The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding the information on possible error sources in the model limit the power of existing Deep Learning-based methods. To overcome these problems, we present a new approach, a hybrid of Capsule Networks and Gaussian Processes. Our method predicts the cleavage efficiency of a gRNA with a corresponding confidence interval, which allows the user to incorporate information regarding possible model errors into the experimental design. We provide the first utilization of uncertainty estimation in computational gRNA design, which is a critical step toward accurate decision-making for future CRISPR applications. The proposed solution demonstrates acceptable confidence intervals for most test sets and shows regression quality similar to existing models. We introduce a set of criteria for gRNA selection based on off-target cleavage efficiency and its variance and present a collection of pre-computed gRNAs for human chromosome 22. Using Neural Network Interpretation methods, we show that our model rediscovers an established biological factor underlying cleavage efficiency, the importance of the seed region in gRNA. Prokaryotic Class 2 CRISPR –Cas (CRISPR-associated) systems possess effector modules that consist of a single multi-domain RNA-Guided Nuclease (RGN) , which min vivo (https://github.com/MicrosoftResearch/Azimuth), WU-CRISPR . ComparU-CRISPR ,20 has sU-CRISPR . AdditioU-CRISPR . NeverthDeep learning allows design of optimal gRNAs without any prior assumptions on the structural features of the target region. Several tools exploiting deep learning have been recently developed for predicting the on-target cleavage efficiency of Cas9 and Cas12a nucleases . Our method is based on a combination of a Hit-Or-Miss capsule network , which acts as a feature extractor, and a Gaussian Process to model the distribution from which the real-valued on-target and off-target activities are sampled. This approach allows estimation of the cleavage efficiency of gRNAs containing mismatches to their target sites using large-scale measurements across multiple cell models from recent studies , but for the purpose of prediction, we scale and shift the labels so the maximum is 1 and minimum is 0. The sequences are strings over an alphabet of four symbols , 20–23 symbols long. The problem is the task of regression which we solve using a neural network-enhanced Gaussian process. We provide the solution that, unlike others, not only gives a point estimate of the cleavage efficiency, but, for the first time, also gives the confidence interval for it.Homo sapiens, Danio rerio, Mus musculus and Xenopus tropicalis. DeepCRISPR dataset (N) where N is the length of the input sequences. For the on-target datasets, the algorithm was applied only once, so that the output is a  image.For on-target Cas9 cleavage efficiency prediction, we used three non-overlapping datasets, geCRISPR, DeepCRISPR and DeepHF. The geCRISPR dataset  consists dataset  consists dataset  provides dataset  with 26  dataset  dataset  dataset  with 156We developed a deep kernel learning model, named GuideHOM (Guide Hit-Or-Miss). GuideHOM employs a Hit-Or-Miss capsule network as an intermediate feature extractor built upon a preprocessing module and a Gaussian Process to estimate the distribution of the cleavage efficiency of RGN programmed with a given gRNA. The estimation is based on the representation produced by the Hit-Or-Miss capsules to predict on-target and off-target cleavage efficiency using gRNA spacer sequence features , where N is the length of gRNA spacer; for gRNA-target preprocessing, the final input is an array of size . To predict on-target cleavage efficiency based only on gRNA, we use the 1D convolutional or recurrent preprocessing modules. To predict cleavage on- and off-target efficiencies based on gRNA and target sequences we use 2D convolutional preprocessing. For a gRNA–target pair, our model estimates the lower and upper bounds of the cleavage efficiency. The recurrent layer is not used for the case with two sequence inputs, in order to minimize unnecessary complexity in the setup. Pytorch  preprocessing subnetwork that extracts low-level sequence features using either a 1D or 2D convolutional layer followed by Leaky ReLU  or an LSTM layer (Long Short Term Memory networks); (ii) encoder subnetwork that extracts high-level sequence features from preprocessed inputs using a set of Hit-or-Miss capsules , and ii that est Pytorch  is used  Pytorch  is used As can be seen from Figure The preprocessing subnetwork provides the extraction of low-level sequence features on the level of k-mers. For the experiments in this work, we used three types of preprocessing subnetworks: (i) 1D convolutional, (ii) 2D convolutional and (iii) LSTM-based. An 1D convolutional preprocessing subnetwork consists of a 1d CoordConv layer  followedi encodes the difference between an input example and a center of the space of possible outputs:C = . In case of classification, when Hit-or-Miss capsules are optimized with their special Centripetal loss, the perfect ‘hit’ is the vector filled with zeros, and the wrong answers accumulate large ‘misses’. In our case, we did not use the classification  loss and classification setup, so the ‘misses’ simply encode the position of the input in the space of all possible inputs, not necessarily the class vectors. Consult the The encoder is used to learn patterns based on the sets of k-mers and to present the learned patterns in a concise manner as a matrix of real numbers. The encoder consists of a number of Hit-Or-Miss capsules applied to the preprocessed input in parallel. Each Hit-Or-Miss capsule is a linear layer followed by batch normalization to accelerate the learning process, and sigmoid activation to constrain the output between 0 and 1 Figure . Hit-Or-x and x′ are the pair of inputs, f(x) is the function we would like to fit, m(x) - mean function and k - covariance function, such as:x is the observed cleavage efficiency, p. A Gaussian process is a probability distribution over possible functions that fit a set of points:er Bound :\documen library , with de library  library r2), Pearson and Spearman correlation coefficients (PCC and SCC respectively), computed using scipy library. To understand whether the confidence intervals the model gives is acceptable, we compute how many real labels for examples from the test set lie between the predicted label and predicted standard deviation:N is the test set size, I is the indicator function which is 1 if the argument is true and 0 othervise, M ∈ . We compute ρ for one, two and three standard deviations and check whether the values are close to 68%, 95% and 99.7%, respectively. We denote those values as ρ68, ρ95 and ρ99.7.To analyze the performance of the regression models, we use three classical metrics - determination coefficient  that are flattened into  or  matrix. The model requires  or , where N is the length of the input, so an intermediate class to reshape the inputs is used. M in our work is set to 10 000. The resulting ALE values have the shape of , where A is the number of feature intervals, in our case, 2 for 0 and 1 of one-hot encoded nucleotides, and B is the number of prediction targets, again, 2 in our case, corresponding to mean efficiency and variance. We are interested only in the component of features that corresponds to the values of 1, because we would like to see the importance of a presence of a certain nucleotide on a certain position.To interpret the predictions of the model, the Accumulated Local Effects  are comThe heatmaps from Figure t is temperature parameter, the less temperature is, the more distinct the output probabilities are, Xi are the features that correspond to ith nucleotide out of four, i and j are the counters. Softmax is applied along the nucleotide axis, the resulting matrix is of  size, and if we sum the vector along the first axis, we get the vector of N ones. We use weblogo to draw the resulting sequences.For logo sequences, constructed out of ALE values, the softmax with additional temperature parameter is calculated:Input: the raw sequence data, downloaded from the UCSC genome browser and respective annotation. Output: the list of possible gRNAs with computed average cleavage efficiency, standard deviation. The input is downloaded with the following options: Chromosome - Download FASTA, Visible range (while whole chromosome is opened in the browser); Annotation – Download csv. The pipeline proceeds as follows:the genes are extracted from the chromosome sequence according to the annotation using a BioPython-based script;Cas-OFFinder  is used N, 2) vector, so the input sequence acts as both gRNA and target;the model is used for each Cas-OFFinder output. For each target sequence, the model computes the average cleavage efficiency and standard deviation (or just the cleavage efficiency in case of ). If thethe guides are sorted by cleavage efficiency in descending order. Each result is saved in a table that combines Cas-OFFinder output and model output.For this analysis we use the best performing Cas9 and Cas12a on-target models. The results are available at a dedicated Zenodo repository, consult the ‘Code and data availability’ section for details.In our study, combination of Hit-Or-Miss networks  and Deep Gaussian Processes  helps toY present within the confidence intervals (with confidence level α) computed on the corresponding test sets was, for all cases, close to the preset confidence level . As the loss function, we use ELBO  or a sun et al.  dataset n et al.  because n et al. .The same performance dynamics is observed for all analyzed datasets: after the training sample size grows to about 10 000 gRNAs, the performance converges at a plateau so that the additional improvement is negligible. Uncertainty reduction follows a similar course: the smallest mean standard deviation is achieved near the 10,000 gRNAs mark and remains approximately constant with larger training sets. These observations suggest that for this version of the GuideHOM architecture, collecting datasets of more than 10 000 gRNAs yields diminishing returns in terms of the model accuracy, so a better strategy is to focus on the reduction of the label noise. However, the cut-off may have to be re-accessed depending on the type of gRNA library used, i.e. those targeting intronic, exonic regions, high/low expressed genes etc. The performance of the method for on-target cleavage efficiency prediction on benchmark datasets tends to be close to that of the best original point estimate models Table  and Kim The performance of all trained models is given in r2, Pearson and Spearman Correlation Coefficients.As can be seen from Table For this test case, we are not interested in off-target effects, therefore DeepCpf1 R E is the model of choice . We also can use all good models as an ensemble, which will result in an improvement of 1-2% in the correlations at the excess of increased uncertainty since we would have to sum the variances to get the correct uncertainty estimation.et al.  by performing in vitro experiments with each of the gRNAs and measuring the levels of proline. All sequences from this set are presented in the 2D visualization of the guide space provides actionable insights into the variety of functional gRNAs available for a gene of interest. Figure For all visualizations in Figures p68, p95 and p99.7. This could be explained by the small size of the dataset, which only includes 1597 gRNA-target pairs, less than half of the next smallest dataset, geCRISPR, with 3619 gRNAs. For the rest of the models, the confidence intervals, on average, remain acceptable. There is also a substantial gap in the performance between the RNN and CNN-based models for the geCRISPR dataset, with RNN ELBO and RNN ELBO+MSE performing worse than the CNN counterparts (Pearson correlation 0.3882 and 0.4403 versus 0.5946 and 0.5926). The remaining models do not yield significant differences in performance either between RNN and CNN or between ELBO and ELBO+MSE. Overall, the cross validation study shows that the GuideHOM architecture is robust to overfitting provided there is enough data to train it on.In addition to reproducing the training routines from previous studies, we conducted a 10-fold cross-validation analysis. Most of the previous studies we based our work on did not perform 10-fold cross validation, so that we cannot compare our performance with that reported in these studies without reproducing them in their entirety, which is outside of the scope of this work. The cross validation results are available in r2 value of 0.625, as compared with 0.617 in the original study, and an acceptable confidence interval . The prediction plots for the test set are shown in Figure Our approach allows not only for the prediction of the on-target cleavage efficiency based on a single gRNA but also, with minimal changes to the architecture , for the prediction of off-target cleavage efficiency from a pair of a gRNA and the target. For the Cas9 gRNA–target pair dataset , we traiThe original Cas9 gRNA-target pair model  is obtaiet al. . The ROC curves for these models are shown in Figure The model trained on the Cas9 gRNA-target pair dataset provides Cas9 cleavage efficiency estimates for guide and target pairs with small numbers of mismatches between the guide and the target. This information can be used to solve the off-target classification task using the Peng et al.  classifiWe next trained a regression model for Cas12a gRNA-target pair dataset. We split the dataset of  into 90%r2 value of 0.625 versus 0.617 for the Jost et al. model, see details above) supports the utility of our approach. As an example of a practical application, we provide the top 5 gRNAs with mean and variance values for each gene in the Homo sapiens reference genome (hg38) chromosome 22. An example of the output for gene SERPIND1 is shown in Table We analyze an empirically validated sgRNA library for 2400 genes that are essential for robust cell growth . The conCas-OFFinder  was usedThe inclusion of uncertainty estimates in the off-target analysis provides for two orthogonal axes that characterize off-target properties: cleavage efficiency and stability of prediction, where cleavage efficiency shows how probable is the cleavage of the DNA strand with this particular target and this particular gRNA, and prediction variance shows how robust the prediction of cleavage efficiency is. Figure Most off-targets with cleavage efficiency variance <0.015 and mean cleavage efficiency <0.15 are located in the left bottom corner of the plot on Figure We use the Accumulated Local Effects (ALE ) to explImportance heatmaps for efficiency and variance tend to mirror each other, not exactly, but very close. For example, in Cas9 Figure  and B, tIn this work, we introduce Deep Kernel Learning-based methods for estimation of confidence intervals for gRNA-target cleavage efficiency. Using a number of publicly available datasets, we construct a set of effector- and cell line-specific models. Our results indicate that deep kernel learning offers substantial advantages over basic deep learning in the estimation of the efficiency of CRISPR-mediated target cleavage. The methods we develop here are distinct from all previously described ones in that an uncertainty estimation is explicitly given. The uncertainty of the model provides valuable clues for the design of gene editing experiments, allowing the end user to select not only gRNAs that are optimal with respect to the on-target and off-target efficiency, but also those that are the most predictable in their behavior in experiments. For example, a researcher probably should pick a low variance gRNA with a lower predicted efficiency compared to a gRNA with a higher efficiency but a wider confidence interval. The confidence interval is important in two cases: when there are many gRNAs to select from and when highly specific editing is required. Given that experimental researchers routinely use at least 10 different gRNAs to target the gene of interest, the confidence intervals become a valuable tool, because they allow the likelihood of an experimental failure to be minimized through optimal selection of gRNAs. As for the specific editing, there are many tasks that require not only silencing a gene, but silencing it using a specific region of the gene as the target.A salient example is analysis of small RNAs, including miRNAs and their targets. In cases when there are many predicted target sites for a small RNA (each can be as short as 8 bp), inducible Cas9 experiments and multiplexed single guide RNAs can be used to generate hundreds of targeted mutations in parallel . When thet al. , ouet al. . For merhttps://github.com/bakirillov/uace. The gRNAs with predicted cleavage efficiencies and variances can be found on Zenodo (https://doi.org/10.5281/zenodo.4773404) along with the logo sequences for gRNA clusters from LOC440792 gene. Demonstration of all models is available on Google Colab (ID: 1VJkhQeqW1OkMG0VJoxcpML7LtnkvTX9e).The code for reproduction of the models, tables and figures, can be found in the following repository: gkab1065_Supplemental_FilesClick here for additional data file.


Pseudomonas putida can tolerate high levels of endogenous and exogenous oxidative stress. Yet, the ultimate reason of such phenotypic property remains largely unknown. To shed light on this question, metabolic network-wide routes for NADPH generation—the metabolic currency that fuels redox-stress quenching mechanisms—were inspected when P. putida KT2440 was challenged with a sub-lethal H2O2 dose as a proxy of oxidative conditions. 13C-tracer experiments, metabolomics, and flux analysis, together with the assessment of physiological parameters and measurement of enzymatic activities, revealed a substantial flux reconfiguration in oxidative environments. In particular, periplasmic glucose processing was rerouted to cytoplasmic oxidation, and the cyclic operation of the pentose phosphate pathway led to significant NADPH-forming fluxes, exceeding biosynthetic demands by ~50%. The resulting NADPH surplus, in turn, fueled the glutathione system for H2O2 reduction. These properties not only account for the tolerance of P. putida to environmental insults—some of which end up in the formation of reactive oxygen species—but they also highlight the value of this bacterial host as a platform for environmental bioremediation and metabolic engineering.As a frequent inhabitant of sites polluted with toxic chemicals, the soil bacterium and plant-root colonizer  Pseudomonas putida is an archetypal instance of a strictly aerobic, Gram-negative soil bacterium characterized by a remarkable metabolic versatility  the specific rate of glucose consumption (qS), and [iv] the molar yield of organic acids on glucose (yP/S). CDW was quantified by harvesting cells by fast filtration in pre-weighed nitrocellulose filters (0.45 μm pore), washed twice with 9 g L−1 NaCl, and dried at 105 °C to a constant weight. Glucose was assayed using a commercial kit from R-Biopharm AG . The concentration of gluconate and 2-ketogluconate in culture supernatants was measured enzymatically  the maximum specific growth rate (μ), [ii] the biomass yield on substrate . Bacterial pellets were immediately frozen in liquid N2. Samples were then extracted three times with 0.5 mL of 60% (v/v) ethanol buffered with 10 mM ammonium acetate (pH = 7.2) at 78 °C for 1 min. After each extraction step, the biomass was separated by centrifugation at 13,000 × g for 1 min. The three liquid extracts were pooled and dried at 120 μbar, and stored at −80 °C thereafter. Samples were resuspended in 20 μL of MilliQ water, distributed in sealed 96-well microtiter plates, and injected into a Waters Acquity UPLC  with a Waters Acquity T3 column  coupled to a Thermo TSQ Quantum Ultra triple quadrupole instrument  with electrospray ionization. 13C-Labeling patterns of free intracellular metabolites were determined for dihydroxyacetone phosphate, fructose-6-phosphate (F6P), fructose-1,6-bisphosphate (FBP), glucose-6-phosphate (G6P), 6PG, PEP, ribose-5-phosphate (R5P), ribulose-5-phosphate (Ru5P), sedoheptulose-7-phosphate (S7P), xylulose-5-phosphate, and Pyr as described previously -glucose or a mixture of 20% (w/w) [U-13C6]-glucose and 80% (w/w) naturally labeled glucose, and 5 mL aliquots of cell broth were harvested by centrifugation at 1,200 × g and −4 °C for 10 min. Bacterial pellets were washed twice with 1 mL of 9 g L−1 NaCl, hydrolyzed in 1 mL of 6 M HCl for 24 h at 110 °C, and desiccated overnight at 85 °C under a constant air stream. The hydrolyzate was dissolved in 50 μL of 99.8% (w/v) dimethyl formamide and subsequently transferred into a new tube. For sample derivatization, 30 μL of N-methyl-N-(tert-butyldimethylsilyl)-trifluoroacetamide was added to the biomass hydrolyzate and incubated at 85 °C for 60 min. The 13C-labeling patterns of proteinogenic amino acids were determined on a 6890 N Network GC system with a 5975 inert XL mass selective detector  -glucose, whereas 6PG from G6P was calculated using data from either 100% [1-13C1]-glucose or 100% [6-13C1]-glucose experiments. Formation of F6P through the PP pathway was estimated using data from 100% [6-13C1]-glucose experiments, and Pyr through the ED pathway was assessed with data from 100% [1-13C1]-glucose experiments. The metabolic model used for net-flux analysis was based on a master reaction network with 45 reactions and 33 metabolites. Fluxes were calculated using [i] the stoichiometric reaction matrix, [ii] constraints accounting for the ratios from FiatFlux analysis and additionally for the ratios in the initial steps of glucose catabolism as described above, [iii] physiological data, and [iv] precursor requirements for biomass. The experimentally determined relative flux ratios were translated into constraints as follows  was estimated as a = v3/(v3 + v16); the fraction of F6P originating through the PP pathway (b) was calculated as b = (v14 + v15)/(v14 + v15 + v17); and the fraction of 6PG originating from G6P (c) was determined as c = v7/(v4 + v6 + v7). For this ratio, the values from the experiments using data from either 100% [1-13C1]-glucose or [6-13C1]-glucose were averaged. The fraction of Pyr originating through the ED pathway (d) was determined as d = v9/(v9 + v22 + v33 + v34). The upper and lower bounds of Pyr originating from malate  were obtained according to e ≥ v34/(v9 + v22 + v33 + v34) and f ≤ v34/(v9 + v22 + v33 + v34). The fraction of OAA originating from Pyr (g) was obtained following g = v32/(v30 + v32). Finally, the fraction of Pyr originating from OAA (h) was derived from h = v33/(v9 + v22 + v33 + v34). The determined linear system of mass balances, flux ratios, quantitative physiology data, and biomass requirements was then solved with the fmincon function from MatLab using the Netto module from FiatFlux to obtain net metabolic fluxes  oxaloacetate (OAA) from Pyr, [ii] glyoxylate shunt, [iii] PEP from OAA, and [iv] the lower and upper bound for Pyr from malate were calculated using the Fiat Flux software . The masc fluxes .g at 4 °C for 10 min. Pellets were suspended in 1 volume of 10 mM phosphate-buffered saline  containing 10 mM 2-mercaptoethanol and centrifuged again. Cells were finally resuspended in 0.3–0.5 volume of the same buffer and sonicated intermittently for 6 min in an ice bath. Sonicated cells were centrifuged at 7,500 × g at 4 °C for 30 min, and the total protein concentration in cell extracts was measured by the Bradford method. The activities of Edd, Eda, Glk, Gcd, and Gad were assayed using standard protocols -Glucose and [6-13C1]-glucose were purchased from Cambridge Isotope Laboratories Inc. , and [U-13C6]-glucose was purchased from Sigma-Aldrich Co. . Other chemicals and enzymes used for in vitro assays were obtained from Sigma-Aldrich Co. and Merck KGaA . H2O2 solutions were freshly prepared from a 30% (w/w) stock solution (Sigma-Aldrich Co.)., to H2O2 at 1.5 mM. Cultures with different 13C-tracers  were grown in parallel to resolve the relative contribution of cyclic EDEMP, PP, and ED pathway to sugar processing. At 1.5 h after the treatment, samples were collected to assess quantitative physiology parameters, flux ratio analysis, and to perform in vitro biochemical assays. This experimental setup resulted in similar growth patterns in the absence and presence of the oxidative stress agent  and the yield of biomass on substrate (YX/S) being essentially the same in control and H2O2-treated experiments. The formation and secretion of organic acids to the extracellular milieu, a typical feature of glucose-grown P. putida, decreased significantly in the presence of the stressor. P. putida typically oxidizes glucose in the periplasm into gluconate and 2-ketogluconate  processing routes of the carbon source. Since a shift from periplasmic to cytoplasmic glucose oxidation would entail higher NADPH formation, we next looked for direct evidence of increased intracellular fluxes through these pathways.In order to capture the metabolic effects of oxidative stress in ent Fig. , and neinditions , 53. In me frame . Quantitluconate , which aluconate . The aboP. putida upon exposure to oxidative stress, the relative abundance of key metabolites was quantified by LC-MS/MS. Out of the metabolites measured, the species that showed the most significant changes in terms of abundance were intermediates of the PP pathway, the EMP route and the TCA cycle—albeit in opposite directions  to 7.6-fold (Ru5P) increase. At the same time, the relative abundance of glycolytic intermediates of the EMP route decreased roughly by half  and up to 70%  when cells were exposed to oxidative stress. Intermediates of the TCA cycle shared the same fate, and their relative abundance was reduced at least by 60%  and up to ca. 90%  in stressed cells. The changes in the relative abundance of other metabolites in the biochemical network of P. putida KT2440 upon exposure to the oxidative stress agent are listed in Dataset P. putida to the pool of glycolytic intermediates.To assess the adaptions within the metabolic network of ons Fig. . In part13C1]- and [6-13C1]-glucose-labeling experiments. Calculation of relative ratios around the hexose node was possible given that the reaction catalyzed by 6-phosphofructo-1-kinase is absent in P. putida [13C1]-glucose as a tracer enables the resolution of fluxes from the cyclic PP and ED pathways back to hexose phosphates because the C6 position is maintained in both routes and can lead to double-labeled hexose phosphate molecules. Flux ratio analysis supported the notion that fluxes through the PP pathway significantly responded to H2O2 addition  flux from triose phosphates or through the PP pathway to the hexose phosphate pool was elucidated by using relative flux ratios derived from -glucose suggested that the contribution of glucose kinase  in stressed P. putida . The full list of flux ratios, including those for reactions in lower catabolism, is presented in Table 2O2. Taken together, these results provided further evidence for a flux rerouting from periplasmic glucose oxidation to the NADPH-generating PP pathway as a direct metabolic response to oxidative stress. We next quantified net fluxes as the basis for accurate estimation of NADPH production in response to oxidative stress.The metabolic network of ida Fig. . The inc13C-labeling data, we determined the in vitro enzyme activities of gluconate kinase  and 2-ketogluconate-6-phosphate reductase  as a proxy. GnuK and KguD activities in cell-free extracts of P. putida KT2440 were similar under control and oxidative stress conditions, and we thus used a GnuK/KguD activity ratio of 0.87/0.13 to constrain the flux split at the gluconate branch point.Combining the quantitative physiology data Fig.  with the2O2, P. putida substantially rerouted its carbon fluxes  and fructose-1,6-bisphosphatase (Fbp) had a small increase (1.5-fold) in cultures exposed to oxidative stress, and flux through the lower, catabolic branch of the EMP pathway  was reduced by ca. 10% under oxidative stress. The diminished input of the ED and the EMP catabolism under oxidative stress propagated into a relatively low flux towards acetyl-CoA  and fluxes within the TCA cycle. No significant activity of the glyoxylate shunt could be detected under any of the experimental conditions tested. Anaplerotic routes within the Pyr shunt, typical of glucose-grown P. putida, had a likewise lower activity in H2O2-stressed cells. The impact of oxidative stress-mediated re-arrangement of carbon fluxes on redox metabolism was investigated next.Under control conditions, glucose was processed mostly through the periplasmic pathway with about 90% of the sugar uptake rate channeled via Gcd Fig. . Most ofxes Fig. . Peripla+-dependent dehydrogenases in the biochemical network, showed a significant increase under oxidative stress, the overall net NADPH production increased substantially. This reconfiguration presumably matches the increased NADPH demand to regenerate glutathione (and other antioxidants) for H2O2 reduction. To assess this impact quantitatively, the net rate of NADPH production (RNADPH) was calculated according to Σi (riF − riC), where riF and riC represent the rates of NADPH formation and consumption, respectively, of all major dehydrogenases potentially carrying significant flux in the biochemical network , that becomes available to counteract ROS accumulation.Considering that the flux through Zwf and GntZ, two of the major NADPork Fig. . Zwf, Gnns Table  was usednts Fig. . When usues Fig. . In partold Fig.  as compaold Fig. . Taken t+-specificity of the dehydrogenase reaction catalyzed by Zwf. The Zwf activity of P. putida KT2440 is represented by three isozymes  could have been affected in the time frame of the experiments, the results of the in vitro enzyme determinations are fully consistent with the previous observations in terms of metabolic flux distribution. Taken together, these results underline the importance of PP pathway for NADPH production when cells are challenged with an oxidative stress agent. We next explored the connection between catabolic NADPH overproduction and mechanisms that counteract oxidative stress such as the glutathione-dependent redox system.To further validate the results of in vivo distribution of metabolic fluxes, in vitro measurements of key enzymatic activities were carried out in cell-free extracts of ion Fig. , there wion Fig. . The obsP. putida has not been explored thus far from a biochemical point of view, the cellular content of both GSH and GS–SG was assessed by means of a biochemical assay in both control and H2O2-stressed cultures  than control experiments, and the composition of the total pool changed significantly upon exposure to oxidative stress. The GSH/GS–SG molar ratio, which reflects the fraction of reduced thiol in the cells, increased by threefold in H2O2–stressed P. putida. This observation suggests that the reduced form of glutathione can be sufficiently replenished by the cellular NADPH sources upon an oxidative stress—directly linking the increased NADPH turnover with the pool of antioxidants.Thiols play several roles in bacteria, which includes maintaining the redox balance, quenching ROS and nitrogen reactive species, and detoxifying other toxins and stress-inducing factors . In mostres Fig. . Cells eP. putida—and thus, the operation of the EDEMP cycle—we explored oxidative stress tolerance when using substrates processed through mainly glycolytic, gluconeogenic, and mixed biochemical routes . While the growth of P. putida with glycolytic substrates, which foster NADPH-forming routes as explained above, was not significantly affected under stressful conditions (with a reduction of μ of only 14%), the other substrates promoted higher levels of sensitivity. The use of a mostly gluconeogenic substrate (α-ketoglutarate) led to μ values in cultures added with H2O2 of around half those observed in control experiments. Glycerol, which is partially processed by gluconeogenic and glycolytic modules of central metabolism [Considering that there is a direct connection between the carbon source on the overall metabolic regime adopted by tes Fig. . To thistabolism , promoteP. putida KT2440 provides a unique experimental system to explore the interplay between stress and metabolic adaption. Broadening this understanding has not only a fundamental interest, but also practical consequences in view of the growing demand of robust agents for in situ environmental catalysis [P. putida KT2440 is naturally primed to endow such circumstances [Environmental microorganisms are exposed to a variety of physicochemical perturbations that require dynamic and concerted responses to ensure cell survival . On thisatalysis  and metaatalysis . In actiatalysis , and a tatalysis . The disatalysis , where smstances .general stress response [E. coli to rapidly respond to H2O2 by increased flux through Zwf and Gnd. It was postulated that inhibition of Zwf caused by NADPH  is lifted under these conditions—releasing ‘reserve’ flux through the oxidative PP pathway. Moreover, the activity of glyceraldehyde-3-phosphate is lowered due to ROS-mediated damage, which causes a metabolic jam in the metabolite pools of the PP route, as previously shown in redox-stressed yeast [P. putida, which lacks a linear, EMP-based glycolysis and processes glucose mostly through the conversion of the sugar into gluconate, reacts to oxidative stress. Since glycolysis is not an alternative glucose degradation route , we hypothesized that the EDEMP cycle could contribute to increased NADPH biosynthesis under oxidative stress via the PP pathway branch.The results of the present study strengthen the notion that network-wide adaption of metabolic fluxes , 66 accoresponse , 67, 68.response , demonsted yeast . Producted yeast , and theed yeast . In this2O2 as compared with untreated controls. In a simplified model of the upper pathways for sugar processing  from de novo transcriptional rearrangements—although the expression level of genes encoding the main NADP+-dependent dehydrogenases in the central catabolism of P. putida KT2440 seem not be overly affected by the H2O2 treatment [2 by mechanisms connected to cellular respiration (i.e. pyruvate dehydrogenase and the TCA cycle) is reduced. P. putida deploys a survival strategy that seems to differ from that reported for the close relative species P. fluorescens, where production of α-ketoacids and ATP formation via converging metabolic mechanisms prevails under oxidative stress [Metabolite changes and flux analysis revealed that the lower (EMP) glycolysis and the PP pathway were affected by Hing Fig. , the out rs Fig. , furthernditions . Such mereatment . One waye stress , 73—yet E. coli takes place within the first 10 s after exposure to the stress agent) is blended with early transcriptional responses . Experimental evidence indicates that there is a multilayered array of defense strategies against oxidative stress in P. putida, including (i) several catalases and superoxide dismutases [+ oxido-reductases [+ and NADH kinases [Increasing evidence points to glutathione peroxidase as a key survival mechanism in stressed bacteria –76. Glutsmutases , 79,  p kinases , and  scenarios. Given the capability of P. putida to adapt to stressful conditions, it cannot come as a surprise that this species has received considerable attention in recent years as a biological agent of choice for in situ bioremediation [Regardless of which carbon source is used, the EDEMP cycle forms the metabolic core of wth Fig. . On the . putida  to withs. putida , organic. putida , starvat. putida , radiati. putida  (includi. putida ), high t. putida , and oth. putida  that oft. putida  beyond tediation  in a timediation .Supplementary InformationData Set 1Data Set 2


Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression  and cancer prevention  by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H Cancer is generally known as a disease of the genome arising out of a combination of genetic mutations, epigenetic modifications and altered signaling pathways. These mutations occur diversely, sometimes with undetermined origins. However, some cancers are associated with infectious agents, and some appear in tissues that are exposed to microbiota  . 14 bacteria, comprising 103 species, in the human colon. This signifies that bacterial genes outnumber human genes in the human body [Microbial agents constitute about 90% of the cells in the human body, and it is estimated that there are 10man body ,3,4,5. Aman body ,7. The transformation of adenoma into carcinoma tumors in colorectal cancer (CRC) requires mutations in the cancer-driver genes, which normally takes up to 10 years, and additional mutations accelerate its progression. There is also a hypothesis that considers the role of the microbiome in CRC, mostly adenomas. The driver–passenger hypothesis states that driver bacteria cause this transformation by triggering DNA damage and persistent inflammation. On the other hand, the tumor microenvironment provides another growth site for opportunistic bacteria called passengers. This even suggests that some probiotic bacteria may take advantage of the tumor microenvironment and prevent the progression of cancer .With the advent of high-throughput technologies, researchers can quantify molecular changes at different cellular levels. These technologies can reveal the complete picture of cell metabolic activity in one snapshot and can unveil the metabolic patterns and function of cells, including the activities of enzymes, gene products, transporters and chemical reactions. Therefore, they would be suitable tools for the metabolic profiling of different cells, both to identify differences between organs and to distinguish metabolic diseases ,11,12,13Genome-scale metabolic models (GEMs) provide quantitative information about the metabolism of large-scale systems . Using GFusobacterium spp. in the production of glutarate and in the suppression of butyrate and acetate levels in feces [Additionally, previous studies have explored changes in the bacterial population and their potential impact on CRC initiation or development, as well as metabolic alterations in the CRC tumor lumen and blood serum. By using the same modeling approach, they highlighted the role of in feces ,24. SincThe community modeling of the microbiome requires the integration of individual bacterial metabolic models (GEMs) based on the proportions of different bacteria present through ecological modeling. In this work, we made use of the MGYS00001248 data , an ecolTo select the GEMs’ best matching with existing metagenome data, we firstly tried to combine both publicly available databases, AGORA and EMBL. In 2016, AGORA models (consisting of 773 GEMs of well-known bacteria) were reconstructed to investigate the reciprocal association of bacterial behaviors and human metabolic diseases . The AGOAlthough using the rarefaction method for metagenomic data analysis is controversial ,30,31, iSince diet can also affect the survival of bacterial models in the community model, we postulated that the environmental conditions of the microbial population were food-intensive, so all the simulations were performed under the rich diet. Finally, from 160 mucosal microbiome samples, 88 microbiome community models were constructed based on AGORA and Pan models .The NMPC , which simulates the capacity of the microbiota to create the intestinal lumen environment, was calculated by FVA . This siStudies have demonstrated that the CRC microbiota and its healthy counterpart have diverse patterns , which aWe used pairwise PLS-DA to find metabolic patterns among the meta-models of each group. PLS-DA is an efficient tool for analyzing metabolomic data . It can p-values < 0.05 were considered significant for the pathways detected in MSEA. Therefore, the results show that microbiota-derived metabolites could be involved in CRC metabolism by altering pathways under different conditions during CRC progression.Metabolite set enrichment analysis (MSEA) was used to determine the role of metabolites in human cell metabolism. For this purpose, VIP was used to extract metabolites that were involved in human metabolism, filtered by the VMH database. This analysis was performed for metabolites that were increased or decreased in a pairwise analysis of groups. l-glutamine, l-tyrosine, pyruvate, tyramine, tryptamine and ten other metabolites were significantly increased in the normal meta-model. Furthermore, metabolites such as taurine, l-serine, chondroitin sulfate, mannose, putrescine and 73 other metabolites were decreased in this comparison  apoptosis and invasion.Several studies have examined the association of metabolites with CRC. In vivo and in vitro environments cannot accurately indicate the sources of metabolic changes, especially in CRC, where the gut microbiome is an inseparable part. Based on the results of our microbiome community modeling and simulating the intestinal lumen metabolic environment  . AlthougThe complete urea cycle converts excess nitrogen to urea and excretes it in the urine. This complete cycle is mainly active in the liver, but enzymes in other cells are responsible for the synthesis of the intermediates of this cycle through the use of nitrogen according to cells’ needs. Extrahepatic urea cycle enzymes are the only intracellular producers of arginine, citrulline and ornithine, which are precursors for the synthesis of polyamines, nitric oxide (NO) and proline. In cancer, unlike in liver cells that secrete nitrogen, urea cycle mediators, including arginine, proline and ornithine, enter anabolic pathways, and changes in their enzymes contribute to tumor growth . As ammoInterestingly, arginine, proline and ornithine, which are important in the urea cycle, were increased in the adenoma models. Furthermore, the increase in arginine and proline metabolism in human cells was increased according to the MSEA results. Arginine and proline replace glucose in the energy supply under glucose deprivation and energy deficiency. The enzyme arginine succinate lyase (ASL) is highly expressed in various cancers, including CRC, which produces arginine, NO and citrulline. Furthermore, the production of intracellular NO from arginine is dependent on this enzyme, so the inhibition of this enzyme has similar effects on the reduction of NO. NO promotes cell proliferation .The amount of secreted glutamine, an important hallmark metabolite of cancer metabolism, was increased in the carcinoma meta-model. Cancer cells use glutamine during glutaminolysis for cell growth and proliferation. This is one of the hallmarks of cancers, and many treatment strategies have been developed based on it. Cancer cells use mediators created in the glutaminolysis cycle to replenish the TCA cycle . GlutamiPyruvate is present in anaplerotic reactions in ovarian cancer and affects mitochondrial functions . By affeDespite the principle of ignoring the TCA cycle and the priority of aerobic glycolysis in cancer cell metabolism, the TCA cycle was increased in the MSEA of the adenoma meta-model. The TCA is involved in the production of required macromolecules and the energy production of cancer cells . From thThe effects of hormones on pathogenicity and its origin are usually considered in the physiology of a human body. The MSEA results revealed the enrichment of thyroid hormone synthesis in the carcinoma meta-model. There have been many studies on the effects of thyroid hormone and nuclear receptors on tumorigenesis and cancer cell proliferation. Despite the heterogeneity in these studies, increased receptor expression in CRC and increased risk in patients with hyperthyroidism may be of interest. It should also be noted that, given the environmental uptake of cancer cells, the role of microbiome metabolism in the production and explanation of the effect of this hormone could be important ,57.Serotonin and dopamine were also increased in the adenoma meta-model. These are neurotransmitters produced by the central nervous system and the gastrointestinal tract. Clinical studies have proven their effects on the growth of tumors such as CRC ,62,63,64Other pathway alterations involved cardiolipin and ETC biosynthesis, which were enriched in the MSEA results for the adenoma meta-model. Cardiolipin is present in the inner membrane of the mitochondria; its overexpression and interaction with ETC proteins in CRC cells increases and optimizes the efficiency of mitochondrial respiration . The inhThe gut microbiome can also accelerate the production of purine and pyrimidine precursors, supporting cell proliferation and energy supply. Our results show significant increases in nucleotides, DNA precursors and purine metabolism in the adenoma and carcinoma models. Adenine, adenosine and deoxy-adenosine, deoxy-cytidine, uridine, guanosine and thymidine were found among the enhanced metabolites for both models. Thymidine catabolism is a metabolic strategy of cancer cells that provides them with energy by supplying carbon to the glycolytic pathway under nutritional deprivation .Phenolic acids and cobalt chloride have anticancer effects ,70. The Our carcinoma meta-data model indicated the prevention of apoptosis due to a significant reduction in the synthesis of taurine, spermidine, phosphatidylethanolamine and phenolic acid metabolites. Taurine stimulates apoptosis, and its secretion in the carcinoma microbiome may be reduced ,72. SperIn this study, we investigated microbiome metabolism in terms of the abundances of microbiota and their distinct metabolomics in different tumors of CRC. For this purpose, we used metabolic modeling and metabolomics analysis approaches including community metabolic modeling (CMM), principal component analysis (PCA) and metabolite set enrichment analysis (MSEA). We used metagenomics and the relative abundances of microbiota data as the input for microbiome metabolic modeling. The relative abundances of microbiota in different tumors of CRC were extracted from the MGnify database (study MGYS00001248). The dataset consists of mucus biopsies from 160 individuals harvested by colonoscopy and examined histologically , and 61 To create a CMM, we require (1) the relative abundances of presented and recognized bacteria, and (2) individual GEMs that match the recognized bacteria. For data consistency, we manually modified the taxonomy nomenclature because metagenomic data often do not have sufficient resolution at the strain, species and genus levels for the bacteria. For the compatibility of the data with the taxonomy assignments, high-resolution taxon data were matched with AGORA GEM models derived from the Virtual Human Metabolism  (VMH) daTo modify the taxonomy nomenclature, we used the Disbiome database . This daData normalization is challenging in this domain. One of the most applicable methods is rarefaction, the applications of which in metagenomics and microbiome analysis have been discussed ,30,31. TLarge-scale community modeling requires the integration of the relative abundances of metagenomics data into metabolic models. For this purpose, we used the mgPipe pipeline that is the part of the microbiome modeling toolbox in the COBRA toolbox. The mgPipe function includes: (1) the analysis of the microbiota abundances per sample; (2) the construction of microbiome models and adding lumen-transport reactions by connection to a specific diet and uptake/secretion behavior; and (3) the simulation of models and the lumen metabolic environment under the given diet by flux variable analysis (FVA). These simulations demonstrate the maximal capability for metabolite production in a lumen environment: the so-called net maximal production capacity (NMPC). The metabolic modeling of the microbiome community was performed using the COBRA toolbox in the MATLAB 2017b environment with the IBM CPLEX 12.8 solver. We used the mgPipe pipeline and AGORA 1.03  models aNMPC data show the maximum microbiota community capacity of each sample in creating the intestinal lumen metabolic environment. This information was used to analyze and compare within groups and between groups.PCA and partial least-squares discriminant analysis (PLS-DA) are two methods that are important in the analysis of metabolomic data . In thisThe variable important projection (VIP) used PLS-DA information to display each of the variables in the first component of each PLS-DA model.The PCA and PLS-DA were performed in the SIMCA software environment of UMETRICS company version 14.1. Autofit was performed for PCA and PLS-DA modeling with default settings.The cross validation of the PCA and PLS-DA was performed using the SIMCA software, by full cross validation. The method used the Krzanowski and PRediction Error Sum of Squares (PRESS) methods and returned the significance of the models ,82.MSEA  is an apOur community metabolic models unveiled the roles of the gut microbiome in CRC development, as well as its significant influence on adenoma and carcinoma tumors, from provoking mutations to facilitating the spread, homeostasis and survival of colorectal cancer cells.


The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level  Measurements of metabolic changes in a variety of biological samples are feasible due to advances in chromatographic separation  and improved detection like nuclear magnetic resonance (NMR) and mass spectrometry (MS). However, the interpretation of complex disease models or patient derived samples can be challenging with large scale metabolomics and when a systems-wide understanding of specific pathways is required. Measuring changes in metabolite concentration do not allow to draw any conclusions on metabolic rates or the direction of a flux. Changes in metabolite levels can either result from differential production or utilization of a given intermediate due to increased flux from synthesizing reactions, decreased flux toward consuming reactions, or alterations in transporter activities. For example, during ischemia-reperfusion injury, glycolytic intermediates accumulate despite a reduction in glucose uptake  and to utilize metabolic vulnerabilities as biomarkers or for the development of therapeutics. Targeted or untargeted measurements of metabolites allow to assess the metabolic state in an organism e uptake –3. Theree uptake . In expein vivo conditions as closely as possible  and are used to study in vivo or ex vivo metabolic changes in the heart. There are several clinically relevant radiopharmaceuticals such as 99mtechnetium-sestamibi  (18F]-Fluorodeoxyglucose ([18F]-FDG)  are less sensitive than radioactive probes, but in a single experiments stable isotope labeling approaches allow to assess multiple pathways simultaneously over longer experimental time periods. Like radiopharmaceuticals, stable-isotope tracers allow flux assessments, but at the same time these probes provide more in-depth analysis of metabolism both using targeted and untargeted discovery oriented analytical approaches. Increased availability and advancements in analytical techniques for metabolomics have made stable isotope tracers increasingly popular in studying cardiac metabolism in vivo and ex vivo. In this review, we describe recent advances in applying stable isotope tracers in cardiovascular research and discuss important aspects to consider during data analysis allowing accurate assessments of metabolic changes  in cost-efficient and safe way. At the same time metabolic conversions of labeled nutrients or small molecules allow to track the incorporation of isotopic label  into downstream products and pathways. The detection of specific metabolic products then allows to assess total metabolite changes alongside enzyme activities, flux rates, and overall contribution of specific pathways to the metabolic profile. Tracer studies in isolated perfused murine hearts are commonly used to evaluate metabolic changes in model systems that resemble possible . In vitrdioLite)  or 18F]8F]-FDG)  for inteF8F]-FDG) studies . Stable  changes .in vivo or ex vivo delivery of stable-isotope tracers including oral administration via diets, gavage or drinking water, direct intravenous infusion, injection, or perfusion of the heart  the Langendorff method and (2) the working heart preparation. The aortic perfusion of Langendorff remains a standard preparation and is popular to this date due to its simpler technical requirements and perfusion apparatus  or human participant. Blood or tissue samples are collected before and after tracer infusion, and isotopic enrichment is then analyzed. Another important factor during stable-tracer experiments is the dynamics of tracers and the determination of metabolite pool sizes , as well as turnover  of metabolites . For exapathways , whereas13C, 15N), number of labeled atoms  and position of labeled atoms will determine which products and pathways will incorporate the label. Any of these options increase the complexity of the experimental design and subsequent data analysis. Selecting the right combination of tracers allows delivering multiple probes and interrogation of multiple pathways simultaneously. Parallel tracing experiments are limited to few applications, but recent advances in high resolution mass spectrometry and development of new probes show promise. For a comprehensive overview of stable-isotope tracers and their readouts, readers are referred to Selecting the right tracer depends on the scope of the study and ultimately which metabolic readout is required for a given biological question. The substrate class , type of atoms  of glucose. To gain insight into the systems-wide response during disease development, it is also necessary to assess how metabolic alterations are linked to protein dynamics and posttranslation modifications .Considering the diversity and versatility of stable isotope tracers, which criteria can we apply to select the optimal probe for a given experiment? The chemical properties, including kinetics, specificity, and turnover, are critical components of tracer selection. Tracer kinetics must be almost identical to unlabeled compounds and probes should not accumulate in tissue. Non-specific binding to other proteins and lipids is generally a concern when using radionucleotides but similar demands need to be met by stable isotope tracers. Lastly, turnover time and tissue concentration of tracers should follow the range of non-labelled metabolites to avoid mass effects and optimal signal-to-noise ratios. Selection of tracers can also be based on metabolic read-outs and targeted toward a pathway of interest. Uniformly labeled tracers of common nutrients  can be used to determine how these nutrients are utilized in multiple pathways. Combination with single labeled tracers in parallel experiments then allows to detect specific metabolic fluxes. Different labeling strategies are best described using glucose because of its high abundance, commercial availability, and ease of use in experimental settings. For example, g pathway  82). Li. Li13C]grogenase , respectications , 84. Stag period . The advA prerequisite for accurate metabolic measurements is the sample collection and preparation. Metabolites have different turnover rates and differences in their abundance can easily lead to obfuscation of experimental data. For example, rapid degradation of NADPH and CoA derivatives during sample preparation have been well-documented , 87. Devin silico. In the context of stable-isotope tracer analysis, models are required to estimate flux distributions and explain labeling states for specific metabolites. These modeling approaches can describe the metabolic state of a biological system, but they cannot predict or explain it by identifying regulatory enzymes or principles   .13C vs. 12C and 2H vs. 1H in carbon- and hydrogen-labeling studies, respectively. Stable-isotopomer measurements are interpreted by defining mass-distribution vectors (MDVs) that reflect the incorporation of label from a tracer into downstream metabolites , 9213C vXj  represents the fraction of a metabolite's pool that corresponds to the jth labeled isotopolog and n is the maximum number of labeled atoms that a metabolite can incorporate. The fractional isotope enrichments, Xj, and their sum equal 1 or 100%. In stable isotope labeling, incorporation of j labeled atoms causes a mass shift of j atomic units nominal to the mass M of the unlabeled metabolite. Therefore, the MDV is often denoted as follows:Where each component IC denotes the isotopolog count for a given metabolite j and TC denotes the sum of all isotopolog counts. For example, the MDV for 13C-labeled glucose can be determined as:Where n = 6, because a glucose molecule has six carbon atoms where 13C-label can be incorporated. Xm0 represents the fraction of unlabeled glucose within the total metabolite pool, and Xm2 = 0.1 would indicate that 10% of the total glucose pool carries two 13C-labeled carbons. Based on the MDV it is not possible to assess where the incorporation of label occurred.with n = 1) are being used. For instance, carbon appears in the form for two stable isotopes, 12C and 13C, with a natural occurrence of ~98.9 and 1.1%, respectively. When using 13C-tracers, measurements need to be corrected for this natural occurrence. Several tools have been developed , highly abundant metabolites , or single labeled tracers (oped see  53–56) –56 n = 1jth row of the matrix jth labeled isotopolog. Determining the actual MDVs for each metabolite is a critical step prior to further data analysis and flux interpretation  is a constrained-based modeling technique that relies on balancing fluxes around metabolites within a network stoichiometry . Flux di13C-Metabolic flux analysis (13C-MFA) is most widely used in assessing flux in stable-isotope tracer experiments  are incorporated into a mathematical model of atom transfers and combined with measurements of nutrient uptakes and secretions, and biomass function. The distribution of fluxes throughout a cellular metabolism is mathematically estimated by iteratively solving a least-square regression problem of isotope labeling measurements and extracellular exchanges rates. The models applied to data regression comprise mass balances and isotopomer balances on all network components, thus often encompasses hundreds of equations. The sum of squared residuals (SSR) is defined as follows:eriments . In 13C-v) are estimated by the assumption of a steady state (equation 7) and minimizing SSRs between simulated (x) and experimentally derived (r) MDVs for a given metabolite S. The stoichiometry matrix of a given metabolic network is represented by N. Uncertainties, σ, can be estimated based on the root-mean square error of unenriched control samples and the standard error of measurement of technical MS replicates. For a metabolite S, steady state implies that the total rate at which it is supplied is equal to the total rate at which it is removed  consists of 64 carbon atom isotopomers , 4,096 hydrogen atom isotopomers , and combined 2.6 × 105 (26 × 212) carbon and hydrogen isotopomers. Therefore, additional methods have been developed to allow the integration of multiple isotopic tracer systems  EMUs are possible. The algorithm allows to identify a minimal set of EMUs to be considered in the simulation model, which reduces the overall number of variables and makes the computational analysis feasible even for multiple-tracer experiments and larger scale models  methods can be used . The output is presented as a mass spectrum where each peak represents a different ion with a specific m/z, and the peak length corresponds with a relative abundance. Combining MS with separation techniques like GC or LC enables analysis of complex samples based on separation times (or retention times) and m/z ratios. The variable ionization of compounds, ion suppression, and instrument cycling times inherently limit the number and type of metabolites that can be distinguished in a single run. Different ionization techniques have been developed including electron ionization (EI) and chemical ionization (CI) for compounds in gas-phase, as well as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) which are suitable for thermally labile and non-volatile analytes . Another advantage over MS is quantifying multiple compounds without the need for calibration curves for each compound. MS and NMR spectrometry have evolved as the most common techniques in stable isotope tracer and metabolomics studies. However, there is no single analytical platform that can achieve a complete quantification and identification of all molecules within a sample. Therefore, more than one method must be employed for comprehensive metabolic profiling. When deciding on an analytical avenue, the choice primarily depends on the focus of the research study and the nature of the samples, and instrument and know-how accessibility.The goal of metabolomics is to comprehensively measure the metabolic composition of a sample in a single analysis. Different analytical platforms combine unbiased, rapid, reproducible, and stable analysis of complex samples in a single run. Each technology has its advantages and disadvantages in terms of sensitivity, throughput, reproducibility, robustness, quantitation, and suitability for specific chemical classes of metabolites. These analytical chemistry techniques range from high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), gas chromatography combustion isotope ratio mass spectrometry (GC-C-IRMS), capillary electrophoresis-mass spectrometry (CE-MS), Fourier transform-ion cyclotron-mass spectrometry (FT-ICR-MS), and nuclear magnetic resonance (NMR). Broadly, analytical chemistry techniques can be divided into (i) separation, (ii) identification, and (iii) quantification of metabolites from a complex sample . The separation of metabolites is accomplished using chromatography  or CE. Sample detection is then achieved using fluorescence, ion conductivity, or spectrometry . MS and NMR are different types of spectrometers that measure physical characteristics over a given spectrum. MS measures masses within a chemical sample through their mass-to-charge ratio (m/z), while NMR measures the variation of nuclear resonant frequencies. Both detection techniques can be combined with a different separation method allowing customized application methods for a specific type of sample and compound. MS and NMR are widely used in metabolomics to detect and analyze stable isotope tracer studies, such as analytes . After iications . ImplemeSingle-cell analysis is a powerful tool to interrogate cellular heterogeneity within the same tissue, allowing for refined assessment of phenotypes and biomarker discovery. Metabolomics at the single-cell level holds the promise to obtain precise spatial and temporal information allowing to assess cell differentiation and division, cell-cell interactions, metabolic cooperation between cell populations and a detailed stress response analysis. In comparison to other omics technology, single-cell metabolomics faces several challenges because of the chemical diversity of metabolites, wide range of concentrations, sample stability, and lack of amplification . Recent in vivo will be critical to identify metabolic vulnerabilities. Ultimately, these efforts may help clinicians to tailor therapeutic interventions based on the metabolic profile of the intact heart.Understanding how reprogrammed metabolism supports cardiac adaptation during stress and diseases requires a systems-wide approach. Translational models that recapitulate cardiac pathophysiology are critical to advance our understanding of CVDs and to utilize metabolic vulnerabilities through the development of novel therapeutics or biomarker identification. Stable-isotope labeling and mathematical modeling require multidisciplinary collaborations to bridging animal models into patients. Likewise, clinical studies should inform animal models for mechanistic hypothesis-driven testing. Challenges persist in accurate estimations of fluxes from stable-isotope tracers and integration into clinical trials. Evidence indicates that metabolic phenotypes are a key determinate of disease development and progression. Advancements in analytical methods to quantify metabolic phenotypes AK designed and wrote the manuscript.This research was supported by R00-HL-141702 and institutional funds from Cedars-Sinai Medical Center to AK.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in  E. coli can mediate and promote population diversification.It is unclear how noise in gene expression propagates to phenotypic heterogeneity in clonal bacterial populations. Here, the authors explore how variability in central sugar metabolism in  While not always beneficial3, population diversification is a key mechanism to adapt to fluctuating environments4, in that it can allow genotypes to persist in the face of adverse conditions. For example, growth rate heterogeneity in bacterial populations contributes to survival upon exposure to antibiotics6, and differences in the onset of cell division can favor tolerance and evolution of antibiotic resistance9. A number of different molecular mechanisms can give rise to phenotypic heterogeneity12. Typically, these are cellular processes such as stochastic gene expression14 and stochastic partitioning of molecules at cell division15, which ultimately manifest in the variation of protein copy numbers per cell. Recent experimental evidence showed that fluctuations in the expression of growth-limiting enzymes can be responsible for growth fluctuations and the transmission of noise to other genes10. While mounting single-cell studies have shown gene expression to vary within a population of genetically identical cells under constant conditions17, which cellular processes are predominantly affected by noisy gene expression and whether noise propagation is shaped by optimality-based principles are still open questions.Heterogeneity between individual bacterial cells growing under macroscopically identical conditions can have important functional consequences19. However, whether and how fluctuations in metabolite levels can permeate the metabolic state of a cell and affect the ability to adapt to changing environments is unclear. While technological advances enabled monitoring levels of metabolites at single cell21, directly probing cell-to-cell variability in the turnover of metabolites remains challenging22, ultimately hampering the study of metabolic heterogeneity and its functional implications. By developing an assay to directly measure variability in the respiratory rates among colonies of Escherichia coli growing on solid media, we show that noisy transcriptional regulation of lower glycolytic and citric acid (TCA) cycle enzymes can be readily transmitted to metabolic heterogeneity. Surprisingly, we found that individual colonies within an isogenic population exhibit largely different respiratory activities, and that while costly23, increased respiratory activity facilitates the early onset of cell growth after starvation by preventing the accumulation of toxic intermediates. We propose that variability in respiro-/fermentative metabolism can be fitness invariant, allowing cells to maintain a greater variation in enzyme expression and potentially employ diverse adaptive strategies to environmental changes.Despite the expectations that stochasticity of biochemical reactions should have negligible effect, theoretical models demonstrate how noise in transcription and translation of metabolic enzymes can affect levels of metabolic reactantsE. coli13   processes affected by standard noise levels consist of lowly expressed genes involved in DNA repair, DNA recombination, and chemotaxis  exhibit larger than expected cell-to-cell variability relative to other proteins with similar expression levels , indicating that there may be selective pressures for noise reduction26. These results suggest that, despite the key role in bacterial fitness of proteins involved in central metabolism, fluctuations in their expression, translation, or degradation may have been preserved by evolution and can be a major source of phenotypic heterogeneity. To test if deviations of proteins from mean-noise levels could be the results of stochasticity in their gene expression regulation, we searched for transcription factors (TFs) regulating the expression of proteins with large deviating noise. We found that Cra, a key regulator of flux in lower glycolysis27, is significantly enriched  for targets that exhibit high deviating noise levels  Fig. 13, we hyxis Fig. , we founxis Fig. . More spls Figs.  and S1. els Fig. . Altoget28. However, whether and which noisy enzymes are able to cause fluctuations in the rates of the corresponding enzymatic reactions, and hence may affect phenotypic heterogeneity, is still unclear. To address this question, we integrated bulk measurements of protein copy-numbers29 and metabolic fluxes30 in E. coli growing in minimal media with seven different carbon sources. By quantifying the linear dependency between changes in the copy number of 40 enzymes and 25 rates of the corresponding metabolic reactions, we found that changes in the abundance of enzymes in lower glycolysis  and citric acid (TCA) cycle  directly scale with changes in fluxes  at the single-cell level are lagging behind. As a result, much less is known about heterogeneity at the level of metabolism, and whether such variability can have direct functional consequences. To overcome this problem, instead of single-cell analytical methods we developed a multiparametric assay that monitors phenotypic and metabolic heterogeneity of thousands of colonies-forming E. coli growing on solid agar media   reduction in the variability of respiratory rates as well as lag times , we observed a continuum of colonies exploiting different metabolic strategies. How can this metabolic diversity be maintained? While in a given constant environment, only one strategy is optimal, in a naturally fluctuating environment the advantages of short lag time or fast growth rate might average out. We hypothesized that variability in respiro-/fermentative metabolism is fitness-neutral—i.e., cells can be equally optimal in spite of different metabolic strategies. Colonies that find an optimal compromise between two competing objectives are called Pareto optimal41—i.e., a colony is Pareto optimal if there exists no other colony that is at least as good in all objectives, but strictly better in at least one objective. By using a simple exponential model of bacterial growth, one can derive that in equally fit cells—i.e., cells able to reach the same number of divisions in a fixed period of time—growth rate and lag time are inversely related (Supplementary text). Hence, according to our hypothesis, the space of feasible growth rates and lag times should be delimited by a Pareto front41 approximating an inverse relationship between the two. Moreover, natural selection of optimal fitness will force colonies to operate metabolism in the proximity of the optimal tradeoffs42, and hence colonies shall not randomly occupy the space of feasible growth rates and lag times. Moreover, because of the already known direct proportionality between fermentative metabolism and growth rate23, we expect that in Pareto optimal colonies, growth rate negatively scales with the respiratory rate (Supplementary text: lag time vs. growth rate). Our theory is consistent with growth kinetics observed in wild-type E. coli colonies growing on glucose as the sole carbon source  operate metabolism so as to require a minimum adjustment between growth on glucose and acetate42, we predicted that FSw would favor respiratory metabolism, while slow-switchers (SSw) would feature higher fermentative metabolism . To test our predictions we used previously published data42 measuring growth rate, glucose-uptake, and acetate secretion for seven FSw and eight SSw randomly selected clones grown in liquid glucose minimal medium. In agreement with our predictions, we found that FSw on average have significantly  higher respiratory activity and slower growth rates than SSw  with an isopropyl β-D-1-thiogalactopyranoside (IPTG) inducible promoter to modulate the arcA expression (arcA+). ArcA is a key repressor of genes involved in the TCA cycle and oxidative phosphorylation. While arcA deletion causes an increased carbon flux into the TCA cycle44, respiration is inhibited and acetate fermentation increased upon arcA overexpression45. Hence, we expect lag times to increase with an increased expression of arcA. To test this hypothesis, we grew wild-type E. coli, the ΔarcA and arcA+ strains in batch liquid cultures of glucose minimal medium up to mid-exponential phase ~1), washed the cells, and incubated them in M9 medium without carbon sources together with the IPTG inducer (0.02 mM) for 2 h. Next, we resuspended cells in M9 medium with glucose and monitored OD600 in a plate reader using different levels of IPTG inducer. Consistent with a previous study45, mild induction of arcA (below 0.03 mM IPTG) is able to improve growth rate with respect to the wild-type  or gluconeogenic  substrate as sole carbon sources  lower levels of biosynthetic intermediates in branched-chain amino acids (BCAA) , including the main precursor pyruvate and the end product valine leucine  often involves negative feedback loops in which metabolites can directly regulate transcription factors activity56. Such type of regulatory interactions can function as negative integral feedback and provide quasi-adaptation to small perturbations58, thereby increasing the number of generations that are necessary for daughter cells to significantly diverge from the transcription factor activity of the ancestor. Therefore, while clearly different from single-cell measurements, by simultaneously monitoring thousands of colonies and their variation in growth kinetic parameters and respiratory rates, we could make experimentally testable predictions on the functional impact of metabolic diversity on phenotypic heterogeneity.Here we monitored phenotypic and metabolic heterogeneity arising at a single colony level. A key advantage of this approach is the ability to directly monitor the variability in metabolic rates rather than reporting on the level of individual metabolites. It is worth noting that bacterial colonies develop in complex 3D structures in which individual bacteria can experience different metabolic gradientsIn this study, we showed that the regulation of key proteins involved in funneling carbon into the TCA cycle plays a fundamental role in the phenotypic heterogeneity of colonies growing on solid media. While not all regulatory solutions can be equally optimal, the existence of multiple and largely diverse metabolic tradeoffs that are fitness-invariant can be a major driving force preserving and shaping phenotypic heterogeneity and plasticity—i.e., the ability to develop varied phenotypes under fluctuating environmental conditions. Independent experimental evidence of planktonic cultures that evolve in coexisting subpopulations exhibiting the same predicted tradeoffs between lag time, growth rate, and respiratory activity, suggests that the same concepts can be generalized to largely diverse growth conditions.arcA deletion significantly suppresses resistance evolution59, provides a basis to link independent experimental evidence relating lag time and energy metabolism61 to antibiotic tolerance and resistance7. Consistent with our findings, mounting evidence associated evolutionary adaptation of bacterial persistence to mutations in enzymes of the respiratory metabolism, such as the proton-pumping NADH:ubiquinone oxidoreductase (Nuo complex)62. We demonstrated that homeostasis in BCAA can play a fundamental role during the early growth phases. We propose that valine/leucine and isoleucine and possibly intermediates in their biosynthesis can function similarly to toxin–antitoxin systems mediating bacterial dormancy. Toxin–antitoxin modules, such as HipAB63, have well-established roles in regulating the bistability of clonal populations64 and in the formation of growth-arrested persisters63. In a similar way, the accumulation of toxic BCAA intermediates in clonal cells with different metabolic activities can lead to cellular stasis by inhibiting BCAA biosynthesis and/or carbon uptake49, ultimately affecting the biosynthesis of essential components for cell growth and division. The existence of different Pareto-optimal metabolic strategies within a clonal bacterial population, on the one hand, could represent a form of metabolic «division of labor»65 and, on the other hand, could provide a bet-hedging strategy to withstand unforeseen challenges, such as periods of nutrient limitations or antibiotic treatments6.Moreover, explaining colony-to-colony variations in growth rate and lag time by changes in respiro-/fermentative metabolism, together with the observation that an 8. The existence of a tradeoff between shortest lag time and maximum growth rate and the ability to shift between these cellular objectives by modulating respiro-/fermentative metabolism can pave the way to alternative therapeutic strategies fighting the emergence of tolerant cells and eventually the appearance of drug resistance67, potentially beyond antimicrobial treatments68.Overall, our findings can have important implications in diverse theoretical and applicative fields. Understanding the origin and functional impact of metabolic heterogeneity can open new opportunities in metabolic engineering and synthetic biology, to incorporate noise transmission in the design of more efficient biosynthetic strategies. Moreover, fluctuations in respiro-/fermentative metabolism can be of particular significance in the design and interpretation of experimental evolutionE. coli BW25113 or mutants listed in Table 2HPO4 2H2O, 3.0 g KH2PO4, 1.5 g (NH4)2SO4, and 0.5 g NaCl and was adjusted to pH 7 before autoclaving. The following components were filter-sterilized separately and then added : 1 mL of 1 M MgSO4, 1 mL of 0.1 M CaCl2, 1 mL 0.1 M FeCl3, and 10 mL of a trace element solution containing (per liter) 180 mg ZnSO4 7H2O, 120 mg MnSO4 H2O, 180 mg CoCl2 6H2O, and 120 mg CuCl2 2H2O. Carbon source solutions were filter-sterilized and added separately to the medium, 5 g/L glucose, fructose, acetate. Agar plates were made by adding 15 g/L of Agar.For all growth experiments, Strains used for the experiments discussed in the main text, their origin, and corresponding reference.600 in 96-well plates with the multiwell reader Infinite  at 37 °C with shaking. The growth rates were extracted by fitting the exponential part of the growth. An iterative approach was used to find the time window of at least 100 min with the maximum exponential growth rate. The intersection between the initial OD of the inoculum and the extension of the exponential growth curve yielded the lag time69.The growth rate and lag time in batch liquid culture were measured by monitoring OD70. When necessary, p values were corrected for multiple tests by q value estimation71. The network of TF-target genes reported in72 was used to identify TF regulating proteins with large deviating noise.Statistical analyses were performed using Matlab R2018b (MathWorks). Gene ontology and metabolic pathway enrichment analysis, and enrichment of noisy enzymes in TF-target genes were based on an iterative hypergeometric test described in Ref. x and y coordinate on the plate, size at each time point, and RGB intensity. Full details and computer codes are available on GitHub https://github.com/Dfernand1795/PetriScanner2.Overnight cultures were diluted in LB or M9 media 1:1000. Cells were grown up to an OD of 1 before plating at appropriate dilutions on solid agar medium. Standard PC scanners  were used to acquire images at 800 dpi resolution of Petri dishes every 10 min. Acquired images were analyzed with an in-house image analysis software implemented in ImageJ IJ1 Macro and Python. In order to improve the accuracy in detection of bacterial colonies the software corrects for changes in light across plates , performs colony segmentation  and tracking. Finally, outliers/artifacts are filtered out. The final data returns for each colony p) abundance and noise from single-cells of E. coli from13. Consistent with other studies, at low copy number (μp) the noise  is dominated by intrinsic noise , while at high expression levels noise is dominated by extrinsic factors, such as fluctuations in ribosomes, polymerase, transcription factors and partitioning of the population between the cell-cycle stages75. To estimate average noise levels at different protein abundance, we used a moving average smoothing method on the data reported for 1018 proteins13. Specifically, we used the malowess function in Matlab, where for each protein, the expected average noise level  and lag-time (Tlag). Fitting was performed in Matlab 2018b using the lsqcurvefit function using the Truest-region algorithm for optimization from 50 multiple start points . To determine the maximum respiratory rate we use a linear model and found the time interval (consisting of at least ten time points) with the largest proportional coefficient between time and average red intensity of a colony. α is the estimate for maximum respiratory rate (AU/h) and β is an offset value.The Gompertz function is able to describe asymmetrical growth curves assuming that at the time of inflection 21 was transformed into E. coli bacterial strains BW25113 and BW25113-ΔarcA (Keio:JW4364) expressing a T7 RNA polymerase (araB::T7RNAP-tetA). For each sample, a single bacterial colony was grown in LB medium for 6 h at 37 °C under agitation (170 RPM). 30 microliters of culture were pelleted, resuspended in 3 ml of M9 medium and further diluted 1:10 in 3 ml of M9 medium supplemented with Glucose 0.5%, ampicillin 50 µg/ml and arabinose 0.01%. Cultures were grown for 16 h at 37 °C under agitation (170 RPM) and typically reached OD600 values between 0.05 and 0.3. For the starvation procedure, cells were washed twice and resuspended in 3 mL of M9 medium only supplemented with ampicillin 50 µg/ml. Samples were brought back to the shaker for two hours before addition of glucose at 0.5%. Cultures were sampled for FACS analysis right before the starvation phase, after 30 and 120 min of starvation and after one and 15 min after addition of glucose. 30 µl of sampled culture was diluted in PBS containing propidium iodide . FACS measurements were performed on a BD FACSaria III Cell sorter. Fluorescence was measured with the following channels Ex488_LP495_BP514/30-H, Ex405_LP502_BP530/30-H and Ex488_LP610_BP616/23-H .The low concentration ATP reporter plasmid pRS-QUE7muΔarcA E. coli overnight cultures growing on M9 minimal medium were diluted in fresh M9 glucose minimal media and grown at 37 °C until exponential phase and an OD600 of 1. Cells were washed twice with M9 medium without carbon source and 700 μL cell cultures were distributed in 96 deep well plates and incubated at 37 °C until shaking at 2 RCF. Samples for metabolomics profiling were taken during exponential growth prior to carbon deprivation and 5, 10, 15, 30, 60, 90 and 120 min after carbon starvation. In total, 50 μl of whole cell broth was directly transferred to 150 μl extraction liquid solution containing 50% (v/v) methanol and 50% (v/v) acetonitrile at −20 °C. The extraction was carried out by incubating the samples for 1 h at −20 °C. Samples were centrifuged for 5 min at 1789 RCFand 80 μl of the supernatant was transferred to 96 well storage plates and stored at −80 °C (Supplementary Data 77. Mass spectra were recorded from m/z 50 to 1000 using the highest resolving power (4 GHz HiRes). All steps of mass spectrometry data processing and analysis were performed with MATLAB . Detected ions were matched to a list of metabolites based on the corresponding molar mass78. For the full list of metabolites used for annotation see Supplementary Data Wild-type and Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary