But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion–cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and  The human body consists of many different interacting systems. The brain would not survive without circulation, and circulation is only possible when energy metabolism is working. Eating food is only safe when the liver extracts the toxic substances and the immune system defends against pathological germs, and so on. In reductionist bioscience, the human physiology is investigated by identifying its parts and focusing on one of it. In controlled experiments, one factor is changed and every other possible controlled as much as possible. This approach has been extraordinarily successful in understanding basic principles of human  physiology. In principle, the reductionist approach assumes that if scientists investigate every part of the human body and its possible interactions with other parts and when they put all together they will understand human physiology.This approach does not necessarily work out. Even reductionists agree that a brain will die within 3 min when circulation breaks down. However, the reductionist approach does not appreciate the tight interconnection of different parts of the body to function in real time. The basic physiological approach based on research in the nineteenth century is still valid when looking at some general functions. Heart function can be described by a linear equation where cardiac output is multiplied with heart frequency. The view changes, however, when the level of detail is increased. Every heart beat is at least slightly different; cardiac output is influenced by factors as simple as preload, afterload, or systemic resistance, or as complicated as a plethora of humoral factors, a variety of efferent signals and the condition of the heart muscle. Neural inputs are never the same; their patterns are depending on afferent signals from the heart to different brain centers and again humoral signals. These are modified by energy metabolism, immunological signal cascades, the internal state of the brain itself. It is possible to identify hundreds of different factors which probably influence the cardiac output in various ways. However, it is also possible to turn it around. The cardiac cycle has effects on brain function beyond simply providing enough blood flow. Oscillations in the brain are coupled to the heartbeat and oscillations in other systems. The state of the immune system has effects on the brain, which again has effects on the circulation system, apparent in sepsis, but relevant also in less dramatic situations. It turns out that the human body consists of closely interconnected parts which communicate closely in real time on the level of changes in time orders between milliseconds and hours, even days.Another part of the picture regards the influence of perturbations. In a linear, reductionist world, a change in one condition will have a straightforward effect on the connected system. Increase adrenaline twice, and the heartbeat increases twice. Take away some blood, and the blood pressure decreases, take away twice as much, and the blood pressure decreases twice as much. The reality is different. A change of internal or external factor will often only induce minor changes. Losing a half liter of blood will probably induce small changes in blood pressure and slightly increase heart frequency. Removing more will eventually lead to sudden changes, at the end to a breakdown of circulation.Meet another paradigm. The human body can be regarded as a system. A system can be defined as a set of interacting or independent components forming a whole. Each system has boundaries defining an inside and an outside. Systems again can consist of a set of systems (subsystems) interacting in the same way. This definition can be applied to physical entities, human bodies, or social constructions in the same way. Systems can be studied by breaking them up into parts as reductionists do, but the notion of the system includes that a system “is more than its parts” . SystemsBiomedical research in the last decades has reached an enormous level of detail. Independent of the area of investigation, the knowledge of genetics, translational mechanisms, intracellular pathways, receptor systems and their ligands, or immunological mechanisms have reached a level where it is challenging to understand the system as a whole. It turned out that simple systems are not as simple, physiological systems are best assumed to be more complex than at first apparent until it can be demonstrated otherwise . All phyAt the end of the last century, the increasing level of details provided by biomedical research provoked debates how to understand the whole system. Similar debates also occurred in other scientific disciplines. System theory and cybernetics gradually evolved to something which was termed complexity science although an exact definition was challenging and no unified theory existed. At the same time period, system theory evolved to dynamic system theory beyond other influences also with the help of inputs from chaos theory which in reality investigates the behavior of deterministic non-linear systems . AnotherIn the beginnings of 2000s systems biology was established. It is a biology-based inter-disciplinary field of study focusing on complex interactions within biological systems on several levels—intracellular, intercellular, hormonal, macroscopic—using a comprehensive approach and as tools data mining, mathematical models, and time series analysis , 5. A tiThis review is organized as follows. After a short introduction of HRV, some physiological systems with influence on HRV will be reviewed. In the next part, different paradigms of HRV are presented—HRV as time series analysis, as a proxy for the autonomic vegetative systems and as a proxy for a complex regulatory system. Finally, implications will be discussed.The principles of HRV are simple. The heartbeat is measured, usually with the help of an ECG signal which is obtained with an adequate device. A minimum sampling rate between at least 250 and 500 Hz is recommended . QRS-disThe autonomic nervous system (ANS) is an important part in the control of different physiological systems, e.g., the heart, smooth muscles, endocrine, and exocrine glands. It has an afferent (sensory) and efferent parts and is distinct from the somatic nervous system in several ways. The main function of ANS is homeostasis, largely regulated by autonomic reflexes,  not under voluntary control. Sensory information is frequently transmitted through afferent vegetative nerve fibers to homeostatic control centers, processed and specific reactions are sent through efferent vegetative fibers. The ANS has as mentioned specific transmitter substances—mostly acetylcholine (ACh) and norepinephrine (NE)—corresponding receptors and can be divided into preganglionic and postganglionic fibers. The central control of the vegetative nerve system has been identified in several subdivisions of the hypothalamus, but several other brain regions including the association areas of the limbic cortex, the amygdala, and the prefrontal cortex are also connected to these hypothalamus nuclei.The hypothalamus itself controls two more systems in addition to the ANS, the endocrine system and an ill-defined neural system involved in motivation  and sociThe sinoatrial node is, of course, the origin for the pace of the heart. It can, however, itself be considered as a system of weakly coupled oscillators with self-organizing properties, synchronized by a mechanism of mutual entrainment or phase locking .Already on the intracellular level, cell organelles behave as weakly coupled oscillators. A combined experimental and simulation study showed with the help of two photon laser scanning microscope an oscillating network behavior of cardiac mitochondria, distinctly different from random behavior in the form of an inverse power law typical for fractal behavior. They might play a role as an intracellular timekeeper and have a long-time memory function of the oscillations, suggested by a calculated fractal dimension close to 1.0 . This kiCardiac neurons are localized both in the heart as intrinsic neurons and intrathoracically. They form a local distributive network, controlled by brainstem and spinal cord neurons and processing both central and local information to control the heart . Major iOne of the leading causes of sinus arrhythmia is probably central coupling of respiratory drive to cardiac vagal motor neurons –36. MeduThe term RSA is used to describe the fluctuation of heart rate during the respiratory cycle. It is highly dependent on the vagal tone in the heart and is observable at a frequency band of 0.15–0.4 Hz. Usually, RSA is interpreted to mirror the vagal activity , involvi2 to a level below the apneic threshold. The hyperventilation pattern consecutively becomes periodic because the diminished arterial CO2 reaches the brainstem delayed due to the low cardiac output. When first the low partial pressure of CO2 is detected, respiration drive is stopped until CO2 increases. This again is detected late, which results in hyperventilation until CO2 is again on a low level and a new cycle begins  and nicotinic (ionotropic). However, like other mediator substances as opioids, ACh is also involved in immune responses. RNA for muscarinic and nicotinic receptors has been detected in mixed populations of lymphocytes and other cytokine-producing cells , 61.via a posttranscriptional mechanism. ACh blocks also the release of other endotoxin-inducible pro-inflammatory cytokines, such as IL-1b, IL-6, and IL-18, by same mechanisms; it does, however, not suppress the anti-inflammatory cytokine IL-10 . Insulin resistance in the heart inhibits the metabolic pathway and stimulates the growth factor-like pathway , 88. ThiThere are several factors which can affect HRV in acute and chronic metabolic changes. The direct involvement of the vegetative nerve system can be attenuated in more chronic conditions, when the diabetic autonomous neuropathy, which necessarily occurs after a certain length of illness . HRV is The LF parameter of HRV has been used to predict hypoglycemia , this miTime series show the variation of a parameter during a period. In complexity theory, they are considered as surrogates of the particular system, giving information about the state of the system. Typical time series can consist of a series of blood samples , physical measurements like heartbeats, blood pressure, EEG; or movements like gait, eye blinking or variability of breathing patterns. Even short time series can also give information about a system. Usually, most properties of time series are ignored. They are summarized by simple statistical calculations like the mean and the SD. In the case of HRV, several linear and non-linear algorithms were eventually introduced. The research literature can be roughly divided into three areas. In the first, HRV parameters are used as a prognostic tool. Short-term or long-term results in trials are associated with  HRV parameters. This approach has been very successful providing studies where an analysis of only 2 min of heartbeat predicted all-cause mortality many years later, e.g., in Ref. –105. Thevice versa. If this is the case, it should be possible to measure consistent patterns in the activity of different vegetative efferents. But this supposed consistency has frequently not been found. Different other parts of the SNS have not necessarily the same kind of oscillations as the cardiac part of SNS if they have oscillations at all. Cutaneous vasoconstrictor fibers, sudomotor fibers, adrenaline-regulating adrenal preganglionic neurons, and nerves supplying the brown adipose have not oscillations as observed in other parts of the SNS  mirroring the function of the executive brain, in particular in the prefrontal cortex has been investigated. As pointed out by Holzman and Bridgett , a smallHeart-rate variability is a fascinating observation and insight in its mechanisms is increasing. One main issue remains that interindividual differences are high and statistical effects are usually shown only on the group level. In particular regarding psychologic functioning, although an association between lower HRV and lower adaptivity is now established , the difFrom a systems science perspective, HRV measurement begins to be a sophisticated and relevant tool for both scientific and clinical insights.GE has elaborated and written the review.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism’s metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.A whole-genome metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of The online version of this article (doi:10.1186/s13068-016-0665-0) contains supplementary material, which is available to authorized users. Trichoderma reesei is a filamentous fungus widely used for commercial scale production of biomass-degrading enzymes. Cellulose is the most abundant organic compound in the biosphere and is used as a raw material by many industries such as paper, food, textile and for biofuel production. Trichoderma reesei is the main industrial source for cellulases and hemicellulases, enzymes that hydrolyze the cellulose component of lignocellulosic materials. There is a need for reducing the cost and maximizing the yield of these cellulose-degrading enzymes.Saccharomyces cerevisiae , A.(iHD666) , K. (Picyzae iWV114 . Extraction of lipids for fatty acid and lipid class analyses was performed with chloroform:methanol . After vortexing and 30 min extraction time at room temperature, the samples were centrifuged at 10,000 rpm for 3 min. The extract (lower layer) was separated and evaporated into dryness under nitrogen flow, and dissolved into 1000 μl of petroleum ether (fatty acid samples) or 100 μl of dichloromethane (lipid class samples).m/z 40–600. After analyzing fatty acid methyl esters by GC-MS, the same samples were trimethylsilylated (TMS) to determine free fatty acid (FFA) and sterol contents. Samples were evaporated, dissolved into 30 μl dichloromethane (DCM) and silylated with 25 μl of MSTFA [N-methyl-N-(trimethylsilyl)trifluoroacetamide] at 80 °C for 20 min. Trimethylsilylated samples were analyzed by GC-MS on a Restek Rtx®-5MS column  were performed on a Waters Alliance HPLC combined with a Cunow DDL21 evaporative light scattering detector (ELSD). Separation of the lipid classes was carried out on a Waters Spherisorb®silica column ] in analysis . The RNAurements . The molP, phosphoethanolamine and phosphocholine needed for the synthesis of triacylglycerols, phosphatidylethanolamines and phosphatidylcholines was estimated based on the amount the lipid classes in the cells, assuming consumption of 1 mol of glycerol-3-P, phosphoethanolamine or phosphocholine per 1 mol of triacylglycerol, phosphatidylethanolamine or phosphatidylcholine, respectively.Esterified and free fatty acids were measured using GC-MS as described above. The amount of triacylglycerols, phosphatidylethanolamines or phosphatidylcholines in the cells was calculated based on the ratio of triacylglycerols : phosphatidylethanolamines : phosphatidylcholines (0.52 : 0.16 : 0.32 (w/w/w)) determined using HPLC-ELSD and the measured amount of esterified fatty acids in the cells. The measured fatty acid residues were assumed to be distributed equally in the lipid classes, three fatty acid residues in triacylglycerols and two fatty acid residues in phosphatidylethanolamines and phosphatidylcholines. Total amount of the esterified fatty acids was 0.0995 mmol/(g CDW) and the average MW of the fatty acid esters in the pool was 274.2 g/mol, which was used for calculation of the average MW of triacylglycerols, phosphatidylethanolamines or phosphatidylcholines and subsequently for calculation of the total amount of lipids in the three classes. The amount of glycerol-3-A. oryzae [ T. reesei model, the amount of chitin is represented as the amount of the monomeric unit, N-acetyl-d-glucosamine. The remaining carbohydrates, other than chitin, were presented as glucose units in the model. Furthermore, a trace amount (1e-06 mmol/(g CDW)) of C00096 GDP-mannose and C01083 Total carbohydrates were measured as described as above. The amount of chitin (g/(g CDW)) in the cells was estimated to be the same as the measured chitin content of . oryzae , but corThe amount of DNA (0.91% (w/w)) was measured as described above. The amount of deoxyribonucleotides in DNA was calculated based on the published GC content of the genome, 52.0% .The amount of RNA in the cells (6.12% (w/w)) was measured as described above. The amount of ribonucleotides in the RNA was estimated based on the nucleotide ratio in 18S-28S pre-rRNA region in the genome .S. cerevisiae model iMM904 [The coefficients for C00001 Water, C00002 Adenosine triphosphate, C00008 Adenosine diphosphate, C00009 Organic phosphorous, C00059 sulfate and C00255 riboflavin copied directly from the l iMM904 . The coeFinally, trace amount (1e-06 mmol/(g CDW)) of typical cofactors were added to the biomass equation to ensure the model’s capability to produce these compounds: C00003 Nicotinamide adenine dinucleotide (NAD), C00010 Coenzyme A, C00016 Flavin adenine dinucleotide (FAD), C00051 Glutathione, C00059 sulfate, C00101 tetrahydrofolate, and C00575 3′,5′-cyclic AMP.In addition to the reaction database, the CoReCo pipeline required as inputs: (1) genomic data input: genome of the organism of interest and genomes of related species as protein sequences, as well as a phylogenetic tree describing the relationships of the organisms, and (2) reconstruction constraints: source compound list of entities available for the organism to build up its metabolome and biomass through the reactions present and exchange reactions .Protein sequences and information about the 56 fungi species were downloaded from JGI.The preprocessing step of the pipeline included adding the protein sequences from the whole-genome models to the file downloaded from Uniprot creating an “augmented SwissProt” as described in "Two-way BLAST was performed against the “augmented SwissProt” for each of the organisms involved in the reconstruction. The BLAST runs were carried out in parallel under our SGE VTT cluster.GTG was run in parallel under VTT cluster for each of the organisms involved in the model reconstruction.INTERPRO results were downloaded from JGI and reformatted adding the latest GO and E.C. ids. For 49 fungi the INTERPRO results were already computed in .A phylogenetic tree was built with CVTree using the protein sequences of all the organisms involved in the model reconstruction.A probabilistic model was built using the results of BLAST, GTG and the phylogenetic tree. Conditional probability distributions were estimated using the E.C. numbers identified with InterProScan in each species as a reference. A score was given to each E.C. for each organism involved in the model reconstruction.Scores were assigned to reactions by copying the score of the E.C. assigned to that reaction. If several E.C.s were annotated to the same reaction, the reaction was assigned the highest score among the scores for the individual enzymes.T. reesei model.During the model reconstruction step, the high scoring reactions were added to each model, sequentially filling the gaps from the source compound list. An atom graph was used to find the pathways from the sources to the added high score reaction. This subgraph contains preferably only high score reactions but low score reactions can be added when needed. This step uses two input parameters: A SBML model was created including all the reactions chosen during the reconstruction step. In addition, two biomass reactions and exchange reactions were added in a post-processing step.Models were tested to identify problems like unrealistic production of compounds and reaction bounds were curated manually. The pipeline was run again from step 5 until no more problems could be identified.The basic steps for the reconstruction of the 56 fungi models using CoReCo pipeline were the following:


Clostridioides difficile are a severe and often lethal risk for hospitalized patients, and can also affect populations without these classical risk factors. For a rational design of therapeutical concepts, a better knowledge of the metabolism of the pathogen is crucial. Metabolic modeling can provide a simulation of quantitative growth and usage of metabolic pathways, leading to a deeper understanding of the organism. Here, we present an elaborate genome‐scale metabolic model of C. difficile 630Δerm. The model iHD992 includes experimentally determined product and substrate uptake rates and is able to simulate the energy metabolism and quantitative growth of C. difficile. Dynamic flux balance analysis was used for time‐resolved simulations of the quantitative growth in two different media. The model predicts oxidative Stickland reactions and glucose degradation as main sources of energy, while the resulting reduction potential is mostly used for acetogenesis via the Wood–Ljungdahl pathway. Initial modeling experiments did not reproduce the observed growth behavior before the production of large quantities of a previously unknown polysaccharide was detected. Combined genome analysis and laboratory experiments indicated that the polysaccharide is an acetylated glucose polymer. Time‐resolved simulations showed that polysaccharide secretion was coupled to growth even during unstable glucose uptake in minimal medium. This is accomplished by metabolic shifts between active glycolysis and gluconeogenesis which were also observed in laboratory experiments.Antibiotic‐associated infections with  CDMMC. difficile defined minimal mediumDFBAdynamic flux balance analysisFBAflux balance analysisGAMgrowth‐associated maintenanceMDMminimal defined mediumNGAMnongrowth‐associated maintenanceODoptical density at 600 nmTCAtricarboxylic acidClostridioides difficile  C. difficile. An initial metabolic model of C. difficile 630 was published by Larocque et al. The prediction of quantitative growth in batch cultures has been done previously for iHD992 of C. difficile 630Δerm, which consist of 992 genes, 786 reactions, 936 metabolites, and 163 transport reactions. It includes time‐resolved substrate uptake and biomass production rates, derived from laboratory data, as well as the production and secretion of a previously undescribed polysaccharide. The identity of the polysaccharide is determined based on combined bioinformatical analysis and laboratory experiments. In addition, DFBA is used for quantitative and time‐resolved simulation of batch cultures in minimal defined medium (MDM) and C. difficile defined minimal medium (CDMM). Both media consist of glucose as well as the same salts and buffers. MDM also contains seven amino acids strictly necessary for growth , while CDMM is a richer medium that includes casamino acids, cysteine, and tryptophan Here, we present an improved and elaborate genome‐scale metabolic model iHD992 of C. difficile 630Δerm consists of 992 genes, 786 reactions, 936 metabolites, and 163 transport reactions. The model is included in the Supporting information (Model S1 and the exemplary Scenario S1). In comparison to the published model iMLTC806cdf of C. difficile 630 C. difficile .The model iMLTC806cdf by 83%, representing a significant improvement.Genome‐scale metabolic models contain reactions based on genome annotations, spontaneous reactions, and reactions neither based on the genome nor known to be spontaneous. While reactions in the first two groups form the core of each model, the number of reactions in the latter group should be low. These reactions are usually added to the model to close gaps in the metabolic pathways which are known or assumed to be functional in the organism. For example, this is the case for described pathways whose enzymes are not yet identified. In our model, we could decrease the number of these reactions compared to C. difficile models are significantly different as they share only 508 metabolic reactions (67% of iMLTC806cdf), while the model by Larocque et al. has 385 (51%) in common with the Clostridium acetobutylicum model iCAC490 iMLTC806cdf, 111 are not existing in iHD992 as they are not sequence based reactions and no other indication could be found for their existence in C. difficile. From the 151 sequence‐based reactions in the model from Larocque et al. not existing in iHD992, 140 are not in iHD992 because of differences in enzyme function predictions, six were excluded from iHD992 as they are metabolically cheaper side reactions compared to the main reactions of the enzymes and five were excluded as these would lead to the cost‐free reduction of NADP+ with NADH.The two iMLTC806cdf l‐leucine uptake as the model predicts C. difficile to be leucine auxotroph. The resulting predicted growth rate of 7.12 h−1 is significantly higher as the experimentally determined 0.414 h−1. This finding indicates the inability of the model iMLTC806cdf to describe the energy metabolism and quantitative growth of C. difficile.The published model C. difficile 630Δerm has a fragmented TCA cycle  and malate dehydrogenase  (EC 1.1.1.38) or used as electron acceptor in the oxidation of L‐aspartate to produce iminosuccinate for the NAD+ biosynthesis. The resulting succinate fills the intracellular succinate/succinyl‐CoA pool for the methionine biosynthesis. Succinate can be recycled by the succinyl‐CoA:acetate CoA‐transferase (EC 2.8.3.18) or degraded to butanoate. A similar TCA cycle was previously described for the close relative C. acetobutylicumC. acetobutylicum. In addition, this organism has putative genes for malate dehydrogenase (EC 1.1.1.37) and 2‐oxoglutarate synthase (EC 1.2.7.3) C. difficile to close the TCA cycle.Based on our genome annotation, cle Fig. , which iC. difficile in detail, the organism was grown in naturally labeled MDM with completely 13C‐labeled glucose. The results of GC/MS analysis of some intracellular metabolites in samples taken at optical density (OD) 0.220 are shown in Fig. The model predicts only glucose and cysteine as carbon sources usable for biomass production in MDM, whereas the other six amino acids are only used for energy generation via Stickland reactions or directly for protein synthesis. To study the carbon metabolism of −1 instead of 0.414 h−1 at 3 h of growth) compared to the experiment. In addition, large amounts of carbon taken up by the organism were not found in the known products or biomass. Due to the viscous flow behavior of the culture supernatant and the temporarily active gluconeogenesis (see previous subsection), we suspected the formation of a polysaccharide. Clostridioides difficile possesses an operon putatively involved in the synthesis of a polysaccharide. The operon consists of genes encoding a putative membrane‐bound O‐acyl transferase (EC 2.3.1.‐), a cellulose synthase (EC 2.4.1.12), an endoglucanase (EC 3.2.1.4), and two membrane proteins of unknown function. We assumed the gene cluster to encode for enzymes responsible for the biosynthesis of acetylated glucose polymer (putatively acetylated cellulose).Initial modeling experiments using the experimental metabolite import and export rates predicted a significant higher growth rate  and alkaline (for acetate) hydrolysis. Table C. acetobutylicumC. difficile, C. acetobutylicum is able to degrade its polysaccharide C. acetobutylicum is unable to grow on cellulose In agreement with the existence of a similar operon Table  in its gC. difficile 630ΔermThe availability of time‐resolved cultivation data of Figure C·molC−1 until 5 h of growth. After the subsequent change of metabolism, the ratio stays constant at ~2.6 molC·molC−1 until 8 h of growth. Constant ratios represent a growth‐associated product formation which indicates in turn the formation of a capsule or biofilm. In fact, capsule C. difficile. We observed stirring and shaking as growth inhibiting effects for C. difficile cultures in our laboratory (data not shown). The growth inhibiting effect can be explained by the capsule formation, as detached polysaccharide is replaced rather than new biomass produced and C. difficile capsules were reported to be fragile The carbon ratio of the polysaccharide subunit secretion rate and biomass production rate predicted by the model Fig. B stays cBiomass h)−1 until 5 h of growth, drops below zero for about an hour when the reaction flux is inverted during gluconeogenesis activity and slowly rises again to about 8.0 mmol·(gBiomass h)−1 until 7.5 h of growth. During unrestricted exponential growth, all fluxes stay constant over time. In contrast, C. difficile 630Δerm generates most of the biomass during a quasilinear growth C. difficile was grown anaerobically in batch culture and all substrates were fully solved, the latter can be ruled out. Growth inhibiting reactions are difficult to identify as other reactions scale down analogously. As the organism shows exponential growth in CDMM  is shown exemplarily in Fig. C. difficile in MDM After the metabolic switch at 5 h of growth, when the amino acid uptake is significantly increased, alanine is secreted by −1 between 1.5 and 3.33 h indicates a strictly exponential growth. The model also predicts 33% of the imported glucose until 4 h of growth to be bound in the secreted polysaccharide. This is in the range of the 36 ± 3% experimentally determined at an OD of 0.725, after 4 h of growth. The predicted amounts of the produced volatile fatty acids , which degrades L‐serine to glycine, or by the glycine‐cleavage‐complex. In addition, the degradation of threonine by L‐threonine aldolase (EC 4.1.2.5) also leads to glycine.In the reductive acetyl‐CoA pathway, also known as Wood–Ljungdahl pathway, acetyl‐CoA is reductively generated from a one‐carbon group and COC. difficile uses the Wood–Ljungdahl pathway as primary electron sink. The activity of the acetyl‐CoA synthase complex (EC 2.3.1.169), the terminal enzyme of the reductive acetyl‐CoA pathway, is nearly constant between 1.0 and 3.0 h of growth  All studies were carried out with DNA, RNA, and protein content were quantified as described earlier et al. m), and hydrolyzed for 24 h at 99 °C. Following neutralization, HPLC measurement of released amino acids and meso‐diaminopimelate was performed as previously described m sodium acetate (pH 6.5) and the gradient was altered to 3% for 5.3 min, 3–6% within 0.05 min, 6–7% within 4.65 min, 7% for 1 min, 7–15% within 1 min, 15% for 10 min, 15–25% within 0.5 min, 25% for 2 min, 25–30% within 2.5 min, 30–100% within 0.1 min, and 100% for 2.9 min. To determine the concentration of released meso‐diaminopimelate, a calibration curve was prepared in the range of 5–300 μm. The gradient was altered between 11 and 22 min as follows: 7–14% within 1 min, 14% for 2.5 min, 14–15% within 0.25 min, and 15% for 7.25 min.Estimation of amino acid composition was adapted from Lee et al. Lipids were extracted according to the protocol of Matyash Cell‐free culture supernatants were obtained during exponential, transient, and stationary growth phase from three individual biological samples.m NaOH and incubated for 2 h at 22 °C followed by a neutralization step using the same volume of 5 m H2SO4. For reference samples without hydrolysis, culture supernatants were diluted with three volumes of an 1 : 1 mixture of NaOH (5 m) and H2SO4 (5 m). Medium samples were used as blanks. Samples were analyzed using the Roche Yellow line Acetic Acid kit . Each biological sample was measured in triplicates.Alkaline hydrolysis was adapted from Häggström and Förberg 2SO4, incubated on ice for 10 min, heated to 99 °C for 20 min, and cooled to 22 °C prior to neutralization with NaOH. For reference samples without hydrolysis, culture supernatant samples were diluted with premixed H2SO4 and NaOH solutions. Medium samples were used as reference for the initial glucose concentrations. Medium samples without glucose addition were used as blanks. Samples were prepared and analyzed using the Roche Yellow line Acetic Acid kit (R‐Biopharm) as described earlier For glucose determination, an acidic hydrolysis was performed to avoid alkaline degradation of glucose Cell extracts were prepared and analyzed as described previously C. difficile 630Δerm were taken from BKM‐react C. difficile or related organisms. Gaps were closed manually and as stingy as possible. Transporters were included whenever annotated, experimental evidence was existent or a transporter was strictly necessary.Reactions corresponding to the annotated gene functions meso‐diaminopimelate was part of the peptidoglycan, corresponding amounts of glutamate, alanine, and glycine according to the peptidoglycan structure of C. difficile 630 meso‐diaminopimelate content of the biomass was corrected and used to calculate amount of peptidoglycan in the biomass (14.5%). The ratio of the peptidoglycan and the cell wall polysaccharide II C. difficile 630ΔermE. coliE. coli, 16S rRNA: C. acetobutylicum, tRNA: Bacillus subtilis) were taken from the MODOMICS database B. subtilisC. difficile isolates C. difficile was assumed. The fatty acid chain length of 16.246 °C and the proportion of unsaturated fatty acids of 16.4% were calculated from literature C. acetobutylicum in the acidogenic phase C. acetobutylicumB. subtilis were used +, Fe2+, Ni2+, Cd2+, and Zn2+ (1% each). The composition of biomass can be found in detail in Table S4.The amount of protein, DNA, RNA, and total lipid in the biomass was determined experimentally. To get the full amino acid content of biomass, the measured amino acid content of hydrolyzed cells were modified assuming the measured glutamate was glutamate and glutamine in a mass ratio of 4 : 1, the aspartate was aspartate and asparagine in the mass ratio of 1 : 1, and the mass ratios of histidine, cysteine, tryptophan, and selenocysteine were 100 : 80 : 80 : 1. Assuming all  biomass .5%. The C. difficile, the NGAM was estimated as the average (2 mmol·(gBiomass h)−1) of five anaerobic rumen bacteria Biomass h)−1, leading to the most in vivo similar results.For a metabolic model capable of describing the energy metabolism and quantitative growth, suitable values for the maintenance energy are required. Growth‐associated maintenance (GAM) and nongrowth‐associated maintenance (NGAM) energies are usually calculated from chemostate cultures at different dilution rates C. difficile 630Δerm were derived from literature originpro 2015g software . The resulting functions of the OD and metabolite concentrations can be found in Table S3 and S4. When necessary, the data of different time frames were fitted separately to get the best mathematical description. The functions were used to compute the concentrations including the OD every 15 min starting at 7.5 min (MDM) or 10 min starting at 5 min (CDMM). Relative substrate concentrations were converted to absolute concentrations setting the concentration at time point zero at the concentration in the initial medium. The biomass was calculated using the correlation of 0.425 g·L−1 biomass dry weight per optical density unit at 600 nm wavelength.Optical densities and relative substrate concentrations during the cultivation of C. difficile 630Δerm. The metano toolbox E. coli during change of the limiting substrate Flux balance analysis, extensively described elsewhere For the modeling of the biosynthesis of the secreted polysaccharide, UDP‐glucose was used as substrate for the polymerization, every second glucose subunit was acetylated with acetyl‐CoA and the hydrolysis of one ATP was assumed as transport cost per glucose subunit across the membrane. To calculate the total conversion of glucose to polysaccharide in MDM, 90% of the taken up glucose prior to the linear growth phase was assumed. The fatty acid synthesis was restricted to use only acetyl phosphate as starter molecule to prevent large‐scale synthesis of fatty acids with up to six carbon atoms via the Stickland reactions and the butanoate pathway.+, EC 1.5.1.5) had to be deactivated after 2.5 h of growth to prevent the refilling of the one‐carbon‐pool from formate. The reason for the inactivation of this pathway in vivo remains unclear. Second, the production of propanoate and hexanoate via the butanoate fermentation pathway had to be limited: the maximal fluxes of the acetyl‐CoA C‐acetyltransferase (EC 2.3.1.9) for the substrates propanoyl‐CoA and butanoyl‐CoA were set to 1/3 and 1/20 of the flux with acetyl‐CoA as the substrate.For the correct reproduction of the experimental data in CDMM, two predefinitions had to be made. First, for an accurate simulation of the side product secretion in CDMM, the methylenetetrahydrofolate dehydrogenase  for the metabolic model iHD992 of C. difficile 630Δerm.Table S1. Isotope labeling of glycolysis and glycolysis‐related metabolites.Table S2. Biomass composition used for the model iHD992.Table S3. Fits of the substrate concentrations and OD over time of MDM cultures with C. difficile 630Δerm.Table S4. Fits of the substrate concentrations and OD over time of CDMM cultures with C. difficile 630Δerm.Click here for additional data file.


Trypanosoma brucei is a parasitic protist that causes significant health burden in sub-Saharan countries endemic for the tsetse fly (Glossina spp.). During the bloodmeal of this insect vector, the flagellate is transmitted to a variety of mammals, including humans, in which T. brucei subs. gambiense and T. brucei subs. rhodesiense cause human African trypanosomiasis. During its life cycle, T. brucei encounters and adapts to very diverse environments that differ in available nutrients. In the mammalian host, it exists in two major forms: the replicating long-slender bloodstream form (LS-BSF) and the nondividing short-stumpy bloodstream form (SS-BSF), the latter being pre-adapted to infect the insect vector [T. brucei during its development require significant morphological and metabolic changes and adaptations [t vector . While tt vector ,3. In thptations ,5.oF1-ATP synthase complex at the expense of ATP [2+ homeostasis are maintained [The seminal work of Keith Vickerman led to the widely accepted model of a highly reduced mitochondrial metabolism in the BSF ,7. Its se of ATP –11. The e of ATP , RNA edie of ATP ,14, and intained ,16. The Because no indications have been obtained yet for the presence of a mitochondrial ATP-producing system in the BSF, the entire cellular ATP pool is considered to be generated solely by highly active glycolysis . The glyClassical metabolic studies performed with trypanosomes purified from the blood of infected rodents or with in vitro–cultured BSF supported the original hypothesis of a drastically simplified mitochondrial metabolism because under aerobic conditions, glucose is almost completely catabolized to pyruvate that is excreted from the cells, indicating no need for the mitochondrial enzymes of the tricarboxylic acid cycle. In the absence of oxygen or when AOX is chemically inhibited, glycerol 3-phosphate is converted into glycerol that is produced in a 1:1 ratio with pyruvate ,20. OccaIn preparation for differentiation into PCF, the SS-BSF up-regulates a subset of mitochondrial and other proteins . MoreoveInterestingly, recent analyses employing the monomorphic LS-BSF strain Lister 427 showed that, in addition to pyruvate, appreciable amounts of other carbon products  are excreted into the cultivation medium , implyinTo map the BSF mitochondrial proteome (mitoproteome), we first used the available mass spectrometry data of purified PCF mitochondria –37 in orAll proteins were then organized into groups based on their Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. No striking qualitative differences were observed in the categories “oxidative phosphorylation” and “core metabolic pathways” comprising many enzymes involved in the carbon, amino acid, and energy metabolism . TypicalThe current metabolic model for BSF excludes a role of the mitochondrion in the ATP production by either oxidative or substrate-level phosphorylation . In contThe second phosphorylation pathway includes the acetate:succinate CoA transferase/SCoAS cycle that contributes to acetate production in the BSF mitochondrion. A substrate for this reaction—acetyl-CoA—is produced by PDH, an enzymatic complex that is present and active in the BSF mitochondrion ,25. MoreThe presence and potential activity of the aforementioned dehydrogenases that produce NADH within the mitochondrion imply that the organelle would require reoxidation of this cofactor. Several possible scenarios for such a capacity can be deduced from the available data . AlthougoF1 ATPase in order to maintain the mitochondrial membrane potential. The classical model presumes that ATP is imported into the organelle via the activity of the ATP/ADP carrier [oF1 ATPase inhibitor oligomycin or by addition of carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP). However, when treated with bongkreic acid, which should halt the activity of FoF1 ATPase by restraining its substrate, the parasite consumes oxygen at the same rate as untreated cells [oF1 ATPase.The possible occurrence of the mitochondrial substrate-level phosphorylation reactions raises an interesting question regarding the mitochondrial bioenergetics of BSF and questions the origin of ATP that is needed by mitochondrial F carrier ,53. Howeed cells ,55. On oCombined, the available data reveal that the metabolic flexibility and adaptability of the BSF mitochondrion are much larger than appreciated so far. Mitochondrial metabolism appears to be controlled at various levels; a developmental program seems to be a major contributor, but recent advances in the field suggest that other cues may also play a role through fine-tuning mechanisms. However, the triggers and signaling pathways of these mechanisms remain to be identified. Furthermore, it should be realized that almost all metabolic studies have been performed with strains well adapted to laboratory conditions. While the proteomic data do not show any significant differences between the monomorphic and pleiomorphic strains, future work combining proteomics and metabolomics with functional genomics should be extended to the mitochondrion of trypanosomes isolated not only from blood but also from other tissues to determine whether their metabolism is tissue specific and, if so, what is/are the mechanism(s) that control(s) the changes. Therefore, the virtually unexplored array of pathways and enzymes begs for attention because it may have important implications for drug target identification and future novel chemotherapeutics. Moreover, a decreased morphological complexity, which is apparently not reflected in metabolic complexity, is an interesting and novel phenomenon that can now be efficiently addressed with emerging, increasingly sensitive methods.S1 TableThe column color coding is green for PCF, dark grey for monomorphic BSF, and light gray for pleiomorphic BSF cells.1, identified; 0, not identified; BSF, bloodstream form; PCF, procyclic form.(XLSX)Click here for additional data file.


Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa,” and their animal host, the whitefly Bemisia tabaci, we apply metabolic modeling to investigate host-symbiont nutrient exchange. Our in silico analysis revealed that >60% of the essential amino acids and related metabolites synthesized by “Candidatus Portiera aleyrodidarum” are utilized by the host, including a substantial contribution of nitrogen recycled from host nitrogenous waste, and that these interactions are required for host growth. In contrast, “Candidatus Hamiltonella defensa” retains most or all of the essential amino acids and B vitamins that it is capable of synthesizing. Furthermore, “Candidatus Hamiltonella defensa” suppresses host growth in silico by competition with “Candidatus Portiera aleyrodidarum” for multiple host nutrients, by suppressing “Candidatus Portiera aleyrodidarum” growth and metabolic function, and also by consumption of host nutrients that would otherwise be allocated to host growth. The interpretation from these modeling outputs that “Candidatus Hamiltonella defensa” is a nutritional parasite could not be inferred reliably from gene content alone but requires consideration of constraints imposed by the structure of the metabolic network. Furthermore, these quantitative models offer precise predictions for future experimental study and the opportunity to compare the functional organization of metabolic networks in different symbioses.An important factor determining the impact of microbial symbionts on their animal hosts is the balance between the cost of nutrients consumed by the symbionts and the benefit of nutrients released back to the host, but the quantitative significance of nutrient exchange in symbioses involving multiple microbial partners has rarely been addressed. In this study on the association between two intracellular bacterial symbionts, “IMPORTANCE The metabolic functions of unculturable intracellular bacteria with much reduced genomes are traditionally inferred from gene content without consideration of how the structure of the metabolic network may influence flux through metabolic reactions. The three-compartment model of metabolic flux between two bacterial symbionts and their insect host constructed in this study revealed that one symbiont is structured to overproduce essential amino acids for the benefit of the host, but the essential amino acid production in the second symbiont is quantitatively constrained by the structure of its network, rendering it “selfish” with respect to these nutrients. This study demonstrates the importance of quantitative flux data for elucidation of the metabolic function of symbionts. The in silico methodology can be applied to other symbioses with intracellular bacteria. The trait of nutrient overproduction has evolved in diverse bacteria that have entered into intimate associations with animals , 2. This1–5–Buchnera  bacteria produce complementary sets of EAAs . For insBuchnera , while iBuchnera –12. In suchnera –, 5, 8.The metabolic contribution of the second symbiont in multipartner symbioses is unambiguous where its metabolic gene content perfectly complements that of the primary symbiont , 13, 14.Bemisia tabaci. These plant sap-feeding insects harbor a primary (obligate) symbiont, “Candidatus Portiera aleyrodidarum”   and, as such, have fewer metabolic genes and reactions than would be expected in a free-living bacterium, such as Escherichia coli  that comprises 76 intracellular reactions and 148 metabolites  with 462 reactions and 469 metabolites (iNA332) comprises 332 genes, 236 intracellular reactions, and 253 metabolites . Additionally, ∼49% of the metabolites in the “Candidatus Portiera aleyrodidarum” iNA94 metabolic network are exchanged with the host. This is in sharp contrast to the 8% of the metabolite pool “Candidatus Hamiltonella defensa” iNA348 exchanges with the host  of each individual organism was determined. A gene was considered essential if its removal from the model resulted in a >99% decrease in growth relative to the wild type. Our simulations showed that 67%, 59%, and 9% of the genes in “Candidatus Portiera aleyrodidarum” iNA94, “Candidatus Hamiltonella defensa” iNA348, and BemisiaiNA332, respectively, were essential for sustaining growth in silico ; (ii) “Candidatus Portiera aleyrodidarum” overproduces tryptophan, threonine, and methionine, which are released to the host bacteriocyte, and tryptophan and methionine are consumed by “Candidatus Hamiltonella defensa”; and (iii) “Candidatus Portiera aleyrodidarum” releases precursors of the remaining 7 EAAs , which are metabolized by the host bacteriocyte to EAAs, with all but lysine required by “Candidatus Hamiltonella defensa” defensa” .Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” require 48 and 23 different inputs from the host, respectively  from the host than “Candidatus Hamiltonella defensa” (0.5 mmol g DW−1 h−1) . Approximately 64% of the material “Candidatus Portiera aleyrodidarum” utilizes from the host is central carbon metabolism intermediates while ∼54% of “Candidatus Hamiltonella defensa” inputs are amino acids . Ten inputs are shared between “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa”: the carbon source glucose, three nonessential amino acids , and six EAAs   .Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” may compete for the 10 shared input metabolites . The exceptional case of glucose arises because “Candidatus Portiera aleyrodidarum” does not use it for either energy production or growth; it is metabolized to 6-phosphogluconolactone (a pentose phosphate pathway intermediate), which is exported back to the host. In summary, these data point to multiple candidate instances of competition between the two bacteria for host resources, but the significance of these competitive interactions in vivo varies with the supply of these compounds from the host.We hypothesized that “abolites , resultiCandidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” export 24 and 14 unique metabolites to the host, respectively .We probed our three-compartment metabolic model for partitioning of metabolic functions between the bacterial partners by examining the metabolic outputs exported to the host from each bacterium. The metabolic networks of “Candidatus Portiera aleyrodidarum” (∼2.4 mmol g DW−1 h−1) to the host is ∼40-fold greater than from “Candidatus Hamiltonella defensa” (∼0.06 mmol g DW−1 h−1) . The two bacteria also differ in the compositions of the outputs, with amino acids and their derivatives accounting for ∼40% of the total quantity of “Candidatus Portiera aleyrodidarum” exports, while ∼93% of “Candidatus Hamiltonella defensa” outputs are central carbon metabolism intermediates . “Candidatus Hamiltonella defensa” has the genetic capacity to synthesize B vitamins, but these micronutrients are retained within the “Candidatus Hamiltonella defensa” cells under the conditions simulated in our model .Our simulations also predict that the total quantity of metabolites exported from “Candidatus Portiera aleyrodidarum” alone supplies threonine and 2,6-diaminoheptanedioate (a lysine precursor) to the host. The amounts of these compounds released by “Candidatus Portiera aleyrodidarum” are proportional to the aspartate supply from the host under physiologically meaningful conditions  for threonine and lysine biosynthesis, our simulations indicate “nditions . , we tracked the fates of ammonia (a nitrogenous waste product of the host), nonessential amino acids, and amino acid derivatives generated in the host bacteriocyte. Our simulations indicate that ∼40% the total flux of N into “Candidatus Portiera aleyrodidarum” is sequestered. Approximately 0.8 mmol g DW−1 h−1 of N-containing metabolites is received by “Candidatus Portiera aleyrodidarum” from the host and 0.5 mmol g DW−1 h−1 of N-rich metabolites, mostly in a form the host cannot synthesize, is released back to the host ; glutamine (22%); other nonessential amino acids (15%); ornithine, carbamoyl phosphate, and homocysteine (15%); and essential amino acids (13%). Aspartate is used by “Candidatus Portiera aleyrodidarum” to synthesize threonine and the lysine precursors 2,3-dihydrodipicolinate and 2,6-diaminoheptanedioate. These three metabolites constitute 58% of the total N released by “Candidatus Portiera aleyrodidarum” to the host. Glutamine is used in three EAA-biosynthetic pathways to make tryptophan, histidinol phosphate (a histidine precursor), and arginosuccinate (an arginine precursor), together accounting for 34% of the total N output. Ornithine, carbamoyl phosphate, and homocysteine are used to make arginosuccinate and methionine, representing 7% of the total N output , glutamine (29%), serine (12%), and essential amino acids (23%)  .Candidatus Hamiltonella defensa”-host model was incapable of supporting host growth. In contrast, removal of “Candidatus Hamiltonella defensa” resulted in an ∼12% increase in the host growth rate , together with 11, 6, and 15% increases in the uptake rates of the shared metabolites glucose, glutamine, and serine, respectively. The uptake rates of the EAAs with host-mediated terminal reactions  remained unchanged, but the export of tryptophan, threonine, and most EAA precursors from “Candidatus Portiera aleyrodidarum” increased by 0.5 to 22%. Exceptionally, the efflux of methionine and the C skeletons of the essential amino acids isoleucine, leucine, and valine were reduced by 2 to 18% relative to the three-compartment model including “Candidatus Hamiltonella defensa” . Altogether, these data highlight the impact “Candidatus Hamiltonella defensa” has on nutrient exchange in this three-partner symbiosis and provide data on the nutritional costs associated with harboring “Candidatus Hamiltonella defensa” by the host.To investigate the metabolic significance of the two bacterial partners to the host, host growth was compared between the three-compartment model and two-compartment models generated by omitting all reactions associated with one bacterium. In the two-compartment models, uptake fluxes for glucose, fructose, ammonium, and phosphate, the main sources of C, N, and P were capped at the observed uptake rates in the three-compartment model. Our simulations indicated that a two-compartment “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” with the whitefly host highlights the distinctive traits of bacterial symbionts with small genomes. Notably, “Candidatus Portiera aleyrodidarum” is highly cooperative, with >60% of essential amino acids synthesized in the system predicted to be allocated to the host   , as welliJO1366) . The fraIn this discussion, we consider these interactions first from the perspective of metabolic inputs to the bacteria and then from the perspective of metabolic outputs from the bacteria to the host.Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” share 10 host-supplied metabolites and that competition for these shared resources decreases bacterial growth rates and may impact the capacity of each symbiont to provide enough nutrients for the host, thereby negatively impacting host fitness. Competition can incur additional costs by driving the evolution or expression of antagonistic traits in the bacteria . This level of recycling is high compared to the aphid-Buchnera symbiosis, where the predicted nitrogen input is 11-fold greater than EAA release  or apparently uncommon (lysine).Our interpretation that “yptophan . The EAAin silico study fast tracks discovery. As metabolic models come to be applied to other associations, it will become feasible to conduct detailed comparisons, with the opportunity to investigate the roles of host and symbiont traits that define the convergent functions of EAA overproduction and release, as well as important differences that may be related to the evolutionary histories of the partners and the ecological circumstance of the different symbioses.This first metabolic model of a multipartner symbiosis in an insect host offers quantitative predictions about the functions of two bacterial symbionts sharing a single host cell. The key experimental advantage is that the models create precise hypotheses for empirical testing. For systems such as whiteflies, where experimental studies are technically demanding and time-consuming because of the very small size of the insects, this Candidatus Portiera aleyrodidarum” (iNA94)  and “Candidatus Hamiltonella defensa” (iNA348)  associated with B. tabaci, reciprocal BLAST searches of symbiont genomes against E. coli strain K-12 substrain MG1655 were used to identify gene orthologs. The criteria for the bidirectional matches included in the model were as follows: an E value of <1e−5, 35% amino acid sequence identity, and match lengths of >70% of the lengths of both query and subject. The list of orthologous genes generated from reciprocal BLAST searches was compared to that of the E. coli strain K-12 substrain MG1655 metabolic model, iJO1366  , reactions capable of generating or consuming dead-end metabolites in each bacterial-symbiont model were identified and incorporated into a draft reconstruction of the host bacteriocyte metabolic network. Additional host reactions completing metabolic pathways for each added reaction and host gene products enriched in host bacteriocytes were incorporated into a final reconstruction of the metabolic network. With the exception of the objective function, exchange, and demand reactions, all reactions included in each model were mass and charge balanced. All the metabolic networks were visualized using Cytoscape_v3.4.0  into a single multicompartment model, reactions and metabolites assigned to each partner were renamed, with reactions in POR , HAM (“Candidatus Hamiltonella defensa”), and BTA (Bemisia) and metabolites in [p] , [h] (“Candidatus Hamiltonella defensa”), and [b] (Bemisia). The stoichiometric matrices of the three models were combined to generate an integrated model, iNA774 . Transport reactions were added to connect the compartments of “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” to the Bemisia compartment, which was adopted as the site for the exchange of metabolites with the external environment.To integrate the three individual models  dead-end metabolites in each bacterial compartment were assigned transport reactions to or from the host bacteriocyte to allow enzymatic reactions mediating their synthesis or consumption to carry flux and (ii) metabolites produced in one species compartment for which another species compartment was auxotrophic were assigned transport reactions to and from the host bacteriocyte. The COBRA toolbox , run in −1 h−1. In the absence of empirical data on the compositions and concentrations of metabolites in the external medium bathing the host bacteriocyte (insect hemolymph), we adopted the most parsimonious strategy of a minimal medium comprising glucose and fructose as carbon sources, ammonia and homocysteine as sources of nitrogen, and hydrogen sulfide as a source of sulfur. Cofactors added to the model medium were thiamine monophosphate, thiamine diphosphate, riboflavin, pyridoxine 5-phosphate, and nicotinate d-ribonucleotide. Water, protons, carbon dioxide, iron(II), iron(III), and phosphate were allowed to freely diffuse across the bacteriocyte cell membrane. The maximum uptake fluxes of all medium components were capped at 100 mmol g DW−1 h−1. The upper bounds of all whitefly intracellular reactions were constrained by the transcript abundances of their respective associated genes, with the exception of asparagine synthase, diaminopimelate decarboxylase, argininosuccinate lyase, histidinol dehydrogenase, phenylalanine hydroxylase, phosphoserine phosphatase, and valine transaminase, which were constrained using the absolute values of amino acid concentrations measured in vivo. Empirically determined concentrations of amino acids were also used to constrain two “Candidatus Portiera aleyrodidarum” reactions, methionine synthase (METS) and threonine synthase (THRS) . The transcriptome (RNA-Seq) data used to constrain the upper bounds of all B. tabaci reactions in the metabolic model were taken from a previous study .All model simulations were performed under aerobic conditions with a maximum oxygen uptake rate of 20 mmol g DWus study . The lowiNA774) model simulations. We selected this community objective function because the primary function of this bacterial-host symbiosis is the provisioning of EAAs that supplement the EAA-deficient plant sap diet of the insect host (see the introduction). Additionally, biomass reactions for “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” were included in each bacterial compartment to represent bacterial growth. The coefficients for the components in the biomass reaction of the bacterial models   were derived from the biomass equation of the metabolic model for another bacteriocyte symbiont, Buchnera iSM199, generated using the experimental data from reference Candidatus Portiera aleyrodidarum,” “Candidatus Hamiltonella defensa,” and Buchnera are all gammaproteobacteria with highly reduced genomes and are endosymbionts in insects of the same suborder, Sternorryncha. The following modifications were made to the Buchnera biomass equation to generate biomass equations for “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa”: (i) the biomass coefficients for each amino acid were recalculated to reflect a “selfish” model where EAAs are produced solely to support bacterial protein synthesis and biomass increase, (ii) cofactors and soluble pool metabolites absent from the Buchnera model but present in the “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” models were given small coefficients (0.00005) to ensure their production by each model, and (iii) H2O was included as a substrate and protons as products in the ATP maintenance equations . First, every transcript in the whole-body transcriptome of an adult whitefly was translated in silico; then, the number of residues of each amino acid in each protein was summed and multiplied by the estimated abundance of the transcript in the transcriptome. The estimated abundance of each transcript was determined by considering the lowest-abundance transcript as present once and normalizing the count for the more abundant transcripts accordingly  . Transcripts with zero abundance were considered to be present once to account for low-abundance transcripts omitted from the transcriptome during sample preparation and processing. Assuming a cellular protein content of 70.6% , as described in reference For of 70.6% , the fraB. tabaci MEAM1   was frozen for subsequent protein quantification. The remaining supernatant was filtered through a 0.45-μm filter plate (Millipore) by centrifugation at 1,500 × g for 10 min. Then, 2.5 μl filtrate was derivatized with AccQ Tag (Waters), following the manufacturer's protocol, and injected into a Waters Acquity ultraperformance liquid chromatograph (UPLC) with a photodiode array (PDA) detector and an AccQ-Tag Ultra 2.1- by 100-mm column. The gradient used was as follows: 0 to 0.54 min, 99.9% A and 0.1% B; 0.54 to 5.74 min, 90.9% A and 9.1% B; 5.74 to 7.74 min, 78.8% A and 21.2% B; 7.74 to 8.04 min, 40.4% A and 59.6% B; 8.04 to 8.64 min, 10% A and 90% B; 8.05 to 8.64 min, 10% A and 90% B; 8.64 to 8.73 min, 99.9% A and 0.1% B; 8.73 to 9.50 min, 99.9% A and 0.1% B (linear between time points), where A is 90% AccQ-Taq Ultra Eluent A in water and B is Accq-Taq Ultra Eluent B. Amino acids were determined by comparing their retention times with standards at 1, 5, 10, 50, and 100 pmol ammonia and protein-amino acids μl−1  and quantified with standard curves. The amino acids were normalized to the protein content determined by DC protein assay (Bio-Rad) with 0 to 10 mg bovine serum albumin ml−1 as a standard.The free amino acid content of bacteriocytes from M507785)  was quanM507785)  to gener


Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as  In order to achieve this, a comprehensive understanding of the metabolic network of the “chassis” organism is required in order to identify the most promising targets to be modified. The availability of complete genome sequences and advances in systems biology allow the analysis of large datasets in an integrated manner to increase our understanding of metabolism and enable model‐driven strain design strategies. Genome‐scale metabolic models (GEMs) can be used to predict the metabolic capabilities of a “chassis” organism, to investigate the changes in the flux distributions under different conditions, to identify metabolic bottlenecks, and to evaluate the outcomes of the possible genetic modifications  is a well‐established host organism used for recombinant protein production  comprising the standard metabolite and reaction IDs and gene‐reaction associations that permit the in silico simulations and analyses. We next investigated the effect of biomass representation on the predictive capability of Kp.1.0 by using published biomass composition datasets for this organism. We then focused on how the changing biomass representations affected the models predictions on targets to be engineered to increase this yeast's productivity. In all, our study has investigated the potential of a new GEM in identifying targets for improving the productivity of K. phaffii cells.In this study, we first reconstructed the metabolic network of 22.1K. phaffii; PpaMBEL1254 , and grown aerobically in chemostat cultures fed with 80:20, 60:40, and 40:60 (w:w) glycerol/methanol mixtures at two dilutions rates (0.05 and 0.16 hr−1) were obtained from Jordà et al.  grown under different oxygenation levels  were adopted from  was prepared as a COBRA‐compliant SBML version 2, level 4  and fold change (FC > 2) analyses. For each case, 100 random values for each flux in the distribution were generated such that each value remained within bounds of the allowable range for that flux, which was determined through flux variability analysis. The Mann–Whitney U test was applied to those values for each individual flux separately, in order to identify those changes, which have appreciable biological impact, the average of the 100 values for each individual flux were employed in all possible pairwise comparisons in fold change analyses. A similar analysis was also conducted for sample sizes of 50, 1,000, and 10,000 to test the effect of the sample size on the significance analysis. Flux values lower than 10−5 were taken as zero. The significance of between‐biomass comparisons under different conditions was calculated using Mann–Whitney U test for each macromolecule.The significance of the effect of biomass composition on the flux distributions was assessed by using both Mann–Whitney K. phaffii annotation file (34378.P_pastoris_GS115.obo) were downloaded from EMBL‐EBI (http://www.ebi.ac.uk) on March 29, 2016. Orthologous genes were obtained from InParanoid 8 database, considering only data that have 100% bootstrap confidence value   in the iPP668 and PpaMBEL1254 models, which precluded the assignment of standard reaction IDs. Following this manual cross‐checking exercise, our initial core network comprised 915 reactions and 965 metabolites. Further analysis revealed gaps and a high proportion (∼45 %) of dead‐end metabolites, and so required further manual curation.An initial core network was constructed considering only reactions found in at least two of the three models. Even some of these were omitted due to the lack of any evidence for their existence in 3.2K. phaffii metabolism in KEGG  was not well defined in the initial core network, was the principal contributor to gaps in the network. The level of detail in the representation of the fatty acid biosynthesis, elongation, and degradation pathways varied between the three extant models. This required the de novo reconstruction of these pathways, using the information for K. phaffii, where they updated both sphingolipid metabolism and the sphingolipid content of the biomass equation based on the literature information. Their representation of sphingolipid metabolism, as reported in iMT1026, was incorporated into the Kp.1.0 model.The inconsistencies in the assignment of reactions involved in glycerophospholipid metabolism to compartments also resulted in the formation of dead‐end metabolites. The localization assignments were performed using the GO  and 1,221 metabolites. Gene‐reaction associations were built on this network. Comparison of the gene‐reaction associations given in the existing models revealed crucial differences in these associations and also in the logical rules applied when multiple genes were associated with a single reaction. There were instances where the reactions in the glycolysis, amino‐acid biosynthesis, purine metabolism, pyrimidine metabolism, and fatty acid elongation pathways were associated with a single enzyme in iPP668 but with more than one enzyme in iLC915 (File S2). For Kp.1.0, we used the reaction‐enzyme associations in KEGG database   were viable. Since there is, as yet, no comprehensive collection ly Table , File S3S. cerevisiae genome. Of the 608 genes in Kp.1.0 with a S. cerevisiae ortholog, 76 genes were identified to have a second, paralogous, copy that arose from the whole‐genome duplication (File S4). For 16 of those genes, model simulations using Kp.1.0 predicted an inviable phenotype, whereas their yeast orthologs were reported to be viable. This is indicative of the lower level of genetic redundancy in the K. phaffii genome, and is entirely congruent with the fact 11 of those 16 genes were reported to have a synthetic lethal interaction with their paralogs in S. cerevisiae  were predicted employing condition‐specific biomass compositions, in the case of Carnicer et al., and using the biomass composition of wild‐type cells under normoxic conditions for the other dataset. The error rates were within a 1–15 % range with one exception, where the CO2 exchange rate (CER) for wild‐type, glucose grown‐cells was predicted to be ∼25% higher than the reported experimental value  in carbon‐limited chemostat culture at a dilution rate of 0.1 hr−1. Glycerol, methanol and oxygen uptake rates were constrained according to the data of  correlations between the flux distributions obtained when the biomass compositions measured experimentally under different conditions were used Figure a, and 22b. We fop‐value < 0.01). We found that the reactions that show high variability did not change when different biomass representations were used.Flux variability analysis was conducted to identify the reactions that display the greatest variability, and the impact of the biomass composition on these changes was investigated. It was observed that the highly variable reactions were catalyzed by enzymes involved in phosphorus, carbohydrate, and ribonucleoside diphosphate metabolism; these associations were significant   and those that showed no significant difference in biomass content.The significance of the change in flux values was evaluated, for each reaction, by comparing each condition with every other. For a sample size of 100, we found that 433 out of the 1,474 reactions in Kp.1.0 showed a significant change in at least one of the comparisons . When the same analysis was conducted with a sample size of 100 using iLC915, incorporating the same biomass equations, it was observed that 693 reactions, which were associated with 452 genes were significantly changed. These genes were identified to be enriched only with the process terms cellular lipid metabolic process and cell cycle, and cellular catabolism  (File S6). The same analysis was repeated for sets of flux distributions of various sample sizes of 50, 1,000, and 10,000, in order to identify any possible biases for sample size, and the results of the analysis indicated that the number of reactions whose fluxes were significantly different and the number of genes associated with those reactions, remained within ±1.25% and ±2.50% of one another, respectively; thus no size‐dependent trends were observed. More than 92% of both the reactions and the genes associated with these reactions were common in all sets and consequently the GO Process Term enrichment analysis highlighted the same processes for all of them, demonstrating the robustness of this analysis .Both the correlation and significance analyses indicated that the effect of biomass representation on flux distribution could not be predicted. There were 215 genes associated with the reactions whose flux was significantly changed by altering biomass composition, independent of the sample size. GO analysis showed that this gene set was significantly enriched for the following biological process terms: steroid biosynthesis, lipid biosynthetic, cellular alcohol metabolic process, and oxidation–reduction .Observation of the impact of biomass representation on flux distributions prompted us to investigate the effect of the biomass content on target identification. Overexpression targets to improve production of human copper/zinc superoxide dismutase (hSOD) were predicted using 12 different biomass representations and, in all, 59 genes were identified as candidates. Among these 59 genes, 37 were classified as targets in all cases, whereas 22 were identified only using some of the biomass representations. The genes encoding the enzymes catalyzing those reactions identified to have an increased flux in all analyses were enriched with histidine and lysine metabolic processes; whereas the genes catalyzing the reactions that showed condition‐dependent increase were enriched with glutamine family amino‐acid biosynthetic process .The effect of the biomass composition on predicting knockout targets to improve hSOD production was evaluated using RobOKoD  in K. phaffii.Kp.1.0 was validated through evaluating its success in predicting whether single‐gene null mutants would be viable or inviable. The essentiality information on K. phaffii GEMS were omitted from Kp.1.0. Furthermore, during the construction of Kp.1.0, enzymes catalyzing reactions that led to an increase in the number of dead‐end metabolites  anchor biosynthesis pathway, and in lipoic acid metabolism) were omitted, together with their cognate genes. This was done because as the number of genes associated with dead‐end reactions increases, the number of genes predicted to be non‐essential will also increase, irrespective of the model structure. The low specificity of the iLC915 GEM in predicting gene essentiality is thought to be a consequence of this fact (Table In order to confidently and unambiguously define gene‐reaction associations, putative genes and genes with unknown function, which comprise 5–10% of the genes included in the other ct Table .K. phaffii is that, in contrast to S. cerevisiae, extensive data on biomass composition of this yeast have been collected under different experimental and genetic conditions. This provided a great opportunity to study the effect of biomass composition on the success of the Kp.1.0 model in predicting growth characteristics, flux distributions, gene essentiality, and the identification of the targets to be engineered for production of a specific r‐protein.The biomass composition of microbial cells is bound to vary between different physiological conditions (e.g., different carbon sources, nitrogen limitation, pH, aeration, temperature, mode of cultivation, genetic manipulation; Aguilar‐Uscanga & Francois, K. phaffii GEM for which gene‐reaction associations are available, also highlighted the lipid metabolic processes as being sensitive to changes in biomass composition. This suggests that the specific effects of changes in biomass composition are not peculiar to a particular network reconstruction but, rather, are intrinsic to the metabolism of this industrial yeast species.These analyses showed that growth characteristics could be predicted with an error rate changing between 2% and 22% depending on the biomass composition used in Kp.1.0. It was observed that the varying the representation of biomass composition did not affect the prediction of gene essentiality. On the other hand, when the flux distributions were investigated, it was seen that the wiring of some parts of the metabolic network changed greatly depending on the biomass composition. The analyses of these significantly altered fluxes, and the genes encoding the enzymes that catalyze the reactions involved, revealed the impact of biomass composition was not random; rather, it affected specific domains of metabolism, including lipid and secondary alcohol biosynthesis and oxidation‐reduction processes. A similar analysis conducted with iLC915, the only other The representation of biomass content also affected the predictions on potential targets to be engineered for improving r‐protein production when flux distribution based approaches such as FSEOF approach were used. Identification of the overexpression targets to improve hSOD production using Kp.1.0 model employing 12 different biomass representations showed that 37% of the targets were classified as targets conditionally. Moreover, further investigation of the effect of biomass representation on predicted beneficial genetic interventions revealed that not only different reactions might be identified as candidates but also the rankings of the candidate targets altered when the biomass content was changed in the model. These results showed that biomass content was a key player in target identification using metabolic modeling.K. phaffii It is suitable to conduct such analyses and has been made, available to the community through the BIOMODELS database (Chelliah et al., We believe that the Kp.1.0 genome‐scale metabolic model represents a useful tool with which to design more productive strains of Additional Supporting Information may be found online in the supporting information tab for this article.Table S1. Effect of Sample Size on Significance AnalysisClick here for additional data file.Supporting Information.S2Click here for additional data file.Supporting Information.S3Click here for additional data file.Supporting Information.S4Click here for additional data file.Supporting Information.S5Click here for additional data file.Supporting Information.S6Click here for additional data file.Supporting Information.S7Click here for additional data file.