In silico evidence for gluconeogenesis from fatty acids in humans

Do co-solvents used in exposure studies equally perturb the metabolic profile of Daphnia magna?

Dissolution methods such as co-solvents are used to solubilize insoluble compounds in exposure experiments. Several exposure studies have followed the guidelines from the Organization for Economic Co-operation and Development where co-solvents are applied at 0.01% v/v of the total exposure volume. Although no observable apical endpoint abnormalities were reported following these guidelines, little is known about the molecular-level impacts of co-solvents used in exposure studies. A targeted metabolomics approach using liquid chromatography coupled with triple quadrupole mass spectrometry was used to assess Daphnia magna responses to four commonly used co-solvents, including acetone (ACT), acetonitrile (ACN), methanol (MeOH), and dimethyl sulfoxide (DMSO), at three different levels (0.01%, 0.05%, and 0.1% v/v) over 48 hr. Based on the observed metabolic disruptions, exposure to MeOH and DMSO induced higher metabolic perturbations in amino acid levels and associated biochemical pathways in comparison to ACT and ACN exposures. However, as with mixtures, when co-solvents are combined with the pollutants under investigation, there is a possibility for additive, synergistic, or antagonistic interactions. Hence, to examine the possible impairments in co-solvent and pollutant mixtures, ACT and ACN applied at 0.01% v/v were chosen to be tested with phenanthridine (PN). Daphnia magna exposure to PN dissolved in ACT had less disruptions; in contrast to PN prepared in ACN, which triggered a higher degree of antagonism in the D. magna metabolic profile. Consequently, exposing D. magna to ACT applied at 0.01% v/v resulted in the lowest metabolic perturbation in both parts of this study, suggesting that it is the least disruptive co-solvent for molecular-level exposure studies involving D. magna.

Amino acid stable carbon isotopes in nail keratin illuminate breastfeeding and weaning practices of mother - infant dyads

Compound-specific stable carbon isotope analysis of amino acids (CSIA-AA) is widely used in ecological studies to analyze food-webs and is gaining use in archaeology for investigating past diets. However, its use in reconstructing breastfeeding and weaning practices is not fully understood. This study evaluates the efficacy of stable carbon isotope analysis of amino acids in early life diet reconstruction by analyzing keratin from fingernail samples of three mother-infant pairs during late gestation and early postpartum periods. Our results show that stable carbon isotope ratios (δ13C) of glycine, and to a lesser extent glutamate, effectively trace the onset of exclusive breastfeeding and the end of weaning in infants. We propose that glycine's 'conditionally essential' metabolic pathway during infancy allows it to reflect maternal glycine δ13C, indicating breastmilk consumption. Subtle changes in glutamate δ13C likely result from its 'non-essential' status. Additionally, δ13C values of glycine and glutamate indicate maternal physiological and pathological stress due to catabolic effects such as gluconeogenesis. These findings have significant implications for ecological and archaeological research using CSIA-AA for dietary reconstructions. They highlight the need to understand how metabolic pathways affecting δ13C of amino acids may change over an individual's lifespan or be altered due to various forms of stress.

Gluconeogenesis unraveled: A proteomic Odyssey with machine learning

The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years. The recognition of both the regulation of the pathway and possible therapeutic applications of proteins depends on accurate identification associated with their gluconeogenesis patterns. This article analyzes the uses of machine learning and deep learning models, to predict gluconeogenesis efficiency. The study also discusses the challenges that come with restricted data availability and model interpretability, as well as possible applications in personalized healthcare, metabolic disease treatment, and the discovery of drugs. The predictor utilizes statistics moments on the structures of gluconeogenesis and their enzymes, while Random Forest is utilized as a classifier to ensure the accuracy of this model in identifying the best outcomes. The method was validated utilizing the independent test, self-consistency, 10k fold cross-validations, and jackknife test which achieved 92.33 %, 91.87%, 87.88%, and 87.02%. An accurate prediction of gluconeogenesis has significant implications for understanding metabolic disorders and developing targeted therapies. This study contributes to the rising field of predictive biology by mixing algorithms for deep learning, and machine learning, with metabolic pathways.

Exploring the lack of articular ends at the Middle Pleistocene site of Qesem Cave, Israel

Biased skeletal part representation is a key element for making inferences about transport decisions, carcass procurement, and use patterns in anthropogenic accumulations. In the absence of destructive taphonomic processes, it is often assumed that the abundance of different anatomical portions represents selective transport and discard patterns of human groups. Because body parts may be transported for specific products such as meat, marrow or grease, a pattern that usually attracts attention in many archaeological sites is the low proportions of appendicular epiphyses. Here we present the case of faunal assemblages from the lower stratigraphic sequence of Qesem Cave, Israel, dated to ca. 430 to 300 ka. All bone accumulations are characterized by a biased skeletal profile including mainly long-limb bones and a virtual absence of epiphyses. The assemblages also show density-mediated attrition not linked to fossil-diagenetic processes, a targeted specific destruction to the most greasy articular ends and an almost total absence of carnivore intervention. Our goal here is to explore the processes that entail the destruction of appendicular epiphyses at Qesem Cave, as well as propose viable hypotheses to explain their underrepresentation on-site. Our results shed light on the domestic activities linked to the processing of bones at the site and support the importance of animal grease in the caloric intake of Middle Pleistocene humans.

Fingerprinting eukaryotic metabolism across the animal kingdom using position-specific isotope analysis (PSIA) 13C/12C measurements

Despite differences in their overall metabolism, eukaryotes share a common mitochondrial biochemistry. We investigated how this fundamental biochemistry supports overall metabolism using a high-resolution carbon isotope approach, position-specific isotope analysis. We measured carbon isotope ¹³C/¹²C cycling in animals, focusing on amino acids that are formed in mitochondrial reactions and are most metabolically active. Carboxyl isotope determinations for amino acids showed strong signals related to common biochemical pathways. Contrasting isotope patterns were measured for metabolism associated with major life history patterns, including growth and reproduction. Turnover of proteins and lipids as well as gluoconeogensis dynamics could be estimated for these metabolic life histories. The high-resolution isotomics measurements fingerprinted metabolism and metabolic strategies across the eukaryotic animal kingdom, yielding results for humans, ungulates, whales, and diverse fish and invertebrates in a nearshore marine food web.

Bigu-Style Fasting Affects Metabolic Health by Modulating Taurine, Glucose, and Cholesterol Homeostasis in Healthy Young Adults

BACKGROUND

Dynamic orchestration of metabolic pathways during continuous fasting remains unclear.

OBJECTIVE

We investigated the physiological effects of Bigu-style fasting and underlying metabolic reprogramming in healthy adults.

METHODS

We conducted a 5-d Bigu trial in 43 healthy subjects [age 23.2 ± 2.4 y; BMI (in kg/m2) 22.52 ± 1.79]. Physiological indicators and body composition were monitored daily during fasting day 1 (F1D) to F5D and after 10-d refeeding postfasting (R10D) and R30D. Blood samples were collected in the morning. Risk factors associated with inflammation, aging, cardiovascular diseases, malnutrition, and organ dysfunction were evaluated by biochemical measurements. Untargeted plasma metabolomics and gut microbial profiling were performed using plasma and fecal samples. Data were analyzed by repeated measures ANOVA with Greenhouse-Geisser correction. Correlation analyses for metabolite modules and taurine were analyzed by Spearman's rank and Pearson tests, respectively.

RESULTS

Heart rate was accelerated throughout the fasting period. Risk factors associated with inflammation and cardiovascular diseases were significantly lowered during or after Bigu (P < 0.05). Body composition measurement detected an overconsumption of fat starting from F3D till 1 mo after refeeding. Metabolomics unveiled a coupling between gluconeogenesis and cholesterol biosynthesis beyond F3D. Plasma taurine significantly increased at F3D by 31%-46% followed by a reduction to basal level at F5D (P < 0.001), a pattern inversely correlated with changes in glucose and de novo synthesized cholesterol (r = -0.407 and -0.296, respectively; P < 0.001). Gut microbial profiling showed an enrichment of taurine-utilizing bacteria at F5D, which was completely recovered at R10D.

CONCLUSIONS

Our data demonstrate that 5-d Bigu is potentially beneficial to health in young adults. A starvation threshold of 3-d fasting is necessary for maintaining glucose and cholesterol homeostasis via a taurine-microbiota regulatory loop. Our findings provide novel insights into the physiological and metabolic responses of the human body to continuous Bigu-style fasting. This trial was registered at http://www.chictr.org.cn as ChiCTR1900022917.

Inulin-grown Faecalibacterium prausnitzii cross-feeds fructose to the human intestinal epithelium

Many chronic diseases are associated with decreased abundance of the gut commensal Faecalibacterium prausnitzii. This strict anaerobe can grow on dietary fibers, e.g., prebiotics, and produce high levels of butyrate, often associated to epithelial metabolism and health. However, little is known about other F. prausnitzii metabolites that may affect the colonic epithelium. Here, we analyzed prebiotic cross-feeding between F. prausnitzii and intestinal epithelial (Caco-2) cells in a "Human-oxygen Bacteria-anaerobic" coculture system. Inulin-grown F. prausnitzii enhanced Caco-2 viability and suppressed inflammation- and oxidative stress-marker expression. Inulin-grown F. prausnitzii produced excess butyrate and fructose, but only fructose efficiently promoted Caco-2 growth. Finally, fecal microbial taxonomy analysis (16S sequencing) from healthy volunteers (n = 255) showed the strongest positive correlation for F. prausnitzii abundance and stool fructose levels. We show that fructose, produced and accumulated in a fiber-rich colonic environment, supports colonic epithelium growth, while butyrate does not.

What Constitutes a Gluconeogenic Precursor?

A gluconeogenic precursor is a biochemical compound acted on by a gluconeogenic pathway enabling the net synthesis of glucose. Recognized gluconeogenic precursors in fasting placental mammals include glycerol, lactate/pyruvate, certain amino acids, and odd-chain length fatty acids. Each of these precursors is capable of contributing net amounts of carbon to glucose synthesis via the tricarboxylic acid cycle (TCA cycle) because they are anaplerotic, that is, they are able to increase the pools of TCA cycle intermediates by the contribution of more carbon than is lost via carbon dioxide. The net synthesis of glucose from even-chain length fatty acids (ECFAs) in fasting placental mammals, via the TCA cycle alone, is not possible because equal amounts of carbon are lost via carbon dioxide as is contributed from fatty acid oxidation via acetyl-CoA. Therefore, ECFAs do not meet the criteria to be recognized as a gluconeogenic precursor via the TCA cycle alone. ECFAs are gluconeogenic precursors in organisms with a functioning glyoxylate cycle, which enables the net contribution of carbon to the intermediates of the TCA cycle from ECFAs and the net synthesis of glucose. The net conversion of ECFAs to glucose in fasting placental mammals via C3 metabolism of acetone may be a competent though inefficient metabolic path by which ECFA could be considered a gluconeogenic precursor. Defining a substrate as a gluconeogenic precursor requires careful articulation of the definition, organism, and physiologic conditions under consideration.

Every-Other-Day Feeding Decreases Glycolytic and Mitochondrial Energy-Producing Potentials in the Brain and Liver of Young Mice

Intermittent fasting is used to reduce body mass in obese adult humans and animals. However, information on the impact of one type of intermittent fasting (IF) called every-other-day feeding (EODF) on young animals is scarce. In this study, 1-month-old mice of both sexes were subjected to a 4-week regimen of EODF using age-matched counterparts fed ad libitum as controls. At the end of EODF exposure, experimental male and female mice weighed 14 and 13% less than the control counterparts. The EODF regimen resulted in lower liver levels of glycogen, glucose, and lactate, but did not affect lactate level in mouse cerebral cortex of both sexes. Activities of key glycolytic enzymes (hexokinase, phosphofructokinase, and pyruvate kinase) in liver of experimental mice were lower than those in controls. In the cerebral cortex, only hexokinase and pyruvate kinase activities were lower than in controls, but phosphofructokinase activity was not affected in IF females and was higher in IF males as compared with ad libitum fed males. Mitochondria isolated from liver of IF mice had lower respiratory control ratios, but those from the cortex had the same values as control animals. The concentration of β-hydroxybutyrate and the activity of β-hydroxybutyrate dehydrogenase were lower in the IF mouse liver, but not changed or enhanced in the IF cerebral cortex. Thus, animal responses to IF do not depend significantly on sex and are directed to decrease energy metabolism to save resources, and the effects are more pronounced in the liver than in the brain.

Stable carbon isotope diagnostics of mammalian metabolism, a high-resolution isotomics approach using amino acid carboxyl groups

The carbon isotopic compositions of amino acids are increasingly measured to characterize diets and metabolic response to diets. We report a new high-resolution system to measure the stable carbon isotopic composition of carboxyl atoms within amino acids. The automated system used HPLC to separate amino acids followed by addition of ninhydrin for decarboxylation and transfer of the evolved CO2 to a stable isotope ratio mass spectrometer for δ13CCARBOXYL measurement. The ninhydrin reaction was conducted at acidic pH (1.5) and elevated temperature (160 oC) giving yields close to 100% for most common amino acids. Eight mammalian keratin samples from herbivores (kudu and caribou), omnivores (humans) and carnivores (bowhead and humpback zooplanktivorous whales) were analysed with this new system. The data provide an initial calibration of reference materials to be used in studies of this type and is the first report of carboxyl carbon isotope distributions in mammals. Results showed widespread 13C enrichments in both essential and non-essential amino acid carboxyl groups, likely linked to decarboxylation of amino acids during normal metabolism. Analyses of non-essential amino acid isotope profiles showed (1) consistent and general taxon-level metabolic differences between the herbivore, human and whale samples, (2) marked differences among individual humans, ruminants and whales (3) evidence for gluconeogenesis in the wildlife samples, and (4) extensive 13C enrichment likely associated with fasting in the humpback whale sample. Future mammalian research related to the metabolism of growth, reproduction, aging and disease may benefit from using this technique. Values obtained for internationally available samples USGS42 and USGS43 (Tibetan and Indian human hair) provide a first characterization of reference materials for δ13CCARBOXYL profiles.

Extracellular palladium-catalysed dealkylation of 5-fluoro-1-propargyl-uracil as a bioorthogonally activated prodrug approach

A bioorthogonal organometallic reaction is a biocompatible transformation undergone by a synthetic material exclusively through the mediation of a non-biotic metal source; a selective process used to label biomolecules and activate probes in biological environs. Here we report the in vitro bioorthogonal generation of 5-fluorouracil from a biologically inert precursor by heterogeneous Pd(0) catalysis. Although independently harmless, combined treatment of 5-fluoro-1-propargyl-uracil and Pd(0)-functionalized resins exhibits comparable antiproliferative properties to the unmodified drug in colorectal and pancreatic cancer cells. Live-cell imaging and immunoassay studies demonstrate that the cytotoxic activity of the prodrug/Pd(0)-resin combination is due to the in situ generation of 5-fluorouracil. Pd(0)-resins can be carefully implanted in the yolk sac of zebrafish embryos and display excellent biocompatibility and local catalytic activity. The in vitro efficacy shown by this masking/activation strategy underlines its potential to develop a bioorthogonally activated prodrug approach and supports further in vivo investigations.

Zonation of hepatic fatty acid metabolism - The diversity of its regulation and the benefit of modeling

A pronounced heterogeneity between hepatocytes in subcellular structure and enzyme activities was discovered more than 50years ago and initiated the idea of metabolic zonation. In the last decades zonation patterns of liver metabolism were extensively investigated for carbohydrate, nitrogen and lipid metabolism. The present review focuses on zonation patterns of the latter. We review recent findings regarding the zonation of fatty acid uptake and oxidation, ketogenesis, triglyceride synthesis and secretion, de novo lipogenesis, as well as bile acid and cholesterol metabolism. In doing so, we expose knowledge gaps and discuss contradictory experimental results, for example on the zonation pattern of fatty acid oxidation and de novo lipogenesis. Thus, possible rewarding directions of further research are identified. Furthermore, recent findings about the regulation of metabolic zonation are summarized, especially regarding the role of hormones, nerve innervation, morphogens, gender differences and the influence of the circadian clock. In the last part of the review, a short collection of models considering hepatic lipid metabolism is provided. We conclude that modeling, despite its proven benefit for understanding of hepatic carbohydrate and ammonia metabolisms, has so far been largely disregarded in the study of lipid metabolism; therefore some possible fields of modeling interest are presented.

Network theory for data-driven epistasis networks

One of the challenges of understanding the genetic basis of complex phenotypes is explaining variability not attributable to individual genes. While most existing methods that investigate variant mutations or differential gene expression focus on individual effects, a complex system of gene interactions (epistasis) and pathways is likely needed to explain phenotypic variation. Herein, we examine methods for treating the interactions in these biological data sets as edges in a network model of the phenotype and review relevant network theory methods for analyzing network structure and identifying important genes. In particular, we review methods for detecting community structure, describing the statistical properties of networks, and computing network centrality of genes that may reveal insights missed by individual genetic effects. We also discuss available tools to facilitate the construction and visualization of epistasis networks of GWAS data.

Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

MOTIVATION

Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps.

RESULTS

Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks.

AVAILABILITY AND IMPLEMENTATION

The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip.

CONTACT

christoph.kaleta@uni-jena.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks

MOTIVATION

The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy.

RESULTS

Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs.

AVAILABILITY AND IMPLEMENTATION

Matlab code is provided in the supplementary online materials

CONTACT

fplanes@ceit.es.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nutri-informatics: a new kid on the block?

From an epistemological point of view, nutritional physiology has been developed, like other factual sciences such as physics, from a purely descriptive to a mechanismic-explanatory scientific discipline. Nowadays, nutritional physiology has entered the molecular stage. Based on this micro-reductionism, molecular targets (e.g., transcription factors) of energy intake, certain nutrients (e.g., zinc) and selected plant bioactives (e.g., flavonoids) have been identified. Although these results are impressive, molecular approaches in nutritional physiology are limited by nature since the molecular targets of nutrients seem to have no ontic priority to understand the nutritional phenotype of an organism. Here we define, to the best of our knowledge, for the first time Nutri-informatics as a new bioinformatics discipline integrating large-scale data sets from nutritional studies into a stringent nutritional systems biology context. We suggest that Nutri-informatics, as an emerging field, may bridge the gap between nutritional biochemistry, nutritional physiology and metabolism to understand the interactions between an organism and its environment.

Gut-derived short-chain fatty acids are vividly assimilated into host carbohydrates and lipids

Acetate, propionate, and butyrate are the main short-chain fatty acids (SCFAs) that arise from the fermentation of fibers by the colonic microbiota. While many studies focus on the regulatory role of SCFAs, their quantitative role as a catabolic or anabolic substrate for the host has received relatively little attention. To investigate this aspect, we infused conscious mice with physiological quantities of stable isotopes [1-(13)C]acetate, [2-(13)C]propionate, or [2,4-(13)C2]butyrate directly in the cecum, which is the natural production site in mice, and analyzed their interconversion by the microbiota as well as their metabolism by the host. Cecal interconversion, pointing to microbial cross-feeding, was high between acetate and butyrate, low between butyrate and propionate, and almost absent between acetate and propionate. As much as 62% of infused propionate was used in whole body glucose production, in line with its role as gluconeogenic substrate. Conversely, glucose synthesis from propionate accounted for 69% of total glucose production. The synthesis of palmitate and cholesterol in the liver was high from cecal acetate (2.8 and 0.7%, respectively) and butyrate (2.7 and 0.9%, respectively) as substrates, but low or absent from propionate (0.6 and 0.0%, respectively). Label incorporation due to chain elongation of stearate was approximately eightfold higher than de novo synthesis of stearate. Microarray data suggested that SCFAs exert a mild regulatory effect on the expression of genes involved in hepatic metabolic pathways during the 6-h infusion period. Altogether, gut-derived acetate, propionate, and butyrate play important roles as substrates for glucose, cholesterol, and lipid metabolism.

A theoretical study of lipid accumulation in the liver-implications for nonalcoholic fatty liver disease

A hallmark of the nonalcoholic fatty liver disease is the accumulation of lipids. We developed a mathematical model of the hepatic lipid dynamics to simulate the fate of fatty acids in hepatocytes. Our model involves fatty acid uptake, lipid oxidation, and lipid export. It takes into account that storage of triacylglycerol within hepatocytes leads to cell enlargement reducing the sinusoids radius and impairing hepatic microcirculation. Thus oxygen supply is reduced, which impairs lipid oxidation. The analysis of our model revealed a bistable behavior (two stable steady states) of the system, in agreement with histological observations showing distinct areas of lipid accumulation in lobules. The first (healthy) state is characterized by intact lipid oxidation and a low amount of stored lipids. The second state in our model may correspond to the steatotic cell; it is marked by a high amount of stored lipids and a reduced lipid oxidation caused by impaired oxygen supply. Our model stresses the role of insufficient oxygen supply for the development of steatosis. We discuss implications of our results in regard to the experimental design aimed at exploring lipid metabolism reactions under steatotic conditions. Moreover, the model helps to understand the reversibility of lipid accumulation and predicts the reversible switch to show hysteresis. The system can switch from the steatotic state back to the healthy state by reduction of fatty acid uptake below the threshold at which steatosis started. The reversibility corresponds to the observation that caloric restriction can reduce the lipid content in the liver.

Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks

Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases.

Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called ‘stoichiometric capacitance’. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes.

Contact:larhlimi@mpimp-golm.mpg.de, or nikoloski@mpimp-golm.mpg.de

Supplementary Information:Supplementary tables are available at Bioinformatics online.

Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

What mRNA Abundances Can Tell us about Metabolism

Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.

Against the stream: relevance of gluconeogenesis from fatty acids for natives of the arctic regions

BACKGROUND

The question whether even-chain fatty acids can be converted into glucose has a long-standing tradition in biochemistry. Since the glyoxylate shunt is absent from mammals, the question has been considered to be solved. It is of particular relevance for understanding the metabolic state of natives of the arctic regions due to the very high fat content of their traditional diet only containing negligible amounts of carbohydrates.

METHODS & RESULTS

Using an in silico approach, we discovered several hitherto unknown routes in human metabolism that allow the conversion of even-chain fatty acids into carbohydrates in humans. These pathways proceed via ketogenesis over the intermediate of acetone and produce the gluconeogenic precursor pyruvate. While these pathways can make a contribution to glucose production during times of limited carbohydrate supply, we found that their capacity might be limited due to a high demand in reducing equivalents in acetone degradation. Considering the traditional diet of natives of the arctic regions, the detected pathways are not only important in order to improve carbohydrate supply, but moreover reduce the amount of protein that needs to be used for gluconeogenesis.

CONCLUSION

In summary, our study sheds new light on our understanding of the metabolic state of natives from the arctic regions on their traditional diet. Moreover, they provide an avenue for new analyses that can reveal how humans have adapted metabolically to a practically carbohydrate-free diet.

Elementary mode analysis: a useful metabolic pathway analysis tool for reprograming microbial metabolic pathways

Elementary mode analysis is a useful metabolic pathway analysis tool to characterize cellular metabolism. It can identify all feasible metabolic pathways known as elementary modes that are inherent to a metabolic network. Each elementary mode contains a minimal and unique set of enzymatic reactions that can support cellular functions at steady state. Knowledge of all these pathway options enables systematic characterization of cellular phenotypes, analysis of metabolic network properties (e.g. structure, regulation, robustness, and fragility), phenotypic behavior discovery, and rational strain design for metabolic engineering application. This chapter focuses on the application of elementary mode analysis to reprogram microbial metabolic pathways for rational strain design and the metabolic pathway evolution of designed strains.

Metabolic network analysis of perfused livers under fed and fasted states: incorporating thermodynamic and futile-cycle-associated regulatory constraints

Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a "unified" metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could be simultaneously applied to both fasted and fed data sets.