Endosymbiosis is extremely rare in bacteria and archaea. Yet, one such endosymbiosis gave rise to eukaryotes and eventually complex multicellular life. While many factors may contribute to the rarity, we lack ways to estimate their influence. Here, we develop a quantitative approach to estimate the influence of one possible factor: metabolic compatibility, which is the ability of both host and endosymbiont to grow on the same resources. Using metabolic networks of existing prokaryotes, we find that more than half of possible endosymbioses would be metabolically viable; however, the resulting endosymbioses are less fit than their ancestors and unlikely to gain adaptations that overcome their fitness disadvantages. Our results provide null models that shed light on the diversity of prokaryotic life. The evolution of the mitochondria was a significant event that gave rise to the eukaryotic lineage and most large complex life. Central to the origins of the mitochondria was an endosymbiosis between prokaryotes. Yet, despite the potential benefits that can stem from a prokaryotic endosymbiosis, their modern occurrence is exceptionally rare. While many factors may contribute to their rarity, we lack methods for estimating the extent to which they constrain the appearance of a prokaryotic endosymbiosis. Here, we address this knowledge gap by examining the role of metabolic compatibility between a prokaryotic host and endosymbiont. We use genome-scale metabolic flux models from three different collections  to assess the viability, fitness, and evolvability of potential prokaryotic endosymbioses. We find that while more than half of host–endosymbiont pairings are metabolically viable, the resulting endosymbioses have reduced growth rates compared to their ancestral metabolisms and are unlikely to gain mutations to overcome these fitness differences. In spite of these challenges, we do find that they may be more robust in the face of environmental perturbations at least in comparison with the ancestral host metabolism lineages. Our results provide a critical set of null models and expectations for understanding the forces that shape the structure of prokaryotic life. The evolution of mitochondria—from independent organism to intracellular organelle—is exceptional in terms of its physiological significance, ecological repercussions, and apparent evolutionary rarity –4. WhileMoreover, after the establishment of the mitochondria, many branches of the eukaryotic lineage acquired other intracellular endosymbionts, including plastids , nitroge14Historically, these questions have been addressed by identifying possible barriers to the initial establishment of a prokaryotic endosymbiosis, such as the rarity of phagocytosis in modern prokaryotes  or issueWhile in principle we could focus on any of the different barriers, one powerful approach is to focus on universal metabolic traits that apply across the biosphere’s ensemble of species and environments , 20. Sin21An important tool in quantifying the ecoevolutionary role of metabolism is the genome-scale metabolic model. Such models use the genomes of an organism to infer its metabolic repertoire and predict its growth in different environments. Genome-scale metabolic models are now available for thousands of species and have been successfully applied to a variety of bioengineering, ecological, and evolutionary problems , 26. Of 27Here, we harness the abundance and flexibility of genome-scale metabolic models to consider putative endosymbioses between random pairs of prokaryotes. By using different collections of metabolic models along with a large number of genomes, we are able to quantitatively study general features of metabolic compatibility in nascent endosymbioses and estimate viability, persistence, and evolvability in several combined outcomes as outlined in Methods: Genome-Scale Metabolic Model Curation). Thus, we obtained metabolic flux models for prokaryotes from three of the largest collections: AGORA (818 models), KBase , and CarveMe . A common feature across collections is that models organize reactions and compounds into compartments; all models have compartments for the cytoplasm and extracellular environment, but CarveMe models are the only ones with an additional compartment for the periplasm. The collections vary in what species, reactions, and compounds they include, as well as their description of how reactions and compounds behave across compartments. Since the collections also differ in their standards for metabolic model creation and formats, even if the same species appears in multiple collections, the metabolic models are not directly translatable. We manage this variation by keeping models from different collections distinct and performing analyses independently on each collection. This reduces the risk of introducing errors and provides an opportunity to assess the extent to which our results depend on metabolic model formats.To assess metabolic compatibility in prokaryotic endosymbioses, we need a broad set of genome-scale metabolic networks (Methods: Assessing Growth and Viability of Metabolisms).With our broad array of metabolic networks, we can assess the metabolic viability, of putative prokaryotic endosymbioses scenario (1) in P value < .001). Such differences may stem from many possible factors including model format, the set of reactions used, the particular compounds available in the environment, and/or the composition of species and their representative environments. We can observe other differences between collections if we evaluate factors that may predict whether an endosymbiosis is viable—such as similarity in reactions or biomass compounds between host and endosymbiont . Yet, despite differences between collections, all 300 sets of samples have a viable percentage between 52 and 71%, indicating a similar scale for viability estimates. Moreover, if we consider both possible configurations of endosymbioses, we find that in each collection of metabolic models the majority of pairs, 84 to 92%, have at least one configuration that is viable  on 10,000 randomly sampled nonviable—yet fixable—endosymbioses from each model collection, we find that the majority of nonviable pairings can be fixed by the addition of a single transport mechanism . Although adding transport mechanisms for all compounds can fix the majority of nonviable endosymbioses, the likelihood that nonviability can be overcome depends on the number of transport mechanisms needed for viability. We estimated the minimum number of missing transport mechanisms by finding a set of compounds that fix viability only through their combined transport, i.e., failure to transport any one of them leads to nonviability. Using a series of linear programs  and (3) in SI Appendix, Fig. S3). Furthermore, we find that the metabolisms act identically in 93 to 97% of perturbations, with the most frequent occurrence being that all survive . If we instead compare the host–endosymbiont pair with the ancestral endosymbiont metabolism, the results depend on the collection such that host–endosymbiont pairs survive more often in KBase models while the reverse is true in AGORA and CarveMe .We observe similar population-level patterns of survival in response to environmental degradation between the host–endosymbiont pair and its ancestral metabolisms . In short, we maximize each metabolism’s growth rate assuming that the total flux of metabolites through the system is maximal. We restrict our analyses to AGORA and CarveMe models because KBase models often yield multiple possible growth rates. Since an endosymbiosis has to sustain two metabolisms, it could be that comparing its growth rate to those of an isolated ancestral host or endosymbiont metabolism is not a fair comparison. We address this possible issue by computing the growth rates of all three metabolisms when grown together in the same environment (Methods: Evolvability Assessment). We focus on mutations that increase the bounds on reaction fluxes, which will either have no effect on a metabolism’s growth rate  or increase it . We randomly sample 1,000 endosymbioses that grow more slowly than their ancestors and compute the effects of all possible single mutations in host and endosymbiont reactions. We identified beneficial mutations in AGORA and CarveMe models but did not find any in KBase models because reaction bounds are not the dominant constraint on growth rates in KBase. The evolvability analyses will thus rely only on models from the AGORA and CarveMe collections, though we did find similar results in KBase after imposing reaction bounds that do limit growth (Methods: Evolvability Assessment).Although endosymbioses grow more slowly than their ancestors, they may be competitive if they are more evolvable. We evaluate this possibility by determining the effects of mutations on metabolisms when they are independent or in an endosymbiosis . Certain ecoevolutionary processes may act to increase the frequency of successful metabolic pairings, such as the frequent exchange of entire metabolic pathways via horizontal gene transfer  in 35transfer –40 or thtransfer . Even intransfer . Since oSI Appendix, sections “Archaea-bacteria pairings” and “Ecological interactions and viability”).Our analysis of metabolic viability was averaged across hundreds of species in different model collections so there may be important factors that influence viability that were masked. For example, many prokaryotes have remarkably sophisticated ecto symbioses, such as aggregates of archaea and bacteria that perform anaerobic methane oxidation by passing electrons extracellularly , 44, theWhile metabolic compatibility did not provide a significant challenge to viability, it did emerge as a substantial filter during the persistence and evolvability stages, when endosymbioses compete with their ancestral metabolisms. Whether metabolisms competed to grow fastest or survive environmental perturbations, scenarios (4) and 5) in  in 5, 6,Examining the rare cases when an endosymbiosis has higher fitness than its ancestors can shed light on the conditions in which it can persist. When subjected to environmental perturbations, the endosymbiosis has a competitive advantage against its ancestral host species. Similarly, when a premium is put on fast growth, the endosymbiosis is more likely to outcompete its ancestral host species than the ancestral endosymbiont species. Taken together, these results suggest that if an endosymbiosis were able to persist, it would likely replace its ancestral host. This result is supported by the current ecological distributions of microbial eukaryotes and prokaryotes. Microbial eukaryotes coexist in abundance with the ancestral lineage of their mitochondrial endosymbiont, the alpha-proteobacteria , 52, but53K-strategists may allow a nascent endosymbiosis to survive when both its ancestors do not. These results are consistent with the argument that compared to bacteria, eukaryotes may have been K-selected with slower rates of reproduction and stable resource supplies . Such differences could stem from many factors including the organisms, environments, or procedures used to construct the networks themselves. Furthermore, the completeness of metabolic models is also an issue as it is likely that many metabolic models are missing reactions and processes. Research into improving metabolic network construction will likely lead to much future development. While we expect such development to produce refinements in our quantitative predictions, it is unlikely to change the broader conclusions that were common across the diverse collections considered here. For instance, our results show that compatibility between metabolisms is common, so if future development in metabolic models adds more reactions, it will likely only lead to increased compatibility, i.e., our results represent a lower bound estimate for metabolic compatibility. Regardless of our expectations, our metabolic endosymbiosis framework could readily accommodate future advances in the field of metabolic networks; it simply requires rerunning the computations with updated networks or different environmental conditions. Thus, our quantitative framework is adaptable and should continue to make useful predictions concerning the role of metabolic compatibility in the evolution and ecology of prokaryotic endosymbioses.Throughout this paper, we have made general predictions about the role of metabolic compatibility in shaping nascent prokaryotic endosymbioses, drawing conclusions from three different metabolic network collections. Although the results from each collection broadly agree about many aspects of metabolic compatibility in endosymbioses, there are some key differences. For example, the outcome of survival competition between a host–endosymbiont pair and its ancestral endosymbiont metabolism depended on the collection . We used the Constraint-Based Reconstruction and Analysis Toolbox (COBRA) (We obtained metabolic models of diverse prokaryotes in .xml format made available in ref.  (COBRA)  to creat (COBRA) . The .ma (COBRA)  to solveS) so that the same row corresponds to the same compound across models. Second, we changed how environmental compounds are made available to metabolic models. Initially, metabolic models access environmental compounds through source/sink reactions whose upper and lower bounds on flux determine how much of the compound is available. We replaced these reactions with mathematically equivalent constraints on the amount of compound available. So, a source/sink reaction flux ri for compound cj with lower and upper bounds li and ui, respectively, would be replaced with the equivalent constraint on the derivative of compound concentration, SH) is the same as if it were growing in isolation. When the metabolism is an endosymbiont, its extracellular compartment is identical to the cellular compartment of the host. Thus, we partitioned the stoichiometric matrix depending on whether the compounds are strictly within the endosymbiont (SE) or within the host (SE → H).For each model collection, we restructured the metabolic models to facilitate analyses of possible endosymbioses. First, we identified all compounds and reactions used within a metabolic model collection and reformatted the stoichiometric matrices  are available in the environment with nonzero amounts, 2) exist in both environmental and cellular compartments, and 3) are usable by the endosymbiont such that at least one reaction has it. We then added extra reactions in the host–endosymbiont joint stoichiometric matrix that enabled those compounds to be transported between the host’s [e] and [c] compartments. We performed flux balance analysis to determine the growth rate. If the growth rate was below the tolerance 0.001, it meant that the lack of viability was also due to the endosymbiont not having access to the external environment. We then confirmed that viability could be restored by providing the endosymbiont access to the external environment through creation of source/sink reactions in rithm in .xλH, ancestral endosymbiont xλE, and host–endosymbiont pair xλP. Second, we maximize the flux through each of the three biomass reactions one at a time while requiring the community flux be held constant at its maximal value , i.e., we add the constraint xλH + xλE + xλP = z. We perform the second step because the solution to a linear program may not be unique, so there may be multiple partitions of growth within a community that give rise to the same total community flux. For our analyses, we considered only those cases in which the growth rates of the host–endosymbiont pair and its ancestral metabolisms are unique when the total community flux is maximal. We determined uniqueness by requiring that the computed growth rates for metabolisms did not vary above 10% between its highest and lowest values. We found that the AGORA and CarveMe collections frequently gave unique values, 99.7% and 98.2% of computations, respectively; however, KBase only met this criterion for uniqueness in 0.02% of computations. Despite the lack of uniqueness in KBase-computed growth rates, the maximal host–endosymbiont pair growth rate rarely exceeded the growth rates of both its ancestral metabolisms, 3 out of 10,000 computations.We compute the growth rates of the host–endosymbiont pair together with its ancestral metabolisms in the same environment through a two-step process. First, we compute the maximal flux through the entire community by maximizing the sum of fluxes through the three biomass reactions: ancestral host xi has a lower bounds li (where li ≤ 0) and an upper bound ui, then we transform its bounds to −1000|li|≤xi ≤ 1000ui. We compute the growth rate following this mutation and then reset the bounds to evaluate another mutation. For host–endosymbiont pairs, we evaluate both mutations in endosymbiont reactions and host reactions separately.We evaluate the adaptive potential of metabolisms by determining how the computed growth rate is affected by mutations that increase the bounds of reactions. Since increasing the bounds of reactions can never decrease growth rates, the mutations are either neutral or beneficial. For ancestral metabolisms, we systematically modify each reaction bounds one at a time by a factor of 1000, effectively removing that reaction as a limiting constraint. Thus, if a reaction flux While our methodology to assess evolvability was able to identify mutations that affected the growth rate in AGORA and CarveMe collections, it did not find any in KBase. The lack of any beneficial mutations in KBase suggests that the reaction bounds do not constrain the growth rate. We tested this hypothesis by decreasing the bounds of all reaction rates by a factor of 100 and recomputing growth rates. We found that the growth rates did decrease and we could identify beneficial mutations in the metabolisms of the host–endosymbiont pair as well as its ancestors. The results are similar to the analyses with AGORA and CarveMe described in the main text: Mutations were more often beneficial in ancestral metabolisms than host–endosymbiont pairs (87.7% for host reactions and 78.0% for endosymbiont reactions).Appendix 01 (PDF)Click here for additional data file.


Consequently, it has wide applications in medicine, food, chemical production, and other burgeoning fields. Due to the shortcomings of chemical syntheses and bio-extraction techniques, alternative biotechnological methods have drawn increasing attention. In this review, we provide the first systematic summary of the progress in the microbial synthesis of heme b. Three different pathways are described in detail, and the metabolic engineering strategies for the biosynthesis of heme b via the protoporphyrin-dependent and coproporphyrin-dependent pathways are highlighted. The UV spectrophotometric detection of heme b is gradually being replaced by newly developed detection methods, such as HPLC and biosensors, and for the first time, this review summarizes the methods used in recent years. Finally, we discuss the future prospects, with an emphasis on the potential strategies for improving the biosynthesis of heme b and understanding the regulatory mechanisms for building efficient microbial cell factories.Heme  Howethetases . Howeverthetases .In evolutionary terms, the CPD pathway can be considered a transitional form between the SHD and PPD pathways . It sharIn the CPD pathway, the six electrons of CPG III are first oxidized to form coproporphyrin III (CP III). This implies that the macrocyclic oxidation in the CPD pathway occurs earlier than in the PPD pathway . This coThe insertion of the ferrous ions to form Fe-coproheme is the next step in the CPD pathway. The iron chelatase involved in this step is coproporphyrin ferrochelatase (CpfC), which is a soluble monomeric protein . It has The last reaction, catalyzed using Fe-coproheme decarboxylase (ChdC), is the oxidative decarboxylation of the two propionic acid side chains on the coproheme pyrrole rings A and B to produce the corresponding vinyl groups . The dec2O2 produced by the earlier pathway enzyme CgoX . S. S30]. Se (ShfC) . In thissiroheme .ahb genes. Firstly, in the presence of AhbA and AhbB, the acetic acid side chains attached to C12 and C18 are decarboxylated to generate 12,18-didecarboxysiroheme (DDSH). The siroheme decarboxylase is either a heterodimer of the two subunits AhbA and AhbB, or a genetically encoded fusion of AhbA and AhbB. AhbC oxidizes and removes the C2 and C7 acetic acid side chains of DDSH to produce Fe-coproheme. Finally, AhbD decarboxylates the propionic acid side chains attached to C3 and C8 to produce heme b [The latter three steps of the pathway are encoded using a series of e heme b . AhbC ane heme b .b  and lactate dehydrogenase (encoded by ldhA) ensures that more carbon enters the TCA cycle [b production in an E. coli strain that overexpressed the genes involved in the heme b synthesis [b production. MacB catalyzed the oxidative decarboxylation of malate to pyruvate in the presence of divalent metal ions [RSalaS gene was overexpressed together with the macB and dctA genes in E. coli, 38.4 mg of intracellular heme b could be extracted from 6 L of the fermentation broth, while it was almost undetectable in the control strain [Since 5-ALA is generated from glycine and succinyl-CoA, which in turn are derived from the metabolites of the TCA cycle, increasing the flux through the TCA cycle can effectively improve the supply of 5-ALA. Blocking the synthesis of the acetate and lactate catalyzed by phosphate acetyltransferase  was 90% higher than the control. It is noteworthy that CoA can inhibit pantothenate kinase by competitively binding to the ATP binding site (Lys101) [In addition to succinate, coenzyme A is also essential for succinyl-CoA synthesis. Pantothenate kinase (CoaA) produces 4-phosphopantothenate using an activated phosphate group from ATP , which u(Lys101) . As PEP (Lys101) , the addb. The AlaS orthologs from Rhodobacter capsulatus  [Rhodobacter sphaeroides  [E. coli  [b production. In addition, the metabolic characteristics of AlaS can be altered to facilitate the heme b accumulation though the modification of the key sites. AlaSfbr was produced by inserting two lysine residues (KK) between the second and third residues of the native AlaS to prevent the unusual proteolysis-mediated feedback inhibition induced by excess heme b [alaS gene was also studied. The saccharomyces cerevisiae strains were constructed based on the random integration of the alaS gene under the control of the PTEF1 promoter at more than 100 δ locus sites in the genome of the CEN.PK2-1C host. Among ten randomly selected strains, the copy number of the integrated alaS genes ranged from two to seven and the highest heme b titer was 6.1-fold higher than that of the control group [The C4 pathway was the first pathway to be applied in the study of the biosynthesis of heme  ,54, Rhod ,60,64 an  were oves heme b . Finallyol group .b in the C5 pathway have been analyzed in detail [C. glutamicum, but only the strain overexpressing the gluTR gene showed a significant 4.6-fold increase in the heme b levels. This indicated that the generation of GSA is a rate-limiting step, which is consistent with the thermodynamic analysis, as it had an extremely positive ΔG0 value. Subsequently, the GluTS, GluTR, and GsaM enzymes from the different sources were expressed in combination, and five different recombinant strains were established. Among them, the strains co-expressing with the gluTR gene from Salmonella arizonae with the gluTS and gsaM genes from E. coli produced the highest heme b titer of 4.22 ± 0.62 mg/L. Since it was shown that overexpression of the gluTS gene may adversely affect the overexpression of the other genes and cell growth [The three genes involved in the synthesis of heme n detail . The shul growth ,68, mostl growth ,54.b. To further increase the flux of the 5-ALA synthesis pathway, the C4 and C5 pathways were overexpressed simultaneously. The strain with both pathways produced 22.95 ± 0.63 mg/L of heme b, which was higher than in the single pathway strains and 4.49-fold higher than the wild-type strain [To some extent, the consolidation of either the C4 or C5 pathway allows for the accumulation of 5-ALA, the crucial precursor of heme e strain . HoweverMetabolic engineering reconfigures the cell’s biochemical network by manipulating the endogenous genes or introducing the heterologous genes to direct the conversion of the renewable raw materials into value-added compounds. However, the readjustment of the native metabolism of microorganisms often leads to serious imbalances in the pathway fluxes, which can have detrimental effects. The accumulation of intermediates can lead to a feedback inhibition of the upstream enzymes and the formation of unwanted by-products. Furthermore, some toxic intermediates may interfere with the cell growth . This reb pathway is mainly focused on multiple modular metabolic engineering (MMME), which relies on plasmids with different copy numbers  catalyzes the degradation of heme b to biliverdin, CO, and free iron. Due to the complexity of the heme b regulatory network, the hmuO transcription is subject to both inhibition by DtxR and activation by HrrA. The latter is required for basal level expression of hmuO, but the DtxR repression imposes an additional threshold for transcriptional activation. DtxR can only dissociate from the promoter of the hmuO gene and allow HrrA to induce the full activation only when the intracellular iron pool is depleted [hmuO gene by producing a nonsense mutation to completely prevent the heme degradation, the specific growth rate, and the heme b production of the engineered strain were significantly lower than the control strain [b concentrations on the strain. Therefore, reducing its expression level rather than knocking it out completely may be the best strategy.In depleted . Howeverl strain . This mab is an important compound in the aerobic electron transport chain, but paradoxically, the high redox potential of heme b makes it a liability, since it is toxic in high concentrations. Although, the underlying mechanisms are still poorly understood, non-iron metal porphyrins have also shown a significant antibacterial activity [b tolerance, the active export of heme or its toxic metabolites to the extracellular space can improve the product tolerance  may be related to the heme export factors [b secretion rate was increased to 63% in the fed-batch fermentation by overexpressing the ccmABC gene [C. glutamicum ATCC 14067, the heme-regulated transporters A and B (HrtA and HrtB), as well as the cytochrome C export protein (CcsA) may be involved in the process of the heme b secretion outside of the cell. A comparison between two recombinant strains of C. glutamicum overexpressing the hrtAB and ccsA genes showed that HrtAB was more conducive for the heme b production, with an extracellular secretion rate of 9.25% [b as a source of iron through an ABC heme transport system consisting of heme-binding proteins and heme transport proteins. In C. glutamicum, HrrS, HtaA, and HmuT are the three typical heme-binding membrane proteins that play key roles in transporting heme into the cytoplasm and regulating the heme biosynthesis [b yield of 35.65 mg/L [In  factors . The hemABC gene . Accordiof 9.25% . Most grynthesis ,80. The .65 mg/L .b [C. glutamicum, and ethambutol (ETB) can inhibit its production, thus promoting the extracellular secretion of heme b [b secretion rate was eventually increased to 91.25% in a 2 L fermenter. However, this modification had a negative effect on the total heme b production and cell growth [In addition to the heme-binding proteins, the fatty acids bound to the cell wall also have a high affinity for heme b . Mycolicf heme b . A modifl growth .E. coli, but also resulted in a 2.5-fold decrease in the intracellular heme b levels compared to the control [b synthesis. Thus, a more stringent regulation of the intracellular iron ion metabolism is required to maintain the balance between the cell growth and the heme b production  mediates the global transcriptional control of iron [fur genes were homologously overexpressed in E. coli, the intracellular iron ion content was reduced and the transcript levels of most of the heme b synthesis genes were increased to varying degrees, except for gluTR. This was due to the large amount of Fur protein that binds to Fe2+ to form the Fur-Fe2+ complex, inhibiting the expression of the iron ion transport system and promoting the expression of the gene encoding ferritin-related proteins. However, the overexpression of the fur gene alone did not increase the heme b production, likely due to an insufficient supply of 5-ALA [2+ is also able to achieve indirect regulation through a small regulatory RNA molecule, called RyhB. When RyhB was constitutively expressed in the engineered E. coli, the 5-ALA production increased by 16%, but the heme b content was significantly reduced. This may be due to RyhB interacting with pbgS and ppfC by binding to specific sites in the DNA, thereby repressing the transcription of these genes [In  of iron . When thof 5-ALA . In addise genes .dtxR gene can only be grown in a low-iron medium [dtxR gene was further overexpressed in addition to the overexpression of alaS, gluTR, gsaM, cgoX, cpfC, and chdC, there was a 3.17-fold increase in the heme b production. This may be because the DtxR protein alleviated the inhibitory effect of the iron overload on heme b. At the same time, the transcriptional analysis showed that the expression of most genes related to the heme b synthesis and those related to the iron metabolism was differentially upregulated following the dtxR overexpression [b production can possibly be further improved by fully elucidating its regulatory mechanism in the future.Gram-positive bacteria with a high genomic GC content use the diphtheria toxin repressor (DtxR) protein as an iron regulatory system instead of Fur. The DtxR proteins operate in a similar manner to the Fur proteins, although they are not phylogenetically related . The mutn medium . When thpression . In addipression . In concb is important for the study of the synthetic pathways, metabolic mechanisms, and practical applications. The measurement methods that have been developed can be divided into the direct and indirect detection of heme b , which binds to the fluorescent proteins (EGFP) and Katushka 2 (mKATE2). Holo-Cyt b562 is a fluorescence resonance energy transfer (FRET) receptor for the EGFP excitation. Subsequently, heme-LBB is used as a fusion structure consisting of a green fluorescent protein (GFP) and hemoglobin (Hb) to reflect the intracellular heme b levels [b is co-translationally incorporated into the hemoglobin part of the biosensor polypeptide and facilitates its correct folding. The GFP-Hb fusion that is bound to heme b is active and fluorescent, while the empty biosensor molecule folds incorrectly and is susceptible to degradation. As a consequence, an increase in the heme b production is correlated closely with the increase in the fluorescence of heme-LBB. The heme b biosensor enables the conversion of the heme concentration into the GFP fluorescence intensity and does not require an additional extraction process for the measurement. This method can be used to quantify the intracellular heme b concentration in real time, but it is not applicable to the extracellular samples.The metabolic regulation is a dynamic and real-time monitoring process of intracellular heme a et al.  construcb levels . Heme b b has significant potential for use in healthcare, food processing, and chemical production. However, the current chemical synthesis and bio-extraction techniques are not sufficient to sustain the market demand. As a consequence, the microbial cell factories for the heme b production have drawn increasing attention over the past two decades. To date, the highest heme b titer for microbial fermentation has reached 1034.3 mg/L [b can be biosynthesized on an industrial scale, and some common problems still need to be addressed. (I) Only two model organisms have been used for the biosynthesis of heme b, and the exploration of the natural high producers as chassis strains is inadequate. (II) The regulatory mechanism of the heme b synthesis is highly complex and not yet fully understood. (III) Screening the metabolic engineering target is mainly limited to the heme b biosynthesis pathway genes. (IV) The long heme b synthesis pathway imposes a certain metabolic burden on a single-cell system.As the most common porphyrin, heme 4.3 mg/L . Howeverde novo synthesis of UPG III, 713 of which  had the ability to synthesize heme b from 5-ALA [b for research include only two model species, E. coli and C. glutamicum. Obviously, many of the natural high-producing strains have not been exploited for the heme b biosynthesis. It can be predicted that in the future, a full screening of the natural hosts with innate advantages in the heme b biosynthesis could lead to a high-yield production of heme b. For example, S. cerevisiae [Pichia pastoris [b to the cell membranes. The analysis of the genetic information on the heme b biosynthesis currently available in research databases allows for a better screening of the natural high producers, and the process can be accelerated by developing a high-quality standardized database. Czajka et al. [Yarrowia lipolytica. Thus, by using species-specific GSMs in combination with machine learning, the range of the heme b-producing strains can be further extended.The analysis of 982 representative prokaryotic genomes showed that 806 organisms  were capable of a om 5-ALA . Howeverrevisiae  and Pichpastoris  play impa et al.  integrata et al.  to develb are now widely recognized, but the regulatory mechanisms are not well understood. Understanding the regulatory properties of the key enzymes of the synthetic pathway, such as PpfC and CpfC, is an important part for achieving their precise regulation. PpfC and CpfC are responsible for a key step in the heme b synthesis, the insertion of the ferrous ions into the porphyrin ring. The regulation of the key enzymes will directly affect the performance of the artificial cell factory. Based on recent advances in artificial intelligence (AI), the AlphaFold2 software has been developed to predict the 3D shape of proteins based on their genetic sequences with, for the most part, pinpoint accuracy [b.Three biosynthetic pathways of heme accuracy . Once thaccuracy , can prob synthesis. However, genome-scale target identification is expected to achieve a higher heme b production by subtly affecting the other nodes in the metabolic network. The genome-scale metabolic modeling (GEM) allows for the expression, transcription, and translation of all the host genes to be taken into account, fully simulating the metabolic changes during growth and identifying the key targets for the heme b biosynthesis through a metabolic flux analysis. Ishchuk et al. [b production by using the S. cerevisiae Yeast8 model. To further optimize the phenotypic prediction of the metabolism, ecYeast8 was applied to the enzyme usage variability analysis and mechanistic simulations for the individual gene modifications for the heme b production, which ultimately resulted in a 70-fold higher intracellular heme b accumulation. The metabolic models with multiple constraints integrating the multi-omics information can lead to more accurate results in pathway analyses and phenotype predictions. For example, the E. coli metabolic models with enzymatic and thermodynamic constraints (ETGEMs) resulted in more realistic production pathways and L-arginine yields [b is subject to complex regulatory mechanisms, and their appropriate integration into the process of solving metabolic network models can better guide the practice of metabolic engineering. It is foreseeable that genome-scale target mining will be much faster than the characterization and functional elucidation of the heme b biosynthetic pathway enzymes.Currently, the engineering targets are limited to the genes encoding the enzymes that are directly responsible for the heme k et al.  identifie yields . The synb has the natural advantage of being biosynthesized by many potential microbial cell factories. Inevitably, the overexpression of the enzymes in the heme b synthesis pathway on a single-cell system can lead to an excessive accumulation of intermediate metabolites that not only cause carbon loss, but also have a toxic effect on the cells. The dynamic regulatory systems, in response to the intermediate metabolites, could couple growth to production, thus alleviating the metabolic burden. On the other hand, MMME was used to regulate the expression of the genes in the heme b synthetic pathway, but the instability of the plasmids required the development of more powerful chromosomal integration techniques or strategies, such as the CRISPR–dCas12a-mediated multiple pathway optimization framework [b (or containing an incomplete pathway) and placing the heme b synthesis module in strains that are tolerant to high concentrations of heme b. These new synthetic strategies not only reduce the metabolic burden of the heme b synthesis in single strains, but also allow for the flexible optimization of the individual modules. The successful construction of artificial microbial consortia often relies on dynamic adjustments between the organisms, so the construction of a UPG III biosensor and a heme b biosensor  is


This promising technology represents an important step toward sustainable development, which can help address some of the most pressing environmental challenges faced by modern society. However, to establish processes competitive with the petroleum industry, it is crucial to determine the most viable pathways for C1 utilization and productivity and yield of the target products. In this review, we discuss the progresses that have been made in constructing artificial biological systems for 3G biorefineries in the last 10 years. Specifically, we highlight the representative works on the engineering of artificial autotrophic microorganisms, tandem enzymatic systems, and chemo-bio hybrid systems for C1 utilization. We also prospect the revolutionary impact of these developments on biotechnology. By harnessing the power of 3G biorefinery, scientists are establishing a new frontier that could potentially revolutionize our approach to industrial production and pave the way for a more sustainable future.The third-generation (3G) biorefinery aims to use microbial cell factories or enzymatic systems to synthesize value-added chemicals from one-carbon (C1) sources, such as CO The projections indicate a potential atmospheric CO2 concentration of 500 ppm by the year 2045 [2 fixation and utilization.The trend of rising atmospheric carbon dioxide  sources such as formaldehyde, formic acid, methanol, methane, and COnd light . Althougnd light –11, somend light –15. Diffproducts –18. Instproducts –25. Addi2 fixation pathways discovered so far include the Calvin–Benson–Bassham (CBB) cycle [2, hydrogen sulfide, hydrogen, acetate, and methane, which are associated with a range of respiratory pathways, including but not limited to desulfurication, hydrogenogenesis, acetogenesis, and methanogenesis [The natural COB) cycle , the redB) cycle , the dicB) cycle , the 3-hB) cycle , the WooB) cycle , and theB) cycle  Fig. 1)2 fixatioB) cycle  .B) cycle   cycle   cycle . Carbon B) cycle . In the ogenesis .Artificial biological systems for C1 fixation are either reconstructed based on the native one or de novo designed. This section will highlight the representative heterotrophic microbes engineered for C1 fixation and in vitro tandem enzymatic systems for assimilating C1 sources to more complex molecules.2 into biomass. Milo and coworkers [E. coli to generate all biomass carbon from CO2 trough the CBB cycle for the first time. Meanwhile, the energy generated from formate oxidation provided extra reducing power for the CBB cycle. Chemostat-based directed evolution was used to convert E. coli from an obligate heterotroph to a full autotroph [P. pastoris to the autotroph by introducing the CBB cycle followed by adaptive laboratory evolution (ALE). Since the methanol oxidation of P. pastoris is localized in the cytoplasm, the entire CBB cycle in the peroxisomes was driven by the energy from the native methanol assimilation pathway [In recent years, there has been a notable advancement in engineering heterotrophic microorganisms for the biomass conversion of COoworkers  successfutotroph . Similarutotroph  engineer pathway . However pathway . Formate pathway , formald pathway . Additio pathway .2, and ammonia are condensed to glycine by the GCS. Finally, glycine is condensed with another methylene-THF to form serine, which is further converted to pyruvate [2 molecule from formate to serine. An alternate pathway for CO2 fixation in heterotrophic microbes involves the use of a carbon capture enzyme called FDH, which facilitates the reversible reaction between CO2 and formate [2 but is sensitive to oxygen [2 but is not affected by oxygen [E. coli to utilize formic acid and CO2 as the sole carbon source by integrating the reconstructed THF cycle and the reverse glycine cleavage reaction, optimizing metabolic fluxes, and adjusting cytochrome bo3 and bd-I ubiquinol oxidase levels. The genetically modified E. coli achieved an optical density at 600 nm of 7.38 within 450 h [2 through the expression of heterologous Candida boidinii FDH and Arabidopsis thaliana fdhmut for redox regeneration [E. coli to grow on formate alone. The energy module operates as follows: Methanol is assimilated through the activity of methanol dehydrogenase (MDH), where formaldehyde is oxidized to formate through native glutathione system activity [E. coli can support a sustainable, formate-based bioeconomy. They achieved this by engineering lactate production from formate and CO2 through ALE and optimization of the rGly pathway [E. coli, rGly has also been proved to work in other heterotrophic microbes such as S. cerevisiae [Cupriavidus necator [Desulfovibrio desulfuricans [Komagataella phaffii [2 utilization for the production of proteins, biomass, or chemicals in heterotrophs.Formate can be used directly as a carbon source. Several studies have focused on engineering microbes to utilize formate via the rGly pathway, a versatile route for C1 biotechnology –40. In tpyruvate . This pa formate ,43. NAD-o oxygen , while Ny oxygen . Lee andy oxygen  engineerin 450 h . In thisneration . Alternaneration  fueled tactivity . After tactivity  demonstr pathway . Except revisiae , Cupriav necator , Desulfofuricans , and Kom phaffii , which pE. coli strain that can synthesize all amino acids from methanol-derived carbon through ALE. In this study, methanol was first oxidized to formaldehyde by expressing the Bacillus stearothermophilus MDH gene and then assimilated through the RuMP cycle [P. pastoris and O. polymorpha, S. cerevisiae does not possess a native methanol assimilation pathway [S. cerevisiae to assimilate methanol through ALE. A system biology approach showed that a significant alteration in central carbon metabolism fluxes and gene expression took place, such as the truncation of an uncharacterized transcriptional regulator, Ygr067cp [S. cerevisiae. Subsequently, Nielsen, Keasling, Chen, and Bai collaboratively engineered S. cerevisiae to grow in minimal medium supplemented with methanol by using a thermodynamic-based module circuit (TMC) strategy followed by ALE. This TMC strategy includes evaluating combinatorial methanol-utilizing pathways in yeast, a compartmentalization strategy that enhances methanol utilization, and a modularization strategy that enhances methanol utilization in methylotrophic S. cerevisiae. The growth was limited, and the maximum OD600 of 0.75 could be achieved after 144 h in a minimal liquid medium where 1% methanol was used as the sole carbon source [S. cerevisiae [Yarrowia lipolytica and O. polymorpha with the native XuMP or RuMP pathway for methanol assimilation have been engineered to efficiently use methanol as the only carbon source to produce valued compounds [XuMP pathway Fig. A and B. MP cycle . Unlike  pathway ,52. To a pathway  engineerYgr067cp . This din source . In addirevisiae –56. In compounds ,58.2 can be electrocatalytically reduced to intermediates, such as methanol, CO, and formate [2 to CO and then a pure acetic acid. Then, they proposed a hybrid electro-biosystem that combined spatially separate CO2 electrolysis with yeast fermentation to efficiently convert CO2 to glucose at a high yield. Additionally, they genetically engineered S. cerevisiae by deleting specific hexokinase genes and overexpressing heterologous glucose-1-phosphatase, enabling the production of glucose from electrogenerated acetic acid in vitro. This platform also demonstrated the potential for easy expansion to produce other products, such as fatty acids, using CO2 as the carbon source [In addition, CO formate ,60, whic formate . Xia, Yun source . These rn source . Some ofn source .2 fixation efficiency of the CBB cycle is limited by the poor activity and selectivity of RubisCO and the imbalanced energy: 9 extra molecules of ATP and 4 molecules of NAD(P)H are consumed through the process [E. coli required approximately 200 days to evolve for CO2 assimilation [P. pastoris took around 275 days [S. cerevisiae to evolve XuMP pathways for methanol assimilation [The abovementioned studies suggest that energy imbalance, oxygen sensitivity, and poor activities of rate-limiting enzymes of C1 fixation pathways (Table) might prevent us from developing efficient chemoautotrophic biological systems for C1 fixation. For example, the CO process . Thus, tmilation , while P275 days . Additiomilation . Considemilation .Constructing heterotrophic microbes for stable growth rates and high carbon yield with C1 sources is challenging due to inherent metabolic inefficiencies and the unfavorable cross-talk between product-forming and growth-sustaining reactions ,65. Esta2 or assimilate glyoxylate to acetyl-CoA without the overall carbon loss  using pyruvate carboxylase, oxaloacetate acetylhydrolase, acetate-CoA ligase, and pyruvate synthase to work as a model system to study the CO2 fixation in the earliest organisms on earth H-dependent dihydrolipoyl dehydrogenase was substituted by biocompatible chemical reduction of protein H using dithiothreitol [2 and NH3 directly. These results create an avenue for the direct production of amino acids and their derivatives from CO2.In recent years, many artificial C1 fixation pathways have been modified based on the natural C1 pathways Fig. . Construoss Fig. A. By utirganisms . After trganisms . Meanwhirganisms  developerth Fig. C. The puoworkers ,76 develthreitol ,76. Substhreitol  develope2 into malate at a rate of 5 nmol per minute per milligram of protein [2-fixing pathways in vitro. The CETCH cycle was designed and constructed based on the freely available databases, such as KEGG [S. cerevisiae into a facultative phototroph by introducing a rhodopsin protein from Ustilago maydis into the vacuole, enabling light-driven proton pumping into the vacuolar compartment without consuming ATP. The findings demonstrated that the yeast engineered with rhodopsins exhibit a selective growth advantage under green light, displaying faster growth rates than their nonphototrophic ancestor and rhodopsin-expressing yeast grown in the dark [2 into value-added compounds continuously, Erb and coworkers [2 to acetyl- and malonyl-CoA that were converted into monoterpenes, sesquiterpenes, and polyketides via the use of varied terpene and polyketide synthases. This work paves the way for engineering synthetic routes for generating complex chemicals from CO2 in the future. To improve the carbon fixation efficiency and reduce the energy consumption in the CBB cycle and CETCH cycle, Erb and coworkers have made further improvements by developing a carboxylation module consisting of a new-to-nature glycolyl-CoA carboxylase (GCC), an engineered glycolyl-CoA synthetase (GCS), and an engineered tartronyl-CoA reductase (TCR) to convert glycolate into glycerate. They predict that the integration of carboxylation module with photorespiration, ethylene glycol conversion, and CETCH cycle can increase the carbon efficiency of all these processes by up to 150% and reduce their theoretical energy demand [2 to starch at a rate of 22 nmol of CO2 per minute per milligram of total catalyst, which is approximately 8.5 times higher than the rate of starch synthesis in maize (Table). Apart from the novel CO2 fixation pathways, C1 assimilation pathways could be designed based on the artificial aldolase (ALS) reactions systematically. Ma and coworkers used comb-flux balance analysis (FBA) algorithm to design 8 carbon-conserving formaldehyde (FALD) assimilation pathways based on an extended metabolic network with nonnatural aldol reactions using the comb-FBA algorithm. In vitro, a new FALD assimilation pathway utilizing allose 6-phosphate, termed the glycolaldehyde-allose 6-phosphate assimilation (GAPA) pathway, was developed with a high carbon yield of 94% . Therefore, developing artificial C1 fixation pathways is necessary to mitigate these issues. Efforts toward creating de novo artificial pathways with superior yields and activity than the natural ones remain a priority. Erb and coworkers constructed the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) synthetic pathway Fig. D, achiev protein , which o as KEGG  and Meta as KEGG . To powe as KEGG . Most rethe dark . To convoworkers  created y demand . Additioy demand  , there is a growing interest to construct electrical-bio and photoelectrical-bio systems for synthesizing value-added compounds from CO2 by combining the synthetic power from both disciplines. By combining the advantages of electrocatalysis  and biocatalysis, chem-bio hybrid systems aim to produce value-added chemicals from CO2 with high conversion efficiency and selectivity [2 fixation.Except for engineered biological systems used for COlization . Photocaatalysis –93. Consectivity ,95. In t2 reduction to C1 chemicals  by either enzymes derived from carbon assimilation pathways or electrocatalysts [2 reduction has several advantages over conventional enzymatic CO2 reduction: It can use renewable electricity as electron source, it can achieve high-energy conversion efficiency, and it can exploit the selectivity and specificity of enzymes or whole cells. Therefore, bioelectrocatalytic CO2 reduction is a promising strategy for replacing petroleum-based feedstocks with sustainable and environmentally friendly alternatives.Bioelectrocatalysis is an interdisciplinary research field that combines electrocatalysis and biocatlaysis by using enzymes or whole cell as catalysts for redox reactions at an electrode . One of atalysts . These Catalysts . Bioelec2 reduction is limited in requiring a high overpotential, leading to reduced selectivity, catalytic yield, and specificity [2 using oxidoreductases is one of the effective solutions. Enzymes can effectively catalyze CO2 reduction at or near its thermodynamic potentials, thus reducing the electrochemical overpotential required to drive the reaction [As discussed before, electrochemical COcificity . Bioelecreaction .2 reduction [2 reduction to methanol using FDH, formaldehyde dehydrogenase , and alcohol dehydrogenase (ADH) by costing 3 NADH molecules. This breakthrough has facilitated the bio-catalytic production of molecules possessing high-energy content, accompanied by an efficient combustion/energy dissipation mechanism. However, the yield of formic acid was low simply because the reverse formic acid oxidation rate is much faster than the forward reduction rate of CO2 [2 reduction system consisting of a conductive polyaniline (PANi) hydrogel conjugated with W-containing FDH from Clostridium ljundahlii (ClFDH), in which CIFDH was regenerated through the direct electron mechanism and a 92.7% Faradaic efficiency (FE) can be achieved -graphene hybrid  . Insteadeduction . In 1999eduction  collabore of CO2 . Moreovee of CO2 . Therefoe of CO2 . Lee ande of CO2  collaborved Fig. A. Later,rid Fig. B. This n2 with the electrons from cathodes as the reducing equivalents [Sporomusa species and Moorella thermoacetica can perform EET [C. necator and E. coli cannot. To overcome this limitation, non-electroactive bacteria have been adopted to indirectly uptake electrons from cathodes via electron shuttles [2 reduction. Electron shuttles have been widely explored to enable the synthesis of various chemicals by different microorganisms in MES. Among them, NR is widely used due to its comparable standard reduction potential (−525 mV versus Ag/AgCl) to that of NADH/NAD+ (−520 mV versus Ag/AgCl) [Microbial electrosynthesis (MES) is a technology that uses microorganisms as biocatalysts to reduce COivalents ,107. Thiform EET ,109, butshuttles ,111, likshuttles , quinoneshuttles , and neushuttles ,115, whiAg/AgCl) , which m2 fixation. For instance, C. necator can use these electron carriers to produce biofuels such as butanol and isobutyl alcohol [C. necator oxidized H2 in the cathodic chamber, producing NADPH and ATP as reducing equivalents to enable the conversion of CO2 to poly-β-hydroxybutyrate (PHB). However, at ambient pressure and room temperature in water, H2 exhibits low solubility (0.00016 g/100 g H2O) when compared to CO2 (0.169 g CO2/100 g H2O) [2 electrocatalytically by using an indium cathode  can also work as electron carriers for CO alcohol . Nocera  alcohol  develope0 g H2O) , which l0 g H2O) ,121 deveode Fig. D. Format necator ,121.Optimizing the electrode surface is also crucial for supporting fast electron transfer rates. The morphology and chemistry of electrodes affect the efficiency of MES in terms of biofilm formation and the electron transfer mechanisms between microbes and electrodes. Electrodes’ materials selections and surface modifications have been done to achieve higher MES efficiency by improving its (a) biocompatibility , (b) ele2 into biomass and secondary metabolites [Photosynthesis is a natural process evolved by cyanobacteria, algae, and green plants to capture solar energy while oxidizing water to oxygen and drive the conversion of COabolites . This prabolites . Howeverabolites . Artificabolites . In thes2 fixation process show immense potential for selectively converting CO2 to value-added chemicals efficiently with low carbon footprint and a low energy input [2O3 photoanode and a BiFeO3 photocathode  tandem cells have the potential to enable the electron transfer from the photosynthetic light reactions to other COoworkers  introducght Fig. B. The coem cells ,142.2, with various oxidoreductases to reduce CO2 to CO, formic acid, formaldehyde, methyl alcohol, and methane [2 reduction  [Besides PEC tandem cells, light-sensitive nanoparticles have also been coupled with various oxidoreductases for different chemical transformations. The major works in this area focused on hybridizing nanoparticles, especially TiO methane –146. In  methane  collaborion Fig. C. By usito 1.6%) .2 fixation at broader spectrum wavelength and higher solar energy conversion efficiency [The photomicrobial system combines photocatalytic material, such as semiconductors, electrodes, polymers, and dyes, with microbes to realize COficiency . The selficiency .2 to formic acid reduction using MV as the mediator. However, the poor biocompatibility of MV led to cell death after the reaction [M. thermoacetica surface  with smaller sizes and better compatibility can cross the cell membrane without killing the microbes. The direct interaction of Au NCs with enzymes improved charge transfer rate and achieved a 33% increase in quantum efficiency for continuous CO2 fixation [E. coli to drive the conversion of CO2 to acetate. The quantum efficiency (QE) of photosynthesis products can achieve 6 to 8%, resulting in a total productivity of approximately 0.89 mmol L−1 h−1. Genetically modified E. coli can subsequently convert acetate into amorphadiene [In semiconductor–microbial hybrid systems, semiconductor materials commonly distributed in medium, immobilized on the cell membrane, or inside the cell. Light-induced excitation of a semiconductor material generates electron-hole pairs that are harnessed by tandem enzymatic systems for chemical synthesis. When the semiconductor is extracellular, it needs a medicator to shuttle the electrons. Zhong and coworkers  designedreaction . Exclusireaction  depositeace Fig. D. The hafixation . Except phadiene .E. coli with organic photosensitizers perylene diimide derivatives and poly(fluorene-co-phenylene) to form a polymer layer with high hole/electron separation efficiency and drive CO2 into acetic acid via the Wood–Ljundahl pathway  nanowire arrays as a light-capturing “Z-scheme” and Sporomusa ovata as the biocatalysts to fix CO2 and generate acetate for up to 200 h under simulated sunlight, achieving energy conversion efficiencies of up to 0.38%. Limited interactions between microorganisms and electrodes resulted in suboptimal CO2 reduction efficiency and increased overpotential. To enhance the performance, they tuned the electrolyte pH for better buffering and constructed a compact nanowire-based cathode integrated with S. ovata to achieve efficient electroreduction of CO2 at a high applied potential (~−1.2 V versus standard hydrogen electrode) and a current density of 0.65 mA cm−2  . MoreoveIn vitro tandem enzymatic systems for C1 fixation have some limitations, such as the requirement of purified enzymes and well-controlled reaction conditions. It may encounter more complex issues in handling large-scale reactions and producing products at a commercially viable rate limited by poor enzymatic selectivity and the external supply of expensive cofactors. Using cell-free systems to incorporate cellular components can provide a more biomimetic environment for enzymatic reactions and improve their activity and specificity . DesigniP. pastoris, the formaldehyde dissimilation pathway is vital for NADH production, but it results in over 40% carbon loss [2 fixation represents a novel research avenue, establishing a new interface between nonphotosynthetic microbes and semiconductors through the confirmation of light-induced intracellular charge transfer, allowing for photoreduction of CO2. However, electron transfer without mediators remains challenging, and the oriented electron transfer between photosensitizers and the targeted biological charge transport chain is barely possible. Thus, further research on material science and molecular biology is needed to elucidate how microbes take up external electrons and possible abiotic electron transfer pathways in the hybrid systems. Finally, chemo-bio hybrids are hard to be scaled up due to their poor compatibility and difficulties in constructing self-assembly and disassembly systems. Designing and engineering self-regeneration and biocompatible living materials with high electroconductive efficiency will be a riveting field in the future.An efficient energy module is crucial to successfully drive the artificial biological systems for C1 utilization. This review discusses three common energy supplies in artificial biological systems: chemical-to-chemical energy conversion, electrical-to-chemical energy conversion, and photo-to-chemical conversion. Introducing an energy module that can provide sufficient energy while minimizing carbon loss is essential for C1 utilization. For natural Aox-dependent methylotrophs like bon loss . Reprogrbon loss ,66,170. In conclusion, developing an artificial biological system that can utilize C1 sources is an immensely impactful research endeavor due to their abundant sources, potential sustainable and carbon-neutral production, and lack of conflict with food security . With re


Quantification of the concentration of particular cellular metabolites reports on the actual utilization of metabolic pathways in physiological and pathological conditions. Metabolite concentration also constitutes the readout for screening cell factories in metabolic engineering. However, there are no direct approaches that allow for real-time assessment of the levels of intracellular metabolites in single cells. In recent years, the modular architecture of natural bacterial RNA riboswitches has inspired the design of genetically encoded synthetic RNA devices that convert the intracellular concentration of a metabolite into a quantitative fluorescent signal. These so-called RNA-based sensors are composed of a metabolite-binding RNA aptamer as the sensor domain, connected through an actuator segment to a signal-generating reporter domain. However, at present, the variety of available RNA-based sensors for intracellular metabolites is still very limited. Here, we go through natural mechanisms for metabolite sensing and regulation in cells across all kingdoms, focusing on those mediated by riboswitches. We review the design principles underlying currently developed RNA-based sensors and discuss the challenges that hindered the development of novel sensors and recent strategies to address them. We finish by introducing the current and potential applicability of synthetic RNA-based sensors for intracellular metabolites. Cell metabolism, the set of enzyme-catalyzed chemical reactions occurring in the cell leading to energy production and biomass formation, works as a coordinated system that results from the interaction of multiple metabolic pathways, where metabolites act as interconnecting nodes among these pathways. Cells regulate metabolic fluxes to adapt their metabolic phenotype to environmental conditions ,2,3,4. Tsensor domain) and a different part that allows converting this interaction into a quantifiable response  (the actuator) . A. ATornadulations . This noulations . For aptulations ,62. In aramework . This liE. coli. With this approach, they experimentally validated two predicted diadenylate cyclases from the human pathogen Clostridium difficile and the archaea Methanocaldococcus jannaschii [Sensors for intracellular metabolites have proved their utility in metabolic engineering. The construction of multi-step pathways requires screening enzymatic activities from multiple sources, including enzyme libraries or biological samples from different origins. Quantification of a specific enzyme-product yield is usually performed by analytical methods, such as HPLC, which is time-consuming and only allows for low to medium throughput. Using an RNA-based sensor for this specific product may expedite the identification of a specific enzymatic activity in biological samples or the variant with the highest activity in large enzyme libraries . Michenennaschii . The enzymes necessary to generate a product of interest are assembled into a synthetic pathway to create a consolidated cell factory, which will still require further optimization. Cell factory optimization entails substantial metabolic engineering, and along this process, the host strain may accumulate mutations, from which only a subset of them contribute to higher product yields, the rest being superfluous or counterproductive. Meyer et al. constructed a whole-cell aptazyme FMN sensor to screen for vitamin B2-secreting cell factories. The authors generated a library of 9000 clones in which a number of DNA segments had been crossed over with wild-type host strain, such that in each clone, a number of the accumulated mutations had been reverted. Individual clones were then co-cultivated with the sensor strain in nanoliter reactor capsules, and B12 producers were selected by FACS . This woLysine Riboselector, where the lysine riboswitch was coupled to the tetA gene, a dual selection system used in E. coli. The Lysine riboselector was utilized to screen a promoter library of an enzyme crucial for diverting metabolic flux toward lysine biosynthesis. After a few rounds of selection cycles, the optimized L-lysine-producing cell factory was enriched to up to 75% of the total population [Finally, along the course of industrial fermentations, cell factories typically lose productivity due to the appearance of sub-optimal phenotypes. The fermentation process pursues a high product yield, which is usually in conflict with the selection pressure for an increased growth rate of clones within the cell factory population ,106. RNApulation . The impToday, the design and functioning of RNA-based sensor frameworks have achieved a higher level of development. Two portable RNA sensor frameworks with different mechanisms of actuation and signal generation have been standardized: aptazyme and light-up-based sensors. In the last few years, an enormous effort has addressed major issues that have been holding back the actual implementation of RNA-based sensors to respond to fundamental questions in biology. However, the number of available sensors for different intracellular metabolites is still very low. In the next few years, it is expected that the developed sensor frameworks will be exploited to scale up the catalog of available sensors.As sensors for different intracellular metabolites become more widely available, their potential applications will also increase. RNA-based sensors could be applied for monitoring different metabolic flux-signaling metabolites. This will provide a rapid, non-invasive, affordable, and straightforward method to measure metabolic fluxes in real-time and at the single-cell level in live cells, which is not feasible today with alternative techniques. Research towards understanding metabolic heterogeneity in human pathologies as relevant as cancer  could hi


Neisseria gonorrhoeae  is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population.The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea. Neisseria gonorrhoeae  is the causative agent of the sexually transmitted infection gonorrhea. Gc is a human specific pathogen that is uniquely adapted to colonize human mucosal surfaces, where it survives despite initiating a robust inflammatory response and influx of innate immune cells, specifically polymorphonuclear leukocytes (PMNs or neutrophils) (rophils) . The mecrophils) . Insteadrophils) . As a hurophils) -11. HoGenome-scale metabolic network reconstructions (GENREs) are a mathematical framework encompassing much of the known metabolic information on an organism. A draft GENRE can be generated with an annotated genome and several automated network reconstruction tools  database GSE123434], from which we identified and characterized unique metabolic features of the bacteria during an innate immune challenge. This GENRE of a clinically important, metabolically fastidious bacterium is a new resource for the Neisseria and microbial metabolic modeling communities. The insights into immune-driven metabolic shifts in Gc revealed by this transcriptionally guided GENRE can inform the future development of therapeutic strategies to combat antibiotic-resistant gonorrhea.Here, we present iNgo_557, a GENRE of Gc metabolism. This model enables the prediction of carbon source utilization and growth yields that recapitulate the behavior of Gc when grown in rich media. Metabolic network coverage in iNgo_557 includes genes, reactions, and metabolites that were initially identified by homology to a model of N. meningitidis M58 (Nmb_iTM560) served as the starting point  knowledge base framework (N. meningitidis M58 (AE002098.2) and N. gonorrhoeae FA1090 (AE004969.1) using a homology matrix–based workflow for generating high-quality multi-strain genome-scale metabolic models , addition of amino acid catabolism pathways, curation of lipooligosaccharide synthesis for Gc and its addition to the biomass equation, modification of the biomass composition for Gc where appropriate, and simplification of lipid biosynthesis , Morse’s Defined Media (MDM), and Roswell Park Memorial Institute 1640 media (RPMI). The metabolites present in each media were assigned to corresponding model exchanges in equivalent amounts and deemed “equally scaled” media (d” media . These sd” media . The bacd” media . All pred” media .As shown in To identify the substrate(s) that were limiting for Gc growth in RPMI compared with MDM or GCBL, we used iNgo_557 to predict the doubling time of Gc in a revised formulation of RPMI where the constraint for uptake of each component was individually increased to fivefold the original medium . In the 13 metabolic flux analysis recently conducted on Gc strain MS11 demonstrated that Gc consumes a variety of amino acids when grown in a glucose-containing medium that is a derivative of MDM  cycle. Thus, a prediction made by our model, that Gc can catabolize amino acids, is supported by experimental evidence. To account for Gc usage of amino acids in the presence of its known carbon sources, amino acid catabolic pathways were left intact in iNgo_557.Gc is reported to be capable of growing on only three carbon sources: glucose, lactate, and pyruvate . While ie of MDM . To furte of MDM  to the Ceference . Our in pyk), was predicted by iNgo_557 as non-essential, but essential by TnSeq. We verified that pyk could be deleted from Gc and that the resulting null mutant could grow in GCBL containing glucose as the sole carbon source, albeit slower than the WT parent or when pyruvate was provided  on GC agar  42). In. In42). provided . While Tprovided , 44. Difin vitro conditions, which can be further validated experimentally.Taken together, there was good concordance between Gc growth predicted using iNgo_557 and experimental results generated in this study and from previously published work. We conclude that iNgo_557 can be used to predict growth dynamics of Gc in various GENREs serve as a tool for scaffolding complex metabolic information in human-interpretable formats. One such application is the integration of transcriptional data with GENREs to develop a comprehensive picture of bacterial metabolism in complex and uncharacterized environments . Given tP. aeruginosa and C. difficile to uncover metabolic contributors to virulence in the context of mucin degradation, biofilm formation, murine infection models, and co-culture with other microbes  algorithm, which uses RNA sequencing (RNA-seq) data to identify the most cost-effective usage of metabolism while also reflecting the organism’s transcriptional investment. In brief, the model is iteratively constrained to a fraction of the maximal possible biomass. The algorithm then assigns linear coefficients to weight model reactions based on transcript abundance. Reactions corresponding to highly abundant transcripts are favored to be retained, and low abundance transcripts are favored to be removed  from the model. Consistency of the model with the transcriptome is assessed by Spearman correlation coefficient. The fraction with the highest correlation is selected as the context-specific model . RIPTiDemicrobes , 19, 46.r = 0.242, P < 0.001 for Gc without PMNs, and r = 0.263, P < 0.001 for Gc with PMNs) –deficient isolate of strain FA1090 Gc, which was cultured in RPMI + 10% fetal bovine serum (FBS) for 1 hour. Gc was cultured in the presence or absence of primary human PMNs that were adherent and treated with the chemokine interleukin-8 to reflect the activated state of immune cells during infection . This Opth PMNs) . These r applied , indicatBiomass flux was significantly increased in the contextualized model of Gc co-cultured with PMNs, compared with Gc cultured without PMNs , suggestd-lactate exchange reactions were unique to Gc co-cultured with PMNs  were identified, and the absolute median activity for each reaction was determined for 1,000 simulations to examine the contribution of each reaction to biomass production . From thith PMNs . Furtherith PMNs , suggestith PMNs , 51. Simith PMNs ; Fig. S7ith PMNs . Althougith PMNs . Togethen = 500). The classifier had an out-of-bag accuracy above 98%, indicating that membership for each contextualized model can robustly be predicted from reaction content within the two contextualized models. We then assessed mean decrease accuracy (MDA) to identify reactions that, when removed from the model, most affected the categorization predictions of the Random Forest. Gc grown in the presence and absence of PMNs was particularly distinguished by flux out of the pyruvate node, through acetate synthesis. Specifically, acetate exchange, acetate transport, acetate kinase, and acetate phosphotransacetylase were identified as reactions that impacted the categorization capabilities of the Random Forest (MDA ~13%) . In. In50). DA ~13%) . AcetateDA ~13%) . VisualiDA ~13%)  demonstrpyk), portions of the pyruvate dehydrogenase complex (dldH), phosphate acetyltransferase (pta), citrate synthase (gltA), aconitase (acnB), and isocitrate dehydrogenase (idh) were all predicted to be essential only for Gc cultured without PMNs.Conditionally essential genes were predicted by conducting essential gene calculations in each model, then comparing them . Twelve pyk), the first enzyme in this pathway. As expected, Δpyk had a growth defect in MDM, GCBL, and RPMI containing glucose as the sole carbon source, while the WT parent grew in these media . On further interrogation, the predicted growth of Gc on MDM without a carbon source was due to the consumption of serine and alanine as carbon sources. Although Gc encodes the genes necessary to catabolize these amino acids (ALATA_L/ NGO_1047 and SERD_L/NGO_1773 and NGO_0444) and does catabolize these amino acids in vitro , it is uin vitro . A potenruginosa  and subspyk) and pathways exiting the pyruvate synthesis node. Metabolic modeling using iNgo_557 predicts that this effect is due to the bypass of pyruvate synthesis through the import of alternative carbon sources, including lactate and pyruvate, when in the presence of PMNs, which is supported by our growth data. PMNs are highly glycolytic cells that consume glucose and secrete lactate when they are activated , citrate synthase (gltA), aconitase (acnB), and isocitrate dehydrogenase (idh) reaction activities were all demonstrated to be high in the presence of glucose but decreased in the presence of pyruvate  and N. gonorrhoeae FA1090 (AE004969.1) to generate a homology matrix for the two species. A secondary comparison using BLAST on nucleotide sequences was conducted to identify potential homologs with poor open reading frame (ORF) annotation. These automated calls were inspected and reassessed for each gene present in Nmb_iTM560 as indicated in https://github.com/aimeepotter/Gc_GENRE_2022).To generate a GENRE for Gc, we used pipeline . In brieModeling assessments, including flux balance analysis, flux variability analysis, single-gene knockout analysis, were conducted using COBRApy . Model qin silico media was recently described, in which metabolite exchanges are scaled based on the maximum possible usage defined by the concentration of metabolites in millimole per liter  and CFU per milliliter, with stationary phase values trimmed .n = 1,000) to produce contextualized models for Gc grown in the presence or absence of PMNs (n = 500 samples per model). Bray-Curtis-based NMDS  and permutational multivariate analysis of variance (PERMANOVA) (perm = 999) analyses were accomplished using the Vegan R package (Transcriptomic data retrieved from the GEO database (GSE123434) for Gc cultured without and with PMNs over the course of 1 hour were mapped to the corresponding FA1090 gene IDs using the conversion table provided in  of PMNs . Flux sa package . Supervi package .Statistical analysis was performed in R v4.1.0. Visualizations of flux balance analysis were performed using Escher .pyk was generated by spot transformation .Opaless Gc is a non-variable Opa-deficient derivative of the FA1090 background constitutively expressing the pilin variant 1-81-S2, which served as the WT for all experiments , 73. Strormation  with an  plasmid  using prormation  and sele2 (pyk strains were grown on GCB plus Kellogg’s supplements with glucose replaced by pyruvate (36 mM) as in reference  plus Kellogg’s supplements at 37°C with 5% CO2 , 77. Δpyeference . For preeference . Spectin7 CFU/mL in 6 mL of media in 15-mL conical tubes (Sarstedt). The bacterial suspension was incubated with rotation at 37°C. Bacterial growth was measured by OD550 and CFU enumeration at specific time points. CFUs are presented relative to 0 hour (100%). Gc was grown in GCBL, HyClone RPMI 1640 media without glutamine  (Cytivia), or carbon-matched Morse’s defined media (MDM) containing either glucose (27 mM), lactate (54 mM), or pyruvate (54 mM)  . Doublin (54 mM)  for the 6 PMN/mL, and Gc was added to each tube at a multiplicity of infection of 10. One millimolar of IPTG was added where indicated to induce the expression of pyk. Six milliliters of the suspension was incubated in 15-mL conical tubes with rotation at 37°C. Bacterial CFUs were enumerated at specified time points and expressed relative to the CFU at 0 hour (100%). Data are expressed as the mean ± SEM of at least three replicate experiments. Significant differences were determined by two-way analysis of variance (ANOVA) with Holm–Sidak correction for multiple comparisons in GraphPad Prism v9.PMNs were isolated from venous blood as previously described and used within 2 hours of isolation . Subject


In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production. The cellular metabolism in microorganisms is the set of biochemical reactions to maintain life, and it can be divided into 2 branches, primary and secondary metabolisms: primary metabolism generates energy and synthesizes cellular biomass, while secondary metabolism mediates microorganisms’ adaptation to the living environment . SecondaBacillus subtilis used as antibacterial agents in both medicine and agriculture . Eq. Eqvgrowvgrowth is the biomass formation rate normalized to 1 gram dry weight, also considered as the growth rate. SM×R is the stoichiometric matrix of the metabolic network with M metabolites and R reactions. A lot of toolboxes have been developed for conducting FBA, such as COBRA Toolbox . kcat is the turnover rate and [E] is the concentration of the enzyme. Fluxes were approximated using unbiased random sampling in the constrained solution space instead of optimizing a defined objective function , kcati are the concentration and turnover rate of the enzyme i, respectively, and [PTOT] is the concentration of total cellular proteins. The proteome allocation of secondary metabolism (The cellular proteome is divided into inflexible housekeeping sector (Q-sector), catabolic sector (C-sector), ribosomal sector for protein translation (R-sector), transportation sector (T-sector), and secondary metabolism sector (U-sector). d to 50% ,105,106 tabolism  can mateFig 4, to resolve the limitation of direct integration of gene expression into FBA. Though not for predicting secondary metabolite production, several integrated regulatory-metabolic models, also called regulatory FBA (rFBA), have been reported for a while [Constrained proteome allocation-embedded FBA models can capture the shift in metabolic states via modeling resource distribution among functional proteome sectors . However a while –115. The a while . The rFBFig 4). The stress response can be appended into rFBA, as the process from stress sensing to regulation can be represented by signal transduction in a network [Enlightened by the idea of dynamic rFBA, if regulatory interactions related to secondary metabolism are elucidated in a Boolean logic network, dynamic simulation of gene expression regulation at metabolic switch can be performed, and thus quantitatively capture the onset of secondary metabolite production  the difficulty in reconstructing the complete biosynthetic route of a secondary metabolite in an enzymatically detailed manner; and (20) the inability of FBA-based modeling techniques to make quantitatively accurate predictions for secondary metabolism under different conditions for engineering and practical purposes. As for the first challenge, except for PKs/NRPs, the pathway reconstruction for secondary metabolites generally lacks template models and databases containing information of biochemical reactions associated with secondary metabolism. Consequently, this process is much less automated. For example, although databases related with EPS biosynthesis are available, e.g., EPS-DB , CAZy 1, the infThe existing FBA-based modeling techniques that are intended to tackle the second challenge have been evaluated in this review. It shows that they are either too simplistic or have restricted application scopes. None of them are predictive enough to capture the production of secondary metabolites. Nevertheless, those modeling techniques show usefulness in certain cases, such as manipulating the objective function and incorporating transcriptomics data. With the aim of building a modeling technique that captures the mechanistic understanding of secondary metabolism, we have suggested the application of 2 potential extensions of FBA-based modeling techniques. They respectively incorporate constrained proteome allocation that explicitly account for the enzyme capacity for secondary metabolism and connect with the regulatory network model for gene expression states that control reaction activities with high resolution. Detailed implementation approaches in those directions remain to be explored, which we envisage will benefit from further and more mechanistic understanding of cellular responses leading to the production of secondary metabolites. Additionally, future curation works of proteomic and gene regulatory information, both in connection with typical or specific secondary metabolic pathways, will contribute to the advancement of those approaches.When a high coverage of biosynthetic pathways for secondary metabolites is achieved in metabolic reaction databases for smGSMM reconstruction, and a modeling technique that accurately captures the mechanism of secondary metabolism is developed, the complex bioprocess of secondary metabolite production will then become more “white-boxed.” Researchers, particularly those in the field of synthetic biology, will be able to conduct more reliable in silico analysis of secondary metabolite production. This advancement will contribute to the development of model-based design and pathway engineering approaches aimed at controlling secondary metabolism, such as enhancing the productivity of high-value secondary metabolites.