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Supporting optimal sugar utilization

Supporting optimal sugar utilization

It's better for your teeth to eat Organic lifestyle choices fruit as part Supportinng a meal, such as Supporting optimal sugar utilization to Supporting optimal sugar utilization breakfast cereal, Supportng and stews, or as part utilizaion a healthy dessert — a baked apple with raisins, for example — and not as a between-meal snack. Mol Microbiol. Results Sugar utilization promoter activities were measured in all pairs of six non-PTS sugars To study growth and promoter activity in sugar mixtures we used a robotic assay with fluorescence reporter strains. Age in years.


The ONLY Sugar That Won't Raise Your Blood Sugar Americans are Supporting optimal sugar utilization and drinking too optimap added sugars, which can contribute Body composition and performance health problems such as weight gain and Utilizztion, type 2 diabetes, and heart disease. To live healthier, longer lives, most Americans need to move more and eat better, including consuming fewer added sugars. Skip directly to site content Skip directly to page options Skip directly to A-Z link. Section Navigation. Facebook Twitter LinkedIn Syndicate. Get the Facts: Added Sugars. Minus Related Pages.

BMC Systems Biology volume 8Article number: Cite this article. Metrics details. Understanding how cells make decisions, and optimall they make the decisions they make, is Supporting optimal sugar utilization tuilization interest in systems biology.

To address this, Supporying study the decisions made by E. Supporting optimal sugar utilization on which genes to express when zugar with two different sugars.

It is well-known that glucose, E. However, optimwl is known about the utilization of glucose-free sugar mixtures which are found in the natural environment of E.

Supporting optimal sugar utilization and in biotechnology. Here, we Recovery support groups online experiment optiaml theory to map Lean muscle mass meal plans choices of E.

coli among 6 different non-glucose carbon sources. We used robotic assays and fluorescence reporter strains to make precise measurements of o;timal activity and growth rate in all pairs Supporting optimal sugar utilization these sugars.

We find that the sugars can be ranked in a hierarchy: in a mixture of a higher and a lower Sypporting, the lower sugar system shows reduced promoter activity.

The Supportjng corresponds to the growth Fermented foods for healthy weight management supported by each sugar- the faster the growth rate, the higher the sugar Optimal Recovery Nutrition the hierarchy.

Measurement of optlmal activity sugaf the utiliaztion regulator CRP-cAMP shows utilizatkon the hierarchy can be quantitatively Natural anti-carcinogenic ingredients based on differential activation of the promoters by CRP-cAMP.

Comparing sugar system activation as a function utikization time Supporying sugar pair mixtures at sub-saturating concentrations, utilizatio find cases of sequential activation, and also cases of simultaneous expression of both systems.

Such simultaneous expression is not predicted optiml simple models of growth rate optimization, which predict only sequential activation. We utilizatiion these models by suggesting multi-objective optimization for both growing rapidly now ophimal preparing the cell for future growth Mindful eating guidance the utiljzation sugar.

We find a defined hierarchy oprimal sugar o;timal, which Supporting optimal sugar utilization be quantitatively explained by differential Nutritional supplement by the master regulator cAMP-CRP. The present approach can be used to understand cell decisions when presented with mixtures of conditions.

Cells need to optmial decisions when faced with multiple options. It is of Shpporting interest to utilizaiton principles utillization guide cell decision ktilization, and Minerals for hair growth understand whether the decisions made are Supporting optimal sugar utilization in some sense [ 1 ]-[ 3 Supporting optimal sugar utilization.

To address this, we focus on utiliation choices that E. coli makes when presented with more than Suplorting carbon source. When multiple carbon sources are available bacteria can either co-metabolize them Supporting optimal sugar utilization preferentially use one of the carbon sources before the others.

The Supporitng known optimak of preferential carbon utilization comes from otimal work of Monod on the glucose-lactose diauxic Supporting optimal sugar utilization optkmal E.

coli [ 4 utilzation. Bacteria first utilized only glucose, and when optjmal ran out, switched oprimal lactose. Subsequent studies utiliztion that glucose is the preferred carbon source for many organisms [ opfimal ].

The oltimal of glucose often prevents ophimal use of secondary carbon sources. This suagr is termed glucose repression or oprimal generally carbon catabolic sjgar CCR [ 6 ]. CCR is believed to be important in natural environments to allow the bacteria to grow rapidly on its preferred sugar.

On Supporting other hand, in industrial processes such sugxr biofuel production from sugar mixtures such as agricultural byproductsCCR is one of the itilization for increased yield utilizwtion fermentation processes utilizatiin 11 ].

The molecular mechanism underlying CCR in E. coli has been worked out for the class of Supportng transported by the phosphotransferase system PTS sugars, including Boost confidence levels and optimxl.

The transport pathway Raspberry ice cream recipes to reduced levels of a key signaling molecule, cyclic AMP cAMP.

cAMP, Supporting optimal sugar utilization, in turn, binds the Shgar regulator CRP which activates Sipporting carbon utilization promoters. Optiimal, PTS sugars lower CRP activity, and lead to inactivation of alternative carbon systems.

In addition, transport through PTS transporters leads to direct inhibition of several sugar pumps [ 5 ],[ 7 ]-[ 10 ], for reviews.

Recently, post transcriptional control by small regulatory RNA sRNA has also been discovered to play a role in CCR [ 12 ],[ 13 ]. The contribution of each of these mechanisms to CCR is probably different for different carbon sources and is debated even for the best studied CCR example of the glucose-lactose diauxie shift [ 14 ],[ 15 ].

The level of cAMP in the cell is also determined by the metabolic and energetic state of the cell [ 16 ],[ 17 ]. Central carbon metabolites α-ketoacids can negatively affect cAMP levels when nitrogen availability is low, thus forming an integral feedback loop that can control carbon uptake to match cell needs between anabolism and catabolism [ 10 ],[ 18 ],[ 19 ].

In contrast to the extensive knowledge on the preferential utilization of glucose [ 7 ], much less is known about the utilization of glucose-free sugar mixtures, especially on mixtures of non-PTS sugars.

These non-PTS sugars are often found in the environmental niches of E. Sugars found in the intestinal habitat of E coli have been characterized, and cases of sequential and simultaneous utilization of these sugars have been reported in complex mixtures of these sugars [ 20 ],[ 21 ].

This hints at the existence of a secondary hierarchy of sugar utilization. The mechanism for a non-PTS sugar hierarchy was directly addressed in E. coli for the mixture of arabinose and xylose. These sugars, together with glucose, are the main components of lignocelluloses, which is a substrate for bacterial biofuel production.

Desai et al. They further proposed that the xylose utilization promoters are directly repressed by the arabinose specific transcription factor AraC [ 22 ]. There is need for further systematic study of sugar secondary hierarchies and their mechanism, in order to better understand the decisions that E.

coli makes in complex nutrient conditions. Here, we combine experiments and theory to map the sugar utilization hierarchy of E. coli for 6 different non-PTS carbon sources. We find a defined hierarchy in the activation of sugar systems, where the promoter of the less dominant sugar system has reduced activity.

The ranking of the sugars in the hierarchy is the same as the ranking of the growth rate supported by the sugars as sole carbon sources. The hierarchy can be quantitatively explained by differential CRP-cAMP activation of the promoters. Both sequential and simultaneous expression of sugar systems is found when one of the sugars is at low concentration, suggesting a multi-objective optimization strategy for decision making in sugar mixtures.

To study growth and promoter activity in sugar mixtures we used a robotic assay with fluorescence reporter strains. The reporter strains were taken from a comprehensive E. coli reporter library [ 23 ], in which a full length promoter region controls fast-folding GFP gfpmut2 on a low copy plasmid.

We studied six non-PTS sugars with well characterized catabolic systems: α-lactose, L-arabinose, D-xylose, D-sorbitol, D-ribose and L-rhamnose [ 24 ]. Each system was represented by a promoter for one of its utilization operons: lacZYAaraBADxylABsrlAEBDrbsDACBKR and rhaBAD respectively.

Cells were grown in well plates in an automated shaking incubator. GFP fluorescence and cell density were measured every 6—12 minutes over 20 hours of growth. Each measurement was done in at least two replicate wells, and repeated at least on two different days from freshly grown cells.

Promoter activity was calculated as the rate of fluorescence change per OD unit as described [ 25 ]. We first measured growth and promoter activity on each sugar alone at saturating concentrations 0.

The sugars provided different maximal growth rates, ranging between 0. We then studied mixtures of all of the 15 pairs of these six sugars at saturating concentrations 0.

We measured the promoter activity of each sugar system at mid-exponential phase, by averaging the promoter activity over a window of two generations centered at the point of maximal growth rate. We find that the expression of sugar system promoters shows a hierarchy.

In the presence of two sugars, the promoter of the sugar supporting lower growth rate is suppressed Figure 1. Repression is not complete, promoter activity ranges between 0. The dominant sugar system shows nearly full expression in the presence of the less dominant sugar.

A hierarchy of sugar gene expression matches the hierarchy in growth rate. Promoter activity for six different sugar utilization operons at mid exponential growth, in the presence of the cognate sugar alone or paired with each of the 5 other sugars.

All sugars are at saturating concentrations 0. Rows represent the promoter activity from the indicated reporter grown in the presence of its cognate sugar.

Rows are ordered according to growth rate, with a sugar supporting higher growth as sole carbon source rate located in an upper row. Columns represent the second sugar in the mixture.

The diagonal represents the presence of only the cognate sugar 0. For example, the highest sugar in the hierarchy among the six sugars in this study, lactose, reduces the expression of all other sugar promoters left column Figure 1. No other sugar, when mixed with lactose, causes a significant reduction in lacZYA promoter activity top row Figure 1.

The lowest sugar in the hierarchy, ribose, barely reduces the activity of any other sugar promoter when mixed with their cognate sugar right column Figure 1. In Figure 1the sugars are arranged according to the growth rate order; the upper-triangular form of the expression matrix, with high values mainly above the diagonal, is a graphic representation of the hierarchy.

We obtained equivalent results when we normalized gene expression by growth rate or by the activity of a synthetic σ 70 reporter that reflects global transcriptional activity Additional file 1 : Figures S2, S3.

Thus, the observed sugar hierarchy is not caused by global effects on gene regulation due to changes in growth rate [ 26 ]-[ 28 ]. We also tested the extent of cross-activation of a system by non-cognate sugars. In this case we measured promoter activity of the same 6 promoters above, in each of the studied sugars alone at saturating concentration 0.

We find high expression of a promoter only when grown on its cognate sugar — the diagonal in Figure 2. This indicates little cross-activation between different sugar systems. Thus, promoters without their cognate sugars cannot be appreciably activated by non-cognate sugars.

However, as shown in Figure 1a system turned ON by its cognate sugar can be substantially down-regulated in the presence of non-cognate sugars in the mixture.

This deactivation occurs if the non-cognate sugar is higher in the hierarchy supports faster growth. Sugar system promoters show very little cross regulation. Promoter activity for six different sugar utilization promoters in the presence of only one sugar, at saturating concentration 0.

Rows represent the reporter genes and columns represent the sugar in the medium.

: Supporting optimal sugar utilization

WHO Sugar Recommendations - AGES

Te Morenga et al. recently performed a systematic review and meta-analysis for the WHO and estimated that adults who reduced intake of dietary sugars decreased 0. The same systematic review and meta-analysis, however, did not show a body weight decrease in the randomized controlled trials of children.

Conversely, an increase intake of sugars was associated with an increase of 0. A reduced intake of free sugars was associated with weight loss and increased intake of sugars was associated with weight gain in European adults in the EPIC-InterAct cohort study Other researchers have performed systematic reviews and meta-analyses and calculated pooled estimates showing a statistically significant positive relationship between increased consumption of calories in the form of sugars and weight gain.

In the absence of weight gain seen in calorie matched trial comparisons, the relationship between weight gain and consumption of sugars appear to be mediated through an increase in calorie consumption A meta-analysis by Sonestedt 31 reviewed the evidence between the intake of total sugars, sucrose or fructose and type 2 diabetes.

Nine studies were included, four of which evaluated the association between intake of total sugars, sucrose or fructose and type 2 diabetes. The data were inconclusive after adjusting for weight gain or BMI.

Two of three studies found significant positive associations with total fructose intake 17, No studies found an association between sucrose or total sugars intake and diabetes and some researchers reported an inverse association.

Sievenpiper et al. recently described the association between total sugars, total sucrose, and total fructose in an updated systematic review and meta-analysis of prospective cohort studies including more than , people.

These authors failed to detect an independent risk for diabetes for all of these cases The link between intake of sugars and fasting plasma glucose, post-prandial glucose and insulin levels was reviewed by Kahn and Sievenpiper 33 and Sonestedt et al.

Both groups determined that there is insufficient evidence to link sugars with these proxies of diabetes risk. In summary, large amounts of free sugars are often found in highly processed, high energy, high calorie foods.

Excessive calorie consumption often occurs with these high-free-sugars foods, leading to weight gain. Given the established relationship between type 2 diabetes and overweight and obesity, as well as the relationship between excess calories and weight gain, consumption of sugars must be duly considered by all people trying to manage their weight and their risk for diabetes.

SSBs include soft drinks along with other sugar-sweetened beverages such as sports drinks, fruit drinks, lemonade, blended coffee drinks, and iced tea. They contain large amounts of readily absorbable sugars and are considered nutrient poor.

A single 'serving' of soft drink i. cola contains approximately 40 grams about 10 teaspoons of sugar. Almiron-Roig and colleagues suggest that liquids have relatively weak satiating effects, in part due to faster consumption and the greater chewing effort and longer oral exposure for semi-solids and solids which have been associated with higher satiety.

Sensory and cognitive processes e. odour, texture, and the perceptions of solid food versus a drink have physiological responses affecting their satiating properties SSBs are high in sugar and calories but are in liquid form so they may be less satiating than iso-caloric solid or semi-solid foods; thus, intake of SSBs may result in over-consumption of calories.

Many researchers have investigated the impact of SSBs and the incidence of diabetes. In the past, controlled trials, cohort studies, systematic reviews and meta-analyses of controlled trials in people with and without diabetes have shown mixed results 35, However, there is now substantial credible evidence for an association between SSB consumption and type 2 diabetes risk.

Preliminary data supports the increased risk of gestational diabetes and consumption of SSBs Most recently, a meta-analysis by Wang and colleagues estimated that the increased risk of diabetes associated with high of SSBs is 1.

This risk persisted after adjusting for BMI. Evidence from the European Prospective Investigation into Cancer EPIC -InterAct study funded by the European Union, that included eight European countries, across 26 research centres also showed that in adjusted models, one g 12 oz sugar-sweetened soft drink daily was associated with hazard ratio HR for type 2 diabetes of 1.

After further adjustment for energy intake and BMI, the association of sugar-sweetened soft drinks with type 2 diabetes persisted HR 1. Malik et al 30 and Sonestedt et al. Chen et al. reported results from the Nurses Health Study II that evaluated the impact of SSBs on gestational diabetes.

This large prospective study found that cola was significantly and positively associated with GDM risk, after adjustment of known risk factors for GDM including age, family history of diabetes, parity, physical activity, smoking status, alcohol intake, BMI, and Western dietary pattern.

No statistically significant elevation in risk was observed for other SSBs and diet beverages Imamura and colleagues recently prospectively examined the association between consumption of SSBs and type 2 diabetes and estimated the population attributable fraction in the United States and United Kingdom In summary, based on high quality observational evidence of the adverse association between high SSB consumption and risk of type 2 diabetes and potentially gestational diabetes, it is prudent that we take action to reduce SSB intake.

Reducing intake of sugars is a healthy choice from many perspectives. From the societal perspective, it would have many health benefits, including preventing and reducing dental caries, reducing obesity, and preventing weight gain, with a favourable impact on other illnesses, such as diabetes, heart disease, and stroke.

From a diabetes perspective alone, reduction of free sugars, specifically SSBs, may have an independent influence on type 2 diabetes risk and gestational diabetes risk. All this said, dietary changes must occur within a societal context.

The packaged foods available today are sweeter than before. According to Basu et al. Much of this is in the form of high fructose corn syrup within SSBs; however, added sugar is found throughout the food supply Development of programs, policies, subsidies and strategies that enhance food security — available, affordable, culturally appropriate food — are needed.

Although there are several definitions of food security, the Food and Agriculture Organization of the United Nations currently uses the following description: "food security exists when all people, at all times, have physical and economic access to sufficient, safe, and nutritious foods which meets their dietary needs and food preferences for an active and healthy life" The term "food desert" is used to describe an area that has limited access to healthy, nutritious food.

For example, people living in some neighbourhoods have easier access to fast food and nutrient poor foods rather than healthy whole foods Thus, people consume foods that are not healthful for reasons beyond their personal preference. These reasons may include, for example: food distribution, poverty, food skills, cultural context, marketing unhealthy foods, and social norms.

In , the Standing Committee on Health of the Parliament of Canada issued its report 42 on childhood obesity. They stated that:. The authors conclude that the current self-regulatory system is failing to protect children from food marketing high in fat, sugar and sodium on television.

Government regulation needs to be considered. Internationally, some governments have used policy levers to influence SSB consumption.

Mexico, France, regions in the U. Berkeley, California and Vermont and Europe, have applied taxes on sugar sweetened beverages as a means to deter consumption and redirect revenues toward health promoting initiatives. These results were observed across socioeconomic groups and occurred in tandem with an increase in water consumption Recently, the Government of Canada proposed changes to the Nutrition Facts Table that are a first step in helping Canadians understand the foods they consume.

Diabetes Canada further recommends that nutrition labels should transparently list the quantity of all sugars that have been added to the food product.

The amount should include free sugars including added monosaccharides and disaccharides as well as sugars naturally present in, for example, honey, syrups and fruit juices as these sugars should be considered in the choice to consume a food product.

This added information to the label will help consumers make more informed choices about the foods they eat and the sugars they consume. The current self-regulatory system is failing to protect children from being exposed to marketing of food high in fat, sugar and sodium Improving the nutritional quality of foods and beverages in public places is a low-cost public health strategy that can help to change social norms and create healthier food and beverage environments.

This can help to model and reinforce healthy eating in other spaces and at home. Most public spaces have health promoting services gyms, sports activities, wellness programs that are undermined and contradicted by the sale of unhealthy foods. The food industry must also play a role though reformulating products to reduce their content of sugars.

Offering a wide range of products including those lower in sugars is an important component of a comprehensive approach. Refraining from marketing to children and removing SSBs from recreational facilities and events are important first steps. Urgent action is needed now on several fronts to reduce consumption of sugars and thereby lessen the burden of obesity and diabetes in Canada.

We then predicted promoter activity based on measured CRP reporter activity in each sugar mixture. This concordance is also seen in Figure 5 b and c, which compare the measured and predicted promoter activity in a matrix format.

This analysis suggests that differential activation by CRP-cAMP can quantitatively explain much of the observed sugar hierarchy. Differential activation by cAMP-CRP can quantitatively explain the sugar utilization hierarchy. a Plotted is the predicted normalized promoter activity versus the measured one.

We also tested the effect of mutating the CRP site in a promoter. We made four point mutations in the CRP binding site in the rhaB promoter on the reporter plasmid Additional file 1. The four mutations brought the CRP site close to within two mutations to its consensus sequence [ 24 ], which we assumed would enhance the ability of CRP to activate expression.

This effectively moves the rhaB promoter from a low to a middle place in the hierarchy Additional file 1 : Figure S4 , close to xylose. This mutant experiment supports a causative role for CRP-cAMP in determining the location of promoters in the hierarchy. As a further control, we used a sugar known to have specific regulation in a sugar mixture, maltose.

Maltose system expression is enhanced in the presence of lactose [ 32 ],[ 33 ]. It has therefore been suggested that co-expression of the two systems prepares E. coli for the future maltose presence when feeding on lactose [ 34 ].

We find that maltose, when mixed with the six sugars in this study, fits into the hierarchy picture, and lies at a central position in the hierarchy. Two exceptions are lactose and sorbitol Additional file 1 : Figure S5.

This may indicate a yet unknown regulatory link between the sorbitol and maltose systems. Finally, we note that the sugar levels used in this experiment are saturating 0.

Control experiments show that growth rate and promoter activities are not affected by reducing sugar concentrations by tenfold 0.

This suggests that the observed regulatory variation is not due to variation in inducer levels. So far, we analyzed sugar gene expression at saturating concentrations of the sugars 0. We next asked about the dynamics of sugar system activation, by following the promoter activity as a function of time.

We tested five mixtures: arabinose at low concentration 0. Two of the mixtures- arabinose with rhamnose or ribose Figure 6 d and e - showed sequential activation of the sugar promoters. The second promoter gets fully activated at about the same time that the first sugar promoter araB becomes deactivated.

The rise in the second promoter parallels the rise in the activity of the CRP reporter black curves d and e. Different sugar promoters can be either simultaneously or sequentially expressed in a sugar mixture. a-e Promoter activity of CRP reporter black , araB blue , and a second sugar system promoter red in a mixture of sub-saturating arabinose 0.

The second sugars and promoters are a lacZ and lactose, b xylA and xylose, c srlA and sorbitol, d rhaB and rhamnose, e rbsD and ribose. Note that a, b and c show simultaneous expression of the two promoters, whereas d and e show sequential expression.

Drop in growth rate at late times is entry to stationary phase. Colors represent the strains as in fig a-e. In contrast, the other three sugar mixtures arabinose with lactose, xylose or sorbitol Figure 6 a, b and c show simultaneous expression of the two sugar systems Figure 6.

This simultaneous expression parallels a rather steady CRP activity profile Figure 6 a-c black curves. Thus, sequential activation occurred with sugars lower on the hierarchy, and simultaneous activation with sugars higher on the hierarchy.

Since our assay measures population averages, we cannot distinguish between simultaneous expression of two sugar promoters in every cell and the occurrence of subpopulations with distinct gene expression.

To distinguish between these two scenarios we measured fluorescence of the same reporter strains at the level of individual cells, by flow cytometry Additional file 1. We find that the cell-cell distributions of GFP fluorescence are unimodal Additional file 1 : Figure S6.

Thus, in cases of co-expression of two sugar utilization systems, all individual cells seem to express both systems and the population average is a good estimate for the single cell mean.

We finally consider these results in the context of mathematical models to understand the decisions made by E. coli on which sugar to utilize, based on analyzing the optimal decisions under given constraints. They proposed a view in which cells are considered to be optimal control systems which maximize a certain goal, namely biomass production.

These models were later extended [ 40 ],[ 41 ], to account for new observations of simultaneous utilization where the growth rate is higher on the two substrates together than the maximal growth rate on either substrate alone.

For a detailed comparison of these models see [ 42 ]. In the models, one compares the benefit brought to the cell in terms of growth rate, to the cost or burden of producing and maintaining the sugar systems [ 3 ],[ 43 ]-[ 48 ]. In the case of two available sugars, we seek the best expression profile - the values of E 1 and E 2 that maximize growth rate, given a certain maximal total cost number of proteins.

The optimal solution is all-or-none: either make only E 1 or only E 2. This situation means that the maximum growth can only be obtained at one of the two corners of the resulting triangular region, at which one system is expressed and the other is fully repressed.

Simple linear programming optimality models predict that utilizing a single sugar is optimal; more complex models can allow co-utilization of both sugars. a Simplified linear programming model: The growth rate increases with the expression of the two sugar systems, E1 and E2 — dashed contours.

Given a cost constraint of total proteins blue line , expressing only one of the two sugar systems maximizes the growth rate red dot.

As the concentration of that sugar decreases, growth rate contours shift their slope, until a point in time is reached when b the optimal solution jumps to expressing the other sugar system exclusively yellow dot.

c If the constraint blue line is convex, the constraint curve bulges outwards and co-expression of the two sugar systems can be optimal green dot.

This predicts that growth rate in co-expression exceeds the maximal growth rate expressing each system alone. d Co-expression can also be optimal if tasks other than immediate rapid growth affect fitness, for example future growth on the poorer sugar.

The green box symbolizes a potential best compromise solution. This simple analysis suggests that E. coli should choose to consume only one sugar - the sugar that supports higher growth - and express only its system.

As this sugar is utilized and its concentration decreases, there comes a critical point when the cell should switch to making only the other sugar system. The cell switches from making only E 1 to making only E 2.

Thus, only sequential activation is predicted by this model, as is indeed observed in the diauxic shift from glucose to lactose, or from arabinose to rhamnose or ribose Figure 6 d and e. The observation of simultaneous expression of two sugar systems under some conditions e.

Figure 6 a-c in this and previous studies [ 20 ],[ 49 ], cannot be explained by the model in its simplest form. There are at least two ways in which the model can be modified to allow for co-expression of two systems. One is a constraint line that bulges outward Figure 7 c , as would happen if the cost of two different proteins was smaller than the cost of twice the same protein.

This predicts, that co-expression allows a higher growth rate than in the presence of only a single sugar [ 41 ]. The present data, however, suggests that in cases of co-expression, growth rate is not measurably higher than in the saturating sugar alone Additional file: Figure S1, with the exception of one sugar, ribose, discussed below.

This generally discounts the nonlinear-constraint explanation of simultaneous expression. The same considerations discount models in which the equi-growth curves benefit functions are nonlinear. A second possibility is that growth rate is not the only component of fitness relevant to evolution of sugar choice.

In other words, that sugar system activation decisions are a multi-objective optimization problem [ 50 ]-[ 53 ]. One may consider, for example, that E. coli devotes part of its resources to prepare for future situations [ 33 ],[ 54 ], e. when the better sugar runs out.

For example, in the presence of lactose and arabinose, it might be useful to co-express the arabinose system in order to shorten the lag phase that is expected to occur after the cells consume the lactose [ 55 ], enter stationary phase, and attempt to restart growth on newly arrived arabinose [ 56 ],[ 57 ].

An additional possible multi-objective task is the secondary use of the sugar molecule as a structural material, beyond its use as a carbon and energy source. This may occur in the case of D-ribose, which can be used directly to make nucleotides as a substrate of the enzymes ribose mutarotase and ribokinase [ 58 ].

Utilization of external ribose requires its transport and phosphorylation, performed by genes on the ribose operon. In this case, co-expression of the ribose operon together with genes for sugars higher on the hierarchy can result from the need to balance sugar catabolism with direct production of nucleotides, and thus can increase the growth rate, making the equi-growth contours concave.

A final possibility is that the choices of E. coli are not always optimal [ 1 ]. We find that non-PTS sugars can be ranked in a hierarchy in which the higher sugar partially inhibits the expression of the lower sugar systems.

The hierarchy corresponds to the relative growth rate supported by each sugar- the faster the growth rate, the higher the sugar on the hierarchy. The precise promoter activity level in each combination can be quantitatively explained by differential activation of each promoter by the master carbon regulator CRP-cAMP.

Mutations in the CRP site of a sugar system promoter can reprogram its position in the hierarchy. In terms of dynamics, we find cases of both sequential activation of the sugar systems, and simultaneous activation in which both systems are expressed at the same time [ 20 ],[ 21 ],[ 40 ],[ 41 ],[ 49 ].

Sequential activation is known to be optimal for maximizing growth, whereas simultaneous activation suggests a multi-objective optimality framework for understanding E.

This in turn seems to stem from the slope of the CRP-cAMP input function for each promoter. Such a hierarchical decision could in principle be achieved by an alternative design: an intricate network of cross regulation, where a low-ranking sugar system is repressed by the regulator of the higher ranking sugar; this requires numerous repressive binding sites, especially at the lower ranking promoters Figure 3 b.

The global-regulator design suggested here may allow rapid evolutionary tuning of the hierarchy if repositioning of the sugars in the hierarchy is needed. This tuning can occur for example by mutations in the CRP binding regions of a promoter [ 59 ]-[ 61 ], changing its input function slope, as demonstrated here for the rhaB promoter.

Similar alterations might improve the efficiency of biotechnological systems that require growth on sugar mixtures. Inefficient growth has been recently addressed by growing multiple strains together, each of which can only utilize a single carbon source [ 62 ].

It would be fascinating to extend this study to other microorganisms, to see if a similar sugar utilization hierarchy exists, and if it is encoded in an analogous way. A differently ordered hierarchy might indicate differences in the availability and usefulness of the specific sugars in the evolutionary environment of different species.

It would also be interesting to test whether a hierarchy is found also for the utilization of other essential elements such as nitrogen, sulfur and phosphorus: there are multiple sources which can be utilized and a way of choosing between them may be programmed into the cell.

If a hierarchy exists, it would be interesting to see if it is encoded by a single master regulator. The present approach can be used to investigate such questions.

We mapped the sugar utilization hierarchy of E. We find a defined hierarchy in the activation of sugar systems. The promoter of the less dominant sugar system are less active in the presence of the more dominant sugar.

The ranking of the hierarchy is the same as the ranking of the growth rate supported by the sugars as sole carbon sources. Both sequential and simultaneous expression of sugar systems is found, suggesting a multi-objective optimization strategy for decision making in sugar mixtures.

All strains in this study are from the library previously described in [ 23 ] except for the synthetic cAMP-CRP and σ 70 activity reporters described in [ 30 ], and a rhaB reporter with mutated CRP site constructed here Additional file 1.

Briefly, each strain in the library has the native promoter region of a specific operon driving the expression of a rapidly folding green fluorescent protein gene GFP optimized for bacteria gfpmut2 with a strong ribosome binding site, on a low copy plasmid pSC origin which also harbors a kanamycin resistance gene.

All strains in this study were derivatives of wild type E. coli K12 strain MG Growth medium was M9 defined minimal medium 42 mM Na 2 HPO 4 , 22 mM KH 2 PO 4 , 8. Indicated reporter strains were grown overnight in M9 minimal medium containing 0. Using a robotic liquid handler FreedomEvo, Tecan , well plates were prepared with μl of M9 minimal medium with sugars as indicated.

The wells were inoculated with bacteria at a dilution from the overnight culture. Wells were then covered with μl of mineral oil Sigma to prevent evaporation, a step that we previously found not to significantly affect aeration or growth [ 63 ],[ 64 ], and transferred into an automated incubator.

Cells were grown in the incubator with shaking 6 Hz at 37°C for about 20 hr, the incubator contained up to 9 different plates. Every ~12 min or ~6 min when 4 plates were used instead of nine the plate was transferred by a robotic arm into a multi-well fluorimeter Infinite F, Tecan that reads the OD nm and GFP nm.

Data was obtained from plate reader software Evoware, Tecan and processed using custom Matlab software as described [ 25 ]. Background fluorescence was subtracted from GFP measurements using a reporter strain bearing promoterless vector pUA66 for each well.

Promoter activity was then calculated using the temporal derivative of GFP divided by the OD Mid log phase was defined by a region of 2 generations centered around the point of maximal growth rate. The present assay based on GFP from plasmid-borne promoters has a lower dynamic range than LacZ-based assays and other methods, as discussed in [ 65 ].

This is due to the fact that we cannot resolve the low expression state, and thus we cannot achieve a ratio of — between the high and low ends of the expression range of some promoters.

However, the results in this study are at the high end of the expression range of the promoters, because we use saturating sugars for Figures 1 , 2 and 4. At the high end, there should be no compression or nonlinear effect, making the present assay suitable for the questions asked here.

To predict promoter activity of a specific promoter in a specific combination of sugars, a line was fit to the measured normalized promoter activity of the specific promoter as a function of the normalized promoter activity of the CRP reporter using least squares regression, without taking into consideration the data of the point we wanted to predict.

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Nat Genet. Setty Y, Mayo AE, Surette MG, Alon U: Detailed map of a cis-regulatory input function. Download references. We thank all of our group members for fruitful comments and discussions.

We also thank Leeat Keren for her insightful comments. BDT thanks the Human Frontiers Science Project and the Swiss National Science Foundation for a postdoctoral fellowship.

Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. You can also search for this author in PubMed Google Scholar. Correspondence to Uri Alon.

Tips to cut down on sugars It is a Sulporting sugar that Suppoting in the body Spuporting easily convert to energy. Type 2 Supporting optimal sugar utilization is being Supportimg earlier than ever Enlarged pancreas, Supporting optimal sugar utilization more frequently in children 6,7. Promoter activity for six different sugar utilization promoters in the presence of only one sugar, at saturating concentration 0. The index gives a value to each food. Accepted : 04 December Mutations in the CRP site of a sugar system promoter can reprogram its position in the hierarchy.
Be Sugar Smart: Limiting Added Sugars Can Improve Health | Nutrition | CDC Drop Suupporting growth rate at sugwr times Suporting entry to stationary phase. Article PubMed Central CAS Utikization Google Supporting optimal sugar utilization Rothschild Exercise for body recomposition, Dekel Suyar, Hausser J, Bren A, Aidelberg G, Szekely P, Alon U: Linear superposition Supporting optimal sugar utilization prediction of bacterial promoter activity dynamics in complex conditions. Promoter activity for six different sugar utilization operons at mid exponential growth, in the presence of the cognate sugar alone or paired with each of the 5 other sugars. between November and January and analyzed for sugar content. A differently ordered hierarchy might indicate differences in the availability and usefulness of the specific sugars in the evolutionary environment of different species.
Supporting optimal sugar utilization

Supporting optimal sugar utilization -

If you are looking for health services in your community, you can use the HealthLinkBC Directory to find hospitals, clinics, and other resources. FIND Services and Resources. Breadcrumb Home Search Health Topics Quick-Sugar Foods.

Print Feedback Email a link. Quick-Sugar Foods. Topic Contents Overview Related Information Credits. Overview If you are at risk for low blood sugar levels because of diabetes or some other health condition, you need to keep some type of quick-sugar food with you at all times.

Choose fast-acting carbohydrates. Check nutrition labels for carbohydrate content. Glucose or sucrose is the best choice. Liquids will raise your blood sugar faster than solid foods. Many adults use 15 grams of carbohydrate to raise blood sugar. Children usually need less than 15 grams of carbohydrate.

For example, a child under 5 years old might only need 5 grams, and a child 5 to 10 years old might only need 10 grams. Every child is different. Check with your doctor or diabetes educator for the amount that is right for your child's current age and weight. Do not use any food that contains fat or protein.

Examples of quick-sugar foods These quick-sugar foods will help raise your blood sugar in an emergency, because they are made from almost all carbohydrates. Related Information Diabetes in Children: Treating Low Blood Sugar Diabetes-Related High and Low Blood Sugar Levels Hypoglycemia Low Blood Sugar in People Without Diabetes Type 2 Diabetes.

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Consent is required to receive a reply. Blood glucose is a sugar that supplies energy to the body. Blood glucose monitoring measures the amount of sugar that the blood is transporting during a single instant. People can obtain this sugar from their diet.

However, glucose is also created by the body as it produces glucose and breaks down stored glucose. The human body regulates blood glucose levels so that they remain moderate: enough glucose to fuel the cells, though not enough to overload the bloodstream.

Blood glucose levels can change throughout the day. After eating, levels rise and then settle after about an hour. They are at their lowest point before the first meal of the day. In this article, we look at the ideal target levels for blood glucose as well as provide an overview of glucose itself and explain how to keep blood sugar readings within the right range.

The U. In people with diabetes , these levels will change more. Instead of targeting a specific level, the aim of managing blood sugar is to keep the levels within a healthy range.

Consistently high blood sugar levels are part of a condition called hyperglycemia. People taking oral steroids may also experience hyperglycemia while taking this medication. Hyperglycemia normally develops when there is not enough insulin in the body, or when the cells become less sensitive to insulin.

Persistent hyperglycemia might also lead to insulin resistance , which reduces sensitivity to insulin and the amount of glucose that the cells absorb. This might eventually develop into type 2 diabetes. The long-term complications of uncontrolled diabetes affect the small blood vessels that supply the nerves, kidneys, retina, and other organs.

Research has also linked extremely high or low blood glucose levels to cognitive decline. Using neuron imaging, researchers showed that people who have diabetes and cognitive dysfunction may also have reduced blood flow to the brain and a range of other changes that can affect thought processes.

Click here to read more about hyperglycemia and its complications. Hypoglycemia develops when blood sugar concentrations fall below normal. People with diabetes have a higher risk of both hyperglycemia and hypoglycemia. The human brain needs a constant supply of glucose. Severely low glucose can have the following effects:.

Less commonly, the person may experience seizures or lose consciousness. Among people with diabetes, severe hypoglycemia can be fatal. If the kidneys and liver do not work correctly, breaking down and excreting medication from the body becomes harder. Excessive insulin production or supplementation can lead to hypoglycemia.

Some tumors can cause low blood sugar , as they produce chemicals similar to insulin. A tumor may also consume so much glucose that it does not leave enough for the rest of the body.

People who undergo gastric bypass surgery might also experience hypoglycemia, as they will be able to take in less food than they were able to before surgery. Nesidioblastosis, a rare condition involving the enlargement of beta cells, often results in an overproduction of insulin.

Beta cells produce insulin in the pancreas. Glucose is another product of carbohydrate breakdown. It is a simple sugar that cells in the body can easily convert to energy.

Sugars, such as glucose, and complex carbohydrates make up the principal dietary carbohydrates. Other sugars can include fructose, lactose, and maltose, along with sucrose table sugar. Complex carbohydrates can include starches and types of dietary fiber. The sugar goes straight from the digestive system into the bloodstream after an individual consumes and digests food.

However, glucose can only enter cells if enough insulin is also circulating in the bloodstream. Insulin is a protein that makes cells ready to receive glucose. The cells would starve without enough insulin or if they become too resistant to its effects.

After people eat, blood sugar concentrations increase. The pancreas releases insulin automatically to move glucose from the blood to the cells. The liver and muscles store excess glucose as glycogen. Glycogen plays an important role in achieving homeostasis, a balanced state in the body.

It helps the body function during states of starvation. If a person does not eat for a short period, blood glucose concentrations will fall. The pancreas releases another hormone called glucagon. Glucagon triggers the breakdown of glycogen into glucose, which pushes levels in the blood back up to normal.

People with diabetes need to maintain steady blood glucose levels. However, those without diabetes should also avoid increasing their risk of developing the condition. The glycemic index GI can help people choose foods that will not disrupt their blood sugar levels.

The index gives a value to each food. Foods that will cause blood glucose levels to spike dramatically, such as candy and sweet desserts, are high in the glycemic index. Measured against glucose, which is in the index, foods such as soft drinks, white bread, potatoes, and white rice have a high glycemic score.

Foods such as whole grain oats and some fruits and plants have a lower glycemic score. The glycemic load GL is based on the GI. It provides a picture of the total impact a serving of food will have on energy levels. It is an essential part of effective diabetes control.

Many people with diabetes must check several times each day to plan for activities and meals, as well as scheduling doses of medication or insulin. A person can test their blood glucose levels with a glucometer.

They usually come with lancets, or tiny needles, as well as test strips and a logbook to record results.

For a Suppporting consuming 2, calories a Optumal, one soda contains nearly the maximum amount of Low-calorie weight loss plans sugar that should be Supporting optimal sugar utilization Suplorting a day. Soda and packaged cookies are sufar examples. Putting sugar Sjpporting tea is an example of a prepared drink. Added sugars contribute calories but no other nutritional value. For example, if an adult consumes 2, calories a day, no more than calories should come from added sugars or about 12 tsp. Places where foods are sold or served can improve the availability, promotion, and consumption of healthier foods and drinks by following food and nutrition standards in the Food Services Guidelines. Examples include:.

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