Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for The online version of this article (10.1186/s13059-019-1730-3) contains supplementary material, which is available to authorized users. Haemophilus influenzae RD was reported in 1999 . . d-pheny iJO1366  to examiYarrowia lipolytica, an eukaryotic microorganism known to accumulate large amounts of lipids [Y. lipolytica, iYLI647, was newly reconstructed and employed to find target reactions that can lead to the enhanced production of dodecanedioic acid [In another example involving f lipids , its GEMf lipids   , 135 andpathogen . P. falce itself . Becausestructed . IntegraAcinetobacter baumannii [Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, A. baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) associated with antimicrobial resistance [A. baumannii GEM, iLP844, was reconstructed and transformed into condition-specific GEMs by integration with transcriptome data. The transcriptome data were obtained from cells treated and untreated with colistin, one of the last-resort antibiotics against multi-drug-resistant pathogens [Condition-specific drug targeting using GEM has also been conducted for aumannii , which isistance . Specifiathogens . Conditiathogens , 140. OnE. coli GEM iJO1366  BLAST, which show their homologous genes from the SEED database. The gene-similarity trees were used to generate a matrix that presents the primary and potential promiscuous functions  of E. coli enzymes encoded by the corresponding genes. Finally, ‘replacer’ genes were identified in the matrix, which have a potential promiscuous function that is identical to the primary function of another conditionally essential gene (‘target’ gene) in E. coli. A potential promiscuous function of a replacer gene can be validated if expression of this gene can prevent cell death upon knockout of the target gene. Among the target–replacer gene pairs predicted using the PROPER method and an E. coli GEM from Model SEED [pdxB–thiG gene pair was experimentally validated   was devedel SEED , the pdxted Fig. f. The pdThese two studies have demonstrated that a high-quality GEM of a target organism allows the prediction of new enzyme functions and enzyme promiscuity, which is extremely useful because not all enzyme functions are experimentally validated.Salmonella strains, spanning 64 serovars, by reconstructing a GEM for each strain [Salmonella serovars and provided information about their evolution. Automatic GEM reconstruction tools have definitely contributed to pan-reactome analysis, and will continue to be applied to various groups of biologically related organisms of high scientific, industrial, and/or medical importance.Computational resources for high-throughput GEM reconstruction are now allowing the metabolic analysis of multiple organisms, including multiple strains of a single species , 142 or h strain . The conPenicillium species that are well-known for the production of secondary metabolites [Penicillium species  , 144 andEscherichia sp., Akkermansia muciniphila, Subdoligranulum variabile, and Intestinibacter bartlettii, was examined using their respective GEMs. The GEMs were obtained from the AGORA models. Upon metformin treatment, Escherichia sp., A. muciniphila, and S. variabile are known to be enriched in the gut, while I. bartlettii is reported to decrease. In the simulation studies, contributing or competing bacteria were predicted through simulation of the GEMs for short-chain fatty acids , amino acids, and gases , all of which play important roles in both intermicrobial metabolic interactions and the regulation of human metabolism. For example, Escherichia sp. and S. variabile were predicted to contribute to the production of short-chain fatty acids under aerobic and anaerobic conditions. In addition, Escherichia sp. was shown to be least affected by the availability of intestinal nutrients. Covering a greater range of microorganisms and the metabolites exchanged among them will add more scientific value to GEM-based studies of a specific microbiota.In a study involving type 2 diabetes patients treated with the drug metformin  Fig. 3bb3b, the In this regard, another recent study also deserves attention. Kumar et al.  examinedSolanum tuberosum) was first reconstructed, and three context-specific GEMs were subsequently created by incorporating transcriptome data from the cells of plants that were infected with Phytophthora infestans, a plant pathogen that causes late blight, at days 0, 1, and 2 after infection  microbial species in a given community. For example, key microorganisms or metabolites in a specific microbiota can be suggested more systematically by examining metabolic interactions among more varied combinations of microorganisms from the microbiota. Here various modeling and simulation algorithms beyond FBA can be developed, depending on the objective of study and the scale of the metabolic modeling to be examined. Second, it is extremely difficult to measure metabolites that are exchanged by microbial species in vivo. Metabolome analyses using food, stool, and/or serum have been the most frequently practiced approaches for characterizing the metabolism of microbiota species, but still have limitations in revealing metabolite exchange by microbial species in vivo. This issue has led to active discussions on the need to identify and apply accurate condition-specific constraints for the GEMs of gut microbiota species and to conduct community-level manual curation of the GEM of each gut microbiota species , 153. FiHuman diseases have also been studied using context-specific GEMs to elucidate metabolic malfunctions in cells that are under chronic or acute disease conditions, and to suggest effective therapeutic targets. Cancers, including cancers of the liver , 154–156Other studies have used GEMs to predict altered intracellular metabolic flux distributions in acute diseases such as sepsis  and viraHuman diseases are associated with highly complex cues and cascading of signals, and thus, the use of GEM-based simulations alone can provide only limited insights into disease. In the future, a number of important studies need to be performed to provide a better understanding of human diseases and to help in designing proper therapies. First, in addition to a metabolic network, regulatory and/or signaling networks should also be considered to allow a more accurate computational description of a diseased cell. These different types of biological network are connected with one another in a highly complex manner. Thus, it will ultimately be necessary to integrate metabolic, gene regulatory, and signaling networks in modeling and simulation. This will require an innovative computational framework that allows simultaneous simulation of material flow (metabolic network) and information flow . Second, it is increasingly recognized that a number of human diseases are significantly affected by patients’ lifestyles. Thus, it will be necessary to develop strategies to integrate human GEMs with a framework of precision medicine that involves not only patient-specific omics data but also personal lifestyle data, such as dietary habits and patterns of various physical activities.Having been established as one of the major modeling approaches for metabolic studies at systems level, the reconstruction and simulation of GEMs continue to be explored for an increasingly wider range of organisms and applications. Advances in the reconstruction and use of GEMs are largely attributed to the greater availability of biological data and information, and to the establishment of automatic GEM reconstruction tools. GEMs will continue to evolve by embracing a greater coverage of GPR associations, reconciling model inconsistencies, and developing novel mathematical modeling techniques that can be used in a high-throughput manner. GEMs will become more powerful by incorporating additional biochemical information that will provide explanations of cellular processes beyond metabolism . AdditioAdditional file 1:Figure S1. A phylogenetic tree at the species level of all of the GEMs reconstructed to date. (PDF 9989 kb)Additional file 2:Table S1. A full list of organisms subjected to the GEM reconstruction and used in the preparation of phylogenetic trees in Fig.


The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach.Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type , during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles.Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro.The online version of this article (10.1186/s12859-019-2825-2) contains supplementary material, which is available to authorized users. Understanding the complex intercellular metabolic interactions during ovarian follicle development requires a systems biology approach. The follicle consists of somatic cells that surround the female germ cell, the oocyte. Metabolic communication between these cell types is necessary for follicle development and oocyte maturation. Yet, most systems-level studies of the follicle to date have focused on signaling and gene regulation  rather tgenome-scale network models to model follicle metabolism [Here we apply tabolism . These ntabolism , includitabolism . The mettabolism . Transcrtabolism –15.To build our cell-type specific metabolic models, we used the mouse metabolic reconstruction , and updA comprehensive mouse metabolic reconstruction based on the most up-to-date metabolic knowledge could increase the accuracy of a reconstruction. Mouse Recon 1 was unable of adequately modelling multiple mouse metabolic functions, several of them associated with key follicle metabolic pathways . Thus, we constructed a high quality and more comprehensive mouse metabolic reconstruction, called Mouse Recon 2, employing the current best practices in systems biology   were the most important metabolites in primordial oocytes, while 6-phospho-D-glucono-1,5-lactone and 2,3-bisphosphonato-D-glycerate, which are involved in the pentose phosphate pathway and oxygen release from red blood cells, respectively, were more prominent in oocytes at later follicle stages . We identified the top 10 metabolites for each follicle stage and cell type Fig. a. Top meges Fig. a.Fig. 4TInterestingly, significant metabolites in the somatic cells were similar at all follicles stages and were primarily folic acid derivatives and L-methionine, which are involved in DNA methylation. L-tryptophan had a high enrichment Z-score in somatic cells as well. This amino acid is the precursor of serotine, which has known effects in follicle maturation . CytosolNos1 (nitric oxide synthase) and Hsd17b4 (hydroxysteroid 17-beta dehydrogenase 4), which is an enzyme part of the peroxisomal beta-oxidation pathway for fatty acids, were the two top enzymes in primordial oocytes; whereas Myo5b (Myosin Vb), an effector for RAB11A required for recycling of transferrin in nonpolarized cells [Akr1b3 , which participates in pyruvate metabolism, and Scl3a2, a glutamine transporter, were among the most enriched genes encoding enzymes in oocytes of all other follicle stages , Fpgs (folylpolyglutamate synthase), and Fdas1 (fatty acid desaturase 1). Slc7a5 encodes an amino acid transporter involved in high-affinity transport of large neutral amino acids such as phenylalanine, tyrosine, leucine, arginine, and tryptophan, while Fpgs encodes an enzyme that establishes and maintains both cytosolic and mitochondrial folylpolyglutamate concentrations and, therefore, is essential for folate homeostasis and the survival of proliferating cells. The enzyme encoding by Fpgs catalyzes the conversion of folates to polyglutamate derivatives allowing to maintain the concentrations of folate components in the cell. Fpgs also facilitates the intracellular retention of these cofactors, which are important substrates for most of the folate-dependent enzymes that are involved in one-carbon transfer reactions in purine, pyrimidine, and amino acid synthesis. Fdas1 Isoform 1, which has its highest Z-score values in the large antral follicles, encodes a component of a lipid metabolic pathway that catalyzes the biosynthesis of highly unsaturated fatty acids and generates arachidonic acid. Arachidonic acid increases the concentration of estrogen and progesterone in granulosa and theca cells [Hsd17b1 (hydroxysteroid 17-beta dehydrogenase 1) encodes an enzyme involved in the metabolism of estrogens, and reduces both estrogens and androgens  in mural cells, a gene that encodes an enzyme that produces retinoic acid, an important vitamin component in ovarian follicle development [In the somatic cells, the top most significant genes encoding enzymes based were ca cells . Hsd17b1ens Fig. b. Highlyelopment .Finally, we determined the most likely secreted and consumed metabolites by each cell type at each follicle stage Fig. . We simuOur analysis also discovered novel metabolic processes during ovarian follicle maturation. For example, our model suggests that the oocyte produces metabolites acetate and inosine. Acetate is the source of cholesterol for cumulus cells , and bashttp://www.mousephenotype.org/). Similarly, OvolFol Recon 1 could seed light into human diseases associated with ovarian follicle development .In this study, we created and analyzed the first metabolic model of the mouse ovarian follicle and its cellular compartments—the oocyte and somatic cells —through the stages of follicle development. We used a systems biology approach to decipher the key exo- and endo-metabolic processes present during mouse ovarian folliculogenesis in vivo. For this purpose, the latest mouse metabolic reconstruction, Mouse Recon 1, was updated based on the recently developed human reconstruction, Human Recon 2. This new model was able to successfully simulate an additional 10% of the metabolic functions compared to the Mouse Recon 1 model. Mouse Recon 2 was then employed to generate the first mouse ovarian follicle metabolic reconstruction, OvoFol Recon 1. Mouse Recon 2 could be applied in future studies to predict mouse phenotypes using IMPC   secreted metabolites may be employed as biomarkers for the stage of follicle development, which is necessary to monitor the growth in vitro of follicles from large mammalian species whose stage cannot be monitored under the microscope. Several metabolites may be useful for determining follicular stage. The transition from primordial follicles to primary follicles could be detected by the decrease of production of nitric oxide, oxalic acid, D-xylose, cholate, or 5-methyltetrahydrofolate. Similarly, the primary to secondary transition could be monitored based on the decrease in production of inosine or the production of (R)-3-hydroxybutyrate; b) consumed metabolites can guide the development of novel media components that will support follicle growth, particularly in early-stage follicles, and in vitro maturation (IVM). For instance, based on the model predictions, supplementation of the α-MEM media that is currently used in in vitro follicle culture with sorbitol and 2-hydroxybutyrate or further increase the concentration of folic acid in the media, as the three metabolites are consumed by the follicles.The metabolome of the oocyte differed greatly from that of the somatic cells at every stage of follicle development. This result is consistent with current thinking regarding the geography of the ovary, where primordial follicles, which are located in the ovarian cortex, and secondary and antral follicles, located in the medulla , are expThe aim of our study was to develop novel data-driven hypothesis that could serve as new areas of research to understand the complex and dynamic intra- and inter-cellular communication between the different ovarian follicular cell types. Our models can be improved by using experimental isolated cell types from all the stages during follicle development. Currently, there is no such a set in mice, especially for granulosa cells, most likely due to the technical difficulties. Similarly, it should be noted that the results from our model have been obtained using transcriptional data from pre-pubertal mice and those differ from pubertal mice, as the dynamics of ovarian follicle development is faster in younger mice and slows with mouse age . FinallyIn conclusion, we successfully applied a systems biology approach to characterize the most important metabolic pathways in the oocyte and somatic cells during various stages of ovarian follicle development. The understanding of the follicle metabolome has been limited by decades of research using bottom-up approaches, which has provided only snapshots of the complex metabolic landscape of the growing follicle. Collectively, metabolic systems approaches were able to model the follicle metabolome, providing a rich set of data that can be applied to generate new hypothesis to test experimentally. Our study can improve in vitro follicle growth and oocyte maturation approaches and support the growth of primordial follicles in vitro.We developed Mouse Recon 2, a general metabolic model based on the latest general human metabolic reconstruction, Human Recon 2.03 , employiWe followed procedures for ovary and follicle isolation as previously established  with slihttp://www.bioconductor.org) for: a) mouse oocytes from primordial to large antral follicles [limma [Microarray data were downloaded from Gene Expression Omnibus (GEO) using the GEOquery  and Arraollicles  ; b) and their fold change in the follicle microarray was below 1.05 from for the primary to two-layered secondary transition- the oocyte transcripts are diluted due to the granulosa cell proliferation. The genes that satisfied these conditions were considered to be only expressed in the oocyte during ovarian follicle development. Genes in the follicle microarrays that were significant in the oocyte and in the follicle microarray but did not follow the patterns described in conditions b and c were classified as present in both cell types, i.e., oocyte and somatic cells  they were significant in the oocyte microarray , cumulus cell only, cumulus cell during acquisition of oocyte competence only, or mural cell only if they were present only in their corresponding arrays and were not significant in any other microarray for the given significance cut-offs  i, fk is the flow calculated with Infomap for element k in the community i, wk is the number of metabolites that are catalyzed by the enzyme k according to the enzyme-metabolite bi-partite graph, Ik is 1 if no microarray data was employed . fp was compared with the background normalized intensity flow of the equal number of randomly selected genes for the entire network using the enzyme-metabolite bi-partite graph. Enzyme Z-scores were established using Eq. fp was determined based on the flow of the enzyme accounting for all the metabolites that are catalyzed by the given enzyme in the entire network, wk. Similarly, Z-scores of the metabolites were established using Eq. fp was determined based on the flow of the enzymes that catalyze the reactions in which the metabolite participates, setting wk to 1.Pathway enrichment within a community, nG, was calculated as follows [Deparaffinized histological sections of the ovaries employed for the in vivo transcriptomic studies were used to determine the number of different cell types over time in each follicle stage .The total number of theca and cumulus cells were estimated using the same approach. The cell type ratios were obtained from slides in which the nucleus of the oocyte could be observed. The average of the maximal and minimal axis of the follicle was employed as an estimation of the cellular ratios. All images were processed with ImageJ  were reconstructed using the FASTCORE algorithm  in the sAdditional file 1: Mice Recon 2, Matlab fileAdditional file 2:Metabolites, reactions and enzymes present in Mice Recon 2. (XLSX 452 kb)Additional file 3:Metabolic model functionalities. (XLSX 66 kb)Additional file 4: OvoFol Recon 1, Matlab fileAdditional file 5:Metabolites, reactions and enzymes present in OvoFol Recon 1. (XLSX 307 kb)Additional file 6:Entrez ID, gene symbol, the community that the gene belongs to according to Infomap and the flow of information that is transferred through the enzymatic node. (XLSX 110 kb)Additional file 7:Z-scores for each metabolic pathway using Infomap information for Mice Recon 2 and OvoFol 1. (XLSX 92 kb)Additional file 8:p-values for each follicle cell type during ovarian follicle development. (XLSX 6750 kb)Mean fold change and fdr-corrected Additional file 9:Supplementary Figures. (DOCX 13578 kb)


Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale metabolic models. We generate an ensemble of models, each equally consistent with experimental data, then perform simulations with them. We apply machine learning to the simulation output to identify model structure variation that maximally influences simulations. These variants are high-priority candidates for curation through removal, addition, or reannotation in the model. We apply this approach, automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS), to 29 bacterial species to improve gene essentiality predictions. We explore targets for individual species and compile pan-species targets to improve the database used during model construction. AMMEDEUS is an automated and performance-driven recommendation system that complements intuition during curation of biochemical knowledgebases. •Prioritizing curation of complex mechanistic models is challenging•Development of curation guidance approach for genome-scale metabolic models•Ensembles and machine learning are used to prioritize possible curation efforts•Application to metabolic models for 29 bacterial species and a biochemical database Large mechanistic models are useful, but their size and connectivity make prioritizing curation effort challenging. Here, we develop a method to identify parts of a metabolic network that contribute to uncertainty in simulations. We apply this method, automated metabolic model ensemble-driven uncertainty elimination using statistical learning (AMMEDEUS), to guide curation of genome-scale metabolic models for 29 bacterial species. We show how AMMEDEUS can be used to guide curation of individual metabolic models and databases through experimentation or targeted literature searches. Escherichia coli and Saccharomyces cerevisiae, but GEMs for most other organisms are much more taxing to create and curate, partially due to the exhaustive and manually driven steps required  are knowledgebases describing metabolic capabilities and their biochemical basis for entire organisms. GENREs can be mathematically formalized and combined with numerical representations of biological constraints and objectives to create genome-scale metabolic models (GEMs). These GEMs can be used to predict biological outcomes  given an environmental context  . GEMs arrequired .The methods used to curate GEMs are nearly universally underreported in the literature. The curation process for a single GEM includes but is not limited to: addition of biochemical activity known experimentally with no identified gene , removal of false-positive annotations of gene-protein-reaction units, refinement of species-specific model components , and removal of database or annotation-induced errors . Curation methods for GEMs that take researchers many months to years to develop are generally summarized qualitatively with limited description or justification. This is not surprising, given the difficulty in prioritizing areas for curation of network-based, highly connected mechanistic models such as GEMs. Systematic, reproducible methods for curation are rare, so GEM curators are left to their own intuition to prioritize curation efforts. Advances in automated generation of GEMs are beginning to make the goal of formalizing the curation process more approachable, but the resulting GEMs are of sufficient quality for very limited purposes and thus still require intensive curation .a priori. In other words, gap filling is a process of fitting a GEM to observed data. This fitting is of tremendous value, but the primary purpose of mechanistic models is to generate in silico predictions for behavior in a previously unobserved environment. In order to efficiently curate a GEM to improve its performance for simulation tasks that have no observed experimental equivalent, a curator needs to understand which portions of the GEM affect the output of the simulation. Existing approaches and tools fall short of this demand because they only improve GEM quality and performance by establishing consistency with modeling standards and previously observed data.In practice, heuristics are typically used to prioritize curation, such as curating portions of the GEM directly involved in the manipulation of a metabolite, gene, or pathway of known interest. These heuristics, combined with targeted literature searches, allow task-based curation and GEM evaluation, which is increasingly supported in software related to genome-scale metabolic modeling . Some emOne way to view this issue is through the lens of a sensitivity analysis, asking how much variation in the parameters of a model will impact a simulation of interest. Such an approach has been developed and applied to dynamic models of biological networks , which rRecently, we developed a framework for the generation of ensembles of GEMs that can be applied to improve predictive performance over that of an individual GEM . This apThe AMMEDEUS approach is summarized as follows. First, we generate many models that are each consistent with experimental data, forming an ensemble of models A. We thein silico growth in each of a set of media conditions  and model structures . We implemented a previously developed iterative gap-filling procedure for generating ensembles of GEMs . First, nditions . Alternanditions . Using tTo validate that the ensembles we generated represent an adequate sampling of the feasible model space, we first subsampled gap-filled reactions in each ensemble for each species and determined the unique reaction content within each subsample C. We fouPseudomonas aeruginosa, in which various aspects of ensemble performance nearly plateaued with only 50 GEMs  and its extensive genomic and physiological characterization from over 100 years of use in biochemistry research  and prediction space  possible, given the input data. This behavior is consistent with previous work examining the performance of ensembles of GEMs for  50 GEMs . Howeverresearch .k-means clustering on the gene essentiality simulations from each species’ ensemble separately. We chose k = 2 to generate two clusters for each species, each of which contain GEMs from the ensemble with similar gene essentiality simulation profiles. The results are visualized for all species in this study using principal coordinate analysis (PCoA) in k = 2 here to illustrate the approach, the separation of models in PCoA space suggests that for many species, determining a larger number of clusters might be advantageous. For example, while k = 2 generates two maximally different simulation clusters, there may be more than two distinct in silico phenotypic clusters that represent meaningful differences in hypothesized model behavior. Accounting for the presence of these smaller clusters may identify important network features that would otherwise only be found through multiple iterations of clustering with k = 2 and refinement of the ensemble.Each species’ ensemble contained 19.27 ± 8.66 genes (mean ± standard deviation) for which at least one GEM’s prediction of essentiality disagreed with another GEM in the ensemble, representing 3.11% ± 1.39 of total metabolic gene content. For the unsupervised machine learning portion of AMMEDEUS, we performed in vitro growth on a rich medium were available. Suitable datasets were identified for Staphylococcus aureus  is sufficient to capture these differences across an ensemble. Having a meaningful degree of variation between simulation clusters is essential moving forward in AMMEDEUS, as we aim to predict the simulation cluster from the network structure of each ensemble member.Our approach is focused on prioritizing curation efforts to reduce uncertainty in model simulations. However, whether the parameters we have used result in clusters with differences in predictive performance is unclear. To investigate this question, we evaluated the performance of a subset of ensembles for which experimental genome-wide gene essentiality datasets derived from s aureus  and Haemfluenzae . Each GEfluenzae B. For boiability . There aEnterococcus faecalis in We next sought to identify the reactions that vary across an ensemble that are associated with membership in each cluster. For this objective, we calculated two metrics for each gap-filled reaction in each ensemble. This process is demonstrated for The intent of the cluster ratio is to capture the value of curating reactions that may be lowly abundant throughout an ensemble yet highly enriched in one of the two clusters . These reactions may make small contributions to classifier performance due to their low abundance, but curating their presence or absence will reduce the uncertainty in the ensemble of GEMs in a straightforward and interpretable way. This strategy contrasts with reactions with high fractional ratios  because the random forest allows for interactions between input variables. As such, curation of individual reactions with high fractional importance may not result in a substantial change in GEM performance; improvements from curating these reactions may be dependent on also curating other reactions that interact with the curated reaction in the trained random forest. Together, the cluster ratio and fractional importance can guide manual curation of a GEM. High cluster ratio reactions represent interpretable “low-hanging fruit” with modest overall value for curation, while high fractional importance reactions represent the highest value curation effort that could be pursued. Reactions with high values for both metrics should be prioritized above all else D.E. faecalis, the reaction with the second highest fractional importance and a cluster ratio of 1 (perfectly enriched in one cluster) was selenocystathionine L-homocysteine-lyase, which generates selenohomocysteine, pyruvate, and ammonia from selenocystathionine , the secondary activity is a less appreciated promiscuous activity (Se-conjugate metabolism by an enzyme primarily known for S-conjugate metabolism), and the primary activity is sparsely annotated in the database used to construct GEMs in this study . Further compounding the issue, the E. faecalis genome within PATRIC contains three annotated cystathionine beta-lyase genes, an enzymatic activity that was recently merged with cysteine-S-conjugate beta-lyase . Given that only the cystathionine beta-lyase activity was annotated in PATRIC, the lack of annotation for cysteine-S-conjugate beta-lyase might be resolved when PATRIC and ModelSEED are updated to take the EC merge into account. This curation vignette highlights the need for improved handling of enzyme promiscuity in biochemical databases and presents an opportunity for targeted curation of E. faecalis annotation in genomic and biochemical databases. Further examples of high-priority curation targets can be found in With a list of prioritized reactions for curation in hand, there are multiple approaches that can be taken to curate their presence or absence, including wet lab experiments, targeted bioinformatic analyses, and literature searches. The optimal approach depends on the scientific history of the organism and data availability. A targeted literature search might reveal information that has not been incorporated into genomic or metabolic databases, especially for well-characterized organisms with a large body of literature. For example, in the ensemble for thionine E; reactithionine . Cysteintivities . Thus, tE. faecalis, with many reactions with high fractional importance at intermediate cluster ratio values, indicating complex interactions between reactions of interest . For these species, reduction of uncertainty in gene essentiality predictions will likely require curation of multiple reactions. Other species have simpler behavior, with a high degree of concordance between cluster ratio and fractional importance for the most important reactions . For these species, each individual reaction of high importance that is curated will result in a substantial and easily predictable decrease in uncertainty for gene essentiality predictions.In addition to the single-species curation guidance enabled by AMMEDEUS, the automated nature of the approach allows meta-analyses that span metabolic models for multiple organisms or entire databases. We performed the AMMEDEUS approach for all 29 species in our study. By compiling these curation target metrics across all 29 species, we are able to identify pan-species or database-wide curation targets. For these reactions, improving the accuracy or coverage of gene-protein-reaction associations could greatly improve the performance of GEMs generated with this database for any species. In glyA) in bacteria  contain a putative SHMT encoded by glyA but no putative TA. For these species, a simple experiment with crude extracts to verify TA activity, as performed previously for C. glutamicum, could verify that the metabolic activity occurs either through promiscuous SHMT activity or an orphan enzyme  that lead to conditional relationships (AND/OR) being captured in the fitted model. While this choice of method can lead to improvements in accuracy, it also results in curation targets that may be conditionally dependent on other targets. Here, we used the cluster ratio to inform the curator of the degree of conditionality between curation targets (or lack thereof). In future implementations of AMMEDEUS, a simpler linear model without covariate interaction may be more suitable for curators only interested in curating individual reactions that will reduce simulation uncertainty.Our approach builds on work in other disciplines in which uncertainty quantification and reduction are applied to understand or improve the behavior of domain-specific models. For example, in petroleum engineering, an ensemble-based approach is used to derive value of information (VOI) estimates for resolving parameter values in models of oil reservoir management . In thispapin@virginia.edu). This study did not generate new unique reagents.Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Jason A. Papin  were generated using the ModelSEED reconstruction pipeline  accessedin silico . For each single carbon source, appropriate identifiers were found in the ModelSEED database. For metabolites with ambiguous chemical identities , only one isomer was selected from ModelSEED to represent the condition. Carbon sources that are complex mixtures of metabolites (gelatin) or polymers (pectin) were excluded from analyses.The base medium for biolog conditions was derived from the ModelSEED media compositions for biolog plates. Flux variability analysis was used to identify metabolites which had essential uptake reactions in all complete media-gap-filled reconstructions from PATRIC. Based on this analysis, we added Heme and H2SO3 to the base biolog composition used Algorithm 1: pFBA-based gap-fillingMin the formulation is identical to the original formulation of pFBA , except  package  that all package . As in B package , the solN ensemble members to be generated for a species, randomly order all M media conditions in which the species grew experimentally. For each single condition m in the shuffled list of conditions M, set the model bounds to represent the media condition m and optimize using pFBA-based gap-filling (Algorithm 1). For all flux-penalized reactions that carry flux in this solution (with >1E−11 units of positive or negative flux chosen as the cutoff), remove the flux minimization penalty. For all m media conditions in M, iteratively repeat the process using the modified flux penalties from all previous conditions when gap-filling in the next condition. After gap-filling to enable growth in all conditions in M, add all gap-filled reactions from all media conditions to the original model to generate an ensemble member. Repeat this process N times with a new ordering of M in each iteration and the original flux penalties set at the beginning of each iteration to generate N ensemble members.For each organism, the approach for generating an ensemble is as follows . All species included in the study grew in at least 10 in vitro single carbon source media conditions ; for each species, all positive growth conditions were used to gap-fill during each cycle. After removing duplicate gap-fill solutions, all species included for further analyses had 970–1,000 members in their ensemble (species not considered after this point are detailed in Organism Selection).Each individual gap-filling step, corresponding to enabling biomass production on a single media source, was performed using Algorithm 1, adapted from our previous work . We perf∗hr uptake allowed for all metabolites with a transport reaction; commonly referred to as “complete medium”). An arbitrarily low cutoff for flux through biomass in gene essentiality screens was used (1E-6 units of biomass/hr), but varying this quantity between 1E-10 and 1E-3 did not substantially affect essentiality results.Ensemble flux balance analysis and ensemble gene essentiality screens were performed using Medusa v0.1.2  and cobrFor all subsampling performed, 1,000 random draws were made with replacement at each subsample ensemble size. Ensemble sizes for each subsampled population ranged from 20 to 1,000, with subsampling performed in intervals of 20 members . When the subsample size exceeded the actual ensemble size , all ensemble members were subsampled.k-means clustering resulting in unbalanced clusters with ~90% of ensemble members in a single cluster. After aggregation of perfectly correlated genes, ensemble gene essentiality predictions were clustered into two clusters using k-means clustering as implemented in the KMeans class of scikit-learn v0.19.2 (∗hr). Random forest classification was performed to predict cluster membership using active features in each ensemble member  . Without this aggregation, these perfectly correlated features heavily biased  v0.19.2  (max ite v0.19.2 ). Gene ectively) . The Ranctively) . To detehttps://github.com/biocore/scikit-bio) was performed using the Hamming distance . In casincluded . In bries aureus  and Haemfluenzae .Subsystem assignment for reactions in ModelSEED were obtained from the ModelSEED biochemistry repository in April 2019 (available from the GitHub repository associated with this study). The highest level subsystem assignment, “class”, was used. For reactions with multiple subsystem assignments at this level, the reaction was considered as a separate observation belonging to both subsystems with the same mean fractional importance and mean cluster ratio . See the included README file within the repository for descriptions of all files and important notes for running and reproducing the analyses. The version of the repository at the time of publication has been deposited in Zenodo (https://doi.org/10.5281/zenodo.3538303, also in All files and code associated with this study are available under the MIT license via GitHub (


Salinibacter ruber DSM13855 is presented here. To our knowledge, this is the first metabolic model of an organism in the phylum Rhodothermaeota. This model, which will be called iMB631, was reconstructed based on genomic and biochemical data available on the strain Salinibacter ruber DSM13855. This network consists of 1459 reactions, 1363 metabolites and 631 genes. Model evaluation was performed based on existing biochemical data in the literature and also by performing laboratory experiments. For growth on different carbon sources, we show that iMB631 is able to correctly predict the growth in 91% of cases where growth has been observed experimentally and 83% of conditions in which S. ruber did not grow. The F-score was 93%, demonstrating a generally acceptable performance of the model. Based on the predicted flux distributions, we found that under certain autotrophic condition, a reductive tricarboxylic acid cycle (rTCA) has fluxes in all necessary reactions to support autotrophic growth. To include special metabolites of the bacterium, salinixanthin biosynthesis pathway was modeled based on the pathway proposed recently. For years, main glucose consumption pathway has been under debates in S. ruber. Using flux balance analysis, iMB631 predicts pentose phosphate pathway, rather than glycolysis, as the active glucose consumption method in the S. ruber.A genome-scale metabolic network reconstruction of  Salinibacter is the type genus of Salinibacteriaceae, a recently described family of halophilic bacteria, living under extreme conditions of hypersaline brines with a high concentration of salt, up to saturation [Salinibacter ruber was the first bacterium shown to be active at hypersaline brines [Halobactreiaceae were considered to be the only prokaryotes active in hypersaline brines [turation , 2. Salie brines –5. Priore brines , 4.S. ruber attracted intensive attention because of its exceptional characteristics. Despites phylogenetic affiliation of S. ruber to bacteria, it has many features in common with members of archaeal family Halobactreiaceae [S. ruber is its haloadaptation mechanism [i.e., the “salt-in” strategy) which is the intracellular accumulation of mineral ions, mostly K+ and Cl-. The salt-in strategy affects the whole proteome structure and goes along with extensive genome wide adjustments to prepare the whole enzymatic system to be functional under hypersaline cytoplasmic conditions [Soon after its description, reiaceae . The mosechanism  which handitions , 9.S. ruber was studied based on sequencing and annotating its genome [The haloadaptation mechanism of s genome . By anals genome , 10, 11.Salinibacter ruber has interesting metabolic characteristics including production of retinal pigments as chloride pump halorhodopsin (HR) and light-driven proton pump, xanthorhodopsin. Furthermore, this bacterium specifically produces certain metabolites including halocapnine derivatives and salinixanthin [ixanthin –19.Sequencing, biochemical, physiological and “omics” data available for a certain organism can be systematized, integrated and mathematically formulated in a well-defined genome-scale metabolic model (GEM). Such a reconstruction is a useful knowledge-base for numerous applications, including modeling and understanding biological communities and interactions, genetic engineering, shedding light to evolutionary traits, and comparative analysis –24. ThouHalobacterium salinarum [Natronomonas pharaonis [Salinibacter ruber, the first reconstructed GEM for members of the bacterial phylum Rhodothermaeota and also the first extremely halophilic bacterial GEM reconstructed to date. Our aim is to shed light on the unusual metabolism and salt adaptation mechanism of S. ruber using the systemic approach of GEM.Extremely halophilic microorganisms are among the least metabolically studied species, to the best of our knowledge. Previously, GEMs of only two archaeal extreme halophiles have been previously reconstructed, namely, alinarum  and Natrharaonis . On the S. ruber DSM13855T was obtained from GenBank under accession numbers CP000159 and CP000160 (chromosome and plasmid respectively). These genomic sequences were re-annotated using RAST [S. ruber available in the literature and also public databases, including BRENDA [Salinibacter. Salinixanthin biosynthesis has the best examples of these kind of reactions. The biosynthesis mechanism of this compound was based on the pathway proposed by Ron et al [For genome-scale metabolic network reconstruction, the genomic sequence of ing RAST  and KAASing RAST  servers ing RAST , MetaCycing RAST  and KEGGing RAST . In caseg BRENDA , KEGG [3g BRENDA , UniProtg BRENDA  and BioCg BRENDA . Duplicag BRENDA , Rhea [3g BRENDA  and BioCg BRENDA . Gene ason et al . Some reIn the next stage, COBRA toolbox , 38 was The biomass composition, which shows the main constituent of dried cell biomass, is typically included in metabolic network models to show their accumulation. Precisely, these metabolites can be considered as reactants of an imaginary reaction, called biomass production. The flux of this reaction, which is a proxy of growth rate, can be used as the objective function in FBA.S. ruber has been published. Here, we defined the biomass composition by integrating data from different literatures referenced here with those taken from the published composition of Escherichia coli model, iAF1260 with some manual adjustments [No detailed information on the biomass composition of ustments , 40–44.e. g., biomass production rate, vbiomass [Z denotes the objective function, C is a row vector showing the influence of individual fluxes on the objective function, v is the reaction flux vector, n denotes the number of reactions. Vi, min and Vi, max are the lower and upper bounds of the flux Vi. Here, FBA was performed using COBRAToolbox-2.0, with the ‘‘optimizeCbModel” function to solve FBA problems using glpk solver version 4.47.Based on steady-state assumption, the concentrations of the cellular metabolites remains constant during the analysis. Flux balance analysis (FBA), a widely used computational method, is based on this assumption. FBA finds a metabolic flux distribution in steady-state that maximizes a defined objective, vbiomass . BrieflyS. ruber DSM13855 was used to prepare preculture, inoculating 100-ml Erlenmeyer flasks containing 50 ml modified growth medium (MGM) with 23% total salt [4, 30 g MgCl2, 7 g KCl and 1 g CaCl2, supplemented with 1% (w/v) peptone and 0.2% (w/v) yeast extract. The pH of the medium was adjusted to 7.2–7.4 with Tris-base (Merck). Carbon source utilization  was tested by omission of peptone from MGM broth and reduction of yeast extract concentration to 0.1 g l-1 [-1 nitrate, DMSO and arginine (fermentation) was tested in MGM broth prepared anaerobically in serum tubes according to the procedures described in references [A frozen stock of tal salt . This me.1 g l-1 . The abiferences –49.S. ruber DSM13855 in the scientific literature. Briefly, we collected experimental data such as amino acid and carbohydrate utilization from literature. Subsequently, for each case, the minimum and maximum limits of exchange reactions were defined to simulate the growth conditions in each case. The predictions of the model were then compared to laboratory observations. Inconsistencies were checked manually and corrected for adding, removing or changing corresponding reactions whenever possible.Evaluation of the model was performed using available data on metabolism of iMB631 model , an archaeal blue copper protein  was previously proposed to play the carrier role in H. salinarum. Mongoldin et al showed that Salinibacter genome has two clusters of cytochrome c oxidase subunit I and II genes, with coxA1 and coxB1 (SRU2099 and SRU2100) and coxA2 and coxB2 (SRU0314 and SRU0313) which coxB2 and coxA2 are most closely related to their haloarchaeal homologs [S. ruber and related reactions were included in the model, no considerable biomass production was achieved when anaerobic growth conditions where simulated. Fluxes in all related reactions remained almost zero in different anaerobic simulations performed.The genes related to oxidative phosphorylation pathway for nibacter , 44. In 2, 8 NADH and/or FADH, and 2 ATP [Chlorobium species, certain sulfate reducing deltaproteobacteria like Desulfobacter hydrogenophilus, some thermophilic Aquificales (Aquifex and Hydrogenobacter thermophiles) and archaea of the family Thermoproteaceae like Thermoproteus have been shown to use reverse tricarboxylic acid cycle [The reverse tricarboxylic acid or reductive tricarboxylic acid (rTCA) cycle is a carbon fixation cycle that synthesize an acetyl CoA by moving the TCA cycle backward, using 2 molecules of COnd 2 ATP . Chlorobid cycle , 55.Chlorobium, a green sulfur phototrophic bacterium, rTCA cycle has many reactions that are TCA ones, just running in reverse direction but there are key enzymes that catalyze energy demanding and critical reactions and make the reversal of the TCA cycle possible. Three Key enzymes have been identified to catalyze the energetically unfavorable reactions and make the rTCA cycle practical, namely, ATP citrate synthase, pyruvate synthase and 2-oxoglutarate synthase [Originally discovered in synthase . Fig 3 sS. ruber is presented in A summary of genes and reactions of the cycle in It should be noted that almost all enzymes of the rTCA cycle can be present in non-autotrophic organisms as parts of other metabolic pathways so the presence of any of these enzymes is not considered as a proof of autotrophic capability of an organism but when most of these genes are tracked in a genome, the possibility of autotrophic lifestyle would be considered. In our model, all but 2 of reactions involved in rTCA could be part of other metabolic pathways. These two reactions are catalyzed by aconitate hydratase and citrate-CoA ligase. We set these reactions’ bounds to zero in our mode unless when the possibility of autotrophic growth was of our concern. All other reactions were considered as part of the metabolic network.iMB631 were assigned into eight main subsystem based on their functional roles. For comparison purpose, the same categorization was adopted for metabolic models of extremely halophilic archaeon Halobacterium salinarum and moderately halophilic bacterium Chromohalobacter salexigens, iOG490 and iOA584 respectively [iMB631 reaction while peptidoglycan metabolism with only 3% of reactions, has the smallest number of related reactions. Amino acid metabolism is more active in Salinibacter and Halobacterium than Chromohalobacter. These two organism were shown to prefer amino acids rather than carbohydrate utilization in laboratory tests and this characteristic is reflected in the models by an increase in the portion of this subsystem in their metabolic models . The larger ration of transport reactions in iOA584 (40%) in comparison to iMB631 and iOG490 (9% and 16% respectively) is presumably related to the differences among these organisms’ osmo-adaptation mechanism. Salinibacter, like Halobacterium and other extremely halophilic archaea, is shown to use the salt-in mechanism which does not include uptake and accumulation of osmolytes. Chromohalobacter, in contrast, is shown to uptake and use different organic osmolytes such as betaine, ectoine and etc. This moderately halophilic organism can also utilize many carbon sources which is not the case for Halobacterium and Salinibacter. Transport reactions to import these carbon sources for further utilization is also a reason for the increased ratio of transport reactions in iOA584. Lipid metabolism comprises 24% of reactions in our model which is the highest in three compared models for this subsystem.The reactions involved in ectively , 50. Thei. e. those with a negative results in experimental tests which incorrectly appeared to be positive in the model, is observed in comparison to false positive results. This can be discussed in the light of previous findings that some enzymes in Salinibacter ruber has salt-dependent activity [S. ruber’s optimal growth salinity  which is routinely used for growth and experimentation of Salinibacter.Our model showed a better function dealing with false positives than false negative results. A higher amount of incorrect predictions in false positive results, activity , 15, 41.iMB631 and iOA584. In the archaeal halophile’s model, iOG490, hydrolase (EC 3) enzymes depleted considerably. It should be noted that many reactions in the models are assigned with more than one EC number in KEGG. For example rxn00154 (KEGG reaction ID R00209) in our model is carried out with E1 component (EC 1) and E2 component (EC 2). In such cases, 2 reactions were considered in total counting, each one with an individual EC number.Distribution of genetically coded enzymes in different enzyme classes is shown and compared in iMB631, overall flux distribution of reactions does not allow it to consume glucose and produce pyruvate in any of the tested fluxes therefore the model predicts that glucose is metabolized mainly through pentose phosphate pathway.Considering the FBA analysis of glucose consumption, it seems that pentose phosphate pathway is the active pathway when glucose uptake is activated. Though Embden–Meyerhof glycolytic pathway is present in iMB631. It is not clear whether Salinibacter conducts a mixotrophic life style or not but iMB631 suggests it as a possibility. Limited number of gene sequences have been reported for citrate-CoA ligase the enzyme related to gap-filled reaction of this pathway and little is known about its structure and gene variation. It is possible that the gene is present but not identified in the genome of S. ruber. The presence of a gene  for photoactive yellow protein (PYP) in Salinibacter’s genome can be considered as a support to this hypothesis. SrPYP is a blue light photoreceptor with an absorption maximum at 431 nm. PYPs are a family of cytoplasmic photoreceptors mostly found in photosynthetic bacteria [An interesting outcome of our model was the prediction of autotrophic life possibility. By including rTCA related reactions in the model, an acceptable biomass production was achieved when autotrophic growth conditions where simulated. As far as we know, no biological data has ever been reported so we put lower and upper limits of zero to suppress two pathway specific reactions in bacteria , 18. MorSalinibacter strains. However, the pathway and mechanism through which these compounds are synthetized are not well defined in terms of cofactor usage, stoichiometry and gene-protein-reaction relations. Therefore, modeling the production of these metabolites via genome-scale network will not be reliable. Further studies are required for obtaining a well-defined description of the reactions involved in these pathways.Salinixanthin and halocapnine derivatives are among the most attractive metabolites in S1 File(XLSX)Click here for additional data file.S2 File(XML)Click here for additional data file.S3 File(XLSX)Click here for additional data file.


More significantly, the typical hexagonal phase of core CdS and shell MoS2 has been obtained. Optical study clearly shows the two steps absorption in the visible region having band gap of 2.4 eV for CdS and 1.77 eV for MoS2. The FESEM of CdS@MoS2 reveals the formation of CdS microsphere (as a core) assemled with 40–50 nm nanoparticles and covered with ultrathin nanosheets of MoS2 (Shell) having size 200–300 nm and the 10–20 nm in thickness. The overall size of the core shell structure is around 8 µm. Intially, there is a formation of CdS microsphre due to high affinity of Cd ions with sulfur and further growth of MoS2 thin sheets on the surface. Considering band gap ideally in visible region, photocatalytic hydrogen evolution using CdS@MoS2 core shell was investigated under natural sunlight. The utmost hydrogen evolution rate achieved for core shell is 416.4 µmole h−1 with apparent quantum yield 35.04%. The photocatalytic activity suggest that an intimate interface contact, extended visible light absorption and effective photo generated charge carrier separation contributed to the photocatalytic enhancement of the CdS@MoS2 core shell. Additional, the enhanced hole trapping process and effective electrons transfer from CdS to MoS2 in CdS@MoS2 core shell heterostructures can significantly contribute for photocatalytic activity. Such core shell heterostructure will also have potential in thin film solar cell and other microelectronic devices.The hierarchical nanostructured CdS@MoS In the past few decades various semiconductor photocatalyst have been demonstrated for conversion of solar energy into electrical and chemical energy due to their unique properties of semiconductor catalyst, such as high extinction coefficient of light absorption, tuneable band gap, and charge carrier multiplication effect2. Recently, in order to look for clean reproducible energy, considerable attention has been paid to the abundant available natural solar light harvesting for hydrogen generation using semiconductor as a photocatalyst3. The transition metal oxide semiconductor catalyst such as ZnO and TiO2 reported for best photocatalytic activity. Along with ZnO and TiO2 some other oxide catalyst such as SnO24, Ta2O55, Nb2O56, Co3O47, WO38, V2O59, MoO310, Bi2MoO411, and CdMoO4 also reported12. But these materials have wide band gap (>3.0 eV) and show good activity only under UV light which is only 5% of the solar spectrum and did not show good photocatalytic activity in the visible light13. Furthermore, there are attempts on development of anion doped metal oxide nanomaterials for visible light active photocatalyst14. However, the stability of these materials limits for its commercial use as an active photocatalyst. Hence, researchers are focused their research on the development of stable and efficient visible light active photocatalyst for H2 generation13.In recent years, The worldwide energy crisis and eco-friendly energy demand call for the generation of advanced photocatalytic materials as one of the major solving strategy16. The conduction band edge of CdS is more negative than the reduction potential of H+/H2, making it more suitable for the H2 generation18. There are reports on the synthesis of CdS/MoS2 composites via various approaches, which shows the good photocatalytic activity as compared to pristine CdS19. However, there is still scope to improve photocatalytic activity of CdS toward water splitting by solving the photo corrosion and faster charge recombination problem20. Therefore, researcher focused much attention mainly on fabricating nanostructure heterojunction to reduce surface recombination, enhanced visible light absorption and modifying surface defects in order to improve the water splitting activity22. Each semiconductor in the heterostructures could be properly chosen and tailored independently to complete the designed functionality. Recently, two dimensional layered molybdenum disulfide (MoS2) has attracted a great deal of attention in the field of photocatalysis and energy applications, due to its excellent photocatalytic performance and cycling stability23. Also the MoS2 and CdS have same hexagonal crystal structure, which ensures the intimate heterojunction formation. Hinnemann B. et al. from density functional theory calculation, revealed that free energy of atomic hydrogen bonding to MoS2 is zero, which is very close to Pt and makes it promising catalyst for hydrogen generation24. The experimental and computational results disclosed that the edges of ultrathin MoS2 nanosheets act as an active sites for the photocatalytic H2 generation25. Yan et al. demonstrated the CdS/MoS2 core shell nano rod synthesis for efficient hydrogen production by a facile chemical deposition method19.The transition metal chalcogenide semiconductor catalyst such as CdS reported as one of the promising semiconductor catalyst for hydrogen generation due to its narrow band gap 2.4 eV, proper valence band position and excellent stability2 core shell fabrication was demonstrated by simple solvothermal route. The hierarchical heterostructures of few layered ultrathin nanosheets of MoS2 uniformly grown on the surface of CdS microsphere is demonstrated. The as-prepared CdS@MoS2 core shell heterostructures show much higher photocatalytic activity and better stability toward water splitting compared with pristine CdS and MoS2 under natural solar light. Our superior photocatalytic hydrogen generation results suggest CdS@MoS2 core shell is promising cost effective photocatalyst for the clean energy fuel.Herein, CdS@MoSAll chemicals were purchased from Fisher Scientific with (Purity 99%). All the chemicals were AR grade and used without any further purification. Cadmium nitrate (4 mmol) and ammonium heptamolybdate tetra hydrate (4 mmol) dissolved in 50 ml methanol separately each. Drop by drop addition of nitric acid in ammonium molybdates solution with constant stirring for 10 minutes to see the clear solution. Followed by drop wise addition of dissolved cadmium nitrate solution into ammonium molybdate solution with constant stirring. After complete addition, it was allow to stir for 15 more minutes with drop wise addition of dissolved thiourea, followed by stirring for 15 more minutes. The solution was then packed in Teflon coated hydrothermal reactor and kept for 48 hours at 150 °C. After completion of reaction, reactor was allowed to cool down to room temperature naturally, followed by washing of the product with distilled water and filtered using 0.41 whatman filter paper. Further, products were washed using AR grade ethanol for several times and then kept for drying at 80 °C for 4 hours in heating oven. The dried powder catalyst again annealed in the tubular furnace at 400 °C for 3 hours in inert atmosphere. After completion of the annealing reaction, allow to cool the product naturally and used for further characterization and photocatalytic study.2 core shell catalyst. The 0.25 M Na2S and 0.35 M Na2SO3 solutions were used as sacrificial reagents. Argon gas was purged through this reaction mixture in order to remove the dissolved gases. The reactor has septum arrangement to collect the evolved gas through the gas tight syringe for analysis using gas chromatography. The all assembly was arranged in an open natural solar light. The quartz reactor has arrangement for water circulation in order to absorb the IR radiation which minimizes the overheating effect. As soon as the reactor was exposed to natural sunlight, the photocatalytic splitting of water was started and generated H2 collected in a head space of the reactor. The amount of H2 evolved and its purity was analyzed by gas chromatograph with time. For comparative study the Photocatalytic activity for H2 generation carried out using Xe-lamp light source  of intensity 300 W. The apparent quantum efficiency of the hydrogen generated was calculated using the following equation.The photocatalytic water splitting was carried out by using 100 ml double distilled water in 250 ml quartz reactor containing 0.1 g CdS@MoS2. The peaks observed at 2θ = 13.9, 33.0 and 58.7 corresponding to the (002), (101) and (008) facets of hexagonal MoS2, respectively and matches well with the previous reports of JCPDS card No. 01-075-153914. From the XRD results, it is revealed that MoS2 prior growth in crystallinity is at the (002) facet. The XRD  with the first overtone mode (2LO), and second overtone (3LO) of CdS, respectively26. Raman spectroscopy is very powerful refined technique, used to access phases present and number of layers of MoS2 material in terms of the position and frequency difference of two characteristic vibrational modes, E12g and A1g27. The CdS@MoS2 core shell structure shows two peaks centered at 378 and 401 cm−1 are assigned as an E12g and A1g vibration mode of MoS2. The E12g mode is attributed to the in-plane vibration of Mo and S atoms, while the A1g mode is related to the out-of-plane vibration of S atoms28. The frequency difference between the E12g and A1g peak of MoS2 measured to be 23 cm−1. This value is smaller than that of the bulk MoS2, indicating presence of few layered shell of MoS2 nanosheets on CdS core29. Dravid et. al. well discussed that Raman spectroscopy has also been utilized to investigate the lattice strain and van der waals interaction at the interface of the 2D MoS2 nanosheet28. The in-plane Raman E12g and A1g mode, is sensitive to the built-in strain of 2D MoS2 and the reflection of interlayer van der Waals interactions, respectively28. Thus, it is reasonable to predict that the shift of E12g and A1g modes of the MoS2 in CdS@MoS2 core shell structures towards the lower frequency side attributed to the effect of lattice strain due to the curving of MoS2 shell31.The Raman measurements were conducted using laser wavelength of 532 nm with laser power 10 mW with a laser spot size 1 μm and the results were shown in Fig. 2 core shell material was recorded, and the result was depicted in the Fig. 32, whereas the second absorption peak observed at 700 nm having band gap 1.77 eV is attributed to MoS233. The band gap of the material was also confirmed from tauc plot as shown in Fig. 2. The sample absorbing more photons with high energy will produce more electron and hole with a high reduction ability to reduce the water molecules into hydrogen. Thus, the sample may exhibit a high hydrogen evolution rate.As the photo absorption properties play a crucial role in determining the photocatalytic activity, UV-visible absorption spectrum of synthesized CdS@MoS13. Photoluminescence measurement for CdS@MoS2 core shell was recorded at room temperature using excitation wavelength 350 nm and results were shown in Fig. 2 and CdS@MoS2 the two emission peaks are observed at 630 and 670 nm corresponds to band edge transition and defects created at the interface of the core shell and its matches well with the previous reports36. The more PL intensity for pristine MoS2 and CdS sample reveals the faster electron and hole pair recombination. Whereas, the significant decline in PL intensity of CdS@MoS2 core shell is due to the efficient photo-carrier separation by band bending (energetic gradient) at hetero-structure interfaces. The CdS@MoS2 Core shell structure facilitates the separation of photo induced electron-hole pairs due to the formation of the hetero junction between the CdS and MoS2 and make it available for the photocatalytic activity. Here, we have optimized the structure of the core-shell to achieve the optimum performance.Photoluminescence (PL) investigations are useful for the analysis of migration, transfer, and recombination processes of photo induced electron–hole pairs in a semiconductor material2 core shell was investigated by FESEM shown in Fig. 2 core shell was found to be around ~8 µm as shown in Fig. 2 shell is observed to be nearly 500 nm in size as observed in Fig. 2 is composed of small ultrathin nanosheets of size 200–300 nm and average measured thickness is around 10–20 nm as shown in Fig. 2 core shell structure using solvothermal process is hitherto unattempted.The surface morphology of the synthesized CdS@MoS2 core shell sample. In a typical TEM image shown in Fig. 2 core shell material. The Fig. 2 is composed of few layered nanosheets. The d-spacing measured from the HRTEM image taken at the edge of the sheet is 0.277 nm corresponding to the (100) plane of the hexagonal MoS2 phase, which in accordance with the XRD results. The selected area electron diffraction taken at the edge of the nanosheet confirms the polycrystalline nature of the MoS2 shell as shown in Supporting Information Fig. Field emission transmission microscopy (FETEM) was used to further analyse the crystallinity, morphology, size of the particle and ultrathin nano sheets in the CdS@MoS2 core shell material further examined by Scanning transmission electron microscopy (STEM) and elemental results as displayed in Fig. 2 seats at outer shell with average thickness of around 500 nm  was used to determine the chemical composition and chemical states of the CdS@MoS eV Fig. , corresp2 core shell material is shown in Fig. 2 core shell was prepared solvothermally using cadmium nitrate, ammonium molybdates and thiourea in the methanol medium. During the solvothermal process, the cadmium, ammonium, molybdenum and sulfur ions are formed. Initially, in the hydrothermal reactor Cd2+ react with the S2− ion to form the CdS nano particles as per the reaction 1 and shown in Fig. 2+ ion towards the S2− ion is more than Mo4+. Also, the energy require for the CdS formation is lower compare to MoS2. The as formed CdS nano particle comes together in order to reduce the surface energy and formation of CdS microsphere takes place  and h+ in valence band (VB) is generated. The photo-generated h+ in VB consumed by sulfide S2− and sulfite SO32− enabling the effective separation of charge carriers40. While, e− in CB reduces the two H2O molecule into one H2 gas molecule12. The advantages and detailed mechanism of S2−/SO32− as scavenger is well discussed in literature by Bahnemann et al.40. The possible photocatalytic water splitting mechanism is as shown below.The photocatalytic activity of CdS@MoS2 generated in µMole with respect to time for CdS@MoS2 core shell material along with CdS and MoS2 were shown in Fig. 2 were synthesized at identical condition and tested for the photocatalytic H2 generation activity via water splitting reaction under natural solar light. It is clear that CdS@MoS2 core shell material shows significantly enhanced H2 generation activity as compare with the pristine CdS and MoS2. For comparative study, the cumulative H2 generation for all the catalysts using solar simulator (xenon lamp) along with H2 generation activity under sunlight were tested and obtained results shown in Fig. 2 generation under xenon and natural solar light i.e., 231 and 416.4 µMole h−1, respectively. The photocatalytic activity results shows less H2 generation using xenon lamp as compare to activity obtained in natural solar light. The H2 generation achieved in solar light is 416.4, 180.6 and 53.0 µMole h−1 for CdS@MoS2 core shell, CdS and MoS2, respectively. It is important to note that all the photocatalysis experiments reported in this manuscript were carried out at an identical condition. The rate of H2 generation for CdS@MoS2 is approximately twice and six times more than CdS and MoS2, respectively. The utmost hydrogen generation rate achieved for core shell is 4,164 µmole h−1 with apparent quantum yield 35.04%, which is much better than other reports for similar materials41. The higher photocatalytic activity obtained for CdS@MoS2 core shell is due to heterostructures of CdS@MoS2 core shell, which facilitate the photo-carrier separation and prevents carrier recombination. Consequently, the core shell heterostructures can increase large light harvesting efficiency leading to an enhanced photocatalytic activity. Furthermore, the defects created at the interface of CdS@MoS2 core shell heterostructures, which ultimately act as a photo-carrier trap and suppresses the recombination significantly. In case of CdS@MoS2 core shell the e− and h+ generated due to irradiation with solar light at CdS and then they transfer to the ultrathin nanosheets of MoS2. The 2D ultrathin nanosheets allows electron to move in the layered structure and make it available for the photocatalytic activity. The ultrathin nanosheets of MoS2 have more number of edges, significantly its acts as active sites for the photocatalytic H2 generation reaction. From the four hours linear H2 generation plot, it is observed there is continuous H2 generation in presence of solar light, concludes the good efficiency of the CdS@MoS2 core shell catalyst. The stability of the CdS@MoS2 catalyst was evaluated by analyzing the XRD of catalyst before and after photocatalysis . Overall, it is concluded that the hydrogen generation achieved is due to CdS@MoS2 core shell catalyst only.This is the only reports which depicts the photocatalytic H− and h+ pair separation, its transfer process from CdS to MoS2 and hydrogen generation mechanism for CdS@MoS2 core shell heterostructures is proposed in Fig. 2 core shell is attributed to the effective electron transfer through the interface formed between the core CdS microsphere and ultrathin nanosheets of MoS2 shell as evidenced by the HR-TEM images in Fig. − and h+ pair recombination of CdS@MoS2 core shell is also evidenced by photoluminescence study as shown in Fig. 2 due to its lower band position13. Whereas the holes from the VB of CdS are trapped at the interface of the CdS@MoS2 core shell, which obviously prevents the recombination of photogenerated carriers10. The photogenerated holes (h+) in the VB of MoS2 and at the interface of the CdS@MoS2 core shell consumed by sulfide S2− and sulfite SO32−. Whereas, the e− from the CB of MoS2 take part in the reduction of two H2O molecule into one H2 molecule. The MoS2 nanosheets shell also act as co-catalyst and obviously enhance the transportation of photgenerated electrons and get it available for photocatalytic activity. This fact also supports for the improvement in the photocatalytic activity of the CdS@MoS2 core shell.The possible schematic illustration for the photo generated e2 nanosheets shell on CdS microsphere, resulting in unusual CdS@MoS2 core shell heterostructures. We observed the photoluminescence quenching phenomenon for CdS@MoS2. This may be attributed to the heterostructures effect resulted from the realigned band structure in CdS@MoS2. The CdS@MoS2 exhibited enhanced photocatalytic hydrogen generation 416.1 µMole h−1, which is much better than that of pristine CdS and MoS2. The core shell structure of the catalysts played an important key role in the enhancement of H2 production. The core shell heterostructures allow faster charge transportation and minimizes the recombination, which subsequently enhance the photocatalytic activity. These finding will open up new avenues for developing low cost heterostructures photo catalyst for water splitting. Our superior photocatalytic hydrogen generation results suggest CdS@MoS2 core shell is promising cost effective photocatalyst for the clean energy fuel.In the summary, a facile solvothermal approach for ultrathin MoSSupporting Information


ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and  Genome-scale metabolic models (GEMs) are congregations of biochemical reactions that occur in an organism or cell/tissue , a tool that uses SLA's results as input to calculate reaction-/gene-specific Essentiality Score (EScore). Unlike topology based methods, ESS relies on the highly valuable biochemical stoichiometric information embedded in GEMs to evaluate the structural properties of reactions/genes. In addition, compared to the DoE, ESS makes the complete use of SLA results and provides higher resolution scores for each reaction/genes.n = 1 implies targeting of a single reaction/gene, whereas n > 1 test for combinations of reactions/genes after excluding single lethal reactions/genes. With these inputs, ESS identifies all essential and synthetic lethal reactions/genes internally using an adapted FastGeneSL  is always 1 regardless of the level of ESS. Figure Figure ESi represents the EScore of reaction/gene i, yij represents the number of combination that reaction/gene i is SL  at level j. X represents number of reactions/genes in the model. In this equation, ESi for an essential reaction/gene is always 1 since for any j > 1, yij would be 0; otherwise, ESi is always no more than 1. In addition, a comparison of EScore among different models is possible since EScore is normalized by the model's X. We used level 3 ESS throughout this study considering the balance between accuracy and computational cost.It is noteworthy that, this principle could be applied to higher level SLA. The increase of SLA level could give a higher resolution of the EScore. Furthermore, the principle could be implemented for calculation of EScore for genes. To calculate EScore for a specific reaction/gene in any level, all possible synthetic lethal combinations involving a reaction/gene should be introduced for normalization purpose. For example, in the case of the 4 reaction/gene model of Figure 2O were removed before generating RRN and GGN. The importance of the nodes  was scored by computing centrality topological parameters i.e., betweenness, eccentricity, closeness, and degree  were retrieved from Avana public datasets 18Q2  environment, and the source code and models (including the cell line models) are available at E. coli with 95 reactions as a proof of concept case. We calculated and compared the EScores and betweenness centralities (BCs) which is one of the most used topology based method for all reactions in the core model , suggesting inconsistency between two metrics although both methods classify these reactions as essential to a certain extent. There are 16 reactions that have non-zero EScores and zero BCs, and 15 of them are responsible for exchanging of very important or essential metabolites, including carbon dioxide, oxygen, glucose, ethanol, formate, lactate, proton, ammonium, phosphate, and succinate. Moreover, among these exchange reactions, those for glucose, proton and ammonium exchange have the maximum EScores , which means they are essential for growth of E. coli in the given condition. This highlights the limitation of topology based methods which missed uptake of glucose as essential because of neglecting stoichiometric info as previously reported  suggests that these 20 reactions are possibly not metabolically important. Moreover, some of them have been reported to be non-essential experimentally, which is the case of FBP in E. coli , and both are cultured in the same medium, RPMI1640. The iIPC298 includes 3414 reactions, 2102 metabolites, and 1827 genes, whereas iNCIH1299 includes 3550 reactions, 2117 metabolites and 1906 genes. Then, we applied level 3 ESS to these two human cancer cell line GEMs to calculate EScores for genes. The computational costs for this analysis were much lower compared to greedy search which was used in most EA analysis Table . To checP < 0.05), whereas, as shown in Table CERES scores are normalized essentiality scores of genes where the median values for essential and non-essential genes are −1 and 0, respectively. Therefore, the correlation between EScores and CERESs should be negative if they agree with each other. Indeed, the respective correlations between EScores and CERESs for iPC298 and iNCIH1299 are −0.194 and −0.205, and both of them being statistically significant , oxidative phosphorylation (NADH16 and CYTBD), uptake of oxygen (EX_o2_e and O2t) and formate transportation (FORt2 and EX_for_e). ACALD and formate transportation are key alternatives for LDH_D in the model which explains these EScore increases. In addition, LDH_D is an important reaction for anaerobic growth, and therefore its deletion will surely increase the importance of aerobic metabolic function. This explains why oxidative phosphorylation and oxygen uptake have the highest increase in EScore since they are key pathways in aerobic growth for E. coli. On the other hand, the reactions with top EScore decreases are D_LACt2 and EX_lac__D_e, both of which are responsible for transportation of lactate. These two reactions are downstream of LDH_D in the model, and their EScores decreased since they become dead-end reactions in the mutant model.As a proof of concept, we applied the method to the core metabolic model of In this study, we proposed a novel method name ESS, which shows as an accurate and efficient method for quantifying reaction and gene EScores. This method was implemented in Matlab and that requires standard constraint-based modeling toolboxes (COBRA or RAVEN). While gene and reaction essentiality have extensively been examined using GEMs, we here introduce a comprehensive scoring system for determining essentiality for single and combined reactions or genes. ESS employs logical transformation of model to refine the gene-protein-reaction relationships, avoiding the potential problem caused by redundant enzymes during the essentiality analysis. We benchmarked ESS against topology based methods and using experimentally derived essentiality scores for reference pan-essential and non-essential genes, and observed that ESS displays higher accuracy than topology-based methods. It should be noted that essentiality in GEMs is dependent on the inputs and objective function. Therefore, EScores could be different for the same GEMs given different inputs and/or objective function. As we showed in the last case study, the proposed method could be easily used to compare GEMs generated from the same reference model. Therefore, it could be a promising tool for future studies that investigate the relationships among gut microbiota, different cells and tissues using resources such as AGORA and HPA where models were generated from a common reference. In this context, we are confident that ESS will have a great application in biotechnology and systems medicine.AM and CZ conducted the study. CZ developed the method. SL, RB, GB, MA and MU helped in development of the method. CZ performed most of the analysis and GB helped in the analysis. CZ wrote the paper and all authors were involved in editing the paper.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.