Do elementary flux modes combine linearly at the "atomic" level?: integrating tracer-based metabolomics data and elementary flux modes

The elementary flux modes (EFMs) approach is an efficient computational tool to predict novel metabolic pathways. Elucidating the physiological relevance of EFMs in a particular cellular state is still an open challenge. Different methods have been presented to carry out this task. However, these methods typically use little experimental data, exploiting methodologies where an a priori optimization function is used to deal with the indetermination underlying metabolic networks. Available “omics” data represent an opportunity to refine current methods. In this article we discuss whether (or not) metabolomics data from isotope labeling experiments (ILEs) and EFMs can be integrated into a linear system of equations. Aside from refining current approaches to infer the physiological relevance of EFMs, this question is important for the integration of metabolomics data from ILEs into metabolic networks, which generally involve non-linear relationships. As a result of our analysis, we concluded that in general the concept of EFMs needs to be redefined at the atomic level for the modeling of ILEs. For this purpose, the concept of Elementary Carbon Modes (ECMs) is introduced.     

System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.     

Metabolic pathway analysis of enzyme-deficient human red blood cells

Five enzymopathies (G6PDH, TPI, PGI, DPGM and PGK deficiencies) in the human red blood cells are investigated using a stoichiometric modeling approach, i.e., metabolic pathway analysis. Elementary flux modes (EFMs) corresponding to each enzyme deficiency case are analyzed in terms of functional capabilities. When available, experimental findings reported in literature related to metabolic behavior of the human red blood cells are compared with the results of EFM analysis. Control-effective flux (CEF) calculation, a novel approach which allows quantification and interpretation of determined EFMs, is performed for further analysis of enzymopathies. Glutathione reductase reaction is found to be the most effective reaction in terms of its CEF value in all enzymopathies in parallel with its known essential role for red blood cells. Efficiency profiles of the enzymatic reactions upon the degree of enzyme deficiency are obtained by the help of the CEF approach, as a basis for future experimental studies. CEF analysis, which is found to be promising in the analysis of erythrocyte enzymopathies, has the potential to be used in modeling efforts of human metabolism.     

Structural robustness of metabolic networks with respect to multiple knockouts

We present a generalised framework for analysing structural robustness of metabolic networks, based on the concept of elementary flux modes (EFMs). Extending our earlier study on single knockouts [Wilhelm, T., Behre, J., Schuster, S., 2004. Analysis of structural robustness of metabolic networks. IEE Proc. Syst. Biol. 1(1), 114-120], we are now considering the general case of double and multiple knockouts. The robustness measures are based on the ratio of the number of remaining EFMs after knockout vs. the number of EFMs in the unperturbed situation, averaged over all combinations of knockouts. With the help of simple examples we demonstrate that consideration of multiple knockouts yields additional information going beyond single-knockout results. It is proven that the robustness score decreases as the knockout depth increases.We apply our extended framework to metabolic networks representing amino acid anabolism in Escherichia coli and human hepatocytes, and the central metabolism in human erythrocytes. Moreover, in the E. coli model the two subnetworks synthesising amino acids that are essential and those that are non-essential for humans are studied separately. The results are discussed from an evolutionary viewpoint. We find that E. coli has the most robust metabolism of all the cell types studied here. Considering only the subnetwork of the synthesis of non-essential amino acids, E. coli and the human hepatocyte show about the same robustness.     

Multiplexed Activity-based Protein Profiling of the Human Pathogen Aspergillus fumigatus Reveals Large Functional Changes upon Exposure to Human Serum

Environmental adaptability is critical for survival of the fungal human pathogen Aspergillus fumigatus in the immunocompromised host lung. We hypothesized that exposure of the fungal pathogen to human serum would lead to significant alterations to the organism''s physiology, including metabolic activity and stress response. Shifts in functional pathway and corresponding enzyme reactivity of A. fumigatus upon exposure to the human host may represent much needed prognostic indicators of fungal infection. To address this, we employed a multiplexed activity-based protein profiling (ABPP) approach coupled to quantitative mass spectrometry-based proteomics to measure broad enzyme reactivity of the fungus cultured with and without human serum. ABPP showed a shift from aerobic respiration to ethanol fermentation and utilization over time in the presence of human serum, which was not observed in serum-free culture. Our approach provides direct insight into this pathogen''s ability to survive, adapt, and proliferate. Additionally, our multiplexed ABPP approach captured a broad swath of enzyme reactivity and functional pathways and provides a method for rapid assessment of the A. fumigatus response to external stimuli.     

MALDI Profiling of Human Lung Cancer Subtypes

Background

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

Methodology/Principal Findings

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.

Conclusions/Significance

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.     

Cerebral microinfarct is emergency consequence of Alzheimer's disease: a new insight into development of neurodegenerative diseases

Imbalance of Aβ and tau protein production and clearance are the key factors among many causes of Alzheimer''s disease that leading to neurons degeneration and cognitive disorders. As a novel approach, glymphatic system quickly clear metabolic waste (especially Aβ and tau) from cerebral environment, and dysfunction of glymphatic system may relate to occurrence of Alzheimer''s disease. Microinfarct is a common histopathologic situation occurring in aging brain and leads to dramatic increase the generation of metabolic by-product after neuronal injury, hindering the operation of glymphatic system and suppress cerebral spinal fluid (CSF) and cerebral interstitial fluid (interstitial fluid, ISF) exchange. Microinfarcts destruct the integrity of microvascular and microstructural tissue, result in Aβ deposition and tau phosphorylation that form neurofibrillary tangles and associated with the cause of Alzheimer''s disease. Currently, it has been found that glymphatic system is involved in the pathological process of Alzheimer''s disease. Improving the function of glymphatic system after cerebral microinfarcts could be developed as a new approach for Alzheimer''s disease prevention and treatment. In this review, we will provide in-depth discussion on functional changes of glymphatic system after cerebral microinfarcts, further reveal pathogenesis of Alzheimer''s disease and provide a potentially more effective method for treatment of Alzheimer''s disease.     

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

The ability of today''s robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.     

基于系统动力学的鄱阳湖区多维福祉时空差异演变与情景模拟

基于资本框架的福祉测度是可持续发展导向下多维福祉研究的重要趋势。基于资本框架构建多维福祉指数,应用基尼系数、空间自相关等方法分析2006-2018年鄱阳湖区38个区县多维福祉指数的时空演化特征,并借助系统动力学方法构建鄱阳湖区多维福祉系统,模拟不同发展情景下鄱阳湖区福祉系统的变化态势。研究发现：（1）2006-2018年鄱阳湖区多维福祉水平得到明显提升,福祉水平空间差异持续缩小。西湖区、东湖区、青山湖区、青云谱区等一直是鄱阳湖区福祉水平高值区,也是福祉水平的高-高集聚区;余干县、九江县、都昌县、庐山市等滨湖地区的福祉水平相对较低,但其后进优势明显,并逐步退出福祉低-低集聚区,福祉水平低-低集聚区的范围大幅缩小。（2）鄱阳湖区多维福祉水平仍有较大的提升空间,可持续发展模式是提升鄱阳湖区福祉水平的优选途径。社会资本与人力资本的投入是提升鄱阳湖区福祉最有效的手段,有助于福祉水平的短期提升;经济资本是鄱阳湖区福祉水平长期发展的不竭动力。注重发挥高福祉集聚区的带动作用,着力激发低福祉区县的福祉后发优势,加强四大资本间的协同作用,发挥社会资本、人力资本对福祉水平的快速提升效应,培育经济资本以实现对区域福祉水平的长期驱动,是推动鄱阳湖区福祉空间均衡发展,减缓人民日益增长的美好生活需要和不平衡不充分发展之间的矛盾的关键。     

Metabolic pathway analysis of glycerol metabolism in Klebsiella pneumoniae incorporating oxygen regulatory system

It is well known that environmental and genetic perturbations have major effects on the metabolic behavior of cells. In this work, a model that utilizes existing knowledge of oxygen and redox sensing/regulatory system to assist elementary flux modes (EFMs) has been developed and was carried out to predict the metabolic potential of Klebsiella pneumoniae for the production of 1,3‐propanediol (1,3‐PD) under anaerobic and aerobic conditions. It was found that the theoretical optimal 1,3‐PD yield could reach to 0.844 mol mol^?1 if the pentose phosphate pathway (PPP), and transhydrogenase had a high flux under anaerobic condition. However, PPP had little influence on the theoretical 1,3‐PD yield, and the flux through tricarboxylic acid (TCA) cycle was high under aerobic conditions. In addition, the effect of oxygen level on the 1,3‐PD and biomass was further analyzed. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Raman–deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota

Human intestinal microbiota is important to host health and is associated with various diseases. It is a challenge to identify the functions and metabolic activity of microorganisms at the single-cell level in gut microbial community. In this study, we applied Raman microspectroscopy and deuterium isotope probing (Raman–DIP) to quantitatively measure the metabolic activities of intestinal bacteria from two individuals and analysed lipids and phenylalanine metabolic pathways of functional microorganisms in situ. After anaerobically incubating the human faeces with heavy water (D₂O), D₂O with specific substrates (glucose, tyrosine, tryptophan and oleic acid) and deuterated glucose, the C–D band in single-cell Raman spectra appeared in some bacteria in faeces, due to the Raman shift from the C–H band. Such Raman shift was used to indicate the general metabolic activity and the activities in response to the specific substrates. In the two individuals' intestinal microbiota, the structures of the microbial communities were different and the general metabolic activities were 76 ± 1.0% and 30 ± 2.0%. We found that glucose, but not tyrosine, tryptophan and oleic acid, significantly stimulated metabolic activity of the intestinal bacteria. We also demonstrated that the bacteria within microbiota preferably used glucose to synthesize fatty acids in faeces environment, whilst they used glucose to synthesize phenylalanine in laboratory growth environment (e.g. LB medium). Our work provides a useful approach for investigating the metabolic activity in situ and revealing different pathways of human intestinal microbiota at the single-cell level.     

Random Walk Simulation of the MRI Apparent Diffusion Coefficient in a Geometrical Model of the Acinar Tree

Apparent diffusion coefficient (ADC) measurement in the lung using gas magnetic resonance imaging is a promising technique with potential for reflecting changes in lung microstructure. Despite some recent impressive human applications, full interpretation of ADC measures remains an elusive goal, due to a lack of detailed knowledge about the structure dependency of ADC. In an attempt to fill this gap we have performed random walk simulations in a three-dimensional geometrical model of the lung acinus, the distal alveolated sections of the lung tree accounting for ∼90% of the total lung volume. Simulations were carried out adjusting model parameters after published morphological data for the rat peripheral airway system, which predict an ADC behavior as microstructure changes with lung inflation in partial agreement with measured ADCs at different airway pressures. The approach used to relate experimental ADCs to lung microstructural changes does not make any assumption about the cause of the changes, so it could be applied to other scenarios such as chronic obstructive pulmonary disease, lung development, etc. The work presented here predicts numerically for the first time ADC values measured in the lung from independent morphological measures of lung microstructure taken at different inflation stages during the breath cycle.     

Gene-targeted metagenomic analysis of glucan-branching enzyme gene profiles among human and animal fecal microbiota

Glycoside hydrolases (GHs), the enzymes that breakdown complex carbohydrates, are a highly diversified class of key enzymes associated with the gut microbiota and its metabolic functions. To learn more about the diversity of GHs and their potential role in a variety of gut microbiomes, we used a combination of 16S, metagenomic and targeted amplicon sequencing data to study one of these enzyme families in detail. Specifically, we employed a functional gene-targeted metagenomic approach to the 1-4-α-glucan-branching enzyme (gBE) gene in the gut microbiomes of four host species (human, chicken, cow and pig). The characteristics of operational taxonomic units (OTUs) and operational glucan-branching units (OGBUs) were distinctive in each of hosts. Human and pig were most similar in OTUs profiles while maintaining distinct OGBU profiles. Interestingly, the phylogenetic profiles identified from 16S and gBE gene sequences differed, suggesting the presence of different gBE genes in the same OTU across different vertebrate hosts. Our data suggest that gene-targeted metagenomic analysis is useful for an in-depth understanding of the diversity of a particular gene of interest. Specific carbohydrate metabolic genes appear to be carried by distinct OTUs in different individual hosts and among different vertebrate species'' microbiomes, the characteristics of which differ according to host genetic background and/or diet.     

Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth

Traditional (genome-scale) metabolic models of cellular growth involve an approximate biomass “reaction”, which specifies biomass composition in terms of precursor metabolites (such as amino acids and nucleotides). On the one hand, biomass composition is often not known exactly and may vary drastically between conditions and strains. On the other hand, the predictions of computational models crucially depend on biomass. Also elementary flux modes (EFMs), which generate the flux cone, depend on the biomass reaction. To better understand cellular phenotypes across growth conditions, we introduce and analyze new classes of elementary vectors for comprehensive (next-generation) metabolic models, involving explicit synthesis reactions for all macromolecules. Elementary growth modes (EGMs) are given by stoichiometry and generate the growth cone. Unlike EFMs, they are not support-minimal, in general, but cannot be decomposed “without cancellations”. In models with additional (capacity) constraints, elementary growth vectors (EGVs) generate a growth polyhedron and depend also on growth rate. However, EGMs/EGVs do not depend on the biomass composition. In fact, they cover all possible biomass compositions and can be seen as unbiased versions of elementary flux modes/vectors (EFMs/EFVs) used in traditional models. To relate the new concepts to other branches of theory, we consider autocatalytic sets of reactions. Further, we illustrate our results in a small model of a self-fabricating cell, involving glucose and ammonium uptake, amino acid and lipid synthesis, and the expression of all enzymes and the ribosome itself. In particular, we study the variation of biomass composition as a function of growth rate. In agreement with experimental data, low nitrogen uptake correlates with high carbon (lipid) storage.     

Alteration of Catecholamine Phenotype in Transgenic Mice Influences Expression of Adrenergic Receptor Subtypes

Abstract: Agonist-induced regulation of adrenergic receptors (ARs) has an important role in controlling physiological functions in response to changes in catecholamine stimulation. We previously generated transgenic mice expressing phenylethanolamine N -methyltransferase (PNMT) under the control of a human dopamine β-hydroxylase gene promoter to switch catecholamine specificity from the norepinephrine phenotype to the epinephrine phenotype. In the present study, we first examined changes in catecholamine metabolism in peripheral tissues innervated by sympathetic neurons of the transgenic mice. In the transgenic target tissues, a high-level expression of PNMT led to a dramatic increase in the epinephrine levels, whereas the norepinephrine levels were decreased to 48.6–87.9% of the nontransgenic control levels. Analysis of plasma catecholamines in adrenalectomized mice showed large amounts of epinephrine derived from sympathetic neurons in the transgenic mice. Subsequently, we performed radioligand binding assays with (−)-[¹²⁵I]iodocyanopindolol to determine changes in binding sites of β-AR subtypes. In transgenic mice, the number of β2-AR binding sites was 56.4–74.9% of their nontransgenic values in the lung, spleen, submaxillary gland, and kidney, whereas the β1-AR binding sites were regulated in a different fashion among these tissues. Moreover, northern blot analysis of total RNA from the lung tissues showed that down-regulation of β2 binding sites was accompanied by a significant decrease in steady-state levels of the receptor mRNA. These results strongly suggest that alteration of catecholamine specificity in the transgenic sympathetic neurons leads to regulated expression of the β-AR subtypes in their target tissues.     

自噬及其在肺部疾病中作用研究进展

自噬作为一种新的细胞程序化死亡方式,在维持细胞内环境稳态中起着重要作用。它由溶酶体介导,对细胞内衰老细胞器或受损蛋白质进行再次利用,以补充细胞在"饥饿"状态下的物质供给。自噬曾被认为是细胞对氧化应激的随机自我保护性反应,然而最近研究发现自噬体的形成具有选择性和高度保守性的特点。目前研究发现自噬在COPD、肺气肿、肺纤维化、肺动脉高压、急性肺损伤、肺肿瘤等肺部疾病中起重要作用。本文通过分析总结自噬信号传导机制及其在肺部疾病中的相关作用,以阐明肺部疾病的可能发生机制,从而指导相关疾病的临床治疗。