When mitochondria fall apart: Unbalanced mitochondrial segregation triggers loss of mtDNA in the absence of mitochondrial fusion

  1. Lisa Dengler
  2. Francesco Padovani
  3. Bianca Lemke
  4. Rebecca Brugger
  5. Benedikt Westermann
  6. Boris Maček
  7. Kurt M. Schmoller
  8. Jennifer C. Ewald

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 biogenesis and inheritance must be carefully regulated alongside cell division to ensure proper mitochondrial function and cell survival. The dynamics of the mitochondrial network, including fusion and fission, play a crucial role in mitochondrial inheritance by facilitating the distribution and quality control of mitochondria. In budding yeast, simultaneous inhibition of both fusion and fission leads to loss of mitochondrial DNA (mtDNA) integrity, resulting in an increased frequency of petite cells. Loss of mitochondrial fusion alone results in the complete loss of mtDNA. While the loss of mtDNA in the absence of mitochondrial fusion has been known for almost 30 years, the reason remained unclear. Here, we investigate the consequences of impaired mitochondrial fusion through depletion of the mitofusin Fzo1. We follow the emerging phenotype by live-cell imaging and the analysis of more than thirty thousand single cells across their cell cycle. Fzo1 depletion causes rapid mitochondrial fragmentation and a reduction in mitochondrial membrane potential, followed by a progressive decline of mtDNA content and cellular growth rate over several cell divisions. During division, Fzo1-depleted daughters obtain an unusually large amount of mitochondria, leaving the mother with too little. This results in a strong disbalance of mitochondrial mass in the population. Additionally, Fzo1-depleted cells lose the ability to adjust mtDNA synthesis to compensate for a low mitochondrial content. The combined effects of unequal distribution and reduced synthesis drive rapid mtDNA loss. These results show how fusion defects lead to mtDNA loss and mitochondrial dysfunction, contributing to understanding diseases linked to fusion defects.

Article activity feed

  1. 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

    We thank the reviewers for their thoughtful comments and overall very supportive feedback.

    Reviewer #1 writes: "The study is very thorough and the experiments contain the appropriate controls. (...) The findings of the study can have relevance for human conditions involving disrupted mitochondrial dynamics, caused for example by mutations in mitofusins." Reviewer #2 writes: "The dataset is rich and the time-resolved approach strong." Reviewer #3 writes: "I admire the philosophy of the research, acknowledging an attempt to control for the many possible confounding influences. (...) This is a powerful and thoughtful study that provides a collection of new mechanistic insights into the link between physical and genetic properties of mitochondria in yeast."

    We address all points below. We have not yet updated our text and figures since we expect substantial additions from new experiments. But we have included Figure R1 with some additional analyses of existing data at the bottom of the manuscript.

    Reviewer1

    1.1 Statistical comparisons are missing throughout the manuscript (with the exception of Fig. 2c). Appropriate statistical tests, along with p-values, should be used and reported where different gorups are compared, for example (but not limited to) Fig. 3d and most panels of Fig. 4.

    We initially decided not to add too many extra labels to the already very busy plots, given that the magnitude of change mostly speaks for itself. However, we will try to find meaningful statistical tests together with a sensible graphical representation for all of the figures. For one example see Figure R1A.

    1.2. I do not agree with the use of Atp6 protein as a direct read-out of mtDNA content. While Atp6 protein levels will decrease with decreasing mtDNA content, the inverse is not necessarily true: decreased Atp6 protein levels do not necessarily indicate decreased mtDNA levels, because they could alternatively or additionally be caused by decreased transcription and/or translation. Therefore, please do not equate Atp6 protein levels to mtDNA levels, and instead rephrase the text referencing the Atp6 experiments in the Results and Discussion sections to measure "mtDNA expression" or "mt-encoded protein" or similar. For example, on p. 14 line 431 should read "mtDNA expression" rather than "decreased synthesis of mtDNA", and line 440 on the same page "mean mtDNA levels" should be "mtDNA expression" or similar.

    All three reviewers agree that using Atp6-NG as a direct proxy for mtDNA requires more validation, or at least rephrasing of the text. We agree that this is the most important point to address. We had previously tried using the mtDNA LacO array (Osman et al. 2015) to directly assess the amount of nucleoids per cell. However, the altered mitochondrial morphology of the Fzo1 depleted cells combined with the LacI-GFP which is still in mitochondria even when mtDNA is gone, increases the noise level to a point that we cannot interpret the signal. However, as this manuscript was in the submission process, the Schmoller lab (co-authors #2 and #7) adapted the HI-NESS system to label mtDNA in live yeast cells(Deng et al. 2025). This system promises much better signal to noise and we expect we can address all concerns regarding the actual count of nucleoids per cell. Should this unexpectedly fail for technical reasons, we will try to calibrate the Atp6-levels with DAPI staining at defined time points and will rephrase the text as the reviewer suggests.

    1.3. In Fig. 3, the authors use the fluorescence intensity of a mitochondrially-targeted mCardinal as a read-out of mitochondrial mass. Please provide evidence that this is not affected by MMP, either with relevant references or by control experiments (e.g. comparing it to N-acridine orange or other MMP-independent dyes or methods).

    Whether or not the import of any mitochondrial protein is dependent on the MMP depends largely on the signal sequence. The preSu9-signaling sequence was previously characterized as largely independent of the MMP compared to other presequences (Martin, Mahlke, and Pfanner 1991), which is why Vowinckel (Vowinckel et al. 2015) and others (Di Bartolomeo et al. 2020; Perić et al. 2016; Ebert et al. 2025) have previously used this as a neutral reference to the strongly MMP-dependent pre-Cox4 signal to estimate MMP. As one control in our own data, we consider that the population-averaged mitochondrial fluorescent signal Figure S3C stays constant in the first few hours, in agreement with the total averaged mitochondrial proteome (Fig R1E). As additional controls, we plan to compare the signal to an MMP independent dye as the reviewer suggests.

    1.4. In Fig. 2e-f, the authors use a promoter reporter with Neongreen to answer whether the reduced levels of the nuclear-encoded mitochondrial proteins Mrps5 and Qcr7 are due to decreased expression or to protein degradation, and find no evidence of degradation of the Neongreen reporter protein. However, subcellular localization might affect the availability of the protein to proteases. Although not absolutely required, it would be relevant to know if the Neongreen fusion protein is found in the same subcellular compartment as Mrps5 and Qcr7 at 0h and 9h after Fzo1 depletion.

    Here, it seems we need to explain the set-up and interpretation of the data better. The key point we are trying to make with the promoter-Neongreen construct is that the regulation is not mainly at the level of transcription. We are showing that the reduction in the levels of the actual protein (orange bars) is not (mainly) explained by a reduction in expression, since the promoter is similarly active at 0 and at 9 hours (grey bars). If expression from the promoter were strongly reduced, the Neongreen would be diluted with growth and would also decrease, but this is not the case. The fluorophore itself is just floating around in the cytosol and is not subject to the same post-translational regulation as Mrps5 and Qcr7, so there is no reason to expect degradation.

    1.5. Fzo1 depletion leads to a very rapid drop in MMP during the first hour of depletion. In the Discussion, can the authors speculate on the possible mechanism of this rapid MMP drop that occurs well before mtDNA or mt-encoded proteins are decreased in level?

    This is indeed an interesting point. We think there are likely three reasons causing this initial drop: Firstly, due to the fragmentation the mixing of mitochondrial content is disturbed and smaller fragments may have suboptimal stoichiometry of components (see also (Khan et al. 2024) who look at this in detail including the Fzo1 deletion); secondly, already fairly early, some mitochondrial fragments may not contain any mtDNA and therefore will be unable to synthesize ETC proteins; thirdly, altered morphological features like changes in the surface-to-volume ratios may play a role. Sadly, mechanistically following up on this is not possible with the tools in our hands and therefore outside of the scope of this manuscript. But we are happy to include these speculations in our discussion.

    1.6. In Fig. 2a, the mtDNA copy number of Fzo1-depleted cells is ca 1.3-fold of the control cells at the 0h timepoint. Why might this be? Is it an impact of one of the inducers? If so, we might be looking at the combination of two different processes when measuring copy number: one that is an induction caused by the inducer(s), and the other a consequence of Fzo1 depletion itself.

    We believe that this 30% increase is within the noise of the experiment rather than an effect of the induction. Since we normalize to t=0 uninduced, the first black data point does not have error bars, emphasizing this difference. None of the protein data suggests that there is an increase in mtDNA encoded proteins (see e.g. 2B, or Atp6 fluorescence data). In the planned HI-NESS experiment, we will see in our single cell data whether there is an actual increase in mtDNA upon TIR induction. Additionally, we will run a qPCR to carefully determine mtDNA levels of untreated wild-type cells, tetracycline treated wild-type cells and tetracycline induced TIR expressing cells to exclude effects of tetracycline as well as the expression of TIR on mtDNA.

    Minor comments:

    1.7. p. 3, line 71: "ten thousands of dividing cells.." should be "tens of thousands of dividing cells".

    Thank you, will correct.

    1.8.-p.4, line 116: please be even more clear with what the "depleted" cells and controls are treated with: are depleted cells treated with both inducers, and controls with neither?

    We will make this more clear. Depleted cells are treated with both inducers, the control cells are not. However, in Figure 1A and in S1 we do controls to show that inducing TIR per se or adding aTC per se does not change growth rate or mitochondrial morphology. We will make this more clear.

    1.9. -p.5, lines 147-148: the authors write "the rate with which the abundance of Cox2 and Var1 proteins decreases was similar to the rate of mtDNA loss" though the actual rate is not shown. Please calculate and show rates for these processes side by side to make comparison possible, or alternatively rephrase the statement.

    Indeed this was not phrased well. We will call it dynamics rather than rates.

    1.10. -Fig. 2d: changing the y-axis numbering to match those in panels a and b would facilitate comparisons.

    Makes sense, we will change this.

    1.11. Fig. 2e: it is recommended to label the western blot panels to indicate what protein is being imaged in each (Neongree,, Mrps5, Qcr7).

    We will adapt the labelling to make it more clear.

    1.12. -p.9, line 262: I suggest referencing Fig. 4e at the end of the first sentence for clarity.

    We will modify the sentence as suggested.

    1.13. -In the sections related to Fig. 3a and Fig. 5a as well as the connected supplemental data, the authors discuss both the median and the mean of mitochondrial mass and Atp6 protein, respectively. For purposes of clarity, I suggest decreasing the focus on the mean (that is provided only in the supplemental data) and focusing the text mainly on the median. The two show differing trends and it is very good that both are shown, but the clarity of the text can be improved by focusing more on the median where possible.

    We will check the phrasing and simplify.

    1.14. -p. 14, line 435: the statement that mt mass is maintained over the first 9h of depletion is only true for the mean mt mass, not for the median. Please make this clear or rephrase.

    We will check phrasing, make it more clear and also point out the extended proteomics data (see Fig R1), which corresponds to the mean of the populations

    1.15.-p.14, line 452: "mitofusions" should be "mitofusins".

    Thanks for catching this.

    Reviewer 2:

    2.1. While inducible TIR is used to reduce background, the manuscript should rigorously exclude auxin/TIR off-targets (growth, mitochondrial phenotypes, gene expression). Please include full matched controls: (plus minus)auxin, (plus minus)TIR, epitope tag alone, and a degron control on an unrelated mitochondrial membrane protein.

    We agree that rigorous controls are crucial for the interpretation of the results. However, we think we have already included most of the controls the reviewer is asking for, but we might have not pointed this out clearly enough. For example, in Fig 1A, we could make it more clear by adding more labels in which samples we added aTC, which is only described in the figure legend.

    Here is a list of all the controls:

    • Each depletion experiment is always matched with an experiment of the same strain without induction. So the genetic background as well as effects such as light exposure, time spent in the microfluidics systems, etc are controlled for.
    • Figure S1D shows that the growth rate is wildtype like in a strain containing either the AID tag or the TIR protein AND upon addition of both chemicals. It also shows that the final genetic background (AID-tag and TIR) also grows like wildtype if the inducers are not added. This conclusively shows that neither the tags/constructs nor the chemicals per se affect growth rate
    • In Figure S1C we show the mitochondrial morphology of the same controls. We will make sure to label them more consistently to match panel D, and include an actual wildtype and a FLAG-AID-Fzo1 strain without TIR treated with both aTC and 5-Ph-IAA as direct comparison
    • In figure 1A we compare the Fzo1 protein levels of a strain with and without TIR. We show that in absence of TIR, adding either aTC or Auxin does not change Fzo1 levels and that the levels are comparable in the strain that is able to deplete Fzo1 directly before addition of 5-Ph-IAA (after 2 h of induction of TIR through addition of tetracycline)
    • Additionally, in Figure S2C we show that two hours after adding aTC, the entire proteome does not change significantly apart from a strong induction of TIR. We can also make this more clear in the figure legend.
    • Additionally, we will run a qPCR to carefully determine mtDNA levels of untreated wild-type cells, tetracycline treated wild-type cells and tetracycline induced TIR expressing cells to exclude effects of tetracycline as well as the expression of TIR on mtDNA. (also in response to 1.6.) In summary, we think we have controlled sufficiently for all confounding parameters and most importantly showed that addition of either aTC or Auxin as well as the FLAG-AID tag per se does not disturb mitochondria or cell growth. We do not see what a degron control on an unrelated protein will tell us. Depending on the nature of the protein, it may or may not have a phenotype that may or may not be related to morphology changes etc.

    2.2. The Mitoloc preSu9 vs Cox4 import ratio is only a proxy of mitochondrial membrane potential (ΔΨm) and itself depends on mitochondrial mass, protein expression, matrix ATP, and import saturation. The authors need to calibrate ΔΨm with orthogonal dyes (TMRE/TMRM) and pharmacologic titrations (FCCP/antimycin/oligomycin) to generate a response curve; show that Mitoloc tracks dye-based ΔΨm across the relevant range and corrects for mass/photobleaching. Report single-cell ΔΨm vs mass residuals.

    We completely agree that the MitoLoc system is only a rough proxy for the actual membrane potential. That is why we make no quantitative claims on the absolute value or absolute difference between groups of cells. We also make very clear in Fig 3B what we are actually measuring and can emphasize again in the text that this is only a proxy. We agree that it is a good idea to compare MitoLoc values to TMRE staining as the reviewer suggests, we will do these experiments in depleted and control cells at different timepoints. Please note though that also dye staining has its caveats, especially in dynamic live cell experiments. TMRM for example is not compatible with the acidic pH 5 medium that is typically used for yeast and subjecting cells to washing steps and higher pH may change both morphology of mitochondria and the MMP, especially in cells that are already “stressed”. We prefer not to complete elaborate pharmacological titration experiments because firstly, this was extensively done in the original MitoLoc paper by the Ralser lab ((Vowinckel et al. 2015), cited 120 times); secondly, the value of the MMP is not the most critical claim of the manuscript. See also 3.12. Please note that in Figure S4D we had already plotted MMP vs mitochondrial concentration.

    2.3. To use Atp6-mNeon as a proxy for mtDNA is an assumption. Interpreting Atp6 intensity as "functional mtDNA" could be confounded by translation, turnover, or assembly. Please (i) report mtDNA copy number time courses (you have qPCR), nucleoid counts (DAPI/PicoGreen or TFAM/Abf2 tagging), and (ii) assess translation (e.g., 35S-labeling or puromycin proxies) and turnover (proteasome/AAA protease inhibition, mitophagy mutants -some data are alluded to- plus mRNA levels for mtDNA-encoded genes). This will support the "reduced synthesis" versus "increased degradation" conclusion.

    We agree with all three reviewers that Atp6 is only a proxy for mtDNA (Jakubke et al. 2021; Roussou et al. 2024) and the correlation should be checked more carefully. We will use the very recently established Hi-NESS system to follow nucleoids/ mtDNA during depletion experiments. See detailed reply to 1.2.

    (ii) in Figure 2C we inhibit mitochondrial translation and show that in this case control and depleted cells have the same level of Cox2, at least suggesting that degradation is not the key mechanism controlling the levels of mtDNA encoded proteins. We cannot do proteasome inhibitor assays since the nature of the AID-TIR systems requires an active proteasome. In figure S5C we show that the Atp6 depletion is similar in an atg32 deletion. This does not completely exclude a contribution of mitophagy to the observed phenotype, but does confirm that mitophagy is not the primary reason for cells becoming petite.

    2.4. The promoter-NeonGreen reporters argue against transcriptional down-regulation of nuclear OXPHOS. Please add mRNA (RT-qPCR/RNA-seq) for representative genes and a pulse-chase or degradation-pathway dependency (e.g., proteasome/mitophagy/autophagy mutants) to firmly assign active degradation. The authors need to normalize proteomics to mitochondrial mass (e.g., citrate synthase/porin) to separate organelle abundance from protein turnover.

    While we are happy to perform qPCR experiments for selected genes, a full RNA-seq experiment seems outside the scope of this study. As explained above, a proteasome inhibitor experiment is not possible in this set-up. Bulk mitophagy/autophagy seems unlikely to be the cause of the decrease of the nuclear-encoded OXPHOS proteins, since most other mitochondrial proteins do not decrease on average on population level in the first hours. This data is now plotted as additional figure (see below) and will be included in the supplementary of the revised manuscript (Fig R1E).

    2.5. Using preSu9-mCardinal intensity as "mitochondrial concentration" is sensitive to expression, import competence, and morphology/segmentation. The authors should provide validation that this metric tracks 3D volume across fragmentation states (e.g., correlation with mito-GFP volumetrics; detergent-free CS activity; TOMM20/Por1 immunoblot per cell).

    We agree that this is an important point and the co-authors discussed this point quite intensively. In figure S3A and B we show (using confocal data) that there is a very strong correlation between the total fluorescence signal and the 3D volume reconstruction. However, the slope of the correlation is different between tubular and fragmented mitochondria (compare panels A and B) and see figure legend. Since we are dealing with diffraction-limited objects it is likely that the 3D reconstruction is sensitive to morphology, especially if mitochondria are “clumping”. We therefore think that the total fluorescence signal is actually a better estimate of mitochondrial mass per cell than the 3D volume reconstruction (especially for our data obtained with a conventional epifluorescence microscope). The mean of the total mitochondrial fluorescence also better matches the population average mitochondrial proteome (Fig R1E). To consolidate this assumption, we will additionally compare our data to a strain with Tom70-Neongreen and to MMP independent dyes.

    Notably, since the morphology is similarly altered in mothers and buds this is of minor impact for our main point – the unequal distribution between mother and buds.

    2.6. The unequal mother-daughter distribution is compelling, but causality remains inferred. Test whether modulating inheritance machinery (actin cables/Myo2, Num1, Mmr1) or altering fission (Dnm1 inhibition) modifies segregation defects and rescues mtDNA/Atp6 decline. Complementation with Fzo1 re-expression at defined times would help order the phenotype cascade.

    We agree that rescue experiments would be very useful. We have some preliminary data for tether experiments, for example with Num1. The general problem is that the fragmented mitochondria clump together. We have not found a method to restore an equal distribution between mother and daughter cells. We will try to optimize the assay, but are not overly confident it will work. Mmr1 deletion aggravates the Fzo1 phenotype, likely also because the distribution becomes even more heterogeneous, but we have not rigorously analyzed this.

    We like the idea of the Fzo1 re-expression and will run such experiments. This will be especially powerful in combination with the new HI-NESS mtDNA reporter. We may be able to track exactly when cells reach the point-of-no return and become petite. This will also help connecting our mathematical model more directly to the data.

    2.7. The model is useful but should include parameter sensitivity (segregation variance, synthesis slopes, initial nucleoid number) and prospective validation (e.g., predict rescue upon partial restoration of synthesis or inheritance, then test experimentally).

    We will refine our model to include the to-be-measured nucleoids/mtDNA values. We will include a parameter sensitivity analysis with the updated model.

    Reviewer 3:

    3.1. About the use of Atp6 as a good proxy for mtDNA content. This is assumed from l285 onwards, based on a previous publication. As the link is fairly central to part of the paper's arguments, and the system in this study is being perturbed in several different ways, a stronger argument or demonstration that this link remains intact (and unchanged, as it is used in comparisons) would seem important.

    We agree, see 1.2.

    3.2. About confounding variables and processes. The study does an admirable job of being transparent and attempting to control for the many different influences involved in the physical-genetic link. But some remain less clearly unpacked, including some I think could be quite important. For example, there is a lot of focus on mito concentration -- but given the phenotypes are changing the sizes of cells, do concentration changes come from volume changes, mito changes, or both? In "ruling out" mitophagy -- a potentially important (and intuitive) influence, the argument is not presented as directly as it could be and it's not completely clear that it can in fact be ruled out in this way. There are a couple of other instances which I've put in the smaller points below.

    Thank you for acknowledging our efforts to show transparent and well-controlled experiments! We address each of the specific points below.

    3.3. full genus name when it first appears

    We will add the full name.

    3.4. I may be wrong here, but I thought the petite phenotype more classically arises from mtDNA deletion mutations, not loss? The way this is phrased implies that mtDNA loss is [always] the cause. Whether I'm wrong on that point or not, the petite phenotype should be described and referenced.

    We can expand the text and cite additional relevant papers. The term “petite” refers to any strain that is respiratory incompetent and leads to small colonies (not necessarily small cells!) (Seel et al. 2023). This can be mutations or gene loss (fragments) on the mtDNA (these are called cytoplasmic petite), or chemically induced loss of mtDNA (e.g. EtBr), or mutations of nuclear genes required for respiration (these are termed nuclear petite; some nuclear petites show loss of mtDNA in addition to the mutation in the nuclear genome) (Contamine and Picard 2000).

    3.5. para starting l59 -- should mention for context that mitochondria in (healthy, wildtype) yeast are generally much more fused than in other organisms

    ok.

    3.6. Fig 1C -- very odd choice of y-axis range! either start at zero or ensure that the data fill as much vertical space of the plot as possible

    True, this was probably some formatting relic. We will adapt the axis to fill the full space. Most of our axes start at 0, but that doesn’t make so much sense here, since we consider the solidity in the control as “baseline”.

    3.7. "wild-type like more tubular mitochondria" reads rather awkwardly. "more tubular mitochondria (as in the wild-type)"?

    Thank you, sounds better.

    3.8. l106 -- imaging artefacts? are mitos fragmenting because of photo stress? -- this is mentioned in l577-8 in the Methods, but the data from the growth rate and MMP comparison isn't given -- an SI figure would be helpful here. It would be reassuring to know that mito morphology wasn't changing in response to phototoxicity too.

    In the methods we just briefly point out that we have done all our “due diligence” controls to check that we do not generate phototoxicity, something that we highlight in the cited review. We do not explicitly have a figure for this, but figure S1A shows that the solidity of the mitochondrial network in control cells stays the same over 9 hours, even though these cells are exposed to the same cultivation and imaging regime as the depleted cells. We will also add a picture of control cells after 9 h. In S1B we show that control cells containing TIR but no AID tag treated with both chemicals imaged over 9 hours also show the same solidity (~mitochondrial morphology) as untreated control. Also, the doubling times of cells grown in our imaging system (Fig R1B) are very similar to the shake flask (Fig R1A). All in all, we are very confident that our imaging settings did not impact our reported phenotypes.

    3.9. para l146 -- so this suggests mtDNA-encoded proteins have a very rapid turnover, O(hours) -- is this known/reasonable?

    Reference (Christiano et al. 2014) suggests that respiratory chain proteins are shorter lived than the average yeast protein. However, based on Figure 2C we think the dynamics mostly speak for a dilution by growth.

    3.10. section l189 -- it's hard to reason fully about these statistics of mitochondrial concentration given that the petite phenotype is fundamentally affecting overall cell volume. can we have details on the cell size distribution in parallel with these results? to put it another way -- how does mitochondrial *amount* per cell change?

    This is a good point. We report mostly on mitochondrial “concentrations” because we think this is what the cell actually cares about (mitochondrial activity in relationship to cytosolic activity). But we will include additional graphs on mitochondrial amount as well as size distributions (Fig R1C, related to Fig 4F). We can already point out that the size distribution of the population does not change much in the first hours. The “petite” phenotype refers to small colonies on growth medium with limited supply of a fermentable carbon source, not to smaller size of single cells.

    3.11. l199 the mean in Fig S3C certainly does change -- it increases, clearly relative both to control and to its initial value. rather than sweeping this under the carpet we should look in more detail to understand it (a consequence of the increased skew of the distribution)?

    This relates somewhat to the previous point. The increase in average concentration is not due to an increased amount in the population, but due to the fact that it is the small buds that get a very high amount of the mitochondria which “exaggerates” the asymmetric/heterogenous distribution. This will be clarified by the figures we mention in the point above.

    3.12. para line 206 -- this doesn't make it clear whether your MMP signal is integrated over all mitochondria in the cell, or normalised by mitochondrial content? this matters quite a lot for the interpretation if the distributions of mitochondrial content are changing. reading on, this is even more important for para line 222. Reading further on, there is an equation on l612 that gives a definition, but it doesn't really clarify (apologies if I'm misunderstanding).

    For each cell, we basically calculate the relative mitochondrial enrichment of the MMP sensitive vs the MMP insensitive pre-sequence.

    So, MMP= (total intensity of mitochondrial pre-Cox4 Neongreen/ total intensity of mitochondrial pre-Su9 Cardinal) / (total cytosolic pre-Cox4 Neongreen/ total cytosolic pre-Su9 Cardinal).

    We calculate this value for each cell, but we do not have the optical resolution to calculate it for individual mitochondrial fragments.

    Both constructs are driven by the same strong promoter, so transcription of the fluorophore should never limit the uptake. Also, in Figure 3D we compare control and depleted cells with similar total mitochondrial concentration, so the difference must be due to a different import of the two fluorophores, see also Fig S4D. The calculated “MMP” value is of course only a crude proxy for the actual membrane potential in millivolts and we do not want to make any claims on absolute values or quantitative differences. But essentially what we are interested in is “mitochondrial health/activity” and we think the system is good at reporting this. See also 2.2.

    3.13. l230 -- a point of personal interest -- low mito concentrations are connected to low "function" (MMP) and give extended division times -- this is interestingly exactly the model needed to reproduce observations in HeLa cells (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002416). That model went on to predict several aspects of downstream cellular behaviour -- it would be very interesting to see how compatible that picture (parameterised using HeLa observations) is with yeast!

    Thank you for pointing out your interesting paper, which we will include in our discussion. Another recent preprint about fission yeast (Chacko et al. 2025) also fits into this picture. Since you were kind enough to disclose your identity, we would be happy to discuss this further with you in person if we can maybe follow-up on this.

    3.14. l239 "less mitochondria" -- a bit tricky but I'd say "fewer mitochondria" or "less mitochondrial content"

    Thanks, we will think about how to best rephrase this, probably less mitochondrial content.

    3.15. Section l234 So here (and in Fig 4) the focus is on overall distributions of mitochondrial concentration in different cells (mother-to-be, mother, bud; gen 1, gen >1). But we've just seen that one effect of fzo1 is to broader the distribution of mitochondrial concentration across cells. Can't we look in more depth at the implications of this heterogeneity? For example in Fig 4F (which is cool) we look at the distribution of all fzo1 mothers-to-be, mothers, and buds. But this loses information about the provenance. For example, do mothers-to-be with extremely low mito concentrations just push everything to the bud, while mothers-to-be with high mito concentrations distribute things more evenly? It would seem very easy and very interesting to somehow subset the distribution of mothers-to-be by concentration and see how different subsets behave

    This is a good point. When analyzing the data, we pretty much plotted everything against everything and then chose the graphs that we think will best guide the reader through the story-line. We can make additional supplementary plots where we show the starting concentrations/amounts of the mother in relationship to the resulting split ratio at the end of the cycle (Fig R1D).

    3.16. l285 -- experimental design -- do we know that Atp6 will continue to be a good proxy for functional mtDNA in the face of the perturbations provided by Fzo1 depletion? Especially if there is impact on the expression of mitoribosomes, the relationship between mtDNA and Atp6 may look rather different in the mutant?

    This is actually our top-priority experiment now. We will use the HI-NESS system and possibly DAPI staining to make a more direct link to mtDNA/ nucleoid numbers, see 1.2.

    3.17. l290 -- ruled out mitophagy. This message could be much clearer. Comparing Fig S5C and Fig 3A side-by-side is a needlessly difficult task -- put Fig 3A into Fig S5. Then we see that when mitophagy is compromised, the distribution of mitochondrial concentration has a lower median and much lower upper quartile than in the mitophagy-equipped Fzo1 mutant? What is going on here? For a paper motivated by disentangling coupled mechanisms, this should be made clearer!

    Thanks for pointing this out. We can of course easily include the control in the corresponding figure. Compromising mitophagy is likely to generally affect mitochondrial health and turnover a little bit, independent of what is going on with Fzo1. The second evidence that speaks against large-scale mitophagy is the proteomics data: On population level the dynamics of the respiratory chain proteins are very different from those of other (nuclear encoded) mitochondrial proteins. We will add additional supplementary figures to make this more clear, see Fig R1E. Most mitochondrial proteins in the proteomics experiment stay constant in the first few hours, consistent with the imaging data showing that the mean mitochondrial content of the population does not change initially. This again highlights that it is the unequal distribution which is the problem and not massive degradation of mitochondria.

    3.18. With the Atp6 signal, how do we know that fluorescence from different cells is comparable? Buds will be smaller than mother cells for example, potentially leading to less occlusion of the fluorescent signal by other content in the cytoplasm

    This is of course a general problem that anyone faces doing quantitative fluorescence microscopy. From the technical side, we have done the best we could by taking a reasonable amount of z-slices and by choosing fluorophores that are in a range with little cellular background fluorescence (e.g. Neongreen is much better than GFP). From a practical standpoint, we are always comparing to the control, which is subject to the same technical limitations as the depleted cells and the cell sizes are very similar. So, even if we are systematically overestimating the Atp6 concentration in the bud by a few %, the difference to the control would still be qualitatively true. We therefore do not think that any of our conclusions are affected by this.

    3.19. l343 -- maintenance of mtDNA -- here the point about l285 (is the Atp6-mtDNA relationship the same in the Fzo1 mutant) is particularly important, as we're directly tying findings about the protein product to implications about the mtDNA

    We will carefully address this, see above.

    3.20. l367 -- on a first read this description of the model feels like lots of choices have been made without being fully justified. Why a log-normal distribution (when the fit to the data looks rather flawed); why the choice of 5 groups for nucleoid number (why not 3? or 8?); the process used for parameter fitting is very unclear (after reading the methods I think some of these values are read directly from the data, but the shapes of the distributions remain unexplained). l705 -- presumably the ratio was drawn from a log-normal distribution and then the corresponding nucleoid numbers were rounded to integers? the ratio itself wasn't rounded? (also l367) How were the log-normal distributions fitted to experiments (Figs. S7A,B)? Just by eye?

    We will update our model based on measured nucleoid counts and then explain more stringently the choices we make/ parameters we select.

    3.21. l711 by random selection -- just at random? ("selection" could be confusing) Overall, it feels like the model may be too complicated for what it needs to show. Either (a) the model should show qualitatively that unequal inheritance and reduced production leads to rapid loss -- which a much simpler model, probably just involving a couple of lines of algebra, could show. Or (b) the model should quantitatively reproduce the particular numerical observations from the experiments -- it's not totally clear that it does this (do the cell-cycle-based decay timescales in Fig 7 correspond to the hour-based decay timescales in other plots, for example). At the moment the model is at a (b) level of detail but it's only clear that it's reporting the (a) level of results.

    If the HI-NESS and Fzo1 re-addition experiments work as explained above, all parameters will have direct experimental data, and we should get much closer to (a).

    3.22. A lot of the discussion repeats the results; depending on editorial preferences some of this text could probably be pared back to focus on the literature connections and context.

    We will think about streamlining the discussion once some of the additional material alluded to above has been added.

    3.23. Data availability -- it looks like much of the data required to reproduce the results is not going to be made available. Images and proteomic data are promised, but the data associated with mitochondrial concentration and other features are not mentioned. For FAIR purposes all the data (including statistics from analysis of the images) should be published.

    We maybe didn’t phrase this clearly. All data will be made available. Where technically feasible, this will be directly accessible in a repository, otherwise by request to the corresponding author.

    On our OMERO server, we have deposited many TB of raw images as well as all the intermediate steps such as segmentation masks, and the csv files with all the extracted data for each cell (including background corrections etc). Additionally, we can include csvs with the data grouped in a way that we used to generate all the box blots etc. As of now, the OMERO data is unfortunately only available by requesting a personal guest login from our bioinformatics facility, but we were promised that with the next technical update there will be a public link available. The proteomics data and the model are already fully accessible. The raw western blot images with corresponding ponceau staining will be included with the final publication either as additional supplementary material or in whatever format matches the journal requirements.

    3.24 l660 -- can an overview of the EM protocol be given, to avoid having to buy the Mayer 2024 article?

    The cited paper is open access. But we can also include more details in our method section.

    References:

    Chacko, L. A., H. Nakaoka, R. Morris, W. Marshall, and V. Ananthanarayanan. 2025. 'Mitochondrial function regulates cell growth kinetics to actively maintain mitochondrial homeostasis', bioRxiv.

    Christiano, R., N. Nagaraj, F. Frohlich, and T. C. Walther. 2014. 'Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe', Cell Rep, 9: 1959-65.

    Contamine, V., and M. Picard. 2000. 'Maintenance and integrity of the mitochondrial genome: a plethora of nuclear genes in the budding yeast', Microbiol Mol Biol Rev, 64: 281-315.

    Deng, Jingti, Lucy Swift, Mashiat Zaman, Fatemeh Shahhosseini, Abhishek Sharma, Daniela Bureik, Francesco Padovani, Alissa Benedikt, Amit Jaiswal, Craig Brideau, Savraj Grewal, Kurt M. Schmoller, Pina Colarusso, and Timothy E. Shutt. 2025. 'A novel genetic fluorescent reporter to visualize mitochondrial nucleoids', bioRxiv: 2023.10.23.563667.

    Di Bartolomeo, F., C. Malina, K. Campbell, M. Mormino, J. Fuchs, E. Vorontsov, C. M. Gustafsson, and J. Nielsen. 2020. 'Absolute yeast mitochondrial proteome quantification reveals trade-off between biosynthesis and energy generation during diauxic shift', Proc Natl Acad Sci U S A, 117: 7524-35.

    Ebert, A. C., N. L. Hepowit, T. A. Martinez, H. Vollmer, H. L. Singkhek, K. D. Frazier, S. A. Kantejeva, M. R. Patel, and J. A. MacGurn. 2025. 'Sphingolipid metabolism drives mitochondria remodeling during aging and oxidative stress', bioRxiv.

    Jakubke, C., R. Roussou, A. Maiser, C. Schug, F. Thoma, R. Bunk, D. Horl, H. Leonhardt, P. Walter, T. Klecker, and C. Osman. 2021. 'Cristae-dependent quality control of the mitochondrial genome', Sci Adv, 7: eabi8886.

    Khan, Abdul Haseeb, Xuefang Gu, Rutvik J. Patel, Prabha Chuphal, Matheus P. Viana, Aidan I. Brown, Brian M. Zid, and Tatsuhisa Tsuboi. 2024. 'Mitochondrial protein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence', Nature Communications, 15: 8274.

    Martin, J., K. Mahlke, and N. Pfanner. 1991. 'Role of an energized inner membrane in mitochondrial protein import. Delta psi drives the movement of presequences', J Biol Chem, 266: 18051-7.

    Osman, C., T. R. Noriega, V. Okreglak, J. C. Fung, and P. Walter. 2015. 'Integrity of the yeast mitochondrial genome, but not its distribution and inheritance, relies on mitochondrial fission and fusion', Proc Natl Acad Sci U S A, 112: E947-56.

    Perić, Matea, Peter Bou Dib, Sven Dennerlein, Marina Musa, Marina Rudan, Anita Lovrić, Andrea Nikolić, Ana Šarić, Sandra Sobočanec, Željka Mačak, Nuno Raimundo, and Anita Kriško. 2016. 'Crosstalk between cellular compartments protects against proteotoxicity and extends lifespan', Scientific Reports, 6: 28751.

    Roussou, Rodaria, Dirk Metzler, Francesco Padovani, Felix Thoma, Rebecca Schwarz, Boris Shraiman, Kurt M. Schmoller, and Christof Osman. 2024. 'Real-time assessment of mitochondrial DNA heteroplasmy dynamics at the single-cell level', The EMBO Journal, 43: 5340-59-59.

    Seel, A., F. Padovani, M. Mayer, A. Finster, D. Bureik, F. Thoma, C. Osman, T. Klecker, and K. M. Schmoller. 2023. 'Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth', Nat Struct Mol Biol, 30: 1549-60.

    Vowinckel, J., J. Hartl, R. Butler, and M. Ralser. 2015. 'MitoLoc: A method for the simultaneous quantification of mitochondrial network morphology and membrane potential in single cells', Mitochondrion, 24: 77-86.

  2. 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

    This article addresses the connection between perturbed mitochondrial structure and genetics in yeast. When mitochondrial fusion is compromised, what is the chain of causality -- the mechanism -- that leads to mtDNA populations becoming depleted? This is a fascinating question, linking physical cell biology to population genetics. I admire the philosophy of the research, acknowledging and attempt to control for the many possible confounding influences. The manuscript describes the context and the research tightly and digestibly; the figures illustrate the results in a clear and natural way.

    For transparency, I am Iain Johnston and I am happy for this review to be treated as public domain. To my eyes my most important shortcoming as a review is my relative lack of familiarity with the yeast fzo1 mutant; while I am familiar with analysis of yeast mito morphology and mtDNA segregation, a reviewer familiar with the nuances of this strain and its culture would be a useful complement.

    I have a few more general points and a collection of smaller points below that I believe might help make the story more robust.

    General points

    1. About the use of Atp6 as a good proxy for mtDNA content. This is assumed from l285 onwards, based on a previous publication. As the link is fairly central to part of the paper's arguments, and the system in this study is being perturbed in several different ways, a stronger argument or demonstration that this link remains intact (and unchanged, as it is used in comparisons) would seem important.
    2. About confounding variables and processes. The study does an admirable job of being transparent and attempting to control for the many different influences involved in the physical-genetic link. But some remain less clearly unpacked, including some I think could be quite important. For example, there is a lot of focus on mito concentration -- but given the phenotypes are changing the sizes of cells, do concentration changes come from volume changes, mito changes, or both? In "ruling out" mitophagy -- a potentially important (and intuitive) influence, the argument is not presented as directly as it could be and it's not completely clear that it can in fact be ruled out in this way. There are a couple of other instances which I've put in the smaller points below.

    Smaller points

    l47 full genus name when it first appears

    l58 I may be wrong here, but I thought the petite phenotype more classically arises from mtDNA deletion mutations, not loss? The way this is phrased implies that mtDNA loss is [always] the cause. Whether I'm wrong on that point or not, the petite phenotype should be described and referenced.

    para starting l59 -- should mention for context that mitochondria in (healthy, wildtype) yeast are generally much more fused than in other organisms

    Fig 1C -- very odd choice of y-axis range! either start at zero or ensure that the data fill as much vertical space of the plot as possible

    l105 "wild-type like more tubular mitochondria" reads rather awkwardly. "more tubular mitochondria (as in the wild-type)"?

    l106 -- imaging artefacts? are mitos fragmenting because of photo stress? -- this is mentioned in l577-8 in the Methods, but the data from the growth rate and MMP comparison isn't given -- an SI figure would be helpful here. It would be reassuring to know that mito morphology wasn't changing in response to phototoxicity too.

    para l146 -- so this suggests mtDNA-encoded proteins have a very rapid turnover, O(hours) -- is this known/reasonable?

    section l189 -- it's hard to reason fully about these statistics of mitochondrial concentration given that the petite phenotype is fundamentally affecting overall cell volume. can we have details on the cell size distribution in parallel with these results? to put it another way -- how does mitochondrial amount per cell change?

    l199 the mean in Fig S3C certainly does change -- it increases, clearly relative both to control and to its initial value. rather than sweeping this under the carpet we should look in more detail to understand it (a consequence of the increased skew of the distribution)?

    para line 206 -- this doesn't make it clear whether your MMP signal is integrated over all mitochondria in the cell, or normalised by mitochondrial content? this matters quite a lot for the intepretation if the distributions of mitochondrial content are changing. reading on, this is even more important for para line 222. Reading further on, there is an equation on l612 that gives a definition, but it doesn't really clarify (apologies if I'm misunderstanding).

    l230 -- a point of personal interest -- low mito concentrations are connected to low "function" (MMP) and give extended division times -- this is interestingly exactly the model needed to reproduce observations in HeLa cells (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002416). That model went on to predict several aspects of downstream cellular behaviour -- it would be very interesting to see how compatible that picture (parameterised using HeLa observations) is with yeast!

    l239 "less mitochondria" -- a bit tricky but I'd say "fewer mitochondria" or "less mitochondrial content"

    Section l234 So here (and in Fig 4) the focus is on overall distributions of mitochondrial concentration in different cells (mother-to-be, mother, bud; gen 1, gen >1). But we've just seen that one effect of fzo1 is to broader the distribution of mitochondrial concentration across cells. Can't we look in more depth at the implications of this heterogeneity? For example in Fig 4F (which is cool) we look at the distribution of all fzo1 mothers-to-be, mothers, and buds. But this loses information about the provenance. For example, do mothers-to-be with extremely low mito concentrations just push everything to the bud, while mothers-to-be with high mito concentrations distribute things more evenly? It would seem very easy and very interesting to somehow subset the distribution of mothers-to-be by concentration and see how different subsets behave

    l285 -- experimental design -- do we know that Atp6 will continue to be a good proxy for functional mtDNA in the face of the perturbations provided by Fzo1 depletion? Especially if there is impact on the expression of mitoribosomes, the relationship between mtDNA and Atp6 may look rather different in the mutant?

    l290 -- ruled out mitophagy. This message could be much clearer. Comparing Fig S5C and Fig 3A side-by-side is a needlessly difficult task -- put Fig 3A into Fig S5. Then we see that when mitophagy is compromised, the distribution of mitochondrial concentration has a lower median and much lower upper quartile than in the mitophagy-equipped Fzo1 mutant? What is going on here? For a paper motivated by disentagling coupled mechanisms, this should be made clearer!

    With the Atp6 signal, how do we know that fluorescence from different cells is comparable? Buds will be smaller than mother cells for example, potentially leading to less occlusion of the fluorescent signal by other content in the cytoplasm

    l336 -- similar to the Jajoo et al. mechanism in fission yeast -- but are you talking about feedback control of the mtDNA or the protein (or mRNA) product?

    l343 -- maintenance of mtDNA -- here the point about l285 (is the Atp6-mtDNA relationship the same in the Fzo1 mutant) is particularly important, as we're directly tying findings about the protein product to implications about the mtDNA

    l367 -- on a first read this description of the model feels like lots of choices have been made without being fully justified. Why a log-normal distribution (when the fit to the data looks rather flawed); why the choice of 5 groups for nucleoid number (why not 3? or 8?); the process used for parameter fitting is very unclear (after reading the methods I think some of these values are read directly from the data, but the shapes of the distributions remain unexplained). l705 -- presumably the ratio was drawn from a log-normal distribution and then the corresponding nucleoid numbers were rounded to integers? the ratio itself wasn't rounded? (also l367) How were the log-normal distributions fitted to experiments (Figs. S7A,B)? Just by eye? l711 by random selection -- just at random? ("selection" could be confusing) Overall, it feels like the model may be too complicated for what it needs to show. Either (a) the model should show qualitatively that unequal inheritance and reduced production leads to rapid loss -- which a much simpler model, probably just involving a couple of lines of algebra, could show. Or (b) the model should quantitatively reproduce the particular numerical observations from the experiments -- it's not totally clear that it does this (do the cell-cycle-based decay timescales in Fig 7 correspond to the hour-based decay timescales in other plots, for example). At the moment the model is at a (b) level of detail but it's only clear that it's reporting the (a) level of results.

    A lot of the discussion repeats the results; depending on editorial preferences some of this text could probably be pared back to focus on the literature connections and context.

    Data availability -- it looks like much of the data required to reproduce the results is not going to be made available. Images and proteomic data are promised, but the data associated with mitochondrial concentration and other features are not mentioned. For FAIR purposes all the data (including statistics from analysis of the images) should be published.

    l660 -- can an overview of the EM protocol be given, to avoid having to buy the Mayer 2024 article?

    Significance

    This is a powerful and thoughtful study that provides a collection of new mechanistic insights into the link between physical and genetic properties of mitochondria in yeast. Cell biologists, geneticists, and the mitochondrial field will find this of potentially deep interest. Because of the mode and dynamics of inheritance in budding yeast, findings here may not be directly transferrable to other eukaryotes, but these insights are still of interest for researchers outside of yeast for their insight into how this well-studied system manages its mitochondrial populations.

  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 #2

    Evidence, reproducibility and clarity

    Dengler and colleagues use an AID-based acute depletion of Fzo1 in budding yeast, coupling microfluidics live imaging, single-cell quantification (>30k cells), proteomics, an mtDNA-encoded Atp6 reporter, and simple modeling to argue that fusion loss causes (i) rapid fragmentation and ΔΨm decline, (ii) progressive mtDNA/RC depletion, and (iii) unequal mother-daughter mitochondrial inheritance; together with a failure of compensatory synthesis, these changes drive petite formation. The time-resolved design is valuable, but several readouts are indirect, and some claims (particularly those regarding membrane potential, synthesis "failure," and causality) appear over-interpreted without additional controls.

    Major points

    1. While inducible TIR is used to reduce background, the manuscript should rigorously exclude auxin/TIR off-targets (growth, mitochondrial phenotypes, gene expression). Please include full matched controls: {plus minus}auxin, {plus minus}TIR, epitope tag alone, and a degron control on an unrelated mitochondrial membrane protein.
    2. The Mitoloc preSu9 vs Cox4 import ratio is only a proxy of mitochondrial membrane potential (ΔΨm) and itself depends on mitochondrial mass, protein expression, matrix ATP, and import saturation. The authors need to calibrate ΔΨm with orthogonal dyes (TMRE/TMRM) and pharmacologic titrations (FCCP/antimycin/oligomycin) to generate a response curve; show that Mitoloc tracks dye-based ΔΨm across the relevant range and corrects for mass/photobleaching. Report single-cell ΔΨm vs mass residuals.
    3. To use Atp6-mNeon as a proxy for mtDNA is an assumption. Interpreting Atp6 intensity as "functional mtDNA" could be confounded by translation, turnover, or assembly. Please (i) report mtDNA copy number time courses (you have qPCR), nucleoid counts (DAPI/PicoGreen or TFAM/Abf2 tagging), and (ii) assess translation (e.g., 35S-labeling or puromycin proxies) and turnover (proteasome/AAA protease inhibition, mitophagy mutants -some data are alluded to- plus mRNA levels for mtDNA-encoded genes). This will support the "reduced synthesis" versus "increased degradation" conclusion.
    4. The promoter-NeonGreen reporters argue against transcriptional down-regulation of nuclear OXPHOS. Please add mRNA (RT-qPCR/RNA-seq) for representative genes and a pulse-chase or degradation-pathway dependency (e.g., proteasome/mitophagy/autophagy mutants) to firmly assign active degradation. The authors need to normalize proteomics to mitochondrial mass (e.g., citrate synthase/porin) to separate organelle abundance from protein turnover.
    5. Using preSu9-mCardinal intensity as "mitochondrial concentration" is sensitive to expression, import competence, and morphology/segmentation. The authors should provide validation that this metric tracks 3D volume across fragmentation states (e.g., correlation with mito-GFP volumetrics; detergent-free CS activity; TOMM20/Por1 immunoblot per cell).
    6. The unequal mother-daughter distribution is compelling, but causality remains inferred. Test whether modulating inheritance machinery (actin cables/Myo2, Num1, Mmr1) or altering fission (Dnm1 inhibition) modifies segregation defects and rescues mtDNA/Atp6 decline. Complementation with Fzo1 re-expression at defined times would help order the phenotype cascade.
    7. The model is useful but should include parameter sensitivity (segregation variance, synthesis slopes, initial nucleoid number) and prospective validation (e.g., predict rescue upon partial restoration of synthesis or inheritance, then test experimentally).

    Significance

    The dataset is rich and the time-resolved approach strong, but key conclusions rely on indirect proxies and need orthogonal validation and at least one causal rescue experiment to avoid over-interpretation.

  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 #1

    Evidence, reproducibility and clarity

    This manuscript by Dengler et al examines the mechanisms underlying the mtDNA depletion observed in cells where mitochondrial fusion is disrupted by depletion of the fusion factor Fzo1. In Saccharomyces cerevisiae, the authors deplete Fzo1 and use live-cell imaging of thousands of cells to follow the effects and their dynamic following Fzo1 depletion. They find that Fzo1-depleted cells show very rapid mitochondrial fragmentation (within 1h of Fzo1 depletion), and also an immediate drop in mitochondrial membrane potential (MMP). MtDNA is lost by 15h, and along with it the expression of mitochondrially-encoded proteins. Nuclear-encoded mitochondrial proteins are also decreased though somewhat later, and the authors find that this is largely due to their degradation (probably a consequence of lack of mitochondrial import into low-MMP cells). Most importantly, the study identifies two separate mechanisms that together contribute to the loss of mt-encoded proteins in Fzo1-depleted cells: unequal distribution of mitochondria during cell division and the reduction of a fusion-dependent compensatory synthesis of mt-encoded proteins. Unexpectedly, Fzo1-depleted cells end up passing an increased (rather than decreased) amount of mitochondria and mitochondria-encoded proteins to their daughters. Over several generations, and combined with the loss of the compensatory synthesis of more mt-encoded proteins, this leads to the progressive loss of mtDNA and mtDNA-encoded proteins in the population.

    The study is very thorough and the experiments contain the appropriate controls. The conclusions are convincing and largely supported by the experimental data that has been appropriately replicated. The data presentation is generally clear although the text could benefit from some streamlining.

    However, addressing the following major comments is required:

    1. Statistical comparisons are missing throughout the manuscript (with the exception of Fig. 2c). Appropriate statistical tests, along with p-values, should be used and reported where different gorups are compared, for example (but not limited to) Fig. 3d and most panels of Fig. 4.
    2. I do not agree with the use of Atp6 protein as a direct read-out of mtDNA content. While Atp6 protein levels will decrease with decreasing mtDNA content, the inverse is not necessarily true: decreased Atp6 protein levels do not necessarily indicate decreased mtDNA levels, because they could alternatively or additionally be caused by decreased transcription and/or translation. Therefore, please do not equate Atp6 protein levels to mtDNA levels, and instead rephrase the text referencing the Atp6 experiments in the Results and Discussion sections to measure "mtDNA expression" or "mt-encoded protein" or similar. For example, on p. 14 line 431 should read "mtDNA expression" rather than "decreased synthesis of mtDNA", and line 440 on the same page "mean mtDNA levels" should be "mtDNA expression" or similar.
    3. In Fig. 3, the authors use the fluorescence intensity of a mitochondrially-targeted mCardinal as a read-out of mitochondrial mass. Please provide evidence that this is not affected by MMP, either with relevant references or by control experiments (e.g. comparing it to N-acridine orange or other MMP-independent dyes or methods).
    4. In Fig. 2e-f, the authors use a promoter reporter with Neongreen to answer whether the reduced levels of the nuclear-encoded mitochondrial proteins Mrps5 and Qcr7 are due to decreased expression or to protein degradation, and find no evidence of degradation of the Neongreen reporter protein. However, subcellular localization might affect the availability of the protein to proteases. Although not absolutely required, it would be relevant to know if the Neongreen fusion protein is found in the same subcellular compartment as Mrps5 and Qcr7 at 0h and 9h after Fzo1 depletion.
    5. Fzo1 depletion leads to a very rapid drop in MMP during the first hour of depletion. In the Discussion, can the authors speculate on the possible mechanism of this rapid MMP drop that occurs well before mtDNA or mt-encoded proteins are decreased in level?
    6. In Fig. 2a, the mtDNA copy number of Fzo1-depleted cells is ca 1.3-fold of the control cells at the 0h timepoint. Why might this be? Is it an impact of one of the inducers? If so, we might be looking at the combination of two different processes when measuring copy number: one that is an induction caused by the inducer(s), and the other a consequence of Fzo1 depletion itself.

    Minor comments:

    • p. 3, line 71: "ten thousands of dividing cells.." should be "tens of thousands of dividing cells".
    • p.4, line 116: please be even more clear with what the "depleted" cells and controls are treated with: are depleted cells treated with both inducers, and controls with neither?
    • p.5, lines 147-148: the authors write "the rate with which the abundance of Cox2 and Var1 proteins decreases was similar to the rate of mtDNA loss" though the actual rate is not shown. Please calculate and show rates for these processes side by side to make comparison possible, or alternatively rephrase the statement.
    • Fig. 2d: changing the y-axis numbering to match those in panels a and b would facilitate comparisons.
    • Fig. 2e: it is recommended to label the western blot panels to indicate what protein is being imaged in each (Neongree,, Mrps5, Qcr7).
    • p.9, line 262: I suggest referencing Fig. 4e at the end of the first sentence for clarity.
    • In the sections related to Fig. 3a and Fig. 5a as well as the connected supplemental data, the authors discuss both the median and the mean of mitochondrial mass and Atp6 protein, respectively. For purposes of clarity, I suggest decreasing the focus on the mean (that is provided only in the supplemental data) and focusing the text mainly on the median. The two show differing trends and it is very good that both are shown, but the clarity of the text can be improved by focusing more on the median where possible.
    • p. 14, line 435: the statement that mt mass is maintained over the first 9h of depletion is only true for the mean mt mass, not for the median. Please make this clear or rephrase.
    • p.14, line 452: "mitofusions" should be "mitofusins".

    Referees cross-commenting

    I think that the reviews of the other two reviewers are both insightful and constructive. Especially the rescue experiment suggested by Reviewer 2 could provide strong support for the interpretations of the study. Note that all three reviewers ask for validation of the use of Atp6p as a read-out of mtDNA function, and that all agree the data is powerful and the study of value to the field.

    Significance

    The fact that disruption of mt fusion leads to mtDNA loss has been known for some time, but the mechanism behind this phenomenon has remained unknown to date. This thorough and precise study by Dengler et al uses state-of-the-art single-cell analysis to dissect the mechanisms underlying the mtDNA loss following the disruption of mt fusion, and convincingly reveal that it is caused by two different mechanisms: i) the inequal inheritance of mitochondria between mother and bud, and ii) the loss of a compensatory mechanism that normally maintains homeostatic mt protein levels. In the process, the authors shed light on the dynamics of the events following Fzo1 depletion, revealing dramatically fast mt fragmentation and a loss of MMP, which in turn can be expected to act as a stress signal and influence a number of cellular processes.

    The findings of the study can have relevance for human conditions involving disrupted mitochondrial dynamics, caused for example by mutations in mitofusins. The study will be of interest to researchers in mitochondrial biology ranging from dynamics and mtDNA maintenance to mitochondrial medicine.

    The field of expertise of this reviewer: mtDNA maintenance. I am not able to properly evaluate the modelling in Fig. 7.

  5. Version published to 10.1101/2025.05.13.653688 on bioRxiv