Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches.

Matcha has been globally valued by consumers for its distinctive fragrance and flavor since ancient times. Currently, the protected designation of origin (PDO) certified matcha, characterized by unique sensory attributes, has garnered renewed interest from consumers and the industry. Given the challenges associated with assessing sensory perceptions, the origin of PDO-certified matcha samples from Guizhou was determined using NIRS and LC-MS platforms. Notably, the accuracy of our established attribute models, based on informative wavelengths selected by the CARS-PLS method, exceeds 0.9 for five sensory attributes, particularly the particle homogeneity attribute (with a validation correlation coefficient of 0.9668). Moreover, an LC-MS method was utilized to analyze non-target matcha metabolites to identify the primary flavor compounds associated with each flavor attribute and to pinpoint the key constituents responsible for variations in grade and flavor intensity. Additionally, high three-way intercorrelations between descriptive sensory attributes, metabolites, and the selected informative wavelengths were observed through network analysis, with correlation coefficients calculated to quantify these relationships. In this research, the integration of matcha chemical composition and sensory panel data was utilized to develop predictive models for assessing the flavor profile of matcha based on its chemical properties.

CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis.

SUMMARY: The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real-time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies. AVAILABILITY AND IMPLEMENTATION: CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Relationship between Functional and Evolutionary Correlations of Enzyme Reactions in Metabolic Network Evolution

Abstract The evolution of species is inevitably accompanied by the evolution of metabolic networks to adapt to different environments. The metabolic networks of different species were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website, and some enzyme reactions with the highest occurrence frequency in all species were found and are reported in this paper. The correlation coefficients of whether the enzyme reactions appear in all species were calculated, and the corresponding evolutionary correlation connection networks were calculated according to different correlation coefficient thresholds. These studies show that, as the evolutionary correlation of enzyme reactions increases, the weighted average of the mean functional concentration ratios of the enzyme reactions also increases, indicating that the functional concentration ratio of enzyme reactions has a certain correlation with the evolutionary correlation. The work presented in this paper enhances our understanding of the characteristics and general rules of metabolic network evolution.

Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale.

BACKGROUND: A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT: Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION: This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

Visual FTBL Generator: Visual Generation Tool for 13C-FLUX File

Abstract 13C metabolic flux analysis (13C-MFA) has achieved increasing significance in quantitative metabolic system analysis in recent years. In 13C metabolic flux analysis, 13C-FLUX software is a major analytical tool. The software's input script is primarily expressed in textual form without visual presentation of the structure of the entire metabolic network, thus error-prone in manual input. To solve this problem, we have developed a visual FTBL generator (VFG, available at http://47.100.98.220/vfg/index.jsp in a Google or Firefox browser)for MFA that eliminates the tedious, error-prone text entry mode and provides a user-friendly graphical interface and simple visual reaction generation functions.

Evaluating the Efficacy of Government Spending on Air Pollution Control: A Case Study from Beijing.

The reform and opening up of the Chinese economy over the last 40 years has led to rapid economic development. However, with the rapid expansion of the economy, increasingly serious air pollution is apparent. In order to control urban air pollution effectively, Chinese governments at all levels have invested large sums every year. However, it has become a difficult issue which influences public government decisions with respect to how and according to what standard to distribute financial funds so as to improve air quality while saving money at the same time. Taking Beijing as an example, this paper investigates the ten-year change in the annual daily mean of inhalable particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) from the year of 2006 to 2015, researches the invested funds in environmental protection in Beijing, and establishes a relationship between the atmospheric indexes of the above three parameters and government-invested funds in environmental protection. According to model analysis, government financial input has an obvious influence on the improvement of air quality. However, during the long period of financial input, the degree of air quality improvement will reduce gradually as time goes by. There exists a direct link between the effectiveness of government financial input to promote air quality and the air quality index, which means when the pollutant standards index is poor (i.e., the corresponding pollutant concentration is higher), the effectiveness will be more apparent. On the contrary, when the index is at a good level, the effectiveness of government financial input is very small. To achieve the best air quality conditions, the government should set the detailed financial input at or over the first-grade standard according to urban air quality standards.

A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.

Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory. However, most biological processes, especially the metabolic reactions, are more sophisticated than simple pairwise interactions and contain three or more participating components. Such multi-lateral relations are not captured by graphs, and computational methods to overcome this limitation are currently lacking. This paper introduces hypergraphs and association hypergraphs to describe metabolic networks and their potential alignments, respectively. Within this framework, metabolic networks are aligned by identifying the maximal Z-eigenvalue of a symmetric tensor. A shifted higher-order power method was utilized to identify a solution. A rotational strategy has been introduced to accelerate the tensor-vector product by 250-fold on average and reduce the storage cost by up to 1,000-fold. The algorithm was implemented on a spark-based distributed computation cluster to significantly increase the convergence rate further by 50- to 80-fold. The parameters have been explored to understand their impact on alignment accuracy and speed. In particular, the influence of initial value selection on the stationary point has been simulated to ensure an accurate approximation of the global optimum. This framework was demonstrated by alignments among the genome-wide metabolic networks of Escherichia coli MG-1655 and Halophilic archaeon DL31. To our knowledge, this is the first genome-wide metabolic network alignment at both the metabolite level and the enzyme level. These results demonstrate that it can supply quite a few valuable insights into metabolic networks. First, this method can access the driving force of organic reactions through the chemical evolution of metabolic network. Second, this method can incorporate the chemical information of enzymes and structural changes of compounds to offer new way defining reaction class and module, such as those in KEGG. Third, as a vertex-focused treatment, this method can supply novel structural and functional annotation for ill-defined molecules. The related source code is available on request.

Comparisons Between Different Methods in Measuring Enzyme Similarity for Metabolic Network Alignment

Metabolic network alignments enable comparison of the similarities and differences between pathways in two metabolic networks and help to uncover the conserved sub-blocks therein. Such analysis is important in the understanding of metabolic networks and species evolution. The fundamental parts of metabolic network alignment algorithms all involve comparisons of the similarity between two enzymes as a similarity measure of network nodes. As a result, the study of methods for measuring enzyme similarity becomes highly relevant. Currently, two approaches are mainly used to measure enzyme similarity. One of the methods is based on similarity measures of gene or protein sequences; the other is based on enzyme classification. In this study, multiple metabolic network alignments were performed using both the methods. The results showed that, in general, the sequence similarity method yielded higher accuracy, especially with respect to reflecting evolutionary distances.

Elementary Flux Mode Analysis Revealed Cyclization Pathway as a Powerful Way for NADPH Regeneration of Central Carbon Metabolism.

NADPH regeneration capacity is attracting growing research attention due to its important role in resisting oxidative stress. Besides, NADPH availability has been regarded as a limiting factor in production of industrially valuable compounds. The central carbon metabolism carries the carbon skeleton flux supporting the operation of NADPH-regenerating enzyme and offers flexibility in coping with NADPH demand for varied intracellular environment. To acquire an insightful understanding of its NADPH regeneration capacity, the elementary mode method was employed to compute all elementary flux modes (EFMs) of a network representative of central carbon metabolism. Based on the metabolic flux distributions of these modes, a cluster analysis of EFMs with high NADPH regeneration rate was conducted using the self-organizing map clustering algorithm. The clustering results were used to study the relationship between the flux of total NADPH regeneration and the flux in each NADPH producing enzyme. The results identified several reaction combinations supporting high NADPH regeneration, which are proven to be feasible in cells via thermodynamic analysis and coincident with a great deal of previous experimental report. Meanwhile, the reaction combinations showed some common characteristics: there were one or two decarboxylation oxidation reactions in the combinations that produced NADPH and the combination constitution included certain gluconeogenesis pathways. These findings suggested cyclization pathways as a powerful way for NADPH regeneration capacity of bacterial central carbon metabolism.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics.

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.