How does fatty acid oxidation contribute to gluconeogenesis

However, acetyl-CoA or acetoacetyl-CoA can be used for ketogenesis to synthesize the ketone bodies, acetone and acetoacetate.

Thus, these amino acids are instead termed ketogenic green. Fatty acids and ketogenic amino acids cannot be used to synthesize glucose.

The transition reaction is a one-way reaction, meaning that acetyl-CoA cannot be converted back to pyruvate. Even if acetyl-CoA enters the citric acid cycle, the carbons from it will eventually be completely oxidized and given off as CO2. The net result is that these carbons are not readily available to serve as keto-acids or carbon skeletons for amino acid synthesis.

Some amino acids can be either glucogenic or ketogenic, depending on how they are metabolized. Some findings imply that free-FAs increase hepatic GNG in both healthy and diabetic individuals [ 12 , 13 ], as well as in the perfused liver [ 14 , 15 ], and in isolated hepatocytes [ 16 , 17 ]. Yet, the mechanism underlies this proposed stimulatory effect is far from being elucidated. Due to correlation between lipid utilization and hepatic GNG rates [ 16 , 18 ], the effect was mostly attributed to increased FAs oxidation.

Other data demonstrate that FAs activate mitochondrial transport of pyruvate independently of their oxidation [ 14 ], thus rule out that acetyl-CoA and NADH levels are involved. Alternatively, FAs oxidation was suggested to directly stimulate gluconeogenic enzymes [ 19 ]. In contrast, an inhibitory effect of FAs on hepatic GNG has also been demonstrated both in vitro [ 20 ] and in vivo [ 21 ].

Another study reported stimulatory as well as inhibitory effects of FAs on glucose production in hepatocytes, depending upon the type of FAs and the type of gluconeogenic precursor used [ 22 ]. Therefore, some of the inconsistency could arise from the diversity of FAs, different exposure times, and variety of experimental models used in various studies. Another explanation to some of controversy might be that FAs activate GNG only in the fed state, but not under fasting [ 16 ] or when GNG is induced [ 23 ], and may even attenuate GNG during fasting [ 24 ].

Our findings, indicating an inhibitory effect of FAs on PEPCK expression in hepatocytes, are seemingly not compatible with several former data.

Those include studies reporting stimulation of PEPCK expression by short-chain FAs in hepatoma cells and in primary hepatocytes, as well as by mid-, and long-chain FAs in primary hepatocytes [ 17 , 25 ]. Stimulatory effect was also demonstrated by oleate in FaO hepatoma cell line [ 26 ], although later experiments have failed to reproduce this result in other hepatoma cell lines or in primary hepatocytes reported in [ 27 ].

Anyhow, performed in absence of gluconeogenic stimulus, all these previous studies reflect the effect of FAs on basal PEPCK expression rather than the effect under inductive conditions which was examined in the current study. Since there is no known post-translational regulation of PEPCK [ 27 ], inhibition of gene induction is expected to be directly reflected in reduction of enzyme activity. G6pase was also previously investigated by a number of studies as an optional site for FAs involvement in GNG regulation, yet much discrepancy exists between different in vitro and in vivo findings of FAs effect on G6pase expression [ 28 ].

Stimulatory effect upon G6pase expression was reported by short-chain FAs in primary hepatocytes and hepatoma cell cultures, and by mid- and long-chain FAs in isolated hepatocytes [ 17 , 29 ]. Another study found no effect of saturated and monounsaturated FAs, but a suppression effect of G6pase expression by polyunsaturated FAs in hepatoma cells [ 30 ]. Yet again, performed in a hormone-free environment, these studies are not representative of FAs effect when GNG is physiologically induced in vivo.

Still, the physiological importance of this observation is unclear, since basal expression levels are very low relatively to the induced expression levels. Since FAs are metabolized by hepatocytes, their effect could be attributed to FAs per se , as well as to various FAs-derived metabolites, acting as a relay. Yet, the rapid effect demonstrated, obtained after 1 hour pre-treatment with FAs, does not support the idea of metabolite-mediated effect.

In addition, the inhibitory effect obtained using FAs-Br, containing the nonmetabolized FA 2-bromopalmitate, is no lesser than the inhibition caused by metabolized FAs. This implies that the effect is generated by FAs themselves. Nevertheless, a quick indirect effect, mediated by a FAs metabolite s , cannot be completely excluded. These results are in agreement with previous studies indicating that long-chain FAs have no stimulatory effect on CPT1 gene expression in primary hepatocytes [ 32 ] and that the response of GPAT gene expression to fasting and refeeding is regulated by insulin [ 33 ].

They imply that the inhibitory effect of free-FAs upon the induction of gluconeogenic genes is direct, rather than created as a secondary result to changes induced in lipid metabolism pathways. Several transcription-related proteins have been suggested to mediate effects of FAs on genes expression [ 41 ], while modification of transcription factors phosphorylation to alternate transactivation capacity is one proposed mechanism [ 42 ].

This hypothesis suggests a novel mechanism that underlies this metabolic aberration. Hepatocytes were isolated according to the method described [ 43 ], with minor modifications. Control cells were treated with appropriate vehicle solution composed of BSA and ethanol.

Gluconeogenic stimuli was induced by 0. D, Sigma added to the culture medium for 6 hours [ 34 , 46 ]. Nile-Red cat. Cells were washed with phosphate-buffered saline PBS , pH 7. Cultured cells were scraped, centrifuged, resuspended in PBS, and sampled for determination of protein concentration by Bradford method cat. B, Sigma. Cells were centrifuged again, resuspended in chloroform:methanol , and shacked gently for 30 minutes. Chloroform phase containing lipids was collected and vacuum-dried.

Pellets were redissolved in chloroform for further analysis. Lipid extraction samples were analyzed relative to standards on a silica gel plate Merck, Germany , as described [ 47 ].

The solvent system comprised petrol ether, diethyl ether, and acetic acid in a volumetric ratio of 80 : 19 : 1. Visualization of the compounds on the plates was performed with iodine staining. Quantitative determination of lipid spots intensity was performed by scanning densitometry. Total RNA was isolated from the cells by using Trizol according to the instructions of the manufacturer.

The results were normalized to the Glyceraldehyde 3-phosphate dehydrogenase GAPDH expression used as endogenous control and fold-change expression was calculated by using Ct values in comparison with experimental controls that received a value of 1.

Culture medium was replaced with 0. After 1 hour the buffer was collected and the glucose concentration was measured by enzymatic colorimetric glucose assay. P, Sigma. Supernatants were collected and used for Western blot analysis. Protein expression was analyzed by standard Western blot techniques.

In brief, protein samples were denatured in electrophoresis buffer cat. Proteins were transferred onto nitrocellulose membranes, and equal loading was confirmed by ponceau red staining. The membranes were blocked in Tris-buffered saline with 0.

Immunodetection was done by using anti-CREB antibody cat. After a subsequent washing step, peroxidase-conjugated anti-rabbit immunoglobulin cat. Visualization of immunoreactive bands was performed by using ECL detection reagents, and the signal was detected by short exposure to x-ray film. Bands were quantified by scanning densitometry and expressed as arbitrary units. D of experiments performed in triplicate. Each experiment was repeated in cells of at least two independent cell isolation procedures.

Thus, the presented pathways are all thermodynamically feasible. In order to analyze the role of the described pathways during prolonged starvation and fasting, we computed for amino acids, fatty acids, lactate and glycerol how much energy in form of ATP is regained in the net balance if they are used for gluconeogenesis and subsequently catabolized.

This quantity, termed gluconeogenic energy efficiency, is defined as the ratio of the above mentioned net amount of energy and the energy that would be obtained by catabolizing the substrate directly Fig. That quantity equals unity if both pathways provide the same amount of ATP. Furthermore, we determined how well amino acids, fatty acids, lactate and glycerol are suited for the storage of glucose. This measure, termed glucose storage efficiency, is defined as the relative amount of glucose regained if these compounds are produced from glucose and subsequently converted back into glucose Fig.

Details on the calculation are given in Text S1. Gluconeogenic energy efficiency and glucose storage efficiencies of a selected list of compounds are displayed in Fig. These values can explain the particular efficiency of carbohydrate reduced and ketogenic diets like Atkins diet for weight reduction.

The reason is likely to be the increased energy loss of gluconeogenesis from fatty acids and ketogenic amino acids in comparison to gluconeogenesis from glucogenic amino acids. Indeed, intermediates of the pathway for gluconeogenesis from fatty acids have been observed in subjects on the Atkins diet [40]. This is also supported by the observation that the traditional diet of inuit does not lead to obesity in spite of the high content in fat. In contrast, new dietary habits of inuit implying a higher consumption in carbohydrates often lead to obesity [48].

Comparing the glucose storage efficiency of the described compounds Fig. Hence, the conversion of glucose to fatty acids and gluconeogenesis from fatty acids results in a loss of half of the glucose.

These values show that fatty acids are not very well suited as glucose storage since their use as such is associated to a higher loss of glucose equivalents of carbohydrates in comparison to glycerol and amino acids that are the major carbohydrate storage compounds besides glycogen. Nevertheless, as discussed above, the utilization of fatty acids as glucose storage gives the body additional flexibility in the utilization of its storage compounds and appears to be used as such in situations during which gluconeogenesis is active.

Moreover, both quantities can be useful for examining the effect of caloric restriction on ageing which is known to extend life span in a large number of organisms [49]. Summarizing our findings, it can be concluded that a thorough, systematic and detailed in-silico investigation of the stoichiometrically feasible routes from fatty acids to glucose based on an experimentally corroborated genome-scale metabolic network provides new insight into human metabolism under glucose limitation.

It confirms earlier, anecdotal evidence and hypotheses about gluconeogenesis from fatty acids via acetone and provides hitherto unrecognized pathways for that conversion.

This provides a plausible explanation for the surprising independence from nutritional carbohydrates over certain periods e. Moreover, we provided a detailed analysis of the energetic balance of these pathways, which explains their limited capacity and their contribution to the particular efficiency of carbohydrate reduced and ketogenic diets. For our analysis we used a genome-scale model of human metabolism [20] in which we split reversible reactions into irreversible forward and backward directions.

We set all external metabolites to internal and added an outflow reaction for each of them. To detect pathways converting even-chain fatty acids into glucose, we furthermore added an influx of mitochondrial acetyl-CoA and an outflow of cytosolic glucose 6-phosphate and mitochondrial coenzyme A.

Furthermore, we added several reactions of amino acid degradation that were not present in the model and replaced reactions of the respiratory chain by a net reaction since the model could produce ATP without consumption of any other metabolite otherwise. Moreover we constrained the flux through several reactions allowing the unconditional production of ATP to zero for further details see Text S1. The final network contains reactions and metabolites. Elementary flux patterns have been introduced as a new theoretical tool for the analysis of metabolic pathways in genome-scale metabolic networks [21].

Within a subsystem of metabolism, that is, a set of reactions of interest, elementary flux patterns correspond to basic route through that subsystem that are compatible with steady-state fluxes through the entire network. Thus, every elementary flux pattern is associated to at least one steady-state flux of the entire system and corresponds to the set of reactions used by this steady-state flux in the subsystem.

The property of elementarity in the definition of elementary flux pattern requires that no elementary flux pattern can be written as set union of other elementary flux patterns. A formal definition will be provided next. Given the stoichiometric matrix of a reaction network we assume for simplicity that the first reactions make up the subsystem of interest.

A flux pattern is defined as a set of reactions within the subsystem that is compatible with at least one steady-state flux of the entire system.

Hence, a set of reaction indices is called a flux pattern if there exists at least one flux vector that fulfills the following conditions:. Furthermore, we call a flux pattern elementary if this set of reactions cannot be written as set union, of other flux patterns.

For more details see Kaleta et al Elementary flux patterns can be used to elucidate all possible pathways consuming a certain compound and producing another. This process builds upon a successive expansion of the subsystem under study to reactions that belong to alternative pathways.

It will be outlined by way of a small example network comprising glycolysis and the pentose phosphate pathway Fig. Within this system we want to find all pathways producing ribosephosphate R5P , a precursor of histidine and nucleotide syntheses from glucose Glc.

Black arrows correspond to reactions belonging to the subsystems. A , D and F Subsystems used in the different iteration steps. B , C and E Selected elementary flux patterns thick black arrows and the reactions used by an associated elementary mode in the remaining system thick gray arrows. If only one direction of a reversible reaction belongs to a subsystem, the reverse direction is omitted for clarity.

G and H Elementary flux patterns of the final system producing R5P with the associated pathways through the entire system. We start with a subsystem encompassing the inflow reaction of Glc and the outflow of R5P Fig. Two elementary flux patterns are found. One of them only contains the inflow reaction of Glc and the other the inflow of Glc as well as the outflow of R5P.

The first elementary flux pattern indicates the existence of a pathway consuming Glc at steady state without using the outflow of R5P. This corresponds to the glycolytic pathway producing glycerolphosphate G3P which is subsequently drained from the system Fig.

The other flux pattern corresponds to a pathway producing R5P Fig. Since we found no elementary flux pattern containing the outflow of R5P without the inflow of Glc we can conclude that the inflow reaction is required for the production of R5P. Otherwise we would have obtained a second elementary flux pattern containing only the outflow of R5P. Next, we need to determine an elementary mode through the entire system using exactly the reactions of the elementary flux pattern in the subsystem.

Such an elementary mode can be obtained using linear programming [21]. This elementary mode corresponds to a first pathway for the production of R5P from Glc. From this initial pathway it is possible to deduce reactions that are essential for the conversion of R5P to Glc see Text S1 for more details. Thus, we find that in addition to the inflow of Glc, the conversion of Glc to glucosephosphate G6P and the conversion of ribulosephosphate Ru5P to R5P are required for production of R5P at steady state.

The knowledge of essential reactions can simplify the analysis in two ways. First, we do not need to include essential reactions into the subsequent subsystems since every pathway producing R5P will use them anyway.

Second, if we find several sequences of essential reactions the task of searching for pathways can be split into sub-tasks. Each sub-task then consists in the search for a pathway connecting a product of a sequence of essential reactions and the educt of the next sequence of essential reactions.

In order to determine reactions that belong to alternative pathways, we include the reactions of the first detected pathway into the subsystem of the next step. As noted above, we need not to add essential reactions and consequently, the subsystem of the second step comprises three reactions Fig. This subsystem gives rise to three elementary flux patterns. This elementary flux pattern corresponds to a pathway for the production of R5P.

This flux pattern is associated to an elementary mode that also uses reactions that do not belong to the subsystem and are not essential reactions. Hence, we have identified reactions that belong to an alternative route.

These reactions are subsequently added to the subsystem of the third step Fig. In this subsystem we find eight elementary flux patterns, two of which contain the outflow of R5P Figs. Determining the elementary modes associated to these elementary flux patterns, we find that both use only reactions that are either essential for the pathway or belong to the subsystem.

Thus, we have identified all pathways producing R5P from Glc. These pathways correspond to the elementary modes associated to each of the two flux patterns containing the outflow of R5P. We computed the shortest elementary flux modes producing glucose 6-phosphate from acetyl-CoA using a previously described method [33].

This method allows one to enumerate elementary flux modes with increasing number of reactions. The entire set of elementary flux modes cannot be computed for the genome-scale metabolic network of humans since their number is too large [32]. We calculated the Gibbs free-energy changes of the described pathways.

We performed these calculations for two scenarios: gluconeogenesis from acetyl-CoA and from palmitate. For the first scenario, we assumed that additionally to the energetic balance as depicted in Fig. For the second scenario, we assumed that biosynthesis starts from 0. We calculated the Gibbs free energy change by where corresponds to the Gibbs free energy of formation, to the universal gas constant, to the temperature, to the stoichiometric coefficient of the th product and to the stoichiometric coefficient of the th substrate of the pathway.

We assumed a pH-value of 7. We used measured Gibbs free-energy of formation when available [50] , [51] and estimated values otherwise [52]. For concentrations we used measured values if they were available. The corresponding values and references are given in Text S1. Author Summary That sugar can be converted into fatty acids in humans is a well-known fact.

Introduction It is well known that excess sugar in the human diet can be converted both into glycerol and fatty acids and, thus, into lipids such as triglycerides. Download: PPT. Figure 1. Classical scheme of the interconversion between glucose and fatty acids in humans. Results Determining pathways for gluconeogenesis from fatty acids Step 1: Elucidating an initial pathway.

Figure 3. Pathway for the conversion of fatty acids into glucose. Step 2: Determining essential reactions.

Step 3: Determining alternative routes. Since these alternative routes have to connect the previously identified sequences of essential reactions we could deduce that they perform one of the following biochemical functions: Conversion of mitochondrial acetyl-CoA into mitochondrial acetoacetate Conversion of cytosolic acetol into mitochondrial pyruvate Conversion of mitochondrial oxaloacetate into cytosolic phosphoenolpyruvate The latter mainly concerns well-known routes for the transport of oxaloacetate and other intermediates of the mitochondrial TCA cycle into the cytosol.

Pathways for gluconeogenesis from fatty acids Overall we detected nine possible pathways for the conversion of acetyl-CoA into mitochondrial acetoacetate see Text S1. Comparison to other pathway detection techniques Besides the concept of elementary flux patterns, other techniques to determine pathways in genome-scale metabolic networks exist. Discussion Evidence for gluconeogenesis from fatty acids We performed a global survey of gluconeogenic routes from fatty acids in human metabolism using a genome-scale metabolic model and elementary flux pattern analysis.

Limited capacity of gluconeogenesis from fatty acids Important aspects related to the presented pathways are energetic requirements that constrain their capacity. Figure 5.

Energetic characteristics of gluconeogenesis from fatty acids We investigated the energetic requirements of the presented pathways in terms of ATP consumption under the assumption that all other metabolites, including NADPH and NADH need to be balanced Fig.

Figure 6. Pathway overview and storage efficiencies of selected compounds. Gluconeogenic energy efficiency and glucose storage efficiency of fatty acids In order to analyze the role of the described pathways during prolonged starvation and fasting, we computed for amino acids, fatty acids, lactate and glycerol how much energy in form of ATP is regained in the net balance if they are used for gluconeogenesis and subsequently catabolized. Figure 7. Gluconeogenic energy efficiency and glucose storage efficiency.

Concluding remarks Summarizing our findings, it can be concluded that a thorough, systematic and detailed in-silico investigation of the stoichiometrically feasible routes from fatty acids to glucose based on an experimentally corroborated genome-scale metabolic network provides new insight into human metabolism under glucose limitation.

Materials and Methods For our analysis we used a genome-scale model of human metabolism [20] in which we split reversible reactions into irreversible forward and backward directions.

Elementary flux patterns Elementary flux patterns have been introduced as a new theoretical tool for the analysis of metabolic pathways in genome-scale metabolic networks [21]. Hence, a set of reaction indices is called a flux pattern if there exists at least one flux vector that fulfills the following conditions: Furthermore, we call a flux pattern elementary if this set of reactions cannot be written as set union, of other flux patterns.

Pathway detection using elementary flux patterns Elementary flux patterns can be used to elucidate all possible pathways consuming a certain compound and producing another.