Regulation of Hepatic Metabolism: Intra- and Intercellular Compartmentation

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We made our modeling analysis represent the liver mainly by constraining the model with the measured metabolic fluxes in this study and fluxes reported in the literature under similar conditions. Specifically, we measured the evolution of key metabolic fluxes in the liver, the liver transcriptome, and plasma metabolite profiles in three in vivo studies during which the rats underwent short-term food deprivation for up to 13 h. We used a recently published algorithm to integrate the measurements with a rat metabolic network model, and predicted the direction of change in extracellular metabolite concentrations resulting from a perturbation of metabolic fluxes in the network Blais et al.

By comparing model predictions of the directions of metabolite changes with measured plasma metabolite profiles, we assessed the contributions of the liver to those changes. Three types of measurements, plasma metabolite profiles, liver gene expression, and stable isotope tracer-based metabolic flux profiles, were made at one or two time points in three experimental studies.

The three studies described here were the vehicle control groups of a larger study involving three different toxicants. The vehicle for each toxicant was different due to their differing physical and chemical properties. The time points also varied slightly because of the differences in their toxicity in the larger study.

Table 1 summarizes the number of animals for each measurement in each study. Table 1. Number of animals used for each measurement per time point in Studies 1—3. Catheter implantation surgery was performed 7 days before each experiment, as previously described Shiota, Rats were anesthetized with isoflurane, after which one of two procedures was performed depending on the type of measurement to be collected during the experiment. To measure changes in gene expression and plasma metabolite profiles, the right external jugular vein was cannulated with a sterile silicone catheter [0.

Metabolic Zonation of the Liver

Alternatively, to measure metabolic flux, both the carotid artery and the right external jugular vein were cannulated with sterile silicone catheters 0. The free ends of the implanted catheters were passed subcutaneously to the back of the neck, where they were fixed. The rats were housed individually after the surgery. Two time points were selected for sampling tissue and blood after vehicle administration in each of the three studies analyzed in the present paper: they were 5 h and 10 h for Studies 1 and 2, and 7 h and 13 h for Study 3.

Following blood collection, animals were given vehicle by oral gavage at 7 a. Then, at 12 p. The same procedures were performed at 2 p. After laparotomy, the liver was dissected and frozen using Wollenberger tongs precooled in liquid nitrogen. Then, after food and water were removed, they were anesthetized with isoflurane at p. Studies 1 and 2 or p. Study 3. Post-awakening, at 1 p.

Following the bolus, [6,6- 2 H 2 ]glucose was administered as a continuous infusion 0.

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All infusates were prepared in a 4. Blood glucose was monitored AccuCheck; Roche Diagnostics, Indianapolis, IN, United States and donor erythrocytes were infused to maintain hematocrit throughout the study. Rats were rapidly euthanized through the carotid artery catheter immediately after the final steady-state sample was collected. Plasma samples were divided into three aliquots and derivatized separately to obtain di- O -isopropylidene propionate, aldonitrile pentapropionate, and methyloxime pentapropionate derivatives of glucose.

Derivatization proceeded as described previously Antoniewicz et al. Derivatizations then proceeded as described previously Antoniewicz et al. Helium flow was maintained at 0. To assess uncertainty, root mean square error was calculated by comparing the baseline MID of unlabeled glucose samples to the theoretical MID computed from the known abundances of naturally occurring isotopes. A detailed description of the in vivo metabolic flux analysis methodology employed in these studies has been previously provided Hasenour et al.

The reaction network defined the carbon and hydrogen transitions for biochemical reactions linking hepatic glucose production and associated intermediary metabolism reactions. Flux through each reaction was estimated relative to citrate synthase fixed at by minimizing the sum of squared residuals between simulated and experimentally determined MIDs of the six fragment ions previously described. Flux estimation was repeated at least 25 times from random initial values.

Relative fluxes were converted to absolute values using the known [6,6- 2 H 2 ]glucose infusion rate and rat weights. Flux estimates for the steady-state samples were averaged to obtain a representative set of values for each rat. Sample preparation was carried out at Metabolon, Inc. All of the methods alternated between full scan MS and data-dependent MS n scans. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards Dehaven et al.

We performed statistical analysis to identify metabolites that changed significantly with the duration of fasting. The raw data consisted of MS counts for each metabolite detected in a given plasma sample. We imputed any missing values with the minimum observed value for each metabolite. We then computed distributions of fold-change values for each metabolite and pooled them across the three studies to resolve changes during short-term fasting above experimental and biological noise.

We pooled these fold-change values across studies for a given metabolite, and calculated 10 5 sample means, which constitute the bootstrapped distribution of the mean fold-change.

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Bcl2fastq2 Conversion Software Illumina was used to generate de-multiplexed Fastq files. Analysis of RNA-seq data consists of two stages: 1 determination of transcript abundance and 2 determination of differentially expressed genes. We determined transcript abundance from Fastq files, consisting of raw sequence reads, using a recently published software tool Kallisto Bray et al.

Using Kallisto, we first generated a reference transcriptome index from cDNA files based on genome assembly Rnor6. We then determined transcript abundance using Kallisto, which is based on pseudoalignment of raw sequence reads to the reference transcriptome index. We used appropriate Kallisto settings for processing single-end sequence reads from Study 1, and paired-end sequence reads from Studies 2 and 3.

Using these transcription data, expressed in units of transcripts per million TPM , we used the analytical tool Sleuth Pimentel et al. Within Sleuth, we applied a likelihood ratio test to identify statistically significant gene expression changes and a Wald test to compute the effect sizes logarithms of the fold-changes , between the two time points in each study, for each test.

From these results, we obtained effect sizes for the genes that were identified by the likelihood ratio test to have changed significantly. Finally, we designated the genes with absolute effect sizes in the top 10th percentile as biologically significant, conditional upon statistical significance. We first updated a recently published functional rat genome-scale network reconstruction iRno , which contains 2, genes and 5, metabolites in 8, reactions and eight compartments connected by Gene-Protein-Reaction rules, and is capable of simulating liver-specific metabolic functions Blais et al.

The updates to iRno included additional reactions or modification of existing reactions based on experimental evidence Supplementary Table S1. For instance, we removed a reaction S -lactate:ferricytochrome-c 2-oxidoreductase, which was determined to be non-existent in mammalian systems. Additionally, we added 90 transport and exchange reactions to iRno to improve its coverage of exchangeable metabolites that were detected in plasma metabolite profiles in the present study.

The updated iRno contains 2, genes and 5, metabolites including 3, unique metabolites in 8, reactions including exchange reactions in eight compartments. Supplementary Table S1 provides the updated iRno. The liver operates in a gluconeogenic mode during the short-term fasting trajectory in the present study.

In this state, the liver takes up amino acids, lactate, and glycerol to produce glucose and urea. The liver also takes up non-esterified fatty acids to produce ketone bodies. We constrained the uptake rates of amino acids, fatty acids, lactate, and glycerol, using values reported in the literature from in vivo measurements in rats undergoing short-term fasting Supplementary Table S2.

Transcriptionally inferred metabolic biomarker response TIMBR is a recently published method developed for predicting changes in extracellular metabolites due to gene expression changes under defined physiological operating conditions by integrating those changes into genome-scale network reconstructions see Blais et al.

In the present study, we applied TIMBR to predict metabolite changes during a 5—6-h window of short-term fasting, where gene expression changes have little influence on metabolic state Ikeda et al. Using this method, we determined the relative production scores for all metabolites X raw from 5 to 7 h X and 10 to 13 h X time points Eq. We refer the reader to the original publication for detailed descriptions of the TIMBR algorithm and the corresponding computer codes Blais et al.

Figure 1. Schematic adapted from Pannala et al. We then calculated a z-transformed TIMBR score X s from the raw metabolite production score X raw for each metabolite, whose positive or negative sign indicated its predicted tendency to increase or decrease in plasma. The TIMBR scores were compared with the measured fold-change values of significantly changed metabolites in the plasma to assess the contributions of liver metabolism to those changes. During fasting, the liver produces glucose by synthesizing it from glycerol, lactate, and amino acids, as well as by breaking down glycogen.

Figure 2 shows a schematic of the liver glucose production pathways, which include reactions of glycogenolysis, gluconeogenesis, and the tricarboxylic acid cycle. The aforementioned fluxes are collectively termed central carbon fluxes. The flux values through individual reactions at 10 and 13 h of fasting Figure 3 , Studies 1—3 were measured by stable isotope tracer studies, and those at 5—7 h of fasting Figure 3 , Est.

In all studies considered for flux values at 5—7 h, food was withdrawn at the beginning of the light cycle. To reduce the influence of potential confounding factors, we first obtained absolute flux of liver glucose production from Rossetti et al.

Compartmentation and Its Role in Metabolic Regulation

We selected fractional contributions of glycerol and lactate from the study of Jin et al. Table 2 shows the fractional contributions of various precursors to liver glucose output at 5—7 h and Figure 3 shows the absolute flux values. Figure 2. Figure adapted from Pannala et al. Unidirectional arrows indicate reactions that operate far from thermodynamic equilibrium and are practically irreversible.

Bidirectional arrows indicate reactions that operate closer to thermodynamic equilibrium and are reversible under physiological conditions. Figure 3.

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Central carbon metabolic pathway fluxes through the reactions illustrated in Figure 2 , measured in Studies 1—3 10 and 13 h time points , and estimated from the literature at 5—7 h time interval. Bars represent mean flux values, and the error bars represent the SE of the means. The numbers of biological replicates in Studies 1—3 were 9, 8, and 8, respectively.

Abbreviated reaction names on the y -axis follow their definitions in the legend for Figure 2. Table 2. Fractional contributions of metabolic precursors glycogen, glycerol, and lactate and amino acids to liver glucose production at varying durations of fasting. Thus, the contributions of the remaining precursors—glycerol, lactate, and amino acids—to glucose output remained nearly constant as absolute values but increased as fractions of glucose output. As a result, the absolute fluxes through the reactions downstream of glycogen breakdown PYGL in Figure 2 , beginning with glucosephosphate isomerase GPI in Figure 2 and ending in the tricarboxylic acid cycle at succinate dehydrogenase SDH in Figure 2 , were nearly equal in magnitude at 10 and 13 h of fasting but higher than the values at 5—7 h of fasting Figure 3.

The major conclusions from the central carbon flux data Figure 3 were that glycogenolysis and overall glucose output decline with fasting duration. A key observation was that the glycogenolysis flux was almost completely depleted after 13 h of fasting. The flux analysis assumption that liver metabolism operated in a pseudo-steady state at 5—7 h and 10—13 h is consistent with numerous observations reported in the literature McGarry et al. The 5—7-h time interval represented the end of an early post-absorptive period—where glycogen breakdown contributed to half of the liver glucose output—which was followed by a steep decline in glycogenolysis and a steep increase in ketogenesis plateauing at the 10—h time interval.

Although the absolute flux of gluconeogenesis from glycerol was nearly equal at all time points, the flux of gluconeogenesis from lactate and amino acids was higher at the 10—h time interval, which indicated the coupling of liver metabolism to extra-hepatic sources of precursors for gluconeogenesis after longer fasting durations.

Finally, a key approximation in the central carbon flux analysis was that the liver provided all of the glucose output. Although the kidney is also known to contribute to overall gluconeogenesis, its contribution is important only at fasting durations beyond 24 h Mithieux et al.

Together with previous evidence, our data suggest the presence of distinct metabolic states after 5—7 h and 10—13 h of fasting. Plasma metabolites changed after short-term fasting Table 3. Given the similarity in liver central carbon fluxes, we treated the 5-h Studies 1 and 2 and 7-h Study 3 fasting durations as early time points, and the h Studies 1 and 2 and h Study 3 durations as later time points for determining metabolite fold-change values and their statistical significance. Of these, 39 metabolites were represented in the rat metabolic network model iRno as exchangeable between liver cells and the extracellular space or plasma.

We compared our model predictions for the direction of change with fasting to those for the 39 metabolites, 33 of which showed an increase and 6 of which showed a decrease. Table 3. Observed changes in metabolites between early 5—7 h and late 10—13 h time intervals, experimentally measured in the plasma, and in the subset that is represented in the rat metabolic network model as exchangeable between the hepatocyte and plasma. We also compared the significant changes in plasma metabolites observed in the present study to those reported in the literature on short-term fasting in the rat McGarry et al.

In terms of major metabolite pathways, most of the changes reported in the literature were in agreement with those found in our study Table 4. Important changes indicative of fasting were a reduction in glucose and phospholipids, and an elevation of ketone bodies, fatty acyl carnitines, corticosterone, and choline. Furthermore, key liver-specific metabolite changes observed here and in the literature were the elevation of primary and secondary bile acids, and the elevation of bile pigments bilirubin and biliverdin.

Supplementary Table S3 provides detailed lists of those metabolites and the entire summary of statistical analysis of all metabolites. Table 4. Concordance of observed changes in plasma metabolite data with reported changes in the literature due to short-term fasting.

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  • Reports on large-scale data on plasma metabolite changes during a short-term fast, the number of biological replicates required to resolve them, and their sensitivity to the type of vehicle administered, do not exist in the literature. The number of metabolites measured in Studies 1, 2, and 3 were , , and , respectively, where the vehicle administered to the rats was different for each study.

    The metabolite fold-change values needed to be pooled across the three studies to resolve metabolite changes above experimental and biological noise during short-term fasting. The sum total of unique metabolites measured in the plasma in all three studies was Table 3 , of which were common to all three studies, were common to exactly any two studies, and were observed in exactly any one study.

    Taken together, there was no study-wise representation bias in the proportion of metabolites among the changed and unchanged groups, nor was there any differential effect of the vehicle on metabolite changes between studies, ensuring that pooling of metabolite fold-change data across studies was not confounded by known experimental differences between studies. Of the remaining four, N -carbamoylaspartate was measured in Study 3, acetylcarnitine in Study 1, inosine in Studies 1 and 2, and isocitrate in Studies 1 and 3. These results underscore the significance of lipids during short-term fasting.

    Gene expression changes in the liver during short-term fasting in all three studies Table 5 revealed that the transcripts from each study mapped to a similar total number of genes about 14, , of which 2, were mapped to 2, in iRno. Based on the criteria of a false discovery rate of less than 0.

    Therefore, we did not use any differential gene expression-based weights in our implementation of the TIMBR algorithm to predict plasma metabolite changes. Supplementary Table S4 shows the results of the gene expression analysis. We integrated liver central carbon flux data, as well as known physiological flux bounds for metabolite exchange fluxes at early after 5—7 h of fasting and late after 10—13 h of fasting time points, with iRno using the TIMBR algorithm.

    We then used the TIMBR algorithm to compute a TIMBR score, whose positive or negative sign indicated the tendency of a metabolite to increase or decrease in the plasma, respectively, owing to changes in the liver metabolic network demand induced by fasting. Table 6. Figure 4. Binary heat map of TIMBR scores of significantly changed exchangeable metabolites in plasma represented in iRno compared with measured fold-change values, grouped by major biochemical pathways: amino acid, carbohydrate, cofactors, and vitamins, TCA cycle, lipid, nucleotide, and peptide.

    The values in the left-hand side column data are measured log 2 fold change values of metabolites, grouped as depressed black background , or elevated white background metabolites. The values in the right-hand side column are the computed TIMBR scores whose negative black background or positive white background sign indicates a predicted tendency of the metabolite to be depressed or elevated in plasma.

    The results in Figure 4 , organized by metabolite pathways, revealed three major pathways represented in our data set: amino acids 8 metabolites , cofactors and vitamins 7 metabolites , and lipids 18 metabolites. The rat metabolic network model, iRno , currently the most comprehensive genome-scale model of rat metabolism, instantiated with physiological flux bounds pertinent to the liver, was tested for satisfying defined liver-specific metabolic functionalities Blais et al.

    The implicit assumption in our model was that overall liver metabolism could be represented by a single network with a representative set of physiological boundary conditions. This assumption seemed to contradict the known metabolic differences in hepatocytes between perivenous and periportal regions in the liver Thurman et al. Despite not representing those different kinds of hepatocytes in our model, the overall satisfaction of liver metabolic tasks attested to a sufficient representation of liver metabolic functions originating in both regions.

    Additionally, the physiological flux bounds and central carbon fluxes employed to constrain the model did not include any metabolic heterogeneity. Finally, a key assumption in analyzing the model was that the network maintained a steady state, which was reasonable given the known metabolic flux conditions at 5—7 h and 10—13 h. A limitation of our modeling analysis was the restricted coverage of metabolites exchanged between the plasma and liver cells. Additional curation of iRno , which included addition of exchange fluxes to improve network coverage of plasma metabolites, was limited by the paucity of literature evidence on the exchangeability of those metabolites.

    Therefore, metabolite changes mapped to the network model are not biased by their limited coverage. The measured changes in the circulating metabolites in plasma reflected the fasting response of the whole body. Our modeling effort sought to investigate plasma metabolite changes that can be associated with changes in liver metabolism under short-term fasting conditions where the primary observation was a decrease in the hormonally regulated flux of liver glycogenolysis and no significant transcriptomic changes of liver enzymes Lin and Accili, Our metabolic network analysis was made liver specific and relevant to liver metabolism by the flux constraints.

    We used the in vivo central carbon fluxes derived from our tracer-infusion studies under short-term fasting conditions coupled with literature data from several studies during short-term fasting that sets the overall metabolite uptake and secretion fluxes of the liver Lopez et al. This analysis assumed that the bulk of the glucose production flux captured by the in vivo metabolic flux analysis was of hepatic origin under these conditions Hasenour et al. Thus, even though the measured metabolite changes were reflective of the overall systemic response, our computational analysis estimated those changes that were in concordance with a hepatic origin.

    To assess the impact of liver transcriptomic changes, we repeated our implementation of the TIMBR method using all of the transcriptomic changes regardless of their statistical significance and found that the predicted directions of metabolite changes were unaltered from those shown in Figure 4 see Supplementary Figures S1 — S3.

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