Microbacterium species were involved in the metabolic ability to tolerate/adapt to local variations within an extreme desert environment. Using an integrative systems biology approach we have carried out construction and comparison of genome-scale metabolic models (GEMs) of two Microbacterium sp., CGR1 and CGR2, previously isolated from physicochemically contrasting soil sites in the Atacama Desert. Despite CGR1 and CGR2 belong to different phylogenetic clades, metabolic pathways and attributes are highly conserved in both strains. However, comparison of the GEMs showed significant differences in the connectivity of specific metabolites related to pH tolerance and CO2 production. The latter is most likely required to handle acidic stress through decarboxylation reactions. We observed greater GEM connectivity within Microbacterium sp. CGR1 compared to CGR2, which is correlated with the capacity of CGR1 to tolerate a wider pH tolerance range. Both metabolic models predict the synthesis of pigment metabolites (β-carotene), observation validated by HPLC experiments. Our study provides a valuable resource to further investigate global metabolic adaptations of bacterial species to grow in soils with different abiotic factors within an extreme environment.The Atacama Desert is the most arid desert on Earth, focus of important research activities related to microbial biodiversity studies. In this context, metabolic characterization of arid soil bacteria is crucial to understand their survival strategies under extreme environmental stress. We investigated whether strain-specific features of two  Actinobacteria from desert areas. As noted by previous researchers, this class possess bacteria with a variety of attractive metabolic qualities that make them good candidates to survive in desert areas1, including their extensive metabolic capability to degrade and utilize compounds from nutrient poor environments and their ability to synthesize secondary metabolites and natural antibiotics2. In fact, Actinobacteria is the dominant class present in desert locations3, and their ability to adapt to this kind of environment is the focus of intense research. For example, Lynch and collaborators found that over 98% of the lineages present in the vicinity of a volcano in the Atacama Desert corresponded to Actinobacteria, and that their metabolisms were adapted to utilize atmospheric gases present in this ecosystem4. One genus that belongs to the class Actinobacteria is Microbacterium, which is present in a vast variety of environments, including desert soils, marine sediments, and the human gut.In recent years, there has been increased interest in the study of Microbacterium sp. CGR1 was isolated from the Andes mountains in the central Atacama Desert5. Its complete genome sequence revealed the presence of putative components related to pH and salinity tolerance, representing the first genome of the genus Microbacterium sequenced and assembled in a single contig. In addition, two other strains of Microbacterium  were isolated and sequenced from desert soils from Saudi Arabia. Biochemical assays highlighted important differences among them in their basal metabolism, mainly related to lipid and carbohydrate (sugar) compositions6. Recently, a second Microbacterium was obtained from the Atacama Desert under a salt crust in the shore of the saline Lejía Lake7. Lejía Lake, which is nested at the base of Lascar Volcano, is one of the highland lakes at risk of disappearing due to climatic change. Despite the extreme conditions present in this lake, a diverse group of bacteria have been found to reside in this environment8. One of these bacteria is Microbacterium sp. CGR2, which has high pH (up to pH 12) and salinity (5% NaCl) tolerance levels7.Recently, Microbacterium from desert soils display interesting and specific features in terms of the configuration of their global metabolism, making it necessary to identify and characterize the complete set of chemical reactions that occur in Microbacterium species in order understand how they survive under extreme dry environments.Research to date demonstrates that 9. GEMs are networks of biochemical reactions of a given microorganism that encompass important metabolic properties of cellular biochemical networks, such as mass and redox balance, and energy requirements10. By using information obtained from annotated genomes and introducing some assumptions, models are capable of: (1) interpreting current biochemical information for metabolic and genetic context, (2) predicting metabolic capabilities, and (3) guiding future experiments to increase the knowledge of the metabolic capabilities of a given organism11. In recent years, several manually curated GEMs have been reconstructed to explore the capabilities of different microorganisms13 with an emphasis on model organisms, such as E. coli14 or Bacillus subtilis15, but also include several species of Actinomycetes16. However, to date there are no manually curated GEMs for organisms from the genus Microbacterium and there is only one GEM of a bacteria from the Atacama Desert17.In this context, a valuable tool for the systematic study of microbial metabolism of new species is the genome scale model (GEM)Microbacterium species. Our analysis shows that the capacity to survive under extreme conditions correlates with the presence of a complete set of metabolic pathways related to the production of osmolytes and the connectivity level of particular metabolites. In addition, Microbacterium models predicted the synthesis of pigment molecules, while an HPLC assay determined the presence of β-carotene, a strongly colored red-orange pigment used in biotechnological applications.In order to generate important information about global and specific metabolic pathways, which can be correlated with the ability of bacteria to survive given the local context of an extreme environment, we reconstructed and characterized the first GEMs of 5; and near Lejía Lake  or tryptic soy broth (TSB)18.CGR1 and CGR2 were previously isolated from the top layer (10 cm depth) of soil samples obtained from the slope of Lascar Volcano  and an electronic thermometer (Orion model 290), respectively. Electrical conductivity (EC) was determined in a 1:5 soil/water extract. Total elemental profiles from soluble soil extracts were determinate by Total Reflection X-ray Fluorescence (TXRF)19.pH and temperature of the Lascar Volcano and Lejía Lake soil samples were determined For soil condition tolerance assays, bacterial growth curves were determined. In order to emulate the specific soil conditions from which CGR1 and CGR2 were isolated, 10 g of soil from Lascar Volcano (Volcano) and Lejía Lake (Lake) were separately solubilized in 20 mL of TSB medium. Each medium (Volcano and Lake soil media) was centrifuged for 10 min at 9000 RMP and the supernatants were used as the growth media for the growth curves assays. Statistical differences were assessed by Student’s t test p < 0.05 or Mann–Whitney test p < 0.05 (GraphPad Prism 4).Microbacterium sp. CGR1 and CGR2 16 S rDNA sequences were obtained from NCBI under accession codes MK110962.1 and KU714726.1, respectively. The Microbacterium sp. CGR1 and CGR2 strains are available under the accession codes RGM2230 and RGM2255 from Colección Chilena de Recursos Genéticos Microbianos—INIA , respectively.Microbacterium species of length longer than 1400 bp present in SILVA database (November 2018). These sequences were aligned using the MAFFT v7.407 software21, with the default algorithm (FFT-NS-2) and subsequently trimmed to 1315 bp of aligned length and realigned using the G-INS-I algorithm. MEGA-X v10.0.5 software22 was used to build a distance tree based on the neighbor-joining method under the Kimura-2-parameters substitution model23. Bootstrap analysis  was used to evaluate statistical nodal support. The tree was visualized and annotated using the online tool Interactive Tree of Life v4.2.324.For the construction of the Neighbor-joining tree, we analyzed 109 sequences corresponding to the best scored representatives of every named Microbacterium sp. CGR1 was obtained from the GenBank BioProject PRJNA2914335. Genome sequencing of CGR2 strain was performed using Illumina GAIIx sequencing technology . A shotgun library with a total of 4,169,378 paired end reads of 300 bp was generated . De novo assembly was conducted under quality-filtered reads using CLC Genomics Workbench version 8.5.1 with length and similarity cutoffs of 80% and 90%, respectively, resulting in four contigs with an N50 size of 3,676,265 and coverage of 238×. Microbacterium sp. CGR2 was annotated using the NCBI Prokaryotic Genome Annotation Pipeline released on 2013, version 4.625 and approved on October 10th, 2018. Gene mining and genomic contexts were visualized using RAST server26.Genome sequence from 27, we identified tRNAs over the draft genomes. Then, in order to find coding sequences, we used glimmer 3.0228. The functional annotation of coding sequences was performed using Blast comparison to several databases including NCBI Non Redundant, SWISSPROT, OMNIOME, KEGG, COG and InterproScan using PROSITE, Pfam, ProDom and SMART. Two GEMs were reconstructed for Microbacterium sp. CGR1 and Microbacterium sp. CGR2, namely Mcgr1 and Mcgr2. In order to minimize random biases from the process, both reconstructions were done following the same steps, which included: (1) Generation of draft models, (2) Generation of a biomass equation, (3) Gap filling, and (4) Test models. These steps were implemented using the following platforms: Aureme29, Pathway Tools30 and, COBRA (Matlab)31, as explained below.Identification of coding regions, annotation and proteome comparisons were performed using previously described protocols. Briefly, a pipeline for prediction of coding regions and annotation was executed for the two strains. Using tRNAscan-SEMicrobacteria, using the softwares Pantograph from AuReMe32 and PathoLogic from Pathway Tools33. Pantograph is based on orthology and GEMs available for Mycobacterium tuberculosis and Streptomyces coelicolor were used as templates35, as they are members of Actinobacteria and their GEMs have been extensively curated  that could account for the synthesis of biomass, considering whether reactions were still essential if individual metabolites were added to the media, and removing those reactions that lacked genome support and could be substituted for single metabolite additions. With this process, we identified that both Microbacterium lacked 11 reactions for the biosynthesis of biotin, and hence, rather than adding those reactions, biotin was added to the media considering that other organism (such as plant or fungi) are able to provide it to the environment37. Transport reactions were also added from template models. Both models included chemical equations consistent with those presented in the BiGG Models database38. Mass balance tests were performed in COBRA, to ensure that all internal reactions would be balanced. Finally, both models were tested to ensure that: (1) their reactions were elementally balanced, (2) they could generate biomass from defined media, and (3) they did not present futile cycles generating energy. Manual curation was done throughout the reconstruction process, as this allows evaluating the need to include different reactions, pathways, or biomass components.Then, the models were used to test the feasibility to produce biomass, and a manual process was implemented to fill gaps. This consisted of evaluating reactions present in the template model  and 1 mL of hexane were added for cell disruption and carotenoid dissolution. Cells were disrupted at room temperature in a BeadBug6 cell homogenizer (Benchmark Scientific) using a program consisting in 4 cycles of 90 seconds of disruption at 3700 rpm followed by a 10-second rest. Cell lysate was then centrifuged and the liquid phase (hexane) was recovered and then evaporated by adding nitrogen on the surface of the liquid. After solvent evaporation, the solid residues in the bottom of the test tube were resuspended in 1 mL of acetone and vortexed until complete dissolution. If a white precipitate appeared, it was discarded by centrifugation. Finally, the homogenous acetone was transferred to a 1.5 mL Eppendorf tube and stored at −20 °C until HPLC analysis.For carotenoid extraction40. Briefly, samples were pre-treated for injection by mixing 450 μL of sample with 50 μL of a 200 mg/L solution of β-apocarotenal, used as internal standard to correct the apocarotenal injection error (retention time tR~6.1 min). After adding the internal standard, 50 μL of sample were injected in duplicate into a LiChrospher® 100, RP C18 (5 μm) column, using Acetonitrile:Isopropanol:Methanol (85:10:5) as a mobile phase. Carotenoids were detected by measuring the optical density of the eluent of the column at 453 nm using a diode array detector (DAD). Chromatograms from the samples were compared with those of known standards to determine the presence of different carotenoids: lycopene (tR ~ 11 min), δ-carotene , ε-carotene (tR ~ 16.5 min) and β-carotene (tR ~ 19.7 min). All standards were purchased from Sigma-Aldrich.The carotenoid analysis was performed by LaChrom L-7000 chromatograph using Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC) for detection41. Based on the particular soil scenario present in the Atacama Desert, two Gram-positive, rod-shaped and yellow-pigmented Microbacterium strains (CGR1 and CGR2) were isolated from soil samples collected from the Andes mountain range in the central region of the Atacama Desert , close to the Lascar volcano, while CGR2 was isolated from an alkaline site with high electrical conductivity  located near Lejía lake. Analysis of available elements showed that the Lake site had a significant higher content of Ca, Mn and K nutrients, supporting its characterization as a salt lake. In contrast, Cu, Fe and Zn were higher at the Volcano site. A similar scenario has been observed in acid mine drainage environments, where the low pH facilitates the solubilization of heavy metals.Microbacterium isolates growing in each of the soil-like media. The results showed that CGR1 grew under the Volcano and Lake media in a similar way. On the other hand, the emulated conditions of the Volcano soil reduced the proliferation of CGR2. As mentioned before, the main differences between both sites, and hence between both media, were the pH and salt concentrations (conductivity). In terms of pH, the Volcano soil media had a final concentration of protons equal to 1E-5, more than four orders of magnitude higher than the concentration of the Lake medium (pH = 9). In general, pH is one of the principal variables highly correlated with soil microbial diversity at the continental scale42, across artificial pH gradients43, in highly managed urban systems and in natural pH gradients19. As observed for other environmental factors, the peak of diversity has been correlated with near-neutral or optimal conditions, showing a trend to decrease towards more acidic and alkaline (extreme) conditions44.Thus, CGR1 and CGR2 were isolated from soils with highly contrasting physicochemical environmental features. In order to test if both bacteria are able to reciprocally growth in each other’s habitat, we generated two soil-like culture media (Volcano and Lake) to emulate the physicochemical variables present in the isolation sites . Figure 45. Bacteria that live in saline environments, especially when they are hypersaline, must overcome the essential problem of maintaining a balance in intra and extracellular water content, and thus avoiding cellular dehydration . For this reason, only osmotolerant species are able to inhabit high saline conditions, leading to a decrease in soil microbial diversity in these environments46.Considering salts, the Lake medium had at least 10 times greater concentration of elements  than the Volcano medium. Salinity has also been known as a factor that influences the microbial diversity of soilMicrobacterium was first proposed by Orla-Jensen in 191947, and was afterwards revised by Collins et al. (1983) and by Takeuchi & Hatano (1998a) in order to include the unification of Microbacterium with its closely related genus Aureobacterium49. At present (November 2018), the genus comprises 109 species (http://www.bacterio.net/microbacterium.html), which occupy a very wide and diverse environmental distribution, including soil50, water51, plants52, rhizosphere53 and human clinical specimens54.The genus Microbacterium isolates from the Atacama Desert was performed, in which the 16 S rRNA sequences of CGR1 and CGR2 strains were compared with the 16 S rRNA sequences from 109 Microbacterium species  could be useful to assess whether metabolic modifications can be linked to adaptations to their particular ecological constraints. For this reason, the global metabolic capabilities of both Streptomyces coelicolor iMK1208 contains 15% of genes35). The Mcgr1 and Mcgr2 models contain 1168 and 1172 reactions, respectively, from which 760 and 764 correspond to metabolic reactions. The similarity of the metabolic capabilities in core pathways is reflected by the high percentage of shared metabolic reactions . These similarities are also reflected by the number of shared metabolites among the two models (98% for both). Table The Mcgr1 and Mcgr2 models contain 632 and 648 genes, which correspond to 19% and 17% of the total genes in each genome, respectively. These values are an expected and sufficient level of representation as they fall within the range of other GEMs  followed by phenylacetate degradation I (aerobic). On the other hand, benzoate compounds are predicted to be metabolized into protocatechuate, and from there utilize pathways protocatechuate degradation II (ortho-cleavage pathway) and 3-oxoadipate degradation. C1 compounds include formaldehyde and methanol. Interestingly, Mcgr1 has three pathways to utilize C1 carbon sources, while Mcgr2 has none, indicating a robust system to deal with these compounds in Mcgr1. This robustness may also ensure that formaldehyde, a toxic metabolite, is rapidly recycled56. Since Mcgr1 was isolated from the Lascar volcano, it is possible that the proximity to the volcano may elevate the availability of methanol and formaldehyde in the environment. For example, Lynch and collaborators identified formaldehyde and other C1 gases present in another volcano in the Atacama Desert, together with biodegradation pathways in bacteria from that area4.Despite many similarities, the two 57, with more particular emphasis in the bacterium Pseudomonas putida58. Other Microbacteria in different locations have been reported to degrade protocatechuate. According to the pathway database available in Pathway Tools30, out of the 48 Microbacteria included, 39 possess protocatechuate degradation pathways. In contrast, only one model in the BiGG database contains this compound 38, indicating underrepresentation in constructed models.Aromatic compounds are another family of substrates belonging to pathways that show differences between Mcgr1 and Mcgr2, as only Mcgr2 can utilize 4-hydroxybenzoate (4-HBA), vanillate and 3,4-hydroxybenzoate (protocatechuate), the latter being also an intermediate compound in the degradation of the former two. These components are considered aromatic pollutants, and their bacterial degradation has been the focus of previous studies59, we theorize that Mcgr2 has adapted to utilize this component produced by other organisms. Mcgr2 was isolated from a salt lake, where it is expected that plants and microorganisms could produce inositol as an osmoprotectant.Another difference between Mcgr1 and Mcgr2 was that only Mcgr2 was capable of degrading inositol. Since this compound is an osmoprotectant for several bacteria and plants60. In order to improve the understanding of this point, we compared the metabolism of both Microbacterium strains against the GEMs of different species with C1 metabolism, which included the models from Methylococcus capsulatus, Bacillus subtilis and Pseudomonas putida63. For this, we identify the presence of 16 enzymes involved in 4 pathways related to C1 metabolism: formaldehyde assimilation II (assimilatory RuMP Cycle), formaldehyde oxidation I (dissimilatory RUMP cycle), formaldehyde oxidation IV and methanol oxidation in the genomes of all five species. We found out that both Microbacterium are the only two species containing all these enzymes, supporting the idea that CGR1 and CGR2 belong to the group of methylotrophic bacteria. Interestingly, by calculating the connectivity of metabolites, we found out that formate, formaldehyde, methanol and CO2 have a lower connectivity in Microbacterium than the other species. This result suggests that the C1 metabolism in the Microbacterium CGR1 and CGR2 presents a high level of specificity, conferring to the systems the a low level of connectivity inside the network64, a characteristic observed in specialized species that live in particular niches, such as Geobacter sulfurreducens, a soil sulphur-reducing proteobacterium65.Microbacterium species have been previous classify as putative methylotrophic bacteriaMicrobacterium against Chromohalobacter salexigens 66, which is a halophilic extremophile commonly used to study osmoadaptation and the methanotrophs bacteria Methylomicrobium buryatense (adapted to metabolize C1 compounds)67. All models contain a similar number of enzymes, reactions and metabolites. In particular, one of the main mechanisms of the latter to cope with the saline environment is the production of osmoprotectants such as ectoine and hydroxyectoine, both metabolites able to be synthesized by Chromohalobacter salexigens and both Microbacterium strains. The model for the methanotrophs bacteria Methylomicrobium buryatense, utilizes the RuMP cycle to fixate C1 just like Microbacterium Mcgr1. This pathway is a highly efficient route for the assimilation of reduced one-carbon compounds . Considering that CGR1 soil contains higher metal concentration  of osmolytes. Some osmolytes that have been reported for bacteria include glycine-betaine, carnitine, proline, trehalose, ectoine and glutamateIn silico analysis showed that both Microbacterium isolates are equally capable of adjusting to the addition or removal of protons . Hence, we hypothesized that their different capacity to deal with acidic conditions could be explained by system-level properties, such as metabolite connectivity 72. Table 2 was one of the top metabolites, with 5 degrees of difference in connectivity, with higher differences in Mcgr1 compared to Mcgr2. Since CO2 is often involved in decarboxylation reactions, and previous studies have shown that decarboxylation reactions could participate in mechanism to handle acidic stress by incorporating protons73, we looked at these reactions in more detail in the models. We found that the gene AKG07_04960 is only present in the Mcgr1 genome. It encodes for a decarboxylase of the amino acids lysine, ornithine and arginine, which, interestingly, have been shown to play a role in the response to acidic stress and survival in other bacteria74.76. In this respect, both Microbacterium strains have a gene, Sodium/Proton antiporter , predicted to be pH-dependent that could be involved in maintaining proper homeostasis under alkaline conditions, as they both have the capability to grow in extreme alkaline conditions . Therefore, we suggest that the connectivity of decarboxylation reactions and proton exchange represent a common metabolic strategy that could explain the presence of both strains in this extreme environment. At the same time, variations in the magnitude of this connectivity could affect their pH tolerance range.Another mechanism that has been described in the literature as an important player in the handling of alkaline and acidic stress is the direct exchange of protons with the external environment through proton pump mechanisms Microbacterium GEMs against the extremely halophilic bacterium Salinibacter ruber in order to find specific mechanisms of adaptation to high concentrations of salt77. S. ruber is characterized by having different osmotic resistance proteins also conserved in Archaea species, such as light-driven proton and chloride pumps, none of them encoded in CGR1 or CGR2 genomes. While the Microbacterium CGR2 lives in a salt environment (Lejia Lake), both not need high concentration of salt to growth, unlike S. ruber, which optimal growth performance requires a 20–30% of salt in the media. In addition, S. ruber are able to produce an unusual carotenoid called salinixanthin, a compound used for transfer energy to light-driven proton pumps. Interestingly, according to the GEMs, both Microbacterium are able to produce carotenoids, however, the absence of light-driven proton pumps in these bacteria suggest that these molecules are not directly related to salt resistant.We also compared the 78. Microbial mechanisms to deal with radiation stress include DNA repair mechanisms and genetic expression of photoprotection molecules80. One group of photoprotection molecules are carotenoids, long chain molecules that provide pigmentation to many organisms and can absorb UV wave length that might otherwise damage cellular structures81. Multiple studies have positively correlated carotenoid pigmentation with UV resistance in bacteria83. It has also been shown that pigmentation by itself does not explain the overall bacterial response to UV radiation in some populations84, a situation that we believe is explained by the presence of different, and possibly complimentary, protective mechanisms.Another relevant stressor in the Atacama Desert is the high UV radiation, which could severely impair growth of certain bacteria, while not having a significant impact in others due to the presence of protective mechanismsMicrobacterium species have been widely used in biotechnological applications; in particular their production of carotenoids has be used for diet supplements, food colorants, animal feed and nutraceuticals85. Interestingly, both Mcgr1 and Mcgr2 exhibited pigmentation on petri plates, and they both possessed biosynthetic gene clusters for carotenes, which strongly suggest that they are producers of carotenes  in the non-mevalonate pathway  may have provided one species with a higher capacity to tolerate a wide range of pH. This comparative global metabolic analysis supports the idea that only a fraction of the encoded proteins takes an active part in metabolic pathways and display strain-specific correlation patterns. These patterns are reflective of the functional differentiation of the two Microbacterium strains described in this work, organisms that co-occur in microbial communities located in spatially close soils.In this work, we presented the first two GEMs for Microbacterium strains encoded the complete pathways required to produce different carotenoids, which are interesting targets for further development. Finally, together with the GEM model of Streptomyces leeuwenhoekii C3417, Microbacterium sp. CGR1 and CGR2 models are the first curated GEMs from bacterial species isolated from desert environments, significantly expanding the current knowledge of global metabolism in extreme bacteria.In addition, analysis of secondary metabolites revealed that both GEM Mcgr1.GEM Mcgr2.Supplementary Figures.Supplementary Tables.


The co-cultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate.Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of C. kluyveri, resulting in a flux of up to 60C. autoethanogenum ethanol production. Blocking either acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) activity or formate transport, in the C. autoethanogenum metabolic model could potentially lead to an up to 150The model suggests that one strategy to optimize the production of medium-chain fatty-acids from syngas would be the addition of succinate. According to the prediction, addition of succinate would increase the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in  This energy-rich syngas can be further used as feedstock for chemocatalytic processes such as Fischer-Tropsh, but microbial fermentation of syngas is gaining more attention recently as a potential production platform 2 ratio in syngas, and are also more resistant to the presence of certain impurities (e.g. sulfides), reducing the need for costly pre-treatment of syngas One of the biggest challenges society faces nowadays is finding alternative processes for the sustainable production of fuels and chemicals. At present, the production of many commodities depends on fossil fuels, which is not sustainable or sugar crops, competing with human and animal food consumption 2 via the Wood–Ljungdahl pathway Clostridium autoethanogenum and Clostridium kluyveri was described to produce medium-chain fatty acids (C4-C6) and their respective alcohols by assimilation of CO or syngas C. autoethanogenum is an acetogenic bacterium able to produce acetate and ethanol when growing on CO or syngas C. kluyveri grows on acetate and ethanol via reverse-C. kluyveri is grown in co-culture with C. autoethanogenum on CO, it produces butyrate and hexanoate, which are further reduced by the acetogen to the corresponding alcohols, butanol and hexanol C. autoethanogenum and C. kluyveri is 0.056 mmol of hexanoate per mmol of CO, whereas the yield obtained in the most recent study Acetogenic clostridia are efficient microbial hosts for syngas fermentation as they can grow on CO and CO/HClostridium thermocellum allowed the design of metabolic strategies to increase ethanol production after identification of bottlenecks in central carbon metabolism that were inhibiting its production Desulfovibrio and archeon Methanococcus maripaludis S2 into a single model with a shared extracellular environment, bringing the use of GEMs to a next level. Since then, this type of community models have been used to describe metabolic interactions among community members and inter-species fluxes C. autoethanogenumC. kluyveriGenome-scale, constraint-based metabolic models (GEMs) attempt to represent the complete set of reactions in a living organism, and have been used to gain better understanding of cellular metabolism, assessing theoretical capabilities or designing media and processes C. autoethanogenumC. kluyveriIn this study, we present a multi-species model built by combining the GEMs of 22.1C. autoethanogenum, the previously described GEM, iCLAU786, was retrieved in SBML (XML) format from the To represent the metabolism of C.kluyveri, iCKL708, was downloaded in table format from its publication The GEM of 2.2C. autoethanogenum and C. kluyveri was generated by combination of single species models: iCLAU786 The multi-species GEM of 2 excreted by C. kluyveri, from the amount of H2 metabolized by C. autoethanogenum. The same was done for acetate. A reaction was included to distinguish the amount of acetate metabolized by C. autoethanogenum, from the amount of acetate produced by C. autoethanogenum. The model also contemplates the possible production of butanol and hexanol via butyrate and hexanoate uptake by C. autoethanogenum. The added reactions are a transport reaction from the external compartment to the internal compartment of C. autoethanogenum, reactions for production of butyraldehyde and caproaldehyde from the corresponding fatty-acids and reactions for production of alcohols from their corresponding aldehydes. Finally, the multi-species model was transformed into SBML level 3 version 1 (see Each species has its own biomass synthesis reaction. An extra biomass metabolite was created and defined in the extracellular compartment for each species: ‘BIOMASSon 1 see .2.3−1 h−1) are integrated as model constraints instead of specific fluxes (mmol gDW−1 h−1), where gDW indicates grams of dry weight. The biomass reaction of each species incorporates as new term, the biomass of the relative species together with the growth rate term. In this way, we can account for species abundance in the community. The biomass of each species is calculated based on the community biomass and the species ratio. In addition to this, steady-state and equal growth rate of species are assumed.In order to model the community, we have followed an approach similar to the one proposed by SteadyCom 2.4C. autoethanogenum and C. kluyveri in co-culture, was estimated from partitioning RNAseq reads and confirmed via cell counting in microscopy observations as two independent methods. Transcriptomic data was obtained from steady-state co-cultures grown in chemostats C. autoethanogenum: DSM 10061  was then derived using: C. autoethanogenum was multiplied by 10 and the dry weight of C. kluyveri was multiplied by 1 (as the observed proportion). Finally, the biomass-species ratio was calculated based on the ratio of the accumulated dry weights. The proportion was observed to be constant among experimental conditions with CO and CO/H2, so we assume that the relative abundances are constant for the rest of conditions too.The second method consisted of direct cell counting under microscopy observations. This led to a proportion between cell numbers of 10 2.5−1 h−1. Product concentrations, measured in mM, were used to compute product secretion rates (mmol l−1 h−1) by using the same hydraulic retention time (HRT) that in laboratory settings. Dilution rate is the inverse of the HRT. In steady-state conditions, growth rate is considered equal to the dilution rate and therefore, it was calculated as the inverse of the HRT and expressed in h−1. In co-culture, the growth rate of both species was assumed to be the same and equal to that of the community, since HRT was kept constant both, in mono-culture and co-culture experiments −1 h−1). Similarly, product formation was set to be at least 80−1 h−1 from the pre-set values in mmol gDW−1 h−1 multiplying by the total biomass and species ratio. In cases where metabolites behave as products that are further metabolized by the other species, the transport reactions of these metabolites are forced to operate in the direction from the external compartment to the other species compartment.Experimental measurements were converted to mmol l2.5.1C. autoethanogenum in mono-culture and co-cultivation of C. autoethanogenum and C. kluyveri grown on CO/H2 and CO/acetate. In co-culture experiments, C. kluyveri was inoculated in the reactor on top of C. autoethanogenum in a 1:20 volume ratio. The organisms were cultivated in chemostat to control environmental conditions such as pH (6.2), temperature (37°), HRT (between 1.5 to 2 days) and medium composition during the entire reactor run. A reactor run starts with inoculation of C. autoethanogenum in mono-culture followed by co-cultivation with C. kluyveri after reaching stationary phase. Total reactor volume is 1.5 l. Working volume was set between 0.75 l to 1 l. Experiments were run on different conditions of CO/H2 and CO/acetate as initial substrates. Concentrations of organic acids and alcohols in the reactor and gas composition in the outflow were tracked during the runs.Experimental data was collected from reactor run 3 and 4 of the recent study 2.6C. kluyveri, unless stated otherwise. For each explored condition, the solution space and the set of fluxes compatible with the measured constraints were sampled using the sample function in the flux_analysis submodule COBRApy. Flux sampling is a method to get a distribution of fluxes Model simulations were done using COBRApy, version 0.17.0 2.7C. autoethanogenumC. autoethanogenum deleting the corresponding reactions. Each mutant was assessed at each experimental condition and compare to the wild type. The media as well as the biomass reaction were constrained using the experimental data of mono-culture experiments for those conditions OptKnock and RobustKnock 3C. autoethanogenum and C. kluyveri. The generated multi-species GEM, together with the GEM of C. autoethanogenum, were used to assess these strategies.The objective of this study is to find optimization strategies for the production of medium-chain fatty-acids from syngas using the co-culture of 3.1C. autoethanogenum, iCLAU786 is composed of 1108 reactions and 1094 metabolites. The model is able to simulate growth on CO or syngas as the sole carbon and energy source, producing acetate and ethanol as the main fermentation products.The GEM of C. kluyveri, iCKL708 has been previously shown to predict growth on ethanol and one other organic acid , propanol and acetate, crotonate, and vinyl acetate, in accordance to published experimental data C. kluyveri, has 993 reactions and 811 metabolites. This updated model also simulates growth on acetate and ethanol uptake producing butyrate and hexanoate as the main chain-elongated products and H2.The GEM of 3.222 excreted by C. kluyveri. Reaction 22 in C. autoethanogenum. Reaction C. autoethanogenum in simulations where acetate acts as additional substrate. This serves to distinguish the fluxes between acetate feed rate  was taken into account resulting in a biomass ratio of 68.5C. kluyveri and 31.5C. autoethanogenum. This cell ratio was observed in CO and CO/H2 conditions and it was assumed to be constant in the model simulations.Microscopy observation of the co-culture led to the estimation of a ratio of 10 cells of 3.3C. autoethanogenum−1 h−1. Experiments were run on CO/H2 and CO/AC (acetate) as initial substrates. Acetate production rate . Ethanol production rate (‘EXC. autoethanogenum minus the ethanol consumed by C. kluyveri (reaction id Rckl837). The model correctly predicts production of medium-chain fatty acids upon introduction of C. kluyveri. Similarly to the mono-culture simulations, there is a slight mismatch between predicted and observed acetate production. The model correctly predicts the increase of medium-chain fatty-acids when more H2 or acetate is added. When H2 feed rate is equal to 5.3 mmol l−1 h−1, butanol is also produced (0.075 mmol l−1 h−1). Also, ethanol accumulation is low in most co-culture conditions, suggesting most of it is metabolized by C. kluyveri. The steady-state production rates of acetate increases with increasing acetate uptake  reaction (R3) and in formate hydrogen lyase reaction (R2 in 2 supply. Part of the formate is excreted and part is further metabolized to acetyl-CoA (ACCOA) following the Wood-Ljungdahl pathway. Pyruvate is partly produced from acetyl-CoA via the pyruvate synthase (R8 in C. kluyveri. Ethanol production rate by C. autoethanogenum (reaction R10) and ethanol uptake by C. kluyveri (R15) indicate that most of ethanol is removed by C. kluyveri and proves the metabolic change to solventogenesis due to addition of C. kluyveri. This can be observed comparing to mono-culture results showed in C. kluyveri (flux through R10). In C. kluyveri ethanol is oxidized to acetyl-CoA (ACCOA) and part of acetyl-CoA is converted to acetate via acetyltransferase and acetate kinase (R16). Acetyl-CoA initiates the reverse 2 to pyruvate via pyruvate synthase (R20) in C. kluyveri.CO or CO and Hon R2 in ) to prodse R8 in  for assiC. kluyveri comes from uptake of succinate produced by C. autoethanogenum (see R28), which could explain the slight mismatch observed in Succinate is converted to Crotonyl-CoA (CROCOA), yielding an additional 2 acetate see , involvi2 production by C. autoethanogenum with increasing H2 feed rate , more protons are released. In contrast, less protons are released when more acetate is fed. ATP synthase increases in both species  when more H2 or acetate is fed to the reactor.The model predicts reduction of COeed rate B. This wurce see . It is o3.4C. autoethanogenum and metabolized by C. kluyveri producing part of the acetate and crotonyl-CoA pool needed for chain elongation and production of fatty-acids. C. autoethanogenum and C. kluyveri growing on CO and H2 as carbon and energy sources. Succinate, acetate and ethanol uptake by C. kluyveri increases with more H2 supply. Succinate uptake decreases when C. kluyveri is less abundant in the co-culture. On the contrary, the amount of ethanol and acetate that is available to be metabolized by C. kluyveri decreases along with the relative abundance of C. autoethanogenum.The model enables detailed inspection of production and uptake profiles. Therefore we investigated the sharing of metabolites between both organisms. Analysis of intracellular fluxes in C. kluyveri .To further investigate the role of succinate, similar simulations were performed but this time preventing the uptake of succinate by kluyveri . The mod3.5We used the experimentally-validated model to simulate alternative scenarios that have so far not been explored experimentally. Accordingly, we present here an analysis of possible strategies to increase medium-chain fatty acid production by the co-culture.3.5.12 feed rates and fixed CO feed rate (4.8 mmol l−1 h−1). C. kluyveri-C. autoethanogenum), respectively. The increased carbon availability has been considered and used to normalize the results, so they are represented as mmol of product per total substrate (CO and succinate) per carbon. As already indicated in 2 uptake by C. kluyveri and changes in H+ balance and ATP synthase.As a strategy to increase the production of desired products, we simulated how the addition of succinate as extra carbon source would affect the production of butyrate and hexanoate at different H2 feed rates.Furthermore, succinate addition allows increased production of both fatty-acids. Hexanoate production can increase up to four times when succinate is added and butyrate has potential to increase around five times with respect to the results obtained with no succinate addition. On the other hand, simulations show no relevant differences upon variations of H3.5.2As a second strategy for co-culture optimization we have predicted and evaluated genetic interventions that could lead to higher medium-chain fatty-acid production.C. autoethanogenum. The three reactions are located in the metabolism of C. autoethanogenum. Mutant 1 refers to the knock out of formate transport reaction via proton symport  and would possibly lead to a further increased production of MCFA and alcohols in conditions with higher H2 influx (−1 h−1), as it has been previously observed. Furthermore, it has already been proven that succinate leads to an increase of MCFA in C. kluyveriAs the model closely predicted obtained experimental values, it can be used to design potential strategies for improved production. Here, we explored a series of strategies to optimize the production of hexanoate. The first strategy relied on the role of succinate, as the model predicts it increases the pool of acetate and crotonyl-CoA, a precursor of the desired fatty-acids, in veri see . The fludded see . The incdded see . Accordidded see . MoreoveC. autoethanogenum. An increasing ratio of ethanol/acetate ratio has been shown to result in increased hexanoate production in C. kluyveriThe second strategy aims to increase hexanoate by increasing ethanol production by C. autoethanogenum is known to increase production of ethanol under acidic or redox overloading conditions C. autoethanogenumC. autoethanogenum we predicted that the individual knock out of one of the following three reactions could increase the production of ethanol: acetaldehyde oxidoreductase (ACALDx), formate transport (FORt2), and the bifurcating formate dehydrogenase (ferredoxin)  (model id leq000001). This functionality of Hyt seems to occur in situations where redox mediators get too reduced 2 via CO dehydrogenase  see . The aceC. kluyveri has been reported to be better at higher pH C. autoethanogenumWhen the reactions of fatty-acids production are not constrained, the model always predicts more hexanoate as compared to butyrate production measured in the actual chemostat experiments 2 uptake by C. kluyveri in cases where hexanoate is more abundant  producing pyruvate that is further converted to formate via formate lyase (model id PFL), for assimilatory purposes. Formate is then assimilated via the tetrahydrofolate pathway to, subsequently, be transformed to various amino acids needed for growth 2 is also metabolized following phosphoenolpyruvate carboxylase reaction (PPC), where CO2 is fixed together with phosphoenolpyruvate (pep) producing oxaloacetate (oaa). Oaa produces aspartate via aspartate aminotransferase (Rckl310). Aspartate is a precursor in the synthesis of threonine involving aspartate kinase (Rckl334), aspartate semialdehyde oxidoreductase (Rckl323), homoserine kinase (Rckl335) and threonine synthase (Rckl336). Then, threonine produces acetaldehyde and glycine via threonine aldolase (Rckl341). Acetaldehyde is converted to acetyl-CoA following aldehyde alcohol dehydrogenase (ADH) reaction. So, the increase in CO2 assimilation could lead to more acetyl-CoA and thus, more fatty-acids. Around 30–402 is also converted to carbonic acid. Carbonic acid produces oxaloacetate via pyruvate carboxylase (Rckl014), which follows the aforementioned route to acetyl-CoA. Simulations made with higher CO2 uptake rates than the ones predicted when hexanoate is more abundant (−1 h−1) however, did not lead to higher production rates of hexanoate or butyrate. This suggest that C. kluyveri metabolizes CO2 up to a maximum value. In fact, this is supported by the observed correlation between growth and the maximum CO2 fixed by C. kluyveri2, close to its maximum capacity.We observe an increase of COdant see  compareddant see . CO2 is 2 , is here predicted to increase the yield of hexanoate up to 0.6, reflecting the potential of this approach to produce medium-chain fatty-acids.Electron yield is calculated based on the amount of electrons going to hexanoate per the total amount of electrons going into the system as carbon and energy sources. Co-cultures of 5C. autoethanogenum and C. kluyveri has provided insights into the fermentation of CO/syngas to medium-chain fatty acids by this co-culture. The prediction of intracellular flux distribution in this consortium enabled to uncover the potential importance of succinate uptake via C. kluyveri to produce butyrate, and suggested an effect of the biomass species ratio on the substrate profile of C. kluyveri. Simulations indicated that succinate addition might result in a substantial increase in hexanoate yield from syngas. In addition, the model of C. autoethanogenum shows that the deletion of reactions FORt2 or ACALDx or FDHC. autoethanogenum potentially increase ethanol production, suggesting a potential increase in hexanoate production when these deletions were to be applied in co-culture experiments. Altogether, our model-driven approach has set a good basis for the systematic design of strategies to modulate and optimize the production of valuable chemicals from syngas.The generation of the multi-species GEM of Sara Benito-Vaquerizo: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft. Martijn Diender: Formal analysis, Investigation, Writing - review & editing. Ivette Parera Olm: Formal analysis, Investigation, Writing - review & editing. Vitor A.P. Martins dos Santos: Funding acquisition, Supervision, Writing - review & editing. Peter J. Schaap: Conceptualization, Writing - review & editing. Diana Z. Sousa: Project administration, Funding acquisition, Conceptualization, Writing - review & editing. Maria Suarez-Diez: Supervision, Conceptualization, Methodology, Visualization, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define “aero-types”, a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of  Escherichia coli genotypes grown under different conditions, which leads to the discovery of a stratified distribution of phenotypes. The observed distribution is determined by distinct ATP generation strategies, defined as “aero-types”, associated with optimal proteome allocation modulated upon differential usage of the electron-transport-chain enzymes. This mechanistic approach offers us a genome-scale understanding of the fitness landscape, and a fundamental interpretation of bacterial physiology and evolution dynamics.Genome-scale models enable quantitative prediction of bacterial phenotypes and a fine-grained description of the underlying optimal proteome allocation. Thus, we can now analyze the phenotypic potential of a large number of  Sewall Wright’s fitness landscape  represenThese well-studied fitness landscape models, whether discrete or continuous, address evolutionary dynamics towards an optimal phenotype based on the rare beneficial mutations that arise historically or in the course of microbial evolution experiments . DirecteAn alternative approach to explore the fitness landscape and phenotypic distribution comes from the solution space of a genome-scale metabolic model (M-model) , 18. GenE. coli, in particular, explicitly incorporate the full reconstruction of transcription and translation pathways to allow for quantitative predictions of proteome allocation at the gene level [E. coli populations on a fitness landscape.Expansion of the M-models to include constraints on the cost of protein biosynthesis has been improving the accuracy of phenotypic predictions for different organisms under various environments –28. The ne level –31 and tne level . A more ne level ), enabliE. coli phenotypic growth data in the rate-yield plane and find consistent non-uniformity in the fitness distribution. Both computationally and experimentally determined phenotypes display multiple distinct phenotypic categories that distribute in stripes on the rate-yield plane and form a landscape with a “stratified” topology. We then show, by detailed analysis of metabolic fluxes and protein expression, that the stratified topography of this phenotypic fitness landscape can be fully described by the energy production strategy, which in turn is determined by a balance between proteome allocation cost and the metabolic efficiency of ATP production. Interestingly, we find that a simple quantity—the fraction of total ATP that is generated by the ATP synthase (fATPS)—is capable of outlining the stratification. Consequently, we define E. coli “aero-types” based on the multimodal distribution of fATPS modulated through the discrete usage of electron-transport-chain enzymes. An aero-type not only describes the cellular respiratory behavior, but also indicates the associated metabolic state and proteomic compositions. Finally, we discuss how the aero-type, as an effective fitness descriptor, can be used to address important biological questions such as the predictability of microbial evolution and the interpretation of the rate-yield tradeoff.Here, we assemble and analyze large amounts of Y) and substrate uptake rate (q), to span the phenotypic space for E. coli (n = 199) includes characterizations of different naturally occurring E. coli strains, evolved gene knock-out mutants, and growth under various nutrient conditions. It is immediately noticeable that both the high and low yield regions are densely populated, yet the regions in between (0.2 < Y < 0.3gDW/g) are almost empty  and thermal stability (ΔG) by a random but small amount  the  at 37°C  and 42°C at 37°C , and conrylation .fATPS, fPGK, fACKr, etc.) formed an eight-element vector that we used as the explanatory variables in a stepwise linear regression analysis. The results showed that six of the ATP production fractions could explain 89.5% of the variation in phenotypic distance  ATP production by the ATP synthase (ATPS4rpp), and reactions catalyzed by the polyphosphate kinase (PPKr and PPK2r) in oxidative phosphorylation; 2) reactions catalyzed by the phosphoglycerate kinase (PGK) and the pyruvate kinase (PYK) in the lower glycolysis pathway; 3) the reaction catalyzed by the acetate kinase (ACKr) in mixed acid fermentation; 4) the reaction catalyzed by the succinyl-CoA synthetase (SUCOAS) in the TCA cycle; and 5) the reaction catalyzed by the ribose-phosphate diphosphokinase (PRPPS) in nucleotide biosynthesis. These quantities  are located within the top two hyperbolic bands on the rate-yield plane  as a simple, yet effective, quantification for the cell’s metabolic state, and key determining factor for the stratified phenotypic distribution on the rate-yield plane.In summary, we introduced the fraction of total ATP produced by the ATP synthase . Second, the average number of subunits per enzyme increases  reactions . For exaatalysis . CompareThese results reveal an intricate balance between proteome complexity and the energy requirements for cell growth. As the energy demand increases, more and more enzyme complexes are necessary to achieve higher ATP yield. However, larger complexes also require significantly more metabolic resources for their biosynthesis. Thus, once activated, these enzyme complexes should be used as much as possible, inducing necessary rewiring of the metabolic network for optimal balance in proteome allocation, and shifting the ATP production strategy to the next discrete state.i to v to describe the five populated phenotypes represented by the five peak values of fATPS (from fATPS = 0 to fATPS ∼ 0.71) observed in the strain sampling simulations. Computationally, we compare aero-type with the P/O ratio, a commonly used parameter that describes the cellular respiratory behavior. We show that the P/O ratio outlines only the local stoichiometry of the oxidative phosphorylation pathways. Aero-type offers a more global description of cellular fitness by representing the metabolic and phenotypic state, and the proteome complexity associated with a specific energy production scheme  and two from the cytochrome oxidase (cyoB from the cytochrome bo oxidase and cydB from the cytochrome bd-I oxidase) for genetic manipulation  would most likely leave the cell in aero-type i and ii, with lower Y and higher qglc. The mutants depleted of cydB and/or nuoB are, in principle, still accessible to all aero-types. However, it is less likely for the ΔnuoB mutant to have higher Y and lower qglc, because the NADH dehydrogenase I is almost always activated for aero-type iv and v.We resorted to the characterization of genetic mutations that may trigger a switch in the aero-type. According to the comprehensive decomposition of the ETC enzyme usage shown in pulation . We woulndh, ΔnuoB, ΔcydB, and ΔcyoB) and double  knockout strains to test the predicted phenotypic effect experimentally  and a quinol monooxygenase gene, ygiN, were removed from the E. coli genome. This mutant strain was characterized by the lowest possible Y and highest qglc, corresponding to aero-type i (fATPS = 0).We constructed the single  to ii (purple).Next, we confirmed the correlation between aero-type and the proteomic state of the mutant strains using RNA-Seq analysis . Hierarcn groups . ConsistIn short, we design mutant strains where the major ETC enzymes are removed combinatorially to perturb the cell’s respiratory potential and ATP production strategy. We show that the phenotypic outcome, proteome re-allocation, and the phenotypic aero-type switch of these strains are consistent with the computational predictions.E. coli is able to thrive under a variety of environmental conditions, from highly oxic to completely anoxic, with its amazingly versatile pool of fifteen primary dehydrogenases and ten terminal reductases [As a facultative anaerobe, ductases . So far,cydAB, cyoABCD, and appBC) and a quinol monooxygenase gene (ygiN) were set to zero. This mutant strain was shown to produce a phenotype that was almost incapable of oxygen utilization and presented fermentative behavior under oxic condition [E. coli under anoxic conditions [To answer this question, we created an in silico strain where the expression of all terminal cytochrome oxidase genes  is a governing parameter describing the discretization; and 3) the discretization is rooted in a balance between the modularity of proteome composition and metabolic functions underlying optimal growth.In this study, we develop a systems biology definition of the phenotypic fitness landscape based on the solution space of the ME model . Simulatandscape , 40. We E. coli “aero-type” to summarize the complex relationships between genotype, metabolic state, proteome allocation, and phenotype. We reason that a switch in the aero-type may occur if differential usage of the ETC enzymes is imposed by genetic mutations or environmental stresses. To confirm the hypothesis, we experimentally construct the mutant strains where major ETC enzymes are removed combinatorially, and show that the measured aero-types of the mutants are consistent with computational prediction.The direct correlation between a cell’s energy production strategy and the phenotypic landscape topography inspires the definition of the fPGK, fPYK and fACKr, With the aero-type defined as a key phenotypic descriptor, it is worth pointing out that discretization of ATP production through other reactions  and genetic perturbations  on the fitness landscape can be rationally evaluated based on how the cell’s energy production is regulated. In principle, such a fitness landscape should be a general and effective framework with which to understand adaptation and evolution of different cell types in a variety of organisms  under diverse conditions.q (qglc), mmol/gDW/h) and the biomass yield (gDW/g) to denote the E. coli phenotypic space.Rate and yield are the most fundamental quantities used to describe bacterial ecology and physiology. The rate can be measured as growth rate, or moles/grams of substrate, ATP, or biomass production per unit time. Yield is usually measured by moles/grams of biomass or ATP per unit of substrate. Regardless of which definition of rate and yield to use, these two physiological parameters are tightly correlated with each other. However, the exact form of the relationship is context-dependent, which may vary according to different experimental procedures and conditions. Here, we aim to resolve the controversy and provide a unified explanation for the condition-dependent rate-yield correlation. Therefore, the particular definition should not affect our investigation and discussion. Without loss of generality, and to compare with the genome-scale model simulations using glucose as carbon source, we choose to use the substrate (glucose) uptake rate (q) and growth rate (μ) are collected from two main types of experimental measurements  the global physiological parameters, 2) the in vivo turnover rate of metabolic enzymes, and 3) the protein-specific thermodynamic parameters. The first two categories of parameters are common to all ME-models, and thus are set to the default values as first developed in O’Brien et al. . ProteinWe showed that the FoldME model improved the precision and scope of prediction for the optimal proteome composition over a wide variety of perturbations, including temperature, nutrient availability, and genetic mutations, and is therefore suitable for the study of phenotypic distribution presented in this paper.E. coli using in silico strains reconstructed to represent the diversity of naturally occurring strains. We assume that adaptation is achieved through gradual accumulation of large amounts of mutations that emerge independently, each with a random small effect on the affected genes. To simulate the genome-scale consequence of this long-term dynamic evolutionary process and estimate its fitness effect, we design a two-step process: 1) select genes for mutation according to the probability of observing a mutation in each gene, and 2) determine the molecular effect of the mutation on the corresponding gene.The purpose of our sampling method is to evaluate the phenotypic distribution of E. coli strains collected from 1) the PATRIC database [E. coli (AIEC) strains. We compiled protein sequences for the 1,566 protein-coding genes present in the FoldME model, and performed pairwise sequence alignments of the protein from each strain against its homologous sequence in E. coli K12 MG1655 [E. coli strains  the database , and 3) 2 MG1655 . We foun strains .keff) of the affected enzyme with an exponentially distributed random number between 0.5 and 2. In the same time, we perturbed the enzyme’s thermo-stability (free energy of unfolding ΔG) by a random amount between -2 to 2 kcal/mol to account for (de)stabilizing mutations with a small effect. The direction of change in the enzyme efficiency and stability was assumed to be opposite considering the pleiotropic effects of mutations [In the second step, we perturbed the catalytic efficiency and the thermo-stability of the enzymes selected for mutation in the first step. The beneficial and deleterious effects of mutations were known to distribute exponentially, with many small‑effect mutations and fewer large‑effect ones , 70. To utations , such thFinally, to introduce environmental stresses, we simulated 100 strain samples at each temperature from 25 to 46°C, resulting in a total of 2,200 simulations.keff or the stability of the enzymes selected for mutation; 3) used a different wild-type keff profile according to the recent machine learning study [keff of the mutated enzyme with larger scaling factors. None of these modifications in the sampling procedure changed our main conclusion regarding a stratified phenotypic landscape determined by the multimodal distribution of fATPS. As an example, we showed the result for sampling simulations in which keff was scaled between 0.1 to 10 fold, and 0.01 to 100 fold  fixed the number of mutated genes to 10%, 20%, or 30% of the total number of modeled genes, and selected mutations assuming uniform mutation fixation frequency for all genes; 2) perturbed only the ng study ; and 4) fATPS and the stratified structure of the phenotypic fitness landscape, we performed sampling simulations where fATPS was constrained to its five most likely values: 0, 0.37, 0.53, 0.64, and 0.71. The constraint was formulated as followed:Vreaction_name denoted the flux of the corresponding reaction and p was the value that fATPS was constrained to. For every fATPS value, 24 sampling simulations were performed at each temperature from 25 to 46°C. However, this strong constraint caused many incompatibilities with the sampled genotype, resulting in a final 2,237 feasible and optimal solutions reported in To confirm the relationship between the multimodal distribution of fATPS was a result of the bias induced by growth rate difference at different temperatures, we performed additional sampling simulations at one particular temperature. In the same time, it was desirable to cover as much on the rate-yield space as possible. Thus, we examined the accessible range of qglc and Y  at each temperature in the previously described 2,200 sampling simulations : 3 around the average growth rate at 30°C , one close to the upper limit for growth at 30°C (0.65), and two slightly lower than the lower bound . Simulation at higher fixed growth rate generated large number of infeasible solutions, thus were not included. The results confirmed that at each simulated growth rate, fATPS showed similar multiple Gaussian distribution that differed only in the relative weight of each Gaussian. Because of the same number of peaks and the mean values, we reported in Next, to maximize instances in every discrete fATPS value for each aero-type to be normally distributed. It followed that fATPS calculated from the sampling simulations should be fitted to a mixture of multiple Gaussian distributions, each representing one aero-type. The number of Gaussian distributions (peaks) should be chosen as the number of aero-types determined based on the distinguishable metabolic (fATPS = 0 (the fully fermentative phenotype) as one “peak”, and fitted the remaining data with four Gaussians using Matlab.We assumed the etabolic  and protetabolic . TherefofATPS distribution constantly showed five peaks, although the heights of the peaks varied. Peaks around 0.0, 0.37 and 0.53 were clearly present throughout all data sets, whereas peaks around 0.64 and 0.71 could be blurred under certain conditions. This final uncertainty likely came from unresolved proteome complexity of the highly respiratory phenotypes, which should not impair the validity of the fitting and the sampling process.To check whether our choice for the number of peaks was consistent, we compared distributions generated from many separate sets of sampling simulations, including those using different sampling strategies for sensitivity analysis. The E. coli electron transport chain contains two types of enzymes: a dehydrogenase that oxidizes an electron donor and a cytochrome oxidase that reduces the electron acceptor (bo oxidase (CyoABCD) and cytochrome bd-I oxidase (CydAB) for the cytochrome oxidase.The acceptor . To creanuoB contains the N2 4Fe-4S cluster, which may play a role in proton translocation activity of the enzyme [bd-I oxidase, although both subunits are required for binding of the heme b595 and heme d components of cytochrome bd-I, subunit II encoded by gene cydB binds a structural ubiquinone-8 cofactor that may have a role in the dimer assembly [cyo operon results in nonfunctional enzymes, yet we choose to disrupt cyoB because it encodes subunit I which is involved in proton translocation [Three of the chosen ETC enzymes are multi-protein complexes, and we aim to choose the gene that maximally disrupts the function of the whole enzyme. For NADH dehydrogenase I, all subunits are required for the assembly or stability of a functional enzyme . The sube enzyme . For cytassembly . Similarlocation .E. coli K-12 MG1655 (ATCC 700926) as the recipient strain. Keio collection strains were used as donor strain for the generation of gene knockout cassettes containing a kanamycin resistance marker [The four single-ETC-gene-knockout and four double-ETC-gene-knockout strains were constructed with the P1 phage transduction method , using Ee marker . Knock-oCharacterizations were performed fully aerated, at 37°C, in 15 mL working volume tubes containing M9 glucose medium, as described in LaCroix et al. . CultureSamples were filtered through a 0.22 micrometer filter  at the same time OD measurements were taken, and the filtrate was analyzed for glucose concentrations using a high-performance liquid chromatography system  with an Aminex HPX-87H column . Glucose uptake rates in exponential growth were determined by best-fit linear regression of glucose concentrations versus cell dry weights, multiplied by growth rates over the same sample range.The oxygen uptake rate of each aerobic culture was determined by measuring the rate of dissolved oxygen depletion in an enclosed respirometer chamber using YSI 5300A Biological Oxygen Monitor System that utilized Clark type polarographic oxygen probes .pgi-deficient E. coli strain, growth-improved clones along the ALE trajectory were isolated and grown in M9 minimal medium supplemented with 4g/L glucose. Cells were then harvested while in exponential growth and genomic DNA was extracted using a KingFisher Flex Purification system previously validated for the high throughput platform mentioned below [To determine the mutations emerged during adaptive laboratory evolution of the ed below . Shotguned below , in whicTotal RNA was sampled from duplicate cultures. All strains were grown in M9 minimal medium supplemented with 4g/L glucose. 3 mL of cell broth (taken at OD600 ∼ 0.6) was immediately added to 2 volumes Qiagen RNAprotect Bacteria Reagent (6 mL), vortexed for 5 seconds, incubated at room temperature for 5 min, and immediately centrifuged for 10 min at 17,500 RPMs. The supernatant was decanted, and the cell pellet was stored at -80°C. Cell pellets were thawed and incubated with Readylyse Lysozyme, SuperaseIn, Protease K, and 20% SDS for 20 minutes at 37°C. Total RNA was isolated and purified using the RNeasy Plus Mini Kit (Qiagen) columns following vendor procedures. An on-column DNase treatment was performed for 30 minutes at room temperature. RNA was quantified using a spectrophotometer  and quality was assessed by running RNA electrophoresis on the Agilent 2100 Bioanalyzer . The rRNA was removed using Illumina Ribo-Zero rRNA Removal Kit (Gram-Negative Bacteria). Stranded RNA-Seq Kit (Kapa Biosystems) was used following the manufacturer’s protocol to create sequencing libraries with an average insert length of around ∼300 bp. Libraries were sequenced on an Illumina HiSeq 4000 instrument.log2(TPM+ 1)), referred to as log-TPM, was taken for the downstream analysis. The log-TPM values of the two biological replicates were highly correlated (R2 > 0.97), expect for the ΔndhΔcydB mutant (R2 = 0.91). Uncertainty of the ΔndhΔcydB mutant might come from partial knockout for one of the replicates, which showed relatively high expression of the cydB gene. We considered the aero-type assignment and other quantifications for this mutant to be less reliable compared to others.Raw sequencing reads were obtained as described above, and mapped to the reference genome NC_000913.3 using Bowtie 2.3.4.3  with theμ,Y ,qglc,qac) and our aero-type definition in the sampling simulations. It turned out that only qac and Y  showed significant correlation. Then, we computed the Pearson correlation between log-TPM and experimentally measured phenotypic parameters, and selected genes that were highly correlated with qac and Y , but not with μ. This process resulted in a set of 391 genes, which were used for generating the clustering diagram shown in Principal component analysis (PCA) of the log-TPM showed that the first four components could explain 84% of the variations throughout the expression profile. The first principal component was highly correlated with exchange rates such as the glucose/oxygen uptake rate and the acetate production rate, and the second with growth rate. These components, although highly explanatory, were enriched in gene clusters  that were non-specific to the conditions and phenotypes that we were interested in. Alternatively, the fourth component, explaining 5.4% of the overall variation, was highly enriched in genes involved TCA cycle, anaerobic respiration and ETC activity. Consequently, we considered selecting genes most representative for aero-type for subsequent hierarchical clustering analysis. First, we calculated the Spearman correlation between five phenotypic parameters (S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S1 FigE. coli strains (top left), for WT or strains with gene knockout (top right), under different nutrient conditions (bottom left), and for different E. coli strains (bottom right).(A) Visualization of a compendium of 199 experimental measurements. Contours of the phenotype density are overlaid on top of the experimental data (gray circles). (B) Experimental phenotypic distribution visualized by whether measurements are taken for WT or evolved (TIF)Click here for additional data file.S2 FigE. coli strains (left) and frequency of mutations per gene in the 2,200 sampling simulations (right).Comparison between the distributions of the number of observed mutations per gene for the 1,765 naturally occurring (TIF)Click here for additional data file.S3 FigY) decreases along the μ-isocline (aero-type decreases from v to i), flux through TCA cycle also decreases. Similarly, fluxes through acetate overflow are negatively correlated with PC1, hence as Y decreases, flux through acetate overflow increases. The opposite sign of correlation between fluxes through the two branches of glycolysis pathways nicely captures the trend that the glycolysis flux slowly switches from oxPPP to EMP as aero-type decreases from v to i. PC2  are not correlated with the flux through ATP synthase, and are not discussed in further details for the purpose of this paper. The name of the reactions are standard reaction IDs available for search on the BIGG database (http://bigg.ucsd.edu/). oxPPP: oxidative pentose phosphate pathway; EMP: Embden–Meyerhof–Parnas pathway; ED: Entner-Doudoroff pathway.The figures illustrate the observed correlation between phenotypic state and the metabolic fluxes, yet they can be best understood with the definition of “aero-type” introduced in later sections. (A) The metabolic states of the five aero-types can be clearly separated by the first principal component (PC1), representing 61.1% of the data variations. PC2 further decomposes metabolic states into sub-types. (B) PC1 is the only component that’s correlated (Pearson correlation = 0.97) with the flux through ATP synthase . Therefore data variations contained in PC1 best represent the observed differences in metabolic states between different aero-types. For example, the fluxes through the TCA cycle are positively correlated with PC1, hence the flux through ATP synthase. This correlation indicates that as the biomass yield ((TIF)Click here for additional data file.S4 FigE. coli strains under glucose minimal medium are highlighted on the rate-yield space. Data includes two wild-type strains at 37°C , one wild-type strain at 42°C , 7 strains evolved at 37°C , and 10 strains evolved at 42°C . (B) Mass fraction of the representative pathway is calculated using all genes involved in the corresponding pathway. The Pearson correlation between the biomass yield and the mass fraction of each pathway is shown. Usage of three pathways that are related to aerobic respiration is significantly correlated with biomass yield . (C) Biomass yield plotted against the mass fraction of genes involved in oxidative phosphorylation. Each point corresponds to a strain, labeled as in (A). (D) Biomass yield plotted against the mass fraction of genes involved in TCA cycle.(A) Phenotypic data of 20 (TIF)Click here for additional data file.S5 FigE. coli. Short and full name for the metabolites are listed as follows. 13dpg: 3-Phospho-D-glyceroyl phosphate; 3pg: 3-Phospho-D-glycerate; actp: acetyl phosphate; pyr: pyruvate; pep: phosphoenolpyruvate; succoa: succinyl-CoA; coa: coenzyme-A; prpp: 5-phospho-alpha-D-ribose 1-diphosphate; r5p: alpha-D-ribose 5-phosphate. The fraction of total ATP produced by each reaction varies significantly. The range of variation in the sampling simulations is indicated in the “Fraction” column. (B) Fraction of variations in the rate-yield phenotypic space explained by the indicated ATP-production reactions. 89.5% of the variations in phenotypic distance can be explained by the first six ATP-producing reactions. Among them, oxidative phosphorylation reactions ATPS4rpp and PPKr contributed the most. (C) Comparison between the actual phenotypic distance on the rate-yield plane and that reconstructed from the six ATP-producing reactions. A simulated phenotype is determined by a four-element vector containing the glucose uptake rate, acetate production rate, growth rate and biomass yield. Other typical phenotypic measurements are highly correlated with one or more chosen quantities, and are thus not included in the calculation. Then phenotypic distance is calculated as the Euclidean distance of this vector with respect to that of the wild-type solution at 37°C. Predictors of the stepwise linear regression are taken as the fraction of ATP produced by each reaction listed in (A).(A) Detailed information for the ATP-producing reactions in (TIF)Click here for additional data file.S6 Figμ-isoclines, which are used in the subsequent analysis shown in panel B, are highlighted. Growth rates shown on the μ-isoclines are computed relative to the wild-type growth rate calculated at 37°C. (B) Along each μ-isocline, the calculated fraction of total cellular ATP produced by ATP synthase (fATPS) is linearly correlated with biomass yield  and glucose uptake rate , with a positive and negative slope, respectively. The intercepts of these linear fitting at the minimum and maximum values of fATPS  provide a way to estimate the feasible range of qglc and Y. The estimations at each growth rate can be connected to draw the boundary of the rate-yield plane (gray shaded area in panel A). The accessible range of the phenotypic space generated this way encompasses the majority of data points from both experiments and model simulations.(A) Fitness distribution of the 2,200 simulated strains on the rate-yield plane (same as shown in (TIF)Click here for additional data file.S7 FigfATPS (hence positively correlated with the aero-type as defined in the later (A) ATP yield through the ATP synthase is positively correlated with (TIF)Click here for additional data file.S8 FigfATPS values: 0, 0.37, 0.53, 0.64 and 0.71. (B) Once fATPS is determined in the energy production strategy, finer structures can be seen on the phenotypic landscape. The distributions of fPGK, fACKr, and fPYK at each fixed fATPS value also show distinct multimodal distributions. The pie charts show the allowable energy production strategy that represents over 95% of the solutions at each fixed fATPS, all fractions shown have standard deviations smaller than 0.02. (C) At fixed fATPS, biomass yield is negatively correlated with the ratio fACKr/fPGK. Because acetate is secreted through the ACKr flux and no biomass is made, increase in fACKr reduces biomass yield. (D) At fixed fATPS, the overall ATP yield is positively correlated with the ratio (fACKr + fPYK)/fPGK. This ratio reflects the relative efficiency of all ATP-producing reactions in terms of ATP production per unit of substrate. The more inefficient reaction PGK is used , the lower the overall ATP yield is. As the yield of oxidative phosphorylation is much higher (∼34 ATP per glucose), the overall ATP yield increases with fATPS at fixed (fACKr + fPYK)/fPGK ratio.(A) Topography of the fitness landscape reconstructed from constrained sampling simulations at 5 most likely (TIF)Click here for additional data file.S9 FigY and qglc calculated for the sampled strains at each temperature are denoted by a circle, then the range of accessible values indicated by horizontal and vertical lines going through the average. For T = 25°C, 30°C, 37°C and 40°C, the optimal wild-type phenotype (square) is shown for reference. For T = 25°C, 30°C, and 40°C, shift in the preferred aero-type is shown by the difference in fATPS distribution. Eight μ-isoclines are drawn, each labeled with the relative growth rate with respect to the simulated optimal WT growth rate at 37°C. Distribution of fATPS at 30°C best captures the features of the full distribution, thus we select this temperature for the down-stream analysis. (B) The fATPS distribution of the 368 sampling simulations performed at 30°C and selected growth rate. Fitting to a mixture of four Gaussian distributions shows consistency with the observed stratified distribution shown in fATPS value shows a similar multi-modal distribution as the maximum fold change in enzyme efficiency increases to 10 and 100 fold in the sampling simulation.(A) The phenoptypic variations at each temperature. On this plot, the average values of (TIF)Click here for additional data file.S10 Figth and 75th percentiles, and whiskers extend to 1.5 times the interquartile range. Sample size in each aero-type is the same as in fATPS and the P/O ratio are tentatively binned into five separate groups based on their multimodal distribution, and mapped to the optimal solutions shown in panel B. (D) Comparison of the experimental and simulated relative abundances of selected genes  with respect to the ATP synthase. Length of the bar and error bar represent the average ratio and standard deviation for each aero-type as defined in (A) Major protein complexes involved in quinone turnover in the sampling simulations. Formate dehydrogenase N and O catalyze the same reaction, hence are designated to the same complex (FDN/O) for simplicity. Box plot of the normalized expression for the indicated protein complexes shows differential usage of the ETC enzyme between different aero-types. To enable direct comparison, the calculated mass fraction of the enzyme complexes is normalized by the total mass fraction of all ribosomal proteins to remove bias coming from different growth rates. The central red line of the box plot shows the median, the bottom and top edges indicate the 25(TIF)Click here for additional data file.S11 FigndhΔcydB, ΔcydB mutants were chosen to represent the higher aero-types v/iv, and the ΔnuoBΔcyoB mutant was chosen to represent a lower aero-type ii. Growth data at 30°C and 37°C nicely recapitulates the expected trend such that ΔndhΔcydB and ΔcydB stay in the region for aero-type iv and ΔnuoBΔcyoB in the region for aero-type ii. At 42°C, all three strains generate a lower biomass due to the temperature stress. However, they maintain well separated on the rate-yield plane representing the aero-type constraints caused by the removal of the respective ETC genes. Thus, the presented data supports the notion that the differential usage of the ETC genes determines the phenotypic aero-type of a cell.The Δ(TIF)Click here for additional data file.S12 Figpgi-deficient strain. (D) Intermediate evolutionary states were chosen at the indicated stages and characterized on the rate-yield plane. Four distinct genotypes were identified along the adaptive trajectory, indicated by red, blue, yellow, and green circles, respectively. Error bars indicate standard deviation of the biological duplicates.(A) The schematic of the proposed hierarchical energy production strategy. Blue and red arrows correspond to the thermodynamic and respiration-fermentation tradeoff, respectively. (B) A coarse-grained representation of the fitness landscape on the rate-yield plane. Color gradient indicates the level of proteome complexity, where blue represents the simpler proteome and red is the more complex proteome. (C) An example adaptive trajectory during the evolution of a (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.