Editors
Reviewing Editor
Bavesh Kana
University of the Witwatersrand, Johannesburg, South Africa
Senior Editor
Bavesh Kana
University of the Witwatersrand, Johannesburg, South Africa
Reviewer #3 (Public Review):
In the revised manuscript by Maio et al, the authors examined the bioenergetic mechanisms involved in the delayed migration of DC's during Mtb infection. The authors performed a series of in vitro infection experiments including bioenergetic experiments using the Agilent Seahorse XF, and glucose uptake and lactate production experiments. Also, data from SCENITH is included in the revised manuscript as well as some clinical data. This is a well written manuscript and addresses an important question in the TB field. A remaining weakness is the use of dead (irradiated) Mtb in several of the new experiments and claims where iMtb data were used to support live Mtb data. Another notable weakness lies in the author's insistence on asserting that lactate is the ultimate product of glycolysis, rather than acknowledging a large body of historical data in support of pyruvate's role in the process. This raises a perplexing issue highlighted by the authors: if Mtb indeed upregulates glycolysis, one would expect that inhibiting glycolysis would effectively control TB. However, the reality contradicts this expectation. Lastly, the examination of the bioenergetics of cells isolated from TB patients undergoing drug therapy, rather than studying them at their baseline state is a weakness.
Author Response
The following is the authors’ response to the original reviews.
Reviewer #1 (Recommendations For The Authors):
Firstly, the authors place a great deal of emphasis on the impact of the Hif1-a inhibitor PX-478. The literature surrounding this inhibitor and its mode of action indicates that it is not a direct inhibitor of activity but that its greatest impact is on the production of Hif1-a. The authors do include another inhibitor as a control, Echinomycin, but it does not appear to be as biologically active and the panel of experiments conducted with this is extremely limited. I would be more comfortable with a full Seahorse experimental panel for Echinomycin, similar to SFig 2.G as performed with PX-478.
We thank the reviewer for their comment highlighting the different mechanisms of action of the HIF-1α inhibitors used in this article. While echinomycin inhibits the binding of HIF-1α to the hypoxia response element (HRE) thereby blocking HIF-1a DNA binding capability, PX-478 inhibits HIF-1α deubiquitination, decreases HIF-1α mRNA expression, and reduces HIF-1α translation. We have included a paragraph explaining this phenomenon in the new version of the manuscript (page 9). In addition, we extended the panel of experiments performed with echinomycin, which confirmed a marked inhibition of the glycolytic pathway when DCs were stimulated with irradiated Mtb in the presence of echinomycin as assessed by SCENITH (new Figure S3H).
Similarly, it would be of value to have Seahorse profiling that directly excludes FAO from the metabolic profile through the use of Etomoxir as an inhibitor of fatty acid oxidation, which one would assume would have no impact on the metabolic response.
In order to estimate the contribution of FAO towards fueling protein synthesis in DCs stimulated with iMtb, the FAO inhibitor etomoxir was incorporated to the SCENITH method as previously described (Adamik et al., 2022). Overall, FAO dependence was found to be less than 10% in DCs, regardless of their activation state. While mitochondrial dependence is reduced after iMtb stimulation, there is no difference in FAO dependence, suggesting that OXPHOS is primarily driven by glucose in iMtb-stimulated cells. This is consistent with HIF1α-induced increase of glucose metabolism-related genes. We have adjusted the results section to include this new result (new Figure S1).
Aside from these minor points, I believe this to be a rigorous study.
Reviewer #2 (Recommendations For The Authors):
In Fig. 1 and Fig. 2, the authors conclude that Mtb rewires the metabolism of Mo-DCs and induces both glycolysis and OXPHOS. The data shows that infection with iMtb or Mtb increases glucose uptake and lactate release, suggesting an increase in glycolysis. However, an increase in lactate is not a measure of glycolysis. Lactate is a byproduct of glycolysis; the end product of glycolysis is pyruvate.
We are grateful for the reviewer's comment, as it gives us the opportunity to explain the conceptual framework on which we based our study. Traditionally, pyruvate has been considered to be the end product of glycolysis when oxygen is present and lactate the end product under hypoxic conditions. Numerous studies have shown that lactate is produced even under aerobic conditions (Brooks, 2018). Therefore, we frame this work in accordance with this view that states that glycolysis begins with glucose as its substrate and terminates with the production of lactate as its main end product (Rogatzki, Ferguson, Goodwin, & Gladden, 2015; Schurr, 2023; Schurr & Schurr, 2017).
Secondly, since the authors have access to the Agilent Extracellular Flux Analyzer, they should have performed detailed ECAR/OCR measurements to conclusively demonstrate that both glycolysis and OXPHOS are increased in Mo. This is especially important for OXPHOS because the only readout shown for OXPHOS is an increase in mitochondrial mass (figure 1 G, H), which is not acceptable. Overall, the data does not indicate that Mtb triggers OXPHOS in the dendritic cells. It only indicates dead iMtb increases the mass of mitochondria in DCs.
The reviewer’s advice is well appreciated. However, we would like to clarify what may be a misunderstanding; that is, the assays alluded to by the reviewer were not performed on monocytes but on DCs. As advised by the reviewer, we now include the OCR measurements by Seahorse and describe the figures according to their order of appearance in the new version of the manuscript.
What happens to the mitochondrial mass when infected with live Mtb?
In response to the reviewer’s question, we determined the mitochondrial mass in infected DCs with live Mtb. In contrast to DCs treated with irradiated Mtb, those infected with live bacteria showed a clear reduction of their mitochondrial mass (modified Figure 1G). This result indicates that, although both Mtb-infected and irradiated Mtb-exposed DCs show a clear increase in their glycolytic activity, divergent responses are observed in terms of mitochondrial mass.
It will be best if the authors indicate in the figure headings that dead Mtb was used.
We agree with the reviewer. For figures 1-3, we applied the term “Mtb” in the figure headings since both irradiated and viable bacteria were used for the corresponding experiments. In figures 4-5, the term “iMtb” (alluding to irradiated Mtb) was used in the figure headings as suggested by the reviewer. For the remaining figures, the term “iMtb” was indicated in their legends when dead bacteria weres used to stimulate DCs.
E.g., Figure 1F; what does live Mtb do to GLUT1 levels etc etc?
In response to the reviewer’s question, we included new data about Glut1 expression in DCs infected with live Mtb in the latest version of the manuscript. In line with the increase in glucose uptake shown in figure 1B, we observed an increase in the percentage of Glut1 positive DCs upon Mtb infection (new Figure 1F, lower panels). The increase in Glut1 expression strengthens the notion that DCs activates their glycolytic activity in response to the infection, as demonstrated by the elevated release of lactate, glucose consumption, HIF-1α expression, LDHA expression (Figure 1) and glycolytic activity (Figure 2, SCENITH results with viable Mtb). Therefore, these data strongly support the induction of glycolysis by Mtb (either viable or irradiated) in DCs.
Also, we found that they were still able to activate CD4+ T cells from PPD+ donors in response to iMtb. This activation of CD4 T cells with iMtb in the presence of a HIF-1alpha inhibitor is expected, as iMtb is dead and not virulent. What happens when the cells are infected with live virulent Mtb?
We would like to clarify the main purpose of the DC-T cells co-culture assays in the presence of the HIF-1α inhibitors. To characterize the impact of HIF-1α on DC functionality, we assessed the capacity of DCs to activate autologous CD4+ T cells when stimulated with iMtb in the presence of HIF-1α inhibitors. To this end, we used iMtb merely as a source of antigens to load DCs and evaluate the effect of HIF-1α inhibition on the activation of antigen-specific T cell. The use of viable Mtb may introduce confounding factors, such as pathogen-triggered inhibitory mechanisms (e.g., EsxH secretion by Mtb, (Portal-Celhay et al., 2016)), which would prevent us from reaching conclusions about the role of HIF-1α. Thus, we consider that the use of live bacteria for this experiment is out of the scope of this manuscript.
The authors demonstrated that CD16+ monocytes from TB patients have higher glycolytic capacity than healthy controls Fig 7. The authors should differentiate TB patient monocytes into DCs and measure their bioenergetics to test if infection alters their glycolysis and OXPHOS.
In agreement with the reviewer, the determination of metabolic pathways in DCs differentiated from monocytes of TB patients is a key aspect of this work. Accordingly, the bioenergetic determinations of DCs generated from monocytes from TB patients versus healthy subjects are now illustrated in Figures 6F (lactate release) and 6G (SCENITH profile).
In the discussion, the authors state that "pathologically active glycolysis in monocytes from TB patients leads to poor glycolytic induction and migratory capacities of monocyte-derived DCs." However, the data from Fig. 1 and 2 show that treatment with iMtb or Mtb induces glycolysis in MoDCs. How do the authors explain these contrasting results?
We thank the reviewer for pointing out this issue. Figures 1 and 2 show DCs differentiated from monocytes of healthy donors (HS). In this case, DCs from HS respond to Mtb by inducing a glycolytic and migratory profile. Yet, in the case of monocytes isolated from TB patients, these cells exhibit an early glycolytic profile from the beginning of differentiation, ultimately yielding DCs with low glycolytic capacity and low migratory activity in response to Mtb. We included this explanation in the discussion (page 18) to better clarify this issue.
Also, the term "pathological" active glycolysis (Introduction and Discussion) is an inappropriate term.
As requested by the reviewer, we excluded the term “pathological” to describe the phenomenon reported in this study.
Lastly, it should be shown whether the DCs generated from CD16+ monocyte from TB patients generate tolerogenic and/or aberrant DCs, which have lower glycolytic and migration capacity compared to the CD16- monocyte population. In Figure 7B, the authors should discuss why the CD16+ monocyte population has lower glycolytic capacity compared to CD16- monocytes in healthy donors. Furthermore, in contrast to the TB patients, do DCs generated from CD16+ monocyte in healthy donors have increased glycolytic and migration capacity compared to CD16- monocyte (because these monocytes showed lower glycolytic capacity)? Furthermore, if there is no difference in glycolytic capacity among the three monocyte populations in TB patients, on what basis was it concluded that DCs generated only from the CD16+ monocyte population may be the cause of lower migration capacity? The authors state in Figure 7F that the DMOG pretreatment matches the situation where the Mo-DCs from TB patients showed reduced migration. Did the authors check the Hif-1alpha levels in monocytes obtained from TB patients?
We appreciate this in-depth analysis by the reviewer because it allows us to clarify some interpretations of the SCENITH results in Figure 7B. It is important to keep in mind that with the SCENITH technique we can only infer about the relative contributions between the metabolic pathways, without alluding to the absolute magnitudes of such contributions. In this regard, it is key to note that the amount of lactate released during the first hours of the TB monocyte culture is much higher than that released by monocytes from healthy subjects (HS, Figure 7A), even when most of monocytes, which are CD14+ CD16-, have comparable glycolytic capacities between HS and TB. Another example to illustrate how to interpret SCENITH results can be found in Figure 2, where a lower mitochondrial dependence is observed in iMtb-stimulated DCs (Figure 2A), while the absolute ATP production associated to OXPHOS is indeed higher as measured by Seahorse (Figure 2D). Therefore, the glycolytic capacity is not a direct readout of the magnitude of glycolysis, but of its contribution to total metabolism. The low levels of lactate released from HS monocytes likely reflects their low activation state and low metabolic activity compared to TB monocytes. In this regard, we have previously demonstrated that monocytes from pulmonary TB patients display an activated phenotype (Balboa et al., 2011). The fact that there is no difference between the glycolytic capacities of TB and HS CD16- monocytes indicates that their proportional contributions to protein synthesis are comparable (again, without inferring about their absolute values, which may be very different).
Beyond the previous clarification, the reviewer's proposal to isolate subsets of monocytes is a very interesting idea. However, the experimental approach is very difficult based on the amount of blood we can obtain from patients. The cohort of patients included in this work comprises very severe patients and we are given up to 15-20 ml of peripheral blood from each. This volume of blood yields up to 10 million PBMC with approximately 1 million monocytes. If we separate the monocyte subsets, the recovered cells per condition will be insufficient to perform the intended assays.
Nevertheless, we incorporate new evidence that TB disease is associated with an increased activation and glycolytic profile of circulating CD16+ monocytes.
i) First, we show that the baseline glycolytic capacity of CD16+ monocytes correlates with time since the onset of TB-related symptoms (new Figure 7C).
ii) Second, we performed high-throughput GeneSet Enrichment Analysis (GSEA) on transcriptomic data (GEO accession number: GSE185372) of CD14+CD16-, CD14+CD16+ and CD14dimCD16+ monocytes isolated from individuals with active TB, latent TB (IGRA+), as well as from TB negative healthy controls (IGRA-). We found enrichments that, unlike oxidative phosphorylation, glycolysis tends to increase in active TB in both CD14+CD16+ and CD14dimCD16+ monocytes (new Figure 7D).
iii) We measured the expression of HIF-1α in monocyte subsets by FACS and found that this transcription factor is expressed at higher levels in CD16+ monocyte subsets from TB patients compared to their counterparts from healthy donors (new Figure 8 A). We consider this result justifies the assays shown in Figure 8B-C, in which we prematurely activated HIF-1α in healthy donor monocytes during early differentiation to DCs and measured its impact on the migration of the generated DCs.
In the Discussion, the authors mention that circulating monocytes from TB patients differentiate from DCs with low immunogenic potential. However, the authors have not shown any immunological defect in any of their data with monocytes from TB patients. In the proxy model mentioned in Figure 7, they have in fact shown that these preconditioned DCs have higher CD86 expression. Can the authors explain/show data to justify the statement in the first paragraph of the Discussion?
We agree with the reviewer on this observation. Our findings are limited to the generation of DCs with low migratory potential (low chemotactic activity towards CCL21 of DC differentiated from TB patient monocytes shown in figure 6H and of DC generated from pre-conditioned monocytes shown in figure 8C). We have modified that part of the discussion to better clarify this point, replacing migratory with immunogenic.
The authors should note that oxamate is a competitive inhibitor of the enzyme lactate dehydrogenase and not glycolysis. Also, LDHA catalyzes the conversion from pyruvate to lactate and not the other way around (Results, page 6).
This comment relates to the first one by the reviewer, in which the dogma of glycolysis was discussed. According to the new conception of glycolysis, it begins with glucose as its substrate and terminates with the production of lactate as its main end product.
The following statements by the authors on page 6 are incorrect: "Because irradiated and viable Mtb induced comparable activation of glycolysis, we subsequently performed all our assays with irradiated Mtb only in the rest of the study due to biosafety reasons." and: "To our knowledge, this is the first study addressing the metabolic status and migratory activity of Mo-DCs from TB patients."
We deleted the first sentence and reworded the second sentence as "To our knowledge, this is the first study to address how the metabolic status of monocytes from TB patients influences the migratory activity of further differentiated DCs".
The Discussion reads as if live Mtb was used in the experiments, which is not the case. This should be corrected.
We changed Mtb for iMtb when it was the case in the discussion. In some cases, Mtb stimulation was used instead of Mtb infection.
Minor Comments:
(1) In Figure 1F legend "Quantification of Glut1+ cells plotted to the right". The underlined part should be "plotted below".
It was corrected.
(2) In Figure 1H. Please describe the quantitation method and describe how many cells or the number/size of fields were used to quantitate mitochondria.
For mitochondrial morphometric analysis, TEM images were quantified with the ImageJ “analyze particles” plugin in thresholded images, with size (μm2) settings from 0.001 to infinite. For quantification, 8–10 cells of random fields (1000x magnification) per condition were analyzed. We included this information in the methods section of the new version of the manuscript.
(3) Please mention the number of independent experimental repeats for each experimental data set and figure.
In each figure, the number of independent experiments is indicated by individual dots.
(4) In Figure 2A legend, "PER; left panel" should be PER; lower panel and "OCR; right panel" should be OCR; upper panel.
It was corrected.
References for reviewers
Adamik, J., Munson, P. V., Hartmann, F. J., Combes, A. J., Pierre, P., Krummel, M. F., … Butterfield, L. H. (2022). Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells. Nature Communications 2022 13:1, 13(1), 1–19. https://doi.org/10.1038/s41467-022-32849-1
Balboa, L., Romero, M. M., Basile, J. I., Sabio y Garcia, C. A., Schierloh, P., Yokobori, N., … Aleman, M. (2011). Paradoxical role of CD16+CCR2+CCR5+ monocytes in tuberculosis: efficient APC in pleural effusion but also mark disease severity in blood. Journal of Leukocyte Biology. https://doi.org/10.1189/jlb.1010577
Brooks, G. A. (2018). Cell Metabolism The Science and Translation of Lactate Shuttle Theory. Cell Metab. https://doi.org/10.1016/j.cmet.2018.03.008
Portal-Celhay, C., Tufariello, J. M., Srivastava, S., Zahra, A., Klevorn, T., Grace, P. S., … Philips, J. A. (2016). Mycobacterium tuberculosis EsxH inhibits ESCRT-dependent CD4+ T-cell activation. Nature Microbiology, 2, 16232. https://doi.org/10.1038/NMICROBIOL.2016.232
Rogatzki, M. J., Ferguson, B. S., Goodwin, M. L., & Gladden, L. B. (2015). Lactate is always the end product of glycolysis. Frontiers in Neuroscience, 9(FEB), 125097. https://doi.org/10.3389/FNINS.2015.00022/BIBTEX
Schurr, A. (2023). From rags to riches: Lactate ascension as a pivotal metabolite in neuroenergetics. Frontiers in Neuroscience, 17, 1145358. https://doi.org/10.3389/FNINS.2023.1145358/BIBTEX
Schurr, A., & Schurr, A. (2017). Lactate, Not Pyruvate, Is the End Product of Glucose Metabolism via Glycolysis. Carbohydrate. https://doi.org/10.5772/66699
