Saccharum spp.), a C4 grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products , and bioelectricity, earning the title of the world’s leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how “omics” advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.Sugarcane ( Saccharum spp., Poaceae) is a perennial C4 grass with an exceptional ability to convert light and nutrients into chemical harvestable energy such as sucrose. It is particularly found in tropical and subtropical climates, in which warm temperatures and water availability underpin maximum growth rates  and increased CW/fiber content, have been developed for this purpose. Since 2G can be produced from the sugar industry residues, this could be a sustainable approach, as well as addressing food security to increase the industrial yield for the same cultivated area. More than a decade of heavy investment in overcoming the technical difficulties of 2G production finally paid off, turning this biofuel into a commercially competitive commodity. Compared to 1G, the addition of the 2G process incremented 50% the production and reduced 35% of C footprint1.Besides the process of first-generation (1G) ethanol production using sucrose accumulated in sugarcane culms, the bagasse (or the whole plant) can provide extra yield through the saccharification of sugars from the cell wall (CW) polysaccharides, named 2G. CW is the raw material for several green chemistry-derived products and the carbon matrix used for 2G production. Such biofuel manufacturing relies on (1) circumvention of the CW recalcitrant by pretreatment of lignocellulosic material, (2) hydrolysis of the CW polysaccharides such as cellulose, hemicellulose, and pectin, and (3) fermentation of the released sugars that will be distilled to ethanol . One of in vivo preferentially cleaves sucrose to generate UDP-glucose and fructose. The transport of sucrose to sink organs occurs through the phloem and its entrance in the sink cells can be mediated by membrane sugar transporters (ST) - and water-use efficiencies relative to other C3 species , and turnover between cell compartment and organs depending on physiological and developmental conditions. In contrast, the metabolic pathways involved in fiber deposition are almost irreversible. The synthesis of cellulose, hemicellulose, and lignin is mediated by different metabolic pathways that physically interact to form the highly complex and compact structure of lignocellulose. This polymer network acts as a physical protective barrier from invaders and abiotic stresses, and thus limits the release of its fermentable sugars at the cell surface, whose collapse will ultimately lead to cell death. Particularly during internode maturation, cell elongation and sucrose accumulation discontinue, while CW thickening and lignification takes place . AlthougLipids, such as triacylglycerols (TAGs), are an alternative form of C storage. However, these lipids are a competing sink for fixed C after photosynthesis, preventing C from being stored as carbohydrates. These fatty acids accumulate in the cytosol as oil bodies that can be metabolized or recycled to form cell structures. TAGs are commercially interesting as a source for biodiesel, presenting the advantage of releasing twice the energy of carbohydrates  and S. spontaneum , but with contributions from other Saccharum spp.  (Current sugarcane breeding programs are mainly focused on developing cultivars with improved yield (biomass production and/or sugar content) and biotic/abiotic stress tolerance. More recently, the existing genetic diversity has been used for selecting energy cane varieties classified as type I and II. Type I has enhanced both sucrose and fiber content, whereas type II varieties are oriented for increasing fiber content only. The modern sugarcane cultivars are hybrids derived from interspecific crosses, mainly between sinense) . Nowadaysinense) . Despitesinense) . As a cosinense) .A typical sugarcane breeding program consists of three major steps: germplasm collection/evaluation, hybridization, and selection . As sugaIn many crops, molecular breeding has fostered the selection of desired traits in a cost- and time-effective manner. The complex sugarcane genetic composition of modern hybrids led to polygenic control of important agronomic traits, hindering the development of genetic maps required for quantitative trait locus (QTL) identification and selection of linked markers for assisted selection in breeding programs. Furthermore, the high heterozygosity of hybrids and inbreeding depression restrict the development of mapping populations derived from inbred lines. Yet, the presence of hom(oe)ologs and close paralogs for most of the sugarcane loci, derived from the highly polyploid and uneven genetic background from sugarcane hybrids, deepens the challenge for a successful genotyping.In contrast to plant species with simpler genomes, segregation occurs at the first generation of a progeny derived from biparental crosses in sugarcane . ConsequBru1 gene, a major dominant resistance locus for the brown rust disease , sorghum (∼150 Kbp), and soybean (∼ 420 Kbp) (Genome-wide association studies (GWAS) are observational studies of existing populations aiming to identify marker-trait associations (MTAs) in genetically diverse populations. One of the requirements for its successful application is the presence of genome-wide linkage disequilibrium (LD), an ideal scenario for modern breeding programs as it relies on relatively recent interspecific crosses and few opportunities for genomic recombination. The estimate of LD in sugarcane is challenging due to difficulties in obtaining a saturated genetic map. Initial work applying amplified fragment length polymorphism (AFLP) markers estimated that hybrids present LD among closely linked markers and that such LD decreased within 0–30 cM window . Barreto420 Kbp) . Yang etAlthough the construction of genetic maps is not required, GWAS can still be affected by the high heterozygosity of segregating loci underrepresented by the currently available SDM. Genomic selection (GS) of individuals by their predicted breeding values estimated from genome-wide DNA marker profiles is a strategy to overcome the small effects of individual QTLs and MTAs. The development of GS models uses a training population, in which the individuals are genotyped and phenotyped. Once the GS model is developed, individuals from the prediction population are selected based on their genomic estimated breeding values and genome-wide marker data . The feaUnlike diploid species, allele dosage can represent a large source of phenotypic variation in sugarcane, which cannot be captured by single-dose markers and the current statistical models. Recent advances have developed mapping models for loci with high allelic dosages . The expS. officinarum and S. spontaneum. These species have distinct traits of great potential: sweetness and robustness that aligned gave rise to the increased yield of the current commercial hybrids. In situ hybridization (GISH) studies have demonstrated that S. officinarum contributed with 80–85% of chromosomes, while S. spontaneum contributed with 10–15% of chromosomes to the current commercial lines. Additionally, around 5% of the chromosomes of commercial hybrids are recombinants of S. saccharum and S. spontaneum chromosomes  collection (SUCEST) that provided around 238,000 high-quality sequences from multiple sugarcane organs and tissues , followe3 to meet upcoming breeding demands , were applied to R570 cultivar demands . The hig demands . The R57 demands . This moin silico evaluation of transcription factor binding sites in the regulatory sequences of this family. This combined approach unraveled regulatory patterns associated with circadian regulation or flowering time  and tandem duplication-caused expansion of polyol transporter (PLT) and STs families are proposed, placing TSTs as promising candidates to achieve higher vacuolar sucrose accumulation in sugarcane culms  China, the Philippines, Indonesia, and Papua New Guinea; (2), and (3) India, Pakistan, and Iran . After s targets . The inc targets .Genome-wise, it is possible to link C partition into valuable compounds and biomass to several genomic regions and specific genes, like those associated with sucrose accumulation , specifiTranscriptomes are a valuable resource to identify the expression behavior of a particular gene in different genotypic backgrounds, especially in a complex polyploid and hybrid system as sugarcane . Also, t2 levels to the atmosphere, which can positively impact yield taking into account the CO2-concentrating mechanisms evolved in C4 species. For example, when the commercial cultivar SP80-3280 was submitted to increased [CO2], a larger C uptake was observed and plants showed 30 and 40% increment on photosynthesis and accumulated biomass, respectively. Increased CO2 levels up-regulated the expression of genes associated with various metabolic pathways such as photosynthesis, sugar transport, and protein metabolism, whereas a few genes related to stress responses were down-regulated  or sucrose seems to be more complex and regulated by diverse SPS, a pattern confirmed by proteomics . Both RB867515 and S. officinarum clustered in all analyzed source and sink organs regarding lignin-associated transcripts  and MYB families (ScMYB52)  . Plus, aScMYB52) .S. officinarum and S. spontaneum and the hybrid cultivar SP80-3280 suggest that the main traits from each ancestor associated with sucrose accumulation and stress tolerance are linked to differential gene and allele expression, expressed haplotypes, and alternative splicing  of multigenic traits that might be under diverse regulators. Sugarcane commercial cultivar SP80-3280 is an excellent candidate for developing a gene expression atlas due to the number of transcriptome projects done in a plethora of environments and organs, in both microarray and RNA-Seq platforms . Such anProteomics offers some advantages over genomics, as it covers the final product of the gene expression (proteins), enrich the number of differential signatures for a given condition , and canSugarcane lignocellulosic biomass has great potential not only for the production of second-generation biofuels, but also for a vast range of high-value compounds. One of the major bottlenecks preventing the use of this feedstock is the recalcitrance to enzymatic hydrolysis due to the multi-scale structure of the plant CW. In contrast to sucrose that can be promptly fermented and converted into ethanol, CW cellulose and hemicelluloses are required to go through a previous hydrolysis step to be transformed into simple sugars prone to alcoholic fermentation  and oxidoreductases (OR) (By adapting protocols designed for the model species ses (OR) . Amongstses (OR)  that perses (OR) .Monocots present a higher percentage of GHs from family 17 compared with dicots, as type II CWs have hemicelluloses primarily composed of mixed -β-D-glucans , which iLignin is a phenolic compound from secondary CWs, fundamental for plant structure and development. Together with some polysaccharides like xylan and hemicelluloses, it is the major cause of the plant CW recalcitrance to 2G production , as ment2 assimilation by photosynthesis. N is also part of the structure of many metabolites including amino acids-building blocks of proteins-, nucleotides and chlorophyll. Therefore, the balance between the C and N metabolism is one of the key determinants for growth and development in plants (4 species have an improved N use efficiency (NUE) thanks to a reduced photorespiration. In sugarcane, however, C and N balance might differ from other well-studied model plants due the unusual prevailing sucrose metabolism  designing new varieties with reduced recalcitrance by lowering lignin content or changing its structure by manipulating Prx and LAC (ii) generating plants that are more accessible to hydrolysis and need less biochemical treatments or improving the enzymatic cocktails by using plant GHs (iii) developing cultivars with increased CW and fiber content by overexpression of glycosyltransferases and cellulose synthases for energy canes  gelant GHs ; (iii) dgy canes . MoreoveThe network controlling plant growth and fitness is largely dependent on metabolism, which is sustained by biochemical reactions through which metabolites are exchanged with the environment to fulfill several cellular and physiological tasks . Not surSimilar to other crops, many yield traits in sugarcane are related to metabolic pathways involved in C partitioning of sucrose to fiber or optimization of metabolic routes generating novel added-value compounds. Despite the potential of metabolomics in molecular phenotyping, currently, there are only a few studies in sugarcane. One of the pioneer studies aimed at investigating whether changes in the metabolic content were associated with sucrose accumulation along with internode development . The graGAI, a component of gibberellin signaling pathway and repressor of DELLAs , a transcription factor (ShSHN1), and a laccase (SofLAC), whose functions were successfully investigated. ShDJ conferred higher water stress resistance and improved saccharification in rice plants  simultaneously while avoiding non-specific (off-target) regions . TherefoSugarcane breeding strategies have long focused on increasing sucrose content and improving stress resistance. The emerging interest in sugarcane biomass, either for 2G or green chemicals, widened the desired breeding goals and the demand on molecular phenotyping. Depicting the molecular physiology of the sugarcane control on C partition and storage, as the regulation of fiber (CW) and sugar partition in the culm, can provide feasible ways to optimize C potential.The sequencing of the ancestors and modern cultivars genomes can enhance the efficiency of biotechnology tools to target specific allelic and regulatory regions. The advance of molecular breeding approaches for polyploids, which combine QTL, GWAS, and GS models with allele dosage information are promising to provide a better incorporation of molecular markers into breeding programs and foster the selection of new cultivars. On the other hand, notable genetics and omics initiatives pointed out several targets as the ones involved in sugar metabolism  or reduced CW recalcitrance  as alternatives to improve product yield. Furthermore, progress of analytical tools has been allowed a broader coverage of vast number of compounds, which differ in their chemical and biological properties. Such approach opens a great perspective not only for the investigation of biochemical pathways and their regulatory networks for the better use of C, but also identification of biomarkers. Finally, sugarcane genetic transformation arrives as a feasible alternative to speed up classical breeding, successfully used for improving stress resistance, increasing sucrose, and rising saccharification rates. Despite being modestly employed in this crop, genome editing is presented as a promising tool. Panoramic approaches as expression atlas and co-expression networks can point out new markers and traits associated with C gain improvements and distribution into valuable compounds that are emerging in the context of sugarcane use as a biorefinery.Altogether, the development of transformation protocols, the increasing knowledge of sugarcane genome, the discovery of new target genes, and the release of genetically modified sugarcane into the market have been paving the way to develop even better new sugarcane varieties that unlock sugarcane C potential in the mid-term.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism’s phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation.Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities.  Its residues are found in soil samples decades after it was last applied and were shown to chronically leach into local aquifers3. As such, efforts are being made to limit and monitor its use4. While atrazine was banned in the European Union and Switzerland since 2003, the United States Environmental Protection Agency still allows its wide use under monitoring6. Areas contaminated with atrazine and other hazardous herbicides are rapidly increasing worldwide introducing a need in remediation approaches. Bioremediation—an environmental bioprocess in which naturally occurring organisms are used for breaking down hazardous substances into less toxic or non-toxic substances—is increasingly acknowledged as a cost-effective feasible alternative for environmental cleaning9. Critical environmental bioprocesses are naturally carried out by bacteria and are related to removal of pollutants from water, soil or air10. Thus, bacterial bioremediation is widely applied for the degradation of various organic pollutants14 including herbicides18. Bioremediation may occur naturally or through the stimulating addition of fertilizers, electron acceptors, etc. These amendments encourage the growth of the degrading microbes within the medium or environment in a process termed biostimulation19. Microbial metabolism has been described as most influential on atrazine degradation, promoting the development of biodegradation strategies20. One of the well-known atrazine degraders is the gram-positive Paenarthrobacter aurescens TC125 (previously known as Arthrobacter aurescens TC1), reported to biodegrade atrazine more efficiently than most other known degraders including Pseudomonas sp. ADP26. The full hydrolytic pathway for atrazine degradation in P. aurescens TC1 was described and reported27. While P. ADP can only use the hydrolytic products of the ring cleavage as a source of nitrogen but not as a source of carbon; TC1 can use the side groups of atrazine as source of carbon, nitrogen and energy consuming up to 3,000 mg of atrazine per liter24. As such, P. aurescens TC1 is an ideal candidate for the study of atrazine bioremediation using a genome scale metabolic model reconstruction.Atrazine  is an herbicide employed to control broadleaf and grass mainly in crops such as rice, wheat, maize, and sorghum. It is also a well-known pollutant of great environmental concern. Atrazine has been shown to have negative effects such as DNA damage, gene expression shifts, cancer and endocrine disruption29. Generally, such approaches follow four key steps: (1) data acquisition—mainly genome sequencing information, basic cell-physiological and biochemical knowledge and some experimental data on cell growth; (2) model reconstruction—translating data into structured mathematical representation; (3) constraint-based optimization simulations—the prediction of growth rate, substrate uptake rates, and byproduct rates under different growth conditions or following knockout mutations, in the absence of kinetic information36; and (4) experimental validation. The potential of such models for the investigation of optimal processing is now acknowledged and practiced40. Examples for applicative use include the optimal production or utilization of industrial compounds such as xylose41, biofuels42, vitamins43 and drug development44. In bioremediation, genome scale metabolic modeling approaches were applied for the design of Geobacter sulfurreducens strain capable of increased electron transfer and a higher Fe(III) respiratory that was beneficial for environmental bioremediation of uranium-contaminated groundwater45. Application of genome scale metabolic modeling for strain design is often based on the Optknock algorithm46 and its derivatives which search for the optimal gene knockouts for a desired metabolic production. However, unlike industrial use of engineered strain for enhanced production in monoculture, introduction of exogenous species into natural habitat is far from trivial and hampers the application of modeling approaches towards bioremediation solutions. An alternative strategy is the use of modeling for the design of optimal conditions that will enhance processes (degradation or synthesis) carried by the endogenous community47. For example, interactions between Geobacter and Rhodoferax—a competitor Fe(III)-reducers in anoxic subsurface environments—were captured and described using metabolic modelling and allowed identification of optimal degradation conditions48. Models can also predict the benefits of cooperative and commensal interactions to degradation efficiency. Xu et al. have demonstrated the use of metabolic models for predicting interactions between P. aurescens TC1 and endogenous soil species49. Exchange metabolites secreted by non-degraders species contribute for enhancing P. aurescens TC1 growth and expediting degradation efficiency50. Here, we directly focused on modeling-based identification of media supplements that can enhance atrazine degradation. We hypothesize that the introduction of specific nutrients into the bacteria’s immediate environment can resemble metabolic exchanges between P. aurescens TC1 and non-degrader species in soil and can lead to an increase in the total biomass and a subsequent increase in the degradation activity. We describe the process of reconstruction of a genome-scale metabolic model of P. aurescens TC1 and its evaluation as a predictive tool for the fast and low-cost screening of potential nutritional supplements that can serve as bio-stimulators of degradation.Modern tools of genomics, transcriptomics, metabolomics, proteomics, signaling systems and synthetic biology have opened new avenues for biotechnological advances and are increasingly applied for promoting better understanding of complex biological systems. In recent years, constraint based metabolic modelling approaches have become widely used as an in silico tool for organism-level phenotyping and the subsequent development of metabolic engineering strategiesP. aurescens TC1 is composed of a circular chromosome of 4.6 Mb coding for 4,222 open reading frames (ORFs) as well as two plasmids, 0.3 Mb each, coding for additional ~ 600 ORFs51. The genome and plasmids were annotated and used to create a draft model reconstruction using the Model SEED pipeline52. The draft reconstruction included an automatic gap-filling step, using default  media options52. This initial reconstruction contained a list of gene-protein-reaction associations that were classified as exchange, transport, and cytosolic as well as a list of all relevant metabolites and a biomass reaction. Based on taxonomy, the biomass reaction was defined to describe a gram-positive bacteria52. The composition of the biomass reaction summarizes the fractional contribution of generalized microbial biomass precursors  to the synthesis of a new cell and is similar to the previously published genome scale reconstruction of Bacillus subtilis53. Unspecified non-metabolic consuming processes in the cell were defined as ATP maintenance and set similarly to Oh et al.53.The genome of 54 were carried out. The model's metabolic functions were compared to genome annotations of P. aurescens TC1 from four established and comprehensive public resources and databases  and missing reactions were added to the draft network. All reactions were screened to verify that they are stoichiometrically balanced. Errors in reaction elemental balance and directionalities were determined according to the KEGG scheme. Model Reactions involved in atrazine degradation pathway and its further conversion into the cellular building blocks were added manually  followed by two hydrolytic deamination reactions catalyzed by hydroxyatrazine hydrolase (AtzB) and N-isopropylammelide isopropylamino hydrolase (AtzC). These enzymes convert atrazine to cyanuric acid and release the alkylamines ethylamine and isopropylamine. Cyanuric acid accumulates stoichiometrically, whereas the alkylamines are further catabolized into cellular building blocks27. Ethylamine and isopropylamine are converted into acetaldehyde and l-alanine  and curation of reaction directionality.Initial simulations were carried for debugging and removing futile or erroneously energy generating loops. To this end, all external fluxes were blocked (upper and lower bounds set to zero). Next, a minimal medium was used, verifying that growth requires the supply of both carbon and nitrogen sources. After establishing no growth under infeasible conditions, we tested growth (biomass production) under experimentally verified conditions as described in Strong et al.C. difficile or B. subtilis (20%)60 and the overall number of reactions is similar to the number of reactions covered by gold standard, updated models of species with similar genome sizes such as iML151561. A detailed description of the network including the reactions, metabolites, genes, and compartments that comprise the network is provided in Supplemental Dataset 62 in Supplemental Dataset The reconstructed metabolic network presented here covers 25% of the ORFs present in the genome and includes 1,179 genes, 2,541 reactions, out of which 134 are exchange, and 2,848 metabolites. The coverage of the genome is similar to those of previously published reconstructed metabolic networks of gram-positive bacteria such as P. aurescens TC1 degrades atrazine and metabolizes its intermediates, isopropylamine and ethylamine that can both be used as sole carbon and nitrogen sources. In silico growth simulations in minimal mineral media supplemented by atrazine or one of its degradation intermediates as a sole carbon and nitrogen source confirm that the model captures these reported physiological capacities. Experimental growth measurements verify model simulations and show that atrazine and its degradation products isopropylamine and ethylamine can be used as sole carbon and nitrogen sources for P. aurescens TC1  and glucose as nitrogen and/or carbon sources respectively was tested. The effect of nutrient ratio on growth rate was predicted by optimizing the production of biomass across uptake gradients while constraining the fluxes through exchange reactions for carbon and nitrogen sources. As expected, increasing glucose uptake increased the growth rate  and high (11.3 h−1). Simulation indicated a media dependent effect of these amino acids , allowing the identification of the ranges of flux variability that are possible within a given mediaets see “”. Atraziets see “ but alsoets see “.Figure 4Arthrobacter sp. HB-5, a close relative of P. aurescens TC168. Phosphate has been previously shown to accelerate atrazine degradation8. To test the effect of phosphate, we used the model to simulate growth and atrazine degradation in high or limited phosphate conditions. In accordance with these reports, simulations showed similar trends, which were confirmed by experimental results. These indicate an effect of phosphate concentrations on P. aurescens TC1 growth . The in-silico simulations were carried on an array of minimal media using atrazine and its interim degradation products as alternative sole carbon and nitrogen sources supplemented by potential enhancers. Simulations predicting and prioritizing specific metabolites as enhancers of growth and degradation were confirmed by in vitro experimental validation. Several limitations of the analysis should be acknowledged. Though the model refinement process included key steps required for construction of high-quality model, further adaptations are required for the current version of iRZ1179 to meet the requirements of gold standard models54. Mainly, the biomass composition and energy maintenance requirements are approximations that rely on automatic protocols and genomic information53. Collection of currently lacking experimental data can be incorporated in future versions of the model to define an accurate biomass objective function using, for example, the BOFdat computational platform69. Another example for potential improvement is modification of maintenance demands and mass transfer regulation based on a recent detailed account of atrazine degradation in P. aurescens at very low concentrations59. Second, data used to construct the model are not consistent at their level of confidence and annotations were gathered from numerous databases. A detailed process of manual inspection of each reaction and determination of its substrate/co-factor specificity54, was not yet carried. Finally, accuracy of predictions is likely to be significantly improved by the addition of other layers of information including metabolomics, transcriptomics and reaction kinetics70. Continuation of the manual curation process, also based on added transcription information, is likely to improve the performances of future versions. Yet, despite limitations, the resulting predictions can successfully capture the relative effect of different media complements on growth and atrazine degradation, as seen in the in vitro experiments performed, hence supporting the use of iRZ1179 as a predictive tool.Here, we reconstructed a genome scale metabolic model, iRZ1179 as a predictive tool enables fast and low (no)-cost screening and ranking of a wide range of potential nutritional supplements that can serve as stimulators. Here, growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different amino acids as efficient, moderate or poor stimulators of degradation activity. Simulation outcomes cannot be pre-determined based solely on the C:N ratio of media supplements but rather consider the complementary stoichiometric effect of multiple chemical reaction cascades, or pathways, that construct the genome scale metabolic network. Thus, these describe the complexity of cellular processes and balance between different metabolic pathways contributing to growth. Simulations per se provide multiple optimal solutions for the internal fluxes involved in primary metabolism and hence are not sufficient for deciphering the mechanism of how environmental (media dependent) conditions affect atrazine degradation. However, integration of simulation data with `omics data  can reduce the solution space and shed light on the fine-tuning of cellular activity considering the need to balance between myriads of constrains. Beyond the current study, the model can serve the contextualization of results from a growing number of high throughput experiments describing the relative abundance of discrete elements  into a higher level understanding of cellular activity. For example, future integration of proteomic data as produced in a recent study exploring adaptation of P. aurescens TC1 to growth in atrazine-fed bioreactors59, can serve for both model refinement and as a predictive tool.Several key findings emerge from this study. First, simulations successfully capture previous reports on the use of carbon and other sources (glucose and phosphate) as bio-stimulators of atrazine degradation, supported by in vitro validation. Second, the confirmation of Finally, model predictions can go beyond supplementing the medium with a single compound. Here, we demonstrated that the efficiency of the combination of different amino acids as growth enhancers depends on other medium components (here –atrazine). Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media.iRZ1179 as a predictive tool and can be considered as a first step towards designing bio-stimulation strategies that can be further tested in vitro and in the field. Soil communities are complex in terms of composition and microbial consortia dynamics, hence imposing greater complexity. Despite the complexity of soil communities, supplementing soil with carbon sources is long known to induce a change in microbial community structure and function. In a recent study we demonstrated the usefulness of modeling approaches for deciphering metabolic complexities within indigenous soil community treated with atrazine49. The integration of species-specific models together with community-based modelling approaches is likely to contribute to our understanding of the biodegradation processes of atrazine in natural environments and promote the development of bioaugmentation and biostimulation strategies. More generally, soil contaminations caused by pesticides and herbicides are considered among the top ten environmental hazards, with no current solutions that support green and cost-effective soil detoxification processes71. Exploiting the exhaustive power of the computational modeling platforms allows for a relatively fast search for bioremediation solutions that optimize the degradation activity of the existing natural communities, by allowing screening of hundreds of thousands of possible solutions and scoring them. Simulations can be designed to screen for metabolites, microbial species and combinations of both. Finally, in order to develop realistic solutions, a potential bio-stimulator should not only be efficient but also cost effective, ruling out expensive degradation enhancing compounds such as dipeptides. The ultimate proof of concept for the advent of computational approaches should be the design of cost-effective bioremediation strategies that outperform solutions that can be found by standard ‘trial and error’ practices.The in vitro validation provides a proof of concept for the use of P. aurescens TC1 was reconstructed using automatic pipeline, as described in Henry et al.52. Briefly, the complete genome sequence of P. aurescens TC1 was retrieved from NCBI , uploaded into RAST55, spliced into genes and annotated. RAST annotations were put through the Model SEED pipeline52 and an initial metabolic network reconstruction was obtained. Model refinement included addition of reactions identified in the specific strain in literature and additional databases, validation of reactions’ elemental balance and directionality, removal of futile loops and gap filling. Simulation conditions were modified to reflect experimentally validated minimal media alternatives that support growth of P. aurescens TC124. All simulations were carried out in a Minimal Mineral Media , supplemented by alternative carbon and nitrogen sources including atrazine, ethylamine, isopropylamine, ammonium, glucose and selected amino acids. The model is available in SBML format and as excel sheet  typically exceeds the number of metabolites (m), the system is under-determined. In order to get a feasible solution space, linear programming (LP), subject to mass balance preservation and flux constraints, was used by introducing an optimization problem. Here, model growth was maximized by maximizing its biomass reaction, which was used as objective function. Specific fluxes for metabolites consumption and secretion were determined following fixation of the respective biomass reaction to its maximal value and then using Flux Variability Analysis (FVA)67, a linear programming method to find the minimal amount of metabolites needed to produce a preset amount of biomass. Dynamic modeling was used for the prediction of the profile of consuming metabolites typical to the biomass increase of P. aurescens TC1 and atrazine degradation across time. To this end, we simulated the behavior of our metabolic model across time. This was done as described in49 and illustrated in Supplemental Dataset 75. Briefly, the model works under the following assumptions: (1) a finite start amount of media components is available; (2) a maximal amount of uptake a single cell can acquire from the media in a given time point is defined ; (3) new substrate concentrations in time t, are determined by the predicted substrate concentration for the previous step augmented with any additional substrates provided or consumed in the current iteration. The maximum uptake was set to a ratio of up to 1 unit of each metabolite available in the media; (4) after each time tick, the biomass amount was updated according to the flux amount of the biomass reaction in the model at this time tick. As biomass production rate serves as a proxy for the size of the population in the simulated environment and substrate uptake/secretion is mainly affected by to population size, the model was used to evaluate the actual substrate uptake/secretion and growth rate given the supplied media across time. Simulations were carried until reaching a state where time cycles did not lead to an increase in biomass. Initial concentration values for all metabolites were set to a fixed amount of 50 units . Simulation parameters and defaults are available in Supplemental Dataset P. aurescens TC1 across time.Flux distributions in the metabolic model were determined using Flux Balance Analysis (FBA) value);  new subs76.All model simulations were done on an Intel i7 quad-core server with 32 GB of memory, running Linux. The development programming language of our simulators was JAVA, and our linear programming software was IBM CPLEX. Surface plots were created using the MATLAB 2019a . Pathway maps were created using Escher mapsPaenarthrobacter aurescens TC1 (ATCCBAA-1386) was grown on R medium  plates with glucose (0.2%) and atrazine  as sole nitrogen. Stock cultures were maintained in 15% glycerol at − 80 °C. Cultures were resuscitated on R medium agar plates (1.5% Difco bacto) and were used as starter colonies for liquid medium growth experiments. The growth of P. aurescens TC1 was tested in R media supplemented with different combinations of carbon and nitrogen sources, including Glucose (0.2%), Atrazine , ammonium chloride (37 mg/l), Ethylamine and Isopropylamine  and the amino acids Histidine, Isoleucine and Methionine . Concentrations of amino acids, ethylamine and Isopropylamine were calculated to provide the same nitrogen content as the atrazine. In addition to the carbon and nitrogen sources, growth in media supplemented by atrazine and atrazine together with glucose was tested under two phosphate concentrations (K2HPO4 in 0.45 and 0.045 g/l). Each substrate was added to sterile R medium and bacteria were grown in triplicate 250 ml flasks incubated at 30 °C with shaking in the dark. One flask contained only R medium and was used as control. A 200 µl sample was taken from each flask every 8 h for measurement of OD in microplate reader  and 1 ml was taken to study atrazine degradation rate using HPLC. The HPLC analysis was performed with Agilent HPLC  equipped with a DAD detector. Samples were separated on Kinetex C18 column  and the mobile phase consisted of 70% methanol flowing at 1 ml/min. Detection and quantitation of atrazine was done at 240 nm with detection limit of 0.1 mg/l. Total duration of each growth experiment was between 152 and 168 h.Supplementary Dataset 1.Supplementary Dataset 2.Supplementary Dataset 3.Supplementary Dataset 4.Supplementary Dataset 5.


Komagataella phaffii (Pichia pastoris) into an emerging cell factory for recombinant protein production (RPP). As a key step of the bioprocess development, this work aimed to demonstrate the importance of tailor designing the cultivation strategy according to the production kinetics of the cell factory. For this purpose, K. phaffii clones constitutively expressing (PGAP) Candida rugosa lipase 1 (Crl1) with different gene dosage were used as models in continuous and fed‐batch cultures. Production parameters were much greater with a multicopy clone (MCC) than with the single‐copy clone (SCC). Regarding production kinetics, the specific product generation rate (qP) increased linearly with increasing specific growth rate (µ) in SCC; by contrast, qP exhibited saturation in MCC. A transcriptional analysis in chemostat cultures suggested the presence of eventual post‐transcriptional bottlenecks in MCC. After the strain characterization, in order to fulfil overall development of the bioprocess, the performance of both clones was also evaluated in fed‐batch mode. Strikingly, different optimal strategies were determined for both models due to the different production kinetic patterns observed as a trade‐off for product titre, yields and productivity. The combined effect of gene dosage and adequate µ enables rational process development with a view to optimize K. phaffii RPP bioprocesses.Its features as a microbial and eukaryotic organism have turned  Komagataella phaffii (Pichia pastoris) recombinant protein production (RPP) of an industrial lipase in chemostat and fed‐batch cultures. Different optimal strategies were designed from a trade‐off for product titer, yields and productivity for two clone models with different gene dosage and production kinetics, from which further transcription analysis supported potential bottlenecks after transcription.The combined effect of strain and bioprocess engineering should be considered during a rational process development towards bioprocess optimization. The synergy between gene dosage and specific growth rate have been evaluated for the  Komagataella phaffii, formerly known as Pichia pastoris, is a widely used yeast for recombinant protein production (RPP), for both biopharmaceuticals and an increasing number of industrial enzymes of interest  has been widely used in K. phaffii bioprocesses by virtue of allowing a strong and tight regulation for the recombinant expression in the presence of methanol , which is involved in a key step of the glycolysis pathway, was the first to emerge as a benchmark for efficient protein expression on various carbon sources. Thus, by avoiding all methanol‐related drawbacks, PGAP‐based bioprocesses present relevant advantages for large‐scale production  and specific growth rate (μ), is considered a key factor to be considered in the bioprocess development. It reflects the equilibrium between the various steps until the product is secreted, as a balance of the different processes involved during the protein synthesis, folding and secretion. This relationship is crucial to bioprocess development and optimization  is one of the most promising lipase enzymes for biocatalytic applications  have been identified; also, all except CRL6 and CRL7 have been identified and sequenced  was selected as model protein to elucidate the differences in the rational design of optimal bioprocess strategies for two clones of K. phaffii with different gene dosage, as example of clone variability in terms of protein production. For this purpose, an accurate characterization of physiological parameters and the production kinetics was firstly performed on chemostat cultures. The obtained results allowed the selection of the optimal bioprocess strategy to maximize the RPP, in order to be applied further in fed‐batch cultivations, which is currently considered as the most used operational mode for industrial RPP , five copies, and to confirm the presence of only one copy of CRL1 gene in the single‐copy clone (SCC).Later, transformed clones were screened in order to identify the best producer clone, which was a clone that integrated five copies of the expression cassette. Thus, it was selected for further studies in which it was compared with a clone with a single copy of gene of interestD). The specific growth rates spanned the range of 0.025–0.15 h−1. The carbon and electron balances were verified and closure found to exceed 95% prior to reconciliation. Fig. D in both strains.The impact of gene dosage on clone production kinetics was assessed with two sets of chemostat cultures grown at different dilution rates (qS) increased linearly with increasing μ. Interestingly, there were no significant differences in qS between clones, which suggests that this rate was unaffected by gene dosage. On the other hand, the overall biomass substrate yield (YX/S) remained fairly constant with a value of about 0.5 gX gS–1, which is consistent with most reported values  possibly due to requirement of more energy for maintenance (ms) than the single‐copy strain (0.029 vs. 0.0023 gS gX–1 h–1). This factor strongly influenced YX/S, which is consistent with some previous studies where biomass production decreased with decreasing μ . This notable change could be related to the RPP. It exerts a strong effect on metabolic fluxes that often leads to an increase in the maintenance requirements  and CO2 production (qCO2) rates increased linearly with μ. Slight differences between both strains were observed at high μ. However, the proportion between these two specific rates are constant, and consequently, the respiratory quotient (RQ) was always about 1.15.In Fig. qS, qO2 and qCO2 showed also to be strongly coupled with μ, therefore, fit into a linear equation pattern , which describes how is distributed a determined bioprocess parameter (specific rate) for cell growth and maintenance.Similarly to qS  and the overall product‐to‐biomass yield (YP/X) were evaluated as main key production parameters. Both are shown in Fig. The specific product generation rate . Some studies have shown protein production to be correlated with gene dosage and hence suggest that the number of gene copies influences qP up an optimum number above which the synergistic effect usually is lost .Unlike SCC, the variation of K. phaffii strains in long‐run chemostats describing a high genetic stability of the recombinant strains .As shown in Fig. µ range that leads to the saturation effect observed in the production kinetic profile. This result is consistent with previous reports where the expression system seemingly saturated during transcription, protein synthesis, post‐translational modifications or even secretion , TDH3 relative transcript levels were essentially identical for the two strains, which suggest that the levels were influenced by D but not by CRL1 gene dosage.Figure r et al.  in whichCRL1 gene expression was initially greater in MC than in SC clones. In SCC, CRL1 relative transcript levels increased linearly with increasing D, which suggests coupling of µ, CRL1 relative transcript levels and qP. These results indicate that PGAP regulates genes TDH3 and CRL1 similarly and hence that the effects are strongly related with cell growth. This was not the case with MCC, however. Thus, although CRL1 gene expression was seemingly not affected by D, qP clearly saturated at high D levels. This may have resulted from CRL1 expression coupling with biomass growth and the total amount of product secreted being limited. Therefore, there might be a bottleneck after transcription precluding conversion of all CRL1 transcripts into proper folded and secreted Crl1.Consistent with the results of the enzymatic activity analysis, TDH3 and CRL1 for different µ is compared by means of plotting the quotient of the relative transcript levels between the MCC and the SCC, can be observed the above‐mentioned trends. Interestingly, while no effect was observed for the TDH3 quotient, the positive effect of CRL1 gene dosage in terms of transcription is confirmed for all the culture conditions tested. According to these results, therefore, no limitation in the transcriptional machinery for CRL1 can be stated. Nevertheless, and despite the CRL1 quotient of transcript levels remains constant regardless the different cultures conditions at different D, there is a significant decrease in the qP quotient when increasing µ. This would support the hypothesis that the relevant increases achieved in terms of transcript levels due to the higher gene dosage of the target gene cannot be therefore converted into functional protein of interest.In Fig. GAP‐driven example of protein expression differs markedly from that reported by Cámara et al. (AOX1‐driven Rhizopus oryzae lipase (Rol) recombinant expression. The increased number of cassettes in Rol attenuated transcription of methanol metabolism and decreased methanol consumption, cell growth and recombinant protein production as a result.Strikingly, the gene dosage effect observed in this Pa et al.  for PAOXK. phaffii may be limited in terms of protein folding, glycosylation and secretion, reduced product yields and productivity could be expected. These limitations were widely discussed by Puxbaum et al.  with MCC.As detailed in Table –1 was used to confirm that Crl1 production would peak at the lowest μ level. As hypothesized, lipolytic activity (1414 UA ml–1) was highest under those conditions and four times greater than the highest value for SCC. A systematic comparison based on the main production parameters obtained at the different operating conditions followed with the two producer clones is presented in Table Based on these results for MCC, an additional fermentation run at 0.025 hqP and YP/X for the fed‐batch cultures at different µ values are depicted. As expected for consistency with the chemostat results, qP increased linearly with increasing µ in SCC but exhibited saturation in MCC. The YP/X variation pattern was similar to that for the chemostat cultures; thus, YP/X decreased exponentially with increasing µ in MCC, but remained fairly constant in SCC.In Fig. qP was higher with MCC than with SCC, and their ratio (3–6) was dependent on the µ applied. Although the overall patterns for the two operational modes were similar, qP and YP/X were substantially higher for the fed‐batch cultures.As in the chemostat cultures, μ is, the longer a bioprocess can be extended to increase product titres until the termination criterion is fulfilled.Fed‐batch cultivation usually ends when biomass production reaches a critical concentration limit imposed by biological and physical restrictions. As a result, growth‐associated biomass in fermentation processes is an unavoidable by‐product and requires controlling the biomass growth in order not to exceed the limiting concentration. Thus, the lower µ and qP, and how these two specific rates affect the bioprocess efficiency.In Table μ on qP, it was demonstrated that the kinetic pattern is markedly different for the MCC and SCC. The MCC exhibited a 30% qP decrease when μ decreased from 0.15 to 0.05 h–1, by effect of its rather flat production kinetic curve at the highest μ, as shown in Fig. qP decreased by 70 % from 0.15 to 0.05 h–1. It is due to the more marked coupling of qP to the cell growth.Regarding the production kinetics, that is, how affects QP) varied on µ similarly to that observed for qP in MCC and SCC. Consequently, the rational selection of µ to optimize the bioprocess performance would require a trade‐off.As expected, productivities .On the contrary, for the MCC, product titre reached a 68 % increase when µ and gene dosage on PGAP‐based K. phaffii clones expressing CRL1 can be used to design optimal operating strategies.The results previously exposed support the idea that combined effect of qP increases linearly with increasing specific growth rate in SCC but exhibits a non‐linear pattern suggestive of saturation of the production kinetics in MCC. Interestingly, qP is considerably higher with MCC than with SCC . These results confirm that gene dosage impacts directly on the production kinetic profile, both in the levels of titre achieved as well as the profile pattern. From the transcriptional analysis performed, one can hypothesize the presence of a potential bottleneck after transcription.In this way, the results obtained from chemostat cultures reveal that μ on the bioprocess efficiency. Although trends were similar as those observed in the chemostat cultures, there were some differences, especially with MCC. Saturation was more marked than in the chemostat tests, and a plateau was reached in the higher µ range.The continuous cultures were used as tool to the design of optimized fed‐batch strategies, since this operational mode is the most widely operational mode used for industrial RPP. Therefore, a rational‐based bioprocess design can be carried out considering the effect of gene dosage and µ has a strong impact on specific production rates and yields (e.g. with SCC), a high µ level should be used to increase qP and YP/X. On the other hand, if the impact of µ is weak (e.g. with MCC), low‐to‐moderate µ values can help to extend the bioprocess time until the limit imposed by a critical amount of biomass is reached. In this case, these differences have been caused by the different gene dosage.When the data for the main production parameters in Table K. phaffii. This work presents how a comprehensively elucidation of the gene dosage effect on production kinetics towards an overall optimization of the RPP bioprocesses can be afforded. Hence, properly understanding these features and their correlation provides a more rational, robust knowledge of the behaviour of cell factories and allows bioprocesses to be engineered for greater efficiency.Summing up, it can be stated as main outcome of this contribution that the production kinetics of the cell factory and specific growth rate must be jointly considered to tailor operating strategies to production clones for maximizing expression in K. phaffii X‐33 from Invitrogen  expressing CRL1 regulated by the GAP promoter were obtained and used. A chimeric vector assembled with the restriction–ligation method was constructed by using commercial pGAPZαA plasmid from Invitrogen and a codon‐optimized synthetic CRL1 coding sequence from GeneScript . Different amounts of plasmid were transformed by electroporation in order to obtain clones with different numbers of integrated expression cassettes. However, only the clones containing 1 or 5 gene copies were used. Both clones can secrete Crl1 to the extracellular medium through the Saccharomyces cerevisiae α‐mating factor signal sequence.Two recombinant clones of et al.  was used to determine the number of cassette integrations in both clones, using a slightly modified version of the method of Cámara et al. . Actin get al. . The speTranscriptional analyses were only done on chemostat cultures, where steady‐state conditions ensured homogeneity in cell population.TM  and 200‐mg glass beads. Total RNA was extracted as per the manufacturer’s instructions. Then, RNA integrity and concentration were checked by agarose electrophoresis and Nanodrop analysis  respectively.Samples of 1 ml were withdrawn under different chemostat conditions and centrifuged at 4 ºC at maximum speed for 2 min. The resulting pellets were resuspended in 1 ml of TRIzolCRL1 (heterologous product); TDH3, which is the gene natively expressed under PGAP control (glycolytic); and PGK gene, phosphoglycerate kinase (glycolytic). Additional genes involved in the unfolded protein response (UPR) and the secretory mechanisms such as KAR2 and HAC1 were also studied. Transcription was assessed by qPCR amplification. For maximum accuracy, mixes were made by the robot EpMotion® . SYBR™ Select Master Mix was used as polymerase mix and a QuantStudio 12K Flex Real‐Timer from Thermo ScientificTM for amplification cycles and data acquisition.cDNA was synthetized by using the iScript™ cDNA Synthesis Kit from Bio‐Rad  according to the manufacturer’s instructions. A set of primers was designed to amplify cDNA for the target genes by qPCR. The set comprised MTH1 glucose‐responsive transcriptional factors, which code for a negative regulator of the glucose‐sensing signal transduction pathway, as housekeeping agents. Rebnegger et al.  and fed‐batch tests (glucose), and of potential fermentation by‐products (arabitol or ethanol), were all determined by HPLC. The column and procedure used for this purpose are described elsewhere . CO2 and O2 mole fractions were measured on‐line with provision for off‐gas pressure and humidity. The data thus obtained were used to estimate the oxygen uptake rate (OUR), carbon dioxide evolution rate (CER), specific rates (qO2 and qCO2) and the respiratory quotient (RQ). RSD was < 5% in all cases.p‐nitrophenyl butyrate (pNPB) % acetone. A volume of 980 µl of buffer was mixed with 20 µl of sample. The absorbance at 348 nm was measured on‐line at 30°C for 2 min on a Specord 200 Plus instrument from Analytic Jena . One unit of activity was defined as the amount of enzyme needed to release 1 mmol of p‐nitrophenol per minute under assay conditions. RSD was < 1%.Crl1 activity was determined by using a modified version of an existing enzymatic assay based on et al., et al., All equations derived from the mass balances used to calculate yields and rates in the chemostat (Garcia‐Ortega µ), glucose uptake (Sq), product generation (Pq), oxygen uptake (qO2) and carbon dioxide production (qCO2). The methodology used is described in detail in a previous paper (Ponte et al., .Measurement consistency was checked by using the standard test with carbon and electron balances as constraints. Both on‐line and off‐line measurements allowed five key specific rates in the black‐box process model to be calculated, namely biomass generation (None declared.Table S1. Primer pairs used for gene dosage and transcript‐level determination by means of ddPCR and qPCR respectively.Click here for additional data file.


Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/gCDW/s) than reported during batch growth (3.3 μmol/gCDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.In this study we applied a repetitive feast–famine regime in an aerobic E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.The adaptation of the physiology and metabolism of  Microorganisms are widely used for the production of chemicals, ranging from small organic acids to large proteins, including biopharmaceuticals, biochemicals and bulk biofuels –3. In orSubstrate gradients are frequently considered as a main reason for performance reduction. Commonly, the substrate concentration should be kept at low levels to avoid overflow metabolism –17. To aEscherichia coli cultures, cyclically circulating from high to low glucose levels, have shown decreased biomass yields and by-product formation  were quantified with GC–MS/MS , as described in  and/or aE. coli to repetitive, dynamic perturbations was evaluated with respect to short and long-term physiological and metabolic characteristics:Comparison between average metabolic rates and yields under repetitive dynamic conditions and steady-state (reference) conditions, at the same dilution rate.Comparison of the metabolic response between repetitive dynamics, single perturbations (pulse experiments) and steady-state levels.The adaptation of 2 and CO2) concentrations  and offgas O and CO2 mentclass2pt{minimKM = 10 μM) of the microorganism  and after glucose or oxygen perturbations [E. coli cultivations in glucose-containing medium, where acetate is produced even when the culture is fully aerated [The extracellular by-product  concentrations  [The biomass specific rates were reconciled using the approach described in , using e g/Cmol)  was used2 production were higher  for the intermittent feeding, compared to the reference continuous regime. More specifically, during the reference chemostat operation, the oxygen to substrate consumption ratio was 2.85 ± 0.05 glc, which is comparable to the values reported in literature for similar cultivation conditions, ranging from 2.74 to 3.62 glc [glc.The most evident observation is that the calculated rates for glucose and oxygen uptake and CO2/molglc , 59–61. Introducing potential cell lysis in the reconciliation, we observed that the lysis rate during the steady-state was calculated as negative (while positive by definition), with a high standard deviation and therefore the steady-state lysis was assumed zero.E. coli, when varying the substrate availability. Many studies attributed this phenomenon either to overflow metabolism, due to high growth rates (> 0.15 h−1) [Saccharomyces cerevisiae , subjected to an intermittent feeding (similar to the regime used here) [The intermittent feeding, resulted in decreased average cell dry weight concentration and subsequent 0.5 log2-fold decrease (− 30.3%) of the total biomass yield. The biomass yield was calculated as the ratio of the total biomass growth rate (including lysed biomass) over the glucose uptake rate. A decrease in biomass yield has also been reported in previous studies for .15 h−1) , 39, or .15 h−1)  observeded here) .2 produced per glucose consumed, during the steady-state regime, was 3.16 ± 0.05 glc, while for the feast–famine a value of 4.05 ± 0.08 glc (+ 28.1%) was observed. Together with the increase in O2 consumption, it indicates that more glucose was used for respiration, rather than biomass production, during the feast–famine cycles [The ratio of COE. coli cells with high time resolution and repeated measurements. Especially, samples can be obtained from repetitive cycles , among various combinations [Special focus was to obtain a high-resolution uptake profile as substrate uptake has a major impact on the intracellular metabolic behaviour of the microorganism. To calculate the short-term glucose uptake profile, a piecewise affine (PWA) rate approximation  was calcinations . The fluCDW/s) immediately after the beginning of the feeding and then decreased slightly until the end of the feeding, followed by significant decrease between 15 s and 18 s, i.e. from 4.40 to 1.04 μmol/gCDW/s.There was no obvious correlation between the glucose uptake rate and the extracellular glucose concentration. The rate reached its highest value (4.68 μmol/gE. coli under glucose excess (Km is in the range of 3–10 μΜ [This contradiction is explained by the activity of the phosphotransferase system (PTS) , which i 3–10 μΜ , 67). ThThe overshoot in the glucose uptake rate has been previously observed in cells exposed to excess of substrate after a starvation period, in different experimental setups , 60, 62.CDW/s), proving that the microorganism has a higher uptake capacity than the one observed under maximum growth. It is, however, puzzling that the glucose uptake rate was decreasing already (mainly after 18 s), while glucose was still in excess (above the glucose affinity constant). An additional test experiment was performed under the same cultivation conditions, with a shorter feeding phase (13 s) but the same cycle length (400 s) and the same total amount of glucose fed over the cycle. The glucose uptake rate calculated (data not shown) exhibited the same decreasing pattern 2 s before the end of the feeding phase (at 11 s), while glucose was still in excess. Raw observations of this test experiment are included in Additional file The highest estimated uptake rate in our work was significantly higher than the maximum observed during batch cultivations for the same strain  cycle, pentose phosphate pathway (PPP) intermediates and amino acids. These measurements originated from one experiment, but metabolites were sampled densely in time, over several cycles. The clear trends of the measurements showed high technical reproducibility .The highly dynamic glucose uptake rate, discussed in the previous section, was expected to result in significant fluctuations in the intracellular metabolite levels. Therefore, it is important to observe how E. coli strains redistribute their metabolic fluxes in such way to minimally divert from the wild-type metabolic network [The intracellular flux profiles over time were estimated by (dynamic) Flux Balance Analysis (FBA) for the glycolytic and pentose phosphate reaction steps. The stoichiometric network included reactions of central carbon metabolism, where the intermediates could be measured . The measured concentrations were significantly higher than the steady-state levels (e.g. up to 20 fold change for FBP), due to the short-term overshoot of carbon flux. The opposite trend was observed for the lower glycolytic metabolites , whose concentration immediately decreased in the first seconds of the feast phase (three to sixfold change from steady-state). The reason behind this drop is based on the PTS system. In order to import and phosphorylate glucose to G6P, PEP needs to be produced and then converted to pyruvate. Unfortunately, pyruvate could not be quantified in this experiment. PEP concentration showed negative correlation with G6P, as it reached its lower concentration after 12.5 s, the same time the maximum concentration of G6P was reached. This behaviour has been observed before in e pulses , 75–77. The metabolites of pentose phosphate pathway related well to the dynamics observed in glycolysis, as they exhibited the same behaviour of rapid accumulation in the beginning of the feast phase and later decrease to the initial levels. The pool of 6PG responded directly to the changes occurring in its precursor, G6P, reaching its maximum concentration at 20 s (7.5 s delay compared to G6P). This peak was observed slightly later in the rest of the metabolites, with the exception of Xyl5P, which responded equally fast.Looking at the flux profiles, we observed that the glucose uptake rate dynamics propagated through glycolysis. The peak observed in the PTS flux right at the beginning of the feeding, also occurred in the succeeding reactions towards the formation of PEP and they all decreased significantly after 15 s. After 110 s all metabolite pools remained constant, while there was no more flux running in glycolysis. The same trend was also observed in all the reaction steps of the pentose phosphate pathway.E. coli culture, which was used to further support the calculated increase in growth rate during the feast phase [E. coli [Compared to the steady-state levels, the immediate increase of the PTS flux (16 fold) led to a higher change in all the glycolytic fluxes (18–19 fold) and an even higher increase in the flux towards PPP (30 fold). This observation, together with the fact that 62.4% of the PTS flux was directed into glycolysis (less than the 70% assumed in steady-state), gives an indication that the cells may increase the flux to pentose phosphate pathway, under these dynamic conditions, therefore enhancing the production of NADPH, assumingly for redox balance purposes. Similar increase was observed after a single-pulse of glucose in an aerobic st phase . NADPH w[E. coli , 79 and [E. coli , 81.In the case of the TCA cycle, only a few metabolites could be precisely quantified, which are shown in Fig. Following glycolysis, also the TCA metabolites showed a dynamic profile over time. We observed that aKG and malate reached their highest concentration 30 s after the beginning of the feast–famine cycle Fig. , displayAmino acids are relevant precursors for protein synthesis. At the same time, several amino acids are closely connected with their respective central carbon metabolism precursor. For example alanine is only one (equilibrium) reaction step from pyruvate. Thus, on the one hand one could expect homeostasis to ensure balanced growth, on the other hand a high dependency on central carbon metabolism  for E. coli [Nucleotide responses to the feast–famine regime are of high interest and especially the ATP/ADP levels, which reflect the adenylate energy state (+ADP+AMP ) of a ce E. coli . Therefo E. coli –86. AMP E. coli K12, where the energy homeostasis was also reported during glucose excess (AEC ranging from 0.8 to 0.85 in the different studies). However, in these studies, the AEC was decreasing during the famine phase, sometimes reaching values even lower than 0.7 [Our findings are in agreement with results from single pulse experiments , 52, 87,than 0.7 , which wthan 0.7  also obsIn contrast to AxP’s, UDP and UTP were not homeostatic. They decreased over the feast phase and increased during the famine phase. Also, GDP and GTP were dynamic with a trend opposite to UxP’s Fig. .Fig. 6In2 was observed over the whole cycle (400 s). The specific glucose uptake rate was higher than at the reference steady-state and there was no significant accumulation of by-products. This suggests high intracellular accumulation of carbon during the feast phase and degradation during the (extracellular) famine phase.From the extracellular observations, it was observed that the carbon uptake and excretion were significantly shifted. While the substrate carbon was consumed only during the first 100 s, excretion in the form of COThe total amount of intracellular carbon during steady-state and feast–famine was calculated from the measured metabolites at every timepoint and is shown in Fig. CDW), at 30 s  until approximately 120 s. Then a constant decrease, until reaching the initial level, was observed. Significantly, the total amount of carbon in the measured metabolome was, at all timepoints, higher than the steady-state levels, resulting from the overshoot in the glucose uptake.The total amount of metabolites was changing over time, during the feast–famine regime. We observed a small decrease in the total metabolome during the first 10 s of the cycle, followed by an increase until the highest point  consumed by the cells in the first 100 s of the feast–famine cycle. In terms of electrons, this accumulation in the feast phase was 6.4 mEmol/LEC. If all the electrons were used during the famine phase, the maximum oxygen consumption observed would be 1.6 EC. In fact, we estimated, from the calculated EC were consumed during the famine phase. Therefore, all of the accumulated intracellular metabolites could have been used as electron donors and could explain the O2 uptake, while the substrate was depleted. Taymaz-Nikerel et al. [Looking at the rest of the intracellular intermediates, the total accumulation, in terms of carbon, during the feast phase was calculated to be 256 μCmol/gLEC Fig. -top. Thil et al.  reached Escherichia coli cultured under dynamic substrate conditions exhibited a different physiology compared to conditions supplying the same amount of substrate steadily. Namely, the average biomass specific consumption  and production rates (CO2) increased under the feast–famine regime, compared to the steady-state, while by-product synthesis remained unaffected. Consequently, biomass formation was adversely disturbed with 30% decrease in yield.These observations suggest that during the intermittent feeding, more glucose was used for respiration than biomass production. This would mean that the excess of ATP produced was used in other cellular processes. If the energy-spilling scenario is correct, it would imply an increase in maintenance or the presence of futile cycles.Alternatively, this change in physiology could be attributed to an increase in competitiveness. Fast consumption of the substrate generates an advantage compared to slow competitors, as an increasing share of the substrate will go to the faster consuming cells .E. coli, the most important maintenance processes described in literature, are protein turnover (synthesis and degradation), switches in metabolic pathways, proteome and RNA repair and cell motility [13C labeling of the amino acids pools and proteasome activity essays [Maintenance is defined as the energy-consuming processes “for functions other than production of new cell material” . In E. cmotility , 94–96. motility . The ATPmotility , 99. Proy essays , 101, whE. coli strain [−1.The reconversion of oxaloacetate to phosphoenolpyruvate by the gluconeogenic PEP carboxykinase . This ATi strain , as welli strain . The indi strain . Yang eti strain  demonstrThe reconversion of fructose-1,6-biphosphate to fructose-6-phosphate by fructose 1,6-bisphosphatase. Usually this futile cycle is tightly regulated by the cells and is therefore minimal under both glycolytic and gluconeogenic conditions , 108.E. coli wild-type on glucose [The reconversion of pyruvate to phosphoenolpyruvate by phosphoenolpyruvate synthase, with the involvement of Enzyme I of the PTS , 110. 10 glucose .The reconversion of acetate to acetyl-coA by acetyl-coA synthetase . ValgepeGlycogen formation and re-consumption. The formation of glycogen, by glycogen synthetase, requires one ATP per glucose, while its consumption does not form any ATP , 113.Futile cycles may also explain the ATP-spilling during the feast–famine regime. Some potential futile cycles, already described in literature, are:E. coli during the feast–famine conditions. It is well possible that the cells up-regulate the enzymes of these futile cycles, in order to rapidly switch the direction of the fluxes, when shifting from feast to famine conditions. The reasons behind this metabolic strategy can be the rapid re-initiation of growth and the tight regulation of the ATP levels in the cell. We indeed observed a constant energy cellular status over the intermittent regime, which enhances the hypothesis of this role of futile cycles. However, the identification and interpretation of these energy-spilling reactions, under alternating feast–famine conditions, has not been studied in literature. Measurements of the enzyme expression and 13C flux determination will be needed to confirm our hypotheses. In addition, knocking out some of the enzymes causing these futile cycles and growing the engineered strains under the same conditions would be a strategy to verify if they are key elements of the ATP homeostasis.All of the above-mentioned futile cycles could be active in E. coli (FBAwMC), including a constraint for the enzyme concentrations, considering the macromolecular crowding. With this model they predicted the maximum growth both in single-substrate, but also in mixed substrate media, accurately representing experimental observations, showing that the growth rate was influenced by the solvent availability in the cytoplasm. In a following study, Vazquez et al. [Based on high time resolution, extracellular glucose concentration measurements and PWA rate approximations, the glucose uptake rate was calculated over cycle time and showed an immediate increase after the feeding was switched on. This rate reached a maximum value, higher than batch maximum rates, and decreased before the concentration decreased. The fact that glucose was still in excess, but the uptake rate could not follow up, indicates the existence of an intracellular metabolic limitation. From our intracellular metabolic analysis, we showed that there was enough PEP during the whole cycle to drive glucose transport in the cell through the PTS, therefore, the import of substrate was not the limiting step. One hypothesis which could explain the switch in the uptake rate is the phenomenon of macromolecular crowding. Macromolecular crowding is occurring in all living organisms, as a large part of the volume of the cell is occupied by high concentrations of macromolecules, such as proteins. Thus, there is limited intracellular volume available for other molecules. This volume exclusion affects various enzymatic reactions, either by increasing or decreasing their rates, depending on the change in size of the reactants –117. Whiz et al.  applied z et al.  or enzymz et al. .Furthermore, we observed the high capacity of intracellular metabolism facing the substrate gradients applied. There was a rapid response in intracellular metabolite concentrations and fluxes, which generally deviated significantly from the steady-state. The dynamics observed were less pronounced moving downstream, from glycolysis to TCA and then to amino acid synthesis, with more modest changes over time.E. coli under nutrient-limited conditions.In addition, during the highly dynamic conditions, applied in this study, we demonstrated the ability of the cells to store an amount of carbon and electrons intracellularly during the feast phase, which were then used when substrate was depleted, therefore, explaining oxygen consumption during the famine phase. This strategy proves to be important for the survival and robustness of E. coli is aerobically cultivated in large-scale bioreactors, facing long-term substrate gradients. Fed-batch regimes are often preferred, since the substrate concentration or the specific growth rate can be controlled in such a way to avoid overflow metabolism [These observations are highly relevant in an industrial context, where tabolism , 125. Thtabolism . Howevertabolism .E. coli culture to substrate perturbations, highlights parameters to take into account for metabolic engineering and process design in relation to large-scale reactor operation.Cells responded immediately to an excess of substrate, by increasing their uptake rate and consequently the intracellular fluxes in tens of seconds. Carbon was stored in intracellular intermediates, during substrate feast and was consumed during a famine phase.Despite, the highly changing dynamics, energy charge homeostasis was observed, as a remarkable fitness characteristic of the response to perturbations, indicating rapid metabolic regulation.More important and highly relevant to industrial fermentations, was the 30% decrease of the biomass yield, occurring during the intermittent feeding, compared to a reference steady-state. Energy-spilling, was therefore, a trade-off for the adaptation of the microorganism in the dynamic environment, seeking for robust growth.Studying the physiological and metabolic responses of an adapted E. coli responds to stress, induced by substrate gradients, by launching a specific metabolic strategy. In order to improve productivity cost-effectively in large-scale bioprocesses, we need to further identify the mechanisms behind stress adaptation, limitations in substrate uptake and respiration, potential energy-spilling pathways and optimal growth targets of the cells, combining multi-omics approaches.The obtained results revealed some reasons for the reduced performance of cell factories during scale-up. Additional file 1. Additional file supporting the results discussed in the text.