ResearchPad - metabolites https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions]]> https://www.researchpad.co/article/elastic_article_7734 Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.

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<![CDATA[Dysregulation of multiple metabolic networks related to brain transmethylation and polyamine pathways in Alzheimer disease: A targeted metabolomic and transcriptomic study]]> https://www.researchpad.co/article/Nf62c48b8-7c01-44cc-9110-a611b974b3f9

Background

There is growing evidence that Alzheimer disease (AD) is a pervasive metabolic disorder with dysregulation in multiple biochemical pathways underlying its pathogenesis. Understanding how perturbations in metabolism are related to AD is critical to identifying novel targets for disease-modifying therapies. In this study, we test whether AD pathogenesis is associated with dysregulation in brain transmethylation and polyamine pathways.

Methods and findings

We first performed targeted and quantitative metabolomics assays using capillary electrophoresis-mass spectrometry (CE-MS) on brain samples from three groups in the Baltimore Longitudinal Study of Aging (BLSA) (AD: n = 17; Asymptomatic AD [ASY]: n = 13; Control [CN]: n = 13) (overall 37.2% female; mean age at death 86.118 ± 9.842 years) in regions both vulnerable and resistant to AD pathology. Using linear mixed-effects models within two primary brain regions (inferior temporal gyrus [ITG] and middle frontal gyrus [MFG]), we tested associations between brain tissue concentrations of 26 metabolites and the following primary outcomes: group differences, Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) (neuritic plaque burden), and Braak (neurofibrillary pathology) scores. We found significant alterations in concentrations of metabolites in AD relative to CN samples, as well as associations with severity of both CERAD and Braak, mainly in the ITG. These metabolites represented biochemical reactions in the (1) methionine cycle (choline: lower in AD, p = 0.003; S-adenosyl methionine: higher in AD, p = 0.005); (2) transsulfuration and glutathione synthesis (cysteine: higher in AD, p < 0.001; reduced glutathione [GSH]: higher in AD, p < 0.001); (3) polyamine synthesis/catabolism (spermidine: higher in AD, p = 0.004); (4) urea cycle (N-acetyl glutamate: lower in AD, p < 0.001); (5) glutamate-aspartate metabolism (N-acetyl aspartate: lower in AD, p = 0.002); and (6) neurotransmitter metabolism (gamma-amino-butyric acid: lower in AD, p < 0.001). Utilizing three Gene Expression Omnibus (GEO) datasets, we then examined mRNA expression levels of 71 genes encoding enzymes regulating key reactions within these pathways in the entorhinal cortex (ERC; AD: n = 25; CN: n = 52) and hippocampus (AD: n = 29; CN: n = 56). Complementing our metabolomics results, our transcriptomics analyses also revealed significant alterations in gene expression levels of key enzymatic regulators of biochemical reactions linked to transmethylation and polyamine metabolism. Our study has limitations: our metabolomics assays measured only a small proportion of all metabolites participating in the pathways we examined. Our study is also cross-sectional, limiting our ability to directly test how AD progression may impact changes in metabolite concentrations or differential-gene expression. Additionally, the relatively small number of brain tissue samples may have limited our power to detect alterations in all pathway-specific metabolites and their genetic regulators.

Conclusions

In this study, we observed broad dysregulation of transmethylation and polyamine synthesis/catabolism, including abnormalities in neurotransmitter signaling, urea cycle, aspartate-glutamate metabolism, and glutathione synthesis. Our results implicate alterations in cellular methylation potential and increased flux in the transmethylation pathways, increased demand on antioxidant defense mechanisms, perturbations in intermediate metabolism in the urea cycle and aspartate-glutamate pathways disrupting mitochondrial bioenergetics, increased polyamine biosynthesis and breakdown, as well as abnormalities in neurotransmitter metabolism that are related to AD.

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<![CDATA[Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells]]> https://www.researchpad.co/article/5c99020dd5eed0c484b97589

Quantitative modeling is useful for predicting behaviors of a system and for rationally constructing or modifying the system. The predictive power of a model relies on accurate quantification of model parameters. Here, we illustrate challenges in parameter quantification and offer means to overcome these challenges, using a case example in which we quantitatively predict the growth rate of a cooperative community. Specifically, the community consists of two Saccharomyces cerevisiae strains, each engineered to release a metabolite required and consumed by its partner. The initial model, employing parameters measured in batch monocultures with zero or excess metabolite, failed to quantitatively predict experimental results. To resolve the model–experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then remeasured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured, including death rate, metabolite release rate, and the amount of metabolite consumed per cell birth, varied significantly with the metabolite environment. Once we used parameters measured in a range of community-like chemostat environments, prediction quantitatively agreed with experimental results. In summary, using a simplified community, we uncovered and devised means to resolve modeling challenges that are likely general to living systems.

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<![CDATA[RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion]]> https://www.researchpad.co/article/5c5df347d5eed0c48458108e

Constraint-based modeling (CBM) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology, biomedicine, and various biotechnological processes. While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models, CBM of communities with balanced growth is more complicated, not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models. Moreover, the solution space of these community models often contains biologically unrealistic solutions, which, even with model linearization and under application of certain objective functions, cannot easily be excluded. Here we present RedCom, a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks. By discarding (single-species) net conversions that violate a minimality criterion in the exchange fluxes, it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts (instead of biomass) to fulfill the requirements of other species. We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants. Compared to full (bilinear and linearized) community models, we found that the reduced community models obtained with RedCom are not only much smaller but allow, also in the largest model with nine species, extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates. Furthermore, the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures. For an enrichment culture for growth on ethanol, we also used metaproteomic data to further constrain the solution space of the community models. Both model and proteomic data indicated a dominance of acetoclastic methanogens (Methanosarcinales) and Desulfovibrionales being the least abundant group in this microbial community.

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<![CDATA[Preliminary results of identification and quantification of paclitaxel and its metabolites in human meconium from newborns with gestational chemotherapeutic exposure]]> https://www.researchpad.co/article/5c76fe5dd5eed0c484e5b96b

Objective

Cancer diagnosis during pregnancy occurs in 1 out of 1000 pregnancies with common malignancies including breast and hematological cancers. Fetal exposure to currently utilized agents is poorly described. We directly assessed fetal exposure by screening meconium from 23 newborns whose mothers had undergone treatment for cancer during pregnancy.

Study design

Meconium was collected from newborns whose mothers were diagnosed with cancer during pregnancy and underwent chemotherapy in the second or third trimester as part of the Cancer and Pregnancy Registry. We conducted screening of 23 meconium samples for chemotherapeutics and known metabolites of chemotherapeutics by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Putative identification of paclitaxel and/or its metabolites was made in 8 screened samples. In positively screened samples, we quantified paclitaxel, 3’-p-hydroxypaclitaxel, and 6α-hydroxypaclitaxel by stable isotope dilution-LC-HRMS.

Results

Mean (standard deviation) levels of paclitaxel in positively screened samples were 399.9 (248.6) pg/mg in meconium samples from newborn born to mothers that underwent chemotherapy during pregnancy. 3’-p-hydroxypaclitaxel and 6α-hydroxypaclitaxel mean levels were 105.2 (54.6) and 113.4 (48.9) pg/mg meconium, respectively.

Conclusion

Intact paclitaxel, 3’-p-hydroxypaclitaxel, and 6α-hydroxypaclitaxel were detected in meconium, providing unambiguous confirmation of human fetal exposure. Variability in meconium levels between individuals may indicate a potential for reducing fetal exposure based on timing, dosing, and individual characteristics. This preliminary study may provide an approach for examining the effects of cancer diagnosis during pregnancy on other outcomes by providing a measure of direct fetal exposure.

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<![CDATA[Pesticide distribution and depletion kinetic determination in honey and beeswax: Model for pesticide occurrence and distribution in beehive products]]> https://www.researchpad.co/article/5c76fe5bd5eed0c484e5b94e

Beehive products such as honey, beeswax and recently pollen have been regarded for many years as appropriate sentinels for environmental pesticide pollutions. However, despite yearly application of hundreds of approved pesticides in agricultural fields, only a minor fraction of these organic compounds were actually detected in honey and beeswax samples. This observation has led us to question the general suitability of beehive products as a sentinel for synthetic organic pesticides applied in the field. The aim of the present study was to experimentally determine the distribution (logarithmic ratio of beeswax to honey pesticide concentration, LogD) and depletion kinetics (half-life) of selected pesticides in honey and beeswax as a measure of the latter matrixes to serve as a pesticide sentinel. The obtained parameters were used to extrapolate to pesticide burden in honey and beeswax samples collected from German and Israeli apiaries. In addition, we aimed to establish a mathematical model, enabling us to predict distribution of selected pesticides between honey to beeswax, by utilizing simple substance descriptors, namely, octanol/water partitioning coefficient, molar weight and Henry coefficient. Based on the present results, it appears that pesticides with LogD values > 1 and half-life in beeswax > 1 day, were likely to accumulate and detected in beeswax samples, and less likely to be found in honey. On the other hand, pesticides with negative LogD values were highly likely to be found in honey and less so in beeswax samples. Finally, pesticides with LogD values between 0–1 were expected to be found in both matrixes. The developed model was successfully applied to predict LogD values, thereby identifying octanol/water partitioning and molar weight as the most prominent substance descriptors, which affect pesticide distribution between honey and beeswax.

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<![CDATA[Pharmacokinetics of morphine in encephalopathic neonates treated with therapeutic hypothermia]]> https://www.researchpad.co/article/5c6f14b9d5eed0c48467a745

Objective

Morphine is a commonly used drug in encephalopathic neonates treated with therapeutic hypothermia after perinatal asphyxia. Pharmacokinetics and optimal dosing of morphine in this population are largely unknown. The objective of this study was to describe pharmacokinetics of morphine and its metabolites morphine-3-glucuronide and morphine-6-glucuronide in encephalopathic neonates treated with therapeutic hypothermia and to develop pharmacokinetics based dosing guidelines for this population.

Study design

Term and near-term encephalopathic neonates treated with therapeutic hypothermia and receiving morphine were included in two multicenter cohort studies between 2008–2010 (SHIVER) and 2010–2014 (PharmaCool). Data were collected during hypothermia and rewarming, including blood samples for quantification of morphine and its metabolites. Parental informed consent was obtained for all participants.

Results

244 patients (GA mean (sd) 39.8 (1.6) weeks, BW mean (sd) 3,428 (613) g, male 61.5%) were included. Morphine clearance was reduced under hypothermia (33.5°C) by 6.89%/°C (95% CI 5.37%/°C– 8.41%/°C, p<0.001) and metabolite clearance by 4.91%/°C (95% CI 3.53%/°C– 6.22%/°C, p<0.001) compared to normothermia (36.5°C). Simulations showed that a loading dose of 50 μg/kg followed by continuous infusion of 5 μg/kg/h resulted in morphine plasma concentrations in the desired range (between 10 and 40 μg/L) during hypothermia.

Conclusions

Clearance of morphine and its metabolites in neonates is affected by therapeutic hypothermia. The regimen suggested by the simulations will be sufficient in the majority of patients. However, due to the large interpatient variability a higher dose might be necessary in individual patients to achieve the desired effect.

Trial registration

www.trialregister.nl NTR2529.

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<![CDATA[Metabolomic profiling reveals correlations between spermiogram parameters and the metabolites present in human spermatozoa and seminal plasma]]> https://www.researchpad.co/article/5c76fe4fd5eed0c484e5b889

In 50% of all infertility cases, the male is subfertile or infertile, however, the underlying mechanisms are often unknown. Even when assisted reproductive procedures such as in vitro fertilization and intracytoplasmic sperm injection are performed, the causes of male factor infertility frequently remain elusive. Since the overall activity of cells is closely linked to their metabolic capacity, we analyzed a panel of 180 metabolites in human sperm and seminal plasma and elucidated their associations with spermiogram parameters. Therefore, metabolites from a group of 20 healthy donors were investigated using a targeted LC-MS/MS approach. The correlation analyses of the amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, sphingomyelins and sugars from sperm and seminal plasma with standard spermiogram parameters revealed that metabolites in sperm are closely related to sperm motility, whereas those in seminal plasma are closely related to sperm concentration and morphology. This study provides essential insights into the metabolome of human sperm and seminal plasma and its associations with sperm functions. This metabolomics technique could be a promising screening tool to detect the factors of male infertility in cases where the cause of infertility is unclear.

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<![CDATA[Differential accumulation of pelargonidin glycosides in petals at three different developmental stages of the orange-flowered gentian (Gentiana lutea L. var. aurantiaca)]]> https://www.researchpad.co/article/5c6b2619d5eed0c484289307

Corolla color in Gentiana lutea L. exhibits a yellow/orange variation. We previously demonstrated that the orange petal color of G. lutea L. var. aurantiaca is predominantly caused by newly synthesized pelargonidin glycosides that confer a reddish hue to the yellow background color, derived from the carotenoids. However, the anthocyanin molecules of these pelargonidin glycosides are not yet fully identified and characterized. Here, we investigated the regulation, content and type of anthocyanins determining the petal coloration of the orange-flowered G. lutea L. var. aurantiaca. Anthocyanins from the petals of G. lutea L. var. aurantiaca were characterized and quantified by HPLC-ESI-MS/MS (High-performance liquid chromatography-electrospray ionization-tandem mass spectrometry) coupled with a diode array detector in flowers at three different stages of development (S1, S3 and S5). Eleven pelargonidin derivatives were identified in the petals of G. lutea L. var. aurantiaca for the first time, but quantitative and qualitative differences were observed at each developmental stage. The highest levels of these pelargonidin derivatives were reached at the fully open flower stage (S5) where all anthocyanins were detected. In contrast, not all the anthocyanins were detected at the budlet stage (S1) and mature bud stage (S3) and those corresponded to more complex pelargonidin derivatives. The major pelargonidin derivatives found at all the stages were pelargonidin 3-O-glucoside, pelargonidin 3,5-O-diglucoside and pelargonidin 3-O-rutinoside. Furthermore, the expression of DFR (dihydroflavonol 4-reductase), ANS (anthocyanidin synthase), 3GT (UDP-glucose:flavonoid 3-O-glucosyltransferase), 5GT (UDP-glucose:flavonoid 5-O-glucosyltransferase) and 5AT (anthocyanin 5-aromatic acyltransferase) genes was analyzed in the petals of three developmental stages, showing that the expression level of DFR, ANS and 3GT parallels the accumulation of the pelargonidin glucosides. Overall, this study enhances the knowledge of the biochemical basis of flower coloration in Gentiana species, and lays a foundation for breeding of flower color and genetic variation studies on Gentiana varieties.

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<![CDATA[Heat stress modifies the lactational performances and the urinary metabolomic profile related to gastrointestinal microbiota of dairy goats]]> https://www.researchpad.co/article/5c6730b8d5eed0c484f37f98

The aim of the study is to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20°C; 40 to 45% humidity) or HS (climatic chamber; 37°C day, 30°C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square—discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from the Human Metabolome Data Base. HS increased rectal temperature (1.2°C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestered aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with a negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat-stressed dairy animals in practice.

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<![CDATA[Association of skeletal muscle and serum metabolites with maximum power output gains in response to continuous endurance or high-intensity interval training programs: The TIMES study – A randomized controlled trial]]> https://www.researchpad.co/article/5c6b2666d5eed0c484289a04

Background

Recent studies have begun to identify the molecular determinants of inter-individual variability of cardiorespiratory fitness (CRF) in response to exercise training programs. However, we still have an incomplete picture of the molecular mechanisms underlying trainability in response to exercise training.

Objective

We investigated baseline serum and skeletal muscle metabolomics profile and its associations with maximal power output (MPO) gains in response to 8-week of continuous endurance training (ET) and high-intensity interval training (HIIT) programs matched for total units of exercise performed (the TIMES study).

Methods

Eighty healthy sedentary young adult males were randomized to one of three groups and 70 were defined as completers (> 90% of sessions): ET (n = 30), HIIT (n = 30) and control (CO, n = 10). For the CO, participants were asked to not exercise for 8 weeks. Serum and skeletal muscle samples were analyzed by 1H-NMR spectroscopy. The targeted screens yielded 43 serum and 70 muscle reproducible metabolites (intraclass > 0.75; coefficient of variation < 25%). Associations of baseline metabolites with MPO trainability were explored within each training program via three analytical strategies: (1) correlations with gains in MPO; (2) differences between high and low responders to ET and HIIT; and (3) metabolites contributions to the most significant pathways related to gains in MPO. The significance level was set at P < 0.01 or false discovery rate of 0.1.

Results

The exercise programs generated similar gains in MPO (ET = 21.4 ± 8.0%; HIIT = 24.3 ± 8.5%). MPO associated baseline metabolites supported by all three levels of evidence were: serum glycerol, muscle alanine, proline, threonine, creatinine, AMP and pyruvate for ET, and serum lysine, phenylalanine, creatine, and muscle glycolate for HIIT. The most common pathways suggested by the metabolite profiles were aminoacyl-tRNA biosynthesis, and carbohydrate and amino acid metabolism.

Conclusion

We suggest that MPO gains in both programs are potentially associated with metabolites indicative of baseline amino acid and translation processes with additional evidence for carbohydrate metabolism in ET.

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<![CDATA[Tolerance and dose-response assessment of subchronic dietary ethoxyquin exposure in Atlantic salmon (Salmo salar L.)]]> https://www.researchpad.co/article/5c57e660d5eed0c484ef2e67

Ethoxyquin (EQ; 6-Ethoxy-2,2,4-trimethyl-1,2-dihydroquinoline) has been used as an antioxidant in feed components for pets, livestock and aquaculture. However, possible risks of EQ used in aquafeed for fish health have not yet been characterized. The present study investigated the toxicity and dose-response of subchronic dietary EQ exposure at doses ranging from 41 to 9666 mg EQ/kg feed in Atlantic salmon (Salmo salar L.). Feed at concentrations higher than 1173 mg EQ/kg were rejected by the fish, resulting in reduced feed intake and growth performance. No mortality was observed in fish exposed to any of the doses. A multi-omic screening of metabolome and proteome in salmon liver indicated an effect of dietary EQ on bioenergetics pathways and hepatic redox homeostasis in fish fed concentrations above 119 mg EQ/kg feed. Increased energy expenditure associated with an upregulation of hepatic fatty acid β-oxidation and induction and carbohydrate catabolic pathways resulted in a dose-dependent depletion of intracytoplasmic lipid vacuoles in liver histological sections, decreasing whole body lipid levels and altered purine/pyrimidine metabolism. Increased GSH and TBARS in the liver indicated a state of oxidative stress, which was associated with activation of the NRF2-mediated oxidative stress response and glutathione-mediated detoxification processes. However, no oxidative DNA damage was observed. As manifestation of altered energy metabolism, the depletion of liver intracytoplasmic lipid vacuoles was considered the critical endpoint for benchmark dose assessment, and a BMDL10 of 243 mg EQ/kg feed was derived as a safe upper limit of EQ exposure in Atlantic salmon.

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<![CDATA[Pilot study of myocardial ischemia-induced metabolomic changes in emergency department patients undergoing stress testing]]> https://www.researchpad.co/article/5c5df30dd5eed0c484580c11

Background

The heart is a metabolically active organ, and plasma acylcarnitines are associated with long-term risk for myocardial infarction. We hypothesized that myocardial ischemia from cardiac stress testing will produce dynamic changes in acylcarnitine and amino acid levels compared to levels seen in matched control patients with normal stress tests.

Methods

We analyzed targeted metabolomic profiles in a pilot study of 20 case patients with inducible ischemia on stress testing from an existing prospectively collected repository of 357 consecutive patients presenting with symptoms of Acute Coronary Syndrome (ACS) in an Emergency Department (ED) observation unit between November 2012 and September 2014. We selected 20 controls matched on age, sex, and body-mass index (BMI). A peripheral blood sample was drawn <1 hour before stress testing and 2 hours after stress testing on each patient. We assayed 60 select acylcarnitines and amino acids by tandem mass spectrometry (MS/MS) using a Quattro Micro instrument (Waters Corporation, Milford, MA). Metabolite values were log transformed for skew. We then performed bivariable analysis for stress test outcome and both individual timepoint metabolite concentrations and stress-delta metabolite ratios (T2/T0). False discovery rates (FDR) were calculated for 60 metabolites while controlling for age, sex, and BMI. We built multivariable regularized linear models to predict stress test outcome from metabolomics data at times 0, 2 hours, and log ratio between these two. We used leave-one-out cross-validation to estimate the performance characteristics of the model.

Results

Nine of our 20 case subjects were male. Cases’ average age was 55.8, with an average BMI 29.5. Bivariable analysis identified 5 metabolites associated with positive stress tests (FDR < 0.2): alanine, C14:1-OH, C16:1, C18:2, C20:4. The multivariable regularized linear models built on T0 and T2 had Area Under the ROC Curve (AUC-ROC) between 0.5 and 0.55, however, the log(T2/T0) model yielded 0.625 AUC, with 65% sensitivity and 60% specificity. The top metabolites selected by the model were: Ala, Arg, C12-OH/C10-DC, C14:1-OH, C16:1, C18:2, C18:1, C20:4 and C18:1-DC.

Conclusions

Stress-delta metabolite analysis of patients undergoing stress testing is feasible. Future studies with a larger sample size are warranted.

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<![CDATA[The Human Cytomegalovirus UL38 protein drives mTOR-independent metabolic flux reprogramming by inhibiting TSC2]]> https://www.researchpad.co/article/5c536b4cd5eed0c484a485ec

Human Cytomegalovirus (HCMV) infection induces several metabolic activities that are essential for viral replication. Despite the important role that this metabolic modulation plays during infection, the viral mechanisms involved are largely unclear. We find that the HCMV UL38 protein is responsible for many aspects of HCMV-mediated metabolic activation, with UL38 being necessary and sufficient to drive glycolytic activation and induce the catabolism of specific amino acids. UL38’s metabolic reprogramming role is dependent on its interaction with TSC2, a tumor suppressor that inhibits mTOR signaling. Further, shRNA-mediated knockdown of TSC2 recapitulates the metabolic phenotypes associated with UL38 expression. Notably, we find that in many cases the metabolic flux activation associated with UL38 expression is largely independent of mTOR activity, as broad spectrum mTOR inhibition does not impact UL38-mediated induction of glycolysis, glutamine consumption, or the secretion of proline or alanine. In contrast, the induction of metabolite concentrations observed with UL38 expression are largely dependent on active mTOR. Collectively, our results indicate that the HCMV UL38 protein induces a pro-viral metabolic environment via inhibition of TSC2.

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<![CDATA[Isolation and characterization of two Acinetobacter species able to degrade 3-methylindole]]> https://www.researchpad.co/article/5c58d644d5eed0c484031a07

3-Methylindole (3MI) or Skatole is a volatile lipophilic organic compound produced by anoxic metabolism of L-tryptophan and associated with animal farming and industrial processing wastes. Pure cultures of bacteria capable of utilizing 3MI were isolated from chicken manure using enrichment culture techniques. The bacteria were identified as Acinetobacter toweneri NTA1-2A and Acinetobacter guillouiae TAT1-6A, based on 16S rDNA gene amplicon sequence data. The optimal temperature and pH for degradation of 3MI were established using single factor experiments. Strain tolerance was assessed over a range of initial concentrations of 3MI, and the effects of initial concentration on subsequent microbial 3MI degradation were also measured. During the degradation experiment, concentrations of 3MI were quantified by reverse-phase high-performance liquid chromatography (HPLC). The strains were capable of degrade initial concentrations of 3MI ranging from 65–200 mg/L. The degradation efficiency was >85% in 6 days for both strains when the initial concentration is less than 200 mg/L. The strains were tested for enzymatic activity using 65 mg/L 3MI. The enzyme extracts of NTA1-2A and TAT1-6A from the 3MI medium degraded 71.46% and 60.71% of 3MI respectively, but no appreciable change in 3MI concentration in the control group was witnessed. Our experiment revealed betaine and choline were identified as 3MI degradation metabolites by both strains while nitroso-pyrrolidine and beta-alaninebetaine formed by NTA1-2A and TAT1-6A strains respectively. The NTA1-2A and TAT1-6A strains removed 84.32% and 81.39% 3MI respectively from chicken manure during fermentation in 8 days and showed a statistically significant difference (P < 0.05) compared with the control group. The optimum temperature and pH were 31°C and 6 respectively, for 3MI degradation by A. toweneri NTA1-2A and A. guillouiae TAT1-6A. We concluded that A. toweneri NTA1-2A and A. guillouiae TAT1-6A are potential strains of interest to degrade 3MI and control odorant in poultry and other livestock industries.

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<![CDATA[Discovery of potential ovicidal natural products using metabolomics]]> https://www.researchpad.co/article/5c79afcfd5eed0c4841e37b2

Plant extracts are a potential source of new compounds for nematode control and may be an excellent alternative for the control gastrointestinal nematodes that are resistant to conventional anthelmintics. However, research involving natural products is a complex process. The main challenge is the identification of bioactive compounds. Online analytical techniques with universal detectors, such as high-performance liquid chromatography-mass spectrometry (HPLC-MS), together with metabolomics could enable the fast, accurate evaluation of a massive amount of data, constituting a viable option for the identification of active compounds in plant extracts. This study focused on the evaluation of the ovicidal activity of ethanol extracts from 17 plants collected from the Pantanal wetland in the state of Mato Grosso do Sul, Brazil, against eggs of Haemonchus placei using the egg hatchability test. The ethanol extracts were obtained using accelerated solvent extraction. The data on ovicidal activity, mass spectrometry and metabolomics were evaluated using HPLC-DAD-MS, partial least squares regression analysis (PLS-DA) and a correlation map (univariate correlation analyses) to detect compounds that have a positive correlation with biological activity. Among the ten metabolites with the best correlation coefficients, six were phenylpropanoids, two were triterpene saponins, one was a brevipolide, and one was a flavonoid. Combinations of metabolites with high ovicidal action were also identified, such as phenylpropanoids combined with the triterpene saponins and the flavonoid, flavonoids combined with iridoid and phenylpropanoids, and saponins combined with phenylpropanoid. The positive correlation between classes of compounds in plants belonging to different genera and biological activity (as previously identified in the literature) reinforces the robustness of the statistical data and demonstrates the efficacy of this method for the selection of bioactive compounds without the need for isolation and reevaluation. The proposed method also enables the determination of synergism among the classes, which would be impracticable using traditional methods. The present investigation demonstrates that the metabolomic technique was efficient at detecting secondary metabolites with ovicidal activity against H. placei. Thus, the use of metabolomics can be a tool to accelerate and simplify bioprospecting research with plant extracts in veterinary parasitology.

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<![CDATA[Detection of MCPG metabolites in horses with atypical myopathy]]> https://www.researchpad.co/article/5c633980d5eed0c484ae68f5

Atypical myopathy (AM) in horses is caused by ingestion of seeds of the Acer species (Sapindaceae family). Methylenecyclopropylacetyl-CoA (MCPA-CoA), derived from hypoglycin A (HGA), is currently the only active toxin in Acer pseudoplatanus or Acer negundo seeds related to AM outbreaks. However, seeds or arils of various Sapindaceae (e.g., ackee, lychee, mamoncillo, longan fruit) also contain methylenecyclopropylglycine (MCPG), which is a structural analogue of HGA that can cause hypoglycaemic encephalopathy in humans. The active poison formed from MCPG is methylenecyclopropylformyl-CoA (MCPF-CoA). MCPF-CoA and MCPA-CoA strongly inhibit enzymes that participate in β-oxidation and energy production from fat. The aim of our study was to investigate if MCPG is involved in Acer seed poisoning in horses. MCPG, as well as glycine and carnitine conjugates (MCPF-glycine, MCPF-carnitine), were quantified using high-performance liquid chromatography-tandem mass spectrometry of serum and urine from horses that had ingested Acer pseudoplatanus seeds and developed typical AM symptoms. The results were compared to those of healthy control horses. For comparison, HGA and its glycine and carnitine derivatives were also measured. Additionally, to assess the degree of enzyme inhibition of β-oxidation, several acyl glycines and acyl carnitines were included in the analysis. In addition to HGA and the specific toxic metabolites (MCPA-carnitine and MCPA-glycine), MCPG, MCPF-glycine and MCPF-carnitine were detected in the serum and urine of affected horses. Strong inhibition of β-oxidation was demonstrated by elevated concentrations of all acyl glycines and carnitines, but the highest correlations were observed between MCPF-carnitine and isobutyryl-carnitine (r = 0.93) as well as between MCPA- (and MCPF-) glycine and valeryl-glycine with r = 0.96 (and r = 0.87). As shown here, for biochemical analysis of atypical myopathy of horses, it is necessary to take MCPG and the corresponding metabolites into consideration.

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<![CDATA[A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism]]> https://www.researchpad.co/article/5c536a77d5eed0c484a4747a

Phototrophic organisms such as cyanobacteria utilize the sun’s energy to convert atmospheric carbon dioxide into organic carbon, resulting in diurnal variations in the cell’s metabolism. Flux balance analysis is a widely accepted constraint-based optimization tool for analyzing growth and metabolism, but it is generally used in a time-invariant manner with no provisions for sequestering different biomass components at different time periods. Here we present CycleSyn, a periodic model of Synechocystis sp. PCC 6803 metabolism that spans a 12-hr light/12-hr dark cycle by segmenting it into 12 Time Point Models (TPMs) with a uniform duration of two hours. The developed framework allows for the flow of metabolites across TPMs while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. The 12 TPMs allow for the incorporation of time-dependent constraints that capture the cyclic nature of cellular processes. Imposing bounds on reactions informed by temporally-segmented transcriptomic data enables simulation of phototrophic growth as a single linear programming (LP) problem. The solution provides the time varying reaction fluxes over a 24-hour cycle and the accumulation/consumption of metabolites. The diurnal rhythm of metabolic gene expression driven by the circadian clock and its metabolic consequences is explored. Predicted flux and metabolite pools are in line with published studies regarding the temporal organization of phototrophic growth in Synechocystis PCC 6803 paving the way for constructing time-resolved genome-scale models (GSMs) for organisms with a circadian clock. In addition, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to temporally separate photosynthesis from oxygen-sensitive nitrogen fixation is also explored using the developed model formalism.

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<![CDATA[Cellular determinants of metabolite concentration ranges]]> https://www.researchpad.co/article/5c536be4d5eed0c484a4947e

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.

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<![CDATA[PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data]]> https://www.researchpad.co/article/5c466521d5eed0c48451791d

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

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