Actinosynnema pretiosum ATCC 31280 is the producer of antitumor agent ansamitocin P-3 (AP-3). Understanding of the AP-3 biosynthetic pathway and the whole metabolic network in A. pretiosum is important for the improvement of AP-3 titer. In this study, we reconstructed the first complete Genome-Scale Metabolic Model (GSMM) Aspm1282 for A. pretiosum ATCC 31280 based on the newly sequenced genome, with 87% reactions having definite functional annotation. The model has been validated by effectively predicting growth and the key genes for AP-3 biosynthesis. Then we built condition-specific models for an AP-3 high-yield mutant NXJ-24 by integrating Aspm1282 model with time-course transcriptome data. The changes of flux distribution reflect the metabolic shift from growth-related pathway to secondary metabolism pathway since the second day of cultivation. The AP-3 and methionine metabolisms were both enriched in active flux for the last two days, which uncovered the relationships among cell growth, activation of methionine metabolism, and the biosynthesis of AP-3. Furthermore, we identified four combinatorial gene modifications for overproducing AP-3 by in silico strain design, which improved the theoretical flux of AP-3 biosynthesis from 0.201 to 0.372 mmol/gDW/h. Upregulation of methionine metabolic pathway is a potential strategy to improve the production of AP-3. Actinosynnema pretiosum ATCC 31280 was isolated in 1977 [A. pretiosum [ in 1977 ,5. Recen in 1977 . Althoug in 1977 . These sretiosum .Esterichia coli GSMM iJR904 [E. coli. The yield of 4-hybroxybutyric acid was increased from 35.2 g/L to 103.4 g/L [iJO1366 [E. coli, resulting in a 5.4-fold increase by the knockout of gene fadE [O-methyltransferase based on genome-scale modeling approach [Actinoplanes ssp. SE50/110 based on the predicted genetic modification strategies [Genome-Scale Metabolic Models (GSMMs) play important roles in systems biology ,13, whicM iJR904 , Choi et03.4 g/L . Kuhn Ip[iJO1366  to exploene fadE . Brochadapproach . Wu et arategies .A. pretiosum ATCC 31280 based on the newly sequenced genome (Genebank accession number: CP029607). Then we integrated the model with time-course transcriptome data of a high-yield mutant strain NXJ-24 [In this study, we reconstructed and validated the first GSMM of n NXJ-24  to invesA. pretiosum ATCC 31280 was reconstructed based on the newly sequenced genome, by a complicated process of annotation, transformation, gap filling, and refinement. Genome annotation was performed through RAST server [A. pretiosum ATCC 31280 has 1282 genes, we called the model Aspm1282.The genome-scale metabolic model of T server , and theT server  with theA. pretiosum, we obtained each component and corresponding proportion through measurement and literature mining. The cellular compositions of A. pretiosum, consisting of lipids, proteins, carbohydrates and so on, were mostly obtained from text books [A. pretiosum ATCC 31280 are listed in Knowledge of the cellular biomass composition is an important prerequisite for the in silico flux analysis, especially during the exponential growth phase, where the primary cellular objective is to maximize growth . There ixt books . The dryA. pretiosum, constraint-based flux analysis was carried out under the assumption of a pseudo-steady state [To perform in silico simulations and predict the metabolic characteristics of dy state . For groj and bj are the minimum and maximum fluxes through reaction j. We also investigated the metabolic capacity of A. pretiosum in AP-3 production by setting AP-3 flux as the objective function. The flux balance analysis (FBA) simulation was performed using COBRA Toolbox [v is a flux vector representing a particular flux configuration, S is the stoichiometric matrix, a Toolbox  with Gur Toolbox  as the lansa30 gene, and overexpressing asm10 gene [ansa30 gene, two 1.5-kb homologous arms for ansa30 disruption were respectively amplified with primers Del30-L-F/R and Del30-R-F/R, sequenced, and together cloned to SpeI/EcoRI-digested plasmid pJTU1278 to give the ansa30-inactivation plasmid pLQ578. In the overexpression of asm10, the kasOp promoter was cloned into BamHI/SpeI-digested plasmid pDR3 [asm10 were amplified with primers asm10-F/R. Subsequently, the sequenced genes were individually inserted into SpeI/EcoRI-digested plasmid pDR3-K under the control of kasOp promoter, generating plasmids pLQ586 and pLQ589. The recombinant plasmids were introduced into NXJ-24 from E. coli ET12567 (pUZ8002) through intergeneric conjugation. The transcriptome data of NXJ-24 during fermentation process of day 1, day 2, day 3, and day 5 were sequenced by Shanghai Biotechnology Corporation, Shanghai, China. We also sequenced the transcriptome of the wild-type strain in day 5, which was used to construct decline-phase specific model of the wild-type for strain optimization.In our previous study, the NXJ-24 mutant was generated by knockout mid pDR3  to generp-value of each pathway (Equation (2)).We used enrichment analysis to identify pathways enriched in essential genes for AP-3 biosynthesis and active reactions during fermentation process significantly. The hypergeometric test was used to calculate the jp is the p-value of subsystem j, N is the total number of reactions/genes in Aspm1282 model, jM is the number of reactions/genes in subsystem j, n is the number of essential genes or active reactions in the whole model, m is the number of essential genes or active reactions in subsystem j.j of each reaction as the expression value of the genes encoding enzymes catalyzing reaction j.E-FLUX is a method for modeling metabolic states under specific conditions by integrating gene or protein expression data, which extends the technique of FBA by modeling maximum flux constraint as a function of measured gene expression . Here wei is the FPKM (fragments per kilobaseof exon per million fragments mapped) of gene i measured by experiment, iexp represents the expression level of gene i, bj represents the upper bound of reaction j, rxnj represents the reaction j.FPKMBy this conversion, we set constraints on both lowly and highly expressed reactions, then FBA will be run by fulfilling the same objective function.Randomly pick up one gene and assign a random mutation code to it. As shown in The expression of genes will be transformed to corresponding reactions in the metabolic model through changing the upper bound or lower bound of it. The range of changes is based on the reference flux values from pFBA. T is the control parameter, α is the attenuation factor (α < 1). When T (k + 1) = T (k) × α will be carried out. When a certain number of iterations is reached, the solution at that moment will be the final strain genetic modification solution.The phenotype of modified strain was simulated using FBA, and the objective value was used to evaluate the mutant. OptRAM will choose to go back to last round of iteration or accept this solution according to the Metropolis criterion :(5)P=eΔOptRAM (optimization of regulatory and metabolic network) is a meta-heuristic strain optimization method based on GSMM, which can be divided into three parts: prediction, random modification, simulation, and evaluation. Firstly, we use parsimonious enzyme usage FBA (pFBA)  to prediAspm1282 is shown in The composition of model Aspm1282 are classified into eight subsystems, including carbohydrate metabolism, amino acid metabolism, energy metabolism, lipid metabolism, metabolism of terpenoids and polyketides, metabolism of cofactors and vitamins, biosynthesis of ansamycins, and other metabolism ). Then we can observe the transformation of flux distribution over time in ansamycin biosynthetic pathway in the high-yield mutant. The coefficients c of AP-3 component in the objective function were determined by the relative ratio between AP-3 titer and biomass measured by experiment in Because simple FBA simulation with the biomass equation in the generic In the conditional specific models of day 1, day 2, day 3, and day 5, c is equal to 0, 0.0013, 0.0068, 0.0084 respectively.We used FBA to calculate the flux distribution of each specific model during cultivation process, as shown in We found the metabolic shift from growth associated pathways to other metabolism started from the second day of cultivation. Although the growth rate was highest on the second day, the active pathway started to transform from growth related pathways to other pathways, such as cysteine and methionine metabolism and secondary metabolism pathway. It indicated that the growth rate might reach maximum at the moment between the two measurements, but the metabolic mode has changed at the second measurement. We hope to get more precise metabolic shift pattern with highly frequent time-course measurements during cultivation in the future.In addition, it has been reported that the production of AP-3 was improved in the pentose phosphate pathway weaken mutant . Here weS-adenosyl-l-methionine), which is the key methyl donor for three steps of methylation reactions in AP-3 biosynthesis pathway. More significantly, one of the methylation reactions, catalyzed by asm10, was reported as the bottleneck in the production of AP-3, the increase in asm10 can increase the AP-3 production by 93% [The metabolic shift during fermentation process indicated that the biosynthesis of AP-3 is initiated when the cells sense stressful conditions, similar with most secondary metabolism. The significant high activity of cysteine and methionine metabolism in the last two days showed that this pathway plays an important role in the biosynthesis of AP-3. Methionine is the precursor of SAM , as shown in Aspm1282 model, we aimed to identify potential engineering strategy for AP-3 overproduction. A series of computational strain optimization methods have been published, such as RobustKnock [Using the ustKnock , OptGeneustKnock , OptORF ustKnock , GDLS [5ustKnock , and FSEustKnock . The metTarget means the average flux value of target product. Range is set to half of the interval between min and max target flux value.Aspm1282 model and the decline-phase-specific model, respectively, integrating expression data of the wild-type strain on day 5 , catalyzed by Asm10, was one of the bottlenecks in AP-3 biosynthesis [Both the significantly active methionine pathway in the latter stage of fermentation and the relationship between methionine pathway and AP-3 biosynthesis indicated that the methionine pathway could be a potentially important pathway for AP-3 overproduction. It was reported that the ynthesis . As a suAspm1282 model and identify a modification strategy for AP-3 overproduction, which enhances the EMP pathway (Embden Meyerhof Parnas pathway), reduces the release of uracil, and improves the biosynthesis of UTP and GTP. EMP is an important pathway that provides energy and carbon skeleton for biomass. The improvement of UTP and GTP biosynthesis can promote the replication of DNA and the transcription of RNA, both of which are essential for biomass. What is more, the biosynthesis of UTP directly affects the production of AP-3 through the biosynthesis of UDP-glucose, one of the precursors for AP-3. All these modification sites demonstrated that the strain optimization by our newly reconstructed model is of great potential for real application in AP-3 overproduction.In actual industrial production, the target production efficiency is determined by both the biomass and flux of the target compound. Therefore, we set Obj as the evaluation of strain modification, which considers biomass and product simultaneously. We used OptRAM method to simulate


Cross-feeding of B vitamins between prototrophic and auxotrophic species is expected to strongly contribute to the homeostasis of microbial communities in the distal gut given the efficient absorption of dietary vitamins in the upper gastrointestinal tract. To confidently estimate the balance of microbiome micronutrient biosynthetic capabilities and requirements using available genomic data, we have performed a subsystems-based reconstruction of biogenesis, salvage and uptake for eight B vitamins  and queuosine  over a reference set of 2,228 bacterial genomes representing 690 cultured species of the human gastrointestinal microbiota. This allowed us to classify the studied organisms with respect to their pathway variants and infer their prototrophic vs. auxotrophic phenotypes. In addition to canonical vitamin pathways, several conserved partial pathways were identified pointing to alternative routes of syntrophic metabolism and expanding a microbial vitamin “menu” by several pathway intermediates (vitamers) such as thiazole, quinolinate, dethiobiotin, pantoate. A cross-species comparison was applied to assess the extent of conservation of vitamin phenotypes at distinct taxonomic levels (from strains to families). The obtained reference collection combined with 16S rRNA gene-based phylogenetic profiles was used to deduce phenotype profiles of the human gut microbiota across in two large cohorts. This analysis provided the first estimate of B-vitamin requirements, production and sharing capabilities in the human gut microbiome establishing predictive phenotype profiling as a new approach to classification of microbiome samples. Future expansion of our reference genomic collection of metabolic phenotypes will allow further improvement in coverage and accuracy of predictive phenotype profiling of the human microbiome.The human gut microbiome harbors a diverse array of metabolic pathways contributing to its development and homeostasis via a complex web of diet-dependent metabolic interactions within the microbial community and host. Genomics-based reconstruction and predictive modeling of these interactions would provide a framework for diagnostics and treatment of dysbiosis-related syndromes via rational selection of therapeutic prebiotics and dietary nutrients. Of particular interest are micronutrients, such as B-group vitamins, precursors of indispensable metabolic cofactors, that are produced  These nSix out of eight B-vitamins analyzed in this study, B1 (thiamin), B2 (riboflavin), B3 (niacin), B5 (pantothenate), B9 (folate) and B6 (pyridoxine), are common biosynthetic precursors of major metabolic cofactors, TPP, FMN/FAD, NAD(P), CoA, THF, and PLP, respectively, essential in all microbes as well as in the mammalian host . VitaminPreviously, we combined genomic and functional context analysis in SEED genome integration and analysis platform to identify novel gene families  and pathway variants involved in microbial B-vitamin metabolism . The earbinary phenotypes, as either having the capability to de novo synthesize a given cofactor (prototrophy) or being strictly dependent on salvage of its precursor(s) from exogenous sources (auxotrophy). Binarization of metabolic phenotypes allows us to develop a computational approach to estimate and compare fractional representation of auxotrophs/prototrophs in publicly available HGM samples. The proposed novel metric, Community Phenotype Index (CPI), provides a probabilistic estimate of fractional representation of organisms  with a particular vitamin production capability in a metagenomic sample.In the current study, we have focused on a detailed reconstruction of all aspects of biogenesis of nine micronutrients (eight B-vitamins and queuosine) including inference of salvage pathways for non-canonical vitamers and specificity for transport systems on a greatly expanded set of reference genomes. This allowed us to establish genomic signatures for numerous functional pathway variants and to classify all studied species by their simplified A predictive phylotype-to-phenotype profiling approach established in this study can be extended to a broader range of metabolic phenotypes and applied for comparative analysis of general metabolic capabilities vs. nutrient requirements of gut microbial communities as a function of various factors defining their healthy vs. pathological development . The obs1  clustering of genes on the chromosome (operons), (ii) co-regulation of genes by a common regulator or a riboswitch, and (iii) co-occurrence of genes in a set of related genomes. Transcriptional regulons for transcription factors (TFs)  and riboswicthes  as captured in the RegPrecise database  that are required for de novo vitamin/cofactor biosynthesis and/or vitamin salvage  or auxotrophy (“0”). These values, combined together for all 2,228 reference genomes, comprise a binary phenotype matrix (BPM), which was used to assess microbiome-wide biosynthetic capabilities and requirements for nine analyzed micronutrients  implemented by different subsets of enzymes, as well as diverse vitamin transporters. For all identified pathway variants, we established  salvage . These wvariants  are tranutrients .4  by 16S rRNA gene copy number to account for its variability between different species and to minimize the strongest biases in estimated values of their relative abundances. For this purpose we used the pan-taxa statistics for the rRNA gene copy number provided by the rrnDB database . For eac5). This tool uses BPM and OTU tables to approximate microbiome-wide metabolic phenotypes while minimizing limitations arising from: (i) imprecise and incomplete correspondence of OTUs to reference genomes; (ii) intrinsic heterogeneity of phenotypes within mapped taxonomic groups. To reach a reasonable compromise between coverage and precision, we use a hierarchical averaging approach. Briefly, in the first step, OTU taxonomic assignments are mapped onto the reference genome collection at three taxonomic levels . For the purpose of further averaging and probabilistic phenotype assignment, every mapped OTU is assigned a mapping weight (w) reflecting representation of corresponding genomes in the collection. Thus, for an OTU precisely mapped at the species level, equal weights are assigned to genomes of all strains/isolates of this species that are present in the collection. For OTUs that could not be mapped at the species level, phenotype averaging is performed at the genus level assigning equal weights to all available species within a genus. A similar approach to averaging/weighting is applied to OTUs mapped only at the family level. OTUs that do not map at the family level  were excluded from phenotype prediction. As a result, a Community Phenotype Matrix (CPM) is computed where an approximate (averaged) phenotype value for every mapped OTU is calculated from respective binary phenotype values (p) multiplied by respective weights (w) of each genome (m). Each value in CPM reflects a probabilistic relative contribution of each OTU (i) to the community-wide phenotype:To assess vitamin production capabilities and requirements for selected HMP and AGP samples, we utilized a beta-version of Phenobiome Profiler tool  multiplied by the relative abundances (A) of all individual OTUs:At the next step, a Community Phenotype Index  for each phenotype in a given sample is then calculated as the total of all respective CPM values (P) in BPM by (1-P). A prediction error for CPI values (reflecting imprecise mapping and phenotype microheterogeneity) was calculated as:CPI provides a probabilistic estimate of fractional representation (from 0 to 100%) of the analyzed phenotype, in this case a particular micronutrient production capability or prototrophy. Computing CPI for auxotrophy, which is more convenient for assessing micronutrient requirements, is performed the same way but replacing all binary phenotype values , while ‘010000000’ is an auxotroph for all vitamins except B2 (mono-prototroph). For each sample, we calculated relative contribution of each of 512 possible vitamin barcodes (Pobs), using binary phenotypes for individual reference genomes from BPM and previously described OTU mapping weights. The same hierarchical averaging/weighting approach was used for calculation of the relative contribution of vitamin barcodes, where each barcode is treated as an individual feature (composite phenotype). To facilitate further manipulations we switched from barcodes to their cumulative representation, termed vitamin prototrophy rank (VPR). For instance, the ‘111111101’ and ‘000000010’ barcodes correspond to ranks 8 and 1, respectively. We further predicted a theoretical distribution for VPRs in each sample assuming that vitamin binary phenotypes in all organisms are independent of each other (the null hypothesis). Stated this way, the null hypothesis follows the Poisson binomial distribution for the expected frequencies (Pexp) of VPRs in a sample, with CPIs acting as independent trials probabilities. To compare the observed vs. expected VPR distributions we computed the ratio of their corresponding probabilities in each sample (Pobs/Pexp) and presented these ratios in logarithmic scale.To analyze the distribution of various combinations of vitamin phenotypes in individual taxa we considered possible combinations of individual binary phenotypes as ordered nine-digit strings, termed 6. To predict MetCyc/MinPath-based abundances of vitamin biosynthetic pathways in the HMP and AGP samples (provided as OTU abundance tables and files with representative sequences), the PICRUSt2 pipeline was used with default parameters. We further used PICRUSt2 to predict phenotype abundances in the same samples using the BPM for vitamin biosynthesis pathways in 2,228 reference genomes. First, we prepared the custom traits table for 2,581 leaves of the PICRUSt2 reference tree that overlapped by NCBI TaxID with the reference HGM genomes analyzed in this work. We then used the PICRUSt2 pipeline with BPM-based custom trait table provided as an input. As the input BPM describes capabilities of reference species to produce vitamins, we did not need to run the MinPath pathway abundance prediction. All pathway and phenotype abundances obtained by PICRUSt2 were normalized by a number of reads in each sample Scattered plots were produced in R using ggplot2 package.The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) pipeline allows one to predict the following functional information for 16S metagenomic samples: (i) functional gene content based on KEGG database annotations for reference genomes ; and ii and MinPWe began this study by selection of the reference collection of bacterial genomes representing the human gastrointestinal microbiota . The selected set included 2,228 genomes representing eleven phyla, 42 orders, 93 families, 226 genera, and 690 species, as well as 173 genomes that have no taxonomically defined species names . The larphenotype rules  that are capable of de novo cofactor synthesis, and (ii) auxotrophic (variant code “A”) that are dependent on the uptake and salvage of a respective vitamin or an alternative metabolic precursor from an exogenous source. Distinct P-variants were assigned to prototrophs with alternative de novo biosynthetic routes. Thus, variations in B1/TPP biogenesis include two alternative routes for the synthesis of a hydroxyethylthiazole (HET) moiety: a common bacterial variant P1 (ThiFGHS signature) and a eukaryotic-like variant P2 (Thi4 signature). Likewise, two alternative routes to synthesize the quinolinate precursor of NAD comprise the most common bacterial variant P1 (NadAB) and a relatively rare eukaryotic-like variant P2 (Tdo-Kmo-Kyn). Similar variations occur in PLP synthesis (PdxAJ vs. PdxST routes) and in the synthesis of pimeloyl-CoA precursor of biotin (via BioC vs. BioW). B12 is synthesized via either anaerobic or aerobic pathway for corin ring biosynthesis that are characterized by either early or late insertion of cobalt via specific chelatases (CbiK/X vs. CobN) and pathway-specific enzymes (CbiDG vs. CobFG).We used a subsystems-based comparative genomics approach  to reconde novo biosynthetic machinery but typically have vitamin uptake, salvage and downstream biotransformation pathways yielding physiologically active cofactors are generally classified as auxotrophic A-variants. Despite substantial variations in salvage of cofactor precursors (see below), this classification is strengthened by indisputable universal essentiality of these cofactors (with the exception of B12 as discussed below). Downstream pathways common for both P- and A-variants that include from one to five conserved enzymatic steps are characteristic of all analyzed cofactors, except PLP  due to their unique capability to uptake a mature cofactor.Many bacterial genomes that completely lack the rlapping . The onlynthesis  in parasde novo or downstream pathways) are characteristic of vitamins B1 , B3  and B6 (PdxK/K2). Other vitamins and metabolic precursors can feed directly into respective cofactor biosynthetic pathways upon uptake from the media via specific transporter systems. In this study, we have performed a comprehensive mapping of all known and predicted components of salvage enzymes and transporters (see below), which in many cases provided important details on metabolic requirements and capabilities of respective species. However, the presence of particular salvage genes is not required for the general inference of auxotrophy (A-variants). Thus, PdxK/K2 kinase was missing in 116 B6 auxotrophs, suggesting existence of yet unknown alternative forms of this essential salvage enzyme. Such requirement would be particularly unsustainable with respect to uptake transporters that represent a substantial bioinformatic challenge due to their evolutionary plasticity  and incomplete knowledge (missing genes).Signature salvage enzymes . It is important to emphasize that the variety of incomplete pathway variants and their representation in the entire reference collection are relatively small confirming that our current understanding and genomic reconstruction of bacterial of vitamin/cofactor biosynthetic machinery are nearly complete.Finally, taking into account that the auxotrophy is inferred based mostly on “negative” evidence, the absence of biosynthetic capabilities, we recognize that, at least in principle, the existence of yet unknown and radically different ∗), which reflect yet unknown non-orthologous gene displacements or alternative biochemical routes, and non-canonical A-variants presents a particular bioinformatic challenge, requiring a detailed case-by-case analysis (Bifidobacterium strains possess an incomplete B1 pathway with missing iminoglycine synthase (ThiO or ThiH), however, all other B1 biosynthetic enzymes are present (∗-variant). In the B3 pathway, NadB  catalyzing the synthesis of iminoaspartate from aspartate is missing in 34 predicted B3 prototrophs  that possess all other de novo B5 synthesis enzymes, suggesting the existence of yet uncharacterized alternative enzyme(s) or biochemical route(s) for β-alanine synthesis in these bacteria. Indeed, alternative routes generating β-alanine unrelated to cofactor metabolism  are known. Moreover, β-alanine can be theoretically salvaged, if available, from the growth media. Based on these considerations, we classify the respective pathway variants as P∗-type prototrophs rather than auxotrophs salvaging non-canonical vitamers (discussed below).Distinguishing between incomplete P-variants with “missing genes”  , may not be considered a true B9 vitamer. At the same time, a number of other identified incomplete pathway variants in metabolism of B1/TPP, B3/NAD, B5/CoA, B7, B12, and queuosine were classified as bona fide auxotrophic A-variants with a potential to salvage alternative vitamers, non-canonical metabolic precursors of respective cofactors . Given the known variability of pathways and isozymes involved in the upstream biotin biosynthesis, we propose the existence of yet uncharacterized enzymes for pimeloyl precursor synthesis in these species. Finally, in the B9 pathway, 454 strains lack the complete pathway for the synthesis of para-aminobenzoate (pABA) precursor, while all other folate biosynthetic enzymes are present in their genomes. There are two potential explanations for these incomplete B9 pathways: (i) presence of yet unknown pABA synthesis routes/enzymes, and/or (ii) salvage of pABA from the diet or other community members. In agreement with the latter hypothesis, it is known that e medium . We chosofactors .thiamine pyrophosphate (TPP) cofactor, which is synthesized via coupling of the phosphorylated hydroxymethylpyrimidine (HMP-PP) and thiazole (HET-P) moieties (de novo biosynthesis of both HMP-PP and HET-P biosynthesis (B1 prototrophs). Auxotrophic variants comprising another half were subdivided into four major sub-variants with distinct biosynthetic/salvage capabilities. The first group of auxotrophs (452 genomes) is unable to synthesize either B1 precursor but possess the ThiM and ThiD kinases, which can convert salvaged HET and HMP precursors into TPP  redox cofactors is also associated with a variety of salvage/recycling pathways from one or both forms of vitamin B3: (i) nicotinic acid (or niacin), the most common form, via PncB salvage enzyme; and (ii) nicotinamide, via one of the three alternative routes, via PncA-PncB or, more rarely in bacteria, via NadV-PncC or NadV-NadM route bypassing otherwise nearly universal downstream enzymes NadD-NadE  synthesis from pantothenate (vitamin B5) is present (with some non-orthologous variations in the first step) in both, prototrophs  and most, but not all predicted B5 auxotrophs . In addition, 91 strains  lacking de novo synthesis have an incomplete universal pathway variant with missing CoaBC bifunctional enzyme catalyzing the conversion of phosphopantothenate to phosphopantetheine. We propose that these bacteria are dependent on an earlier hypothesized “pantetheine shunt,” a salvage of an alternative vitamer pantetheine (CoA degradation product) via a secondary activity of pantothenate kinase converting it directly to phosphopantetheine, thus by-passing both steps requiring a missing CoaBC enzyme (de novo pathways are two variants lacking pantoate biosynthesis: (i) Apt-variant in 40 genomes that contain two de novo enzymes, PanD and PanC; (ii) Apt∗-variant in 16 genomes that retain only one de novo enzyme, PanC. Similar to an Aq-variant in NAD biogenesis, this genomic signature suggests the possibility of salvaging pantoate  in addition to vitamin B5.A five-step universal downstream pathway  of CoenzC enzyme . Indeed,C enzyme . NotablyC enzyme  also canB7 auxotrophs (836 genomes) lack all four enzymes responsible for the conversion of pimeloyl-CoA precursor to biotin  truncations are observed makes this interpretation more consistent with pathway topology. Second, a sporadic phylogenetic distribution of these variants among diverse Actinobacteria, Proteobacteria and Firmicutes genomes is more consistent with gene loss rather than non-orthologous replacement.More than 80% of identified o biotin . The remo biotin  charactederivatives of B12 are not universally essential in bacteria. While most bacteria have B12-dependent methionine synthase and ribonucleotide reductase, these enzymes are often dispensable due to the presence of alternative B12-independent enzymes and metabolic routes  lack all enzymes of anaerobic or aerobic upstream corin ring synthesis, as well as most downstream enzymes, except BtuR/PduO family adenosyltransferase converting vitamin B12  to an active coenzyme B12 . Likewise and following the same reasoning, the three observed incomplete pathway variants . Of no less importance, it revealed a set of well-defined open problems, such as missing genes and yet unknown biochemical routes, a subject of bioinformatics-driven gene and pathway discovery. Additionally, this analysis tentatively implicated several vitamers as additional or alternative metabolic precursors of respective cofactors as a subject of non-canonical salvage, at least in the species carrying particular partial pathways. These metabolic precursors along with canonical B-vitamins are a likely subject of cross-feeding between donors (at least some prototrophs) and acceptor (auxotrophs) mediated by a variety of specialized transport systems.Overall, the current analysis of incomplete pathways allowed us to reconcile most of the gaps and inconsistencies in the A paramount importance of transporters for vitamin uptake and salvage notwithstanding, especially in auxotrophs, we typically did not include them in gene signatures defining major phenotype rules (for the reasons discussed above). On the other hand, specificity assignment of candidate transporters can be achieved with higher accuracy in the genomic and functional context of auxotrophs (as constrained by their well-defined metabolic requirements) and then conservatively projected to prototrophs. Vitamin/vitamer transport systems that are present in many prototrophs can contribute to their opportunistic (energy-saving) lifestyle and, potentially, to cross-feeding as discussed below. All identified and tentatively assigned transport systems are captured in respective subsystems  and corrThus, the analyzed distribution of potential uptake transporters for B1 vitamers allowed us to establish the presence of candidate HMP transporters in most HMP auxotrophs (97%), while the HET transporter ThiW was identified in only 64% of HET auxotrophs. In the group of dual HMP/HET auxotrophs, both HET and HMP transporters are present in 259 genomes (57%), and additional ∼100 genomes from this group contain one of the two transporter types. Incomplete knowledge of transporters for thiamine precursors notwithstanding, this analysis suggests that salvage of B1 and its vitamers plays a prominent role in metabolic interaction within gut microbial communities. These transporters are also present in many genomes with P-variants providing the respective species with additional salvaging and, potentially, cross-feeding capabilities.Escherichia coli K12, which is well-known to have a robust nicotinate and nicotinamide salvage capability via PncA-PncB pathway seamlessly compensating for gene deletions in its de novo NadB-NadA-NadC pathway  in HGM.Uptake of vitamin B5 in bacteria is mediated by either PanF or PanT transporters, whereas PanS was characterized as a pantoate transporter in lmonella . OrtholoE. coli and many other enterobacteria was found in 13 genomes of ε-proteobacteria from the Campylobacterales order that are characterized by either A1 or A3 incomplete pathway variants. These observations suggest that both types of biotin transporters may have a wider specificity for at least some B7 vitamers. So, for the closest B7 precursor, dethiobiotin, this conjecture is supported by their uptake competition observed in E. coli (The majority of B7 auxotrophs (78%) contain the Energy-Coupling Factor (ECF) family BioY transporters. An even higher fraction of auxotrophs with incomplete biotin salvage pathways  also harbor one or two copies of BioY-family transporters. In addition, a secondary transporter YigM enabling an optional B7 salvage in prototrophic  E. coli . The rep E. coli  supportsOrthologs and paralogs of known B12 uptake transporters, namely BtuFCD(B) of ATP-binding Cassette (ABC) family and CbrT of the ECF family, were found in one or several copies in nearly half of A-variants including 268 genomes with incomplete pathways suggesting their involvement in the uptake of alternative B12 vitamers. Indeed, representatives of both B12 transporter families were experimentally shown to import both B12 and Cbi . TherefoC. difficile . Besides, a previously uncharacterized transporter family YhhQ (COG1738) was implicated in the import of queuosine precursors  were observed only in B2 prototrophs, whereas the primary active transporters, RibU (ECF family) and RibXY (ABC superfamily) are present in both, prototrophs and auxotrophs. Among similar, albeit less contrasting trends are: (i) the presence of the biotin permease, YigM in the majority of B7 prototrophs contrasting with BioY (ECF family) preferential occurrence in auxotrophs; (ii) more frequent occurrence of pantothenate facilitator transporter PanF in B5 prototrophs vs. the opposing trend of PanT (ECF family) to occur mostly in B5 auxotrophs; (iii) similar co-occurrence trends for thiamine permease PnuT vs. ThiT (ECF family). The observed trends, with the caveats pertaining to incomplete data, statistical significance and possible alternative interpretations, are consistent with a possible role of at least some secondary transporters in the excretion and community-wide sharing of respective vitamins/vitamers. If experimentally confirmed, these trends may provide presently unknown genomic signatures distinguishing potential vitamin donors from other prototrophs with opportunistic salvaging rather than sharing capabilities.Lactobacillus plantarum, Clostridium sporogenes, Clostridium tyrobutyricum, Clostridium botulinum A str. Hall, and Veillonella parvula required folate for growth. According to our reconstruction, all these species were assigned a special B9 phenotype variant (P∗), which corresponds to a conditional prototrophy in the presence (but not in the absence) of the pABA precursor  phenotype  in 30 bacterial strains revealed that 75 out of 89 experimentally determined phenotypes (85%) matched the predicted vitamin auxotrophies . Approxirecursor . Likewishenotype  due to tStaphylococcus spp. that lack any B12-dependent enzyme and Bacillus cereus that has a single B12-dependent enzyme, the methionine synthase MetH, and its B12-independent analog MetE  or “0” (auxotrophy). For the purpose of this analysis, we have converted all the detailed variant codes  are capable of synthesizing all vitamins except pantothenate (B5) and queuosine. The Verrucomicrobia phylum represented by five Akkermansia strains, as well as a single HGM strain from the Planctomycetes phylum, have biosynthetic capabilities for all vitamins except B12. The Lentisphaerae phylum represented by a single HGM species, Victivalis vadensis, produces all vitamins except B5. Both HGM species from the Spirochaetes phylum are auxotrophic for all vitamins except riboflavin (B2). The Ureaplasma and Mycoplasma strains from the Tenericutes phylum are auxotrophic for all nine micronutrients. The Firmicutes, Actinobacteria and Synergistetes phyla contain the largest number of auxotrophs for the majority of vitamins. Among Actinobacteria, vitamin B12 is synthesized by species from three orders , whereas among Firmicutes the majority of B12 producers belong to the Clostridiales, Selenomonadales and Veillonellales orders. Finally, queuosine is not synthesized by most Actinobacteria except two Coriobacteria species, whereas among Firmicutes it is mostly synthesized by Bacillales and Negativicutes. Overall, this analysis reveals general correlational trends between phylogeny (even at higher taxonomic ranks) and vitamin phenotypes, but also points to substantial phenotype variations as visualized by fractional averaged phenotype values (between 0 and 1).First, it provides a simple way to assess a phylogenetic distribution of micronutrient requirements and production capabilities across a broad range of HGM genomes. For this purpose, we used BPM to calculate d phylum . These ad phylum , also thd phylum . The Pronumber of variable phenotypes ; and (ii) overall phenotype variability score calculated as a sum of variances for each vitamin phenotype  . To assess these variations within species or at higher taxonomic levels we used two metrics: (i) notypes) . The higRuminococcus torques represented by two strains in the analyzed set of genomes shows the highest variability of vitamin phenotypes , suggesting that these strains could be incorrectly classified. Indeed, based on phenotypic, biochemical, phylogenetic and genomic evidence one of these strains has recently been reclassified as Mediterraneibacter torques ATCC 27756  demonstrate highly variable vitamin phenotypes. At the phylum and class levels, Actinobacteria and Firmicutes  demonstrate the highest degree of phenotype variability (OPVS > 2) with 8 and 9 variable phenotypes, respectively.The same metrics were used to assess interspecies variations and variations at higher taxonomic levels . At the We also calculated vitamin-specific variability scores that take into account variability of phenotypes in all taxa at a given taxonomic rank . At the Based on this analysis, we conclude that an accurate phylogeny-based vitamin phenotype projection from reference genomes to OTUs or phylotypes mapped by 16S rRNA gene profiling of HGM samples, is generally impossible at the taxonomic level higher than a family. Moreover, the projection at lower levels, of families, genera and even species may be affected by varying phenotype heterogeneity within respective groups. Therefore, to optimize the accuracy and coverage of microbiome-wide phenotype predictions, we have to account for phenotype heterogeneity characteristic of each phylotype/OTU (see next section).We applied the obtained BPM that captures vitamin/cofactor production capabilities and requirements across a collection of curated reference genomes in a simplified digital form for prediction of community-wide phenotypes from HGM 16S samples profiling data. This analysis is performed in two stages, each enabling a particular type of community phenotype profiling .first stage is to tentatively assign prototrophy/auxotrophy phenotypes to all OTUs (or phylotypes) identified in a sample (above a certain abundance threshold) that are subsequently confidently mapped to our reference collection at least at the family level. As discussed in the previous section, unlike the case of reference genomes, some of these assignments  are bound to have fractional values (between 0 and 1). In the current implementation of the Phenotype Profiler pipeline (by Phenobiome Inc.), this is achieved by OTU taxonomic assignment mapping to reference genomes using a hierarchical averaging approach . A final product at this stage of the analysis is a Community Phenotype Matrix (CPM) containing nine columns with respective weighted average phenotype values computed for all mapped OTUs. Similar to BPM, each line in CPM is a nine-digit string reflecting a metabolic potential, biosynthetic capabilities (prototrophy) vs. micronutrient requirements (auxotrophy = 1-prototrophy) of each OTU.The goal of the second stage is a value, which is obtained via transformation of the OTU-abundance-phenotype table into a string of nine values reflecting fractional representation (%) of nine prototrophy phenotypes in the analyzed HGM sample. For each phenotype, this value, termed CPI, is computed as a sum of probabilistic phenotypes in each of the nine columns weighted (multiplied) by relative abundance of respective OTUs. A nine-value CPI string reflects the vitamin prototrophy/auxotrophy phenotype representation extended from an individual OTU to the entire community. Using CPI strings as simplified microbiome-wide metrics of vitamin/cofactor metabolic capabilities and requirements, enables a comparative analysis of multiple samples in model studies [as applied in the accompanying publication ] or across large datasets, as illustrated here by the analysis of HMP and AGP collections.The main product of the We applied the Phenotype Profiler pipeline to the comparative analysis of two large HGM 16S rRNA datasets from the HMP and AGP projects containing 245 and 2,863 samples, respectively. Both datasets were pre-processed using the QIIME2 pipeline and the obtained OTUs were taxonomically assigned using our custom heuristic-based procedure . The OTU-abundance values were further renormalized to take into account substantial variations in 16S rRNA gene copy number between different species of HGM bacteria . CPI valOverall, the phenotype profiling approach proposed in this study complements the established taxonomic profiling approach commonly used for description and comparative analysis of microbial communities. Thus, vitamin-specific CPIs provide useful metrics to compare and contrast micronutrient requirements of HGM communities, while the community-wide distribution of phenotype strings points to major vitamin acceptors and potential donors in these communities.vitamin prototrophy rank (VPR), ranging from 0 (complete auxotrophy) to 9 (complete prototrophy) with respect to all nine analyzed micronutrients. Intermediate VPR values, 1 through 8, are assigned to groups of species showing prototrophy with respect to 1 through 8 (out of 9) micronutrients. For each sample in the analyzed HMP and AGP communities, we calculated two values: (i) the observed VPR frequencies (Pobs), and (ii) the expected VPR probabilities obtained in the assumption of independence of individual phenotypes in each organism (Pexp)  of various combinations of vitamin phenotypes in individual species from HGM samples representing different “lifestyles” , we calculated relative contribution of ordered nine-digit strings of individual binary phenotypes . As a first approximation of such lifestyles, we introduced a simple metric, m (Pexp) .Pobs/Pexp) reveals enrichment for both extreme lifestyles, multi-prototrophy and multi-auxotrophy (VPR ≤ 3). This trend, which is observed in both datasets, may reflect fundamental properties of organization of syntrophic microbial communities where species with an extreme “parasitic” lifestyle are not only tolerated but gain certain selective advantages as long as multi-prototrophic donors of micronutrients are sufficiently abundant. Major genera contributing to the extreme prototrophic, auxotrophic and intermediate lifestyles are enlisted in Bacteroides spp. are major contributors to a multi-prototrophic lifestyle in both HMP and AGP datasets. Among major multi-auxotrophs, Faecalibacterium spp. are common in both datasets representing the most successful B-vitamin acceptors in microbial gut communities. It is tempting to consider major taxa representing multi-prototrophic lifestyles as potential vitamin donors enabling sustainability of numerous auxotrophic acceptors. Identity of such donors and genomic features distinguishing sharing from non-sharing prototrophs  remain an important direction of future research, which would impact our understanding of metabolic interactions in microbial communities.The results of this analysis are illustrated by ratios of the observed over expected frequency of nine aggregated lifestyles across all samples in HMP and AGP datasets . Despitevitamin phenotype abundances in HGM samples from HMP and AGP datasets by using PICRUSt algorithm . We selected a subset of metabolic pathways in MetaCyc database that most closely describe the reconstructed in this work  vitamin . The obt vitamin  were com vitamin . For mos vitamin . A weakein silico phenotype predictions and published experimental data.By applying the subsystem-based genomic reconstruction, we have analyzed pathways for biosynthesis, salvage and uptake of eight B-group vitamins (and queuosine) for HGM bacteria represented by a diverse reference set of 2,228 genomes. Overall, nine reconstructed metabolic subsystems include over two hundred functional roles encoded by distinct protein families . Specifidigital binary phenotypes with 1 or 0 values corresponding to prototrophy and auxotrophy, respectively. These values computed for each of the nine vitamin phenotypes across 2,228 HGM genomes capture our reference collection in a compact form of a Binary Phenotype Matrix (BPM). Overall, auxotrophic phenotypes are very common in HGM species, and only a small subset of microorganisms can synthesize all vitamins further supporting the micronutrient sharing hypothesis  and high confidence (estimated average error < 10%) of microbiome-wide phenotype predictions. The output of this analysis, in the simplest aggregated form of Community Phenotype Indexes (CPIs), provides an estimate for a fractional representation  of auxotrophy vs. prototrophy for all nine analyzed phenotypes in each analyzed HGM sample. Substantial variations of CPI values are observed between individual samples as well as between these two data sets  enables a new computational approach to ata sets  potentiaata sets  and founata sets . Quite lOverall, in addition to further supporting a hypothesized role of syntrophic micronutrient metabolism in HGM communities, a comparative phenotype profiling approach established in this study has provided a computational framework for the experimental testing of this hypothesis . This approach is extending methodology of a functional description of microbial communities, which, presently, is not as established as phylogenetic profiling methods. A side-by-side comparison of our phenotype-based approach with the most advanced existing approach, PICRUSt2, predicting pathway abundance via a combination of MinPath algorithm with MetaCyc pathways, revealed a generally good agreement  deeper coverage of relevant microbial communities by complete reference genomes and (ii) increased coverage and resolution of phylogenetic profiling , is expected to substantially improve the accuracy of predictive phenotype profiling of complex microbial communities. This approach can be further expanded via inclusion of additional digitized phenotypes covering other nutrient requirements , utilization capabilities  , catabolAO and DR conceived and designed the research project. MK and AA performed the comparative genomic analysis, reconstruction of pathways and subsystem encoding. AA compared the obtained phenotypes to experimentally published data. DR provided the quality control of the obtained data. SI, MK, and SL analyzed metabolic phenotypes in the 16S datasets. DR, AA, PN, SP, and AO wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Respiration by soil bacteria and fungi is one of the largest fluxes of carbon (C) from the land surface. Although this flux is a direct product of microbial metabolism, controls over metabolism and their responses to global change are a major uncertainty in the global C cycle. Here, we explore an in silico approach to predict bacterial C-use efficiency (CUE) for over 200 species using genome-specific constraint-based metabolic modeling. We find that potential CUE averages 0.62 ± 0.17 with a range of 0.22 to 0.98 across taxa and phylogenetic structuring at the subphylum levels. Potential CUE is negatively correlated with genome size, while taxa with larger genomes are able to access a wider variety of C substrates. Incorporating the range of CUE values reported here into a next-generation model of soil biogeochemistry suggests that these differences in physiology across microbial taxa can feed back on soil-C cycling. Microbial respiration releases carbon from the soil. Here, the authors estimate bacterial carbon use efficiency in soils for over 200 species using constraint-based modeling, incorporate the values into an ecosystem model, and find that shifts in community composition may impact carbon storage. Heterotrophic respiration (RH) by soil bacteria and fungi can account for a large proportion of total global soil respiration (35–69 Pg C/year4). Although this flux is a direct product of microbial metabolism, controls over physiology and their responses to global change are a major uncertainty in the global C cycle6.Soil respiration is one of the largest exchanges of carbon (C) from the land surface to the atmosphere, releasing an estimated amount of 98 ± 12 Pg C/year from soil as CO5. Variations in C partitioning among microbial taxa is poorly characterized, yet critical to understanding relationships between physiology, community composition, and soil C cycling.Soil respiration rates are influenced by microbial physiology because cellular metabolism dictates what fraction of the C consumed by soil bacteria and fungi is allocated to respiration, biomass, extracellular enzyme production, and other metabolic functions. Thus, the partitioning of microbial C can have a direct impact on the global C cycle7. Empirical estimates of microbial CUE range from near zero to over 0.89. Most biogeochemical models use a fixed value selected between 0.15 and 0.6, typically without careful parameterization8. Some of the variation observed in CUE may be attributed to the sensitivity of CUE to abiotic factors such as temperature and pH10. However, an additional and often neglected source of variation in CUE may be due to physiological differences between soil microbial groups and their differential capacities for accessing particular substrate types12. This single parameter in microbial biogeochemistry models has direct impacts on estimates of greenhouse gas emissions and terrestrial C storage6, and the sustainability of bioenergy cropping systems13, making it necessary to survey how CUE varies both among taxa and across substrate types.Carbon use efficiency (CUE) measures the partitioning of C between microbial biomass and respiration14. Previous work on functional and physiological variability suggests that defining bacterial taxa along a spectrum of copiotrophy  to oligotrophy  may be one useful approach for understanding how groups respond to changes in temperature and resource availability16. Classification schemes based on trophic strategy may be useful from a biogeochemical perspective if differences in growth strategies correspond to variation in CUE. For example, copiotrophs are hypothesized to show lower CUE than oligotrophs17, and this could potentially alter the CUE of microbial communities observed to shift toward a greater proportion of copiotrophic bacteria in response to global change manipulations that increase substrate availability, such as soil warming19.Characterizing microbial C metabolism is particularly important in the context of global change, which may alter the structure and activity of microbial communities and their access to substrates14. However, these phylum-level classifications are not consistent across studies21. An improved understanding of the phylogenetic structure of biogeochemically relevant traits is needed to identify how microbial community structure impacts C cycling23. Many functional genes show strong conservation within prokaryotes, leading to the possibility for strong phylogenetic structure in functional traits, particularly those that emerge from the coordinated activity of multiple genes24. For example, bacterial traits such as growth rates in the presence of labile C show strong phylogenetic signals, whereas other traits such as responses to priming show shallow phylogenetic signals23. Thus, the level of phylogenetic resolution required to characterize variation in CUE across bacterial taxa remains unclear.Observations from global change experiments and genome-based estimates of minimal generation times have shown some support for the classification of particular phyla, as oligotrophic or copiotrophic12. Similarly, genomic traits have been useful for predicting microbial trophic strategies and biogeography, with bacterial taxa with larger genomes occupying a wider range of habitat types25 and dominating communities where resources are available in diverse forms but limiting concentrations26. Comparable efforts for predicting CUE from genomic traits are necessary to help overcome challenges with measuring taxa-specific CUE for highly diverse soil bacterial communities.In addition to understanding the phylogenetic structure of variation in CUE, it may be useful to explore whether particular genomic traits predict CUE. For example, copy numbers of ribosomal RNA operons are inversely related to growth efficiency in bacteria, providing a method for predicting growth efficiencies from genomes17. These patterns are overlaid with differences between taxa, with potentially oligotrophic groups showing less responsivity to limiting resource availability than copiotrophic taxa17. Thus, estimates of CUE must consider both biotic and abiotic sources of variability, including bacterial physiology, substrate availability, and substrate chemistry.In the environment, microbial taxa are exposed to variations in substrate chemistry and supply rates that impact rates of C uptake and growth. These abiotic factors are likely to interact with intrinsic differences in physiology among taxa to ultimately determine CUE. For example, observations from bacterial cultures show that CUE increases with limiting resource concentration and with the free energy content of available resources17. Direct measurements of CUE have been made using a wide range of methods including calorespirometry11 and stable isotope approaches27. CUE has also been estimated indirectly for whole communities based on environmental variables such as resource stoichiometry28. These methods can lead to CUE estimates that vary by a factor of two or more, making direct inter-comparisons challenging10.Prior work on estimating CUE is limited to a small set of individual microbial taxa, or involves mixed, whole communities29 and can be used to estimate taxa-specific biological fluxes, including biomass growth and C uptake30. Here, we explore an in silico approach to generate theoretical predictions of CUE for over 200 taxa using genome-scale constraint-based metabolic modeling. We find that intrinsic physiological differences between taxa can lead to >300% variation in CUE, which is far greater than that assumed in global models where CUE is either fixed or varies solely in relation to abiotic factors. We find that CUE is primarily structured at subphylum phylogenetic levels and is correlated negatively with genome size and GC content. These findings provide a framework for predicting CUE from genomic traits and for inferring potential impacts of shifts in bacterial community composition on C cycling. Using a recent ecosystem model of heterotrophic soil respiration (DAMM-MCNiP), we demonstrate that accounting for the observed variation in microbial physiology across taxa alone can have persistent implications for estimates of soil C emissions and soil C pool sizes.Using a consistent methodology to measure CUE across a broad range of microbial taxa is necessary to determine how physiological variation in resource use between taxa impacts CUE. Metabolic models of bacterial physiology can be generated from annotated genomes31. Flux balance analysis was performed for each metabolic model with C supplied exclusively through one of 14 individual C-containing metabolites, and CUE was calculated as the proportion of C assimilated into biomass relative to C uptake. We observe a mean CUE of 0.53 ± 0.25 (S.E.) across taxa and substrate types, suggesting that nearly half of consumed C is lost via respiration on average , soil organic C pool sizes were nearly twice as large following 100 years of simulation compared to the low efficiency scenario  can be used to quantitatively analyze the complete set of metabolic reactions in an organism. This approach has been successfully used to represent bacterial metabolism and growth patterns31. We utilized the following thirteen taxa in this analysis: Clostridium ljungdahlii DSM 13528, Staphylococcus aureus subsp. aureus N315, Saccharomyces cerevisiae S288c, Methanosarcina barkeri str. Fusaro, Bacillus subtilis subsp. subtilis str. 168, Thermotoga maritima MSB8, Synechocystis sp. PCC 6803, Escherichia coli str. K-12 substr. MG1655, Shigella boydii Sb227, Salmonella enterica subsp. enterica serovar Typhimurium str. LT2, Klebsiella pneumoniae subsp. pneumoniae MGH 78578, Geobacter metallireducens GS-15, and Mycobacterium tuberculosis H37Rv. Mean CUE was calculated from CUE on growth on each of the following C-sources individually: D-Glucose, Fumarate, Acetate, Acetaldehyde, 2-Oxoglutarate, Ethanol, Formate, D-Fructose, L-Glutamine, L-Glutamate, D-lactate, L-Malate, Pyruvate, Succinate. Due to the limited number of manually curated microbial metabolic models, we expanded our approach to include models generated using automated pipelines (described below) for over 200 bacterial taxa from phyla commonly observed in soil environments.Genome-scale metabolic models were selected for analysis from two separate databases. Thirteen microbial models were selected from the Biochemically, Genetically, and Genomically structured knowledgebase of metabolic reconstructions (BiGG), which contains a small set of manually curated metabolic models from diverse environments47, which are of particular interest due to their major contributions to global respiration. We queried the Department of Energy’s kBase for over 200 taxa and used automated pipelines to construct a large set of draft metabolic models.The BiGG database primarily includes microbial models associated with the human microbiome, limiting our capacity to extrapolate our findings from these well-curated metabolic models to environmental microbial communities. We expanded our approach to target bacterial genomes belonging to phyla commonly observed in soil environments51 (kBase) was searched in March 2016 for bacterial genomes belonging to phyla that have been observed to dominate forest soil bacterial community composition based on 16S ribosomal RNA and DNA sequencing47. A total of 23,530 genomes belonging to the six selected phyla were identified in kBase, corresponding to 1064 unique genera. For each phylum, at least 25 genomes were selected for analysis. For phyla with more than 50 available genomes, the full list of unique genera was scanned to target genera that have been observed in soil environments when possible  genome annotation pipeline and the model SEED framework52. Draft metabolic models were gapfilled using the Gapfill Metabolic Model tool in kBase to add the minimal set of reactions required to produce biomass on complete media, which contains all possible metabolites available for uptake53. Gapfilling on complete media results in conservative gapfilling by assuming that metabolites necessary for growth but not produced intracellularly based on genome annotation are available in the environment.The Build Metabolic Model tool was used in kBase to generate metabolic models from 231 selected genomesoptimizeCbModel command in the COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox54. All FBA analyses were set to maximize bacterial biomass production in this analysis, in accordance with standard FBA assumptions33.Flux balance analysis (FBA) allows for an estimation of metabolic fluxes, such as rates of C uptake and utilization, through a metabolic model based on linear optimization of a specified objective function, such as biomass production. FBA makes a steady-state assumption, circumventing the need for knowledge of kinetic parameters, and uses the stoichiometry of metabolic reactions to determine the feasible space of all possible combinations of reaction rates. By prescribing an optimization scheme, it is possible to identify specific points in this feasible space, resulting in putative predictions of all metabolic reaction rates in the organism, including uptake and secretion fluxes and growth. This approach requires specification of (1) a flux or set of fluxes to maximize (or minimize) and (2) upper and lower bounds for all reactions within the metabolic model. Upon specification of these inputs, FBA is able to estimate the particular combination of fluxes through all reactions in the model that satisfy the given conditions. FBA was performed in MATLAB R2014a using the n exchange reactions, and where C is equal to the number of C atoms taken up or secreted in a given reaction:−1 h−1, where grDW indicates the cellular biomass dry weight in grams. As CUE was calculated as a ratio of fluxes, values were not sensitive to the order of magnitude of maximum flux bounds as long as these were consistent across reactions. Certain models produced a respiration flux of 0 mmol grDW−1 h−1 and were excluded from subsequent analyses of CUE.C use efficiency (CUE) is calculated as the proportion of C retained in biomass relative to total C uptake Eq. . For a m−1 h−1 and FBA was performed again to identify reactions that were essential for biomass production. Finally, maximum uptake for all essential reactions was individually set to 5% of the maximum uptake flux for all other metabolites (50 mmol grDW−1 h−1), and FBA was performed again to detect the impact of constraining particular essential reactions. Reactions that resulted in a reduction of the biomass flux by at least 5% were classified as constraining, meaning that the biomass production flux showed a direct response to the availability of metabolites dictated by these reactions.To calculate CUE under conditions of limited substrate availability, reactions in each metabolic model were first classified according to the following hierarchy: 1) exchange, (2) C-containing, (3) utilized when available, (4) essential to biomass production, and (5) constraining to biomass production . For 18 of the most commonly constraining reactions, the uptake flux corresponding to a 75% reduction in the biomass flux was identified for each model. For all models containing a given constraining reaction, FBA was performed after setting the maximum uptake flux for the constraining reaction to this reduced value while leaving all other exchange reaction fluxes potential. Constrained CUE was then calculated according to equation (−1 h−1 while a second set was more heavily gapfilled to achieve a (default) minimum biomass flux of 1000 mmol grDW−1 h−1. A total of 246 exchange reactions, including 211 C-containing exchange reactions, were observed across 231 models gapfilled to the lower biomass threshold. A total of 318 exchange reactions, including 279 C-containing exchange reactions, were observed across 231 models gapfilled to the higher biomass threshold. On average, models gapfilled to the higher biomass threshold had only 8 additional C-containing exchange reactions. Gapfilling intensity had a significant impact on subsequent calculations including CUE, but inter-model comparisons and rank order were not strongly affected by gapfilling intensity. Potential CUE values calculated from models gapfilled at the two intensities were strongly correlated (Pearson’s rank correlation coefficient = 0.7).To test the sensitivity of our results to the method of gapfilling, two parallel sets of models were constructed for each taxon. One set of models were gapfilled to achieve a minimum biomass flux of 0.1 mmol grDW57. Branch lengths were computed according to the Grafen method58 using the compute.brlen command in the ape package59 in R Studio. To test for phylogenetic signals, Blomberg’s K statistic60 was calculated using the multiPhylosignal function in the Picante package61 in R Studio. Mean differences in potential CUE between phyla were compared using phylogenetic ANOVA with the phylANOVA function in the phytools package62 in R Studio.The Build Phylogenetic Tree tool was used in the DOE kBase to generate a phylogenetic tree for 220 of the 231 genomes analyzed based on 49 highly conserved clusters of orthologous group (COG) families and the FastTree maximum likelihood method63. The CI indicates how much a particular node on the phylogeny accounts for the total variation in potential CUE64. After calculating the CI for all 191 nodes in our analysis, we classified the subset of nodes where collective contributions accounted 90% of the variation in potential CUE based on the taxonomic level at which descendent species diverged.In order to determine the taxonomic level which best describes variation in potential CUE, we used Phylocom to calculate the contribution index (CI) for each of the 191 nodes in the bacterial phylogenynlme package65 in R Studio. The proportion of variance explained by each predictor was estimated using a pseudo R2 value designed for nonlinear regression66 using the r.squaredLR function in the MuMIn package67, which estimates the improvement of the fit model relative to a null model based on a likelihood ratio test. The stepAIC function in the MASS package68 was additionally used to determine the simplest regression model with multiple predictors.The relationship between potential CUE and (1) the number of exchange reactions, (2) the number of C-containing exchange reactions, (3) genome size, (4) guanine-cytosine (GC) content, and (5) number of genes was assessed using phylogenetic generalized least-squares regression with the 69. Specifically, the model uses Michaelis-Menten kinetics to describe the depolymerization of soil organic C and soil organic Nitrogen by microbial extracellular enzymes to produce dissolved organic C (DOC) and dissolved organic N (DON). Maximum reaction velocities are governed by temperature-sensitive Arrhenius functions. Uptake of DOC and DON by microbial biomass is governed by a second series of Arrhenius and Michaelis-Menten kinetic equations, which are sensitive to moisture-mediated O2 availability. Following uptake, the parameter CUE is used to determine the partitioning of C between microbial biomass and soil respiration. DAMM-MCNiP has been parameterized to describe seasonal patterns in heterotrophic soil respiration at a temperate forest site, and is able to capture 56% of variation in empirical observations of seasonal heterotrophic respiration at an hourly scale69 . The published model uses a default CUE value of 0.3 as used in several other ecosystem models. We modified the parameterization of CUE in this model  model was used to estimate potential impacts of the observed variation in CUE on ecosystem-level C fluxes. DAMM-MCNiP models the effects of soil moisture and temperature on coupled C and nitrogen fluxes through soil pools and microbial biomassFurther information on research design is available in the Supplementary InformationReporting Summary


Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in  E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for  A central goal of computational biology is to develop the ability to predict the genome-scale proteome allocation that leads to the highest fitness—or, as a proxy, growth rate—subject to a given environment and protein budget.In order to prevail in a given environment, living cells have to allocate a finite amount of protein into diverse cellular functions. Understanding optimal global proteome allocation is a central problem in systems biology and underlies important cellular properties like growth rate4 search for the optimal growth rate that can be achieved given a set of uptake fluxes and metabolic network stoichiometric constraints, but do not account for the protein allocation problem. In order to extend FBA accordingly, a variety of genome-scale models (GEMs) of metabolism have been developed that consider the cost of expressing metabolic enzymes. Some GEMs extend the approach of FBA with an additional constraint on the total amount of protein the cell has available to catalyze metabolic fluxes that maximize cell growth7. Other more detailed GEMs include the entire gene expression machinery to explicitly model the proteome composition as a consequence of translation rates and growth-dependent dilution of macromolecules to daughter cells11.Traditional approaches like flux balance analysis (FBA)keff . Thus, GEMs that account for proteome allocation rely heavily on estimates of effective turnover rates. In the past, these estimates were either obtained by random sampling7, parameter fitting13, or, in most cases, by using in vitro measurements of enzyme turnover numbers, kcat6. In theory, in vitro kcat measurements should provide a reasonable upper limit on keff, where incomplete saturation, thermodynamic effects, posttranslational modifications, and allosteric regulation will render keff in vivo lower than kcat in vitro14. Nevertheless, in practice, in vitro assays of enzyme activity are sensitive to a variety of extraction and assay parameters, leading to noisy estimates and rendering large-scale estimation of kcat in vitro difficult . To address this issue and to provide estimates of keff in vivo, proteomic data across diverse growth conditions was recently combined with in silico flux predictions to calculate kapp,max, the maximal keff across conditions14. This in vivo estimate is a promising candidate for parameterization of all GEMs that account for enzyme kinetics. Nevertheless, the scope of datasets on both in vitro kcat and kapp,max is far from genome-scale, with a coverage of direction-specific reactions in E. coli of about 12% for kcat in vitro and 8% for kapp,max  methods for the development of complex statistical models have been successfully applied to modelling bacterial physiology20, enzyme specificity22, and enzyme affinity23, with applications in metabolic engineering and synthetic biology25. Here, we combine known correlates of kcat with novel features for enzyme structure, biochemical mechanism, network context, and assay conditions to build ML models of kcat in vitro and kapp,max that can predict these parameters at the genome scale. Application of these ML models to the parameterization of mechanistic GEMs enables improved predictions of proteome allocation.It would thus be desirable to understand the underlying genome-scale patterns of catalytic enzyme turnover rates—a major part of the kinetomeIn order to build predictive ML models of enzyme catalytic turnover rates, we compiled a diverse set of features that include network properties, enzyme structural properties, biochemical mechanism information, and assay conditions Fig. .Fig. 1MaE. coli K-12 MG1655, iML151526: The average flux across diverse growth conditions was obtained with a Monte Carlo sampling approach and parsimonious FBA27 (see Methods). The propensity of an enzyme component to participate in multiple reactions—the generalist property—was in the past found to be associated with lower catalytic turnover rates28. We thus quantified the tendency of an enzyme to catalyze multiple reactions from the gene-protein-reaction (GPR) rules of iML1515. Furthermore, the number of enzyme substrates was extracted from the stoichiometric matrix of iML1515.Network properties were extracted from a GEM of 29 and homology models from the I-TASSER modelling pipeline31 (see Methods). Global structural disorder and molecular weight were used as ML model features. The relative occurrence of secondary structures classes are highly correlated with the fraction of structural disorder, and we decided not to include them in the ML model to avoid co-linear features. We further expected properties of the catalytic site structure to be particularly informative about enzyme turnover and thus extracted catalytic site information from the Catalytic Site Atlas32. In particular, we used active site depth, active site solvent exposure, active site hydrophobicity, the number of residues contributing to the active site, and active site secondary structure as model features , and metabolite concentrations (see Methods).Further information on enzyme biochemistry was included in the form of EC numbers, thermodynamic efficiency, Michaelis constants  as compared to the more complex models  for the kcat in vitro models . In silico flux is the most important feature for both in vitro kcat and in vivo kapp,max, confirming the hypothesized significant role of evolutionary selection pressure on enzyme turnover numbers17. We confirmed this important role of flux by using fluxes based on experimental metabolic flux analysis (MFA) data instead of in silico fluxes, leading to very similar model performances , whereas both in vitro and in vivo data sets are limited to a few hundred of these measurements  suggesting that an ensemble approach may improve ML model accuracy. We thus used the average prediction across these twelve models as the final ensemble model. Experimental data on kapp,max and kcat in vitro was then extrapolated to the genome scale using the respective ensemble model.The high cross-validated accuracy of the ML models for max Fig.  suggests5 and a GEM of metabolism and gene expression (ME model)9, to predict quantitative proteomics data37 and to compare predictive performance across different genome-scale parameterization strategies: known in vitro kcat with missing values simply replaced by the median of known values (median-imputed), in vitro kcat extrapolated with the ensemble ML model, median-imputed kapp,max, and kapp,max extrapolated with the kapp,max ML ensemble model del Fig. . Furtherkapp,max-based parameter sets than for those based on kcat in vitro, where the prediction error is on average 43% lower in MOMENT and also 43% lower in the ME model. The ensemble ML model further improves the predictive performance of kapp,max-based GEMs consistently across growth conditions and mechanistic modelling techniques, with an average reduction in root mean squared error (RMSE) of 34% and 20% for MOMENT and the ME model, respectively , a result consistent with diffusion-limited theory of catalysis in enzymes with buried active sites, which predicts a decrease with tunnel depth38. Similarly, a negative impact of solvent accessibility on enzyme turnover rates is inferred in the kapp,max model. This result is in agreement with multiple observations of the importance of selective barriers that prevent water access of the active site for enzyme function . The tendency of enzyme components to catalyze multiple reactions  was identified as a major contributor to predictions in models of kapp,max and kcat in vitro, where multifunctional components tend to decrease catalytic turnover rates  is a very important feature in the model for kcat in vitro, but plays no significant role in the kapp,max model. One possible reason is that kapp,max, as an estimator of kcat, is expected to be biased in terms of Km, where the bias acts in the opposite direction from the effect estimated for kcat in vitro.The diversity of biochemical reactions renders genome-scale experimental characterization of enzyme kinetics a task of prohibitive complexity. We show that ML models of enzyme structure, network context, and biochemistry can be utilized for the in silico prediction of catalytic turnover numbers, particularly in the case of in vivo estimates of apparent enzyme turnover, kcat and kapp,max originate from disparate sources. Thus, the agreement between the ML models for in vitro kcat and kapp,max in terms of feature importance hierarchy . The most promising output, kapp,max, is currently limited to unique homomers—i.e., the enzyme subunit is only used in one reaction—and to reactions that have proteomics data and flux predictions available. Our current ML model of kapp,max is thus likely biased toward unique homomeric enzymes. Careful extension of the kapp,max protocol to non-unique and heteromeric proteins, flux estimation of non-essential reactions, or extension of the scope of expression data via ribosome profiling could be used to further improve genome-scale estimation; learning curves for the complex random forest model confirm that additional data is likely to increase model performance  as close to the MFA-estimated values (vdata) as possible using a quadratic programming (QP) problem:As an alternative to the flux sampling using parsimonious FBA, experimental data on metabolic flux obtained from metabolic flux analysis (MFA) was utilized  problem:Measured fluxes were then constrained to their QP-optimized values, and FBA was once again run with an ATP maximization objective (termed the ATP maintenance reaction or ATPM)vlb* and vub* are the standard flux bounds augmented with the QP-optimized values from Eq.  to be mapped to their corresponding UniProt protein sequence entries  and annotated sequence features52. Next, UniProt identifiers were mapped to structures in both the Protein Data Bank29 and homology models from the I-TASSER modelling pipeline31. These structures were then scored and ranked53 to select a single representative structure based on resolution and sequence coverage parameters. For the cases in which only PDB structures were available, the PDBe best structures API was queried for the top scoring structure. If no more than 10% of the termini were missing along with no insertions and only point mutations within the core of the sequence, the structure was set as representative. Otherwise, a homology model was selected by sequence identity percentage or queued for modelling53. It is important to note that the structure selection protocol results in a final structure that is monomeric, and thus parameters which may be impacted by quaternary complex formation are not currently considered. This is a limitation in both experimental data and modelling methods, as complex structures remain a difficult prediction to make. Furthermore, for global and local calculations (described below), all non-protein molecules  were stripped before calculating the described feature. Out of the 1515 proteins, 729 experimental protein structures and 784 homology models were used in property calculations. Finally, we added annotated active site locations from the Catalytic Site Atlas SQL database32 for any matching PDB ID in the analysis.To gather protein-specific features, global properties of catalytic enzymes and local properties of their active sites were calculated using the ssbio Python package54 and the Biopython ProtParam module55. Local properties for secondary structure and solvent accessibilities were predicted from sequence using the SCRATCH suite of tools56 and additionally calculated from set representative structures using DSSP57 and MSMS58. Predicted hydrophobicities of amino acids were calculated using the Kyte-Doolittle scale for hydrophobicity with a sliding window of seven amino acids59. For a full list of obtained properties, see Supplementary Table Global protein properties were classified as properties that were derived from the entire protein sequence or structure , and local properties were those that described an annotated catalytic site . From the protein sequence, global properties were calculated using the EMBOSS pepstats package60, and extended with additional EC number data from KEGG61 and MetanetX62 where available.Reaction EC numbers were obtained from the Bigg databaseE. coli was obtained from literature48. Reaction equilibrium constants (Keqs) were estimated using the latest group contribution method63. Then, a thermodynamic FBA problem64 was solved constraining only high flux reactions (>0.1 mmol/gDW/h), subject to uncertainty. Once a feasible set of fluxes, metabolite concentrations (x), and Keqs was identified, convex sampling was used to obtain a distribution of x and Keq values that accounts for measurement gaps and uncertainty. These sampled x and Keq values were used to calculate the reaction Gibbs energies using the definition:To estimate reaction Gibbs energies, metabolite data for eight growth conditions for Q is the reaction quotient defined as the product of the metabolite concentrations (or activities) to the power of their stoichiometric coefficient in the reaction (S). The thermodynamic efficiency parameter ηrev used in this study was then calculated from this ΔG using its definitionηrev is 0 at equilibrium and 1 at perfect forward efficiency). For consistency, we considered each reaction as the forward direction stoichiometry for this calculation. Average ηrev across the eight growth conditions was used as model input feature.Note that this expression is bounded between 0 and 1 for reactions in the forward direction (Kms) were extracted from the BRENDA33 and the Uniprot52 resource and manually curated. When multiple values exist for the same constant, in vivo-like conditions, recency of the study, and agreement among values were used as criteria to select the best value.Michaelis constants  5-fold cross-validation. In the case of neural networks, hyperparameters were optimized using 3000 iterations of random discrete search and 5-fold cross-validation. Details on implementation and hyperparameter ranges are given in Supplementary Table Statistical models of turnover rates were trained using the caret packageiML1515 using turnover rates obtained from the respective data source or ML model. In the case of membrane proteins, which were not in the scope of the ML model, a default value of 65 s−1 was used. Linear programming problems were constructed using the R45 packages sybil46 and sybilccFBA47, and problems were solved using IBM CPLEX version 12.7. Enzyme molecular weights were calculated based on the E. coli K-12 MG1655 protein sequences (NCBI Reference Sequence NC_000913.3), and the total weight of the metabolic proteome was set to 0.32 gprotein/gDW in accordance with the E. coli metabolic protein fraction across diverse growth conditions44. Aerobic growth on each substrate in Schmidt et al.37 was modeled by setting the lower bound corresponding to the uptake of the substrate and oxygen to −1000 mmol gDW−1 h−1, effectively leaving uptake rates unconstrained.In order to validate the ability of different vectors of catalytic turnover rates to explain quantitative protein data, proteome allocation was predicted using the MOMENT algorithm. We calculate MOMENT solutions for 9 was applied to validate the predicted enzyme turnover rates. For these simulations the iJL1678b ME-model of E. coli K-12 MG1655 was used70. Like in the MOMENT predictions, a default value of 65 s−1 was used for the keffs of membrane proteins, and aerobic growth on each substrate in Schmidt et al.37 was modeled by setting the lower bound corresponding to the uptake of the substrate and oxygen to −1000 mmol gDW−1 h−1, effectively leaving uptake unconstrained. The keffs of all processes in iJL1678b-ME that fell outside the scope of iML1515 were also set to 65 s−1. The model was optimized using a bisection algorithm and the qMINOS solver, a solver capable of performing linear optimization in quad-precision72, to find the maximum feasible growth rate within a tolerance of 10–14. The unmodeled protein fraction, a parameter to account for expressed proteins that are either outside the scope of the model or underutilized in the model, was set to 0. Further, mRNA degradation processes were excluded from the ME-model for these simulations to prevent high ATP loads at low growth rates.In addition to MOMENT, a GEM of metabolism and gene expression (ME model)kapp,max and kcat in vitro prediction approaches. Thus these genes were not considered when comparing predicted and measured protein abundances (Fig. Genes that are subunits in membrane localized enzyme complexes and genes involved in protein expression processes were out of the scope of the ces Fig. . In siliρ correlations was tested using the AS 89 algorithm73 as implemented in the cor.test function of the R environment45. Permutation tests for feature importance in the random forest models were conducted using the R package rfPermute using 500 permutations of the respective response variable per model.The statistical significance of Spearman’s R code for model training and analysis, and Python code for ME modelling are available from the authors upon request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary