Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing  and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions  of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health. The scientific community has only recently begun to realize and fully appreciate the significant role of the microbiome in human health . Increasin silico means of predicting metabolic capacity from nucleic acid sequences would embolden drug discovery by generating testable hypotheses sans cost-prohibitive upstream metabolome profiling analyses.Untargeted metabolomic analyses greatly facilitate the detection and characterization of a wide range of metabolites, affording researchers a comprehensive understanding of the metabolic pathways invoked within a microbial community. Such techniques have bolstered and accelerated mechanistic studies and biomarker identification strategies across a variety of diseases . With maRecent years have seen advances in linking microbiome sequencing data to metabolome data. One such strategy relies on the network of connections linking a given gene to reactions and compounds in a database. These linkages are used to infer molecular compound identities from the genetic information housed within a microbial community. A method called predicted relative metabolomic turnover (PRMT) was used to predict metabolites from a coastal marine metagenomics dataset, and the predicted metabolites correlated strongly with environmental factors . MIMOSA Via MelonnPan, a model trained from paired microbiome and metabolome datasets can be used to predict metabolites from a novel microbiome dataset san a priori knowledge regarding relationships between genes and metabolites. This approach circumvents the limitations of the reference-based methods discussed above and has been used to generate promising results between two inflammatory bowel disease (IBD) cohorts  consisted of samples other than stool or intestinal tissue, (4) had microbiome data other than NGS sequencing, (5) had targeted metabolomics data, and (6) had fewer than 30 microbiome-metabolome paired samples. Notably, as different metabolome generation and processing methods have significant impact on results, we only selected studies which applied untargeted metabolome profiling with multiple liquid chromatography-mass spectrometry (LC-MS) methods or nuclear magnetic resonance (NMR) for a comprehensive view of the metabolite pool. Eighteen studies were retained and six were used in this evaluation due to data accessibility  using USEARCH (de novo OTU clustering) and determined a representative consensus sequence per de novo OTU. Piphillin (Raw sequencing data were either downloaded from NCBI Sequence Read Archive (SRA) for public studies or generated in-house using the Illumina platform . We anal at 0.97 . For 16S USEARCH . We assiiphillin  was thenFor metagenomic and metatranscriptomics reads, adapter sequences and low-quality ends were trimmed with Trimmomatic <Q20; . We then1, and the Chemical Translation Service2 were used to identify and convert metabolites.Detailed LC-MS/NMR conditions, software and library databases for metabolite data generation per each study are listed in A relationship table linking KEGG orthologs (KO) and BioCyc reactions (RXNs) to the compounds housed in KEGG (release date 2019 May) and BioCyc (version 23.0) databases was compiled. Specifically, if KOs have corresponding KEGG reactions which produce or consume KEGG compounds, we directly linked those KOs to KEGG compounds. An example is that K24443 corresponds to two reactions R02428, R02526, which produce or consume compounds C02753, C00001, C00502, C01114, and C00545. Thus, K24443 is linked to those compounds. There are cases when KOs do not have corresponding reactions but KEGG Enzyme numbers, and we would link those enzymes to reactions and further to compounds. An example is that K00046 has no corresponding reactions but the enzyme number EC:1.1.1.69, through which we linked to reactions R01738, R01740 and the reactions produce or consume compounds C00257, C00003, C01062, C00004, C00080, C00006, C00005, thus those compounds are linked to K00046. Manual effort was also made to link transporter KOs (BRITE class: ko02000) to their associated compounds. For the BioCyc database, RXNs and their associated compounds were inferred from “core_description_of_RXNs” directly, which included reactions from both MetaCyc and all PGDBs in BioCyc. This collection of reactions is referred to as “BioCyc reactions” in the remainder of the text. Reaction directionality was not taken into consideration when constructing the relationship table because many biochemical reactions are reversible depending on the reactant and product concentrations.via DESeq2 analysis  between case and control samples 3, we updated the community metabolic network template to the May 2019 version of the KEGG database. Microbiome functional abundance data were normalized by means of MUSiCC using default settings . As the abundance matrix generated with MIMOSA includes both positive and negative values, we elected to apply Wilcoxon rank sum tests with Benjamini-Hochberg FDR correction . Wilcoxon signed rank tests were used to comparatively evaluate precision, recall, and F1 scores across pipelines and databases between paired datasets.Prevalence filtering was applied to measured metabolome data, as well as MIMOSA and MelonnPan predicted metabolome data, and metabolites detected or predicted in fewer than 5% of the samples were omitted from consideration. Data resulting from empirically measured- and MelonnPan predicted-profiles were log transformed after imputing zeros with the minimum non-zero value per metabolite. We then conducted Student’s Random sampling analysis was also performed and compared to the pipeline prediction. For coverage evaluation, the same number as the predicted metabolites were randomly selected from the in-house chemical dictionary and compared with the pipeline predicted results. For differential metabolite identification, gene labels were randomly shuffled prior to DESeq2 and used for prediction for Mangosteen. For MIMOSA and MelonnPan, predicted metabolite labels were shuffled in the predicted matrices, followed by the same procedure for differential metabolite identification. The results were further evaluated against the measured metabolites and performance was represented in precision, recall and F1 score. The procedure was repeated for 99 times.n = 648 from five studies), while a smaller subset (n = 83) arose from intestinal tissue samples of the CRC study. All samples were subjected to metabolomic analysis via either mass spectrometry  or NMR spectroscopy . For microbiome profiling, half of the datasets originate from 16S rRNA gene amplicon sequencing (four datasets), followed by shotgun metagenomic analysis (two datasets), and metatranscriptomic sequencing (two datasets).Among the 6 studies that fit our selection criteria as described in n = 1,273) and NMR the least  identified the greatest number of metabolites . While Mangosteen-B predicted significantly fewer unique microbial functions than Mangosteen-K , it linkMetabolites bearing KEGG identifications were then assigned to their corresponding BRITE classifications . Melonnpp = 0.01) and overall F1 scoring  compared to its BioCyc-referenced counterpart. With respect to ML-based MelonnPan, these two reference databases used for model building performed equally well and yielded no significant differences in precision, recall, or F1 score. We also compared the prediction results to random sampling, which randomly predicted the same number of metabolites, and all pipelines showed significantly better performance regarding precision, recall and F1 score (p < 0.01).Predicted metabolite profiles were compared to empirically measured metabolite profiles. MelonnPan exhibited the greatest precision  in predicting the presence/absence of metabolites in the human gut, followed by MIMOSA (0.08) and Mangosteen , with squared m12 values  compared to random prediction while MIMOSA and MelonnPan did not .We evaluated these pipelines on their ability to identify differentially abundant metabolites between case and control groups as shown in a priori microbiome sequencing data affords researchers the ability to generate hypotheses in a rapid, cost-effective, and relatively reliable fashion. We systematically compared the ability of metabolite predictions by two published tools alongside a newly developed reference-based pipeline using more than 900 paired microbiome-metabolome stool/intestinal tissue samples from six different studies on various human diseases. Resulting metabolite profile predictions were compared to one another and contrasted alongside an empirically measured metabolome dataset, focusing primarily on occurrence  and identification of differentially abundant metabolites.Gaining insight into the metabolite pool of a microbial community is of paramount importance to understand its ecological role(s), not to mention its potential as a source of therapeutics and other invaluable molecules . As an avia these reference-based pipelines likely contributed to the elevated recall values. In addition, any given KO may participate in any number of distinct reactions, bearing the potential to dramatically increase the number of associated metabolites. With regard to reference-based methods, MIMOSA surpassed Mangosteen-K in both precision and F1 score. This speaks to the benefit of considering the directionality as well as the network of connections between reactions within a community. Although Mangosteen showed worse performance in metabolite prediction compared to MIMOSA, it was able to link more metabolites and could be useful for mining metabolites outside of known metabolic network. While the type of microbiome data examined also affects prediction performance, we lacked an appropriate number of independent datasets to evaluate this aspect in our study (one study for metagenomic to metatranscriptomic and another for 16S rRNA amplicon to metatranscriptomic sequencing comparisons). A pipeline’s ability to accurately distinguish differential metabolites between case and control groups is typically a sound foundation from which hypotheses are structured and biomarker and therapeutics discovery initiatives expound. Such evaluations are predicated upon accurate predictions of metabolite abundance, and as such, are much more stringent than occurrence prediction. While we did not observe promising results in any of the pipelines, MelonnPan predictions yielded the highest mean F1 scores (around 0.1 in both MelonnPan-K and MelonnPan-B).When used to predict the presence or absence of given metabolites within a community, the ML-based MelonnPan approach significantly outperformed its reference-based competitors, with respect to overall precision and F1 score. However, the reference-based Mangosteen and MIMOSA pipelines did exhibit high recall despite their lower precision and F1 scores. As recall is calculated based on true-positive and false-negative tallies, the high number of metabolites predicted Machine-learning-based MelonnPan outperformed the reference-based methods in predicting both occurrence and differential metabolites. In contrast to the reference-based pipelines, this powerful technique considers the impact of host metabolism and hitherto undiscovered interactions that do not exist in reference databases. Like all ML strategies, MelonnPan relies heavily on the accuracy of training data. As such, while untargeted metabolome profiling strategies generate the most comprehensive overview of the metabolites present within a sample, coverage is unavoidably limited due to the inherent technical biases associated with either MS or NMR . Hence, While conducting the differential metabolite evaluation, we observed the highest F1 score in the MelonnPan-predicted metagenomic dataset of via conventional untargeted metabolome profiling as well as ML-based prediction , either differential ones between case and control or any features the researchers are interested in. Ultimately, caution and common sense go a long way in interpreting the predicted metabolite profiles resulting from reference-based pipelines.As opposed to ML-based and data-driven approaches, reference-based methods can potentially identify/predict metabolites whose concentration or specific chemical properties precluded them from detection ediction . Referenediction  and BioCediction  are two ediction , but thiediction . There aThere are several limitations in the current evaluation that need to be taken into consideration when researchers try to choose the optimal prediction tool for their research. While we treated the empirically measured, untargeted metabolome as a “gold standard” and directly compared all pipeline prediction results to this profile, biases in technology render even this portrayal imperfect in its attempt to unveil the true and complete metabolite pool . In realPredicting metabolic capacity and metabolite diversity is of paramount consequence to better understanding and manipulating the function(s) of microbial communities, all of which bears immense significance to improving human health and empowering environmental microbiome research . UltimatPRJNA668188.Metadata, 16S rRNA gene amplicon and metatranscriptomic sequencing data generated in this study (SG_IBD) have been deposited in the NCBI database under BioProject ID The studies involving human participants were reviewed and approved by University of Calgary, Conjoint Health Research Ethics Board (ID:18142 and 14-2429). The patients/participants provided their written informed consent to participate in this study.SI, TD, and KD conceived and supervised the project. XY, TA, TD, and SI designed the study. XY, ER, KW, YW, JC, PB, and GK performed data collection and processing. XY conducted the analysis and wrote the first draft of the manuscript. All authors edited and approved the manuscript.XY, ER, KW, YW, JC, KD, TD, and SI are employees of Second Genome Inc., which provides salaries and stock options. Second Genome Inc. is an independent therapeutics company with products in development to treat intestinal disorders and other human diseases. TA is an employee of Altman Analytics LLC. The remaining 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.


Chlorella sorokiniana (C. sorokiniana) was investigated. At the early stage of mixotrophic growth (day 0–1), autotrophy contributed over 70% of the total metabolism; however, heterotrophy contributed more than autotrophy after day 1 due to the rapid increase in cell density, which had a shading effect in the photo-bioreactor. Heterotrophy continued to have a higher contribution until the available organic carbon was depleted at which point autotrophy became dominant again. Overall, the increase in algal biomass and light conditions in the photo-bioreactor are important factors in determining the contribution of autotrophy and heterotrophy during a mixotrophic culture.The contribution ratio of autotrophic and heterotrophic metabolism in the mixotrophic culture of  Microalgae are a promising source of alternative fuels because they perform photosynthesis by fixing atmospheric carbon dioxide and have high growth rates and areal productivity . MoreoveMany algal species are not only capable of growing autotrophically, using photons of light as a sole energy source, but are also capable of growing heterotrophically, using organic carbon as an energy and carbon source for metabolism . FurtherIn terms of wastewater treatment, the mixotrophic culture is the most efficient culture because not only nitrogen and phosphorus but also organic compounds from wastewater can be removed simultaneously.However, the culture conditions that cause a shift between autotrophy and heterotrophy and the consequent growth efficiency of mixotrophic algae are not fully understood. Thus, in order to use mixotrophic cultures to remove nutrients and organic matter from wastewater, the preferable contribution ratio of organic to inorganic carbon must be investigated.The stoichiometries of autotrophic, heterotrophic and mixotrophic metabolism were revealed by a previous study , which aAutotrophic metabolism:Heterotrophic metabolism:Mixotrophic metabolism:Generally, in autotrophic cultivation, IC consumed by algae can be calculated by measuring the initial and residual concentrations of bicarbonate. Likewise, in heterotrophic cultivation, consumed OC can be determined by estimating the difference between the initial and residual OC concentrations provided. In mixotrophic cultivation, however, IC is produced by heterotrophic respiration (Equation (2)) and is added to the residual IC concentration as an available carbon source. This is the main difficulty in defining the ratio of autotrophic to heterotrophic contributions.Karlodinium micrum and found that it was dominated by heterotrophic metabolism [Chlorella sorokiniana (C. sorokiniana) was used as a microalgae strain instead of Chlorella vulgaris because it was identified as an ideal candidate for mixotrophic cultivation [C. sorokiniana also has a higher specific growth rate, biomass productivity and cell doubling time than Chlorella vulgaris for the post treatment of dairy wastewater plant effluent [Despite the significance of distinguishing between autotrophic and heterotrophic contributions in mixotrophic metabolism, this topic has been rarely studied mainly due to the complex characteristics of mixotrophy compared with autotrophy or heterotrophy alone. A study investigated the balance of autotrophy and heterotrophy in a mixotrophic growth of tabolism . This sttivation . C. soroeffluent .C. sorokiniana (AG20740) was obtained from KCTC .The microalgae strain 3 0.5 g-C L−1 and glucose 0.5 g-C L−1. The flasks were agitated at 120 rpm using an orbital shaker and controlled at 25 ± 2 °C under a continuous photosynthetic photon flux density (PPFD) of 120 μmolm−2s−1 from a light emitting diode measured by a photo-radiometer . The composition of the BG11 medium was NaNO3 (1500), K2HPO4 (40), MgSO4·7H2O (75), CaCl2· 2H2O (36), Citric acid·H2O (6), Ferric ammonium citrate (6), EDTA (1), H3BO3 (2.86), MnCl2·4H2O (1.81), ZnSO4·7H2O (0.22), NaMoO4·2H2O (0.39), CuSO4·5H2O (0.079) and Co(NO3)2·6H2O (0.049) as mg L−1.In order to acclimatize in a mixotrophic culture, the microalgae were pre-cultured in 250 mL Erlenmeyer flasks containing a modified BG11 medium with NaHCOAfter four days of pre-culturing, the microalgae were inoculated in the 2.5 L photo-bioreactor (working volume of 2 L) with the initial inoculum at 0.1 optical density (OD). Other operating conditions are described in a was the concentration of chlorophyll˗a in the extract and OD664, OD647 and OD630 were the corrected optical densities at the respective wavelengths.The residual concentration of glucose was measured with a dinitrosalicylic acid (DNS) reagent . The resThe amount of pigment per unit volume was calculated as follows (Equation (5)):The contribution ratio of autotrophy and heterotrophy during the mixotrophic culture was calculated based on the carbon consumption as below (Equations (6) and (7)):3 and glucose were used as IC and OC sources, respectively, for mixotrophic metabolism, the stoichiometry equation of mixotrophic metabolism can be expressed as follows [As NaHCO follows   and sufficient light penetration. Therefore, photosynthesis was the main metabolism for C. sorokiniana in the culture system. At a low light intensity, the microalgae synthesized photosynthetic pigments to absorb photons more actively [660 0.7) within 24 h. This cell density might be high enough to decrease the light penetration through a photo-bioreactor, resulting in insufficient light intensity for proper photosynthesis. It has been reported that a high chlorophyll-a content and algal density (at concentrations of 105 cells/mL or above) caused a self-shading effect that limited the light for photosynthesis [On day 0–1, the autotrophic contribution was higher due to the low initial cell density . After day 3, as available organic carbon became scarce, autotrophy became completely dominant. The results suggest that increased algal biomass and consequent self-shading are important factors in determining the contribution of autotrophy and heterotrophy during a mixotrophic culture. The study contributed to clarifying the dynamic of mixotrophic metabolism, which is useful for the experts of the sector to understand algae growth.The contribution ratio of autotrophy and heterotrophy changed dramatically during a mixotrophic culture. Autotrophy dominated the growth metabolism during the early culture period (day 0–1). However, increased algal biomass might inhibit factor photosynthesis through self-shading. Heterotrophic metabolism became dominant as


Previous studies have discussed the importance of an optimal range of metabolic activity during preimplantation development. To avoid factors than can trigger an undesirable trajectory, it is important to learn how nutrients and metabolites interact to help launching the correct developmental program of the embryo, and how much the in vitro culture system can impair this process. Here, using the bovine model, we describe a factorial experimental design used to investigate the biochemical and molecular signature of embryos in response to different combinations of morphological features—i.e. speed of development—and external stimuli during in vitro culture—i.e. different oxygen tensions and glucose supplementation. Our analyses demonstrate that the embryos present heterogeneous metabolic responses depending on early morphological phenotypes and the composition of their surroundings. However, despite the contribution of each single stimulus for the embryo phenotype, oxygen tension is determinant for such differences. The lower oxygen environment boosts the metabolism of embryos with faster kinetics, in particular those cultured in lower glucose concentrations. This proposal was further developed2 to introduce molecular components such as “the quiet range” of nutrients turnover, and then revisited many times to include different categories of “quietness” such as levels of intrinsic metabolic activity and loss of quietness due to environmental stress, particularly the oxidative stress often observed in culture systems that use high oxygen tension2. In 2011, Gardner3 suggested that, in human embryos, a significant increase in glucose consumption—indicating a highly active metabolism—is necessary during initial development to sustain embryo viability. Later on, Leese discussed the existence of an optimal range of metabolic activity, or, in other words, the embryo ability to successfully carry out the developmental program with the minimum amount of energy4.Mammalian preimplantation development follows a generally accepted metabolic pattern. Yet, there is no consensus on how we can successfully use metabolism or metabolic biomarkers to predict embryos viability. According to the “quiet embryo” hypothesis5, certain precautions are required. Besides, in vitro based technologies for farm animals exhibit a number of side effects rarely described in other species5, reinforcing the importance of specific studies unraveling the interplay between in vitro culture and metabolism switches in the bovine model.The basis of our knowledge on preimplantation metabolism comes from studies on mouse and human embryos. When extrapolating to large animals, which have a better buffering capacity against metabolic insults6. As embryonic development progresses, glucose consumption gradually increases. Oxidative metabolism has its first mark in the stage of 12–16 cells, when major genome activation occurs8. Within the cells, glucose follows different metabolic pathways, e.g., the pentose phosphate pathway, which generates both the ribose for DNA and RNA synthesis, and the NADPH for lipids biosynthesis and reduction of intracellular glutathione, a powerful antioxidant6. Internalized glucose can also be partially converted to lactate (known as the Warburg Effect), which, in vivo, could facilitate key events such as invasion, angiogenesis and modulation of the immune response during the implantation process6. This plasticity is particularly important for the initial embryo development given that highly proliferative cells have other metabolic requirements beyond the production of ATP. However, studies measuring simultaneously energy efficiency and nutrients uptake/production are still lacking.In the bovine developing embryo, prior to morulae compactation, the production of energy is based on the oxidation of pyruvate, which is preferred due to the low energy requirements at this point9. Such mechanism has been considered responsible for supporting the increase in energy demand for blastulation and hatching, besides contributing to the synthesis of biomass and favoring the redox system10. Numerous evidences show that embryos produced in vitro carry the marks of energetic stress, mainly due to inadequate support offered by existing culture systems11. Thus, providing conditions to foster metabolism, while avoiding, neutralizing or minimizing stress factors, may be the key to ensure proper development.Although it is largely accepted that carbohydrate metabolism  is crucial for embryonic development, the role played by the metabolism of endogenous lipids in this process remains poorly known. Lipids are a potential energy source for the embryo through the β-oxidation of fatty acids12, is a robust predictor of blastocyst formation in humans14. In the bovine, the kinetics of the first 2 cell divisions is associated with distinct phenotypes at the blastocyst stage15. Molecular and biochemical characterization demonstrated that fast-developing embryos exhibit improved ability to trigger adaptive responses17 and distinct profiles of epigenetic reprogramming (more DNA methylation)18.Likewise, the embryonic kinetics seem to be related to the embryos viability and ability to respond to stress. The timing to complete the first cell divisions, in particular from 2 to 5 cellsThe scenario described above allows us to recognize the heterogeneity of embryonic metabolism and responses to external factors when fast and slow embryos are compared. Such heterogeneity should be considered as we explore some overarching questions:(a) Is there a range to up/downregulate embryo metabolism while still sustaining its viability?(b) How does heterogeneity affect metabolism and the ability to respond to environmental stressors?(c) Can we produce embryo with an efficient energy metabolism using the available in vitro culture systems?1. Thus, in the present paper we investigate the biochemical and molecular signature of Fast and Slow bovine embryos in response to combined stressful stimuli during in vitro culture.Avoidance of factors triggering undesirable trajectories should become easier as we learn about the influences of specific nutrients and metabolites on the developmental programming of the embryosBovine embryos were in vitro produced following the experimental design as described on Fig. Bovine ovaries were obtained from a commercial slaughterhouse and transported in warm sterile saline [0.9% (w/v) NaCl] for about 3 h. In the laboratory the ovaries were washed several times again with sterile saline to remove blood and debris. Cumulus-oocyte complexes (COCs) were collected by aspiration of follicles with diameter between 2 and 8 mm using syringe and needle.2 in air and high humidity for 24 h.For in vitro maturation (IVM), groups of 25–30 oocytes were placed in 90 μL drops of the maturation medium  under mineral oil. Maturation of COCs followed in an incubator at 38.5 °C, 5% CO6 sperm/mL. Matured COCs were transferred to 90 μL drops of fertilization media19 and covered with mineral oil. Oocytes and sperm were incubated at the same conditions of air and temperature for 18 h. This study did not require direct handling of animals.Frozen commercial semen straws from 2 bulls were thawed in a water bath at 37 °C for 30 s and their contents were mixed and centrifuged on a discontinuous Percoll gradient (45% and 90%) as a strategy to avoid the influence of the bull in the kinetics of embryo development. Sperm concentration was adjusted to 1 × 10Approximately 18 h post insemination (hpi), remaining granulosa cells were completely removed, presumptive zygotes were transferred in groups of 25–30 to 90 µL drops of Synthetic Oviductal Fluid  and distributed to the study groups following the experimental design . Then, we calculated the necessary quantities to reach the final concentrations of 2 mM and 5 mM of our treatment. At 98 hpi, the embryos were reallocated to these treatments in smaller groups (15–20 per drop).21, where they remained for the last 48 h of culture. Expanded blastocysts and their respective culture media were collected at 168 hpi and kept at − 80 °C for further evaluation.Finally, at 120 hpi, embryos were individually transferred to 20 μL drops of fresh SOFaa inside manually carved microwells, an adaptation from the well of the well system2 [20] or 5% O2 [5]). Final groups are: 0F20, 0S20, 2F20, 2S20, 5F20, 5S20, 0F5, 0S5, 2F5, 2S5, 5F5, 5S5  the amount of glucose in the culture media ; (ii) the speed of first cleavages (Fast [F] or Slow [S]) and (iii) the oxygen tension in the culture system  using the EnzyChrom Pyruvate Assay . The production of lactate was quantified in the same culture media  using EnzyChrom Lactate Assay Kit . Both evaluations are based on enzymatic reactions with fluorometric detection and were performed in accordance with the manufacturer’s instructions.Expanded blastocysts were collected at 168 hpi and analyzed by means of ROS content  with CellROX Green Reagent (CRG) (Thermo Fisher) and MMP  with MitoTracker Red CMXRos (Thermo Fisher). Briefly, blastocysts from all groups were individually transferred to a 50 µL drop of PBS containing CRG (5 µM) + MitoTracker (0.05 µM) and incubated in the dark for 30 min in the same conditions of IVC. After incubation, embryos were washed in PBS, mounted on slides in glycerol drops and immediately evaluated under fluorescence microscopy . Stained blastocysts were individually photographed at 40 × magnification using the Leica Application Suite (LAS) software (Leica Microsystems Brazil).Pools of 2 expanded blastocysts per group were collected at 168hpi and used in 4 replicates for the quantification of ATP using the ApoSENSOR ADP/ATP Ratio Bioluminescent Assay Kit . Briefly, after 5 min in Nucleotide Releasing Buffer, embryonic ATP was converted to light by the catalyzing activity of the enzyme luciferase (ATP Monitoring Enzyme). Luminescence intensity was measured, and a standard curve was used to convert the luminescence output to ATP concentration.22. We selected 3 classes of lipids to include in the present analysis: Free Fatty Acids , Triacylglycerols (99 transitions) and Cholesteryl esters (49 transitions). A total of 178 lipids were monitored and are described in Supplementary Table Lipids were extracted from individual blastocysts  collected at 168hpi using the Bligh & Dyer method and subsequently analyzed by Multiple Reactions Monitoring Mass Spectrometry (MRM-Profiling) following the protocol described inPools of three blastocysts per group were collected at 168 hpi and used in 4 replicates. Gene expression analysis was performed using inventoried TaqMan assays , specific for Bos taurus. We verified the mRNA abundance of 84 target genes  for total RNA extraction and the High-Capacity cDNA Reverse Transcription Kit  for cDNA synthesis. Both protocols were performed following the manufacturer’s instructions. Each RNA sample was quantified with NanoDrop  and final amount was adjusted to 50 ng prior to reverse transcription.23, following the guidelines of the manufacturer . Briefly, samples were submitted to sequence-specific preamplification in a mix containing 1.25 µL assay mix , 2.5 µL TaqMan PreAmp Master Mix (Applied Biosystems) and 1.25 µL cDNA. The reactions were initiated at 95 °C for 10 min followed by denaturing at 95ºC for 15 s, annealing and amplification at 60ºC for 4 min for 14 cycles. The preamplified products were diluted sixfold prior to qPCR.Pre-amplification and qPCR were performed as previously describedThe 96 × 96 Dynamic Array Integrated Fluidic Circuits chip (Fluidigm) was loaded according to the manufacturer's protocol with each sample solution ; and each assay solution . The qPCR thermal cycling was performed in the Biomark HD System  using the protocol TaqMan GE 96 × 96 Standard. All the analysis was performed in four biological replicates and Ct values were calculated from the system’s software .The experimental design proposed for this work originated 12 study groups as previously described. Thus, we opted for a more descriptive approach to analyze the resulting data. When necessary to show the significance of the results, GraphPad Prism 8.0 was used to verify normality and then compare groups by Student t test or ANOVA + Tukey, considering an alpha of 5%. Descriptive results are summarized on Supplementary table Total cleavage rates were obtained from the number of cleaved embryos divided by the total number of presumed zygotes  to set a threshold for the background subtraction; resulting values for integrated density data were used to calculate the fluorescence intensity.The results extracted from the mass spectrometer were processed using a script especially constructed to obtain the absolute intensity of each ion. Then ion intensities from transitions of interest were recovered for all groups and normalized by the total ion current (TIC) to obtain relative ion abundances.24 as stable endogenous controls (GAPDH and PPIA). Gene expression results were expressed as ΔCt and then normalized  for the creation of heatmap graphs.For the relative quantification of the transcripts of interest, we obtained ΔCt values for each sample, calculated from the difference between the Ct values (Cycle Threshold) of each target gene and the geometric mean Ct values of the genes selected by the software NormFinder25, and can be used to organize any kind of comparative data from different species or specimens . The basic requirement for a parsimony analysis is to translate the observed information into characters. In this study, we included 93 characters among molecular and biochemical evidences. More specifically, they are 84 genes , as well as blastocyst rates, were gathered to create a database containing information from a total of 585 blastocysts (or their respective culture media). For every analyzed character, we verified the distribution plot, and embryos were allocated into quartiles. The 1st or 4th quartiles, representing the extreme of phenotypes, were given a score of 0 or 2, as described on Table 2 [Node A] opposing to 5% O2 [Node B]), suggesting this category superimposes the others (glucose concentration and kinetics of development). Groups 5S20 and 5S5 present particular characteristics that prevented them from being included in any of the nodes.The data matrix analysis of the 93 characters from 12 study groups resulted in a single, fully resolved tree , groups cultured in lower glucose concentration (0.6 mM and 2 mM) clustered opposing to those cultured in a high glucose environment (5 mM). On the other hand, in 5% O2 (Node C), the kinetics of development was more decisive. As a consequence, the Fast groups clustered in opposition to the Slow groups regardless of the glucose supplementation.In a second step, we isolated the oxygen tension factor and reanalyzed the matrix. The two resulting dendrograms were fully resolved Fig. . StrikinOur next effort was to identify which attributes were responsible for the clusterization in each of the situations and to understand their roles in the study groups. Thus, the attributes contained in each node and their respective behavior in the main clusters are discussed below.2 presented increased amounts of free radicals  were represented in the groups cultured at a lower oxygen environment, which is also in accordance with the higher relative amounts of cholesteryl esters and TAGs found in those groups Fig. D.2 were submitted to cluster analysis, we observed an evident separation according to the kinetics of development. Out of the 16 attributes responsible for this clustering  from the groups cultured in low or physiological levels (0.6 mM and 2 mM).In terms of gene expression, the cluster analysis indicates a more active transcription in the groups cultured with lower glucose concentrations. In this case, the node formation  were pointing to this direction. A more detailed analysis of fatty acids behavior showed that relative amount of total FFA was increased in groups culture in 2 and 5 mM of glucose , and lipid metabolism towards esterification of fatty acids instead of oxidation, as we propose on the schematics of Fig. 28. Therefore, both extremes received a low score regarding mitochondrial activity. Besides, blastocyst rates were included as a marker for the efficiency of each culture system.It is important to keep in mind that too high or too low levels of mitochondrial membrane potential may induce unwanted loss of cell viabilityRemarkably, when the scores were assigned, groups clustered in a very similar way as previously happened for hierarchical analysis Fig. A. Groups2, this relationship becomes more evident. Offering a low glucose concentration (~ 0.6 mM in this study) in a high oxygen environment may generate fair blastocyst rates, but not without a compromised metabolic efficiency.Confirming the results obtained by cluster analysis, the amount of glucose certainly influences the energy efficiency of the embryos. At 20% O2, there is still some heterogeneity in embryonic response.On the other side, the lower oxygen tension seems to foster the metabolism of fast-developing embryos. In terms of lipid metabolism, these embryos prefer the esterification of fatty acids, which can either be stored inside lipid droplets in the form of cholesterol esters and TAG or enter the phospholipids pathway to help build membrane for cell proliferation. Moreover, whilst there is no difference in blastocyst rates among Fast groups cultured at 5% OFinally, Fast embryos cultured with lower glucose (0.6 mM) in a hypoxic environment were the ones responding more accordingly to a presumable efficient metabolism. These embryos are able to produce a high ATP supply with decreased ROS quantities, thereby indicating functional mitochondria. These results were summarized in Fig. 18. Other studies provided evidences that such differences were related to the metabolic stress caused by the culture system17. Based on these assumptions, the present study was initially designed to investigate how Fast and Slow embryos respond to stressors commonly found during in vitro culture, i.e., non-optimized oxygen tension and high glucose concentration.Although other studies have tackled issues such as how culture under low oxygen produces more efficient embryos and why Fast embryos perform better, the broad approach we address here is unique. Previous reports stated numerous differences in the transcription pattern and DNA methylation profile between bovine embryos developing at different cleavage speeds29. Similarly, the lumen of the female reproductive tract provides an oxygen concentration of 2–8%, differing from the atmospheric level of 20%6. The oviduct also provides an efficient oviduct red/ox system, which guarantees an ideal environment for the passage of the embryo30.The concentration of glucose in the bovine oviduct fluid (~ 2 mM) is lower than in blood plasma (~ 5.5 mM), indicating a differential secretion of this substrate for adaptation to embryonic metabolism31. Therefore, by combining environmental stressors and embryos with different phenotypes, we were able to better visualize this plasticity during initial development. When all the study groups were analyzed together, cluster analysis revealed an evident metabolic signature, showing the modulation of embryonic metabolism is highly related to the oxygen tension in the culture system.The relevance of energy metabolism to control gene expression, since cells constantly adjust their metabolic state in response to extracellular signaling and nutrient availability, was previously demonstrated34. According to these same reports, the exposure to different oxygen concentration generates embryos with distinct transcription profiles. For the bovine embryos herein examined, we observed that not only the profile of gene expression differs, but a greater number of genes are responsible for such differences on groups cultured in high oxygen. This may be related to the activation of adaptive responses, as described by Leese2 for human embryos. Thus, while mammalian embryos are quite susceptible to the effects of high oxygen concentration, having an active metabolism could help them compensate for damages caused by environmental injuries and restore developmental trajectory.Previous studies performed with in vitro produced embryos from different species, including cattle, indicated that lower oxygen tension results in better embryonic development, with increased cleavage and blastocyst rates, and higher post-implantation success rate36 and Heat Shock Proteins (HSF1 and HSP90)37. The exposure to high levels of oxygen is a known source of oxidative stress33 and has also been associated to the activation of pro-apoptotic signaling cascades 34.Furthermore, the genes represented in nodes A and B were mostly related to oxidative and reticulum stress response pathways such as those mediated by GPX1, PRDX12. Among possible damages caused by the excess of ROS, we highlight changes in the expression of transcription factors, such as REST, PAF1, STAT3, CDX2, KEAP1 and TFAM. In general, mRNAs and other nucleic acids are easily targeted by free radicals, which leads to detrimental effects in cellular machinery and protein synthesis2. These results also reinforce recently published data describing the differences in the control of gene transcription mediated by the oxygen tension23.Supporting these findings, an expected higher presence of ROS was confirmed in the groups cultured at 20% O2, which corroborates the representation of glycolysis-related genes (PFKP and PGK1) in those groups. However, this effect was clearly not widespread. Only the groups cultured in physiological glucose concentration (2 mM) presented improvements in mitochondrial activity and ATP amounts, suggesting there is an optimal amount of energy substrates that must be available to the embryos. This is in agreement with Leese et al.4, who proposed that embryos on an intermediary range of pyruvate consumption are more likely to form blastocysts.Along with the metabolism of the embryos cultured in high oxygen, our analysis clearly indicated the importance of energy substrates availability. The results show a higher consumption of pyruvate at 20% O39. Hypoxia also enhances anaerobic energy production. To reduce mitochondrial function, and therefore decrease oxygen consumption and ROS production, hypoxia induced factors block the conversion of pyruvate. Then, the flow of pyruvate into the mitochondria decreases, fueling the production of lactate and reducing mitochondrial metabolic processes40. Such events are evident in the embryos cultured at low oxygen that presented lower pyruvate consumption coupled to an increased lactate production.When oxygen is sparse, cells adapt to hypoxia by reprogramming the expression of a number of genes involved in carbohydrate metabolism2. The excess of intracellularly accumulated free fatty acids can cause lipotoxicity40. To avoid this, cells can convert FFA into neutral TAGs that are stored in lipid droplets and serve as energy depots41. Indeed, the activation of such mechanism was observed in our embryos cultured in low oxygen and it is also typical in tumoral cells. It prevents the formation of free radicals, with the lipid droplets serving as building blocks for the production of essential sterol esters and phospholipids required in high proliferative cells for the biogenesis of new membranes35.Groups cultured in a high oxygen tension also presented differential lipid metabolism. A number of genes responsible for the synthesis of fatty acids were found to be important in those groups. Accordingly, we verified increased relative amounts of total free fatty acids, and particularly of stearic and oleic acids when compared to groups cultured in 5% O2 environment, which is depicted by the higher mitochondrial activity and ATP content when compared to Slow embryos. This suggests a very thin threshold separating two types of embryos with high metabolic demands: (i) embryos with higher activity due to loss of transcriptional control; and (ii) embryos with a certain degree of damage but still able to activate adaptive pathways, surviving even with poor quality, which may lead to future implications.Results indicate that the metabolism of Fast embryos is favored in a 5% O18, we speculate that the loss of transcriptional control and the lower ability to activate adaptive pathways may be explained by perturbations in epigenetic mechanisms.We believe Slow embryos belong to the first type. Despite having a more active metabolism, the conversion rates to blastocyst were lower in all Slow embryos when compared to the faster counterparts. Considering that the DNA from Slow blastocysts is significantly less methylated than DNA from Fast blastocyst42. Therefore, we propose that more viable embryos are those capable of producing more energy with minimum disturbances to the energy production system as whole, i.e. the mitochondria and lipid metabolism.Finally, the score system herein created reflects how the external environment impacts and helps defining the energy efficiency of the embryos. Moreover, it allows us to propose the conditions that better support the development of these embryos in vitro. In accordance with the Dynamic Energy Budget theory, our scores consider “efficiency” in the energy sense, meaning that for any given substrate, there is an optimal range of metabolic activity to support embryonic demand, but when exposed to a challenge or stress, the resulting response will have an energy cost that will make them shift out of this metabolic range43. If further resources are required, development might be compromised as indicated by their metabolic phenotype.Although the high oxygen tension still allows the formation of blastocysts, the energy production of these groups is clearly distressed; hence, they presented the lowest score values. In other words, the metabolism is able to rectify the stress enough to maintain these embryos alive (somatic maintenance), however, it may lead substrates away from other essential processes or even boost additional activity to increase embryonic complexity (i.e. differentiation), as suggested by previous publications2. Under an oxygen condition apparently favoring energy metabolism, the heterogeneous response between Fast and Slow embryos becomes more obvious. The lowest scores belong to Slow embryos suggesting their deficient energy metabolism is consequence of other phenotype-related mechanisms and not driven by external environment. Among the Fast groups, the one more closely matching the expected characteristics of an efficient metabolism is the group 0F5. The combination of lower glucose and oxygen tension offered to this group are probably just the right amount to boost their energy production and sustain the demands of a healthier developmental program.The highest scores belong to the groups cultured at 5% OIt is relevant to consider that these characteristics are representative of the physiology, but do not represent the potential of these embryos to establish pregnancy in the event of a transfer to recipient cows. It is possible that embryos produced in vitro could show some compensation after entering the uterine environment that would allow them to implant and develop into a calf. The metabolic imbalance suffered in vitro could be overcome, but likely not without consequences for the future offspring.In conclusion, our analyses depict how embryonic metabolic response can be quite heterogeneous depending on early phenotypes and composition of the culture media surrounding the embryos. This is a pioneer study that simultaneously shows the capacity embryos have to switch to other substrates when needed, and to modulate molecular machinery, ensuring their survival even in the face of adverse and non-optimized external conditions. We believe that the information presented here may greatly contribute to decision making in terms of choosing the more suitable culture system or performing interventions that favor specific characteristics and rescue embryos that would otherwise perish. Future studies must focus on determining whether these embryos have the same implantation potential and what are the consequences of an inefficient energy metabolism for the blastocyst in a short term, and also for the future offspring in a long term.Supplementary Information.


Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the  The Arabidopsis thaliana REIL proteins are involved in the late cytosolic steps of 60S ribosome subunit maturation and are required for growth under low temperature2. The reil1-1 reil2-1 double mutant is deficient for both REIL paralogs and, unlike Arabidopsis Col-0 wild type, does not resume growth after cold shift, even to moderate 10 Ribosomes are the workplaces of protein biosynthesis, and defects in the pathway of ribosome biogenesis have an effect on many cellular processes, like metabolism, which critically depend on enzymatic proteins. While metabolism is known to affect ribosome function via the target of rapamycin (TOR) signalling pathway, little is known about how defects in ribosome biogenesis feed back on metabolism3. They also depend on the enzymatic setup of a cell, which is in turn governed by gene regulatory and signalling networks that affect protein activity4. However, determination of metabolic fluxes is a tedious and labour-intensive task7. A targeted analysis that predicts relevant fluxes for hypothesis generation based on integration of available high-throughput data sets from systems biology studies may streamline the planning of such time-consuming experimental flux studies.One possibility to investigate the feedback of ribosome biogenesis defects on metabolism is the characterization of reaction fluxes. Metabolic fluxes depend, in part, on the metabolite pools8. In general, metabolic fluxes of a system are predicted under the assumption that this system operates in steady-state and optimizes an objective (e.g. biomass yield). If feasible, the resulting mathematical approach often results in a non-unique flux distribution. To this end, constraints defined through integration of high-throughput data can reduce the solution space of feasible flux distributions11. Such approaches have been shown to result in a more accurate prediction which is closer to the actual physiological state12. Despite the availability of methods that integrate high-throughput data, their full potential has yet to be realized13.In this regard, constraint-based approaches have proved as a valuable tool for hypotheses generation regarding flux distributions and their differential behaviour. For instance, the simplest of these approaches, flux balance analysis (FBA), can predict steady-state fluxes in bacteria at exponential growth14 as well as approaches that use time-series data . iReMet-flux18 is the only constraint-based approach to date that can integrate relative metabolite levels to investigate differential flux behaviour between two scenarios. It relies on a mass-action-like description of reaction rates (i.e. flux). In contrast to uFBA, iReMet-Flux does not require data on absolute quantification of metabolite levels and therefore allows for a broader application due to the availability of relative metabolomics data. In contrast to TREM-Flux, it does not assume a linear scaling with the change of metabolite levels between two time points. In addition, iReMet-Flux differs from a recent approach in which the relative metabolomics data are integrated on a qualitative level (i.e. increases or decreases)14. Similar to the objective on which MOMA is based19, iReMet-flux minimizes the flux differences between two scenarios, but does not rely on pre-calculated flux distributions for a reference scenario. Additionally, iReMet-flux allows for the integration of relative enzyme levels either by direct usage of quantitative or qualitative proteomics data, or via gene expression ratio that can serve as a proxy21. However, if employed to time-series data, it does not account for the magnitude of possible flux changes between time steps. To address this problem, we extended iReMet-flux to account for temporal changes, while keeping the possibility of multi-level high-throughput data integration.Of particular interest are approaches which allow integration of relative metabolite levels, since these datasets are easier to obtain in contrast to absolute metabolite concentrations used in thermodynamic flux balance analysisA. thaliana Col-0 wild type and reil1-1 reil2-1 double mutant plants before and after cold shift. Finally, we provided directly testable hypotheses about the impact of REIL-mediated deficiency in ribosome biogenesis on metabolism.Here, we aimed to develop a novel constraint-based approach, termed TC-iReMet2, that facilitates the integration of relative metabolite and transcript levels while accounting for temporal change of physiological parameters. We used TC-iReMet2 to investigate differential flux behaviour of A. thaliana. Unlike genome-scale metabolic networks22, we refer to large-scale models as those reconstructed following a bottom-up approach23.We propose Time Course Integration of Relative Metabolite and Transcript levels (TC-iReMet2) that estimates fluxes based on the integration of time-course data on relative metabolite and transcript levels. The key feature of TC-iReMet2 is that it accounts for the possible magnitude of flux changes between time points and thus could provide a more accurate explanation of flux rerouting over time. We show that TC-iReMet2 can be applied to study flux redistributions in pathways in a large-scale metabolic network of S of the considered metabolic model. The rows of the stoichiometric matrix correspond to metabolites, and columns stand for reactions. The integer entries denote the molarity of a product (positive entry) or a substrate (negative entry) in a reaction, ensuring mass and charge conservation. In the following, we assume that the investigated metabolic network contains P reactions and n metabolites, and that its functioning is compared between two experimental scenarios, denoted by A and B (e.g. mutant and wild type) over to time points t. Furthermore, we denote by Similar to other constraint-based approaches, TC-iReMet2 uses a stoichiometric matrix i, j, j enters a reaction i as a substrate, A: B: Under mass action kinetics, a flux through an irreversible reaction j as i as i as To simplify the notation, we will refer to the ratio of metabolite levels of 25. For metabolite ratios, only a small portion of the metabolome, and hence metabolite ratios, can be quantified. To account for the case that a metabolite ratio cannot be measured, general upper and lower boundaries for metabolite ratios are introduced. If the ratio of metabolite j is experimentally quantified, it is indicated by Determining the entirety of metabolite and enzyme concentrations is not possible with the existing technologiesi is defined, it is indicated by In the absence of enzyme ratios, we use the Gene Protein Reaction (GPR) rules of metabolic models to approximate enzyme ratios using transcriptomic data. The GPR roles are defined by a set of Boolean expressions that describe which genes encode an enzyme. For example, gene products encoding for isoenzymes or isoforms are linked by an OR operator. Conversely, protein subunits that must be present simultaneously to form an active enzyme are linked by an AND operator. In case of an enzyme encoded by one gene, the enzyme concentration is approximated by its expression value. For each reaction that is catalyzed by a complex requiring multiple genes, the enzyme concentration is set to the minimum expression value of gene products connected by the AND operator. For the OR operator, the sum of expression values for the respective genes is used. These rules were applied to each reaction in both scenarios, fractioned and assigned as the corresponding enzyme ratio. Therefore, an enzyme ratio is represented by a ratio of gene expression levels following the GPR rules. Equivalently to metabolite ratios, if a GPR rule for reaction A. thaliana. This has been verified by performing flux variability analysis at a fixed flux through the biomass reaction, to specify that 80% of reactions operate in only one direction18. A ratio constraint for reaction i is included if not only the enzyme ratio, but also at least one of the substrate ratios corresponding to that reaction is available. For metabolites or enzymes whose ratios could not be determined we use the extremal values found at that specific time point. Let F(i) denote the set of substrates of reaction i. Additionally, let the set of irreversible reactions with at least one experimentally quantified metabolite ratio and approximated enzyme ratio be denoted by j and transcript ratio of i are indicated by In this setup, we only consider constraints for irreversible reactions, since more than 80% of reactions that are assumed to follow mass–action-like kinetics  are irreversible in the analyzed model of opt, is calculated for both scenarios via FBA. A biomass fraction B to its respective value derived from FBA. In contrast, biomass flux in scenario A is fixed to a fraction Similarly, a ratio constraint for the biomass reaction can be formulated. To this end, a time-point specific biomass fraction, denoted by A and B respectively. (ii) the biological system aims to maintain an optimal state given by the enzymatic setup. This assumption is captured by making sure that the flux distributions between the two scenarios at a given time point t. We model this assumption by accounting for the magnitude of possible physiological changes by assuring that the difference of flux distributions between time points is as small as possible, i.e. A, weighted by B, weighted by Furthermore, we assume that: (i) the metabolic network to operate in quasi-steady state at every time point. Hence, In summary, the TC-iReMet2 approach is cast as a quadratic program (QP) as follows:A. thaliana REIL deficiency. To this end, we compared predicted flux differences between Col-0 wild type and reil1-1 reil2-1 double mutant with deficiency in cytosolic 60S ribosome biogenesis. The REIL proteins are required for growth when plants are shifted to cold . Along with the morphometric data, the relative changes of metabolite pools and transcripts were profiled30  and non-growth-associated maintenance functions (NGAM22).The experimental setup and the availability of transcriptomics data and data on relative metabolite levels allowed the application of TC-iReMet2The overall flux distance of wild type compared to mutant across all predicted reactions differed before cold shift, with the wild type having a higher overall flux Fig. A. This pWe next considered the flux differences for each reaction in the metabolic model. Additionally, we investigated reactions displaying large changes in flux differences at early time points, as the most interesting to understand the changes in the metabolic network functionality in response to the cold shift.We focussed on differential behaviour of all reactions between mutant and wild type, excluding transport reactions and artificial reactions to avoid bias due to lack of gene association for these reactions. To this end, we applied K-means clustering to group reactions . In contrast, cluster 2, consisting of 46 reactions, exhibited mainly decrease of relative flux changes (DDD). This inverse behaviour is best captured by the function of RuBisCO as it exhibits strong flux changes for its carboxylation function (cluster 5) and oxygenation function (cluster 2). Reactions in cluster 3 mainly exhibited no changes (NNN). Similarly, cluster 6 summarizes reactions that exhibit constant positive flux change over all time points. The remaining clusters 1, 4 and 7 group reactions that exhibit an inverse shift in behaviour at day 21.If we consider the top 10 reactions  did not find a solution after 1000 trials. Therefore, this analysis indicates that the findings are specific, in the sense that alternative optima are unlikely, and significant as there are no other possible flux distributions in optimal solution.14. With the advances in the proteomics profiling, TC-iReMet2 has the potential to provide further applications closer to the assumptions of the approach.Here, we proposed a computational approach, termed TC-iReMet2, and showed that it provides the means for time-resolved predictions of fluxes while keeping the simplicity of the constraint-based modelling framework and allowing for the integration of relative metabolomic, transcriptomic, and morphometric data. The findings of this study indicate that TC-iReMet2, a differential flux profiling method, can be used to identify differential fluxes between wild type and mutants over time. It is important to note that TC-iReMet2 uses the ratio of transcripts as a proxy for the ratio of enzyme abundance (following GPR rules). This is a strong assumption, knowing that post-translational modifications and translational efficiency have a large effect on both the abundances and ratios of proteins. However, since transcript ratios are used as one component of the constraints, such an approach provides a better coverage of metabolic networks than proteomics data33. One possible approach to overcome this issues is to use a power-law formalism34, which would allow the constraints to remain linear, at the cost of making assumptions about which regulators affect a reaction rate and with what strength. For this reasons we have decided that TC-iReMet2 is formulated based on mass-action-like kinetic, while allowing for discrepancy to model possible effects due to the mentioned saturation and regulation.Moreover, the enzyme kinetic assumed in TC-iReMet2 does not consider saturation effects no presence of regulators (e.g. activators or inhibitors) of enzyme activity. Inclusion of a saturation effect, like in Michaelis-Menten kinetic, would not allow casting the problem with only linear constraints, rendering application to large-scale networks computationally challenging. Similar problem arises when considering the inclusion of regulation, which is additionally problematic due to the lack of information on how the effect of the regulator is captured in the enzyme kinetic form used, particularly for plantsreil1-1 reil2-1 double mutant to A. thaliana Col-0 wild type before and after cold shift strongly supported for the previously suggested theory stating that REIL mediated ribosome biogenesis deficiency feeds back into metabolism. Overall, we find that flux differences are more similar during hibernation phase, with strong flux redistributions occurring at day 21. This is evident from the data from the morphometric analysis  as a cofactor. These changes may indicate either an altered redox state of these cofactors or more likely differential use of NAD and NADP after cold shift in the mutant. For example, when considering the predicted inverse flux changes of the mitochondrial iCit dehydrogenase isozyme reactions, iCitDHNAD_m and iCitDHNADP_m, we can deduce in agreement with our metabolic model that the mutant switches to preferential use of NAD rather than NADP for this reaction. Inversely, a preferential use of NADP is predicted for 6-phosphogluconic dehydrogenase reactions, 6PGDHNADP_h and 6PGDHNAD_h, and for the H-serine dehydrogenase isozyme-reactions, HserDHNADP_h and HserDHNAD_h. Taken together with the additional indicated flux changes of glutamate synthetase (GluSNAD_h), and of the mitochondrial malate dehydrogenase , or glutamate dehydrogenases, GluDH1NADP_m and GluDH2NAD_m, we hypothesize that the Generation of this hypothesis would not have been possible by stand-alone analysis of the transcriptome data alone. When we compare the results of TC-iReMet2 with a differential analysis of the transcriptomics data reaction per reaction following GPR rules, only the increase of isocitrate dehydrogenase (iCitDHNAD_m) flux in the mutant can be found overlapping with TC-iReMet2’s predictions .z denotes the value of TC-iReMet2’s objective function and A linear program with a single quadratic constraint is defined to find possible alternative solutions. For this, the solution space has to be defined. In addition to the defining constraints given by TC-iReMet2, a single quadratic constraint, due to quadratic objective function TC-iReMet2 is based on, has to be introduced. It fixes the value of the objective function to be the same as in optimal solution, forcing the optimization to find alternative optima. Here, 2. Select a random direction 3. Find the extreme point in solution space described by 4. If there is a solution for the optimization program at step 3, a new point at the edge of the solution space can be determined to form a line segment with the initial point.5. Randomly choose a point on this line segment to create a sample, which is in turn updated as a new initial point 6. Repeat steps 1. to 5. until the defined number of samples is collected.The multiplication of relative metabolite levels, substitution of unmeasured metabolite ratios with their respective minimum and maximum ratio value together with approximated enzyme ratios can lead to immense ratio constraints, which in turn could lead to numerical instabilities. Determining a maximum considered ratio constraint is therefore crucial to ensure numerical stability. To this end, we calculated the flux distributions allowing for a maximum ratio constraint ranging from 10We used R statistical programming languages implementation of the K-means algorithm with seven assumed clusters . The morphometric parameters apparent planar leaf area and perimeter underestimate biomass accumulation, since rosette leaves could slightly overlap. We contrasted these estimates by using the sum of diameter 1 and 2 as proxies of biomass accumulation. The diameter may slightly overestimate biomass because only the longest leaves are considered. Accordingly, we integrated the four biomass proxy parameters by giving equal weight to each one of them (Supplementary Table To model the fraction of biomass yield in scenario mentclass2pt{minimhttps://www.ncbi.nlm.nih.gov/geo/) and are available through accession number GSE101111. The metabolomics data are obtained from the Supplemental Table 30. The morphometric data are obtained from Schmidt et al., 20132. All data are included in the provided GitHub repository (https://github.com/tciremet2/TC-iReMet2) as well as in the Supplementary Tables to ensure easy access and reuse of the provided implementation.The transcriptomics data are already published and are uploaded to the Gene Expression Omnibus (36 in conjunction with the Tomlab optimization environment37. Statistical analysis and creation of figures was done with R38 programming language and R’s ggplot2 library39 and “MATLAB 2017b, The MathWorks”36. The implementation is available at https://github.com/tciremet2/TC-iReMet2.For implementation of TC-iReMet2 we used “MATLAB 2017b, The MathWorks”Supplementary Information 1.Supplementary Information 2.


The characterization of plant enzymes by expression in prokaryotic and eukaryotic (yeast and plants) heterologous hosts has widely been used in recent decades to elucidate metabolic pathways in plant secondary metabolism. Yeast and plant systems provide the cellular environment of a eukaryotic cell and the subcellular compartmentalization necessary to facilitate enzyme function. The expression of candidate genes in these cell systems and the identification of the resulting products guide the way for the identification of enzymes with new functions. However, in many cases, the detected compounds are not the direct enzyme products but are caused by unspecific subsequent reactions. Even if the mechanisms for these unspecific reactions are in many cases widely reported, there is a lack of overview of potential reactions that may occur to provide a guideline for researchers working on the characterization of new enzymes. Here, an across-the-board summary of rearrangement reactions of sesquiterpenes in metabolic pathway engineering is presented. The different kinds of unspecific reactions as well as their chemical and cellular background are explained and strategies how to spot and how to avoid these unspecific reactions are given. Also, a systematic approach of classification of unspecific reactions is introduced. It is hoped that this mini-review will stimulate a discussion on how to systematically classify unspecific reactions in metabolic engineering and to expand this approach to other classes of plant secondary metabolites. Escherichia coli  to [M(ST) + 324] (the mass of two glucose units minus the mass of two water units) .10) and a decrease of the rearrangement products costic acids and ilicic acid (11–14) by comparing extracts from buffered versus unbuffered yeast cultures on a GC-MS and a LC-MS system  to [M(ST) + 18]. This was observed for the rearrangement from germacrene A acid  to β-elemene (16). However, germacrene A (15) elutes later (at higher temperatures) from the GC-MS column than its heat-induced rearrangement product β-elemene (16) [Similar to acid-induced rearrangements, heat-induced rearrangement does not change the total mass and only slightly changes the MS-MS pattern for the conversion from germacrene A (ene (16) .All the above-mentioned unspecific reactions could theoretically occur in the natural plant except for the heat-induced Cope rearrangement. Why are these reactions rarely observed in nature, where plants can accumulate highly reactive enzyme products in high concentrations in a way that provides protection without poisoning the host plant cell? In the case of STL, several biosynthetic enzymes that have so far been characterized are naturally located in secretory cells of glandular trichomes ,11,13 orFor researchers engaging in the reconstruction of metabolic pathways, it is crucial to have a perception about the unspecific reactions the presumed enzyme product may undergo. Here, a systematic approach to categorize and explain the most frequent unspecific reactions for sesquiterpenes is presented. Expanding this approach to other classes of natural compounds such as mono-, di- and triterpenes as well as polyphenols and alkaloids could create a store of knowledge to better plan and interpret the reconstruction of the metabolic pathways of plant specialized metabolites.


Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis. Candida species are by far the most common cause of invasive fungemia in humans [Candida species, Candida albicans is the main etiological agent of invasive candidiasis [In the last few decades, a significant increase in nosocomial fungal infections has been observed, and n humans ,2. Amongdidiasis ,4, beingdidiasis ,6, its adidiasis ,8,9 makeCandida infections , and only some azoles and echinocandins are recommended as first-line agents [C. albicans, but even more so for other emerging non-albicans Candida species, such as C. glabrata, C. krusei and C. auris. For example, in recent studies, almost 40% of the Candida glabrata isolates shown to be resistant to at least one echinocandin were also resistant to fluconazole [albicans pathogenic Candida species, the scenario is even more frightening, as several of them display, either intrinsic or easily acquired resistance to several of the available antifungal agents. For example, in a recent case, Candida auris isolates were identified as resistant to the three classes of available antifungal drugs, further raising public concern on the future efficacy of current antifungal therapeutic options [Only three classes of antifungal drugs are licensed to treat e agents . Currentconazole ,12. In n options . The idePlasmodium falciparum, enabling the identification of potential drug targets that could be used as both, anti-malarial drugs and transmission-blocking agents [Genome-scale metabolic models have the potential to provide a holistic view of cell metabolism. Historically, these global mathematical descriptions of cell metabolism have mostly been linked to metabolic engineering of microbial cell factories, given their potential to simulate global metabolic behavior and provide hints to guide experimental optimization of such organisms for the production of added-value compounds . Howeverg agents .C. albicans, the iRV781. This model is provided in the well-established SBML format and can easily be read in most metabolic engineering platforms such as OptFlux [Here, we present the first validated in silico genome-scale metabolic reconstruction of  OptFlux  and COBR OptFlux . The modCandida albicans iRV781 genome-scale metabolic model was developed following the methodology represented in merlin 4.0.2 [Merlin is a platform that enables the semi-automatic reconstruction of metabolic models, providing a user-friendly interface that assists the user in the manual curation process [The in 4.0.2  for the in 4.0.2 , and Optin 4.0.2 , for the process .Candida albicans SC5314 was obtained from NCBI’s Assembly database, accession number ASM18296v3 (www.ncbi.nlm.nih.gov/assembly) [www.ncbi.nlm.nih.gov/taxonomy) [merlin to univocally identify the organism under study throughout the reconstruction process. In order to establish a proximity between species, the 16S rRNA gene of several known closely related species was used to construct a Phylogenetic tree, the sequences being retrieved from NCBI’s database and aligned using MEGA X 10.0.5  [merlin based on taxonomy and frequency of similar sequences trough remote Basic Local Alignment Search Tool (BLAST) [http://www.UniProt.org/) and HMMER [merlin, using genome annotation to determine which reactions will be included in the model, based on an algorithm described in detail elsewhere [The genome sequence of the reference strain ssembly)  and the axonomy) , which iPA, USA) . The evoPA, USA)     [In order to ensure that all reactions in the network are balanced, unbalanced reactions were identified, using the corresponding (BRENDA)  as refer(BRENDA) . Since t(BRENDA) , UniProt(BRENDA)  and KEGG(BRENDA) ) and litThis model includes four compartments: Extracellular, cytoplasm, mitochondrion and cytoplasmic membrane. The prediction of compartments for each enzyme and carrier was performed using the WoLF PSORT protein subcellular localization predictor .merlin’s TranSyT [2O, CO2, and NH3, which are often carried by facilitated diffusion, were added to the model with no gene association.Given the existence of compartments in the model, it is necessary to create transport reactions for the metabolites. Transport reactions were generated using genomic information together with the public database TCDB  by merli TranSyT . TranspoThe biomass formation was represented by an equation that includes proteins, DNA, RNA, lipids, carbohydrates, and cofactors, and detailed information for the composition of each one of these macromolecules. The content of each component was determined based on the literature or using experimental data. All the calculations were performed as described previously S. cerevisiae iMM904 metabolic model was applied, 1.5.This ratio represents the relationship between ATP synthesis and oxygen consumption, indicating the number of orthophosphate molecules used for ATP synthesis per atom of oxygen consumed during oxidative phosphorylation [merlin, and were updated to replicate the same ratio as in the iMM904 model:For the phosphorus to oxygen ratio the same theoretical ratio used in the rylation . Three gReaction R00081_C4:Reaction T02161_C4:Reaction T00485_C4:The final balance reaction:S. cerevisiae, adjusted for the composition in macromolecules of the biomass equation [This model also includes ATP requirements for biomass formation and maintenance (non-growth). The growth ATP requirements, 23.346 mmoles ATP/g Dry Cell Weight (DCW), were introduced directly into the biomass equation; this value was calculated based on ATP requirements for biosynthesis of cell polymers for equation .Candida tropicalis [Non-growth associated ATP maintenance, the amount of ATP required by the cell even when it is not growing, was represented in the model by an equation that forces ATP consumption via a specific flux. The boundaries of this flux were inferred from opicalis . See FilThroughout the curation process, reactions were edited, manually added to, or removed from the model to correct some gaps in the network, using KEGG pathways, MetaCyc Database, and literature data as standards.Candida albicans reference strain SC5314 was batch-cultured at 37 °C, with orbital agitation (250 rpm) in Yeast Nitrogen Base (YNB) medium without amino acids: 5 g/L glucose (Merck), 6.8 g/L YNB (Difco). Solid media contained, besides the above-indicated ingredients, 20 g/L agar (Iberagar).C. albicans. For the few carbon or nitrogen sources for which the model predictions were not consistent with literature data, wet-lab experiments were conducted. Specifically, the utilization of cellobiose, D-Ribose, and mannitol as carbon source, by the C. albicans reference strain SC5314, was evaluated in solid YNB medium containing either 5 g/L glucose as control, or 5 g/L of either one of the mentioned carbon sources. C. albicans cell suspensions used to inoculate the agar plates, were mid-exponential cells grown in YNB medium with 5 g/L glucose, until culture OD600nm = 0.5 ± 0.05 was reached and then diluted in sterile water to obtain suspensions with OD600nm = 0.05 ± 0.005. These cell suspensions and subsequent dilutions  were applied as 4 µL spots onto the surface of solid YNB media, with the indicated carbon sources. Growth was assessed after incubation at 37 °C for 24 h.The capability of utilizing different carbon and nitrogen sources for cell growth was assessed by comparing in silico predictions to literature data for All the phenotype simulations were performed with Flux Balance Analysis (FBA) in OptFlux 3.0  using thThe final version of the iRV781 model includes 781 genes associated with 1221 reactions, among which, 174 are transport reactions, and 196 are external drain reactions , involving 927 metabolites and four different compartments. Analyzing the distribution of proteins by compartments, 205 are plasma membrane proteins, 521 cytoplasmatic proteins and 139 mitochondrial proteins.C. glabrata [S. cerevisiae [C. glabrata, S. cerevisiae or both.In order to elucidate the characteristics of our model we selected well-characterized genome-scale metabolic models of glabrata  and S. crevisiae  as a comS. cerevisiae or C. glabrata). Furthermore, about 65% of the proteins are shared by the three models while about 20% are unique in iRV781 (Although our model has common standard identifiers for reactions (KEGG ID), it is not possible to assess how the reactions differ among the three models, since the remaining two models do not possess the same identifiers. However, considering only the proteins associated with an EC number, it is possible to make a comparison across the existing models. More than 80% of the proteins with an associated EC number in our model are also present at least in one of the other 2 models , but absent in C. glabrata.The enzyme 1.13.99.1, inositol oxygenase, responsible for the conversion of myo-inositol into D-glucuronate. This enzyme seems to be involved in resistance to toxic ergosterol analogs , is alsoC. albicans to use L-sorbose as carbon source [S. cerevisiae.The enzyme 1.1.1.289, sorbose reductase, responsible for the interconversion of L-sorbose into D-sorbitol. In fact, the presence this enzyme allows n source , unlike N-Acylsphingosine. This protein is involved in sphingolipid metabolism, with possible impact in azole resistance in C. albicans [Candida species, being present in C. parapsilosis, C. dubliniensis, C. auris, but not in C. glabrata.The enzyme 1.14.19.17, sphingolipid 4-desaturase, responsible for the conversion of dihydroceramide into albicans . The preThe enzyme 1.1.99.2, L-2-hydroxyglutarate dehydrogenase, is a metabolite repair enzyme responsible for the conversion of (S)-2-hydroxyglutarate into 2-oxoglutarate. In other organisms such as plants  or humanN-acetylglucosamine kinase, responsible for the conversion of N-acetyl-D-glucosamine into N-acetyl-D-glucosamine 6-phosphate. Many yeast species, including S. cerevisiae have lost their ability to utilize N-acetyl-D-glucosamine as carbon source, however, genetically altered yeasts are able to use it, based on expression of C. albicans genes [C. albicans to utilize this carbon source, a feature that is particularly important for its survival inside the phagosomes [The enzyme 2.7.1.59, ns genes . In factagosomes .N-acetylglucosamine-6-phosphate deacetylase, responsible for the conversion of N-acetyl-D-glucosamine 6-phosphate into D-glucosamine 6-phosphate. Like 2.7.1.59, this enzyme is also involved in N-acetyl-D-glucosamine metabolism.The enzyme 3.5.1.25, C. albicans.The enzyme 1.4.3.3, D-amino-acid oxidase, responsible for the conversion of a D-amino acid into a 2-oxo carboxylate and ammonia, is the first enzyme involved in the catabolism of D-amino acids and may allow the utilization D-amino acids as a source of carbon or nitrogen in some yeasts . It may In most cases, the observable differences in EC numbers were related to outdated EC numbers or were compensated with other enzymes that are responsible for the same reactions in the model. However, some cases stand out as potential unique features of S. cerevisiae. On the other hand, 336 reactions were removed from the initial model, have been removed for being unconnected reactions, general reactions, reactions using metabolites that are not included in the model, or reactions for which it was manually verified that the model does not have the enzyme coding gene. Additionally, the compartment of 79 reactions was changed, and 94 reactions were altered to become balanced. The complete list of alterations can be found in During the process of manual curation described in the methods section, a total of 66 reactions were manually added to the initial model obtained from the results of re-annotation to fill gaps. Additionally, evidence from the literature was always considered, or of the well-studied The biomass equation  includesS. cerevisiae [Carbohydrate , Lipid , Sterol revisiae .C. albicans strains, other than the reference SC5314 strain, was also considered in the analysis to increase the number of carbon and nitrogen sources tested.Based on the literature, phenotypic growth data were collected from different sources. Data related to C. albicans CBS562 strain seems not to be able to use cellobiose or D-ribose as sole carbon sources, contrary to the model’s prediction. Therefore, the utilization of cellobiose and D-ribose by C. albicans SC5314 was evaluated experimentally to assess whether the prediction failure could result from a different metabolic capacity exhibited by the reference strain. The results confirmed the model’s prediction regarding the utilization of cellobiose and D-Ribose  Fungal Biodiversity Centre webpage , the C. D-Ribose , suggest4 strain ,62 .Experimental data obtained elsewhere  from synhe model . Additiove yeast , under aC. albicans is unable to grow in anaerobic conditions in minimal media. However, C. albicans colonization is known to spread into anaerobic niches of the gastrointestinal tract or in the inner sections of biofilms where the oxygen availability is scarce or null. Dumitru et al., 2004 reported a defined anaerobic growth medium for studying Candida albicans. In this medium (GPP) oleic acid and nicotinic acid were added as required growth factors for anaerobic growth [S. cerevisiae under anaerobiosis also requires growth factors, such as ergosterol and Tween 80, a source of oleic acid, if growing in a defined medium (SMM), it should be noticed that SMM medium also contains nicotinic acid in its composition despite not being a required growth factor in anaerobic conditions [Glucose = 6.58 mmol g−1 dry weight h−1, to compare the growth parameters with the reported values for S. cerevisiae. Indeed, for the same anaerobic conditions, the specific growth rate of the model and the ethanol production are similar to data reported for S. cerevisiae [c growth . Interesnditions . Cultivarevisiae , though C. albicans essential genes [A set of al genes , were usal genes  , a very well-known class of drugs, used to treat infectious diseases [C. albicans has not been sufficiently investigated. However, it seems that sulfa-fluconazole combination results in increased antifungal activity against C. albicans, leading to the reversal of azole resistance in previously resistant strains [Another promising example of a predicted diseases . The eff strains .Since a reaction can be catalyzed by a protein encoded by more than one gene, and genes may encode more than one protein, we decided to analyze the model’s critical reactions. This analysis allowed to increase the number of confirmed drug targets predicted as essential enzymes.As an example, the FKS genes stand out. They are not considered essential as the enzyme beta-1,3-glucan synthase can be encoded by more than one FKS/GSC/GSL genes . HoweverC. albicans’ model (Candida species. In fact, if we search the 12 genes present in albicans Candida species, they all have orthologous genes in C. parapsilosis and C. dubliniensis, 11 of them have orthologs in C. auris and 8 of them in C. glabrata.Other drugs, such as ethionamide, sulfacetamide, azelaic acid, cerulenin or trimethoprim, were identified as targeting proteins from various organisms. These proteins are homologous to the proteins encoded by genes identified as essential in RPMI, in the s’ model . AltogetDespite the methodology for the reconstruction of genome-scale metabolic models being standardized, eukaryotic models remain a challenge, due to their large genomes and complexity . These mCandida albicans in systemic infections. However, it is important to highlight that in theses reconstructions, it is not considered that cells may need time to adapt to genetic perturbations or environmental variability [Genome-scale metabolic reconstructions are effective in drug target prediction and are expected to continue to expand in the future . Gene esiability . Additioiability . Despiteiability .C. albicans is presented in this study. The model was manually curated and validated thoroughly, constituting a powerful platform for the study of C. albicans metabolic potential and weaknesses. The iRV781 model includes 781 genes associated with 1221 reactions, the number of reactions in the main pathways being similar to those in C. glabrata and S. cerevisiae models. However, about 20% of the proteins associated with EC numbers in iRV781 are unique in relation to these models. The model proved accurate when predicting the utilization of different carbon and nitrogen sources, and in anaerobic growth in defined anaerobic media. In silico growth parameters are also in agreement with the experimental data. We were able to identifyas essential genes in RPMI medium some which are already known targets antifungal agents and other antimicrobial agents used in clinical practice. This observation suggests that the C. albicans global stoichiometric model, presented herein, may be a promising platform for the identification of further targets for new antifungal drugs.The first validated global metabolic model for the human pathogen