B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis.Glycerol is a desirable alternative substrate for 2,3-butanediol  production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study.The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.The online version contains supplementary material available at 10.1186/s12934-021-01688-y. Biodiesel is a liquid biofuel derived from natural fats through a chemical reaction process (esterification or transesterification). Biodiesel has advantages over fossil diesel in terms of health and environmental concerns, such as lower sulfur and carbon dioxide emissions, which may contribute to a reduction in global warming. In 2019, 24% of the global carbon dioxide emissions from fossil fuel consumption were related to the transport section, and approximately 75% of the total carbon dioxide emissions from this section was from commercial vehicles [Biodiesel production generates large amounts of crude glycerol as a by-product; every ton of biodiesel produced generates approximately 100 kg of crude glycerol. Despite the increasing purification capacity and the development of new applications, biodiesel production has been associated with increased glycerol generation over the past few decades. Hejna et al.  reportedCurrently, sugar, starch, and molasses are the major raw materials for commercial biological products, such as proteins and biochemical monomers. Recent studies have focused on the use of renewable and non-food competitive materials as alternative resources; glycerol is a promising substrate for use as a renewable and low-cost substrate for biological production platforms.-2,3-Butanediol  is a valuable bio-based chemical compound that has a wide range of potential applications, such as in the manufacturing of printing inks, antifreeze agents, foodstuffs, chemicals, and pharmaceuticals . In addiBacillus subtilis, a “generally recognized as safe” organism, is an ideal platform organism for engineering bacterial pathways for the biological production of various useful metabolites, such as lactate, acetate, acetoin, and 2,3-BD [B. subtilis is a superior host for bioproduction as compared to Escherichia coli because it is more tolerant to fermentation products, such as butanol and ethyl acetate [E. coli and other gram-negative bacteria [Among industrial microbes, d 2,3-BD , 10. B.  acetate , and lacbacteria .B. subtilis. Thus, in silico simulation with FBA is available for efficient metabolic pathway design of B. subtilis for glycerol assimilation and 2,3-BD production in a complex metabolic system. In preliminary experiments, we found that the B. subtilis model described by Oh et al. [Bioinformatics tools have become increasingly attractive. Various techniques have been used to simulate and analyze biochemical systems. In silico genome-scale metabolic models (GSMs), which can be used to evaluate all metabolic reactions in a cell, are formulated on the basis of genome annotation data and experimental data. GSMs computationally describe gene-protein-reaction associations for a whole set of metabolic genes in an organism and can be used to simulate metabolic fluxes . GSMs cah et al. , iYO844 B. subtilis has focused mostly on improving the production of 2,3-BD from sugars such as glucose [Klebsiella oxytoca [Klebsiella pneumoniae [Bacillus amyloliquefaciens [B. subtilis to improve 2,3-BD production from glycerol represents an alternative for applications in biorefineries. The iBsu1147 metabolic network model of B. subtilis has been used to generate an in silico metabolic engineering design of reactions to increase the production of 2,3-BD from glucose [B. subtilis and evaluate the effects of step-by-step gene knockouts by validating the in silico design with experimental data.Previous bioengineering research on  glucose –19, not  oxytoca , Klebsieeumoniae , and Bacefaciens . Bioengi glucose ; howeverB. subtilis using in silico analysis to identify gene knockout targets in order to improve the metabolic pathway. A prototype of the metabolic pathways of B. subtilis for 2,3-BD production from glycerol was designed using the iYO844 model, considering growth in M9 medium. Changes in the bio-fermentation profiles caused by four gene knockouts are presented, and fermentation products were periodically collected and analyzed. Furthermore, the in silico analysis results were compared with experimental fermentation profiles.The aim of this study was to enhance 2,3-BD production from glycerol in B. subtilis model iYO844 [. Briefly, the environmental conditions in the iYO844 model were modified to the use of M9 medium with glycerol and an oxygen consumption rate of 10 mmol/gCDW/h, and biomass flux was the objective function. The regulation of glycerol uptake and metabolism in the model was optimized using the glycerol kinase (glpK) pathway . Fluxes of other volatile fatty acids (VFAs) were not observed.We modified and optimized the l iYO844 , which iway see “. The priackA) and phosphate acetyltransferase (encoded by pta) reactions, were set to zero   increased from 0 to 1.86 mmol/gCDW/h, and the fluxes of other fermentation products were not affected. The results from this simulation showed that inactive ACKr and PTAr reactions did not promote or contribute to 2,3-BD production.To test the inhibitory effect of acetic acid production, the metabolic fluxes of the ACKr and PTAr reactions, which refer to the acetate kinase  deletion , whereas the LDH_L reaction flux became zero. The flux of acetoacetate production decreased to 1.5 mmol/gCDW/h. Interestingly, 2,3-BD production became a major by-product (1.43 mmol/gCDW/h), whereas the production of other VFAs remained zero. The results of this simulation clearly showed that suspension of the LDH_L reaction can enhance 2,3-BD production from glycerol in B. subtilis.Next, we inactivated the LDH_L reaction to evaluate the effect of ion Fig. C. The remmgA, was a suitable target for inactivation , and the metabolic fluxes of the OCOAT1 and ACACT1 reactions became zero. As expected, the PDH flux was reduced by about half, and 2,3-BD production increased to 2.54 mmol/gCDW/h. Importantly, 2,3-BD was the only major product at this stage, whereas other products, such as ethanol, diacetyl, and propionic acid, were not detected. In the next phase of our study, the order of the simulations was applied to step-by-step gene modification in B. subtilis to validate the hypothesis from these simulations.From the simulation results of the three inactivated reactions, we inferred that the major fermentation products were acetoacetate and 2,3-BD. Since the target of this study was 2,3-BD production alone, we had to eliminate the metabolic flux of acetoacetate production. The conversion of pyruvate into acetoacetate involves three reactions catalyzed by pyruvate dehydrogenase (PDH), acetyl-CoA C-acetyltransferase ACACT1), and 3-oxoacid CoA-transferase (OCOAT1). The PDH reaction is an essential metabolic reaction that cannot be disabled, and the OCOAT1 reaction involves multiple genes, including BSU38990 and BSU38980. Therefore, the reaction catalyzed by ACACT1, encoded by , and 3-oB. subtilis GSM (iBsu1147) and flux variability analysis simulations to identify target reactions of genes to overexpress or underexpress to allow 2,3-BD production from glucose. The micro-aerobic condition was applied in the simulation (0.0005 mmol/gCDW/h), referring to the constrained oxygen condition at 0.5% saturated dissolution. Candidate reactions were predicted by using the flux variability analysis. The simulation results suggested that the first four genes to underexpress were pyc (encoding pyruvate carboxylase), ytiB or yvdA (encoding putative carbonic anhydrase), ndk (encoding nucleoside diphosphate kinase), and gmk (encoding guanylate kinase). These four genes are entirely different from the target genes and not related to those of the simulation for gene deletion in this study. Also, the biomass flux was zero when FBA simulation was conducted with knockout of these four genes in the iYO844 model. The difference in the prediction results may be due to the use of different models, environmental conditions, and prediction methods.Hao et al.  used a BB. subtilis step-by-step knockout strains  were performed to validate the predictions based on the GSM simulation. The growth, glycerol utilization, and fermentation characteristics of each strain are shown in Fig. –1, and the glycerol consumption rate was 7.26 mmol/gCDW/h. The fermentation profile of W168  as mentioned in the previous section, we found that acetic acid can also be produced by N-acetylglucosamine-6-phosphate deacetylase  and cysteine synthase , which correspond to metabolic reaction of B. subtilis 168 in the Kyoto Encyclopedia of Genes and Genomes . Moreover, there is an indirect pathway of B. subtilis 168 in which pyruvate is converted into acetic acid via pyruvate oxidase  and acylphosphatase .Figure u et al.  reported–1), but the glycerol consumption rate was higher (8.7 mmol/gCDW/h). In addition, some VFAs were produced at slightly lower final concentrations than by W168, which may be explained by the decrease in glycerol consumption after the exponential phase. These results indicated that knockout of ackA and pta did not improve 2,3-BD production  reaction might compete with the -butanediol dehydrogenase  reaction for NADH, which is mainly produced by glycerol-3-phosphate dehydrogenase  and glyceraldehyde-3-phosphate dehydrogenase . Hence, the deletion of lctE eliminates the NADH competitive reaction, which results in an increase in 2,3-BD production from glycerol. In addition, lctE deletion has been reported to increase the production of 2,3-BD in other platforms, including B. subtilis [K. oxytoca [Enterobacter cloacae [K. pneumoniae [lctE deletion can be regarded as key in improving 2,3-BD production in many microorganisms.When considering the metabolic pathway of glycerol dissimulation to 2,3-BD, we found that the L-lactate dehydrogenase , is involved in the conversion of acetyl-CoA into acetoacetyl-CoA, which is a precursor of acetoacetate production for pyruvate and butanoate metabolism. Thus, mmgA deletion leads to the suppression of the metabolic flux from pyruvate to acetyl-CoA, which may contribute to an increase in the fluxes associated with 2,3-BD production. We conducted a micro-aerobic fermentation experiment to ensure the promotive effect of mmgA knockout on 2,3-BD production from glycerol. Under the same conditions, W168∆3 produced 1.93 ± 0.10 g/L 2,3-BD, whereas W168∆4 produced 2.13 ± 0.05 g/L after 96 h of incubation. This result confirmed that mmgA knockout contributed to the increase in 2,3-BD production.Remarkably, B. subtilis and other microorganisms [The theoretical yield of the conversion of glycerol into 2,3-BD is 0.5 mol/mol glycerol . The 2,3rganisms , 28, 29.–1) and glycerol consumption rate  derived from the experimental data of the B. subtilis strains were applied in the in silico FBA simulation to determine the oxygen consumption rate , AHSERL3 (cysteine synthase), and ALDD2x , were zero. Moreover, other reactions involved in acetic production, such as those catalyzed by pyruvate oxidase  and acylphosphatase , are not represented in this model. While we found that many reactions are missing the iYO844, this model provided the most reliable result of biomass production from glycerol, as mentioned above. Therefore, further development and improvement of the B. subtilis model are necessary to allow for more accurate in silico simulations in future. To date, substantial research has focused on reconstructing GSMs of B. subtilis as a representative model for other gram-positive bacteria [In the FBA, the highest acetic production rate was obtained for the W168 strain, which corresponded to the experimental result of acetic production of W168 during the exponential phase. However, in the FBA, after inactivation of the ACKr and PTAr reactions, the acetic production rate became 0.09 mmol/gCDW/h, which was not in line with the experimental findings after bacteria .–1), whereas the glycerol consumption rates were higher (8.7 mmol/gCDW/h). However, the final concentrations of some of the major VFAs were slightly lower for W168∆2 than for W168, which may have been due to the decrease in glycerol consumption after the exponential phase. For ackA and pta knockout in W168∆2, the FBA results predicted that the fluxes of 2,3-BD production were zero. Indeed, knockout of these two genes did not affect 2,3-BD production  was knocked out in previous studies in order to enhance 2,3-BD production in B. subtilis [B. subtilis W168 in M9 medium containing glucose or glycerol did not indicate ethanol to be a significant product. Therefore, knockout of acoA was not required. This demonstrates that the use of in silico simulation can save time, energy, labor, and costs when compared to trial-and-error experiments.We found that subtilis , 24. HowB. subtilis strains was substantially higher than that of the wild-type strain. The promotive effect of mmgA knockout on 2,3-BD production from glycerol, which was predicted based on the FBA simulation results, was demonstrated for the first time in this study.Although the FBA results were not exactly the same as the experimental results, the guidance of gene knockout based on in silico simulations was very accurate, and the trends in the simulation results were consistent with the experimental results. 2,3-BD production from glycerol by the modified B. subtilis was applied to design and develop the metabolic pathways. FBA results provided precise target reactions to guide gene knockouts to improve the metabolic pathway for 2,3-BD production from glycerol. Furthermore, the use of in silico simulation of FBA could provide a novel platform solution for the bioengineering of B. subtilis to enhance 2,3-BD production from glycerol, which was the guidance of mmgA knockout. Additionally, the prediction results can save time and costs from unnecessary gene knockouts. The methodology of this study can be applied to increase other target fermentation products through the use of FBA in GSMs of other microorganisms. We are currently focusing on the development of GSMs and prediction strategies to provide better simulation results in future studies. The experimental knowledge and updated information derived from these experiments can be used to develop accurate and efficient models and predictions.Glycerol is a renewable and low-cost substrate that has potential as a raw material for industrial biological production processes. 2,3-BD is a valuable bio-based chemical compound for a wide range of potential applications and was selected as the target product in this study. The iYO844 genome-scale metabolic model of B. subtilis wild-type 168 (W168), was obtained from the Microbe Division at the RIKEN BioResource Research Center, Japan. We used the CRISPR-Cas9 system for gene knockout in B. subtilis [Escherichia coli NovaBlue .The strains and plasmids used in this study are listed in Table subtilis . PlasmidackA gene knockout (pJOE_ackA), plasmid pJOE8999 was cut at the BsaI restriction site and purified by gel purification . The primers used in this study are listed in Table BsaI sites (P_ackA01 and P_ackA02 primers) was introduced into pJOE8999 by polymerase chain reaction (PCR) using KOD FX Neo DNA polymerase . The PCR product was digested with DpnI restriction enzyme to remove the methylated DNA templates and gel-purified. Plasmid pJOE8999 containing a 20-nucleotide sgRNA for ackA deletion was cut using the SfiI restriction enzyme and purified. Meanwhile, two homology arms for ackA deletion were PCR-amplified from B. subtilis W168 chromosomal DNA using the P_ackA03 and P_ackA04, and P_ackA05 and P_ackA06 primers. After PCR product purification, these two PCR fragments were combined by overlap extension PCR using the P_ackA03 and P_ackA06 primers. The overlap extension PCR fragment was integrated into the SfiI site of the vector using NEBuilder HiFi DNA Assembly Master Mix , and transformed into E. coli NovaBlue for plasmid construction and replication. Transformants were selected on 25 μg/mL kanamycin in Luria–Bertani (LB) medium. pJOE_pta, pJOE_lctE, and pJOE_mmgA were constructed using the same protocol as that used for pJOE_ackA plasmid construction.For plasmid construction for B. subtilis W168 by natural transformation [cas9 under the control of PmamP. The plate was incubated at 30 °C for 2 days, and colonies were selected and transferred to LB agar without antibiotics and incubated at 50 °C overnight. The replication of pJOE8999 derivatives was temperature-sensitive, so the plasmid was removed during this process. The colonies were streaked on LB agar and incubated at 42 °C to obtain single colonies. The colonies were checked for target gene deletion by PCR using the above primers; for example, P_ackA07 and P_ackA08 primers were used to check for ackA deletion. Loss of the plasmid was evaluated by plating single colonies onto LB agar with and without kanamycin. Finally, target gene deletion was confirmed using DNA sequencing.Plasmids pJOE_ackA, pJOE_pta, pJOE_lctE, and pJOE_mmgA were sequentially introduced into ormation  for multLB liquid medium contains (per L) 10.0 g tryptone, 5.0 g yeast extract, and 10.0 g sodium chloride, and for plates, 1.5% bacto-agar is added for solidification. LB medium was used for the maintenance and preservation of all strains, and for plasmid construction. All strains were preserved at − 80 °C and revived by inoculation on LB agar plates.B. subtilis strains. Colonies of B. subtilis strains were cultured in 5 mL of M9 medium at 37 °C under rotation at 180 rpm overnight. Then, 1% (v/v) of the culture medium was activated by culture in 5 mL of M9 medium at 37 °C and 180 rpm for 18 h. Subsequently, 0.5% (v/v) liquid culture was inoculated into a 300-mL flask with 150 mL of M9 medium at 37 °C and 180 rpm for 72 h. Bacterial growth was periodically observed by measuring the optical density of the liquid culture at 600 nm (OD600). At the same time, liquid samples were collected and analyzed for residual glycerol and fermentation products, including acetic acid, lactic acid, pyruvic acid, succinic acid, diacetyl, acetoin, 2,3-BD, and ethanol. To confirm the effect of mmgA knockout on 2,3-BD production, micro-aerobic fermentation was performed using 120 mL of M9 medium at 37 °C and 100 rpm for 96 h.M9 medium  containiB. subtilis strains was periodically observed by measuring the OD600 using a UVmini-1240 spectrophotometer . The cell dry weight (CDW) was converted from the OD600 value; 1 OD600 is equal to 0.325 of g CDW/L [2SO4 at a flow rate of 0.5 mL/min.The growth of  g CDW/L . The conB. subtilis strain 168 in this study referred to the iYO844 model constructed by Oh et al. [http://bigg.ucsd.edu/). The last version of the iYO844 model contained 990 metabolites, 1250 metabolic reactions, and 844 genes. For simulations to determine the metabolic flux distributions, FBA was performed with the commercial GLPK (GNU Linear Programming Kit) package, which supplies a solver for large-scale linear programming (LP) and mixed integer programming (MIP), and the COBRA toolkit [B. subtilis was applied by setting a lower bound to allow unlimited uptake on the exchange reactions of the necessary minerals. The ALCD19_D and ALCD19_L reactions, which involve alcohol dehydrogenases, were deleted from the model because of the determined roles of YhdN in methylglyoxal resistance [The GSM of  toolkit  using MAsistance .B. subtilis strains under limited oxygen conditions, the glycerol and oxygen consumption rates were set at 10 mmol/gCWD/h (the maximum uptake rate), and the biomass objective function was set to maximize the target product for the growth simulation. Gene knockout simulations were performed by setting the minimum and maximum fluxes of the related reactions of the target genes to zero. The available reactions for gene knockout did not include exchange reactions, transporter reactions, or essential gene reactions. Details of the gene targets for knockout are listed in Table B. subtilis wild-type strain 168, which was originally used in the iYO844 model. The modified strains correspond with the gene knockout strains of W168: W168∆2 (deletion of the ACKr and PTAr reactions), W168∆3 , and W168∆4 .To evaluate the growth and metabolic pathways of glycerol utilization as the sole substrate in the simulation of −1) and glycerol consumption rate  were evaluated from experimental data during the logarithmic phase using Eqs. The specific cell growth rate μ, h− and glycAfter deriving the experimental results of the specific cell growth and glycerol consumption rates, FBA simulations of each strain were performed again to compare the in silico results with the experimental results. The biomass flux was used as an objective function. Glycerol consumption rates were fixed according to the experimental results of each strain, and oxygen consumption rates were varied to obtain the specific cell growth rates based on the experimental results. The production of the target VFAs was compared between the experimental results and FBA simulation results.Additional file 1. FBA analysis results from the experimental data.


Given that approaches for compartment-specific metabolomics exist, the benefits they offer compared to conventional non-compartmented 13C flux studies remain to be determined. Using compartment-specific labeling information of IgG1-producing Chinese hamster ovary cells, this study investigated differences of flux patterns exploiting and ignoring metabolic labeling data of cytosol and mitochondria. Although cellular analysis provided good estimates for the majority of intracellular fluxes, half of the mitochondrial transporters, and NADH and ATP balances, severe differences were found for some reactions. Accurate flux estimations of almost all iso-enzymes heavily depended on the sub-cellular labeling information. Furthermore, key discrepancies were found for the mitochondrial carriers vAGC1 (Aspartate/Glutamate antiporter), vDIC , and vOGC . Special emphasis is given to the flux of cytosolic malic enzyme (vME): it could not be estimated without the compartment-specific malate labeling information. Interesting enough, cytosolic malic enzyme is an important metabolic engineering target for improving cell-specific IgG1 productivity. Hence, compartment-specific 13C labeling analysis serves as prerequisite for related metabolic engineering studies.The online version contains supplementary material available at 10.1007/s00449-021-02628-1. First, applications dealt with prokaryotic cells -d-glucose as a carbon tracer with an average isotopic ratio of 25% [U-12C6]- and 75% [U-13C6]-d-glucose. Experiments were performed as biological duplicates. In addition to carbon labeling experiments, bioreactor cultivations with [U-12C6]-d-glucose were performed using the same conditions for metabolome profiling.Bioreactor cultivations were performed in a two-fold parallel CellFerm Pro bioreactor system  equipped with pitched blade impellers and a process control system. Bioreactor cultivations were started with a VCD of about 0.4 × 10d-glucose and ʟ-lactate were monitored offline with LaboTRACE, an amperometric biosensor system . Extracellular antibody (IgG1) concentrations were measured using ELISA as reported previously  were defined: q for cellular uptake and secretion rates, k as inter-compartment transport, and v as compartment-specific reaction. The balance of metabolite i participating in reaction j localized externally, in cytosol, or in mitochondria was described by Eqs. ci denotes the concentration of metabolite i [mol L−1], cx denotes VCD [cell L−1], t denotes time [h], and Qi,feed denotes the feed-rate of metabolite i [pmol L−1 h−1].The two-compartment CHO-cell model comprises the stoichiometric matrix q for metabolite i by time-series analysis of extracellular concentrations ci. Therefore, the metabolic steady-state was defined as mirrored in the constraint 13C flux analysis. Both stationary and non-stationary labeling patterns were analyzed, originating from the metabolic steady-state condition.The process model describing the batch cultivation is given in Eq. S considering the following constraints: (i) pool sizes of cytosolic and mitochondrial metabolites were in a steady-state and (ii) the entire system was (over)-determined because of the ample 13C labeling information. Fluxes were estimated according to:M is the measurement matrix containing the stoichiometric coefficients of qmeas (measured rates [pmol cell−1 h−1]) and p contains the estimated fluxes using mass-isotopomer data [pmol cell−1 h−1]).MFA was performed using the metabolic network 13C tracers into intracellular metabolite carbon skeletons -d-glucose after 2.5 days, revealing no phenotypic changes, i.e., no alterations of cellular metabolism.During the exponential growth phase, cells grew with 0.025 ± 0.001 h13C MFA was performed using compartment-specific metabolome data reflecting subcellular pools of cytosol and mitochondria together with isotopomer profiles of the said compartments. Flux estimations were performed at least 100 times with randomized input values and rational boundary values for each parameter . Finally, the chi-square tests achieved 228.87, which served the statistical constraint of 232.92 on a 95% significance level.−1 h−1 of hexokinase) and extremely low PPP fluxes (0.008 ± 0.001 pmol cell−1 h−1 of G6P dehydrogenase) were found. The latter accounted for 6.68% of the d-glucose consumed. These observations are in agreement with the findings of Ahn & Antoniewicz [13C MFA in adherent CHO-K1 cells. In addition, approximately 15% (0.016 ± 0.002 pmol cell−1 h−1) of intracellular G6P was continuously in exchange with endogenous glycogen.High glycolytic  mitochondrial citrate carrier  served as a citrate exporter to provide cytosolic acetyl-CoA for the de novo lipid biosynthesis pathway; (ii) the malate-aspartate shuttle comprising 2-oxoglutarate carrier  and aspartate-glutamate carrier (aspartate/glutamate antiporter), which is often described as an indirect NADH shuttle because imported malate is oxidized to oxaloacetate, releasing NADH, fulfilled a different function; malate was net exported from mitochondria to fuel cytosolic ME.Glycolytic carbon fueled into mitochondria via two transport mechanisms; 77% entered via the mitochondrial pyruvate carrier (MPC1/2) and 23% via a putative cyt) was identified as the primary provider serving NADPH needs. Compartment-specific 13C MFA estimated that about 86% of the NADPH requirement was fulfilled by MEcyt (0.09 ± 0.01 pmol cell−1 h−1).NADPH is a key electron donator for anabolic pathways and is essential for monoclonal antibody biosynthesis. Ahn & Antoniewicz, Templeton et al. , 7 sugge13C MFA was performed to investigate the importance of distinct sub-cellular information to elucidate proper in vivo subcellular flux patterns. Analyzing the merged data .An additional data Eq.  via 13C This study was performed using the same model consisting of 42 intracellular biochemical reactions. Figure 13C. This is also true for citrate synthase vCS, although identifiability was poor. Similar results were observed for cytosolic-based reactions: pyruvate carboxylase (vpc) and PEP carboxykinase (vpepck)  Fig. C. These vmdh), ME (vme), aspartate amino-transferases (vast), and alanine amino-transferases  are localized in cytosol and mitochondria, respectively. Of the eight iso-enzymes analyzed, seven conversion rates were significantly different. The only exception is the mitochondrial malate dehydrogenase  which revealed statistical similarity although fluxes even reversed. On contrary, the cytosolic malate dehydrogenase  also disclosed flux reversion but with a sound statistical identifiability.Special emphasis is laid on the so-called iso-enzymatic reactions of Fig. vast), namely alanine amino-transferases  and aspartate amino transferases (vast). The analysis of whole-cell data resulted in flux overestimation compared to compartment-specific analysis. Notably, the substrate aspartate occurred in cytosol and mitochondria and is a key player of the aspartate-malate shuttle. Moreover, alanine was involved in the co-transport of glycolytic carbon into mitochondria with the MPC1/2. In this case, proper localization and labeling information of the compound is key to estimate fluxes correctly.Non-compartmented data were not able to properly reflect real fluxes of the amino-transferases (vme) as displayed in Fig. 13C flux estimations using non-compartmented data identified significantly lower (about 30%) cytosolic vme,cyt than the non-compartmented data. Regarding mitochondria, the opposite was found. The finding for vme using non-compartmented data is consistent with the observations of Ahn & Antoniewicz, Templeton et al. [13C MFA with cellular data. Importantly, cytosolic ME activity via vme,cyt was identified as a key supplier for NADPH needed for IgG production in CHO cells .vAGC1), malate carrier (vDIC), α-ketoglutarate/malate antiporter (vOGC), and the putative alanine carrier (vmAla)  and the corresponding reactions . Shuttle estimations regarding vDIC and vOGC using non-compartment-specific data contradict flux calculations using compartment-specific information estimation. The sub-cellular labeling information of malate is essential to get accurate flux estimates. Interestingly, the flux estimation of putative asparagine carrier (vmAsn) was not biased by the use of whole-cell labeling data only. This may reflect that vmAsn heavily depends on the measured ʟ-asparagine uptake rate (qAsn) irrespective of the existence of additional subcellular information.Comparing shuttle activities using sub-cellular and cellular labeling information reveals significant differences for half of the inter-compartment transporters, namely the aspartate/glutamate antiporter (la) Fig. D. Similaf factors were estimated for each metabolite and compared with the measurements of Junghans et al. [f) were poorly identified with pyruvate showing the smallest difference of 8.59% only. On average, 59.71% difference was found compared to the real labeling fraction. Notably, the best estimates of pyruvate and asparagine also enabled improved flux values for the corresponding biochemical reactions, e.g. vMPC1/2, vpdh for pyruvate, and vasns, vmAsn for asparagine.Using Eq. s et al.   outlines that very similar key messages are obtained analyzing the new data set: Glycolytic fluxes are fairly similar irrespective whether subcellular or cellular 13C MFA with compartment-specific metabolome data compared to exploiting cellular labeling information not distinguishing between cytosol and mitochondria.This study challenges the information gain when performing vpgi, vGAPdh vG6Pdh, and vphdgh) located in a single compartment (here: cytosol) disclose equal values irrespective of the analytical approach selected. Interestingly, this also holds true for vcs, located in mitochondria, primarily due to poor flux identifiability. Furthermore, vpepck and vpc revealed such high flux variances that no distinction could be found whether cellular or subcellular 13C data were used. Apparently, both reactions depend on cytosolic (OAAcyt) and mitochondrial oxaloacetate (OAAmit). They act at the interphase of the two compartments and rely on proper sub-cellular measurement information (τ13C) for correct identification. Distinct OAA measurements were not available in the current study due to challenging analytical access to the compound. Accordingly, flux estimations might be biased by the quality of OAA pool estimations.Figure vmdh,mit) and the pyruvate carrier vMPC1. Both disclose large error bars rendering a discrimination between cellular versus subcellular approaches hardly possible  flux estimation failed to estimate the mitochondrial and cytosolic fluxes of the amino-transferases vAGC1 and vmAla are also reflected by the results of vast and valt. In addition, the poor estimation of the malate carriers vDIC and vOGC depended on vme. In general, fluxes of transporters and bioreactions heavily relied on the labeling dynamics measured in the related metabolites. Regarding vMPC1, the reduced shuttle activity based on non-compartmented data reflects the missing malate exported into cytosol . Interestingly, without information about compartment-specific metabolomics, cytosolic 13C signals obtained from simulations are pretty similar to those of the whole-cell. This is mainly due to the relatively low information gain with respect to the key mitochondrial metabolites malate and aspartate. Compartment-specific labeling information and turnover of the latter are decisive to resolve activities of mitochondrial transporters.To check whether the additional use of labeled glutamine  might haOften, cellular analysis achieved similar flux estimations as subcellular studies by fitting measured cytosolic labeling fractions for the sake of estimating pool sizes properly Table . In othevme. Remarkably, this flux was found to be a promising metabolic engineering target to maximize the formation of heterologous proteins by improved NADPH supply [Among the fluxes with the largest discrepancies is the cytosolic ME H supply . AccordiH supply . WhetherH supply .In general, most of the flux estimations using either non-compartmented or compartmented data led to similar values. Even global cell qualifications, such as rates of total ATP formation and NADH production, were similar. However, two main findings should be considered:cis-aconitate (in mitochondrion) to correct for mitochondrial leakage. In general, fast metabolic inactivation, standardized sample processing and use of internal standards are prerequisites for any compartment-specific metabolomics approach that might be used in future applications.To date, the compartment-specific analytical approach has shown its suitability for multiple metabolomic studies investigating CHO cells under in vivo-like conditions , 24–30. 13C labeling data, the study revealed that non-compartmented data enabled to identify most fluxes involving single compartment metabolites. Besides, half of the mitochondrial shuttle fluxes and global properties, such as ATP and NADH formation, were fairly well estimated without requiring further subcellular labeling information. However, there is a number of sensitive fluxes that could only be identified properly if compartment-specific pool information was used. Among those were mitochondrial shuttles that rely on alanine, aspartate and malate. Furthermore, key metabolic engineering targets, such as the cytosolic ME flux for NADPH formation, were severely underestimated using  cellular data. This may disguise their role as promising metabolic engineering target if non-compartmented pool analysis is performed, only. The finding underlines the necessity to apply subcellular data for flux estimation, not only to quantify cytosolic/mitochondrial shuttle activities but also to identify metabolic engineering targets and obtain valid values for real pool sizes.Investigating the need for using subcellular Supplementary file1 (PDF 137 kb)Supplementary file2 (PDF 158 kb)Supplementary file3 (PDF 169 kb)Supplementary file4 (PDF 1935 kb)Supplementary file5 (PDF 73 kb)Supplementary file6 (DOCX 178 kb)Below is the link to the electronic supplementary material.


Lactococcus lactis. Here, we present a proteome‐constrained genome‐scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose‐limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model‐based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient‐rich environments and thus form targets of fitness improvement.Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade‐offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient‐rich conditions, i.e., multiple available nitrogen sources, such as  Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement.A proteome‐constrained genome‐scale metabolic model of  We compared a glucose‐rich condition and a glucose‐limited condition in which the reference growth rate is 0.5/h. Of the 845 parameters, only 24 led to variations > 1% under the glucose‐rich condition  that was selected during long‐term cultivation in glucose‐limited chemostat conditions at D = 0.5/h and that harbors a point mutation in the global carbon catabolite repression regulator (CcpA)   Fig , confirm001) Fig , while o001) Fig . These mP = 0.030 compared with random distribution of significantly changed proteins over membrane and cytosolic protein fraction, Appendix Table We did not find a considerable change in the total protein membrane fraction  and inhibits glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), which has NADH as a product   than for arginine catabolism can be explained by the lower protein costs for serine than for arginine , a tool that reconstructs genome‐scale metabolic networks based on previous manually curated ones by homology between genes, and subsequently manual curation. For non‐spontaneous reactions with missing GPRs, we assigned a “dummy” protein as their catalysts to eliminate potential bias toward using them in simulations. Then, we split reactions with isozymes into multiple reactions that each is catalyzed by one isozyme, and reversible reactions into forward and reverse direction. In addition to updating and reformulating metabolic reactions, we formulated reactions for transcription, stable RNA cleavage, mRNA degradation, tRNA modification, rRNA modification, tRNA charging, ribosomal assembly, translation, protein maturation, protein assembly, enzyme formation, and protein degradation. Additionally, we formulated dilution reactions for RNA and enzymes to represent their dilution to daughter cells during cell division. Lastly, we modified the biomass equation of the metabolic model, i.e., removed protein and RNA from the equation as they were represented by dilution reactions, and added an unmodeled protein to account for all the other proteins that are not synthesized by the model. The genome‐scale metabolic model was updated using CBMPy (https://doi.org/10.5281/zenodo.3485023), while pcLactis was constructed using the COBRA toolbox .The binary search solved successive individual linear programs, which maximizes the production of the dummy protein subject to stoichiometric and coupling constraints. Lower and upper bounds of some reactions were also constrained. Notably, amino acid exchange fluxes were constrained by growth rate‐dependent upper bounds based on experimental data (iq/iµ). We imposed a small increase in the uptake rate of each amino acid Δq = 0.01 to investigate the change in growth rate Δµ.We performed the scaled reduced cost analysis (ic/iµ). With pcLactis, we imposed a small increase Δc = 0.01 in the glucose transporter and modeled proteome to investigate the change in growth rate Δµ at different conditions. We also performed sensitivity analysis with the small model for glucose uptake, proteome allocated to the small model and arginine uptake using the small increase Δc = 0.01 to investigate the change in ATP production rate ATPq, i.e., the sensitivity score iS is calculated as: iS = (ΔATPq/Δc)(ic/ATPq).We performed sensitivity analysis for two constraints with pcLactis, i.e., the glucose transporter and modeled proteome, on growth rate. The sensitivity score Lactococcus lactis ssp. cremoris MG1363 is a plasmid cured derivative of strain NCDO712 , while standing in 30°C water baths. The headspace was continuously flushed with 5% COTo start up a chemostat culture, 5 ml CDMpc with 25 mM glucose was inoculated with glycerol stocks of the respective strains, taken from a −80°C. This was directly added to the reactor containing 265 ml fresh 25 mM glucose CDMpc. The reactor was operated in batch mode  for 24 h, allowing the cells to reach the stationary phase. This was verified by observing that the pH remained constant without the addition of 2 M NaOH. After 24 h, the medium pump was switched on. The chemostat was operated for 8 volume changes before samples were taken. Samples were taken from the effluent of the reactor. Retention time in the effluent tube was < 2 min. Sample tubes were kept on ice while sample was being collected. For each strain, four replicate chemostats were cultivated.g for 3 min at 4°C, and the supernatant was filtered through a 0.22 µm polyether sulfone filter  and stored at −20°C until further analysis. Extracellular concentrations of lactate, acetate, formate, ethanol, and glucose were determined by high‐performance liquid chromatography (HPLC) as described previously , 2 ml samples were centrifuged at 27,237 4, 0.381% w/v Na2B4O7·10H2O, 0.0325% w/v sodium azide in deionized water (DI water); and eluent 2 had composition: 45% v/v methanol, 45% v/v acetonitrile in DI water. Samples were prepared by mixing 25 µl sample with 875 µl DI water, 25 µl Borate buffer , and 25 µl 1 mM Norvaline as internal standard. For amino acid derivation, 3 µl phthaldialdehyde (Sigma‐Aldrich), i.e., OPA, was automatically added 3 min before sample injection (5 µl) into the column. Concentrations were determined by comparison of sample peak areas to those of a calibration curve identically run with representative amino acid concentrations.Extracellular concentrations of amino acids were determined by HPLC on a Shimadzu system with: LC‐20AD pumps, DGU‐14A degasser, SIL‐10ADvp autosampler, CTO‐10ASvp oven, SCL‐10Avp system controller, and RF‐10AXL fluorescence detector. Separation occurred on an Agilent Zebra Eclipse plus solvent saver 3.0 × 150 × 3.5 column over a period of 38 min per sample, with an isocratic flow of two eluents both at a flow rate of 0.64 ml/min. Eluent 1 had composition: 0.142% w/v NaHPOg for 3 min at 4°C. The supernatant was discarded, and the pellet was frozen in liquid N2 prior to storage at −20°C until further analysis.Proteome samples were collected in low protein binding tubes, centrifuged at 27,237 For dry weight measurements, 10 ml broth was collected and filtered over a pre‐dried  pre‐weighed 0.2 µm cellulose nitrate filter (Whatman GmbH). The filter was then washed once with 10 ml demineralized water and dried , before weighing again.iq in mmol/gCDW/h) were calculated as: qi=D·Ci,supernatant‐Ci,medium/Xbiomass, where D is the dilution rate (/h), C the concentration of compound i (mM) and Xbiomass the biomass concentration (g/l).Fluxes  at room temperature. The digested peptides were eluted by centrifugation for 30 min, subsequent addition of 100 µl 1 ml/l HCOOH in water, and again centrifugation for 30 min. pH was adjusted to pH 3 using 10 v/v% trifluoroacetic acid.Proteins were isolated using the FASP method as described previously  and frequently observed contaminants. In addition to the standard settings, Trypsin/P with a maximum of two missed cleavages was set as the digestion mode, acrylamide modifications on the cysteines were set as a fixed modification, and methionine oxidation, protein N‐terminal acetylation, and asparagine or glutamine deamidation were set as variable modifications. A false discovery rate of 1% at protein and peptide level was allowed, and the minimum required protein length was set at 7. At least two peptides were required for protein identification of which at least one peptide was required to be unique in the database. Identified proteins were quantified with MaxQuant’s LFQ algorithm  and variation  to make calculations possible. We then performed a 2‐sided two‐sample Study conception: BT, HB, and JN; Modeling, simulations, and data analysis: YC and EvP‐K; Experiments: BvO and SD; Data analysis: DM and SB; Manuscript writing: YC, EvP‐K, and BT; Manuscript editing: All authors.The project is organized by and executed under the auspices of TiFN, a public–private partnership on precompetitive research in food and nutrition. The authors have declared that no competing interests exist in the writing of this publication. Funding for this research was obtained from Friesland Campina, CSK Food Enrichment, the Netherlands Organisation for Scientific Research and the Top‐sector Agri&Food.AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Review Process FileClick here for additional data file.


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.