Natural Killer Cell Receptor Signaling and Activation Depend on Cell Cycle Stages

  1. Darren Wethington
  2. Indrani Nayak
  3. Helle Jensen
  4. William C. Stewart
  5. Oscar A. Aguilar
  6. Shih-Yu Chen
  7. Garry P. Nolan
  8. Gregory K. Behbehani
  9. Lewis L. Lanier
  10. Jayajit Das

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Abstract

Receptor signaling in Natural Killer (NK) cells leads to post-translational modification (e.g., phosphorylation) of sub-cellular signaling proteins within minutes of receptor stimulation that eventually give rise to diverse effector functions including cell proliferation. Recent single-cell mass cytometry (i.e., CyTOF) experiments in macrophages showed variations of abundances of phosphorylated signaling proteins across cell cycle states indicating a dependence of cell signaling kinetics on an order of magnitude slower kinetics (~ several hours) of cell cycle transitions. We investigated cell cycle dependence of NKG2D signaling kinetics in NK cells by CyTOF measurements performed on IL-2-treated NKG2D-stimulated primary human CD56 dim NK cells. The CyTOF experiments revealed monotonic or semi-monotonic increases of the average protein abundances of the majority of signaling proteins such as pCrkL, pPLCγ2, and pErk, and the degranulation marker protein CD107a with progressing cell cycle states at specific time points post-NKG2D stimulation; however, several proteins such as pVav1, pS6, and pAkt, and early activation marker protein CD69 also showed non-monotonic variations in the average abundances with progressing cell cycle states. We used minimal mathematical and computational models coupling signaling and cell cycle processes to show that non-monotonic variations in the signaling protein abundances with progressing cell cycle stages are likely to arise in situations where protein synthesis and degradation and signaling kinetics are actively regulated by cell cycle processes.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary

    In this study, Weethington et al investigate how the abundance/activity of signaling proteins change over time following stimulation of NK cells and if the dynamics of these changes are coupled to cell cycle progression. Using CyTOF to measure these proteins in single cells and using several NK cell models, the investigators categorize proteins by the dynamics of these changes as cells progress through G1, S, and G2/M. The investigators indicate that the majority of proteins increase monotonically or semi-monotonically during cell cycle progression, while others exhibit non-monotonic changes - increasing from G1 -> S and then decreasing form S -> G2/M or vice-versa. The authors then use these data to inform mathematical models to identify the cellular processes that may give rise to these non-monotonic changes, identifying protein synthesis, degradation, or signaling kinetics as potential mechanisms.

    Major comments

    I do not understand the rationale for comparing time points (post-stimulation) between progressive cell cycle phases. Although there is a fixed temporal ordering to cell cycle phases (G1 -> S -> G2/M), there is no temporal relationship between protein abundance measurements at a post-stimulation time point in different cell cycle phases. For example, take CD69 in Fig 2E,G: the authors cite non-monotonous changes occurring at the 32, 64, and 256 min timepoints and semi-monotonic changes at all other time points. The abundance of CD69 at 32 min post-stimulation in G1 has no temporal relationship to the 32 min time point in S or G2 phase, so it is not clear how a statement about monotonicity can be made in this context? I believe the appropriate analysis strategy to interrogate the question posed by the authors in this paper is to compare the entire time-course of protein abundance between phases (i.e. the shape/magnitude of change in protein abundance in G1 vs S vs G2). Through this lens, the CD69 data in Fig 2G would suggest that the decrease in protein abundance at later time points (relative to untreated within the same phase) is larger in S phase than in G1 or G2. It should also be noted that the CD69 dynamics following stimulation is completely different in primary cells (Fig 2) vs the NK cell line (Fig S3), making interpretation and generalization very difficult. It is also difficult to assess the magnitude of differences in protein abundance given that there are often no measures of variance indicated in the bar plots visualizing these changes (e.g. Fig 2G, Fig S2B). I am aware that the authors use a pair of one-sided t tests to make statements of statistic significance for these comparisons. However, in single-cell assays of this scale with hundred to thousands of data points per condition, t tests are prone to Type I error and often overpowered to identify truly meaningful differences. Is a >5% decrease in mean abundance from G1 to S phase in a single experiment (independent replicates do not appear to have been performed) and no follow-up validation experiments sufficient to make the statement that this decrease is biologically meaningful? And then stratify proteins into classes based on these relatively small changes?

    Significance

    Our current knowledge of the mammalian cell cycle comes mostly studies in epithelial and fibroblast cells. A better understanding of the cell cycles of other cell types, how it is regulated, and how it influences other cell biological events would be a significant benefit to the field

    General assessment: I believe that this study has fundamental concerns (described above) that must be addressed before this manuscript should advance to publication

    Audience: Basic research, cell cycle and immunology audiences.

    My background is in experimental and computational cell cycle biology

  3. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    Summary:

    Wethington, Nayak, Jensen et al. investigated changes within protein abundances in distinct NK cell cycle stages after NKG2D stimulation of primary human NK cells and the NK cell line NKL. In addition the authors use mathematical models to define distinct patterns of signaling protein abundances across different cell cycle stages.

    Overall, the manuscript is well written and of interest for the scientific community. However, the manuscript could benefit from additional improvements.

    Major comments

    1. It remains unclear how many replicates were used within the manuscript throughout. Please state the number of replicates clearly. Since there is considerable variation between different human donors an n=3-5 would be preferable for the NKG2D stimulation of primary human data to draw valuable conclusions.
    2. Did the authors compare non-reactive vs reactive NK cells after NKG2D stimulation and if yes, how does the pattern look for the signaling molecules between distinct cell cycle phases when comparing those? It would be interesting to see the distribuition of CD107a negative and positive NK cells within the different cell cycle stages upon stimulation. This would potentially also provide an internal negative control as the signaling proteins within the CD107a negative population are expected to go through less changes.
    3. The link between the first part (NKG2D stimulation) and second part (mathematical modeling) remains a bit unclear. Was any of the NKG2D stimulation data used to train the mathematical modeling? If not a potential way to improve the link would be to describe the mathematical modeling first and subsequently validate certain patterns in the NKG2D modeling or to compare cytokine only induced changes (only IL-2) to receptor signaling changes (NKG2D stimulation).

    Minor comments:

    1. The level of NKG2D is not shown within manuscript and could be added as an additional supplementary figure.
    2. The authors mention CDKs influencing cell signaling. Did the authors track the abundance of CDK molecules upon NK cell stimulation?
    3. Figure 2E shows a lot of information and is a bit crowded. Potentially it would be easier to split the information up? Show a heatmap of the expression of the significant proteins at all different timepoints and then show the abundance changes in detail for a few proteins for specific timepoints.

    Significance

    General assessment:

    The manuscript provides an interesting mathematical modeling as well as CyTOF data from NK cell stimulations about differences in protein abundances throughout different cell cycle stages of NK cells. The data of the NK cell stimulation could be better linked to the mathematical modeling to make a stronger case for the robustness of the model and for more mechanistic conclusions. The manuscript contains a lot of data which is sometimes presented to condensed (Figure 2), the manuscript could benefit from a clearer red line throughout/focus on key molecules.

    Audience:

    The data presented is of interest for the specialized NK cell community but the discussion section could be improved by making a stronger case of how the herein presented data/model will benefit further studies within the NK cell or general immunology field.

    My field of expertise: NK cell biology, tissue-resident NK cells.

  4. Review Commons

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    Referee #1

    Evidence, reproducibility and clarity

    Summary: The paper reports the results of a study that examines how cell cycle stages influence NK cell receptor signaling. The authors find that while most signaling proteins increase monotonically with cell cycle progression, a subset shows non-monotonic variations. Simple computational models are used to explore mechanisms to qualitatively explain the observations.

    Major comments:

    I am not convinced that the use of models here substantially contributes to the understanding of the observations. The reason is that the results are fairly intuitive and actually to a good extent already well known to those who construct models of reaction kinetics including cell-cycle dynamics. So for example the observation that protein numbers increase mononotically with cell-cycle progression is the obvious thing to expect because since most proteins have a lifetime that exceeds the mean cell-cycle duration then it follows that naturally the protein numbers have to increase during the cell-cycle. This already explains the bulk of the observations. For those proteins where there is non-monotic behaviour, indeed there is something more complex going on but here there are many possibilities. As they say, if we have a 2nd order reaction then the firing rate of bimolecular reactions could increase or decrease with cell-cycle progression because it decreases with cell volume and increases with abundance, both of which factors vary with cell-cycle progression. A model is not quite needed to see that this may lead to non-monotic behaviour. If the model was fitted to the data, i.e. the experimental distributions of protein abundance with cell-cycle progression were fitted to the model and then these are used to constrain possible mechanisms, then yes I would agree that the model brings in some added benefit. Another criticism is the modelling approach itself involves strong simplifications that may not be entirely realistic." : (i) the volume does not seem to change within one cell-cycle stage, e.g. it is 1.3 for all times within the S phase.. "This assumption may be questionable, particularly for cell-cycle stages that occupy a large portion of the cycle." The cell volume generally should vary continuously with time within the cell-cycle and because the propensities are time-dependent then the SSA is not anymore exact and hence one needs to use modifications of it which account for such phenomena. (ii) the doubling of gene copy number due to DNA replication seems to have been omitted from the model. This is expected to lead to a considerable change in the protein numbers at the point in the cell-cycle where DNA replication occurs and hence appears to be an important factor for this study. (iii) how do we reconcile protein concentration homeostasis with the models described in this paper? This is a well known phenomenon, see for e.g. Nature communications 9.1 (2018): 4496 and references therein. (iv) cell-size control mechanisms are not included in the model (adder, sizer, timer); the choice is known to crucially alter protein dynamics across the cell-cycle so difficult to see how one can ignore the inclusion of these. See for e.g. PLoS computational biology 18.10 (2022): e1010574.

    Minor comments:

    The literature on models of gene expression (mRNA and protein dynamics) including cell-cycle dynamics is extensive and the discussion of this paper would benefit from including more of this. Some of these papers include Biophysical Journal, 107 (2014), 301-313; Journal of theoretical biology 348 (2014): 1-11.; PLoS computational biology 12.8 (2016): e1004972; Plos one 15.1 (2020): e0226016; J. Chem. Phys. 159, 224102 (2023).

    Significance

    This is an interesting paper with both data and modelling. However, presently, the connection between them does not appear strong enough to fully support the conclusions drawn.

  5. Version published to 10.1101/2025.02.24.639882 on bioRxiv