Computational optimization can effectively aid the designprocess, but current methods are generally unsuited for systems withmultiple temporal or concentration scales, as these are slow to simulatedue to their numerical stiffness. Here, we present a machine learningmethod for the efficient optimization of biological circuits acrossscales. The method relies on Bayesian optimization, a technique commonlyused to fine-tune deep neural networks, to learn the shape of a performancelandscape and iteratively navigate the design space toward an optimalcircuit. This strategy allows the joint optimization of both circuitarchitecture and parameters, and provides a feasible approach to solvea highly nonconvex optimization problem in a mixed-integer input space.We illustrate the applicability of the method on several gene circuitsfor controlling biosynthetic pathways with strong nonlinearities,multiple interacting scales, and using various performance objectives.The method efficiently handles large multiscale problems and enablesparametric sweeps to assess circuit robustness to perturbations, servingas an efficient  Butsince multiscale systems often cannot be simulated at such scale,the computational costs limit the applicability of current optimizationmethods.Previous work on computationalcircuit design has largely focusedon genetic circuits that operate in isolation from other layers ofthe cellular machinery 1A. A ran26 These systems are receiving substantial attentionthanks to several successful implementations that improved yieldsas compared to classic techniques in metabolic engineering.28 The key principle is to put enzymatic genes under the control ofmetabolite-responsive mechanisms that couple heterologous expressionto the concentration of a pathway intermediate.3 This creates feedback loops between enzyme expression andpathway intermediates that allow the control of pathway activity inresponse to upstream changes in growth conditions or precursor availability.Such dual genetic-metabolic systems are particularly challenging tosimulate efficiently because metabolites and enzymes vary in differenttime scales, from milliseconds (enzyme kinetics) to minutes (enzymeexpression), and they also appear in vastly different concentrations;in bacteria enzymes are typically expressed in nanomolar concentrations,while metabolites are found typically above the millimolar range.29 Moreover, the implementation of these systemsis costly and requires substantial experimental fine-tuning. As aresult, a central task prior to implementation is the choice of asuitable feedback control loop between metabolites and enzymatic genes,and the strength of interactions between metabolites and actuatorsof gene expression such as transcription factors30 or riboregulators.31 The designof control architectures is particularly important, because thereare many ways of building similar control loops,32 for example by employing combinations of transcriptionalactivators and repressors,34 that may differ intheir performance and cost of implementation.A notable example of this challenge appears ingenetic circuitsfor dynamic control of metabolic pathways.35 Using a toy example for a simple pathway, we first show that themethod converges rapidly and outperforms other optimizers by a substantialmargin. We then consider real world models of metabolic pathways in Escherichia coli for the production of several relevantprecursors: glucaric acid,36 fatty acids,33 and p-aminostyrene.34 We use these pathways to illustrate how thespeed of our method enables screening optimal designs in realisticdesign tasks that would otherwise be infeasible to compute, includingthe impact of uncertain enzyme kinetic parameters, the use of layeredarchitectures that combine metabolic and genetic control, and theoptimization of a complex model with 23 differential equations, 27candidate control architectures, and 16 parameters to be optimized.The method can help speed up the design of synthetic biological circuitsand presents a novel approach to explore the design space ahead ofimplementation.Here, we presenta fast and scalable machine learning approachfor optimization of multiscale circuit architectures and parameters1A. The m22.1J is a performance objective to be optimizedover a space of continuous parameters pc and a discrete set of circuit architectures pd. The ordinary differentialequation (ODE) in h(x). Common examples of continuous parametersin applications are binding affinities between DNA and regulatoryproteins, or the strength of protein-protein interactions. Conversely,circuit architecture would typically involve various combinationsof positive and negative feedback loops among molecular species. Wehave stated the problem as minimization of J, butsimilar formulations can be posed as a maximization problem.In general, a circuitdesign task can be stated as the following mixed-integer optimizationproblem:35 where the objective function requires measuringdata with costly and/or slow experimental work. In deep learning,BayesOpt is widely employed for model selection, as traditional gridsearch approaches require large compute resources to train many architectureswith combinations of various layer sizes and other hyperparameters.38In this paper, we propose to solve the design problem in J requires solving a stiffODE at many locations of the mixed-integer search space, which canrapidly become infeasible. To illustrate the utility of BayesOpt ina range of design problems, we focus on genetic control circuits formetabolic pathways that synthesize high-value products. In this case,the ODE in s and e are vectors of metabolite and enzyme concentrations, respectively.The term f describes the biochemical reactions between pathway intermediates,while the parameter λ models the dilution effect by cell growth.The vector u describes the enzyme expression rates controlled by somepathway intermediates, and typically take the form of sigmoidal dose-responsecurves that lump together processes such as metabolite-TF or metabolite-riboregulatorinteractions.30 The continuous parameters pc model the dose-response curvesof the feedback mechanisms, whereas the discrete parameters pd specify the gene controlarchitecture. The number of heterologous enzymes determines the numberof genes in the control circuit. In pathways under dynamic controlas in 39 whileenzyme expression changes in the scale of minutes or longer. Moreover,metabolites and enzymes are also present in different ranges of concentrations,from nM for enzymes to mM and higher for metabolites.29 As a result, simulation of the ODE in For the circuit design task in J can beflexibly usedto model common design goals such as production flux, yield or titer,as well as cost-benefit tasks that balance production with the deleteriousimpact of the pathway on the physiology of the host. To first establisha baseline for the performance of our method, we employed a simpletoy pathway model that displays common features found in real metabolicpathway  and landscapes with flat basins wheremost optimization algorithms would struggle to find the optimum (e.g.downstream activation). When searching over the space of architecturesand parameters simultaneously, the problem becomes a mixed-integer,nonconvex optimization that is extremely challenging to solve withtraditional approaches.We considered the four control architectures shown in pd) and dose-responseparameters (pc) thatminimize the performance objective in We implemented a BayesOpt routine tojointly compute the architecture in upper glycolysisand contains three enzymatic steps . Doong andcolleagues implemented a dynamic control circuit using the dual transcriptionalregulator IpsA which responds to the intermediate myoinositol (MI).26 The pathway enzyme MIOX is allosterically activatedby its own precursor, and one intermediate (MI) can be exported tothe extracellular space. We employed a previously developed ODE model10 that was parametrized using a combination ofenzyme kinetic data and omics measurements, and considered the samefour control architectures as in the previous example, including variousalternative implementations of negative feedback control.We focused on a pathwayfor synthesis of glucaric acid in E. coli 2A, a keySupporting Information), together withthe fraction of samples in which the algorithm explored each controlarchitecture across the successive iterations. The optimal architecture was found quickly and the algorithm wasable to further decrease the value of the objective function by exploringthe space of dose-response parameters of IpsA. We observe that asthe iterations progress, the algorithm shows a remarkable abilityto explore other architectures despite their larger objective functionvalues, thus highlighting the global nature of the algorithm.The results in Vm,MIOX isthe maximum rate of reaction, km,MIOX isthe Michaelis-Menten constant, and ka,MIOX and aMIOX are allosteric activationconstants. We solved the optimization problem for 1,000 combinationsof these three parameters, which took under 16 hours on a MacbookAir with Apple M1 processor and 8 GB of RAM running MacOS Monterey.Perturbing the kinetic parameters of the glucaric acid pathway didnot significantly affect the minimum objective function value achieved,indicating that the optimum is robust to uncertainty in the kineticparameters  and regulatorythresholds (θ) control the shape of the dose-response curves.As shown in 1 are low  and compressed into a narrow range as compared to thelarger standard deviation of the downstream repression threshold θ2 . This is reflected on a larger variationin the shape of the dose response curve for the downstream loop. Experimentalfine-tuning of a dual control circuit might target parameters withoptimal values with a wide range, such as k1, as varying these parameters is less likely to impair circuit function.Overall, these results show the robustness of the glucaric acid dualcontrol system to kinetic parameter uncertainty and demonstrate thepossibilities enabled by the speed of BayesOpt.The maximal enzyme expression rates  architecture in 33 , an industrially relevantvinyl aromatic monomer, in E. coli (45 using a cost-benefit objective similar to Supporting Information). This model has two possible metabolites that can regulate geneexpression, namely p-amminocinnamic acid (p-ACA)and p-aminophenylalanine (p-AF), both of which canact as ligands for aptazyme-regulated expression device (aRED) transcriptionfactors,46 and three genes to be controlled.The aRED transcription factors can also act as dual regulators (activatorsor repressors) on any of the three promoters involved in the pathway.For simplicity, we limit the design space to control architectureswithout positive feedback loops, as these are prone to bistability.47 This results in 27 possible control architecturesand 16 continuous parameters to be optimized. The model also has anumber of additional complexities. It contains operon-based gene expressioncommonly found in bacterial systems , it includes a detailed description of mRNAdynamics and protein folding, which results in a large model with23 differential equations, and it can also display oscillatory dynamics.Our previous case studies have been limited to circuits with a singlemetabolite controlling gene expression and a relatively small numberof control architectures. We now study a large model for the synthesisof  E. coli 4A45 usin45 The intermediate p-ACA and theefflux pump used to remove p-ACA from cells are both cytotoxic, whileanother intermediate, p-AF, leaks from cells.34 The pathway enzyme L-Amino Acid Oxidase (LAAO) depletes key aromaticamino acid metabolites and creates toxic hydrogen peroxide as a byproduct.The model incorporates these various types of toxicity in the formof a toxicity factor τ. This toxicity factor is of the formtl, ta, and tp are chemical-specific toxicityfactors. Enzyme-induced toxicity tl scales the key metabolite depletion rate driven by the enzymeLAAO. Metabolite-induced toxicity ta scales the impact of toxic intermediate p-ACA concentration.Finally, protein-induced toxicity tp reflects the toxicity caused by efflux pump expression. Thetoxicity factor acts as a scaling coefficient on the pathway synthesis,degradation, and folding reaction rates.In addition to expression of heterologous enzymes,the accumulationof toxic intermediates is another major source of genetic burden tohost organisms. The p-AS model has several sources of toxicity presentin the pathway.48 and adding alarge regularization term to the loss.Despite the complexityand size of the p-AS model, we observe thatBayesOpt explores many of the 27 possible architectures and convergesto a low value of the objective function 4B; this ta and protein-induced toxicity tp in 49 The p-AS pathway liesat the far end of what is currently possible to build experimentallyand thus illustrates the broad applicability of BayesOpt to realisticdesign tasks in metabolic engineering.To investigate the robustnessto chemical toxicity, we perturbedthe metabolite-induced toxicity 3Progress in synthetic biologyallows the construction of circuitsof increased complexity across various levels of biological organization.However, large design spaces and multiple scales can become substantialchallenges for the design of functional systems. In this paper, wepresented the use of Bayesian optimization for the design of biologicalcircuits. The method can rapidly find circuit architectures and parametersthat optimize a performance objective that captures the target circuitfunctionality.50 We have shown that Bayesian optimization can aid the design of suchsystems prior to implementation and serve as tools for insilico screening of competing designs that may have similarperformance but entail different cost of wetlab implementation. Weshowed the efficiency and scalability of the method in several realworld case studies from metabolic engineering. In particular, the p-aminostyrene pathway is more complex than systems typicallyimplemented in literature so far, which suggests that the method isapplicable across a range of relevant design tasks.The method is particularly well suited for casesin which the multiplescales prevent efficient simulation of ODE models. Gene circuits designedto control metabolic pathways are an excellent example of such multiscalesystems, as they combine fast metabolic time scales with the muchslower dynamics of gene expression. Moreover, the choice of regulators,control points, and control architectures adds multiple degrees offreedom that are infeasible to explore experimentally. Previouslyimplemented metabolic control systems have been built primarily basedon application-specific knowledge of pathway features.51 and is gaining substantialinterest in other areas of synthetic biology.53 In this paper we have shown how such methods can also benefit dynamicpathway engineering by using optimization as a means to navigate thedesign space prior to system prototyping.We anticipateseveral novel applications of this work to otherproblem areas where discovery or tuning of multiscale circuits hasbeen previously infeasible. For instance, this method could be employedto fit temporal circuit dynamics to data or discern which of severaldiscrete circuit mechanisms most closely matches observed behavior.As with other design strategies based on ODE models, a challenge ofour approach is the significant domain knowledge required to constructmodels for a target pathway, both in terms of the enzyme kineticsand the downstream metabolic processes that affect pathway activity.Machine learning has already proved useful in a range of metabolicengineering tasks44.137 Bayesian optimization is commonlyemployed for hyperparameter tuning in deep neural networks. We employedExpected Improvement as an acquisition function and a tree-structuredParzen estimator (TPE) as a nonparametric statistical model for theloss landscape. We performed a grid search over the TPE hyperparameterγ which controls the balance between exploration and exploitationbut found little impact on the algorithm performance; we thus usedthe default value of γ = 15 .We employedthe Bayesian optimization routine implemented in the Python HyperOptpackage.HyperOpt search space. At each run of the Bayesian optimizationroutine, the initial guess for the continuous decision variables weresampled from uniform distributions, with upper and lower bounds weretaken from literature.44 Architectures were chosen uniformly from the set of architectureswithout positive feedback loops.Constraints on the continuous and discretedecision variables were incorporated directly into the 4.2p-aminostyrene pathway in pd in pc in p-aminostyrene pathways, expressionrates were parametrized with bespoke nonlinear functions describingspecific biochemical processes. The discrete control architectureswere defined in two different ways. For the toy, glucaric acid, and p-aminostyrene models, the architectures were defined usinga binary matrix to encode the mode of transcriptional control. Forthe fatty acid model we instead defined each architecture as a categoricalchoice and switched between model functions correspondingly. We notethat the p-aminostyrene pathway also contains ODEsfor mRNA abundance and folded/unfolded proteins. All models and theirparameters are described in the Supporting Information.We considered fourexemplar pathways modeled via ordinary differential equations (ODEs):the toy system in 54 In allcases, the initial concentrations of heterologous pathway enzymeswere assumed to be zero. Initial concentrations for native metaboliteswere determined by first solving a model without the heterologousenzymes up to steady state. Simulation times and initial conditionsare detailed in the Supporting Information for each model.The ODE models were solved with scikit-odes, a Pythonwrapper forthe SUNDIALS suite of solvers.4.3J in 1 and α2 across many model simulations, andchose values that led to similar values for both components; thisprevents the optimizer from biasing the search towards low loss valuescaused by the scaling effects alone. For the fatty acid model in Supporting Information.In all cases the lossfunction


Pichia pastoris, glycerol and methanol cultivation conditions were studied. Additionally, in-silico metabolic modelling analysis of growth-coupled enzyme quantity, suggests metabolic gene up/down-regulation strategies for heme production. First, cultivations and metabolic modelling analysis of P. pastoris were performed on glycerol and methanol in different growth media. Glycerol cultivation uptake and production rates can be increased by 50% according to metabolic modelling results, but methanol cultivation—is near the theoretical maximum. Growth-coupled metabolic optimisation results revealed the best feasible upregulation (33 reactions)  and 66 downregulation/deletion  reaction suggestions. Finally, we describe reaction regulation suggestions with the highest potential to increase heme production yields.Soy leghemoglobin is one of the most important and key ingredients in plant-based meat substitutes that can imitate the colour and flavour of the meat. To improve the high-yield production of leghemoglobin protein and its main component—heme in the yeast  Recommendations to limit meat consumption have been made because meat is a critical source of protein and in numerous high-income countries, protein utilisation outperforms dietary necessities3. Plant-based meat can be an efficient alternative to livestock, as for the production of the primary plant proteins, fats, carbohydrates and vitamins are used. Plant-based meat is defined as a meat-like substance made from vegetarian-friendly ingredients like proteins (from soy and potatoes), fat (from coconut and sunflower oils), carbohydrates , nutrition additives  and flavours 4. The market for plant-based meat is anticipated to reach a significant milestone of $30.9 billion by 20265 due to the rise in the number of vegetarians, vegans, and flexitarians in Europe during the past several years6. The switch from an animal to a non-animal diet is also influenced by religious considerations and high production costs7.Meat is one of the significant sources of dietary protein. It is frequently recognised as a high-quality protein source due to its nutritional qualities and favourable sensory properties such as texture and flavour. However, the rising global population has led to a rise in the production and consumption of meat around the world8. Also, hemoprotein addition to artificial meat or its analogues has been proven to display great potential to mimic the taste and smell similar to that of traditional meat. There are a number of hemoglobin analogues across many organisms. Leghemoglobins, are present in the root nodules of leguminous plants. Leghemoglobin (legH) is approved by the United States Food and Drug Administration (FDA) as a food colouring additive and can be used equivalently to hemoglobin and myoglobin in both vegetarian and non-vegetarian diets9.Heme-containing proteins are considered one of the crucial flavour and colourant contributors in animal red meat10. The use of legH protein as a food additive also circumvents the contradictions of animal material used in the diet, especially for vegetarians.In plant-based meats legH unfolds upon cooking, releasing its heme cofactor to catalyse reactions that can transform the same biomolecules, isolated from plant-based sources, into the array of compounds that comprise the unique flavour and aroma of traditional meat12 was studied, furthermore, other groups studied extracellular hemin addition, which provided only 5–10% increase of heme-containing protein mass in respect to the total protein content13. In heme-containing protein production, extracellular precursor addition seems to not be an economically feasible approach and further studies should be focused to achieve commercially viable intracellular hemoglobin, myoglobin and legH production through genetic engineering.Many attempts in biotechnology have already been made to increase hemoprotein yields, thus ensuring the feasibility of such technology for industrial-scale production. For example, to improve heme-containing protein production the addition of 5-aminolevulinic acid (ALA) to the growth medium and overexpression of heme transporter genesCandida spp., Hansenula spp., Pichia spp., or Toruplosis spp. representatives, which are methylotrophic yeast and can utilize alcohols, also methanol, have been previously used for accomplishing the mentioned tasks14. Furthermore, alternative microorganisms, like Kluyveromyces lactis, which are capable of utilising cheap dairy industry by-products, like cheese whey, have been studied15. Another study reported that it was possible to increase legH secretion by 83-fold in a genetically engineered P. pastoris with respect to the wild-type strain, by overexpressing heme pathways and globin synthesis genes simultaneously16. A recent study demonstrated that by combining systems biology and genetic engineering approaches, heme production can be increased by 70-fold in Saccharomyces cerevisiae. This was done using metabolic modelling coupled with growth-coupled heme production analysis on the genome level, where the most promising analysis results were genetically implemented and experimentally tested17.Currently, microorganism platforms are the most perspective way of manufacturing legH in commercial amounts, for further integration into plant-based meat. Several legH-producing recombinant microorganisms are 18 and explain genotype–phenotype interaction in unicellular and multi-cellular organisms20.In the last decade, genome-scale metabolic modelling is increasingly being used as an auxiliary tool for facilitating various genetic engineering tasks and for predicting potential effects. Metabolic models effectively encode gene-protein-reaction (GPR) associations21, and predicts environmental perturbation impacts on the metabolism and potential carbon distribution23. Genome-scale metabolic model (GEM) application in biotechnology already has proven its importance and helped to develop new nutrient uptake innovations24, novel software developments26, created new biotechnology applications28.Metabolic modelling allows us to predict the organisms phenotypes biotechnological properties and responses30.GEM investigation has guided the development of strains with optimised yields of industrial metabolites 31. This method is very effective in new strain design because metabolic production becomes a necessary by-product for cell growth. Furthermore, to increase desired metabolite production yields and production capabilities it is possible adapting laboratory evolution by selecting for maximum growth33.One of the most common approaches to metabolic engineering and computational biology during the last decade has spread to link the production of the desired metabolite with coupled growthPichia pastoris expression system has been extensively used to produce various recombinant proteins for research and industrial applications. This methylotrophic yeast is highly suitable for heterologous protein expression due to several key features: ease of genetic manipulation, high-frequency DNA transformation, cloning by functional complementation, high levels of intracellular and extracellular protein expression, and the ability to perform higher eukaryotic protein modifications 35. In addition, relatively low levels of native secreted proteins facilitate the purification of the secreted recombinant proteins. Taking into account the economic factors  and the advanced genetic techniques available, P. pastoris is the system of choice for heterologous protein expression37. The versatility of P. pastoris is reinforced by the high yields of various recombinant proteins achieved in this microorganism40.Over the past 20–30 years, the Thus, to increase the production of legH protein to economically feasible quantities, global intracellular reactions/gene manipulations must be carried out.Pichia pastoris. We exploited fermentation data and FBA  to estimate the potential of the P. pastoris biomass yield. To identify particular reactions, we developed a growth coupled analyses algorithm to systematically explore the P. pastoris genome-scale metabolism model to identify reactions with the highest impact on heme production.This study aimed to perform biomass growth-coupled analyses to determine a list of the reactions necessary to up or down-regulate to increase heme biosynthesis in P. pastoris. In the first step, P. pastoris is grown on glycerol to produce biomass. In the second step, P. pastoris is cultivated on methanol as a sole carbon source and legH synthesis is induced. We introduced the soya (Glycine max) leghemoglobin coding gene into P. pastoris X-33 strain genome under control of methanol inducible promoter . We performed P. pastoris biomass production followed by legH induction and growth on methanol. We analysed legH production using polyacrylamide gel.Process in P. pastoris growth on glycerol or methanol are optimal. Then we built an algorithm to find out reaction sets linked to P. pastoris growth and heme production.First, we performed FBA to test if Pichia pastoris strain X-33 containing genome-integrated legH gene . In GEM analyses we assumed that each legH protein contains one heme molecule41. Therefore, legH production is linked to biomass growth and is limited by intracellular heme production.We tested the GEM ability to simulate physiologically relevant conditions and analyse heme biosynthesis growth-coupled production. We performed experimental fermentations with P. pastoris reactions regulation design sets for genetic engineering purposes and increase intracellular heme production rate, we used P. pastoris iMT1026 GEM, which consists of 1708 metabolites, 2243 reactions, 2 different biomass compositions for glycerol and methanol fermentation optimisations, has 9 different compartments and transport intercompartment42. iMT1026 model was updated and missing mandatory information filled to be compatible with CobraToolbox 3.043.To develop GEM includes metabolite name, charged formula and compartment name in the cell. As iMT1026 has no metabolite or reaction database references, thus it was difficult to test new functionality in comparison with scientific literature. GEM is considered to work at pH 7.2, 30 °C environment temperature.P. pastoris cultivation in synthetic media on glycerol and methanol and measured the specific growth rate (μ). We found, that the growth rate of glycerol was 6.3–4.75-times higher (μ ~ 0.19 h−1) than consuming methanol (μ ~ 0.04–0.03 h−1), which is in the scope of previously published data (1.7–8.5 times faster)44. The O2 consumption rate on glycerol growth was 2.17–2.29 mmol*g−1 * h−1, but CO2 production was a little less (1.61–1.7 mmol*g−1 * h−1) than O2 consumption  or permittivity probe . Both sensors delivered similar tendencies during the first stage of the fermentation (glycerol). However, culture permittivity results started to decrease gradually during the methanol fermentation stage, thus indicating loss of viability when methanol feed was increasing . The same conclusions have been made by earlier studies13, where it was concluded, that extracellular addition of substrates is a less effective strategy and has its limitations in comparison to genetic engineering potential. For example, 70 times increase in heme production was achieved in S. cerevisiae when using GEM-driven metabolic engineering17.After obtaining growth, OP. pastoris GEM to make a systemic reaction impact analysis on potential heme production. Previously described experimental and GEM optimisation results comparisons suggested a potential to increase heme production at the expense of biomass production. This could be one of the GEM modelling bottlenecks in the biomass reaction itself. In GEM, a biomass objective function45 exists, which defines necessary metabolic resources for cell process machinery and proliferation needs. But the biomass objective function data, experimentally measured in strict cultivation conditions, is expensive to measure and sometimes even has a large error distribution. Also, it is known that biomass objective function composition and macromolecule weighting factors can change during different environmental conditions46.Thus, the next step in this study was to use In this study, we improved the previously published growth-coupled GEM algorithm and adjusted it to new GEM requirements. To exclude biomass objective function limitations, we decided that growth-coupled GEM algorithm functionality  will systematically analyse all metabolic reactions' impact on heme production.The weighted factor shows how much percentage each flux value changes versus the mean value and is summed together. As not all reaction flux changes are in linear    (ID in the Metacyc).P. pastoris cell growth with legH production—we listed all reactions whose rate increased when the biomass rate decreased. To increase heme intracellular production, the algorithm has suggested upregulating reactions from the dihydroxyacetone cycle combined with glycolysis reactions  (5-AMINOLEVULINIC-ACID-SYNTHASE-RXN), where Succinyl CoA (succoa) (SUC-COA) is consumed in large amounts as a 5-Aminolevulinate (5aop) (5-AMINOLEVULINIC-ACID-SYNTHASE-RXN) precursor. In the mitochondrial TCA cycle- (PWY-5690) Succinate–CoA ligase (ADP-forming) reaction (SUCCCOASYN-RXN), which is one of the suggested upregulation reactions is used to produce Succinyl CoA (SucCoA) (SUC-COA) and consume large amounts of ATP (atp) (ATP), which could be a limiting step for improved intracellular heme production if less ATP (atp) (ATP) is available in mitochondria  was identified as rate limiting49, but recent studies in P. pastoris showed that HEM1 (in GEM ALASm)16 is limiting. They analysed single gene upregulation of heme b biosynthesis I pathway (heme-BIOSYNTHESIS-II) and tetrapyrrole biosynthesis II (from glycine) (PWY-5189) pathways and concluded that ALASm reaction upregulation had the most impact on legH production compared to other heme biosynthesis pathway reactions. The same conclusions indirectly suggest GEM pyrroline-5-carboxylate reductase step-weighted factor algorithm results. Thus, different results are found in different yeast species, which suggests heme production in various species is differently regulated. More the study suggested that HEM1 (ALASm), HEM2 (PPBNGS), HEM3 (HMBS) and HEM12 (FCLTm) upregulation showed a 41% increase and all heme production genes overexpression led to 1.5-fold increased heme production, leaving the potential for much more improvements as it was recently found in S. cerevisiae17.To increase intracellular heme bioproduction the most perspective upregulation reactions are 5-aminolevulinate synthase (ALASm), porphobilinogen synthase (PPBNGS), hydroxymethylbilane synthase (HMBS), uroporphyrinogen-III synthase (UPP3S) from heme b biosynthesis I pathway (heme-BIOSYNTHESIS-II)  are (a) formyltetrahydrofolate dehydrogenase (FTHFDH) and methylenetetrahydrofolate dehydrogenase (NADP) (MTHFD)  Phosphoglycerate dehydrogenase (PGCD), phosphoserine transaminase (PSERT), and phosphoserine phosphatase (L-serine) (PSP_L)  and Dihydroxyacetone kinase (DHAKx) from the methanol metabolism pathway, which together form formaldehyde assimilation III (dihydroxyacetone cycle) (P185-PWY) .The largest group of downregulation/deletion candidates are different amino acids and their intermediate interconversion-related metabolism reactions with step-weighted factor 17.3 ;Threonine and Lysine pathway: 6 reactions Tyrosine, Tryptophan and Phenylalanine pathway: 5 reactions ;Cysteine pathway: 3 reactions ;Methionine pathway: 3 reactions The largest amount of candidate reactions are:P. pastoris amino acid metabolism for biomass growth competes with heme biosynthesis for glycine. Additionally, P. pastoris upregulate the C1 metabolism pathway in mitochondria to increase glycine synthesis necessary for heme (heme-BIOSYNTHESIS-II and PWY-5189) biosynthesis (see chapter “upregulations”).We assume that in our experimental model, The downregulation of amino acid metabolism could be related to the allocation of resources towards glycine and heme synthesis. Threonine is starting point for leucine and isoleucine synthesis, it is produced by the threonine aldolase from glycine. By lowering leucine, valine and isoleucine metabolism—the system retains more glycine to allocate for heme synthesis. Detailed biochemical pathways and reactions description is found in Supplementary Materials 51. Our results reveal, that DHFRim  should be downregulated to achieve max heme synthesis. There are 2 reaction chains from a 5, 10-methylenetetrahydrofolate to tetrahydrofolate in yeasts and DHFRim is glycine dependent, while the parallel reaction chain is not.The next largest group is the nucleotide metabolism pathway 11  reactions, in which the step-weighted factor is 17.3–18.1% Fig. . The algTherefore, by keeping upregulating C1 metabolism and glycine synthesis and downregulating their consumption elsewhere—heme yield can be improved. Moreover, purine synthesis uses 4 ATP per 1 purine molecule; if down-regulating the purine synthesis pathway, some ATP could be reallocated to heme synthesis.The third large group consists of 23 reactions from different metabolic pathways , HEM2 (PPBNGS), HEM3 (HMBS) and HEM12 (FCLTm) upregulation leads to a 41% increase in intracellular heme production and all HEBE biosynthesis reactions upregulation lead only to 1,fivefold increase.For upregulation, the best candidates are heme-BIOSYNTHESIS-II and PWY-5189, which include all heme biosynthesis pathways. Previous studies showed that HEM3 (HMBS reaction) is a rate-limiting step in S. cerevisiae, the latest report shows 70-fold intracellular heme increase potential by upregulating heme biosynthesis pathways and deleting not essential carbon or nitrogen-consuming reactions like serine hydroxymethyltransferase (GHMT2r), heme oxygenase (biliverdin-producing) (heme-OXYGENASE-DECYCLIZING-RXN in MetaCyc53) and glycine cleavage system (GLYCLm). Heme-OXYGENASE-DECYCLIZING-RXN is not found in the iMT1026 GEM, thus hasn’t been calculated step-weighted factor, but in other organisms, the reaction converts heme to bilirubin, which would rapidly decrease intracellular heme concentration.In comparison with GLYCm reaction converts glycine and THF to 5, 10-Methylenetetrahydrofolate and is used as a response to high concentrations of the glycine, thus diverting and decreasing it from heme production.GHMT2r is responsible for converting serine to glycine using thf as a co-factor. Our model suggests it as an upregulation candidate, but published results showed that deletion will in total increase intracellular heme. This is explained because our algorithm does not include quantitative omics measurements which more precisely explain enzyme activity, available amount, and indirect effects on other metabolic processes. Nevertheless, comparing within literature published data algorithm shows some inconsistencies, still, it shows feasible upregulation (33 reactions)  and 66 downregulation/deletion  reaction suggestions results. P. pastoris GEM analysis that demonstrates the potential directions for metabolic engineering to improve P. pastoris as an expression platform for legH production.Our work is the first in silicoP. pastoris metabolism, although well adapted to heterologous protein production, it’s biomass flux can be increased. When comparing our fermentation data with in silico P. pastoris GEM optimisation results, we found, that the biomass growth rate on glycerol could be increased by 50%. However, P. pastoris growth on methanol is close to the theoretical maximum. Our experiments showed that the specific growth rate (µ) is 0.19 h−1, which is a little higher than Looser et al.—0.15 h−154 and 0.06–0.16 h−155.A higher biomass growth rate on the glycerol is an opportunity to shorten the biomass production phase before switching to methanol induction. Since legH production is linked to biomass growth, legH production is limited with the “free heme” bioavailability. Thus, if part of the growth resources could be rerouted to the heme production—an increase in heme and legH production would be expected.P. pastoris GEM response to the decrease in biomass growth rate and identified 500 reactions which had an impact on heme production. As the most promising targets—the algorithm filtered out 33 reactions for upregulation and 66—as downregulation targets.We developed an algorithm to systematically explore For upregulation, the algorithm suggests that heme-BIOSYNTHESIS-II and PWY-5189 are the best candidates and formaldehyde assimilation III (dihydroxyacetone cycle) (P185-PWY) is the less effective candidate. For downregulations/deletion suggestions, most potential targets are identified within amino acid and nucleotide metabolism. Heme biosynthesis competes with amino acid metabolism for common NH2 donor glutamine and methyl group donor glycine.56, where Yeast8 genome-scale metabolic model and ecYeast8 enzyme-constrained metabolic model combinatorial analysis were done to find out the best up/down-regulation gene candidates and experimental validation. In enzyme-constrained metabolic models, reaction rates are constrained by the corresponding intracellular enzyme concentration multiplied by its turnover number (kcat) by keeping fluxes at physiologically relevant levels57. Compared with GEM, enzyme-constrained GEM has a large reduction in flux variability for most of the metabolic reactions. Since P. pastoris GEM is less detailed than Yeast8 metabolic model58 and our best knowledge has less available enzymes concentration experimental data, thus we chose to use the algorithm not related to enzyme concertation-based GEM analysis. This would force to implement data from other yeast species and could lead to biologically irrelevant results. Our results showed the 33 best upregulation candidate reactions comparing with 40 upregulation gene candidates in S. cerevisiae, which are quite a similar number of candidates.A similar approach has been done by Ref.P. pastoris growth rate is tightly regulated to translation rate59. The next engineering task involving modelling and genome-wide engineering would be to decouple translation machinery from growth and to reallocate more resources for heterologous protein production. We speculate, that our analyses would help to choose strategies for metabolic engineering for a number of heme-containing proteins like peroxidases, catalases, etc. when heterologous expressed in P. pastoris.In P. pastoris iMT1026 GEM, which consists of 1708 metabolites, 2243 reactions, 2 different biomass compositions for glycerol and methanol fermentation optimisations, has 9 different compartments and transport intercompartment for Cobra toolbox 3.0 compatibility42.To analyse GEM for growth-coupled reactions and reaction manipulation impact on heme biosynthesis, we improved 60. The main concept of the framework is to select reaction amplification targets for improved product formation. The framework concept is to search for the candidate reaction, which flux changes have the most impact on target metabolite production under maximising biomass reaction flux. During analysis 3 different enzymatic reaction types were determined: flux-increasing reaction, flux-decreasing reaction and unaffected. In the end, algorithm develops an MS Excel file with exact results in specific tables  changing fluxes;“Negative_contra_proportional”—reactions with flux value from right to left and are inversely proportionally to forced product (heme) changing fluxes;“Positive_directly_proportional”—reactions with flux value from left to right and are directly proportionally to forced product (heme) changing fluxes;“Negative_directly_proportional”—reactions with flux value from right to left and are directly proportionally to forced product (heme) changing fluxes.After growth-coupled GEM modelling algorithm optimisations, we made Excel additional reactions data filtering because not all irreversible reactions stoichiometry in GEM are in the same direction, which can lead to incorrect or incomplete optimisation results. Also, not all reactions had linearly changing flux values or even steady-state results, thus only reactions with all 5 results and steadily changing reaction flux values were chosen for the next analysis steps. As a result, the algorithm generates 4 additional MS Excel data tables:When all upregulation and downregulation/deletion reactions are filtered out and saved in separate data tables , which was calculated as (Formula ):1\documThe weighted factor shows how much percentage each flux value changes versus the mean value and is summed together. As not all reaction flux changes are in constant value  was designed by GenScript and synthesized by BioCat GmbH . The gene was cloned into the pPICZC vector (Invitrogen) using EcoRI and NotI restriction sites , were inoculated with 100 mL of inoculum grown in BMGY medium  at 30 °C for 18–22 h in a shake flask at 250 RPM. Two feeding solutions were used—glycerol fed-batch solution  and methanol fed-batch solution .Batch cultivation and feeding media solutions used in this study were prepared according to the “Pichia fermentation process guidelines” by Invitrogen CorporationThe bioreactor vessel was filled with distilled water and sterilized at 121 °C for 30 min, while the cultivation mediums and glycerol fed-batch solutions were autoclaved separately at the same conditions. The PTM1 and methanol-fed-batch solutions were sterilized by filtration through a 0.2 µm filter.4OH solution before starting the cultivation process and maintained at the set value during fermentation. The temperature was controlled at 30.0 ± 0.1 °C, using a temperature sensor and by adjusting the temperature in the vessel jacket. The dissolved oxygen (DO) level was measured using a DO probe  and kept above 30 ± 5% by varying the stirrer speed (200–1000 RPM) (Cascade 1) or enriching the inlet air with pure O2 (Cascade 2). A constant air or air/oxygen mixture at a flow rate of 3.0 slpm was maintained throughout the process. A condenser was used to condense moisture from outlet gasses, and antifoam 204 (Sigma) was added when necessary to control excessive foam formation. Substrate feed solutions were pumped using a high-accuracy peristaltic pump .The fermentations were conducted in a 5 L bench-top fermenter  with a working volume of 2–4 L. The pH was monitored using a calibrated pH sensor probe  and adjusted to 5.0 ± 0.1 with a 28% NH2 and CO2 concentration were measured in the reactor exhaust gas using an O2/CO2 analyser . Culture turbidity was estimated using a turbidity probe  measuring the light absorption (transmission) within the 840–910 nm wavelength range. Finally, a permittivity probe  was used to estimate the viable cell concentration during cultivation.Process OThe cultivations began with a glycerol batch phase. After 18–24 h, the batch glycerol was depleted, and a glycerol fed-batch solution was fed into the reactor at a rate of 0.61 mL/min for 4 h, or until an optical density of 100–120 was achieved. After a brief feeding pause of 10–30 min, to allow the cells to consume all of the residual glycerol, the feed substrate was switched to methanol, and the solution was fed into the reactor at a rate of 0.12 mL/min for 5 h, followed by 0.24 mL/min for 2 h, and finally 0.36 mL/min for the remainder of the cultivation.® tubes and centrifuged at 15′500 g for 3 min. Afterwards, the supernatant was discarded, the cells resuspended in distilled water and centrifuged once more. The liquid phase was discarded and the remaining wet cell biomass was weighed. Dry cell weight (DCW) was determined using a previously-determined empirical formula  at a wavelength of 590 nm . Wet cell weight (WCW) measurements were determined gravimetrically. Biomass samples were placed in pre-weighted EppendorfSupplementary Information 1.Supplementary Information 2.


Pseudomonas aeruginosa and performing continuous metabolic activity measurements in the calScreener. We detail simple principal component analysis to differentiate between metabolic states of different populations and probabilistic logistic classification to assess resemblance to wild-type bacteria. This protocol for fine-scale metabolic measurement can aid in understanding microbial physiology.Here, we present a protocol for assessing metabolic activity of bacterial populations by measuring heat flow using isothermal calorimetry. We outline the steps for preparing the different growth models of For complete details on the use and execution of this protocol, please refer to Lichtenberg et al. (2022).  •Isothermal microcalorimetry (IMC) for fine-scale metabolic measurements•Non-invasive metabolic measurements demonstrated for different bacterial growth models•Novel data analysis approach distinguishes microbial populations based on thermogram Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Pseudomonas aeruginosa and performing continuous metabolic activity measurements in the calScreener. We detail simple principal component analysis to differentiate between metabolic states of different populations and probabilistic logistic classification to assess resemblance to wild-type bacteria. This protocol for fine-scale metabolic measurement can aid in understanding microbial physiology.Here, we present a protocol for assessing metabolic activity of bacterial populations by measuring heat flow using isothermal calorimetry. We outline the steps for preparing the different growth models of  Increased tolerance to antibiotics of biofilms, persister cells, spores and dormant cells have been correlated with lower metabolic activity of the bacteria.Non-invasive measurements of metabolic activity in live cells are challenging and most methods only measure isolated factors of the metabolic output, such as metabolites, oxygen consumption, or acidification. By analyzing metabolic activity in living cells, it is possible to identify energetically demanding processes and to distinguish different growth stages of bacteria.Here we describe a calorimetry method in biological context  using the calScreener™  for multiplexed, label-free, and direct population-level measurement of metabolic activity in live samples. Early protocols have been established for measuring a variety of bacterial species and mammalian cell types.,,The transition of planktonic to biofilm phenotype as well as the production of extracellular matrix components is regulated by second messenger molecule cyclic diguanylate (c-di-GMP).Pseudomonas aeruginosa with varying internal levels of (c-di-GMP) as an example.Here, we have established a protocol to measure and analyze metabolic activity in different growth models for strains of Isothermal calorimetry measures dissipated energy from biochemical reactions within cells by monitoring released heat, a direct measure of metabolic activity. The measurement is thus unspecific to a certain process. Providing information on the total enthalpy changes over time (heat flow), which are not only related to biomass formation, but also physiological changes.Note: The protocol below describes the specific steps for setting up different growth models of Pseudomonas aeruginosa for planktonic, surface attached biofilm and alginate-beads embedded cells. Furthermore, we present a sensitive method for continuously measuring the metabolic activity of these models by biocalorimetry using the calScreener™ . For analysis, raw heat-flow data are used for principal component analysis (PCA) and logistic regression to distinguish the different metabolic states.Note: Here we will describe the method using lysogeny broth (LB) as growth media due to its ease of access and universal use in microbiology, but we have also used other growth media like R2A, tryptic soy broth (TSB) etc.The output data, a thermogram, displays heat flow  over time. Thermograms of different strains can be grouped by principal component analysis to detect differences in metabolic states of samples and further classified with logistic regression. Moreover, the non-invasive nature of the method allows for subsequent experiments on the same sample.Timing: 1 day1.Pseudomonas aeruginosa .Prepare overnight (ON) cultures of strains of interest. Here we describe 2.Transfer cells from −80°C glycerol stock with an inoculation loop into 5 mL sterile LB in a 15 mL cell culture tube with dual position lid to allow air entering the tube.3.Incubate for 24 h at 37°C on a shaking platform at 180 rpm.The steps below are prepared one day prior to experimental setup.Timing: 2 h4.Sterilize all equipment that is not single-use and already sterile by autoclaving.5.a.Sterilize titanium vials and lids by heat treatment in oven (160°C > 2 h). Alternatively sterilize by autoclaving, ethanol 70% (minimum 30 s) or UV light (minimum 30 min).b.Note: Keep vials dry! Condensation, evaporation or residual liquid can interfere with the signal.Keep in a sterile box until usage.c.Place all 48 titanium vials in the metal holder .Prepare calPlate™ and calWells™•2% alginate solution: Add 2 g of Sodium alginate  to 98 mL milliQ water under stirring on a heated magnetic stirrer set to 500 rpm and 100°C and allow to completely dissolve (∼30 min).In this protocol, metabolic activity is measured in up to 32 samples simultaneously using a calScreener™  biocalorimeter. For production of alginate beads, surface biofilms, and planktonic culture, prepare the following solutions prior to experiments.•0.25 M CaCl2solution: Add 36.755 g CaCl2 (dihydrate) in 1,000 mL milliQ water.Autoclave and store at 4°C. Use within one month and ensure that solution is sterile before use.•20% L-arabinose: Add 2 g of L-arabinose to 8 mL milliQ water and sterile filter through a 0.22 μm syringe filter.Autoclave and store at 4°C up to 36 months.•0.9% saline solution: Add 9 g NaCl to 100 mL milliQ.Store at 4°C. Use within one week.•LB broth: Weigh 25 g LB powder and suspend in 1,000 mL milliQ water and mix until everything is dissolved.Filter sterilize and store at +4°C for up to 24 months.Autoclave and store at 4°C. Use within one month and ensure that medium is sterile before use.Timing: 2 daysP. aeruginosa growth models for surface attached biofilm, alginate bead embedded cells and planktonic cells. Surface biofilms is incubated directly inside non-activated plastic inserts  to allow further metabolic measurements, while alginate beads are incubated in Erlenmeyer flasks and planktonic cultures in cell culture tubes and then transferred to plastic wells upon measurements.This section describes the preparation of ,Surface attached biofilm formation has been optimized for the plastic inserts and the planktonic culture preparation has been adapted. The encapsulation of bacteria in alginate beads is done using a modification of previously described protocols.Note: Two of the strains used here have an arabinose-inducible promoter. Thus, 0.2% L-arabinose  should be added to induce the promoter. Here, we mixed LB with arabinose for inoculation into the calWells™ just prior to insertion in the calScreener™.Note: Use plastic inserts of calWells™ for setting up the growth model; that allows a direct measurement of the samples.The full workflow is illustrated in Timing: 1 h1.Filter the ON culture through a 10 μm syringe filter to ensure a mainly planktonic population.2.600 of 0.005 in sterile LB supplemented with 0.2% arabinose.Measure the OD of the filtered ON culture on a spectrophotometer and adjust to a final OD3.Add an aliquot of 200 μL to each plastic insert.4.Position plastic inserts into titanium vial of the calPlate™ Timing: 25 h 5.Filter the ON culture through a 10 μm syringe filter to ensure a mainly planktonic population.6.600 of 0.005 in sterile LB.Measure the OD of the filtered ON culture on a spectrophotometer and adjust to a final OD7.Fill the sterile plastic inserts  with 200 μL of the OD-adjusted ON culture.8.Cover the plastic inserts with parafilm, position them in a 48-well plate and incubate for 24 h at 37°C, 120 rpm for biofilm to develop on the sides and bottom of the insert.9.Following incubation, remove planktonic biomass by removing supernatant gently, then washing gently with 200 μL sterile saline solution (0.9% NaCl).10.After washing, position inserts into titanium vials of the calPlate™ .11.Add 200 μL fresh LB media supplemented with 0.2% L-arabinose to each plastic insert.Timing: 26 h P. aeruginosa in alginate beads follows the procedure as described elsewhere.,12.Filter the ON culture through a 10 μm syringe filter to ensure a mainly planktonic population.13.600 of 2.Measure the OD of the filtered ON culture on a spectrophotometer and adjust to an OD14.600 of 0.1.Dilute the OD adjusted culture 20× in a sterile alginate solution (2% w/v) to a final OD15.2 in a 250 mL beaker glass on a magnetic stirrer with a sterile magnet (∼3 cm length).Place 150 mL of sterile 0.25 M CaCl16.2 solution, and ∼1.5 cm from the edge of the beaker.Set the magnetic stirrer to 150 rpm and place a 21-gauge needle perpendicular to- and ∼3 cm from the surface of the CaCl17.Connect the needle to a 20 mL syringe containing the alginate-bacterial solution by sterile silicone tubing .18.Mount the syringe into a syringe pump and set the flow rate to 30 mL/h.19.2 solution.Dispense droplets of the alginate-bacteria solution into the stirred CaCl20.2 solution.Harden the spherical beads for 1 h in the 0.25 M CaCl21.2 and wash the beads twice by swirling the beads carefully in 50 mL sterile 0.9% saline solution in a beaker.Discard the CaCl22.Transfer 20 beads to 50 mL prewarmed LB medium in a 250 mL Erlenmeyer flask.23.Incubate at 37°C and 100 rpm for 24 h.24.Transfer beads to a new beaker and rinse them gently by swirling in 50 mL 0.9% sterile NaCl for <1 min to remove non-embedded bacteria.25.Place a single bead into a plastic insert .26.Position plastic inserts into titanium vials of the calPlate™ .27.Add 190 μL fresh LB medium supplemented with 0.2% L-arabinose resulting in 200 μL total volume in each well.Note: Handle the filled inserts carefully to avoid spilling. It is advised to use sterile tweezers for insert handling to prevent contamination. Liquid on the rim of the vial can interfere with the signal In this step, we set up the calPlate™ with the sample-containing plastic inserts and perform live cell metabolic measurements. Isothermal calorimetry with the calScreener™ allows evaluation of overall metabolic activity of a sample by continuous measuring of heat flow . Samples are prepared prior to insertion and measurement, which can be done for unlimited time, typically 24 h for fast growing cells.We have optimized planktonic culture and a surface attached biofilm model for instrument specific plastic inserts, including cell number and pre-incubation times.Timing: 1–2 days (or longer)28.Place 48 sterile titanium vials into the metal holder of the calPlate™ on the sample station.29.Place plastic inserts to vials in reference positions (A 1–8 and F 1–8).30.Fill reference vials with equal volume and media as used for the sample vials, here 200 μL LB medium.Note: Use gloves and/or sterile tweezers to minimize the risk of contamination.31.For biofilm and alginate bead samples, transfer the plastic inserts containing the samples in the titanium vials on the plate.32.For planktonic cultures, place empty plastic inserts into titanium vials on the calPlate™ and subsequently add 200 μL aliquots of diluted samples to the vials.33.Place the lids loosely on the vials B.34.Tighten the lids using the torque wrench set on 40 cNm force.Setup the calPlate™  and inse35.a.Check the temperature setting on the temperature control unit; it should be set at 37°C.b.Open calView™ software and start new experiment .Figure 3Prepare the system for measurement.36.a.Retract the plate insertion arm from the calScreener™.b.Place the calPlate™ on the bridge (column 8 facing insertion opening).c.Optional: annotate sample content in software for individual vials.Gently push the calPlate™ on position one using the plate insertion arm and let the system stabilize for 10 min.d.Push plate insertion arm to position two and wait for 20 min for temperature equilibration of the system.e.Reaction Start in calView™.Push plate to final position 3 (measuring position) and activate f.Retract plate insertion arm to Start position C.g.Note: Check that the plate is correctly placed on the heat sensor. CRITICAL: Make sure you do not move the plate from the sensor when retracting the arm.Run experiment for desired time .Stepwise insertion of the calPlate™ with temperature equilibration steps .a.Retrac37.a.Stop in calView™ to end the experiment and save the file for further analysis.Press b.Insert the plate insertion arm completely and retrieve the calPlateTM with help of the magnets D.c.Open the lids using the torque and retrieve the sample for further analysis if required, otherwise discard.Remove calPlate™.Timing: 1 h38.Open the experiment file in calView™.39.Normalize data with baseline correction .40.Export ∗csv file .41.Check data quality and see if there were any issues with the measurement; see Optional: For tailored metabolic parameter analysis upload data to webtool calData. https://symcel.com/analysis-tools/calorimetric-growth/  for different growth models, as well as species and growth condition, shows differences in shape, regardless of preincubation time. An example for a thermogram is shown in Alteration of internal c-di-GMP levels contribute to metabolic activity changes where for example high c-di-GMP levels lead to an increase of the maximum metabolic activity as shown for planktonic and biofilm samples.,,IMC allows for testing fast antibiotic susceptibility in real time, with dose dependent increase of time to signal detection.To run analysis code in Python you must install Python using either the official binary found at:https://www.python.org/downloads/or the Anaconda version found at:https://www.anaconda.com/products/distribution.Subsequently, required packages can be installed by running the command in a command prompt/terminal:pip install numpy matplotlib scikit-learn python-ternaryorconda install numpy matplotlib scikit-learn python-ternarydepending on whether Python was installed using anaconda or not.Note: Ensure that all columns have equal length or trim the data accordingly if this is not the case.Collect all data in a comma-separated spreadsheet. We provide example data (data.csv) and example scripts that assume the following format:1.Gather the metabolic signal data in a single matrix of shape EXPERIMENTS × TIMESTEPS. See 2.Run PCA decomposition of the data using sklearn.decomposition.PCA.3.Transform the data to the two principal components.4.Plot the data as a labeled scatter plot.To analyze the outcome of the experiments using principal component analysis, run the included script pca.py, which executes the following steps:5.Select all wild-type data and create an input matrix of shape WT_EXPERIMENTS × TIMESTEPS of metabolic signal and a vector of length WT_EXPERIMENTS storing the type of model used in the experiment.6.Train a probabilistic logistic classifier on the wild-type data.7.Use the classifier to evaluate all probability of wild-type model on all data.8.Plot the data, which, in the case of three classes, can be done efficiently as a ternary plot.To classify the strains in terms of wild-type models, run the included script classification.py, which executes the following steps:Biocalorimetry measures the thermal activity of a sample, a measure that is proportional to metabolic activity. The method is particularly useful for applications in microbiology and in this protocol, we describe the measurement of metabolic activity of different growth models. IMC is a robust and sensitive method, however, there are certain limitations to consider.IMC measures heat released at the population level and sums all metabolic processes within a sample. Despite the high sensitivity of the measurement and its ability to detect minute changes, the non-specific nature of the signal poses certain challenges for interpretation.In the case of surface biofilm, we must consider that not only biofilm embedded cells but also planktonic cells will contribute to the signal, and this cannot directly be distinguished from the signal. This is also the case for co-culture experiments of mixed species, where every single cell, therefore species, contributes to the overall signal. Although the thermograms are species and condition specific, the data can benefit from complementing experiments. Therefore, it is of advantage that the technique is non-invasive and non-disruptive which allows for endpoint assays on the same sample. Online measurements in a replicate sample are possible.For comparison of principal component analysis of different samples, a determination of growth rate may be required when focusing on metabolic changes only. That way we can exclude growth rate effects on the metabolic signal.During the measurements, samples are kept in a closed system. This will eventually lead to an oxygen depletion, acidification, and accumulation of metabolic byproducts may occur which has to be taken into consideration for analysis. Although, the design of the vials totaling 600 μL internal volume and working with volumes of 100–200 μL allow sufficient oxygen diffusion from the headspace of the vial for an extended amount of time.The measurement can be performed for an unlimited time, especially when observing physiological rearrangements in stationary phase. However, to determine when to stop the experiment depends on the system one is working with. The possibility for online monitoring of metabolic activity is very useful to determine the experimental endpoint, for instance when the heat flow values of the samples decay and reach close to zero values.Mycobacteria, Cutibacterium acnes, filamentous fungi etc.To achieve maximum sensitivity and accuracy, the samples when introduced to the system must equilibrate to the assay temperature which takes approximately 45 min until data can be collected. In microbiology experiments, one typically starts with a low inoculum where the lag phase until signal detection exceeds equilibration time. For most experimental setups, this time lag is not a significant disadvantage, unless acute responses are being measured. Therefore, the method is applicable for a wide range of systems (organisms), including slow growing organisms such as for example For samples and experiments that require temperatures below 30°C the system cannot be directly applied. The calScreener™, has an active heating but not active cooling system and requires a temperature difference (delta T) of at least 10°C to provide a stable signal. For low temperature measurements an external climate chamber is required.Instruments must be placed in a temperature stable environment, ideally a temperature-controlled room, to ensure accurate measurements for temperature fluctuations that can lead to baseline variability  after insertion of the plate.•The thermograms should move to negative values of the heat flow (μW) and plateau for a couple of minutes before going up and stabilizing at zero value again see C.•If the signal does not plateau, re-enter the plate directly and make sure that the plate is pushed all the way in.•The insertion arm should be retracted and not touch the plate during the run.•To avoid plate retraction, hold the silver handles when retracting the arm see C.Negative initial signal due to leakage .Figure 8•When lids are fully tightened, the titanium vials resemble a closed system. In case of leakage from the vial, condensation, an endothermic process, occurs. This leads to a negative initial signal that stays constant throughout the measurement B.•○Displaced or brittle O-rings.○Not fully tightened lid.○Liquid residual on O-ring.Leakage can have several different sources:•Retract the plate and check the lid of the vial where the signal disturbance occurred.•If the lid was not fully tightened, tighten it again and reinsert the plate for measurement or start new experiment.•If an O-ring is missing, displaced, or worn out it must be replaced.Note: It is advised to check the lids regularly to see that the O-rings are not damaged.Moisture under the lids A.•Moisture under the lid changes the signal in a particular way which can be recognized in the data A. Exampl•Make sure to remove spills on the vials to avoid moisture under the lid.•When transferring the vials to the plate or when carrying the plate, be careful to not move it too much.•Before closing the lids, check if the O-ring is in place.•Screw the lid on with the appropriate torque and do not use force.•Before analyzing data, check for data quality.•Discard data with signal interference from moisture and open lid.Contaminations in reference vials.•Thermodynamic references contain medium, here LB, and should give a steady signal. In case of contamination in reference vials, negative heat flow values occur, and it will impact referring sample vials.•An example is shown in •Check the thermogram of the reference wells (A 1–8 and F 1–8). The heat flow should plateau around zero throughout the course of the experiment.•If the values move to negative values, that indicates microbial contamination of the reference and connected samples should be discarded.•The references refer to the two lower samples in the same column, e.g., reference A1 to sample B1 and C1. Or to the reference row F, the two upper ones, e.g., D1 and E1 for reference F1.Noisy or fluctuating signal in the references.•Noise in the references could indicate that the room where the instrument is placed has temperature fluctuations.•To find out whether that is the reason, measure the temperature over a longer time interval of a couple of days.mlichtenberg@sund.ku.dk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the Mutant strains generated for use in this study will be made available on request, but we may require a completed Materials Transfer Agreement if there is potential for commercial application.https://doi.org/10.5281/zenodo.7541038).All original code has been deposited at Zenodo and is publicly available as of the date of publication (


Rimicaris is the dominant organism living in hydrothermal vents. However, little research has been done on the functions of their intestinal flora. Here, we investigated the potential functions of Deferribacterota, which is dominant in the intestine of Rimicaris kairei from the Central Indian Ridge. In total, six metagenome-assembled genomes (MAGs) of Deferribacterota were obtained using the metagenomic approach. The six Deferribacterota MAGs (Def-MAGs) were clustered into a new branch in the phylogenetic tree. The six Def-MAGs were further classified into three species, including one new order and two new genera, based on the results of phylogenetic analysis, relative evolutionary divergence (RED), average nucleotide identity (ANI), average amino acid identity (AAI) and DNA–DNA hybridization (DDH) values. The results of the energy metabolism study showed that these bacteria can use a variety of carbon sources, such as glycogen, sucrose, salicin, arbutin, glucose, cellobiose, and maltose. These bacteria have a type II secretion system and effector proteins that can transport some intracellular toxins to the extracellular compartment and a type V CRISPR–Cas system that can defend against various invasions. In addition, cofactors such as biotin, riboflavin, flavin mononucleotide (FMN), and flavin adenine dinucleotide (FAD) synthesized by R. kairei gut Deferribacterota may also assist their host in surviving under extreme conditions. Taken together, the potential function of Deferribacterota in the hydrothermal R. kairei gut suggests its long-term coevolution with the host.The genus  There are at least ten species in this genus, including R. chacei, R. paulexa, R. parva, R. susannae, R. exoculata, R. falkorae, R. hybisae, R. kairei, R. vandoverae, and R. variabilis. They mainly live in the hydrothermal fields with some difference in distribution among these species. For examples, R. exoculata is mainly living at the hydrothermal vent of the Mid-Atlantic Ridge (MAR), R. kairei is mainly resident at the Central Indian Ridge  as electron acceptors; D.autotrophicu used molecular hydrogen, acetate, lactate, succinate, pyruvate and complex proteinaceous compounds as electron donors, and Fe (III), Mn (IV), nitrate or elemental sulfur as electron acceptors; D. thermophilus obtained energy from the reduction of manganese (IV), iron(III), and nitrate in the presence of yeast extract, peptone, casamino acid, tryptone, hydrogen, malate, acetate, citrate, pyruvate, lactate, succinate, and valerate. D. desulfurican is a heterotrophic bacterium, while all of the other three species are chemolithoautotrophic bacteria. In addition, Flexistipes sinusarabici, which was most closely related to Deferribacter and isolated from the Atlantis Deep brines of the Red Sea, is tolerant to high temperature, high salt concentration and heavy metals, and strictly anaerobic. This organism prefers complex growth substrates such as yeast extract, meat extract, peptone, and trypsin, while formate, lactate, citrate, malate, carbohydrate, amino acid, and alcohol do not support cell growth  pathway and a nonoxidative pentose phosphate pathway as well as a complete tricarboxylic acid cycle. M. schaedleri can also alter gene expression in mucosal tissues, suggesting an intimate interaction with the host . Sampling sites were located in Edmond  and Kairei  at depths of 3,281 m and 2,421 m, respectively  following the manufacturer’s procedures. The quality and quantity of genomic DNA were checked by gel electrophoresis. The DNA concentration was determined by using a Qubit dsDNA HS assay kit with a Qubit 2.0 fluorometer . A total of 100 ng DNA was used for library preparation. High-throughput sequencing was performed with the Novaseq 6000 platform to produce 2 × 150 bp paired-end reads (Illumina).The samples were identified as  article . The int2.3.Metagenomic DNA sequencing produced a total of 154,387,934 reads, with a total length of 45 Gbp. Trimmomatic v0.36 was used for trimming with parameters  , and Fas2.4.There were 79 genomes in total for phylogenetic analyses, including 57 genomes from Tenericutes, Firmicutes, Acidobacteria, Chrysiogenetes, and Proteobacteria, and the others were from the phylum Deferribacterota. Firstly, 43 ribosome proteins were obtained by CheckM analysis from all of these genomes and then aligned by MAFFT v7.487  with the2.5.D. autotrophicus, D. desulfuricans SSM1, F. sinusarabici DSM4947, and M. schaedleri ASF457, of which D. autotrophicus, D. desulfuricans SSM1 and F. sinusarabici DSM4947 were free-living bacteria from hydrothermal environments, and M. schaedleri ASF457 was from the mouse intestine. The studied MAGs and reference genomes were annotated using Prokka v1.14.6, and the parameters were set to metagenome and kingdom bacteria , genomic bins with each longer than 2 kbp, completeness >50%, and contamination <10% were selected. There were 18 MAGs obtained in total, including Firmicutes, Bacteroidetes, Spirochaetota, Campylobacterota and Deferribacterota, based on the analysis by GTDB-tk  with four related reference genomes, including D. autotrophicus, D. desulfuricans SSM1, F. sinusarabici DSM4947, and M. schaedleri ASF457, are shown in Def_J6 is close to 90%, and its genome size is 2.5 Mb, similar to the four reference MAGs with a genome size of 2.2–2.5 Mb. The completeness of other Def-MAGs ranged from 55.33 to 88.76%, with genome sizes ranging from 0.9 to 1.8 Mb. Notably, the average G + C content of the six Def-MAGs ranged from 46.4 to 50.7%, much higher than that of the reference genomes, in which the G + C content ranged from 30 to 38%. We also listed the number of tRNAs and rRNAs and the number and percentage of genes annotated by each genome in the KEGG and COG databases  values are 0.467, 0.460, 0.467, and 0.455 for Def_J3, Def_J5, Def_J6, and Def_A7, respectively. Def_J1 and Def_A4 are classified as the Mucispirillaceae family but could not be further classified into any known genus. The RED values were 0.747 and 0.749 for Def_J1 and Def_A4, respectively , fumarate hydratase (EC4.2.1.2), and succinate dehydrogenase (ubiquinone) flavoprotein subunit (EC1.3.5.1) were found, and most of the enzymes, including key enzymes such as citrate synthase (EC2.3.3.1) and ATP citrate lyase (EC2.3.3.8), were absent. In Def-MAG and M. schaedleri ASF457, starch or glycogen has 33–66% integrity for the synthesis pathway and 100% integrity for the degradation pathway. However, both pathways were absent in the three environmental reference genomes , salicin (EC2.7.1.-), arbutin (EC2.7.1--), cellobiose (EC2.7.1.205), maltose (EC2.7.12.08), and other carbon sources into cells (3 to citrulline and over the process reversibly. Although no nitric oxide synthase (EC1.14.14.17) was found for the conversion from citrulline to arginine, arginine deiminase (EC3.5.3.6), which catabolizes arginine to citrulline, is present and can irreversibly hydrolyse L-arginine to L-citrulline and ammonia, suggesting that Def-MAGs could provide energy by arginine metabolism generating ATP.Six Def-MAGs and four reference MAGs were used to comparatively analyse the metabolic pathways by KEGG decoding. Based on the heatmap, both groups have a similar integrity of metabolism for the glycolysis and gluconeogenesis pathways, but the gluconeogenesis pathway was almost absent in the genome of i ASF457 . Genes ri ASF457 . Taking  genomes . We found Def_A4 . Notablyntestine . β-N-aceto cells . The larto cells , both of3.3.2.M. schaedleri, among which CBM13 and GH57 were specific , CBM (carbohydrate-binding modules), CE (carbohydrate esterases), GH (glycoside hydrolases) and GT (glycosyltransferases). Among them, a total of 21 carbohydrate enzymes were in six De-MAGs, nine of which were specific in the six Def-MAGs, including one CBM, CBM67; one CE, CE9; and 7 GHs, GH1, GH133, GH19, GH3, GH4, GH42, and GH78 . A totalspecific . A totalspecific . In addispecific . These t3.3.3.M. schaedleri ASF457, the Deferribacterota from the mouse intestine, only the alanine and tryptophan synthesis pathways were absent  pathway or twin arginine translocation (Tat) pathway , which then polymerizes by glycogen synthase (GlgA) to produce linear glucans. Finally, a non-reducing-end oligoglucan transfer mediated by branching enzymes (GlgB or GBE) to the 6-position of residues within a chain generates side branches that convert it to glycoge  can be found in the article/QL and L-SH conceived and designed the experiments. QL performed the experiments. QL, MS, F-CZ, and C-AL analysed the data. QL and L-SH wrote the manuscript with input from all other authors. L-SH directed and supervised all of the research. All authors contributed to the article and approved the submitted version.This study was supported by the general projects of National Natural Science Foundation of China (42176125); the Scientific research and manufacture projects of Sanya City, Grant (NO. 2020KS01); Hainan Provincial Natural Science Foundation of China, grant (322CXTD531); the major scientific and technological projects of Hainan Province (ZDKJ2019011); and the major scientific and technological projects of Hainan Province (ZDKJ2021028).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Escherichia coli (E. coli). Gene cluster for polysaccharide antigen biosynthesis encodes various glycosyltransferases (GTs), which drive the process of SP synthesis and determine the serotype.Surface polysaccharides (SPs), such as lipopolysaccharide (O antigen) and capsular polysaccharide (K antigen), play a key role in the pathogenicity of E. coli genomic sequences were chosen for systemic data mining. The monosaccharides in both O and K antigens were dominated by D-hexopyranose, and the SPs in 70–80% of the strains consisted of only the five most common hexoses (or some of them). The linkages between the two monosaccharides were mostly α-1,3 (23.15%) and β-1,3 (20.49%) bonds. Uridine diphosphate activated more than 50% of monosaccharides for glycosyltransferase reactions. These results suggest that the most common pathways could be integrated into chassis cells to promote glycan biosynthesis. We constructed a database  for browse this information, such as monosaccharide synthesis pathways. It can also be used for serotype analysis and GT annotation of known or novel E. coli sequences, thus facilitating the diagnosis and typing.In this study, a total of 7,741 Summarizing and analyzing the properties of these polysaccharide antigens and GTs are of great significance for designing glycan-based vaccines and the synthetic glycobiology.The online version contains supplementary material available at 10.1186/s12864-023-09269-6. Escherichia coli (E. coli) consists of commensal and pathogenic strains, some of which can cause serious infectious diseases. Polysaccharides are important extracellular polymeric substances of E. coli. They usually maintain their physical and chemical properties and the biological functions of cells, thus playing a protective role in bacteria. Among them, lipopolysaccharide (LPS) and capsular polysaccharide (CPS) have specific immunogenicity and are strongly associated with the pathogenicity of E. coli [ E. coli –3.E. coli is usually surrounded by a large amount of CPS, the structural basis of its biological adhesion. It is generally believed that CPS attenuates the complement-mediated immune process and leads to immune escape [E. coli. Many vaccines based on these surface polysaccharides (SPs) have been developed [Compared with Gram-positive bacteria, Gram-negative bacteria have a thinner peptidoglycans layer but can generate an additional outer membrane with LPS as the main component , 5. LPS e escape –12. Thereveloped –15. Seveeveloped , 17.The structure of O antigens is diverse, with variations in chain length, the sugar residue, or glycosidic bond among different serotypes , 18, 19.E. coli. Based on the database, an online platform  was developed for information retrieval and typing analysis of E. coli polysaccharide antigens. It is significant for clinical diagnosis, microbial resource development, vaccine development, and epidemiological investigation.Enzymes encoded by antigen-synthesis genes are core drivers in the synthesis and assembly of SPs. These genes are mainly classified into three categories , 11, 23:E. coli strains deposited in the NCBI database were downloaded . Then, we evaluated the quality of those assemblies using CheckM [The genome assemblies of all g CheckM  and Fastg CheckM . Subsequg CheckM , 26 are http://csdb.glycoscience.ru/snfgedit/snfgedit.html), which can visualize the oligosaccharide chain by using Symbol Nomenclature for Glycans (SNFG), was used to draw the structure of each antigen based on the relevant information. The enzymes encoded by the antigen-synthesis genes were also recorded with their relevant information. The information of donors, acceptors, and glycosidic bonds was linked to GTs, while the information of precursors, products, and synthetic pathways was linked to nucleotide-sugar precursor synthase. All strains were linked to the antigen information and each specific serotype derived from the process described above; so, the contained information became a knowledge graph. Each screened information was manually checked. The validated E. coli O antigen gene cluster sequences were used as the reference sequences for identifying the serotype of the uploaded sequence.Antigen information was mainly collected from published literature and combined with NCBI, MetaCyc  and GTDB–5).For easy storage and access, these data were organized into a database. Additionally, effective analysis tools were also developed. Several website modules were constructed to explore the database, including antigenic type browsing, antigenic type retrieval, polysaccharide synthesis retrieval, monosaccharide synthesis retrieval, and sequence upload and analysis modules. Gene prediction of the user-uploading strain genome was performed by Prodigal . Basic LE. coli genome assemblies were collected in this study. These assemblies date back to 2004, with a significant increase in number since 2014, which may be strongly related to the continuous development of sequencing techniques , GalNAc, Glc or GlcNAc are particularly common, and notably, they occur frequently in various antigens or strains with many different types of linkages Fig. A. ConnecThe linkage type of the SP showed some preferences Fig. . In the Monosaccharides are the material basis of antigen synthesis; some have several modifications. The types of monosaccharides differ between O antigen and K antigen.O antigen structure is based on combining a total of 113 monosaccharides. In terms of the carbon number, hexoses account for the majority, but there are also a certain number of pentoses, heptoses, and nonoses Fig. A. These Because the biological synthesis of O antigens is well studied, we further explored the sugar composition in O antigens. The occurrence frequency of each monosaccharide was sorted from high to low to discover the least combination of monosaccharides with the highest coverage for strains and antigens Fig. A. We fouCertain sugars may be difficult to assimilate from the environment. Therefore, sugar biosynthesis pathways that use common simple sugars to produce the required sugars for antigen synthesis are also important. A total of 38 pathways are included in this study, each with detailed step-by-step information, including substrates, products, enzymes, and involved serotypes and strains. Glucose plays an important role in the biosynthesis of various sugars because the initial precursors of 32 pathways are its derivatives . As glucose is the most common monosaccharide in organisms with steady content, it is often used as the metabolic precursor of various sugars. Fructose-6-phosphate is an important intermediate in glycolysis and thus can effectively ensure the precursor supply involved in four pathways [http://ecosp.dmicrobe.cn/) is a manually curated resource that catalogs majority of the reported E. coli SPs. This information comprises three sections: GT, antigen (O antigen and K antigen), and pathway.A database was constructed based on the collected data to store and display the information centrally. The EcoSP database , a genome sequence of an unindexed E. coli strain (E. coli PSU0569 strain), and an indistinguishable close-related non-E. coli strain (Shigella flexenri 2a301 strain). The results  was split into fragments every 200 kb , and then, fragments were randomly assembled into an artificial genome. GT annotation of this artificial genome sequence matched correspondingly after the recombination and could still accurately predict the previous serotype  and E. coli 6409 (O101). First, those two sequences were split at 2.4 Mb to create four partial genomes . Second, partial genomes were assembled to generate two heterologous recombinant genomes (101A-162B and 162A-101B). In these partial genomes, 101A could be predicted as the serotype (O101), while the other three partial genomes could not. For the heterologous recombinant genomes, the 101A-162B recombinant sequence could be predicted as serotype O101, while 162A-101B failed. To verify the sensitivity, we added a reference O162 antigen gene cluster to 162A-101B, and the sequence could be predicted as the serotype (O162). This suggests that the corresponding antigen-synthesis gene cluster is sufficient for serotype prediction and that recombination between different strains (or perhaps just DNA contamination) has no effect.Considering that these genomes have been identified and included in NCBI, and new clinical isolates or artificial strains may have different mutations or recombination, we used several designed genome sequences to conduct a simulation test. The first analysis was performed with a disordered genome. The genome sequence of Acinetobacter baumannii K09-14 (Table SIn addition, GT prediction can also be performed for genomes that fail to predict serotypes. For example, GT prediction was performed on sequence 162A-101B to obtain the GT loci, which were compared with the annotated information of the original sequence Fig. C. Before4 Table S.The results could be exported as tables, which can facilitate subsequent sorting. Given that the analysis of genome annotations is time-consuming, users can choose to leave an email address to receive a notification along with a result file when the analysis is complete. These two analyses can discover GTs in the target strain and thus have important implications for constructing our chassis cells and synthesizing the desired antigens using the chassis cells with several specific GTs. With these analysis tools, in-silico validation for whether the designed strain can produce the desired antigen also becomes possible.E. coli SPs. Summarizing the data, we found similarities and differences between O and K antigens. Most of the sugars in the O antigen belong to hexose, and the main type is D- pyranose. For the K antigen, the situation is similar, except that there are significantly fewer types of sugars compared to the O antigen. This may be why the number of O antigens is larger than that of K antigens. Connections between Glc, Gal, Man, or Rha are the most common in both antigens, but the connection of Rib-Kdo is somewhat important in K antigens. In both O antigen and K antigen, the C1 carbon of the donor is the main link site, and it is usually connected to the receptor's C2, C3, or C4 carbon. Glc and its derivatives are mainly used as substrates for synthesizing the specific sugar, and UDP- or dTDP- activation is the major form in the synthesis process. This information led us to think about whether we could engineer desired strains. The genome of the engineered strain should contain at least the important monosaccharide synthesis pathways and some of the most common GTs, and thus have sufficient potential to synthesize target antigens with less and cheaper common sugars supplied during fermentation. Based on this consideration, we are also trying to construct the engineered strain and the corresponding GT gene library.We manually curated a relation database for E. coli [Several databases related to SPs have been built, but each has advantages and disadvantages. KEGG and MetaCyc are classical databases for biological research that have recorded many metabolic pathways in E. coli , 34. NevE. coli , 35. TheE. coli genomes and make a prediction for unknown genomes. In general, they tend to be more of the chemical structure, and don't go far enough in the biological direction.On the contrary, the ECODAB database collects only antigen-related information from the perspective of the O antigen. Its information on antigen structure is relatively complete but lacks information on GT or gene clusters . SimilarE. coli SP research. In general, this database only focuses on the information related to SPs. It combines multiple relevant pieces of information so that the results are sufficient but not redundant. The advantage of this design is that researchers from different fields can easily use the database without expertise in SPs. We hope EcoSP will effectively facilitate gene mining and promote the construction of various polysaccharide synthesis engineered strains.In contrast, EcoSP is a platform dedicated to In the future, it is envisaged that the expanding data will provide more insights into synthetic pathways and antigen types. EcoSP is expected to regularly incorporate up-to-date information from the latest publications to ensure accuracy and coverage for representative antigens and GTs.Additional file 1: Supplementary figures.Additional file 2: Supplementary table 1.Additional file 3: Supplementary table 2.Additional file 4: Supplementary table 3.