Detailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology. This heterogeneity largely stems from the stochastic nature of cellular processes such as transcription and translation and, more importantly, of their interactions3. Whole-cell simulations are promising avenues for understanding how complex interactions between cellular processes can shape the mapping between genotype and phenotype in a stochastic context5. However, the full stochastic simulation of a whole cell remains intractable7, since it would necessitate the simulation of more than 9. Instead, whole-cell simulations rely on hybrid schemes15, where abundant biological species are handled with an ordinary differential equation scheme, whereas low-abundance species are described by stochastic differential equations. For example, in the whole-cell simulator for the organism Mycoplasma genitalium4, the cell is decomposed into modules operating together, each module corresponding to a specific cellular processes such as metabolism or protein translation. The bottleneck due to the computational tractability was by-passed by simulating each module differently, e.g., the metabolism by a deterministic model, non-metabolic processes such as the protein translation by a simplified and well-established stochastic model. Having different simulation modules in interaction is an important step towards whole-cell simulation, as it allows to focus modeling efforts on one cell process at a time.The amount of single cell data available is rapidly increasing, providing an unprecedented access to the heterogeneity of phenotypes within a population of cellsM. genitalium simulator is an incontestable milestone in whole-cell modeling, process descriptions were hard-coded, which complicates the adaptation of the simulator to other organisms. An alternative to integrated whole-cell models is to rely on models that follow a standardized representation and can be inter-operated16. For example, metabolic processes can be represented as a network of reactions17 described by the Systems Biology Markup Language (SBML)18. SBML is a flexible format where every compartment, every chemical, every reaction is defined in a format that is both biochemically informative and easy to parse for simulation. SBML models can be simulated by different types of solvers, such as constraint-based models21 or stochastic simulators such as COPASI22, which proposes deterministic solvers based on ordinary differential equations (ODEs) and several implementations of Gillespie’s Stochastic Simulation Algorithm (SSA)23 for efficient stochastic simulation. However, a SBML file containing all reactions involved in detailed non-metabolic processes  would contain several gigabytes of information with at least Escherichia coli (more than 4000 genes with average length 1 kb)24. Generating, storing and loading such a file in any software is unrealistic.Although the 26 such as BNGL27, Kappa28, or rxncon29, circumvent this problem by defining rules that apply to multiple species (including species involved in a complex) or states that match a pre-defined pattern. These languages allow to define complex regulatory events or transduction processes with a minimal set of rules. Another alternative to reduce the number of reactions are meta-languages such as Antimony30 and PySB31, which define reaction modules that can later be imported or expanded into an SBML or a rule-based model. However, for simulation, rule-based models must be converted into standardized reactions. In network-based approaches, rules are used to exhaustively enumerate possible reactions and reactants32. For example, in the Fc33, 24 rules become 58,276 reactions34. These reactions can then be stored in a standardized format, such as SBML, or a solver-specific format, such as CPS for COPASI. In network-free approaches37, reactants are represented as individual particles; the matching of rules with particles allows to determine which reactions can be performed dynamically. While network-free approaches avoid the need to exhaustively list reactions and species, they tend to be less efficient than network-based approaches34. Particle-based representations are time and memory-intensive, particularly for large systems35. The Hybrid Particle/Population (HPP) model35, integrated in the BioNetGen framework36, offers an interesting trade-off: while reactants are modeled as particles by default, the user can lump related particles  into populations, enabling memory and time savings.Such combinatorial complexity typically arises in non-metabolic processes because reactants can adopt multiple states, such as the phosphorylation of protein residues. If each state is modeled as a separate chemical species, biochemical reactions must be duplicated, as each reaction only applies to one state. Rule-based languages24. Indeed, the relevant biological annotations, such as the position of promoters or terminators, are provided as global positions along the DNA or RNA and cannot be easily handled by rules, which are based on local motifs. As a result, transcription and translation are never explicitly modeled as polymerization processes, instead only the regulation of transcription and translation is considered38. The polymerization is represented in a one-step model, which makes it difficult to accurately represent sequence-dependent regulation based on known biological annotations, such as the position of T-box regulation sites39. While the detailed description of transcription and translation involves millions of possible states, all polymerization processes share a common pattern: the step-by-step addition of new elements specified by a template sequence defining the polymer in production. Ideally, we would like to represent these processes in a format that provides the composability of SBML-based models, but have more freedom in the type of reactions that we can represent, such that reactions related to the same process  do not need to be represented more than once 40.While rule-based models provide concise representations for regulatory events, template-based polymerization processes, such as transcription, translation, or DNA replication, currently lack strong representations and simulation standards, with difficulties arising from gene specificity , and dependency on spatial information 41 which compares very favorably with more standard implementations of the exact Gillespie’s SSA. Finally, we exemplify how processes can be extended, simulating the injection of a drug targeting the elongation phase of translation and the annotation of a T-box regulation process for the tyrS gene.In this paper, we introduce BiPSim, a flexible stochastic simulation framework for template-based polymerization processes designed to serve as an intermediate between rule-based models and SBML-based models. Like SBML-based models, cell processes are declared as a set of reactions in input files (limited hard-coded information about the cell), but BiPSim defines new classes of molecules and reactions that handle sequence-based processes. Like rule-based approaches, only a limited number of reactions (one reaction per type of nucleotide or amino acid) is necessary to describe sequence-based processes in generic terms. To ensure maximal scalability, BiPSim uses a hybrid simulation scheme similar to HPP, where freely diffusing chemicals and template sequences are modeled as populations, while chemicals that are bound to a sequence are represented as lightweight particles. For these particles, the set of realizable reactions is computed on the run, depending on the current pool of chemicals and their current location along the template sequence. BiPSim reactions are simple enough to be easily understood  but abstract enough to represent complex processes at various levels of description with a limited number of reactions. All reactions are integrated by a standard Gillespie approach, facilitating extension by adding new reactions or combining multiple sets of reactions. We show how BiPSim can be used to define and simulate processes involved in gene expression (replication/transcription/translation) at various levels of detail. We show that fully stochastic simulations of a cell cycle remain tractable for a detailed version of the gene expression processes, as simulations rely on a constant-time implementation of Gillespie’s SSABiPSim is an open-source command-line tool for the stochastic simulation of sequence-based processes based on the Gillespie algorithm. The software is implemented in C++, runs on Linux/Mac and is licensed under the GNU GPL licence.BiPSim relies on a set of computer classes managing both chemical and sequence-based reactions efficiently Fig. . In ordeBriefly, the ChemicalReaction represents a standard  reaction applied to freely diffusing chemicals . SequenceBinding represents the binding of a freely diffusing FreeChemical to a ChemicalSequence (e.g. DNA or RNA) through an unoccupied BindingSite, creating an instance of a BoundChemical (implemented using particle-based modeling). A Translocation moves a specific instance of a BoundChemical along its ChemicalSequence; the movement occurs according to a predefined step . A Loading consumes and releases FreeChemicals depending on the sequence that is being read by a specific particle of a BoundChemical . The previous reactions can be combined arbitrarily, enabling binding of multiple instances of BoundChemicals to the same ChemicalSequence, potentially through multiple BindingSites. A Release reaction creates a new ChemicalSequence that depends on the BindingSite and current position of a BoundChemical (e.g. an RNA product corresponding to a specific promoter/terminator combination). A ChemicalReaction can also transform an instance of a BoundChemical into freely diffusing FreeChemicals, implying that the BoundChemical is detached from its ChemicalSequence.In Fig. mentclass2pt{minimThe sequence-based reactions defined by BiPSim are based on lightweight BoundUnit particles Fig. b. AlthouTechnically, every time a FreeChemical binds to a BindingSite, a particle is created. This particle stores two internal states: its location  and its position Fig. b. When aCompared to rule-based models, all particles have the same representation  and are unable to form complexes. Another key difference is that, in order to compute propensity, there is no pattern matching between particles and rules. Instead, sequence-based reactions and reactants use various bookmarking mechanisms to map particles with the reactions in which they can participate. For example, in the simplified transcription process shown in Fig. Simulated models are completely defined by input files, which declare all molecules and reactions. While the classes of reactions and reactants necessarily constrain the types of processes that can be represented, the simulation system starts empty and all biological processes must be completely defined from scratch. We used a text format for input files where declaration of reactions can be easily interpreted as classical biochemical descriptions Fig. . The reaBiPSim contains three main modules: (1) The Input module instantiates reactants and reactions according to input files. 2) In the Simulation module, a Solver controls the following simulation loop : the direct method23 (complexity O(R), where R is the number of reactions [O(f(n)) indicates that the running time is a linear function of f(n) where n is the parameter of interest. O(1) means that the running time is independent of the size of the input.)], a binary search method43 (complexity O(logR)), and a composition rejection method41 (complexity O(1)). By default, it uses the latter algorithm, whose computational effort does not depend on the number of reactions  in bacteria for three levels of granularity in the process description Fig. . We usedBacillus subtilis , realistic parameters from literature , where the T-box motif was first identified46. T-box regulation acts through the binding of tyrosine tRNAs to the leader region of the tyrS RNA. This mechanism leads to abortive transcription through an early terminator that can be counter-acted by the formation of an antiterminator in the presence of uncharged tRNAs  the annotation of the regulatory site, (b) the reactions occurring when a chemical reaches the regulatory site, (c) events allowing to simulate tyrosine shortage  with implementations from other simulation tools. Because different tools have different applications, we emphasize that we focus on performance in a general setting, i.e., models that can be described in SBML format and can thus be simulated by numerous simulation tools. As there are no sequence-related models involved, all models have been implemented in BiPSim using the pool-based FreeChemicals and ChemicalReactions and none of the particle-based chemical entities or reactions.34 comparing 16 implementations of the SSA over five simulation models with increasing combinatorial complexity. In the original publications, the authors concluded that, even in datasets with high combinatorial complexity, network-based implementations (exhaustive representation of reactant states and reactions) outperformed network-free implementations (particle-based) for large numbers of molecules. We found that BiPSim performed on par with the best network-based implementations . Given that network-free implementations proved to be inefficient in the original benchmark, one may naturally worry that performance drops when particle-based reactions are used. However, we found that the number of reactions per second between the gene expression models (780k–2M reactions/s) and this benchmark (200k–3M reactions/s) was similar, suggesting that BiPSim efficiently integrates both pool-based and particle-based reactions. We carefully tailored our particle-based reactions to retain an efficiency that is similar to pool-based reactions. More specifically, we implemented particles that only contain minimal information and can be recycled over time, while particle-based reactions are used to implicitly group similar reactions together  with increasing length, which more closely resemble polymerization-like processes. An n-cascade is a linear pathway characterized by its length  and the number of molecules for the initial chemical of the cascade. For our use-cases Table , every nWe found that BiPSim’s implementations dramatically outscaled COPASI and BioNetGen’s implementations Table . For an 41 is essentially constant time  complexity. More surprisingly, BiPSim’s implementation of the binary tree method, which has a theoretical As expected from theoretical perspectives, BiPSim’s implementation of the composition-rejection SSAme Table , althoug41, BiPSim is able to simulate large-scale sequence-based processes stochastically. As we have shown on the three gene expression models, BiPSim could describe different cell processes with a few types of reactants/reactions only, store the description in few input files of reasonable size  maximize the number of sequence-based processes that can be represented, in particular through the use of sequence annotations, (2) minimize the number of new reaction and reactant types to limit the amount of hard-coded information, (3) ensure that all new reactions types can be performed efficiently. BiPSim uses an efficient hybrid simulation scheme based on pools, to represent freely diffusing chemicals, and lightweight particles, to represent sequence-bound chemicals. Combined with a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm with respect to reactions maximize16 can be represented stochastically. In our benchmarks, we obtained significant speed-ups Beyond the performance issues of the simulation algorithms, BiPSim shows that the use of an adequate description of cellular processes (in particular for non-metabolic molecular processes) is central, especially in the context of the development of whole cell simulators. Nevertheless, we expect that new types of reactions or reactants (and thus new classes) may be required. However, since the design of BiPSim is modular, adding new reactions and/or reactants should require a limited number of BiPSim modifications . BiPSim’s design focused on concise representations of sequence-based processes, which can theoretically be combined with rule-based models in future work. On another note, while BiPSim monitors binding site occupancy to correctly compute binding rates, the current version does not implement interactions between bound chemicals, such as the collisions between RNA polymerases49 or ribosomes50. Finally, 52), generates a combinatorial complexity that must be represented by duplicating the definition of sequences or subsequences.BiPSim provides concise and efficient descriptions of polymerization processes through the implementation of a hybrid pool/particle modeling scheme. However, because BiPSim is not rule based, it shares the same limitations as network-based approaches, in particular regarding combinatorial regulatory events. While we illustrated the simplicity of the representation of T-box regulation, other processes, such as the combinatorial control of gene expressionWe expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in sequence-based processes. For instance, the impact of specific genomic features such as pauses in transcription or translation elongation, regulatory sites of mRNAs, can be finely simulated for one gene only as well as genome-wide. BiPSim offers the ability to handle multi-level descriptions of processes simultaneously. A few set of processes can be finely detailed while others are coarsely described. BiPSim thus offers the capability of tuning a compromise between simulation speed and model granularity.16. They enable the fine representation of a wide variety of cellular processes, including transcription, translation and signaling processes. However, cellular processes such as the metabolism or transcription regulation are better represented using different types of models. The key question then is how to combine Gillespie-based models with models based on another solver strategy, such as ODE models, constraint-based models21 and boolean models17. While hybrid schemes have been proposed12, the whole-cell modeling field would greatly benefit from a dedicated and standardized solving strategy combining all the popular solvers, and where a wide variety of models can be plugged in. Such a representation would enable exceptional freedom in model choices, as modules would be interchangeable, and in granularity, enabling to efficiently combine coarse-grained representations for intensive processes and fine-grained representations for specific signaling, regulation or sequence-based events.BiPSim models, rule-based models and SBML models can all be simulated using the same core simulator, paving the way to a modular representation of whole cells23. New types of reactions and reactants are implemented as separate C++ classes. Reaction classes inherit from a common interface which can be queried by the solver to determine reaction propensities and executing reactions, enabling seamless integration with traditional implementations of the SSA. Similarly, reactant classes share common properties, such as the possibility to add or remove instances of the reactant. While freely diffusing chemicals are simple pools of molecules, sequence-bound molecules are represented individually as particles.BiPSim is an open-source command-line tool implemented in C++, designed to run on UNIX-based operating systems (Linux and Mac). Along with traditional chemical reactions, BiPSim defines new types of reactions and reactants that can be simulated using Gillespie’s Stochastic Simulation Algorithm (SSA)A typical simulation goes through an initialization phase and a solving phase. During initialization, user-defined reactions and reactants are loaded in the simulation system, along with initial conditions. During solving, the solver loops through SSA steps: 1) update reaction propensities, (2) select a reaction, (3) execute the selected reactions. If the selected reaction only involves freely diffusing chemicals, BiPSim behaves similarly to any network-based simulator: the quantities of the affected molecules are updated. However, if a sequence-bound reactant is involved, a random particle from this reactant is drawn. A reaction may change the internal state (position) of the particle. Alternatively, the particle may become a new type of sequenced-bound reactant. In this case, the particle is detached from its current reactant and re-attached to the new reactant type. Some sequence-based reactions only apply to a subset of particles from a given reactant, which are monitored through the use of filters  includes all gene-specific sequence annotations, such as promoters, transcription start and termination sites (one set of annotations per gene).We generated three gene expression models for The two other models are simplifications of the ge_detailed model, obtained by aggregating transcription and translation sequence-based reactions as a single reaction for all genes (ge_aggregated) or a subset of genes . More specifically, the initial binding process of the polymerase is still sequence-based. The particle is then translocation to an arbitrary position within the gene (+50 bases compared to the binding site) in order to make the binding site available to other polymerases. The gene product is then generated in one step and the polymerase is released from its template sequence. As a result, there are two additional reactions per gene represented using the aggregated process. These reactions have been generated using a custom Python script and added to transcription.in and translation.in. All sequence annotations and other reactions in the system remained identical.Bacillus subtilis was taken from53: DNA sequences, position of promoters, transcription starting sites, terminators, ribosome binding sequences (RBS), start and stop codons. A Transcription Unit (TU) can contain several genes, rRNAs or tRNAs. Conversely, a gene can belong to several TUs. The total number of mRNAs in the cell is given by the sum of TUs carrying genes. Models ge_detailed, ge_aggregated and ge_hybrid use native TUs.Sequence information for B. subtilis measurements54 when available. Otherwise, we used Escherichia coli values for a doubling time close to chromosome replication time, that is 44.Parameters were set to match known values from Global replication rate is known to be around 750 bp/s. For simplicity, we set translocation as the limiting step of replication, other steps were set to occur approximately 10–100 times faster. Translocation rate was set to 750 s56. This means that, on average, the replication fork recruits a new DNA polymerase every 1000 bp. Because replication advances at 750 bps/s, a new polymerase is recruited every 1.33 s. The number of DNA polymerases and the recruitment rate were set according to the following mathematical relation58. We estimate For the lagging strand, the length of Okazaki fragments is known to be around 1000 bpInitiation and termination were arbitrarily set to last around 1 s. Promoters (resp. ribosome binding sites) all have the same strength (recruitment rate of RNA polymerases resp. ribosomes). Note that initiation time is negligible compared to elongation time. These rates do not affect the total protein production rate.Transcription rate is known to be around 50 nucleotides/s. For simplicity, we set translocation as the limiting step of transcription, other steps were set to occur approximately 10–100 times faster. Translocation rate was set to 50 sTranslation rate is known to be around 20 residues/s. Loading was set as the limiting step of translation, other steps were set to occur approximately 10 times faster. Loading rates were set to approximately 20 sInputs of the simulator are given in number of molecules. For simplicity, the volume of the cell is set to be constant at a value of mentclass2pt{minimThe tetracycline model was obtained by extending the ge_detailed model. We added a file describing how tetracycline interacts with the ribosome, as well as tetracycline injection and purification Fig. b. We simtyrS transcription units (U2301 and U2303), ensuring that their production is simulated nucleotide-by-nucleotide. We then added two files annotating aborted gene products and the T-box terminator site (tbox_regulation.in), the interaction between the transcribing RNA polymerase and tyrosine tRNAs (tbox_regulation.in), and the depletion and re-injection of tyrosine (tyrosine_depletion.in). All other files of the ge_hybrid model remained unchanged.The T-box regulation model was obtained by extending the ge_hybrid model. First, we removed the aggregate reactions describing the production of We started by downloading the benchmark results and SBML models provided by the authors in the original publication’s Supplementary File n reactions n molecules of chemical We started by generating n-cascades in BiPSim format, SBML format and BNGL format using custom Python scripts. An n-cascade contains n to 50,000. We implemented a script that modified the COPASI model to implement a “Task” of type “timeCourse” with a “Method” of type “DirectMethod”  and set the “Random Seed” parameter to 1. For BioNetGen’s sorted direct method, we ran the BNGL model in BioNetGen 2.5.0 with the function “simulate” with the “method” parameter set to “ssa”. Due to prohibitive run time, we limited n to 10,000. For NFSim, we ran the BNGL model in BioNetGen 2.5.0 with the function “simulate” with the “method” parameter set to “nf”. Due to prohibitive run time, we limited n to 100,000.The SBML model was converted to a COPASI model using the command line CopasiSE version 4.27. Due to memory issues in CopasiSE during this conversion step, we limited We measured simulation time using Python’s time library for COPASI and BiPSim. Note that simulation time did not include conversion time (conversion from SBML to COPASI model). For BioNetGen and NFSim, we extracted the number following “CPU TIME: simulate” from the standard output as the simulation time (to avoid including conversion time).All simulations were run on a server with an Intel(R) Xeon(R) Gold 6134 CPU @ 3.20 GHz processor and 188 GB of RAM. BiPSim was compiled under CentOS 7 using gcc 4.8.5 and BOOST 1.71. All simulations were run on a single thread and used less than 4 GB of RAM and could be reproduced on a standard laptop with similar performance .Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Oryzias latipes), is a widely used genetic vertebrate model with several isogenic inbred laboratory strains. We used liver extracts of medaka iCab and HO5 strains as a paradigm to test the feasibility of distinguishing the metabolome of two different inbred strains. Fifteen metabolites could be detected in uni- and multivariate analyses that showed strain-specific levels. Differences could be assigned to specific metabolic pathways. Our results show that NMR spectroscopy is a suitable method to detect variance of the metabolome caused by subtle genetic differences. Thus, it has the potential to address genotype–phenotype associations in medaka, providing an additional level of phenotypic analysis.The relationship between genetic variation and phenotypic traits is often poorly understood since specific genotypes do not always easily translate into associated phenotypes, especially for complex disorders. The genetic background has been shown to affect metabolic pathways and thus contribute to variations in the metabolome. Here, we tested the suitability of NMR metabolomics for comparative analysis of fish lines as a first step towards phenotype-genotype association studies. The Japanese rice fish, medaka ( The advent of whole genome sequencing stimulated hope that the genetic variations in a population could be associated with pathology, especially for multi-causal diseases such as diabetes or cardiovascular diseases. The study of single gene mutations in the past forty years had revealed the impact of single genes and their protein products on disease etiology. Prominent examples include Huntington’s disease  or DucheMetabolomic studies aim to identify and quantify comprehensively metabolites that are the products and/or effectors of cellular pathways. These studies are based on the assumption that the metabolic state of an organism provides crucial insight into its physiological state. For example, in many congenital metabolic disorders, the function of a single enzyme is perturbed and abnormal metabolite concentrations of the respective enzymatic reactions indicate a metabolomic disorder. Using this approach, characteristic metabolite patterns were detected in a multi-center clinical neonate metabolomics study in Turkey .Furthermore, in cases where genomic approaches cannot reveal a unique genotype–phenotype correlation, differences in metabolites may correctly identify the pathological state. MODY5 is a specific form of maturity onset diabetes of the young, which causes multisystem disorder with a wide spectrum of clinical symptoms. Although more than 100 different mutations of the underlying causal gene, hepatocyte nuclear factor 1 homeobox B, have been reported, genomic approaches have not yet been able to reveal a genotype–phenotype correlation . MetabolOryzias latipes) and zebrafish (Danio rerio) are widely used vertebrate models due to economical husbandry, high fecundity and short generation time. High evolutionary conservation of genes and their function permits using teleost models as disease models [Teleost models such as medaka , enve models  or dailye models ,15, and e models .Inbred strains are of particular importance as their fixed genomes permit experimental replication and analysis of how environmental factors impact on a given trait. Medaka offer a number of isogenic inbred strains, providing a paradigm to study the influence of the genomic background on the metabolism . DespiteWe used NMR spectroscopy as a non-targeted analytical method, which allows the analysis of complex mixtures within a single experiment without prior elaborate processing. Although less sensitive than mass spectrometry, its excellent reproducibility, simple sample preparation and inherent quantitative nature make it perfectly suited for exploratory studies such as our comparative approach. Our analysis shows that strain-specific differences in the metabolic profile can be identified with high statistical power using liver extracts. Separation according to sex was less pronounced. The detection of explicitly distinct metabolic liver pattern by NMR spectroscopy shows that our analytical methodology can be applied for future genotype–phenotype studies of the metabolome in medaka.Upon dissecting the livers, we observed that livers from the inbred strain HO5 were generally bigger than those of iCab. Similarly, livers of female fish were larger than their male counterparts within both strains, but since it was essential to immediately suppress metabolic pathways, livers could not be weighed after dissection, but were immediately snap frozen and lyophilized. Because of the variable sizes of livers, spectra showed different overall intensity ranges . HoweverFour representative spectra of male and female medaka fish of the inbred strains HO5 and iCab are shown in A clear separation between the strains was already observed in a principal component analysis (PCA) which does not use knowledge of group membership for clustering . Strains2: 0.88, Q2: 0.81) showed that the predictive power of the PLS-DA model was high. A 1000-fold permutation analysis resulted in a p-value < 0.001. Separation by sex did not alter this result, but females showed slightly lower quality parameters . Thus, the metabolic profile of liver could reliably distinguish the two inbred strains.Partial least squares-discriminant analysis (PLS-DA) which, as a supervised method, takes group membership into account, revealed a clear separation between the two fish strains . QualityMetabolites responsible for the classification were identified using the variable importance in projection (VIP) scores of the PLS-DA analysis , Table 1t-tests as univariate analyses to identify differentially expressed metabolites between HO5 and iCab  adjusted p-value < 0.01). Analyzing all samples together resulted in smaller p-values than when male and female fish were separately analyzed , compared to 21 for male fish (data not shown). Therefore, a less stringent p-value criterion (p < 0.05) was used for female fish.We alternatively performed fold change (FC) analysis and two-sample analyzed . One plap-value was significantly higher than for their male counterparts (p = 0.04 compared to p < 0.0001). In contrast, only concentrations of lactate/threonine, ATP and an unknown substance with its resonance at 8.51 ppm, which is presumably also a nucleotide, were lower in HO5. In addition, some minor sex-specific differences in metabolite concentrations were found between the two inbred strains. Levels of malate and adenosine monophosphate (AMP) were elevated mainly in female HO5, whereas sarcosine and choline levels were lower. In contrast, alanine levels were decreased only in male HO5.The most significant metabolites identified in this way overlapped with the most important metabolites identified in the PLS-DA models . In part2: 0.93 and Q2: 0.67, p < 0.001) showed that the model was reliable. When sex impact was analyzed separately for HO5 and iCab, separation could be further enhanced by PLS-DA. Quality control parameters showed that the metabolic profile could reliably distinguish male and female fish of the same strain. . A 1000-fold permutation analysis resulted in p-values of 0.002 and 0.001, respectively. However, the separation was not as pronounced as the one we observed between strains  was used, levels of nicotinamide also appeared to be higher, and levels of reduced glutathione, ornithine, hypotaurine and formate lower in male fish. Moreover, strain-specific differences between male and female were observed. For example, levels of glutamine and alanine were decreased only in males of the HO5 strain. When considering only iCab, glycerophosphocholine and ATP were increased and aspartate decreased in males.Using univariate analysis, we compared the metabolite levels between male and female fish. Here, most of the metabolites with fold changes >1.3 or <0.77 with an associated FDR adjusted DA model . SignifiAs one of the main “omics-” approaches, metabolomics—the comprehensive study of metabolites within cells, tissues, or body fluids—is important for the analysis of physiological processes. Metabolic pathways are complex interplays involving the activity of many genes. Changes in cellular activities are directly translated into altered metabolite patterns. Importantly, in addition to the genes encoding metabolic enzymes, the genetic background also directly influences the metabolome, especially when the metabolite pattern of single organs such as livers or brains were investigated, as was demonstrated in metabolome studies of mouse strains . SimilarIt has previously been shown that sex-specific differences of the medaka metabolome can be detected . The aimBoth multivariate and univariate statistical analysis showed that strain-specific metabolic states could be identified with high statistical power in medaka liver extracts. We determined a panel of 15 metabolites that distinguish the 2 strains.Interestingly, several of the metabolites with strain-specific relative amounts are key molecules of specific metabolic pathways in the liver . PathwayElevated levels of aspartate and malate in HO5 could also indicate an affected malate aspartate shuttle in the mitochondria. ATP and AMP levels are connected to both pathways, but as liver preparation was time-consuming and we did not take explicit precautions to quench enzymatic reactions, these two metabolites are probably less significant. Fumarate is also involved in the phenylalanine degradation pathway. Interestingly, both phenylalanine and tyrosine levels were higher in HO5 fish. Tyrosine, on the other hand, is an intermediate metabolite of melanin synthesis. HO5 fish show reduced melanin pigmentation . We therefore speculate that melanin synthesis is affected in this inbred strain, leading to an accumulation of the upstream intermediates of melanin synthesis. Sarcosine and choline, which are both lower in HO5, could indicate a distinct hepatic methylamine osmolyte metabolism in this strain. Striking is the enormous fold change of hypotaurine observed for both sexes in HO5. Hypotaurine is an intermediate in the biosynthesis of taurine. Lactate, on the other hand, is severely reduced in HO5, which hints at an altered gluconeogenesis/cori cycle in liver. Thus, our comparative metabolome analysis identifies strain-specific changes of key metabolites between strains. Future experiments will be designed to test our hypothesis concerning affected metabolic pathways.As PCA revealed that male and female samples showed some variation, we also analyzed the sexes separately for strain-separating metabolites. This allowed us to assess the robustness of the method. Overall, however, the same metabolites contributed to the statistical separation of the strains, regardless of whether sex was taken into account or not. Very few extra metabolites stood out that were different in iCab or HO5 when male and female group membership was used as additional criterion.To prove that the identified metabolite pattern was truly strain-specific, we assessed whether the liver extracts could also be grouped according to sex only. Among the 14 metabolites that contributed to sex differentiation, 50% were entirely different from the ones that separated the strains. The most striking one was 3-Methylhistidine, with a fold change between 12 and 13 in male liver, followed by creatine. Among the metabolites that already showed up for strain separation, hypotaurine levels were elevated in females. Taken together, separation on the basis of sex was possible, but yielded an inferior separation with lower quality values than a separation on the basis of strain. This was surprising given that the requirements for egg production in females should lead to marked different metabolic requirements in the liver. Our findings are in good agreement with published data, reporting sex-specific differences in metabolite patterns in rodents ,22,23, hGenetic screening, performed with the aim to understand the association of genotype and phenotype, has experienced a dramatic increase in the past years due to ever lower genome sequencing costs. However, often when comparing specific genotypes the associated phenotypes differ in subtle ways. Expression analyses at the mRNA level also give only an approximation of gene function, as they do not, per se, allow us to assess the effect on the organism as mRNA stability and/or protein translation product or further regulations also determine the activity of a gene product. Thus, metabolomics—the comprehensive study of small molecules within cells, tissues, or body fluids—provides important complementary insights. In summary, we show that NMR spectroscopy of adult liver tissue can detect quantitative strain specific differences in medaka fish. Therefore, our approach provides a tool to address genotype–phenotype associations in this vertebrate model system that will further our understanding of how the metabolome is influenced by the genetic background.The inbred, isogenic medaka strains iCab and HO5 were kept in closed stocks at the Institute of Biological and Chemical Systems, Biological Information Processing (IBCS-BIP) at the Karlsruhe Institute of Technology (KIT), as previously described in recirculatory systems, under 14 h light/10 h dark conditions at 26 °C . Fish huArtemia nauplii were fed at noon. Seven month old fish of both sexes were used for this study. Fish were divided into four groups according to inbred strain and sex. A group size of 20 fish was chosen , resulting in a total of 79 fish. After fish were sacrificed by hypothermic shock, individual livers were immediately dissected and collected in empty homogenization tubes , Germany). Samples were placed on dry ice for transport, subsequently lyophilized and stored at −25 °C until extraction.All fish used for this study were fed the same way: equal amounts of dry flakes  were fed in the morning and afternoon, equal amounts of fresh Based on an established extraction protocol , we deve1H-NMR spectroscopy were prepared containing six metabolites commonly found in tissues. The quality control contained 1 mM glucose and 0.5 mM each of the following: valine, glutamate, choline, acetate and phenylalanine, which were dissolved in the same phosphate buffer as the tissue samples. Aliquots of QC samples were kept at −25 °C until measurement.Quality control (QC) samples for 2O containing 0.6 mM trimethylsilylpropanoic acid (TSP) as an internal standard and 0.2 mM sodium azide, pH 7.2). The samples were centrifuged for 10 min at 21,382 g and 4 °C. A total of 600 µL were then transferred to a 5-mm NMR tube and analyzed immediately by 1H-NMR. 1H NMR spectra were acquired using a Bruker Avance II + 600 MHz spectrometer  equipped with a double resonance 5-mm BBI probe or a triple resonance broad band 5-mm TBI probe. Temperature was calibrated to 300 K on a daily basis. QC samples were included once a day. One-dimensional spectra were recorded using the Bruker pulse sequence noesygppr1D with 64 k data points, 512 transients and a relaxation delay of 4 s over a spectral width of 20 ppm. Water suppression was achieved through presaturation with a selective pulse B1 field strength of 25 Hz. Prior to Fourier transformation, spectra were multiplied with an exponential function with a 0.3 Hz line broadening factor. NMR spectra were automatically phased and baseline corrected and manually referenced to internal TSP (δ 0.0) using Topspin . In addition, 2D 1H-1H correlation spectroscopy (COSY), and 1H-13C heteronuclear single-quantum correlation (HSQC) experiments were recorded for identification and assignment of metabolites.Aqueous phase extracts were dissolved in 700 µL phosphate buffer . Metabolites were assigned based on the Human Metabolome Database (HMDB) and the evaluation version of Chenomx NMR Suite 8.5 . To confirm the assignments, representative samples were spiked with small amounts of the particular substances. Spectra were divided into variable sized buckets leading to 46 buckets that contained only signals of one specific metabolite whenever possible. Some of the metabolites were characterized by separate buckets for protons of different functional groups. Glucose was not included in the analysis as we noticed some increase in its intensity when we repeated measurements, which was probably due to residual glycogen degradation. A table with all ppm values can be found in the Statistical analysis was performed using MetaboAnalyst 5.0 . Prior t2  and R2 (explained variance). Between-group comparisons were performed using univariate fold change (FC) analysis. Student’s t-tests were performed to determine statistical significance in MetaboAnalyst which yield FDR adjusted p-values based on the Benjamini–Hochberg procedure. We used the KEGG database for pathway analysis in metaboanalyst [To observe intrinsic metabolic variations, multivariate analyses including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed. To assess the quality of the resulting PLS-DA models, permutation testing with 1000 permutations, as well a 10-fold internal cross-validation, were performed. The predictive ability of the models was judged by the quality parameters Qoanalyst .


Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses.Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies. Various experimental data sets exist for this strain at different stages of symbiosis with its native host pea. By integrating transcriptome, proteome, and gene essentiality data with the GSM, we perform a detailed investigation of nutrient uptake and metabolic pathway usage of Rlv3841 in the rhizosphere, as nodule bacteria and as nitrogen-fixing bacteroids. This genome-scale approach for data integration reproduced experimentally observed phenotypes and particularly highlighted the role of different carbon sources and amino acids throughout the different stages of symbiosis. The metabolic model developed herein provides a valuable resource for targeted investigation of metabolic requirements of the different rhizobial lifestyles and may enable the identification of strategies for engineering strains that are metabolically advantaged at all stages of symbiosis formation.In this study, we reconstruct and extensively curate a genome-scale metabolic model (GSM) for B. japonicum (S. meliloti (S. meliloti as a template and reactions from our previously reconstructed bacteroid model of Rlv3841 (23)  . Extensi841 (23) , 31 and 841 (23) . Comparihia coli  as well hia coli , was furhia coli . Because . Extenshia coli , and washia coli . Clusterhia coli  analysishia coli . The quahia coli , where i10.1128/mSystems.00975-21.1FIG S1https://doi.org/10.1186/1471-2164-12-444). Download FIG S1, EPS file, 1.3 MB.COG classification of model genes. Pie chart showing the distribution of COG classifications for the genes in iCS1224. COG classification was performed with WebMGA  in the rhizosphere, (ii) as undifferentiated nodule bacteria, and (iii) as nitrogen-fixing bacteroids . We chosP < 0.001) between metabolic fluxes and transcript abundances was obtained with the biomass reaction constrained to carry at least 77.5% of its maximum flux, and the rhizosphere-specific model contained 606 reactions and 576 metabolites. Remarkably, out of the 134 nutrients available for uptake before data integration, only 51 were present in the rhizosphere-specific model.Generation of a rhizosphere-specific model thus required information about available nutrients as well as gene expression data. Nutrient availability in the rhizosphere is mainly determined by plant root exudates, and plants modulate the composition of their root exudates to select for specific soil microbes , 46. How10.1128/mSystems.00975-21.5TABLE S2in silico rhizosphere representation. Download Table S2, DOCX file, 0.02 MB.Compounds included in the Copyright © 2022 Schulte et al.2022Schulte et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the dctA biosensor in Rlv3841 in the pea rhizosphere  family transporter (RL3840) was 2.6-fold upregulated in the pea rhizosphere compared to free-living cells , which smeliloti  and may iae VF39 .leuD mutant of Rlv3841 requiring the addition of 1 mM leucine to nodulate pea  with those for alfalfa (non-host legume)  and sugaOverall, both the context-specific model obtained by transcriptome data integration and the reporter metabolite analysis were in good agreement with experimental data for rhizobial metabolism in the rhizosphere without forcing the uptake of any compound through arbitrary constraints. Instead, insights into nutrient uptake were facilitated by the integration of gene expression and gene essentiality data with iCS1224. If biomass production were simply maximized with unlimited availability of all root exudate compounds, this would result in uptake of all available compounds that are required for biomass formation, which would not reflect a biologically meaningful scenario.2 into ammonia. The distinction between these developmental stages is required in the context of gene essentiality analyses because genes required for the differentiation process may not be essential for nitrogen fixation and vice versa. Similar to the approach for the rhizosphere model, we used RIPTiDe to obtain models for nodule bacteria and bacteroids and performed flux sampling to identify those reactions that are most likely to be active in each contextualized model.We next sought to develop models for Rlv3841 inside the nodule environment. For this purpose, it is important to differentiate between nodule bacteria at the tip of the nodule, which are dividing and undergoing differentiation, and bacteroids in the central nitrogen fixation zone of the nodule . While nP < 0.001) when the objective value was constrained to 65% of its maximum. The observation that higher correlation was obtained for lower flux through the objective reaction (compared to the rhizosphere) indicates that the metabolism of nodule bacteria is not oriented toward maximum growth. This agrees with experimental data showing that growth of infection threads proceeds at highly variable rates controlled by the plant host  were all predicted to be taken up by nodule bacteria (S. meliloti suggesting the use of sucrose-derived sugars as a carbon source by differentiating nodule bacteria (Myo-inositol is present in the rhizosphere (myo-inositol catabolism have strongly reduced competitiveness compared with wild-type Rlv3841 (myo-inositol catabolism is very low in mature bacteroids (myo-inositol catabolism were not disadvantaged during growth in the rhizosphere compared to wild-type Rlv3841 (60). In addition, it has been proposed that catabolism of rhizopines, which are inositol derivatives, by undifferentiated nodule bacteria may be important as a kin selection strategy  when nitrogenase activity was constrained to 65% of its maximum. The reduced model size compared to both the rhizosphere and the nodule bacteria model is in agreement with the reduced physiological complexity of the non-dividing bacteroids  enrichment of histidine in the Nif and Fix proteins, which could at least partly explain why histidine biosynthesis is required in bacteroids.Low levels of uptake were predicted for most amino acids to support the required synthesis of protein, but no significant catabolism of any amino acid was observed. Mutant studies have shown a requirement for branched-chain amino acid supply to bacteroids , and theium etli  and RNA-oli 4292 . In addin clover . To inven clover . We foun10.1128/mSystems.00975-21.8TABLE S5Table S5, DOCX file, 0.01 MB.Enrichment of amino acids in Nif and Fix proteins. The amino acid compositions of all coding sequences in the Rlv3841 genome and the Nif and Fix proteins were compared using a hypergeometric test followed by Benjamini-Hochberg false discovery rate correction. Download Copyright © 2022 Schulte et al.2022Schulte et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 13C labeling and observed high agreement between model predictions and experimental data.In this study, we present the first curated GSM for Rlv3841, a model strain for investigating rhizobium-legume interactions and a natural symbiont of the agriculturally important crop pea. GSMs have emerged as promising tools for informing experimental design, addressing fundamental research questions, and contextualizing experimental data . In ordeWe further used the GSM to elucidate metabolic changes in Rlv3841 as it transitions from a free-living soil bacterium in the rhizosphere to an undifferentiated nodule bacterium and finally to a nitrogen-fixing bacteroid. While significant advances in determining metabolic requirements for successful symbiosis formation have been made using transcriptome data  and geneRhizobium sp. from root exudates of Arabidopsis  as a teodelSEED , followeodelSEED . Gene-prodelSEED  and comp48  as a odelSEED .S. meliloti  and theR. leguminosarum bv. trifolii ANU843 . When using the optimizeCbModel function, the Taxicab norm was minimized to avoid loops in the calculated flux distributions. All scripts are available on Github (https://github.com/CarolinSchulte/Rlv3841-lifestyles).Standard metabolic modeling computations were performed in MATLAB R2020b (Mathworks) using scripts from the COBRA Toolbox v3.0  and the To evaluate the agreement between model predictions and experimentally determined carbon source utilization, we limited our analysis to those compounds that were either present in the model or showed a positive growth phenotype in the phenotype microarray experiment. The lower bounds for the exchange reactions were then adjusted according to the composition of universal minimal salts (UMS) media  with ammTP: true positives    FP: false positivesTN: true negatives   FN: false negativessingleGeneDeletion with the minimization of metabolic adjustment (MOMA) option in the COBRA Toolbox, while all components of UMS media with succinate and ammonia were available without constraints on their uptake rate. The predictions were compared with gene essentiality data for Rlv3841 determined by INSeq  (The Python implementation of RIPTiDe (riptide)  was usedin silico representation of each environment. If an exchange and transport reaction for a compound already existed in the model, the lower bound of the exchange reaction was set to –1,000. If a compound was only present as an intracellular metabolite, a sink reaction for this metabolite with lower bound set to –1,000 and upper bound set to 0 was added. This was done to avoid erroneous exclusion of metabolites which are present in the environment, but for which transporters have not been identified. Some cofactors and central intermediates of carbon metabolism, such as glyceraldehyde 3-phsophate, were excluded from environmental representations because their uptake would result in unspecific predictions for metabolic pathway activity.In addition to the nutrient availability determined based on experimental data, trace elements and vitamins required for flux through the objective function were added to the For the rhizosphere model, compounds that have been detected in pea root exudates , 51, 52 reporterMetabolites function from the RAVEN Toolbox 2.0 . A liquid culture of Rlv3841 was grown at 28°C in UMS media supplemented with 10 mM glucose, 10 mM ammonium chloride, and a vitamin solution as previously described . Cells wMetabolomics data were obtained in a previous study , where oPisum sativum cv. Avola seeds were surface sterilized and sown in sterilized boiling tubes with fine vermiculite and nitrogen-free rooting solution. Pea seeds were grown in the dark for 3 days and then transferred to a controlled environment room, where they were grown at 25°C with a 16:8-h photoperiod for another 4 days. On day 7, 1 mL (108 CFU/mL) of washed Rlv3841 cells was added near the root. At 7 days postinoculation, rhizobial cells were harvested from the rhizosphere as previously described  and any remaining adapters and low-quality reads were filtered using cutadapt .All data needed to evaluate the conclusions in this article are present in the article and/or the supplementary materials. RNA-Seq data for Rlv3841 in the pea rhizosphere are available on the NCBI SRA database, BioProject number


The modeling correctly predicted the more efficient reduction of 10-hydroxywarfarin over warfarin but not the order of the remaining hydroxywarfarins. Experimental studies did not indicate any clear trends in the reduction for rac-hydroxywarfarins or 10-hydroxywarfarin into alcohol 1 and 2. The collective findings indicated the location of the hydroxyl group significantly impacted reduction selectivity among the hydroxywarfarins, as well as the specificity for the resulting metabolites. Based on studies with R- and S-7-hydroxywarfarin, we predicted that all hydroxywarfarin reductions are enantioselective toward R substrates and enantiospecific for S alcohol metabolites. CBR1 and to a lesser extent AKR1C3 reductases are responsible for those reactions. Due to the inefficiency of reactions, only reduction of 10-hydroxywarfarin is likely to be important in clearance of the metabolite. This pathway for 10-hydroxywarfarin may have clinical relevance as well given its anticoagulant activity and capacity to inhibit S-warfarin metabolism.Coumadin (R/S-warfarin) anticoagulant therapy is highly efficacious in preventing the formation of blood clots; however, significant inter-individual variations in response risks over or under dosing resulting in adverse bleeding events or ineffective therapy, respectively. Levels of pharmacologically active forms of the drug and metabolites depend on a diversity of metabolic pathways. Cytochromes P450 play a major role in oxidizing R- and S-warfarin to 6-, 7-, 8-, 10-, and 4′-hydroxywarfarin, and warfarin alcohols form through a minor metabolic pathway involving reduction at the C11 position. We hypothesized that due to structural similarities with warfarin, hydroxywarfarins undergo reduction, possibly impacting their pharmacological activity and elimination. We modeled reduction reactions and carried out experimental steady-state reactions with human liver cytosol for conversion of  This anticoagulant effect of warfarin is derived from the inhibition of vitamin K oxidoreductase complex 1 (VKORC1), which is involved in the recycling of vitamin K during the maturation of coagulation factors  and alcohol 2 (major metabolite) (9R-11R-hydroxywarfarin and 9S-11S-hydroxywarfarin), whereas alcohol 2 was the combination of alcohols with mixed chiral centers (9R-11S-hydroxywarfarin and 9S-11R-hydroxywarfarin). Nevertheless, R-warfarin was more efficiently metabolized than S-warfarin and both reactions resulted mainly in formation of the S alcohol by CBR1 and AKR1C3 , and 9R-11S-hydroxywarfarin . Given access only to rac-hydroxywarfarins, we anticipated similar metabolic profiles of diastereomeric isomers from reductive reactions. How the regiospecificity of warfarin hydroxylation would impact those subsequent reductive reactions remained unknown.As an alternative to glucuronidation, we hypothesized that hydroxywarfarin reduction is an alternate, competing pathway for elimination of these primary metabolites. For each enantiomer, this process involves reduction of the C11 carbonyl into two possible alcohols or four for a racemic substrate mixture. Historically, studies carried out with abolite) . Our preabolite) . We deted AKR1C3 . Consequin vitro metabolic studies. For a rapid initial analysis, we predicted the likelihood for warfarin and hydroxywarfarin reduction at the C11 carbonyl using the publicly available Rainbow Model for metabolism developed by our group . Human liver cytosol pooled from 150 donors (HLC150) was purchased from Corning Gentest .Unless otherwise stated, common reagents were ACS grade and purchased from Sigma-Aldrich. Toronto Research Chemicals was the source for in vitro and in vivo records, including 1590 reduction reactions, from literature-curated databases. This powerful tool is available for use free and online through our secure server at http://swami.wustl.edu/xenosite/p/phase1_rainbow. For each atom or bond site on the molecule, the model generates a score from 0 to 1.0, with higher values corresponding to greater likelihood for a reaction to occur at that location. This model output is analogous to a probability and provides a strategy to scale reaction relative likelihoods. In our case, we used this metric to rank hydroxywarfarin reductions in comparison to parent drug warfarin. We could then compare the findings to those from experimental kinetic studies, which are more labor and resource intensive.For rapid insights on possible reactions, we predicted hydroxywarfarin reductions at the C11 carbonyl using our deep neural Rainbow Model that simultaneously labels sites of metabolism and classifies them into five key reaction classes: stable and unstable oxidations, dehydrogenation, hydrolysis, and reduction . The modrac-hydroxywarfarin was then resuspended in 50 mM potassium phosphate buffer pH 7.4 and sonicated for 5 min. HLC150 was added to the substrate to final concentrations of 0.25–1.0 mg/ml protein. The mixture was incubated for 10 min at 37°C with 350 rpm rotation and then the reaction was initiated with the addition of 1 mM NADPH. Reactions were quenched after 10, 20, 40, or 60 min using ice cold acetonitrile containing 1 µM coumatetryl  as an internal standard. Quenched reactions were centrifuged at 5°C at 2,500 rpm for 15 min with the supernatant transferred to a 96 well half-area plate for injection. Those studies identified conditions leading to linear response as a function of time and protein concentration, 40 min and 1.0 mg/ml protein, respectively, that were used in subsequent steady-state kinetic studies. Initial rates were measured for substrate concentrations ranging from 50 to 1,000 µM. Hydroxywarfarin alcohol initial rates were plotted as a function of substrate concentration and analyzed by comparing the fit of the data to the Michaelis-Menten equation (hyperbolic curve) and Hill equation (nonhyperbolic curve) using GraphPad Prism 9.2 . The best fit was determined using extra sum-of-squares F test.Steady-state studies involved a two-stage approach to first identify suitable reaction conditions and then to use them for determining kinetic profiles for reactions. Initially, control experiments were conducted to determine the optimal reaction time and protein concentration to ensure steady-state conditions. Hydroxywarfarin stocks were prepared in methanol solutions. For steady-state kinetic reactions, hydroxywarfarin aliquots were dried down using an evaporator under nitrogen using an Organomation Microvap Nitrogen Evaporator System  to remove the carrier solvent. The residual For structural characterization, sample reactions were injected onto a Shimadzu UHFLC equipped with an SPD-10A UV–Vis (280 nm) and RF-10AXL fluorescence  detectors.−3 torr. Detection was achieved using MS scan from 100 to 400 m/z for fragments of parent ions, m/z 311.2 for warfarin alcohols, and m/z 327.2 for hydroxywarfarin alcohols.Waters Acquity LC series  and analytical separation were achieved on a 2.1 × 50 mm Hypersil Gold 5 µm column (Thermo Scientific). Parent compounds and metabolites were separated using a linear binary gradient . The flow rate was 0.4 ml/min, and the gradient was: initial 20% (B), 0–2.0 min 20–40% (B), 2.0–2.75 min 40 to 55% (B), 2.75–3.5 min 55–65% (B), held, and then returned to 20% (B) at 3.6 min. The total run time was 5.0 min. Subsequent mass spectrometry involved a Quattro Premier triple quadrupole mass spectrometer (Waters-Millipore Corp.), which used electrospray ionization to interface the LC system. Positive ions were generated using a cone voltage of 25 V. Product ions were generated using argon collision induced disassociation at a collision energy of 15 eV while maintaining a collision cell pressure of 1.2 × 10flu/abs). Division of the metabolite response ratio by that for substrate yielded a conversion factor, i.e., CF = Metabolite Rflu/abs ÷ Substate Rflu/abs. Metabolite fluorescence responses could then be multiplied by the conversion factor to generate a response comparable to that for substrate making its quantitation by inference using substrate fluorescence possible.Samples were injected onto a 4.6 × 150 mm Zorbax Eclipse 3.5 µm XDB-C18 column (Agilent) heated to 45°C using a Shimadzu UHFLC equipped with SPD-10A UV–Vis and RF-10AXL fluorescence detectors. Analytes including warfarin, hydroxywarfarins, reduced metabolites, and coumatetryl  were resolved with an isocratic method of 25 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer pH 6.5 and 3:1 methanol:acetonitrile. The elution of analytes was monitored by absorbance at 325 nm and fluorescence . Peak areas were normalized to internal standard and quantified by inference using the substrate response. Due to the absence of authentic metabolite standards, we inferred their quantitation based on the corresponding responses. Molecular fluorescence was more sensitive than absorbance; however, carbonyl reduction impacted fluorescence but not absorbance based on initial studies with warfarin and warfarin alcohols. Consequently, for substrate and metabolite in each reaction, we calculated the response ratio as the normalized peak area for the fluorescence response divided by that for absorbance (Rrac-7- and 10 hydroxywarfarin attain the highest levels in patients (rac-hydroxywarfarin or warfarin (positive control) along with either methanol alone (control) or with inhibitor . Reactions were conducted using three different inhibitors to selectively inhibit individual and classes of cytosolic reductases as discussed in a previous study  at a lower concentration (2 µM) to more selectively inhibit AKR1C3 and at a high concentration (10 µM) to inhibit all Aldo-keto reductase family 1C members (AKR1C). Similarly, indomethacin (IND) at lower concentration (5 µM) selectively blocks AKR1C3 activity with only limited effects on CBR1 inhibition, but at a high concentration (180 µM), indomethacin broadly inhibits all AKR1C members and Carbonyl Reductase 1 (CBR1). Lastly, we used 60 µM quercetin to target only CBR1 activity for inhibition.As major oxidized warfarin metabolites, patients , and thuus study  and revius study . We emplWarfarin and the hydroxywarfarins were all predicted to undergo reduction at the C11 carbonyl bond with a moderate to low likelihood compared to the rest of the Rainbow Model training set. rac-hydroxywarfarins for the potential for reduction by pooled human liver cytosolic fractions (HLC150) with rac-warfarin serving as a positive control  followed by a major metabolite . Unlike warfarin, the addition of a hydroxyl group impacted specificity of reduction rates and their relative values for alcohols 1 and 2. In fact, the minor alcohol 1 metabolite was not observable for rac-4′-hydroxywarfarin reactions. In general, rac-10-hydroxywarfarin underwent the highest rates of reduction for both alcohols followed by warfarin and 6-hydroxywarfarin with 7-, 8-, and 4′-hydroxywarfarin showing the lowest rates. Lastly, differences in these initial rates for rac-warfarin and the hydroxywarfarins were not the same at 100 and 1,000 μM, suggesting differences in the respective binding specificities, e.g., Km, among reactions. Subsequent LC-MS characterization provided evidence for the expected metabolite structures, namely, 311 m/z for warfarin alcohols (positive controls) and 327 m/z for hydroxywarfarin alcohols  and major  metabolites. For almost all alcohols, kinetic profiles fit best to the Michaelis-Menten mechanism with the exception of alcohol 1 for rac-6-hydroxywarfarin, which did not reach saturation by 1,000 μM, so initial rates in the linear range up to 400 µM were fit to a line yielding a slope to approximate reaction specificity (Vmax/Km). The corresponding kinetic parameters, Vmax and Km, and calculated specificities (Vmax/Km) for reactions are reported in rac-4′-hydroxywarfarin, the minor pathway for alcohol 1 was not even measurable. In general, lower Vmax values accompanied lower Km values suggesting substrate recognition comes at the cost of substrate turnover. Overall substrate recognition was moderate to very poor as reflected in large Km values, and the maximal reaction rates (Vmax) varied widely among the hydroxywarfarins and their alcohol metabolites. Moreover, the relative specificities for the pairs of alcohols for each hydroxywarfarin differed in magnitude. Taken together, these findings indicated that introduction of the hydroxyl group led to regioselectivity in metabolism but no clear patterns, when compared to warfarin. For both alcohols, rac-10-hydroxywarfarin had the highest specificities, and yet the order of the less specific reactions depended on the location of the hydroxyl group with rac-warfarin always the second most efficient.Steady-state reactions in HLC150 were conducted to determine mechanism and kinetics for reduction of hydroxywarfarin isomers compared to the warfarin parent drug. Initial reaction studies showed linearity in initial rates as a function of time and protein concentration ensuring steady-state conditions (data not shown). in vivo clearance pathways, and so we carried out phenotyping assays using reductase inhibitors to identify enzymes responsible for the reduction of those hydroxywarfarins , Km 220 µM (190–260), and specificity 0.14 (Vmax/Km). For S-7-hydroxywarfarin, the values were Vmax 2.4 pmol/min/mg protein (1.9–3.2), Km 290 µM (160–550), and specificity 0.0083 (Vmax/Km). Taken together, these findings demonstrate that 7-hydroxywarfarin undergoes enantioselective reduction favoring to the R isomer and regiospecific product formation yielding a single metabolite for either substrate.We sought insights on the impact of chirality toward reductase selectivity and specificity during reactions. Studies with racemic hydroxywarfarins yielded chromatographically resolved but ambiguous alcohol metabolite peaks. Individual hydroxywarfarin isomers are not commercially available; however, we had previously synthesized nidation . In thisnidation  that posabolites . Those rtrations . When corvations . Both darac-hydroxywarfarins or 10-hydroxywarfarin that varied up to six-fold for overall specificity of reductions. rac-6-Hydroxywarfarin demonstrated similar metabolic kinetics as rac-warfarin followed by rac-8-hydroxywarfarin and lastly, rac-7-hydroxywarfarin. Moreover, the specificity of reactions for alcohol 1 and 2 differed among the hydroxywarfarin reactions. The collective findings indicate the location of the hydroxyl group significantly impacted reduction selectivity among the hydroxywarfarins, as well as the specificity for the resulting metabolites. Those effects may be due to structural discriminations of individual reductases or the collection of reductases present in cytosolic fractions.We report the first evidence for reductases catalyzing the conversion of the C11 carbonyl of hydroxywarfarins into an alcohol as a novel pathway for their elimination. As a rapid screen for metabolism, our Rainbow metabolism model predicted that for the warfarin metabolites, reduction would increase with closer proximity of the hydroxyl group to the site of metabolism. That trend correctly ranked 10-hydroxywarfarin, as the most likely to undergo reduction followed by warfarin. Nevertheless, the pattern did not hold true for the remaining hydroxywarfarins whether considering the specificity for individual alcohol metabolites or their combination. In fact, the experimental studies did not indicate any clear trends in the specificity of reduction for the R-warfarin reduction and a preference for formation of S alcohols regardless of substrate chirality (rac-warfarin in the literature (9S-11S-hydroxywarfarin) and a major metabolite . This trend may also apply to hydroxywarfarin reactions. We explored the impact of chirality on 7-hydroxywarfarin reduction and observed many similarities to warfarin reactions  and 9R-7,11S-dihydroxywarfarin . These studies suggest that the hydroxyl group at position 7 did not alter chiral bias in metabolism. If this trend extends to the other substrates in this study, then all hydroxywarfarin reductions are enantioselective toward R substrates and enantiospecific for S alcohol metabolites.The reliance on racemic substrates for reactions masked potentially important roles for chirality in metabolic flux for individual hydroxywarfarins to the respective alcohol metabolites. For comparison, our previous work with warfarin revealed significant chiral bias in reactions . CBR1 anhirality . In follterature  then yieeactions . First, S-7-hydroxywarfarin, a metabolite of the main pathway for S-warfarin elimination  as an approximation of model predictions for reactions to occur. The consistency of modeling and experimental results for the most efficient reactions but not the others may indicate the scaling limitations of model predictions to experimental data and/or performance of the model itself. Retraining of the model with larger, more diverse sets of reduction reactions would aid in resolving performance issues. Lastly, experimental studies relied on racemic substrates and inference for quantitating mixtures of alcohol isomers due to the absence of pure, authentic reagents. Nevertheless, interpretation of our results was possible by using our prior work with warfarin and alcohol standards along with exploratory studies with R- and S-7-hydroxywarfarin. Our results showed enantiospecificity and enantioselectivity of 7-hydroxywarfarin reduction that matched the general trend observed previously for warfarin reduction. Based on this finding, we predicted a similar trend occurs with the other hydroxywarfarin isomers.The challenges in handling chirality in reactions were the main limitations of this work. The Rainbow Model lacked chiral descriptors for substrates and its application to possible metabolites. Additionally, model outputs were qualitative predictions for likelihood of a reaction to occur that did not scale to quantitative experimental kinetic mechanisms and constants. Consequently, we compared reaction specificities (Vrac-warfarin) is a highly efficacious drug that poses challenges to adequate dosing due to a narrow therapeutic range and sometimes unpredictable responses in patients. Critical determinants of that uncertainty are gaps in our understanding about how metabolism impacts the potency and levels of pharmacologically active parent drugs and metabolites. The studies reported herein revealed a previously unknown reduction pathway for the elimination of 10-hydroxywarfarin that may play a role in the uncertainty in patient response to warfarin therapy. Knowledge of pathways leading to 10-hydroxywarfarin by CYP3A4 and elimination by reductases provides a more complete picture of the metabolic flux for this metabolite. The resulting 10-hydroxywarfarin levels would then mediate its direct and indirect effects on the anticoagulant response that would be the foundation for important follow up clinical studies based on this research.Coumadin (