Glycolipid MPIase is essential for the TAT (twin-arginine translocation) pathway

  1. Hanako Nishikawa
  2. Nachi Yamamoto
  3. Hayate Kunezaki
  4. Yui Shirogane
  5. Kotoka Kanno
  6. Katsuhiro Sawasato
  7. Miwa Yamada
  8. Ken-ichi Nishiyama

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    eLife Assessment

    This important study provides a detailed analysis of the transcriptional landscape of the mouse hippocampus in the context of various physiological states. The main conclusions are convincingly supported: that most transcriptional targets are generally stable, with notable exceptions in the dentate gyrus and with regard to circadian changes. The authors comprehensively addressed the concerns raised in the first round of review.

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Abstract

TAT (twin-arginine translocation) is a preprotein translocation system dedicated to membrane translocation of prefolded proteins in plants and bacteria. The TAT translocon, consisting of the TatABC subunits, drives translocation using proton motive force. However, there have been no reports on the successful reconstitution of the TAT system. In this report, we show that MPIase, a glycolipid that has been known to catalyze membrane protein integration, is essential for the TAT system. Our findings in recombinant Escherichia coli demonstrate that overproducing TatABC increases MPIase levels, and that depleting MPIase results in TAT precursor accumulation in the cytosol. Furthermore, co-reconstitution of MPIase with TatABC revealed the translocation activities of TAT substrates in a proton motive force-dependent manner. This is the first successful reconstitution of the TAT system and will be advantageous for understanding its mechanisms.

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  1. Version published to 10.7554/elife.112170.4 on eLife
  2. Version published to 10.7554/elife.112170 on eLife
  3. eLife

    eLife Assessment

    This important study provides a detailed analysis of the transcriptional landscape of the mouse hippocampus in the context of various physiological states. The main conclusions are convincingly supported: that most transcriptional targets are generally stable, with notable exceptions in the dentate gyrus and with regard to circadian changes. The authors comprehensively addressed the concerns raised in the first round of review.

  4. eLife

    Reviewer #1 (Public review):

    Olmstead et al. present a single-cell nuclear sequencing dataset that interrogates how hippocampal gene expression changes in response to distinct physiological stimuli and across circadian time. The authors perform single nucleus RNA sequencing on mouse hippocampal tissue after (1) kainic acid-induced seizure, (2) exposure to an enriched environment, and (3) at multiple circadian phases.

    The dataset is rigorously collected, and a major strength is the use of the previously established ABC taxonomy from Yao et al. (2023) to define cell types. The authors further show that this taxonomy is largely independent of activity-driven transcriptional programs. Using these annotations, they examine activity-regulated gene expression across neuronal and glial subclasses. They identify ZT12, corresponding to the transition from the light to the dark period, as transcriptionally distinct from other circadian time points, and show that this pattern is conserved across many cell types. Finally, they test how circadian phase influences activity-dependent gene expression by exposing mice to an enriched environment at different times of day, and report no significant interaction between circadian phase and enriched environment exposure.

    Comments on revised version.

    The authors have addressed the comments from the first round of review.

  5. eLife

    Reviewer #2 (Public review):

    Olmstead et al. present ACT-DEPP, a single-nucleus RNA-sequencing atlas of the mouse hippocampus. The atlas profiles transcription after environmental enrichment, after kainate-induced seizure, and at four circadian time points, and all nuclei are registered to the Allen Brain Cell taxonomy. The authors report three main findings. First, seizure and novel experience induce transcriptional programs that differ greatly in magnitude. Second, ZT12 is a transcriptional inflection point shared across cell types. Third, induction of classical immediate early genes (IEGs) is broadly maintained across circadian phases, with only a few genes showing circadian-activity interactions.

    The study is ambitious in scope, and the dataset is rigorously collected. Registering the data to the ABC taxonomy makes them easy to reuse and to integrate with other brain-wide resources. In the revision, the authors substantially strengthened the analyses and refined their interpretations. Overall, the descriptive conclusions are well supported.

    Locomotor activity was not recorded, so some ZT-dependent effects may partly reflect behavioral state as well as intrinsic clock regulation. The authors clearly acknowledge this in the Limitations section. With relatively few replicates per ZT, small circadian-activity interactions may go undetected. The pathways highlighted in Fig. 6I-J are best viewed as hypothesis-generating. These points are appropriately framed and do not detract from the value of the work. This study will be a useful reference for investigators studying IEG biology, experience-dependent plasticity, and circadian regulation in the brain.

  6. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    Olmstead et al. present a single-cell nuclear sequencing dataset that interrogates how hippocampal gene expression changes in response to distinct physiological stimuli and across circadian time. The authors perform single-nucleus RNA sequencing on mouse hippocampal tissue after (1) kainic acid-induced seizure, (2) exposure to an enriched environment, and (3) at multiple circadian phases.

    The dataset is rigorously collected, and a major strength is the use of the previously established ABC taxonomy from Yao et al. (2023) to define cell types. The authors further show that this taxonomy is largely independent of activity-driven transcriptional programs. Using these annotations, they examine activity-regulated gene expression across neuronal and glial subclasses. They identify ZT12, corresponding to the transition from the light to the dark period, as transcriptionally distinct from other circadian time points, and show that this pattern is conserved across many cell types. Finally, they test how circadian phase influences activity-dependent gene expression by exposing mice to an enriched environment at different times of day, and report no significant interaction between circadian phase and enriched environment exposure.

    A crucial consideration for users of this dataset is the potential confounding effect between circadian phase and locomotor activity. This is particularly relevant because dentate gyrus activity is strongly modulated by locomotion. The authors acknowledge this issue in the Discussion and provide useful guidance for how to interpret their findings, considering this confound.

    We thank the reviewer for their accurate and thoughtful summary of the ACT-DEPP dataset and the key experimental design and analytical choices that form the basis of the study. We agree that circadian phase and locomotor activity are major confounders that are irreconcilable in the current study. We have modified our Discussion of this point to consider future studies which would be able to delineate the two (lines 403-406)

    Taken together, this dataset represents a useful resource for the neuroscience community, particularly for investigators interested in how novel experience and circadian phase shape activity-related and immediate early gene expression in the hippocampus

    Reviewer #2 (Public review):

    This manuscript presents the ACT-DEPP dataset, a comprehensive single-nucleus RNA-sequencing atlas of the mouse hippocampus that examines how activity-dependent and circadian transcriptional programs intersect. The dataset spans multiple experimental conditions and circadian time points, clarifying how cell-type identity relates to transcriptional state. In particular, the authors compare stimulus-evoked activity programs (environmental enrichment and kainite-induced seizures) with circadian phase-dependent transcriptional oscillations. They also identify a transcriptional inflection point near ZT12 and argue that immediate early gene (IEG) induction is broadly maintained across circadian phases, with minimal ZT-dependent modulation.

    Strengths:

    The study is ambitious in scope and data volume, and outlines the data-processing and atlasregistration workflows. The side-by-side treatment of stimulus paradigms and ZT sampling provides a coherent framework for parsing state (activity) from phase (circadian) across diverse neuronal and non-neuronal classes. Several findings - especially the ZT12 "inflection" and the differential sensitivity of pathways across subclasses - are intriguing.

    Weaknesses:

    (1) The authors acknowledge, but do not adequately address, the fundamental confounding factor between circadian phase and spontaneous locomotor activity. The assertion that these represent "orthogonal regulatory axes," based on largely non-overlapping DEGs, may be overstated. The absence of behavioral monitoring during baseline is a major limitation.

    We agree that circadian phase and locomotor activity are irreconcilable confounders in the current study. We have removed the phrase “orthogonal regulatory logic.” This point is disclaimed as the leading point in our Limitations section. We expand on this section to consider future studies which would be able to delineate the two (lines 403-406).

    (2) The statement "Thus, novel experiences and seizures trigger categorically distinct transcriptional responses-with respect to both magnitude and specific genes-in these hippocampal sub regions" is overstated, given the data presented. Figure 2A-B shows that approximately one-third of EE-induced DEGs at 30 minutes overlap with KA DEGs, and this overlap increases substantially at 6 hours in CA1 (where EE and KA responses become "fully shared"). This suggests the responses are quantitatively different rather than "categorically distinct."

    We have modified the sentence (lines 127-129) to state: “Thus, novel experience and seizure induce transcriptional programs that differ greatly in magnitude and, apart from CA1 after 6 hours, contain stimulus-specific genes.” We have additionally modified the section title to de-emphasize stimulus-specificity.

    (3) In Figure 4B, "active cells" are defined as those with >=3 of 15 IEGs above the 90th percentile, with thresholds apparently calibrated in CA1. Because baseline expression distributions differ across subclasses, this rule can bias activation rates across cell types.

    The baseline expression thresholds were computed for each CA1 Supertype, e.g. CA1 69-1, 702, etc. (Figure 4D).

    (4) Few genes show significant ZT × stimulus (EE or seizure) interactions, concentrated in neuronal populations. Given unequal nucleus counts and biological replicates across subclasses, small effects may be underpowered.

    We agree with the observation that our measurements may be underpowered to observe small effects. The nuances of our DGE methodology are discussed below under Reviewer 2’s Recommendation #3.

    (5) In Figure 6 I, J, the relationship between the highlighted pathways/functions and circadian phase is not yet explicit.

    We agree that there is no obvious relationship for ZT-based changes in expression among genes in shared sets highlighted in Figures 6I-J. However, their enrichment among the small set of 13 significantly interacting genes genome-wide merits remark as we have done so here. What functional implications these observations have is the subject of future experiments.

    (6) Line 276-280: The enrichment of lncRNAs at ZT12 in CA1 is intriguing but underdeveloped. What are these lncRNAs, and what might they regulate?

    Gene symbols were ascertained by mapping reads to the GRCm39-113 mouse genome in Parse Bioscience’s Trailmaker pipeline (Methods). GRCm39 returns gene biotype annotations, which is how we determined these genes were lncRNAs. A literature search for these gene symbols found that all 12 are predicted or uncharacterized loci without functional experimental data.

    Overall, most descriptive conclusions are supported (e.g., broad phase-robustness of classical IEGs; an inflection near ZT12). Claims about the separability/orthogonality of activity vs circadian programs, and about categorical distinctions between EE and KA responses, would benefit from more conservative wording or additional analyses to rule out behavioral and power-related alternatives.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the authors):

    I have a few suggestions for additional analyses and clarifications that could further strengthen the study:

    (1) KA responsive populations and spatial context

    In Figure 2, the authors show that KA-responsive cells cluster within two supertypes, which may 99 represent seizure-specific populations. Could the authors leverage the spatial transcriptomic data 100 from Yao et al. to ask whether there is a specific cell type or subregion within the dentate gyrus 101 that is predisposed to express the seizure-associated gene programs identified in Figure 2?

    We thank the reviewers for this interesting analysis suggestion. We have added Figure 2—figure supplement 6 as an exploration of the spatial distribution of DG Supertype 138-3. We find that these cells are predominantly located in dorsal hippocampus, although are sparsely distributed throughout all of hippocampus.

    (2) Baseline circadian modulation of canonical IEGs

    In the Discussion, the authors note that canonical immediate early genes do not overlap with circadian differentially expressed genes, citing results from Figure 5. However, the current 109 analyses do not directly show how canonical IEG expression varies across the circadian cycle at 110 baseline. It would be helpful to include a focused visualization, for example, a heatmap or line plot 111 of IEG expression across circadian time points, to more directly support this conclusion.

    We have added Figure 6—figure supplements 1-2. These plots demonstrate low variation of IEG expression within the standard environment across ZTs. EE-induction is likewise similar across ZTs in CA1. DG demonstrates less consistent induction relationships, likely due to dramatically low abundances leading to noisy expression estimates.

    (3) Neurogenesis-related signatures

    Given that dentate gyrus cells are included in the dataset, it may be informative to comment on neurogenesis-related transcriptional programs. For example, can the authors report whether markers of adult-born granule cells or progenitor populations are detected, and whether genes associated with neurogenesis are modulated by experience (seizure or enriched environment) or circadian phase? Even a brief analysis or summary would likely be of interest to the community.

    We do capture a small population of immature neuronal cells (IMNs) in our dataset that are likely the product of dentate neurogenesis (Figure 1B). Our experiment was not adequately designed to detect the origin of these cells, nor determine whether EE induces changes in rate of neurogenesis or cell fates. However, we can measure the effects of acute EE on the preexisting IMN population present at the time of experimentation. We have added Figure 2—figure supplement 7 to examine this topic. We observe that hours of EE exposure instead of minutes is necessary to induce transcriptional changes. Interestingly, many of the DEGs observed encode neurotransmitter receptor subunits or intercellular adhesion proteins.

    Reviewer #2 (Recommendations for the authors):

    Behavioral confounding factor: Please provide locomotor activity measures aligned to ZT for the relevant cohorts (e.g., wheel counts or video tracking), or at minimum, discuss how ZT-dependent changes in arousal/locomotion could contribute to the observed transcriptional differences and how your analyses mitigate this confounding factor. Including locomotion as a covariate in pseudobulk models or presenting matched restraint controls would be helpful.

    We did not collect locomotion data on the mice and so are unable to include it as a covariate in any analyses. We place the Limitations subsection at the beginning of our Discussion and disclose the confounding effect of locomotion/arousal as a main limitation of this study.

    It is well established that mice increase locomotor activity in the hour preceding the dark phase (i.e. during ZT 11-12) [1]. Similarly, in vivo neurophysiological studies of hippocampus during locomotion consistently demonstrate that neuronal cells in hippocampus exhibit spatially tuned activity that increases as an animal navigates an environment compared to when it is stationary [2]. If locomotion were to exhibit major influence on gene expression in our experiment, it is reasonable to assume that we would observe such effects in IEGs. Nevertheless, we do not observe changes in baseline IEG expression across ZTs (Figure 6—figure supplement 1-2). Based on this albeit circumstantial evidence, we conclude that circadian influences on gene expression are likely to predominate over locomotor ones in the absence of other manipulations.

    What may explain this lack of IEG induction across dramatically different locomotor phases? It is possible that locomotion-dependent effects on neuronal activity are insufficient to drive activity-dependent changes without instructive learning signals within the hippocampal circuit. One speculative mechanism for this could be that, in the standard environment (i.e. the animal’s home cage), the lack of strong plasticity events such as dendritic plateau potentials [3] fails to overcome the signaling threshold(s) necessary to induce activity-dependent transcriptional programs. It also remains possible that locomotion-induced transcriptional changes involving hundreds of genes such as that observed in Figure 5 are coordinated through processes independent of canonical IEG induction.

    (2) "Active cell" definition: Please provide (i) a sensitivity analysis varying the percentile cutoff (e.g., 80/85/95%) and the number of IEGs required (2-4), and (ii) a complementary analysis using continuous activity scores (e.g., AUCell or ssGSEA) with concordance to the binary call reported (ROC/PR or agreement metrics). Subclass-specific thresholding or normalization would further mitigate bias; reporting the fraction of "active" cells per subclass under each parameter set would be helpful.

    We have added Figure 4— figures supplements 1-2 to incorporate all these requested analyses. Rather than rely on an external library to construct an IEG module score, we employ a simple z-scoring index of the 15 IEGs used in Figure 4B, D, E to overcome the technical challenges unique to this set of low-abundance genes.

    From these requested analyses, we find that ROC curves were extremely accurate at classifying active and inactive cells based on a continuous activity IEG score (AUC > 0.985, Figure 4 — figure supplement 2). Precision-recall curves were also constructed, which demonstrated decreased—although still robust—classification of cell activity state in SE. This is consistent with the performance of precision/recall tradeoff between conditions when there is an imbalance of active/inactive cells in the underlying populations (see Figure 4B). When investigating the decreased PR-accuracy in SE, we observed that discordant classifications were likely to arise when a single IEG was elevated enough to significantly increase the composite score, leading to a spurious classification as “active” with the continuous scoring method when it would not meet the 3-gene threshold in the other method.

    Finally, we note that we already employed supertype-specific thresholding to set baseline thresholds for each CA1 sub-population.

    (3) Interaction effects and power: Please (a) report per-subclass effective sample sizes and replicate numbers, (b) re-analyze with a complementary framework (limma-voom or edgeR) and, where possible, (c) include sample-level mixed-effects models (animal as a random effect; pseudobulk counts or cell-fraction outcomes). Presenting effect sizes with uncertainty (e.g., forest plots) would improve interpretability beyond binary FDR thresholds.

    We have added Figure 2—figure supplements 1-5; and Figure 3—figure supplement 1 to address these requests and included a new paragraph in the Results section (lines 130-146) describing how these different methods perform comparatively on our dataset. We observe that these methods perform similarly under most conditions, but that DESeq2 shrinkage—the method used for all DGE analysis in the main figures—aggressively forces estimated fold-change toward zero in certain conditions with high variance. We maintain our original conclusion that Type I error rate is adequately minimized. However, Type II error rate in our DGE analyses may be higher with DESeq2 under certain conditions compared to other frameworks.

    We modified our Discussion to expand on these findings (lines 415-424) and disclose to the reader that “Our findings should be interpreted as a conservative observation of activity- and circadian-dependent changes in gene expression.” We thank the reviewer for these suggestions which have significantly improved the analytic rigor of the project and revealed its potential technical shortcomings.

    (4) Figure 6 I,J : Please make clear whether these pathways are simply enriched among DE genes or whether they exhibit bona fide circadian modulation.

    The 13 significant genes ascertained through the interaction analysis presented in Figure 6I-J were manually annotated through literature review, given the feasibility from such a small number of DEGs. The pathways described—endocannabinoid synthesis, signaling and excitability, and chromatin remodeling/gene expression—were all cellular processes implicated by literature review of the gene list. From this, we conclude that these processes are influenced by an interaction between circadian- and activity-dependent induction of gene expression after controlling for their individual effects.

    References

    (1) Eckel-Mahan, K. L. et al. Circadian oscillation of hippocampal MAPK activity and cAMP: implications for memory persistence. Nat Neurosci 11, 1074–1082 (2008).

    (2) Buzsáki, G. Theta Oscillations in the Hippocampus. Neuron 33, 325–340 (2002).

    (3) Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).

  7. Version published to 10.7554/elife.112170.3 on eLife
  8. eLife

    eLife Assessment

    This study demonstrates a critical role of the glycolipid membrane protein insertase (MPIase) in the twin-arginine translocation (TAT) pathway, a protein translocation system that is conserved across all domains of life. The successful reconstitution of the bacterial TAT system in both bacteria-derived and artificial liposomes provides solid experimental evidence that MPIase has a broader role in membrane protein translocation than previously recognized. By revealing the essential function of a nonproteinaceous membrane component in catalyzing a core cellular process, this work offers fundamental insights into the molecular mechanisms of protein translocation.

  9. eLife

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

    Strengths:

    The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation.

    Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway.

    Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

  10. eLife

    Author response:

    The following is the authors’ response to the previous reviews.

    Public Reviews:

    [Editors' note: Overall, the reviewing editor and reviewers agreed that authors rigorously addressed reviewers' major scientific concerns.]

    Reviewer #1 (Public review):

    Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

    Comments on revised version from Reviewer 1 and the Reviewing Editor:

    In Reviewer 1's points 1 and 2 regarding statistical significance of immunoblotting data (in recommendation to authors), authors stated that they repeated the experiments three times in the rebuttal letter but not in the figure legends. We recommend authors to add a statement such as "errors denote +-SD (N = 3)" in the final version, which will make the data more solid and reliable.

    Thank you for the comments. We added the suggested sentence.

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

    Strengths:

    The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    Comments on revised version from Reviewer 2 and Reviewing Editor:

    (Reviewer 2) It is nice to find the mistake in Fig. 4B (RR, EK413, (not +MPIase)) and corrected it in the revised version. However, the data of Fig. 4B and the related modified text on P.9 L256-269 are not very easy to understand what the authors are trying to argue. This reviewer believes that the magnitudes of MPIase expression level are Ek413 > +MPIase > delta-MPIase. Therefore, the means of supernatant in the delta-MPIase and EK413 represents mostly cytosol and periplasm, respectively. On the other hand, the +MPIase shows less supernatant that means a large part of TorA-GFP are trapped nearby membrane but not aggressively transported. Similar results are found also in (KK) case, whereas the sup level in EK413 is lower than that of (RR) case due to the lack of twin-arginine motif in the substrate.

    Although these results are convincing in explaining the model in Figure 5, authors might be able to attempt to refine them more understandable. Especially, the revised text on P.9 L256-269 is not enough for leading readers to the author's claim. So that, the authors should revise the text about Fig. 4B again and the related description in the manuscript. Another option is it would be possible to delete whole Fig. 4B, because Fig. 4A, C already well support the model in Fig. 5.

    (Reviewing Editor) I agree with Reviewer 2. The first paragraph in the section "MPIase serves as the TAT signal receptor" runs very long and the clarity of the descriptions on Fig. 4B could be further improved. Fig. 4A, C would sufficiently support author's arguments. I recommend that authors consider these options for the final version.

    We appreciate the critical comments. In Fig. 4B, we aimed to show that the amount of membrane-targeted TorA-GFP would increase due to the overproduction of MPIase caused by TatABC. At the same time, TorA-GFP translocation was also stimulated. However, since the translocated TorA-GFP was recovered in the same fractions as the cytosolic TorA-GFP, interpreting the results would be complicated. Therefore, we deleted Fig. 4B, as suggested by the reviewer and editor. The corresponding text and the right-hand side of Fig. S9 were also deleted.

  11. Version published to 10.7554/elife.112170.2 on eLife
  12. eLife

    eLife Assessment

    This study demonstrates a critical role of the glycolipid membrane protein insertase (MPIase) in the twin-arginine translocation (TAT) pathway, a protein translocation system that is conserved across all domains of life. The successful reconstitution of the bacterial TAT system in both bacteria-derived and artificial liposomes provides solid experimental evidence that MPIase has a broader role in membrane protein translocation than previously recognized. By revealing the essential function of a nonproteinaceous membrane component in catalyzing a core cellular process, this work offers fundamental insights into the molecular mechanisms of protein translocation.

  13. eLife

    Reviewer #1 (Public review):

    [Editors' note: Overall, the reviewing editor and reviewers agreed that authors rigorously addressed reviewers' major scientific concerns.]

    Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

    Comments on revised version from Reviewer 1 and the Reviewing Editor:

    In Reviewer 1's points 1 and 2 regarding statistical significance of immunoblotting data (in recommendation to authors), authors stated that they repeated the experiments three times in the rebuttal letter but not in the figure legends. We recommend authors to add a statement such as "errors denote +-SD (N = 3)" in the final version, which will make the data more solid and reliable.

  14. eLife

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

    Strengths:

    The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    Comments on revised version from Reviewer 2 and Reviewing Editor:

    (Reviewer 2) It is nice to find the mistake in Fig. 4B (RR, EK413, (not +MPIase)) and corrected it in the revised version. However, the data of Fig. 4B and the related modified text on P.9 L256-269 are not very easy to understand what the authors are trying to argue. This reviewer believes that the magnitudes of MPIase expression level are Ek413 > +MPIase > delta-MPIase. Therefore, the means of supernatant in the delta-MPIase and EK413 represents mostly cytosol and periplasm, respectively. On the other hand, the +MPIase shows less supernatant that means a large part of TorA-GFP are trapped nearby membrane but not aggressively transported. Similar results are found also in (KK) case, whereas the sup level in EK413 is lower than that of (RR) case due to the lack of twin-arginine motif in the substrate.

    Although these results are convincing in explaining the model in Figure 5, authors might be able to attempt to refine them more understandable. Especially, the revised text on P.9 L256-269 is not enough for leading readers to the author's claim. So that, the authors should revise the text about Fig. 4B again and the related description in the manuscript. Another option is it would be possible to delete whole Fig. 4B, because Fig. 4A, C already well support the model in Fig. 5.

    (Reviewing Editor) I agree with Reviewer 2. The first paragraph in the section "MPIase serves as the TAT signal receptor" runs very long and the clarity of the descriptions on Fig. 4B could be further improved. Fig. 4A, C would sufficiently support author's arguments. I recommend that authors consider these options for the final version.

  15. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

    My major points are listed below for the authors to consider:

    (1) The authors successfully reconstituted the TAT system using the purified TatA/B/C, but the translocation efficiency was much lower than that of native INV. The authors partly attributed this to the reason that "MPIase recovery would be too low to detect the TAT activity" in the Discussion part. So, what would happen to the translocation efficiency if you added more MPIase to the reconstituted system? How about the abundance of MPIase from the INV and reconstituted proteoliposomes?

    Fig. 3A shows that proteoliposomes reconstituted with active INV were inactive. However, TAT activity was observed when the proteoliposomes were fused with MPIase-containing liposomes. This indicates that MPIase was not sufficiently recovered during reconstitution. Typically, we added MPIase to proteoliposomes at 5% to phospholipids. However, increasing the amount of MPIase to exceed that of INV did not significantly increase the activity. The amounts of MPIase were stated in the text (P17, L3-4).

    (2) Why were only TatC levels measured in Figure 2C, whereas the expression levels of TatA were not detected? Also, from my observation, the amount of TatC in the third lane is lower than that in the previous two lanes.

    We also wanted to check the TatA levels, but the antibody was no longer available. Here, we confirmed that inactive INVs still contain Tat components.

    (3) The authors should explain why the TatA/B/C ratios in Figure 3C (1:1:1) and Figure 3D (10:1:1) are inconsistent.

    We examined various TatA/B/C ratios. We found that the ratio of 10:1:1 was better than 1:1:1. This was added to the text (P8, L11-16).

    (4) ~30% of the fluorescence was recovered in the membrane fraction (Figure 4A) both in the functional TAT signal sequence (RR) and in the inactivating mutant signal sequence (KK), which suggests that MPIase acts as a relatively broad recognition factor. Given that MPIase does not discriminate between RR and KK, why do un-translocated substrates remain in the cytoplasm rather than non-specifically adhering to the membrane when MPIase is depleted in vivo?

    Our results strongly suggest that MPIase acts as a signal sequence receptor. Therefore, in the absence of MPIase, the targeting of TAT precursors should be inhibited. Furthermore, because the TorA signal is less hydrophobic than the Sec signal, the level of nonspecific membrane binding would decrease.

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

    Strengths:

    The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    Weaknesses:

    (1) To show the importance of the Tat system in bacterial cells, it would be good to describe in the introduction how many proteins are translocated via the Tat system.

    The E. coli K12 cells possess 27 TAT substrates. This was added to the text (P2, L14).

    (2) Figure 2B and D show that a sufficient amount of MPIase is important in SufI translocation. However, the reason why MPIase level was upregulated in the BL21 strain but not in the KS46 strain remains unexplained. The authors should address this point.

    CdsA is a rate-limiting enzyme in MPIase biosynthesis. When MPIase production needs to increase, the cdsA promoters are activated. In KS46, however, cdsA is under the control of the arabinose promoter on a plasmid, so CdsA cannot be induced even when MPIase is necessary. This was added to the text (P6, L26-27).

    (3) In Figures 4A and B, the authors explain that MPIase first works as a receptor of TorA-GFP without recognizing the RR motif. This conclusion is based on the results of the fractionation assays, where "sup" indicates the cytoplasmic and periplasmic fractions, and "ppt" indicates the membrane fraction. In Figure 4B, under the TatABC+++, (RR), +MPIase condition, the substrate is secreted most efficiently via the Tat pathway and should therefore be recovered in the periplasm fraction (sup). However, the authors point out that efficiently processed substrate was recovered in the ppt fraction rather than the sup fraction. The authors should explain why this occurred.

    We found a mistake in the processing of the results for the sample of the TatABC+++, (RR), +MPIase condition, in Fig. 4B. Therefore, we remeasured the sample and corrected the figure. The relevant text was also modified (P9, L16-29). It was found that a large part of fluorescence was recovered in the supernatant fraction. We also uploaded the raw data for the fluorescence values.

    Recommendations for the authors:

    Reviewing Editor Comments:

    Overall, I agree with the reviewers that the conclusions are reasonably well supported by the data. I have additional scientific/editorial concerns and recommendations:

    (A) - Scientific:

    (1) P.4 lines 10-15 and Fig. 1 top-left (the effect of overexpressed TatABC).

    The authors state that the extent of Surf1 maturation increased upon overexpression of TatABC, but the band intensities for mature Surf1 in the TatABC-induced vs uninduced lanes are very similar.

    The expression was weakened: ' The level of the mature form increased....' was changed to ' The level of the mature form slightly increased....'

    (2) P.7 lines 23-24, Figure 3A. (the TAT system reconstituted in liposomes).

    The authors state that a small portion of Surf1 is successfully translocated and protected by ProK. But unlike the assay results on IMVs, the size of the mature form (i.e., translocated form) appears to be the same as the untranslocated form. Some explanation seems to be needed.

    In INV, the signal sequence is cleaved off by Lep, however, the cleavage is not coupled with translocation. In the reconstituted proteoliposomes, such cleavage is hardly observed since the membrane proteins were diluted by fusing with MPIase-containing liposomes. This was added to the text (P7, L26-29).

    (3) Based on the model (Figure 5), the interaction between the RR motifs and MPI appears non-electrostatic as the substrate binds to the sugar moieties of the MPIase, not to the pyrophosphate part. Then what is the molecular nature of the substrate-MPI interaction? Some explanation/speculation seems to be needed.

    MPIase has been identified as a factor that drives membrane protein insertion. Through the analysis of the mechanisms of insertion, we found that the glycan chain interacts directly with the transmembrane region of the membrane proteins through the numerous acetyl residues on the glycan. Moreover, we found that the positive charges of substrate membrane proteins interact with the pyrophosphate residue through the electrostatic interaction. Therefore, it is reasonable that the h region of the TAT signal binds to the MPIase glycan through the hydrophobic interactions, and the n region including the RR motif binds to the pyrophosphate residue. This was added to the text (P8, L25-27; P11, L14-17).

    (B) - Editorial:

    (1) P.2. lines 5-10. (Introduction)

    The description of the TAT-targeting signal needs to be more clearly described for a broader readership. For example, what are "h" and "c"?

    The signal sequence is composed of three regions, 'n', 'h', and 'c' from N-terminus. The n region contains positive charges including the RR motif. The next h region contains a hydrophobic stretch. The c region contains a cleavage site. This was added to the text (P2, L4-11).

    (2) P.7 lines 23-24.

    What is the rationale for using Pm-Fob-His as a control?

    This is a control for a membrane protein unrelated to the TAT system to reveal that MPIase is specifically interacts with TatABC.

    (3) P.8 lines 7-8.

    CCCP is not defined.

    CCCP (Carbonyl cyanide 3-chlorophenylhydrazone) is a protonophore. This was added to the text (P8, L18).

    Reviewer #1 (Recommendations for the authors):

    (1) The conclusions derived from Figures 1-3 heavily rely on representative immunoblotting images. Given that representative tracks can inherently introduce selection bias, how do the authors ensure the statistical robustness of these findings without quantitative bar graphs and rigorous significance analysis? To fully substantiate these interpretations, it is recommended that these immunoblotting assays be quantified across at least three independent biological replicates.

    We did not quantify the results in sections where we presented qualitative discussions. However, we performed all experiments at least three times.

    (2) Is it possible that the error values be added after the statistical values of translocation efficiency in Figures 2 and 3?

    We added the SD values to some results in Fig. 3.

    (3) "MPIase depletion of was then confirmed", an extra "of".

    Corrected.

    (4) The capitalization style of "TAT" in the whole text is suggested to be unified.

    Corrected.

  16. Version published to 10.64898/2026.06.05.730299v4 on bioRxiv
  17. Version published to 10.64898/2026.06.05.730299v3 on bioRxiv
  18. eLife

    eLife Assessment

    This study demonstrates a critical role of the glycolipid membrane protein insertase (MPIase) in the twin-arginine translocation (Tat) pathway, a protein translocation system that is conserved across all domains of life. The successful reconstitution of the bacterial Tat system in both bacteria-derived and artificial liposomes provides solid experimental evidence that MPIase has a broader role in membrane protein translocation than previously recognized. By revealing the essential function of a nonproteinaceous membrane component in catalyzing a core cellular process, this work offers fundamental insights into the molecular mechanisms of protein translocation.

  19. eLife

    Reviewer #1 (Public review):

    Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

    My major points are listed below for the authors to consider:

    (1) The authors successfully reconstituted the TAT system using the purified TatA/B/C, but the translocation efficiency was much lower than that of native INV. The authors partly attributed this to the reason that "MPIase recovery would be too low to detect the TAT activity" in the Discussion part. So, what would happen to the translocation efficiency if you added more MPIase to the reconstituted system? How about the abundance of MPIase from the INV and reconstituted proteoliposomes?

    (2) Why were only TatC levels measured in Figure 2C, whereas the expression levels of TatA were not detected? Also, from my observation, the amount of TatC in the third lane is lower than that in the previous two lanes.

    (3) The authors should explain why the TatA/B/C ratios in Figure 3C (1:1:1) and Figure 3D (10:1:1) are inconsistent.

    (4) ~30% of the fluorescence was recovered in the membrane fraction (Figure 4A) both in the functional TAT signal sequence (RR) and in the inactivating mutant signal sequence (KK), which suggests that MPIase acts as a relatively broad recognition factor. Given that MPIase does not discriminate between RR and KK, why do un-translocated substrates remain in the cytoplasm rather than non-specifically adhering to the membrane when MPIase is depleted in vivo?

  20. eLife

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

    Strengths:

    The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    Weaknesses:

    (1) To show the importance of the Tat system in bacterial cells, it would be good to describe in the introduction how many proteins are translocated via the Tat system.

    (2) Figure 2B and D show that a sufficient amount of MPIase is important in SufI translocation. However, the reason why MPIase level was upregulated in the BL21 strain but not in the KS46 strain remains unexplained. The authors should address this point.

    (3) In Figures 4A and B, the authors explain that MPIase first works as a receptor of TorA-GFP without recognizing the RR motif. This conclusion is based on the results of the fractionation assays, where "sup" indicates the cytoplasmic and periplasmic fractions, and "ppt" indicates the membrane fraction. In Figure 4B, under the TatABC+++, (RR), +MPIase condition, the substrate is secreted most efficiently via the Tat pathway and should therefore be recovered in the periplasm fraction (sup). However, the authors point out that efficiently processed substrate was recovered in the ppt fraction rather than the sup fraction. The authors should explain why this occurred.

  21. Version published to 10.7554/elife.112170.1 on eLife
  22. Version published to 10.64898/2026.06.05.730299v2 on bioRxiv
  23. Mentioned by @biorxiv-biochem.bsky.social on Bluesky

    Glycolipid MPIase is essential for the TAT (Twin-Arginine Translocation) pathway https://www.biorxiv.org/content/10.64898/2026.06.05.730299v1

  24. Mentioned by @biorxivpreprint.bsky.social on Bluesky

    Glycolipid MPIase is essential for the TAT (Twin-Arginine Translocation) pathway https://www.biorxiv.org/content/10.64898/2026.06.05.730299v1

  25. Version published to 10.64898/2026.06.05.730299v1 on bioRxiv