Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism  is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of  E. coli grows relatively fast on a wide variety of nutrition sources and oxygen concentrations under standard environmental conditions , it is frequently used in experimental microbiological studies. This study focuses on the growth dynamics of Escherichia coli K-12 MG1655 on glucose. Abundant information is available about this nonvirulent E. coli substrain from systems biology and experimental microbiology, due to its frequent application as a host organism for recombinant DNA  in biochemical models [YX/S [gDW/mol nutrient].Microorganisms take up and consume nutrients from their environment, in the first place to fulfill physiological maintenance requirements and, secondly, to support biomass growth and cell reproduction. Maintenance processes comprise osmoregulation, proofreading and internal turnover of macromolecular compounds, cell motility, and defense mechanisms . For thel models . In addiμ [h−1] and the specific nutrient uptake rate qS [mol nutrient/(gDW⋅h)] of the microbial cells in a linear way , and KS the half-saturation Monod constant for nutrient uptake [mol nutrient/L]. Generally, the half-saturation constant has a very low value (in the order of 10−6 M). Hence, it only plays an important role at very low nutrient concentrations.The specific nutrient consumption rate is a function of the extracellular nutrient concentration kinetics :qS=qS,mqP [mol product/(gDW⋅h)] is the specific secretion rate of metabolic product P, qP the specific secretion rate at zero cellular nutrient uptake, and YP/S [mol product/mol nutrient] the yield coefficient of product P on nutrient substrate S.Analogously to the linear growth law in E. coli on glucose. Information about the intracellular metabolism and flux distribution is obtained from metabolic flux balance analyses.The mathematical expressions in Eqs S. This matrix maps the intracellular metabolic fluxes vector (v) onto a vector containing the time derivatives of the metabolite concentrations (x):Flux balance analysis is a constrained-based mathematical method, based on systems biology concepts, to analyze metabolic reaction networks for the prediction of cellular phenotypes . In systb), according to the concept of the exchange stoichiometric matrix Sexch , and (iii) supplementary maintenance requirements due to the presence of undissociated lipophilic acids [Ui] in the environment.In this equation, the maintenance coefficient is split up in three terms: (A(1) and qG ≈ qG,max) without weak acids, the microbial growth rate decreases to zero when the environmental proton concentration reaches a minimum inhibitory value [H+]min. Hence, the coefficient of the proton maintenance term A(1) is calculable from To determine the value of the parameters i in an aerobic nutrient-rich system. Decreases in the intracellular pH due to weak acid dissociation in the cell are associated to significant increases in the cellular glucose uptake rate .In the normal physiological temperature range (NPTR) for kinetics :μ(1)=μ:The maximum specific glucose uptake rate at infinite temperature, At temperatures higher than the NPTR, the maintenance coefficient increases rapidly according to the Arrhenius kinetic model, while the biomass yield coefficient is not significantly affected , 46. Thiperature :mG(1),∞E. coli K-12 MG1655 of , and activation energies E1 and E2  the specific growth rate at reference conditions, and P . The specific metabolite secretion rates are linearly related to the specific glucose uptake rate qG by means of a product yield coefficient Similar to the procedure in Case Study I, anaerobic growth dynamics of +/NADH redox balance, approximately equal amounts of acetic acid and ethanol are generated min has the same value under aerobic and anaerobic conditions  are the concentrations of growth-inhibiting substances, μmax is the maximum specific growth rate under nutrient-rich conditions, and μopt is the maximum specific growth rate under optimal pH, temperature and weak acid concentrations. The gamma hypothesis has been used in, e.g., the model of  and [DL] the respective concentration of undissociated and dissociated lactic acid in the system, and  and  the minimal concentrations of both lactic acid forms inhibiting growth. From the mathematical structure of In this formula, min) is less critical when the other environmental conditions are more optimal for growth.In the current paper, a novel approach has been developed to incorporate the synergistic effects of different stress factors. Instead of extending the expression for the specific growth rate with an interaction factor, adjustment terms and factors are applied to the maintenance coefficient and the specific glucose uptake rate of The main advantage of the developed approach is that the synergistic effect of environmental factors on the maintenance coefficient and specific glucose uptake rate is explicitly incorporated in the metabolic models of the case studies. This makes the developed metabolic models particularly suitable to incorporate in spatially-explicit individual-based models taking local cellular glucose uptake from the environment into account.Salmonella Typhimurium for which a genome-scale model is available from [In this work, a metabolic model has been developed for E. coli. The model may probably also be used for other food pathogens which are similar to E. coli, such as the gram-negative rod-shaped ble from , or evenble from , 73).In addition, to model microbial dynamics under glucose-limited conditions, the uptake of acetic and lactic acid as carbon source has to be taken into account. However, when the presence of organic acids in the food medium is only the result of glucose fermentation , the concentrations of produced acids are too low to cause significant additional biomass growth.Finally, the developed model is only valid for acid pH values and in a temperature range above 21°C. To model microbial growth under 21°C, an additional function has to be added to the piecewise expression in Eqs In predictive microbiology, accurate descriptions of microbial growth dynamics are the primary goal. However, some other, more practical concerns need to be taken into account, like the required simulation time, model complexity and genericness. Fast simulation run times are indispensable for real-time monitoring and contol of microbial bioprocesses, implying that the cellular metabolism needs to be modeled in the least complex way that is still accurate enough for the considered biosystem, as complex metabolic models lead to high computational loads and simulation run times. In the same way, the least complex metabolic model needs to be applied to individual-based simulations of multiple cell systems, as each cell is simulated separately. Furthermore, when the individual-based simulation is spatially-explicit, attention should be paid to the genericness of the metabolic model in order to simulate the impact of heterogeneous and dynamic environmental conditions on the cellular growth dynamics accurately.E. coli growth dynamics under aerobic, micro-aerobic, and anaerobic conditions, based on the linear dependency between the specific microbial growth and nutrient consumption rate of [iAF1260 model. In this way, the intended trade-off between model complexity, accuracy and genericness is achieved.In this paper, metabolic models are built for the description of  rate of . AlthougiAF1260 are only valid for specific reference environmental conditions, viz., a neutral M9 medium enriched with glucose at a temperature of 37 °C. The influences of low pH values, weak acid cell products and high temperatures as growth-inhibiting environmental factors have been incorporated in the form of adjustment terms or factors on the maintenance coefficient or specific nutrient uptake rate in Pirt’s law. For anaerobic conditions, the shift to a lactic acid producing metabolism at low pH values has been implemented as well. This leads to a novel and more intuitive approach to simulate the synergistic effect of the considered microbial stress conditions on microbial growth. As a result, this approach is an excellent low-complexity tool within the context of hurdle technology to find combinations of growth-inhibiting conditions optimizing quality, safety and, economic value of food products.Flux balance analyses with S1 Text(PDF)Click here for additional data file.


Terpenoids are a group of natural products that have a variety of essential and non-essential roles in metabolism, in biotic and abiotic interactions, as well as commercial applications such as pharmaceuticals, food additives, and chemical feedstocks. Economic viability for commercial applications is commonly not achievable by using natural source organisms or chemical synthesis. Engineered bio-production in suitable heterologous hosts is often required to achieve commercial viability. However, our poor understanding of regulatory mechanisms and other biochemical processes makes obtaining efficient conversion yields from feedstocks challenging. Moreover, production from carbon dioxide via photosynthesis would significantly increase the environmental and potentially the economic credentials of these processes by disintermediating biomass feedstocks. In this paper, we briefly review terpenoid metabolism, outline some recent advances in terpenoid metabolic engineering, and discuss why photosynthetic unicellular organisms—such as algae and cyanobacteria—might be preferred production platforms for the expression of some of the more challenging terpenoid pathways. Terpenoids are a chemically diverse class of natural products that are present in all domains of life and number in the tens of thousands of compounds isolated to date . The wealth of terpenoid structures provides a variety of roles in central cellular processes . The greatest terpenoid diversity can be found in plants where they have a myriad of roles including mediating in complex interactions with the biotic and abiotic environment ,2.Terpenoid biosynthesis starts with two non-homologous metabolic routes including the mevalonate (MVA) and the methyl-D-erythritol (MEP) pathways. These pathways produce the universal 5-carbon prenyl phosphate precursor molecules isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) . IPP andThe MEP pathway is present in most bacteria, in plastids of photosynthetic organisms, and in some eukaryotic organisms, including pathogens such as the malaria parasite. The MVA pathway appears in archaea and the cytosol of most eukaryotic organisms. Consequently, plants and other eukaryotic organisms—including some algae species—have both pathways present but in different compartments and with distinct roles  ,3,4. ThePlant-derived specialized terpenoids are often produced in low quantities or in mixtures of similar compounds , which makes extraction from natural sources commercially unviable and chemical synthesis very challenging, due to the complexity of the structures of interest ,6,7. ConEscherichia coli and Saccharomyces cerevisiae (yeast) have been the primary target hosts. They are well-characterized heterologous systems with a defined terpenoid background, which facilitates the analytics process and allows the expression of enzymes one by one, or in combinations. This involves the biosynthetic steps of known terpenoids, and even allows the production of new-to-nature compounds [The industrial workhorses ompounds ,11,12,13ompounds  and taxaompounds —precursoompounds ,18,19,20E. coli and yeast. We then explore advances in engineering photosynthetic microorganisms and discuss their potential as alternative production platforms for heterologous terpenoids.In this review, we summarize key advances that used metabolic engineering to both rewire the host’s primary metabolism to maximize the production of isoprenoid precursors and to enable the production of the desired terpenoid product in tractable, well studied hosts Metabolic engineering is the application of systems and synthetic biology approaches to engineering cells for the production of industrially useful biochemicals. These factories can be designed and engineered for optimal resource allocation towards the desired pathway. Terpenoid precursor and biosynthetic pathways are well characterized in different organisms and have been manipulated in a multitude of studies, which renders them model pathways for metabolic engineering. The biosynthesis of terpenoids has a distinctive modular structure, which makes their pathways particularly suited to synthetic biology-based engineering approaches: a natural grouping of biochemical reaction types creates four distinct modules that can be independently manipulated . PathwayE. coli, they work in synergy and both have a higher flux when compared to having only one pathway present [Production hosts commonly have insufficient availability of the C5 prenyl pyrophosphate precursors IPP and DMAPP to provide an adequate flux to the heterologous pathways. Successful examples of terpenoid production hosts include maximization of the metabolic flux towards the formation of IPP and DMAPP through the overexpression or manipulation of genes of the endogenous MVA and MEP pathways as well as the insertion of the MEP pathway in organisms with only an MVA pathway to supplement flux ,26,27,28 present .In the MEP pathway, the first two steps catalyzed by deoxy-xylulose 5-phosphate synthase and deoxy-xylulose 5-phosphate reductoisomerase  as well as the isomerization of C5 prenyl phosphates by the isopentenyl-diphosphate delta-isomerase (IDI)  are rateIn the presence of the MVA/MEP pathway overexpression/augmentation, over-accumulation of prenyl phosphates is problematic and causes cellular toxicity . For thiDifferent classes of terpenoids are produced from prenyl phosphates with different chain-length carbon skeletons . HemiterE. coli and S. cerevisiae preferentially produce FPP, which is a key precursor for the primary isoprenoids produced by these organisms . As a consequence, sesquiterpene (C15) production in these organisms has yielded relatively high titers especially in yeast [Both in yeast ,35. The in yeast ,36,37,38S. cerevisiae and E. coli, the primary prenyl transferase converts GPP immediately to FPP by the addition of a second IPP unit without releasing GPP from the active site under normal conditions [Directing productivity towards the C10 and C20 branches faces different challenges. In nditions . This menditions ,41. An aS. cerevisiae whereby the GPP/FPP synthase was engineered to expand its substrate binding site, which allows it to add an IPP unit to catalyze GGPP formation [Accumulation of GGPP requires an uninhibited flux through GPP and FPP and usually a strong “metabolic pull” strategy is used by overexpressing GGPP synthase ,42,43. Aormation . This alThe next two modules comprise the specific pathway for each target compound and are where the true terpenoid diversity is unlocked. Terpene synthases (Module III) form a wide variety of carbon skeletons. These skeletons are the substrates of the decorating enzymes of Module IV. This chemistry is highly complex and results in massive diversity. In this case, we outline generic information to provide (a) a more complete overview of terpenoid biosynthesis and (b) context for the following section on terpenoid production by photosynthetic organisms.Physcomitrella patens) to over a hundred per genome [Terpene synthases are often multimeric and slow enzymes . These fr genome . They arr genome ,50. Whilr genome ,12,22,51Decorating enzymes—that alter the basic carbon skeleton—are usually the final step of terpenoid biosynthesis. Various enzymes such as oxidative enzymes , methyltransferases, acyltransferases, and prenyltransferases diversify the already numerous terpene skeletons . The cytE. coli strain variants, selecting the ones with higher NADPH regeneration rates [As discussed above, precise control of the heterologous biosynthetic pathways is often crucial to increase production. One way to help balance pathway flux across metabolic modules is to apply metabolite biosensors linked to feedback control mechanisms. Such a strategy can tie enzyme production  with internal metabolite pools (such as the enzyme’s substrate or a key precursor molecule), which enables the production of the relevant enzyme exactly when required. Moreover, biosensors can be used to implement directed evolution methodologies, which connect compound concentration with growth or other responses. Two successful examples are the use of an NADPH sensor for neurosporene production enhancement and an IPP sensor for lycopene production enhancement ,59. Ng aon rates . This alFlux reporters such as the carotenoids lycopene and beta-carotene are commonly used as indicators of the terpenoid yield since they give a colored phenotype that can be assessed by absorption or fluorescence. This has been exploited in studies to identify elements such as transcription factors that have an effect on production, coupled with directed evolution or high throughput engineering approaches ,61,62. TIt should be noted that MEP and MVA pathways both consume reducing power by using NADPH and NADH, respectively. The availability of these redox partners as well as haem molecules (serving as enzyme cofactors) and the precursors acetyl-coA (for the MVA pathway) and pyruvate and glyceraldehyde 3-phosphate (of the MEP pathway) are limiting steps. Therefore, the introduction of pathways that enable the recycling of cofactors or increase their availability have been used successfully to increase terpenoid production ,65,66,67E. coli was optimized to produce taxadiene, which was oxygenated by a yeast expressing taxadiene 5α-hydroxylase (a cytochrome P450) and the P450 reductase [Often, it is not easy to optimize all four nodes and produce a terpenoid of interest in a single organism. The use of dual-organism production systems is an interesting alternative. An example has been implemented in the production of oxygenated products of taxadiene. eductase . This miE. coli and S. cerevisiae are currently the main hosts for terpenoid production chosen for their high growth rates, relative ease of engineering, scalability, bioprocess technology, and—in particular—for the advanced knowledge available on their metabolism and genetic resources. However, these two organisms have not evolved to produce a wide variety of complex terpenoid metabolites.The heterologous production of terpenes requires the expression of foreign enzymes and often entire metabolic pathways. E. coli. Theses isoprenoids are also required in higher amounts to satisfy metabolic demands for the production of photosynthetic pigments, sterols, isoprene, and secondary metabolites. Extensive engineering is required to get reasonable flux to isoprenoids in both yeast and E. coli, which suggests that isoprenoid metabolism is relatively inefficient in these organisms [2 as a feedstock (rather than secrete it in the atmosphere), and offer the ability to directly link photosynthesis with heterologous metabolite production via electron transfer proteins [Photosynthetic organisms produce a much greater diversity of isoprenoids and use them for many more metabolic processes than yeast and rganisms ,69. Convrganisms . Moreoveproteins ,71. CyanE. coli, they are attractive production hosts due to their photosynthetic growth and relatively simple cellular organization.Cyanobacteria are receiving increased attention as potential photosynthetic production hosts due to their simple cellular organization and their metabolic repertoire, which is characterized by high plasticity ,86,87,88Synechocystis sp. PCC 6803 [−1 to 0.25 mg g−1) with the heterologous expression of the MVA pathway [E. coli [E. coli is due to a low innate pathway flux in this organism relative to Synechocystis sp. PCC 6803, which provides a larger dynamic range for improvement. It may also reflect availability of central carbon intermediates to feed into the pathways, differential enzyme behavior in the different organisms, or different availability of DMAPP for isoprene synthase. Isoprene production is limited by the amount of the isoprene synthase expressed. Fusion of the isoprene synthase with the c-phycocyanin beta subunit, which is a highly expressed protein, provided a yield increase to 5.4 mg g−1 [−1 [The first study in cyanobacterial terpenoid engineering involved the production of the hemiterpene isoprene in PCC 6803 . The rel pathway . It is i[E. coli . This hi4 mg g−1 . Overexp g−1 [−1 .Synechococcus elongatus PCC 7942, which resulted in the accumulation of 1.26 g L−1 (approximately 540 mg g−1) isoprene after three weeks of growth [Synechocystis sp. PCC 6803 by studying the effect of overexpressing the MEP pathway enzymes in isoprene production [A different approach was used in isoprene production in f growth . This isoduction . Many  was achieved in Synechocystis sp. PCC 6803 when two enzymes of the pentose phosphate cycle (the ribose 5-phosphate isomerase and ribulose 5-phosphate 3-epimerase) together with a GPP synthase to increase precursor availability were overexpressed [An early effort to engineer monoterpenoid production in rene L−1 . Fusion  L−1 [−1 . Davies ectively . An incrxpressed .Anabaena sp. PCC 7120 where farnesene titres reached 0.08 mg g−1 after two weeks of growth [Synechococcus elongatus 7942, which reached 19.8 mg L−1 amorphadiene [E. coli DXS, FPP synthase, and the IPP isomerase. In Synechocystis sp. PCC 6803, squalene, a triterpenoid derived from FPP by the action of squalene synthase, accumulated to approximately 1.2 mg g−1 when the downstream squalene hopene cyclase was knocked out [Synechococcus elongatus 7942, which reached 4.6 mg L−1 alpha-farnesene after seven days of cultivation [Sesquiterpenoid production was explored in f growth . The higphadiene . The autcked out . The oveSynechocystis sp. PCC 6803 with reported titres of 0.36 mg g−1 [−1 [−1 by codon-optimizing the terpene synthases and expressing them in a self-replicating vector under a strong promoter [Diterpenoid expression in cyanobacteria is less studied. Geranyllinalool, which is a diterpene alcohol present in tobacco, was produced in 6 mg g−1 . Englund g−1 [−1 . Manoyl promoter .−1 (or µg g−1) to low mg L−1 (or mg g−1)—with the notable exception of isoprene production in Synechococcus elongatus , Ko et al. note that, in most cases, the cyanobacterial titres are disappointingly low in comparison [S. elongatus almost doubled. An increased biomass productivity caused by a sink effect has been observed in other metabolic engineering studies . In an mparison . The effmparison , the cars . Cyanobs  and in Module IV (decorations) require electron donors as redox partners. Therefore, the redox availability increases flexibility and makes redox enzyme expression more straightforward. One example is the expression of cytochrome P450s in cyanobacteria by using ferredoxin as a direct link to a photosynthetic redox power. This strategy not only results in active enzymes but also may increase overall photosynthetic capacity ,106,107.These studies highlight that, while native MEP pathway flux in cyanobacteria may be relatively high, engineering heterologous terpenoid production still faces challenges in these organisms. Competition with native terpenoid production as well as a relatively poor understanding of regulatory mechanisms are two key challenges that require further attention. Despite this, the ability of these organisms to tolerate and compensate for heterologous terpenoid production is extremely promising.Microalgae are unicellular, (mostly) photosynthetic eukaryotes and form an enormously diverse group that spans across several branches of the eukaryotic tree of life. Because of their huge diversity, this group of organisms is largely unexplored. However, their terpenoid repertoire is estimated to be vast and diverse . MicroalRecent advances in sequencing technologies, metabolomics, and proteomics have paved the way to the metabolic characterization at the genetic level of representative microalgal species. The development of increasingly efficient genetic toolboxes for model algal species ,111,112 C. reinhardtii is historically the longest and most widely used model organism for algal genetics. Efforts in engineering this species have provided proof-of-concept for its suitability as a single-celled factory for the synthesis of many bio-products from recombinant therapeuticenzymes to small molecules. The generation of the mutant C. reinhardtii strains that display to consistently high levels of transgene expression such as UMV4 and UMV11 [C. reinhardtii to produce the sesquiterpenoid patchoulol by expressing a plant patchoulol synthase gene [−1 in photoautotrophic growth. By comparing previously published sesquiterpenoid yield in cyanobacteria with those of C. reinhardtii, Lauersen et al. hypothesized that C. reinhardtii might have a relatively larger availability of farnesyl pyrophosphate [C. reinhardii. To address this, terpenoid metabolism was engineered by RNAi-mediated silencing of genes involved in competing pathways that used FPP as a substrate including a squalene synthase and geranyl-geranyl-pyrophosphate synthase. Down-regulation of these competing enzymes resulted in bisabolene yields up to 4.8 mg L−1. This was further improved to 11.0 mg L−1 by cultivating the engineered algae in mixotrophic light/dark (16 h/8 h) regimes and providing both 3% CO2 and acetate in the growth medium [−1) [C. reinhardtii. Further carbon flow optimization by fusing the terpene synthases to a mutated GGPP synthase resulted in the production increase to 25 mg L−1 manoyl oxide [The green alga nd UMV11 , which gase gene , which ihosphate ,83. Howeh medium . Yields ium [−1) . Very reyl oxide .C. reinhardii to improve terpenoid yields, but it also underscored that terpenoid production is strongly influenced by cultivation conditions. Therefore, understanding the basis of the endogenous regulation of the isoprenoid pathways will be key toward designing efficient metabolic engineering and bioprocessing strategies. Nuclear transformation in C. reinhardtii occurs by random chromosomal integration of the transgene. Therefore, transgene expression is highly variable across independent cell lines (due to the position-of-integration effects) and may be subjected to currently poorly-understood endogenous silencing mechanisms possibly both at the transcriptional and translational level. Therefore, high-throughput screening platforms are required for identifing the best performing strains. In addition, a targeted gene integration technique would improve reproducibility.This work demonstrated the feasibility and efficacy of fine-tuning of isoprenoid metabolism in Thalassiosira pseudonona and Phaeodactylum tricornutum have emerged in recent years as models for the two main diatom morphological sub-groups (centric and pennate).Diatoms are taxonomically distant from higher plants but, being eukaryotic like higher plants, they have both MVA and MEP pathways for isoprenoid precursor production. Diatoms are known to produce high amounts of carotenoids , and a Phaeodactylum tricornutum is a potentially promising candidate because of its peculiar organization of central carbon metabolism, which comprises three different glycolytic pathways. Besides the Emden Meyerof Parnas glycolysis, P. tricornutum possesses a putative phosphoketolase pathway and a functional eukaryotic Entner-Doudoroff pathway [P. tricornutum, have been heterologously expressed and employed by engineered yeasts—where they are absent—for increased availability of isoprenoid precursors [ pathway . Althougecursors .P. tricornutum revealed several unusual features including the absence of a conventional squalene epoxidase enzyme and the combination of the enzymatic activities of IPP isomerase and squalene synthase in a fusion enzyme [An investigation of the genetic basis of isoprenoid metabolism in n enzyme . These uP. tricornutum resulted in the 2.4 fold increase in the yield (24.4 mg g−1 DW) of the native high-value carotenoid fucoxanthin [Diatoms benefit from the most advanced genetic toolbox among algae and the availability and the efficiency of the molecular tools to genetically manipulate diatoms has recently progressed enormously. High-efficiency genetic transformation can be achieved by several means ranging from biolistic DNA-coated particle bombardment, electroporation, and bacteria-mediated conjugation in which the latter is compatible with the transfer of large DNA constructs . Methodsoxanthin , which hP. tricornutum. The expression of a Lotus japonicus lupeol synthase in P. tricornutum enabled the conversion of 2,3-epoxysqualene into lupeol to the maximum titre of 0.1 mg l−1 and the conversion of this to trace amounts of betulin by expressing a Medicago truncatula CYP450 oxidase (MtCYP716A12) and its reductase (Mt71CPR) [To date, there is only one example of heterologous terpenoid production in diatoms. This was aimed at the production of plant triterpenoid saponigenin by installing a synthetic branch in the sterol biosynthesis of Mt71CPR) . The proMt71CPR) .These initial efforts in terpenoid engineering in algae clearly showed the potential and the feasibility of the production of plant terpenoids in algae while underscoring that key bottlenecks provide sufficient pools of substrates and metabolic pull from heterologous production with competing essential pathways such as that from sterol biosynthesis. This points towards the need to characterize and understand endogenous isoprenoid metabolism for efficient engineering. The feasibility of transferring large extrachromosomal genetic constructs provides the opportunity for more complex and multiplexed engineering approaches for heterologous production in diatoms, which makes this group of organisms one of the most promising candidates among photosynthetic eukaryotic microbes.Even though there has been considerable progress in their metabolic engineering, heterologous production of terpenoids is far from straightforward in photosynthetic microorganisms. However, with the expansion of available hosts, engineering tools, and understanding of terpenoid metabolism regulation, increased product titre and diversity will become available. Synthetic and systems biology are expected to contribute greatly to this effort by providing more options and helping in more holistic—taking into account not just the heterologous pathway and the connected metabolic branches, but the whole metabolism—engineering.Synechococcus UTEX 2973, algae members of the genus Nannochloropsis (Eustigmatophyceae), or more non-model species may reveal strains more suitable for high terpenoid productivities. Ultimately, photosynthetic terpenoid ‘chassis cells’ that deliver reasonable base rates, titers, and yields [E. coli and yeast will become available.Presently, cyanobacteria and algae are limited by small genetic engineering toolboxes compared to model heterotrophs, have a lack of knowledge about regulatory mechanisms, and have relatively slow growth. They also require non-standard equipment (light growth cabinets and photobioreactors). Production under different growth conditions (hetero/mixo/autotrophic) and light regimes (continuous light/photoperiod) has shown some promise but needs to be further evaluated. As toolboxes for manipulation develop further and our knowledge of regulatory systems expands, engineering will become easier in these organisms. Moreover, expanding our engineering efforts to more algal and cyanobacterial species such as the cyanobacterium d yields  similar E. coli. It is probable that increasing the numbers of groups will engage with these microorganisms as tools and understanding improves. This is drawn by the benefits of CO2 fixation as a carbon feedstock and the potential for improved production of specific target terpenoids especially decorated plant terpenoids. Photosynthetic microorganisms may provide more production options including for the expression of more complex plant pathways.A better understanding of flux regulatory mechanisms will improve our ability to successfully redirect carbon to heterologous pathways without impinging unduly on essential metabolic processes. These mechanisms are bound to be different than the ones in heterotrophic mechanisms, given the different origin of metabolic precursors (photosynthetic) and the more complex redox regulation. Historically, terpenoid engineering in photosynthetic microbes has only just begun. The first report was just eight years ago  and rela


S-adenosylmethionine pathway, the Krebs cycle, as well as CO2 fixation. It now feels that it is only a question of time until essential parts of metabolism can be replicated non-enzymatically. Here, I review the ‘accidents’ that led to the discovery of the non-enzymatic glycolysis, and on the example of a chemical network based on hydrogen cyanide, I provide reasoning why metabolism-like non-enzymatic reaction topologies may have been missed for a long time. Finally, I discuss that, on the basis of non-enzymatic metabolism-like networks, one can elaborate stepwise scenarios for the origin of metabolic pathways, a situation that increasingly renders the origins of metabolism a tangible problem.Until recently, prebiotic precursors to metabolic pathways were not known. In parallel, chemistry achieved the synthesis of amino acids and nucleotides only in reaction sequences that do not resemble metabolic pathways, and by using condition step changes, incompatible with enzyme evolution. As a consequence, it was frequently assumed that the topological organisation of the metabolic pathway has formed in a Darwinian process. The situation changed with the discovery of a non-enzymatic glycolysis and pentose phosphate pathway. The suite of metabolism-like reactions is promoted by a metal cation, (Fe(II)), abundant in Archean sediment, and requires no condition step changes. Knowledge about metabolism-like reaction topologies has accumulated since, and supports non-enzymatic origins of gluconeogenesis, the  Orgel To postulate one fortuitously catalyzed reaction, perhaps catalyzed by a metal ion, might be reasonable, but to postulate a suite of them is to appeal to magic. A century of biochemical research has revealed a stunning conservation of the topological organisation and functional principles of the metabolic network, the biochemical system that enables all life processes –5. This in vitro selected ribozymes obtain their catalytic activity via the binding of metal ions such as zinc . In. In47]. We speculated that small changes in pH could have enabled early forms of metabolic regulation. Furthermore, another point to mention here is the temperature dependency. While we have shown the initial experiments at 70°C, we also tested lower temperatures. Non-enzymatic glycolytic and PPP reactions already achieve reasonable rates at temperatures of 40°C and lower . Hence, In the reducing atmosphere of early Earth, iron stayed in its reduced form and was water soluble. Iron is by far the most concentrated transition metal in Archean sediment. Recent estimates indicate that the Archean oceanic iron concentration may have reached millimolar levels ,52,53. TBack to the question of how thermophiles can evolve metabolism that includes metabolites that are unstable at the temperature they live in, such as the small carbon phosphates glyceraldehyde 3-phosphate or dihydroxyacetone phosphate. The solution is non-enzymatic reactions that resemble metabolic pathway topologies: if glyceraldehyde 3-phosphate would decompose randomly, it would indeed be a problem in the evolution to metabolism: first because the metabolite would be ‘lost’ for the cell, and second because no topological organisation of the network would be maintained. The situation is, however, completely different if the heat-induced reaction converts the intermediate into another metabolite that is important for the organism. Under heat, pyruvate forms from glyceraldehyde-3 phosphate . At low Of note, studies based on sequence comparison have often found PPP enzymes lacking in thermophiles and concluded on this basis that they ‘lack’ a PPP . Our datIn an online search, I came across The Sandwalk blog  in which provocative language is used against a good number of high-profile scientific papers, many of them in the origin-of-life and evolution field. If still online, a blog entry named ‘more primordial soup nonsense’ makes nonetheless an interesting read, because it points to a common misconception about the origins of metabolism: While there is an ongoing scientific debate about the many unsolved problems about the origins of catabolic processes ,8,56, thIrrespective of whether the carbon bond-forming reaction of gluconeogenesis indeed seems to have originated in non-enzymatic chemistry as well , the anaFor understanding the evolution of enzymatic pathways, one has to keep in mind that selective advantage of an anabolic product is only given if a metabolite can be used by the biological system. Certainly, glycolysis could only evolve because biological systems can form glucose, but equally, gluconeogenesis does only provide a selective advantage because the biological system can metabolise the glucose formed.2 and H2 has been discussed as a basis of autotrophic carbon fixation [The other point is that metabolism is a far from equilibrium system. In the context of the origins of life, one likely disequilibrium between COfixation ,58; but fixation . If earlfixation .The third important point is that thermodynamic properties in a system like in a metabolic network are coupled to one another. A single reaction, or a single pathway, plays its role in life by being part of the metabolic system, which involves both ecological and environmental interactions. What matters are the properties of the system as a whole, that is a sum of the properties of all of its intermediate reactions. It has, for instance, been argued that the Wood–Ljungdahl might be the oldest among carbon fixation pathways because of its exergonic nature . GlycolyIf a non-enzymatic process enabled the evolution of glycolysis and the PPP — but the two pathways alone are not sufficient to form a functional system — this means that additional, perhaps many, non-enzymatic metabolism-like reactions still need to be discovered. Indeed, over the last two years, a plethora of additional metabolism-like chemical networks have been elaborated. I discuss some of these in brief.The biological importance of glycolysis requires an evolutionary origin of a process that can also form the intermediate of upper glycolysis. A key candidate is gluconeogenesis, an essential pathway that operates opposite to the catabolic functionality of glycolysis. A recently discovered aldol condensation can form fructose 1,6-bisphosphate from the otherwise unstable glycolytic three-carbon intermediates dihydroxyacetone phosphate and glyceraldehyde-3-phosphate, preventing the metal-induced catabolic reaction that otherwise would form pyruvate . The reaS-adenosylmethionine (SAM), the key donor of cellular methyl groups. Enzymatically, the formation of SAM is highly sophisticated, but the Tawfik laboratory recently discovered that ATP and adenosine react non-enzymatically with methionine to yield SAM spontaneously. Again, a non-enzymatic metabolism-like reaction may have primed the emergence of the complex metabolic pathway [Another recently discovered important non-enzymatic reaction leads to the formation of  pathway .2+, Cr3+ and Fe0 [Another key metabolic pathway that is canonically referred to as central metabolism is the Krebs cycle. As different from glycolysis and the PPP, the origin of the Krebs cycle has long been discussed in the context of the early evolution of metabolism. In the origin-of-life literature it is typically argued that the early Krebs cycle was most probably reductive, a perception that emerges from the fact that the Early Earth atmosphere was reductive ,64. If t and Fe0 .More recently, the Moran laboratory also used a similar approach to test for non-enzymatic carbon fixation. This approach was led to the identification of a non-enzymatic Acetyl-CoA pathway . Despite3+ and/or Fe0 concentrations if introduced to central metabolism would affect the topological organisation of the metabolic network by catalysing additional reactions that are not part of metabolism. More so, because the reactions require step changes in conditions, a situation arises that forestalls the Darwinian selection of an enzymatic network which essentially requires co-occurrence of the reactions in a single milieu, so that a functional, selectable unit is formed.The existence of environmental reductive-non-enzymatic Krebs cycle-like reactions is exciting. At the same time, one has to note, however, that despite the intensive three-decade spanning search for reductive Krebs cycle-like non-enzymatic reactions, the ones that have been identified still fall short of providing a precursor for a Darwinian selection process. For the minor part, because these reactions are driven by catalysts such as semiconductor particles, strong UV light, high CrRather than optimizing individual reactions, and aiming to synthesize specific products at optimal yield, we instead screened hence for such unifying reaction conditions. The rationale for the screen was that that if indeed the Krebs cycle emerged from non-enzymatic reactions, a single condition in which many of them can co-occur, must exist. Taken the other way, the approach we choose differs from classic organic-chemistry experimentation  in a funIt is in the nature of a single-condition approach that, like in metabolism, one expects reactions to occur at different rates, ranging from fast to slow reactions, which requires the application of sensitive analytical techniques with a high dynamic range. The key aspect here is that, for serving as a precursor to the selection process for an enzymatic reaction, a metabolite does not need to be present at high concentrations, but it is important that the reaction can be accelerated by enzymatic evolution to provide an advantage .We did constrain the screen of a broad range of both reductive and oxidative environments, by the fact that the biology of the Krebs cycle is tightly bound to the presence of iron and sulfur . LiteralThe condition identified was broadly seen as a surprise however. While in the case of glycolysis and the PPP the screening approach yielded a condition that met expectations generated by the geosciences (i.e. the driver of the reactions are soluble Fe(II) cations kept reductive in a low- oxygen atmosphere), the unifying condition in which all Krebs-cycle intermediates react in a Krebs cycle-like manner was a solution containing sulfate radicals that are formed from peroxydisulfate — hence an oxidative, and not a reductive condition. In follow-up experiments, we identified also another condition that enables a large fraction of Krebs-cycle non-enzymatic reactions, and also this was oxidative: a solution containing hydrogen peroxide . More re2O2 and hydroxyl radicals, are essential in metabolism and signalling of every cell [Initially, were unsure how to interpret this result. While a large fraction in the origin-of-life community currently assumes the Krebs cycle to descend from a reductive pathway, our results as well as the results of Springsteen et al.  indicateery cell . As discHow strong is actually the evidence that the enzyme-catalysed Krebs cycle originated as a reductive pathway? Phylogenetic data strongly indicate that the Krebs cycle was, at least in parts, already present in LUCA . PhylogeAn often repeated argument centres around the situation that an early atmosphere was reductive ,67. In a2 [2 fixation function that is the common function of the Krebs cycle. Indeed, rather than gaining carbon from the Krebs cycle, the typical problem higher organisms have with the Krebs cycle is actually the loss of CO2 induced by its oxidative activity. They use complex mechanisms, including the Cori cycle, rather than a reductive Krebs cycle, to overcome extensive carbon loss. The evolution of complex and costly compensatory mechanisms for the CO2 loss as induced by the Krebs cycle seem to be in contrast with the interpretation that its evolutionary precursor was actually providing selective advantage exactly through providing CO2 to these species.What does the Biological record say about the nature of the early Krebs cycle? First, one notices that many species possess only part of the Krebs cycle. In these cases, the Krebs cycle is typically oxidative, and barely fixes CO2 ,84. In g2 fixation. Particularly illustrative are photosynthetic species that, despite possessing a fully functional Krebs cycle, use the much slower Calvin cycle for CO2 fixation. Keeping in mind that Darwinian evolution can only select for an enzymatic pathway if it provides a selective advantage above the existing metabolic system, this fact is indicative about one of two scenarios. Scenario one is that the Krebs cycle - at least in these species - was not CO2 fixating, at least at the time when the Calvin cycle evolved. Scenario two is that photosynthetic organisms lost the ability of fixating CO2 through the reductive Krebs cycle at some point, paving the way of the slower Calvin cycle to evolve later. The second scenario would be rather unusual; evolution typically does not take steps back to lose an efficient pathway to replace it with a less efficient one. There remains the possibility that an enzyme-catalysed reductive Krebs cycle could not be maintained mechanistically upon the oxygenation of the atmosphere. But here are photosynthetic species that use a reductive Krebs cycle, indicating the pathway is not necessarily dysfunctional in photosynthetic species [2 ; the essential function of the Krebs cycle in modern organisms is, however, explained by its role of providing intermediates, mainly for the synthesis of amino acids — a process in which the Calvin cycle is not involved. In parallel, a difficulty is that it would dictate the defining reaction of the Calvin cycle, catalysed by Ribulose-1,5-bisphosphate carboxylase (Rubisco), to emerge only upon the oxygenation of the atmosphere. The enzyme can, however, accept not only CO2 but also oxygen as a substrate, and even the highly sophisticated modern Rubisco enzyme can only partially prevent this side reaction, explaining why despite immense evolutionary pressure causing Rubisco to have become the most abundant enzyme in the biosphere, the reaction remains so slow in the oxygen-rich atmosphere [Particular attention should be placed on species that possess the Krebs cycle in parallel to other pathways of CO species . Third, mosphere –87.In other words, the biological evidence about the nature of the early versions of the Krebs cycle sends mixed messages. In particular, it is important to keep in mind that the fact that an early atmosphere was in a reductive equilibrium is no convincing argument towards a claim that all early metabolic reactions were reductive too. This situation underlined by the existence of multiple oxidative pathways like glycolysis, which originated pre-oxygenation by participating in the far from equilibrium metabolic network. More than anything, the debate about the nature of the early Krebs cycle shows that the theoretical fundament about the origins of metabolism, and the conditions that led to the origins of metabolic pathways, are currently far too weak to dismiss systematically obtained experimental evidence, specifically if it is in conflict with some of current hypothetical assumptions.Last but not least, I would like to briefly speculate about the role of non-enzymatic metabolism-like reactions on the origin of the metabolic network. Basically, there are two questions to answer, and these may not be the same. The first one is, were these the processes that also supplied the early cells with their required metabolites? And second, did such reactions enable the evolutionary origins of enzymes, metabolic pathways and, hence, the metabolic network? Obviously, there is no answer to the first question (yet), and indeed, the proof of this principle is still missing. The problem here is that non-enzymatic glycolysis, PPP and oxidative Krebs cycle non-enzymatic reactions ,47,74,88in ice [d-sugars and l-amino acids [One might be much closer to an answer to the second question. A key answer the bottom-up approaches must provide is how the chemical network serves in the evolution of enzyme-catalysed metabolism. Non-enzymatic reaction systems may be an elegant solution to overcome the end-product problem: the existence of non-enzymatic precursors converts the origin of a metabolic pathway into a stepwise problem , which min ice  has showno acids , or provno acids . The firUntil recently, non-enzymatic, prebiotic reaction sequences that resemble metabolism were not known. The recent discovery of a non-enzymatic glycolysis and PPP, followed by the discovery of several other non-enzymatic and prebiotically plausible metabolism-like processes, has changed this situation. Hence, it appears that the topology of metabolism is rooted in the non-enzymatic chemistry accessible to early life forms that existed irrespective of  the Darwinian selection process. For glycolysis and the PPP, the key enabling agents are Fe(II) cations, broadly available on the primordial planet, as evidenced from the sedimentary record. Hence, it appears that mundane conditions were implicated in shaping the topological organisation of metabolism, and yet, the existence of non-enzymatic metabolism-like reactions may explain why modern thermophiles can persist at temperatures that are beyond the thermal stability of some of their key metabolites. By serving as a ‘template’ in the Darwinian selection processes, the existence of non-enzymatic metabolism-like reaction sequences may have allowed life to overcome the end-product problem and facilitated the origin of metabolic pathways. Furthermore, there is growing evidence that enzymes might have exploited amino acid-based as well as metal ion catalysis since the beginnings of metabolism. Hence, the origin of life, often dismissed as a ‘philosophical problem’, is increasingly becoming a problem that is experimentally tangible.


One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from eukaryotes, so models trained on eukaryotes may not apply to prokaryotes.Escherichia coli. The network was trained and tested on the recently-released sgRNA activity dataset. Our convolutional neural network achieved excellent performance, yielding average Spearman correlation coefficients of 0.5817, 0.7105, and 0.3602, respectively for Cas9, eSpCas9 and Cas9 with a recA coding region deletion. We confirmed that the sgRNA prediction models trained on prokaryotes do not apply to eukaryotes and vice versa. We adopted perturbation-based approaches to analyze distinct biological patterns between prokaryotic and eukaryotic editing. Then, we improved the predictive performance of the prokaryotic Cas9 system by transfer learning. Finally, we determined that potential off-target scores accumulated on a genome-wide scale affect on-target activity, which could slightly improve on-target predictive performance.We designed and implemented a convolutional neural network to predict sgRNA activity in We developed convolutional neural networks to predict sgRNA activity for wild type and mutant Cas9 in prokaryotes. Our results show that the prediction accuracy of our method is improved over state-of-the-art models. Gene editing allows modification of the genome and transcription products on target sites. The CRISPR-Cas9 system is a bacterial adaptive immune system, which includes CRISPR-associated nuclease Cas9 (SpCas9), a specificity-determining CRISPR RNA (crRNA), and an auxiliary trans-activating RNA (tracrRNA) –3. The cCRISPR-Cas9 using a specific sgRNA can precisely edit the target site (i.e. on-target editing), though it may bind and edit at other additional sites (i.e. off-target editing). Off-target effects are undesired and should be minimized. Moreover, widely varying sgRNA on-target activity limits further application of CRISPR-Cas9 gene editing –22. PoorE. coli [recA) [Some studies indicate that the CRISPR-Cas9 system is affected by chromatin structures in eukaryotic cells. Chromatin openness and CRISPR-Cas9 mutagenesis efficiency are correlated, indicating that CRISPR-Cas9 mutagenesis is influenced by chromatin accessibility in zebrafish embryos . MappingE. coli . A gradiE. coli . Althougi [recA) , indicatRecently, a deep-learning framework, DeepCRISPR , was preIn this study, we developed a convolutional neural network with five convolution layers to predict sgRNA activity in prokaryotes. We created a sgRNA activity predictor for wild type and mutant Cas9 in prokaryotes, surpassing available state-of-the-art models. We confirmed that sgRNA activity prediction models trained on prokaryotes are not appropriate for eukaryotes. Then, we trained our convolutional neural network with eukaryotic data, similarly surpassing available state-of-the-art eukaryotic models. We next adopted perturbation-based approaches to analyze biological patterns between prokaryotic and eukaryotic editing. Then, we improved predictive performance of prokaryotic Cas9 by transfer learning. Finally, we observed that genome-wide potential off-target effects influence on-target activity, and utilized genome-wide accumulative potential off-target scores and sgRNA guide sequence fold scores to further improve predictive performance.recA). We removed redundancy in Set 1 with similarity threshold 0.8, which established another dataset  with good signal-to-noise ratio and low bias, including Cas9, eSpCas9, and Cas9 (△ with goorecA), respectively, in Set 1 , respectively, in Set 1. The p-values in Set 2 were similar to those in Set 1. We found that the performance of simple CNN_2layers . All Spearman correlation coefficients are less than 0.11. Among prokaryotes, we used the eSpCas9 model to predict Cas9 activity, with a Spearman correlation coefficient of 0.5822. These results from these two different results demonstrate our model is valid within domains, but not valid between domains.Eukaryotic sgRNA activity prediction models are almost invalid for prokaryotes , 40. We We trained CNN_5layers with eukaryotic data, which produced a eukaryotic model. Then, we compared our eukaryotic model with other models including DeepCRISPR , DeepCasp-values ∈, Additional file recA) scenario. The melting temperatures are listed in Additional file We next calculated target sequence melting temperatures T1_7), T8_15), T16_20), T(1_20), T(-5_-1) and T(21_+2) (see Methods). The Spearman correlation coefficients between on-target activity and melting temperatures Table  are all _7, T, free energy of the thermodynamic ensemble (FETE), frequency of the minimum free energy structure in the ensemble (FMSE), and ensemble diversity (ED) to characterize the secondary structure of 20 nt-long guide RNAs using ViennaRNA  see Met. We alsoTo understand invalidity across domains, we interpreted the trained CNN models and analyzed the learned biological features with perturbation-based approaches –48. FiguRegion I of the three scenarios shows extreme base preference and Region II takes second place Fig. . We founWe calculated importance scores by calculating the absolute values of four differences at each position. The normalized cumulative value of 41 points on each curve is shown in Fig. The predictive performance in prokaryotic eSpCas9 scenario is more accurate than prokaryotic Cas9 and eukaryotic scenarios. According to the ideology of transfer learning  and to irecA). We found that all Spearman correlation coefficients between the four POSs and sgRNA activity for eSpCas9 were lower than for Cas9  for every sgRNA. Cropit_POS, Cctop_POS, Mit_POS, and Cfd_POS were potential off-target scores calculated by CROP-IT , CCTop , MITScors9 Table , which cs9 Table  to predirecA), only RNA fold scores (f_c) improved performance. In all three scenarios, melting temperatures did not greatly improve performance, indicating that CNN_5layers is more able to learn melting temperature features than RNA fold score features. Additionally, melting temperature features were extracted more easily , T(8_15), T16_20), T(1_20), T(-5_-1), T(21_+2), MFE, FETE, FMSE, and ED. We tested several feature combinations. The feature combinations are described in Table 0, T1_20. We obse_7, T(8_1N4N20NGGN3) is so small that important feature information is omitted. As shown in Fig. We compared our CNN_5layers with other published networks, such as CNN_Lin , DeepCasrecA). However, the performance improvements are very small. Multi-modal network architectures have been widely reported, so we attempted to make POSs a CNN input by adding them as an input channel or concatenating them into flattened fully-connected layers. However, fluctuations in predictive performance made it difficult to determine exactly whether performance improved. Our two-step training method shows improved performance. The main advantage of CNN is abstracting features layer by layer [Genome-wide potential off-target effects influence on-target activity and utilized genome-wide accumulative potential off-target scores to further improve predictive performance for Cas9, eSpCas9 and Cas9 , eSpCas9, and Cas9 (△recA). The eSpCas9 is a reengineered Cas9 derivative with improved specificity, containing K810A, K1003A, and R1060A mutations [recA) was developed in Escherichia coli by knockout of recA blocking DSBs repair. Three datasets included 44,163, 45,070, and 48,112 sgRNAs, respectively [The main datasets that we used in training and testing were from ∼70,000 sgRNA activity maps, which were systematically profiled by co-expressing a genome-scale library with a pooled screening strategy in utations . Cas9 , ensuring that target regions were flanked by a 3’NGG PAM site. Then, we extended the target DNA sequences to 43 nt, namely N10N20NGGN10 . There were several reasons for these extensions: (1) some studies indicate that sequences upstream and downstream of sgRNA target sites have favored and unfavored nucleotides in human and mouse cells [Ciona cells [We mapped these 20 nt sgRNA guide sequences to the se cells , 24, espna cells ; (2) thena cells –62; (3) na cells , 49. DeerecA)  Table .For model inputs, we converted 43 nt-long DNA sequences into one hot code representation. Each position had a four-element vector with one component set to one and the others set to zero. Each sample was represented as a 4 ×43×1 three-dimensional matrix, where 4 represents four channels. The model output is a value which predicts on-target activity.To achieve the desired performance, we adopted advanced algorithms such as batch normalization and a leaky rectified linear unit (LeakyReLU). Batch normalization (BN) addresses the internal covariate shift. This method normalizes each training mini-batch, allows much higher learning rates, and makes model more easily initiated . The ReLn×3×1 and stride 1 convolution kernel size, a maximum pooling layer with 2 ×1 and stride 2 window size, and two fully-connected layers to build CNNs with various convolution layers and multiple scales of fully-connected layers  [We compared our algorithms with three recently published prediction algorithms based on deep learning. The first network architecture is DeepCRISPR , which i20NGGN3) . Another20NGGN3) . We dist20NGGN3)  and Deep20NGGN3)  by refer20NGGN3) , CNN_Lin20NGGN3) , DeepCas20NGGN3) , and ourWe used fourteen eukaryotic datasets which were collected and arranged by Haeussler et al. . We chosN10N20NGGN10 in a certain order, -10 to -1 for the first N10 (upstream), 1 to 23 for N20NGG, and +1 to +10 for the last N10 (downstream). Then, we calculated the melting temperatures of 1 to 7 (Region III), 8 to 15 (Region II), 16 to 20 (Region I), 1 to 20, -5 to -1, and 21 to +2 (T(1_7), T(8_15), T(16_20), T(1_20), T(-5_-1), and T(21_+2), respectively; Table Previous studies showed that the predictive scores were most influenced by melting temperature, which is determined by Watson-Crick base pairing. In other words, the melting temperatures in different regions are effective predictive metrics , 28. We Escherichia coli by Cas-OFFinder [Off-target effects were detected in multiple cell types. Off-target effects possibly happen at any region on a genome-wide scale as long as the region contains a PAM and 20 nt-long protospacer sequence with minor mismatch. Different sgRNAs have different number of candidate off-target sites. Moreover, different candidate off-target sites have different off-target efficiencies for a given sgRNA. To research potential off-target influence on on-target activity for various sgRNAs, we found out genome-wide potential off-target sites  for every sgRNA in OFFinder . We founOFFinder , CCTop [OFFinder , MITScorOFFinder , and CFDOFFinder  to respeOFFinder  and did β1 = 0.9, β2 = 0.999, ε = 1e - 08. The batch size is set to 128. As shown in Fig. We trained our CNNs and other networks on a single NVIDIA Quadra P6000 GPU. Our CNNs were based on PyTorch framework. For DeepCRISPR , DeepCasrecA) was not involved in research of models interpretability and transfer learning.We adopted perturbation-based approaches, which changed a part of the input and observed its impact on the model output –48. We un is the number of data points of the two variables and di is the difference in the ranks of the ith element of each random variable considered. The two variables are on-target activity value and its prediction. We used t-test to test the Spearman correlation coefficients. We calculated the Spearman correlation coefficients and the p-values by SciPy library in Python. Moreover, we used a Steiger test to compare the Spearman coefficients between two models, which was performed by psych package in R.We used the Spearman correlation coefficient to evaluate the model performance, which is defined as Additional file 1 More detailed tables. Statistical significance of Spearman correlation coefficients in this paper and more details about 5-fold cross-validation. Table S1. Performance of all modes including DeepCRISPR, CNN_Lin, DeepCas9, CNN_2layers, CNN_3layers, CNN_4layers, CNN_5layers, and CNN_7layers. Table S2. The Spearman correlation coefficients and significances between eukaryotic sgRNA activity and predictions based on our prokaryotic models and eukaryotic models. Table S3. Significance between on-target activity and six melting temperatures, four RNA fold scores, and four POSs. Table S4. Detailed information of 5-fold cross-validation for several network architectures.Additional file 2Figure S1. Real-time performance comparison under 5-fold cross-validation. The figure shows the real-time average Spearman correlation coefficients changes during being trained for several network architectures. Horizontal x-axis is training epochs, and vertical y-axis is average test Spearman correlation coefficients under 5-fold cross-validation. a, b and c represent Cas9, eSpCas9 and Cas9 (△recA), respectively. The light pink dashed are respectively corresponding to Spearman correlation coefficients of 0.542, 0.682 and 0.328 for Cas9, eSpCas9 and Cas9 (△recA), which are performances of gradient boosting regression trees.Additional file 3 CNN architectures for comparison. CNN architectures from CNN_2layers to CNN_7layers.Additional file 4 Eukaryotic datasets. Eleven independent eukaryotic test and training set of DeepCRISPR, DeepCas9, TSAM, and our CNN_5layers.Additional file 5 Melting temperatures. The melting temperatures of 43 nt-long target DNA sequences.Additional file 6Figure S2. Real-time performance comparison between raw and transfer learning scenario. The figure shows the real-time average Spearman correlation coefficients changes during being trained in raw and transfer learning scenario. Horizontal x-axis is training epochs, and vertical y-axis is average test Spearman correlation coefficients under 5-fold cross-validation.Additional file 7 Set 1 dataset. Set 1 samples including 44,163, 45,070, and 48,112 sgRNAs for Cas9, eSpCas9 and Cas9 (△recA), respectively.Additional file 8 RNA fold scores. Four physicochemical properties characterizing 20 nt-long guide sequences fold.Additional file 9 All sgRNAs POSs. POSs calculated by CROP-IT, CCTop, MITScore, and CFD-Score.


Tomato is a model organism to study the development of fleshy fruit including ripening initiation. Unfortunately, few studies deal with the brief phase of accelerated ripening associated with the respiration climacteric because of practical problems involved in measuring fruit respiration.Because constraint‐based modelling allows predicting accurate metabolic fluxes, we investigated the respiration and energy dissipation of fruit pericarp at the breaker stage using a detailed stoichiometric model of the respiratory pathway, including alternative oxidase and uncoupling proteins. Assuming steady‐state, a metabolic dataset was transformed into constraints to solve the model on a daily basis throughout tomato fruit development.CO2 released and an excess of energy dissipated at 40 d post anthesis (DPA) just before the onset of ripening coinciding with the respiration climacteric. We demonstrated the unbalanced carbon allocation with the sharp slowdown of accumulation (for syntheses and storage) and the beginning of the degradation of starch and cell wall polysaccharides. Experiments with fruits harvested from plants cultivated under stress conditions confirmed the concept.We detected a peak of We conclude that modelling with an accurate metabolic dataset is an efficient tool to bypass the difficulty of measuring fruit respiration and to elucidate the underlying mechanisms of ripening. Solanum lycopersicum) has been studied intensively with respect to genetics, physiology and biochemistry, and became an important model for fruit development. For climacteric fruit species, such as tomato, fruit maturation is characterized not only by a metabolic shift from normal development conditions toward the fully mature state, but also by a brief phase of accelerated ripening with a sharp increase in respiration , and a distinctive feature of UCPs is their activation by free fatty acids  system  allows the prediction of metabolic steady‐state fluxes by applying mass balance constraints to a stoichiometric model. Although metabolic modelling in plant cells is not as advanced as in other organisms such as microorganisms and mammalian cells  that modified the fruit carbon content. Metabolic modelling appears to be a way to fill the gap in our knowledge about fruit respiration because it gets around the difficulty of measuring gas exchange of fruits attached to the plant.The lack of technically reliable devices applicable to attached fruits has led to only few studies dealing with fruit respiration from a physiological prospect Atkin, . The aimSolanum lycopersicum L. Moneymaker cultivar as described in Biais et al. (−1). Photosynthetically active radiation (PAR) and air temperature were measured using a PAR Quantum Sensor  and resistance temperature detectors (PT‐100) located in a ventilated shelter at the top of the canopy, respectively. The temperature of eight growing fruits per condition  was measured using thermocouples (type K) positioned in the pericarp on the bottom of the first fruit of the fifth truss during three successive periods: from 12 to 26 August, from 3 to 21 September, and from 22 September to 3 October.Experiments were performed with the s et al. . BrieflyFruits at nine developmental stages  were harvested on three different trusses . For each sample, three biological replicates were prepared with a minimum of four fruits per replication. Samples were prepared by first removing the seeds, jelly and placenta, and then cutting the fruit pericarp into small pieces and immediately freezing them in liquid nitrogen. Samples were ground and stored at −80°C until analysis.et al. , total and individual amino acids, organic acids , starch and protein content are reported in Biais et al. . Total c .et al., atlab  and the optimization toolbox, solver quadprod with interior‐point‐convex algorithm for the minimization.Concentrations of accumulated metabolites and biomass components were fitted in order to calculate, by derivation, the corresponding fluxes used as boundaries in the flux balance model. To this end, a regression method was used to evaluate the derivative as precisely as possible at each stage of development. The best fit in the least‐squares sense which minimizes the sum of squared residuals, that is, the difference between observed and fitted values, was provided by a polynomial fitting method , fatty acid synthesis  and nucleotide synthesis . The other accumulated compounds were described as simple vacuolar storage: (1) organic acids, that is, malate  and citrate (Vac‐cit); (2) soluble sugars, that is, glucose (Vac‐glc), fructose (Vac‐fru) and sucrose (Vac‐suc); and (3) four groups of free amino acids, that is, glutamate (Vac‐Glu), aspartate (Vac‐Asp), alanine (Vac‐Ala) and serine (Vac‐Ser). Compared with the previous version  in addition to cell wall synthesis (Vcws), in order to split synthesis and degradation similarly to starch . These 16 fluxes linked to accumulated metabolites and biomass components were defined as outfluxes and were evaluated by fitting the experimental concentrations as described in Colombié et al.  involved ubiquinone (UQ/UQH2) which was reoxidized mainly by the cytochrome pathway (Vrcc34) associated with proton transfer from the matrix (Hi) to the intermembrane space (He). Back‐diffusion of protons to the matrix is coupled to the synthesis of ATP from ADP and Pi, through the activity of ATP‐synthase plus ATP/ADP and Pi carriers (Vatpsynthase). In our model, bypasses of the classical pathway were set by adding AOXs (Vaox) which directly couples the oxidation of ubiquinol with the O2 reduction and UCPs (Vucp) which dissipates protons similarly as proton leak. The non‐proton‐pumping NADH dehydrogenases were also taken into account for energy dissipation. Based on differences in capacity and affinity of complex I and alternative NADH dehydrogenases  that would represent at least a part of cellular maintenance. Thus, in the model respiratory metabolism was described by a set of seven equations:Because biosynthesis of the main biomass components  requires both ATP and NAD(P)H, energy costs were explicitly taken into account. The cofactors NADP/NADPH were linked to biomass production, and the cofactors NAD/NADH and FAD/FADH were linked to ATP synthesis via a cascade of reactions associated to the miETC described by the ‘standard’ pathway also termed the ‘cytochrome pathway’. The first reactions regenerating the cofactors  in order to take into account fruit expansion. Subsequently, to enable comparisons between, for example, environmental conditions, all fluxes were converted and expressed on a gram basis, by using the fruit growth curve (Table S2).The whole fruit was considered as the modelled system and all of the fluxes were first calculated on a fruit basis . At steady‐state, the mass balance equation is expressed by Xint=i=1…mint the vector of mint internal metabolites, V = (vi)j = 1…n the flux vector composed by the rates of n reactions of the network, N=(nij)i=1…mint,j=1…n the stoichiometry matrix where nij is the stoichiometric coefficient of metabolite xiint, in reaction j.With s (with s = 10 … 50), denoted, respectively, by Vmins=j=1…n and Vmaxs=j=1…n such that the flux V satisfies:A lower and an upper bound at each stage vmin,js≤vj≤vmax,js for j = 1…n.which means et al. .Second, as detailed in Colombié et al.  constraiet al., −1, were used to limit the corresponding flux, vmax,js, in the metabolic network, considering that they correspond to maximal enzyme capacity. A regression‐fitted curve was built in order to give a constraint value at each day for modelling (see Table S2). The same values, but negatives, were used as lower bounds for reversible enzymes. When the capacity of a given enzyme was not known (< 20 reactions), the bounds were set to infinity.Third, the experimentally determined activities of 30 enzymes of central metabolism (Biais next) denoted by vext,ks (with k = 0 …next − 1), were evaluated on a daily basis throughout development using a previously described fitting method , to enforce these vk to attain these fitted values vkext,, we set: Finally, from experimental concentrations of accumulated metabolites and biomass components, 16 fluxes .In order to take into account the variability of the experimental data, a bootstrap method was used to construct a number of resamples of the observed dataset obtained by random sampling with replacement from the original dataset. Once the transformation and the fit were applied to all of the resamples, a large set of polynomials was obtained. A 95% interval of prediction for the derivatives was used to fix the boundaries of the corresponding external fluxes denoted by In order to solve the system describing the development of the tomato fruit, the same objective function – that is, the minimization of the sum of the squares of all internal fluxes – was used for each stage in agreement with the principle of ‘minimal effort’ .From a computational point of view, as et al., et al.  and CO2 release from decarboxylation . This peak, which corresponds to high catabolic activity  with a high respiratory flux (CO2 released) was clearly visible when fluxes were expressed on a fruit basis, but also when fluxes were expressed on a gram basis , which describes a part of energy dissipation, showed a similar profile with a progressive increase starting before 30 DPA and a peak at 40 DPA. The maintenance flux (Vnga_ATP) also displayed a peak at 40 DPA. Considering the experimental variability, an interval of flux prediction followed the same behaviour except for the total ATP flux and the maintenance for which the peak at 40 DPA tends to disappear when all outfluxes were maxima. To check whether the peak of energy dissipation found at 40 DPA was not due to the bypassing reactions added in the model through the alternative pathways, the two reactions Vaox and Vucp were removed. In consequence, flux profiles still displayed a high metabolic and respiratory activity at 40 DPA whereas the maintenance flux was clearly increased . To sum up, the constraint‐based metabolic model predicted that a respiratory peak and a simultaneous dissipation of excess energy would occur at the breaker stage, and thus at the very moment of the occurrence of the respiration climacteric.The system Eqn  was solv, et al.  revealeddel Fig. ; note thsis Fig. . The calc. 7% of all the carbon stored at the end of fruit expansion (150 Cmmol per fruit at 50 DPA). To confirm that the origin of the energy peak observed at 40 DPA corresponded to the excess of degraded carbon polymers, we performed virtual experiments by increasing fluxes of both cell wall and starch degradation . Taken together these results indicate that the degradation of stored carbon contributes substantially to the respiration climacteric. This explains why the variability calculated with all outfluxes at minimum increased the peak, whereas all outfluxes at maximum tended to make the peak ATP flux and maintenance disappear   changed during development . At the breaker stage, the CO2 from the TCA cycle (Vidh +Vkgdh) reached 30% and 50% of total CO2 released under control and water‐stressed conditions, respectively, at the expense of Vpdh and Vg6pdh. Finally, cell wall degradation took place under all conditions and both galactose and galacturonate, two intermediates of cell wall degradation, were accumulated at the last stages of development, after 49 DPA . As expected and according to the data fits, cell wall degradation started before galactose and galacturonate would accumulate . Taken together these results: (1) confirm that cell wall and starch degradation supplies carbon for the respiration climacteric, and (2) show that environmental growth conditions, that is, water stress and shading, modulate the intensity of the energy peak observed before tomato fruit ripening.As previously reported the composition of the tomato fruit pericarp is not constant during fruit development and can be influenced by the environment  Fig. . Howeveret al., et al., Finally, for 30 fluxes, ratios between calculated fluxes and enzymatic capacities were calculated to estimate how much enzyme capacity is readily involved in a given metabolic flux. As previously reported for tomato fruit, most of these ratios have been shown to be very small  the main exothermic reaction is respiration; we estimated the heat production rate (Q in W or J s‐1) per fruit as follows :Assuming that in aerobic and nonfermentative tissue ; and ∆r H′0, molar reaction enthalpy of NADH‐linked O2 reduction , it was about thousand times lower than the fruit heat variation due to environment (c. 0.6 W). Thus, the tomato fruit thermogenesis due to the respiration climacteric appeared negligible.Finally, with a maximal estimated heat produced by metabolic activities close to 0.7 × 10et al.  was still present to balance energy cofactors and consequently can be considered as an adjustment variable. Energy costs of maintenance  can vary widely depending on the tissue, the growth conditions or the developmental stage. The increase in ATP, which in many fruits occurs during the respiration climacteric  the cytochrome pathway of miETC ) and/or (2) the proton‐linked oxidative phosphorylation ). Moreover, the nongrowing associated ATP maintenance  this peak is due to unbalanced carbon allocation just before ripening, (2) environmental stress modulates the quantity of carbon stored and hence the intensity of fruit respiration climacteric, and (3) heat from the metabolic activity of respiration contributes negligibly to fruit temperature change. Our constraint‐based metabolic modelling proved to be a good tool with which to estimate fruit respiration, which is difficult to measure in fruits still attached on the mother plant.Climacteric behaviour has been widely studied in fruits but the corresponding fruit respiration is difficult to measure. Using a constraint‐based model describing the cytochromic respiration and its bypasses, we predicted metabolic fluxes of tomato fruit development on a daily basis. The model revealed a peak of COIn order to go further in developing this tool, as there are a number of fleshy fruit species, including tomato, with different cultivars exhibiting climacteric or nonclimacteric behaviour  should be directed to the Fig. S1 Fumarate concentration throughout tomato fruit development.Fig. S2 Fit of the accumulated metabolites and biomass compounds.Fig. S3 Importance of dissipating fluxes, AOX and UCP, on calculated fluxes.Fig. S4 Plant culture and fruit characterization.Fig. S5 Fruit composition throughout development.Fig. S6 Origin of CO2 released throughout tomato fruit development.Fig. S7 Galactose and galacturonate throughout the tomato fruit development.Fig. S8 Estimated heat from metabolic activities.Fig. S9 Fruit temperature and fruit energy variations.Notes S2 Mathematical demonstration of unicity.Click here for additional data file.Table S1 Model descriptionClick here for additional data file.Table S2 Raw data and fitsClick here for additional data file.Notes S1 Stoichiometric model in sbml.Click here for additional data file.Video S1 Flux maps of water stress compared with control.Click here for additional data file.