In an attempt to understand the interactions between microbial species and the phages inside rumen, a genome-scale metabolic modeling approach was utilized by using key members in the rumen microbiome  and the viral phages associated with them. Individual microbial host models were integrated into a community model using multi-level mathematical frameworks. An elaborate and heuristics-based computational procedure was employed to predict previously unknown interactions involving the transfer of fatty acids, vitamins, coenzymes, amino acids, and sugars among the community members. While some of these interactions could be inferred by the available multi-omic datasets, our proposed method provides a systemic understanding of why the interactions occur and how these affect the dynamics in a complex microbial ecosystem. To elucidate the functional role of the virome on the microbiome, local alignment search was used to identify the metabolic functions of the viruses associated with the hosts. The incorporation of these functions demonstrated the role of viral auxiliary metabolic genes in relaxing the metabolic bottlenecks in the microbial hosts and complementing the inter-species interactions. Finally, a comparative statistical analysis of different biologically significant community fitness criteria identified the variation in flux space and robustness of metabolic capacities of the community members. Our elucidation of metabolite exchange among the three members of the rumen microbiome shows how their genomic differences and interactions with the viral strains shape up a highly sophisticated metabolic interplay and explains how such interactions across kingdoms can cause metabolic and compositional shifts in the community and affect the health, nutrition, and pathophysiology of the ruminant animal.The complex microbial ecosystem within the bovine rumen plays a crucial role in host nutrition, health, and environmental impact. However, little is known about the interactions between the functional entities within the system, which dictates the community structure and functional dynamics and host physiology. With the advancements in high-throughput sequencing and mathematical modeling,  These three organisms are responsible for fiber digestion, starch and protein digestion, and methane production, respectively. These species were chosen because of their high abundance levels in the rumen, co-abundance reported in previous studies, and the availability of genome annotation , P. ruminicola , and M. gottschalkii  were integrated into a community model using a multi-level optimization framework  on the metabolic capacity of the community members. We found that the flux ranges of the microbial species are robust irrespective of the choice of a community objective. Hence, maximizing the community biomass is a rational choice since a stable community in the rumen needs to survive and grow at a reasonable rate to perform its necessary role in host nutrition and pathophysiology despite constant washout events like fecal secretion.P. ruminicola, R. flavefaciens, and M. gottschalkii using the ModelSEED database  had thermodynamically inconsistent production of redox cofactors, which warranted an extensive manual curation to be performed on the models (see methods section for details). The manual curation steps ensured that there is no chemical or charge imbalance present in the models. The numbers of reactions that carries unrealistically high fluxes without any nutrient uptake (thus defined as reactions with unbounded fluxes) also decreased substantially . The rest of the unbounded reactions from the nucleotide degradation pathways were not fixed since they do not critically affect the biological significance of the models and are usually common in existing metabolic models . The simulated community biomass flux (0.513 h−1) was comparable to the experimentally observed values of passage/dilution rates of 0.043–1.0 h−1 in the rumen depending on dietary regimen and other factors  are absorbed by the rumen epithelium. M. gottschalkii accepts hydrogen, formate, carbon di-oxide from the other two members, and releases methane to the atmosphere, similar to what was described in previous reports , the models of R. flavefaciens, P. ruminicola, and M. gottschalkii were amended with 29, 26, and 18 metabolic reactions, respectively. For a detailed result of the alignment search  growth, (ii) SCFA production, (iii) feed utilization, (iv) methane and carbon-di-oxide release, and (v) small sugar molecule production, the appropriate community-level objective functions were chosen and optimized . The simulated growth rates were 0.086, 0.065, and 0.362 hr−1 for R. flavefaciens, P. ruminicola, and M. gottschalkii, respectively. While an experimental growth rate observation for these microbes are not available, the growth rates are comparable to the reported dilution rates in the rumen  pathway for lysine synthesis, methanogenesis, methionine biosynthesis, peptidoglycan biosynthesis, pentose phosphate pathway, SCFA production, and purine and pyrimidine conversion pathways carried higher fluxes compared to the post-gapfilled model. These show the important role of viral AMGs in cell growth and replication for the host .The addition of Auxiliary Metabolic Genes (AMGs) from the viruses associated with each of the host community members had profound impact on shifting and relaxing the flux ranges of key rate-limiting steps in the host organisms, termed as “metabolic bottlenecks.” As seen in R. flavefaciens, P. ruminicola, and M. gottschalkii are shown in M. gottschalkii could produce biotin, nitrite, and octadecanoate at a high rate, P. ruminicola became less reliant on these metabolites after addition of viral AMGs, possibly due to the complementation in the galacturonan and pectin digestion ability and alanine, aspartate, and glutamate metabolism. Galacturonate and Deoxyadenosine were not consumed by R. flavefaciens at the same rate they were being produced by P. ruminicola because R. flavefaciens was able to digest the complex carbohydrates in the plant feed more efficiently after addition of viral AMGs. The transfer of mannose and fructose sugars to M. gottschalkii from R. flavefaciens was not identified as interactions, which ultimately resulted in reduction of the number of de novo interactions in the community to 20 when viral AMGs were present. Upon inspection of the functionalities coming from the phages associated with M. gottschalkii, it became apparent that the D-glyceraldehyde-3-phosphate glycolaldehyde transferase and D-mannose-6-phosphate aldose-ketose-isomerase system that drive the conversion between fructose and mannose became unnecessary in the presence of virome functionalities in the models. At the same time, the phosphofructokinase/sedoheptulokinase (EC 2.7.1.14) enzymes, including Sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase and ATP:Sedoheptulose 7-phosphate 1-phosphotransferase could shift the carbon flux from glycolysis (via fructose-6-phosphate) to Pentose Phosphate pathway through tetrose sugars and therefore facilitate the adaptation of M. gottschalkii even without the support of sugars from R. flavefaciens. The ability to maintain species fitness without these interactions leads to better community fitness in situations when these interactions are threatened .The inclusion of viral AMGs as metabolic functions resulted in noticeable changes in the inter-species transports, as manifested by the change in the flux ranges of the shared metabolite transactions  was directly related to the metabolic profile of M. gottschalkii, and a very low percentage (6%) of reactions had varied flux ranges under all the five different community-level objectives in this species.While a large number of reactions had varying flux ranges under different community-level objective functions, the change was very negligible, with an average standard deviation of 0.0001. The variation in the number of reactions with non-zero standard deviation between different metabolic models could possibly be correlated to their metabolic roles in the community and the choice of the community-level objective function. For example, Although living organisms have evolved to maximize their survival in varying environments, it is difficult to assess the primary driver of the flux distributions of primary metabolism . The models included reactions for central carbon metabolism, secondary biosynthesis pathway, energy and cofactor metabolism, lipid synthesis, elongation and degradation, nucleotide metabolism, amino acid biosynthesis and degradation. Flux Balance Analysis (FBA) was employed during model testing, validation, and analyzing flux distributions at different stages of our work  is a predetermined threshold value of the appropriate objective flux vapp−obj to ensure that the feasible flux space satisfy the targeted value.One of the limitations of constraint-based genome-scale models is that the mass balance constraints only describe the net accumulation or consumption of metabolites, without restricting the individual reaction fluxes. While biochemical conversion cycles like TCA cycle or urea cycle are ubiquitous in a metabolic network model, there can be cycles which do not consume or produce any metabolite. Therefore, the overall thermodynamic driving force of these cycles are zero, implying that no net flux can flow around this cycle  represents the steady-state FBA problem for each of the community members R. flavefaciens, P. ruminicola and M. gottschalkii. A local alignment search (BLAST) of the viral proteomes downloaded from several databases .In addition to the syntrophic, mutualistic, and competitive microbial interactions, viruses impact microbial populations through cell lysis and reprogramming of host metabolism by Auxiliary Metabolic genes (AMGs). Most of the bacterial species in the cattle rumen have their own associated viruses  optimization procedures (named GapFind and GapFill) were used to identify and eliminate network gaps in these reconstructions  version 24.7.4 with IBM CPLEX solver was used to run all the optimization algorithms in this work. For each of the algorithms, the required optimization framework was scripted in GAMS and then run on a Linux-based high-performance cluster computing system at the University of Nebraska-Lincoln. The models were parsed from Systems-Biology Markup Language (SBML) level 2 document using standard programming languages  to generate the input files required by GAMS.All datasets generated or analyzed for this study are included in the manuscript and the RS and SF conceived the study. RS supervised the study. MI performed all the experiments and analyzed the results. MI, SF, and RS wrote the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fragaria sub-species can be identified solely based on the gathered metabolic profiles. We also showed that domesticated accessions displayed highly similar metabolic changes due to shared domestication history. Differences between domesticated and wild accessions were detected at the level of metabolite associations which served to rank metabolites whose regulation was mostly altered in the process of domestication. The discovery of comprehensive metabolic variation among strawberry accessions offers opportunities to probe into the genetic basis of variation, providing insights into the pathways to relate metabolic variation with important traits.High-throughput metabolomics technologies can provide the quantification of metabolites levels across various biological processes in different tissues, organs and species, allowing the identification of genes underpinning these complex traits. Information about changes of metabolites during strawberry development and ripening processes is key to aiding the development of new approaches to improve fruit attributes. We used network-based methods and multivariate statistical approaches to characterize and investigate variation in the primary and secondary metabolism of seven domesticated and seven wild strawberry fruit accessions at three different fruit development and ripening stages. Our results demonstrated that  Fragaria x ananassa Duch.) fruit is characterized by its large size1, red color2, distinct aroma3, sweet fruity flavor4, and reduced firmness5. The flesh of the strawberry is a swollen receptacle, a false fruit, and the seeds (achenes) are the true fruit6. During the early stages of strawberry fruit development, auxin, synthetized in the achenes, promotes fruit growth8. Receptacle growth follows either a single or a double sigmoid curve, depending on the cultivar, and involves gains in diameter and fresh weight. This change is mainly a result of cell enlargement; however, cell division accounts for 15–20% of the total growth10.Modern domesticated ripe strawberry  in inter-species crosses41. In their earlier study24, the authors showed that the fruit of F. virginiana accessions have significantly higher antioxidant capacity, total phenol and anthocyanins contents than fruit from different lines of F. chiloensis and F. x ananassa.The potential for breeding of cultivars that are more nutritious, better-tasting and more resistant to biotic/ abiotic stress depends to an extent of the variability and heritability of the bioactive compounds. The biotechnological approach is also an option to supplement this improvement, via modification of specific biosynthetic pathwaysStrawberry breeding has become an area of substantial economic importance, mainly as a result of the royalties paid for commercial protected cultivars. Current methods for accurate detection of erroneous or illegal propagation are time-consuming and expensive because they involve the cultivation and morphological characterization of controversial cultivars for one to two years under controlled conditions.42. Metabolomics can take a snapshot of the current biochemical status, useful to compare varieties and evaluate changes in metabolic pools47. These metabolic phenotypes can be used in comparative analysis of metabolic processes across accessions to obtain temporal aspects of biochemical regulation. These large datasets are analyzed with multivariate statistical analysis aimed at determining biological components that show differential behavior under various scenarios within a single species. While the field of using molecular markers for breeding has experimented a revolution due to the advent of next-generation sequencing technologies51. Evolutionary metabolomics studies including wild and domesticated accessions have been carried out for few selected species52. The aim of this work is to provide a detailed comparative analysis of wild and domesticated accessions with respect to primary and secondary metabolism in fruit ripening stages of 14 Fragaria accessions (seven domesticated and seven wild accessions) and to determine if data from metabolomics technologies have the power to distinguish between two accession types. In addition, if the latter is feasible, we are also interested in identifying the metabolites which contribute most to the distinction between the two groups of accessions. Thus, the selected metabolites can be used either as markers for selection, certification of different species or for the physiological evaluation of plant genotypes for breeding new strawberry cultivars.Metabolomic approaches, including those utilizing Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS) allow the single-experiment profiling and quantification of numerous metabolites that are generally conserved across the kingdoms of lifeTo assess the metabolic profiles reflecting the interplay between primary and secondary metabolisms in the fruit of domesticated as well as wild strawberry accessions, we selected three stages of ripening (Methods). For the comparative analysis of metabolite profiles, we used the profiles of 43 primary metabolites, from GC-MS technology, and 87 polar secondary metabolites, from UPLC-Orbitrap-MS/MS in seven domesticated and seven wild strawberry accessions over three different ripening stages. First, we examined the quality of the normalized data sets by principal components analysis (PCAs) on the combined metabolic profiles from domesticated and wild accessions together Fig. .Figure 1The major diagonal passing through  and  separates the green from the other stages, whereas the diagonal passing through  and  separates the domesticated from wild strawberry accessions Fig. . This an53.The dendrogram produced by hierarchical cluster analysis (HCA) of the strawberry accessions based on the variation in 43 primary metabolites and 87 polar secondary metabolites  over three ripening stages in each accession. The compromise of the tables is based on the Rv coefficients determined for every pair of tables. The Rv coefficient takes values between 0 and 1, and small values of the coefficient indicate large differences between the data sets. The compromise allows us to make statements about the concordance (i.e. similarity) between each pair of tables.In the following, we investigated how similar are the entire data sets of metabolite relative abundance between different accessions. This allows us to arrive at a compromise between the different data sets, which provided additional insights in the relationship between the accessions. To this end we applied analysis based on STATIS which, unlike PCA, allows simultaneous investigation of multiple tables . In our analysis, we used altogether F. vesca; 196, 191, 660, and F. chiloensis; 156 and 282).Figure S (see Methods). To visualize the contribution of tables to the compromise we plotted the weight of each table on the x-axis and the similarity between the compromise and each individual table (per Rv2 coefficient) on the y-axis  are concentrated in the top right corner and, therefore, most affect that compromise; in contrast, those of wild strawberry are spread out  exhibit the smallest effect, manifested in the low Rv2 coefficients and weights of tables. What is apparent from the contribution of each table to the compromise is that the domesticated accessions are very similar to each other, in contrast to the wild accessions, with the exception of 595. This finding may partly be due to the fact that F. chiloensis red accession was likely contributed as one parental of F. x anannassa strawberry (domesticated) accessions54. Therefore, this analysis points at a greater variability of the metabolome in wild compared to domesticated accessions.We next investigated the extent to which data sets from different accessions determine the compromise xis Fig. . We founout Fig. . This inSince, in this analysis, we consider two metabolites data sets, from primary and secondary metabolites, we next ask if the obtained compromise differ when the two classes of metabolites are analyzed separately. To this end, we determined the contribution of each table based on each class of metabolites, separately. As show in Fig. F. chiloensis accessions) and 591 (F. mochata) contribute the most of the PC1 while showing minor contribution to PC2  and 196, 660, 191 (F. vesca accessions) data sets show reciprocal contribution to PC2. Finally, when comparing the contribution to PC2 and PC3, we observe a reciprocal contribution to PC2 of the F. vesca accessions , F. mochata (591), F. chiloensis accessions (156 and 282) on one side, and all domesticated accessions (except for 595) on the other  in the contribution to PC1 and PC2 may be attributed to the fact that the ripe fruit from the two subspecies 282 and 156 are white, while for the subspecies 595, it is red  of the compromise space. By jointly considering primary and secondary metabolites, the data tables of 282, 156  in correlation-based networks. Metabolites undergoing major changes in metabolic co-regulation are expected to exhibit differential connectivity (DC) across networks, and can be employed in understanding of metabolic differentiation in the process of domestication of strawberry. Here we employed a differential correlation-based network approach to compare domesticated (D) and wild (W) accessions. Each of the two metabolic networks, denoted as network D and network W, comprises metabolite-metabolite associations based on the significance of Pearson correlation coefficients, adjusted using a false discovery rate (FDR) of 0.05. In addition, we conducted the analysis separately for the three maturation stages  aiming at understanding whether such changes are stable as the developmental process unfolds. After quality checks (see Methods), a total of 39 primary metabolites (GC-MS) and 81 secondary metabolites (LC-MS) were used in the differential correlation-based network approach.We first observed that the resulting metabolic networks were systematically denser for W compared to D. Density denotes the proportion of significant correlation between pair of metabolites (i.e. edges present in the network) from the total number of metabolite pairs, and it has been suggested to reflect the level of regulation in the network. Network D has densities of 0.124, 0.109 and 0.114 when green, white or red stages are considered, respectively. The observation about density result in the values of 0.267, 0.318 and 0.280 for the network W for the three progressive ripening stages, respectively Fig. . Interest-tests to determine differences in relative abundance in D and W. P-values relative to DC were computed from a metabolite-specific permutation scheme (see Methods), and both lists of P-values (DC and t) were separately adjusted using a FDR of 0.05. Finally, we combined the two metrics to screen metabolites that simultaneously display significant changes in co-regulation and relative abundance  gain of connectivity and increased abundance (top-right quadrant), ii) gain of connectivity and decreased abundance (top-left quadrant), iii) loss of connectivity and decreased abundance (bottom-left quadrant), or iv) loss of connectivity and increased abundance (bottom-right quadrant).For every metabolite, we determined its DC by subtracting the number of edges incident on the corresponding node in the network W to that in network D. To understand how DC relates to the difference in the levels of each metabolite, we also performed multiple nce Fig. . A totalP of 0.007, 0.002 and 0.046 for green, white and red strawberries, respectively). The investigated categorization of metabolites based on changes in their relative abundance and connectivity in the network demonstrates that the ripening process in wild and domesticated accessions is controlled by different mechanisms which shape the abundance of selected metabolites. To this end, we ask if there are metabolites which show differential behavior between the populations of wild and domesticated accessions.We have identified a total of 13, 40 and 32 significant changes in green, white and red strawberries . We identify a total of 29 metabolites that exhibited significant domestication status effect .The aims of the present study were to provide a comparative metabolome analysis of seven domesticated and seven wild strawberry accessions, in particular primary and secondary metabolism during fruit ripening and also, to determine if these data have the power to distinguish between accessions. If the latter is feasible, we were interested in identifying the metabolites which contribute most to the distinction between the two groups of accessions, which can be used to increase the narrow genetic base of domesticated strawberry.F. vesca and F. chiloensis accessions. (Sub)species-specific differentiation was mainly due to qualitative and quantitative differences in accumulation of secondary metabolites, reflecting variation in the activities and substrates specificities of corresponding enzymes. Thus, it may be expected that the observed metabolic differences form the basis for species-specific differences in important traits  and opens up possibilities to breed for novel these novel traits in cultivated domesticated strawberry backgrounds. To strengthen this claim, we conducted a clustering analysis of the wild and domesticated accessions based on different sets of other traits , all of them have been selected for similar breeding purposes and therefore may have been exposed to similar selection history, including selection for desirable traits during breeding. Therefore, the differences among compound classes and single metabolites are strongly determined by the accessions group, indicating that genetic differences between accessions are reflected in metabolic differences. This may be due to differences in the regulation of metabolic pathways, differences in the activity of key enzymes determining the flux through the pathway, or the activity or substrate specificity of specific modifying enzymes.57 and grapevine58, here we also consider that higher metabolic co-regulation reflects a prioritization of the affected metabolic pathways. Upon complementation with t-tests, we were then able to simultaneously identify changes in co-regulation and abundance between the two groups and the corresponding differences in the three developmental stages, for each metabolite. For example, while xylose is more abundant in wild accessions in all three stages, it is less co-regulated in domesticated varieties in green and red stages, but in between it becomes more tightly regulated in white domesticated stage. This might suggest that, in spite of no discernible change (stage-wise) in the difference (accession-wise) in abundance, the associated metabolic processes gained more relevance in the strawberry ripening, specifically at the white stage, by means of domestication. On the other end of the spectrum, compounds such as tyrosine and procyanidin trimer (isomer 5), associated with tighter co-regulation and higher abundance in domesticated varieties, could represent those that were selected or co-selected for. However, we still can identify metabolites that are more tightly co-regulated in domesticated varieties and still less abundant, compared to wild accessions. These too could be favorable traits, including sugar-containing ellagitannins such as ellagic acid deoxyhexose, galloyl−bis(HHDP)−glucose found to be particularly abundant during early achene development13. Interestingly, important secondary metabolites playing a role in plant adaptation to the environment59 were associated with tighter co-regulation and higher abundance in wild accessions, such as triterpenoid-hexose, quercitin di-hexose [isomers 2 and 3], tri-galloyl-hexose, kaempferol-hexose and naringenin chalcone hexose. Moreover, these metabolites were identified as the most contribute to distinguish between the wild and domesticated accessions, being clear candidate to be markers for selection.In general, correlation network analysis suggests that metabolism during strawberry fruit ripening is highly coordinated. We observed that the metabolic networks in wild accessions were denser than the networks from the domesticated ones in all three stages of development. However, when primary and secondary metabolites were separately analyzed, the latter were largely responsible for the difference, suggesting that the process of domestication of strawberry lead to a general deregulation of the secondary metabolism and maintenance of the core processes that include primary metabolites. This observation was supported by the significant predominance of primary and secondary metabolites among metabolites displaying gain and loss of connectivity in the domesticated varieties, respectively, in all three stages of development. Altogether, these findings are consistent with the selective pressure of specific environmental stresses, elicitors of secondary metabolism, being no longer exerted in the process of strawberry domestication. Furthermore, we note that the difference of density between the two networks does not affect the subsequent results from the differential connectivity analysis, as the reported metric (DC) is normalized for the maximum value in each case (see Methods). The differential connectivity approach highlights the changes in metabolic co-regulation underlying strawberry maturation by examining the differences between domesticated and wild accessions across each individual developmental stage. Following the leading attempts in potatoCorrelation-based network analysis highlighted an extent of connectivity, building accession/stage-specific metabolite nodules. The metabolites allowing this degree of connectivity are probably different across the accessions, but this type of evidence suggests some common mechanism among domesticated and wild strawberry fruit at the basis of metabolic regulation involving a high connectivity of primary metabolites mainly in early developmental stages. One of the disadvantages of using degree of connectivity, is that it is not possible to distinguish direct or indirect interactions between metabolites, since a third metabolite can be responsible of this connectivity.These results of this study clearly demonstrate that there is a large metabolic variation present in the strawberry germplasm collection analyzed. Our analysis based on STATIS indicates that the wild and domesticated strawberry accessions differ with respect to the secondary metabolites. Moreover, STATIS was able to distinguish and classify together the accessions from the same species. By applying a mixed-effect model we were able to identify the metabolites which contribute most to the distinction between wild and domesticated strawberry accessions, which can be used to increase the narrow genetic based of domesticated strawberry. We also observed that the relative abundance of the analyzed metabolites varied greatly among fruits of wild accessions, demonstrating the potential of these germplasm collection for genetic improvement of metabolic traits, although the success of the breeding program is strictly related to combining attitude of the different parents.The correlation network analysis demonstrated that overall, the secondary metabolism of wild strawberry accessions was more tightly coordinated than the domesticated accessions, which suggests that the domestication process lead to a deregulation of the secondary metabolism. The differential connectivity analysis, on the other hand, shed light on individual metabolites that exhibit a significant change of coordination with other metabolites, which in the latter case translates into the prioritization of key metabolic processes. In association with the differences in relative abundance then, the two metrics helped in concurrently determining how levels of metabolite and their control change across the three stages of maturation described herein.These analyses formed are an effective tool to understand metabolic variation between strawberry accession, and may serve as a bridge to understanding the manifestation of economically important traits. For this reason, this type of experiment can be considered important for the production of new genotypes with improved specific metabolic traits, and for the identification of accessions that can perform in the same way as the most useful parents. A combined use of high throughput genotyping and metabolomics techniques should provide a new avenue to discover novel associations between allele frequency and metabolic variation in strawberry populations. Such integration will enable us to conduct targeted molecular breeding of the metabolites that contribute to the adaptive or nutritive value of new improved strawberry varieties.Fragaria x ananassa accessions from different breeding programs were chosen to represent a large proportion of commercial strawberry acreage in South Spain from both public  and private (Candonga) breeding programs. Additionally, North American accessions were added to enhance the range of diversity (Camarosa and Benicia). Also, seven wild strawberry accessions from different origins were included in this study .A panel of seven All strawberry plants were grown and maintained under glasshouse conditions . Fruits were harvest in three different developmental and ripening stages corresponding to green (G), white (W) and ripe (R) Fig. . These f60. The mass spectra were cross-referenced with those in the Golm Metabolome database61. Full documentation of metabolite profiling data acquisition and interpretation is provided in Supplementary Table Metabolites analysis by GC-MS was carried out as previously described62. In brief, 250 mg of each biological replicates was extracted with a cold mixture of methyl-tert-butyl-ether: methanol (3:1). To facilitate cell disruption samples were incubated in a cooled sonic bath for 10 min. The subsequent addition of a water:methanol (3:1) mixture to the extract resulted in a formation of two liquid phases. A fixed volume of polar phase was transferred to a fresh Eppendorf tube before concentrating it to dryness in a Speed-vac .Metabolite extraction was performed as previously described18 reverse phase column  which was operated at a temperature of 40 °C. The mobile phases consisted of 0.1% formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile (Solvent B). The flow rate of the mobile phase was 400 µl/min, and 2 µl of sample was loaded per injection. The UPLC was connected to an Exactive Orbitrap (Thermo Fisher Scientific) via a heated electrospray source (Thermo Fisher Scientific). The spectra were recorded alternating between full-scan and all ion-fragmentation-scan modes, covering a mass range from 100 to 1500 m/z. The resolution was set to 35,000, and the maximum scan time was set to 250 ms. The sheath gas was set to a value of 50, while the auxiliary gas was set to 20. The transfer capillary temperature was set to 250 °C, while the heater temperature was adjusted to 350 °C. The spray voltage was fixed at 3 kV, with a capillary voltage and a skimmer voltage of 25 and 15 V, respectively. MS spectra were recorded from minute 0 to 19 of the UPLC gradient. The raw data acquired were processed using Xcalibur 2.1 software (Thermo-Fisher). Processing of chromatograms, peak detection, and integration were performed using REFINER MS 7.5 . The obtained features (m/z at a certain retention time) were queried against the biological databases . The mass error for the database searches and the fragment assignments was always <2 parts per million  resulting in confident elemental composition assignments. The MS/MS fragmentation of the metabolites was compared with candidate molecules found in databases, and verified with earlier literature on similar compounds, especially when the presence of the metabolite was reported in strawberry. Full documentation of metabolite profiling data acquisition and interpretation is provided in Supplementary Table Chromatographic separation was performed using a Waters Acquity UPLC system equipped with a HSS T3 Ci.e., metabolites), corresponding to the columns, measured over i.e., stages), given by the rows. As previously stated, jth metabolite in the ith stage of the lth accessions. Each tables was normalized and centered based on published recommendations64. Each row was assigned the same weight, so that all weights over rows sum of to 1 and are gathered in a so-called vector of masses STATIS operates on the analysis of We then compute the relationship between the rows of each matrix by determining65:The first eigenvector The second step of STATIS consists of a generalized eigenvalue decomposition of the compromise, whereby one can obtain the loadings of the variables.66. We used stage, replicate (rep), and population (pop) factors as fixed effects and the genotype (gen) as a random effect nested in population to model the level of each metabolite (meta). For completeness, we include the code snippet:Mixed-effect model was implemented for every metabolite separately by using the R package lme67. To support the identification of these metabolites, the MS and MS/MS spectrum have been included as Supplementary Fig. Statistical testing for the fixed effects was conducted with the lmerTest function from the same packageet al., 2007- http://bioconductor.org/packages/release/bioc/html/pcaMethods.html). Metabolic networks (W for wild varieties and D for wild varieties) were re-constructed from metabolite-metabolite Pearson correlation coefficients (r) associated with FDR-adjusted P-values , based on the resulting metabolic networks, was calculated as:Supplementery InformationTable S1Table S2Table S3Table S4


There are large differences between men and women of child-bearing age in the expression level of 5 key enzymes in one-carbon metabolism almost certainly caused by the sex hormones. These male-female differences in one-carbon metabolism are greatly accentuated during pregnancy. Thus, understanding the origin and consequences of sex differences in one-carbon metabolism is important for precision medicine.We have created a mathematical model of hepatic one-carbon metabolism based on the underlying physiology and biochemistry. We use the model to investigate the consequences of sex differences in gene expression. We give a mechanistic understanding of observed concentration differences in one-carbon metabolism and explain why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and we show how to use the model to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes.Our model of hepatic one-carbon metabolism is a useful platform for investigating the mechanistic reasons that underlie known associations between metabolites. In particular, we explain how gene expression differences lead to metabolic differences between males and females.The online version of this article (doi:10.1186/s12918-018-0621-7) contains supplementary material, which is available to authorized users. There are significant sex differences in one-carbon metabolism (OCM) and these differences are accentuated in pregnancy –3. WomenThe reaction diagram for the folate and methionine cycles in OCM is very complicated consisting of loops within loops see Fig.  below. FIn  we creatThe schematic diagram of the mathematical model is shown in Fig. β-synthase (CBS) reaction flux is called the transsulfuration flux. Note that, at steady state, the remethylation flux plus the methionine input flux must equal the transmethylation flux and the methionine input flux must equal the transsulfuration flux. All concentrations and Km values are in micromolar. All fluxes are in micromolar/hour.We follow standard nomenclature and refer to the sum of the fluxes of the 5 methyltransferase reactions from SAM to SAH as the transmethylation flux. The sum of the methionine synthase (MS) and betaine-homocysteine methyltransferase (BHMT) reaction fluxes is called the remethylation flux, and the cystathionine The complete mathematical model consists of 17 differential equations for 17 metabolites. Each differential equation tracks the concentration change of the metabolite, by adding the sum of the rates of the arrows coming into that variable and by subtracting the sum of the rates of the arrows leaving the variable. Contributions of the mitochondria to one-carbon metabolism have been studied in , the traKm values from the ranges in the literature. Then we adjust the Vmax values so that the metabolite concentrations at steady state are consistent with the concentrations in the literature. In fact, the Vmax values are proportional to enzyme expression levels and enzyme expression levels vary by up to 25% from individual to individual   \documenKm=1000μM for Hcy taken from [μM) and equals one otherwise. Thus, the fourth term equals one when betaine is below 315 and increases up to 1.2 as betaine concentration rises.The kinetics of CBS are standard Michaelis-Menten with ken from . The secken from , 36 and ken from , suggestWe note that once one has the concentrations of the metabolites, then one obtains the metabolite fluxes by putting the concentrations into the above formulas.Vmax value of MTHFR by 0.3 and 0.68, respectively. Similarly, total folate concentration is a parameter in the model. We test the effects of different values of total folate by multiplying this parameter by appropriate scale factors.The C677T and A1289C polymorphisms of MTHFR reduce the activity of MTHFR by 70% and 32%, respectively , 38. In Five enzymes in one-carbon metabolism, BHMT, MS, MTHFR, serine hydroxymethyltransferase (SHMT), and PEMT, are differentially expressed in males and females and the differences are not small. In Fig. Vmax values of the enzymes PEMT, BHMT, SHMT, MS, MTHFR by the factors shown in Table The model described in Methods is a model for male one-carbon metabolism. The resulting concentrations and (some) velocities at steady state are shown in Fig. After making these changes, we ran the model to its new steady state. The resulting concentrations and velocities for the female model steady state are shown by the red numbers in Fig. There are several interesting and important questions. The first is what are the mechanisms that cause these differences in enzyme expression levels in females? A good deal is known. The sex hormones estrogen, testosterone, and progesterone are found in both the males and females but in different proportions. Men have around 19 times more testosterone than women and women of childbearing age have up to 9 times more estrogen than men. Estrogen concentration increases even more throughout pregnancy . It has Other hormones also cause sex dimorphism in liver enzymes. Waxman showed that the pulsatile release of growth hormone (GH) by the pituitary in males and the almost constant release in females cause differences in cytochrome P450 expression in rats . DNA micThe second question is what are the mechanisms by which the enzymes expression changes lead to the concentration changes. These questions are easy to state but not easy to answer, because the reaction network is complicated and the long range interactions, through which a substrate concentration at one location can affect enzymes at distant locations in the network , then SAM will inhibit BHMT more preventing remethylation of Hcy to Met and SAM will activate CBS more sending more flux down the transsulfuration pathway and both effects limit how much SAM will rise. If SAM starts to fall  then the inhibition of BHMT is withdrawn and the activation of CBS is withdrawn thus remethylating more Hcy to Met and limiting the decline in SAM. As a result, over wide ranges, methionine input does not affect choline concentration very much. Since methionine input changes a lot during the day because of meals this may be an important evolutionary adaptation.Panel c shows the homocysteine concentration at steady state in the female model as a function of MTHFR activity and methionine input. As above, the white dot is wild type and the magenta dots indicate the steady state concentration of Hcy in the presence of the polymorphisms. Both polymorphisms substantially raise Hcy concentration. However, as can be seen in Panel D, in the presence of a folate deficiency that is 20% of normal, the Hcy concentration of even the wild type is higher than those caused by the polymorphisms in case of no folate deficiency (Panel c). This is another indication of the importance of folate. The mechanism by which folate affects Hcy concentration is discussed in the “The purpose of this study was to understand sex differences in one-carbon metabolism and for that purpose we added the synthesis pathway of choline and betaine to a previous mathematical model . AlthougRecently, a large scale semi-quantitative model of sex differences in liver metabolism has been published –47 basedThe enzyme PEMT catalyzes the production of PC, the first substrate on the pathway that synthesizes choline and betaine. Choline and betaine are essential nutrients obtained from the diet or from the synthesis pathway on the right side of Fig. The upregulation of PEMT by estrogen in women of child-bearing years greatly increases the concentrations of choline and betaine as we saw in the “It is important to remember that the mathematical model in this paper is for liver one-carbon metabolism. We explained, in the “This study uses mathematical modeling to investigate sex differences in one carbon metabolism, but it is only a first step because there are many other issues to be analyzed. Since estrogen levels vary during the menstrual cycle, our results suggest that Hcy levels should vary too, and they do , 67. KalWe have created a new mathematical model of one-carbon metabolism in the liver including the synthesis pathway for choline and betaine. The model was used to understand how observed enzyme expression differences lead to metabolite concentration differences. In particular, we explained why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and show how the model can be used to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes. Future work will explore how one-carbon metabolism changes during pregnancy.Additional File 1Supplementary Material for Sex Differences in One-Carbon Metabolism. (PDF 710 kb)


Nitrification and sulfur-based autotrophic denitrification processes can be used to remove ammonia from wastewater in an economical way. However, under certain operational conditions, these processes accumulate intermediate compounds, such as elemental sulphur, nitrite, and nitrous oxide, that are noxious for the environment. In order to predict the generation of these compounds, an analysis based on the Gibbs free energy of the possible reactions and on the oxidative capacity of the bulk liquid was done on case study systems. Results indicate that the Gibbs free energy is not a useful parameter to predict the generation of intermediate products in nitrification and autotrophic denitrification processes. Nevertheless, we show that the specific productions of nitrous oxide during nitrification, and of elemental sulphur and nitrite during autotrophic denitrification, are well related to the oxidative capacity of the bulk liquid. Then, the OXC was calculated according to (2/L), [NO2−-N] is the nitrogen concentration as nitrite (mg NO2−-N/L), [NO3−-N] is the nitrogen concentration as nitrate (mg NO3−-N/L), and [SO4−2-S] is the sulphur concentration as sulphate (mg SO4−2-S/L).To determine the oxidative capacity for the different operating conditions, first, in order to make a “redox ladder” , the redrding to  and 3)  4+ was s2−, N2O, and S0 was normalized using the measured biomass as volatile suspended solids (VSS) [In order to obtain a specific rate of production intermediate compounds for each operational condition analyzed, the production of NOds (VSS) , 12.p-values and R2) and standardized residuals were studied. The confidence and prediction intervals were calculated by using the F distribution and analysis of variance (ANOVA) test. P‐value ≤ 0.05 was considered significant. A 95% prediction interval was determined, being the range in which one can expect any individual value to fall into 95% of the time.Simple linear regression analysis between the calculated OXC (explanatory variable) and the measured production of intermediate species (dependent variable) was performed using XLSTAT® software . In order to evaluate the fitting of the regression models, regression characteristic , and lirt chain . Also, irt chain . In the ompounds . Therefo0 accumulation was observed. On the other hand, despite the fact that the reduction of nitrate into nitrogen gas provides more energy than its reduction to nitrite  was an appropriate parameter to predict the accumulation of intermediate products at least for autotrophic denitrification. Nevertheless, the discrepancies between experimental data and those obtained by the theoretical calculations indicate the opposite.According to Seto and Iwasa , the beh2O specific production increases with the increase of the OXC of the bulk liquid . That is, N2O formation is promoted by both high DO and nitrite concentration. This agrees with the results of He et al. [2O production was favored by high redox potential inside nitrifying biofilms. However, some studies reported that N2O formation decreased when the system was operated at high DO levels [2O production during nitrification in soils [Analyses by linear regression showed significance and a strong to very strong relationship between the OXC of the bulk liquid and the production of intermediate compounds, in nitrification and autotrophic denitrification processes . In the e et al.  who obseO levels . This caO levels  found an2 = 0.791 and 0.929, p < 0.05). Generally, the formation of both intermediate compounds is related to the feeding S/N ratio: an excess of electron donor causes the accumulation of elemental sulphur while an excess of electron acceptor leads to the generation of nitrite [o can be accumulated even under sulphide limitation conditions [o during autotrophic denitrification is also related to high pH values (> 8.5) [2S/HS−/S2- equilibrium, since higher pH values lead to lower redox potentials (lower oxidative capacity). This agrees with the results obtained in sulfide-oxidizing bioreactors showing that the formation of elemental sulfur is optimal at low redox potentials [o increases as the DO concentration decreases due to the decrease of the redox potential [In the case of autotrophic denitrification, sulphur generation seems to be promoted by less oxidative environments while the opposite trend is observed for nitrite (Figures  nitrite , 14. Nevnditions , 12 and,nditions . Accumultentials . A similotential .+/NADH ratio within cells. This ratio controls gene expression and enzyme synthesis for the overall cell metabolic activities [The redox potential corresponds to the activity of the electrons present in the bulk liquid that influences the NADtivities . Therefotivities . Also, etivities .In summary, the results show that there is good agreement between calculated OXC values and the specific rate of production of nitrous oxide, elemental sulfur, and nitrite, as intermediate compounds in the analyzed nitrification and autotrophic denitrification systems. This suggests that the OXC calculation can be used to assess and predict the generation of these intermediate compounds.The value of the Gibbs free energy calculated for the evaluated operational conditions cannot be used in order to predict the formation of nitrous oxide, elemental sulphur, and nitrite during nitrification and autotrophic denitrification processes. Nevertheless, the oxidative capacity of the bulk liquid appears as a useful tool to predict the accumulation of these intermediates. The oxidative capacity is a parameter simple to calculate and may provide a valuable starting point for the evaluation of the accumulation of undesirable intermediate compounds in wastewater treatment systems.


Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces.The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in The online version of this article (10.1186/s12864-018-4905-5) contains supplementary material, which is available to authorized users. Streptomyces species are usually soil-dwelling bacteria, which have adapted to their competitive ecological niches by developing a notably diverse secondary metabolism . Currently, more than two thirds of the antibiotics used have been derived from natural products discovered in Streptomyces and related species [Streptomyces coelicolor A3(2) is a well-studied model organism for the production of antibiotics in this genus. The genome of this soil-dwelling bacterium encodes more than 20 secondary metabolite biosynthetic gene clusters (BGCs) [Streptomyces’ secondary metabolism diversity to discover novel antibiotics and natural product-derived drugs [ species ; howevericolor A3 is a weled drugs , 5. Howeed drugs , and maned drugs , heteroled drugs , or accued drugs .Streptomyces metabolism have been developed for this purpose [Streptomyces strains. In 2005, the first generation GSMM of S. coelicolor, iIB711, was published [S. coelicolor [Genome-scale metabolic models (GSMM) have been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization –12, and  purpose –17. The  purpose . Informaublished , which wublished , which iublished ; this moelicolor .Streptomyces strains have been reconstructed since the first S. coelicolor model, iIB711. A model of the Streptomyces tenebrarius metabolic network, which was derived from the iIB711 model of Borodina, Krabben & Nielsen has been used to identify targets to optimize production of tobramycin [Saccharopolyspora erythraea has been reconstructed based on the iMA789 model of Alam et al. to improve the production of erythromycin [S. coelicolor iMK1208 model was used for model-guided engineering of ethylmalonyl-CoA pathways in Streptomyces hygroscopicus to increase production of ascomycin [Streptomyces strains and other actinomycetes was used to evaluate potential host strains for overproducing different chemical classes of secondary metabolites using comparative multi-objective modelling [Furthermore, several genome-scale metabolic models for other biotechnologically relevant bramycin . A modelhromycin . One of scomycin . A largeodelling .Streptomyces metabolism and technical progress in the concepts of computational model building, we constructed and validated an updated GSMM of S. coelicolor, iAA1259, to provide a more precise metabolic flux and biomass predictions and to facilitate the integration of metabolomics, proteomics, and transcriptomics information with the model predictions.Based on recent advances in our understanding of S. coelicolor A3(2), iAA1259, was based on all three previously published iterative reconstructions of S. coelicolor metabolic models [The construction of the updated GSMM of c models , 21, 22,S. coelicolor [Streptomyces  can be activated through these small diffusible molecules, and they are an interesting target for synthetic biology engineering [S. coelicolor (MetaCyc) [Mycobacterium tuberculosis data [Multiple pathways were added or updated. 1) Polysaccharide degradation pathways  were introduced to enable simulated growth in complex media containing these carbon sources. 2) The biosynthetic pathway for the secondary metabolite yCPK –28 was aelicolor . 3) The elicolor , 32; secineering , 34. 4) ineering , has nowineering , 36. 5) MetaCyc) , and orgMetaCyc) , as wellsis data   , 41, andelicolor ; the metelicolor –48. Finaelicolor –51 and kelicolor –54, iAA1for S. coelicolor in a glucose-limited minimal defined media [2 uptake rates as constraints on the model, along with the production rates of CO2 and γ-actinorhodin, the extracellular lactone form of actinorhodin [As the first step in model validation, chemostat data collected by Melzoch et al. ed media  were usenorhodin . BiomassA more substantial improvement in prediction quality is observed when comparing the dynamic growth predictions of the metabolic models iAA1259, iMK1208, and iMA789, to published experimental growth data . The CDA genes . The second NADH dehydrogenase, NDH-II, encoded by three copies of the ndh gene, is preferentially expressed during exponential phase and switched off after the transition phase; hence, the ndh genes show high correlation with the predicted flux. While regulatory phenomena like this are not considered in this type of model, the misprediction highlights an interesting phenomenon for future study, i.e. the impact of the relative role of the two sources of reducing co-factors on secondary metabolism in S. coelicolor.Regarding the second major anti-correlation, which is seen for the genes encoding the 14 subunits of NADH dehydrogenase I (NDH-I), the nuo complex, the disagreement between gene expression and predicted flux is due to a regulatory phenomenon, which in general is difficult to capture in a constraint-based model: two isoenzyme complexes are present in D+ ratio . Of thesy phase, ). So, whS. coelicolor primary and secondary metabolism, iAA1259; this model shows improved predictive abilities compared to previous model generations for metabolic changes at different scales, from overall biomass dynamics to fluxes through individual reactions.Here, we have presented an updated computational model of Streptomyces strains using a synthetic biology approach [Another important improvement is that the model has been also updated to enable integrative multi-omics data analysis, to be used for designing and debugging of engineered approach , and is approach , 54.Streptomyces strains. The principled construction of the model using standard identifiers will facilitate the transfer of information to related strains beyond S. coelicolor . Ithia coli  and Saccrevisiae .S. coelicolor model. The standard protocol for reconstruction of high-quality constraint-based GSMMs was followed when adding new genes, reactions, and metabolites [The model reconstruction was initiated by updating the iMK1208 abolites .In summary, the initial stoichiometric matrix was generated by comparing and using the iMK1208 model , KEGG 6, ScoCyc Streptomyces strains using comparative reconstruction and modelling methods. The final model has been named iAA1259 and is compliant with current metabolic model standards [Finally, multiple database identifiers were added either by automatic matching or by manual curation when necessary. The metabolites were annotated with multiple database identifiers; BiGG and KEGG identifications were already present in iMK1208, and other databases relevant to metabolomics data analysis were added: ChEBI, HMDB, CAS, IUPAC, ChemSpider, Metlin and PubChem identifications, wherever available. Chemical structure-related annotations (SMILES or InChi) were also introduced for all metabolites. Furthermore, when available, all reactions were annotated with EC code and CAS registry number, in addition to the BiGG annotation used in iMK1208. Additional gene annotations have been included to facilitate transcriptomics data integration with identifiers for Gene Ontology (GO), RefSeq, EMBL-ENA and Ensembl. For integrated proteomics analysis, annotations have been expanded to include identifiers for UniProt, Pfam and Panther, as well as data on protein length, mass, and amino acid sequence to support the direct mapping of mass-spectrometry-based proteomics data in the future. The expansion of these annotations also aims at helping fast reconstruction of metabolic models for other tandards , 54. TheThe model was analysed by using Flux Balance Analysis (FBA) and parsimonious FBA (pFBA) to predict optimal in silico growth and metabolic flux using the COBRA toolbox in Matlab and Python , 72 and 2, O2, glucose, phosphate, and glutamate uptake based on fermenter time-course data [Despite the fact that FBA is not a dynamic modelling approach (its basic assumption being a steady-state flux distribution), using dynamic constraints on COrse data  enabled rse data .Multiple omics data types have been used to validate the model; the proteomics data  have beeAdditional file 1:Table S3. Summary table of the updates and new features added to the iAA1259 model compared to the previous generations. Table S4. Table of the new reactions added to the iAA1259 model. Table S5. Table of the new metabolites added to the iAA1259 model. Table S6. Table of the new genes added to the iAA1259 model. Figure S1. Correlation analysis between gene expression and predicted fluxes for iMA789 and iMK1208 (gene expression showing a variation superior to 25%). Figure S2. Mapping of observed metabolites in an untargeted metabolomics dataset onto the metabolic network. Table S7. Constraints used and predicted growth rates of the different models from the Fig. 1. Figure S3. Comparison of the normalized growth prediction of the metabolic models to the experimental data. (DOC 4121 kb)Containing the Additional file 2:Table S1. Biomass modifications and recalculation of ATP consumption. (XLS 81 kb)Additional file 3:iAA1259 metabolic model in SBML format. (XML 4249 kb)Additional file 4:Excel file specifying metabolites, reactions, genes contained, and databases IDs present in the iAA1259 metabolic model. (XLS 2531 kb)Additional file 5High-resolution version of Fig. Additional file 6Table S2. Full detailed data used for the Fig.


Arabidopsis when directly inferred at system level from transcriptome data. A metabolite-centric reporter pathway analysis approach enabled the computation of metabolites associated with transcripts at four time points of cold treatment. Tripartite networks of gene-metabolite-pathway connectivity outlined the response of metabolites and pathways to cold stress. Our metabolome-independent analysis revealed stress-associated metabolites in pathway routes of the cold stress response, including amino acid, carbohydrate, lipid, hormone, energy, photosynthesis, and signaling pathways. Cold stress first triggered the mobilization of energy from glycolysis and ethanol degradation to enhance TCA cycle activity via acetyl-CoA. Interestingly, tripartite networks lacked power law behavior and scale free connectivity, favoring modularity. Network rewiring explicitly involved energetics, signal, carbon and redox metabolisms and membrane remodeling.The study of plant resistance to cold stress and the metabolic processes underlying its molecular mechanisms benefit crop improvement programs. Here we investigate the effects of cold stress on the metabolic pathways of  Environmental stresses constrain the growth and production of plants. Abiotic stresses include drought, cold, salt, and osmotic shock and biotic stresses include wounding and pathogen attack. Both kinds of stresses can form reactive oxygen species (ROS) leading to oxidative damage of proteins, nucleic acids, and lipids Mittler, . Stress Arabidopsis  method is one of several approaches capable of revealing significant operating pathways from microarray and RNAseq data. These methods identify statistically significant gene sets that are differentially expressed in the gene or pathway lists. While they are often sufficient, there are some biological cases where only few differentially expressed genes are found amid large perturbations. Examples include diagnosis-relapse and tumor tissues . The data comprises cold stress ID series GSE5620 (Control plants) and GSE5621 (Cold stress). These datasets were from experiments carried out using Affymetrix ATH1 microarray chip technology, which included an array of over 22,000 genes  were sown on Magenta boxes containing MS-Agar media and grown as previously described  to reduce data dimensionality and help identify outliers. We extracted metabolic genes from AraCyc and then structured a custom-based Excel query to identify metabolic genes of interest within the Affymetrix ATH1 gene information file. The resultant metabolic gene set included 4,730 genes.The gene expression data of Arabidopsis genome scale network by collecting annotated metabolic genes and their biochemical reactions from AraCyc v.14 , and especially UniProt (https://www.uniprot.org). In particular, we focused on significantly changed metabolites and pathways for accuracy. Differentially expressed genes between control and stress samples were identified in the Matlab environment using the ttest2 command based on two-sample t-test. Subsequently, P-values were calculated for each gene.First, we constructed a draft consensus of an P-value data file, a gene metabolite association file, and a pathway metabolite association file for MATLAB. In RPAm, a metabolite-score must be computed first by the Reporter Metabolic Centric algorithm . The use of this distribution function converts uniformly distributed P-values for each probe set into normally distributed standard Z score random variables. For isozymes, the lowest P-values of isozymes were selected.We constructed three files, a Z-value, a Zmetabolite score was calculated for each metabolite as an aggregate Z score of its k neighboring genes in the metabolic network scale. In this step, the Z scores of genes connected to a metabolite in the metabolic model were averaged to obtain a final Z score for the metabolite  and standard deviation (σk) of a background Z score distribution resulting from 10,000 rounds of random sampling of k metabolites of metabolic networks . Furthermore, the reporter metabolite analysis is similar to the method of Stouffer. However, the statistical parameters of the gene set were corrected for the background distribution prior to the estimation of significance  vs. log of k. The exponent γ of the power law [P(k) ~ k−γ] and the determination coefficient (R2) were derived from linear regression models. γ is the negative of the slope of the log linear model. R2 explains how much of the data fits the linear model. The scale free behavior of the network should be considered strongly supported when both γ and R2 are high. The Kolmogorov-Smirnov (KS) fit statistic compares the fitted distribution with the input degree vector and the KS P-value  and Newman-Girvan (NG) algorithms  ~ O(n log2n), with m, n, and d representing edges, vertices and the depth of the dendrogram describing the structure of the community, respectively  analyzing differentially expressed genes in two transcriptome datasets (GSE5620 and GSE5621), (ii) associating these genes to metabolites, metabolic reactions, and metabolic pathways, and (iii) visualizing genes, metabolites and pathways using a tripartite graph representation  that can be divided (partitioned or colored) into k disjoint sets (partitions or colors) and connections (graph edges) that always connect nodes belonging to different sets. Closed tripartite graphs do not impose restrictions on the tripartite structure of connected nodes  , metabolites (Mi), and pathways (Pi), in which allowed connections are only between Gi and Mi nodes and between Mi and Pi nodes  Figure . In conts Figure . We descs Figure . One benArabidopsis.An initial exploration of the differential expression of over 22,000 genes with PCA analysis showed samples clustering into groups and a time resolved response to cold stress occurring at 4 time points, 3, 6, 12, and 24 h of cold treatment Figure . We alsom better represents the effects of environmental perturbations at network-level since it considers all connecting reactions that consume or produce the metabolites of the pathway   regulated in the 3, 6, 12, and 24 h cold acclimated plants, respectively . Moreover, the glycolytic pathway that is linked to perturbation of energy was significantly identified. On the other hand, ethanol (P = 0.01) was a significant metabolite which leads to membrane lipid composition for stabilization -trisphosphate biosynthesis. All of these pathways suggest stress related changes occur in cell membrane lipid composition at all time points through signal metabolism. Moreover, the regulatory role of sucrose in cold acclimation was also reported . In contrast, sucrose biosynthesis was significantly regulated at all time points, indicating a key role of sucrose in cold perturbation , which is an important intermediate in several major carbon fluxes, including those involved in starch, sucrose, and cellulose biosynthesis , organic acids  and carbohydrates   metabolite  is an important signal molecule of the D-myo-inositol-5-phosphate biosynthesis pathway, which is involved in abiotic stress, pollen tube growth, ion channel activity, and guard cell movement . The 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate (PIP2) metabolite (P = 0.017 for 12 h), a precursor for the synthesis of nucleotides. Significantly changed activities of the pentose phosphate (oxidative branch) I and pentose phosphate (non-oxidative branch) pathways are also the source of carbon skeletons for the synthesis of aromatic amino acids, phenylpropanoids, and their derivatives  and glycolysis IV (plant cytosol) pathways facilitate the conversion of carbohydrates to oil. Pyruvate, which is the end product of glycolysis, is further decarboxylated into acetyl-CoA, which is a precursor for fatty acid and oil biosynthesis , which were reprogrammed for cold acclimation. This occurs in three stages: (1) the conversion of hexose phosphates to ribulose-1,5-bisphosphate by phosphoribulokinase (the non-oxidative reaction); (2) the conversion of ribulose-1,5-bisphosphate and CO2 (mostly produced by PDH3) to PGA by Rubisco ; and (3) the metabolism of PGA to pyruvate and then to fatty acids , poly-hydroxy fatty acids biosynthesis, phospholipid remodeling, phospholipid desaturation, glycolipid desaturation, phospholipases, and phosphatidylcholine biosynthesis I and II. Cold, heat, and osmotic stress change the physical properties of lipids in cell membranes  accounts for 60% of the total pectin in primary cell walls of plants  was a key metabolite in redox metabolism  and lutein metabolism , functioning as antioxidants. Similarly, Coffea canephora showed higher contents of the zeaxanthin and lutein in cold perturbation , L-ascorbate biosynthesis V, ascorbate glutathione cycle, glutathione redox reactions I and II, gamma-glutamyl cycle, formaldehyde oxidation II (glutathione-dependent), sulfate reduction II, formaldehyde oxidation II and thioredoxin pathway in cold stress  and II (PS II) and Rubisco are considered to act as primary stress sensors of the chloroplast , suggesting it functions as an endogenous IAA precursor  system  of 0.023, 0.021, 0.026, and 0.028 at 3, 6, 12, and 24 h of cold acclimation, respectively  that a node has k connections. For inhomogeneous networks, highly connected nodes get more connections. In these random networks, the probability P(k) of a node being connected to k other nodes decays as a power law, P(k) ~ k−γ, without a characteristic scale  vs. log k for all tripartite networks  of ~ 70% support the linear model. KS fit statistic higher than >0.10 and low P-values of the KS test (< 0.05) , which describes the ratio of connected triplets in the graph normalized over all vertices and ignoring the direction and weights of the edges  were highly connected and located in the center of the network while individual vertices were located toward the periphery  Figure . Variatice Table . This prce Table . Indicesce Table . High iny Figure . This fe2 fixation, including activating the Rubisco shunt. This involves maintaining a steady-state of energy metabolism . For example, extraction of a sub-network from the 3 h cold acclimation network showed redox metabolisms linked to each other Figure . Interesm Figure .Arabidopsis. Notably, cold stress levies great demands on energy production and biochemical requirements. Initially, plants mobilize glycolysis to rapidly produce ATP and divert the main carbon flux routes to amino acid and lipid metabolic pathways. In this process, many metabolic pathways are activated to produce cellular materials needed to sustain stress. We find that energy rich metabolites, such as ethanol, may help the functioning of the TCA cycle under perturbation. Signal pathways and structural integrity related pathways were also activated. Clearly, our metabolite-centric reporter pathway analysis provides a new framework with which to help scientists achieve a deeper understanding of the co-regulation of metabolites and metabolic pathways. Our analysis also revealed modularity and rejection of the power law behavior in the structure of tripartite networks of gene-metabolite-pathway connectivity.A metabolite-centric reporter pathway analysis of transcriptomes uncovered a number of metabolites and pathways that differentially modulate the plant response to cold stress in IK and IY performed experiments. IK, IY, and GC-A analyzed the data. IK and GC-A wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics. The measurement of metabolites in the diagnosis of inborn errors of metabolism (IEMs) was established 55 years ago by Dr. Robert Guthrie for phenylketonuria (PKU) after its discovery by Dr. Asbjørn Følling three decades earlier . PKU is In parallel to measurements by mass spectrometry, genetic screening of IEMs started in the 1990s . Today, Nowadays, targeted metabolite assays represent the key technology in NBS worldwide . HoweverThe use of untargeted metabolomics could complement current targeted metabolite assays. Untargeted metabolomics has the prospect of studying a wider range of metabolic pathways and to provide a broader view of the true metabolic phenotype of diseases . UntargeIn this review, we will discuss IEM diseases with emphasis on screening and diagnosis using targeted versus untargeted mass spectrometry (MS) approaches. We will give an overview of studies, including application of matrices analyzed, instrumentation, and data processing methods. We will also discuss challenges and obstacles to adapt untargeted mass spectrometry in IEMs screening and diagnosis.IEM diseases are a group of inherited genetic diseases resulting from total or partial absence or deficient activity of an individual enzyme, structural protein, or transporter molecule . Recent A recent attempt for classification associated with the prevalence of each category of IEMs has been reported by Ferreira and his colleagues, . They us. These discrepancies of IEM categories appear in overall incidence but also for specific categories including organic acid disorders, lysosomal storage disorders, fatty acid metabolism disorders, mitochondrial disorders, urea cycle disorders, amino acid metabolism disorders, and carbohydrate metabolism disorders.Studies from different countries have found wide disparities in the reported incidence rates of IEM diseases . These dHowever, there is a lack of data regarding the causes of disparities in overall incidence and rates of individual disorders worldwide. Higher rates of consanguineous marriage in some countries or regions can increase the incidence rates up to 50-fold . RegionaMost IEMs are inherited in an autosomal recessive (AR) manner . In AR dIEM disorders are often characterized by alteration of multiple overlapping metabolic pathways. Different classifications have emerged to categorize IEM diseases to enable easier clinical and laboratory diagnosis and facilitate treatment  such as The Society for the Study of Inborn Errors of Metabolism (SSIEM) considers clinical aspects of IEMs in a specific classification. The SSIEM classification assorts large numbers of individual disorders according to their biochemical pathways and common pathophysiological mechanisms ,44. MetaIEMs may present at various ages in different ways. Clinical presentation of the disease can occur even before birth, at birth, or during the first days of life as deterioration after normal birth and delivery . Errors This phenomenon is caused by changes in catabolism which occurs normally in the first days of life, leading to an accumulation of metabolites that cause toxic manifestations. The rate of deterioration is variable according to disease type, the extent of the disease and the most affected organ. For example, inborn errors in metabolism may appear as neurological symptoms, disorders of acid-base balance, unexplained hypoglycemia, cardiomyopathy, hepatic deterioration, or sudden death. Other diseases have more subtle presentations, such as a characteristic odor which is not commonly detected ,57. In gClinical manifestations of IEMs are overlapping. Therefore, clinical approaches to diagnosing IEMs are very difficult, especially for rarer disease variants. Presenting symptoms in infants can have a wide variety of possible causes. Distinctive facial abnormalities are linked to a group of diseases such as lysosomal storage disorders, pyruvate dehydrogenase deficiency, glutaric aciduria type II, cholesterol biosynthesis defect, glycolysalation disorders, and disorders of peroxisomal biogenesis . InfantsDiagnosis of IEMs is based on biochemical tests that are divided into two approaches: (1) screening tests to detect possible abnormal levels of metabolic biomarkers in blood or urine before the disease manifests and (2) tests to detect specific pathognomonic biomarkers . All bioScreening is an important tool for primary disease prevention. In contrast to diagnostic investigations, the screening tests aim to detect diseases in the latent asymptomatic stage of the disease to facilitate intervention and improve outcomes . IEM scrIn the symptomatic stage of the disease, severity and onset of symptoms influence the investigations and diagnostic methods used. However, as many IEMs present with very similar symptoms, it is necessary to perform multiple diagnostic investigations concurrently . The invMetabolomics seeks to measure the complement of endogenous and exogenous small molecules in a given sample. Metabolomics analysis in cells, tissues, and body fluids reflects the biochemical status of the sample a direct readout of the current phenotype  of the living organism ,81. ThesMetabolomics analysis in clinical medicine is most commonly performed using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) . NMR speMass spectrometry is used in two different modes in IEMs screening: either by direct injection of the sample to the ionization source of the mass spectrometer or by separation mixtures of compounds by gas chromatography (GC) or liquid chromatography (LC) prior to MS detection. GC or LC methods reduce the complexity of chemical mixtures prior to mass spectrometry analysis  and is wThe introduction of ultrahigh-performance liquid chromatography (UHPLC) and tandem mass spectrometry (MS/MS) have improved sample throughput and analytical sensitivity of a wide range of metabolites ,94,95. RMetabolomics in biomedical studies generally uses biofluids, cells, and tissues as the primary matrix to generate metabolic signatures. In clinical practice, urine, serum, and plasma are easy to collect and to prepare. They present the most commonly used biofluids ,100,101.In addition, other biosamples could be used, for example cerebrospinal fluid (CSF)  and saliMetabolomics is divided into two approaches: studies looking only at specific compounds (targeted metabolomics) and studies that aim at measuring all compounds (untargeted metabolomics), Traditionally, the study and diagnosis of IEMs is performed using a panel of targeted analyses with dedicated analytical protocols. These protocols cover a selected panel of diseases by quantifying the metabolites in a disease pathway and metabolite levels are then compared to the range of healthy  metabolic concentrations ,119,120.Despite its unquestionable role, current target-based newborn screening programs yield only a snapshot of all metabolic alterations. Many IEMs cannot be identified by current routine targeted metabolite analyses . Even whFalse interpretation of disease manifestations by the parents or family doctors as well as incomplete description of patient symptoms by the parents may lead to inappropriate diagnostic panels. Hence, there is a high risk that improper tests are performed by metabolite target screening methods, increasing the rate of false negative results and delaying accurate diagnoses ,124. UsiThe economic evaluation of targeted newborn screening programs has been studied in different areas over the last decade. Thiboonboon et al. described extended metabolic screening as not cost-effective in Thailand . In the In addition to limits of testing policies, targeted metabolomic analysis in classic newborn screening programs limits the discovery of novel metabolic defects due to its focus on a single panel of known metabolic pathways. Providing a broader understanding of the pathophysiology and the metabolic interactions behind the phenotype of the disease is important to design better treatment regimens ,137. Cur3) and globotriaosylceramide (Gb3), has proven challenging to be correctly diagnosed. Auray-Blais et al. analyzed plasma and urine samples of Fabry disease patients using LC-time of flight mass spectrometry and found that more than 20 isoforms of Gb3 were responsible for the disease severity and prognosis. Such detailed results were previously missed by targeted approaches [With recent characterizations of novel IEMs, researchers are beginning to recognize the drawbacks of targeted metabolite methods in newborn screening program, especially for diseases that are difficult to diagnose. Lysosomal storage diseases have a high degree of phenotypic and genetic variability and are characterized by multisystem effects . Fabry dproaches ,145,146.proaches .In contrast to targeted metabolomics approaches, untargeted metabolomics aims to measure all detectable analytes in a sample, including unidentified metabolites . Untargem/z) and MS/MS fragmentation spectra. Chromatographic retention times represent the degree of interaction of chemicals with the column adsorbent material, while accurate mass m/z and MS/MS spectral data can be compared to authentic chemical standards to verify claims on compound identification in IEM reports [Sample preparation for untargeted metabolomics aims at broad scale metabolome coverage without discriminating against specific classes of small metabolites while still removing proteins and other macromolecules . Typical reports ,159.For untargeted assays, high resolution mass spectrometers provide the best sensitivity, specificity and coverage. Quadrupole time-of-flight MS (QTOF MS or TTOF MS) and quadrupole Orbitrap MS (Q-Exactive) instruments have full scan modes and ability to fragment ions to provide a panoramic view of metabolites in biological samples. Triple quadrupole MS (QQQ MS) are most useful for targeted metabolite newborn screening, especially for absolute quantifications . Yet, onUntargeted metabolomics produces large and chemically diverse datasets. Such datasets comprise signals of both of known and unknown chemical structures and require careful interpretations. Statistical analyses of large datasets must account for false discovery rates to adjust for multiple testing. In addition to univariate analysis, data can also be processed by multivariate data analysis, including enrichment statistics . StatistMany clinical and biomedical studies have reported using global untargeted metabolomics ,165, TabUntargeted metabolomics widens the range of metabolites associated with IEMs and discovers new compounds that could be potential biomarkers. Using untargeted metabolomics in phenylketonuria of patient plasma led to the identification of two new biomarkers, glutamyl-glutamyl-phenylalanine, and phenylalanine-hexose . These nMetabolites can be divided per their physicochemical properties such as water-soluble (hydrophilic) and lipid-soluble (lipophilic) molecules. Hydrophilic molecules are the domain of primary metabolism such as sugars, amino acids, organic acids, or nucleotides. Screening lipophilic compounds has been termed lipidomics . It routAcylcarnitines are important classical targets for diagnosis of IEMs such as organic aciduria, mitochondrial and fatty acid oxidation defects . Few stuWith current advances in analytic methods, thousands of peaks and metabolites with good sensitivity are revealed using different platforms. The need to identify these peaks  is a grand challenge for untargeted metabolomics researchers . The firA number of studies compared results between targeted and untargeted techniques ,203. A rO-sulfate, vanillylmandelate, and 3-methoxytyramine sulfate that are not screened in targeted assays [Conversely, classic NBS using targeted metabolomic approaches did not show much improvement over the past years with respect to the magnitude of false positive results. Two distinct studies reported one false positive case per every 50–300 true positive cases in the USA ,205. Thed assays .If targeted metabolomics yields inconclusive diagnosis results, additional tests are required such as analysis of further targets, DNA analysis, or more invasive techniques such as individual enzyme assay in cells. Such analyses require additional costs and time to achieve the accurate diagnosis . These pScreening of IEM diseases using untargeted metabolomics gives a broad overview on aberrant pathways. A large screen of multiple metabolic pathways provides more diagnostic confidence, . For exaTargeted metabolomics in screening and diagnosis of IEMs give a narrow view of the diseases assayed. We need to consider untargeted metabolomics as a new tool to improve the scope of IEM disease categories associated with pathophysiology, early symptoms, therapy options and follow-up strategies . Untarge


Although a number of theories exist, there is currently little consensus on drivers of thermal performance in ectotherms, and fish in particular. We present a general size and trait based model that shows that thermal performance can be explained by an interplay of physiological, ecological, and behavioural constraints. Increasing temperatures under climate change are thought to affect individual physiology of fish and other ectotherms through increases in metabolic demands, leading to changes in species performance with concomitant effects on species ecology. Although intuitively appealing, the driving mechanism behind thermal performance is contested; thermal performance (e.g. growth) appears correlated with metabolic scope (i.e. oxygen availability for activity) for a number of species, but a substantial number of datasets do not support oxygen limitation of long-term performance. Whether or not oxygen limitations via the metabolic scope, or a lack thereof, have major ecological consequences remains a highly contested question. size and trait-based model of energy and oxygen budgets to determine the relative influence of metabolic rates, oxygen limitation and environmental conditions on ectotherm performance. We show that oxygen limitation is not necessary to explain performance variation with temperature. Oxygen can drastically limit performance and fitness, especially at temperature extremes, but changes in thermal performance are primarily driven by the interplay between changing metabolic rates and species ecology. Furthermore, our model reveals that fitness trends with temperature can oppose trends in growth, suggesting a potential explanation for the paradox that species often occur at lower temperatures than their growth optimum. Our model provides a mechanistic underpinning that can provide general and realistic predictions about temperature impacts on the performance of fish and other ectotherms and function as a null model for contrasting temperature impacts on species with different metabolic and ecological traits. Temperature, through its effects on individual physiology, is a dominant driver of species ecology and biogeography  spent on metabolically costly activity and resting/hiding to optimize the net energy gain relative to mortality . In the P (mass per time) is the difference between supply S and metabolic demands D, each being functions of body weight w and temperature T:Net energy gain f is the activity-dependent (i.e. a function of τ) feeding level as a fraction between 0 and 1. The supply is discounted by the loss due to specific dynamic action β , and φ is the fraction of food excreted and egested.For supply, The feeding level is given by a Holling type II functional response:The feeding level is therefore determined by the fraction of time spent foraging τ (henceforth the activity fraction), foraging rate D) are standard metabolism \docuT) specifies the temperature dependency of T), and l the oxygen concentration at 5°C. To specify λ(T), we define Here λ.• Production efficiency, here defined as available energy for both growth and reproduction, relative to the food consumption  energy into reproduction and growth. The change in allocation to reproduction with size and age in variable environmental conditions is described by the maturation reaction norm (MRN), which is generally defined as the probability of maturing at a certain age under different growth conditions . We defiThe slope of MRNs is evolutionarily determined by the strength of the covariation between growth and mortality for a given population in a given environment. Strongly positive covariation leads to the relatively flat reaction norms observed for most fish populations , althougc determines how rapidly energy allocation shifts from somatic growth to reproduction.The allocation was parametrized asAs growth is also fundamentally driven by resource availability, we contrast the growth response to temperature at the baseline level , with a Fitness consequences for particular life-history strategies  at different temperatures can be investigated if one considers the timescales in the model to be short relative to evolutionary timescales . On these timescales, we assume that adaptive responses are negligible ) and temperature T via temperature-driven activity. Fitness is the integral over energy allocated to reproduction at age t and corresponding weight w(t), t.f) at the cost of increased metabolism and mortality, and at the opposing end minimizes mortality and metabolic costs at the expense of production . This axis leads to an approximately constant ratio of production to mortality and corresponds to a line of equal size in the life-history space proposed by To ensure a level of generality beyond existing, species-specific ecophysiological models, we explored ecological impacts of optimized behaviour at different temperatures in a trait-based context. In doing so, we hoped to bridge the existing gap between detailed species-specific models, and general, largely conceptual theory describing temperature impacts. Specifically, we contrast species along a gradient of life history that, at the one end, maximizes production  have a higher standard metabolism and lower weight scaling of metabolic costs . In pracWe further contrasted species with oxygen limitation at high temperatures  with species that do not experience oxygen limitation at high temperatures . This assumption is in line with observations that the aerobic scope often exceeds energetic requirements from swimming alone and is adapted to provide oxygen for digestion (SDA), the oxygen demand of which can be as high or higher than that of locomotion alone . Model cIncreasing metabolic demands at higher temperatures leads to increased activity levels in order to optimize energy gains relative to mortality risk . This diP to M. Available energy shows a dome-shaped response to temperature in slow strategists and is maximized at relatively high temperatures. However, it is limited by oxygen availability only at high temperatures in species with a dome-shaped MOS. Production efficiency follows a near opposite trend due to the relatively flat response in the feeding level f, but temperature-driven increases in maximum consumption.Temperature and metabolic demand-driven adjustments to the activity level lead to substantial changes in performance-related metrics in both trait scenarios . For sloP peaks at much lower temperatures. Given the relatively flat mortality levels, the ratio of P/M largely follows the trend in P.For fast strategists, the relatively modest response in activity levels at all but the smallest sizes leads to a decline in feeding levels, which causes a largely dome-shaped response of available energy and growth efficiency to warmer temperatures . Again, Simulated growth curves illustrate the ontogenetic consequences of higher temperatures . For botResource availability strongly modulates this growth response to temperature; low-resource availability leads to strong differences in asymptotic size, whereas high food availability leads to fast growth and larger asymptotic length at high temperatures  and 7. IP/M  and has the same pay-off for all life histories. This approximation may not reflect actual activity cost as swimming at MMR in fast predatory fish is far more efficient than swimming of sluggish fish, such as flatfish . Such anOptimal activity is predicted to be higher at warmer temperature in nearly all cases, but this finding is sensitive to the cost of activity—at higher cost relative to the potential pay-off, activity may even decline at high temperatures. Predictions of increased activity are supported by many observations in experimental and field conditions for both larval and adult fish  that includes similar physiological constraints to our model predicted relatively high fitness at high temperatures (In order to provide a general framework, our model set-up is deliberately minimalist, and probably under-parametrized to reflect ecological and life-history aspects of particular species, such as migrations, social behaviour or seasonal energy requirements. As such, this framework provides a null model to assess the diversity of possible responses in fish, and other ectotherms, to temperature in a highly simplified system. Nevertheless, it provides a starting point from which to explore the importance of costs and benefits of particular life histories and thermal adaptations. For instance, a recent species-specific ecophysiological model for cod (eratures , 2015. Aeratures  assumed The importance of the interaction between ecology, bioenergetics and oxygen limitations in deriving realistic predictions about temperature impacts on ecological rates and fitness calls into question predictions for climate change impacts based on simple models of growth alone . We suggFigureS1Click here for additional data file.FigureS2Click here for additional data file.FigureS3Click here for additional data file.FigureS4Click here for additional data file.FigureS5Click here for additional data file.FigureS6Click here for additional data file.FigureS7Click here for additional data file.