Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage–T cell axis in glioma

  1. Zehui Du
  2. Menghan Li
  3. Brandon H Bergsneider
  4. Andy P Tsai
  5. Kwang Bog Cho
  6. Lily H Kim
  7. John Choi
  8. Gordon Li
  9. Tony Wyss-Coray
  10. Michael Lim
  11. Xinnan Wang

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Mitochondrial regulators are increasingly recognized for their influence on immune signaling within the tumor microenvironment (TME). In glioma, where immunosuppression limits therapeutic efficacy, we investigate how targeting the mitochondrial protein MIRO1 alters the TME. We combine single-nucleus RNA sequencing of murine gliomas treated in vivo with an MIRO1-binding compound and bulk RNA sequencing of human glioma resections treated with the same compound ex vivo. Cross-species transcriptomic integration reveals an MIRO1-responsive program in the TME. Among shared targets, we identify PARP11/Parp11 as a consistently up-regulated gene in glioma, which is down-regulated after MIRO1-binding compound treatment in both human and mouse gliomas. Cell–cell communication analysis shows that a specific cluster of macrophages (MAC1), which exhibits robust Parp11 and Pdl1 (encoding PD-L1) expression, sends immunosuppressive signals to CD8 + cytotoxic T cells, and may receive prostaglandin E 2 signals from another cluster of macrophages (MAC4). Targeting MIRO1 eliminates this cell circuitry and reduces the tumor cell population. Our study provides a transcriptomic framework for understanding mitochondria-immune crosstalk and nominates MIRO1-PARP11 as a potential effector axis of brain immune dysfunction.

Article activity feed

  1. Version published to 10.26508/lsa.202603749
  2. Review Commons

    Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

    Learn more at Review Commons

    Reply to the reviewers

    Reviewer #1

    Evidence, reproducibility and clarity

    __Summary of findings and key conclusions This manuscript asks how pharmacologic targeting of the outer mitochondrial membrane protein MIRO1 (RHOT1) with a MIRO1-binding compound (MR3) reshapes immunosuppressive programs in the glioma tumor microenvironment (TME). The core of the paper is a cross-species transcriptomic comparison that combines an in vivo mouse dataset with an ex vivo human perturbation dataset. Model systems and approach (as described): • Mouse in vivo: GL261-Luc intracranial glioma in C57BL/6J mice; MR3 is administered intracranially at the implantation site (10 µM in 5 µL DMSO) on days 11 and 18, and tumors are harvested on day 22 for single-nucleus RNA-seq (snRNA-seq). • Mouse snRNA-seq: NeuN-based nuclei sorting, 10x Genomics v3.1; alignment to mm10; Seurat-based integration and annotation. Tumor-cell calling is supported by CNV inference (SCEVAN/CopyKAT). One MR3-treated sample is excluded after QC, leaving 3 control vs 2 MR3-treated samples (11,940 NeuN− nuclei). • Human ex vivo: freshly resected glioma cores from 3 patients are cultured with 10 µM MR3 or DMSO for 24 h, followed by bulk RNA-seq (STAR alignment to hg19; DESeq2 for differential expression). • Cross-species integration: the analysis is restricted to 1:1 orthologs and protein-coding genes shared across datasets; inferred cell-cell signaling is explored with CellChat. Main findings (as presented): • MR3 shifts expression of a subset of glioma-associated genes toward a non-tumor-like direction ("rescued genes") and is associated with large changes in inferred cell-type composition in the mouse snRNA-seq dataset (including a marked drop in the fraction of nuclei annotated as tumor: 44.5% to 4.3%; Fig. 1E). • Across TCGA-vs-GTEx (glioma-upregulated genes) and three MR3 response analyses (mouse snRNA-seq, mouse pseudo-bulk, and human bulk RNA-seq), PARP11/Parp11 is reported as the only gene that is consistently upregulated in glioma and consistently downregulated by MR3 (Fig. 2B). • Within the mouse myeloid compartment, Parp11 is most enriched in MAC4 and MAC1, while MAC1 shows high Cd274 (Pdl1/PD-L1). MR3 reduces Parp11 in MAC4/MAC1 and reduces Cd274 in MAC1 (Fig. 2H). • CellChat analysis suggests that in controls MAC1 is the dominant sender of PD-L1/PD-1 signaling to CD8+ T cells (Fig. 3C), and that this PD-L1/PD-1 interaction is strongly diminished after MR3 (Fig. 3E). • The authors propose a paracrine model in which MAC4-derived PGE2 (via Ptges3) sustains Parp11 expression in MAC1 through cAMP/PKA/CREB, promoting PD-L1-mediated T-cell suppression; MR3 disrupts this circuitry (Fig. 4). Major comments

    1. Strength of the conclusions Two parts of the story felt well supported by the data as shown. First, the cross-species convergence on PARP11/Parp11 is a clear and potentially useful result (Fig. 2B). Second, the myeloid subclustering plus CellChat analysis makes a coherent case that PD-L1/PD-1 signaling in this model is dominated by a specific macrophage subset (MAC1) and changes after MR3 (Fig. 2H, Fig. 3). Where I was less convinced is when the manuscript moves from "transcriptomic and modeling evidence" to causal statements such as "MIRO1-mediated axis driving immunosuppression" and "MR3 reduces tumor burden by reactivating immunity." At the moment, several central inferences remain indirect: • Causality is inferred primarily from transcriptomic shifts and ligand-receptor inference rather than functional immune readouts.__

    -We thank the Reviewer for the constructive evaluation. We have toned down the claims throughout the manuscript with tracking.

    __ On-target attribution to MIRO1 hinges on MR3 being a MIRO1 binder; the study does not include a genetic MIRO1 perturbation or a target-engagement/epistasis test in the relevant immune compartments (and the authors acknowledge this limitation in the Discussion).__ -We have examined on-target activity of MR3 in our other papers. For example, by depleting Miro1 with CRISPRi in glioma cells (Miro1 KD cells), we found that it phenocopied the effect of MR3. We also expressed Miro1-7A, a drug-resistant mutant of Miro1 predicted to be unable to bind MR3 (1) in Miro1 KD glioma cells, which rendered glioma cells insensitive to MR3 treatment. These data demonstrate that in cellular glioma models, Miro1 is the target of MR3 and MR3 exerts its functions via directly binding to Miro1.

    We have also excluded off-target effect of MR3 by examining other mitochondrial GTPases (1, 2) including Miro2.

    We agree these data were not done specifically in immune compartments, and have acknowledged it in Discussion and added more explanation in Introduction citing our published papers.

    __ The very large reduction in "tumor cell proportion" (Fig. 1E) is striking but is still a composition measure of recovered nuclei; it is not, on its own, a direct measurement of tumor size/burden and could be sensitive to differential nuclei recovery or cell loss during processing.__ -We agree that the "tumor cell proportion" in Fig. 1E represents the composition of recovered nuclei and is not, by itself, a direct measurement of tumor size or burden. We have removed "tumor burden" throughout the manuscript to avoid confusion.

    To determine whether the observed reduction might reflect technical bias, we examined the quality control metrics across all samples. Of the six initial samples (three control and three treated), one treated sample (TN1) showed clear quality concerns and was therefore excluded from downstream analysis.

    For the remaining samples, the distributions of detected genes per nucleus and total RNA counts per nucleus were similar between groups. The percentage of mitochondrial reads was consistently low, and only a small fraction of nuclei was removed during filtering, indicating overall comparable nuclei quality. Notably, the treated samples yielded similar or even higher total numbers of recovered nuclei, despite showing a lower tumor cell proportion. Please refer to new Fig. S1A for these results.

    Together, these observations suggest that the decrease in tumor cell proportion is unlikely to be explained simply by differential nuclei recovery, sequencing depth, or filtering effects. That said, we recognize that compositional differences in single-nucleus RNA sequencing data do not provide a direct measurement of tumor burden. We have revised the manuscript to clarify this point and to indicate that independent future approaches would be required for definitive assessment.

    I think the paper can go forward in its current scope, but the strength of the claims should match the level of evidence. If the authors want to keep strong, causal language in the title/abstract ("driving immunosuppression," "reduces tumor burden"), then I consider one or two targeted validation experiments essential (see below). Alternatively, the authors can temper the language and position the mechanistic model more explicitly as a hypothesis generated from the transcriptomic analysis.

    -We thank the Reviewer! We have toned down the claims throughout the manuscript to make the data consistent with the conclusion.

    __ Statements that should be labeled as preliminary/speculative (unless additional validation is added) • MAC4-derived PGE2 as the upstream driver of MAC1 Parp11/PD-L1: plausible and nicely consistent with Ptges3 being MAC4-high in controls and reduced with MR3 (Fig. 4A), but not demonstrated.__

    -We have changed the conclusion of this part to:

    Together, these bioinformatic findings suggest that MAC4 may produce PGE₂, which could act on nearby MAC1 cells in a paracrine manner to increase* Parp11* expression, although this model needs to be functionally validated.

    __ MIRO1 ____→____ mtDNA ____→____ cGAS/STING ____→____ Ptges3 as a mechanistic chain: interesting, but currently framed largely by pathway knowledge plus modest expression changes (Supplementary Fig. S5).__ -We have added: "which requires future functional investigation."

    __ "MR3 reactivates anti-tumor immunity to reduce tumor burden": the gene set enrichment and CellChat shifts are consistent with immune activation, but immune-mediated tumor control is not directly tested.__ -We have toned down these claims on tumor burden and only conclude as: MR3 may enhance anti-tumor immune responses.

    __ Replication and statistics Mouse snRNA-seq replication is limited after QC (3 control vs 2 MR3-treated animals). With n=2 treated, it is hard to know whether some of the biggest composition and cluster-level changes are robust to animal-to-animal variability.__

    -As also explained to Rev 2, we originally planned 3 mice per group. Despite losing one after QC, sample-level pseudobulk PCA analysis (treating each mouse as one replicate) of the mice shows clear separation of treated from untreated groups (new Fig. S2C), supporting technical reproducibility despite a small n. The two MR3-treated samples clustered together and were clearly separated from controls, indicating that the transcriptional effect of MR3 exceeds inter-animal variability (new Fig. S2C). The reduction in tumor cell proportion was also observed in both treated animals (new Fig. S2F). We have added this description to the Results (Page 5, lines 116-118) and included a new figure showing the tumor cell proportion for each animal (new Fig. S2F).

    We acknowledge this is a limitation, but as the Reviewer also pointed out that our paper's significance is to transcriptomically link Miro1 to well-known immune suppression factors in glioma TME and integrate 3 glioma databases which will facilitate researchers in the field to advance their own research. Thus, our methods and resource should be still valid and useful to the community.

    Relatedly, the snRNA-seq differential expression is performed with Seurat FindMarkers (Wilcoxon rank-sum). Per-cell testing can inflate significance if biological replicate structure is not accounted for (pseudoreplication). I suggest the authors clarify exactly how they handled sample-level replication for the key DE results and, where possible, re-run the main DE comparisons using a sample-aware approach (e.g., pseudo-bulk within cell types/subclusters).

    -We thank the reviewer for raising this important point. In the original analysis, differential expression was performed using Seurat's FindMarkers function which performs per-cell testing. We acknowledge that this approach can overestimate significance if biological replicate structure is not explicitly accounted for.

    To address this, we re-ran the key differential expression analyses using a pseudo-bulk approach: counts were aggregated per cell type/subcluster per sample, and DE testing was performed across samples rather than individual cells. The main results and conclusions remain consistent with the original analysis, while this approach ensures that statistical significance properly reflects biological replication (new FigS3. D-F).

    For the human bulk RNA-seq, the methods indicate 3 patient tissues split across MR3 vs DMSO for 24 h. In DESeq2, a paired design (including patient as a blocking factor) would be important to avoid patient-to-patient variability dominating the treatment signal; the manuscript should confirm whether the design formula accounted for this.

    -In the revised manuscript, we re-ran the DESeq2 analysis using a paired design with patient as a blocking factor and compared DMSO and MR3 within each patient (P1-P3). The results are consistent with our previous analysis. PARP11 remains significantly downregulated (raw p-value Finally, several places in the Methods define significance using p-value cutoffs (e.g., GEPIA3 TCGA/GTEx analysis uses p 1; human DE uses p = 1). Because multiple testing is substantial in all of these analyses, I recommend reporting FDR-adjusted values consistently (and being explicit about whether figures/tables show raw or adjusted p-values).

    -We have now used FDR-adjusted values for the TCGA/GTEx analysis and have updated Fig. 1C (top left), Results, and Methods accordingly. PARP11 remains significant after FDR correction.

    For the human bulk RNA-seq, very few genes pass an adjusted p 2FC| > 1 across all four differential expression analyses and updated the corresponding description in Methods.

    __ Do the data support the macrophage-to-CD8 suppression claim? The CellChat PD-L1/PD-1 network figures are suggestive (Fig. 3C/E), but ligand-receptor inference is not the same as demonstrating functional T-cell inhibition. At minimum, I would like to see one orthogonal readout (flow or immunostaining) showing that PD-L1__ protein on myeloid cells and PD-1 on CD8 T cells change in the expected directions after MR3, and that CD8 T cells show an activation/effector signature at the protein level.

    -We agree this would be clearly the next step in functional studies, but the current manuscript is focused on transcriptomic analysis and method building, so we have toned down any claims at the functional level.

    In addition, we have observed that T cells after MR3 treatment show upregulation of cytotoxicity- and IFN-response-related genes consistent with enhanced effector function at the transcriptional level. We have added new Fig. S6A and explanation in Result.

    __ PARP11: mediator vs marker The cross-species PARP11 result is the most convincing and potentially generalizable finding in the manuscript (Fig. 2B). However, in the specific context of this study, PARP11 is still best supported as a conserved MR3-responsive candidate rather than a demonstrated causal driver of PD-L1-mediated suppression. If the authors want to argue PARP11 is an effector of the pathway (rather than a marker), they should either soften the language or add a minimal functional linkage experiment within the existing scope (see "Optional" experiments below).__

    -We have softened the overall language throughout the manuscript to emphasize the correlation and PARP11 as a marker and to reflect the bioinformatic nature of the study. As this paper's main goal is method development and resource building, with already 11 figures, we think functional experiments could be done in another paper.

    __ Reproducibility and clarity of methods I appreciate that the authors provide a code/data portal (MiroScape) and a GitHub link. To make the study as reproducible as possible, I recommend: • Deposit raw sequencing reads for both mouse and human datasets (GEO/SRA) and include accession numbers in the manuscript.__

    -We have just deposited all raw data. Accession numbers will be provided once it is public.

    __ Provide a short, consolidated "computational reproducibility" note with software versions and key parameters (Seurat, CellChat, STAR, DESeq2, etc.).__ -Added

    __ Clarify pseudo-bulk construction (what is aggregated, at what level, and how many biological replicates contribute to each pseudo-bulk comparison).__ -Added

    __ Add a brief summary of MR3 provenance/validation and what "MIRO1-binding" means operationally in the context of these experiments (especially for readers outside the MIRO1 field).__ -We have added this in Introduction.

    Experiments requested (kept within the existing claims) I am intentionally not suggesting new lines of experimentation. The experiments below are aimed only at supporting the paper's current central claims. I separate them into items I consider essential vs optional, depending on how strongly the authors want to phrase mechanistic conclusions.

    -We thank the Reviewer. We have toned down the claims to reflect the bioinformatic nature of the paper. We will perform suggested experiments below in another paper.

    Essential if the title/abstract continue to use strong causal language • Protein-level validation of the PD-L1/PD-1 axis and CD8 activation in the GL261 model. A focused flow cytometry panel (myeloid PD-L1; CD8 PD-1 plus one or two effector markers such as GZMB/IFNG/Ki67) or multiplex IF/IHC on tumor sections would substantially strengthen the central MAC1 ____→____ CD8 claim. • An orthogonal measure of tumor burden in the same treatment paradigm. The manuscript currently treats the drop in the fraction of nuclei annotated as tumor (Fig. 1E) as a reduction in tumor burden; I recommend including IVIS longitudinal data and/or histologic tumor area/volume at harvest to support this statement. • If feasible, modestly increase in vivo biological replication (the snRNA-seq analysis currently has n=2 treated after QC). Even adding one additional treated animal that passes QC would help. Feasibility (rough guidance only; core pricing varies widely by institution): a repeat GL261 cohort to harvest tumors for flow and/or histology typically takes ~3-6 weeks end-to-end. A small flow panel plus core time is often on the order of a few thousand USD (antibodies and cytometry), while basic histology/IF quantification might be in the hundreds to low-thousands. If the authors already have stored tissue from the existing cohort, some of this could be faster/cheaper. Optional (only if the authors want the MAC4 ____→____ PGE2 ____→____ Parp11 mechanism to be more than a model) • Measure PGE2 (ELISA or targeted lipidomics) in tumor lysates/conditioned media from control vs MR3-treated samples, or provide a closer proxy for PGE2 pathway engagement in the relevant clusters. Optional (only if the authors want to argue PARP11 is an effector) • A minimal functional linkage experiment (in vitro) testing whether PARP11 perturbation phenocopies the relevant aspect of MR3 in macrophages (e.g., PD-L1 levels and/or the ability to suppress CD8 activation in a co-culture). This could be done with a PARP11 inhibitor or knockdown. I do not think in vivo genetics are required for this manuscript, but some functional tie would prevent overinterpretation.

    __ Minor comments A. Analysis/experimental clarifications that seem straightforward • Human DESeq2: please clarify whether the DESeq2 design was paired by patient (i.e., patient as a blocking factor).__

    -See above. We re-ran the human differential expression analysis using a paired design with patient as a blocking factor and explained in Methods.

    __ snRNA-seq DE: please clarify whether any sample-aware method was used for the key DE conclusions (especially Parp11/Cd274 changes) rather than per-cell statistics alone.__ -See above. The key DE results are based on sample-level pseudobulk (each mouse as one replicate). The two MR3-treated samples cluster together in pseudobulk PCA (new Fig. S2C), and the tumor reduction is seen in both animals (new Fig. S2F), supporting robustness to animal variability.

    __ CellChat: because min.cells filtering is used (min.cells = 20), please note this explicitly in figure legends where subclusters appear only in one condition, so readers understand why certain labels are missing.__ -We have edited the Fig 3 legend accordingly.

    __ Figure and text consistency issues I noticed several figure/legend/citation issues that look like simple fixes: • Fig. 3 legend panel labeling: the legend text refers to the PD-L1/PD-1 chord plot as (C) MR3− and (D) MR3+, but (D) is the heatmap panel; the chord plots are (C) and (E). This should likely read (C) MR3− and (E) MR3+.__

    -Yes, and corrected.

    __ Fig. 5 panel reference: the Results text refers to the Cross Species module as Fig. 5F, but the Fig. 5 legend defines panels (A-E) and labels (E) as "Cross Species module." Please reconcile (either change the text to Fig. 5E or add a panel F).__ -Changed to "E".

    __ Discussion figure citation: the Discussion cites Ptges3/PGE2 evidence as "(Figure 3)," but Ptges3 is shown in Fig. 4A and the model is in Fig. 4B.__ -Added "Figure 4A-B" there.

    __ Fig. 1D numbers: the Results text states 509/1,602 (mouse) and 15/106 (human) "rescued" genes (Fig. 1D), but the Fig. 1D pie charts are labeled with different totals (mouse total 3490; human total 104). Please reconcile the denominators and ensure the figure matches the text and analysis choice (bulk vs snRNA vs filtered gene sets).__ -For the cross-species analysis, we only counted genes with human-mouse orthologs so that the two datasets were compared in the same gene space. This avoids inflation from species-specific genes. We have added a clarification in the figure legend.

    __ Fig. 2 legend: there is a stray quote in "lymphoid subclusters" (appears as subclusters").__ -removed.

    __ Presentation and framing • Tone down or carefully qualify statements equating snRNA-seq composition shifts with reduced tumor burden (or add an orthogonal tumor-burden measurement as suggested above).__

    -We have removed "tumor burden" throughout the manuscript.

    __ Where possible, tie mechanistic language explicitly to the level of evidence ("consistent with," "suggests," "model proposes") so readers do not over-interpret the transcriptomic inference.__ -done.

    __ Consider adding a small schematic in the Results or a short "interpretation" sentence in the figure legends explaining what the CellChat plots do and do not show, since non-specialists can misread these as direct interaction measurements.__ -We have added explanations in Fig 3 legends for CellChat and emphasized the transcriptomic nature of the data.

    __ Prior literature The PARP11 immunotherapy literature is cited appropriately. For the PGE2 angle, it may help readers if the authors add one or two glioma-focused references on PGE2-mediated myeloid/T-cell suppression (if not already in the full reference list).__

    -We have added two more papers showing PGE2 may induce MDSCs and immunosuppresion in glioma (3) (4).

    Significance

    Nature and significance of the advance The advance here is primarily conceptual and resource-oriented. Conceptually, the work connects a mitochondrial regulator (MIRO1) to a specific, testable immunosuppressive circuit in the glioma TME. Technically, the cross-species perturbation framework and the accompanying MiroScape portal should be useful to groups looking for conserved, drug-responsive immune programs.

    Context within the existing literature Immunosuppression in glioma and the importance of tumor-associated myeloid populations are well established, as is the limited success of checkpoint blockade in GBM. The manuscript's proposed MAC4/MAC1 paracrine model and its emphasis on PD-L1/PD-1 signaling adds a focused, hypothesis-generating view of how particular macrophage states might sustain CD8 dysfunction. The identification of PARP11 as a conserved MR3-responsive gene also fits with emerging work implicating PARP11 in immunoregulatory programs and response to immunotherapy.

    Audience • Neuro-oncology and glioma TME researchers (myeloid heterogeneity, immune suppression). • Tumor immunology groups interested in myeloid-driven checkpoint resistance. • Researchers working on mitochondrial stress signaling and immunometabolism. • Computational biologists building cross-species or multi-modal integration frameworks. Reviewer expertise and limitations Keywords: glioma microenvironment; macrophage/microglia biology; tumor immunology; single-cell/nucleus transcriptomics; computational ligand-receptor inference. Limitations: I am not a medicinal chemist, so I cannot deeply evaluate MR3 chemistry, PK/PD, or specificity beyond what is presented. I also did not evaluate the full web-portal implementation beyond the manuscript description.

    Reviewer #2

    Evidence, reproducibility and clarity

    __The authors study responses to MIRO1 inhibition in a mouse model of GL261 GBM and in human tissue pieces treated ex vivo. They provide an interesting link between mitochondrial function and potential therapeutic outcomes in a tumor type that is typically challenging to treat. The manuscript is written clearly, in correct English language and figures are well structured and easy to interpret. __-We thank the Reviewer for the positive comments. We want to clarify that the compound binds to Miro1 and doesn't inhibit Miro1's GTPase activity (1). We have now added explanation in Introduction.

    __ Major critique:

    1. However, I need to stress that study is based of few experiments with low robustness. The predominant experiment is single-nuclei RNAseq analysis of GL261 tumors implanted into mice, constituting 3 CTRL and 2 treated mice, due to removal of 3rd animal following sequencing (low recovery of high quality nuclei). Therefore, the sample group is small. This is understandable for snRNA-seq experiment (although 3 animals in treated group is somewhat necessary), but the efficiency of treatment with MR3 should be better documented in a larger cohort of animals. Crucial changes in distribution of cell types or polarisation of myeloid cells should be confirmed with flow cytometry, which is more feasible on a larger cohort.__

    -We agree. As explained to Rev 3, the current paper is focused on conceptual and methodical advances and providing a resource to the community, which is already big with 11 figures. As Rev 1 mentioned, our paper's significance is to transcriptomically link Miro1 to well-known immune suppression factors in glioma TME and integrate 3 glioma databases which will facilitate researchers in the field to advance their own research. Importantly, PCA analysis of the mice at the animal level showed clear separation of treated from untreated groups and the reduction in tumor cell proportion was also observed in both treated animals (new Fig. S2C, F), supporting technical reproducibility despite a small n. Thus, our methods and resource should be still valid and useful to the community. Exploring the tumor-reducing efficacy of MR3 or combined treatments (e.g. with anti-PD-L1 or PARP11 inhibitor) in larger cohorts is an exciting next step.

    __ Human model does not seem robust (also, only 3 patients). Very few genes are affected by treatment (incomparably less than in mice), which poses a question if the model is sufficient to study the effect of the treatment. This should be at least discussed and arguments should be stated why such model is suitable.__

    -We agree and the observed variability in treatment response is actually expected and consistent with the well-established molecular and phenotypic heterogeneity of human glioma. Importantly, despite this diversity, we identified one gene (PARP11) consistently altered across all patient's samples and mouse model. This cross-species reproducibility supports the biological and translational relevance of the finding of PARP11. We have now added this to Discussion.

    In addition, we reanalyzed the human bulk RNA-seq using a paired design with patient as a blocking factor as suggested by another reviewer, which increased the number of DE genes (new Fig. 1C).

    __ Fig. S1E shows that actually few genes are commonly affected between human and mouse experiments. So conclusion about "conserved" modulation by MR3 seem an overstatement.__

    -We meant "Parp11" is conserved. We have deleted "conserved" throughout the manuscript when we didn't refer specifically Parp11 to avoid confusion.

    __ Mechanistic conclusions about PARP11, PGE, PD-L1 etc are not documented by any wet lab experiments, just by bioinformatic modelling.__ -We have scrutinized the Main Text to emphasize this.

    __Minor:

    1. Authors should discuss choice of GL261 model. It is immunogenic and does not resemble human GBM ideally, so the choice should be explained.__

    -Although GL261 model demonstrates higher immunogenicity compared to human GBM, this feature enables evaluation of immune-modulating therapies and mechanisms in an immune-competent setting. This model still preserves critical aspects of glioma biology, including immunosuppressive TME, invasive behavior, and intracranial growth (5). Thus, this model provides a suitable platform for our study of mechanistic investigation of immune cells in the TME. We have now added this to Method.

    __ In clustering of mouse snRNAseq data, T cells seem underclustered, e.g. Treg cluster clearly constitutes half of Il2ra-positive and negative cells, the latter probably being conventional CD4+ T cells (usually CD4+ T cells in GL261 are 50:50 Treg and conventional). This can affect further conclusions on cell:cell interactions.__

    -We thank the reviewer for this important observation. We agree that in the former annotation, it was improper to annotate all the CD4+ T cells as Treg cells, given the limited expression of Foxp3, Il2ra and other Treg marker genes. Consequently, the previously annotated "Treg cluster" likely includes both regulatory-like and conventional CD4+ T cells.

    We have further clustered the CD4+ T cell population and found that if we divided CD4+ T cells into conventional CD4+ T and Treg cells, it yielded few Treg cells for downstream analysis (~50). This would compromise the robustness and reliability of our following analysis (CellChat/DEA/etc).

    To address this, we have revised our annotation and now refer to this population more conservatively as "regulatory-like CD4+ T cells" rather than bona fide Tregs. Importantly, this subset still exhibits elevated expression of immunoregulatory molecules and is associated with CD8+ T cell dysfunction, preserving the main conclusions regarding immune suppression within the tumor microenvironment. We have updated the Results, Figures, and Discussion accordingly to clarify this revised annotation and its implications for cell-cell interactions.

    Please refer to following new figures for the updated annotation and associated results:

    Fig. 2G-H, Fig. 3A-G, Fig. S4C-D,G, Fig. S5-B-G, Fig. S6A.

    Significance

    The study provides an interesting conclusion and potentially relevant discovery. However, in opinion of this reviewer, the performed experiments do not strengthen this sufficiently, especially in terms of mechanical insights and weak data on human samples. In the line of general literature on new treatments of GBM and testing thereof in mouse model, this study lacks mechanistic insights and solid data on therapeutic efficiency.

    -As mentioned above, the goal of this paper is to provide novel methods to integrate datasets, resource building, and identify markers in the glioma TME. It will serve as useful resources to the community and form the foundation for future therapeutic validation in larger cohorts. We have acknowledged the limitations in the revised manuscript.

    __Reviewer #3 (Evidence, reproducibility and clarity (Required)): ____

    The authors of "Cross-Species Transcriptomic Integration Reveals a Conserved, MIRO1-Mediated Macrophage-to-T-Cell Signaling Axis Driving Immunosuppression in Glioma" present transcriptomic, both bulk RNA Seq and single nucleus RNA Seq, from GL261 murine gliomas treated with the Miro1 targeting compound MR3. RNA Seq data from human tumor explants treated with MR3 is also presented. The authors compared DEGs from their treated tissues with publicly available RNA Seq data sets comparing DEGs from normal tissue and Glioma tumors. The goal being to identify genes modulated by MR3 that may be underlying glioma growth, TME changes, and immunosuppression. There is a significant amount of data presented, with in-depth analysis conducted on the sequencing data sets. The manuscript is lacking in mechanistic depth and this reviewer feels that the results are over-interpreted, especially without any additional conformational assays run to confirm the interpretation of the sequencing data. There were many bold statements made (lines 109-110, 117, 130-131, 142-144, 163-165) that I felt did not have enough evidence to back up their claims. __

    -We have toned down these places mentioned above:

    Line 109-110: Deleted now

    Line 117: Deleted now

    Line 130-131: Deleted now

    Line 142-144: Deleted: "highly differentially expressed", the rest of the sentence is supported by our data.

    Line 163-165: Deleted now

    As explained later, our paper is focused on bioinformatic analysis and resource and method building. In-depth functional studies will be performed in another paper.

    __A significant concern is the lack of conformation that MR3 is targeting Miro1 in these models. __

    -We have done this in another manuscript where we show that in cellular glioma models, Miro1 is the target of MR3 and MR3 exerts its functions via directly binding to Miro1.

    __Previous publications from the authors have shown evidence that MR3 reduces Miro1 expression in cell and fly models. Sometimes this requires the co application of FCCP or antimycin A. Thus, the results attributed within cannot be attributed to Miro1 changes but rather any on or off-target effect of MR3. __

    -We originally discovered MR3 by ligand-based in silico modeling and thermal shift direct binding assay (1, 2). Thus, MR3 is a Miro1 binder (stated in Abstract and Introduction too, now we have added more background in Introduction). Indeed, sometimes we saw MR3 reduced Miro1 protein levels under certain conditions, for example, in vivo in flies after days of feeding (1, 2), or in PD cells upon Antimycin A or CCCP treatment (1, 2, 6, 7). MR3 mostly likely exerts its function via altering Miro1 protein-protein interactions (8) and Miro1 protein is subsequently degraded in proteasomes following complex dissociation or after posttranslational modifications (1, 2) (8). We have stated this hypothesis in Result section (page 10, possible model).

    In our other papers we have excluded off-target effect of MR3 by examining other mitochondrial GTPases (1, 2) including Miro2, and by showing Miro1 KD glioma cells phenocopied the effects of MR3 and drug-resistant Miro1 mutant in glioma cells rendered insensitivity to MR3. These data show Miro1 is the main target of MR3.

    We have added more explanations to the Introduction.

    __Understanding that mouse studies are expensive and time-consuming, and the acquisition of human tissue is not trivial, the sample sets are still small. Further confirmation of findings in cell models, organoids etc. would strengthen the findings and justify the smaller sample size of mice and human tissue. __

    -We agree and we have another in-depth study. However, the current paper is focused on conceptual and methodical advances and providing a resource to the community, which is already big with 11 figures. As Rev 1 mentioned, our paper's significance is to transcriptomically link Miro1 to well-known immune suppression factors in glioma TME and integrate 3 glioma databases which will facilitate researchers in the field to advance their own research. Thoroughly understanding Miro1's role in glioma TME is our next goal as stated in Discussion and is beyond the scope of the current study.

    __The website MiroScape will be a very useful tool in the proper hands. ____

    1. Confirm activity of MR3 on Miro1 in relevant samples. Direct downregulation? Modulation of other targets known to be altered by MR3? __

    -As mentioned above, we have shown in tumor cells, MR3 disrupts pathogenic Miro1-protein interactions without the need to reduce Miro1 protein. There is currently no other target known to be altered by MR3, not even Miro2, demonstrated before (1, 2). We have added more explanations in Main Text.

    __ Conduct further mechanistic work to validate claims inferred by differentially expressed genes.__

    -As mentioned above, our current paper is focused on bioinformatic methods and resource building. Further mechanistic work will be performed in another paper.

    __ Significantly temper claims related cell targeting, direct communication between cells and overarching responses inferred from Sequencing data. -Done. See above and Main Text.

    Reviewer #3 (Significance (Required)):

    My laboratories expertise lies in signaling related to mitochondrial structure and function. We have investigated the Miro1 protein and effects on cellular responses related to Miro1 expression. We have tested the MR3 compound in our own systems with limited success. Therefore my major concerns lie in validating the on-target activity of the compound in their models. __-As explained above, in our other papers we have thoroughly examined on-target activity of MR3 by courter-screening other Miro1 related/similar proteins (1, 2, 6, 7) and by using Miro1 KD cells. We have now added more explanations in Main Text.

    __ With additional mechanistic validation this could be a very significant study. Using advanced model systems as the authors do allows for a comprehensive understanding of tissue responses. This is far advanced from simple single cell line culture studies but also adds significant complexity to the interpretation of the data. I am a strong believer that Sequecing data must be validated with functional assays.__

    -We agree and are actively conducting those studies. However, bioinformatic analysis and method and resource building are sometimes too comprehensive to combine with functional data which may take years to obtain. We think our paper's method, markers identified in TME, and resources will be very useful to the community.

    References

    1. Hsieh CH, Li L, Vanhauwaert R, Nguyen KT, Davis MD, Bu G, Wszolek ZK, Wang X. Miro1 Marks Parkinson's Disease Subset and Miro1 Reducer Rescues Neuron Loss in Parkinson's Models. Cell metabolism. 2019;30(6):1131-40 e7. Epub 2019/10/01. doi: 10.1016/j.cmet.2019.08.023. PubMed PMID: 31564441; PMCID: PMC6893131.
    2. Li L, Conradson DM, Bharat V, Kim MJ, Hsieh CH, Minhas PS, Papakyrikos AM, Durairaj AS, Ludlam A, Andreasson KI, Partridge L, Cianfrocco MA, Wang X. A mitochondrial membrane-bridging machinery mediates signal transduction of intramitochondrial oxidation. Nat Metab. 2021. Epub 2021/09/11. doi: 10.1038/s42255-021-00443-2. PubMed PMID: 34504353.
    3. Mi Y, Guo N, Luan J, Cheng J, Hu Z, Jiang P, Jin W, Gao X. The Emerging Role of Myeloid-Derived Suppressor Cells in the Glioma Immune Suppressive Microenvironment. Front Immunol. 2020;11:737. Epub 2020/05/12. doi: 10.3389/fimmu.2020.00737. PubMed PMID: 32391020; PMCID: PMC7193311.
    4. Dean PT, Hooks SB. Pleiotropic effects of the COX-2/PGE2 axis in the glioblastoma tumor microenvironment. Front Oncol. 2022;12:1116014. Epub 20230126. doi: 10.3389/fonc.2022.1116014. PubMed PMID: 36776369; PMCID: PMC9909545.
    5. Mathios D, Kim JE, Mangraviti A, Phallen J, Park CK, Jackson CM, Garzon-Muvdi T, Kim E, Theodros D, Polanczyk M, Martin AM, Suk I, Ye X, Tyler B, Bettegowda C, Brem H, Pardoll DM, Lim M. Anti-PD-1 antitumor immunity is enhanced by local and abrogated by systemic chemotherapy in GBM. Science translational medicine. 2016;8(370):370ra180. Epub 2016/12/23. doi: 10.1126/scitranslmed.aag2942. PubMed PMID: 28003545; PMCID: PMC5724383.
    6. Bharat V, Durairaj AS, Vanhauwaert R, Li L, Muir CM, Chandra S, Kwak CS, Le Guen Y, Nandakishore P, Hsieh CH, Rensi SE, Altman RB, Greicius MD, Feng L, Wang X. A mitochondrial inside-out iron-calcium signal reveals drug targets for Parkinson's disease. Cell Rep. 2023;42(12):113544. Epub 2023/12/07. doi: 10.1016/j.celrep.2023.113544. PubMed PMID: 38060381.
    7. Bharat V, Hsieh CH, Wang X. Mitochondrial Defects in Fibroblasts of Pathogenic MAPT Patients. Front Cell Dev Biol. 2021;9:765408. Epub 2021/11/23. doi: 10.3389/fcell.2021.765408. PubMed PMID: 34805172; PMCID: PMC8595217.
    8. Kwak CS, Du Z, Creery JS, Wilkerson EM, Major MB, Elias JE, Wang X. Optogenetic Proximity Labeling Maps Spatially Resolved Mitochondrial Surface Proteomes and a Locally Regulated Ribosome Pool. bioRxiv. 2025. Epub 2026/01/07. doi: 10.64898/2025.12.21.693523. PubMed PMID: 41497653; PMCID: PMC12767525.
  3. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #3

    Evidence, reproducibility and clarity

    The authors of "Cross-Species Transcriptomic Integration Reveals a Conserved, MIRO1-Mediated Macrophage-to-T-Cell Signaling Axis Driving Immunosuppression in Glioma" present transcriptomic, both bulk RNA Seq and single nucleus RNA Seq, from GL261 murine gliomas treated with the Miro1 targeting compound MR3. RNA Seq data from human tumor explants treated with MR3 is also presented. The authors compared DEGs from their treated tissues with publicly available RNA Seq data sets comparing DEGs from normal tissue and Glioma tumors. The goal being to identify genes modulated by MR3 that may be underlying glioma growth, TME changes, and immunosuppression. There is a significant amount of data presented, with in-depth analysis conducted on the sequencing data sets. The manuscript is lacking in mechanistic depth and this reviewer feels that the results are over-interpreted, especially without any additional conformational assays run to confirm the interpretation of the sequencing data. There were many bold statements made (lines 109-110, 117, 130-131, 142-144, 163-165) that I felt did not have enough evidence to back up their claims. A significant concern is the lack of conformation that MR3 is targeting Miro1 in these models. Previous publications from the authors have shown evidence that MR3 reduces Miro1 expression in cell and fly models. Sometimes this requires the co application of FCCP or antimycin A. Thus, the results attributed within cannot be attributed to Miro1 changes but rather any on or off-target effect of MR3. Understanding that mouse studies are expensive and time-consuming, and the acquisition of human tissue is not trivial, the sample sets are still small. Further confirmation of findings in cell models, organoids etc. would strengthen the findings and justify the smaller sample size of mice and human tissue. The website MiroScape will be a very useful tool in the proper hands.

    1. Confirm activity of MR3 on Miro1 in relevant samples. Direct downregulation? Modulation of other targets known to be altered by MR3?
    2. Conduct further mechanistic work to validate claims inferred by differentially expressed genes.
    3. Significantly temper claims related cell targeting, direct communication between cells and overarching responses inferred from Sequencing data.

    Significance

    My laboratories expertise lies in signaling related to mitochondrial structure and function. We have investigated the Miro1 protein and effects on cellular responses related to Miro1 expression. We have tested the MR3 compound in our own systems with limited success. Therefore my major concerns lie in validating the on-target activity of the compound in their models.

    With additional mechanistic validation this could be a very significant study. Using advanced model systems as the authors do allows for a comprehensive understanding of tissue responses. This is far advanced from simple single cell line culture studies but also adds significant complexity to the interpretation of the data. I am a strong believer that Sequecing data must be validated with functional assays.

  4. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #2

    Evidence, reproducibility and clarity

    The authors study responses to MIRO1 inhibition in a mouse model of GL261 GBM and in human tissue pieces treated ex vivo. They provide an interesting link between mitochondrial function and potential therapeutic outcomes in a tumor type that is typically challenging to treat. The manuscript is written clearly, in correct English language and figures are well structured and easy to interpret.

    Major critique:

    1. However, I need to stress that study is based of few experiments with low robustness. The predominant experiment is single-nuclei RNAseq analysis of GL261 tumors implanted into mice, constituting 3 CTRL and 2 treated mice, due to removal of 3rd animal following sequencing (low recovery of high quality nuclei). Therefore, the sample group is small. This is understandable for snRNA-seq experiment (although 3 animals in treated group is somewhat necessary), but the efficiency of treatment with MR3 should be better documented in a larger cohort of animals. Crucial changes in distribution of cell types or polarisation of myeloid cells should be confirmed with flow cytometry, which is more feasible on a larger cohort.
    2. Human model does not seem robust (also, only 3 patients). Very few genes are affected by treatment (incomparably less than in mice), which poses a question if the model is sufficient to study the effect of the treatment. This should be at least discussed and arguments should be stated why such model is suitable.
    3. Fig. S1E shows that actually few genes are commonly affected between human and mouse experiments. So conclusion about "conserved" modulation by MR3 seem an overstatement.
    4. Mechanistic conclusions about PARP11, PGE, PD-L1 etc are not documented by any wet lab experiments, just by bioinformatic modelling.

    Minor:

    1. Authors should discuss choice of GL261 model. It is immunogenic and does not resemble human GBM ideally, so the choice should be explained.
    2. In clustering of mouse snRNAseq data, T cells seem underclustered, e.g. Treg cluster clearly constitutes half of Il2ra-positive and negative cells, the latter probably being conventional CD4+ T cells (usually CD4+ T cells in GL261 are 50:50 Treg and conventional). This can affect further conclusions on cell:cell interactions.

    Significance

    The study provides an interesting conclusion and potentially relevant discovery. However, in opinion of this reviewer, the performed experiments do not strengthen this sufficiently, especially in terms of mechanical insights and weak data on human samples. In the line of general literature on new treatments of GBM and testing thereof in mouse model, this study lacks mechanistic insights and solid data on therapeutic efficiency.

  5. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    Summary of findings and key conclusions

    This manuscript asks how pharmacologic targeting of the outer mitochondrial membrane protein MIRO1 (RHOT1) with a MIRO1-binding compound (MR3) reshapes immunosuppressive programs in the glioma tumor microenvironment (TME). The core of the paper is a cross-species transcriptomic comparison that combines an in vivo mouse dataset with an ex vivo human perturbation dataset.

    Model systems and approach (as described):

    • Mouse in vivo: GL261-Luc intracranial glioma in C57BL/6J mice; MR3 is administered intracranially at the implantation site (10 µM in 5 µL DMSO) on days 11 and 18, and tumors are harvested on day 22 for single-nucleus RNA-seq (snRNA-seq).
    • Mouse snRNA-seq: NeuN-based nuclei sorting, 10x Genomics v3.1; alignment to mm10; Seurat-based integration and annotation. Tumor-cell calling is supported by CNV inference (SCEVAN/CopyKAT). One MR3-treated sample is excluded after QC, leaving 3 control vs 2 MR3-treated samples (11,940 NeuN− nuclei).
    • Human ex vivo: freshly resected glioma cores from 3 patients are cultured with 10 µM MR3 or DMSO for 24 h, followed by bulk RNA-seq (STAR alignment to hg19; DESeq2 for differential expression).
    • Cross-species integration: the analysis is restricted to 1:1 orthologs and protein-coding genes shared across datasets; inferred cell-cell signaling is explored with CellChat.

    Main findings (as presented):

    • MR3 shifts expression of a subset of glioma-associated genes toward a non-tumor-like direction ("rescued genes") and is associated with large changes in inferred cell-type composition in the mouse snRNA-seq dataset (including a marked drop in the fraction of nuclei annotated as tumor: 44.5% to 4.3%; Fig. 1E).
    • Across TCGA-vs-GTEx (glioma-upregulated genes) and three MR3 response analyses (mouse snRNA-seq, mouse pseudo-bulk, and human bulk RNA-seq), PARP11/Parp11 is reported as the only gene that is consistently upregulated in glioma and consistently downregulated by MR3 (Fig. 2B).
    • Within the mouse myeloid compartment, Parp11 is most enriched in MAC4 and MAC1, while MAC1 shows high Cd274 (Pdl1/PD-L1). MR3 reduces Parp11 in MAC4/MAC1 and reduces Cd274 in MAC1 (Fig. 2H).
    • CellChat analysis suggests that in controls MAC1 is the dominant sender of PD-L1/PD-1 signaling to CD8+ T cells (Fig. 3C), and that this PD-L1/PD-1 interaction is strongly diminished after MR3 (Fig. 3E).
    • The authors propose a paracrine model in which MAC4-derived PGE2 (via Ptges3) sustains Parp11 expression in MAC1 through cAMP/PKA/CREB, promoting PD-L1-mediated T-cell suppression; MR3 disrupts this circuitry (Fig. 4).

    Major comments

    1. Strength of the conclusions Two parts of the story felt well supported by the data as shown. First, the cross-species convergence on PARP11/Parp11 is a clear and potentially useful result (Fig. 2B). Second, the myeloid subclustering plus CellChat analysis makes a coherent case that PD-L1/PD-1 signaling in this model is dominated by a specific macrophage subset (MAC1) and changes after MR3 (Fig. 2H, Fig. 3). Where I was less convinced is when the manuscript moves from "transcriptomic and modeling evidence" to causal statements such as "MIRO1-mediated axis driving immunosuppression" and "MR3 reduces tumor burden by reactivating immunity." At the moment, several central inferences remain indirect:
      • Causality is inferred primarily from transcriptomic shifts and ligand-receptor inference rather than functional immune readouts.
      • On-target attribution to MIRO1 hinges on MR3 being a MIRO1 binder; the study does not include a genetic MIRO1 perturbation or a target-engagement/epistasis test in the relevant immune compartments (and the authors acknowledge this limitation in the Discussion).
      • The very large reduction in "tumor cell proportion" (Fig. 1E) is striking but is still a composition measure of recovered nuclei; it is not, on its own, a direct measurement of tumor size/burden and could be sensitive to differential nuclei recovery or cell loss during processing. I think the paper can go forward in its current scope, but the strength of the claims should match the level of evidence. If the authors want to keep strong, causal language in the title/abstract ("driving immunosuppression," "reduces tumor burden"), then I consider one or two targeted validation experiments essential (see below). Alternatively, the authors can temper the language and position the mechanistic model more explicitly as a hypothesis generated from the transcriptomic analysis.
    2. Statements that should be labeled as preliminary/speculative (unless additional validation is added)
      • MAC4-derived PGE2 as the upstream driver of MAC1 Parp11/PD-L1: plausible and nicely consistent with Ptges3 being MAC4-high in controls and reduced with MR3 (Fig. 4A), but not demonstrated.
      • MIRO1 → mtDNA → cGAS/STING → Ptges3 as a mechanistic chain: interesting, but currently framed largely by pathway knowledge plus modest expression changes (Supplementary Fig. S5).
      • "MR3 reactivates anti-tumor immunity to reduce tumor burden": the gene set enrichment and CellChat shifts are consistent with immune activation, but immune-mediated tumor control is not directly tested.
    3. Replication and statistics Mouse snRNA-seq replication is limited after QC (3 control vs 2 MR3-treated animals). With n=2 treated, it is hard to know whether some of the biggest composition and cluster-level changes are robust to animal-to-animal variability. Relatedly, the snRNA-seq differential expression is performed with Seurat FindMarkers (Wilcoxon rank-sum). Per-cell testing can inflate significance if biological replicate structure is not accounted for (pseudoreplication). I suggest the authors clarify exactly how they handled sample-level replication for the key DE results and, where possible, re-run the main DE comparisons using a sample-aware approach (e.g., pseudo-bulk within cell types/subclusters). For the human bulk RNA-seq, the methods indicate 3 patient tissues split across MR3 vs DMSO for 24 h. In DESeq2, a paired design (including patient as a blocking factor) would be important to avoid patient-to-patient variability dominating the treatment signal; the manuscript should confirm whether the design formula accounted for this. Finally, several places in the Methods define significance using p-value cutoffs (e.g., GEPIA3 TCGA/GTEx analysis uses p < 0.05 and |log2FC| > 1; human DE uses p < 0.05 and log2FC >= 1). Because multiple testing is substantial in all of these analyses, I recommend reporting FDR-adjusted values consistently (and being explicit about whether figures/tables show raw or adjusted p-values).
    4. Do the data support the macrophage-to-CD8 suppression claim? The CellChat PD-L1/PD-1 network figures are suggestive (Fig. 3C/E), but ligand-receptor inference is not the same as demonstrating functional T-cell inhibition. At minimum, I would like to see one orthogonal readout (flow or immunostaining) showing that PD-L1 protein on myeloid cells and PD-1 on CD8 T cells change in the expected directions after MR3, and that CD8 T cells show an activation/effector signature at the protein level.
    5. PARP11: mediator vs marker The cross-species PARP11 result is the most convincing and potentially generalizable finding in the manuscript (Fig. 2B). However, in the specific context of this study, PARP11 is still best supported as a conserved MR3-responsive candidate rather than a demonstrated causal driver of PD-L1-mediated suppression. If the authors want to argue PARP11 is an effector of the pathway (rather than a marker), they should either soften the language or add a minimal functional linkage experiment within the existing scope (see "Optional" experiments below).
    6. Reproducibility and clarity of methods I appreciate that the authors provide a code/data portal (MiroScape) and a GitHub link. To make the study as reproducible as possible, I recommend:
      • Deposit raw sequencing reads for both mouse and human datasets (GEO/SRA) and include accession numbers in the manuscript.
      • Provide a short, consolidated "computational reproducibility" note with software versions and key parameters (Seurat, CellChat, STAR, DESeq2, etc.).
      • Clarify pseudo-bulk construction (what is aggregated, at what level, and how many biological replicates contribute to each pseudo-bulk comparison).
      • Add a brief summary of MR3 provenance/validation and what "MIRO1-binding" means operationally in the context of these experiments (especially for readers outside the MIRO1 field). Experiments requested (kept within the existing claims) I am intentionally not suggesting new lines of experimentation. The experiments below are aimed only at supporting the paper's current central claims. I separate them into items I consider essential vs optional, depending on how strongly the authors want to phrase mechanistic conclusions. Essential if the title/abstract continue to use strong causal language
      • Protein-level validation of the PD-L1/PD-1 axis and CD8 activation in the GL261 model. A focused flow cytometry panel (myeloid PD-L1; CD8 PD-1 plus one or two effector markers such as GZMB/IFNG/Ki67) or multiplex IF/IHC on tumor sections would substantially strengthen the central MAC1 → CD8 claim.
      • An orthogonal measure of tumor burden in the same treatment paradigm. The manuscript currently treats the drop in the fraction of nuclei annotated as tumor (Fig. 1E) as a reduction in tumor burden; I recommend including IVIS longitudinal data and/or histologic tumor area/volume at harvest to support this statement.
      • If feasible, modestly increase in vivo biological replication (the snRNA-seq analysis currently has n=2 treated after QC). Even adding one additional treated animal that passes QC would help. Feasibility (rough guidance only; core pricing varies widely by institution): a repeat GL261 cohort to harvest tumors for flow and/or histology typically takes ~3-6 weeks end-to-end. A small flow panel plus core time is often on the order of a few thousand USD (antibodies and cytometry), while basic histology/IF quantification might be in the hundreds to low-thousands. If the authors already have stored tissue from the existing cohort, some of this could be faster/cheaper. Optional (only if the authors want the MAC4 → PGE2 → Parp11 mechanism to be more than a model)
      • Measure PGE2 (ELISA or targeted lipidomics) in tumor lysates/conditioned media from control vs MR3-treated samples, or provide a closer proxy for PGE2 pathway engagement in the relevant clusters. Optional (only if the authors want to argue PARP11 is an effector)
      • A minimal functional linkage experiment (in vitro) testing whether PARP11 perturbation phenocopies the relevant aspect of MR3 in macrophages (e.g., PD-L1 levels and/or the ability to suppress CD8 activation in a co-culture). This could be done with a PARP11 inhibitor or knockdown. I do not think in vivo genetics are required for this manuscript, but some functional tie would prevent overinterpretation.

    Minor comments

    A. Analysis/experimental clarifications that seem straightforward

    • Human DESeq2: please clarify whether the DESeq2 design was paired by patient (i.e., patient as a blocking factor).
    • snRNA-seq DE: please clarify whether any sample-aware method was used for the key DE conclusions (especially Parp11/Cd274 changes) rather than per-cell statistics alone.
    • CellChat: because min.cells filtering is used (min.cells = 20), please note this explicitly in figure legends where subclusters appear only in one condition, so readers understand why certain labels are missing.

    B. Figure and text consistency issues

    I noticed several figure/legend/citation issues that look like simple fixes:

    • Fig. 3 legend panel labeling: the legend text refers to the PD-L1/PD-1 chord plot as (C) MR3− and (D) MR3+, but (D) is the heatmap panel; the chord plots are (C) and (E). This should likely read (C) MR3− and (E) MR3+.
    • Fig. 5 panel reference: the Results text refers to the Cross Species module as Fig. 5F, but the Fig. 5 legend defines panels (A-E) and labels (E) as "Cross Species module." Please reconcile (either change the text to Fig. 5E or add a panel F).
    • Discussion figure citation: the Discussion cites Ptges3/PGE2 evidence as "(Figure 3)," but Ptges3 is shown in Fig. 4A and the model is in Fig. 4B.
    • Fig. 1D numbers: the Results text states 509/1,602 (mouse) and 15/106 (human) "rescued" genes (Fig. 1D), but the Fig. 1D pie charts are labeled with different totals (mouse total 3490; human total 104). Please reconcile the denominators and ensure the figure matches the text and analysis choice (bulk vs snRNA vs filtered gene sets).
    • Fig. 2 legend: there is a stray quote in "lymphoid subclusters" (appears as subclusters").

    C. Presentation and framing

    • Tone down or carefully qualify statements equating snRNA-seq composition shifts with reduced tumor burden (or add an orthogonal tumor-burden measurement as suggested above).
    • Where possible, tie mechanistic language explicitly to the level of evidence ("consistent with," "suggests," "model proposes") so readers do not over-interpret the transcriptomic inference.
    • Consider adding a small schematic in the Results or a short "interpretation" sentence in the figure legends explaining what the CellChat plots do and do not show, since non-specialists can misread these as direct interaction measurements.

    D. Prior literature The PARP11 immunotherapy literature is cited appropriately. For the PGE2 angle, it may help readers if the authors add one or two glioma-focused references on PGE2-mediated myeloid/T-cell suppression (if not already in the full reference list).

    Significance

    Nature and significance of the advance

    The advance here is primarily conceptual and resource-oriented. Conceptually, the work connects a mitochondrial regulator (MIRO1) to a specific, testable immunosuppressive circuit in the glioma TME. Technically, the cross-species perturbation framework and the accompanying MiroScape portal should be useful to groups looking for conserved, drug-responsive immune programs.

    Context within the existing literature

    Immunosuppression in glioma and the importance of tumor-associated myeloid populations are well established, as is the limited success of checkpoint blockade in GBM. The manuscript's proposed MAC4/MAC1 paracrine model and its emphasis on PD-L1/PD-1 signaling adds a focused, hypothesis-generating view of how particular macrophage states might sustain CD8 dysfunction. The identification of PARP11 as a conserved MR3-responsive gene also fits with emerging work implicating PARP11 in immunoregulatory programs and response to immunotherapy.

    Audience

    • Neuro-oncology and glioma TME researchers (myeloid heterogeneity, immune suppression).
    • Tumor immunology groups interested in myeloid-driven checkpoint resistance.
    • Researchers working on mitochondrial stress signaling and immunometabolism.
    • Computational biologists building cross-species or multi-modal integration frameworks.

    Reviewer expertise and limitations

    Keywords: glioma microenvironment; macrophage/microglia biology; tumor immunology; single-cell/nucleus transcriptomics; computational ligand-receptor inference. Limitations: I am not a medicinal chemist, so I cannot deeply evaluate MR3 chemistry, PK/PD, or specificity beyond what is presented. I also did not evaluate the full web-portal implementation beyond the manuscript description.

  6. Version published to 10.1101/2025.11.10.686781 on bioRxiv