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  1. Author Response:

    Reviewer #1:

    In multicellular eukaryotes, reproduction usually proceeds through a single-cell stage via propagule cells (germ cells) of some kind, like the zygotes resulting from gamete fusion in animals and flowering plants. In such organisms, inheritance of nuclear genomes from one generation to the next is a relatively straightforward problem when compared to that of inheriting non-nuclear genomes (e.g. mitochondrial or chloroplast genomes), which often exist at very high copy numbers that are not always the same throughout the life cycle of the reproductive cell lineage that gives rise to the gametes. This complex problem is nevertheless important in evolution because allelic changes in these non-nuclear genomes can impact the phenotypes, and therefore potentially the fitness, of the cells, tissues, and organisms that house them.

    In animals, the observations that (a) the gamete precursor cells (primordial germ cells = PGCs) in embryos, or postembryonic gamete precursors (oogonia that have not yet become mature oocytes) typically have far fewer copies of mitochondria than the oocyte that will give rise to the zygote and (b) mitochondrial genome allelic variance is typically higher in embryonic PGCs than in post-embryonic germ cells, have led to the acceptance that some kind of regulated mitochondrial culling occurs at some point between initial PGC specification and the end of gametogenesis. What is less clear is exactly when along this germ cell life cycle trajectory this culling takes place, what the specific evolutionary, cellular or molecular mechanisms are that regulate it, and which mechanism(s) best explain the observed pattern of inherited mitochondrial genomes in populations.

    This manuscript addresses these problems with the approach of developing a computational evolutionary model to see how well different assumptions about when and how mitochondrial culling takes place, are able to predict the observed distribution of mitochondrial mutations in some human populations for which data are available. The authors test the fit of three hypotheses to these data: (1) imposing a bottleneck at the PGC stage by limiting the number (and variance) of mitochondria at PGC stages; (2) selection against oogonia that have "bad" mitochondria; (3) preferential accumulation of "good" mitochondria, pooled from multiple oogonia, into those oogonia that will go on to complete oogenesis. They find that the third model fits the data better than the first two. They then compare these hypotheses in a multi-generational model. They report that the third model fit the data better over a wider range of selective pressures, than the first two, although all three models have some explanatory power within the range of mutation rates explored.

    This problem is an important one and the modeling approach could add important complementary perspective to existing empirical data, or suggest new avenues of experimentation for the future. The authors have tried to extract much biological data from the empirical data to inform their parameter and boundary choices for the model, and explained quite clearly their choices, which is an excellent approach. However, a weakness of the study is that the parameters that inform the model, and many of the assumptions that underlie the logic they use to interpret their results, are drawn from a wide range of different biological systems, but the model aims to test the fit of specific hypotheses to human data only. There are many differences in every aspect of germ line segregation, PGC development, oogenesis, and mitochondrial behaviour across animals, and which aspects of these things have strong evidence for universal conservation remains unclear. Nevertheless, in this MS the authors make broad claims about universality of conclusions in some cases, and in others appear to be restricting their conclusions to explaining human data only. A second area for improvement is that some well-documented observations on mitochondrial and germ line biology that are relevant to interpreting their observations, are not considered or claimed to be absent or irrelevant (e.g. paternal mitochondrial inheritance, germ lineage separation in flowering plants), and the existing empirical literature providing evidence for these things in at least some systems is not discussed at all, not even to explain why the authors deem this evidence unimportant for their model or for the conclusions they draw from it.

    We thank the referee for their fair summary and comment on our submission. Whilst we believe that our modelling approach should apply to many systems, it is perhaps better to limit our main claims to the systems where there is a high density of information – in particular mammalian systems, humans and mice. We have rewritten the MS in this light, and restrict our comment on non-mammalian systems to the Discussion. The details of particular systems are well worth further investigation to test the generality of our conclusions.

    Reviewer #2:

    Colnaghi, Pomiankowski and Lane develop models to investigate the effects of population genetic forces on mtDNA variation within germline cells to address unanswered questions about the selective pressures on mitochondrial genomes. The models are based on updated information about germline development in mammals, including humans. Realistic parameters of mutation, selection and sampling drift are applied to the demography of cells from stem cell through mature oocytes. Three selective processes are considered: at the level of the individual (zygote), the cell, and the mitochondria. The results indicate that selection among mitochondria is the most likely process to match empirical, clinical data for mitochondrial mutation loads. This is based on modeling the mixing of mitochondria following cytoplasmic transfer of cellular contents among individual oogonia in germline cysts into the emergent primary oocyte. The proportion of mutant mtDNAs, or the strength of selection on mutant vs. wild type mtDNAs, proved to have the most impact on model outcomes and correspondence to clinical data.

    The paper is clearly written and addresses controversies that have emerged in earlier studies. Notably, the results suggest that the bottleneck effects on the mtDNAs population during germline development has less of an effect that previously thought on the selective landscape that may permit mtDNA to persist despite the consequence of Muller's ratchet decay. A pleasant aspect of the paper is its clear presentation of quantitative approaches used in both the computational and evolutionary models presented. The paper presents an advance of interest to a general readership.

    We thank the referee for this summary.

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  2. Evaluation Summary:

    Non-nuclear genomes, such as those of mitochondria, contribute to many aspects of cellular function, organismal function, and fitness. Understanding their biology and evolutionary dynamics is thus an essential component eukaryotic evolution. The manuscript addresses an important and complex problem regarding the relationship between mitochondrial mutations, their impacts on gamete function, and the attendant evolutionary processes. The authors present a computational approach to distinguish between three hypotheses about the level of selection most likely to explain the distribution of mitochondrial mutations in human populations. They propose that selection among mitochondria is the most likely process to match empirical, clinical data, for mitochondrial mutation loads. There is, however, currently a mismatch between the fact that the data are derived from numerous different species whose biology is not always comparable, the model, and the title of the paper.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  3. Reviewer #1 (Public Review):

    In multicellular eukaryotes, reproduction usually proceeds through a single-cell stage via propagule cells (germ cells) of some kind, like the zygotes resulting from gamete fusion in animals and flowering plants. In such organisms, inheritance of nuclear genomes from one generation to the next is a relatively straightforward problem when compared to that of inheriting non-nuclear genomes (e.g. mitochondrial or chloroplast genomes), which often exist at very high copy numbers that are not always the same throughout the life cycle of the reproductive cell lineage that gives rise to the gametes. This complex problem is nevertheless important in evolution because allelic changes in these non-nuclear genomes can impact the phenotypes, and therefore potentially the fitness, of the cells, tissues, and organisms that house them.

    In animals, the observations that (a) the gamete precursor cells (primordial germ cells = PGCs) in embryos, or postembryonic gamete precursors (oogonia that have not yet become mature oocytes) typically have far fewer copies of mitochondria than the oocyte that will give rise to the zygote and (b) mitochondrial genome allelic variance is typically higher in embryonic PGCs than in post-embryonic germ cells, have led to the acceptance that some kind of regulated mitochondrial culling occurs at some point between initial PGC specification and the end of gametogenesis. What is less clear is exactly when along this germ cell life cycle trajectory this culling takes place, what the specific evolutionary, cellular or molecular mechanisms are that regulate it, and which mechanism(s) best explain the observed pattern of inherited mitochondrial genomes in populations.

    This manuscript addresses these problems with the approach of developing a computational evolutionary model to see how well different assumptions about when and how mitochondrial culling takes place, are able to predict the observed distribution of mitochondrial mutations in some human populations for which data are available. The authors test the fit of three hypotheses to these data: (1) imposing a bottleneck at the PGC stage by limiting the number (and variance) of mitochondria at PGC stages; (2) selection against oogonia that have "bad" mitochondria; (3) preferential accumulation of "good" mitochondria, pooled from multiple oogonia, into those oogonia that will go on to complete oogenesis. They find that the third model fits the data better than the first two. They then compare these hypotheses in a multi-generational model. They report that the third model fit the data better over a wider range of selective pressures, than the first two, although all three models have some explanatory power within the range of mutation rates explored.

    This problem is an important one and the modeling approach could add important complementary perspective to existing empirical data, or suggest new avenues of experimentation for the future. The authors have tried to extract much biological data from the empirical data to inform their parameter and boundary choices for the model, and explained quite clearly their choices, which is an excellent approach. However, a weakness of the study is that the parameters that inform the model, and many of the assumptions that underlie the logic they use to interpret their results, are drawn from a wide range of different biological systems, but the model aims to test the fit of specific hypotheses to human data only. There are many differences in every aspect of germ line segregation, PGC development, oogenesis, and mitochondrial behaviour across animals, and which aspects of these things have strong evidence for universal conservation remains unclear. Nevertheless, in this MS the authors make broad claims about universality of conclusions in some cases, and in others appear to be restricting their conclusions to explaining human data only. A second area for improvement is that some well-documented observations on mitochondrial and germ line biology that are relevant to interpreting their observations, are not considered or claimed to be absent or irrelevant (e.g. paternal mitochondrial inheritance, germ lineage separation in flowering plants), and the existing empirical literature providing evidence for these things in at least some systems is not discussed at all, not even to explain why the authors deem this evidence unimportant for their model or for the conclusions they draw from it.

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    Colnaghi, Pomiankowski and Lane develop models to investigate the effects of population genetic forces on mtDNA variation within germline cells to address unanswered questions about the selective pressures on mitochondrial genomes. The models are based on updated information about germline development in mammals, including humans. Realistic parameters of mutation, selection and sampling drift are applied to the demography of cells from stem cell through mature oocytes. Three selective processes are considered: at the level of the individual (zygote), the cell, and the mitochondria. The results indicate that selection among mitochondria is the most likely process to match empirical, clinical data for mitochondrial mutation loads. This is based on modeling the mixing of mitochondria following cytoplasmic transfer of cellular contents among individual oogonia in germline cysts into the emergent primary oocyte. The proportion of mutant mtDNAs, or the strength of selection on mutant vs. wild type mtDNAs, proved to have the most impact on model outcomes and correspondence to clinical data.

    The paper is clearly written and addresses controversies that have emerged in earlier studies. Notably, the results suggest that the bottleneck effects on the mtDNAs population during germline development has less of an effect that previously thought on the selective landscape that may permit mtDNA to persist despite the consequence of Muller's ratchet decay. A pleasant aspect of the paper is its clear presentation of quantitative approaches used in both the computational and evolutionary models presented. The paper presents an advance of interest to a general readership.

    Was this evaluation helpful?