Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model. Metabolomics is an expanding field of research that involves the characterization of small molecules in cells, tissues, and other biological systems. Metabolites are direct products of enzymatic reactions that provide a functional readout of cellular state ,2. Compam/z) and relative intensities for the parent compound and its fragments. Mass spectrometry (MS) techniques coupled with liquid or gas chromatography separation techniques, LC–MS or GC–MS, respectively, have become standard analytical platforms for untargeted metabolomics . The LC 20H22N2O4) [Interpretation of metabolomics data is facilitated by assigning putative chemical identities to the features. Relying on the mass of the ionized parent compound for annotation is problematic, as a particular mass may be associated with many chemical formulas  . The speH22N2O4)  and NISTH22N2O4) , cover oH22N2O4) ,13,14,15In recent years, computational tools have become available to recommend a ranked list of chemical structures that best explain a spectral signature. This ranked list is selected amongst a pre-specified candidate set, a listing of metabolites with formula weights that match the measured masses of parent compounds in the sample. Earlier tools used rule-based approaches to generate fragmentation patterns of candidate metabolites, e.g., . SubsequWe propose a novel annotation workflow for untargeted metabolomics that addresses current limitations regarding spectral database coverage and computational cost of annotation. The goal of this workflow is to engineer a candidate set that can be used for putative identification using database searches or other annotation tools. The key step is to filter the detected masses through a metabolic model that we call an extended metabolic model (EMM). An EMM includes not only the defined substrates and products of enzymes cataloged for the organism(s) associated with the sample, but also additional metabolites reflecting the potential for promiscuous enzymatic activities. The central premise is that an EMM can be used to define a candidate set that is more comprehensive than a standard genome-scale metabolic model, but still enforces a degree of specificity for the system of interest. Our workflow, termed EMMF (EMM-based filtering), broadens the search space for annotation beyond compounds in a reference metabolic model assembled from catalog definitions of enzymatic reactions, thus enhancing discovery while avoiding the computational cost of analyzing every compound in large chemical structural databases. We demonstrated the utility of EMMF on untargeted LC–MS data from cultures of Chinese hamster ovary (CHO) cells and bacterial isolates from murine cecum. We compared the candidate sets from reference metabolic models, EMMs, and a large structural database (PubChem). EMMF suggested biologically relevant chemical identities for almost a quarter of measured features, providing a > 7-fold increase in the candidate set size when compared to using a reference metabolic model. Importantly, EMMF allowed the discovery of novel relevant putative identities that are not currently catalogued in PubChem. Targeted LC–MS experiments confirmed the presence of a predicted CHO cell metabolite that had previously not been cataloged as a Chinese hamster enzyme substrate or product.To describe and evaluate the EMMF workflow, we presented it alongside two other annotation workflows . In descSelecting the candidate set from a large database can potentially enhance annotation by increasing the number of measured masses that have a match in the candidate set B. This dEscherichia coli [Our novel annotation workflow C, EMMF, hia coli . From rehia coli , PROXIMAhia coli  of this m/z), and a set of associated product ion (fragment) masses and their relative intensities, that is, the MS/MS signature. The reference metabolic models for CHO cell and murine cecal microbiota were derived from genomes in the KEGG database. For the CHO cell, we obtained lists of metabolites and reactions cataloged in KEGG that are associated with the organism code cge. The cecal culture is a consortium of many species. We assembled a community-level model based on the taxonomic groups detected in the culture using a previously described procedure [We compared the annotation workflows in rocedure . The numThe EMM for each sample was generated using biotransformation operators derived from each model  matching to a larger number of measured masses, and (b) suggesting a larger set of putative chemical identities. Using these two metrics, we compared the size of the “biologically relevant candidate sets” in the model- and EMMF-based workflows and compared that with the size of the candidate set using PubChem . A smallThe quality of the EMMF candidates with known PubChem or KEGG identities  were evaAs the KEGG database is largely a small subset of PubChem, using all of the KEGG compounds as a candidate set for annotation may not be as computationally prohibitive as using PubChem. Further, using only a biological database such as KEGG for annotation guarantees biological relevance of candidate metabolites. A question that often arises regards the benefits of utilizing a general database for annotation compared to when employing a database that mostly comprises biomolecules. Using the EMMF workflow as a reference and restricting derivatives to those with chemical identities in PubChem, we were able to explore and quantify the benefits. Specifically, we utilized the EMMF workflow to identify candidate sets for our datasets. We then compared the EMM candidates against those obtained using the database-based workflows using KEGG and PubChem . Many caThe table reports the number of EMMF derivatives, percentage of EMMF derivatives that had non-zero CFM-ID scores, and the average score. These results are reported for EMMF candidates that had a matching identity in the KEGG and for PubChem databases.To generate the candidate set as the input to in silico annotation analysis in database-based workflow B, we ide6. When using KEGG as a database for annotation, CFM-ID ranked six of the eight derivatives as the highest ranked candidates, whereas two of the derivatives were not in KEGG  and 4-hydroxyphenyllactate:NADP+ oxidoreductase (R03338) are both catalyzed by D-hydrogenase  and hydroxyphenylpyruvate reductase (E.C. 1.1.1.237). Reaction 3-(4-hydroxyphenyl)lactate hydro-lyase (4-coumarate-forming) (Reaction R08766 in the KEGG database) is associated with an enzyme that has yet to be characterized (E.C. 4.2.1.-). It is unlikely that the source of 4-hydroxyphenyllactate in our sample was exogenous, as our cell culture medium was chemically defined and did not include this metabolite. Further evidence that the metabolite is endogenously derived was provided by a recently updated genome-scale metabolic model (GEM) for the CHO cell in the BiGG database . This GEWe further analyzed the spectral signatures comprising our experimental validation set using the Global Natural Products Social Molecular Networking (GNPS) spectral library . There wWe further explored the annotation of the experimental validation set using the Human Metabolome Database (HMDB) . There wWe report the number of PubChem compounds that matched in mass to the measured spectra , group FOur EMMF workflow addressed the challenge of creating an annotation candidate set that is enzymatically relevant to the sample under study and that includes metabolites beyond what is already catalogued in reference metabolic models. One important contribution of the work is conceptually separating the engineering of the candidate set from annotation, as EMMF creates a biologically relevant candidate set that can be utilized for putative identification. Prior works provided limited engineering of candidate sets. These works focused on exclusions of particular elements, substructures, or compounds , or on iResults from comparing the three workflows emphasized the need for optimizing the engineering of candidate sets. We demonstrated for our two case studies that using candidate sets from large databases is computationally prohibitive, as others have also noted . We alsoEMMF relies on a reference metabolic model for annotation. Other recent studies have also exploited the metabolic network to enhance annotation. One method, iMet, suggests that neighboring metabolites within a metabolic network have similar MS/MS spectra and trains a classifier to predict if two spectra belong to neighboring metabolites . The claPROXIMAL to rank predicted derivatives on the basis of enzyme designations as generalists or specialists [http://hassounlab.cs.tufts.edu/proximal. This work did not evaluate the quality of candidates that did not have a match in PubChem or KEGG. A thorough evaluation of these candidates may have yielded biologically relevant matches. This work presents the first in vivo experimental evidence for a computationally predicted metabolite derived through promiscuous action of an enzyme. Using a chemical standard, we confirmed the presence of 4-hydroxyphenyllactate in a CHO cell culture, even though there is no documented gene associated with the CHO cell metabolism that can catalyze the reaction with 4-hydroxyphenyllactate as product. We were, however, able to confirm only one out of the eight predicted metabolites. This could be due to inaccuracies in the rankings by the annotation tools. Analyses of the tested compounds using the GNPS spectra database, HMDB, CFM-ID, and MetFrag showed significant variations in the annotation results. The low confirmation rate can also be due to the assumption that all enzymes are promiscuous. As an enhancement, we are currently investigating methods to improve cialists  and partcialists . The curIt is possible to utilize other tools or databases to identify metabolites that could occur due to enzyme promiscuity. For example, BioTransfomer utilizes a knowledgebase (MetXBioDB) and a reasoning engine to predict enzyme products . MetXBioDespite limitations due to the underlying potentially incomplete metabolic models and to the accuracy of current annotation tools, EMMF demonstrates utility in creating an expanded, biologically relevant candidate set and in utilizing it to enhance annotation. This utility is demonstrated via the discovery of 4-hydroxyphenylpyruvate and in high annotation scores using CFM-ID for some EMMF derivatives. Importantly, EMMF promises to offer annotation opportunities beyond those possible with metabolic models without the high computational cost of searching large structural databases that contain many non-biological compounds.


Escherichia coli Nissle 1917 (EcN) is an intestinal probiotic that is effective for the treatment of intestinal disorders, such as inflammatory bowel disease and ulcerative colitis. EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics. Escherichia coli strain Nissle 1917 (EcN) is among the most studied probiotic bacteria [The nonpathogenic bacteria . Over 10bacteria . EcN is Pseudomonas aeruginosa in Caenorhabditis elegans and mouse infection models [EcN has been genetically engineered to diagnose, prevent, and treat diseases owing to its facile genetics and biosafety profile . Recombin models . EcN cann models ,7,8. EcNn models  and hypen models . With adn models .E. coli strains [A genome-scale metabolic network model is a computational framework that allows the prediction of metabolic flux values for a whole set of metabolic reactions using optimization techniques, such as flux balance analysis (FBA) ,12. Meta strains . In part strains  and BL21 strains . Althoug strains , the proIn this study, we reconstructed a comprehensive and highly curated metabolic network model of EcN based on comparative genome and phenome analyses. A phenotype microarray (PM) test was performed to validate and update the metabolic model, and the reconstruction was extensively manually curated. Combined with results from the phenome analysis, metabolic simulation using the model identified genetic determinants of EcN-specific phenotypes. Further, genetic and metabolic factors responsible for intestinal colonization of EcN were identified.E. coli metabolic models that are the most highly curated K-12 MG1655 (model id: iML1515) [E. coli strains, uropathogenic CFT073 (ic_1306) [E. coli K-12 intestinal commensal strain MG1655 were used as the gold standard for the reconstruction of the EcN metabolic model.The EcN genome (RefSeq accession number NZ_CP007799)  is poorlic_1306)  and asymic_1306) . Genome ic_1306)  and iML1The reconstruction was initiated from the homology searches of the genomes of K-12 MG1655 (NC_000913) , CFT073 There were many missing reactions owing to incorrect, missing, or low-quality reactions listed in CFT073 and ABU83972 reconstructions, which were generated through the automatic pipeline . Thus, tN-acetyl-d-galactosamine, D-arabinose, 2-deoxy-d-ribose, L-glutamic acid, β-hydroxy-butyric acid, γ-hydroxy-butyric acid, D-lactitol, L-proline, D-psicose, D-raffinose, L-sorbose, and D-tagatose) and nine nitrogen sources , whereas it did not grow on five carbon sources  and five nitrogen sources  (We used PM to identify a wide range of digestible and inhibitory substrates through growth behavior . To identhosine) .d-glucose-6-phosphate, and cytidine-3′-monophosphate). Both grew on all the cells of PM5 (nutrient supplements), including that of the negative control. EcN grew on the majority of peptide nitrogen sources (PM6–PM8); however, K-12 could not utilize various valine-containing dipeptides owing to the frameshift mutation of ilvG, which encodes the subunit of acetohydroxy acid synthase II responsible for valine resistance [EcN and K-12 grew similarly on phosphorus and sulfur sources (PM4), with the exception of K-12 growth on three phosphorus sources  at any concentration . Of the 240 different inhibitory compounds tested, EcN and K-12 grew in the presence of 237 and 225 compounds with at least one of four concentrations, respectively. Compared to K-12, only EcN grew in wells containing at least one concentration of 15 compounds . EcN did not grow in the wells containing three antibiotics , whereas both K-12 and CFT073 do , and that the GntII system is primarily involved in the catabolism of L-idonic acid in which D-gluconate is an intermediate [Many of the phenotypic differences between EcN and K-12 could be correlated with the genotypic differences. Genome comparison of EcN, K-12, and CFT073 identified the genomic regions of EcN that differed from those of K-12, possibly responsible for the phenotypic differences . The reltabolism  is intaclization   by insertion element [The comparison of gene clusters for O-antigen biosynthesis revealed that EcN has a nonfunctional ase gene E, which  element . Thus, i element   and 95 nitrogen sources (PM3), differences in the simulated and experimental growths were identified in the utilization of 25 carbon and 19 nitrogen sources , which cN-acetyl-d-glucosamine, N-acetyl-β-d-mannosamine, fructose, sorbitol, mannitol, mannose, and glucosamine, and these are carbohydrates transported by the sugar phosphotransferase system (PTS). Among the genes associated with PTS, we found that ptsI encoding PTS enzyme I was frameshifted in the EcN genome (NZ_CP007799). As PtsI activity is essential for E. coli growth on these PTS sugars [ptsI gene, and its DNA sequence was found to be intact and the same as that of E. coli ABU83972. Thus, we added transport reactions for these PTS sugars. We added one transport reaction and two intracellular reactions associated with the sorEMABFDC operon genes associated with L-sorbose metabolism. We added 13 dipeptidase reactions  to break down 13 dipeptides and one dipeptidase reaction  specifically hydrolyzing dipeptides containing a proline residue at the carboxy-terminal end [The incongruences in the simulated and experimental growths were reflected in the modification of the model . False nS sugars , we sequinal end   were observed for the utilization of 12 substrates  as the sole carbon or nitrogen source. To match the predictions and experimental results, six reactions were removed from the draft model, as no relevant genes were retrieved in the MetaCyc  and/or KpotE) was reported to show weak gene expression levels under normal culture conditions [Eight reactions were reported to be active only under anaerobic , was sequenced to be identical to that in the CFT073 genome. The aceF encodes the subunit of pyruvate dehydrogenase (PDH), catalyzing a preparatory step to the tricarboxylic acid (TCA) cycle by converting pyruvate from glycolysis into acetyl-CoA during aerobic respiration, and thus, it should be intact to explain the observed rapid growth of EcN under aerobic conditions.EcN has fully functional pathways for central carbon metabolism. It is also fully functional in the de novo syntheses of all essential nucleotides, amino acids, fatty acids, and LPS. The growth kinetics of EcN using diverse carbon sources was comparable to that of K-12 , demonstAll the entities of the metabolic model, such as metabolic reactions, genes, metabolites, and gene–protein–reaction rules, were manually curated. The final EcN metabolic model, referred to as iDK1463, comprised 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions . The simargI, can, leuB, and purN) that were not predicted to be essential in K-12, and two of them (can and leuB) were experimentally validated. Ornithine carbamoyltransferase is involved in the L-arginine biosynthetic pathway, which is encoded by two genes in K-12 (argF and argI) and one gene in EcN (argI) [can and cynT) and one gene in EcN (can) [leuB and dmlA) and one gene in EcN (leuB) [purN and purT) and one gene in EcN (purN). A single deletion of purN in K-12 did not lead to purine auxotrophy [purN and purT resulted in purine auxotrophy [FBA was performed to predict the reactions essential for cell growth of EcN and K-12 strains under aerobic conditions using glucose as the sole carbon source. A total of 195 genes were predicted to be essential for each EcN and K-12, of which 191 genes were shared. The predictions were compared with 249 essential genes determined by experimental genome-wide gene-knockout screens performed for K-12 under glucose growth conditions . The preN (argI) . CarbonicN (can) . 3-isoprN (leuB) . Phosphoxotrophy .gltA encoding citrate synthase and icd encoding isocitrate dehydrogenase) were predicted to be essential from FBA using iML1515 of K-12 [lpd, gapA, eno, fbaA, pgk, pfkA, and tpiA) represent false positives . False growth prediction can be used to refine the model when more biochemical information is available [Of the 69 metabolic genes involved in the central catabolic pathways , nine were reported to be essential for K-12 aerobically growing on glucose . Of the N-acetylgalactosamine, N-acetylglucosamine, N-acetylneuraminic acid, L-fucose, D-galactose, and D-gluconate) [d-ribose, L-lactic acid, L-sorbose, and AKG), and D-glucose  supported EcN colonization, while N-acetylglucosamine and D-ribose were unnecessary for the intestinal colonization. For FBA under anaerobic conditions, the upper limits of oxygen and each of the carbon sources were set to zero and 20 mmol gDCW−1 h−1, respectively. Fluxes of six reactions that were deactivated for aerobic FBAs   ,52,53, f-glucose . Glyceroytogenes . From thytogenes , five ofAGMT7pp)  were uncnditions .N-acetylglucosamine, N-acetylneuraminic acid, and L-sorbose), and D-glyceraldehyde 3-phosphate . D-gluconate was metabolized to D-glyceraldehyde 3-phosphate and pyruvate almost exclusively via the Entner–Doudoroff (ED) pathway. L-lactic acid and AKG were subsequently converted to pyruvate. The predicted growth yield, ATP production, and NAD+ regeneration according to the carbon sources are summarized in −1) when N-acetylneuraminic acid was used as the sole carbon source, followed by N-acetylglucosamine (0.554) > D-glucose (0.504) ≈ N-acetylgalactosamine (0.5) > D-galactose (0.455) > D-gluconate (0.407) = L-sorbose (0.407) > 2-deoxy-d-ribose (0.258) ≈ L-fucose (0.251) > glycerol (0.210) > AKG (0.14) > L-lactic acid (0.014)  . The utite lyase A, and th+ regeneration by malate dehydrogenase (MDH). Malate was converted to pyruvate as a part of gluconeogenesis. Pyruvate was converted to acetyl-CoA with concomitant generation of NADH by PDH, whereas the catabolism of the other eight carbon sources employed anaerobically induced pyruvate formate lyase (PFL), which catalyzes the non-oxidative cleavage of pyruvate to acetyl-CoA and formate [+ regeneration resulted in the lowest ATP production for FBA using L-lactate as the sole carbon source.The catabolism of AKG and L-lactic acid did not include glycolysis, thereby yielding the lowest growth rate. Intracellular AKG was converted to citrate by running the TCA cycle in reverse, then to oxaloacetate and acetate by citrate lyase of an anaerobic enzyme . Oxaloac formate . IntraceE. coli strains of Nissle and K-12 in the utilization of diverse nutrient substrates were identified  and two carboxylic acids , which were reported to be present in the gastrointestinal tract [E. coli strains grow predominantly in the mucus layer [N-acetyl-d-galactosamine. Furthermore, it could not grow on 2-deoxy-d-ribose, which promotes pathogenic E. coli strains during host colonization because deoxyribose is derived from DNA degradation and is abundant in the intestine [The phenotypic profiles in this study provide insights into the colonizing capabilities of EcN. Nutrient sources supporting cell growth are important for ntestine . EcN canal tract ,53. Commus layer . They caus layer ,62. Of tntestine .E. coli and absent from commensal E. coli and intestinal pathogenic E. coli [E. coli strains is repressed under anaerobic conditions [E. coli [Mycobacterium tuberculosis in human macrophages [While EcN and K-12 showed high similarity in aerobic respiration, they were different in the anaerobic fermentative pathway. Remarkably, EcN has a large gene cluster for L-lactate biosynthesis, anaerobic utilization of AKG, and their two-component regulatory system F. To dat E. coli . As AKG  E. coli  and actinditions , the ana[E. coli . The int[E. coli , and AKG[E. coli . In addirophages . Furtherrophages , and Lldrophages . Our metE. coli to colonize the mouse intestine as it is the primary route for the catabolism of gluconate and other sugar acids which are available in the mucus layer [E. coli MG1655 to colonize in the mouse intestine [The ED pathway is important for us layer . Especiantestine . In metantestine .E. coli strains cannot synthesize methionine from 5-methylthioribose, which is exported into the culture media as waste [E. coli S88 (O45:K1:H7) [Owing to the incomplete MSP, as waste . Interes5:K1:H7)   . MTRu-1-5:K1:H7) ,72.Coxiella burnetii, Chlamydia trachomatis, Legionella pneumophila, and Listeria monocytogenes [The growth of intracellular pathogens relies on host metabolites and depends on the efficient usage of the nutrients at various spatial locations and stages of intracellular growth . The bipytogenes . Little ytogenes , studiesLactobacillus and Bifidobacterium species [In general, the most common probiotics are Gram-positive strains, such as  species . EcN is  species . EcN has species . In the E. coli strains of K-12 MG1655 (NC_000913) [The complete genome sequence of EcN (RefSeq accession number: NZ_CP007799)  was used_000913) , CFT073 _000913) , and ABU_000913) , the proGenes with potential metabolic roles were identified from the list of genes contained in the metabolic models of K-12 MG1655 (model id: iML1515), CFT073 (ic_1306), and ABU83972 (iECABU_c1320), which were retrieved from the BiGG models database , and theE. coli strain Nissle 1917  was provided by Ardeypharm GmbH, . PM tests were performed as previously described [E. coli K-12 MG1655, which were purchased from American Type Culture Collection (ATCC), were retrieved from our previous study [escribed . The PM us study .Cells were grown overnight at 37 °C on a Biolog universal growth (BUG) + B agar plate. Colonies were picked from the agar surface and suspended in inoculating fluid (IF) containing the indicator dye tetrazolium violet. IF-0 media were used for plates PM1 to PM8 and IF-10 for plates PM9 to PM20. Sodium succinate was added with ferric citrate to the inoculation solution of plates PM3 to PM8. All PM plates were inoculated with cell suspensions at 100 μL/well and incubated in an OmniLog incubator  at 37 °C for 48 h. Four independent PM tests were performed using sodium succinate as the sole carbon source for the plates PM3 to PM8. The PM data were analyzed using the R package opm .−1 h−1 of EcN were used as growth-associated maintenance energy (GAM) and non-growth-associated maintenance energy (NGAM), respectively [−1 h−1, respectively, unless otherwise mentioned.FBA was performed using the COBRApy software . The maxectively . The upp−1 h−1. For PM3 simulations, the maximum uptake rates of succinate and a nitrogen source were set to 10 and 5 mmol gDCW−1 h−1, respectively. A substrate was considered to be not utilized if the growth rate was <5% of the growth objective value calculated for the wild-type strain.For simulation of cell growth on PM plates for testing utilization of carbon (PM1 and PM2) and nitrogen (PM3) sources, the composition of defined media used in the PM plates was retrieved from BioCyc , and theTo identify the reactions that are essential for cell growth, the maximum growth rate was simulated when each reaction was removed from the reconstructed metabolic model using COBRApy . An unreptsI and aceF genes, three sets of primers were prepared for each gene divided into three parts (For DNA sequencing of the ee parts . ConsideptsI and aceF of E. coli Nissle 1917 were deposited in GenBank under accession numbers MW239125 and MW239126, respectively.The sequences of


Microbial activity mediates the fluxes of greenhouse gases. However, in the global models of the marine and terrestrial biospheres used for climate change projections, typically only photosynthetic microbial activity is resolved mechanistically. To move forward, we argue that global biogeochemical models need a theoretically grounded framework with which to constrain parameterizations of diverse microbial metabolisms. Here, we explain how the key redox chemistry underlying metabolisms provides a path towards this goal. Using this first-principles approach, the presence or absence of metabolic functional types emerges dynamically from ecological interactions, expanding model applicability to unobserved environments.“Nothing is less real than realism. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.” –Georgia O’Keefe Marine microbial activities fuel biogeochemical cycles that impact the climate, but global models do not account for the myriad physiological processes that microbes perform. Here the authors argue for a model framework that reinterprets the ocean as physics coupled to biologically-driven redox chemistry. Since these transformations respond to, and feedback on, changes in climate  reactions that supply energy for metabolisms can provide an additional organizing principle for explicit descriptions of microbial populations in ecosystem models. This redox basis can be exploited to quantitatively resolve chemical transformations in terms of assimilatory and respiratory fluxes. While not yet incorporated into earth system models, this view has been advocated for such applications20, and has been embraced and employed in the field of environmental biotechnology, such as in the interpretation and modeling of wastewater bioreactors21. Just as models of ocean and atmospheric circulation are constrained by conservation of energy and potential vorticity, complementing mass balance with powerful redox and energetic constraints enables self-consistent descriptions of diverse microbial metabolisms.As we expand models to include the full metabolic potential of microorganisms, how can we organize and reduce the complexity of the descriptions of metabolic diversity? Non-photosynthetic organisms oxidize chemical species for energy, and thus their respiration is biogeochemically significant9. In analogy to the use of redox chemistry, trait-based functional type models of phytoplankton have used cell size as an organizing principle for understanding phytoplankton biogeography, biodiversity, and impact on biogeochemistry23. These types of theoretical constraints allow for the inclusion of more functional types without introducing as many degrees of freedom as would be necessary if each were empirically described. The guiding perspective is that organizing complex biological behavior by its underlying chemical and physical constraints gives more universally applicable descriptions of large-scale biogeochemistry.This approach aims to advance ecological modeling beyond species-specific descriptions to those that matter for biogeochemical function, in line with trait-based modeling approachesWhen incorporating a redox-balanced approach into ecosystem models, microbial function emerges from underlying chemistry as a consequence of interactions between populations modeled as metabolic functional types and their environment. Resulting theoretically grounded ecosystem models independently simulate microbial growth, respiration, and abundances in ways that we can compare with observations such as sequencing datasets. Thus, sequencing datasets are used as critical tests for the models, as external constraints rather than as input to the models, allowing for an iterative relationship between theory, observations, and models.In contrast with empirically informed models, this approach involves constructing a model of microbial activity theoretically, and then comparing the results with the observations in order to gain an understanding of the system. The goal is to understand why biology functions as it does, in addition to anticipating global impacts. From a first-principles biogeochemical perspective with respect to physical and chemical forcing, genes are an intermediate step between forcing and function, with the detailed complexity of biological reality following the underlying chemical and physical constraints . This does not equate to thinking that biology (or genetic information) does not matter or can be replaced entirely in models by physics and chemistry. Rather, we want to fundamentally understand biological activity as an integrated part of an ecosystem, and physics and chemistry become tools for doing so.Here, we outline the basis for using redox chemistry as an organizing principle and its translation into quantitative descriptions of microbial activity that are simple enough for global earth system models. We then discuss the benefits of this approach in the context of their implications for improved understanding and projections of global change impacts. Finally, we discuss limitations and possible future developments.28. The information from sequencing in particular has allowed for a huge expansion of insight into the detailed in situ activity of uncultivated species. When investigating global-scale impacts, how do we decide which of these details may be bypassed for simplicity? Or, if this simplification is impossible, must we incrementally construct a microbial ecosystem model that incorporates all known micro-scale detail?From one perspective, microbial communities are characterized by interactions at the micro-scale: gene expression, enzymatic capabilities, metabolites, species-specific interdependencies, etc., as well as the physical and chemical environment surrounding small cells30. For example, it is well known that microbial communities in sediments and anoxic zones organize according to the redox tower – the ranking of half-reactions by electrochemical potential32. Furthermore, respiration by living organisms increases the entropy of the environment by dissipating concentrated sources of chemical energy in accordance with the Second Law of Thermodynamics34.Another way forward arises from a macro-scale perspective, which examines how ecosystem function relates to the chemical potential utilized by organisms for energy35 demonstrated that external conditions cause the seemingly random fluctuations observed in microbial growth, rather than stochastic variation in gene expression. This provides direct evidence of deterministic behavior, and so the authors conclude that microbial systems can in principle be determined by macroscopic laws.This perspective suggests that chemical potential can be used to predict the activity of microbial communities and their biogeochemical impact. However, given the notorious complexity of microbial cells and systems, which is many steps away from governing chemical or physical equations, how can we be sure that this activity is indeed predictable? Frentz et al.37, as captured in the phrase “everything is everywhere, the environment selects”38. Furthermore, recent evidence also shows that gene acquisitions and deletions happen quickly enough to allow for horizontal gene transfer to dominate bacterial adaptation43, implying that evolution can occur within few generations and thus on timescales similar to ecological interactions. Perhaps consequentially, similar geochemical environments have been demonstrated to have high-microbial functional redundancy despite different taxonomic compositions44. This may be interpreted with the hypothesis that physics and chemistry selects for metabolic traits, and that these traits can be housed in different organisms with taxonomic composition shaped by micro-scale or biotic interactions45.How is this determinism manifested? If microbial communities can respond relatively quickly to changes to their local environment, they may predictably optimize the exploitation of locally available resources. In the ocean, dispersal in microbes is thought to be a highly efficient process such that microbial communities can in effect draw from an extensive seed bank52, and is conceptually similar to other redox-balanced approaches to understanding microbial activity in sediments, soils, subsurfaces, and aquatic systems58. Illustrating the power of these approaches, anticipating metabolism from chemical potential resulted in a prediction that anaerobic ammonia oxidation (anammox) should exist decades before it was observed60. Quantitatively understanding microbially mediated rates of conversion of substrates has practical implications for wastewater treatment, and thus the field of biotechnology has established methodologies for an approach in textbook form21. Flux balance analysis (FBA) models can be considered as much more highly detailed analogs of this approach that resolve the mass and electron balances among a multitude of chemical reactions within a cell62.The prediction of microbial activity from environmental chemical potential has a long history in microbiologyWe can resolve microbial activity in global ecosystem models using the underlying redox chemistry of diverse metabolisms as a constraint. One specific way forward is to model distinct metabolisms as populations of metabolic functional types. This systematically quantifies relative rates of substrate consumption, biomass synthesis, and excretions of transformed products associated with each metabolism. Coupled with estimates of substrate uptake, this replaces implicit parameterizations of processes such as organic matter consumption, oxygen depletion, and denitrification with electron-balanced respiratory fluxes of dynamic microbial populations. Box 48. The ratio of anabolism and catabolism can then be represented by the fraction f of electrons fueling cell synthesis vs. respiration for energy, following ref. 21. This provides a yield y (moles biomass synthesized per mole substrate utilized) of each required substrate that reflects two inputs: electron fraction f and the coefficients of the half-reactions , or the oxidation of ammonia or nitrite using oxygen (chemoautotrophic nitrification) as exemplified in Table 2 produced by heterotrophic metabolisms as well as the CO2 fixed by chemoautotrophic metabolisms  redox reaction Fig. . Such agBox 1 Incorporating metabolic functional types into ecosystem modelsμ is described asVi is the specific uptake rate of substrate i, and yield yi is the biomass yield with respect to that substrate. Yields for the different substrates and elements are interlinked in the metabolic budget derived from the underlying redox chemistry. Yields reflect Gibbs free energies of reaction among other factors. In the simplest model, non-limiting substrates are consumed in proportion to the limiting resource according to the metabolic budget, although in reality they may accumulate in the form of storage molecules.A metabolic functional type can be represented as a population with a growth rate that is limited or co-limited by multiple required substrates. If Liebig’s Law of the Minimum is employed, the limiting growth rate Each metabolic functional type population can be incorporated into a multi-dimensional environmental model  with physical transport asB, loss rate L, velocity u, and diffusion coefficient κ. The loss rate function varies with biomass and represents a combination of processes, including predation, viral lysis, maintenance, and senescence. These processes remain largely unconstrained, although efforts have been made to relate losses to ecological dynamics108.for biomass concentration Viyi, versus that exiting the cell in modified form as a waste product, Vi(1 − yi)  photoprotective cellular machinery.Descriptions of microbial growth that reflect underlying chemical potential can enable predictions of many other metabolic transitions, such as nitrogen fixation, nitrification, and the transition to sulfur oxidation and reductionnts Fig. , as wellReplacing implicit descriptions of organic matter remineralization: The fate of organic matter dictates the amount of carbon sequestered in the marine and terrestrial biospheres. Microbial consumption mediates the carbon stored in soils, the carbon stored in the ocean as dissolved organic matter (DOM), and the sinking flux of organic carbon that constitutes the marine biological carbon pump4, without which atmospheric CO2 would be 100–200 ppm higher than current levels. We want to understand how these carbon reservoirs respond to changes in climate, such as increased temperatures and changes in precipitation patterns. However, in biogeochemical models, simple rate constants often dictate the remineralization of elements from organic back into inorganic constituents.Replacing simplistic parameterizations with dynamic metabolic functional types means that electron-balanced descriptions of growth and respiration instead drive the fate of organic matter in earth system models Fig. . In addi2 excreted and the absolute amount of biomass sustained on a given substrate, independent of uptake kinetics . Untangling the relationship between abundances and biogeochemical function is also necessary for interpretation of genetic evidence that provide insight into this complex ecosystem structure.71.Redox-balanced metabolic functional type modeling links rates of biomass synthesis associated with a particular metabolism to its rate of respiration as well as the standing stock of limiting nutrients. As functional type modeling is coupled with estimates of population loss rates due to grazing, viral lysis, or other mortality, simulations also resolve the standing stocks of functional biomass. This quantifies the relationship between biomass concentrations and volumetric rates of chemical transformations, emphasizing how relatively low biomass may be associated with relatively high bulk rates18  and nitrite (NO2−) concentrations is consistent with observed differences75, and it reflects that the oxidation of one mole of NH4+ generates three times more electrons than the oxidation of one mole of NO2−, with differences in cell size further contributing to differences in abundances  themselves are not necessarily the most concise or useful currency given functional redundancies, unattributed function, and variation in gene dosage from horizontal gene transfer as well as growth rate40. Recent gene-centric models aim to resolve the abundances of key genes as proxies for a predetermined set of metabolic pathways17. However, the parameters used to describe metabolic pathways in these models are estimated similarly to the redox-balanced yields and efficiencies described here.17 resolves biomass and nutrient concentrations prognostically, and then uses a three-part formula—representing constitutive, regulated, and steady state transcription—to diagnostically calculate transcription rates from modeled biomass and growth rates17. Thus, the two types of modeling are complimentary, with redox chemistry providing estimates of metabolic activity from fundamental principles, and the careful calibrations between activity and sequencing providing a comparative metric.The innovation of gene-centric models is the sophisticated conversion of estimates of biogeochemical activity and biomass to genes. For example, the model of Coles et al.Nitrospina-like 16S rRNA genes with two conversion factors: the cell elemental quota (fmol N cell−1) and the number of cellular gene copies. Conversion error arises since cell mass and size vary with growth rate82. Maintaining transparency of the conversion from predicted microbial activity to genes and transcripts allows interdisciplinary audiences to understand and critique the models.The examples here externalize the conversion between modeled activity and sequencing information as a transparent process. In Fig. Using chemical potential as a theoretically grounding organizing principle for the resolution of diverse metabolisms can greatly improve microbial descriptions in global biogeochemical models. However, the approach does have its limitations, which generally increase in significance with increased temporal or spatial resolution.83. On one hand, this is beneficial for resolution of microbial processes in fine-grained ocean circulation models where flow can vary on the order of days. However, incorporating another constraint, such as proteome allocation69, is necessary to inform these choices. For example, considering enzymatic allocation in combination with energetics allowed for the prediction of both the division of nitrification into a two-step process in mixed environments and the combined, complete pathway in one organism (comammox) in biofilms, which preceded observations of the latter86.Modeling metabolic diversity with functional type populations requires choosing how metabolisms are distributed among the populations. This has consequences when interpreting time-varying states: model solutions become dependent on the partitioning of metabolism among the functional types as the timescales of physical change approach the timescales of microbial growth . Such versatility characterizes key players in large-scale biogeochemistry, such as nitrite-oxidizing bacteria and photoheterotrophs92. Some seemingly inactive populations may slowly metabolize over long timescales, requiring longer model integration times and careful attention to their loss rates for resolution, while some populations are periodically active as revealed by high-resolution observations in time37.Also, the metabolic functional type approach resolves only active functional biomass, while evidence suggests that less than 10% to more than 75% of the microbial community may be inactive66 have been empirically supported93. For organic matter, future work is needed to develop suitable descriptions of consumption rates, whether empirical or theoretical. For example, descriptions require attention to the hydrolysis of organic compounds by extracellular enzymes and the ecology of sinking marine particles—the diffusion of monomer away from the particle, within-particle transport, and dynamic ecological interactions on particle surfaces, among other processes95.The proposed modeling approach relies on estimates of the limiting uptake rates of required substrates. In lieu of suitable theoretical descriptions, the use of empirically derived uptake kinetic parameters still employs the benefits of the redox-informed yields (Supplementary Note 97. This could be used to improve the phenomenological description of organic matter in models as labile vs. non-labile, for example, with a more mechanistic underpinning.In Fig. 98. Incorporating this excretion into an energetic framework would enhance studies of phytoplankton ecology, such as studies of photoautotrophic-heterotrophic interactions in the ocean surface or photoautotrophic-chemoautotrophic interactions at the base of the euphotic zone where some phytoplankton excrete nitrite due to incomplete reduction of nitrate99.Descriptions of phytoplankton are currently much more sophisticated than of bacteria and archaea in models, reflecting a longer history of comprehensive sets of observations. However, further work could develop simple descriptions of photoautotrophic metabolisms from underlying energetics by connecting the supply of photons to available energy for biosynthesis within the cell. Many biogeochemical models account for an inefficiency of phytoplankton metabolism with a parameter that dictates their excretions of dissolved organic matter100? This may improve resolution of time-varying metabolic versatility. However, if both steps of nitrification were a part of such a consortium, would the characteristic accumulation of nitrite be predicted (Supplementary Fig. As a more radical extension, can we progress past population modeling and model microbial consortia as one aggregate community biomassWe have described a useful approach for understanding and anticipating microbial control of biogeochemical cycling that is suitable for global applications. The approach aims to represent microbial growth and respiration explicitly and consistently from knowledge of chemical gradients in the environment, towards a goal of building an independently constructed theoretical ecosystem model that can then be compared to observations. Describing microbial communities with underlying energetic constraints connects metabolisms dynamically with global geochemical distributions, such as those of carbon dioxide, oxygen, and biologically available nitrogen. This deepens our understanding of microbial ecosystems and enables the incorporation of the feedbacks of microbial activity to changes in global biogeochemistry and the climate system.Supplementary InformationPeer Review File


Escherichia coli being the best-studied. Several chemicals derived from acetate have already been successfully produced in E. coli on a laboratory scale, including acetone, itaconic acid, mevalonate, and tyrosine. As acetate is a carbon source with a low energy content compared to glucose or glycerol, energy- and redox-balancing plays an important role in acetate-based growth and production. In addition to the energetic challenges, acetate has an inhibitory effect on microorganisms, reducing growth rates, and limiting product concentrations. Moreover, extensive metabolic engineering is necessary to obtain a broad range of acetate-based products. In this review, we illustrate some of the necessary energetic considerations to establish robust production processes by presenting calculations of maximum theoretical product and carbon yields. Moreover, different strategies to deal with energetic and metabolic challenges are presented. Finally, we summarize ways to alleviate acetate toxicity and give an overview of process engineering measures that enable sustainable acetate-based production of value-added chemicals.Ecological concerns have recently led to the increasing trend to upgrade carbon contained in waste streams into valuable chemicals. One of these components is acetate. Its microbial upgrading is possible in various species, with  Escherichia coli, acetate formation can pose challenges. It has long been established that E. coli produces acetate together with other fermentation products like lactate and ethanol under anaerobic conditions [When producing value-added chemicals or recombinant proteins in nditions . Howevernditions . Additionditions ,4,5.E. coli strains. On the one hand, E. coli W exhibits only low amounts of accumulated acetate [E. coli strains JB101, JM105, and B on 20 g/L glucose and a dissolved oxygen level maintained above 20% were reported to be 0.88 g/L, 1.20 g/L, and 1.75 g/L, respectively [The amount of accumulated acetate may vary between different  acetate ,7. On thTo avoid the negative effects of acetate accumulation, extensive research on the cause of overflow metabolism has been done. For example, studies have identified the influence of acetyl-CoA synthetase, the cellular redox ratio, and the proteome efficiency of fermentative pathways ,10,11,12E. coli BL21(DE3) exhibits a reduced acetate accumulation in glucose batches, recombinant protein production has shifted towards this host strain [The implementation of different strategies helped to circumvent the challenges posed by acetate. Since t strain . Moreovet strain ,19,20,21E. coli processes, acetate is a part of many low-cost feedstocks such as lignocellulosic hydrolysates [Although often avoided as a substrate for olysates ,23,24. Folysates ,26.2 fixation via microbial electrosynthesis [In other cases, acetate is not only an additional part of feedstocks, but it is actively produced for further application in industrial processes. Potential acetate sources range from syngas fermentation to COynthesis ,30,31,32As a result, the implementation of acetate as a novel substrate in industrial biotechnology has been attempted in recent years and is showing promising results on the small scale. Recent achievements suggest that insights into the acetate metabolism of different microbial species combined with metabolic engineering of industrial host organisms seem to be the way towards sustainable production of value-added chemicals ,35,36,37E. coli. Therefore, this review will give an overview of acetate metabolism of different species and will then focus on acetate utilization in E. coli. We will present products that have already been successfully produced, highlight energetic aspects of growth on acetate, and discuss challenges that need to be overcome by metabolic and process engineering.Even though a wide variety of microorganisms are capable of growing on and metabolizing acetate, the focus for further process and strain development is E. coli, Vibrio natriegens, Pseudomonas aeruginosa, and the purple non-sulfur bacterium Rhodobacter capsulatus, members of the phylum of firmicutes , cyanobacteria  and sulfate-reducing bacteria  have exhibited growth on acetate [Chlamydomonas reinhardtii and Chlorella sorokiniana) can use acetate as a carbon source [The ability to use acetate as a carbon source is present in numerous microorganisms ranging from eukaryotes to bacteria. In the latter, growth on acetate is possible across different phyla. Next to proteobacteria, which include  acetate ,41,42,43n source ,45. Whet+ and NADPH/NADP+), and the formation of precursors for other metabolites  are distinctly different compared to sugar fermentation. As these factors have a major influence on how well cells grow and produce metabolites, we will focus our overview of the acetate metabolism in different organisms in these areas.When acetate constitutes the sole carbon source, the central energy metabolism (generation of ATP), the redox balance of the cells  and uses nitrate or fumarate as electron acceptors [Under anaerobic conditions, cceptors ,79. Onlycceptors ,81.C. kluyveri and the group of sulfate-reducing bacteria.Other anaerobic microorganisms exhibit a vastly different metabolism. Lacking oxygen as an electron acceptor, they use a wide range of alternative electron acceptors, including hydrogen, sulfur, sulfate and nitrite ,83,84,85C. kluyveri has gained biotechnological importance due to its ability to produce medium-chain organic acids from acetate and ethanol [ ethanol ,87. The 2 in the process [In this pathway, ethanol acts as the electron donor, whereas acetate assumes the role of an electron acceptor . Acetyl- process . The sec process .Once the conversion of crotonyl-CoA to butyryl-CoA is complete, butyryl-CoA can either leave the reverse β-oxidation cycle or be elongated further to caproate in another round .C. kluyveri to produce medium-chain organic acids in this way is frequently utilized in microbial consortia, where the respective substrates are produced by other microorganisms [The ability of rganisms ,86,87.2 [There are two main groups of sulfate-reducing bacteria. One group can metabolize organic compounds incompletely and produces acetate while the other one can completely convert organic compounds to CO2 . The latDepending on the organism, different metabolic adaptations  enable tDesulfobacter postgatei, acetate is completely metabolized to CO2 via the citric acid cycle , yeasts and fungi (Y. lipolytica and Cryptococcus curvatus), and even microalgae (Auraniochytrium limacinum SR21) [As can be seen, the range of microorganisms for the production of lipids is diverse, including bacteria (um SR21) ,99,100.Dinoroseobacter sp. JL1447, the cyanobacterium Synechocystis PCC 6803, and the bacteria Pseudomonas putida KT2440 and Aeromonas hydrophilia [Dinoroseobacter sp. JL1447, acetate as carbon source was even found to sustain a higher PHA production rate than others like glucose, glutamate, pyruvate, or citrate [Polyhydroxyalkanoates (PHAs) and especially polyhydroxybutyrate are another important class of value-added chemicals. Their production from acetate has been reported from the phototrophic bacterium rophilia ,102,103. citrate .Other chemicals such as histidine, caproate, malic acid, molecular hydrogen, and methane/biogas have a narrower range of hosts for production from acetate cf.  36,105,,110,111.Most organisms listed in However, coupling the consumption of acetate to the production of value-added chemicals in their natural producers can be challenging, especially if genetic or metabolic information on the organism is incomplete, the cultivation conditions are challenging or tools and procedures for genetic manipulation are lacking. Thus, the range of efficient, industrially applicable producers as well as products in these mostly non-model organisms is somewhat limited.E. coli, as a well-studied model organism, has become the focal point for the production of acetate. While some chemicals, like lipids and gases, are not commonly produced in E. coli, small organic acids, alcohols, and amino acids derived from metabolic intermediates can readily be generated. Other possible products include polyhydroxybutyrate, phloroglucinol, and β-caryophyllene . However, calculating the MTYs can be challenging and there seem to be two general ways by which they are obtained.In some instances, MTYs are calculated by considering only the carbon stoichiometry of reactions necessary to convert the carbon source to the product, regardless of the amounts of ATP, NADH, or NADPH required for the generation of the target molecule  ,114,125.2 [In other instances, it is taken into account that for the continuous supply of ATP, NADH, and NADPH, additional molecules of the carbon source need to be taken up and metabolized, which might also result in the release of CO2 ,119. In In a few cases  the production pathways are inherently energy-balanced. In these instances, follow-up reactions of the acetate molecules that are required for the carbon balance compensate for the energy consumption of acetate uptake and product generation. As an example, the energy expense for the formation of itaconic acid is covered by acetyl-CoA entering the TCA and glyoxylate cycle, where NADH, FADH, and subsequently ATP is regenerated . TherefoThis diversity in the calculations complicates comparability as shown in If acetone is produced from acetate, two molecules of acetate need to be taken up. The resulting two acetyl-CoA are subsequently condensed to acetoacetyl-CoA and react further to acetoacetate. In the last step of the acetone pathway, acetoacetate is converted to acetone by decarboxylation. Considering only these reaction steps, the carbon-balanced formation of acetone from acetate may be described by Equation (1). Molecules with no impact on the carbon or energy balance were omitted.As a result, the maximum theoretical yield of acetone from acetate is 0.50 mol/mol or 0.49 g/g and the theoretical carbon yield amounts to 75%.However, it should also be taken into account that the acetate uptake consumes ATP. Here, we assumed that acetate was taken up via the energetically more expensive acetyl-CoA synthetase, which requires two ATP per acetate. Therefore, Equation (1) needs to be amended towards Equation (2).2. As a result, we can formulate Equation (3).The consumed ATP may be replenished if another molecule of acetate is taken up and enters the TCA cycle. With the assumptions on ATP equivalents made above, the two ATP for the uptake are balanced out by 9 ATP gained from TCA cycle associated reactions, leaving a surplus of 7 ATP. This is enough to balance the ATP-requirement for the acetone synthesis, however, the reactions of the TCA cycle also produce two additional molecules of CO2, the final energy balanced reaction is given in Equation (4).To finalize the equation, we want to eliminate the ATP. Since we have established above that one molecule of acetate equals 7 ATP and two COThe new energy-balanced yield constitutes 0.39 mol/mol or 0.38 g/g and the theoretical carbon yield is 58%.2, while the other one follows the reactions of the glyoxylate shunt. In the glyoxylate shunt, the third molecule of acetyl-CoA needs to condense with glyoxylate to form malate. In the end, the reactions replenish the two molecules of oxaloacetate that were required at the start of the TCA cycle. Therefore, the carbon-balanced synthesis of itaconic acid can be given in Equation (5).Next, we examine the production of itaconic acid. First of all, it requires the uptake of three molecules of acetate. Two of the three resulting acetyl-CoA enter the TCA cycle and together with two molecules of oxaloacetate, they form two molecules of citrate and subsequently isocitrate. From here, one isocitrate can react to itaconic acid and COIf we examine the energy requirement, we can see that 6 ATP are needed for the uptake of 3 molecules of acetate. However, the subsequent reactions of the glyoxylate shunt generate two NADH and two FADH, which amounts to a total of 6 ATP. As a result, Equation (5) is inherently energy-balanced with the yield from acetate constituting 0.33 mol/mol or 0.73 g/g and the theoretical carbon yield 83%.The different calculation strategies can have a considerable impact. Firstly, it should be noted that no biological system can surpass an energy balanced yield, meaning if acetate is used as the sole carbon source, the MTY of acetone can never be above 0.39 mol/mol. Therefore, the highest reported yield for acetone (0.29 mol/mol) is already relatively close to the biological maximum . This shAnother issue might be the comparison of two products from acetate, especially, if the maximum theoretical carbon yield (MTCY) is involved as we outlined above for the two metabolites acetone and itaconic acid. If a non-energy-balanced calculation is applied, it might seem that acetone and itaconic acid have similar MTCYs, 75% and 83%, respectively. However, this is only because the pathway for itaconic acid is inherently energy-balanced while the one for acetone is not ,113. If Therefore, to avoid these pitfalls, it would be advisable to work with energy-balanced MTYs.+/ATP-ratio [Whether the MTY is energy-balanced or not, might not be the only challenge, though. If an energy-balanced approach is chosen, there is still the issue of how to weigh ATP, NADH, NADPH, and FADH against each other. In aerobic cultivations with acetate as the sole carbon source ATP is mainly gained via oxidative phosphorylation. The yield of ATP per oxygen is given by the phosphate/oxygen (p/o) ratio, which in turn depends on the electron transport efficiency and the HTP-ratio ,131.E. coli, different kinds of dehydrogenases with varying H+/e−-ratios have been identified. They are responsible for proton translocation across the membrane. The ratio of H+ to ATP is also a topic for debate, with reports ranging from 2 to 4 resulting in p/o-ratios between 1,5 and 3 [In ,5 and 3 ,132,133.,5 and 3 ,134.Based on the assumptions made above, acetate has an MTY of 7 ATP. Two ATP are needed for acetate uptake (irreversible uptake via acetyl-CoA synthase was assumed) while 9 ATP are generated during the oxidation of acetyl-CoA in the TCA cycle. Compared to other carbon sources like glycerol and glucose, the ATP yield of acetate is rather low.E. coli like glucose or glycerol are more favorable in terms of ATP yield under aerobic conditions. For glucose uptake and conversion to fructose-1,6-bisphosphate requires 2 ATP. Then, the conversion of two glyceraldehyde-3-phosphate to two 1,3-bisphosphoglycerate generates 2 NADH, and 2 ATP are recovered from two 1,3-bisphosphoglycerate to glycerate-3-phosphate. The reactions of 2 phosphoenolpyruvate to pyruvate finally yield two additional ATP. The remaining 18 ATP originate from 2 acetyl-CoA entering the TCA cycle according to the equivalence assumptions for the conversion of FADH, NADH, and NADPH to ATP. In total, 24 ATP are gained from glucose this way.As can be inferred from For the uptake of glycerol via glycerol-3-phosphate, one ATP is needed. Two ATP equivalents are generated in the next step, yielding dihydroxyacetone phosphate. The subsequent reactions towards pyruvate and the TCA cycle are the same as with glucose. However, this time only one 3C-unit is processed. This leads to an overall ATP gain of 14 ATP per glycerol.In terms of energy content, the main challenge concerning acetate is the carbon’s high oxidation state. By using glucose or glycerol some energy equivalents are generated independently of the reactions of the TCA cycle. As portrayed in Moreover, since most acetyl-CoA has to enter the TCA cycle for sufficient energy supply, the production of sugars can only be fueled by activation of the glyoxylate shunt . Even thAnother important aspect of the growth of any carbon source is the availability of cofactors. While the oxidation of NADH and NADPH can be used to build up a proton gradient across the membrane to generate ATP, there are also several other vital reactions in the cell, which require reduction equivalents ,137,138.However, despite all the challenges that acetate entails energetically, one of its advantages might be that its entry point into the metabolism (acetyl-CoA) is very close to the potential products’ branching points from the central carbon metabolism. With additional metabolic engineering, this “metabolic proximity” could allow for tight regulation of production.E. coli for better productivity, yields, and product titers on acetate can be conducted on two main levels.Engineering E. coli host strains, which is the main prerequisite for acetate-based production [The first level is metabolic engineering, which comprises several major aspects. Most importantly, it enables the introduction of non-native pathways into the oduction ,120,125.oduction ,123. Anooduction .The second level of engineering is process engineering. As process conditions like pH may substantially affect growth and metabolism on acetate, tight regulation is often necessary . In otheE. coli concerning the production of value-added chemicals from acetate is the introduction of the metabolic pathways leading to the desired product. Naturally, the first aspect of metabolic engineering of E. coli host strain, but the expression levels of the involved enzymes were optimized to favor tyrosine production [E. coli, resulting in the construction of expression cassettes containing extra copies of the E. coli genes and the heterologous ones [E. coli host strain [The pathways branching from the central carbon metabolism can be constructed in different ways. In the case of tyrosine, the pathway is natural to the oduction ,144. Forous ones ,121,123.ous ones ,125,145.t strain ,127,128.In some cases, additional gene deletions are necessary to disable unfavorable pathways, in other instances pathway genes are deleted and reintroduced into the cells under a promoter which allows expression control ,127,128.The types of deletions and modifications depend on the branching point from the central carbon metabolism. However, the starting point for all products is acetate uptake. Therefore, the strategy of enhancing acetate uptake to improve productivity has been employed with products from different branching points, such as acetone, isopropanol, tyrosine, succinate, and glycolate ,127,128.E. coli acetate can either be taken up via the reversible ackA/pta pathway or the irreversible acs-mediated way, these are the two points of action [ackA/pta pathway to improve acetate uptake and production of acetone, isopropanol, succinate, or glycolate [acs to enhance acetate uptake with products like succinate, phloroglucinol, tyrosine, or mevalonate [As for the question of how to increase acetate uptake, there are two approaches to tackle this challenge. As in f action ,63. Somelycolate ,117,125.valonate ,127,128.ackA/pta pathway usually include the lower energy requirement compared to uptake via acetyl-CoA synthetase (Acs) and its prevalence on high acetate concentrations [It is extremely difficult to tell if either option is generally better or worse than the other one. Arguments for choosing the trations ,117,125.trations .ackA/pta pathway instead of acs was detrimental to cell growth and did not improve production [acs-overexpression but rather worked better via enhancing the ackA/pta pathway [ackA/pta pathway which did increase acetate uptake and thereby the product titer and yield [ackA/pta pathway, plays an important regulatory role in the cells. Acetyl-phosphate affects protein acetylation and in extension glucose metabolism and acetyl-CoA pools as well as the phosphorylation of numerous response regulators like CheY, PhoB, OmpR, and CpxR [In the case of itaconic acid, it has been reported that overexpression of the oduction . Similar pathway . These fnd yield . The reaand CpxR ,147. Theand CpxR ,150,151.and CpxR .maeB  and pckA  increases isobutanol production [Apart from acetate uptake, with products branching from pyruvate, it seems that the most efficient way to increase the yield is enhancing pyruvate availability. It has been shown that the overexpression of oduction . The addoduction .For phosphoenolpyruvate-derived chemicals, the situation is less clear, since the only reported product is tyrosine. In this case, the expression of the glyoxylate cycle enzyme AceA was altered. With this strategy, it was possible to find a balance between carbon conservation via the glyoxylate shunt and energy generation via the TCA cycle thereby increasing the tyrosine yield .gltA) improved productivity [Some products branching from acetyl-CoA seem to benefit from enhancing the glyoxylate shunt. Flux through the glyoxylate shunt can either be increased by direct upregulation  or by blocking the TCA cycle via deletion of isocitrate dehydrogenase ,119. In uctivity . In a fuuctivity . As an euctivity . Other auctivity ,114,118.sdhAB is also disabled to prevent flux away from succinate and strains may also be lacking maeB and pckA to reduce losses of oxaloacetate [To successfully produce chemicals derived from the TCA cycle, the reactions leading away from them or their precursors need to be blocked in most cases. For glycolate, any pathway decreasing the glyoxylate pool, except the glycolate production, must be stopped . To obtaoacetate ,127.Next to that, it might also be necessary to actively replenish TCA cycle intermediate pools. For instance, the production of itaconic acid can be improved by enhancing the flux of the glyoxylate shunt. That way, isocitrate as the precursor for itaconic acid is replenished .In conclusion, pathway engineering strategies are highly product dependent and must also be adapted to the branching point from the central carbon metabolism.A repeatedly encountered challenge when using acetate as a carbon source is its potential toxicity to the cells. In concentrations above 5 g/L it can inhibit cell growth substantially, thereby complicating possible production processes . As a reThe question of how to deal with acetate is not only encountered when it is used as the sole carbon source but also when cells are grown on glucose . In someThere are several approaches to explain acetate or acid toxicity in general. One of the theories addressing this issue is the uncoupling theory. The reasoning behind this theory is that acetic acid in its protonated form can traverse the cell membrane and dissociate inside the cell. To maintain the proton gradient across the membrane, the resulting proton must be expelled from the cell again, which requires energy (ATP). Subsequently, this energy is not available for growth .A closely related explanation for acid toxicity is anion accumulation. It assumes that when acetic acid diffuses into the cells and dissociates, the intracellular pH decreases. This decrease leads to an accumulation of anions with potentially detrimental osmotic consequences for the cells .E. coli actually has a number of natural acid tolerance systems, which have been thoroughly reviewed elsewhere [To counter excessive acid stress, lsewhere . The moslsewhere ,157.E. coli strains, which is possibly the cause of strain-dependent acid resistance [However, different studies dealing with the production of n-heptanoic and octanoic acid showed that the responsible enzymes are expressed differently in various sistance ,159. ConE. coli strains have already been characterized several times. On defined medium and with an acetate concentration of 85 mM (approx. 5 g/L), E. coli C (ATCC8739) was found to have the highest maximum specific growth rate (µmax) with 0.41 ± 0.01 h−1, followed by E. coli W (0.37 ± 0.01 h−1). At the same conditions, E. coli BL21 and MG1655 exhibited a µmax of 0.30 ± 0.01 h−1 and 0.29 ± 0.01 h−1 respectively [E. coli W reached a µmax of 0.46 h−1, while E. coli BL21(DE3) and MG1655 grew at 0.36 h−1 and 0.23 h−1 respectively [Acetate tolerance of different ectively . When grectively .The expression and regulation of genes conferring acetate resistance was examined in a study about the cAMP receptor protein (CRP). There, it was discovered that one particular mutation (D138Y) led to an improved growth rate under acetate stress, albeit with a significantly longer lag-phase . UnfortuE. coli have identified the inhibition of methionine biosynthesis as a cause for reduced growth. When MetE, the first enzyme of the methionine synthesis pathway, is blocked, the toxic intermediate homocysteine accumulates [E. coli [Other studies concerning acid stress in umulates . Accordi[E. coli .Another possibly influencing factor is the intracellular concentration of acetyl-phosphate . Acetyl-E. coli under anaerobic conditions on glucose, it can be assumed that similar experiments can also lead to more acetate tolerant E. coli under aerobic conditions [To find further mechanisms for acetate tolerance and to create tolerant strains, adaptive laboratory evolution is a means that can be employed. As it was already successfully used to improve acetate tolerance and growth of nditions ,166.E. coli W strain [A topic related to acetate tolerance is product tolerance. For acidic products like 3-hydroxypropionic acid, the issue of acid tolerance arises too. While this has been addressed in the production of glycerol, the utilization of an acid-tolerant strain for 3-hydroxypropionic acid has yet to be implemented for acetate as the substrate ,167. ConW strain .E. coli [Phloroglucinol is another product that has a toxic effect on E. coli . To alleE. coli . While tE. coli ,169.relA, marC, proQ, yfgO, and rraA) that could enhance isopropanol tolerance [For other products, different solutions to improve tolerance are possible. In the case of isopropanol, adaptive laboratory evolution led to insights on genes involved in alcohol tolerance. The study identified mutations in five genes  [This issue seems to arise predominantly for the generation of alcohols. Concerns over the NADPH supply have led to the introduction of an additional NADPH regeneration system in the acetate-based production of isobutanol (E. coli) . The proE. coli) .pntAB or nadK was introduced into the cells and pntAB was deemed especially suitable to improve the yield as it replenished NADPH which was consumed by isopropanol production most effectively [Contrarily, cofactor engineering was successfully applied to increase the product yield of acetate-based isopropanol. Additional ectively .Another example is the pathway for mevalonate synthesis which also demands NADPH. In a recent study, the importance of NADPH regeneration for the production of mevalonate was revealed . HoweverTo facilitate cofactor engineering, helpful tools for metabolic engineering have been developed recently. In silico cofactor, balance estimation provides the possibility to evaluate different routes to a target product and allows a more efficient strain design .E. coli strain capable of being a platform for testing different enzymatic NADH regeneration systems might also be useful for future metabolic engineering. The strain in question is dependent on the introduction of an NADH regeneration system to grow on acetate. Subsequent determination of the growth rates of cells with different NADH regeneration systems enables the identification of a suitable cofactor regeneration strategy under the applied conditions [The emergence of an nditions .In the case of acetate-based production, process engineering has to deal with a few key factors to ensure stable and reproducible results.The first issue of special importance seems to be the tight regulation of the pH. Recent results show that the difference between pH 6 and 7 can have a major impact on the expression of acetate metabolism genes . AlthougAnother challenge is the reproducibility of biological replicates. Our own acetate-based preliminary shake flask and bioreactor experiments have shown that growth and productivity can differ significantly between replicates . This isAdditionally, process engineering can offer solutions for the issue of potentially toxic products. For instance, gas stripping has already been used to remove isopropanol from glucose-based fed-batch cultivations .In situ extraction could be a means to obtain more hydrophobic chemicals as has been demonstrated with the sesquiterpene alcohol α-bisabolol . Such anThe final challenge for process engineering is the coupling of acetate-based production to other processes. Syngas fermentation as a possible source for acetate was successfully tested with isopropanol . Other pSeveral topics still need to be addressed when it comes to acetate-based production.The first issue concerns media additives. As of yet, yeast or beef extract in different concentrations is utilized for isopropanol, acetone, itaconic acid, or β-caryophyllene to name a few examples ,113,114.E. coli.Investigations concerning metabolic engineering approaches constitute another field of potential future work. Obtaining knowledge on factors influencing the productivity of chemicals derived from different metabolic intermediates is certainly paramount ,153. ForE. coli, its reaction to oxygen, or the transition from glucose to acetate as growth substrate have already been established [Additionally, the use of metabolomics combined with precise metabolic models on acetate might identify future targets for metabolic engineering. Models for the acetate overflow metabolism in ablished ,176,177.ablished .To improve acid tolerance in general, further research and application of existing techniques could be a promising way. This includes studies on the expression of genes conferring acid tolerance, investigations concerning global regulators, or adaptive laboratory evolution experiments, which would also be applicable to enhance product tolerance ,159,170.Finally, developments that facilitate the industrial implementation of acetate-based production will be necessary. The establishment of continuous processes would be essential to make acetate industrially attractive. The combination of continuous acetate-based production with in situ product recovery would allow for high productivity while also maintaining high product titers. Such measures will be necessary to compete with petrochemical production, which already often employs continuous processes . HoweverE. coli is still required. In combination with efficient process design, this could lead the way towards “green” production of industrially relevant platform chemicals.Microbial conversion of acetate into value-added products can pose an ecologically sustainable alternative to fossil fuels. To be economically competitive, further research on the metabolism and metabolic engineering of potential host organisms like