A measure of biological robustness in microbial metabolic systems and its approximated estimation

The variability of physical performance in representatives of various biorhythmotypes at different times of the day was studied. It has been revealed that the efficiency of metabolic systems during hours of functional optimum makes it possible to carry out large (by volume and power) physical loads, which indicates greater efficiency of mechanical work in the corresponding period. Taking these circumstances into account, planning of training loads avoids the effect of overtraining and leads to an increase in the overall level of physical performance

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Learning from metabolic networks: current trends and future directions for precision medicine.

Current Medicinal Chemistry ◽

10.2174/0929867328666201217103148 ◽

2020 ◽

Vol 28 ◽

Author(s):

Ilaria Granata ◽

Mario Manzo ◽

Ari Kusumastuti ◽

Mario R Guarracino

Keyword(s):

Systems Biology ◽

Precision Medicine ◽

Metabolic Networks ◽

Information Modeling ◽

Model Systems ◽

Specific Information ◽

Important Indicator ◽

Predictive Values ◽

Metabolic Systems ◽

Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

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β1-blocker in sepsis

Journal of Intensive Care ◽

10.1186/s40560-021-00552-w ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Daisuke Hasegawa ◽

Ryota Sato ◽

Osamu Nishida

Keyword(s):

Heart Rate ◽

Oxygen Delivery ◽

Sinus Tachycardia ◽

Meta Analysis ◽

Lactate Production ◽

Volume Index ◽

Initial Resuscitation ◽

Randomized Controlled ◽

Metabolic Systems

Abstract Background The use of ultrashort-acting β1-blockers recently has attracted attention in septic patients with non-compensatory tachycardia. We summarized the metabolic and hemodynamic effects and the clinical evidence of ultrashort-acting β1-blockers. Main body A recent meta-analysis showed that ultrashort-acting β1-blockers reduced the mortality in septic patients with persistent tachycardia. However, its mechanism to improve mortality is not fully understood yet. We often use lactate as a marker of oxygen delivery, but an impaired oxygen use rather than reduced oxygen delivery has been recently proposed as a more reasonable explanation of hyperlactatemia in patients with sepsis, leading to a question of whether β1-blockers affect metabolic systems. While the stimulation of the β2-receptor accelerates glycolysis and lactate production, the role of β1-blocker in lactate production remains unclear and studies investigating the role of β1-blockers in lactate kinetics are warranted. A meta-analysis also reported that ultrashort-acting β1-blockers increased stroke volume index, while it reduced heart rate, resulting in unchanged cardiac index, mean arterial pressure, and norepinephrine requirement at 24 h, leading to an improvement of cardiovascular efficiency. On the other hand, a recent study reported that heart rate reduction using fast esmolol titration in the very early phase of septic shock caused hemodynamic instability, suggesting that ultrashort-acting β1-blockers should be started only after completing initial resuscitation. While many clinicians still do not feel comfortable controlling sinus tachycardia, one randomized controlled trial in which the majority had sinus tachycardia suggested the mortality benefit of ultrashort-acting β1-blockers. Therefore, it still deems to be reasonable to control sinus tachycardia with ultrashort-acting β1-blockers after completing initial resuscitation. Conclusion Accumulating evidence is supporting the use of ultrashort-acting β1-blockers while larger randomized controlled trials to clarify the effect of ultrashort-acting β1-blockers are still warranted.

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SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BMC Bioinformatics ◽

10.1186/s12859-021-04281-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Justin Y. Lee ◽

Britney Nguyen ◽

Carlos Orosco ◽

Mark P. Styczynski

Keyword(s):

Machine Learning ◽

Metabolic Networks ◽

Sampling Frequency ◽

Low Noise ◽

Training Data ◽

High Noise ◽

Regulatory Interactions ◽

Learning Framework ◽

Metabolic Systems ◽

Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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Endothelial glycocalyx degradation is associated with early organ impairment in polytrauma patients

BMC Emergency Medicine ◽

10.1186/s12873-021-00446-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Feng Qi ◽

Hao Zhou ◽

Peng Gu ◽

Zhi-He Tang ◽

Bao-Feng Zhu ◽

...

Keyword(s):

Injury Severity ◽

Early Stage ◽

Endothelial Glycocalyx ◽

Control Group ◽

Metabolic System ◽

Metabolic Systems ◽

Organ Systems ◽

Severity Scores ◽

Polytrauma Patients ◽

Trauma Group

Abstract Background Endothelial glycocalyx (EG) abnormal degradation were widely found in critical illness. However, data of EG degradation in multiple traumas is limited. We performed a study to assess the EG degradation and the correlation between the degradation and organ functions in polytrauma patients. Methods A prospective observational study was conducted to enroll health participants (control group) and polytrauma patients (trauma group) at a University affiliated hospital between Feb 2020 and Oct 2020. Syndecan1 (SDC1) and heparin sulfate (HS) were detected in serum sample of both groups. In trauma group, injury severity scores (ISS) and sequential organ failure assessments (SOFA) were calculated. Occurrences of acute kidney injury (AKI), trauma-induced coagulopathy (TIC) within 48 h and 28-day all-cause mortality in trauma group were recorded. Serum SDC1 and HS levels were compared between two groups. Correlations between SDC1/HS and the indicators of organ systems in the trauma group were analyzed. ROC analyses were performed to assess the predictive value of SDC1 and HS for AKI, TIC within 48 h, and 28-day mortality in trauma group. Results There were 45 polytrauma patients and 15 healthy participants were collected, totally. SDC1 and HS were significantly higher in trauma group than in control group (69.39 [54.18–130.80] vs. 24.15 [13.89–32.36], 38.92 [30.47–67.96] vs. 15.55 [11.89–23.24], P < 0.001, respectively). Trauma group was divided into high degradation group and low degradation group according to SDC1 median. High degradation group had more severe ISS, SOFA scores, worse organ functions (respiratory, kidney, coagulation and metabolic system), and higher incidence of hypothermia, acidosis and shock. The area under the receiver operator characteristic curves (AUC) of SDC1 to predict AKI, TIC occurrence within 48 h and 28-day mortality were 0.838 (95%CI: 0.720–0.957), 0.700 (95%CI: 0.514–0.885) and 0.764 (95%CI: 0.543–0.984), respectively. Conclusions EG degradation was elevated significantly in polytrauma patients, and the degradation was correlated with impaired respiratory, kidney, coagulation and metabolic systems in early stage. Serum SDC1 is a valuable predictive indicator of early onset of AKI, TIC, and 28-day mortality in polytrauma patients.

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Enzyme kinetics and metabolic control. A method to test and quantify the effect of enzymic properties on metabolic variables

Biochemical Journal ◽

10.1042/bj2690697 ◽

1990 ◽

Vol 269 (3) ◽

pp. 697-707 ◽

Cited By ~ 10

Author(s):

L Acerenza ◽

H Kacser

Keyword(s):

Enzyme Concentration ◽

Time Dependent ◽

Experimental Systems ◽

State Approximation ◽

Metabolic Systems ◽

Steady State Approximation ◽

Before And After ◽

Factor Alpha ◽

Metabolic Variables ◽

Time Invariant

It is usual to study the sensitivity of metabolic variables to small (infinitesimal) changes in the magnitudes of individual parameters such as an enzyme concentration. Here, the effect that a simultaneous change in all the enzyme concentrations by the same factor alpha (Co-ordinate-Control Operation, CCO) has on the variables of time-dependent metabolic systems is investigated. This factor alpha can have any arbitrary large value. First, we assume, for each enzyme measured in isolation, the validity of the steady-state approximation and the proportionality between reaction rate and enzyme concentration. Under these assumptions, any time-invariant variable may behave like a metabolite concentration, i.e. S alpha = Sr (S-type), or like a flux, i.e. J alpha = alpha Jr (J-type). The subscripts r and alpha correspond to the values of the variable before and after the CCO respectively. Similarly, time-dependent variables may behave according to S alpha (t/alpha) = Sr (t) (S-type) or to J alpha (t/alpha) = alpha J r (t) (J-type). A method is given to test these relationships in experimental systems, and to quantify deviations from the predicted behaviour. A positive test for deviations proves the violation of some of the assumptions made. However, the breakdown of the assumptions in an enzyme-catalysed reaction, studied in isolation, may or may not affect significantly the behaviour of the system when the component reaction is embedded in the metabolic network.

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Abstract 280: Increased Metabolite Levels of Glycolysis and Pentose Phosphate Pathway in Rabbit Atherosclerotic Arteries and Hypoxic Macrophage

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.34.suppl_1.280 ◽

2014 ◽

Vol 34 (suppl_1) ◽

Author(s):

Atsushi Yamashita ◽

Yan Zhao ◽

Yunosuke Matsuura ◽

Kazuaki Yamasaki ◽

Sayaka Moriguchi-Goto ◽

...

Keyword(s):

Glucose Uptake ◽

Pentose Phosphate Pathway ◽

Nuclear Translocation ◽

Tricarboxylic Acid Cycle ◽

Pentose Phosphate ◽

Plasminogen Activator Inhibitor 1 ◽

Hexokinase Ii ◽

Femoral Arteries ◽

Metabolic Systems ◽

18F Fdg

Aims: Inflammation and possibly hypoxia largely affect glucose utilization in atherosclerotic arteries, which could alter many metabolic systems. However, metabolic changes in atherosclerotic plaques remain unknown. The present study aims to identify changes in metabolic systems relative to glucose uptake and hypoxia in rabbit atherosclerotic arteries and cultured macrophages. Methods: Macrophage-rich or smooth muscle cell (SMC)-rich neointima was created by balloon injury in the iliac-femoral arteries of rabbits fed with a 0.5% cholesterol diet or a conventional diet. THP-1 macrophages stimulated with lipopolysaccharides (LPS) and interferon-γ (INFγ) were cultured under normoxic and hypoxic conditions. We evaluated comprehensive arterial and macrophage metabolism by performing metabolomic analyses using capillary electrophoresis-time of flight mass spectrometry. We evaluated glucose uptake and its relationship to vascular hypoxia using 18F-fluorodeoxyglucose (18F-FDG) and pimonidazole, a marker of hypoxia. Results: The levels of many metabolites increased in the iliac-femoral arteries with macrophage-rich neointima, compared with those that were not injured and those with SMC-rich neointima (glycolysis, 4 of 9; pentose phosphate pathway, 4 of 6; tricarboxylic acid cycle, 4 of 6; nucleotides, 10 of 20). The uptake of 18F-FDG in arterial walls measured by autoradiography positively correlated with macrophage- and pimonidazole-immunopositive areas (r = 0.76, and r = 0.59 respectively; n = 69 for both; p < 0.0001). Pimonidazole immunoreactivity was closely localized with the nuclear translocation of hypoxia inducible factor-1α and hexokinase II expression in macrophage-rich neointima. The levels of glycolytic (8 of 8) and pentose phosphate pathway (4 of 6) metabolites increased in LPS and INFγ stimulated macrophages under hypoxic but not normoxic condition. Plasminogen activator inhibitor-1 protein levels in the supernatant were closely associated with metabolic pathways in the macrophages. Conclusion: Infiltrative macrophages in atherosclerotic arteries might affect metabolic systems, and hypoxia but not classical activation might augment glycolytic and pentose phosphate pathways in macrophages.

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