This creates an evolutionary paradox due to the existence of PKET pathway in obligatory photoautotrophs. We aim to answer the paradox with the aid of bioinformatic analysis along with metabolic, transcriptomic, fluxomic and mutant data integrated into a multi-level kinetic model. We discussed the problems linked to neglected isozyme, pket2 (sll0529) and inconsistencies towards the explanation of residual flux via PKET pathway in the case of silenced pket1 (slr0453) in Synechocystis sp. PCC 6803. Our in silico analysis showed: (1) 17% flux reduction via RuBisCO for Δpket1 under AC-auto, (2) 11.2–14.3% growth decrease for Δpket2 in turbulent AC-auto, and (3) flux via PKET pathway reaching up to 252% of the flux via phosphoglycerate mutase under AC-auto. All results imply that PKET pathway plays a crucial role under AC-auto by mitigating the decarboxylation occurring in OPP pathway and conversion of pyruvate to acetyl CoA linked to EMP glycolysis under the carbon scarce environment. Finally, our model predicted that PKETs have low affinity to S7P as a substrate.Phosphoketolase (PKET) pathway is predominant in cyanobacteria (around 98%) but current opinion is that it is virtually inactive under autotrophic ambient CO Synechococcus elongatus PCC 79421 and butanol from Synechocystis sp. strain PCC 68032. However, certain metabolites or overproduction of intermediates can be lethal. There is also a possibility that required mutation(s) might be unstable or the target bacterium may even be able to maintain the flux distribution for optimal growth balance due to redundancies in the metabolic network, such as alternative pathways. The current dominant “trial and error” approach implies that our understanding of cellular metabolic regulatory mechanisms, including those involving isozymes, post-translational modifications or allosteric regulations, is secondary to achieve the desired effect/product. Integrating computational biology methods may help both to understand complex big data and to improve our understanding of cellular processes, improving potential biotechnological applications in the long term.Metabolic engineering of cyanobacteria provides many options for producing valuable compounds, e.g., acetone from Synechocystis sp. strain PCC 6803 (hereafter referred to as Synechocystis) is one of the more complex prokaryotes, known for its metabolic plasticity3. All known glycolytic pathways that exist in nature are accommodated in its central carbon metabolism: the Embden–Meyerhof–Parnas (EMP) pathway, the oxidative pentose phosphate (OPP) pathway, the phosphoketolase (PKET) pathway and the Entner–Doudoroff (ED) pathway. However, our knowledge about regulatory roles of PKET in Synechocystis is still limited.Cyanobacterium 4, and it is known that deactivation of PKET pathway leads to 11.5% slower growth under autotrophic ambient CO2 (AC-auto) condition4. Moreover, an engineered strain of Synechocystis with blocked glycogen synthesis and enabled xylose catabolism was reported with over 30% total carbon flux directed via PKET4 under mixotrophic condition on xylose and CO2. Furthermore, overproduction of acetyl phosphate, caused by overexpression of pket (PKET gene), can negatively influence the pentose sugar metabolism5 but may increase lipid synthesis6. Finally, recent 13C labelling study concluded that there is virtually zero flux via PKET under AC-auto7 in Synechocystis. This finding is in direct disagreement with the previous theoretical stoichiometric8 and kinetic9 studies which estimated the flux via PKET pathway to be 11.6% and 5.3% of flux via RuBisCO, respectively. Thus, we have decided to investigate this issue and to review the flux via PKET pathway based on all available data.Phosphoketolase converts xylulose 5-phosphate to glyceraldehyde 3-phosphate and acetyl phosphate, or, fructose 6-phosphate to erythrose 4-phosphate and acetyl phosphate, see Fig. Synechocystis: (1) if PKET pathway is present in cyanobacteria, especially in obligatory photoautotrophs, why it is inactive in autotrophic conditions during day, (2) what is the role of PKET2 and can we quantify its potential benefits and (3) does the role of PKET pathway change in different environmental conditions?Our study aims to provide answers to the following questions with the help of multi-level kinetic model of central carbon metabolism for Nostoc/Anabena PCC 7120 (Nostoc)10 but our multiple sequence alignment showed 78% homology among PKETs in cyanobacteria with annotated PKET isozymes (see pket1 (slr0453) of Synechocystis and pket1  of Nostoc. Interestingly, the secondary PKETs of cyanobacteria, including pket2 (sll0529) of Synechocystis and pket2  of Nostoc, are clustered also with fungi, bacteria and algae, implying a possible horizontal gene transfer. The common feature of photosynthetic and non-photosynthetic species in this heterogenous cluster is a complex metabolism, allowing to survive on organic source of carbon. Strikingly, PKET2, firstly mentioned in 201011, was ignored in the metabolic studies despite the residual (approximately 10%) flux via PKET pathway in the case of silenced pket1 under both mixotrophic4 and autotrophic7 condition. It was suggested that this residual flux is caused by the activity of phosphate acetyl-transferase (PTA) activity7. The problem with this explanation is that it considers only reactions towards TCA cycle, i.e., reversibility of PTA, even though the same residual flux was detected also towards glyceraldehyde-3-phosphate (GAP) synthesis4. Since GAP synthesis is not connected to PTA activity  whereas aldolase 2 (class I) supplies only 10% of total due to allosteric inhibition13. It can thus be speculated that, after pket1 is silenced, PKET2 is behind the residual activity of PKET pathway and PKET2 contribution does not increase due to allosteric inhibition. However, despite other known cases among isozymes in Synechocystis, allosteric inhibition is only one of possible explanations and we cannot prioritize it or exclude other options such as post-translational modification or regulation by signalling cascade(s), etc.We propose that neglected PKET2 of Synechocystis. Fortunately, our method of multi-level kinetic modelling  and ambient CO2 mixotrophic condition (AC-mixo), varying slightly in dependence of preferred substrate  PKET2 has a positive impact on growth rate in turbulent, carbon scarce environment and (iii) PKET2 is currently the only explanation of the residual fluxes via PKET pathway in the case of silenced pket1, both towards TCA cycle and GAP synthesis.In order to test the impact of PKET2 on growth in case of ΔSynechocystis was made with the aid of stoichiometric model based on flux balance analysis, suggesting flux of 14.9 (11.6% of flux via RuBisCO)8. At the time, it was believed that the model accurately describe HC-auto due to very good agreement with labeling experiment performed in HC-auto14. However, it was suggested later that the majority of fluxes are scalable in response to a changing environment9. Thus, explaining the stoichiometric model, designed for AC-auto, nearly fitting the fluxomic data for HC-auto. The rescaled value for PKET flux under AC-auto from stoichiometric model is then 4.1. This value is more than double of our previous estimate 9. However, 13C labeling experiment was recently performed for AC-auto which concluded virtually zero flux via PKET pathway7. This result implied that both theoretically estimated values of PKET flux were incorrect. The problems with such implication were: (1) stoichiometric modeling approach assumes the flux balance for maximal growth benefits so non-zero flux via PKET pathway indicates at least a potential growth benefit and (2) our previous kinetic model9, validated for different environmental conditions, also predicted non-zero flux via PKET pathway in order to improve the match between the experiments and simulations.The first attempt to assess the flux via PKET pathway in pket13. The simulated flux via PKET pathway was 3.16 (7.2% of flux via RuBisCO) under AC-auto which strongly out-performed (up to 2.5-fold) the flux via phosphoglycerate mutase, the exit point of EMP glycolysis from Calvin-Benson cycle, see Fig. 7. Since more accurate simulation actually increased the estimated flux via PKET pathway, we compared these values with original fluxes from labeling experiment7. The data from labeling experiment calculated a virtually non-existent flux via Xu5P branch (10–6) and high flux  via F6P branch of PKET pathway7 which agrees with our predicted value perfectly. However, this result was not mentioned in their discussion, probably due to zero lower bound value for flux via both branches of PKET pathway, implying theoretically inactive PKET pathway. The lower bound of PKET flux might be explained only by reversibility of PTA. In the case of PTA flux, lower bound was 16.86 which was the same as calculated flux via F6P branch of PKET pathway. Since we have already rejected the reversibility of PTA as the source of residual flux via PKET pathway (see the section above), we can conclude that the flux via PTA is mostly, if not fully, originated from PKET pathway.Therefore, we decided to elaborate the role and benefits of PKET pathway in detail. We tailored the flux via PKET1 and PKET2 to match 11.5% growth decrease under AC-auto for Δpket1 has negative impact over all fluxes, especially via RuBisCO, under AC-auto, see Fig. pket1, less amount of carbon is available in the system due to decarboxylation in OPP and conversion of pyruvate to acetyl CoA linked to EMP glycolysis. This conclusion is further supported by the data from AC-mixo  and only 0.42% of total PKET activity maintained by S7P branch15. PKET breaks down S7P into ribose-5P and acetyl-P . The target of our in silico experiment was to maximize the growth with any combination of the substrates  for PKETs. Firstly, the statistical analysis of results implies significantly higher variability in growth rate when S7P is metabolized via PKET pathway (Table pket1) is the same for both scenarios (with or without S7P), the reason behind the same value is that parameter estimation routine applied zero flux via S7P branch of PKET pathway due to its lower efficiency. Thus, our model predicts that the affinity of PKETs towards S7P in cyanobacteria is very low, similarly as shown for E. coli15. Nevertheless, experimental verification is required as the role of S7P branch of PKET pathway might be more significant under certain stress conditions.Recently, sedoheptulose-7P (S7P) has been identified as a substrate of PKET in  KM = 68. mM and oay Table . When thay Table . Thus, iSynechocystis: (1) bacteria have PKETs that are regulated by allosteric inhibition12 and pket2 is in the cluster with bacteria (in contrast to pket1), opening the possibility for horizontal gene transfer; thus we can speculate that PKET2 is also allosterically inhibited by phosphoenolpyruvate, oxaloacetate or glyoxylate which provides currently the only explanation for the residual flux via PKET pathway in Δpket1 and clarifies why PKET2 does not display any higher activity in such scenario; however, other alternative explanations such as post-translational modification cannot be currently excluded, (2) our in silico experiment of Δpket1 mutant with active PKET2 showed the same impact on growth as reported previously in AC-auto for silenced pket1 which was believed at the time to be a total inhibition of PKET pathway, (3) there are other known cases of allosteric inhibition in Synechocystis, including the proposed 90/10 flux contribution of isozymes, e.g., aldolase 1 and 2, and (4) we showed a clear benefit of having PKET2 in the central carbon metabolism of Synechocystis, namely, an 11.2–14.3% growth increase in the turbulent AC-auto. However, an experimental verification, if the product of gene sll0529 (pket2) indeed has phosphoketolase activity, as well as what, if any, is the impact of phosphate acetyl-transferase on residual flux via PKET pathway, is needed. Similarly, here proposed allosteric regulation of PKET should be experimentally verified.We presented the following support for the existence and role of two PKETs in 13C labelling experiment which revealed virtually no flux via PKET pathway under AC-auto. However, we have found a critical error in their analysis and their own labelling data supports our conclusion of the great importance of PKET pathway. Furthermore, we have shown that silencing pket1 has a negative impact over all fluxes, including RuBisCO and PGM in AC-auto but not in HC-auto and AC-mixo. We conclude that high flux via PKET pathway mitigates the decarboxylation occurring in OPP pathway and conversion of pyruvate to acetyl CoA linked to EMP glycolysis under AC-auto, i.e., in the carbon scarce environment. Finally, we have tested S7P as a substrate for PKETs as it was reported for E. coli. Our analysis predicted low efficiency of S7P branch of PKET pathway and low affinity towards S7P, similarly as shown for E. coli.In addition to analysing the putative PKET2 isozyme, we have also focused on the flux distribution of PKET pathway under various conditions. We showed that PKET pathway could be the major carbon flux exit point from Calvin–Benson cycle in AC-auto, overcoming the flux via phosphoglycerate mutase by up to 252%. This finding is in direct disagreement with recent Synechocystis.In the end, we did not analyse the role of PKET pathway under heterotrophic condition because its impact has already been shown and because heterotrophic condition is substantially more difficult to integrate into our light-on-only multi-state model. Our main goal was to address the purported negligible impact of PKET pathway in autotrophic condition; however, we plan to analyse the role of PKET in heterotrophic or photoheterotrophic conditions in the future, together with other isozymes within the central carbon metabolism of 16.HC-auto to AC-auto17.AC-auto to AC-mixoTranscriptomic data13C labeling data14.HC-auto7.AC-auto18.AC-mixo14, there is a line of evidence that this particular labeling experiment is describing HC-auto. Firstly, the growth rate of culture was 0.09 h−114 which is higher rate than the one observed for 3% CO2 enriched medium (0.06 h−1)7. Secondly, authors used a high concentration of bicarbonate in their study and demonstrated that level of photorespiration is very small under high CO2 conditions14; study was performed under single environmental condition, i.e., HC-auto. Finally, we showed that majority of metabolic fluxes scales based on the level of carbon9 which explains why most of fluxes are very similar for AC-auto and HC-auto after normalization. However, key exit fluxes, such as phosphoglycerate mutase, OPP or PKET pathway do not scale9 which explains significant difference for exit fluxes between14 and7, e.g., ratio between fluxes via RuBisCO and OPP is 7.8 for HC-auto14 but 1.7 for AC-auto7.We note that, despite its not clearly stated in the original work as authors are talking only about photoautotrophic condition21.HC-auto20.AC-auto17.AC-mixoMetabolic datapket1 mutant4.ΔMutant dataSynechocystis was developed and simulations were executed using the SimBiology toolbox, Optimization toolbox, Global optimization toolbox and Parallel toolbox of MATLAB . The routine for parameter estimation was a hybrid genetic algorithm. The model for HC-auto is available in the Supplement in SBML format L2V4 compatible with MATLAB 2010b and higher. We recommend to open the model either in MATLAB or user friendly COPASI (free academic license). The weight factors (applied transcriptomic data) necessary to run the model in other conditions (AC-auto and AC-mixo) is provided in Supplement.The multi-level kinetic model for 9. This simplified approach allowed to estimate the CO2 fixation and glucose uptake by comparing to relative experimental fluxes in all tested conditions as well as to keep the track of growth rate, matching the experimental levels from literature9. Moreover, the biomass in the model is assumed to be equal to the amount of fixed carbon, whereas nitrogen was not a limiting nutrient factor in the experimental setups9. The definition of biomass in the model is provided by following equation, based on the model content, in which upper index s stands for accumulation of respective metabolite in the sink  and carbohydrate synthesis. These metabolic reactions were coupled to simplified light reactions and Ci and glucose uptake as the main input parameters. Biomass production is estimated as weighted sum of sink reactionsink Fig.  and a paSynechocystis9, which was modified and extended in order to describe AC-mixo condition. We note that the justification of using fold changes in the mRNA levels as a proxy between different environmental conditions, assuming a 1:1 ratio between a change in transcriptome and enzymatic amount, is provided in our previous study, including the support from experimental studies, can be found in our previous study9.The model consists of 61 reactions, 48 metabolites and 214 kinetic parameters. The enzymatic reactions are described by Michaelis–Menten kinetics, except for the light reactions, Ci and glucose uptake, which are described by mass action kinetics. All reactions, except for the import of external carbon, are localized in a single compartment. The starting point of modelling was our previous model of max. Then, we fit the experimental fluxomics and metabolic data from HC-auto. In this process of parameter estimation, we obtain a set of parameters which will be used in all following steps or trashed and search for new set will begin. If there is a match between simulation and experiment in HC-auto, metabolic and fluxomics data are saved (including the set of kinetic parameters). Then, we simulate a transition from HC-auto to AC-auto, i.e., applying the weight factors (mRNA), run the simulation for the steady state AC-auto, save the metabolic and fluxomic data and do the same for AC-auto to AC-mixo shift. At this point, we do semi-automated check for the reasonable match between simulated and experimental data for AC-auto and AC-mixo , followed by manual curation of results. If acceptable match is found, the final step is a fine tuning of parameters and observing the changes in all three conditions. Manual tuning and curation of kinetic parameters at the different levels of parameter estimation and data integration is an essential part of modelling, because it provides a deeper understanding of system behavior and helps to detect errors in the algorithms, routines and model structure, e.g., when a new pathway is added.The integration of various omics data occurs in several steps. In the first step, transcriptomic data from HC-auto to AC-auto and AC-auto to AC-mixo are included into the model in the form of mean values of measured mRNA changes as weight factors for each Vmax and kf parameters, running it 100-times and calculating the mean value and standard deviation. Two scenarios, with one or two PKET, were tested, in all three environmental conditions.We note that we shifted our priority from metabolic levels to fluxomic data as a key reference for match fitting. The main reason is that we are analyzing now 3 environmental conditions and planning to add more. However, there is no study providing metabolic data for the particular conditions of our interest and combining multiple metabolic data sets from different studies proved to be extremely challenging. We continue observing and analyzing the metabolic levels but in the case of parameter fitting, we currently only check if metabolic levels are in the physiological range and analyze the homeostatic stability of metabolites. Finally, simulation of fluctuating environment was done by applying up to ± 20% random variation in all V9. The first application of the method, the case study of phosphoglycerate mutase, discussed the potential of this method, e.g., the verification of gene annotation22.More information about the method, parameter estimation and other details can be found in our previous studySupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Postoperative delirium is the most common complication among older adults undergoing major surgery. The pathophysiology of delirium is poorly understood, and no blood-based, predictive markers are available. We characterized the plasma metabolome of 52 delirium cases and 52 matched controls from the Successful Aging after Elective Surgery (SAGES) cohort (N = 560) of patients ≥ 70 years old without dementia undergoing scheduled major non-cardiac surgery. We applied targeted mass spectrometry with internal standards and pooled controls using a nested matched case-control study preoperatively (PREOP) and on postoperative day 2 (POD2) to identify potential delirium risk and disease markers. Univariate analyses identified 37 PREOP and 53 POD2 metabolites associated with delirium and multivariate analyses achieved significant separation between the two groups with an 11-metabolite prediction model at PREOP (AUC = 83.80%). Systems biology analysis using the metabolites with differential concentrations rendered “valine, leucine, and isoleucine biosynthesis” at PREOP and “citrate cycle” at POD2 as the most significantly enriched pathways . Perturbations in energy metabolism and amino acid synthesis pathways may be associated with postoperative delirium and suggest potential mechanisms for delirium pathogenesis. Our results could lead to the development of a metabolomic delirium predictor. The economic impact of delirium associated health care expenditures is over $164 billion per year in the U.S. alone7.Delirium is a condition characterized by an acute change and fluctuation in attention, thinking, and consciousness. Postoperative delirium complicates the courses of 15–53% of older adults undergoing major surgical procedures12; (2) alterations in plasma levels of precursor amino acids leading to dysfunction in neurotransmitter systems13; (3) oxidative stress leading to cerebral dysfunction16; (4) acute or chronic stress response resulting in aberrantly high levels of glucocorticoids18; and (5) dysregulation of the circadian rhythm21.Currently, delirium is solely a clinical diagnosis, and there is no laboratory test or biomarker to facilitate its diagnosis. Additionally, delirium etiology and pathogenesis are poorly understood. Previous research has suggested several mechanisms for delirium pathogenesis, which include: (1) increased expression of inflammatory markers found in the blood and central nervous system22. Differences in their concentration levels provide an efficient way to monitor and detect alterations in specific cellular pathways. Metabolomics can reveal transient biochemical changes that are closely aligned with the disease state of a system22.Metabolomics is a high-throughput quantitative approach to study the metabolome—the collection of small metabolites found in a system. These smaller metabolic molecules are often the end products of the biochemical processes in a cell and are particularly sensitive to endogenous and exogenous stimuli25. Targeted metabolomics approaches have been employed previously in identifying novel biomarkers and distinguishing between diseased and healthy cohorts27. Only a few prior studies have explored metabolic changes associated with delirium and illuminating potential underlying biological mechanisms31.Metabolomics has been extensively used to characterize cellular changes in disease phenotypes and facilitate identification of disease-specific markersTo obtain more insights into the pathophysiological mechanisms of delirium as well as to identify potential risk and disease markers, we performed targeted metabolomic profiling both at preoperative and postoperative time points. We identified multiple metabolites linked to delirium before and after surgery and demonstrated enrichment of several metabolic pathways. Our associative, predictive, and systems analysis may help to identify pathophysiologic pathways to prioritize for development of diagnostic and therapeutic regimens for postoperative delirium.In Fig. 32, with cases experiencing slightly more sleep disruption. The prevalence of diabetes medication use was low and similar in cases and controls. We did not control for the variables that were not used in the matching process because of the risk of over-controlling for variables that are potentially along the causal pathway to delirium34.Table 39. After data imputation, signal drift correction, and normalization, the percent relative standard deviation (RSD) was less than 5 for 87% and less than 10 for 98% of all metabolites in the pooled QC samples . Missing data accounted for 3.15% of total data acquired from present metabolites  when compared with CNT , which included three of the four metabolites that showed a binomial test BH-corrected p value < 0.05. A support vector machine (SVM) prediction model40 using these 11 metabolites as predictors yielded an area under the curve (AUC) of 83.80% for the associated receiver operating characteristic (ROC) curve  = 0.848 and predictive performance with R2Q = 0.344  at PREOP: 2-oxobutanoate, isoleucine, and valine. All three metabolites were downregulated in DEL  Table . This caAt POD2, three pathways were found to be significantly enriched with our significant metabolites N = 53) .Metabolomics provides new insights into metabolic phenotypes and dysregulations of metabolic pathways underlying human disease and adds an important avenue for biomarker and therapeutic target discovery. However, experimental and computational challenges such as sample size, batch effect, peak drift, and data normalization hamper wide-spread use30. In a blood-based metabolomics study of delirium, Guo et al. took an untargeted metabolomics approach in characterizing preoperative serum metabolites of older surgical patients (between 65 and 80 years) undergoing hemiarthroplasty for hip fractures. They identified four metabolites associated with a higher risk of postoperative delirium  with alterations in energy metabolism and oxidative stress pathways29. In a follow-up study, Guo et al. profiled both PREOP and POD1 samples and reported imbalances between branched-chain and aromatic amino acids with perturbations in tricarboxylic cycle and oxidative stress pathways31. Although the specific metabolites and direction of dysregulation were not always consistent within the three studies  metabolome of older patients (≥ 65 years) who developed delirium following elective hip or knee arthroplasty. They found that spermidine, putrescine, and glutamine were significantly upregulated in delirium patients compared to controlsl-alanine, nicotinamide, Creatine, 2-oxobutanoate, Creatinine) are involved in metabolism of amino acids as well as 2 metabolites  are linked to pyrimidine ribonucleosides degradation. Uracil was the only metabolite linked to delirium by Guo et al.31. Disruption in amino acid metabolism has been linked to Alzheimer’s disease and dementia47. Another one of these metabolites, N-acetyl-glutamate (NAG), is a crucial enzymatic cofactor for the first step in the urea cycle, essential for liver ureagenesis and reduced levels are associated with Alzheimer’s disease48.  class, which also includes the aromatic amino acids (AAA), tyrosine, tryptophan, and phenylalanine. LNAAs are key precursors for neurotransmitters that have been implicated in delirium, including glutamate, serotonin, dopamine, and norepinephrine14. BCAAs and AAAs compete for access to the central nervous system (CNS) through the large neutral amino acid transporter 1 (LAT1)52. With the absence of BCAAs, there is greater opportunity for the AAAs to reach CNS and generate their associated neurotransmitters: serotonin, dopamine, and norepinephrine.Our systems biology approach indicates possible downregulation of the valine, leucine, isoleucine biosynthesis pathway at PREOP, for those patients who would go on to develop delirium following surgery. Valine, leucine, and isoleucine are essential branched-chain amino acids (BCAA). Essential amino acids are not naturally synthesized in the body and are obtained through diet. BCAAs contribute to energy production, and are key precursors in glutamate synthesis54. Importantly, there is a well characterized reduction in activity of the rate limiting citrate cycle enzyme α-ketoglutarate dehydrogenase which converts α-ketoglutarate to succinyl-CoA56 that may be a key event in AD pathogenesis as well as various other neurodegenerative diseases due to oxidative stress54. Reduced  α-ketoglutarate dehydrogenase may result in accumulation of α-ketoglutarate. A disruption of energy homeostasis and increased oxidative stress because of increased citrate cycle activity may be a crucial event during delirium manifestation.We identified distinct metabolites and metabolic pathophysiological pathways linked to delirium development at POD2. Most significantly, potential upregulation in the citrate cycle pathway in delirium cases was predicted. This is a key pathway for energy metabolism. Six metabolites within the citrate cycle  were all increased in delirium cases, as was itaconic acid, which is generated by decarboxylation of aconitate. Interestingly, both plasma and CSF metabolomics comparison of patients with Alzheimer’s disease (AD) to matched cognitive healthy controls identified a similar increase of several citrate cycle intermediates such as citrate, aconitate, and α-ketoglutarate in AD57. Kealy and colleagues showed that energy metabolism dysregulation was sufficiently capable of triggering an acute cognitive dysfunction in a delirium mouse model44. Under normal conditions in humans, these ketones are present in plasma in an approximate 2:1 βOHB:AcAc ratio58. At PREOP, the delirium cohort displayed a decrease in βOHB , and then an upregulation of AcAc  at POD2, elucidating an alteration in energy metabolism. At POD2 our participants were in a fasting state, which supports an increase in acetoacetate, but this upregulation should be uniform across both phenotypes. This disparity points to potential energy pathway alteration in the delirium group that is not observed in the control group. These findings support previous hypotheses that energy metabolism dysregulation is a possible driver in delirium pathophysiology44.Glucose is the main fuel for the brain, but under glucose-depleted conditions like strenuous exercise, fasting, or starvation; the ketone bodies, β-hydroxybutyrate (βOHB) and acetoacetate (AcAc), are the main alternative fuel sources. These small lipid-derived molecules are converted to acetyl-CoA and then oxidized in the citrate cycle to produce adenosine triphosphate and carbon dioxidel-tryptophan, is a neuromodulator interacting with NMDA, nicotinic, and GPR35 receptors. It plays a role in various neurophysiological functions as well as neurological diseases and inflammatory processes. Overexpression of kynurenic acid in several neurological diseases is associated with confusion58, while reduction of kynurenic acid in mice improves cognitive function60. Both kynurenine and tryptophan are differentially quantified concentrations at POD2 in delirium samples at nominal, not BH-corrected, p value  with kynurenine, like kynurenic acid, being increased and tryptophan being decreased in delirium samples. Thus, kynurenic acid may be one key metabolite associated with delirium development.While most of the metabolites identified in this study have not been previously linked to delirium risk or development, a few of these metabolites have been previously linked to inflammation, cognitive function, or neurological diseases. Most prominent is kynurenic acid, which our data show to be significantly increased in delirium cases at POD2. Kynurenic acid, a degradation product of Despite our strengths in experimental design, established cohort, developed workflow, and data analysis methods, several limitations of this study are of note. The statistical power at PREOP was still insufficient to yield metabolites that passed two or more BH-corrected statistical tests implying the need for more samples. Second, the SAGES study was conducted within a single geographic region, limiting the generalizability of our results across other populations. Third, since only plasma samples were analyzed, it is difficult to assess whether similar metabolic changes occur in the brain and have functional and causal consequences with regard to delirium risk or development. Therefore, the identification of disruptions in several metabolic pathways and the link to delirium pathogenesis and pathophysiology are putative and need to be further validated. Fourth, since delirium had already occurred in most of our participants at the time of the POD2 blood draw, we are unable to completely disentangle metabolic changes that are on the causal pathway to delirium from those that result from delirium. Finally, targeted metabolomics is restricted by detection of the predefined metabolites established by the specific protocol that may result in the omission of metabolites involved in delirium.In conclusion, we have identified metabolites associated with delirium at both pre- and post-operative time points that may enhance our understanding of the etiology and pathophysiology of delirium. Our findings highlight potential dysregulation in energy production pathways at both PREOP and POD2 paving the way for future studies, which should expand upon these findings through exploration of key enzymes involved in these pathways. The metabolomic findings at PREOP have the potential to be combined with other biomarkers to develop a predictive signature, which can be used to target personalized, preventative interventions to reduce delirium incidence. Additionally, our analysis of the metabolome at POD2 could improve our understanding of the disease mechanism, which may lead to better strategies to ameliorate the delirium process. Overall, the established workflow and data analysis provide a two-faceted approach to delirium metabolomics providing a comprehensive assessment of the biochemical activity associated with this syndrome.60. SAGES enrolled patients ≥ 70 years old scheduled for major noncardiac surgery, including total knee or hip replacement, cervical or lumbar laminectomy, abdominal aortic aneurysm repair, lower extremity vascular bypass, or colectomy. Patients received either general or spinal anesthesia. Major inclusion and exclusion criteria have been published60. Patients underwent a detailed screening process to exclude dementia based on medical record review, capacity assessment, patient or family report of dementia diagnosis, and cognitive testing using the Modified Mini-Mental State Examination61. In addition, patients underwent a neurocognitive battery at baseline, which was used to compute the general cognitive performance (GCP) summary measure62. Comorbidities were identified from medical record review by trained physician abstractors and scored based on the Charlson Comorbidity Index.The Successful Aging after Elective Surgery (SAGES) study is a prospective observational study of older adults undergoing major elective non-cardiac surgery and was designed to understand novel risk factors and long-term outcomes of deliriumInformed consent for study participation was obtained from all subjects according to procedures approved by the institutional review boards of Beth Israel Deaconess Medical Center (BIDMC) and Brigham and Women’s Hospital, the two surgical sites, and Hebrew SeniorLife, the study coordinating center, all located in Boston, Massachusetts. All experiments, methods, and analyses conducted in the current manuscript were performed in accordance with relevant guidelines and regulations of the institutional review board (IRB) of all participating institutions.64. The presence of delirium by chart review was adjudicated by at least two delirium experts, and discordance was resolved through consensus65. Patients were considered delirious if delirium was present on either the CAM or the chart review method on any postoperative day; otherwise, patients were considered non-delirious. If delirium was absent during the entire hospitalization, subsyndromal delirium was defined as (i) an acute change in mental status or fluctuation, (ii) at least one CAM core feature , and (iii) at least one other CAM supporting feature 66.Postoperative delirium was determined daily throughout hospitalization, supplemented with a validated chart review to identify cases that may have been missed during daily interviews. Delirium was assessed using the Confusion Assessment Method (CAM) diagnostic algorithm68. Delirium cases (DEL) were defined as patients with delirium on POD2. No delirium controls (CNT) were defined as patients with no delirium or no subsyndromal delirium during their hospital stay. DEL/CNT pairs were matched on six variables   Table . Vascula59 as previously described68. PREOP and POD2 timepoint plasma was utilized in the present study. For all of the samples, blood was collected in a non-fasting state at PREOP and in a fasting state at POD2. A pooled control sample was created from twenty PREOP samples  from patients de-enrolled from the SAGES study. Metabolites were extracted from equal volumes of heparinized plasma (100 µl) by ice-cold methanol precipitation added to a final concentration of 80% (vol/vol).Heparinized plasma samples were collected and stored as part of the SAGES study69. This protocol utilizes a targeted selected reaction monitoring (SRM), positive/negative ion-switching, mass spectrometry-based metabolomics platform suitable for bodily fluids, cells, and fresh and fixed tissue69.Equal volumes per sample of methanol extracted metabolites were run on a 5500 QTRAP  mass spectrometer using a previously published targeted methodology developed by the BIDMC Mass Spectrometry CoreTo accommodate for the maximum sample loading capacity of the 5500 QTRAP 60 samples, six unique sample preps and mass spectrometry runs were performed for a total of 52 matched DEL/CNT pairs at PREOP and POD2 that resulted in analysis of a total of 208 samples. Each experimental run—except for the final one, which was abridged due to sample limitations—included ten matched pairs, three internal spike-in standards, thirteen pooled quality control plasma samples (QC), three conditioning samples and four blanks Fig. . In the 69. A metabolite was considered “present” if it was measured in at least 50% of the samples within a phenotypic group .Overall, 315 metabolites were measured using previously published targeted methodology  method72.Signal drift was corrected with statTarget using a quality control-based machine learning algorithm: random forest signal correction (QC-RFSC)74.NormalizeMets R package was used to normalize the metabolomics data matrix to previously selected internal standards using the NOMIS: normalization using optimal selection of multiple internal standards technique75. These results were further tested for robustness using tenfold split analysis at a 5% significance level, with 80% power, and run 10,000 times , the Benjamini–Hochberg (BH) procedure was applied to correct for multiple hypotheses testing77. Potential outliers were identified using principal component analysis (PCA) and Mahalanobis distance78 and were omitted prior to the application of orthogonal partial least squares-discriminant analysis (OPLS-DA), a supervised multivariate separation method79. The model was validated through sevenfold cross-validation, cross validation-analysis of variance (CV-ANOVA)80, and response permutation testing (n = 1000) using the fit metrics R2X, R2Y, and Q2.Multivariate statistical analysis was performed using the ropls package in the statistical program R83, which takes a bifurcated analytic approach that includes metabolite over-representation and pathway topology84. A metabolite was included in biological systems analysis if it showed significant differential concentration , BH p value < 0.05 (POD2)) in two or more statistical tests. At PREOP, nominal p value was used in lieu of BH p value because systems analysis requires more metabolites than those that met the BH significance cut-off < 0.05.Systems biology analyses of delirium-related metabolites were performed using MetaboAnalyst 4.0 82. A pathway was considered enriched if the false discovery rate (FDR) adjusted p value was < 0.05. In topology analysis, perturbed metabolites located in critical or central positions are deemed to have a greater impact on a pathway.Hypergeometric testing was used to assess if delirium-related metabolites were overrepresented in a pathway more than expected by chanceSupplementary Tables.Supplementary Figures.


Microbacterium species were involved in the metabolic ability to tolerate/adapt to local variations within an extreme desert environment. Using an integrative systems biology approach we have carried out construction and comparison of genome-scale metabolic models (GEMs) of two Microbacterium sp., CGR1 and CGR2, previously isolated from physicochemically contrasting soil sites in the Atacama Desert. Despite CGR1 and CGR2 belong to different phylogenetic clades, metabolic pathways and attributes are highly conserved in both strains. However, comparison of the GEMs showed significant differences in the connectivity of specific metabolites related to pH tolerance and CO2 production. The latter is most likely required to handle acidic stress through decarboxylation reactions. We observed greater GEM connectivity within Microbacterium sp. CGR1 compared to CGR2, which is correlated with the capacity of CGR1 to tolerate a wider pH tolerance range. Both metabolic models predict the synthesis of pigment metabolites (β-carotene), observation validated by HPLC experiments. Our study provides a valuable resource to further investigate global metabolic adaptations of bacterial species to grow in soils with different abiotic factors within an extreme environment.The Atacama Desert is the most arid desert on Earth, focus of important research activities related to microbial biodiversity studies. In this context, metabolic characterization of arid soil bacteria is crucial to understand their survival strategies under extreme environmental stress. We investigated whether strain-specific features of two  Actinobacteria from desert areas. As noted by previous researchers, this class possess bacteria with a variety of attractive metabolic qualities that make them good candidates to survive in desert areas1, including their extensive metabolic capability to degrade and utilize compounds from nutrient poor environments and their ability to synthesize secondary metabolites and natural antibiotics2. In fact, Actinobacteria is the dominant class present in desert locations3, and their ability to adapt to this kind of environment is the focus of intense research. For example, Lynch and collaborators found that over 98% of the lineages present in the vicinity of a volcano in the Atacama Desert corresponded to Actinobacteria, and that their metabolisms were adapted to utilize atmospheric gases present in this ecosystem4. One genus that belongs to the class Actinobacteria is Microbacterium, which is present in a vast variety of environments, including desert soils, marine sediments, and the human gut.In recent years, there has been increased interest in the study of Microbacterium sp. CGR1 was isolated from the Andes mountains in the central Atacama Desert5. Its complete genome sequence revealed the presence of putative components related to pH and salinity tolerance, representing the first genome of the genus Microbacterium sequenced and assembled in a single contig. In addition, two other strains of Microbacterium  were isolated and sequenced from desert soils from Saudi Arabia. Biochemical assays highlighted important differences among them in their basal metabolism, mainly related to lipid and carbohydrate (sugar) compositions6. Recently, a second Microbacterium was obtained from the Atacama Desert under a salt crust in the shore of the saline Lejía Lake7. Lejía Lake, which is nested at the base of Lascar Volcano, is one of the highland lakes at risk of disappearing due to climatic change. Despite the extreme conditions present in this lake, a diverse group of bacteria have been found to reside in this environment8. One of these bacteria is Microbacterium sp. CGR2, which has high pH (up to pH 12) and salinity (5% NaCl) tolerance levels7.Recently, Microbacterium from desert soils display interesting and specific features in terms of the configuration of their global metabolism, making it necessary to identify and characterize the complete set of chemical reactions that occur in Microbacterium species in order understand how they survive under extreme dry environments.Research to date demonstrates that 9. GEMs are networks of biochemical reactions of a given microorganism that encompass important metabolic properties of cellular biochemical networks, such as mass and redox balance, and energy requirements10. By using information obtained from annotated genomes and introducing some assumptions, models are capable of: (1) interpreting current biochemical information for metabolic and genetic context, (2) predicting metabolic capabilities, and (3) guiding future experiments to increase the knowledge of the metabolic capabilities of a given organism11. In recent years, several manually curated GEMs have been reconstructed to explore the capabilities of different microorganisms13 with an emphasis on model organisms, such as E. coli14 or Bacillus subtilis15, but also include several species of Actinomycetes16. However, to date there are no manually curated GEMs for organisms from the genus Microbacterium and there is only one GEM of a bacteria from the Atacama Desert17.In this context, a valuable tool for the systematic study of microbial metabolism of new species is the genome scale model (GEM)Microbacterium species. Our analysis shows that the capacity to survive under extreme conditions correlates with the presence of a complete set of metabolic pathways related to the production of osmolytes and the connectivity level of particular metabolites. In addition, Microbacterium models predicted the synthesis of pigment molecules, while an HPLC assay determined the presence of β-carotene, a strongly colored red-orange pigment used in biotechnological applications.In order to generate important information about global and specific metabolic pathways, which can be correlated with the ability of bacteria to survive given the local context of an extreme environment, we reconstructed and characterized the first GEMs of 5; and near Lejía Lake  or tryptic soy broth (TSB)18.CGR1 and CGR2 were previously isolated from the top layer (10 cm depth) of soil samples obtained from the slope of Lascar Volcano  and an electronic thermometer (Orion model 290), respectively. Electrical conductivity (EC) was determined in a 1:5 soil/water extract. Total elemental profiles from soluble soil extracts were determinate by Total Reflection X-ray Fluorescence (TXRF)19.pH and temperature of the Lascar Volcano and Lejía Lake soil samples were determined For soil condition tolerance assays, bacterial growth curves were determined. In order to emulate the specific soil conditions from which CGR1 and CGR2 were isolated, 10 g of soil from Lascar Volcano (Volcano) and Lejía Lake (Lake) were separately solubilized in 20 mL of TSB medium. Each medium (Volcano and Lake soil media) was centrifuged for 10 min at 9000 RMP and the supernatants were used as the growth media for the growth curves assays. Statistical differences were assessed by Student’s t test p < 0.05 or Mann–Whitney test p < 0.05 (GraphPad Prism 4).Microbacterium sp. CGR1 and CGR2 16 S rDNA sequences were obtained from NCBI under accession codes MK110962.1 and KU714726.1, respectively. The Microbacterium sp. CGR1 and CGR2 strains are available under the accession codes RGM2230 and RGM2255 from Colección Chilena de Recursos Genéticos Microbianos—INIA , respectively.Microbacterium species of length longer than 1400 bp present in SILVA database (November 2018). These sequences were aligned using the MAFFT v7.407 software21, with the default algorithm (FFT-NS-2) and subsequently trimmed to 1315 bp of aligned length and realigned using the G-INS-I algorithm. MEGA-X v10.0.5 software22 was used to build a distance tree based on the neighbor-joining method under the Kimura-2-parameters substitution model23. Bootstrap analysis  was used to evaluate statistical nodal support. The tree was visualized and annotated using the online tool Interactive Tree of Life v4.2.324.For the construction of the Neighbor-joining tree, we analyzed 109 sequences corresponding to the best scored representatives of every named Microbacterium sp. CGR1 was obtained from the GenBank BioProject PRJNA2914335. Genome sequencing of CGR2 strain was performed using Illumina GAIIx sequencing technology . A shotgun library with a total of 4,169,378 paired end reads of 300 bp was generated . De novo assembly was conducted under quality-filtered reads using CLC Genomics Workbench version 8.5.1 with length and similarity cutoffs of 80% and 90%, respectively, resulting in four contigs with an N50 size of 3,676,265 and coverage of 238×. Microbacterium sp. CGR2 was annotated using the NCBI Prokaryotic Genome Annotation Pipeline released on 2013, version 4.625 and approved on October 10th, 2018. Gene mining and genomic contexts were visualized using RAST server26.Genome sequence from 27, we identified tRNAs over the draft genomes. Then, in order to find coding sequences, we used glimmer 3.0228. The functional annotation of coding sequences was performed using Blast comparison to several databases including NCBI Non Redundant, SWISSPROT, OMNIOME, KEGG, COG and InterproScan using PROSITE, Pfam, ProDom and SMART. Two GEMs were reconstructed for Microbacterium sp. CGR1 and Microbacterium sp. CGR2, namely Mcgr1 and Mcgr2. In order to minimize random biases from the process, both reconstructions were done following the same steps, which included: (1) Generation of draft models, (2) Generation of a biomass equation, (3) Gap filling, and (4) Test models. These steps were implemented using the following platforms: Aureme29, Pathway Tools30 and, COBRA (Matlab)31, as explained below.Identification of coding regions, annotation and proteome comparisons were performed using previously described protocols. Briefly, a pipeline for prediction of coding regions and annotation was executed for the two strains. Using tRNAscan-SEMicrobacteria, using the softwares Pantograph from AuReMe32 and PathoLogic from Pathway Tools33. Pantograph is based on orthology and GEMs available for Mycobacterium tuberculosis and Streptomyces coelicolor were used as templates35, as they are members of Actinobacteria and their GEMs have been extensively curated  that could account for the synthesis of biomass, considering whether reactions were still essential if individual metabolites were added to the media, and removing those reactions that lacked genome support and could be substituted for single metabolite additions. With this process, we identified that both Microbacterium lacked 11 reactions for the biosynthesis of biotin, and hence, rather than adding those reactions, biotin was added to the media considering that other organism (such as plant or fungi) are able to provide it to the environment37. Transport reactions were also added from template models. Both models included chemical equations consistent with those presented in the BiGG Models database38. Mass balance tests were performed in COBRA, to ensure that all internal reactions would be balanced. Finally, both models were tested to ensure that: (1) their reactions were elementally balanced, (2) they could generate biomass from defined media, and (3) they did not present futile cycles generating energy. Manual curation was done throughout the reconstruction process, as this allows evaluating the need to include different reactions, pathways, or biomass components.Then, the models were used to test the feasibility to produce biomass, and a manual process was implemented to fill gaps. This consisted of evaluating reactions present in the template model  and 1 mL of hexane were added for cell disruption and carotenoid dissolution. Cells were disrupted at room temperature in a BeadBug6 cell homogenizer (Benchmark Scientific) using a program consisting in 4 cycles of 90 seconds of disruption at 3700 rpm followed by a 10-second rest. Cell lysate was then centrifuged and the liquid phase (hexane) was recovered and then evaporated by adding nitrogen on the surface of the liquid. After solvent evaporation, the solid residues in the bottom of the test tube were resuspended in 1 mL of acetone and vortexed until complete dissolution. If a white precipitate appeared, it was discarded by centrifugation. Finally, the homogenous acetone was transferred to a 1.5 mL Eppendorf tube and stored at −20 °C until HPLC analysis.For carotenoid extraction40. Briefly, samples were pre-treated for injection by mixing 450 μL of sample with 50 μL of a 200 mg/L solution of β-apocarotenal, used as internal standard to correct the apocarotenal injection error (retention time tR~6.1 min). After adding the internal standard, 50 μL of sample were injected in duplicate into a LiChrospher® 100, RP C18 (5 μm) column, using Acetonitrile:Isopropanol:Methanol (85:10:5) as a mobile phase. Carotenoids were detected by measuring the optical density of the eluent of the column at 453 nm using a diode array detector (DAD). Chromatograms from the samples were compared with those of known standards to determine the presence of different carotenoids: lycopene (tR ~ 11 min), δ-carotene , ε-carotene (tR ~ 16.5 min) and β-carotene (tR ~ 19.7 min). All standards were purchased from Sigma-Aldrich.The carotenoid analysis was performed by LaChrom L-7000 chromatograph using Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC) for detection41. Based on the particular soil scenario present in the Atacama Desert, two Gram-positive, rod-shaped and yellow-pigmented Microbacterium strains (CGR1 and CGR2) were isolated from soil samples collected from the Andes mountain range in the central region of the Atacama Desert , close to the Lascar volcano, while CGR2 was isolated from an alkaline site with high electrical conductivity  located near Lejía lake. Analysis of available elements showed that the Lake site had a significant higher content of Ca, Mn and K nutrients, supporting its characterization as a salt lake. In contrast, Cu, Fe and Zn were higher at the Volcano site. A similar scenario has been observed in acid mine drainage environments, where the low pH facilitates the solubilization of heavy metals.Microbacterium isolates growing in each of the soil-like media. The results showed that CGR1 grew under the Volcano and Lake media in a similar way. On the other hand, the emulated conditions of the Volcano soil reduced the proliferation of CGR2. As mentioned before, the main differences between both sites, and hence between both media, were the pH and salt concentrations (conductivity). In terms of pH, the Volcano soil media had a final concentration of protons equal to 1E-5, more than four orders of magnitude higher than the concentration of the Lake medium (pH = 9). In general, pH is one of the principal variables highly correlated with soil microbial diversity at the continental scale42, across artificial pH gradients43, in highly managed urban systems and in natural pH gradients19. As observed for other environmental factors, the peak of diversity has been correlated with near-neutral or optimal conditions, showing a trend to decrease towards more acidic and alkaline (extreme) conditions44.Thus, CGR1 and CGR2 were isolated from soils with highly contrasting physicochemical environmental features. In order to test if both bacteria are able to reciprocally growth in each other’s habitat, we generated two soil-like culture media (Volcano and Lake) to emulate the physicochemical variables present in the isolation sites . Figure 45. Bacteria that live in saline environments, especially when they are hypersaline, must overcome the essential problem of maintaining a balance in intra and extracellular water content, and thus avoiding cellular dehydration . For this reason, only osmotolerant species are able to inhabit high saline conditions, leading to a decrease in soil microbial diversity in these environments46.Considering salts, the Lake medium had at least 10 times greater concentration of elements  than the Volcano medium. Salinity has also been known as a factor that influences the microbial diversity of soilMicrobacterium was first proposed by Orla-Jensen in 191947, and was afterwards revised by Collins et al. (1983) and by Takeuchi & Hatano (1998a) in order to include the unification of Microbacterium with its closely related genus Aureobacterium49. At present (November 2018), the genus comprises 109 species (http://www.bacterio.net/microbacterium.html), which occupy a very wide and diverse environmental distribution, including soil50, water51, plants52, rhizosphere53 and human clinical specimens54.The genus Microbacterium isolates from the Atacama Desert was performed, in which the 16 S rRNA sequences of CGR1 and CGR2 strains were compared with the 16 S rRNA sequences from 109 Microbacterium species  could be useful to assess whether metabolic modifications can be linked to adaptations to their particular ecological constraints. For this reason, the global metabolic capabilities of both Streptomyces coelicolor iMK1208 contains 15% of genes35). The Mcgr1 and Mcgr2 models contain 1168 and 1172 reactions, respectively, from which 760 and 764 correspond to metabolic reactions. The similarity of the metabolic capabilities in core pathways is reflected by the high percentage of shared metabolic reactions . These similarities are also reflected by the number of shared metabolites among the two models (98% for both). Table The Mcgr1 and Mcgr2 models contain 632 and 648 genes, which correspond to 19% and 17% of the total genes in each genome, respectively. These values are an expected and sufficient level of representation as they fall within the range of other GEMs  followed by phenylacetate degradation I (aerobic). On the other hand, benzoate compounds are predicted to be metabolized into protocatechuate, and from there utilize pathways protocatechuate degradation II (ortho-cleavage pathway) and 3-oxoadipate degradation. C1 compounds include formaldehyde and methanol. Interestingly, Mcgr1 has three pathways to utilize C1 carbon sources, while Mcgr2 has none, indicating a robust system to deal with these compounds in Mcgr1. This robustness may also ensure that formaldehyde, a toxic metabolite, is rapidly recycled56. Since Mcgr1 was isolated from the Lascar volcano, it is possible that the proximity to the volcano may elevate the availability of methanol and formaldehyde in the environment. For example, Lynch and collaborators identified formaldehyde and other C1 gases present in another volcano in the Atacama Desert, together with biodegradation pathways in bacteria from that area4.Despite many similarities, the two 57, with more particular emphasis in the bacterium Pseudomonas putida58. Other Microbacteria in different locations have been reported to degrade protocatechuate. According to the pathway database available in Pathway Tools30, out of the 48 Microbacteria included, 39 possess protocatechuate degradation pathways. In contrast, only one model in the BiGG database contains this compound 38, indicating underrepresentation in constructed models.Aromatic compounds are another family of substrates belonging to pathways that show differences between Mcgr1 and Mcgr2, as only Mcgr2 can utilize 4-hydroxybenzoate (4-HBA), vanillate and 3,4-hydroxybenzoate (protocatechuate), the latter being also an intermediate compound in the degradation of the former two. These components are considered aromatic pollutants, and their bacterial degradation has been the focus of previous studies59, we theorize that Mcgr2 has adapted to utilize this component produced by other organisms. Mcgr2 was isolated from a salt lake, where it is expected that plants and microorganisms could produce inositol as an osmoprotectant.Another difference between Mcgr1 and Mcgr2 was that only Mcgr2 was capable of degrading inositol. Since this compound is an osmoprotectant for several bacteria and plants60. In order to improve the understanding of this point, we compared the metabolism of both Microbacterium strains against the GEMs of different species with C1 metabolism, which included the models from Methylococcus capsulatus, Bacillus subtilis and Pseudomonas putida63. For this, we identify the presence of 16 enzymes involved in 4 pathways related to C1 metabolism: formaldehyde assimilation II (assimilatory RuMP Cycle), formaldehyde oxidation I (dissimilatory RUMP cycle), formaldehyde oxidation IV and methanol oxidation in the genomes of all five species. We found out that both Microbacterium are the only two species containing all these enzymes, supporting the idea that CGR1 and CGR2 belong to the group of methylotrophic bacteria. Interestingly, by calculating the connectivity of metabolites, we found out that formate, formaldehyde, methanol and CO2 have a lower connectivity in Microbacterium than the other species. This result suggests that the C1 metabolism in the Microbacterium CGR1 and CGR2 presents a high level of specificity, conferring to the systems the a low level of connectivity inside the network64, a characteristic observed in specialized species that live in particular niches, such as Geobacter sulfurreducens, a soil sulphur-reducing proteobacterium65.Microbacterium species have been previous classify as putative methylotrophic bacteriaMicrobacterium against Chromohalobacter salexigens 66, which is a halophilic extremophile commonly used to study osmoadaptation and the methanotrophs bacteria Methylomicrobium buryatense (adapted to metabolize C1 compounds)67. All models contain a similar number of enzymes, reactions and metabolites. In particular, one of the main mechanisms of the latter to cope with the saline environment is the production of osmoprotectants such as ectoine and hydroxyectoine, both metabolites able to be synthesized by Chromohalobacter salexigens and both Microbacterium strains. The model for the methanotrophs bacteria Methylomicrobium buryatense, utilizes the RuMP cycle to fixate C1 just like Microbacterium Mcgr1. This pathway is a highly efficient route for the assimilation of reduced one-carbon compounds . Considering that CGR1 soil contains higher metal concentration  of osmolytes. Some osmolytes that have been reported for bacteria include glycine-betaine, carnitine, proline, trehalose, ectoine and glutamateIn silico analysis showed that both Microbacterium isolates are equally capable of adjusting to the addition or removal of protons . Hence, we hypothesized that their different capacity to deal with acidic conditions could be explained by system-level properties, such as metabolite connectivity 72. Table 2 was one of the top metabolites, with 5 degrees of difference in connectivity, with higher differences in Mcgr1 compared to Mcgr2. Since CO2 is often involved in decarboxylation reactions, and previous studies have shown that decarboxylation reactions could participate in mechanism to handle acidic stress by incorporating protons73, we looked at these reactions in more detail in the models. We found that the gene AKG07_04960 is only present in the Mcgr1 genome. It encodes for a decarboxylase of the amino acids lysine, ornithine and arginine, which, interestingly, have been shown to play a role in the response to acidic stress and survival in other bacteria74.76. In this respect, both Microbacterium strains have a gene, Sodium/Proton antiporter , predicted to be pH-dependent that could be involved in maintaining proper homeostasis under alkaline conditions, as they both have the capability to grow in extreme alkaline conditions . Therefore, we suggest that the connectivity of decarboxylation reactions and proton exchange represent a common metabolic strategy that could explain the presence of both strains in this extreme environment. At the same time, variations in the magnitude of this connectivity could affect their pH tolerance range.Another mechanism that has been described in the literature as an important player in the handling of alkaline and acidic stress is the direct exchange of protons with the external environment through proton pump mechanisms Microbacterium GEMs against the extremely halophilic bacterium Salinibacter ruber in order to find specific mechanisms of adaptation to high concentrations of salt77. S. ruber is characterized by having different osmotic resistance proteins also conserved in Archaea species, such as light-driven proton and chloride pumps, none of them encoded in CGR1 or CGR2 genomes. While the Microbacterium CGR2 lives in a salt environment (Lejia Lake), both not need high concentration of salt to growth, unlike S. ruber, which optimal growth performance requires a 20–30% of salt in the media. In addition, S. ruber are able to produce an unusual carotenoid called salinixanthin, a compound used for transfer energy to light-driven proton pumps. Interestingly, according to the GEMs, both Microbacterium are able to produce carotenoids, however, the absence of light-driven proton pumps in these bacteria suggest that these molecules are not directly related to salt resistant.We also compared the 78. Microbial mechanisms to deal with radiation stress include DNA repair mechanisms and genetic expression of photoprotection molecules80. One group of photoprotection molecules are carotenoids, long chain molecules that provide pigmentation to many organisms and can absorb UV wave length that might otherwise damage cellular structures81. Multiple studies have positively correlated carotenoid pigmentation with UV resistance in bacteria83. It has also been shown that pigmentation by itself does not explain the overall bacterial response to UV radiation in some populations84, a situation that we believe is explained by the presence of different, and possibly complimentary, protective mechanisms.Another relevant stressor in the Atacama Desert is the high UV radiation, which could severely impair growth of certain bacteria, while not having a significant impact in others due to the presence of protective mechanismsMicrobacterium species have been widely used in biotechnological applications; in particular their production of carotenoids has be used for diet supplements, food colorants, animal feed and nutraceuticals85. Interestingly, both Mcgr1 and Mcgr2 exhibited pigmentation on petri plates, and they both possessed biosynthetic gene clusters for carotenes, which strongly suggest that they are producers of carotenes  in the non-mevalonate pathway  may have provided one species with a higher capacity to tolerate a wide range of pH. This comparative global metabolic analysis supports the idea that only a fraction of the encoded proteins takes an active part in metabolic pathways and display strain-specific correlation patterns. These patterns are reflective of the functional differentiation of the two Microbacterium strains described in this work, organisms that co-occur in microbial communities located in spatially close soils.In this work, we presented the first two GEMs for Microbacterium strains encoded the complete pathways required to produce different carotenoids, which are interesting targets for further development. Finally, together with the GEM model of Streptomyces leeuwenhoekii C3417, Microbacterium sp. CGR1 and CGR2 models are the first curated GEMs from bacterial species isolated from desert environments, significantly expanding the current knowledge of global metabolism in extreme bacteria.In addition, analysis of secondary metabolites revealed that both GEM Mcgr1.GEM Mcgr2.Supplementary Figures.Supplementary Tables.


The co-cultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate.Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of C. kluyveri, resulting in a flux of up to 60C. autoethanogenum ethanol production. Blocking either acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) activity or formate transport, in the C. autoethanogenum metabolic model could potentially lead to an up to 150The model suggests that one strategy to optimize the production of medium-chain fatty-acids from syngas would be the addition of succinate. According to the prediction, addition of succinate would increase the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in  This energy-rich syngas can be further used as feedstock for chemocatalytic processes such as Fischer-Tropsh, but microbial fermentation of syngas is gaining more attention recently as a potential production platform 2 ratio in syngas, and are also more resistant to the presence of certain impurities (e.g. sulfides), reducing the need for costly pre-treatment of syngas One of the biggest challenges society faces nowadays is finding alternative processes for the sustainable production of fuels and chemicals. At present, the production of many commodities depends on fossil fuels, which is not sustainable or sugar crops, competing with human and animal food consumption 2 via the Wood–Ljungdahl pathway Clostridium autoethanogenum and Clostridium kluyveri was described to produce medium-chain fatty acids (C4-C6) and their respective alcohols by assimilation of CO or syngas C. autoethanogenum is an acetogenic bacterium able to produce acetate and ethanol when growing on CO or syngas C. kluyveri grows on acetate and ethanol via reverse-C. kluyveri is grown in co-culture with C. autoethanogenum on CO, it produces butyrate and hexanoate, which are further reduced by the acetogen to the corresponding alcohols, butanol and hexanol C. autoethanogenum and C. kluyveri is 0.056 mmol of hexanoate per mmol of CO, whereas the yield obtained in the most recent study Acetogenic clostridia are efficient microbial hosts for syngas fermentation as they can grow on CO and CO/HClostridium thermocellum allowed the design of metabolic strategies to increase ethanol production after identification of bottlenecks in central carbon metabolism that were inhibiting its production Desulfovibrio and archeon Methanococcus maripaludis S2 into a single model with a shared extracellular environment, bringing the use of GEMs to a next level. Since then, this type of community models have been used to describe metabolic interactions among community members and inter-species fluxes C. autoethanogenumC. kluyveriGenome-scale, constraint-based metabolic models (GEMs) attempt to represent the complete set of reactions in a living organism, and have been used to gain better understanding of cellular metabolism, assessing theoretical capabilities or designing media and processes C. autoethanogenumC. kluyveriIn this study, we present a multi-species model built by combining the GEMs of 22.1C. autoethanogenum, the previously described GEM, iCLAU786, was retrieved in SBML (XML) format from the To represent the metabolism of C.kluyveri, iCKL708, was downloaded in table format from its publication The GEM of 2.2C. autoethanogenum and C. kluyveri was generated by combination of single species models: iCLAU786 The multi-species GEM of 2 excreted by C. kluyveri, from the amount of H2 metabolized by C. autoethanogenum. The same was done for acetate. A reaction was included to distinguish the amount of acetate metabolized by C. autoethanogenum, from the amount of acetate produced by C. autoethanogenum. The model also contemplates the possible production of butanol and hexanol via butyrate and hexanoate uptake by C. autoethanogenum. The added reactions are a transport reaction from the external compartment to the internal compartment of C. autoethanogenum, reactions for production of butyraldehyde and caproaldehyde from the corresponding fatty-acids and reactions for production of alcohols from their corresponding aldehydes. Finally, the multi-species model was transformed into SBML level 3 version 1 (see Each species has its own biomass synthesis reaction. An extra biomass metabolite was created and defined in the extracellular compartment for each species: ‘BIOMASSon 1 see .2.3−1 h−1) are integrated as model constraints instead of specific fluxes (mmol gDW−1 h−1), where gDW indicates grams of dry weight. The biomass reaction of each species incorporates as new term, the biomass of the relative species together with the growth rate term. In this way, we can account for species abundance in the community. The biomass of each species is calculated based on the community biomass and the species ratio. In addition to this, steady-state and equal growth rate of species are assumed.In order to model the community, we have followed an approach similar to the one proposed by SteadyCom 2.4C. autoethanogenum and C. kluyveri in co-culture, was estimated from partitioning RNAseq reads and confirmed via cell counting in microscopy observations as two independent methods. Transcriptomic data was obtained from steady-state co-cultures grown in chemostats C. autoethanogenum: DSM 10061  was then derived using: C. autoethanogenum was multiplied by 10 and the dry weight of C. kluyveri was multiplied by 1 (as the observed proportion). Finally, the biomass-species ratio was calculated based on the ratio of the accumulated dry weights. The proportion was observed to be constant among experimental conditions with CO and CO/H2, so we assume that the relative abundances are constant for the rest of conditions too.The second method consisted of direct cell counting under microscopy observations. This led to a proportion between cell numbers of 10 2.5−1 h−1. Product concentrations, measured in mM, were used to compute product secretion rates (mmol l−1 h−1) by using the same hydraulic retention time (HRT) that in laboratory settings. Dilution rate is the inverse of the HRT. In steady-state conditions, growth rate is considered equal to the dilution rate and therefore, it was calculated as the inverse of the HRT and expressed in h−1. In co-culture, the growth rate of both species was assumed to be the same and equal to that of the community, since HRT was kept constant both, in mono-culture and co-culture experiments −1 h−1). Similarly, product formation was set to be at least 80−1 h−1 from the pre-set values in mmol gDW−1 h−1 multiplying by the total biomass and species ratio. In cases where metabolites behave as products that are further metabolized by the other species, the transport reactions of these metabolites are forced to operate in the direction from the external compartment to the other species compartment.Experimental measurements were converted to mmol l2.5.1C. autoethanogenum in mono-culture and co-cultivation of C. autoethanogenum and C. kluyveri grown on CO/H2 and CO/acetate. In co-culture experiments, C. kluyveri was inoculated in the reactor on top of C. autoethanogenum in a 1:20 volume ratio. The organisms were cultivated in chemostat to control environmental conditions such as pH (6.2), temperature (37°), HRT (between 1.5 to 2 days) and medium composition during the entire reactor run. A reactor run starts with inoculation of C. autoethanogenum in mono-culture followed by co-cultivation with C. kluyveri after reaching stationary phase. Total reactor volume is 1.5 l. Working volume was set between 0.75 l to 1 l. Experiments were run on different conditions of CO/H2 and CO/acetate as initial substrates. Concentrations of organic acids and alcohols in the reactor and gas composition in the outflow were tracked during the runs.Experimental data was collected from reactor run 3 and 4 of the recent study 2.6C. kluyveri, unless stated otherwise. For each explored condition, the solution space and the set of fluxes compatible with the measured constraints were sampled using the sample function in the flux_analysis submodule COBRApy. Flux sampling is a method to get a distribution of fluxes Model simulations were done using COBRApy, version 0.17.0 2.7C. autoethanogenumC. autoethanogenum deleting the corresponding reactions. Each mutant was assessed at each experimental condition and compare to the wild type. The media as well as the biomass reaction were constrained using the experimental data of mono-culture experiments for those conditions OptKnock and RobustKnock 3C. autoethanogenum and C. kluyveri. The generated multi-species GEM, together with the GEM of C. autoethanogenum, were used to assess these strategies.The objective of this study is to find optimization strategies for the production of medium-chain fatty-acids from syngas using the co-culture of 3.1C. autoethanogenum, iCLAU786 is composed of 1108 reactions and 1094 metabolites. The model is able to simulate growth on CO or syngas as the sole carbon and energy source, producing acetate and ethanol as the main fermentation products.The GEM of C. kluyveri, iCKL708 has been previously shown to predict growth on ethanol and one other organic acid , propanol and acetate, crotonate, and vinyl acetate, in accordance to published experimental data C. kluyveri, has 993 reactions and 811 metabolites. This updated model also simulates growth on acetate and ethanol uptake producing butyrate and hexanoate as the main chain-elongated products and H2.The GEM of 3.222 excreted by C. kluyveri. Reaction 22 in C. autoethanogenum. Reaction C. autoethanogenum in simulations where acetate acts as additional substrate. This serves to distinguish the fluxes between acetate feed rate  was taken into account resulting in a biomass ratio of 68.5C. kluyveri and 31.5C. autoethanogenum. This cell ratio was observed in CO and CO/H2 conditions and it was assumed to be constant in the model simulations.Microscopy observation of the co-culture led to the estimation of a ratio of 10 cells of 3.3C. autoethanogenum−1 h−1. Experiments were run on CO/H2 and CO/AC (acetate) as initial substrates. Acetate production rate . Ethanol production rate (‘EXC. autoethanogenum minus the ethanol consumed by C. kluyveri (reaction id Rckl837). The model correctly predicts production of medium-chain fatty acids upon introduction of C. kluyveri. Similarly to the mono-culture simulations, there is a slight mismatch between predicted and observed acetate production. The model correctly predicts the increase of medium-chain fatty-acids when more H2 or acetate is added. When H2 feed rate is equal to 5.3 mmol l−1 h−1, butanol is also produced (0.075 mmol l−1 h−1). Also, ethanol accumulation is low in most co-culture conditions, suggesting most of it is metabolized by C. kluyveri. The steady-state production rates of acetate increases with increasing acetate uptake  reaction (R3) and in formate hydrogen lyase reaction (R2 in 2 supply. Part of the formate is excreted and part is further metabolized to acetyl-CoA (ACCOA) following the Wood-Ljungdahl pathway. Pyruvate is partly produced from acetyl-CoA via the pyruvate synthase (R8 in C. kluyveri. Ethanol production rate by C. autoethanogenum (reaction R10) and ethanol uptake by C. kluyveri (R15) indicate that most of ethanol is removed by C. kluyveri and proves the metabolic change to solventogenesis due to addition of C. kluyveri. This can be observed comparing to mono-culture results showed in C. kluyveri (flux through R10). In C. kluyveri ethanol is oxidized to acetyl-CoA (ACCOA) and part of acetyl-CoA is converted to acetate via acetyltransferase and acetate kinase (R16). Acetyl-CoA initiates the reverse 2 to pyruvate via pyruvate synthase (R20) in C. kluyveri.CO or CO and Hon R2 in ) to prodse R8 in  for assiC. kluyveri comes from uptake of succinate produced by C. autoethanogenum (see R28), which could explain the slight mismatch observed in Succinate is converted to Crotonyl-CoA (CROCOA), yielding an additional 2 acetate see , involvi2 production by C. autoethanogenum with increasing H2 feed rate , more protons are released. In contrast, less protons are released when more acetate is fed. ATP synthase increases in both species  when more H2 or acetate is fed to the reactor.The model predicts reduction of COeed rate B. This wurce see . It is o3.4C. autoethanogenum and metabolized by C. kluyveri producing part of the acetate and crotonyl-CoA pool needed for chain elongation and production of fatty-acids. C. autoethanogenum and C. kluyveri growing on CO and H2 as carbon and energy sources. Succinate, acetate and ethanol uptake by C. kluyveri increases with more H2 supply. Succinate uptake decreases when C. kluyveri is less abundant in the co-culture. On the contrary, the amount of ethanol and acetate that is available to be metabolized by C. kluyveri decreases along with the relative abundance of C. autoethanogenum.The model enables detailed inspection of production and uptake profiles. Therefore we investigated the sharing of metabolites between both organisms. Analysis of intracellular fluxes in C. kluyveri .To further investigate the role of succinate, similar simulations were performed but this time preventing the uptake of succinate by kluyveri . The mod3.5We used the experimentally-validated model to simulate alternative scenarios that have so far not been explored experimentally. Accordingly, we present here an analysis of possible strategies to increase medium-chain fatty acid production by the co-culture.3.5.12 feed rates and fixed CO feed rate (4.8 mmol l−1 h−1). C. kluyveri-C. autoethanogenum), respectively. The increased carbon availability has been considered and used to normalize the results, so they are represented as mmol of product per total substrate (CO and succinate) per carbon. As already indicated in 2 uptake by C. kluyveri and changes in H+ balance and ATP synthase.As a strategy to increase the production of desired products, we simulated how the addition of succinate as extra carbon source would affect the production of butyrate and hexanoate at different H2 feed rates.Furthermore, succinate addition allows increased production of both fatty-acids. Hexanoate production can increase up to four times when succinate is added and butyrate has potential to increase around five times with respect to the results obtained with no succinate addition. On the other hand, simulations show no relevant differences upon variations of H3.5.2As a second strategy for co-culture optimization we have predicted and evaluated genetic interventions that could lead to higher medium-chain fatty-acid production.C. autoethanogenum. The three reactions are located in the metabolism of C. autoethanogenum. Mutant 1 refers to the knock out of formate transport reaction via proton symport  and would possibly lead to a further increased production of MCFA and alcohols in conditions with higher H2 influx (−1 h−1), as it has been previously observed. Furthermore, it has already been proven that succinate leads to an increase of MCFA in C. kluyveriAs the model closely predicted obtained experimental values, it can be used to design potential strategies for improved production. Here, we explored a series of strategies to optimize the production of hexanoate. The first strategy relied on the role of succinate, as the model predicts it increases the pool of acetate and crotonyl-CoA, a precursor of the desired fatty-acids, in veri see . The fludded see . The incdded see . Accordidded see . MoreoveC. autoethanogenum. An increasing ratio of ethanol/acetate ratio has been shown to result in increased hexanoate production in C. kluyveriThe second strategy aims to increase hexanoate by increasing ethanol production by C. autoethanogenum is known to increase production of ethanol under acidic or redox overloading conditions C. autoethanogenumC. autoethanogenum we predicted that the individual knock out of one of the following three reactions could increase the production of ethanol: acetaldehyde oxidoreductase (ACALDx), formate transport (FORt2), and the bifurcating formate dehydrogenase (ferredoxin)  (model id leq000001). This functionality of Hyt seems to occur in situations where redox mediators get too reduced 2 via CO dehydrogenase  see . The aceC. kluyveri has been reported to be better at higher pH C. autoethanogenumWhen the reactions of fatty-acids production are not constrained, the model always predicts more hexanoate as compared to butyrate production measured in the actual chemostat experiments 2 uptake by C. kluyveri in cases where hexanoate is more abundant  producing pyruvate that is further converted to formate via formate lyase (model id PFL), for assimilatory purposes. Formate is then assimilated via the tetrahydrofolate pathway to, subsequently, be transformed to various amino acids needed for growth 2 is also metabolized following phosphoenolpyruvate carboxylase reaction (PPC), where CO2 is fixed together with phosphoenolpyruvate (pep) producing oxaloacetate (oaa). Oaa produces aspartate via aspartate aminotransferase (Rckl310). Aspartate is a precursor in the synthesis of threonine involving aspartate kinase (Rckl334), aspartate semialdehyde oxidoreductase (Rckl323), homoserine kinase (Rckl335) and threonine synthase (Rckl336). Then, threonine produces acetaldehyde and glycine via threonine aldolase (Rckl341). Acetaldehyde is converted to acetyl-CoA following aldehyde alcohol dehydrogenase (ADH) reaction. So, the increase in CO2 assimilation could lead to more acetyl-CoA and thus, more fatty-acids. Around 30–402 is also converted to carbonic acid. Carbonic acid produces oxaloacetate via pyruvate carboxylase (Rckl014), which follows the aforementioned route to acetyl-CoA. Simulations made with higher CO2 uptake rates than the ones predicted when hexanoate is more abundant (−1 h−1) however, did not lead to higher production rates of hexanoate or butyrate. This suggest that C. kluyveri metabolizes CO2 up to a maximum value. In fact, this is supported by the observed correlation between growth and the maximum CO2 fixed by C. kluyveri2, close to its maximum capacity.We observe an increase of COdant see  compareddant see . CO2 is 2 , is here predicted to increase the yield of hexanoate up to 0.6, reflecting the potential of this approach to produce medium-chain fatty-acids.Electron yield is calculated based on the amount of electrons going to hexanoate per the total amount of electrons going into the system as carbon and energy sources. Co-cultures of 5C. autoethanogenum and C. kluyveri has provided insights into the fermentation of CO/syngas to medium-chain fatty acids by this co-culture. The prediction of intracellular flux distribution in this consortium enabled to uncover the potential importance of succinate uptake via C. kluyveri to produce butyrate, and suggested an effect of the biomass species ratio on the substrate profile of C. kluyveri. Simulations indicated that succinate addition might result in a substantial increase in hexanoate yield from syngas. In addition, the model of C. autoethanogenum shows that the deletion of reactions FORt2 or ACALDx or FDHC. autoethanogenum potentially increase ethanol production, suggesting a potential increase in hexanoate production when these deletions were to be applied in co-culture experiments. Altogether, our model-driven approach has set a good basis for the systematic design of strategies to modulate and optimize the production of valuable chemicals from syngas.The generation of the multi-species GEM of Sara Benito-Vaquerizo: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft. Martijn Diender: Formal analysis, Investigation, Writing - review & editing. Ivette Parera Olm: Formal analysis, Investigation, Writing - review & editing. Vitor A.P. Martins dos Santos: Funding acquisition, Supervision, Writing - review & editing. Peter J. Schaap: Conceptualization, Writing - review & editing. Diana Z. Sousa: Project administration, Funding acquisition, Conceptualization, Writing - review & editing. Maria Suarez-Diez: Supervision, Conceptualization, Methodology, Visualization, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.