, however during the COVID-19 pandemic we couldn’t perform new experiments and we are finally getting a chance to restart our work. We did remove the re-infection data within the “trained immunity” context without losing the message of the manuscript. We hope that our work will satisfy the review process.

Sincerely,

A. Murat Kaynar

Journal Requirements:

Critique: We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data.

Response: We did add the survival graph for the unmanipulated and sham groups as an Supporting Information file.

Critique: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly.

Response: We added the captions accordingly.

Critique (9/26/2020):

a) Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution.

Response: Dr. Kaynar received research grant support from NIH (HL126711).

b) State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Response: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

c) If any authors received a salary from any of your funders, please state which authors and which funders.

Response: Dr. Kaynar received salary support from NIH (HL126711).

Reviewers' comments:

We thank the reviewer for constructive input and we will address these in an itemized fashion.

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: No

Response: We thank the reviewers and have rewritten the discussion avoiding overinterpretation of the data.

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Response: Our co-author, Dr. Chung-Chou H. (Joyce) Chang, is Professor of Biostatistics and Clinical and Translational Science in the University of Pittsburgh Graduate School of Public Health and we have been collaborating on various models of sepsis for more than 10 years. Individual statistical responses will follow the specific statistical questions.

Reviewer #1:

Critique: The manuscript would benefit from a better outline of the experiments, such as a schematic of the timing of the treatment of the flies and at which time point samples are taken for analysis.

Response: We thank the reviewer and added a schematic for experimental outline and read-outs as Figure 1 hoping it clarifies the study. Our message with this paper is the long-term effects of a 1-week treatment with DCA. After 1-week exposure to diet with (+) or without (-) DCA, all sepsis-surviving flies were switched to regular diet.

Critique: The concept of “sepsis” needs to be better introduced. Are there any data that show the extent of infection of the flies after pricking them with bacterial solution?

Response: “Sepsis” was recently defined by the SEPSIS 3* consortium as a ‘life-threatening organ dysfunction caused by a dysregulated host response to infection.’ While acknowledging the limitations of pre-clinical models, others# and we@ defined “sepsis” in Drosophila, where the flies were infected with Staphylococcus and then treated with orally available antibiotics. The antibiotic exposure eliminated the bacterial burden, however inflammation (“dysregulated host response”) persisted. In our original work, we showed decrease and subsequent elimination of bacterial burden with antibiotic treatment.

*[https://pubmed.ncbi.nlm.nih.gov/26903338 The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) JAMA 2016 Feb 23;315(8):801-10.]

#[https://pubmed.ncbi.nlm.nih.gov/16940158/ The Nitric Oxide Scavenger Cobinamide Profoundly Improves Survival in a Drosophila Melanogaster Model of Bacterial Sepsis. FASEB J 2006 Sep;20(11):1865-73]

@[https://pubmed.ncbi.nlm.nih.gov/26791145/ Cost of Surviving Sepsis: A Novel Model of Recovery From Sepsis in Drosophila Melanogaster - Intensive Care Med Exp 2016 Dec;4(1):4]

Critique: What is the specificity of DCA? Has it been used in other experiments that investigate the Warburg effect?

Response: DCA has ben used to manipulate metabolic and inflammatory changes in other infection models. Work by Yamane et al did show in a murine model of influenza that both DCA as well as a DCA analog (diisopropylamine dichloroacetate) improved survival (Figure 8) and had similar inhibition of pyruvate dehydrogenase kinase 4 (Table 3).

#[https://pubmed.ncbi.nlm.nih.gov/24865588/ Diisopropylamine Dichloroacetate, a Novel Pyruvate Dehydrogenase Kinase 4 Inhibitor, as a Potential Therapeutic Agent for Metabolic Disorders and Multiorgan Failure in Severe Influenza. FASEB J 2006 Sep;20(11):1865-73] PLoS One 2014 May 27;9(5):e98032]

Critique: As an output of activation of the immune response (as an approximation of the concept of inflammation? – needs to be explained), only a few AMP genes and Toll are selected. This seems too limited in order to have a good idea about the status of “sepsis survivor” in the treated flies.

Response: As the reviewer rightly suggested, we used the studied AMP’s as an approximation of inflammation based on our earlier research. In our prior work using the same model*, we explored the components of the canonical transcription factor NF-κB (dorsal, Dif, Relish) and a wider panel of receptors, signaling molecules, and antimicrobial peptides (PGRP-SD, Toll, Metchnikowin, Cecropin A, JNK, Drosomycin, Defensin, InR, IRS, PTEN, Akt1, Foxo, mTORC1, and ratio of p-Akt/Akt) as surrogates for sustained inflammation among flies surviving sepsis.

In our original work*, we observed persistent elevation of NF-κB gene expression as well as activation (Figure 2 in Intensive Care Med Exp 2016) and AMP’s (Figure 3 in Intensive Care Med Exp 2016) in the absence of any obvious infection as shown by culturing flies surviving sepsis.

This was the basis of us establishing the “sepsis survivor” phenotype with the goal of mimicking patients surviving sepsis, yet having ongoing inflammation even at hospital discharge. We distilled our prior experience and used these AMP’s and Toll in the current manuscript. We revised our manuscript accordingly and added the reasoning for the few AMP’s and Toll used.

*[https://pubmed.ncbi.nlm.nih.gov/26791145/ Cost of Surviving Sepsis: A Novel Model of Recovery From Sepsis in Drosophila Melanogaster - Intensive Care Med Exp 2016 Dec;4(1):4]

Critique: The purpose of the geotaxis experiments is not explained and should be elaborated.

Response: Our goal of using a functional outcome* was to mimic the human experience surviving sepsis.

*[https://pubmed.ncbi.nlm.nih.gov/26109398/ Physical Activity, Muscle Strength, and Exercise Capacity 3 Months After Severe Sepsis and Septic Shock - Intensive Care Med 2015 Aug;41(8):1433-44]

Critique: The purpose of the experiments of untargeted metabolomics is not explained and should be elaborated. Particular metabolites have the highest weight in principal component analysis: explain and provide evidence.

Response: Univariate data analysis as shown in Fig. 5 in the revised manuscript and multivariate analysis such as PCA analysis provide different information from the data measured. Univariate analysis suggests that the variable (here a metabolite) presents significantly different amounts in a sample from another sample, control vs. treated for example. Otherwise, multivariate analysis, using all variables in the same calculation gives information about the relationship between variables.

Here, we have chosen the unsupervised PCA method, which is considered a basic analysis in metabolomics, not susceptible to overfitting, and robust method when clustering samples, as it is the case in Figure 5.

[https://link.springer.com/article/10.1007/s11306-013-0598-6 Reflections on univariate and multivariate analysis of metabolomics data. - Metabolomics. 2014;10(3):361–374.]

PCA results are evidenced in score-plot figures (Fig. 5) calculated by reducing the number of dimension (or variables) while preserving the initial information (metabolites variability).

The two principal components of the score plot may be roughly considered as a combination of the initial variables, each of them having a different “weight” in the calculation of the principal component equation. Therefore, the weights of each variable on the PC1 and PC2 describe the metabolites variation responsible for the discrimination of the clusters in the PCA score-plots. In addition, the values of metabolite weight on PC1 and PC2 are given in the table below.

wpc1 wpc1 Wpc2

Acetate 0.32 0.45

AKG 0.38 0.1

Cit/isoCit -0.35 0.4

Fumarate -0.38 0.08

Lactate 0.3 0.55

Malate -0.35 0.35

Pyruvate 0.38 0.15

Succinate -0.35 0.32

Critique: In the re-infection experiments, unmanipulated flies are compared with sepsis survivors. But sham-treated animals should be used for comparison?

Response: We agree with the reviewer and eliminated the data on reinfection model from the revised manuscript.

Better description of experiments:

Critique: The number of biological repeats should be clearly mentioned in text and figure legends. The data in the supplementary figures correspond to technical or biological repeats (e.g. qPCR)?

Response: We thank the reviewer, the data in the supplementary figures correspond to biological repeats and added this information into the main text as well as figure legends.

Critique: The criteria for the identification of the targeted metabolites (pyruvate, lactate, TCA intermediates) needs to be described (m/z values, retention times in chromatography…), perhaps as supplementary data.

Response: The methods for LC-MS metabolites vary between each laboratory leading to variations in retention times. In addition, we derivatized the metabolites, so the m/z values will not be the masses of the parent metabolite. For instance, lactate has a mass of 89, but will be 89 + the mass of 3 NP. We did cite the relevant methods in the main text and added a supplemental table (Table S2) as rightly requested.

Critique: Primers and conditions for qPCR need to be provided.

Response: We added a supplementary table with RT-qPCR information.

Critique: Supplementary Figure 6 needs to be explained. There are no legends for the supplementary data in general.

Response: We agree with the reviewer and eliminated the data on reinfection model from the revised manuscript.

Reviewer #2: We thank Prof. Bajgar for the expertise and criticism shared with us

1. Summary of the research and my overall impression

...Even though I like the ideas behind the design of the experiment and building of the story, the data do not entirely adhere to their interpretation and do not support the general conclusions. Therefore, I have to declare here that data seems to me to be over-interpreted ...

Response: We appreciate the candid and constructive input from Prof. Bajgar. We re-worded our discussion remaining loyal to the data present.

2. Discussion of specific areas for improvement

Major points:

Critique: 1) It is known that bacterial infection causes polarization of macrophages towards aerobic glycolysis in drosophila. Nevertheless, all the detected changes (gene expression and metabolites) have been measured on the whole fly lysates.

Response: We agree that macrophage polarization is well established in the literature both for mammalian as well as insect models. We think of sepsis as a systemic disease with multiple tissues/cell types involved, so we decided to use whole insect analytes, however we got the inspiration to follow our work with macrophage specific deletion models.

Critique: Additionally, the treatment affecting the metabolic rearrangement of cells towards oxidative phosphorylation is systemic. The presented data, in my opinion, do not support the metabolic shift towards aerobic glycolysis. The mechanism of the DCA effect on the life span is thus elusive and cannot be interpreted as a Warburg effect reversal. Therefore the mechanism of action of this drug is entirely unknown may influence many different signaling pathways in various tissues and organs.

Response: We agree with the criticism that one specific pathway for DCA improvement on sepsis. One way to demonstrate this more definitively would have been to do the 13C-glucose labeling experiments that we are discussing now to start after the Covid-19 pandemic normalizes the laboratory environment.

Critique: Moreover, Drosophila - as a model organism, enables to solve particularly the question of tissue-specific effects of such treatment due to versatile genetic tools that were omitted entirely. For me, the whole model of survivors of sepsis and the long-term persisting Warburg effect in flies is a little bit confusing. Authors claim that flies surviving the infection still show increased activation of the immune system and metabolic reprogramming. Nevertheless, in this manuscript, there are completely missing data about the level of bacteria and how long they are persisting in the flies. While trying to find the answer to this question, I found the answer to this question in supplementary data of previously published research publication establishing this experimental model (citation 23).

Response: Our current data did not show any residual infection after the antibiotic treatment, yet we would also like to remind the reviewers that both the DCA+ as well as the DCA- groups had the same experimental conditions (i.e. infection followed by antibiotics).

Critique: Fig.1 – the number of individuals is not sufficient to claim anything about survivors of infection; it is not clear to me whether more than one biological replicate has been done.

Response: We had biological replicates and went through this survival analysis with Prof. Chang, one of our co-authors. We agree with the difficulties on performing survival analyses in Drosophila as we keep these animals in vials and one can argue that vials are like clusters. Thus, adding all of the flies together across vials makes it a challenge. For example, two flies in one vial could be regarded to be more alike than one fly from one vial and one from another. We performed Kaplan-Meier survival analyses for the groups adjusting and not adjusting for clusters (vials). The two groups are statistically different (DCA has better survival) at the alpha=0.05 level with and without adjusting.

The IQR of survival days for the two groups were:

Group A (regular diet): median=12 days (IQR: 7-35 days) and Group B (DCA diet): median=20 days (IQR: 11-58 days). The DCA diet groups showed better survival throughout of the study period, especially after day 12 the separation becomes profound.

Critique: Fig.2 – I would say that there are not enough individuals used in this assay, and the number of individuals in compared groups is strikingly different.

Response: Our analyses suggested that the lifespan was statistically significant.

Critique: Fig.3 – Several significant differences do not have displayed the significances in the plots even though they are discussed in the text. Moreover, the data show a striking difference even in sham control groups in comparison to untreated ones. How can the authors explain that the expression level of cecropin-A and drosomycin is increased more than 50-times just in response to sham treatment?

Response: Figure 3 (now Figure 4) has the data in the figures in the following order:

Sham regular diet – Sepsis survivors regular diet – Sham DCA diet – Sepsis survivors DCA diet

The 50-fold increase was in response to infection, not to sham treatment.

Critique: Fig.4 – I like the PCA analysis (although it would be nice to see data also in RDA), but have some problems with some interpretations. How is figure 4A, showing no difference between all three groups, support the notion that infection induces the Warburg effect? Why these groups diversify because of DCA feeding (because one would instead expect that DCA treatment would diminish previously observed changes)?

Response: The Warburg effect is based on the increased of lactate content (which is shown in the Figure 5). The Figure 5A is a multivariate analysis not only based on lactate concentration but on several metabolites aiming to reveal the relationships between the variables measured. The PCA score plot shows that the variability of all metabolites within the 3 groups (unsupervised method) does not reach the clustering of the groups. Nevertheless, when flies are treated with DCA as in figure 5B, the 3 groups tend to separate and when regular diet- and DCA- survivors are compared a clear clustering is obtained in PCA loading plot.

Critique: Further, the variance is explained mainly by metabolites of the TCA cycle metabolites (as it is mentioned in the results), but the typical changes for the Warburg effect are instead increased glycolysis and pentose phosphate pathway while the TCA cycle undergoes complicated rewiring.

Response: Most of the metabolites measured as those of the TCA cycle and we agree with the reviewer that TCA cycle undergoes complicated inter-connected modulations as shown in the figures 5 and 6.

Critique: Are these metabolites changing as well. How are these data in adherence with previously published observation, where for instance, lactate has not been altered in the same treatment? (https://www.mdpi.com/22181989/6/4?view=abstract&listby=date&page_no=1).

Response: There are a large number of differences between this study and the previous one.

First, the analytical methods are completely different in the physical principles and aims. NMR is a non-targeted method evaluating a large number of variables that will be statistically analyzed (including what could be considered as “noise”). In the present study, the mass spectrometry, far more sensitive, is a targeted method here aimed to measure metabolites of the TCA cycle.

Secondly, the statistical analyses are completely different as, in the present study, univariate analysis was performed showing the variation of lactate content and unsupervised multiparametric analysis was applied to investigate the possible relationships between these variables. In the previous paper, lactate was not the variable with the highest correlation in the supervised models, as several other metabolites with higher correlation were found.

As far as the suggested RDA, a supervised method, our number of samples and number of variables could make interpretation difficult (same argument could be made for PLS in this case).

Critique: Fig.5 – According to my opinion, the data are not interpreted correctly since the authors claim that DCA leads to increased TCA cycle rather than the conversion of pyruvate to lactate. Still, there is almost no difference in most of the TCA metabolites in the sham group of flies in response to DCA feeding.

Response: The figure is now Fig 6. In the sham group, there is no lactate increase and no particular glycolysis over-function, the TCA cycle is running at its usual turn-over.

Acetyl-CoA did increase with DCA treatment and pyruvate is converted into acetyl-CoA in the mitochondria suggesting that more pyruvate is entering into TCA and less is being converted to lactate. However, as the reviewer rightly suggested, 13C labeling would make this more visual and mechanistic. I think the changes in lactate are not very substantial because there was just so much to start with (mM range). And maybe this is more of a shift from anaerobic to aerobic glycolysis? We overall agree that “Warburg” effect can't be proven with the current studies.

Critique: Fig.6 – The analysis has performed to show whether gain of primed immunity to S. aureus reinfection rely in metabolic rearrangement and can be thus disturbed by DCA feeding. Unfortunately, I have to admit that this experiment failed to show that there is the primed immunity in analyzed flies. The number of individuals in this assay is insufficient. By adding the sham and manipulated group that was excluded from the figure but can be found in data files, it is clear the flies did not gain the primed immunity at all. The rest of the experiment is thus irrelevant.

Response: We agree with the reviewer, while it is the logical next step to test if trained immunity was activated with the DCA treatment, our current data is not conclusive. We thus eliminated the data and associated interpretations.

Minor points:

Critique: Line4 – I do not entirely understand the last sentence of the abstract, but it may be due to my English.

Response: We modified the last sentence in the abstract to align with the message of the manuscript.

Critique: Introduction – I lack in the introduction the information about what is known about the Warburg effect and polarization of macrophages in Drosophila. Moreover, many of the references are not very well supporting the notions (for example – 17,19, etc.).

Response: Clinicians managing long-term outcomes from sepsis, such as increased risk for mortality, do not have good pre-clinical models to address the clinical questions. Thus, our goal was to have a model relevant to the human experience and we probably used disproportionately more clinical references. However, we agree with Prof. Bajgar using appropriate references in Drosophila and we did add literature on Drosophila macrophage polarization.

Critique: The paragraphs - Fly reinfection and Fly survival and lifespan - should be described more into the detail.

Response: We did eliminate the reinfection data from the revised manuscript. We described the survival and lifespan in more detail.

Critique: Even though there is mentioned the source of primers, it would be valuable to have the sequences of primers and probes used in the appendix. Further, the genes, included in the publication, should be identified by their FLY base FBGN identifier.

Response: We added a supplementary table with qPCR primer information. We did order probes and primers from Applied Biosystems as Assays-on-Demand (AoD). The company provides AoD gene assays with context sequence surrounding the assay location and not the primer sequences.

We added the FBGN identifiers into the manuscript.

Critique: Discussion – the references should be revisited. The notion on line 331-334 seems to me excessive and directly connected with the published data.

Response: We thank Prof. Bajgar, we revised the references and made the conclusion statement based solely on our data suggesting an association. As both reviewers suggested, this model lends itself for future mechanistic approaches, such as selective genetic manipulation.

9 Oct 2020

Dichloroacetate-induced metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.

PONE-D-20-05383R1

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16 Oct 2020

PONE-D-20-05383R1

Dichloroacetate-induced metabolic reprogramming improves lifespan in a Drosophila model of surviving sepsis.

Dear Dr. Kaynar:

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