A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

  1. Luis Fernando Montaño-Gutierrez
  2. Marc Sturrock
  3. Iseabail Farquhar
  4. Kevin Correia
  5. Vahid Shahrezaei
  6. Peter S. Swain

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Abstract

A common cellular task is to match gene expression dynamically to a range of concentrations of a regulatory molecule. Studying glucose transport in budding yeast, we determine mechanistically how such matching occurs for seven hexose transporters. By combining time-lapse microscopy with mathematical modelling, we find that levels of transporters are history-dependent and are regulated by a push-pull system comprising two types of repressors. Repression by these two types varies with glucose in opposite ways, and not only matches the expression of transporters by their affinity to a range of glucose concentrations, but also the expression of some to how glucose is changing. We argue that matching is favoured by a rate-affinity trade-off and that the regulatory system allows yeast to import glucose rapidly enough to starve competitors. Matching expression to a pattern of input is fundamental, and we believe that push-pull repression is widespread.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    Cells need to adjust their gene expression pattern, including nutrient transporters and enzymes to process the available nutrient. How cells maintain the coordination between these processes is one of the most critical questions in biology. In this work authors elegantly combined a range of relevant experimental techniques, ranging from time-lapse microscopy, microfluidics, and mathematical modelling to address this question. Combining these methods, authors proposed a push-pull like mechanism, involving two pairs of repressors (Mth1, Std1 and Migs) in the glucose sensing network. In budding yeast there are multiple hexose transporter genes with varying affinity and transport rate. Authors postulated that on sensing glucose, cells switch between expressing high affinity glucose transporters (when extracellular glucose is low), and low affinity glucose transporters (in high extracellular glucose), and these processes are mediated by the pairs of repressors as mentioned earlier. Following the expressing patterns of fluorescently tagged hexose transporters and varying the extracellular glucose concentrations in media, authors proposed that pairs of repressors switch their activity depending on extracellular glucose level, and which is matched by the promoters of the hexose transporter genes to achieve optimality of glucose transport.

    This study is elegantly designed and addressed an interesting question. The mechanism (push-pull involving two pairs of repressors) is plausible and justified by the data. Authors also presented a mathematical model and made predictions, which are also verified. We will recommend the publication of this work with minor modifications.

    Major comments:

    This study is well designed and experiments performed accordingly. We have only minor comments for revision.

    Minor comments:

    1. Although authors covered a wide array of literature, but while discussing tradeoffs and nutrient sensing, it will be good to include bacterial growth law and related literature, and physiological level tradeoffs should be discussed. Moreover, authors vouched that the push-pull mechanism helps to circumvent the rate-affinity tradeoff of the transporter, whereas expressing genes to more precisely corelate with the extracellular glucose level brings out physiological optimality. This rate-affinity tradeoff and its physiological role should be discussed clearly.
    2. Authors described the ALCATRAS device in their previous publication, but for better clarity, a supplementary figure with schematic diagram and experimental plan should be included.
    3. Microscopic images of transporter expression pattern should be shown as kymographs in the supplementary, in this version of the manuscript plots from processed microscopy images are shown only.
    4. GFP was used to tag HXT1-7 as mentioned by the authors and expression of these genes are evaluated in separate experiments. We suggest including a schematic diagram describing the experimental design while using the microfluidic device and the experimental plan should be written in more detail in general. We found this part confusing. Did authors considered tagging two separate transporters with different fluorescent tag from either end of the affinity spectrum and showing the expression pattern in one experiment? Authors mentioned co expression of receptors at a particular glucose concentration over time, is this inferred from separate timelapse experiments? This need to be more clearly stated.
    5. Please mark the second phase of media glucose concentration in panel 1C, 1% glucose phase is marked, please mark the other phases for clarity.
    6. For the repressors to sense glucose and to initiate the push pull mechanism, there should be baseline glucose flux, which is not clearly mentioned in the manuscript. Authors mentioned that minimal intracellular glucose in absence of extracellular glucose and deployed a logistic function to increase intracellular glucose. The baseline glucose level is crucial, and authors should comment on this. Also, glucose mediated protection of HXT4 should be discussed in this context.
    7. Figure 3B and 3C, details of the error bars should be mentioned in the figure legend.

    Referee cross-commenting

    All other reviewers also identified this study insightful and interesting, similar to our comments. We also agree with the suggestions made by other reviewers. Suggested changes and modifications can be addressed within a month as mentioned by most of the reviewers. Excellent point raised by other reviewers on technicalities and addressing those points will improve the readability of this work even more.

    Significance

    General assessment:

    Use of innovative microfluidics platform to trap mother cells and following the gene expression pattern by fluorescence microscopy and combining the experimental approach with mathematical model are the strengths of this work. Whereas the proposed push-pull mechanism is not generalizable to other carbons. Model is merely used to fit the data, rather than making interesting predictions. Also how does the mechanism holds when cells are switched from other nutrient sources is also not clear in this work, which are the limitations of this work.

    Advance

    This work involves experimental technique and mathematical model to test the hypothesis. Use of custom-built microfluidics set up and live cell imaging to track gene expression levels in varying nutrient condition. This study links single cell level gene expression pattern to model and predict system level behavior. Nutrient sensing and subsequent rearrangement of gene regulatory network is an important question to address, and the proposed push-pull mechanism in this study adds up to the existing body of literature.

    Audience:

    This work is interdisciplinary and researchers across multiple fields will be interested in this work, including researchers interested in microbial nutrient sensing, systems biology, topology of gene regulatory network, metabolism, and general microbiology.

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    Referee #2

    Evidence, reproducibility and clarity

    Summary:

    The yeast Saccharomyces cerevisiae possesses a large family of hexose transporters, the HXT genes. Some of these transporters play known roles in transport to feed metabolism while others seem to respond to glucose levels but have differing cellular functions, acting more as sensors than as drivers of carbon and energy production. The authors use single cell fluidics to monitor steady state expression of specific transporters under controlled glucose levels. The authors then used published information on the regulatory network of HXT4 gene expression to predict expression levels and confirm the role of the prior identified regulators. Thus, this work confirms prior work as to the levels of substrate leading to optimized expression of transporters and confirms the role of the identified regulatory network. The fact that the main single cell fluidics findings confirm the prior culture analyses affirms the utility of the prior work.

    Major Comments:

    1. The analysis uses protein expression levels (HXT4-GFP) as a proxy for transcriptional regulation. This study assumes no regulation of protein expression beyond transcription under steady state conditions. This seems like a reasonable assumption. However, for the dynamic change analysis (Figure 1 C, lines 70-78) loss of GFP-tagged protein from a single cell would be due not just to absence of transcription but also to differential rates of endocytosis and degradation, which could vary across the different HXTs. In cell populations the plasma membrane composition of the bud can be dynamically different from that of the mother cell and will reflect changes in transcription patterns. Meaning that cells with buds might have reduced expression due to the presence of the bud versus non-budding cells. And if buds are washed away during the time course of the experiment this could impact assessment of GFP signal - I am assuming controls were done to address this and should be included in the presentation. Did the authors consider this in their experimental design and interpretation?
    2. The modeling was based upon the assumption of the validity of prior work and observations and authors show that models based upon that prior knowledge work to explain the single cell data. One wonders what perturbing prior modes of action would do to fit the data. That is, if the role of one regulator was downplayed or modified in concert with another would data still fit in a reasonable way? My concern again is that loss of signal (protein) is equated exclusively with transcription and not post-transcriptional regulation. This timeline in 1C and in fig 2 of 20 hours certainly would accommodate post-transcriptional regulation of protein levels.
    3. Lines 142-150: two models are proposed: Std1 activating Snf1 with std1 deletion therefore hyperactivating Mig1. The second model is for Std1 to repress Mig2 with deletion of std1 then leading to hyperactivation of Mig2. It seems this could be directly tested using multiple deletant strains, or modified repressor proteins. For example, is the effect lost in a std1 mig1 double mutant?
    4. Lines 121-122 the comment that comparing expressing GFP from the HXT4 promoter to GFP tagged HXT4 protein allows glucose to protect HXT4 from degradation needs to be explained.
    5. Line 180-186: this is an important analysis - I assume binding sites for repressors/inducers of the HXT genes have been mapped -then the comparison to known promoter structure (lines 214-246) is a great test of the model. It seems the finding are consistent with previously published data on differential regulation of these promoters in full-culture studies.
    6. Lines 293-299: one thing the authors should highlight is the contrast between these single cell studies and prior population studies that are influenced strongly by the heterogeneity between bud and mother cell plasma membrane composition. The mother cell can of course benefit from the differential expression in the daughter cell and the daughter cell benefits from the differential composition of the mother cell. This study shows that mother cells adapt membrane composition as well, but perhaps the potential role of cell membrane protein turnover should also be included.

    no Minor Comments

    Significance

    It has been known for quite some time that glucose transport in the yeast Saccharomyces cerevisiae is dynamically regulated to optimize sugar depletion to sugar metabolism. This intricate system involves a family of hexose transporters of differing affinities for substrate, the timing and level of expression of which is regulated by both eternal hexose levels and internal ability to metabolize keeping cytoplasmic sugar levels low. Since facilitated diffusion systems can transport in both directions, the consumption of substrate assures the direction of uptake will be dominant. The authors demonstrate in this paper that differential expression of the known major regulators of HXT gene expression work in concert to adjust the expression patterns of transporters of differing affinities leading to optimization of hexose uptake. The study monitored changes in single cells and findings confirm prior work conducted in cell populations. One assumption has always been that the mother cell might "sacrifice" itself by not being able to dynamically clear the membrane of environmentally unmatched hexose transporters relying on the altered membrane composition of the bud. This work's focus on "mother cells" demonstrates that regulation still occurs if cells are allowed to reach a steady state. The timeline may be slower than bud adaptation, but these authors confirm that mother cells respond dynamically to glucose levels.

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    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication.

    Yeast relies on seven passive hexose transporters (Hxt1-Hxt7) to import glucose, its preferred sugar; deleting all seven abolishes growth on glucose. The underlying regulatory network is exceptionally intricate, reflecting yeast's evolutionary priority for glucose. Two membrane sensors-Snf3 (high affinity) and Rgt2 (low affinity)-detect extracellular glucose and thereby inactivate two co-repressors, Mth1 and Std1, which modulate the DNA-binding factor Rgt1. Concurrently, intracellular glucose activates the SNF1 kinase, phosphorylating and exporting the repressor Mig1, while Mth1/Std1 also govern the transcription and stability of Mig2, another DNA-binding repressor. Together, Rgt1, Mig1, and Mig2 integrate these inputs to control HXT promoter activity (Fig. 2A). Importantly, Mth1 and Std1 do not directly bind to DNA and this complication - the protein-protein interaction that one cannot get from DNA sequence - is just one source of difficulty that the authors overcame.

    To map the network's behavior, the authors used microfluidic "cages" housing single cells expressing GFP-tagged HXTs, monitoring fluorescence under three constant glucose levels-low (0.01%), medium (0.1%), and high (1%) (Fig. 1B-C). The authors confirm that steady-state Hxt abundances rank by transporter affinity. But the more important and surprising discovery is that when the cells were subjected to gradual glucose up-shifts and down-shifts, they discovered that some transporters transiently spike only when [glucose] rises and others only when [glucose] falls (Fig. 1C and Fig. S1F). This discovery establishes that the HXT network not only "senses" the absolute external [glucose] concentration but also the direction of the temporal change in external [glucose].

    To understand how the regulatory network yields such intricate temporal changes in HXT expression, the authors first focused on the medium-affinity transporter, Hxt4. Targeted knockouts of Mig1/Mig2 versus Mth1/Std1 confirmed that Hxt4 dynamics arise from differential repressor kinetics. To formalize these findings, the authors built an ODE model grounded in literature-based constraints (pg. 13 of the Supplement) with explicit separation of repressor timescales. They rigorously fit the model to wild-type and knockout time series-exploring parameter sensitivity in depth (Fig. S5).

    The authors discovered that their model and experiments converged on a push-pull mechanism: fast-acting Mig1/Mig2 dominate during glucose up-shifts, while slower Mth1/Std1 govern down-shifts, determining whether each HXT gene is repressed or de-repressed (i.e., "who gets there first"). Extending this analysis across all seven HXTs via approximate Bayesian computation revealed the most likely repressor-promoter interactions for each transporter, reducing a vast parameter space to unique or small sets of plausible regulatory schemes. The authors thus revealed what could be happening and which regulations are improbable - a more nuanced and comprehensive view than giving just one outcome for each HXT.

    Overall, this work represents a role model - textbook-worthy - for quantitative systems biology. Beyond the rigor and novelty of its findings, the authors explain complex mathematical concepts with clarity, and the narrative flows logically from experiment to model to inference. This study provides a definitive mechanistic resolution of the HXT network and establishes a broadly applicable framework for dissecting dynamic and complex gene circuits.

    Major points:

    I don't recommend any new experiments or modeling; the major claims are already well supported by the data and models. Below are comments and questions intended to improve clarity and facilitate the reader's understanding. Please feel free to disregard any that you find not sensible or beyond the scope of the current work.

    1. Preconditioning (Fig. 1B-C): What medium were cells in immediately before t = 0? Were they in log-phase or stationary-phase growth just prior to the glucose addition?
    2. Transporter ranking in medium glucose: In the medium [glucose] regime, why is a low-affinity Hxt the second-most highly expressed, rather than the next-highest-affinity transporter? Could co-expression of multiple affinities (e.g., as a bet-hedging strategy) be advantageous? The Discussion section already mentions bet-hedging but I think you could further discuss ideas such as evolutionarily trained "Pavlovian" response or what the 2nd-ranking says about what the yeast anticipates as an upcoming change in the environment.
    3. Defining low/medium/high regimes: Low = 0.01%, Medium = 0.1%, and High = 1%. This is indeed in line with the standard classification of [glucose] in the literature regarding HXTs. But how might your results change at intermediate concentrations - those between these three levels. Using the model, could you comment on whether HXT expression dynamics "sharply" change as a function of either the [glucose]/time or the final concentration of [glucose] after the ramping-up phase?
    4. Rate-affinity trade-off (Lines 18-20): Give a brief explanation of the rate-affinity trade-off. Why does higher affinity necessarily entail a lower maximal transport rate (Vₘₐₓ) for passive transporters? Perhaps you can give an intuitive explanation backed by mass-action kinetics (e.g., to attain a higher affinity, the glucose-binding pocket on Hxt cannot be flipping rapidly back-and-forth between facing cytoplasm and extracellular space -- the binding pocket must allow sufficient time for molecule to find and bind it).
    5. Single-transporter expression (Lines 39-40): It's unclear to me why cells would express only the "optimal" Hxt and suppress all others. For instance, a bet-hedging strategy might favor simultaneous expression of multiple affinities. Consider revising these lines or adding a brief explanation. Related to above is a subtle point I think that was glossed over: there must be a fitness cost associated with making too many copies of Hxtn. After all, why not make as many transporters as possible? Is the cell operating near the upper limit of Hxt abundance, beyond which there's a fitness cost? Is there a pareto-optimal-type front in the space of expression level and another axis? I think this could go into the Discussion section.
    6. Hxt5 exception (Fig. 1B): Although Hxt5 follows a distinct regulatory scheme, it is most highly expressed at medium [glucose] (0.1%), consistent with its affinity like the other Hxts. I think you could mention this in lines 51-58.
    7. Glucose-ramp details (Fig. 1C; Lines 66-67): You state that [glucose] rises from 0 to 1 % over 15 min and reaches 1 % at t = 3 h. However, the actual ramp slope ([glucose]/time) and when the [glucose] starts to increase from zero aren't specified. The Hxt5-GFP behavior and differing Hxt6/7 levels at t = 0 vs. t = 20 h suggest the ramp may begin later than t = 0. Please clarify these details in the caption and main text, and consider adding a [glucose] vs. time schematic above the panel in Fig. 1C (like in Fig. 1B).
    8. Pre-t < 0 incubation (Fig. 1C): Related to point 1, how long were the cells incubated in pyruvate (or other medium) before t < 0? The Hxt6-GFP level at t = 20 h does not match that at t = 0; what is the timescale for Hxt6-GFP and Hxt7-GFP decay to steady state after glucose removal?
    9. Hxt-GFP localization: Does the reported Hxt#-GFP level include fluorescence from both the plasma membrane and internal compartments (e.g., vacuole)? Clarifying which pools of fluorescence are quantified would help interpretation, even if they don't change the main conclusions are unchanged.
    10. Predominantly transcriptional" wording (Lines 90-92): The phrase "...the regulation is predominantly transcriptional" should specify that it refers to the induction of HXT4 transcription during glucose down-ramping, rather than the subsequent decrease in Hxt4-GFP. The experiments do not rule out post-translational regulation (e.g., endocytosis) once glucose levels fall below a threshold.
    11. Glucose "protection" of Hxt4 (Lines 121-122): The statement "we allowed glucose to protect Hxt4 from degradation" is unclear. First, Hxt4-GFP likely degrades at a different rate than free GFP-you could estimate its half-life from Fig. S3. Second, please explain precisely what "protection" means in the model or experiment.
    12. Quantifying repressor kinetics (Lines 158-162): The push-pull mechanism is compelling, but it would be helpful to report the quantitative separation of timescales-e.g., how much faster do Mig1/Mig2 respond compared to Mth1/Std1? Including fold-difference would strengthen this explanation.
    13. Mechanism of repressor regulation (Lines 197-213): Be clearer about whether and how changes in extracellular glucose alter the expression levels of Mth1, Std1, Mig1, and Mig2, as opposed to modulating say, how Mth1 and Std1 bind to Rgt2 protein. I think you could be clearer here about which regulatory steps (transcriptional, post-translational, or binding-affinity changes) are assumed in the model and supported by the data.

    Minor points:

    1. Abstract: Original: "...how an HXT for a medium-affinity transporter can be made to respond like the HXTs for the other transporters." Suggestion: "...how the gene-expression regulation of a medium-affinity HXT can be rewired to respond like that of any other HXT." (You might also generalize beyond "medium-affinity" if the converse holds.)
    2. Lines 64-66: Please emphasize that the "synthetic complete medium" used for pre-conditioning contains no glucose.
    3. Line 143: The phrase "low expression of the std1\Delta strain in glucose" is ambiguous-low expression of which gene or reporter? Please specify.
    4. Line 240: Change "should weakened" to "should weaken."
    5. Fig. S9 caption (typo) Change "Rtg1 sites are..." to "Rgt1 sites are...."

    Hyun Youk.

    Referee cross-commenting

    I agree with the other reviewers' comments. The other reviewers noticed important points I have missed. But like them, I'm still supportive of the work being published with < 1 month spent on revision. I still don't recommend any further experiments or modeling.

    Significance

    This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication via Review Commons.

  5. Version published to 10.1101/2021.04.20.440667 on bioRxiv