Soldiers in active military service need optimal physical fitness for successfully carrying out their operations. Therefore, their health status is regularly checked by army doctors. These inspections include physical parameters such as the body-mass index (BMI), functional tests, and biochemical studies. If a medical exam reveals an individual’s excess weight, further examinations are made, and corrective actions for weight lowering are initiated. The collection of urine is non-invasive and therefore attractive for frequent metabolic screening. We compared the chemical profiles of urinary samples of 146 normal weight, excess weight, and obese soldiers of the Mexican Army, using untargeted metabolomics with liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). In combination with data mining, statistical and metabolic pathway analyses suggest increased S-adenosyl-L-methionine (SAM) levels and changes of amino acid metabolites as important variables for overfeeding. We will use these potential biomarkers for the ongoing metabolic monitoring of soldiers in active service. In addition, after validation of our results, we will develop biochemical screening tests that are also suitable for civil applications. Many professionals require a certain level of physical fitness for their work, particularly first-line responders such as firefighters, paramedics, and military personnel. To ensure their operability, they require, in addition to training, good eating habits and periodic review of their health status.Overweight and obesity are present in most populations and are the origin of numerous metabolic diseases . The WorIn Mexico, the prevalence of overweight and obesity is dramatically high at about 75% . Thus, tSoldiers of the Mexican Army have regular exams of their health state by a military doctor. Since overweight and obese soldiers could present risks for their own health and missions, mainly in the special bodies such as paratroopers, they are sent to lose weight in particular training camps such as the “Center for improving lifestyle and health” in Mexico City. Furthermore, the social security institute’s law for the Mexican Armed Forces considers soldiers with a Body Mass Index (BMI) greater than 30 as incapable of active service . This meMetabolites in body fluids can be analyzed to assess the nutrition and endogenous changes associated with overweight and obesity, using techniques such as nuclear magnetic resonance (NMR) and mass spectrometry (MS) . UsuallyAnalyzing urine would be more convenient for patients and provide information on the metabolism and pathways involved in particular conditions . Urine iArtificial intelligence and machine learning algorithms can support medical diagnosis . ClassifThis study used untargeted metabolomics based on mass spectrometry to analyze urine from military personnel with normal and excess weight (overweight and obesity). Using Ada Boost data mining, we created a classification model and identified possible biomarkers for monitoring the metabolic state of soldiers and the early diagnosis of deviations.Participants were recruited from the Military Medical Sciences Center, Mexico City, Mexico. Inclusion criteria were: both sexes, active military service, and signed consent to participate voluntarily. Participants answered a questionnaire to identify risk factors for obesity; the next day, nutritional status was assessed by bioelectrical impedance.The Body-Mass-Index (BMI) was calculated using with the person’s weight measured in kilograms (kg) and the person’s height in meters (m).Following the WHO system, soldiers with a BMI equal to or higher than 25 were classified as ‘overweight,’ and those with a BMI equal to or above 30 as ‘obese’ .v/v) and transferred to vials for UPLC-MS analysis.The first urine of the day was collected at 6 am, and the samples were frozen at −60 °C until their processing. Urine samples were thawed and centrifuged at 850 g for 5 min for metabolomics analysis. Ten L of each sample were diluted in 90 L of chromatography-mass spectrometry (LC-MS) grade water . Samples were analyzed with a Dionex UltiMate 3000 HPLC  coupled to an Orbitrap Fusion Tribrid Mass Spectrometer (Thermo Scientific) with an electrospray ionization source. We used an AccuCore C18 column  to separate metabolites using a binary gradient elution of solvents A and B, similar to the method described by m/z in Full Scan mode with an AGC target of 2.0E5. Before the analysis, the mass spectrometer was calibrated with LTQ ESI Positive Ion Calibration Solution .Data were acquired in positive electrospray ionization (ESI+) mode with the capillary voltage set to 3.5 kV, the Ion Transfer Tube Temperature to 350 °C, and Vaporizer Temp to 400 °C. The desolvation gas was nitrogen with a flow rate of 50 UA (arbitrary units). The detector type was Orbitrap at a resolution of 120,000. Data were acquired from 50–2,000 msconvert tool (https://proteowizard.sourceforge.io/) (zlib compression:We used the docker version of the ProteoWizard rge.io/) . To reduhttps://www.knime.com) , using the CSV vim plugin (<chrisbra/csv.vim>).For mass spectrometry raw data processing and generation of an aligned feature matrix, we employed the OpenMS nodes  of the Kime.com) . Figure https://www.metaboanalyst.ca/)  . We applThe MetaboAnalyst report for the uploaded data is provided as a i.e., the values were mean-centered and divided by the standard deviation of each variable.First, we filtered the raw data by the interquartile range (IQR), normalized it by the median, and applied a square root transformation. Further, we used auto-scaling, https://hmdb.ca) . For the Mummichog algorithm, we set a p-value cutoff of 0.25 (default top: 10% peaks). We used the pathway library of Homo sapiens MFN pathway/metabolite sets (a meta library) with at least five entries.For identifying metabolic pathway enrichment and likely involved metabolites, we used the Functional Analysis (MS peaks) tool of MetaboAnalyst . We spechmdb.ca) , with 10https://www.genome.jp/kegg/compound/)   , BiGG (holites/) , the Ediolites/)  and the After clean-up, 52 samples of healthy, 47 overweight, and 21 obese individuals were left. We used these 120 data sets for further analysis. The healthy group showed 5,717 to 9,657, the overweight group 5,559 to 10,447, and the obese group 5,575 to 9,436 features.First, we applied a cluster analysis with the sparse PLS-DA (sPLS-DA) algorithm , which iTo test if we could distinguish between healthy participants and others, we joined the overweight and obese groups and applied an orthogonal projection to latent structures data analysis (OPLS-DA) . As a reThe classification is imperfect; however, the graphics represent the medical situation of clearly healthy, obviously sick, and patients in transition. Consequently, we can discriminate between two metabolic identities of normal-weight and overweight/obese soldiers.i.e., healthy and obese-overweight, we created the Volcano plot shown in p-value threshold of 0.1 (raw), with equal group variance.Using the same parameters for uploading the data (see ‘Methods’), but only defining two groups,  Two hundred twenty-five significant differential variables were detected and subjected to an Adaptive Boost data mining analysis.The preselected 225 variables were loaded into R/Rattle  for furtConsequently, the classification between healthy and obese-overweight persons based on urinary metabolomics profiles is highly reliable, considering natural variations.The important variables that contribute most to correct classification are shown in To explore affected metabolic pathways and facilitate the identification of metabolites, we performed a Mummichog analysis in MetaboAnalyst (see ‘Methods’).As indicated in  •Urea cycle/amino group metabolism •Alanine and aspartate metabolism •Drug metabolism—cytochrome P450 •Aspartate and asparagine metabolism •Ubiquinone biosynthesis.Especially the appearance of urea cycle/amino group metabolism as the first hit gives confidence to the Mummichog algorithm since no information about the origin of the samples was given to the MetaboAnalyst platform.Thus, ions assigned to metabolites of enriched pathways have increased confidence in our further discussion.To develop a predictive classification model, we used the untargeted LC-MS features with at least a 1.3-fold change. The features correspond to ions with a particular retention time. Although a 30% increased or decreased metabolite level might not be critical for health, it can indicate a disturbed pathway.Identifying compounds corresponding to the features is theoretically possible. However, the reliable assignment of metabolites is tedious , and theThe OPLS-DA analysis already indicated distinct metabolic identities  for normThe Adaptive Boost model demonstrates metabolic differences between normal weight and overweight-obese individuals, which can be used for classification. Further, the Adaptive Boost could provide a sensitive method to estimate the metabolic state and the tendency of a person to gain weight. However, additional studies are necessary to evaluate the performance of Adaptive Boost models with untargeted metabolic data as a predictive tool in clinical diagnostics and treatment.Compiling the biomarker candidate ions with likely metabolite identifications resulted in Several ions and the metabolic pathway integration-derived metabolites hint at S-adenosyl-L-methionine (SAM). A previous study reported a 42% increase of SAM in the serum of test persons who were overfed by 1,250 kcal per day and gained weight above the median . SAM is Several of these reactions have been reported to be altered in obesity. For example, high serum levels of homocysteine have been correlated with reduced high-density lipoprotein (HDL) levels. The accumulation of homocysteine comes with lower SAM and SAH levels, leading to a diminished production of phosphatidylcholine, which is essential for the production of low-density lipoproteins (LDL) and very-low-density lipoproteins (VLDL) . HyperliAnother altered SAM-related pathway, we detected, is related to nicotinamide metabolism. Nicotinamide-N-methyl transferase (NNMT) methylates nicotinamide, using SAM as a methyl donor . As a reThe possibility of detecting excess food energy intake in urine by measuring SAM would provide a non-invasive method for monitoring patients during weight-loss diets and professionals who require high physical fitness, such as soldiers. Thus, the level of SAM will be assayed in the following study during the treatment of obese military personnel.In addition, several ions that putatively correspond to compounds from amino acid metabolism were identified. Changes in amino acid levels and related metabolites in obese patients have been reported in several studies . TherefoTherefore, besides the SAM level, we will investigate the role of amino acid metabolism in obesity and weight reduction in future studies.An Ada Boost model based on urinary metabolomics data could discriminate obese and overweight from healthy military personnel with a low overall error rate of 5.5%, indicating a metabolic signature related to the excessive ingestion of food.Important variables from data mining, statistical analyses, and metabolic pathway enrichment analysis suggest S-adenosyl-methionine (SAM) as a possible urine biomarker for overfeeding. Increased SAM levels were found for overfed people in plasma, but monitoring SAM in urine could be used daily for close follow-up of patients, for example, in the treatment of losing weight or persons that need a high level of physical fitness, such as soldiers.As well, the amino acid metabolism showed significant changes.Therefore, in ongoing studies, we include SAM, amino acid metabolism compounds, and acylcarnitines for evaluating the metabolic state of military personnel. In the future, our results will support the design of low-cost biochemical assays for the broad public.10.7717/peerj.13754/supp-1Table S1The article uses compounds’ KEGG and BiGG Models identifiers. This table lists the common chemical names of the metabolites.Click here for additional data file.


Organisms have the capacity to alter their physiological response to warming through acclimation or adaptation, but the consequence of this metabolic plasticity for energy flow through food webs is currently unknown, and a generalisable framework does not exist for modelling its ecosystem-level effects. Here, using temperature-controlled experiments on stream invertebrates from a natural thermal gradient, we show that the ability of organisms to raise their metabolic rate following chronic exposure to warming decreases with increasing body size. Chronic exposure to higher temperatures also increases the acute thermal sensitivity of whole-organismal metabolic rate, independent of body size. A mathematical model parameterised with these findings shows that metabolic plasticity could account for 60% higher ecosystem energy flux with just +2 °C of warming than a traditional model based on ecological metabolic theory. This could explain why long-term warming amplifies ecosystem respiration rates through time in recent mesocosm experiments, and highlights the need to embed metabolic plasticity in predictive models of global warming impacts on ecosystems. Organisms can alter their physiological response to warming. Here, the authors show that the ability to raise metabolic rate following exposure to warming is inverse to body size and provide a mathematical model which estimates that metabolic plasticity could amplify energy flux through ecosystems in response to warming. Most of these studies assume, based on the Metabolic Theory of Ecology (MTE), that whole-organism metabolic rate increases with size  raised to the three-quarter power, and with temperature according to the Boltzmann–Arrhenius equation with an activation energy of ~0.65 eV3. The assumed universality of these specific values has been questioned more recently, however, given that they vary both within and across species5. Furthermore, species may have the capacity to alter their metabolic traits through acclimation, evolutionary adaptation or both10. We refer to this flexibility in species-level thermal responses henceforth as ‘metabolic plasticity’, which can be thought of simply as a group of similar organisms altering their metabolic rate in a similar way when the environment changes. Metabolic plasticity should ultimately have consequences for ecosystem functioning by altering energy flow through the food web11, but evidence for such changes across species and trophic levels in natural systems is still lacking.A crucial, but currently unresolved, question in climate change research is how the functioning of complex ecosystems will respond to long-term warming. This challenge has typically been approached using general relationships for the size- and temperature-dependence of species-level metabolic rates in ecosystem-scale mathematical models12) of freshwater invertebrates in a large-scale natural warming experiment. Our study site, the Hengill catchment in Iceland18, consists of multiple streams, each with a characteristic temperature regime  under acute exposure (experiments lasting 10–60 min) to temperatures of 5, 10, 15, 20 or 25 °C in the laboratory (see Methods). This response of metabolic rate to short-term temperature change is a fundamental measure of organismal physiology and also governs the daily effects of temperature on energy fluxes through ecosystems1. Metabolic rate was only quantified for each individual at one acute temperature, after which we measured its body mass and confirmed its species identity via microscopy. After quality-control procedures (see Methods), this yielded data on 1359 individuals from 16 species representing 44 different populations  altered both the size- and (acute) temperature-dependence of metabolic rate in two fundamental ways Table . First, 25. We parameterised the model with previously published empirical biomass and dietary data18 from the study system and physiological rates that incorporated either metabolic plasticity . In contrast, tropical organisms may find it more beneficial to downregulate their metabolism to minimise energy loss and avoid heat stress14. This suggests that thermal adaptation plays a weaker role in driving metabolic plasticity in our space-for-time study than might occur in response to long-term warming. Our results may thus be a conservative estimate of future change if genetic adaptation further increases the scope for metabolic plasticity over time.The levels of metabolic plasticity we report here Fig.  are like29. Organisms in our experiments were confined to small glass vials, and thus their activity was constrained during the experiments compared to normal activity. As such, our measurements are much closer to resting than maximum metabolic rate, and temperature effects are thus likely to indicate differences in energy allocated to growth, rather than activity. Nevertheless, quantification of activity levels and/or measurement of resting and maximum metabolic rates would be needed to disentangle the relative contributions of behavioural and physiological plasticity to the observed changes in rates of energy expenditure. Follow-up studies should prioritise this research gap.Activity levels can be an important source of variation in estimates of temperature effects on metabolic rate30. Previous research in our study streams has shown that this is not necessarily the case and that larger organisms can thrive in warmer conditions if the production of resources is sufficient to meet their higher energy demands17. Thus, the scope for metabolic plasticity to increase energy flux from the base of the food web could help sustain large predators at higher trophic levels in the face of global warming. Understanding all these effects will require timescales of observation sufficient to disentangle adaptation from acclimation via decadal-scale warming experiments31 and meta-analyses of time series data that incorporate long-term temperature changes32. In the meantime, embedding metabolic plasticity in individual-to-ecosystem projections of climate change impacts may improve realism and help to account for some of the ecological surprises in response to warming that have been reported recently27.Our findings have important implications for top predators, which are widely predicted to decline due to global warming18 , which contains many streams of different temperature due to geothermal heating of the bedrock or soils surrounding the springs .The study was conducted in the Hengill valley, Iceland17, describing food web structure18, and measuring whole-stream respiration15 (described in detail below).Invertebrates were collected from nine streams spanning a temperature gradient of 5–20 °C across the entire study system  experimental temperature over a 15 min period to avoid a shock response. Glass chambers ranged in volume from 0.8–5 ml and scaled with the size of the organism. The glass chambers were filled with water from the Hengladalsá, which was filtered through a 0.45 µm Whatman membrane after aeration to 100% oxygen saturation. A magnetic stir bar was placed at the bottom of each chamber and separated from the organism by a mesh screen. In each experiment, one individual organism was placed in each of seven chambers and the eighth chamber was used as an animal-free control to correct for potential sensor drift. The chambers were sealed with gas-tight stoppers after the 15 min acclimatisation period, ensuring there was no headspace or air bubbles.Experiments were carried out to determine the effects of body mass, acute temperature exposure , and chronic temperature exposure  on oxygen consumption rates as a measure of metabolic rate37. A total of 330 s measurement periods were recorded for each individual, where dissolved oxygen was measured every second. Oxygen consumption rate was calculated as the slope of the linear regression through all the data points from a single chamber, corrected for differences in chamber volume and the background rate measured from the control chamber (which was never >5% of the measured metabolic rates). We converted the units of this rate (µmol O2 h−1) to energetic equivalents (J h−1) using atomic weight (1 mol O2 = 31.9988 g), density (1.429 g L−1), and a standard conversion38 (1 ml O2 = 20.1 J). Organisms generally exhibited some activity during experiments, thus these measurements can be classified as routine metabolic rates12, which are more reflective of energy expenditure in field conditions. Nevertheless, activity levels were minimal due to the space constraints of the chambers , indicating that the measured rates were likely to be closer to resting metabolic rates. Oxygen concentrations were never allowed to decline below 70% to minimise stress and avoid oxygen limitation. The system was cleaned with bleach at the end of each measurement day to avoid accumulation of microbial organisms on the insides of glass chambers and the water bath. In total, oxygen consumption rates were measured for 1819 individuals, none of which were ever reused in another experiment, thus every data point in the analysis corresponds to a single new individual .Oxygen consumption by individual organisms was measured using an oxygen microelectrode , fitted through a capillary in the gas-tight stopper of each chamber39. A linear dimension was precisely measured for every individual using an eyepiece graticule and converted to dry body mass using established length-weight relationships , metabolic rate, I, depends on body mass and temperature as:I0 is the intercept, M is dry body mass (mg), b is an allometric exponent, EA is the activation energy (eV), and TA is a standardised Arrhenius temperature:All statistical analyses were conducted in R 4.0.2 . According to the Metabolic Theory of EcologyTacute is an acute temperature exposure (K), T0 sets the intercept of the relationship at 283.15 K , and k is the Boltzmann constant (8.618 × 10−5 eV K−1). We performed a multiple linear regression (‘lm’ function in the ‘stats’ package) on the natural logarithm of Eq.  substituted for Tacute in Eq. (gls’ and ‘lme’ functions in the ‘nlme’ package) containing all possible subsets of the following random effects structure40:Here, e in Eq. . To deteHere, we are accounting for the possibility that metabolic rate could be different for each species  and that the effect of body mass, acute temperature exposure, or chronic temperature exposure on metabolic rate could also be different for each species .lme’ function, and performed AIC comparison on all possible combinations of the fixed-effect structure40  and TA performed similarly . We set ‘method = ‘REML’’ before extracting model summaries and partial residuals from the best-fitting model40. Note that the models were always fitted to the raw metabolic rate data, with residuals only extracted for a visual representation of the best-fitting models, excluding the noise explained by the random effect of species identity  was used to test for spatial autocorrelation in the temperature gradient, by comparing pairwise temperature difference between streams to the pairwise distance between streams. Pairwise distances were calculated from GPS coordinates taken at the confluence of each stream with the main river and the ‘earth.dist’ function in the ‘fossil’ R package. This analysis revealed no significant relationship between pairwise temperature and pairwise distance between sites .A Mantel test .In addition, we explored for spatial autocorrelation in the residuals of our optimal model Table  by gener41; (ii) tree topology information from the Open Tree of Life42 ; and (iii) previously reported divergence time estimates between pairs of genera from the TimeTree database43. More precisely, we were able to obtain COI-5P nucleotide sequences for 15 out of 16 species (Supplementary Table 44 (v. 7.490). To constrain the topology of our phylogeny based on the results of previous studies, we queried the OTL via the ‘rotl’ R package45 (v. 3.0.11). This yielded topological information for all 16 species. Finally, we manually queried the TimeTree database to obtain node age estimates. We only used three such estimates that (a) were based on more than five previous studies and (b) did not force any tree branches to have a length of zero.To examine the influence of evolutionary relatedness on metabolic rate measurements, we reconstructed a time-calibrated phylogeny of the 16 species in our final dataset (Supplementary Table 46 (v. 3.2.7a) to obtain a time-calibrated phylogeny based on the sequence alignment, the OTL topology, and the node ages from TimeTree. For this, we first determined the most appropriate nucleotide substitution model using ModelTest-NG47 (v. 0.1.7). This was the General Time-Reversible model with Gamma-distributed rate variation across sites and a proportion of invariant sites. To allow branches of the phylogeny to differ in their rate of sequence evolution, we specified the Independent Gamma Rates model48 and used a normal distribution with a mean of 0.00003 and a standard deviation of 0.00001 as the prior for the mean clock rate. Finally, we executed four MrBayes runs with two chains per run for 100 million generations, sampling from the posterior distribution every 500 generations. Samples from the first ten million generations were treated as burn-in and were discarded. We examined the remaining samples to ensure that the four MrBayes runs had converged on statistically indistinguishable posterior distributions  and the parameter space was sufficiently explored . We summarised the sampled trees into a single time-calibrated phylogeny by calculating the median age estimate for each node  to 1 , with intermediate values indicating deviations from a pure random walk. To estimate phylogenetic heritability, we fitted a generalised linear mixed-effects model using the ‘MCMCglmm’ R package50 (v. 2.32). We set the natural logarithm of metabolic rate as the response variable and only an intercept as a fixed effect. We also specified a phylogenetic species-level random effect on the intercept, using the phylogenetic variance-covariance matrix obtained from our time-calibrated phylogeny. We used the default  prior for the fixed effect, an uninformative Cauchy prior for the random effect, and an uninformative inverse Gamma prior for the residual variance. We then executed four independent runs for 500,000 MCMC generations each, with parameter samples being obtained every 50 generations after the first 50,000. We verified that sufficient convergence was reached, based on potential scale reduction factor and effective sample size values, as described earlier. Phylogenetic heritability was calculated as the ratio of the variance captured by the species-level random effect to the sum of the random and residual variances. The mean posterior phylogenetic heritability estimate of the natural logarithm of metabolic rate was 0.48. This means that nearly half (48%) of the variation can be explained by the evolution of metabolic rate along the phylogeny . The optimal model .To describe the remaining unexplained variation, we fitted a series of models using s in Eq.  of the ms in Eq.  of the mIt should be noted, however, that a definitive empirical quantification of the relative strength of evolutionary and acclimatory processes would require population genetics (to determine evolutionary divergent populations among streams), transcriptomics , and exhaustive common garden experiments . Such an undertaking was logistically unfeasible in this study, but should be a focus for follow-up research on this topic.25 to predict the effects of climate warming on ecosystem-level energy fluxes. We began by assuming that each stream ecosystem is at energetic steady state, i.e., for all n consumer species in the system:Gi and Li are the energy gain and loss rates [J h−1], respectively, of the ith species in that stream. All basal species are implicitly assumed to be at energy balance. The two terms in Eq. , and wij is the consumer’s preference for that species . Thus, the effective flux through a trophic link is n linear equations (corresponding to the n consumer species) of the form:Zi’s, eij’s, and wij’s are known.Here, for the fluxing’ function in the ‘fluxweb’ R package, parameterised with: (1) binary predation matrices for 14 stream food webs, characterised by 49,324 directly observed feeding interactions18; (2) biomasses for every species in each food web, characterised by 13,185 individual body mass measurements17; (3) assimilation efficiencies (eij’s) based on an established temperature-dependence and resource type 53; (4) preferences (wij’s) depending on resource biomasses; and (5) metabolic rates estimated using Eqs. (I approximates Z). We treated TA in Eqs.  are an underestimate.For this, we used the ‘ed with:  binary ped with:  binary pC in Eq.  implies 18. Briefly, this involved three stone scrapes per stream for benthic diatoms, five Surber samples per stream for macroinvertebrates, and three-run depletion electrofishing for fish. All individuals in the samples were identified to species level where possible and counted. Linear dimensions were measured for at least ten individuals of each species in each stream, with body masses estimated from length-weight relationships17. The population biomass of each species in each stream was calculated as the total abundance [individuals m−2] multiplied by the mean body mass [mg dry weight]. Food web links were largely assembled from gut content analysis of individual organisms collected from the streams , but additional links were added from the literature when yield-effort curves indicated that the diet of a consumer species was incomplete18.Biomass and food web data were sampled in August 2008, with extensive protocols described in previous publicationsF . Oxygen concentrations were measured during 24- to 48 h periods from 6th to 16th August 2008, i.e., the exact same time period during which biomass and food web data were sampled to parameterise the energy flux model15. Dissolved oxygen concentrations were measured every minute with optic oxygen sensors . Hourly ecosystem respiration was calculated from the net metabolism at night, i.e., when no primary production occurs due to lack of sunlight.To test whether our model of energy fluxes through trophic links was empirically meaningful, we calculated the sum of all energy fluxes through each stream food web to get the total energy flux, F, we also calculated a modified total energy flux, F*, for each food web after considering a global warming scenario, where we added 2 °C to TA in Eq.  model with metabolic plasticity . To determine whether our results were consistent for all major trophic groupings in the system, we repeated the analysis after calculating the change in energy flux to herbivores (ΔFH = FH* – FH), detritivores (ΔFD = FD* – FD), and predators (ΔFP = FP* – FP) in each stream.In addition to total energy flux, A in Eq.  and to bC in Eq. . We calccity Eq.  predicteFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the effect of IDH1/2-mutations in cancer using a combination of experimental metabolic flux data and patient-derived gene expression data. Our models demonstrate the potential of computational exploration to reveal biologic behavior: they show that an exogenously-mutated IDH1 experimental model utilizes glutamine as an alternative carbon source for lactate production under hypoxia, but does not fully-recapitulate the patient phenotype under normoxia. We also demonstrate the utility of using gene expression data as a proxy for relative differences in metabolic activity. We use the approach of probabilistic model checking and the freely-available Probabilistic Symbolic Model Checker to construct and reason about model behavior. This designation recognizes both the important role that reprogramming of energy metabolism plays in cancer, as well as the need to better understand the mechanisms by which this reprogramming is achieved, its relation to other signaling-related hallmarks, and ultimately how it can be exploited to develop new therapies.Deregulation of cell metabolism has recently been designated an emerging hallmark of cancer2, is characterized by a shift toward relying on aerobic glycolysis—an inefficient way of metabolizing glucose to supply cellular energetics that is typically deployed in hypoxic conditions. The Warburg phenotype has been observed in many aggressive cancers, and is thought to be an adaptation that enables fast-growing cells to fuel biosynthesis through glycolytic intermediates, and thrive in the tumor micro-environment characterized by gradients of hypoxia.The classical cancer metabolic phenotype, known as the Warburg phenotype3. These mutations are found in more than 80% of lower grade gliomas and secondary glioblastomas, as well as in acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms, and cholangiocarcinoma5. The accumulation of In some cancers metabolism is directly affected through mutation of key metabolic enzymes, most notably the IDH family of enzymes. IDH1, IDH2, and IDH3 catalyze the conversion of (iso)citrate to 8.The discovery of the oncogenic role of 9. How these metabolically-defective cells adapt to the high energy demands of fast-growing cells is not known. The feedback loop between the microenvironment pressures (such as hypoxia or nutrient scarcity) and the shift in metabolic phenotypes is not well-understood.Many unanswered questions remain on the role of IDH mutations in cancer. For example, while typically slow growing, IDH-mutant gliomas eventually progress to an aggressive phenotype with Warburg-like metabolism10. Next-generation sequencing, mass spectrometry and other high-throughput assaying technologies are used to determine the quantities of gene-products and infer pathway activity. However, assaying the metabolic profiles under varying, physiologically-relevant conditions, and integrating the obtained information remains difficult.Advances in technologies for profiling the metabolome are making it possible to glimpse at the cancer metabolic profiles both in a wide range of biological models as well as directly in patients. Metabolic Flux Analysis (MFA), for example, can be used to determine the flux of molecules in a metabolic pathway through tracing of quantities of  (http://cancergenome.nih.gov/) to predict the metabolic profiles of normal brain and IDH-mutant gliomas. Surprisingly, the models derived from patient data point to differences due to IDH-mutation status that are not recapitulated in the experimental models. The model is publicly available in the Biomodels repository with MODEL22071800001 identifier.In this article, we report on the use of probabilistic model checking, an approach developed for modeling and reasoning about systems with inherent stochastic behavior, to model and reason about glucose and glutamine metabolism in cancer and how it is affected by mutations in IDH. Specifically, we use the Probabilistic Symbolic Model Checker (PRISM)A metabolic pathway is a set of biochemical reactions that transform substrates into products in the presence of enzymes. Each reaction is characterized by a rate, which depends on the concentration of the substrates and a rate constant dependent on the characteristic of the enzymes catalyzing the reaction.k is the reaction constant. The reaction also requires one molecule of Chemical reactions can be described following the law of conservation of mass. For example, the first reaction of glycolysis—the conversion of one molecule of glucose to one molecule of glucose-6-phosphate—is captured by this simple expression of the law:Enzyme kinetics are often complex and dependent on the experimental conditions  and relative concentrations of enzymes and substrates. However, a frequently-used simplifying assumption, known as the law of mass action is that the rate of reactions is proportional to the concentration of substrates and the reaction constant.16 to modeling molecular entities and reactions as computer processes. The molecular entities are modeled as discrete variables, and the reactions as guarded actions of the form:We follow Kwiatkowska et al.’s approachThe first reaction of glycolysis at  can thusEach reaction can thus be modeled as a guarded command. The set of resulting guarded commands is executed in parallel: in particular, when two or more reactions are enabled, a race between the enabled reactions ensues, and the first to be triggered determines how the variables are updated. The reactions that share a label are executed synchronously, as one joint command: their guards must all hold for the command to be enabled, the rates are multiplied, and the updates take place simultaneously. The language also provides mechanisms that enable modular specifications.Properties that can be specified over such models are temporal and quantitative in nature. Some examples are: “What is the probability of reaching a state in which lactate is depleted/saturated?” “What is the quantity of glucose at a given instant in time?” and “How many glucose-to-glucose6-phosphate reactions happen on average over a period of time?”Our focus is on modeling glycolysis and the tricarboxylic acid (TCA) cycle and how they are affected by mutations in IDH. We abstract over the compartment where the reactions happen and therefore assume that 14 studied the metabolic phenotype of cell lines that differ only with respect to their IDH1-mutation status. Grassian et al.14 were used to instantiate our model of glycolyis and TCA with four phenotypes: The flux rates from Grassian et al.17, who used relative expression of genes that code for key metabolic enzymes to estimate the relative rates of key metabolic reactions.The https://cancergenome.nih.gov/)18 were downloaded and processed using R’s19 Bioconductor20 package TCGAWorkflow21. The samples were split into cohorts as follows: 140 glioblastomas (GBMs) with wild type IDH formed the Warburg phenotype; 419 lower grade gliomas (LGGs) with mutant IDH formed the RNA-sequencing data from 665 gliomas with known IDH-mutation status, and 5 normal brain patient samples generated from The Cancer Genome Atlas (TCGA) Research Network  of the gene expression.Preprocessing, normalization, and filtering of the raw transcript counts were performed as recommended in TCGAWorkflow14 of the normoxia The fold changes of isoenzymes—enzymes catalyzing the same reaction—were aggregated by averaging to obtain a single fold change for the corresponding reaction. The flux rates for the Normal phenotype were computed by dividing the flux rates of the Warburg phenotype , which are extensions of Discrete Time Markov Chains (DTMCs). DTMCs model events that happen in lockstep in discrete steps, whereas CTMCs model events that happen in continuous real time, with the time required to make a transition being a random variable drawn from an exponential distribution.S is a finite set of states, AP is a set of atomic propositions, and A Discrete-Time Markov Chain (DTMC) is a tuple i. The Markovian assumption, which states that In a DTMC model, transitions between states are denoted by discrete probability distributions, which represent the likelihood of moving to a target state. The behavior of a DTMC is the set of the execution paths S, AP, and L are defined as for DTMCs and A Continuous-Time Markov Chain (CTMC) is a tuple s to s and t time units is s have positive rates, the first transition to be triggered determines the next state of the CTMC. A path of C is a finite or infinite sequence s is the sum of the rates of all the transitions leaving from s, s before a transition is exponentially distributed in E(s). A transition probability matrix can be defined such thatThe exit rate from a state 23, or continuous-time formulations, for example, Continuous Stochastic Logic (CSL)24. The properties that can be specified fall under two categories: state properties, denoted with a if S: a steady state property Properties are expressed in some flavor of probabilistic temporal logic, for example, Probabilistic Computation Tree Logic (PCTL)s satisfies s satisfies s if with probability greater than or equal to p the next state we transition to from s will satisfy PCTL and CSL also contain a probabilistic state operator which operates on path formulas. The probabilistic formulas are interpreted as follows: a state Quantitative versions of the properties, denoted as The problem of model checking is that of deciding if a DTMC/CTMC model satisfies a property expressed as a PCTL/CSL formula. The model checking problem for CTMCs is typically transformed into the simpler problem of model checking of the embedded DTMCs. For the rest of the present paper, the underlying models are CTMCs.26.Efficient methods for automatic model checking of temporal logics and their probabilistic variants have been developed using a combination of graph algorithms, fixpoint computations, and techniques for computing optimal solutions of systems of equations. For very large models for which model checking is infeasible, their behavior can be explored through simulation or statistical model checking: generating a large number of random paths through the model, evaluating the result of the given properties for the sampled paths, and using the information to estimate approximately correct results13 was used for model checking. Results were plotted using Python’s matplotlib library. Scipy’s interpolation functionality was used to perform smoothing on the 3D plots.Command-line PRISMIn this study, we constructed a model of a simplified metabolic network consisting of components from glycolysis, glutamine metabolism, TCA cycle, and several reactions feeding into PPP, and fatty acid synthesis. The resulting pathway diagram is depicted in Fig. The model construction workflow is depicted in Fig. The same model can be used to study the behavior of different phenotypes, based on how the reaction rates are instantiated. The unique ability of We study the behavior of phenotypes by model checking several types of properties on the amount of molecular quantities over time including computing transient amounts (which give snapshots of the pathway at instants in time), cumulative amounts over intervals of time, as well as the probability of reaching certain qualitative states. This is possible due to the expressiveness of the property specification language based on PCTl/CSL and the model checking machinery, which enables their automatic verification or simulation. Moreover, the PRISM framework also allows model checking of these properties under different initial concentrations enabling reasoning about how these transient amounts and cumulative amounts change as a function of nutrient concentration.14, who profiled isogenic colorectal carcinoma cell lines (that differed only on the IDH-mutation status) under normoxia and hypoxia. To our knowledge, this is the only publicly available dataset derived from direct measures of metabolic fluxes from We instantiated our metabolic model with MFA data published by Grassian et al.We studied the behavior of the phenotypes of the entclass1pt{minima14 is a patient-derived cell line, whereas the The 14 MFA data was that the authors of the study applied standard deterministic kinetic approach to modeling, the preferred mathematical modeling approach for metabolic pathways. Deterministic kinetic models are based on Ordinary Differential Equations (ODEs), which can be used to describe the change in molecular concentrations, and are modeled as continuous functions. The systems of ODEs describing a molecular network can be explored through mathematical analysis, when the constraints on them are simple, or through computational simulation of how the quantities change over time while satisfying any imposed constraints. Grassian et al.’s work therefore enabled us to compare and validate our results against the deterministic kinetic approach.Another appeal of utilizing Grassian et al.’sWe reproduced the trends observed by Grassian et al., in both the effect of hypoxia on parental entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minima14 normalized the 14.Grassian et al.entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaHypoxia induces faster rates of nutrient utilization in both cell lines Fig. . The difThe dependence on glucose and glutamine as carbon sources is better understood through experiments that study the effect of varying initial concentrations of nutrients on the transient amounts and cumulative quantities of the metabolites. The results are shown in Fig. Hypoxia conditions lead to notable differences in dependence on glutamine: The dependence of lactate on glutamine is better understood by looking at how the probability of lactate saturation—lactate amount reaching the maximum amount set in the model—is affected by the initial concentration of glucose and glutamine. We model check the probability that lactate is saturated and show the results in Fig. The models constructed with cell line profiling data showed the limitations of the biological models based on overexpression of mutant IDH. We set out to explore whether patient data would reveal a different story for 17.The lack of direct measures of metabolic activity in patient samples precludes direct modeling of their metabolism. Similarly, proteomic and phospho-proteomic profiling remains sparse, or has limited coverage  therefore being of limited utility in our context. However, transcriptomic profiling has been extensively applied in patient samples, and can provide estimates on relative differences in metabolic activity of specific enzymes between phenotypes based on the differences in expression of the genes encoding those enzymes17, we used the fold change of expression for genes coding for key metabolic enzymes as a proxy for the relative rates of the corresponding reactions; when multiple genes encoded the same enzyme or isoenzymes, these genes expression’ was aggregated to obtain differences at the enzyme level.Since no absolute rates can be determined based on transcriptomic information, and given that the 27.Cohorts from glioma and normal brain samples from TCGA were constructed as follows: 140 The differences in expression levels of the key glucose metabolism enzymes and transporters, aggregated at the functional category level, are shown in Table The resulting models were studied as before for behavior over time, as well as how the behavior is affected by initial nutrient concentration  and add or remove components, makes it possible to perform complex in silico experiments. Crucially, these experiments can offer predictions on how the biological systems might behave when tested or perturbed in analogous ways.Our models of a metabolic network with components from glucose and glutamine metabolism provided several important insights. First, they showed that biological models in which IDH mutations are induced exogenously do not fully recapitulate the metabolic profile of cells endogenously expressing mutant-IDH. Under normoxia, the MFA models of the isogenic 28 found that glycolysis in gliomas was elevated to an average of 3-fold compared to normal brain. The accuracy of using mRNA expression as a proxy for protein levels depends on good correlation between mRNA expression and protein levels, which is generally not true. In particular, a model based on mRNA expression data does not account for phosphorylation events, and although differences in the phenotypes could be observed, we expect the differences in enzymatic activities to be even more pronounced because of phosphorylation and posttranslational modifications. Encouragingly, however, a recent study in an ovarian cancer xenograft model found that differentially expressed mRNAs correlate significantly better with their protein product than non-differentially expressed mRNAs29.Nevertheless, we showed the utility of integrating MFA data from in vitro experiments with patient-derived mRNA data by predicting the overall metabolic profile of 30. Lactate and to a lesser degree pyruvate produced by astrocytes are released in the extracellular space to be utilized by the neurons. It has been noted that neurons have the ability to fully oxidize lactate, thereby supporting the role of astrocytes providing lactate as an alternative fuel.The normal brain phenotype that we model is based on data obtained at the whole organ level. While at the whole organ level normal brain is glycolytic, a more complex picture has emerged at the cellular level, complicated also from the fact that most experiments at the cellular level are done in vitro on preparations enriched in astrocytes, neurons, or endothelial cells and therefore do not fully recapitulate the in vivo scenario. Neurons rely on oxidative metabolism to meet their high energy needs and produce minimal lactate, whereas astrocytes are highly glycolytic and produce lactate31 reversed the Warburg effect in glioblastoma through the use of methylane blue in an in vitro glioblastoma model: the reversal was evidenced by an increase in oxygen consumption and reduction of lactate production. The overall effect of reduction in the biosynthesis of intermediates feeding proliferation led to slower proliferation of the glioblastoma cells. Another reason to target the Warburg effect is because this phenotype may be an adaptation to the gradients of hypoxia present in the microenvironment of fast growing tumors, and which may drive reliance of tumor cells on glycolysis for their ATP-production needs32.Our models can also help reason about how changing the parameters of the model affects their behavior. An intriguing question is whether formulation of phenotypic shifts  can be formulated as an optimization problem, and solved to predict which reactions could be targeted to achieve such shifts. Unfortunately, our approach has shed light on the fact that the Warburg phenotype cannot be reversed by simply introducing the IDH1 mutation in the parental glioblastoma phenotype. However, it is possible in this approach to explicitly change the rates of the reactions (simulating knock-down or over-expression experiments) and study the resulting behavior. Affecting the metabolic phenotypes of cancer—driving them towards normal or maladapted phenotypes—has been proposed as a treatment strategy. For example, Poteet et al.14, who used a deterministic kinetic modeling approach, therefore serving as validation of the model checking approach as a suitable alternative to the kinetic deterministic modeling of metabolism. Computational approaches in addition to model checking include Petri Nets, process calculi, and hybrid approaches12. Baldan et al.33, for example, review modeling of metabolic pathways using Petri Nets. The Petri Net representation closely resembles the graphical representations of metabolic pathways as networks: tokens flow in a network of connected places according to transition firing rules that, depending on the flavor of the formalism, may be governed by quantitative rules like timed rates, or stochasticity. Petri Nets are amenable to qualitative structural analyses, which in a biological setting can be used, for example, to establish their continuous operation/presence of steady states, or quantitative analyses that are often synonymous with simulation33. Reddy et al.34, and Hardy and Robillard35 modeled glycolysis and pentose phosphate pathways in erythrocytes using several flavors of Petri Nets, and showed (upper) boundaries for certain molecular species, conservation properties, and situations leading to deadlocking of the system. Finally, Fisher and Henzinger12 overview the fundamental differences between mathematical and computational approaches and the appeal of choosing one approach over the other given a biological modeling task. They explain that mathematical models are difficult to obtain when relationships between the variables change over time and depend on certain qualitative events (such as a saturation concentration being reached), making the computational approaches effective in such contexts. Moreover, computational approaches allow for specification at different levels of abstraction, and can therefore be useful even when a process that is being described is not understood in detail.Other mathematical and computational approaches exist for constructing models of metabolism. Our model checking results are in agreement with the published work by Grassian et al.36. Possible extensions of the model that can be handled within the PRISM framework could include explicit modeling of compartments and molecular transporters. A more complete picture of metabolism will also inevitably require modeling how the metabolic pathways interface and interact with other major signaling pathways.We abstracted over compartment information and therefore did not model the shuttling of the molecules in and out of the cytosol or mitochondria. However, metabolism in cancers is often regulated through regulation of transporters of molecules and their isoforms. For example, mitochondral pyruvate carrier (MPC), which carries pyruvate into mitochondria, is composed mainly of two isoforms: MPC1 and MPC2. Lower levels of MPC1 (whether by deletion of its genomic locus or lower expression) are associated with poorer prognosis in gliomas and stronger Warburg effectSupplementary Information.


Finally, we analyzed differential representation of individual SCFA pathway genes across several WGS metagenomic datasets. The obtained collection of SCFA pathway genes and phenotypes enables the predictive metabolic phenotype profiling of HGM datasets and enhances the in silico methodology to study cross-feeding interactions in the gut microbiomes.Short-chain fatty acids (SCFAs) including acetate, formate, propionate, and butyrate are the end products of dietary fiber and host glycan fermentation by the human gut microbiota (HGM). SCFAs produced in the column are of utmost importance for host physiology and health. Butyrate and propionate improve gut health and play a key role in the neuroendocrine and immune systems. Prediction of HGM metabolic potential is important for understanding the influence of diet and HGM-produced metabolites on human health. We conducted a detailed metabolic reconstruction of pathways for the synthesis of SCFAs and L- and D-lactate, as additional fermentation products, in a reference set of 2,856 bacterial genomes representing strains of >800 known HGM species. The reconstructed butyrate and propionate pathways included four and three pathway variants, respectively, that start from different metabolic precursors. Altogether, we identified 48 metabolic enzymes, including five alternative enzymes in propionate pathways, and propagated their occurrences across all studied genomes. We established genomic signatures for reconstructed pathways and classified genomes according to their simplified binary phenotypes encoding the ability (“1”) or inability (“0”) of a given organism to produce SCFAs. The resulting binary phenotypes combined into a binary phenotype matrix were used to assess the SCFA synthesis potential of HGM samples from several public metagenomic studies. We report baseline and variance for Community Phenotype Indices calculated for SCFAs production capabilities in 16S metagenomic samples of intestinal microbiota from two large national cohorts , the Hadza hunter-gatherers, and the young children cohort of infants with high-risk for type 1 diabetes. We further linked the predicted SCFA metabolic capabilities with available SCFA concentrations both for  Acetate  and propionate producers encoding the propanediol pathway (such as Roseburia and Blautia species).Butyrate-producing bacteria have been extensively studied largely by cultural studies, however their entation . Three oentation . In continiphila . Propioniniphila . The latin vitro fermentation studies to measure the influence of prebiotics and other compounds on HGM samples  of a specific metabolite . For met samples . In our  samples . It was In the current study, we established the bioinformatics approach for assessing the metabolic potential for SCFA synthesis in HGM metagenomic samples in both 16S rRNA gene sequencing format and whole genome sequencing (WGS) datasets. For this purpose, a detailed analysis and reconstruction of the SCFA synthesis pathways was carried out in the reference set of HGM microbial genomes. Genomic signatures were established for many variants of enzymatic pathways and bacterial species were classified according to their ability (or inability) to produce SCFAs using a simplified binary matrix of phenotypes. We also reconstructed reference metabolic pathways for production of D- and L-lactate as an additional end product of microbial fermentation. The obtained matrix of binary phenotype values for SCFAs and lactate was used for the predictive phenotype profiling of the human gut microbiome with the publicly available 16S and WGS metagenomic datasets. As result, we compared the distribution of distinct pathway variants in HGM metagenomics datasets, compared the predicted metabolic potentials with metabolomics data for butyrate and propionate and demostrated the age-dependent increase of butyrate producers in childrens microbiomes. The observed correlations and dependencies are expected to guide the development of personalized nutritional supplements and health food products.The initial reference set of 2,228 human gut bacterial genomes from our previous study of vitamin metabolism  was expaWe used a subsystem-based comparative genomic approach implemented in the SEED genomic platform/database , 2014 foThe reconstructed SCFA and lactate production pathways  were furin vivo and in vitro fermentation studies of HGM microbiota   and the crobiota . The 16Scrobiota . Brieflycrobiota  using thcrobiota  databasecrobiota .Ai and PIi are relative abundance and phenotype index for each ASV.We used a development version of the Phenotype Profiler tool provided by PhenoBiome Inc  to assess the metabolic potential for SCFA production see , we mappA prediction error for CPI values (reflecting imprecise mapping and phenotype microheterogeneity) was calculated as:For ASV mapping to the HGM reference genomes, we used the threshold for nucleotide identity 90% that roughly corresponds to the taxonomic resolution on the family level. ASVs with identity to the HGM reference organisms below this threshold were discarded. According to our previous analyses, PI values were calculated by phenotype averaging across all genomes mapped with 90% threshold, resulting in PI predictions of sufficient accuracy for the majority of phenotypes, including SCFAs .Metagenomic samples with less than 75% abundance coverage to the HGM reference genomes were discarded from further analysis due to higher prediction error for CPI values. The choice of this threshold is a trade-off between 1) keeping a large fraction of initial samples for better statistical significance and 2) using CPI predictions with the smallest CPI error values. For all analyzed 16S datasets except Hadza, application of 75% threshold for the abundance of “mapped” ASVs has resulted in filtering out no more than 10% of samples  the Integrative Human Microbiome Project (iHMP) that includes WGS and other multi-omics data on functional dysbiosis in the gut microbiome during IBD activity ; 2) and For gene-based functional profiling of trimmed and filtered WGS data files we implemented a pipeline including the following public domain tools: a metagenome assembly with MEGAHIT ; gene prTo compare the CPI-based phenotype profiling approach with a state of the art predictive metabolic pathway abundance approach we used ASV sequences and abundance tables obtained for the AGP and UKT datasets and run PICRUSt2 with default parameters . The defThe trimmed mean M (TMM) approach  implemenTo detect changes of bacterial species in the butyrate-producing communities at different stages of child development in the TEDDY 16S dataset, it was performed linear discriminant analysis with effect size (LEfSe) . For LEfAlpha diversity (AD) and beta diversity (BD) metrics for 16S metagenomic samples were calcucated by QIIME2  as the Fp < 0.05.All correlation analyzes were performed using Spearman’s rank correlation by R software 4.1.2. Statistical significance was set at We used a subsystem-based comparative genomics approach implemented in the SEED genomic database and analysis platform  to reconvia the universal reaction by the butyryl-CoA dehydrogenase electron-transferring flavoprotein complex Bcd-EtfAB  that start from acetyl-CoA (derived from pyruvate), succinyl-CoA (derived from succinate), 2-oxoglutarate (derived from L-glutamate), and L-lysine, respectively . Despitecd-EtfAB . P1 pathFusobacterium species. Taxonomically, pathways of butyrate synthesis are most widely distributed among the Firmicutes (mainly from the Clostridia class), and, to a much lesser extent, among a few genera from the Bacteroidetes, Fusobacteria, and Spirochaetes phyla . Alternatively, S-methylmalonyl-CoA can be converted into propionyl-CoA using pyruvate-dependent transcarboxylase MMCT that contains two subunits . In P2 pathway, lactate is converted through lactoyl-CoA into acryloyl-CoA via CoA-transferase Pct and dehydratase LcdABC, and it is further reduced to propionyl-CoA by reductase Acr. Final release of propionate from propionyl-CoA is catalyzed by the same upstream pathway CoA-transferases both in P1 and P2 pathways . P3 pathway includes propane-1,2-diol dehydration to propionaldehyde via B12-dependent dehydratase PduCDE, next step carried out by dehydrogenase PduP yields propionyl-CoA, which is converted to propionate by consecutive actions of transferase PduL and kinase PduW.The reconstructed propionate production pathways in reference HGM bacteria include three previously described variants (P1-P3) that make propionate from either succinate, lactate, or propanediol metabolic precursors, respectively . LactateBacteroides, Proteobacteria and Actinobacteria phyla. The propanediol pathway P3 is the second most abundant variant found in over 300 genomes represented by ∼100 taxonomically diverse species from the Firmicutes phylum , Proteobacteria , and Fusobacteria. In addition, the pdu gene locus encoding the P3 pathway was found in a single representative from the Bacteroidetes phylum, Bacteroides xylanolyticus, suggesting it was a subject to a horizontal transfer from Firmicutes. The lactate (acrylate) pathway P2 was identified in 90 genomes corresponding to 30 species from the Firmicutes phylum (mostly from the Clostridia class), as well as in a single Fusobacterium species.Among 2,856 reference genomes, complete pathways of propionate synthesis were found in 826 genomes . The sucPropionibacterium, Corynebacterium). Noteworthy, all three alternative enzymes do not co-exist in any studied HGM genome. Two alternative forms of methylmalonyl-CoA mutase were identified: 1) MutAB, where both alpha and beta subunits contain fused catalytic and B12-binding domains, i.e., MutAB from Propionibacterium shermanii  rikoshii . We alsominicola , suggestlcdX orthologs always co-occur and co-localize with acr and/or pct genes encoding two other enzymes from the P2 pathway of propionate synthesis. Based on these observations we predicted LcdX function as lactoyl-CoA dehydratase (EC:4.2.1.54).The lactate pathway P2 contains two alternative forms of lactoyl-CoA dehydratases, LcdABC and LcdX. The previously characterized three-subunit enzyme LcdABC is present in 62 genomes possessing P2 pathway, whereas the novel predicted dehydratase LcdX was found in 43 genomes , including 18 genomes possessing both alternative isozymes . LcdX beE. coli strains, Rhodospirillum rubrum and Bacteroides xylanolyticus (pathway variant P3*). These include 28 Clostridiales genomes and Bacillus massiliosenegalensis posessing both alternative forms of propanediol dehydratase. The B12-independent propanediol dehydratase PduC2 is a glycyl radical enzyme that belongs to the COG 1882 (PflD) family, which includes pyruvate formate lyase 2 and B12-independent glycerol dehydratase. In all analyzed genomes, PduC2 is accompanied by the radical SAM-family proteins PduD2 that belongs to the COG1180 (PflA) family, including glycerol dehydratase activating protein, and thus is likely to function as an PduC2 activating enzyme. The predicted new propanediol dehydratase PduC2 and the PduD2 activating enzyme have been recently biochemically characterized in Roseburia inulinivorans, and confirmed to utilize 1,2-propanediol as a substrate in strictly anaerobic conditions , transcriptional repression of the propionate/butyrate pathway genes in the analyzed experimental conditions, or by a significantly larger metabolic flow to generate alternative fermentation product(s). For propionate production, we also observed a few cases of inconsistencies in the opposite direction, when three HGM species that are experimentally confirmed to produce propionate, appear to lack any propionate synthesis pathway in their genomes . Finally, in the process of searching for experimental SCFA phenotype evidences in published sources, were found a few other characterized SCFA producers, including Clostridium neopropionicum, Anaerotignum neopropionicum, Alistipes dispar,Alistipes communis, Anaerococcus octribumium, Butyricimonas faecalis, Clostridium phytofermentans,Corynebacterium avidum, Peptoniphilus ivorii, Clostridium sphenoides. These HGM organisms have not been described in our reference genomic collection, we thus plan to add them to our collection in the future. Overall, the examined experimental data on SCFA production are in good agreement with our in silico reconstruction and prediction of propionate and butyrate production phenotypes.To validate the predicted SCFA production capabilities in reference HGM genomes, we collected the published experimental data on SCFAs synthesis by each analyzed HGM species. As result, we found experimentally described SCFA production phenotypes for 210 out of 823 reference HGM species . Among 1binary phenotypes introduced in our previous study of vitamin synthesis capabilities in the human gut microbiome (Kosakonia sacchari). Most of the 504 analyzed genomes from the Actinobacteria phylum (47 genera) are producing acetate, formate, and L-lactate, whereas propionate and D-lactate can be produced by 6 and 3 genera, respectively, and the butyrate production pathway was found only in a single genome (Micromonospora aurantiaca). The Fusobacteria  and Bacteroidetes  phyla contain mostly producers capable of synthesizing all described SCFAs. The Verrucomicrobia phylum as represented by six Akkermansia strains have biosynthetic capabilities for formate, propionate and D-lactate. Three reference genomes from the Synergistetes phylum are capable of formate and propionate synthesis. The Lentisphaerae phylum represented by a single HGM species  produces only acetate and formate. Both strains of Brachyspira pilosicoli from the Spirochaetes phylum are producers of acetate, butyrate and L-lactate. The Tenericutes phylum members (6 Mycoplasma and 19 Ureaplasma strains) are able to produce acetate and L-lactate. A single HGM strain from the Planctomycetes phylum  can produce only L-lactate.To apply the reconstructed SCFA synthesis pathways in reference genomes to metagenomic HGM samples we use the concept of crobiome . Binary crobiome  allows ucrobiome . The larnumber of variable phenotypes (NVP), and 2) overall phenotype variability score (OPVS) calculated as a sum of variances for each SCFA phenotype (C. bolteae (7 strains) and C. clostridioforme (9 strains) possessing highly variable butyrate and propionate production phenotypes. Other phylogenetically diverse HGM species including Acinetobacter baumannii (21 strains), Enterococcus faecalis (62 strains), Fusobacterium nucleatum (21 strains) and Fusobacterium periodonticum (6 strains) demonstrated high OPVS values due to variability of the acetate, L-, D-lactate and propionate production phenotypes. Similar metrics were used to assess SCFA phenotype variations at the genus level. Out of 296 analyzed HGM genera, 163 were represented by more than one genome, and 102 of them (∼34%) demonstrated various degrees of phenotype variability. Majority of genera with the highest variability of SCFA binary metabolic phenotypes  belong to the Clostridia class from the Firmicutes phylum  henotype . The higs phylum . In contWe used the metabolic phenotype profiling approach and the obtained reference BPM (see Methods) to predict SCFA production capabilities for HGM samples from three published 16S rRNA gene sequencing datasets, namely the American Gut Project (AGP) , the UK To study relationship between SCFA production phenotypes and microbial alpha diversity (AD) of the gut microbiome samples, we binned all samples into groups of similar AD and constructed CPI-vs.-AD scatterplots for each SCFA metabolic phenotype. The UKT dataset demonstrated characteristic phenotype-specific dependences between CPI and AD values . First, The gut microbiota is passed on to newborns from their mothers and develops with age . To analF. prausnitzii, a major butyrate producing bacterium in HGM   at diffem in HGM , as welle groups . Each ofFinally, we compared microbial composition of samples within and between groups of samples from the TEDDY dataset with available metadata . In order to measure the overall scale of the observed differences, we performed beta diversity (BD) analysis and considered the average pairwise distances between samples for each comparison scenario. We found that for both intergroup and intragroup comparisons their respective BD values were of the same order of magnitude see , suggestin vivo study of HGM modulation with discrete dietary fiber structures in randomized controlled trial in humans   . Measureectively , providivia the amino acid pathway than children. The P1 (succinate) pathway is the most abundant propionate synthesis pathway in both datasets, followed by the P3 (propanediol) pathway in children from the TEDDY dataset  based on 16S or WGS metagenomic data is important for microbiome research and development of personalized prebiotics and probiotics. Isolating and culturing of individual HGM bacterial species to identify their metabolic products is not feasible due to unknown carbon sources and growth medium conditions for a significant number of HGM species. Thus, development of bioinformatics approaches for prediction of SCFA production potential is important for predictive functional profiling of microbiomes. In our previous works, we developed the metabolic phenotype profiling approach to estimate the relative abundance of HGM bacteria encoding functional variants of amino acid and vitamin biosynthetic pathways , while hTo obtain the reference collection of SCFA metabolic genes and phenotypes we utilized the subsystems approach and analyzed a set of 2856 HGM genomes representing 823 bacterial species. As result, the comparative genomics analysis allowed us to reconstruct all previously described variants of metabolic pathways for production of butyrate (4 variants), propionate (3 variants), formate, acetate, and lactate . Completpdu gene cluster was identified only in 9% of 144 analyzed Lactobacillus genomes, at that this pathway is present in 2 out of 3 strains of L. brevis and 8 out of 10 strains of L. reuteri. To minimize the CPI prediction error due to the observed phenotype variations we used the sequence-based mapping scheme for functional profiling of 16S microbiome datasets. As result, mean CPI prediction error values were no more than 1% for most analyzed 16S datasets and SCFA phenotypes  and overall metabolic pathway abundance (for WGS). Each CPI value represents a probabilistic estimate of the fraction microbial cells that are capable to produce a specific SCFA. To enable the phenotype profiling approach, we obtained the simplified binary phenotypes corresponding to producers (“1”) and non-producers (“0”) of each SCFA. These binary values were combined together for 2,856 analyzed genomes to make a BPM that was further analyzed across taxonomically-related genomes to determine the variability of SCFA phenotypes at the species and genus levels . Comparienotypes .Analysis of the AGP and UKT datasets showed significant similarity between the obtained SCFA phenotype profiles , which mvia metabolomics reflects steady state metabolite levels, however 90–95% of SCFAs produced in the colon lumen are absorbed by the intestinal mucosa  serve as substrates for formation of propionate (via P2 pathway) and butyrate  by numerous other HGM species. These metabolic interactions were confirmed in a few in vitro co-culturing studies, including 1) the co-growth of B. adolescentis, which uses starch and fructose oligosaccharides as substrates, and strains of Eubacterium (Anaerobutyricum) hallii and Anaerostipes caccae, that use lactate and acetate (formed by B. adolescentis) and produce butyrate as a final SCFA product  was previously mainly studied in several Firmicutes bacteria from the Negativicutes class (via the P2 pathway) was demonstrated in Porphyromonas gingivalis and Clostridioides difficile   acetate . The secevotella . Succinaevotella . The cones class , while tifficile . The thiifficile . 1,2-proa genera . The othlglyoxal . Severalhimurium  and Rosenivorans , are capopionate . Finally acetate .Taking together, the reconstructed biochemical pathways and binary metabolic phenotypes for SCFA and lactate production in 2,856 reference HGM strains allowed us to expand the previously reported BPMs for amino acid and vitamin biosynthesis and to perform a quantitative analysis of SCFA phenotype profiles in HGM metagenomes. The predicted SCFA synthesis phenotype and pathway abundances can find its practical application in the diagnosis and treatment of syndromes associated with dysbiosis through a rational and personalized choice of probiotics and food supplements. Future expansion of the reference genome set by newly sequenced HGM isolates and metagenome-assembled genomes will improve the accuracy and reliability of our functional phenotype profiling approach.


Escherichia coli harboring unbranched ETS that pump 1, 2, 3, or 4 proton(s) per electron and characterized them using a combination of synergistic methods . We report that: (a) all four ETS variants evolve to a similar optimized growth rate, and (b) the laboratory evolutions generate specific rewiring of major energy-generating pathways, coupled to the ETS, to optimize ATP production capability. We thus define an Aero-Type System (ATS), which is a generalization of the aerobic bioenergetics and is a metabolic systems biology description of respiration and its inherent plasticity.The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. There is a detailed understanding of the structural and biochemical features of respiratory enzymes; however, a holistic examination of the system and its plasticity is lacking. Here we generate four strains of  The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. Here the authors examine the systems level properties of aerobic electron transport system using adaptive laboratory evolution and multi-omics analyses. However, evolution has produced variations within the ETS which modulate the overall energy efficiency of the system even within the same organism3. The systems level impact of these variations and their individual physiological optimality remain poorly determined. To mimic varying ETS efficiency we generated four Escherichia coli deletion strains , each with one of the four unbranched ETS variants that pump 1, 2, 3, or 4 proton(s) per electron, respectively. We then performed systems level characterization of these ETS variants. We observe that: (a) adaptive laboratory evolution (ALE) enables all four ETS variants to evolve to a similar growth rate; (b) the evolution of ETS variants is supported by specific rewiring of major energy-generating pathways that couple to the ETS to optimize their ATP production capability; (c) proteome allocation per ATP generated is the same for all the variants, (d) the aero-type, that designates the overall ATP generation strategy4 of a variant, remain conserved during its laboratory evolution, with the exception of the ETS-4H variant; and (e) integrated computational analysis of the data supports a proton-to-ATP ratio of 10 protons per 3 ATP for ATP synthase for all four ETS variants.Respiration requires organisms to have an electron transport system (ETS) for the generation of proton-motive force across the membrane that drives ATP synthase. Although the molecular details of the ETS are well-studied and constitute textbook material, few studies have appeared to elucidate its systems biology. The most thermodynamically efficient ETS consists of two enzymes, an NADH: quinone oxidoreductase (NqRED) and a dioxygen reductase  are less electrogenic compared to Cytochrome bo3 O2REDs (CYO). There are two CBDs, bd-I and bd-II, and both functions similarly to generate proton-motive force (PMF) by a vectorial movement of protons involving transmembrane charge separation. The similar PMF generation strategies make bd-I and bd-II O2REDs equivalent when choosing gene knockout strategies8.To examine the contributions of individual oxic respiratory pathways to bioenergetics, we sought to design unbranched pathways through the ETS. The oxic component is contributed by both proton pumping and non-pumping NqREDs  along with three types of OEDs Fig. . Cytochrn = 1, 2, 3, 4). The designs of the four ETS variants are illustrated in Fig. Based on these characteristics, we designed four ETS variants with unbranched electron flows representing all alternate oxic respiratory routes translocating 1, 2, 3, or 4 proton(s) per electron  showed different growth rates that had no clear association with their H+/e− value. While the loss of activity of NDH-I showed a lesser growth rate retardation, the deletion of NDH-II significantly compromised the growth rate of the deletion strains.Next, we analyzed their growth phenotype Fig. . IntereseETS-nHm with the replicate evolutionary endpoints indexed as m = A, B, C, D)9. We evolved all variants until their growth rate plateaued. ETS-1H, 2H & 3H required evolution for approximately 400 generations, while ETS-4H required approximately 700 generations. In spite of the different number of protons pumped per electron, all four ETS variants evolved to a similar optimized growth rate in replicate evolutions (~0.85 h−1)  Fig. .10) to interpret the potential impact of the identified mutations. The mutation calling revealed only a few genetic changes in the evolved strains except for eETS-4HC which acquired 15 genetic changes 20. Almost all mutations in sdhA were either in or near interface surfaces and seem to be working to disrupt its functionality by either disrupting a substrate-binding site or causing a structural-functional perturbation  of each variant, but the correlation between pre-and post-evolved variants was lower Fig. . Notably21. Therefore, we examined if the surrogate NqRED or O2RED compensated for the loss of function resulting from deleted ETS enzymes 25. We observed a high correlation in the metabolic flux distributions of the four evolved replicates of each strain, further supporting a similar evolutionary pathway followed by replicates of each variant  increased the flux through the proteome conservative ED pathway. Thus, we observed a compensatory selection of the preferred pathway to achieve a balanced proteome.The finite resource carrying capacity of a cell creates metabolic tradeoffs on how to partition the proteome to support metabolic pathways best suited for a given growth condition. With an approximately 3.5-fold higher protein cost, the Embden–Meyerhoff–Parnass (EMP) pathway consumes a larger proportion of proteome as compared to the Entner–Doudoroff (ED) pathwaynuoB deletion (ETS-1H and ETS-2H) increased metabolic flux through complex II of ETS, ETS-3H appeared to minimize the flux through complex II  allows for redundancy in the We observed that ETS-1H, ETS-2H, and ETS-3H did not show a major shift in their biomass yield during evolution and thus preserved their respective aero-types Fig. . The evoeETS variant, and the total ATP output of each proteome expressed was almost constant  +/ATP) is technically challenging and, therefore, it is still an area of active research32. The rotational catalysis-based calculation suggests the H+/ATP value to be 3.3, due to the symmetry mismatch between the Fo and F1 complexes of ATP synthase: threefold symmetry of α3β3 in F1 and tenfold symmetry of the c-ring in Fo34. The proton-to-ATP ratio may vary depending upon any change in the number of c-subunits and this modulation allows tailoring to meet the bioenergetic demand of various organisms35. The H+/ATP value derived using a synthetically reconstituted membrane system was found to be 436. With our comprehensive definition of the state of the ATS amongst the variants, we could address the issue of ATP synthase proton-to-ATP ratio. We used data generated on the variants to computationally estimate the most likely proton-to-ATP ratio for E. coli ATP synthase32. We constrained the ME-model using the observed metabolic exchange rates and gene expression data and optimized for the H+/ATP value of ATP synthase that produces the experimentally estimated growth rates of the variants. The ME-model calculates the median value of the H+/ATP to be 3.25, a value close to 3.3 supporting the rotational catalysis hypothesis  was used as the wild-type strain. P1 phage transduction method was used to generate the knockout strains41, and strains from the Keio collection were used as a donor for the gene knockout cassettes42. uETS-1H and uETS-3H were generated and used for validation purposes in an earlier study4. uETS-2H and uETS-4H were generated here and all four ETS variants were evolved for this study.600 of 0.3 . Cultures were always maintained in excess nutrient conditions assessed by non-tapering exponential growth. The evolution was performed for a sufficient time interval to allow the cells to reach their fitness plateau.ALE was performed using 4 independent replicates of each ETS variant. Cultures were serially propagated on M9 minimal medium with 4 g/L glucose at 37 °C and well-mixed for proper aeration using an automated system that passed the cultures to fresh flasks once they had reached an A10.The ALE mutation datasets supporting the conclusions of this article is available in the following open-access archive repository: 10.5281/zenodo.5431595. These datasets are also available in the ALEdb database43. The visualizations for the 3D protein structures were generated using the NGL software package44. The software implementation of these actions is available in the following open-access archive repository: 10.5281/zenodo.5431595.Mutated DNA sequence data processing was performed using Python 3. The mutations from ALEdb are described according to their experiment, evolution replicate, sample, and technical replicate. Some evolutions include midpoint samples that could inflate the frequency a mutation is observed. Unique ALE mutations were therefore only considered once per ALE. Starting strain mutations and hypermutator samples were filtered out of the ALE experiment mutation datasets according to their publications. Mutation needle plots were generated using the trackViewer R software package19. The predicted structural destabilization effects of SNPs were assumed according to predicted significant ΔΔG scores (ΔΔG > 2)20. SIFT and ΔΔG scores were acquired from Mutfunc45. Functional annotations were acquired from UniProt46 and Mutfunc.Mutation effects were predicted according to multiple methods. Truncations were predicted according to the potential effect of mutations on the function of start codons and their potential to introduce a premature stop codon. The predicted deleterious effects of SNPs were assumed according to significant SIFT (sorting intolerant from tolerant) scores (SIFT score < 0.05)600 ~0.6. Nucleic acid isolation, library preparation, and subsequent analysis were performed as previously described47. Briefly, genomic DNA was isolated using a Nucleospin Tissue kit including treatment with RNase A. Resequencing libraries were prepared following the manufacturer’s protocol using Nextera XT kit. RNA was isolated using the Qiagen RNeasy Mini Kit following suggested protocol. Ribosomal RNA was removed using Illumina Ribo-zero kit and a KAPA Stranded RNA-Seq Kit (Kapa Biosystems KK8401) was used to prepare sequencing libraries. Sequencing was performed on an Illumina HiSeq and/or NextSeq.A clone from the endpoints of evolved strains was picked for DNA sequencing and RNA sequencing. The strains were grown in an M9 minimal medium supplemented with 4 g/l glucose. Total DNA was sampled from an overnight grown culture and total RNA was sampled from a culture at an A2SO4 mobile phase set to a flow rate and temperature of 0.5 mL/min and 45 °C, respectively. The phenotype dataset was used for the aero-type classification of the strains as described previously4.Phenotype characterization was performed using two independent biological replicates. Samples for the substrate uptake and secretion rate were collected at regular intervals and filtered using a 0.22 μm filter . The measurements were performed using refractive index detection by HPLC (Agilent 12600 Infinity) with a Bio-Rad Aminex HPX87-H ion exclusion column. The HPLC method was the following: injection volume of 10 μL and 5 mM H48. The same FoldME model was used for estimating the H+/ATP value for ATP synthase within each ETS variant and replicates. The model was constrained with phenotypic data  and expression data was layered on using the same methods used for the flux mapping48. In addition to these constraints, the necessary ETS genes for each variant were knocked out. Proton pumping ratios from 2.5 to 4.5 were sampled by changing the stoichiometry of the ATPS4rpp reaction in the ME-model, and then the proton pumping ratio was optimized so that the model produced a biomass dilution rate that matched the experimentally determined growth rate.Flux mapping was done as previously described using a genome-scale model of metabolism and protein expressionith protein in the model. Total proteome allocated to the ATS was calculated as follows: ith protein in the ATS . The list of 209 ATS genes was generated based on Clusters of Orthologous Groups (COG) and Gene Ontology (GO) categories to include as many relevant genes as possible to represent pathways involved in ATP production, then filtered to remove genes that are never expressed in the multiple model simulations. Mass fraction of proteome allocation to the ATS was calculated as a ratio of the two values for each strain.The same FoldME model was used for the proteome allocation calculation as the flux mapping and ATP synthase estimation calculations. The model was constrained with phenotypic data  and expression data was layered on using the same methods used for the flux mapping. Solutions from the fully constrained ME-models were then used for calculating proteome allocation. Total proteome allocation for each strain was calculated as follows:ith ATS associated reaction in the table below.Calculation of the total ATP produced by the ATS used the same fully constrained ME-model. A list of all metabolic reactions associated with ATS genes was curated. Reactions that consumed or produced ATP were noted and the stoichiometric coefficient associated with ATP was used as a modifier for calculating the total ATP production as follows Table :\documenTotal ATP Production/Total Proteome Allocated was calculated as a ratio of the total ATP production to the mass fraction of proteome allocated to the ATS for each strain.6. The only genes included in the dataset were those contained in the list of 209 ATS genes. The dataset consisted of all unevolved strains, uETS-1H through 4H, and all evolved replicates eETS-1HA through eETS-4HD. Additionally, the unevolved and evolved wild-type strains were included with the former being used as a reference to center the data. The final and resulting dataset that was used for ICA contained 209 genes by 22 conditions.Independent component analysis was performed on an RNA-seq dataset with steps described inFurther information on research design is available in the Supplementary informationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesDataset 1Dataset 2Dataset 3