Glycolytic flux-signaling controls mouse embryo mesoderm development

  1. Hidenobu Miyazawa
  2. Marteinn T Snaebjornsson
  3. Nicole Prior
  4. Eleni Kafkia
  5. Henrik M Hammarén
  6. Nobuko Tsuchida-Straeten
  7. Kiran R Patil
  8. Martin Beck
  9. Alexander Aulehla  Is a corresponding author
  1. Developmental Biology Unit, European Molecular Biology Laboratory, Germany
  2. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Germany

Abstract

How cellular metabolic state impacts cellular programs is a fundamental, unresolved question. Here, we investigated how glycolytic flux impacts embryonic development, using presomitic mesoderm (PSM) patterning as the experimental model. First, we identified fructose 1,6-bisphosphate (FBP) as an in vivo sentinel metabolite that mirrors glycolytic flux within PSM cells of post-implantation mouse embryos. We found that medium-supplementation with FBP, but not with other glycolytic metabolites, such as fructose 6-phosphate and 3-phosphoglycerate, impaired mesoderm segmentation. To genetically manipulate glycolytic flux and FBP levels, we generated a mouse model enabling the conditional overexpression of dominant active, cytoplasmic PFKFB3 (cytoPFKFB3). Overexpression of cytoPFKFB3 indeed led to increased glycolytic flux/FBP levels and caused an impairment of mesoderm segmentation, paralleled by the downregulation of Wnt-signaling, reminiscent of the effects seen upon FBP-supplementation. To probe for mechanisms underlying glycolytic flux-signaling, we performed subcellular proteome analysis and revealed that cytoPFKFB3 overexpression altered subcellular localization of certain proteins, including glycolytic enzymes, in PSM cells. Specifically, we revealed that FBP supplementation caused depletion of Pfkl and Aldoa from the nuclear-soluble fraction. Combined, we propose that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization.

Editor's evaluation

How can generic changes in metabolism program specific changes in signaling and cell fate? To date it has been difficult to distill plausible models for specificity in this arena, but now Miyazawa et al. demonstrate a link between glycolytic flux, mesoderm segmentation and Wnt signaling. Enhanced flux translated into failures in segmentation and suppression of Wnt target gene expression, as well as inducing alterations to subcellular localization of glycolytic enzymes, suggesting pivotal links between glycolytic flux, signaling and lineage specification. Through careful work on presomitic mesoderm, the authors work suggests important new links between metabolism and differentiation.

https://doi.org/10.7554/eLife.83299.sa0

Introduction

Living systems have the critical ability to sense environmental cues, and to integrate this information with cellular functions by modulating their metabolic activity (Efeyan et al., 2015; Kaelin and Ratcliffe, 2008). The changes in metabolic activity, in turn, are sensed by multiple mechanisms to ensure that metabolic state matches cellular demands. Such mechanisms, referred to as metabolite sensing and signaling, generally consist of ‘sentinel metabolites’ and ‘sensor molecules’ (Litsios et al., 2018; Wang and Lei, 2018). Sentinel metabolites mirror nutrient availability or cellular metabolic state by their levels. These metabolites can, in addition, potentially induce cellular responses, if their levels are linked to the activity of sensor molecules, such as proteins and RNAs. Well known examples of metabolite sensing and signaling include the mechanistic target of rapamycin (mTOR), which responds to altered levels of amino acids and couples nutritional availability with cell growth (Saxton and Sabatini, 2017), or AMP-activated protein kinase (AMPK), which senses adenosine monophosphate (AMP) levels and ensures that cellular bioenergetic demand matches cellular energetic state (González et al., 2020).

Importantly, the role of metabolite signaling is not limited to detecting nutrient availability to match metabolic activity and cellular demands. Recent work has highlighted the emerging link between central carbon metabolism and other cellular programs, such as gene regulation. For instance, by controlling the abundance of rate-limiting substrates used for post-translational modificiations, such as acetyl-CoA, metabolic activity can directly impact gene expression (Campbell and Wellen, 2018; Reid et al., 2017; Miyazawa and Aulehla, 2018). Glycolytic metabolites can also serve as signaling molecules that impact signal transduction directly. In yeast, for example, the glycolytic metabolite fructose 1,6-bisphosphate (FBP) has been shown to regulate the pro-proliferative RAS signaling cascade by interacting with the guanine nucleotide exchange factor Sos1 (Peeters et al., 2017). Notably, the connection between metabolic activity and other cellular programs can also occur at the level of metabolic enzymes with non-canonical, moonlighting functions (Snaebjornsson and Schulze, 2018; Boukouris et al., 2016; Miyazawa and Aulehla, 2018). In situations when moonlighting and canonical enzyme function are inter-dependent, a direct link between cellular metabolic state and moonlighting function is established. One such example is the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (Gapdh), which moonlights as an RNA-binding protein regulating translation when not engaged in its glycolytic function (Chang et al., 2013). While these studies highlight an intricate link between central carbon metabolism and other cellular functions, knowledge of metabolite signaling in more complex physiological settings, such as embryonic development, is still limited.

There are both classic (Spratt, 1950) as well as more recent findings (Bulusu et al., 2017; Oginuma et al., 2017; Miyazawa et al., 2017; Bhattacharya et al., 2020; Djabrayan et al., 2019; Rodenfels et al., 2019; Chi et al., 2020; Miyazawa and Aulehla, 2018) indicating that glucose metabolism and developmental programs are indeed linked. For instance, in mouse and chick embryos, the presomitic mesoderm (PSM) shows intrinsic differences in the expression levels of glycolytic enzymes, leading to the establishment of a glycolytic activity gradient along the anterior-posterior axis (Bulusu et al., 2017; Oginuma et al., 2017). The key question that remains largely unanswered is how a change in cellular metabolic activity is sensed and mechanistically linked to developmental programs.

To address this fundamental question, we focused on mouse embryos at the organogenesis stage following gastrulation, when glucose metabolism is rewired dynamically in time and space in response to extrinsic environmental cues and intrinsic developmental programs (Miyazawa et al., 2017; Bulusu et al., 2017; Oginuma et al., 2017). At this stage, the PSM is periodically segmented into somites, the precursors of vertebrae and skeletal muscles in vertebrates (Hubaud and Pourquié, 2014). PSM patterning and somite formation is controlled by the Wnt, FGF, and retinoic acid-signaling pathways, which show a graded activity along the anterior-posterior axis. In addition, PSM segmentation is linked to a molecular oscillator, the segmentation clock, comprised of several, interconnected signaling pathways (Notch, Wnt, Fgf) that show rhythmic activation cycles in PSM cells, with a period matching the rate of somite formation, for example ∼2 hr in mouse embryos (Aulehla et al., 2008; Yoshioka-Kobayashi et al., 2020; Soroldoni et al., 2014; Matsuda et al., 2020; Diaz-Cuadros et al., 2020; Chu et al., 2019; Sonnen et al., 2018). The interplay between graded and oscillatory signaling dynamics within the PSM controls somite formation in time and space. Previously, a link between glycolytic activity and graded signaling activities has been found (Bulusu et al., 2017; Oginuma et al., 2017; Oginuma et al., 2020). In particular, evidence was found that glycolysis is part of a feedback loop linking (graded) FGF- and Wnt-signaling pathway activities (Oginuma et al., 2017; Oginuma et al., 2020). Although these studies revealed a link between glycolysis and morphogen signaling during PSM patterning, it remains unclear how a change in glycolytic activity is sensed and mechanistically linked to signaling.

In this study, our goal was therefore to first determine in vivo sentinel metabolites during mouse embryo PSM development. We then combined genetic, metabolomic and proteomic approaches to investigate how altered glycolytic flux and metabolite levels impact developmental signaling and patterning processes.

Results

Steady state levels of FBP mirror glycolytic flux within PSM cells

In order to identify sentinel metabolites whose levels reflect glycolytic-flux within PSM cells, we quantified steady state metabolite levels in PSM samples cultured in various concentrations of glucose. We first verified that higher glucose concentrations led to higher glycolytic flux in PSM cells (Figure 1A). Throughout this study, we used quantification of secreted lactate as a proxy for glycolytic flux due to the inability to directly measure flux in embryonic tissues. We also analyzed somite formation and PSM patterning at different glucose concentrations using real-time imaging of the segmentation clock as a dynamic readout (Figure 1—figure supplement 1). PSM patterning proceeded normally, at least qualitatively, at glucose concentrations from 0.5 mM to 12.5 mM, with ongoing periodic morphological segmentation, axis elongation, and oscillatory clock activity throughout the PSM. Below or above this glucose range, morphological changes such as defects in PSM segmentation and axis elongation started to appear.

Figure 1 with 1 supplement see all
Identifying sentinel metabolites that mirror glycolytic flux.

The amount of secreted lactate and intracellular metabolites within PSM explants were measured by gas chromatography mass spectrometry (GC-MS; n=3 biological replicates for each condition). The explants were cultured for 3 hr ex vivo in 0.5 mM, 1.0 mM, 2.0 mM, or 10 mM glucose. (A) The relative amount of secreted lactate (shown in black circles) and intracellular fructose 1,6-bisphosphate (FBP; shown in red triangles) under various glucose conditions. The gray and red lines show the Michaelis-Menten fit (Vmax = 4.7 arbitrary unit, Km = 1.5 mM) for secreted lactate and the linear regression line for intracellular FBP, respectively. (B) Pearson correlation analysis between intracellular metabolite levels and extracellular glucose levels. Metabolites showing significant correlation (p-value < 0.01) are shown in black. Those with a |Pearson correlation coefficient|>0.9 are highlighted in red. Abbreviations: G6P, glucose 6-phosphate; F6P, fructose 6-phosphate; R5P, ribose 5-phosphate; FBP, fructose 1,6-bisphosphate; 3 PG, 3-phosphoglycerate; PEP, phosphoenol pyruvate; αKG, α-ketoglutarate. (C) Hierarchical clustering heatmap of metabolites detected in the PSM explants. Fold changes were calculated using 0.5 mM glucose condition as the reference. Hierarchical clustering was performed using Ward’s method with Euclidean distance.

We hence focused on a glucose range between 0.5 and 10 mM to analyze steady state levels of metabolites in central carbon metabolism by gas chromatography mass spectrometry (GC-MS). Amongst the 57 metabolites quantified, 14 metabolites showed significant linear correlation (p-value <0.01) with extracellular glucose levels (Figure 1B). Fructose 1,6-bisphosphate (FBP) showed the highest correlation with extracellular glucose and also fold-change response to glucose titration (Figure 1A, Figure 1C). These results identify several sentinel metabolites, notably FBP, which had been shown to serve as a sentinel metabolite from bacteria to eukaryotic cell lines (Kochanowski et al., 2013; Zhang et al., 2017; Peeters et al., 2017; Tanner et al., 2018), in mouse embryos.

Altered mesoderm development caused specifically by FBP supplementation

To test for a potential functional role of those sentinel metabolites that we identified, we next performed medium-supplementing experiments with the goal of altering intracellular metabolite levels. To this end, we supplemented the control culture medium with high levels of either fructose 6-phosphate (F6P), FBP, or 3-phosphoglycerate (3 PG) and scored the effect at the level of morphological segment formation, elongation, and also oscillatory segmentation clock activity, using real-time imaging quantifications. Interestingly, FBP supplementation impaired mesoderm segmentation and elongation and disrupted segmentation clock activity in the posterior PSM (Figure 2A, Figure 2—figure supplement 1A, Figure 2—figure supplement 1B). Immunostaining of active caspase-3 in explants did not reveal a major difference in cell death between control and FBP-treated explants (Figure 2—figure supplement 1C).

Figure 2 with 2 supplements see all
FBP supplementation impacts mesoderm segmentation and elongation in a dose-dependent manner.

(A) Kymographs showing dynamics of the Notch signaling activity reporter LuVeLu in PSM explants treated with 20 mM of the indicated metabolite. (B) Whole mount in situ hybridization analysis for the FGF (i.e. Dusp4) and the Wnt target (i.e. Msgn1) gene expression in the PSM. PSM explants were incubated for 12 hr in the presence of F6P or FBP. Expression domains of Dusp4 and Msgn1 are indicated by yellow squares. Shh and Uncx4.1 were used as markers for the neural tissue and posterior somite boundary, respectively. Scale bar, 200 µm. (C, D) The number of newly formed somites (C) and the length of PSM explants (D) after 12 hr ex vivo culture (one-way ANOVA with Tukey’s post-hoc test, *p-value <0.05, **p-value <0.01, ***p-value <0.001 versus control).

In contrast to the effects seen with FBP, glycolytic metabolites upstream (i.e. F6P) or downstream (i.e. 3 PG, pyruvate) of FBP did not cause such effects (Figure 2A, Figure 2—figure supplement 1A, Figure 2—figure supplement 1B; the effect of pyruvate supplementation was described in Bulusu et al., 2017). We also tested the effect of FBP supplementation on gene expression, focusing on an FGF-target gene Dusp4 (Niwa et al., 2007) and a Wnt-target gene Msgn1 (Wittler et al., 2007). Supplementation of FBP, but not F6P, caused a downregulation of Dusp4 and Msgn1 mRNA expression in a dose-dependent manner (Figure 2B), accompanying reduction of mesoderm segmentation and elongation (Figure 2C, Figure 2D). Of note, at intermediate concentration (10 mM) of FBP supplementation, only the Wnt-taget gene Msgn1 was downregulated, while the Fgf-target gene Dusp4 showed expression comparable to control samples, indicating potential dose-specific effects of FBP.

To validate the effects seen upon exogenous addition of FBP, we investigated the uptake of FBP by stable isotope (13C) tracing. We cultured PSM explants in medium supplemented with fully 13C-labelled FBP (13C6-FBP) and analyzed 13C-labelling of intracellular metabolites by liquid chromatography mass spectrometry (LC-MS). Following three hours of incubation with 13C6-FBP, 13C-labeling was detected in glycolytic intermediates downstream of FBP (Figure 2—figure supplement 1D), confirming the uptake of labeled carbons by the explants. Since we also detected that a small fraction of 13C6-FBP broke down to 13C6-fructose monophosphate (F6P and/or fructose 1-phosphate (F1P)) in the culture medium during incubation (data not shown), we performed additional control experiments by culturing PSM explants in F1P-supplemented medium. Similar to F6P, supplementation of F1P did not cause any detectable phenotype at the level of segmentation clock activity or elongation (Figure 2—figure supplement 1E).

As a related finding, we observed that upon glucose titration, the expression of Wnt-signaling target genes in PSM explants is anti-correlated with glucose availabilty/glycolytic activity: while lowering glucose concentration (from 5.0 mM to 0.5 mM) correlated with an upregulation of several Wnt target genes, such as Axin2, Ccnd1, and Myc, the opposite effect was found when glucose concentration was increased (from 5.0 mM to 25 mM) (Figure 2—figure supplement 2).

Combined, our findings hence suggest that FBP, but not other glycolytic intermediates such as F6P, F1P, or 3 PG, is a flux-sentinel and signaling metabolite, as it impacts mesoderm development and gene expression in a dose-dependent manner.

Generating a conditional cytoPFKFB3 transgenic mouse line as a genetic tool to increase glycolytic flux

Our findings thus far show that intracellular FBP levels respond dynamically to an alteration in glycolytic flux (Figure 1), and importantly, that FBP, but not its precursor metabolite F6P, impacts PSM development in a dose-dependent manner (Figure 2). Based on these observations, we next sought a way to manipulate glycolytic flux at the level of the phosphofructokinase (Pfk) reaction and importantly, in a genetic manner (Figure 3A). Pfk converts F6P into FBP, the first committed step in glycolysis, and plays a critical role in regulating glycolytic flux (Tanner et al., 2018; Mor et al., 2011). We generated transgenic mice enabling conditional overexpression of a mutant PFKFB3 i.e. PFKFB3(K472A/K473A) (Yalcin et al., 2009). PFKFB3 generates fructose 2,6-bisphosphate (F2,6BP), a potent allosteric activator of Pfk (Figure 3A). A previous study showed that PFKFB3(K472A/K473A) localises exclusively to the cytoplasm, and that this cytoplasmically-localized PFKFB3 (hereafter termed as cytoPFKFB3) activates glycolysis (Yalcin et al., 2009). Indeed, in PSM explants from transgenic embryos with ubiquitous overexpression of cytoPFKFB3, we found increased glycolysis based on the analysis of lactate secretion (Figure 3B). In addition, we found that in cytoPFKFB3 embryos, lactate secretion changed in a glucose-dose dependent manner (Figure 3B). Next we investigated steady state metabolite levels in control and transgenic PSM explants cultured in 10 mM glucose condition. Among the 57 metabolites quantified by GC-MS, FBP and lactate were significantly increased in transgenic PSM explants, while aspartate, glucose 6-phosphate, and glutamate were significantly decreased (Figure 3C, Figure 3—figure supplement 1A). These findings mirrored the results in wild-type PSM explants upon glucose titration (Figure 1).

Figure 3 with 1 supplement see all
cytoPFKFB3 overexpression causes an increase in glycolytic flux and FBP levels within PSM cells.

(A) Conditional cytoPFKFB3 transgenic mice were generated to activate glycolysis through allosteric activation of Pfk. (B) Quantification of secreted lactate in control and cytoPFKFB3 transgenic PSM explants cultured for 12 hr under varying concentrations of glucose (unpaired Welch’s t-test, *p-value <0.05, **p-value <0.01). (C, D) Measurement of steady state metabolite levels by GC-MS (n=4 biological replicates for each condition) in control (Ctrl) and cytoPFKFB3 (Tg; crossed to HprtCre line) explants cultured for 3 hr in medium containing 10 mM glucose. SAM (Significance Analysis for Microarrays) analysis was performed using a significance threshold δ = 0.9, which corresponds to a false discovery rate (FDR)=0.012. G6P, glucose 6-phosphate. FBP, fructose 1,6-bisphosphate. 3 PG, 3-phosphoglycerate. PEP, phosphoenol pyruvate. Asp, aspartate. Asn, asparagine. (E) Targeted metabolomics analysis by liquid chromatography-mass spectrometry (LC-MS). Relative FBP levels were determined in control and cytoPFKFB3 explants cultured for 3 hr in various culture conditions (2 G: 2.0 mM glucose, 10 G: 10 mM glucose, 25 G: 25 mM glucose, F6P: 2.0 mM glucose plus 20 mM F6P; n=3 biological replicates for each culture condition). Unpaired Welch’s t-test (***p-value <0.001 vs. Ctrl-10G).

It is notable that cytoPFKFB3 overexpression enables glycolytic flux to reach a level that is not achievable in control embryos (Figure 3B). Consistently, we found that cytoPFKFB3 overexpression lifted the upper limit of FBP levels in PSM cells (Figure 3E, Figure 3—figure supplement 1B, Figure 3—figure supplement 1C). In control explants, FBP levels did not increase further when glucose concentration was increased from 10 mM to 25 mM. It was also the case when control explants were cultured in 20 mM of F6P (Figure 3E). These results indicate that the Pfk reaction carries a (rate-)limiting role for glycolytic flux and FBP levels, and that cytoPFKFB3 overexpression hinders the flux-regulation function of Pfk. As a possible indicator of dysregulated flux at the level of Pfk reaction, we observed that the ratio between FBP and glucose mono-phosphate (G6P/F6P) was increased in cytoPFKFB3 embryos compared to control even when FBP levels were comparable between them (Figure 3—figure supplement 1D).

We hence conclude that the overexpression of cytoPFKFB3 leads to activation of glycolysis at the level of Pfk in a glucose-dose dependent manner. More generally, the cytoPFKFB3 transgenic mouse line represents a potentially powerful new genetic model to study the role of glycolysis.

Functional consequence of cytoPFKFB3 overexpression on PSM development

We then investigated the functional consequences of cytoPFKFB3 overexpression on mesoderm development. Constitutive overexpression of cytoPFKFB3 from fertilization caused embryonic lethality, as no transgenic pups were recovered (n=30 pups, N=6 litters). We have not yet investigated the precise timepoint and cause of lethality. At embryonic day 10.5 (E10.5), cytoPFKFB3 transgenic embryos were morphologically indistinguishable from their littermates, but had slightly fewer somites (Figure 4A; control: 38±1.5 somites, transgenic: 35±3.9 somites).

Figure 4 with 2 supplements see all
cytoPFKFB3 overexpression impacts mesoderm development in a glucose-concentration dependent manner.

(A) Total number of somites in E10.5 embryos (mean ±s.d; unpaired Welch’s t-test; **p-value <0.01). Ctrl, control embryos; Tg, cytoPFKFB3 embryos (crossed to HprtCre line). (B,C) Number of formed somites and quantification of PSM explant length after 12 hr in vitro culture. (D) Whole mount mRNA in situ hybridization analysis for Lfng, Shh, and Uncx4.1 in PSM explants after 12 hr in vitro culture at varying glucose concentrations. Asterisks denote somites that formed during the in vitro culture. Scale bar, 100 µm. (E,F) Effect of mesoderm-specific overexpression of cytoPFKFB3 on PSM segmentation and elongation (12 hr incubation). The PSM explants were cultured in medium containing 10 mM glucose. Bar graphs show the number of newly formed somites during the culture (E), and the length of explants after the culture (F; mean ±s.d; unpaired Welch’s t-test; ***p-value <0.001). (G) Real-time quantification of segmentation clock activity using Notch signaling activity reporter LuVeLu in PSM explants, shown as kymographs. Note that oscillatory reporter activity ceased in cytoPFKFB3/T-Cre samples during the experiment, while control samples showed ongoing periodic activity.

To analyze the impact of cytoPFKFB3 overexpression on mesoderm development in a more dynamic and quantitative manner, we analyzed mesoderm segmentation, elongation, and oscillatory clock activity in cytoPFKFB3 and control explants cultured at various glucose concentrations. Consistent with our previous findings (Figure 1—figure supplement 1), control explants proceeded segmentation and PSM patterning in a qualitatively comparable manner, even when cultured at higher glucose concentrations (Figure 4B). In contrast, we found that somite formation was impaired in explants from cytoPFKFB3 embryos in a glucose-dose dependent manner (Figure 4B). Overall growth during this 12 hr incubation seemed comparable or even increased in cytoPFKFB3 transgenic explants, based on the size of explants after culture (Figure 4C). We also tested whether a mesoderm-specific cytoPFKFB3 overexpression has a similar effect on somite formation. Indeed, mesoderm specific cytoPFKFB3 overexpression, using Cre-expression driven by the promoter of the pan-mesoderm marker Brachyury (i.e. T-promoter-driven Cre Perantoni et al., 2005), showed similar reduction in segment formation, compared to control explants (Figure 4E, Figure 4F). The real-time imaging quantification of segmentation clock activity revealed that in cytoPFKFB3 explants cultured at 10 mM glucose, clock oscillations ceased after few cycles, in contrast to control samples (Figure 4G; Figure 4—video 1).

Molecularly, we found that the expression of the Wnt signaling target gene Msgn1 was downregulated in cytoPFKFB3 explants, again in a glucose-concentration dependent manner (Figure 5A). Of great interest, about 30% of cytoPFKFB3 explants showed reduced expression of Msgn1 even under 2.0 mM glucose condition where their FBP levels are within the range of wild-type explants (Figure 3—figure supplement 1B). In contrast, we did not find an obvious change in the expression of Dusp4, an Fgf signaling target, which was maintained even at 25 mM glucose (Figure 5B).

Effect of cytoPFKFB3 overexpression on Wnt and FGF target gene expression.

(A,B) Whole mount mRNA in situ hybridization for Msgn1 (Wnt-target gene) and Dusp4 (FGF-target gene) in the PSM explants. Explants were cultured for 12 hr under various glucose conditions, as indicated. Shh and Uncx4.1 were used as markers for neural tissue and posterior somite boundary, respectively. Expression domain of Msgn1 and Dusp4 is indicated by yellow rectangles. Note the glucose-dose dependent loss of Msgn1 expression in cytoPFKFB3 explants (Tg; crossed to HprtCre line). In contrast, Dusp4 expression appeared unaffected in cytoPFKFB3 explants. Asterisks mark somites that formed during the culture. Scale bar, 100 µm. (C–E) Transcriptome analysis of PSM explants cultured for 3 hr in vitro (n=3 biological replicates for each culture condition). Gene expression profiles were compared between control and cytoPFKFB3 explants (C), control and FBP (20 mM)-treated explants (D), or control and F6P (20 mM)-treated explants (E). Among differentially expressed genes (adjusted p-value <0.01; shown in red), Wnt-target genes and PFKFB3 were marked by blue.

To elaborate these findings, we next performed a transcriptome analysis of control and cytoPFKFB3 explants cultured in 10 mM glucose for three hours. We identified 568 genes as differentially expressed genes (DEGs; adjusted p-value <0.01; Supplementary file 1): 210 genes were upregulated in cytoPFKFB3 explants, while 358 genes were downregulated (Figure 5C). Genes associated with transcription, anterior-posterior patterning, and the Wnt signaling pathway were enriched among the downregulated DEGs (Supplementary file 2), and those DEGs included many Wnt-target genes (Figure 5C). No gene ontology (GO) term was enriched among the upregulated DEGs.

To examine whether FBP addition would mirror effects on gene expression and in particular Wnt -signaling target genes, we then performed a transcriptome analysis of explants cultured with FBP (Figure 5D). While FBP supplementation caused upregulation of cell cycle- or metabolism-related genes, it led to downregulation of genes associated with transcription and anterior-posterior patterning (Supplementary file 3; Supplementary file 4). Of great importance, these downreguated DEGs included many Wnt-targets (Figure 5D), most of which were also downregulated in cytoPFKFB3 explants. F6P-treated explants did not show such feature (Figure 5E, Supplementary file 5; Supplementary file 6). Therefore, these results indicate that an increase in glycolytic flux or FBP levels leads to suppression of Wnt signaling activity.

Combined, these results show that cytoPFKFB3 overexpression results in reduced segment formation, arrest of the segmentation clock oscillations and downregulation of Wnt signaling, in a glucose-dose dependent manner. As glucose concentration impacts, in turn, glycolytic flux (Figure 3B), these findings suggest that these phenotypes are flux-dependent and are not a mere result of cytoPFKFB3 overexpression. In addition, we found that exogenous FBP-supplementation likewise causes a dose dependent effect on clock oscillations and downregulation of Wnt-signaling target gene expression (Figure 2, Figure 5D), implicating FBP as a mediator of flux-sensitive effects on development and signaling.

In vivo phenotype of cytoPFKFB3 embryos is sensitive to maternal environment

As noted above, cytoPFKFB3 embryos were morphologically indistinguishable from control littermates when dissected at E10.5. This contrasts with the PSM phenotype we found when cytoPFKFB3 PSM explants were cultured in vitro (Figure 4, Figure 5). As this phenotype is glucose dose dependent, we reasoned that the absence of an obvious in vivo phenotype at E10.5 could reflect low in vivo glucose concentrations, which have been reported to be lower than in maternal circulation (Renfree et al., 1975). To test this possibility, we performed whole embryo roller-culture (WEC) experiments with cytoPFKFB3 embryos at E8.5, exposing them to ∼5 mM glucose (50% rat serum / DMEM with 1.0 g/L glucose). Indeed, while all control embryos completed cranial neural tube closure (NTC) (n=12/12) after 24 hr WEC, about 40% of the transgenic embryos (n=7/18) failed to complete this process, showing a developmental delay as well (Figure 4—figure supplement 1A–C).

In order to further test the hypothesis in vivo we next used the maternal diabetes mouse model Akita (Wang et al., 1999; Yoshioka et al., 1997). Akita mice carry a point mutation in the Ins2 gene, which leads to a diabetic phenotype including hyperglycemia. Akita heterozygous females indeed showed elevated blood glucose levels (i.e. ∼450 mg/dl) compared to control (i.e. ∼150 mg/dl). On the maternal diabetic background, 50% of cytoPFKFB3 embryos (n=5 out of 10 embryos) showed neural tube defects (NTDs) with developmental delay in vivo, while less than 10% of control embryos (n=1 out of 13 embryos) showed NTDs (Figure 4—figure supplement 1D, Figure 4—figure supplement 1E). In addition, they had fewer somites than control embryos. This provides in vivo evidence for a glycolytic flux-dependent impact on embryonic development in cytoPFKFB3 embryos.

Perturbation of glycolytic-flux and FBP levels alters subcellular localization of glycolytic enzymes

Our data thus far suggest that altered glycolysis, caused by either nutritional or genetic means, impairs PSM development, possibly mediated via the sentinel metabolite FBP. To probe for potential underlying mechanisms, we turned to the role of glycolytic enzymes. Interestingly, we had found that several glycolytic enzymes are localized in the nucleus in PSM cells, based on cell-fractionation analysis (Figure 6—figure supplement 1C, Figure 6—figure supplement 1D). It had been proposed previously that the subcellular localization of glycolytic enzymes can change dynamically in response to altered glycolytic flux (Kwon et al., 2010; Hu et al., 2016; Zhang et al., 2017). We therefore aimed to systematically investigate the changes in subcellular protein localization in response to altered metabolic state in mouse embryos. To this end, we performed a proteome-wide cell-fractionation analysis in PSM explants cultured in various metabolic conditions.

Proteins were extracted from cytoplasmic, membrane, nuclear-soluble, chromatin-bound, and the remaining insoluble (labeled as ’cytoskeletal’) fractions. We found that in samples cultured for three hours in FBP-supplemented medium (and to a lesser extend in F6P-supplemented medium), proteins part of the glycolytic pathway (12 combined glycolytic enzymes) were reduced in the cytoskeletal and, to a lesser extent, the nuclear soluble fraction, relative to samples cultured in control medium (Figure 6A, Figure 6—figure supplement 1A, Figure 6—figure supplement 1B). For several glycolytic enzymes detected in the nuclear soluble fraction, that is aldolase A (Aldoa), phosphofructokinase L (Pfkl), glyceraldehyde 3-phosphate dehydrogenase (Gapdh), and pyruvate kinase M (Pkm) (Figure 6—figure supplement 1E), we performed a targeted analysis using Western blotting (Figure 6B). Interestingly, we found that amongst those tested enzymes, Aldoa and Pfkl were significantly depleted from the nuclear soluble fraction upon incubation in FBP-supplemented medium.

Figure 6 with 1 supplement see all
Subcellular localization of glycolytic enzymes are responsive to FBP treatment.

(A) Effects of FBP treatment on subcellular localization of glycolytic enzymes. PSM explants were cultured for 3 hr in media containing 2.0 mM glucose and supplemented with 20 mM F6P or FBP. In addition to whole cell lysates (WCL), protein extracts were prepared from cytoplasmic (CYT), membrane (MEM), nuclear-soluble (NUC), chromatin-bound (CHR), and cytoskeletal (SKEL) fractions (n=3 biological replicates). Abundance ratios (log2(F6P/FBP-treated/control)) of glycolytic enzymes (in blue) were compared to those of non-glycolytic proteins (the rest, in gray) for statistical analysis (unpaired two-sample Wilcoxon test, *p-value <0.05, ***p-value <0.001, ****p-value <0.0001, n.s., not significant). (B) Effects of FBP on the abundance of glycolytic enzymes in the nuclear soluble fraction. Subcellular protein fractionation was performed following 1 hr incubation of PSM explants in the media containing 0.5 mM glucose and supplemented with 20 mM FBP (n=6 biological replicates; paired t-test, **p-value <0.01, n.s., not significant).

We next asked whether subcellular localization of glycolytic enzymes is also altered upon cytoPFKFB3 overexpression, which we showed leads to an increase in glycolytic flux and FBP levels (Figure 3B–D). We hence performed subcellular proteome analysis of both control and cytoPFKFB3 transgenic PSM explants, cultured for 1 hr in 10 mM glucose-containing medium. Due to the limited material obtained from transgenic embryos, proteins from nuclear-soluble, chromatin-bound, and cytoskeletal fractions were collected as a single, nuclear-cytoskeletal fraction. We found that cytoPFKFB3 overexpression altered the nuclear-cytoskeletal abundance of 12 proteins among 2813 detected proteins (adjusted p-value <0.05 and |log2(fold change)|>0.5) (Figure 7A–C). One of these proteins showing a pronounced depletion in the nuclear-cytoskeletal fraction in transgenic explants turned out to be the glycolytic enzyme Pfkl (Figure 7C). Using western blotting, we confirmed that Pfkl was depleted in the nuclear-cytoskeletal fraction in transgenic explants cultured at 10 mM glucose (Figure 7D). Importantly, under 2.0 mM glucose condition, nuclear-cytoskeletal Pfkl was not depleted in transgenic explants, suggesting that subcellular localization of Pfkl changes in a glucose-dose-dependent manner. In addition, we found that, in cytoPFKFB3 explants, the overall abundance of glycolytic machinery was decreased in the cytoplasmic and membrane fraction (Figure 7A–B).

Subcellular localization of Pfkl responds to cytoPFKFB3 overexpression in a glucose-concentration dependent manner.

(A–C) Effects of cytoPFKFB3 overexpression on subcellular protein localization assessed by mass spectrometry. Following 1 hr incubation of PSM explants in 10 mM glucose, protein extracts were prepared from cytoplasmic (A), membrane (B), and nuclear-cytoskeletal (C) fractions (n=3 biological replicates for each culture condition). Proteins whose abundance showed significant changes (adjusted p-value <0.05 and |log2(fold change)|>0.5) are marked red in the volcano plots. Top violin plots show distribution of abundance changes of glycolytic (blue) and non-glycolytic (gray) proteins. Statistical comparison as in Figure 6. **p-value <0.01. Ctrl, control explants; Tg, cytoPFKFB3 explants (crossed to the HprtCre line). (D) Western-blot analysis of subcellular localization of Pfkl under different glucose conditions. Subcellular protein fractionation was performed following 1 hr incubation of PSM explants under 2.0 mM or 10 mM glucose (n=3 biological replicates for each culture condition). CYT, cytoplasmic fraction; MEM, membrane fraction; N-S, nuclear-cytoskeletal fraction.

Combined, our results hence reveal that an alteration in glycolytic-flux/FBP levels, either by direct supplementation of metabolites or by genetic means using cytoPFKFB3 overexpression, changes the distribution of glycolytic enzymes in several subcellular compartments. While we have not been able to address the functional consequence of specific changes in subcellular localization, such as the nuclear depletion of Pfkl or Aldoa when glycolytic flux is increased, these results pave the way for future investigations on the mechanistic underpinning of how metabolic state is linked to cellular signaling and functions.

Discussion

Identifying FBP as a sentinel metabolite for glycolytic flux in developing mouse embryos

In this work, we investigated how glycolytic flux impacts mouse embryo mesoderm development, seeking to decipher the underlying mechanisms. First, we aimed to identify sentinel metabolites whose concentrations mirror glycolytic flux in mouse embryos (Kochanowski et al., 2013; Zhang et al., 2017; Cai et al., 2011; Peeters et al., 2017). The identification of sentinel metabolites is critical, as steady state metabolite levels are generally poor indicators of metabolic pathway activities (Jang et al., 2018). By investigating how steady state metabolite levels respond to an alteration in glycolytic flux upon glucose titration, we identified aspartate, FBP, and lactate as potential sentinel glycolytic metabolites whose steady state levels were either positively (i.e. FBP and lactate) or negatively (i.e. aspartate) correlated with extracellular glucose levels (Figure 1B). Similar changes were observed upon glycolytic activation by cytoPFKFB3 overexpression (Figure 3C). Remarkably, we found that FBP levels exhibit a strong linear correlation with a wide range of glucose concentrations, showing a 45-fold increase from 0.5 mM to 10 mM glucose conditions (Figure 1A). In addition, FBP levels showed a linear correlation with lactate secretion in control explants, and such a correlation was maintained even in cytoPFKFB3 explants (Figure 3—figure supplement 1C). Previous studies suggested that the reversible reactions between FBP and PEP allow coupling of FBP to lower glycolytic flux (Kochanowski et al., 2013), and importantly that feedforward activation of pyruvate kinase by FBP enables the cell to establish a linear correlation between FBP and glycolytic flux over a wide range of FBP concentrations (Kotte et al., 2010; Kochanowski et al., 2013). Such properties of lower glycolytic reactions may allow FBP to function as a generic sentinel metabolite for glycolytic flux in various biological contexts, from bacteria to mammalian cells (Kochanowski et al., 2013; Zhang et al., 2017; Peeters et al., 2017; Tanner et al., 2018). This study extends such a finding of FBP as a glycolytic sentinel metabolite to in vivo mammalian embryos.

FBP as a flux signaling metabolite connecting glycolytic-flux and PSM development

Interestingly, in addition to being a sentinel for glycolytic flux, FBP has been shown to carry signaling functions, hence relaying flux information to downstream effectors, such as transcription factors and signaling molecules (Kochanowski et al., 2013; Zhang et al., 2017; Peeters et al., 2017). To test if such a flux-signaling function exists also in mouse embryos, we combined two complementary approaches, that is medium-supplementation of FBP (Figure 2) and, importantly, a genetic mouse model to increase glycolytic flux (Figures 35).

First, we revealed that high doses of FBP impaired mesoderm segmentation, disrupted the segmentation clock activity and led to downregulation of Wnt and Fgf target gene expression in the PSM (Figure 2). Using 13C-tracing experiments, we showed that exogenous FBP could be taken up by PSM cells (Figure 2—figure supplement 1D), an important control considering the debate regarding the permeability of this highly charged metabolite through the cell membrane (Alva et al., 2016). Interestingly, the effect of FBP appear most pronounced in the posterior, most undifferentiated PSM cells, while segmentation clock activity persists in the anterior PSM cells upon medium-supplementation of FBP (Figure 2A). This argues against a pleiotropic, toxic effect of FBP and suggests a more specific effect triggered by increased FBP levels.

As a second, complementary approach to alter glycolytic flux and hence FBP levels, we aimed to increase the activity of Pfk, the rate limiting glycolytic enzyme, in a genetic manner (Figure 3). To this end, we generated conditional transgenic mice which overexpress cytoPFKFB3 in a Cre-dependent manner. We showed that cytoPFKFB3 overexpression was indeed effective in increasing glycolytic flux in PSM explants, with a two-fold increase in secreted lactate (Figure 3B). Such a strong activation of glycolysis has been shown to be difficult to achieve by overexpression of single, wild-type glycolytic proteins in mammalian cell lines (Tanner et al., 2018; Yalcin et al., 2009). Of note, GC-MS analysis showed that cytoPFKFB3 overexpression was effective in increasing intracellular FBP levels (Figure 3C, Figure 3D). Because the extent of glycolytic activation by cytoPFKFB3 was dependent on glucose concentration in the culture media (Figure 3B), we can titrate the effects of cytoPFKFB3 overexpression by increasing glucose. Therefore, the cytoPFKFB3 transgenic mouse line that we generated is a powerful, genetic mouse model to study the function of glycolysis and, more importantly, that of a sentinel glycolytic metabolite FBP, in various biological contexts.

Functionally, overexpression of cytoPFKFB3 led to impairment of PSM segmentation at 10 mM or higher glucose concentrations, while wild-type PSM developed properly, at least qualitatively, at this glucose concentration (Figure 4, Figure 1—figure supplement 1). The abnormal PSM development accompanied disruption of the segmentation clock activity and suppression of Wnt-target gene expression, while expression of FGF-target gene remained comparable to control. These phenotypes are reminiscent of our observation that intermediate levels (10 mM) of exogenous FBP suppressed mRNA expression of Msgn1 but not of Dusp4 (Figure 2B). This data hence indicate that cytoPFKFB3 overexpression phenocopies the effect of the FBP-supplementation on PSM development.

Combined, these findings provide evidence that the sentinel glycolytic metabolite FBP exerts a signaling function in PSM development.

The role of regulated flux at the level of Pfk

Our findings suggest that flux-regulation at the level of Pfk is critical to keep FBP steady state levels within a range compatible with proper PSM patterning and segmentation. In agreement with such a rate-limiting function for Pfk, we found in glucose titration experiments that FBP levels saturated and did not further increase at glucose levels above 10 mM (Figure 3E). Along similar lines, the supplementation of high concentrations of the Pfk substrate F6P did not result in a significant increase of FBP levels, again compatible with a rate-limiting function at the level of Pfk (Figure 3E). The upper limit of glycolytic flux and FBP levels can be experimentally increased by cytoPFKFB3 overexpression (Figure 3B, Figure 3E). We interpret the data as evidence that cytoPFKFB3 overexpression compromises the flux-control function of Pfk and hence much higher FBP (and secreted lactate) levels are reached. Such a drastic increase in glycolytic flux and FBP levels correlates with a severe PSM patterning phenotype (Figure 4), which resembles the phenotype induced by supplementation of high dose of FBP (Figure 2). Our results in mouse embryos hence provides evidence that flux regulation by Pfk, an evolutionary conserved role present from bacteria to humans, serves to maintain FBP levels below a critical threshold.

In addition to this threshold function, we find evidence that a change in glycolytic flux and FBP levels within the physiological range also correlates with functional consequences. For instance, we reveal flux-dependent quantitative gene expression changes, such as a control of Wnt-signaling target genes, during glucose titration experiments (Figure 2—figure supplement 2). Accordingly, a modest increase in glycolytic flux in cytoPFKFB3 transgenic embryos cultured at 2.0 mM glucose also exhibits Wnt signaling target gene downregulation (Figure 5A). Of note, it remains unclear whether changes in the levels of FBP alone or if, in fact, changes in several sentinel metabolites underlies this gene expression change in cytoPFKFB3 embryos (Figure 3—figure supplement 1D). Given the advent of technology detecting metabolite-protein interactions, including allosteric effects, in a proteome-wide manner (Savitski et al., 2014; Feng et al., 2014; Piazza et al., 2018), the fundamental challenge to reveal allosteromes for several metabolites should now be tackled.

Wnt signaling as a link between glycolytic-flux and PSM patterning

While the detailed mechanism of flux-regulated gene expression in PSM cells has not yet been revealed, the response to changes in flux clearly involves the Wnt signaling pathway: lowering glucose concentration correlates with an upregulation of Wnt target genes, while the opposite effect was found when glucose concentration was increased (Figure 2—figure supplement 2). Consistent with such an anti-correlation, we found that Wnt target gene expression was decreased in conditions of FBP supplementation and cytoPFKFB3 overexpression (Figure 2B, Figure 5). Previously, it was shown that Wnt signaling can promote glycolysis directly or indirectly (Oginuma et al., 2017; Pate et al., 2014). Therefore, our findings suggest that in the PSM there is a negative-feedback regulation from glycolysis to Wnt signaling. Contrary to our findings, a previous study performed in cultured chick embryos has suggested that inhibition of glycolysis decreases Wnt signaling (Oginuma et al., 2020). This discrepancy could relate to the time point of analysis: while Oginuma et al. mainly focused on analyzing samples 16 hr after metabolic changes, we chose to score the effects of altered glycolytic flux/FBP levels already after a 3-hr incubation, with the goal to capture the primary response of PSM cells. Whether the difference in sampling time underlies the observed difference is yet unknown, but both studies highlight that Wnt signaling is responsive to glycolytic flux, supporting a tight link between metabolism and PSM development. Given the central function of Wnt signaling in development, stem cells and disease, a future key interest will be to reveal its link to metabolism and in particular glycolytic flux in these different contexts. In addition, as FBP can be considered as a universal sentinel for glycolytic-flux in living organisms, it will be crucial to reveal the mechanisms of how cells integrate steady state FBP levels in these different contexts.

Impact of altered glycolytic-flux and FBP levels on subcellular protein localization

As one mechanism by which FBP levels are integrated into cellular programs, we propose that FBP levels impact subcellular localization of proteins, some of which might function as FBP sensor molecules. Here, we revealed that several glycolytic enzymes including Aldoa and Pfkl are amongst those proteins altering their subcellular localization in response to FBP supplementation or cytoPFKFB3 overexpression in high glucose/flux conditions (Figure 6, Figure 7).

While we do not have any direct functional evidence so far for a functional role of nuclear localized glycolytic enzymes, our findings do raise the question whether their subcellular compartmentalization is linked to a non-metabolic, moonlighting function (Enzo et al., 2015; Cieśla et al., 2014; Ronai et al., 1992; Yang et al., 2011; Yang et al., 2012).

Additionally recent evidence in several biological systems highlights that subsets of metabolic reactions, for instance, from the mitochondrial TCA-cycle, take place also in the nucleus in order to maintain local supply of substrates for epigenetic modifications (Nagaraj et al., 2017; Kafkia et al., 2020). Thus, one possibility is that specific glycolytic reactions are taking place also in the nucleus, for instance to provide a local source of co-factors (e.g. NAD+) and/or substrates (e.g. acetyl-CoA, O-GlcNAc) for post-translational modifications of proteins. This emerging view of compartmentalized, local metabolic reactions as a way to regulate cellular functions has been recently supported by experimental evidence (De Bock et al., 2013; Hu et al., 2016; Jang et al., 2016; Ryu et al., 2018; Bulusu et al., 2017).

While future studies will need to reveal if nuclear localization of glycolytic enzymes is linked to their moonlighting functions or metabolic compartmentalization, our finding that their subcellular localization is glycolytic flux-sensitive reveals a potentially general mechanism of how metabolic state is integrated into cellular programs. Of note, the translocation of proteins was observed only when high levels of FBP were reached upon direct FBP supplementation or cytoPFKFB3 overexpression with high glucose (Figure 6, Figure 7). Future studies hence need to investigate whether flux-dependent change in protein localization also occurs upon moderate and more physiological changes in glycolytic-flux/FBP levels. To this end, the development of more quantitative approaches, such as live-imaging of tagged enzymes and the development of metabolite biosensors, are needed.

Outlook

Using mouse embryo mesoderm development as a model system, our study identifies FBP as a sentinel, flux-signaling metabolite connecting glycolysis and developmental signaling pathways. Interestingly, FBP has been implicated as an allosteric regulatorregulation of a multitude of proteins involved in either metabolic as well as non-metabolic processes in Escherichia coli (Feng et al., 2014; Piazza et al., 2018). Revealing the FBP allosterome and investigating the impact of allosteric interactions on protein localization and, more generally, on protein function, is of central importance and a key future objective (Lindsley and Rutter, 2006). Excitingly, emerging techniques now start to enable a more comprehensive interrogation of metabolite-protein interaction (Savitski et al., 2014; Feng et al., 2014; Piazza et al., 2018). We are currently exploring the possibility to decipher metabolite-protein allosteromes in complex biological samples, such as in developing embryos.

Lastly, it is notable that the role of FBP as a flux-signaling metabolite has been demonstrated in microbes (Litsios et al., 2018) and hence predates the origin of signaling pathways involved in multicellular organism development, such as the Wnt signaling pathway, which appeared in metazoa (Holstein, 2012). It is hence of great interest to investigate how metabolic flux-signaling has been integrated into signaling pathways involved in multicellular organism development in the course of evolution.

Materials and methods

Mice

All animals were housed in the EMBL animal facility under veterinarians’ supervision and were treated following the guidelines of the European Commission, revised directive 2010/63/EU and AVMA guidelines 2007. All the animal experiments were approved by the EMBL Institutional Animal Care and Use Committee (project code: 21–001_HD_AA). The detection of a vaginal plug was designated as embryonic day (E) 0.5, and all experiments were conducted with E10.5 embryos.

Generation of conditional cytoPFKFB3 transgenic mouse line

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Flag-PFKFB3(K472A/K473A) (hereafter termed as cytoPFKFB3) from Yalcin et al., 2009 was amplified by PCR using the following primers: Forward 5’-TAGGCCGGCCGCCACCATGGACTACAAGGACGACGACG-3’ and reverse 5’-TGGGCCGGCCGGAAATGGAATGGAACCGACAC-3’. The resulting amplicon was then cloned into the Rosa26 targeting vector Ai9 (Madisen et al., 2010) using FseI restriction enzyme to generate the loxP-stop-loxP-cytoPFKFB3 (LSL-cytoPFKFB3) construct. Conditional cytoPFKFB3 transgenic mouse line was generated by standard gene targeting techniques using R1 embryonic stem cells. Briefly, chimeric mice were obtained by C57BL/6 blastocyst injection and then outbred to establish the line through germline transmission. Rosa26LSL-cytoPFKFB3 mouse line was maintained by crossing to CD1 mouse strain.

Genotyping

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The following mice used in this study were described previously and were genotyped using primers described in these references: T-Cre (Perantoni et al., 2005), HprtCre (Tang et al., 2002), LuVeLu (Aulehla et al., 2008). Akita mice (Wang et al., 1999; Yoshioka et al., 1997) were imported from the Jackson Laboratory (stock #003548) and were genotyped using the following primers: Forward 5’-TGCTGATGCCCTGGCCTGCT-3’ and reverse 5’-TGGTCCCACATATGCACATG-3’ (restriction digestion of PCR products by Fnu4HI produce 140 bp and 280 bp bands for wild-type and mutant alleles, respectively). The primers used for genotyping of Rosa26LSL-cytoPFKFB3 mice were as follows: Bofore Cre-recombination, forward 5’-GAGCTGCAGTGGAGTAGGCG-3’ and reverse 5’-CTCGACCATGGTAATAGCGA-3’ (predicted product size, 580 bp); After Cre-recombination, forward 5’-GGCTTCTGGCGTGTGACCGG-3’ and reverse 5’-ACTCGGCTCTGCGTCAGTTC-3’ (predicted product size, 340 bp). For polymerase chain reaction (PCR), OneTaq 2 X Master Mix with Standard Buffer was utilized (New England Biolabs).

Ex vivo culture of PSM explants

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PSM explants with three intact somites were collected using micro scalpels (Feather Safety Razor, No. 715, 02.003.00.715) in DMEM/F12 (without glucose, pyruvate, glutamine, and phenol red; Cell Culture Technologies) supplemented with 0.5–25 mM glucose (Sigma-Aldrich, G8769), 2.0 mM glutamine (Sigma-Aldrich, G7513), 1.0% (w/v) BSA (Cohn fraction V; Equitech-Bio, BAC62), and 10 mM HEPES (Gibco, 15360–106). The explants were then washed with pre-equilibrated culture medium (DMEM/F12 supplemented with 0.5–25 mM glucose, 2.0 mM glutamine, and 1.0% (w/v) BSA) and were transferred to eight-well chamber slides (Lab-Tek, 155411) filled with 160 µl of the pre-equilibrated culture medium. When assessing the impacts of glycolytic intermediates on PSM development, culture medium supplemented with a glycolytic intermediate i.e. fructose 1-phosphate (Sigma-Aldrich, F1127), fructose 6-phosphate (Sigma-Aldrich, F3627), fructose 1,6-bisphosphate (Santa Cruz, sc-221476), 13C6-fructose 1,6-bisphosphate (Cambridge Isotope laboratories, CLM-8962), 3-phosphoglycerate (Sigma-Aldrich, P8877) was prepared with pre-equilibrated culture medium right before dissection. Basal culture condition was 0.5 mM glucose at the beginning of this study but was later switched to 2.0 mM glucose which yields a slightly improved reporter gene expression. No major difference was observed in the effects of FBP between these glucose conditions. Following ex vivo culture under 5% CO2, 60% O2 condition, the explants were washed with PBS and were fixed overnight with 4% (v/v) formaldehyde solution (Merck, 1040031000) at 4 °C for further analyses.

Time-lapse imaging of LuVeLu embryos

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Imaging was performed as described before (Lauschke et al., 2013). In brief, samples were excited by 514 nm-wavelength argon laser or 960 nm-wavelength Ti:Sapphire laser (Chameleon-Ultra, Coherent) through 20×Plan-Apochromat objective (numerical aperture 0.8). In some experiments, samples were placed into agar wells (3% low Tm agarose, Biozyme, 840101) with 600 nm-width to restrain tissue movements during imaging. Image processing was done using the Fiji software (Schindelin et al., 2012).

In situ hybridization

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Fixed PSM explants were dehydrated with methanol and were stored at –20 °C until use. Whole mount in situ hybridization was performed as described in Aulehla et al., 2008.

Immunostaining

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Immunostaining with anti-cleaved caspase-3 antibody (Cell Signaling, #9661, RRID:AB_2341188; 1:200 dilution) was performed as described in Bulusu et al., 2017. Goat anti-rabbit-Alexa-488 antibody was used as a secondary antibody (Invitrogen, #A-11034; 1:1000 dilution). Samples were imaged on a LSM780 laser-scanning microscope (Zeiss) using 10×EC Plan-Neofluar objective lens (numerical aperture 0.3).

Gas chromatography-mass spectrometry (GC-MS) analysis

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Wild-type and cytoPFKFB3 transgenic PSM explants with no somite were cultured ex vivo for three hours under different glucose conditions, as described above. After washing twice with ice-cold PBS, the explants were snap frozen by liquid N2, and were stored at –80 °C until use. Metabolites were extracted from the 25 x explants by mechanically dissociating tissues by pipetting in 100 µl ice-cold methanol supplemented with ribitol (5.0 µg/mL) as an internal standard. For metabolite extraction from the conditioned medium, 20 µl of the medium was mixed with 40 µl of ice-cold methanol supplemented with ribitol. After incubation at 72 °C for 15 min, one volume of ice-cold MilliQ water was added, followed by centrifugation at 14,000 rpm at 4 °C for 10 min. The supernatants were transferred to amber glass vials (Agilent, 5183–2073) and were dried by centrifugal evaporator EZ-2 Plus (SP Scientific) (30 °C, Medium Boiling Point). The dried metabolite extracts were derivatized with 40 µL of 20 mg/mL methoxyamine hydrochloride (Alfa Aesar, 593-56-6) solution in pyridine (Sigma-Aldrich, 437611) for 90 min at 37 °C, followed by addition of 80 µL N-methyl-trimethylsilyl-trifluoroacetamide (MSTFA) (Alfa Aesar, 24589-78-4) and 10 hour incubation at room temperature (Kanani and Klapa, 2007; Blasche et al., 2021). GC-MS analysis was performed using a Shimadzu TQ8040 GC-(triple quadrupole) MS system (Shimadzu Corp.) equipped with a 30mx0.25 mm x 0.25 μm ZB-50 capillary column (7HG-G004-11; Phenomenex). One μL of the sample was injected in split mode (split ratio = 1:5) at 250 °C using helium as a carrier gas with a flow rate of 1 mL/min. GC oven temperature was held at 100 °C for 4 min followed by an increase to 320 °C with a rate of 10 °C/min, and a final constant temperature period at 320 °C for 11 min. The interface and the ion source were held at 280°C and 230°C, respectively. The detector was operated both in scanning mode (recording in the range of 50–600 m/z) as well as in MRM mode (for specified metabolites). For peak annotation, the GCMSsolution software (Shimadzu Corp.) was utilized. The metabolite identification was based on an in-house database with analytical standards utilized to define the retention time, the mass spectrum and marker ion fragments for all the quantified metabolites. The metabolite quantification was carried out by integrating the area under the curve of the MRM transition of each metabolite. The data were further normalized to the area under the curve of the MRM transition of ribitol.

Liquid chromatography-mass spectrometry (LC-MS) analysis

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After three-hour culture in the presence of 20 mM 13C6-FBP, PSM explants were washed with cold 154 mM ammonium acetate, snap frozen in liquid N2 and then dissociated in 0.5 mL ice-cold methanol/water/ACN (50:20:30, v/v) containing 0.20 μM of the internal standard lamivudine (Sigma-Aldrich, PHR1365). The resulting suspension was transferred to a reaction tube, mixed vigorously and centrifuged for 2 min at 16,000×g. Supernatants were transferred to a Strata C18-E column (Phenomenex, 8B-S001-DAK) which were previously activated with 1 mL of CH3CN and equilibrated with 1 mL of MeOH/H2O (80:20, v/v). The eluate was dried in a vacuum concentrator. The dried metabolite extracts was dissolved in 50 μL 5 mM NH4OAc in CH3CN/H2O (75:25, v/v), and 3 µL of each sample was applied to an amide-HILIC (2.6 μm, 2.1x100 mm, Thermo Fisher, 16726–012105). Metabolites were separated at 30 °C by LC using a DIONEX Ultimate 3000 UPLC system and the following solvents: solvent A consisting of 5 mM NH4OAc in CH3CN/H2O (5:95, v/v) and solvent B consisting of 5 mM NH4OAc in CH3CN/H2O (95:5, v/v). The LC gradient program was: 98% solvent B for 1 min, followed by a linear decrease to 40% solvent B within 5 min, then maintaining 40% solvent B for 13 min, then returning to 98% solvent B in 1 min and then maintaining 98% solvent B for 5 min for column equilibration before each injection. The flow rate was maintained at 350 μL/min. The eluent was directed to the hESI source of the Q Exactive mass spectrometer (QE-MS; Thermo Fisher Scientific) from 1.85 min to 18.0 min after sample injection. The scan range was set to 69.0–550 m/z with a resolution of 70,000 and polarity switching (negative and positive ionisation). Peaks corresponding to the calculated metabolites masses taken from an in-house metabolite library (MIM +/− H+ ±2 mmU) were integrated using the El-MAVEN software (Melamud et al., 2010). For the targeted quantification of FBP, extraction was performed as stated above with the following exceptions: samples (25 PSM) were dissociated in 0.5 mL ice-cold methanol/water/ACN (50:20:30, v/v) containing 0.25 uM U-13C6 FBP (Cambridge isotope laboratories, CLM-8962). After drying the samples were dissolved in 30 uL 5 mM NH4OAc in CH3CN/H2O (75:25, v/v), and 13 µL of each sample was applied to the amide-HILIC column. The LC gradient program was: 98% solvent B for 2 min, followed by a linear decrease to 30% solvent B within 3 min, then maintaining 30% solvent B for 15 min, then returning to 98% solvent B in 1 min and then maintaining 98% solvent B for 5 min for column equilibration before each injection. The scan range was set to 200–500 m/z with a resolution of 70,000 and only done in negative mode.

Extracellular lactate measurement

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Condition medium was collected following 12 hr ex vivo culture of PSM explants, and was stored at –80 °C until use. Fluorometric lactate measurements were performed with the Lactate Assay Kit (Biovision, K607) following manufacturer’s instructions with a slight modification. The reaction volume was reduced to 50 µl, and 0.5–1.0 µl of the conditioned medium was used for the analysis.

Whole embryo roller-culture and TUNEL staining

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Embryos were collected with the intact yolk sac at E8.5 in DMEM (1.0 g/L glucose, without glutamine and phenol red) (Gibco, 11880–028) supplemented with 2.0 mM glutamine, 10%(v/v) FCS, and 1%(v/v) penicillin/streptomycin (Gibco, 15140–122). The embryos were cultured for 24 hours using the roller bottle culture system in 50% rat serum/DMEM (supplemented with 2.0 mM glutamine and 1% (v/v) penicillin/streptomycin) under 8% CO2, 20% O2, and 72% N2 (flow rate, 20 mL/min) condition (Rivera-Pérez et al., 2010). Following the whole embryo culture, the embryos without the yolk sac and amniotic membrane were fixed with 4% formaldehyde overnight at 4 °C. TUNEL staining was done with In Situ Cell Death Detection Kit (Roche, 12156792910) following manufacturer’s instructions, followed by DAPI (0.5 µg/mL) staining. Images were acquired with a LSM780 laser-scanning microscope (Zeiss) using 10×EC Plan-Neofluar objective lens (numerical aperture 0.3).

Subcellular proteome analysis by mass spectrometry

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PSM explants (without somites) were cultured in desired culture conditions. The explants were washed twice with ice-cold PBS and subjected to subcellular protein extraction using a Subcellular Protein Fractionation for Cultured Cells kit (Thermo Fisher Scientific, #78840). 8–11 x PSMs were used for each condition in each replicate. PSMs were dissociated in 10 µl of CEB buffer per PSM by pipetting, after which 10 µl (i.e. 1×PSM worth) of uncleared lysate was taken as the whole-cell lysate (WCL) sample. The rest of the extraction was carried out following manufacturer’s instructions using buffer amounts scaled according to the number of PSMs in the sample. When using cytoPFKFB3 and control explants, subcellular protein extraction was performed with the following exceptions: After extraction of the MEM fraction, protein from the remaining pellet (constituting the nuclear and cytoskeletal fractions) was extracted with the NEB buffer (with micrococcal nuclease) plus 1×SDS lysis buffer [50 mM HEPES-NaOH (pH 8.5), 1% SDS, 1 x cOmplete protease inhibitor cocktail (Roche, 11873580001)]. The resulting fractions were stored at –80 °C before further processing. Subsequently, CYT and MEM fractions were reduced in volume to ∼50 µl in a speedvac, and each subcellular protein fraction was denatured with 1% SDS at 95 °C for 5 min, after which residual nucleic acids were degraded with benzonase (EMD Millipore, #71206-25KUN; final concentration 0.1–1 U/µl) for 45 min at 37 °C and 300 rpm until samples were no longer viscous.

All samples were prepared for MS using a modified SP3 protocol (Hughes et al., 2014). Briefly, protein samples were precipitated onto Sera-Mag SpeedBeads (GE Healthcare, #45152105050250 and #65152105050250) in the presence of 50% ethanol and 2.5% formic acid (FA) for 15 min at room temperature, followed by four washes with 70% ethanol on magnets. Proteins were digested on beads with trypsin and Lys-C (5 ng/µl final concentration each) in 90 mM HEPES (pH 8.5), 5 mM chloroacetic acid and 1.25 mM TCEP overnight at room temperature shaking at 500 rpm. Peptides were eluted on magnets using 2% DMSO and dried in a speedvac. Dry peptides were reconstituted in 10 µl water and labelled by adding 4 µl TMT label (20 µg/µl in acetonitrile (ACN)) (TMT10plex, Thermo Fisher Scientific #90110, comparison of FBP and F6P treatment; or TMTsixplex, #1861431, comparison of TG to Ctrl) and incubating for one hour at room temperature. Samples were multiplexed as follows: for comparison of FBP and F6P treatment all conditions (FBP, F6P, untreated) of each biological replicate were run in two separate TMT sets: one set including WCL, CYT and NUC fractions, and the other MEM, CHR and SKEL, for a total of six TMT sets for the experiment. For the comparison of Tg and Ctrl, both conditions (Tg, Ctrl) and all replicates were run in a single TMTsixplex experiment for each subcellular fraction (nuclear-cytoskeletal, cytoplasmic, membrane). Labeling was quenched with hydroxylamine (1.1% final concentration), and samples were dried in a speedvac. Each sample was then resuspended using 100 µl LC-MS H2O, and 10% of each sample was taken, pooled to make a full TMT set, and desalted on an OASIS HLB µElution plate (Waters 186001828BA); washing twice with 0.05% FA, eluting with 80% ACN, 0.05% FA, and drying in a speedvac. The resulting sample was run on a 60 min LC-MS/MS gradient (see details below) to estimate relative amounts of protein in each channel. A second TMT set was then pooled using equalized amounts based on the median intensity of each channel from the first run to create an analytical TMT set with approximately equal labelled input protein in each channel. The analytical TMT set peptides were desalted using OASIS as described above and dried in a speedvac. Dried peptides were taken up in 20 mM ammonium formate (pH 10) and prefractionated offline into six (comparison of FBP, F6P to untreated) or 12 (comparison of Tg to Ctrl) fractions on an Ultimate 3000 (Dionex) HPLC using high-pH reversed-phase chromatography (running buffer A: 20 mM ammonium formate pH 10; elution buffer B: ACN) on an X-bridge column (2.1x10 mm, C18, 3.5 µm, Waters). Prefractionated peptides were vacuum dried.

For LC-MS/MS analysis, peptides were reconstituted in 0.1% FA, 4% ACN and analyzed by nanoLC-MS/MS on an Ultimate 3000 RSLC (Thermo Fisher Scientific) connected to a Fusion Lumos Tribrid (Thermo Fisher Scientific) mass spectrometer, using an Acclaim C18 PepMap 100 trapping cartridge (5 µm, 300 µm i.d. x 5 mm, 100 Å) (Thermo Fisher Scientific) and a nanoEase M/Z HSS C18 T3 (100 Å, 1.8 µm, 75 µm x 250 mm) analytical column (Waters). Solvent A: aqueous 0.1% FA; Solvent B: 0.1% FA in ACN (all LC-MS grade solvents are from Thermo Fisher Scientific). Peptides were loaded on the trapping cartridge using solvent A for 3 min with a flow of 30 µl/min. Peptides were separated on the analytical column with a constant flow of 0.3 µl/min applying a 120 min gradient of 2–40% of solvent B in solvent A. Peptides were directly analyzed in positive ion mode with a spray voltage of 2.2 kV and a ion transfer tube temperature of 275 °C. Full scan MS spectra with a mass range of 375–1500 m/z were acquired on the orbitrap using a resolution of 120,000 with a maximum injection time of 50ms. Data-dependent acquisition was used with a maximum cycle time of 3 s. Precursors were isolated on the quadrupole with an intensity threshold of 2e5, charge state filter of 2–7, isolation window of 0.7 m/z. Precursors were fragmented using HCD at 38% collision energy, and MS/MS spectra were acquired on the orbitrap with a resolution of 30 000, maximum injection time of 54ms, normalized AGC target of 200%, with a dynamic exclusion window of 60 s.

The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD029988. Mass spectrometry raw files were processed using IsobarQuant (Franken et al., 2015) and peptide and protein identification was obtained with Mascot 2.5.1 (Matrix Science) using a reference mouse proteome (uniprot Proteome ID: UP000000589, downloaded 14.5.2016) modified to include known common contaminants and reversed protein sequences. Mascot search parameters were: trypsin; max. 2 missed cleavages; peptide tolerance 10 ppm; MS/MS tolerance 0.02 Da; fixed modifications: Carbamidomethyl (C), TMT10plex (K); variable modifications: Acetyl (Protein N-term), Oxidation (M), TMT10plex (N-term). IsobarQuant output data was analyzed on a protein level in R using in-house data analysis pipelines. In brief, protein data was filtered to remove contaminants, proteins with less than 2 unique quantified peptide matches as well as proteins, which were only detected in a single replicate. Subsequently, protein reporter signal sums were normalized within each TMT set using the vsn package (Huber et al., 2002). Significantly changing proteins between the treated and untreated sample were identified by applying a limma analysis (Ritchie et al., 2015) on the vsn-corrected values. Replicates were treated as covariates in the limma analysis for the comparison of FBP to F6P, as biological replicates were run as separate TMT sets. Multiple-testing adjustment of p values was done using the Benjamini-Hochberg method.

Western blot analysis

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PSM explants (without somites) were cultured in desired culture conditions and were subjected to subcellular protein extraction, as described above. Primary antibodies used in the study are as follows: Anti-Aldolase A (Proteintech, 11217–1-AP, RRID:AB_2224626, 1:5000), anti-Tpi (Acris, AP16324PU-N, RRID:AB_1928285, 1:5000), anti-Gapdh (Millipore, MAB374, RRID:AB_2107445, 1:5,000), anti-Pkm1/2 (Cell signaling, 3190, RRID:AB_2163695, 1:5000), anti-Histone H2B (Millipore, 07–371, RRID:AB_310561, 1:10,000), anti-beta-Tubulin (Millipore, 05–661, RRID:AB_309885, 1:10,000), anti-Hsp90 (Cell signaling, 4874, RRID:AB_2121214, 1:1000). Mouse monoclonal antibody against Pfkl was generated by EMBL Monoclonal Antibody Core Facility using full-length Pfkl as an antigen. For protein expression and purification, full-length Pfkl transcript was amplifed by reverse transcription (RT)–PCR using mouse embryo total RNA as a template and cloned into pET28M-SUMO3 vector (EMBL Protein Expression and Purification Core Facility) using AgeI and NotI restriction enzymes. Following primers were used for RT-PCR: forward 5’-TCATCTACCGGTGGAATGGCTACCGTGGACCTGGAGA-3’ and reverse 5’-TCATCTGCGGCCGCTCAGAAACCCTTGTCTATGCTCAAGGT-3’.

Gene expression analysis by NanoString nCounter analysis system

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A custom probe set was designed to include 237 genes involved in glucose metabolism, Notch-, Wnt-, and FGF-signaling pathways. In addition, six positive controls, eight negative controls and housekeeping genes for normalisation (housekeeping genes used: Cltc, Gusb, Hprt1 and Tubb5) were included in the probe set. Following three-hour culture with the specified glucose concentration, the PSM explants were further dissected immediately posterior to the neural tube to isolate the posterior PSM. Five posterior PSM samples were pooled per replicate and snap frozen by liquid N2. Total RNA was isolated using TRIzol reagent (Invitrogen) according to manufacturer’s instructions and concentrated using RNA Clean & Concentrator-5 Kit (Zymo research). RNA was hybridized to the probes at 65 °C, samples were inserted into the nCounter Prep Station for 3 hr, the sample cartridge was transferred to the nCounter Digital Analyzer, and counts were determined for each target molecule. Counts were analysed using nSolver Analysis Software Version 4.0, and sequentially subjected to background correction, positive control (quality control) and normalisation to housekeeping genes.

RNA sequencing analysis

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Libraries for RNA sequencing analysis were prepared following the Smart-seq2 protocol with small modifications (Picelli et al., 2014). Following three hour incubation, tail buds (posterior to the end of neural tube) were isolated from the PSM explants, washed twice with cold PBS (0.01% BSA), and mechanically dissociated in cold PBS (0.01% BSA) in micro wells (ibidi, #80486; one tail bud in 2 µl PBS). Cell suspensions (1.75 µl) were mixed with cell lysis buffer (4.25 µl; 0.02% Triton-X with RNasin), snap frozen by liquid N2, and stored at –80 °C until cDNA synthesis.

cDNAs were synthesized using SuperScript IV Reverse Transcriptase (Thermo Fisher Scientific) and amplified by PCR (9 cycles) with HiFi Kapa Hot start ReadyMix (Kapa Biosystems, KK2601). After clean-up with SPRI beads, concentrations of cDNA (50–9000 bp) samples were determined by the Bioanalyzer (Agilent, High Sensitivity DNA kit). A total of 250 pg cDNAs were then used for tagmentation-based library preparation. Size distribution and concentrations of the libraries were determined by the Bioanalyzer (Agilent, High Sensitivity DNA kit) and the Qubit Fluorometer (dsDNA High Sensitivity Kit), respectively. Twelve multiplexed libraries were sequenced in one lane using NextSeq 500 (Illumina) with 75 bp single-end readings. Sequencing reads were aligned to Mus musculus genome (GRCm38) with the STAR aligner (version 2.7.1 a) (Dobin et al., 2013). For Pfkfb3, reads from wild-type and mutant (cytoPFKFB3) transcripts were counted separately and summed up as Pfkfb3 read counts. Differential gene expression analysis was performed with the DEseq2 package (Love et al., 2014) using gene count tables produced during alignment with GRCm38.101 annotation. Orphan genes with no gene symbol were excluded from the downstream analysis. Gene ontology (GO) term analysis was performed with DAVID. All RNA sequencing data used for this study have been deposited to the European Nucleotide Archive (ENA) under the accession number PRJEB55095.

Statistical analysis

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Statistical analysis was performed with GraphPad PRISM 9 software. For the metabolomics data, statistical analysis was performed with the Statistical Analysis for Microarray (SAM) package (Tusher et al., 2001) using R. For Pearson correlation analysis, numpy (Harris et al., 2020), pandas (McKinney, 2010), and scipy (Virtanen et al., 2020) libraries were used. For data visualization, matplotlib (Hunter, 2007) library was utilized.

Data availability

RNAseq data have been deposited to the European Nucleotide Archive (ENA) under the accession number PRJEB55095. Proteomics data have been deposited to the ProteomeXchange Consortium under the accession number PXD029988.

The following data sets were generated

References

    1. Renfree MB
    2. Hensleigh HC
    3. McLaren A
    (1975)
    Developmental changes in the composition and amount of mouse fetal fluids
    Journal of Embryology and Experimental Morphology 33:435–446.

Decision letter

  1. Joshua M Brickman
    Reviewing Editor; University of Copenhagen, Denmark
  2. Kathryn Song Eng Cheah
    Senior Editor; University of Hong Kong, Hong Kong

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

[Editors' note: this paper was reviewed by Review Commons.]

https://doi.org/10.7554/eLife.83299.sa1

Author response

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The paper by Miyazawa and colleagues addresses a key question: How is changed metabolic activity sensed and to induce changes in developmental programs. In recent years, there is more and more indication that metabolism is not only a dull workhorse synthesizing the building blocks for new cells and providing chemical energy, but that metabolic activity itself has also a regulatory role. How this precisely works is largely unknown and even also unexplored in higher cells. From early insights obtained in microbes, it seems that certain metabolites – possibly reflecting metabolic activity (i.e. flux) – could be metabolic signals that feedback into cellular regulation.

The current paper takes this idea now to developmental processes, where the authors found that the glycolytic metabolite fructose-1,6-bisphosphate is a flux-dependent signal that interferes with developmental processes. This is a very exciting finding, as it indicates that this metabolite not only has a regulatory function in microbes but also in mouse during mesoderm development.

Answering the question how such a flux-dependent metabolite mechanistically interferes with the developmental processes is an enormously difficult. Compared to other mechanistic studies, where deleting genes, modifying genes, and changing protein expressions will usually do the trick, here, perturbing metabolite levels is extremely challenging, particularly if such perturbations need to be carried out in a way that nothing else is perturbed. Researchers, who are not overly familiar with metabolism, usually underestimate the difficulty with targeted and insightful perturbation of metabolism.

To this end, the authors of this paper need to be congratulated for a very well carried out study with very solid data, and excellent control experiments. The authors open up a new path towards understanding how embryo mesoderm development is regulated by metabolic activity. In particular, they show that glycolytic flux, FBP and important developmental phenotypes as well as protein localization changes are linked. As normal with a complex metabolism-based story as this one, there is always more that could be done. Yet, the results are highly important to be reported now such that the field as a whole can build on these interesting results and to explore the exciting path further that has been opened by the authors. Thus, I strongly recommend publishing these findings: The data generated by the authors are accompanied by the required control experiments. The conclusions drawn are very solid. I do not have any major concerns but just a number of minor suggestions that the authors could consider in a revised version of the manuscript.

Minor:

1. At the end of the introduction, the authors stated their original goal. As it is phrased, it is unclear whether this goal has been obtained or not. They might want to consider replacing the last introductory sentence by a sentence stating what the reader can find in this paper.

#1. We agree with the reviewer and have rephrased accordingly (line 90–93):

“In this study, our goal was therefore to first determine in vivo sentinel metabolites during mouse embryo PSM development. We then combined genetic, metabolomic and proteomic approaches to investigate how altered glycolytic flux and metabolite levels impact developmental signaling and patterning processes.”

2. Data from Figure 3: If you plot the lactate secretion vs the FBP levels of the controls and the overexpression experiment, would the control and the overexpression data lie on one line (maybe if combined with the data shown in Figure 1A)?

#2. As the reviewer suggested, it is of great interest to check whether lactate secretion and FBP levels show a similar correlation in control and cytoPfkfb3 embryos, considering that cytoPfkfb3 overexpression lifts the upper limit of glycolytic capacity and FBP levels (revised Figure 3B, 3E). As the reviewer suggested, we plotted FBP levels against lactate secretion and fitted a linear regression line onto control samples (please see Figure 3—figure supplement 1C ). The new plot shows that lactate secretion and FBP levels in cytoPfkfb3 embryos lie on the linear regression line derived from wild-type samples, highlighting that a correlation between lactate secretion and FBP levels is maintained even in cytoPfkfb3 embryos. We now included this new plot in the revised Figure 3—figure supplement 1 and modified the text accordingly (line 326-328):

“In addition, FBP levels showed a linear correlation with lactate secretion in control explants, and such a correlation was maintained even in cytoPfkfb3 explants (Figure 3—figure supplement 1C).”

3. Maybe the authors could attempt an experiment like the following one: Chose the strongest phenotype observed and test a combination of overexpressing cytoPfkfb3 and reducing extracellular glucose level at the same time?

#3. We agree this suggested experiment is important to show that the phenotype in cytoPfkfb3 embryos is indeed dependent on glycolytic flux and have already addressed this specific point in our manuscript, see results in Figure 4B and 5A in our original manuscript. The results show that the phenotypes in cytoPfkfb3 explants, i.e. reduction in somite formation and downregulation of Msgn mRNA expression occur in a glucose dose-dependent manner. Since in this embryonic context, we show that glucose concentration impacts glycolytic flux (see increased lactate production upon glucose titration in Figure 3B), our findings support the conclusion that the effect of cytoPfkfb3 overexpression is flux-dependent and not due to the overexpression per se. Based on the reviewer's feedback, we have modified the text to clarify and highlight this critical point (line 238–242):

“Combined, these results show that cytoPfkfb3 overexpression results in reduced segment formation, arrest of the segmentation clock oscillations and downregulation of Wnt signaling, in a glucose-dose dependent manner. As glucose concentration impacts, in turn, glycolytic flux (Figure 1A, 3B), these findings suggest that these phenotypes are flux-dependent and are not a mere result of cytoPfkfb3 overexpression.”

4. Can the proteomics experiments shown in Figure 6 be repeated with high and low extracellular glucose? High glucose should yield high FBP levels and one would then expect to see the same as with the experiment where at 2 mM glucose 20 mM extracellular FBP were added. Is this the case?

#4. We agree with the reviewer that based on the findings, one would expect the phenotype, i.e. in this case translocation of proteins, to correlate with FBP levels. Two of our results are of note in this regard.

First, our data indicates that in order to see the effect on protein localization, high levels of FBP have to be reached. Accordingly, we find that Pfkl becomes depleted from the nuclear-cytoskeletal fraction in cytoPfkfb3 explants when cultured in 10 mM glucose but not (visibly) in 2.0 mM glucose (Figure 7D). Corresponding to this, FBP levels in cytoPfkfb3 explants show a significant increase (about 3-fold) from 2.0 to 10 mM glucose conditions (revised Figure 3E).

Second, in control samples, FBP levels saturate in high glucose conditions. FBP levels in control samples do not further increase when glucose concentration is increased from 10mM to 25mM, and thus it does not become as high as in cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E).

Therefore, in order to reveal the translocation, it requires an experimental strategy that leads to significantly increased FBP levels, such as in cytoPfkfb3 explants with high glucose condition, or alternatively, direct supplementation of FBP.

As also pointed out by the other reviewers, we are experimentally generating controlled conditions that exceed the physiological range which the embryo is exposed to. Accordingly, our data does not constitute evidence that under physiological conditions an alteration of protein localization in response to change in glycolytic flux and FBP levels occurs, at a smaller scale.

We regard our approach as a first step to reveal potential mechanisms and so far hidden possible responses to changes in metabolic flux. In order to see minor changes in translocation upon small changes in glycolytic-flux/FBP levels, more quantitative approaches, such as live-imaging of tagged proteins, will need to be developed. We hence decided to include these discussions in our revised manuscript (line 446-452):

“Of note, the translocation of proteins was observed only when high levels of FBP were reached upon direct FBP supplementation or cytoPfkfb3 overexpression with high glucose (Figure 6, 7). Future studies hence need to investigate whether flux-dependent change in protein localization occurs upon moderate and more physiological changes in glycolytic-flux/FBP levels. To this end, the development of more quantitative approaches, such as live-imaging of tagged enzymes and the development of metabolite biosensors, are needed.”

5. While the authors quantified proteins in different compartments, I was wondering whether they also looked for whole-embryo protein expression changes?

#5. We have not done protein expression analysis using whole embryos, or other isolated tissues in this study. This is indeed a potentially interesting future experimental comparison.

6. Throughout the manuscript, the authors state the glucose levels or cytoPfkfb3 changes the glycolytic flux. While I tend to agree with this, it is important to note that the authors have not directly measured glycolytic flux, but use the amount of accumulated lactate as a proxy. I think it is important to add this disclaimer at important points in the manuscript, such that readers are aware of this point.

#6. We fully agree with the reviewer and now have added the following sentence in the first result section to make this point clearer to the reader (line 99-100):

"Throughout this study, we used quantification of secreted lactate as a proxy for glycolytic flux due to the inability to directly measure flux in embryonic tissues."

7. Another aspect for changing FBP levels could be connected on what was found in yeast, where the FBP levels were found to oscillate with the cell cycle (https://pubmed.ncbi.nlm.nih.gov/31885198/). Could this be connected with the pattern formation here?

#7. This is indeed an interesting aspect to discuss; in the absence of experimental evidence connecting the observed pattern formation and cell cycle (though some classic work had suggested its existence) we have decided to omit the discussion of this potential link.

8. Line 606: The mentioned review article also covers yeast. As such, maybe the authors should replace the term "bacteria" with "microbes"?

#8. We modified our manuscript accordingly.

Reviewer #1 (Significance (Required)):

**Referees cross-commenting**

As I mentioned in my comment, targeted metabolic perturbations are extremely difficult. Perturbing a metabolite level without at the same time perturbing the flux through this pathways is difficult (of not impossible). Also, the opposite is the case.

I am not sure whether experiments as the one suggested by reviewer 2 (comment 1) will really lead to results from which further conclusions can be drawn. Furthermore, there does not need to be a linear correlation between the extracellular glucose concentration and metabolic flux/FBP levels (as my reviewer colleague implies). Thus, I am not sure whether doing this experiment makes sense, or would lead to strengthened conclusions.

Reviewer 2 also states "The lack of proven mechanism for the activity of FBP might restrict the real general impact of this work." I agree that we do not know the downstream targets of FBP, but finding them would likely require many years of additional work. Such work will not be initiated if this paper is not published, and it would be a pity if it would be further delayed. I feel that the evidence is strong enough that FBP has an important role and with this paper published, it will motivate others to look for the downstream targets.

Reviewer 3 makes the point: "Given that FBP levels are highly correlated with extracellular glucose levels (which impact glycolytic flux )(TeSlaa and Teitell, 2014) the authors should elaborate on why progressive increase in extracellular glucose does not affect PSM patterning, in the same way that increasing FBP levels does. " Here, I feel my reviewer colleague might be overlooking that in biochemistry molecular interactions typically reach a saturation at some point. The correlation between extracellular glucose and glycolytic flux has likely only a range where these two measures linearly correlate. Similarily, the correlation between glycolytic flxu and FBP likely also exists only within a certain range, and finally FBP levels and the downstream targets likely also only linearly interact within bounds. Thus, the absence of a correlation at "extremes" does by no mean mean that what the authors propose is incorrect. In fact, it just shows what you expect from biomolecular interactions that there a limits to linear correlations.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary.

The work described in this paper first searches for potential sentinel metabolites of glycolytic flux, focusing on the process of somitogenesis during mouse embryonic development. By measuring the levels of different metabolites in the presomitic mesoderm (PSM) of E10.5 mouse embryos cultured in the presence of three different glucose concentrations, the authors identify 14 metabolites whose concentration rises with increasing glucose concentration in the culture medium. Among them, they selected fructose 1,6-bisphosphate (FBP) for further analyses, as it showed the highest linear correlation with extracellular glucose concentrations. They then show that addition of FBP to the incubation medium of cultured embryo tails interfere with somitogenesis and tail extension in a concentration-dependent fashion. In addition, they show that this effect is exacerbated when extracellular glucose levels are increased. By analyzing specific targets of Wnt and FGF signaling, the authors also show that addition of FBP down-regulates both signaling pathways in the PSM. They then use a genetic trick (ubiquitous overexpression of cytoPfkfb3) to increase FBP levels by allosteric activation of Pfk (the enzyme that produces FBP) in developing embryos. When tails from these transgenic embryos were cultured in vitro and exposed to various glucose concentrations somitogenesis was affected in a way resembling the effects of FBP on cultured tails from wild type embryos. The authors then go on to determine the subcellular localization of different proteins in tails incubated in the presence of various FBP concentrations to identify that some enzymes involved in the glycolytic pathway (and they specifically focus on Pfkl and Aldoa) are excluded from nuclear fractions at high FBP concentrations. The authors conclude that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization.

Major comments

I think that in general the work described in this manuscript has been performed to the highest technical standards. However, I do not think that I can agree with the authors' conclusions (that FBP connects glycolysis with PSM patterning and that subcellular localization of glycolytic enzymes play a role in this process), which in my opinion go way beyond what can be proven by the data provided.

1. Explants incubated with external glucose concentrations up to 25 mM have no obvious defects on somitogenesis or on the segmentation clock as determined by LuVeLu cycling activity. Under these conditions, explants are expected to contain very high FBP levels if this metabolite keeps its linear relationship with external glucose (in this work it was not measured beyond 10 mM glucose in the medium, where FBP concentration was already very high). This contrasts with the phenotypes observed upon exogenous supplementation of FBP, which affects somitogenesis already at 2 mM glucose. These latter results are at odds not only with the lack of phenotypic alterations under high glucose conditions, but also with the observation that exogenous addition of fructose 6-phosphate (F6P), the substrate of Pfk enzymes to generate FBP, does not alter somitogenesis. The authors take the absence of effects by incubation with F6P as a control of the specificity of FBP. However, as F6P is the natural substrate of Pfk, it is possible that supplementation of F6P also leads to an increase of FBP but in a way closer to a physiological condition. Therefore, I find it essential to determine FBP levels in tails incubated in the presence of increasing amounts of F6P, as if it increases FBP levels, similarly to what the authors described for the tails incubated with increasing glucose concentrations, it will have important implications to the interpretation of the work presented in this manuscript.

#9. We agree with the reviewer and to directly address this central point, we have performed an extended, additional experiment, collecting 375 embryos to quantify FBP levels under five conditions with three biological replicates.

There are two major results that we highlight here: First, we found that addition of F6P did not lead to increased FBP levels compared to control samples cultured in 10 mM glucose, which is in stark contrast to cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E). Second, while increasing glucose concentration is mirrored by elevated FBP levels as we reported, we find clear evidence of saturation above a concentration of 10mM glucose: increasing glucose to 25mM does not increase FBP levels further (revised Figure 3E).

This saturation effect seen in glucose titration, but also the absence of elevated FBP upon F6P addition, might be expected outcomes because, as also the reviewer 1 pointed out in the response, Pfk is commonly considered to be a rate-limiting enzyme in the glycolytic pathway. We now have the direct experimental data supporting this hypothesis and thank the reviewers to have initiated this additional (very involved) experiment.

This new data allows us to conclude more firmly on the correlation between FBP levels and phenotype: at high FBP levels, which are seen in cytoPfkfb3 samples, we observe PSM patterning defects. These high levels are not reached even at 25mM glucose or upon F6P addition, due to the saturation at the level of PFK enzymatic step. Hence, while glucose titration does elevate FBP significantly until this saturation, FBP levels are not as high as in cytoPfkfb3 samples. As a correlative finding, we see that only those conditions with very high FBP levels, or the direct addition of high levels of FBP, cause the arrest of segmentation clock activity. At moderately elevated FBP levels, observed in control explants with high glucose or in cytoPfkfb3 explants with low glucose, clock activity continues and we find a quantitative effect at the level of gene expression, i.e. Wnt signaling target downregulation (Figure 2—figure supplement 2, Figure 5A).

The new data has been included in the revised manuscript and the text has been adjusted accordingly:

– (Result Part, line 178–184) "Consistently, we found that cytoPfkfb3 overexpression lifted the upper limit of FBP levels in PSM cells (Figure 3E, Figure 3—figure supplement 1B, Figure 3—figure supplement 1C). In control explants, FBP levels did not increase further when glucose concentration was increased from 10 mM to 25 mM. It was also the case when control explants were cultured in 20 mM of F6P (Figure 3E). These results indicate that the Pfk reaction carries a (rate-)limiting role for glycolytic flux and FBP levels, and that cytoPfkfb3 overexpression hinders the flux-regulation function of Pfk."

– (Discussion Part, line 378–391) “Our findings suggest that flux-regulation at the level of Pfk is critical to keep FBP steady state levels within a range compatible with proper PSM patterning and segmentation. In agreement with such a rate-limiting function for Pfk, we found in glucose titration experiments that FBP levels saturated and did not further increase at glucose levels above 10 mM (Figure 3E). Along similar lines, the supplementation of high concentrations of the Pfk substrate F6P did not result in a significant increase of FBP levels, again compatible with a rate-limiting function at the level of Pfk (Figure 3E). The upper limit of glycolytic flux and FBP levels can be experimentally increased by cytoPfkfb3 overexpression (Figure 3B, 3E). We interpret the data as evidence that cytoPfkfb3 overexpression compromises the flux-control function of Pfk and hence much higher FBP (and secreted lactate) levels are reached. Such a drastic increase in glycolytic flux and FBP levels correlates with a severe PSM patterning phenotype (Figure 4), which resembles the phenotype induced by supplementation of high dose of FBP (Figure 2). Our results in mouse embryos hence provides evidence that flux regulation by Pfk, an evolutionary conserved role present from bacteria to humans, serves to maintain FBP levels below a critical threshold.”

The main difference between the experiments involving FBP supplementation and those involving high glucose concentrations or exogenous F6P addition is that in the later two cases increase in FBP would be restricted to the tissue(s) expressing Pfk, whereas upon FBP supplementation this metabolite would hit any tissue, regardless of whether or not it would ever be physiologically exposed to this molecule. In the case of the PSM, this might be relevant because it has been shown that there is a gradient of glycolysis, being high at the caudal tip and becoming lower at more anterior regions of the PSM, most likely mirroring the distribution of Pfk activity. Exogenous administration of FBP would flatten the gradient, which could lead to alterations in PSM patterning, whereas glucose (and eventually F6P) would not as they would increase FBP locally in the area where it is normally activated, keeping the natural gradient.

On the basis of these arguments, to which extent does FBP connect glycolysis and somitogenesis under physiological conditions?

#10. First, we would like to clarify that while indeed glycolytic activity is graded along the PSM, as other and we reported previously (reported in Bulusu et al., 2017 and Oginuma et al., 2017), the baseline expression of the entire glycolytic machinery (from glucose transport to lactate production) is very high, in all PSM cells. Hence, we see that cells all along the entire PSM have very active glycolysis, the posterior PSM being even more active.

For this and related reasons, our interpretation about the difference seen between glucose titration/F6P addition on one side, and FBP addition/cytoPfkfb3 addition on the other side, is based on the role of Pfk in controlling either flux levels or dynamics in all PSM cells.

Hence, while we agree that we generate experimental conditions that allow FBP levels to surpass those found in control embryos, we would like to highlight the fact that even moderate changes in flux does result in very robust functional consequences on gene expression (Figure 2—figure supplement 2, Figure 5), as we show in this work.

We can currently not fully address the first point raised, i.e. the role of graded flux/graded metabolite levels, due to the experimental limitations. Such a study requires, for instance, the generation of metabolite biosensor reporter lines in order to be able to monitor these changes dynamically, in space and time.

Essential additional experiment related to point #1: Measure FBP from PSM explants incubated under various exogenous concentrations of F6P.

#11. We have performed this suggested experiment, which required the collection of n=375 embryos cultured under the various conditions and analysis by LC-MS to quantify metabolites. The outcome was indeed very informative (please refer to our response #9).

Another experiment that could be informative: measure FBP levels in PSM incubated under different glucose concentrations but instead of using the whole PSM together, dividing the PSM in posterior, medium and anterior parts (similarly to what was done in Oginuma et al., 2017, reference in the manuscript) to see if there is a gradient in FBP activation.

#12. While in principle we agree that this experiment could be informative, we consider the proposed experiment beyond the scope of this work and technically very challenging (although possible). With a similar motivation, the development of metabolite biosensors is an alternative route that we are pursuing for future studies (for the detail, please refer to our response #10).

2. A similar argument could be presented for the results with the cytoPfkfb3 transgenics, as they are based on global artificial overactivation of Pfk, in addition to other possible effects of the ectopic activity of cytoPfkfb3, which were not controlled. Also, while the phenotypic alterations in the PSM in vitro, most particularly in the experiments involving incubation of the tails, are rather strong, the reported effects on somitogenesis in vivo are minor, also questioning the contribution of the in vitro conditions to the final phenotypic effects observed throughout the manuscript.

#13. First of all, we would like to emphasize that the phenotype seen in cytoPfkfb3 embryos, i.e. the reduction of segmentation and downregulation of Wnt-target gene expression, occurs in a glucose dose dependent manner (Figure 4B and 5A). Hence, it is not the overexpression of cytoPfkfb3 per se that can account for the effects seen. But rather, increased glycolytic flux caused by the combination of transgene expression with high glucose results in functional consequences.

In addition, ‘other possible effects’ that the reviewer is referring to should be evident in all transgenic embryos, irrespective of glucose dose. To the contrary, transgenic embryos cultured in low glucose conditions appear unaltered to control embryos.

Second, we agree that we need to distinguish between strong phenotypes, visible at the level of clock arrest, and milder phenotypes, visible at the level of quantitative gene expression changes. It is important to note that the moderate phenotype, i.e. the quantitative gene expression changes seen in posterior PSM, are seen upon the addition of FBP at moderate levels and upon in glucose titration within the physiological concentration range, as well as in cytoPfkfb3 embryos. We take this as evidence that the effects seen in cytoPfkfb3 transgenic embryos reflect a common response also seen under physiological conditions.

To extend this argument to the in vivo setting, we have performed additional experiments using a genetic mouse model for diabetes. As shown in our previous submission, cytoPfkfb3 transgenic animals do not exhibit a drastic in vivo phenotype when dissected at embryonic day 10.5. One interpretation of this finding is that since the cytoPfkfb3 phenotype is glucose and flux-dependent, the in vivo flux is low, reflecting low glucose concentrations described in vivo. To test the effect of increased flux in cytoPfkfb3 embryos in vivo, we therefore crossed the transgenic mice into a diabetic model called Akita, in which a point mutation in the Insulin2 gene causes high maternal glucose levels (Yoshioka et al., 1997; Wang et al., 1999). Using this experimental setup, we tested whether transgenic embryos in Akita diabetic females would manifest in vivo phenotypes.

Indeed, we found that cytoPfkfb3 transgenic embryos developing in Akita diabetic females showed significantly increased cases of neural tube closure defects (50% of cytoPfkfb3 embryos) and developmental delay (control: 38 somites vs. cytoPfkfb3: 34 somites at E10.5), defects not seen in transgenic cytoPfkfb3 embryos from control females (please refer to Figure 4—figure supplement 1D-E). This dependency of the in vivo phenotype on maternal glucose conditions again highlights that the defects observed in cytoPfkfb3 embryos are not due to the expression of cytoPfkfb3 per se, but are rather directly linked to increased/unregulated glycolytic flux.

We included the new in vivo data in the revised Figure 4—figure supplement 1D-E and modified the text accordingly.

In conclusion, combining the arguments in the two previous comments, to which extent the results from the addition of FBP or from the transgenic activation of Pfk are not artefactual phenotypes without real physiological relevance?

#14. In our view, two main conclusions, both in vivo and in vitro, can be drawn based on the result we obtained:

First, we find that a moderate increase in glycolytic flux, within the physiological range, leads to a quantitative and consistent change in gene expression, such as downregulation of Wnt target genes (Figure 2—figure supplement 2, Figure 5). Such a phenotype was the result of either glucose titration or culturing cytoPfkfb3-transgenic embryos in low glucose concentration.

In these conditions, while overall PSM patterning is qualitatively normal, we do find consistent changes at quantitative level, i.e. gene expression changes, which are also mirrored by a reduced rate of segmentation (Figure 4B). A detailed analysis of the quantitative changes at the level of segmentation clock dynamics is being carried out and will be presented in a dedicated follow up study.

Second, we find that a very significant increase in FBP levels, i.e. when cytoPfkfb3 transgenic animals are cultured in high glucose conditions or when samples are cultured in high levels of FBP, PSM patterning is qualitatively altered and segmentation clock ceases to oscillate. In this case, we agree that it is not a physiological condition, as such high levels of flux and FBP are not reached in control samples which have intact flux regulation by Pfk. Nevertheless, such an experimental condition can be insightful, as it very clearly reveals the potential link between glycolysis, clock activity, PSM patterning and the Wnt signaling pathway.

It is the combination between the moderate and the more severe effects, observed both in vitro, and now also in vivo using the Akita model (see above), that we take as evidence for an intrinsic, physiological link between glycolytic activity, PSM patterning and signaling.

3. The authors seem to give a strong functional meaning to the absence of Pfkl and Aldoa from the nuclear fraction in tails incubated with exogenous FBP, suggesting a "moonlighting" function of these enzymes under FBP regulation. In addition to the purely speculative nature of this interpretation (there is no proof for such activity or even an attempt to test it), the data provided is also difficult to interpret for various reasons.

#15. We fully agree that we do not show a functional role for either the nuclear localization of enzymes or their dynamic change in sub-cellular localization and have tried to express this clearly in the original manuscript:

– (Result Part, line 382-388) “While we have not been able to address the functional consequence of specific changes in subcellular localization, such as the nuclear depletion of Pfkl or Aldoa when glycolytic flux is increased, these results pave the way for future investigations on the mechanistic underpinning of how metabolic state is linked to cellular signaling and functions.”

– (Discussion Part, line 575-577): “While future studies will need to reveal if nuclear localization of glycolytic enzymes is linked to their moonlighting functions or metabolic compartmentalization…”

Based on this comment by the reviewer, we have further emphasised this point in the revised manuscript (line 430-433):

“While we do not have any direct functional evidence so far for a functional role of nuclear localized glycolytic enzymes, our findings do raise the question whether their subcellular compartmentalization is linked to a non-metabolic, moonlighting function.”

The protein levels in nuclear fractions are clearly much lower than those in the cytoplasm (this is best seen in the blots of Figure 6D). Does this represent similar subcellular distribution of these enzymes throughout the tissue or the different levels result from the presence of the enzymes in the nucleus of only a subset of the cells? This might be of importance to understand the possible relevance of the subcellular distribution of those enzymes. All the analyses were done on bulk tissue and, therefore, it is not possible to distinguishing between these possibilities. As the authors have antibodies for these enzymes, they could try to perform immunofluorescence analyses, which would provide spatial data.

#16: We agree that a spatially resolved analysis of the subcellular localization of these various enzymes is needed. Unfortunately, the immunofluorescence experiments that we performed did not yield clear, reliable results and hence we can’t provide the answer at this time.

In addition to this, it would be important to determine Pfkl and Aldoa subcellular localization in explants incubated with different external concentrations of glucose, which in a way reproduces better possible physiological effects (see point 1), to see if under those conditions high FBP also affects subcellular distribution of those enzymes.

#17: Please find our response under #4 (below), as this important point was also raised by the reviewer 1.

(Our response #4)

#4. We agree with the reviewer that based on the findings, one would expect the phenotype, i.e. in this case translocation of proteins, to correlate with FBP levels. Two of our results are of note in this regard.

First, our data indicates that in order to see the effect on protein localization, high levels of FBP have to be reached. Accordingly, we find that Pfkl becomes depleted from the nuclear-cytoskeletal fraction in cytoPfkfb3 explants when cultured in 10 mM glucose but not (visibly) in 2.0 mM glucose (Figure 7D). Corresponding to this, FBP levels in cytoPfkfb3 explants show a significant increase (about 3-fold) from 2.0 to 10 mM glucose conditions (revised Figure 3E).

Second, in control samples, FBP levels saturate in high glucose conditions. FBP levels in control samples do not further increase when glucose concentration is increased from 10mM to 25mM, and thus it does not become as high as in cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E).

Therefore, in order to reveal the translocation, it requires an experimental strategy that leads to significantly increased FBP levels, such as in cytoPfkfb3 explants with high glucose condition, or alternatively, direct supplementation of FBP.

As also pointed out by the other reviewers, we are experimentally generating controlled conditions that exceed the physiological range which the embryo is exposed to. Accordingly, our data does not constitute evidence that under physiological conditions an alteration of protein localization in response to change in glycolytic flux and FBP levels occurs, at a smaller scale.

We regard our approach as a first step to reveal potential mechanisms and so far hidden possible responses to changes in metabolic flux. In order to see minor changes in translocation upon small changes in glycolytic-flux/FBP levels, more quantitative approaches, such as live-imaging of tagged proteins, will need to be developed. We hence decided to include these discussion in our revised manuscript (line 446-452):

“Of note, the translocation of proteins was observed only when high levels of FBP were reached upon direct FBP supplementation or cytoPfkfb3 overexpression with high glucose (Figure 6, 7). Future studies hence need to investigate whether flux-dependent change in protein localization occurs upon moderate and more physiological changes in glycolytic-flux/FBP levels. To this end, the development of more quantitative approaches, such as live-imaging of tagged enzymes and the development of metabolite biosensors, are needed.”

Suggested additional experiments related to point #3:

3a. Analysis of subcellular localization of Pfkl and Aldoa by Immunofluorescence. This analysis is not limited by the amount of biological material available, so it could be applied to different experimental conditions.

#18. We addressed this point in our response #15.

3b. Subcellular distribution of Pfkl and Aldoa in explants exposed to different exogenous glucose concentrations. As this involves wild type embryos, it can be done following similar protocols as in figures 6 and 7 of the manuscript.

#19. We addressed this point in our response #16.

4. The results from the work presented in this manuscript would indirectly indicate a negative relationship between glycolysis and somitogenesis. This contrasts with previous reports indicating the essential role of aerobic glycolysis for the same process. There is no explanation for this apparent (and important) contradiction (the authors only comment the discrepancy between the data provided in this paper and previous reports in what concerns the relationship between glycolysis and Wnt signalling, although they also do not provide an explanation).

#19. We cannot resolve this discrepancy, but now offer a more detailed discussion, also based on the additional data we obtained.

First, it is important to point out that we have performed additional experiments to substantiate this part of the work, i.e. a transcriptome analysis with control and cytoPfkfb3 explants cultured in 10 mM glucose. We decided to focus on an early time point, i.e. three-hour after incubation, in order to increase the chance to score the primary response of PSM cells upon changes in glycolytic flux. In addition, our nanostring data in Figure 2—figure supplement 2 shows that glucose titration can change the expression levels of some Wnt-targets in both directions, i.e. decreasing glucose upregulates their expressions while increasing glucose downregulates their expressions. Again, this analysis was done at short time-scales to score the immediate effect.

One possible explanation regarding the difference to Oginuma et al. could indeed be the late time point of analysis in their study, i.e. 16-hour after culture. This difference in sampling time, i.e. 3-hour vs. 16-hour after culture, is of particular importance given the dynamic nature of metabolic and signaling responses.

We have added a sentence to explain this point in more detail (line 414-419):

“This discrepancy could relate to the time point of analysis: while Oginuma et al. mainly focused on analyzing samples 16-hour after metabolic changes, we chose to score the effects of altered glycolytic flux/FBP levels already after a three-hour incubation, with the goal to capture the primary response of PSM cells. Whether the difference in sampling time underlies the observed difference is yet unknown, but both studies highlight that Wnt signaling is responsive to glycolytic flux, supporting a tight link between metabolism and PSM development.”

Minor comments.

It was not specified the tissue used for the Western blot analyses (was it the PSM alone, the whole tails including somites, etc). This is of relevance to comment #3.

#20. PSM explants without somites were cultured for one/three-hour and were subjected to subcellular protein fractionation. This information is now included in the revised method section.

Reviewer #2 (Significance (Required)):

– The work described in this manuscript identifies FBP as a sentinel metabolite for the glycolytic flux. This, itself has the potential to be important for different processes in which differences in glycolysis makes a difference, although I do not think that this will be relevant for the developmental process on which the authors focused their study (see major comments #1 and 2). Indeed, the lethality of global transgenic cytoPfkfb3 expression (although it was not analyzed if it was during development of in postnatal stages, or the cause of this lethality) but with very minor effects on somitogenesis in vivo supports this conclusion.

#21. Please see our detailed comments also based on the newly added in vivo experiments done with the Akita diabetic mouse model in our responses #9–14.

– The potential moonlighting activity of Pfk (connected with specific subcellular localization), is an interesting idea but so far does not go beyond pure speculation. This is prone to the typical double edged effect of stimulating research in that direction but also the potential negative effect of being taken for granted without rigorous proof.

#22: We have added a statement to highlight the nature of this finding and the requirement for follow up studies both in this and other contexts. Please refer to our response #15 for the details.

– The importance of metabolism in general and glycolysis in particular for somitogenesis and axial extension has been recently reported (the relevant papers are cited in the manuscript) and therefore the work described in this manuscript extends those studies. Also, the recent observations that metabolic process can influence cell activity beyond their participation on the classical pathways in which they are involved, including processes apparently as distant as epigenetic regulation of gene activity (see for instance Tarazona and Pourquie, 2020, Dev Cell 54, 282-292), is opening new perspectives to the study of the influence of metabolism on physiological and pathological processes (championed by cancer and immunological response). It also provides a link between control mechanisms across large scale phylogeny, from procaryotes to eukaryotes.

– In principle, the potential audience for this work could be wide, as the interest in understanding the involvement of metabolism in the regulation of physiological and pathological processes has been growing over the last years. However, the lack of proven mechanism for the activity of FBP might restrict the real general impact of this work. In this regard, the suggestion that it might control some type of still unknown moonlighting activity of Pfk is so far totally speculative.

– I am a developmental biologist with strong focus on mechanisms of somitogenesis and axial extension in vertebrate embryos. There is no part of this work for which I do not feel competent to evaluate.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary

In the present manuscript, Miyazawa and colleagues explore the role of glycolytic flux on embryonic development by using presomitic mesoderm (PSM) patterning as a model.

First, the authors examined the steady-state levels of central carbon metabolism metabolites in PSM explants. Explants were cultured in various concentrations of glucose and subjected to gas chromatography mass spectrometry (GC-MS). These experiments allowed the identification of metabolites (such as lactate, 3PG, and FBP) that exhibit a linear correlation with glucose levels and can therefore serve as sentinel metabolites for glycolytic flux in PSM cells. Among the metabolites identified, fructose 1,6-bisphosphate (FBP) showed the strongest linear correlation with glucose levels and was used to inform the design of subsequent experiments.

Second, to elucidate the functional role of FBP on PSM patterning, the authors supplement the media used to culture PSM explants with various concentrations of FBP and:

- analyze the dynamics of Notch signaling (a critical player in mesoderm segmentation during embryogenesis) using real-time imaging of the LuVeLu reporter;

- assess gene expression patterns using in situ hybridization of candidate genes.

The authors find that supplementation with FBP, but not F6P or 3PG, impairs mesoderm segmentation and disrupts the activity of the segmentation clock in the posterior PSM. Furthermore, FBP supplementation led to the reduced expression of FGF- and WNT-target genes Dusp4 and Msgn, respectively.

Third, the authors generate a conditional cytoPfkfb3 transgenic mouse line in which a cytoplasmic form of the Pfkfb3 enzyme is overexpressed. Pfkfb3 can promote glycolysis, and more importantly, leads to increased levels of FBP in a glucose-dependent manner. The authors find that cytoPfkfb3 transgenic PSM explants contain higher levels of FBP and secrete lactate at higher levels when compared to control explants. Importantly, cytoPfkfb3 transgenic PSM explants exhibit impaired somite formation and reduced expression of Msgn (but not Dusp4) in a glucose-dependent manner when compared to control explants.

Finally, the authors investigate changes in protein subcellular localization in their pharmacological and genetic models of FBP-driven glycolytic flux activation. This was prompted by previous reports on the changes in subcellular localization of glycolytic enzymes (Hu et al., 2016). To this end, the authors perform proteome-wide cell-fractionation analyses in drug-treated and cytoPfkfb3 transgenic PSM explants and find that certain glycolytic proteins exhibit altered subcellular localization in both cases (albeit in different fractions).

Major concerns:

(Re: Results from Figure 2 and Figure S1.)

– Given that FBP levels are highly correlated with extracellular glucose levels (which impact glycolytic flux )(TeSlaa and Teitell, 2014) the authors should elaborate on why progressive increase in extracellular glucose does not affect PSM patterning, in the same way that increasing FBP levels does. This is especially important given the claim that FBP is a sentinel metabolite of glycolytic flux.

#23. This important point was also addressed by the reviewer 2, so please see our responses that are also listed under #9, #10, #14 (below).

(Our response #9)

We agree with the reviewer and to directly address this central point, we have performed an extended, additional experiment, collecting 375 embryos to quantify FBP levels under five conditions with three biological replicates.

There are two major results that we highlight here: First, we found that addition of F6P did not lead to increased FBP levels compared to control samples cultured in 10 mM glucose, which is in stark contrast to cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E). Second, while increasing glucose concentration is mirrored by elevated FBP levels as we reported, we find clear evidence of saturation above a concentration of 10mM glucose: increasing glucose to 25mM does not increase FBP levels further (revised Figure 3E).

This saturation effect seen in glucose titration, but also the absence of elevated FBP upon F6P addition, might be expected outcomes because, as also the reviewer 1 pointed out in the response, Pfk is commonly considered to be a rate-limiting enzyme in the glycolytic pathway. We now have the direct experimental data supporting this hypothesis and thank the reviewers to have initiated this additional (very involved..) experiment.

This new data allows us to conclude more firmly on the correlation between FBP levels and phenotype: at high FBP levels, which are seen in cytoPfkfb3 samples, we observe PSM patterning defects. These high levels are not reached even at 25mM glucose or upon F6P addition, due to the saturation at the level of PFK enzymatic step. Hence, while glucose titration does elevate FBP significantly until this saturation, FBP levels are not as high as in cytoPfkfb3 samples. As a correlative finding, we see that only those conditions with very high FBP levels, or the direct addition of high levels of FBP, cause the arrest of segmentation clock activity. At moderately elevated FBP levels, observed in control explants with high glucose or in cytoPfkfb3 explants with low glucose, clock activity continues and we find a quantitative effect at the level of gene expression, i.e. Wnt signaling target downregulation (Figure 5A, Figure 2—figure supplement 2).

The new data has been included in the revised manuscript and the text has been adjusted accordingly:

– (Result Part, line 178–184) "Consistently, we found that cytoPfkfb3 overexpression lifted the upper limit of FBP levels in PSM cells (Figure 3E, Figure 3—figure supplement 1B, Figure 3—figure supplement 1C). In control explants, FBP levels did not increase further when glucose concentration was increased from 10 mM to 25 mM. It was also the case when control explants were cultured in 20 mM of F6P (Figure 3E). These results indicate that the Pfk reaction carries a (rate-)limiting role for glycolytic flux and FBP levels, and that cytoPfkfb3 overexpression hinders the flux-regulation function of Pfk."

– (Discussion Part, line 378–391) “Our findings suggest that flux-regulation at the level of Pfk is critical to keep FBP steady state levels within a range compatible with proper PSM patterning and segmentation. In agreement with such a rate-limiting function for Pfk, we found in glucose titration experiments that FBP levels saturated and did not further increase at glucose levels above 10 mM (Figure 3E). Along similar lines, the supplementation of high concentrations of the Pfk substrate F6P did not result in a significant increase of FBP levels, again compatible with a rate-limiting function at the level of Pfk (Figure 3E). The upper limit of glycolytic flux and FBP levels can be experimentally increased by cytoPfkfb3 overexpression (Figure 3B, 3E). We interpret the data as evidence that cytoPfkfb3 overexpression compromises the flux-control function of Pfk and hence much higher FBP (and secreted lactate) levels are reached. Such a drastic increase in glycolytic flux and FBP levels correlates with a severe PSM patterning phenotype (Figure 4), which resembles the phenotype induced by supplementation of high dose of FBP (Figure 2). Our results in mouse embryos hence provides evidence that flux regulation by Pfk, an evolutionary conserved role present from bacteria to humans, serves to maintain FBP levels below a critical threshold.”

(Our response #10)

#10. First, we would like to clarify that while indeed glycolytic activity is graded along the PSM, as other and we reported previously (reported in Bulusu et al., 2017 and Oginuma et al., 2017), the baseline expression of the entire glycolytic machinery (from glucose transport to lactate production) is very high, in all PSM cells. Hence, we see that cells all along the entire PSM have very active glycolysis, the posterior PSM being even more active.

For this and related reasons, our interpretation about the difference seen between glucose titration/F6P addition on one side, and FBP addition/cytoPfkfb3 addition on the other side, is based on the role of Pfk in controlling either flux levels or dynamics in all PSM cells.

Hence, while we agree that we generate experimental conditions that allow FBP levels to surpass those found in control embryos, we would like to highlight the fact that even moderate changes in flux does result in very robust functional consequences on gene expression (Figure 2—figure supplement 2, Figure 5), as we show in this work.

We can currently not fully address the first point raised, i.e. the role of graded flux/graded metabolite levels, due to the experimental limitations. Such a study requires, for instance, the generation of metabolite biosensor reporter lines in order to be able to monitor these changes dynamically, in space and time.

(Our response #14)

In our view, two main conclusions, both in vivo and in vitro, can be drawn based on the result we obtained:

First, we find that a moderate increase in glycolytic flux, within the physiological range, leads to a quantitative and consistent change in gene expression, such as downregulation of Wnt target genes (Figure 2—figure supplement 2, Figure 5). Such a phenotype was the result of either glucose titration or culturing cytoPfkfb3-transgenic embryos in low glucose concentration.

In these conditions, while overall PSM patterning is qualitatively normal, we do find consistent changes at quantitative level, i.e. gene expression changes, which are also mirrored by a reduced rate of segmentation (Figure 4B). A detailed analysis of the quantitative changes at the level of segmentation clock dynamics is being carried out and will be presented in a dedicated follow up study.

Second, we find that a very significant increase in FBP levels, i.e. when cytoPfkfb3 transgenic animals are cultured in high glucose conditions or when samples are cultured in high levels of FBP, PSM patterning is qualitatively altered and segmentation clock ceases to oscillate. In this case, we agree that it is not a physiological condition, as such high levels of flux and FBP are not reached in control samples which have intact flux regulation by Pfk. Nevertheless, such an experimental condition can be insightful, as it very clearly reveals the potential link between glycolysis, clock activity, PSM patterning and the Wnt signaling pathway.

It is the combination between the moderate and the more severe effects, observed both in vitro, and now also in vivo using the Akita model (see above), that we take as evidence for an intrinsic, physiological link between glycolytic activity, PSM patterning and signaling.

(Re: Figure 2A and Figure 2B)

– The authors should be consistent with the glucose concentrations for the experiments where they assess the dynamics of Notch signaling (Figure 2A) and gene expression (Figure 2B) or otherwise elaborate on why different concentrations are used for these assays.

#24: We agree that ideally the experimental parameters should be as consistent as possible. In regards to the control glucose concentration used in this study, both 0.5 mM and 2.0 mM glucose were used. It reflects that over the years, minor adjustments in the experimental protocol were made, i.e. we now use 2.0 mM glucose as standard setting for all experiments, while previously, 0.5 mM glucose was used (see Bulusu et al., 2017). This change is based on the observation of a slightly improved culture outcome, in terms of reporter gene expression. We have confirmed that the developmental outcome and also effects seen upon addition of FBP are consistent at 0.5 mM and at 2.0 mM glucose. We made a note in the methods section to explain this point (line 513-515):

“Basal culture condition was 0.5 mM glucose at the beginning of this study but was later switched to 2.0 mM glucose which yields a slightly improved reporter gene expression. No major difference was observed in the effects of FBP between these glucose conditions.”

(Re: Results from pharmacological and genetic models of increased FBP levels)

– The authors state that FBP-driven impairment of mesoderm segmentation is most pronounced in the undifferentiated PSM cells (in the posterior-most end of the explants) and is, therefore, unlikely to be due to a toxic effect that might otherwise affect the whole explant. While this is a reasonable assumption, it does not discount the possibility that the spatial specificity of the effect of FBP could be driven primarily by increased cell death in the posterior end of the explant. Thus, the authors should test whether cell death underlies the mesoderm patterning defects seen in PSM explants subjected to increased FBP levels.

#25. We have performed immunostaining of active caspase-3 in explants cultured for three-hour in medium containing 0.5 mM glucose and 20 mM FBP and found no difference between control and FBP-treated explants (please refer to Figure 2—figure supplement 1C). This qualitative result does not indicate a major effect via cell death in the tail bud region (i.e. posterior PSM) as the underlying reason for the observed phenotype. We included the new data in the revised Figure 2—figure supplement 1C and adjusted the text accordingly.

(Re: Gene expression experiments/analyses)

– This study would benefit greatly from transcriptomic analysis of wt and cytoPfkfb3 transgenic PSM explants (and/or transcriptomic characterization of FBP-treated vs. control PSM explants). The candidate approach used to assess gene expression (through in situ hybridization) may not be sufficient to conclude that cytoPfkfb3 over-expression leads to the downregulation of Wnt signaling (a claim the authors make at the beginning of the manuscript).

#26. We fully agree with the reviewer’s comment. We have now performed RNA-sequencing (RNAseq) analysis using control and cytoPfkfb3 explants cultured in 10 mM glucose, importantly after three hours of incubation in order to score early effects at transcriptome level (please refer to Figure 5C–E).

We found clear evidence that many Wnt-target genes (i.e. Axin2, Cdx4, Dact1, Dkk1, Mixl1, Msgn1, Sp5, Sp8, T) were significantly downregulated in cytoPfkfb3 explants, supporting the conclusion that Wnt signaling activity is downregulated in cytoPfkfb3 explants under high glucose condition.

Furthermore, in order to examine similarities between the effects of cytoPfkfb3 overexpression and FBP supplementation, we also performed RNAseq analysis with explants treated with high dose of FBP or F6P. FBP supplementation resulted in downregulation of Wnt target gene expression (i.e. Dact1, Dkk1, Mixl1, Lef1, Sp5, T, Tbx6), mirroring the effects seen in cytoPfkfb3 samples. Such a response was not detected in F6P-treated explants.

Combined, these new data significantly strengthen our conclusion that an increase in glycolytic flux and FBP levels leads to downregulation of Wnt signaling activity. The new data is now included in the revised Figure 5C–E and adjusted the texts accordingly.

(Re: Results related to the neural tube closure defects in cytoPfkfb3 transgenic embryos)

– The section of the manuscript describing the neural tube closure defects in cytoPfkfb3 transgenic embryos is superficial, lacks detail, and distracts from the focus of the study. Perhaps the data and text on neural tube closure defects should be included as supplemental information.

#27: We agree with the reviewer that in the previous version, this data appeared isolated. It also connects with the point raised by the reviewer 2 about the in vivo significance of our findings. To address both these points, we have now performed additional in vivo experiments using a diabetic mouse model (Akita) to directly test the in vivo consequence of cytoPfkfb3, which interestingly links to the previous findings of neural tube defects. Please see our response #13 for the details (below):

(Our response #13)

First of all, we would like to emphasize that the phenotype seen in cytoPfkfb3 embryos, i.e. the reduction of segmentation and downregulation of Wnt-target gene expression, occurs in a glucose dose dependent manner (Figure 4B and 5A). Hence, it is not the overexpression of cytoPfkfb3 per se that can account for the effects seen. But rather, increased glycolytic flux caused by the combination of transgene expression with high glucose results in functional consequences.

In addition, ‘other possible effects’ that the reviewer is referring to should be evident in all transgenic embryos, irrespective of glucose dose. To the contrary, transgenic embryos cultured in low glucose conditions appear unaltered to control embryos.

Second, we agree that we need to distinguish between strong phenotypes, visible at the level of clock arrest, and milder phenotypes, visible at the level of quantitative gene expression changes. It is important to note that the moderate phenotype, i.e. the quantitative gene expression changes seen in posterior PSM, are seen upon the addition of FBP at moderate levels and upon in glucose titration within the physiological concentration range, as well as in cytoPfkfb3 embryos. We take this as evidence that the effects seen in cytoPfkfb3 transgenic embryos reflect a common response also seen under physiological conditions.

To extend this argument to the in vivo setting, we have performed additional experiments using a genetic mouse model for diabetes. As shown in our previous submission, cytoPfkfb3 transgenic animals do not exhibit a drastic in vivo phenotype when dissected at embryonic day 10.5. One interpretation of this finding is that since the cytoPfkfb3 phenotype is glucose and flux-dependent, the in vivo flux is low, reflecting low glucose concentrations described in vivo. To test the effect of increased flux in cytoPfkfb3 embryos in vivo, we therefore crossed the transgenic mice into a diabetic model called Akita, in which a point mutation in the Insulin2 gene causes high maternal glucose levels (Yoshioka et al., 1997; Wang et al., 1999). Using this experimental setup, we tested whether transgenic embryos in Akita diabetic females would manifest in vivo phenotypes.

Indeed, we found that cytoPfkfb3 transgenic embryos developing in Akita diabetic females showed significantly increased cases of neural tube closure defects (50% of cytoPfkfb3 embryos) and developmental delay (control: 38 somites vs. cytoPfkfb3: 34 somites at E10.5), defects not seen in transgenic cytoPfkfb3 embryos from control females (please refer to Figure S4—figure supplement 1D-E ). This dependency of the in vivo phenotype on maternal glucose conditions again highlights that the defects observed in cytoPfkfb3 embryos are not due to the expression of cytoPfkfb3 per se, but are rather directly linked to increased/unregulated glycolytic flux.

We included the new in vivo data in the revised Figure S4—figure supplement 1D-E and modified the text accordingly.

(Re: Conclusions of the study)

– A previous study by Oginuma et al., 2020 provided strong evidence for a mechanism underlying the positive regulation of Wnt signaling by glycolysis (initiated by the elevation of intracellular pH) in the chick embryo tailbud. As mentioned in the discussion, the results of the present study are not consistent with this mode – and this contradiction is not sufficiently resolved. This is a concern, given that the evidence that cytoPfkfb3 inhibits Wnt signaling is sparse (see above).

#28: This important point was also raised by the reviewer 2, please see our response as listed under #19 (below).

(Our response #19)

We cannot resolve this discrepancy, but now offer a more detailed discussion, also based on the additional data we obtained.

First, it is important to point out that we have performed additional experiments to substantiate this part of the work, i.e. a transcriptome analysis with control and cytoPfkfb3 explants cultured in 10 mM glucose. We decided to focus on an early time point, i.e. three-hour after incubation, in order to increase the chance to score the primary response of PSM cells upon changes in glycolytic flux. In addition, our nanostring data in Figure 2—figure supplement 2 shows that glucose titration can change the expression levels of some Wnt-targets in both directions, i.e. decreasing glucose upregulates their expressions while increasing glucose downregulates their expressions. Again, this analysis was done at short time-scales to score the immediate effect.

One possible explanation regarding the difference to Oginuma et al. could indeed be the late time point of analysis in their study, i.e. 16-hour after culture. This difference in sampling time, i.e. 3-hour vs. 16-hour after culture, is of particular importance given the dynamic nature of metabolic and signaling responses.

We have added a sentence to explain this point in more detail (line 414-419):

“This discrepancy could relate to the time point of analysis: while Oginuma et al. mainly focused on analyzing samples 16-hour after metabolic changes, we chose to score the effects of altered glycolytic flux/FBP levels already after a three-hour incubation, with the goal to capture the primary response of PSM cells. Whether the difference in sampling time underlies the observed difference is yet unknown, but both studies highlight that Wnt signaling is responsive to glycolytic flux, supporting a tight link between metabolism and PSM development.”

– Another discrepancy lies in the lack of an observable phenotype when culturing mouse PSM explants at very low glucose concentrations (e.g., 0.5 mM in Figure 2A). Oginuma et al. observed clear disruptions to embryonic elongation and somite formation at a glucose concentration equal to 0.83 mM. Would this be due to species-specific mechanisms? Furthermore, while the authors focus on sentinel metabolites (such as FBP), experiments involving direct manipulation in glycolysis could resolve some of these inconsistencies.

#29: Indeed species specific differences in the requirement for glucose are to be expected. Our extensive analysis shows that at 0.5mM glucose, segmentation and elongation proceeds (Bulusu et al., 2017).

Regarding the second point, we have outlined several strategies to directly perturb glycolysis, i.e. glucose titration (mirrored by increase in lactate secretion) and by genetic targeting of the rate-limiting enzyme, Pfk. Glucose titration in wild-type embryos corresponds to the experiment the reviewer suggested, and we again found that higher glucose (i.e. higher flux) leads to down regulation of several Wnt-target genes (Figure 2—figure supplement 2). Of note, also in cytoPfkfb3 explants the effects are glucose-dose dependent (again mirrored by increase of lactate secretion), clearly indicating that we successfully and directly controlled glycolysis.

References

1. Hu, Hai, et al. "Phosphoinositide 3-kinase regulates glycolysis through mobilization of aldolase from the actin cytoskeleton." Cell 164.3 (2016): 433-446.

2. TeSlaa, Tara, and Michael A. Teitell. "Techniques to monitor glycolysis." Methods in enzymology 542 (2014): 91-114.

3. Oginuma, Masayuki, et al. "Intracellular pH controls WNT downstream of glycolysis in amniote embryos." Nature584.7819 (2020): 98-101.

Reviewer #3 (Significance (Required)):

The experimental results reported in this study enhance our understanding of how cellular metabolic states regulate cellular behaviors during embryonic development. The study provides insight into how PSM elongation is controlled by morphogenetic mechanisms that are modulated by glycolytic flux. One of the strengths of the study is the use of an interdisciplinary approach that includes GC-MS, in vivo imaging and mouse transgenic lines. It should be noted that some of the conclusions of the study diverge from previous papers that examine the role of metabolism in developmental patterning (e.g., Oginuma et al., 2020).

https://doi.org/10.7554/eLife.83299.sa2

Article and author information

Author details

  1. Hidenobu Miyazawa

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Contribution
    Conceptualization, Investigation, Writing - original draft, Writing – review and editing
    Contributed equally with
    Marteinn T Snaebjornsson
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6164-5134
  2. Marteinn T Snaebjornsson

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    German Cancer Research Center, Heidelberg, Germany
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Contributed equally with
    Hidenobu Miyazawa
    Competing interests
    No competing interests declared
  3. Nicole Prior

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    School of Biological Sciences, University of Southampton, Southampton, United Kingdom
    Contribution
    Conceptualization, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2856-7052
  4. Eleni Kafkia

    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    Novo Nordisk Foundation Center for Stem Cell Biology, University of Copenhagen, Copenhagen, Denmark
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Henrik M Hammarén

    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8534-2530
  6. Nobuko Tsuchida-Straeten

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    University Hospital Heidelberg, Heidelberg, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared
  7. Kiran R Patil

    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    The Medical Research Council (MRC) Toxicology Unit,University of Cambridge, Cambridge, United Kingdom
    Contribution
    Supervision, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6166-8640
  8. Martin Beck

    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Present address
    Molecular Sociology, Max Planck Institute of Biophysics, Frankfurt, Germany
    Contribution
    Supervision, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7397-1321
  9. Alexander Aulehla

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing
    For correspondence
    aulehla@embl.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3487-9239

Funding

European Molecular Biology Laboratory

  • Kiran R Patil
  • Martin Beck
  • Alexander Aulehla

H2020 Marie Skłodowska-Curie Actions (664726)

  • Hidenobu Miyazawa

Japan Society for the Promotion of Science

  • Hidenobu Miyazawa

Sigrid Juséliuksen Säätiö

  • Henrik M Hammarén

Deutsche Forschungsgemeinschaft (331351713)

  • Alexander Aulehla

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Theodore Alexandrov for helpful discussion and Jonathan Rodenfels and Joel I Perez-Perri for critical comments on the manuscript. We thank Jason Chesney for kindly sharing the Flag-PFKFB3(K472A/K473A) plasmid, Yvonne Petersen for performing blastocyst injection of ESCs, Jana Kress for generating anti-Pfkl antibody, Vladimir Benes and Laura Villacorta for technical advice and supports for RNA-sequencing analysis, Jonathan Landry for helping RNAseq data analysis, the nCounter core facility at the university of Heidelberg for expression analysis using NanoString technology, Bernd Klaus for helping with statistical analysis of the NanoString data and all the member of the Aulehla group for their support and helpful discussion. This work was supported by the EMBL Advanced Light Microscopy Facility (ALMF), Genomics Core Facility and the Laboratory Animal Resources (LAR).

Ethics

All animals were housed in the EMBL animal facility under veterinarians' supervision and were treated following the guidelines of the European Commission, revised directive 2010/63/EU and AVMA guidelines 2007. All the animal experiments were approved by the EMBL Institutional Animal Care and Use Committee (project code: 21-001_HD_AA).

Senior Editor

  1. Kathryn Song Eng Cheah, University of Hong Kong, Hong Kong

Reviewing Editor

  1. Joshua M Brickman, University of Copenhagen, Denmark

Publication history

  1. Preprint posted: December 23, 2021 (view preprint)
  2. Received: September 7, 2022
  3. Accepted: November 6, 2022
  4. Accepted Manuscript published: December 5, 2022 (version 1)
  5. Version of Record published: December 21, 2022 (version 2)

Copyright

© 2022, Miyazawa, Snaebjornsson et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hidenobu Miyazawa
  2. Marteinn T Snaebjornsson
  3. Nicole Prior
  4. Eleni Kafkia
  5. Henrik M Hammarén
  6. Nobuko Tsuchida-Straeten
  7. Kiran R Patil
  8. Martin Beck
  9. Alexander Aulehla
(2022)
Glycolytic flux-signaling controls mouse embryo mesoderm development
eLife 11:e83299.
https://doi.org/10.7554/eLife.83299

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