Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia. Malassezia species are lipophilic and lipid-dependent yeasts that are frequently encountered in the human and animal microbiota. Usually, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis (SD), pityriasis versicolor, atopic dermatitis, and folliculitis -based models  can be uMalassezia species and an atypical M. furfur strain. To this end, we used metabolic modeling, using genomics and proteomics data. The results show flux differences in the production of steroid in M. furfur and the metabolism of butanoate in M. pachydermatis. In addition, defects in the assimilation of palmitic acid in Malassezia globosa, Malassezia sympodialis, M. pachydermatis, and the atypical variant of M. furfur (4DS), were suggested, but not for M. furfur CBS 1878. These predictions were validated via culturing on defined media.The aim of this work was to investigate the genomic and metabolic differences between reference strains of four Malassezia strains M. globosa CBS 7986, M. sympodialis CBS 7222, M. pachydermatis CBS 1879, and M. furfur CBS 1878 purchased from Fungal Biodiversity Center , as well as a previously reported isolate of M. furfur with atypical assimilation of Tween 80 (4DS)  , 20 g L-1 Ox-bile , 36 g L-1 malt extract , 2 mL L-1 glycerol , 2 mL L-1 oleic acid , and 10 mL L-1 Tween 40  for 4–5 days at 33°C   were use at 33°C . The gen globosa , M. pachdermatis , and M. podialis  have bees genome  were useM. furfur strains that were sequenced in this study were recovered in modified mDixon for 4–5 days at 33°C (The  at 33°C . Genomic at 33°C .M. furfur strains was sequenced at the Beijing Genomics Institute (Shenzhen) using the Illumina HiSeq 2000 platform. Two runs of 100-bp paired-end reads and 200-bp insert-size libraries were undertaken following standard Illumina protocols. The quality of the reads was analyzed using FastQC software  genes were compared with the predicted genes and proteins of the genomes using blastp, blastn, tblastn from the blast suite version 2.2.29  was obtained from the metabolic network using in-house Perl scripts to obtain a system of linear equations, S × v = 0, where v is the flux vector. System constraints included the lower and upper bounds of reaction fluxes. The system allowed 0.000–1,000 mmol gDW-1 h-1 for irreversible reactions and -1,000 to 1,000 mmol gDW-1 h-1 for reversible reactions. The system was solved to identify the theoretical limits for different fluxes in the metabolic system using GAMS  software. The linear programming presented here was developed with solver CPLEX 12.6.0.0 with an optimization tolerance of 10-6. A modified biomass production reaction (Supplementary Table S. cerevisiae as an objective function (S. cerevisiae (-1 h-1) in anaerobic conditions (oxygen uptake rate of 2 mmol gDW-1h-1) was used for the simulation. This optimization problem aimed to solve the maximization of the flux through the reaction of biomass used as objective functions. The resulting flux distributions were filtered with a cutoff of ±0.05 mmol gDW-1 h-1 to allow a better visualization of the reactions that can carry fluxes and plotted as a heatmap in R , 36 g L-1 malt extract , 6 g L-1 peptone , 2 mL L-1 glycerol , 2 mL L-1 oleic acid , 10 mL L-1 Tween 40 , and 500 mg L-1 chloramphenicol . Aliquots of 5 mL were taken in the early exponential and early stationary phase and centrifuged at 26,000 g for 10 min. The resulting pellet was washed three times with PBS, after which it was resuspended in extraction buffer . Cells were disrupted by vortexing for 10 min with 4-mm silica beads and centrifuged at 26,000 g and 4°C for 10 min. Proteins in the supernatant were precipitated with 20% TCA  (Protein extraction was carried out as described . Strains States) . The amo3. Protein profiling for each sample was carried out using the mass spectrometer Michrom HPLC Paradigm type, the Q-mass spectrometer ionization Proxeon Exactive nano-spray, and the Easy-LC II HPLC.The extracted proteins were sent to the proteomic center of the University of California at DavisMalassezia spp. predicted proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE  and proteins were compared against the Uniprot Ustilaginomycotina proteins and the he PRIDE  partner he PRIDE . To vali-1 K2HPO4; Sigma–Aldrich, United States), 145 g L-1 KH2PO4 , 20 mL M-N , 15 g L-1 NaCl , 1 mL 1% CaCl2.2H2O  (w/v), 10 mL 20% glucose  (w/v), 10 mL 0.01% FeSO4  (w/v), 5 mL spore elements , 100 mg L-1 CuSO4.5H2O , 100 mg L-1 H3BO3 , 100 mg L-1 MnSO4.H2O , l00 mg L-1 Na2MoO4.2H2O , and 2.5 mL 20% NH4NO3  (w/v)] containing 4 mM Tween , or 4 mM oleic acid (Carlo Erba), or palmitic acid (Merck) supplemented with 1% Brij-58 , an emulsifier that is not metabolized. It did not support the growth of the FAS mutant of the yeast S. cerevisiae  contained 98% palmitic acid and 2% elaidic acid, an unsaturated acid. The oleic acid (Carlo Erba) contained 78% oleic acid and, in addition, we detected polyunsaturated fatty acids (10% of linoleic acid), unsaturated fatty acids , and saturated fatty acids .To determine the growth on different Tween varieties and with fatty acids, the strains were first grown on mDixon at 33°C for 7 days. The fungal cells were suspended in 3 ml of water with 0.1% Tween 80 used to inoculate 27 mL of minimal medium (MM) [containing per liter: 10 mL K-buffer pH 7.0 , with 30.8 and 7% of the genomes of M. furfur and of the atypical M. furfur strains, respectively, representing multiple matches or repetitions. In contrast, less than 1% of the genomes of M. globosa, M. pachydermatis, and M. sympodialis corresponded to multiple matches.A pairwise Nucmer comparison at the nucleotide level showed that M. globosa, which only showed 0.8% identity using exact matches of at least 20 bp when compared to M. furfur or the atypical M. furfur genomes. This was <1.2% when the M. globosa genome was compared to M. pachydermatis and M. sympodialis. As expected, the most similar strains were M. furfur and the atypical M. furfur strains with a similarity >80% (Table 2). A higher degree of similarity was observed when evaluating the six translation frames using Promer . The biggest differences were found when M. furfur and M. sympodialis were compared, with 70.7% zero matches. The second highest difference was between M. globosa and M. furfur, with approximately 48% of zero matches. As expected, the highest similarity was observed in the case of the M. furfur strains with only 3.4% of zero matches.The Nucmer alignment showed significant sequence divergence between the four species. The biggest difference was observed for M. furfur sequences in this study with the already published Malassezia genomes, we built a phylogenetic tree using highly conserved genes . This phylogeny showed that atypical M. furfur clustered with M. furfur CBS 7982 and our sequence of M. furfur CBS 1878 clustered with the previously reported sequence of this strain.To establish the taxonomic position of the two reported M. furfur and the atypical M. furfur strain using multiple lines of evidence . Functional annotation showed that both M. furfur strains contained twice the amount of proteins when compared to the other Malassezia species. Yet, the number of EC numbers and reactions were similar, as can be seen in Figure 2. The average genome length, exon length, and the number of exons per gene for the five genomes analyzed are presented in Figure 2.A total of 10,203 and 12,131 protein-encoding genes were predicted for M. furfur and the atypical M. furfur contained 7,570 and 10,434 protein clusters, respectively, as predicted with CD-Hit  were found . The PKSs from the five species shared most of the domains, with the exception of M. globosa, which lacked the S-adenosyl-L-methionine-dependent methyltransferase domain, and M. furfur, which lacked the ketoreductase domain, acyl carrier protein-like domain, NAD-binding domain, and the thioester reductase-like domain in its three predicted PKSs. Out of 954 fungal homologs, the PKS of the basidiomycete Paxillus involutus was the most similar to that of the atypical M. furfur, with an identity of 31%. The most similar PKSs to those of Malassezia were found in basidiomycetes. The closest homolog of the PKS of M. furfur was from Hydnomerulius pinastri, with an identity of 29.8%, while an Auricularia delicata homolog was most similar to the PKS of M. pachydermatis and M. sympodialis, showing 29.9 and 31% similarity, respectively. In the case of M. globosa, the most similar PKS was that of Coniophora puteana, with an identity of 26.8%.To search for genes related to lipid biosynthesis, a total of 2,382 FASs  were compared with the predicted proteins of the  tblastn  and phmmMalassezia strains was similar when analyzing 13 categories of metabolic genes . The metabolic core  .The distribution of the genes in the genomes of the five Malassezia strains were irreversible. Compartmentalization analyses showed a similar distribution of reactions among the species. However, differences were observed in the number of reactions when the M. furfur strains were compared to the other species. This corresponded to cytoplasmic and mitochondrial reactions . After the gap-filling approach to reduce the network pathologies, around 250 reactions were added to M. globosa and M. sympodialis, 300 to M. pachydermatis, 600 to M. furfur, and 660 to the atypical M. furfur strain. The final networks ended up with 2,162 metabolites in M. globosa, 2,303 in M. sympodialis, 1,838 in M. pachydermatis, 3,103 in M. furfur, and 3,642 in the atypical M. furfur strain. The final metabolic networks . The clustering coefficients were around 0.14 and 0.18 and the diameter of the networks ranged between 10 and 15 nodes.The database search and the group contribution method used to calculate the Gibbs free energy predicted that 15–26% of the reactions of the five Table 4. The highest biomass production was found in the atypical M. furfur strain at 2.84 mmol gDW-1 h-1 and the lowest was in M. sympodialis at 0.089 mmol gDW-1 h-1. The number of reactions that that carried flux varied among the species, ranging from 36% in the atypical M. furfur strain to 42% in the M. sympodialis strain.FBA was carried out and the resulting predicted biomass fluxes that were used as objective functions are shown in Figure 7 for visualization as a heatmap, the resulting clusters were found based in the flux carried by each reaction, with M. globosa and M. sympodialis having the most similar flux distribution. The main differences between M. furfur and the other strains were reactions involved in valine, leucine, and isoleucine degradation, and pyrimidine and purine metabolism. Differences in the lipid-metabolism reactions were also found. M. furfur displayed high fluxes in reactions related to fatty acid degradation and elongation, which involved the conversion of hydroxyacyl-CoA to trans-2-enoyl-CoA. These were higher in comparison to those observed in the atypical M. furfur strain. Several reactions associated with fungal steroid biosynthesis were found in the atypical M. furfur and M. furfur isolates. Despite the atypical M. furfur isolate having a larger number of these reactions, they had higher fluxes in the M. furfur strain. In the atypical M. furfur strain, higher fluxes were found in reactions involved in the degradation of maltose, fructose, and starch. M. globosa and M. sympodialis showed similar metabolic behavior. However, some differences in M. sympodialis corresponding to changes in the pyruvate and gluconeogenesis pathways, as well as the pathways involved in the degradation of long-chain fatty acids into acyl-CoA, and in the conversion of hydroxyacyl-CoA to trans-2-enoyl-CoA were found. Finally, in M. pachydermatis, there were differences in the core metabolism of pyruvate and butanoate, and in the biosynthesis of phenylalanine, tyrosine, and tryptophan.The flux distribution of each network is represented in Malassezia species, and lipid metabolism may be related to the pathogenesis of these yeasts, differences in lipid assimilation among the species was further evaluated in the model. In reactions involve in fatty acid biosynthesis such as MNXR2003 , involved in the conversion of hexadecanoic acid to palmitoyl-CoA, higher fluxes were found in M. furfur, even though all the strains’ genomes contain the genes that codify the enzyme that catalyzes these reactions.As lipid assimilation differs among M. furfur peptides were found during the exponential phase (T1), corresponding to an average of 1,865 ± 154 proteins. The lowest number of peptides (828 ± 456) was obtained with M. globosa in the stationary phase (T2), corresponding to an average of 116 ± 60 proteins. Consensus proteins were defined as proteins present in at least two replicates with a quantitative identity of ≥95%. The number of these proteins differed among the strains. M. furfur in the stationary phase had the highest number , while M. globosa presented the lowest number in the exponential phase (201 consensus proteins). The number of reactions was higher in atypical M. furfur in the exponential phase, with 841 reactions, as compared to the other species (Table 5). The principal component analysis (PCA) showed that the proteome profiles behaved differently among the species, with the samples from atypical M. furfur and M. globosa having the most similar profiles. In addition, the clustering and PCA analyses also showed that the replicates behaved similarly within each species in both the stationary and exponential phases .An average number of 8,864 ± 2,400 Figure 9). Based on the validation and taking into account the percentage of the total reactions that can be experimentally validated . The most successful model was that of M. pachydermatis, where ∼40 and 30% of the predicted proteins were expressed in the exponential and stationary phase, respectively. The less accurate models were those of M. globosa and of the atypical M. furfur, with ∼10% of the predicted proteins being found. Similarly, when evaluating the number of predicted enzymes that were expressed, M. pachydermatis showed the best prediction model with ∼90% of its expressed enzymes predicted by the flux distribution in both the exponential and stationary phases. M. globosa, on the contrary, only showed ∼50% of its expressed enzymes, as predicted by the model under both growth phases.Protein validation of the models was conducted and expressed as the percentage of proteins predicted by the model that were detected by proteomic profiling (expressed proteins) as well as the percentage of expressed enzymes that were found to be catalyzing an reaction carrying flux in the model  declined rapidly in the second growth step  in comparison to that of the same strain reported by Table 1); a similar size to the previously reported size of 13.5 Mb. Phylogenetic analysis conducted in this study established that our M. furfur strains are part of the Malassezia cluster A that has been previously described. This cluster consists of several strains of this species that were suggested to be a species complex (M. furfur (10.3 Mb) clustered with M. furfur 7982 (7.7 Mb), thus showing them to be distantly related to other strains, and suggesting that they are implicated in divergence events  7,570 and 10,434 protein clusters, respectively, were detected. Thus, although there are indeed protein duplications, most of the predicted proteins represent unique proteins in the genomes of M. furfur and the atypical M. furfur strains. This is further supported in the case of enzymes by the fact that the unique EC numbers and reactions in both M. furfur strains were higher than those in the other species studied. This diversification in the number of predicted proteins may provide evidence for the metabolic versatility found in M. furfur. The increase in unique proteins and the genetic diversification present in the M. furfur strains may be due to mating of the yeast, since bipolar mating systems have been previously proposed in Malassezia spp. .As expected, the FAS was not found in the genomes of either e domain , and thenjugates , and in c lipids . FurtherAspergillus oryzae  the production of steroid-like fungal hormones in M. furfur for sexual reproduction, as happens in ascomycetes  fun joedes ; and mos ellisii . Finallys strain . In addi. furfur .M. sympodialis and of the M. furfur strain (M. furfur CBS 1878) networks imply a more complex network. Yet, the clustering and diameter of these two networks were lower than for other yeast metabolic networks such as those of S. cerevisiae   in the cytosol and all Malassezia spp. lack a FAS complex and are not able to produce palmitate  mutants of S. cerevisiae  , but becnoyl-CoA  than thealmitate . This phty acids . These drevisiae . FurtherM. globosa and M. restricta are not capable of synthesizing unsaturated fatty acids due to the lack of a Δ 9-desaturase (EC 1.14.19.2) gene  gene , which idruff/SD . In thisM. sympodialis but not in M. globosa or M. pachydermatis. These differences may be associated with the phenotypic differences of each species, reflected by the differential lipid assimilation or the differential use of Tweens. We should, however, explore more deeply the physiology and regulation of β-oxidation in this yeast  as well as to the fatty acid metabolism (M. pachydermatis may be reflected in the production of lipids that may not be produced by the other Malassezia species and that may be corroborated by lipid profiling of the yeasts.The higher number of reactions related to the butanoate metabolism found in tabolism . Thus, tThe proteomic profiling allowed us to validate, on average, 30% of the predicted proteins of the model, implying that the other enzymes that are supposed to be expressed according to the FBA are not present or that their concentrations are substantially low. An average of 70% of the proteins expressed in all the samples were in fact predicted. Given that this technique is still not widely used to validate these kinds of models, the cutoff points and expected percentages are not well known. Furthermore, issues such as the identification of large-scale protein datasets, small protein concentration detection, and the extraction of low soluble proteins such as membrane proteins may affect the profiling .Malassezia. Flux differences were suggested in the production of steroids in M. furfur and in butanoate metabolism in M. pachydermatis. The assimilation defects of palmitic acid were suggested in M. globosa, M. sympodialis, M. pachydermatis, and in the atypical variant of M. furfur. The capability of M. furfur to assimilate palmitic acid was confirmed via culturing on defined media.Together, the metabolic reconstruction and modeling showed versatility within the genus of ST, AC, AG, HdC, and SR contributed to the design of the work. ST, AC, and RO performed the experiments. All authors were involved in the analysis and interpretation of data. ST and AC wrote the manuscript. HdC, HW, SR, and AG made revisions. All authors approved the version to be published and agreed to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer LK and handling Editor declared their shared affiliation.


Microorganisms oxidize organic nitrogen to nitrate in a series of steps. Nitrite, an intermediate product, accumulates at the base of the sunlit layer in the subtropical ocean, forming a primary nitrite maximum, but can accumulate throughout the sunlit layer at higher latitudes. We model nitrifying chemoautotrophs in a marine ecosystem and demonstrate that microbial community interactions can explain the nitrite distributions. Our theoretical framework proposes that nitrite can accumulate to a higher concentration than ammonium because of differences in underlying redox chemistry and cell size between ammonia- and nitrite-oxidizing chemoautotrophs. Using ocean circulation models, we demonstrate that nitrifying microorganisms are excluded in the sunlit layer when phytoplankton are nitrogen-limited, but thrive at depth when phytoplankton become light-limited, resulting in nitrite accumulation there. However, nitrifying microorganisms may coexist in the sunlit layer when phytoplankton are iron- or light-limited (often in higher latitudes). These results improve understanding of the controls on nitrification, and provide a framework for representing chemoautotrophs and their biogeochemical effects in ocean models. Nitrite tends to peak at the base of the sunlit zone in the ocean, but the ecological drivers of the local and global distributions of nitrite are not known. Here, Zakem et al. use a marine ecosystem model to show how the interactions of nitrifying microbes mediate nitrite accumulation. Most fixed nitrogen in the ocean is in the oxidized form of nitrate (3) and nitrite  due to anaerobic activity2; however, we focus here only on the dynamics of aerobic environments.The primary nitrite maximum (PNM), an accumulation of mentclass2pt{minimmentclass2pt{minim23: (i) excretion of 25, and (ii) chemoautotrophic 26. Here, we focus on outstanding questions of the latter hypothesis, since isotopic evidence suggests that 21.Despite the widespread occurrence of the PNM, the mechanisms that determine the locations and magnitude of accumulated 30 and incorporated into ecosystem models32, but observations of nitrification in the euphotic zone33 and close to the sea surface36 suggest that this is not universally the case. An alternate (but not exclusive) hypothesis is that phytoplankton outcompete slow-growing, chemoautotrophic nitrifiers for 38, but not deeper where light limits photoautotrophy.First, why does peak nitrification occur at the base of the euphotic zone? Photoinhibition of nitrifying microorganisms has been documented40 and differential temperature sensitivity of their rates41, though some observations conflict with the latter42. Here, we will suggest that this reflects differences in the metabolisms of the distinct clades of nitrifiying microorganisms.Second, while both Third, why does nitrification sometimes occur in the euphotic zone, and why is surface [Here we synthesize and address these questions and hypotheses using a hierarchy of mathematical models and simulations that are grounded in theoretical and laboratory evaluations of the kinetics and efficiencies of marine nitrifying microorganisms. The models are used to interpret both existing and new water column observations in the subtropical North Pacific as well as the global distribution of We first present a general population dynamics model for Bi (mol L−1) is the biomass of type i with population growth rate μi (d−1), loss rate Li (d−1), and physical transport. Loss rate is a function of the population density of grazers Z and other factors (Methods). We describe population growth rate μi using Monod kinetics with limiting resource Rj (determined by Liebig’s Law of the minimum). Growth rate depends on the yield of biomass with respect to the limiting resource yij (mol biomass mol R−1), the maximum specific resource uptake rate R per mol biomass per day), and a half-saturation concentration Kij (mol L−1) as:We consider the clades of microorganisms carrying out the two steps of nitrification as two metabolic functional types: ammonia-oxidizing and nitrite-oxidizing organisms . We describe the population dynamics of each type as:43. For the two nitrifying metabolisms, we assume that DIN and oxygen are the potentially limiting resources (Rj). Over most of the subtropical thermocline, oxygen exists at sufficiently high concentrations (greater than nanomolar) to serve as the terminal electron acceptor for AOO and NOO45.The uptake parameters together give an expression for the specific uptake affinity (Equations  and 2)  2) proviRj that limits the growth of nitrifier type i as:Why does t the PNM,12,13? Ci47. In theory, at a steady state, the population with the lowest R* excludes all others limited by the same resource , and the environmental concentration is set to this 48, we derive stoichiometrically balanced equations to describe catabolic and anabolic processes as a function of the redox chemistry that underlies each step of nitrification. Constraining thermodynamic efficiency with published laboratory observations of marine AOO and NOO growth, we approximate the population-level growth stoichiometries of ammonia-oxidizing biomass BAOO and nitrite-oxidizing biomass BNOO as:4 (Methods). The predicted stoichiometry of 2 demand for the AOO is 1:1.45, close to that observed in culture37 (1:1.52) indicating that the model captures key aspects of real metabolisms. In Eqs. . If we assume a 10-fold larger cell volume for NOO, this estimates a 4.6-fold lower specific affinity.Second, we speculate that NOO specific uptake affinity may be lower than that of AOO if (as is observed) yij) and affinity (Li) are low relative to maximum growth rates (and thus negligible in the denominator), then we predict that [12. If yields are similar, but affinity is 4.6-fold different, we predict a similar order of enhanced Using these differences in yield . For simplicity, we neglect other limitations such as iron availability  also correlate with [12.For comparison with the model, we show relevant observations collected at several stations in the North Pacific subtropical gyre in 2014  to diagnose ecological control , to cal52. However, for picophytoplankton, the effective half-saturation concentrations for nitrogen uptake with respect to growth rate are significantly lower57, and maximum growth rate is higher. This reflects that nitrifiers use DIN for energy production, which is relatively inefficient, while the phytoplankton use it as a nitrogen source for synthesis with a much higher biomass yield.We find that the modeled phytoplankton exclude the chemoautotrophic nitrifiers in the euphotic zone, where both are limited by nitrogen. This is because the photoautotrophs have lower subsistence concentrations for R*s , peak [13. This suggests that either the yield or the affinity difference, or a smaller combination of both, may best represent natural assemblages of nitrifiers.We further demonstrate the control of [AOO Fig. . With bo6. Observed pelagic [13 than the deep concentration in the model here (tens of nanomolar). Assuming no other sinks for [R*s of the NOO, but of a diverse community. Affinities and efficiencies may vary with clades, such as those with maximum growth rates too low for survival at the PNM, or may be plastic in nature. A slower-growing, efficient, deep NOO clade could deplete Even deeper in the water column, [Thaumarchaea) and Nitrospina in the California Current19, a 1–5-fold lower abundance of Nitrospina-like bacteria compared to amoA gene and MG1 abundances in Monterey Bay58, and a 1–4-fold difference at Station ALOHA58. Thus, observed abundances are quantitatively consistent with observed [Modeled nitrifier abundances also reflect the distinctions between the AOO and NOO Fig. . With bo2, though differences in coastal waters have been documented42. Thus, differences in the modeled AOO and NOO emerge in nutrient distributions and nitrifier abundances, but not in subsurface nitrification rates. This reinforces our understanding that exported organic matter determines the rate of all steps in the sequence of remineralizing metabolisms below the euphotic zone22.Rates of nts Fig. . This maR* from the peak of the PNM to about 175 m depth. This indicates that vertical mixing is non-negligible there. In other model simulations, particularly in less stratified water columns, this effect is stronger, and DIN accumulates to concentrations much higher than R*  with the phytoplankton at some locations , nitrifiers may locally sustain growth if sufficient DIN is supplied. This characterizes the base of the euphotic zone, and subpolar surface environments during winter, where phytoplankton are light-limited. Surface nitrification rates may also be significant in high-nutrient, low-chlorophyll regions where iron limits phytoplankton growth, if nitrifiers are less iron-limited. Observations suggest that this may be the case for AOO, which require copper rather than iron for redox machinery47 suggests that the degree of coexisting nitrification and primary production in the euphotic zone reflects the degree of limitation of phytoplankton growth. Less iron or less light availability should allow for a proportionally higher rate of coexisting nitrification.Future observations could test these hypotheses. While timescales of marine nitrifier growth are too long for nitrification to become competitive at the surface at night  can be associated with much higher DIN concentrations where losses and mixing are significant. Our models suggest that this is often the case. This highlights the utility of a dynamic ecosystem model in synthesizing complex interactions. Resolving the fluxes of all of the components results, at times, in unintuitive relationships between the standing stocks of nutrients and biomass.14. Laboratory observations25 and observed associations of Prochlorococcus with the PNM68 support this mechanism. Such excessive 21. Rather, we provide explanations for In the current model, nitrification is sufficient to simulate global distributions of To conclude, we have hypothesized the main controls on the distributions of 48, three half-reactions combine to form the catabolic and anabolic full reactions for each nitrifier metabolism: (1) the oxidation of f represents the fraction of electrons from the electron donor that are channeled into biomass synthesis vs. respiration. Combined, these reactions inform a “whole organism” stoichiometry. The resulting correlation of yield y and growth rate yV (Eq. (y and growth rate may be important in modifying further characteristics).Following established methodologyentclass1pt{minima69, and that both AOO and NOO use reduced nitrogen , the three half-reactions, for generic biomass CcHhOoNn, and their electron-partitioning coefficients, are:d normalizes the biomass synthesis reaction to one electron (see definition below). The sum gives the full metabolism for BAOO (ignoring water and lumping bicarbonate into the CO2 pool for simplification), as a function of f:For the BNOO as:For the y, defined as moles biomass N synthesized per mole DIN used, are:The resulting nitrifier yields d for both functional types. Following previous methodology, d represents the number of electron equivalents that correspond to the oxidation states of the inorganic constituents of that synthesis48. Assuming generic microbial biomass composition of C5H7O2N and d = 4(5) + 1(7) − 2(2) − 3(1) gives d = 20.Since we assume that the f). Smaller f equates to a lower growth efficiency and a lower yield, y. While thermodynamics can provide some theoretical constraints on f, the efficiency of energy production in marine microbes is not well known. Thermodynamics and wastewater treatment studies suggest that f is very similar for the two nitrifying types48—the difference in the free energy of the oxidation of − eq−1, respectively, at standard state with pH = 7.048) does not significantly impact theoretical estimates—so we employ published laboratory observations of marine AOO and NOO growth to constrain a common f for both metabolisms73 −1 mol biomass N per mol −1 mol biomass N synthesized per mol f values corresponding to these yields are 0.030 and 0.032, respectively. A value of 0.03 is about one-fifth of the value of f inferred for wastewater bioreactors48, perhaps explained by the need for marine organisms in oligotrophic environments to have higher-affinity, energetically-expensive nutrient transport systems.Our analysis supports the assumption that f = 0.03, d = 20 ± 4, with uncertainty in d from the 1 mol/mol s.d. of the C:N69, where changes in H and O stoichiometry were neglected to give the largest impact on the resulting stoichiometric uncertainty. We examine the sensitivity of the yields to variation in f and d in Supplementary Note The stoichiometries presented assume Nitrosopumilis provide values for the parameters for the uptake of 37 (Supplementary Table Vmax (in mol 37 of 2 per mg protein per hour: (3.35 × 24.2)36.29−1 = 2.23). Assuming a 16% N content of protein gives a specific maximum Vmax we use in the model. . We then use established empirical and theoretical allometric relationships52 to predict the kinetic parameters for the NOO relative to those of the AOO. Allometric theory predicts that though the cellular uptake rate should increase with cell size, the specific uptake rate should decrease due to a decrease in the surface to volume ratio: the cellular rate scales with surface area as cell radius r2, volume increases as r3, and so the specific rate scales as r−1. Theory also predicts that the half-saturation concentration increases with cell size. The diffusion-limited cellular uptake rate (which explains the steep slope of the Michaelis–Menton form) increases as r. The cellular affinity equates to the quotient of the cellular uptake rate and KN. This suggests that KN scales as r2r−1 = r. Together, allometry thus suggests that the specific affinity decreases with cell size as r−2, which is supported by more detailed analysis of nutrient uptake models43.Known 1/3 = 2.2, and so for the NOO, we estimate a specific uptake rate of about half, and a KN of about double that of the AOO. This gives a 4.6-fold lower specific affinity −1 = 88.2 L per μmol biomass N per d). This literature52 also suggests that picophytoplankton affinity for Prochlorococcus-sized cell for inorganic nitrogen is in the range of 100–1000 L per μmol biomass N per d, bracketing that of the nitrifiers. Thus, couched only in terms of affinity for Prochlorococcus and AOA may be close competitors.Empirical results linking cell size to affinity76, using a Varian Cary 100 Bio ultraviolet-visible spectrophotometer. Ammonia oxidation was measured using 1515NH4Cl, 99%, Cambridge Isotope, 100 nM) incubated for 24 h in 500 mL polycarbonate bottles in the dark and at close to in situ temperatures. A 30 mL sample at the beginning and at the end of the incubation (T0 and T24) was extracted from each bottle, filtered through 0.2 μm pore-sized nylon filters, and frozen. Thawed aliquots were treated with sodium azide to convert all of the 2O gas77. The 14N:15N ratio of the N2O gas was analyzed on an isotope ratio mass spectrometer by the University of California Davis Stable Isotope Facility. Unlabeled carrier −1) was calculated as a function of the atom % 15N (a) of initial and final samples as:V is the sample volume (L), and Δt is the incubation time (d).Measurements were made in the subtropical North Pacific on NEMO (“Nutrient Effects on Marine Microorganisms”) Cruise NH1417 in August and September 2014. Using satellite data, stations thought to be at productive locations were chosen for nitrification rate measurements to increase chances of detectable concentrations and rates. Measurements were taken at stations 44, 58, 70, and 99  measurements, seawater was filtered onto 25 mm Whatman GF/F filters . Filters were extracted in the dark in 5 mL of 90% acetone for 24 h at +3 °C prior to measurement78 on a Turner Designs TD-700 fluorometer calibrated with pure Chl a (Sigma-Aldrich). Abundances of thaumarchaeal ammonia monooxygenase aubunit A (amoA) genes were measured with quantitative PCR.For the Chlorophyll BAOO, nitrite-oxidizing organisms BNOO, phytoplankton P, heterotrophic bacteria Bhet, and microzooplankton grazer Z), three inorganic nutrients , with limitation by nutrients (γN), modification by temperature (γT), and limitation by light. Light limitation was parameterized using an exponential form as a function of the instantaneous photosynthetic rate Γ (d−1) and the Chl a to Carbon ratio θ (g/g)79 as:Phytoplankton grow as a function of a maximum growth rate Γ was computed as a function of photosynthetically active radiation I, the maximum quantum yield of carbon fixation ϕ (mol C mol−1 photons), and the absorption of light by phytoplankton 2 (mgChl)−1) representing a mean value over all wavelengths, as:Photosynthetic rate θ varies with photoacclimation, and is computed using a steady-state solution79 as:θmax is a maximum ratio. In the water column model, θ is allowed to reach an arbitrary minimum value of 0.1θmax, which does not affect solutions of any state variables, but does set the minimum modeled Chl a concentration in Fig. The Chl:C Nutrient limitation is a function of the total concentration of all species of DIN:V (d−1) of each DIN species by the phytoplankton type are resolved as:The inhibition of 52. Cell volume v was converted from diameter assuming a spherical cell, and traits calculated with generic form:Values for the maximum growth rate and the half-saturation constants were computed as functions of cell size following data-based allometric relationshipsP in the water column model), a and b are chosen for small cells80, with v computed assuming the average Prochlorococcus cell diameter of 0.6 μm55. For the larger additional phytoplankton types in the global model, a and b are representative of larger phytoplankton cells using published values54.For the picophytoplankton functional type  as:yD partitions consumption of D into biomass synthesis (as yD) and its remineralization into yD − 1). The growth efficiency yD is assumed as the average bacterial growth efficiency of 0.14 (s.d. 0.14) (mol biomass synthesized mol D−1) for the open ocean81. The maximum uptake rate VDmax and half-saturation constant KD are best estimates that constrain the model heterotrophic bacterial growth rate to about 0.1 d−1, matching the average bulk bacterial growth rate82.The bacterial heterotrophic functional type grows as a function of organic matter , heterotrophic bacteria, and the chemoautotrophic nitrifiers. This parameterization was chosen based on the assumption that grazing preferences are predominantly governed by size83, and that Prochlorococcus and the microbial functional types are all roughly the same size.  The total amount of grazing is calculated as a saturating function of total prey biomass, giving the rate of grazing g (L mol−1 d−1), which is then multiplied by the biomass of each prey in Eqs.  using a formulation that follows the Arrhenius equation54 as:T is the ambient temperature, T0 is a reference temperature, AE regulates the temperature modification, and τ normalizes the maximum value.  and the resulting steady-state gZ (d−1), asg (L mol−1 d−1) is the above grazing rate (equation Z (mol L−1) is the population density of predators. In the model here, the loss rate Li is the same for all of the microbial prey populations, since mB is assumed constant and g depends on the sum of all prey biomass.  boundary conditions result in some accumulation of D at the bottom of the 2000 m domain, conceptually representing a sediment layer. To smooth over numerical error, vertical mixing was allowed to increase there with a 100 m length scale, simulating a bottom boundary mixed layer. KZ (m2 s−1) is thus calculated at cell faces as:H is the height of the domain (2000 m).In the water column model, the mixed layer was imposed by varying the vertical diffusion coefficient I decreases with depth according to the attenuation coefficients for water kw and for chlorophyll kChl, following a previous approach84 as:TChl is the sum of the concentrations of chlorophyll, and kChl is an upper estimate of the absorption by chlorophyll to account for additional absorption by colored dissolved organic matter54. Iin is the incoming irradiance, which is 0.5Imax, or for resolution of the daily cycle, Iin(t) = 0.5Imax(cos(ωt) + 1) where ω = 2π d−1.Light energy T in °C.A temperature curve was fit to the mean observations from the four stations sampled on Cruise NH1417 . Advection for the sinking organic matter pool was carried out using the QUICK advection scheme, consisting of a linear interpolation between points weighted by an upstream second order curvature, resulting in third order accuracy. Fluxes were calculated at the faces of each grid cell, and concentrations at the centers.37. The relative larger size of the NOO as compared to AOO was varied from a factor of 1 to a factor of 20, thus, impacting the relative values of Vmax and KN by a factor of 11/3 to 201/3. Because of their large uncertainty, the following parameters were sampled randomly over the linear range of ±50% of the default value: the maximum growth rate μmax and nutrient half-saturation constants VDmax, KD, and yD; the grazing parameters gmax and Kg, mortality rates mB and mZ, and zooplankton efficiency ζ.For the ~1000 model solutions in Fig. 85, and has a horizontal resolution of 1° × 1° and 23 levels of vertical resolution, from 10 m at the surface to 500 m at depth. The model was numerically integrated until rates of microbial activity equilibrated throughout the thermocline (200 years in the illustrated model).The present configuration of the 3D MITgcm biogeochemical model resolves a total of six phytoplankton populations with parameters that represent the traits of the following six functional types: diatoms, picoplankton, diazotrophs, coccolithophores, and other large and other small phytoplankton. Four zooplankton types graze on the phytoplankton: one each specifically on the picoplankton , the other small phytoplankton, and coccolithophore types, and the fourth grazes on the diatom, the other large phytoplankton, and diazotroph type. The three-dimensional ocean circulation state estimate (the ECCO-GODAE state estimate) is from the configuration of the MITgcm as constrained by observationsIn addition to the six phytoplankton types, six microbial metabolic functional types are included in the global ecosystem model, and are responsible for all organic matter remineralization, nitrification, and denitrification. Particulate and dissolved organic matter (POM and DOM) are consumed and subsequently remineralized by the aerobic heterotrophic bacterial functional type (as in the water column model), an anaerobic nitrate-reducing  is available at http://mitgcm.org and the ecosystem component including the bacteria is available from git://gud.mit.edu/gud1.Fortran code for the ecosystem model and the one-dimensional water column configuration is available at Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion–cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and  The human body consists of many different interacting systems. The brain would not survive without circulation, and circulation is only possible when energy metabolism is working. Eating food is only safe when the liver extracts the toxic substances and the immune system defends against pathological germs, and so on. In reductionist bioscience, the human physiology is investigated by identifying its parts and focusing on one of it. In controlled experiments, one factor is changed and every other possible controlled as much as possible. This approach has been extraordinarily successful in understanding basic principles of human  physiology. In principle, the reductionist approach assumes that if scientists investigate every part of the human body and its possible interactions with other parts and when they put all together they will understand human physiology.This approach does not necessarily work out. Even reductionists agree that a brain will die within 3 min when circulation breaks down. However, the reductionist approach does not appreciate the tight interconnection of different parts of the body to function in real time. The basic physiological approach based on research in the nineteenth century is still valid when looking at some general functions. Heart function can be described by a linear equation where cardiac output is multiplied with heart frequency. The view changes, however, when the level of detail is increased. Every heart beat is at least slightly different; cardiac output is influenced by factors as simple as preload, afterload, or systemic resistance, or as complicated as a plethora of humoral factors, a variety of efferent signals and the condition of the heart muscle. Neural inputs are never the same; their patterns are depending on afferent signals from the heart to different brain centers and again humoral signals. These are modified by energy metabolism, immunological signal cascades, the internal state of the brain itself. It is possible to identify hundreds of different factors which probably influence the cardiac output in various ways. However, it is also possible to turn it around. The cardiac cycle has effects on brain function beyond simply providing enough blood flow. Oscillations in the brain are coupled to the heartbeat and oscillations in other systems. The state of the immune system has effects on the brain, which again has effects on the circulation system, apparent in sepsis, but relevant also in less dramatic situations. It turns out that the human body consists of closely interconnected parts which communicate closely in real time on the level of changes in time orders between milliseconds and hours, even days.Another part of the picture regards the influence of perturbations. In a linear, reductionist world, a change in one condition will have a straightforward effect on the connected system. Increase adrenaline twice, and the heartbeat increases twice. Take away some blood, and the blood pressure decreases, take away twice as much, and the blood pressure decreases twice as much. The reality is different. A change of internal or external factor will often only induce minor changes. Losing a half liter of blood will probably induce small changes in blood pressure and slightly increase heart frequency. Removing more will eventually lead to sudden changes, at the end to a breakdown of circulation.Meet another paradigm. The human body can be regarded as a system. A system can be defined as a set of interacting or independent components forming a whole. Each system has boundaries defining an inside and an outside. Systems again can consist of a set of systems (subsystems) interacting in the same way. This definition can be applied to physical entities, human bodies, or social constructions in the same way. Systems can be studied by breaking them up into parts as reductionists do, but the notion of the system includes that a system “is more than its parts” . SystemsBiomedical research in the last decades has reached an enormous level of detail. Independent of the area of investigation, the knowledge of genetics, translational mechanisms, intracellular pathways, receptor systems and their ligands, or immunological mechanisms have reached a level where it is challenging to understand the system as a whole. It turned out that simple systems are not as simple, physiological systems are best assumed to be more complex than at first apparent until it can be demonstrated otherwise . All phyAt the end of the last century, the increasing level of details provided by biomedical research provoked debates how to understand the whole system. Similar debates also occurred in other scientific disciplines. System theory and cybernetics gradually evolved to something which was termed complexity science although an exact definition was challenging and no unified theory existed. At the same time period, system theory evolved to dynamic system theory beyond other influences also with the help of inputs from chaos theory which in reality investigates the behavior of deterministic non-linear systems . AnotherIn the beginnings of 2000s systems biology was established. It is a biology-based inter-disciplinary field of study focusing on complex interactions within biological systems on several levels—intracellular, intercellular, hormonal, macroscopic—using a comprehensive approach and as tools data mining, mathematical models, and time series analysis , 5. A tiThis review is organized as follows. After a short introduction of HRV, some physiological systems with influence on HRV will be reviewed. In the next part, different paradigms of HRV are presented—HRV as time series analysis, as a proxy for the autonomic vegetative systems and as a proxy for a complex regulatory system. Finally, implications will be discussed.The principles of HRV are simple. The heartbeat is measured, usually with the help of an ECG signal which is obtained with an adequate device. A minimum sampling rate between at least 250 and 500 Hz is recommended . QRS-disThe autonomic nervous system (ANS) is an important part in the control of different physiological systems, e.g., the heart, smooth muscles, endocrine, and exocrine glands. It has an afferent (sensory) and efferent parts and is distinct from the somatic nervous system in several ways. The main function of ANS is homeostasis, largely regulated by autonomic reflexes,  not under voluntary control. Sensory information is frequently transmitted through afferent vegetative nerve fibers to homeostatic control centers, processed and specific reactions are sent through efferent vegetative fibers. The ANS has as mentioned specific transmitter substances—mostly acetylcholine (ACh) and norepinephrine (NE)—corresponding receptors and can be divided into preganglionic and postganglionic fibers. The central control of the vegetative nerve system has been identified in several subdivisions of the hypothalamus, but several other brain regions including the association areas of the limbic cortex, the amygdala, and the prefrontal cortex are also connected to these hypothalamus nuclei.The hypothalamus itself controls two more systems in addition to the ANS, the endocrine system and an ill-defined neural system involved in motivation  and sociThe sinoatrial node is, of course, the origin for the pace of the heart. It can, however, itself be considered as a system of weakly coupled oscillators with self-organizing properties, synchronized by a mechanism of mutual entrainment or phase locking .Already on the intracellular level, cell organelles behave as weakly coupled oscillators. A combined experimental and simulation study showed with the help of two photon laser scanning microscope an oscillating network behavior of cardiac mitochondria, distinctly different from random behavior in the form of an inverse power law typical for fractal behavior. They might play a role as an intracellular timekeeper and have a long-time memory function of the oscillations, suggested by a calculated fractal dimension close to 1.0 . This kiCardiac neurons are localized both in the heart as intrinsic neurons and intrathoracically. They form a local distributive network, controlled by brainstem and spinal cord neurons and processing both central and local information to control the heart . Major iOne of the leading causes of sinus arrhythmia is probably central coupling of respiratory drive to cardiac vagal motor neurons –36. MeduThe term RSA is used to describe the fluctuation of heart rate during the respiratory cycle. It is highly dependent on the vagal tone in the heart and is observable at a frequency band of 0.15–0.4 Hz. Usually, RSA is interpreted to mirror the vagal activity , involvi2 to a level below the apneic threshold. The hyperventilation pattern consecutively becomes periodic because the diminished arterial CO2 reaches the brainstem delayed due to the low cardiac output. When first the low partial pressure of CO2 is detected, respiration drive is stopped until CO2 increases. This again is detected late, which results in hyperventilation until CO2 is again on a low level and a new cycle begins  and nicotinic (ionotropic). However, like other mediator substances as opioids, ACh is also involved in immune responses. RNA for muscarinic and nicotinic receptors has been detected in mixed populations of lymphocytes and other cytokine-producing cells , 61.via a posttranscriptional mechanism. ACh blocks also the release of other endotoxin-inducible pro-inflammatory cytokines, such as IL-1b, IL-6, and IL-18, by same mechanisms; it does, however, not suppress the anti-inflammatory cytokine IL-10 . Insulin resistance in the heart inhibits the metabolic pathway and stimulates the growth factor-like pathway , 88. ThiThere are several factors which can affect HRV in acute and chronic metabolic changes. The direct involvement of the vegetative nerve system can be attenuated in more chronic conditions, when the diabetic autonomous neuropathy, which necessarily occurs after a certain length of illness . HRV is The LF parameter of HRV has been used to predict hypoglycemia , this miTime series show the variation of a parameter during a period. In complexity theory, they are considered as surrogates of the particular system, giving information about the state of the system. Typical time series can consist of a series of blood samples , physical measurements like heartbeats, blood pressure, EEG; or movements like gait, eye blinking or variability of breathing patterns. Even short time series can also give information about a system. Usually, most properties of time series are ignored. They are summarized by simple statistical calculations like the mean and the SD. In the case of HRV, several linear and non-linear algorithms were eventually introduced. The research literature can be roughly divided into three areas. In the first, HRV parameters are used as a prognostic tool. Short-term or long-term results in trials are associated with  HRV parameters. This approach has been very successful providing studies where an analysis of only 2 min of heartbeat predicted all-cause mortality many years later, e.g., in Ref. –105. Thevice versa. If this is the case, it should be possible to measure consistent patterns in the activity of different vegetative efferents. But this supposed consistency has frequently not been found. Different other parts of the SNS have not necessarily the same kind of oscillations as the cardiac part of SNS if they have oscillations at all. Cutaneous vasoconstrictor fibers, sudomotor fibers, adrenaline-regulating adrenal preganglionic neurons, and nerves supplying the brown adipose have not oscillations as observed in other parts of the SNS  mirroring the function of the executive brain, in particular in the prefrontal cortex has been investigated. As pointed out by Holzman and Bridgett , a smallHeart-rate variability is a fascinating observation and insight in its mechanisms is increasing. One main issue remains that interindividual differences are high and statistical effects are usually shown only on the group level. In particular regarding psychologic functioning, although an association between lower HRV and lower adaptivity is now established , the difFrom a systems science perspective, HRV measurement begins to be a sophisticated and relevant tool for both scientific and clinical insights.GE has elaborated and written the review.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism’s metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.A whole-genome metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of The online version of this article (doi:10.1186/s13068-016-0665-0) contains supplementary material, which is available to authorized users. Trichoderma reesei is a filamentous fungus widely used for commercial scale production of biomass-degrading enzymes. Cellulose is the most abundant organic compound in the biosphere and is used as a raw material by many industries such as paper, food, textile and for biofuel production. Trichoderma reesei is the main industrial source for cellulases and hemicellulases, enzymes that hydrolyze the cellulose component of lignocellulosic materials. There is a need for reducing the cost and maximizing the yield of these cellulose-degrading enzymes.Saccharomyces cerevisiae , A.(iHD666) , K. (Picyzae iWV114 . Extraction of lipids for fatty acid and lipid class analyses was performed with chloroform:methanol . After vortexing and 30 min extraction time at room temperature, the samples were centrifuged at 10,000 rpm for 3 min. The extract (lower layer) was separated and evaporated into dryness under nitrogen flow, and dissolved into 1000 μl of petroleum ether (fatty acid samples) or 100 μl of dichloromethane (lipid class samples).m/z 40–600. After analyzing fatty acid methyl esters by GC-MS, the same samples were trimethylsilylated (TMS) to determine free fatty acid (FFA) and sterol contents. Samples were evaporated, dissolved into 30 μl dichloromethane (DCM) and silylated with 25 μl of MSTFA [N-methyl-N-(trimethylsilyl)trifluoroacetamide] at 80 °C for 20 min. Trimethylsilylated samples were analyzed by GC-MS on a Restek Rtx®-5MS column  were performed on a Waters Alliance HPLC combined with a Cunow DDL21 evaporative light scattering detector (ELSD). Separation of the lipid classes was carried out on a Waters Spherisorb®silica column ] in analysis . The RNAurements . The molP, phosphoethanolamine and phosphocholine needed for the synthesis of triacylglycerols, phosphatidylethanolamines and phosphatidylcholines was estimated based on the amount the lipid classes in the cells, assuming consumption of 1 mol of glycerol-3-P, phosphoethanolamine or phosphocholine per 1 mol of triacylglycerol, phosphatidylethanolamine or phosphatidylcholine, respectively.Esterified and free fatty acids were measured using GC-MS as described above. The amount of triacylglycerols, phosphatidylethanolamines or phosphatidylcholines in the cells was calculated based on the ratio of triacylglycerols : phosphatidylethanolamines : phosphatidylcholines (0.52 : 0.16 : 0.32 (w/w/w)) determined using HPLC-ELSD and the measured amount of esterified fatty acids in the cells. The measured fatty acid residues were assumed to be distributed equally in the lipid classes, three fatty acid residues in triacylglycerols and two fatty acid residues in phosphatidylethanolamines and phosphatidylcholines. Total amount of the esterified fatty acids was 0.0995 mmol/(g CDW) and the average MW of the fatty acid esters in the pool was 274.2 g/mol, which was used for calculation of the average MW of triacylglycerols, phosphatidylethanolamines or phosphatidylcholines and subsequently for calculation of the total amount of lipids in the three classes. The amount of glycerol-3-A. oryzae [ T. reesei model, the amount of chitin is represented as the amount of the monomeric unit, N-acetyl-d-glucosamine. The remaining carbohydrates, other than chitin, were presented as glucose units in the model. Furthermore, a trace amount (1e-06 mmol/(g CDW)) of C00096 GDP-mannose and C01083 Total carbohydrates were measured as described as above. The amount of chitin (g/(g CDW)) in the cells was estimated to be the same as the measured chitin content of . oryzae , but corThe amount of DNA (0.91% (w/w)) was measured as described above. The amount of deoxyribonucleotides in DNA was calculated based on the published GC content of the genome, 52.0% .The amount of RNA in the cells (6.12% (w/w)) was measured as described above. The amount of ribonucleotides in the RNA was estimated based on the nucleotide ratio in 18S-28S pre-rRNA region in the genome .S. cerevisiae model iMM904 [The coefficients for C00001 Water, C00002 Adenosine triphosphate, C00008 Adenosine diphosphate, C00009 Organic phosphorous, C00059 sulfate and C00255 riboflavin copied directly from the l iMM904 . The coeFinally, trace amount (1e-06 mmol/(g CDW)) of typical cofactors were added to the biomass equation to ensure the model’s capability to produce these compounds: C00003 Nicotinamide adenine dinucleotide (NAD), C00010 Coenzyme A, C00016 Flavin adenine dinucleotide (FAD), C00051 Glutathione, C00059 sulfate, C00101 tetrahydrofolate, and C00575 3′,5′-cyclic AMP.In addition to the reaction database, the CoReCo pipeline required as inputs: (1) genomic data input: genome of the organism of interest and genomes of related species as protein sequences, as well as a phylogenetic tree describing the relationships of the organisms, and (2) reconstruction constraints: source compound list of entities available for the organism to build up its metabolome and biomass through the reactions present and exchange reactions .Protein sequences and information about the 56 fungi species were downloaded from JGI.The preprocessing step of the pipeline included adding the protein sequences from the whole-genome models to the file downloaded from Uniprot creating an “augmented SwissProt” as described in "Two-way BLAST was performed against the “augmented SwissProt” for each of the organisms involved in the reconstruction. The BLAST runs were carried out in parallel under our SGE VTT cluster.GTG was run in parallel under VTT cluster for each of the organisms involved in the model reconstruction.INTERPRO results were downloaded from JGI and reformatted adding the latest GO and E.C. ids. For 49 fungi the INTERPRO results were already computed in .A phylogenetic tree was built with CVTree using the protein sequences of all the organisms involved in the model reconstruction.A probabilistic model was built using the results of BLAST, GTG and the phylogenetic tree. Conditional probability distributions were estimated using the E.C. numbers identified with InterProScan in each species as a reference. A score was given to each E.C. for each organism involved in the model reconstruction.Scores were assigned to reactions by copying the score of the E.C. assigned to that reaction. If several E.C.s were annotated to the same reaction, the reaction was assigned the highest score among the scores for the individual enzymes.T. reesei model.During the model reconstruction step, the high scoring reactions were added to each model, sequentially filling the gaps from the source compound list. An atom graph was used to find the pathways from the sources to the added high score reaction. This subgraph contains preferably only high score reactions but low score reactions can be added when needed. This step uses two input parameters: A SBML model was created including all the reactions chosen during the reconstruction step. In addition, two biomass reactions and exchange reactions were added in a post-processing step.Models were tested to identify problems like unrealistic production of compounds and reaction bounds were curated manually. The pipeline was run again from step 5 until no more problems could be identified.The basic steps for the reconstruction of the 56 fungi models using CoReCo pipeline were the following:


Clostridioides difficile are a severe and often lethal risk for hospitalized patients, and can also affect populations without these classical risk factors. For a rational design of therapeutical concepts, a better knowledge of the metabolism of the pathogen is crucial. Metabolic modeling can provide a simulation of quantitative growth and usage of metabolic pathways, leading to a deeper understanding of the organism. Here, we present an elaborate genome‐scale metabolic model of C. difficile 630Δerm. The model iHD992 includes experimentally determined product and substrate uptake rates and is able to simulate the energy metabolism and quantitative growth of C. difficile. Dynamic flux balance analysis was used for time‐resolved simulations of the quantitative growth in two different media. The model predicts oxidative Stickland reactions and glucose degradation as main sources of energy, while the resulting reduction potential is mostly used for acetogenesis via the Wood–Ljungdahl pathway. Initial modeling experiments did not reproduce the observed growth behavior before the production of large quantities of a previously unknown polysaccharide was detected. Combined genome analysis and laboratory experiments indicated that the polysaccharide is an acetylated glucose polymer. Time‐resolved simulations showed that polysaccharide secretion was coupled to growth even during unstable glucose uptake in minimal medium. This is accomplished by metabolic shifts between active glycolysis and gluconeogenesis which were also observed in laboratory experiments.Antibiotic‐associated infections with  CDMMC. difficile defined minimal mediumDFBAdynamic flux balance analysisFBAflux balance analysisGAMgrowth‐associated maintenanceMDMminimal defined mediumNGAMnongrowth‐associated maintenanceODoptical density at 600 nmTCAtricarboxylic acidClostridioides difficile  C. difficile. An initial metabolic model of C. difficile 630 was published by Larocque et al. The prediction of quantitative growth in batch cultures has been done previously for iHD992 of C. difficile 630Δerm, which consist of 992 genes, 786 reactions, 936 metabolites, and 163 transport reactions. It includes time‐resolved substrate uptake and biomass production rates, derived from laboratory data, as well as the production and secretion of a previously undescribed polysaccharide. The identity of the polysaccharide is determined based on combined bioinformatical analysis and laboratory experiments. In addition, DFBA is used for quantitative and time‐resolved simulation of batch cultures in minimal defined medium (MDM) and C. difficile defined minimal medium (CDMM). Both media consist of glucose as well as the same salts and buffers. MDM also contains seven amino acids strictly necessary for growth , while CDMM is a richer medium that includes casamino acids, cysteine, and tryptophan Here, we present an improved and elaborate genome‐scale metabolic model iHD992 of C. difficile 630Δerm consists of 992 genes, 786 reactions, 936 metabolites, and 163 transport reactions. The model is included in the Supporting information (Model S1 and the exemplary Scenario S1). In comparison to the published model iMLTC806cdf of C. difficile 630 C. difficile .The model iMLTC806cdf by 83%, representing a significant improvement.Genome‐scale metabolic models contain reactions based on genome annotations, spontaneous reactions, and reactions neither based on the genome nor known to be spontaneous. While reactions in the first two groups form the core of each model, the number of reactions in the latter group should be low. These reactions are usually added to the model to close gaps in the metabolic pathways which are known or assumed to be functional in the organism. For example, this is the case for described pathways whose enzymes are not yet identified. In our model, we could decrease the number of these reactions compared to C. difficile models are significantly different as they share only 508 metabolic reactions (67% of iMLTC806cdf), while the model by Larocque et al. has 385 (51%) in common with the Clostridium acetobutylicum model iCAC490 iMLTC806cdf, 111 are not existing in iHD992 as they are not sequence based reactions and no other indication could be found for their existence in C. difficile. From the 151 sequence‐based reactions in the model from Larocque et al. not existing in iHD992, 140 are not in iHD992 because of differences in enzyme function predictions, six were excluded from iHD992 as they are metabolically cheaper side reactions compared to the main reactions of the enzymes and five were excluded as these would lead to the cost‐free reduction of NADP+ with NADH.The two iMLTC806cdf l‐leucine uptake as the model predicts C. difficile to be leucine auxotroph. The resulting predicted growth rate of 7.12 h−1 is significantly higher as the experimentally determined 0.414 h−1. This finding indicates the inability of the model iMLTC806cdf to describe the energy metabolism and quantitative growth of C. difficile.The published model C. difficile 630Δerm has a fragmented TCA cycle  and malate dehydrogenase  (EC 1.1.1.38) or used as electron acceptor in the oxidation of L‐aspartate to produce iminosuccinate for the NAD+ biosynthesis. The resulting succinate fills the intracellular succinate/succinyl‐CoA pool for the methionine biosynthesis. Succinate can be recycled by the succinyl‐CoA:acetate CoA‐transferase (EC 2.8.3.18) or degraded to butanoate. A similar TCA cycle was previously described for the close relative C. acetobutylicumC. acetobutylicum. In addition, this organism has putative genes for malate dehydrogenase (EC 1.1.1.37) and 2‐oxoglutarate synthase (EC 1.2.7.3) C. difficile to close the TCA cycle.Based on our genome annotation, cle Fig. , which iC. difficile in detail, the organism was grown in naturally labeled MDM with completely 13C‐labeled glucose. The results of GC/MS analysis of some intracellular metabolites in samples taken at optical density (OD) 0.220 are shown in Fig. The model predicts only glucose and cysteine as carbon sources usable for biomass production in MDM, whereas the other six amino acids are only used for energy generation via Stickland reactions or directly for protein synthesis. To study the carbon metabolism of −1 instead of 0.414 h−1 at 3 h of growth) compared to the experiment. In addition, large amounts of carbon taken up by the organism were not found in the known products or biomass. Due to the viscous flow behavior of the culture supernatant and the temporarily active gluconeogenesis (see previous subsection), we suspected the formation of a polysaccharide. Clostridioides difficile possesses an operon putatively involved in the synthesis of a polysaccharide. The operon consists of genes encoding a putative membrane‐bound O‐acyl transferase (EC 2.3.1.‐), a cellulose synthase (EC 2.4.1.12), an endoglucanase (EC 3.2.1.4), and two membrane proteins of unknown function. We assumed the gene cluster to encode for enzymes responsible for the biosynthesis of acetylated glucose polymer (putatively acetylated cellulose).Initial modeling experiments using the experimental metabolite import and export rates predicted a significant higher growth rate  and alkaline (for acetate) hydrolysis. Table C. acetobutylicumC. difficile, C. acetobutylicum is able to degrade its polysaccharide C. acetobutylicum is unable to grow on cellulose In agreement with the existence of a similar operon Table  in its gC. difficile 630ΔermThe availability of time‐resolved cultivation data of Figure C·molC−1 until 5 h of growth. After the subsequent change of metabolism, the ratio stays constant at ~2.6 molC·molC−1 until 8 h of growth. Constant ratios represent a growth‐associated product formation which indicates in turn the formation of a capsule or biofilm. In fact, capsule C. difficile. We observed stirring and shaking as growth inhibiting effects for C. difficile cultures in our laboratory (data not shown). The growth inhibiting effect can be explained by the capsule formation, as detached polysaccharide is replaced rather than new biomass produced and C. difficile capsules were reported to be fragile The carbon ratio of the polysaccharide subunit secretion rate and biomass production rate predicted by the model Fig. B stays cBiomass h)−1 until 5 h of growth, drops below zero for about an hour when the reaction flux is inverted during gluconeogenesis activity and slowly rises again to about 8.0 mmol·(gBiomass h)−1 until 7.5 h of growth. During unrestricted exponential growth, all fluxes stay constant over time. In contrast, C. difficile 630Δerm generates most of the biomass during a quasilinear growth C. difficile was grown anaerobically in batch culture and all substrates were fully solved, the latter can be ruled out. Growth inhibiting reactions are difficult to identify as other reactions scale down analogously. As the organism shows exponential growth in CDMM  is shown exemplarily in Fig. C. difficile in MDM After the metabolic switch at 5 h of growth, when the amino acid uptake is significantly increased, alanine is secreted by −1 between 1.5 and 3.33 h indicates a strictly exponential growth. The model also predicts 33% of the imported glucose until 4 h of growth to be bound in the secreted polysaccharide. This is in the range of the 36 ± 3% experimentally determined at an OD of 0.725, after 4 h of growth. The predicted amounts of the produced volatile fatty acids , which degrades L‐serine to glycine, or by the glycine‐cleavage‐complex. In addition, the degradation of threonine by L‐threonine aldolase (EC 4.1.2.5) also leads to glycine.In the reductive acetyl‐CoA pathway, also known as Wood–Ljungdahl pathway, acetyl‐CoA is reductively generated from a one‐carbon group and COC. difficile uses the Wood–Ljungdahl pathway as primary electron sink. The activity of the acetyl‐CoA synthase complex (EC 2.3.1.169), the terminal enzyme of the reductive acetyl‐CoA pathway, is nearly constant between 1.0 and 3.0 h of growth  All studies were carried out with DNA, RNA, and protein content were quantified as described earlier et al. m), and hydrolyzed for 24 h at 99 °C. Following neutralization, HPLC measurement of released amino acids and meso‐diaminopimelate was performed as previously described m sodium acetate (pH 6.5) and the gradient was altered to 3% for 5.3 min, 3–6% within 0.05 min, 6–7% within 4.65 min, 7% for 1 min, 7–15% within 1 min, 15% for 10 min, 15–25% within 0.5 min, 25% for 2 min, 25–30% within 2.5 min, 30–100% within 0.1 min, and 100% for 2.9 min. To determine the concentration of released meso‐diaminopimelate, a calibration curve was prepared in the range of 5–300 μm. The gradient was altered between 11 and 22 min as follows: 7–14% within 1 min, 14% for 2.5 min, 14–15% within 0.25 min, and 15% for 7.25 min.Estimation of amino acid composition was adapted from Lee et al. Lipids were extracted according to the protocol of Matyash Cell‐free culture supernatants were obtained during exponential, transient, and stationary growth phase from three individual biological samples.m NaOH and incubated for 2 h at 22 °C followed by a neutralization step using the same volume of 5 m H2SO4. For reference samples without hydrolysis, culture supernatants were diluted with three volumes of an 1 : 1 mixture of NaOH (5 m) and H2SO4 (5 m). Medium samples were used as blanks. Samples were analyzed using the Roche Yellow line Acetic Acid kit . Each biological sample was measured in triplicates.Alkaline hydrolysis was adapted from Häggström and Förberg 2SO4, incubated on ice for 10 min, heated to 99 °C for 20 min, and cooled to 22 °C prior to neutralization with NaOH. For reference samples without hydrolysis, culture supernatant samples were diluted with premixed H2SO4 and NaOH solutions. Medium samples were used as reference for the initial glucose concentrations. Medium samples without glucose addition were used as blanks. Samples were prepared and analyzed using the Roche Yellow line Acetic Acid kit (R‐Biopharm) as described earlier For glucose determination, an acidic hydrolysis was performed to avoid alkaline degradation of glucose Cell extracts were prepared and analyzed as described previously C. difficile 630Δerm were taken from BKM‐react C. difficile or related organisms. Gaps were closed manually and as stingy as possible. Transporters were included whenever annotated, experimental evidence was existent or a transporter was strictly necessary.Reactions corresponding to the annotated gene functions meso‐diaminopimelate was part of the peptidoglycan, corresponding amounts of glutamate, alanine, and glycine according to the peptidoglycan structure of C. difficile 630 meso‐diaminopimelate content of the biomass was corrected and used to calculate amount of peptidoglycan in the biomass (14.5%). The ratio of the peptidoglycan and the cell wall polysaccharide II C. difficile 630ΔermE. coliE. coli, 16S rRNA: C. acetobutylicum, tRNA: Bacillus subtilis) were taken from the MODOMICS database B. subtilisC. difficile isolates C. difficile was assumed. The fatty acid chain length of 16.246 °C and the proportion of unsaturated fatty acids of 16.4% were calculated from literature C. acetobutylicum in the acidogenic phase C. acetobutylicumB. subtilis were used +, Fe2+, Ni2+, Cd2+, and Zn2+ (1% each). The composition of biomass can be found in detail in Table S4.The amount of protein, DNA, RNA, and total lipid in the biomass was determined experimentally. To get the full amino acid content of biomass, the measured amino acid content of hydrolyzed cells were modified assuming the measured glutamate was glutamate and glutamine in a mass ratio of 4 : 1, the aspartate was aspartate and asparagine in the mass ratio of 1 : 1, and the mass ratios of histidine, cysteine, tryptophan, and selenocysteine were 100 : 80 : 80 : 1. Assuming all  biomass .5%. The C. difficile, the NGAM was estimated as the average (2 mmol·(gBiomass h)−1) of five anaerobic rumen bacteria Biomass h)−1, leading to the most in vivo similar results.For a metabolic model capable of describing the energy metabolism and quantitative growth, suitable values for the maintenance energy are required. Growth‐associated maintenance (GAM) and nongrowth‐associated maintenance (NGAM) energies are usually calculated from chemostate cultures at different dilution rates C. difficile 630Δerm were derived from literature originpro 2015g software . The resulting functions of the OD and metabolite concentrations can be found in Table S3 and S4. When necessary, the data of different time frames were fitted separately to get the best mathematical description. The functions were used to compute the concentrations including the OD every 15 min starting at 7.5 min (MDM) or 10 min starting at 5 min (CDMM). Relative substrate concentrations were converted to absolute concentrations setting the concentration at time point zero at the concentration in the initial medium. The biomass was calculated using the correlation of 0.425 g·L−1 biomass dry weight per optical density unit at 600 nm wavelength.Optical densities and relative substrate concentrations during the cultivation of C. difficile 630Δerm. The metano toolbox E. coli during change of the limiting substrate Flux balance analysis, extensively described elsewhere For the modeling of the biosynthesis of the secreted polysaccharide, UDP‐glucose was used as substrate for the polymerization, every second glucose subunit was acetylated with acetyl‐CoA and the hydrolysis of one ATP was assumed as transport cost per glucose subunit across the membrane. To calculate the total conversion of glucose to polysaccharide in MDM, 90% of the taken up glucose prior to the linear growth phase was assumed. The fatty acid synthesis was restricted to use only acetyl phosphate as starter molecule to prevent large‐scale synthesis of fatty acids with up to six carbon atoms via the Stickland reactions and the butanoate pathway.+, EC 1.5.1.5) had to be deactivated after 2.5 h of growth to prevent the refilling of the one‐carbon‐pool from formate. The reason for the inactivation of this pathway in vivo remains unclear. Second, the production of propanoate and hexanoate via the butanoate fermentation pathway had to be limited: the maximal fluxes of the acetyl‐CoA C‐acetyltransferase (EC 2.3.1.9) for the substrates propanoyl‐CoA and butanoyl‐CoA were set to 1/3 and 1/20 of the flux with acetyl‐CoA as the substrate.For the correct reproduction of the experimental data in CDMM, two predefinitions had to be made. First, for an accurate simulation of the side product secretion in CDMM, the methylenetetrahydrofolate dehydrogenase  for the metabolic model iHD992 of C. difficile 630Δerm.Table S1. Isotope labeling of glycolysis and glycolysis‐related metabolites.Table S2. Biomass composition used for the model iHD992.Table S3. Fits of the substrate concentrations and OD over time of MDM cultures with C. difficile 630Δerm.Table S4. Fits of the substrate concentrations and OD over time of CDMM cultures with C. difficile 630Δerm.Click here for additional data file.


Trypanosoma brucei is a parasitic protist that causes significant health burden in sub-Saharan countries endemic for the tsetse fly (Glossina spp.). During the bloodmeal of this insect vector, the flagellate is transmitted to a variety of mammals, including humans, in which T. brucei subs. gambiense and T. brucei subs. rhodesiense cause human African trypanosomiasis. During its life cycle, T. brucei encounters and adapts to very diverse environments that differ in available nutrients. In the mammalian host, it exists in two major forms: the replicating long-slender bloodstream form (LS-BSF) and the nondividing short-stumpy bloodstream form (SS-BSF), the latter being pre-adapted to infect the insect vector [T. brucei during its development require significant morphological and metabolic changes and adaptations [t vector . While tt vector ,3. In thptations ,5.oF1-ATP synthase complex at the expense of ATP [2+ homeostasis are maintained [The seminal work of Keith Vickerman led to the widely accepted model of a highly reduced mitochondrial metabolism in the BSF ,7. Its se of ATP –11. The e of ATP , RNA edie of ATP ,14, and intained ,16. The Because no indications have been obtained yet for the presence of a mitochondrial ATP-producing system in the BSF, the entire cellular ATP pool is considered to be generated solely by highly active glycolysis . The glyClassical metabolic studies performed with trypanosomes purified from the blood of infected rodents or with in vitro–cultured BSF supported the original hypothesis of a drastically simplified mitochondrial metabolism because under aerobic conditions, glucose is almost completely catabolized to pyruvate that is excreted from the cells, indicating no need for the mitochondrial enzymes of the tricarboxylic acid cycle. In the absence of oxygen or when AOX is chemically inhibited, glycerol 3-phosphate is converted into glycerol that is produced in a 1:1 ratio with pyruvate ,20. OccaIn preparation for differentiation into PCF, the SS-BSF up-regulates a subset of mitochondrial and other proteins . MoreoveInterestingly, recent analyses employing the monomorphic LS-BSF strain Lister 427 showed that, in addition to pyruvate, appreciable amounts of other carbon products  are excreted into the cultivation medium , implyinTo map the BSF mitochondrial proteome (mitoproteome), we first used the available mass spectrometry data of purified PCF mitochondria –37 in orAll proteins were then organized into groups based on their Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. No striking qualitative differences were observed in the categories “oxidative phosphorylation” and “core metabolic pathways” comprising many enzymes involved in the carbon, amino acid, and energy metabolism . TypicalThe current metabolic model for BSF excludes a role of the mitochondrion in the ATP production by either oxidative or substrate-level phosphorylation . In contThe second phosphorylation pathway includes the acetate:succinate CoA transferase/SCoAS cycle that contributes to acetate production in the BSF mitochondrion. A substrate for this reaction—acetyl-CoA—is produced by PDH, an enzymatic complex that is present and active in the BSF mitochondrion ,25. MoreThe presence and potential activity of the aforementioned dehydrogenases that produce NADH within the mitochondrion imply that the organelle would require reoxidation of this cofactor. Several possible scenarios for such a capacity can be deduced from the available data . AlthougoF1 ATPase in order to maintain the mitochondrial membrane potential. The classical model presumes that ATP is imported into the organelle via the activity of the ATP/ADP carrier [oF1 ATPase inhibitor oligomycin or by addition of carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP). However, when treated with bongkreic acid, which should halt the activity of FoF1 ATPase by restraining its substrate, the parasite consumes oxygen at the same rate as untreated cells [oF1 ATPase.The possible occurrence of the mitochondrial substrate-level phosphorylation reactions raises an interesting question regarding the mitochondrial bioenergetics of BSF and questions the origin of ATP that is needed by mitochondrial F carrier ,53. Howeed cells ,55. On oCombined, the available data reveal that the metabolic flexibility and adaptability of the BSF mitochondrion are much larger than appreciated so far. Mitochondrial metabolism appears to be controlled at various levels; a developmental program seems to be a major contributor, but recent advances in the field suggest that other cues may also play a role through fine-tuning mechanisms. However, the triggers and signaling pathways of these mechanisms remain to be identified. Furthermore, it should be realized that almost all metabolic studies have been performed with strains well adapted to laboratory conditions. While the proteomic data do not show any significant differences between the monomorphic and pleiomorphic strains, future work combining proteomics and metabolomics with functional genomics should be extended to the mitochondrion of trypanosomes isolated not only from blood but also from other tissues to determine whether their metabolism is tissue specific and, if so, what is/are the mechanism(s) that control(s) the changes. Therefore, the virtually unexplored array of pathways and enzymes begs for attention because it may have important implications for drug target identification and future novel chemotherapeutics. Moreover, a decreased morphological complexity, which is apparently not reflected in metabolic complexity, is an interesting and novel phenomenon that can now be efficiently addressed with emerging, increasingly sensitive methods.S1 TableThe column color coding is green for PCF, dark grey for monomorphic BSF, and light gray for pleiomorphic BSF cells.1, identified; 0, not identified; BSF, bloodstream form; PCF, procyclic form.(XLSX)Click here for additional data file.