Glucose metabolism regulation mechanisms -
On the other hand, a functional interplay between HMGA1 and the homeodomain transcription factor PDX-1 a key regulator of pancreatic islet development and beta cell function has been shown previously in the context of the INS gene and other pancreatic islet-specific genes The possibility for HMGA1 to play a role also in this context, was substantiated by the fact that binding of PDX-1 to the INS gene promoter was reduced in Hmga1- knockout mice Subsequent studies added more details in our understanding of the INS gene regulation.
In the insulin-secreting beta-cell line INS-1, as demonstrated by chromatin immunoprecipitation experiments, glucose stimulated binding of HMGA1 to the INS promoter, resulting in a significant increase in insulin production and secretion Coherently, when INS-1 cells were treated with HMGA1 siRNA, a significant reduction in glucose-induced insulin secretion was observed, thereby confirming the importance of HMGA1 in this scenario Even in the absence of HMGA1-DNA binding sites on the INS gene promoter, the assembly of a transcriptionally active multiprotein-DNA complex involving HMGA1, PDX-1 and the transcription factor MafA, was required for proper transcription of both human and mouse INS genes In line with this observation, the deficit in HMGA1 compromised binding of PDX-1 and MafA to the INS promoter, thereby imparing INS gene transcription and glucose-induced insulin secretion However, given that substantial interspecies differences exist in pancreatic islet development and function 19 , 97 , 98 , any parallelism between human and mouse at this level must be considered carefully and further details on this should be provided.
For example, based on our recent observations highlighting a novel relationship between HMGA1 and FoxO1 99 , further investigation in this field could deliver deeper information on the possibility that an interplay among HMGA1 and FoxO1 can be a component of this regulation, as an overarching role of FoxO1 in pancreatic beta cell function has been already outlined 6 , — Besides being required for both insulin and INSR gene transcription, HMGA1 plays an important role in the regulation of the insulin signaling cascade The gluconeogenic genes phosphoenolpyruvate carboxykinase PEPCK and glucosephosphatase G6Pase , as well as the IGFBP1 gene which plays a glucose counterregulatory role by preventing the potential hypoglycemic effects of IGF1 are known to be inhibited by insulin for example, after a meal.
In fact, by triggering the phosphorylation of HMGA1 at the level of the three serine residues, Ser, Ser, and Ser, insulin promotes the detachment of HMGA1 from promoter target genes and its corresponding nuclear localization in the inactive heterochromatin.
Thus, HMGA1 acts as a downstream modulator of insulin action, and is an important key player in insulin and nutritionally-regulated transcription of genes involved in glucose metabolism and homeostasis. Given that the role of the transcription factor FoxO1 in the control of gluconeogenesis is well established 6 , , as for the regulation of pancreatic beta cell function, a cross-talk between HMGA1 and FoxO1 can be hypothesized also in this case and investigated in future studies.
Data from the Hmga1 -knockout mouse model evidenced a complex metabolic phenotype, in which peripheral insulin hypersensitivity paradoxically coexisted with a condition of impaired glucose tolerance and overt diabetes 19 , thus supporting the existence of alternative insulin signaling pathways ensuring peripheral glucose utilization and disposal by insulin-independent mechanisms.
Further studies in vitro confirmed that HMGA1 has a role in the activation of both IGFBP1 and IGFBP3 gene transcription 16 , Therefore, it is plausible that under physiological circumstances e. The counter-regulatory hormone glucagon, which acts in opposition to insulin, binds its cognate G-protein coupled receptor on liver cell membrane and stimulates the transmembrane adenylyl cyclase to produce cyclic AMP cAMP as second messenger.
This, in turn, leads to the activation of protein kinase A PKA , which, among many other proteins, phosphorylates the Cyclic AMP Responsive Elements Binding Protein CREB transcription factor , The final event is the assembly of a functional transcriptional machinery on the promoter regions of gluconeogenic genes Some observations in cultured hepatic cells indicate that cAMP also increases HMGA1 protein expression 17 , Consistently, Hmga1 RNA levels were significantly increased in liver of mice following systemic administration of glucagon.
In agreement with the observations mentioned above, upregulation of FoxO1 expression via the glucagon-cAMP-PKA signaling has been reported in liver of fasting mice to maintain fasting euglycemia Thus, upregulation of HMGA1 during fasting when glucagon peaks may contribute to the mechanisms necessary to prevent hypoglycemia, through activation of FoxO1 99 and gluconeogenic gene expression.
The opposite occurs after a meal, when insulin peaks, and glucagon declines Figure 3. In this metabolic scenario, inactivation of HMGA1 by insulin-induced HMGA1 phosphorylation, by causing the detachment of FoxO1 from DNA and its nuclear exclusion, inhibits gluconeogenesis and contributes to restoration of postprandial euglycemia Figure 3.
Figure 3. The increase of glucagon during fasting Left turns on the cAMP-PKA-CREB pathway, allowing HMGA1 gene activation and protein expression. In turn, HMGA1 activates the FoxO1 gene and promotes transactivation of G6Pase and IGFBP1 promoters by FoxO1, thereby maintaining fasting euglycemia through elevation of hepatic gluconeogenesis and attenuation of IGF1 bioactivity.
Under feeding conditions Right , binding of insulin to its receptor initiates a series of events culminating in the sequential phosphorylation p of HMGA1 and FoxO1, which reduces FoxO1 gene expression, promotes the detachment of FoxO1 from G6Pase and IGFBP1 gene promoters, and leads to FoxO1 nuclear exclusion, thereby ensuring postprandial euglycemia through inhibition of hepatic gluconeogenesis and augmentation of IGF1 bioactivity.
In a recent paper, after 3-day fasting or restriction diet in mice, renal gene expression, assayed by microarray, demonstrated, among other transcription factors, an increment in HMGA1 expression These findings are coherent with previous findings in the liver, in which an effect of HMGA1 on gluconeogenic genes has been described Another glucose metabolism-related gene, which has been shown to be regulated by HMGA1, is the one encoding for the retinol binding protein 4 RBP RBP-4 is mostly produced by the liver, although adipose tissue also contributes, and plays a role in systemic insulin resistance.
RBP-4 expression in fat and its levels in blood inversely correlated with the adipose-specific glucose transporter GLUT-4 in obesity and type 2 diabetes In vitro studies with human HepG2 and murine Hepa 1 hepatoma cells have demonstrated that HMGA1 binds to and increases transcription of the RBP-4 gene promoter both in basal and in cAMP-induced conditions 17 , , while in vivo , in whole mice, injection of glucagon, by inducing increased intracellular cAMP, activates both HMGA1 and RBP-4 expression in liver and fat.
Consequently, under physiological circumstances, this loop has an important relapse in conditions of low glucose availability, in which intracellular cAMP increases. Interestingly, the brain-type GLUT-3 facilitative glucose transporter has also been shown to be transcriptionally regulated by HMGA1 , thereby supporting further the relevance of this factor in multiple settings of energy demand.
Both muscle and fat play relevant roles in maintaining euglycemia. In this regard, previous studies from our group demonstrated that INSR expression is reduced in muscle and adipose tissues from both Hmga1 -knockout mice and in individuals with reduced levels of HMGA1 19 , The physiological role of HMGA1 in adipogenesis has been investigated in vitro and in vivo 63 , , and a critical role of HMGA1 in adipocytic cell growth and differentiation has been demonstrated in murine 3T3-L1 adipocytes Also, HMGA1 may exert a negative role in adipose cell growth by balancing the effects of the cognate HMGA2 protein, another member of the HMGA family Indeed, transgenic mice, overexpressing HMGA1 in both white and brown adipose tissues, showed reduced fat mass and impaired adipogenesis with respect to wild-type mice 63 , and were protected against high-fat diet induced obesity and systemic insulin resistance, thus supporting the role of HMGA1 in the maintenance of glucose homeostasis.
In addition to RBP-4, whose regulation has been discussed, other adipokines have been demonstrated to be under the control of HMGA1.
Several reports have also indicated that HMGA1 plays a role in muscle tissue, and HMGA1 is present in mouse C2C12 cultured muscle cells, in which HMGA1 overexpression increases cell proliferation and prevents myotube formation Downregulation of HMGA1 is an early and necessary step for the progression of the myogenic program.
In fact, mice overexpressing Lin28a and Lin28b show an insulin-sensitized state, with protection against high-fat diet induced diabetes In contrast, muscle-specific loss of Lin28a and overexpression of let-7 resulted in insulin resistance and impaired glucose tolerance As Lin28a directly promotes HMGA1 translation , it has been postulated that in muscle-specific Lin28a knockout mice, insulin resistance is, at least in part, due to reduced HMGA1 levels and consequently impaired INSR expression Insulin resistance, defined as a subnormal biological response to the glucose-lowering effect of insulin, is a characteristic of many common disorders, including type 2 diabetes, the metabolic syndrome, fatty liver disease, and obesity — However, severe forms of insulin resistance may occur as uncommon syndromes, either congenital or acquired, in patients with impaired INSR signaling or lipodistrophy , Congenital disorders include the Type A syndrome of insulin resistance, the Rabson-Mendenhall syndrome, leprechaunism, and some syndromes of generalized or partial lipodystrophy.
Type A syndrome is an autosomal dominant disorder characterized by the triad of hyperinsulinemia, acanthosis nigricans, and ovarian hyperandrogenism — Hyperglycemia is not always present at diagnosis.
Female patients appear lean and without lipodystrophy, even if a variant of this syndrome has been reported in obese women , Male patients may initially exhibit acanthosis nigricans and hypoglycemia, while overt diabetes may not occur until the fourth decade or later As a step toward understanding the molecular basis of regulation of the INSR gene, a nuclear binding protein that specifically interacted with, and activated the INSR gene promoter, was identified previously, during muscle and adipose cell differentiation Later, this DNA binding protein was identified as HMGA1, and its expression was markedly reduced in two unrelated patients with either the Type A syndrome or the common form of type 2 diabetes, in whom cell surface INSRs were decreased and INSR gene transcription was impaired despite the fact that the INSR genes were normal, thus indicating defects in INSR gene regulation 15 , 89 , Subsequent investigations in both these patients allowed the identification of a novel genetic variant, c.
In other two patients mother and daughter with the type A syndrome of insulin resistance, a hemizygous deletion of the HMGA1 gene was also identified Restoration of HMGA1 protein expression in these subjects' cells enhanced INSR gene transcription and restored cell-surface INSR protein expression, thus confirming that defects in HMGA1, by decreasing INSR protein production may indeed induce severe insulin resistance The mechanistic linkage between HMGA1, insulin resistance and certain less common forms of type 2 diabetes has been further supported by a study in two diabetic patients, in whom aberrant expression of a pseudogene for HMGA1, HMGA1-p , caused destabilization of HMGA1 mRNA with consequent loss of INSR and generation of insulin resistance These findings demonstrate, therefore, that HMGAl is necessary for proper expression of the INSR.
In its common form, type 2 diabetes is a heterogeneous complex disease in which concomitant insulin resistance and beta-cell dysfunction lead to hyperglycemia , From a pathogenetic point of view, both predisposing genetic factors and precipitating environmental factors contribute importantly to the development of the disease , So far, about gene variants have been associated with an increased risk for type 2 diabetes Most of these variants are presumed to negatively affect pancreatic beta-cell function and insulin secretion, while some of them appear to impact peripheral insulin sensitivity, thereby impairing tissue glucose uptake As it concerns HMGA1, on the basis of its involvement in insulin resistance, a role for this nuclear factor in type 2 diabetes has also been postulated and studies in this direction have been performed by us and others 20 , — In circulating monocytes and cultured lymphoblasts from diabetic patients carrying these variants, HMGA1 and INSR expressions were markedly decreased and these defects were corrected by transfecting HMGA1 cDNA The most frequent HMGA1 rs variant previously named rs , was significantly higher in type 2 diabetic patients from three populations of white European ancestry: Italian, American and French populations Although not replicated in a heterogeneous French population , the rs variant was later associated with type 2 diabetes among Chinese and Americans of Hispanic ancestry , thus providing evidence for the implication of the HMGA1 gene locus as one conferring a cross-race risk for the development of type 2 diabetes.
More recently, the credibility of an association between the HMGA1 rs variant and type 2 diabetes was confirmed also in a transethnic meta-analysis that included all available published articles examining this association in different populations The metabolic syndrome is a common multicomponent disorder, which is associated with increased risk for type 2 diabetes, cardiovascular disease CVD , and nonalcoholic fatty liver disease , As insulin resistance plays a pivotal role in the pathophysiology of metabolic syndrome , , the impact of HMGA1 has been investigated in two large Italian and Turkish populations, both affected by metabolic syndrome Findings indicated that the HMGA1 rs variant was significantly associated with metabolic syndrome in both populations, in which this association occurred independently of type 2 diabetes, thus lending credence to the hypothesis that this variant may independently associate with other insulin resistance-related traits.
Consistent with this assumption, a strong association of the rs variant with certain metabolic syndrome-related traits i. Interestingly, as CVD is a major risk for both type 2 diabetes and the metabolic syndrome, the association of HMGA1 rs variant with acute myocardial infarction, independently from diabetes and other cardiovascular risk factors, has been reported previously , , suggesting that HMGA1 may also represent a novel genetic marker of cardiovascular risk.
An important issue that deserves to be discussed is to which extent Hmga1 -knockout mice reflect findings in humans. Although in the broader context of glucose metabolism similarities between the two species are well known i.
At a molecular level, previous known beta cell species-specificities in ion channel components and membrane transporters, as well as in insulin secretion, have been recently further enriched by data from transcriptome profiles in single human and murine beta cells , , while evidence of heterogeneity of pancreatic beta cells has been proved to occur in both humans and mice However, interspecies differences do not exclude that in some instances, like in the case of lack of the KCNJ11 gene, the mouse phenotype well recapitulates human neonatal diabetes Focusing on HMGA1 loss-of-function, three biochemical and metabolic conditions are common to humans and mice: reduced insulin receptor expression, impaired insulin signaling, and insulin resistant diabetes.
Instead, insulin levels in humans hyperinsulinemic and mice hypoinsulinemic are clearly discrepant In fact, in Hmga1 -knockout mice, both beta cell mass and insulin secretion are impaired. Differences in pancreatic islet ontogenesis and differentiation, as well as differences in nongenetic environmental elements and susceptibility to genetic modifiers, have been postulated to explain these dissimilarities On the other hand, Hmga1 -knockout mice have proved to be insulin hypersensitive, despite the deficit in INSRs.
Although the latter has proved to be more effective to reduce glycemia in mice than in humans , the importance of these systems in both species still deserves further investigations.
As an example, recent findings obtained in genetically engineered mice with a specific deletion of the RBP4 gene in the liver, indicating that circulating RBP4 derives mainly from hepatocytes , need to be confirmed in humans.
At present, HMGA1 is known to be involved in multiple biological processes. Based on the above-mentioned findings, among the many tasks that HMGA1 can perform, there is its role in the transcriptional regulation of gene and gene networks involved in INSR signaling and glucose metabolism.
In this review, we provided an overview of the major contributions that have been made in this area over the last years. Overall, the data obtained so far well support the role of HMGA1 in the regulation of genes implicated in the maintenance of glucose homeostasis and metabolic control, providing new insight into the regulation of glucose metabolism and disposal.
Clinically, the importance of HMGA1 gene variability in glucose metabolism is emphasized in a wide range of clinical conditions ranging from rare insulin resistance syndromes to type 2 diabetes and the metabolic syndrome.
Besides, being a multifunctional protein, HMGA1 may constitute a molecular link between metabolism and other distinct biological processes, including cell proliferation, and differentiation, viability, autophagy, cell cycle, apoptosis, that need to be sustained by cell energy.
New insights may come from epigenetic studies, including miRNAs, whose common role in both malignancy and metabolism is recently emerging. On the other hand, disentangling the pleiotropic nature of HMGA1 by the identification of distinct molecular partners and networks uniquely implicated in metabolism, still represents a big challenge.
A contribution could come from studies on the relationship between HMGA1 and the yet unexplored nuclear metabolic sensors. Apart from the intrinsic biological and clinical interest of these findings, a deeper understanding of the mechanisms that regulate glucose metabolism in health and disease is of importance for the development of more effective therapies.
To fill the gap of our knowledge in this regard, future directions based on the omics-related technologies, combined with bioinformatic tools, can help identify novel proteins and their networks, as well as genes and gene products regulated by, or interacting with HMGA1. To the best of our knowledge, this is the first review article exclusively dedicated to the role of HMGA1 in this context, and we hope that it will serve as a quickly accessible reference in this important clinical field.
EC and DF prepared the first draft of the manuscript. RS, SP, MG, and GM contributed to critical revision of the manuscript. BA and FB were involved in the literature search.
AB critically revised and edited the final version of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The handling editor is currently co-organizing a Research Topic with one of the authors AB, and confirms the absence of any other collaboration.
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Third, we incorporated the estimated uptake and secretion rates in a constraint-based approach to resolve dynamic intracellular metabolic responses to changes in nutrient availability. Akin to other dynamic FBA modeling approaches 8 , 17 , 32 , bacterial growth was divided into N intervals at equidistant optical densities ODs and fluxes were assumed to be constant within these intervals.
To account for the variability in estimated rates, lower and upper bounds for each exchange flux i. As changes in OD can be routinely measured at high time-resolution and accuracy using standard plate readers, experimentally measured growth rates are used as hard constraint in the model.
Hence, maximization of the biomass objective does not need to be invoked. For metabolites without external calibration, we introduce an auxiliary time-independent variable representing the proportional scaling factor c between measured MS intensities and actual concentrations.
Instead of solving fluxes v t at each time point independently 33 , our method is formulated as a one-step global linear optimization problem to generate time dependent flux maps.
Time-dependent prediction of metabolic fluxes. a Percentage of inactive purple and active green reactions throughout the entire time course and reactions that carry flux for a limited time yellow. c Predicted time-dependent fluxes through the NAD- and NADP-dependent malic enzymes MaeA and MaeB, respectively.
Assembling the set of constraints generated at each time point in one global optimization problem allows us to solve the vector of scaling factors c and time-dependent fluxes v t at once, such that the flux solutions for each time interval depend on each other.
To test uncertainty in flux estimates we performed flux variability analysis FVA of the optimal flux solution: for each reaction, we calculated the maximum and minimum sum of fluxes over time. Most of these reactions were involved in transport, exchange and catabolism of amino acids, nucleotide precursors, and intermediates of central metabolism Fig.
Among the transiently active reactions we found the NAD- and NADP-dependent malic enzymes, which catalyze the anaplerotic reaction converting malate into pyruvate Fig. While these anaplerotic reactions are inactive in glucose minimal medium 34 , time-dependent estimates of flux through the malic enzymes MaeA and MaeB suggest an early transient activation of the flux from malate to pyruvate in the presence of CAA Fig.
Consistent with model predictions of an earlier and stronger activation of the NAD-dependent MaeA enzyme Fig.
To test this possibility, we repeated the growth assay in M9 glucose medium supplemented with different aspartate concentrations Fig. This experiment confirmed our hypothesis and showed that, in the presence of aspartate, Δ maeA has a reduced growth rate with respect to the wild-type.
Overall, we demonstrated that constraining an FBA model with a combination of absolute and relative measurements of metabolite concentrations in the supernatant allows to estimate dynamic intracellular flux rearrangements during the sequential utilization and depletion of nutrients in a complex medium.
Our model-based analysis revealed mainly two phases of growth, approximately before and after the culture reaches an OD of 1. During this phase, the culture exhibited low-glucose uptake, high acetate overflow, and an excess consumption of nitrogen Figs.
This excess consumption of nitrogen is predicted to be balanced by secretion of ammonia, a phenomenon also observed in the presence of large quantities of glutamine Upon near depletion of these amino acids, glucose and ammonia became the main carbon and nitrogen sources, respectively Fig.
Estimated rates of amino acid uptake. Measured amino acid concentrations in the supernatant crosses and interpolated values blue lines.
The black line represents the calculated instantaneous uptake rate, and the gray shaded region the confidence intervals. The flux requirements for protein biosynthesis green line was estimated from the stoichiometry of biomass composition multiplied by the instantaneous growth rate.
Red shaded regions delineate lower and upper bounds of amino acid requirements for protein biosynthesis estimated from experimental measurements of protein abundance in E. Dashed red lines are direct linear interpolations from the upper and lower bounds multiplied by the instantaneous growth rate.
The chart backgrounds of all non-degradable amino acids are highlighted in yellow and the font color is red.
Amino acids degraded into the same product are grouped by different colors, according to the central schematic metabolic network. Throughout these two phases, the rates of amino acid consumption varied approximately one order of magnitude Fig.
Uptake rates for all seven non-degradable amino acids Fig. Akin to uptake of methionine that is inhibited by internal methionine levels 38 , it appears that uptake rates of all non-degradable amino acids are tightly regulated by internal feedback loops, possibly to avoid accumulation of toxic intermediates In contrast, more than half of the degradable amino acids i.
Consistent with previous evidence 40 , we found that the average amount of amino acid consumed per unit change in OD i. Our data hence supports the hypothesis that amino acid biosynthetic cost imposes a selective pressure to encode less costly amino acids in highly abundant proteins 41 , 42 Supplementary Fig.
To test whether the observed amino acid uptake rates depend on their absolute or relative concentration in the medium, we supplemented the medium with seven mixes of all amino acids at different quantities and determined their individual average uptake rates Fig.
For most amino acids, varying amounts of their concentrations in the media did not affect our previous conclusions Supplementary Figs. However, the average uptake rates of some amino acids e. We sought to find potential regulatory dependencies between amino acids by correlating uptake rates between all pairs of amino acids across the seven conditions Fig.
On average, serine and aspartate exhibited the strongest correlation with uptake rates of other amino acids, suggesting for a prominent role of these amino acids in the regulation of nutrient consumption Fig. Consumption vs. cost of amino acids. a Each dot represents one amino acid, while the red line is the instantaneous cellular growth rate.
For each amino acid, the initial concentration is related to the OD at which the amino acid has been depleted from the medium. b For each amino acid, its metabolic cost, i.
c For each amino acid, its metabolic cost 41 is compared to an average estimate of its uptake rate, calculated as the initial amino acid concentration divided by the hours and culture gram of dry biomass gDW at time of amino acid depletion. d Distribution of amino acid average uptake rates across seven media containing different initial quantities of amino acids see also Supplementary Fig.
e Heatmap of pairwise correlation between average uptake rates of amino acids across seven tested conditions. Boxplot of pairwise correlation for each amino acid. Box edges correspond to 25th and 75th percentiles, whiskers include extreme data points, and outliers are shown as red plus signs.
g Average uptake rates of asparagine against glutamine. Noteworthy, several low-cost amino acids that were consumed at a rate not exceeding the requirement for protein biosynthesis, namely proline, arginine, glutamine, and asparagine, cannot be directly degraded, but first need to be converted into glutamate or aspartate.
The remaining amino acids that could be directly degraded but are instead taken up at a relatively low rate, are alanine, tryptophan, and lysine. However, differently from the other amino acids, alanine is also an essential component of cell wall peptidoglycan, and tryptophan and lysine have high biosynthetic costs Fig.
Altogether these results suggest for a complex tradeoff between the cost of degrading amino acids—i. Above, we found that some amino acids are catabolized and even reduce glucose catabolism during the early stages of growth.
While glucose-based catabolite repression of less preferred substrates is relatively well characterized in E. coli 40 , 44 , 45 , 46 , much less is known about the influence of other nutrients on glucose consumption 47 , Thus, we next investigated how E.
coli coordinates catabolism of amino acids and glucose. As a key regulator of carbon uptake and catabolism, the transcription factor Crp regulates the expression of many alternative substrate uptake systems and genes involved in amino acid degradation and carbon catabolism in E.
coli By measuring Crp activity with a GFP reporter plasmid, we verified that glucose strongly represses Crp activity 44 and that the addition of amino acids does not influence this repression Fig.
Thus, our results suggest that transcriptional regulation by Crp is not responsible for the reduced glucose consumption. Coordination of glucose and amino acids catabolism. b Dynamic changes of the ratio between phosphoenolpyruvate and pyruvate upon CAA supplementation.
c Dynamic relative changes in the abundance of pyruvate blue , oxaloacetate red , and α-ketoglutarate yellow upon supplementation with the different amino acid mixtures and glucose as the main carbon source.
the ratio between acetate secretion and glucose consumption. e In vitro activity of PtsI in the presence of only the reactant phosphoenolpyruvate PEP , or PEP with the addition of glutamate GLT , alanine ALA , aspartate ASP , fructose bis-phosphate FBP , succinate SUC , glyoxylate GOX , α-ketoglutarate AKG , oxaloacetate OXA , and oxamate OXM.
An alternative mechanism for more rapid control of glucose uptake relies on changing transporter activity either via phosphorylation 49 or small molecule binding 5. Dephosphorylation of the first step of the sugar—phosphoenolpyruvate phosphotransferase system PTS , EIIA Glc , leads to the transport inhibition of several non-PTS carbon sources.
According to the current model, a rapid increase of the ratio between phosphoenolpyruvate and pyruvate levels would correspond to increased phosphorylation of EIIA Glc 49 , and hence a reduced glucose uptake. We monitored immediate changes in the intracellular ratio between phosphoenolpyruvate and pyruvate after supplementation of CAA, using a targeted Liquid Chromatography—Mass Spectrometry LC—MS method 50 Fig.
We observed a rapid increase of intracellular pyruvate and almost steady levels of phosphoenolpyruvate Fig. While we do not have direct experimental evidence, reduced phosphorylation of EIIA Glc seems implausible because EIIA Glc is already completely dephosphorylated in the presence of glucose Moreover, even higher EIIA Glc dephosphorylation would correspond to an additionally increased glucose uptake, contrary to our observation.
Thus, collective evidence suggests that the coordination between glucose and amino acid catabolism is achieved by intracellular signaling metabolites; the most parsimonious explanation being modulation of glucose uptake through degradation products of amino acid catabolism.
To test this hypothesis and to identify putative effector metabolites, we supplemented E. coli cultures during mid-exponential growth on glucose minimal medium with eight different amino acid mixtures.
Each mixture was deprived of one class of amino acids that are catabolized into either of the three final degradation products, namely the α-keto acids: pyruvate, α-ketoglutarate, or oxaloacetate.
Generally, addition of amino acids affected glucose consumption and acetate secretion Table 1 and caused large concentration changes in intermediates of central metabolism, primarily pyruvate, and oxaloacetate see Supplementary Discussion and Supplementary Fig.
Relative concentration changes determined by FIA-TOFMS were consistent with previous absolute concentration measurements by LC—MS Supplementary Figs.
glucose consumption. We found a similar correlation in previously published data 52 monitoring metabolite and flux changes in gene deletion mutants Supplementary Fig. The rapid accumulation and subsequent depletion of intracellular pyruvate levels upon CAA supplementation were compatible with the initial growth phase during which glucose consumption was reduced, and the subsequent phase, after depletion of low-cost amino acids, characterized by increased glucose uptake and decreased acetate secretion Figs.
In media lacking amino acids such as serine, glycine, threonine, tryptophan, cysteine, and alanine that are degraded into pyruvate, we observed no suppression of glucose uptake Table 1 , showing that pyruvate levels change in response to catabolism of amino acids. Moreover, previous results from chemostat experiments have shown that increasing glucose uptake corresponds to increased intracellular levels of pyruvate 53 , which is opposite to the negative correlation i.
Hence, pyruvate changes are unlikely to be a mere indirect consequence of changes in glucose uptake. Collected evidence suggests pyruvate as a candidate for the regulation of glucose uptake and acetate secretion.
Activation of acetate secretion by pyruvate was already known because pyruvate is a strong activator of phosphotransacetylase, catalyzing the reversible interconversion of acetyl-CoA and acetyl phosphate Furthermore, E.
Moreover, pyruvate represses the activity of PdhR, a transcriptional regulator that negatively regulates formation of pyruvate dehydrogenase complex PDHc.
These results suggest that PdhR residual activity in glucose M9 is completely abolished when amino acids are supplemented to the medium and intracellular pyruvate levels are increased up to fold.
The combined increase of pyruvate levels and decreased PdhR activity can potentially support a higher flux into acetyl-CoA and possibly acetate. To test whether pyruvate can also directly regulate glucose uptake, we purified PtsI, the component of the PTS system that catalyzes the phosphorylation of glucose to glucosephosphate with phosphoenolpyruvate PEP as phosphate donor.
We determined PtsI in vitro activity in the presence of nine different intermediates of central metabolism, using the known inhibitor α-ketoglutarate as a positive control 5 , Since pyruvate is one of the reaction products and a readout of the assay, the pyruvate-analog oxamate had to be used to investigate inhibition by pyruvate Fig.
To further demonstrate that increased pyruvate can selectively inhibit the PTS uptake system, we tested for a potential growth inhibitory effect by addition of extracellular pyruvate to E.
coli growing with either the PTS carbon source glucose or the non-PTS carbon source succinate. Since Crp-mediated catabolic repression would prevent pyruvate uptake during exponential growth on glucose, we first grew E. coli in glucose M9 media overnight to stationary phase when Crp activity is high, enabling uptake of alternative carbon sources.
Consistent with the pyruvate inhibition of the PTS system hypothesis, we observed that pyruvate addition caused lower growth rates during adaptation to the reappearance of glucose Fig. On the contrary, extracellular pyruvate facilitated the adaptation to succinate, resulting in faster growth Fig.
Consistent with these findings, while succinate consumption is also strongly reduced upon addition of amino acids Fig.
Differently from glucose, where Crp activity is already repressed, succinate uptake is directly controlled by Crp 58 , and addition of amino acids coincide with proportional reduction in Crp activity Fig.
Thus, collective evidence suggests that rapid catabolism of serine, glycine and aspartate reduces glucose catabolism and increases acetate secretion via accumulation of α-keto acids, mainly pyruvate and oxaloacetate.
On the other hand, while amino acid catabolism modulates glucose uptake via post-translational regulation, transcriptional adaptation seems to be at the basis of global carbon flow regulation in the presence of a non-PTS carbon source. Coordination of succinate and amino acid catabolism. a Changes in the instantaneous growth rate of E.
coli in glucose M9 black , with 20 blue , or 40 red mM of pyruvate. b Changes in the instantaneous growth rate of E. coli in succinate M9 black , with 20 blue or 40 red mM of pyruvate. c Relative succinate uptake rates of E.
d Dynamic relative changes in the abundance of pyruvate blue , oxaloacetate red and α-ketoglutarate brown upon supplementation with the amino acid mixtures and succinate as the main carbon source. Resolving dynamic flux variations in continuously changing and nutritionally complex environments has remained a major challenge Growing in rich media, bacteria cannot consume all nutrients at once, but must decide which to consume first and how to utilize them.
Changing nutrient concentrations force bacteria to continuously adapt their uptake, which in turn requires continuous and rapid redirection of intracellular fluxes. Here, we developed an experimental and computational approach that allows to infer dynamic metabolic adaptation phases of E.
coli during growth in complex medium. Exo-metabolome patterns revealed complex dynamics in the consumption of nutrients and secretion of metabolites. Somewhat unexpectedly, maximum growth occurred at relatively low-glucose uptake and high acetate secretion.
Constraint-based model predictions resolved the overall contribution of consumed nutrients to biomass formation and the intracellular fluxes, unveiling underlying metabolic strategies during the different growth phases with consecutive depletion of amino acids.
Low-cost amino acids provided most of the carbon and nitrogen needed during the initial phase of rapid exponential growth that featured relatively low rates of glucose uptake.
In particular, serine, aspartate, and glycine were consumed at much higher rates than required for protein synthesis and therefore contributed substantially to energy and biomass generation. To understand how E. coli coordinates the consumption of different nutrients and adapts its intracellular fluxes, we monitored the intracellular metabolome response to sudden addition of different combinations of amino acids.
Pronounced changes in pyruvate levels and their strong correlation with the ratio between glucose consumption and acetate secretion, suggested pyruvate as an important regulator of glucose uptake and acetate secretion. We demonstrated that the pyruvate-analog oxamate inhibits the activity of the glucose uptake system protein PtsI, and that extracellular pyruvate hampers growth resumption of E.
coli on glucose, but not on a non-PTS carbon source. Altogether with the previous experimental evidence of pyruvate as an activator of acetate overflow in E. coli 60 , our results suggest that catabolism of amino acids and glucose is coordinated through changes in the levels of α-keto acids, primarily pyruvate, and oxaloacetate.
Since our experiments were not performed under nitrogen limitation, the minor changes in α-ketoglutarate levels do not contradict previous results on the role of α-ketoglutarate in regulating glucose uptake in response to nitrogen limitation, and extended our understanding on the functional regulatory role of other keto acids in central metabolism, like pyruvate and oxaloacetate.
The presented experimental and computational method can quantitatively model intracellular flux rearrangements during growth of microbes and potentially higher cells in complex environments beyond the here investigated amino acids.
We envisage that this new approach coupled with the high time-resolution and coverage achievable with direct infusion mass-spectrometry 24 , 51 , 61 , in combination with more quantitative methods such as LC—MS or nuclear magnetic resonance 62 , 63 , has the potential to derive testable predictions on regulatory mechanisms that underlie the investigated metabolic phenotypes, even outside of the restricted laboratory conditions.
For all growth experiments, E. The M9 medium contained, per liter of deionized water: 7. Carbon source solutions were filter-sterilized and added separately to the medium. Growth of E. Cell dry-weight was inferred from a predetermined conversion factor of 0. Absolute concentrations of glucose and acetate in culture supernatants were determined with enzymatic kits Megazyme.
In parallel, we used a direct flow-injection mass-spectrometry method FIA-Q-TOF 21 to profile relative changes of metabolite concentrations in the supernatant.
Specifically, culture supernatants were injected into an Agilent Series Quadrupole Time-of-flight mass spectrometer Agilent, Santa Clara, CA , operated in negative mode. The experiment was performed in triplicates and each sample of the ten time points was injected twice.
From the spectrum of detected ions, amino acids and other metabolites were annotated as mono-deprotonated ions with the genome-scale model of E.
coli by Orth et al. Absolute amounts of amino acids were estimated by comparing peak intensities measured in the culture supernatant with intensities of a dilution series of pure amino acids in M9 minimal medium see supplementary materials for further details.
The validity of the estimated concentrations was confirmed using an alternative standard method for amino acid quantification ACCQ-Tag Ultra Derivatization Kit, Waters see Supplementary Text and Supplementary Fig. Time-dependent profiles of each individual metabolite detected in the supernatant were interpolated using multivariate adaptive regression splines MARS Error estimates were calculated as the standard deviation over the fittings obtained from the bootstrapping procedure.
Next, we estimated relative instantaneous uptake and secretion rates by calculating the difference of metabolite levels between two consecutive time points, divide by the time interval i. It is worth noting that an intrinsic problem of data interpolation is the filtering of high frequencies in the data i.
To monitor rapid metabolic changes in response to diverse mixes of amino acids and to facilitate dynamic sampling at high-throughput, reducing the risk of sample processing artifacts, we used a well plate whole-culture broth extraction protocol Aliquots of cell culture were extracted without cell separation using cold solvent extraction and then directly injected into a time-of-flight mass spectrometer We supplemented exponentially growing E.
coli cells with synthetic amino acid SAA mixtures, containing the same concentrations of individual amino acids as measured in CAA, but deprived of groups of amino acids catabolized into pyruvate e.
Pure M9 glucose medium containing 0. To validate that whole-cell broth WCB measurements were representative for intracellular metabolome changes, we also used a fast filtration protocol 4 to exclusively monitor intracellular changes upon CAA and SAA perturbations and showed that most of the patterns we obtained in WCB samples resembled measured intracellular changes Supplementary Data 2.
For the spiking experiments, E. At an OD of 0. coli 13 C extract were added as internal standard to the extraction solution 66 , immediately after filter addition. Metabolites were detected on a tandem mass spectrometer Thermo TSQ Quantum Triple Quadropole with Electron-Spray Ionization; Thermo Scientific, Waltham, MA, USA.
Standard FBA couples mass balance, thermodynamic i. Linear constraints define a space of feasible steady state flux solutions, and the flux distributions that maximize an objective function, typically biomass production i.
where, v represents the vector of fluxes, S the stoichiometric MxR matrix i. Typically, only one substrate is limiting and maximum growth corresponds to maximum yield.
The in-silico medium is defined by allowing the import of a metabolite from the external compartment to the intracellular ones e.
Often, the flux distribution that optimizes biomass production and is the most parsimonious—i. The genome-scale model of E. coli used in this study accounts for exchange reactions When the medium becomes complex and the limiting resource is not known, the space of feasible solutions increases and simple objective functions as pure maximization of biomass are no longer appropriate 17 , In classical constraint-based modeling e.
To overcome these limitations, uptake rates can be experimentally determined and used as constraints in the model to reduce the space of feasible solutions. However, broad coverage comes at the expenses of only relative and not absolute quantitative readouts.
For all 20 amino acids, we obviated to this limitation using external calibration curves derived from pure compounds full details can be found in the Supplementary. Notably, the two isomers leucine and isoleucine, cannot be distinguished by FIA-TOF analysis.
Hence for these two amino acids we estimated only upper bounds. Independent measurements of glucose uptake and acetate secretion were performed using enzymatic kits. To incorporate the direct flow-injection mass-spectrometry measurements of the remaining metabolites in a genome-scale metabolic model of E.
coli , we implemented an optimization procedure that includes auxiliary unknown variables reflecting the linear proportionality factors between measured MS intensities and concentrations.
To this end, we divided the experimentally observed growth time course in N intervals at equidistant ODs, from 0. A quasi-steady state assumption is crucial to reduce problem complexity and maintain linearity in the constraints. For each time interval, the absolute rate of consumption and secretion of glucose, acetate and amino acids are estimated from absolute experimental measurements, together with confidence intervals v measA.
Subsequently, we minimize the L1-norm distance between the relative estimates of exchange rates for metabolites without a calibration curve, multiplied by a scaling factor c.
To this end we introduce an auxiliary variable D. Results are robust to small changes of this parameter. For each exchangeable metabolite in the model, the uptake at time t cannot exceed the sum of produced and consumed metabolite up to that time.
It is worth noting that all measured rates absolute and relative are used as soft constraints, besides growth rate, which is here imposed as a hard constraint:. Solutions were calculated using the CPLEX IBM optimization software.
The three minimizations were carried out one after the other in the order described above. Specifically, after solving the first optimization, the minimum sum of absolute differences between absolute estimated and predicted fluxes obj 1 is used as a constraint in the second optimization.
Finally the objective value obj 2 is used as constraint to find the flux solution that minimizes the sum of absolute fluxes. To find the full range of possible flux values v t we used flux variability analysis Specifically, we minimize and maximize the flux through each metabolic reaction under the constraint that solutions has to lie in the Results from flux variability analysis are reported in Supplementary Data 1 and in Figs.
Cultivation of the E. coli strain bearing a transcriptional reporter plasmid for monitoring Crp activity, as well as the calculation of promoter activity as the OD normalized GFP production rate, were performed using a previously described protocol 71 , Steady state promoter activities in M9 minimal medium with varying external amino acid composition were determined during the first rapid growth phase window during which the cultures exhibited the maximal growth rate.
PtsI enzyme assays were performed as previously described by Doucette et al. PtsI enzyme I containing an N-terminal histidine tag was purified from ASKA library strain b 73 and its identity verified by SDS page.
After drying the samples and resuspension in water, the fraction of 13 C-labeled PEP in the total PEP pool was determined by LC—MS analysis. Similarly to 5 , for each time course weighted nonlinear least-squares regression was used to fit the following equation:.
to the mean and s. of three experimental replicates. L is the 13 C-labeled fraction of the PEP pool at time t , and k is the fitted parameter representing the rate constant of the labeling reaction; 0. The estimated k- values of each assay were then compared using t -test analysis to identify compounds with a significant inhibitory effect Fig.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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