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Barik, Dr Bibhuti Prasad. "In Silico Observations and Analysis of Metabolic Pathways." International Journal of Scientific Research 2, no. 11 (2012): 44–48. http://dx.doi.org/10.15373/22778179/nov2013/14.
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Katz, J. "Mathematical analysis of metabolic pathways." American Journal of Physiology-Endocrinology and Metabolism 252, no. 4 (1987): E571—E572. http://dx.doi.org/10.1152/ajpendo.1987.252.4.e571.
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Forst, Christian V., and Klaus Schulten. "Phylogenetic Analysis of Metabolic Pathways." Journal of Molecular Evolution 52, no. 6 (2001): 471–89. http://dx.doi.org/10.1007/s002390010178.
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Moreno-Sánchez, Rafael, Emma Saavedra, Sara Rodríguez-Enríquez, and Viridiana Olín-Sandoval. "Metabolic Control Analysis: A Tool for Designing Strategies to Manipulate Metabolic Pathways." Journal of Biomedicine and Biotechnology 2008 (2008): 1–30. http://dx.doi.org/10.1155/2008/597913.
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The traditional experimental approaches used for changing the flux or the concentration of a particular metabolite of a metabolic pathway have been mostly based on the inhibition or over-expression of the presumed rate-limiting step. However, the attempts to manipulate a metabolic pathway by following such approach have proved to be unsuccessful. Metabolic Control Analysis (MCA) establishes how to determine, quantitatively, the degree of control that a given enzyme exerts on flux and on the concentration of metabolites, thus substituting the intuitive, qualitative concept of rate limiting step. Moreover, MCA helps to understand (i) the underlying mechanisms by which a given enzyme exerts high or low control and (ii) why the control of the pathway is shared by several pathway enzymes and transporters. By applying MCA it is possible to identify the steps that should be modified to achieve a successful alteration of flux or metabolite concentration in pathways of biotechnological (e.g., large scale metabolite production) or clinical relevance (e.g., drug therapy). The different MCA experimental approaches developed for the determination of the flux-control distribution in several pathways are described. Full understanding of the pathway properties when working under a variety of conditions can help to attain a successful manipulation of flux and metabolite concentration.
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Mattei, Gianluca, Zhuohui Gan, Matteo Ramazzotti, Bernhard O. Palsson, and Daniel C. Zielinski. "Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways." Metabolites 13, no. 11 (2023): 1127. http://dx.doi.org/10.3390/metabo13111127.
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Pathway analysis is ubiquitous in biological data analysis due to the ability to integrate small simultaneous changes in functionally related components. While pathways are often defined based on either manual curation or network topological properties, an attractive alternative is to generate pathways around specific functions, in which metabolism can be defined as the production and consumption of specific metabolites. In this work, we present an algorithm, termed MetPath, that calculates pathways for condition-specific production and consumption of specific metabolites. We demonstrate that these pathways have several useful properties. Pathways calculated in this manner (1) take into account the condition-specific metabolic role of a gene product, (2) are localized around defined metabolic functions, and (3) quantitatively weigh the importance of expression to a function based on the flux contribution of the gene product. We demonstrate how these pathways elucidate network interactions between genes across different growth conditions and between cell types. Furthermore, the calculated pathways compare favorably to manually curated pathways in predicting the expression correlation between genes. To facilitate the use of these pathways, we have generated a large compendium of pathways under different growth conditions for E. coli. The MetPath algorithm provides a useful tool for metabolic network-based statistical analyses of high-throughput data.
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Liao, James C. "Modelling and analysis of metabolic pathways." Current Opinion in Biotechnology 4, no. 2 (1993): 211–16. http://dx.doi.org/10.1016/0958-1669(93)90127-i.
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Arias-Méndez, Esteban, Diego Barquero-Morera, and Francisco J. Torres-Rojas. "Low-Cost Algorithms for Metabolic Pathway Pairwise Comparison." Biomimetics 7, no. 1 (2022): 27. http://dx.doi.org/10.3390/biomimetics7010027.
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Metabolic pathways provide key information for achieving a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on pathway comparison, using graph data structures. Metabolic pathway comparison has been defined as a computationally complex task. In a previous work, two new algorithms were introduced to treat the problem of metabolic pathway pairwise comparison. Here we provide an extended analysis with more data and a deeper analysis of metabolic pathway comparison as listed in the discussion and results section.
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Huang, Yan, Rong Chen, Shuci Yang, Ye Chen, and Xiaoying Lü. "The Mechanism of Interaction Between Gold Nanoparticles and Human Dermal Fibroblasts Based on Integrative Analysis of Transcriptomics and Metabolomics Data." Journal of Biomedical Nanotechnology 18, no. 6 (2022): 1562–76. http://dx.doi.org/10.1166/jbn.2022.3365.
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The aim of this paper was to combine transcriptomics and metabolomics to analyze the mechanism of gold nanoparticles (GNPs) on human dermal fibroblasts (HDFs). First, 20-nm GNPs were prepared, and the differentially expressed genes in HDFs were subsequently screened by transcriptome sequencing technology after 4, 8, and 24 h of treatment with GNPs. By comparing the metabolic pathways in which the metabolites obtained in a previous study were involved, the pathways involving both genes and metabolites were filtered, and the differentially expressed genes and metabolites with upstream and downstream relationships were screened out. The gene–metabolite–metabolic pathway network was further constructed, and the functions of metabolic pathways, genes and metabolites in the important network were analyzed and experimentally verified. The results of transcriptome sequencing experiments showed that 1904, 1216 and 489 genes were differentially expressed in HDFs after 4, 8 and 24 h of treatment with GNPs, and these genes were involved in 270, 235 and 163 biological pathways, respectively. Through the comparison and analysis of the metabolic pathways affected by the metabolites, 7, 3 and 2 metabolic pathways with genes and metabolites exhibiting upstream and downstream relationships were identified. Through analysis of the gene–metabolite–metabolic pathway network, 4 important metabolic pathways, 9 genes and 7 metabolites were identified. Combined with the results of verification experiments on oxidative stress, apoptosis, the cell cycle, the cytoskeleton and cell adhesion, it was found that GNPs regulated the synthesis of downstream metabolites through upstream genes in important metabolic pathways. GNPs inhibited oxidative stress and thus did not induce significant apoptosis, but they exerted effects on several cellular functions, including arresting the cell cycle and affecting the cytoskeleton and cell adhesion.
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Mashima, Izumi, Yu-Chieh Liao, Chieh-Hua Lin, et al. "Comparative Pan-Genome Analysis of Oral Veillonella Species." Microorganisms 9, no. 8 (2021): 1775. http://dx.doi.org/10.3390/microorganisms9081775.
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The genus Veillonella is a common and abundant member of the oral microbiome. It includes eight species, V. atypica, V. denticariosi, V. dispar, V. infantium, V. nakazawae, V. parvula, V. rogosae and V. tobetusensis. They possess important metabolic pathways that utilize lactate as an energy source. However, the overall metabolome of these species has not been studied. To further understand the metabolic framework of Veillonella in the human oral microbiome, we conducted a comparative pan-genome analysis of the eight species of oral Veillonella. Analysis of the oral Veillonella pan-genome revealed features based on KEGG pathway information to adapt to the oral environment. We found that the fructose metabolic pathway was conserved in all oral Veillonella species, and oral Veillonella have conserved pathways that utilize carbohydrates other than lactate as an energy source. This discovery may help to better understand the metabolic network among oral microbiomes and will provide guidance for the design of future in silico and in vitro studies.
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Leiser, Scott, Christopher Choi, Ajay Bhat, and Charles Evans. "A Metabolic Stress Response." Innovation in Aging 4, Supplement_1 (2020): 123. http://dx.doi.org/10.1093/geroni/igaa057.404.
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Abstract An organism’s ability to respond to stress is crucial for long-term survival. These stress responses are coordinated by distinct but overlapping pathways, many of which have been found to also regulate longevity in multiple organisms across species. Despite extensive effort, our understanding of these pathways and how they affect aging remains incomplete and thus is a key area of study in Geroscience. Our previous work identified flavin-containing monooxygenase-2 (fmo-2) as a key longevity-promoting gene downstream of at least three longevity promoting pathways, including the hypoxic response, the pentose phosphate pathway, and the dietary restriction pathway. Based on the commonalities of these pathways, we hypothesized that fmo-2, a classically annotated xenobiotic enzyme, might play a key endogenous role in responding to metabolic stress. Our resulting data, using metabolic profiling and further epistatic analysis, both support this hypothesis and link fmo-2’s mechanism to modifications to one-carbon metabolism (OCM), a key intermediate pathway between the nucleotide metabolism, methylation, and transsulfuration pathways. Using mathematical modeling and a novel metabolomics approach, we were able to further identify the likely mechanism of fmo-2-mediated metabolic effects, and connect them to both OCM and downstream components. We propose a model whereby nematode fmo-2 represents a class of enzymes that are able to modify large aspects of metabolism, similar to how transcription factors modify gene expression, and that fmo-2 is a key member of a conserved metabolic stress response.
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Rise, Kjersti, May-Britt Tessem, Finn Drabløs, and Morten Beck Rye. "FunHoP analysis reveals upregulation of mitochondrial genes in prostate cancer." PLOS ONE 17, no. 10 (2022): e0275621. http://dx.doi.org/10.1371/journal.pone.0275621.
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Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.
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Thurley, Kevin, Christopher Herbst, Felix Wesener, et al. "Principles for circadian orchestration of metabolic pathways." Proceedings of the National Academy of Sciences 114, no. 7 (2017): 1572–77. http://dx.doi.org/10.1073/pnas.1613103114.
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Circadian rhythms govern multiple aspects of animal metabolism. Transcriptome-, proteome- and metabolome-wide measurements have revealed widespread circadian rhythms in metabolism governed by a cellular genetic oscillator, the circadian core clock. However, it remains unclear if and under which conditions transcriptional rhythms cause rhythms in particular metabolites and metabolic fluxes. Here, we analyzed the circadian orchestration of metabolic pathways by direct measurement of enzyme activities, analysis of transcriptome data, and developing a theoretical method called circadian response analysis. Contrary to a common assumption, we found that pronounced rhythms in metabolic pathways are often favored by separation rather than alignment in the times of peak activity of key enzymes. This property holds true for a set of metabolic pathway motifs (e.g., linear chains and branching points) and also under the conditions of fast kinetics typical for metabolic reactions. By circadian response analysis of pathway motifs, we determined exact timing separation constraints on rhythmic enzyme activities that allow for substantial rhythms in pathway flux and metabolite concentrations. Direct measurements of circadian enzyme activities in mouse skeletal muscle confirmed that such timing separation occurs in vivo.
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Lundy, Thomas, and Asok K. Sen. "Sign Pattern Analysis of Control Coefficients of Metabolic Pathways." SIAM Journal on Matrix Analysis and Applications 16, no. 3 (1995): 828–42. http://dx.doi.org/10.1137/s0895479892228201.
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Siriwan, Wanwisa, Nattachai Vannatim, Somruthai Chaowongdee, et al. "Integrated Proteomic and Metabolomic Analysis of Cassava cv. Kasetsart 50 Infected with Sri Lankan Cassava Mosaic Virus." Agronomy 13, no. 3 (2023): 945. http://dx.doi.org/10.3390/agronomy13030945.
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Cassava mosaic disease (CMD) is a major disease affecting cassava production in Southeast Asia. This study aimed to perform an integrated proteomics and metabolomics analysis of cassava cv. Kasetsart 50 infected with Sri Lankan cassava mosaic virus (SLCMV). Proteomics analyses revealed that 359 proteins were enriched in the plant–pathogen interaction, plant hormone signal transduction, and MAPK signaling pathways. A total of 79 compounds were identified by metabolomics analysis of the healthy and SLCMV-infected cassava plants. Integrated omics analysis revealed that 9 proteins and 5 metabolites were enriched in 11 KEGG pathways. The metabolic pathways, plant hormone signal transduction, and plant–pathogen interaction pathway terms were specifically investigated. The findings revealed that caffeic acid and chlorogenic acid were associated with the plant–pathogen interaction pathway, histidine (HK3) was involved in plant hormone signal transduction, while citric acid and D-serine were associated with the metabolic pathways. KEGG functional enrichment analysis revealed that plant–pathogen interaction, plant hormone signal transduction, and metabolic pathways were linked via the enriched protein (protein phosphatase 2C) and metabolites (cyclic nucleotide-binding (AT2G20050) and D-serine). The available information and resources for proteomics and metabolomics analyses of cassava can elucidate the mechanism of disease resistance and aid in cassava crop improvement programs.
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SINGH, SHAILZA, B. K. MALIK, and D. K. SHARMA. "METABOLIC PATHWAY ANALYSIS OFS. PNEUMONIAE: ANIN SILICOAPPROACH TOWARDS DRUG-DESIGN." Journal of Bioinformatics and Computational Biology 05, no. 01 (2007): 135–53. http://dx.doi.org/10.1142/s0219720007002564.
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The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.
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Hu, Hejing, Qiuling Li, Lizhen Jiang, Yang Zou, Junchao Duan, and Zhiwei Sun. "Genome-wide transcriptional analysis of silica nanoparticle-induced toxicity in zebrafish embryos." Toxicology Research 5, no. 2 (2016): 609–20. http://dx.doi.org/10.1039/c5tx00383k.
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Silica nanoparticle-induced toxicity in zebrafish embryos affected expression of 2515 genes. Pathway analysis and Signal-net analysis indicated that the gap junction, vascular smooth muscle contraction, and metabolic pathways, apoptosis, the MAPK signaling pathway, the calcium signaling pathway and the JAK-STAT signaling pathway were the most prominent significant pathways in SiNP-induced toxicity in zebrafish embryos.
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Moiz, Bilal, Jonathan Garcia, Sarah Basehore, et al. "13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity." Metabolites 11, no. 4 (2021): 226. http://dx.doi.org/10.3390/metabo11040226.
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Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with 13C metabolic flux analysis (13C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) 13C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased 13C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed 13C enrichment differences in UDP-GlcNAc. 13C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, 13C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.
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AY, FERHAT, TAMER KAHVECI, and VALÉRIE DE CRÉCY-LAGARD. "A FAST AND ACCURATE ALGORITHM FOR COMPARATIVE ANALYSIS OF METABOLIC PATHWAYS." Journal of Bioinformatics and Computational Biology 07, no. 03 (2009): 389–428. http://dx.doi.org/10.1142/s0219720009004163.
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Pathways show how different biochemical entities interact with one another to perform vital functions for the survival of an organism. Comparative analysis of pathways is crucial in identifying functional similarities that are difficult to identify by comparing individual entities that build up these pathways. When interacting entities are of single type, the problem of identifying similarities by aligning the pathways can be reduced to graph isomorphism problem. For pathways with varying types of entities such as metabolic pathways, alignment problem is even more challenging. In order to simplify this problem, existing methods often reduce metabolic pathways to graphs with restricted topologies and single type of nodes. However, these abstractions reduce the relevance of the alignment significantly as they cause losses in the information content. In this paper, we describe an algorithm to solve the pairwise alignment problem for metabolic pathways. A distinguishing feature of our method is that it aligns different types of entities, such as enzymes, reactions and compounds. Also, our approach is free of any abstraction in modeling the pathways. We pursue the intuition that both pairwise similarities of entities (homology) and the organization of their interactions (topology) are important for metabolic pathway alignment. In our algorithm, we account for both by creating an eigenvalue problem for each entity type. We enforce the consistency while combining the alignments of different entity types by considering the reachability sets of entities. Our experiments show that our method finds biologically and statistically significant alignments in the order of milliseconds. Availability: Our software and the source code in C programming language is available at .
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Klesmith, Justin R., and Timothy A. Whitehead. "High-throughput evaluation of synthetic metabolic pathways." TECHNOLOGY 04, no. 01 (2016): 9–14. http://dx.doi.org/10.1142/s233954781640001x.
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A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.
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Ashida, Yuta, Tomonobu Ozaki, and Takenao Ohkawa. "A Comparative Analysis of Metabolic Pathways Based on Metabolic Steady States." IPSJ Transactions on Bioinformatics 2 (2009): 83–92. http://dx.doi.org/10.2197/ipsjtbio.2.83.
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Fell, David A. "Increasing the flux in metabolic pathways: A metabolic control analysis perspective." Biotechnology and Bioengineering 58, no. 2-3 (1998): 121–24. http://dx.doi.org/10.1002/(sici)1097-0290(19980420)58:2/3<121::aid-bit2>3.0.co;2-n.
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Brister, Danielle, Brianna A. Werner, Geoffrey Gideon, et al. "Central Nervous System Metabolism in Autism, Epilepsy and Developmental Delays: A Cerebrospinal Fluid Analysis." Metabolites 12, no. 5 (2022): 371. http://dx.doi.org/10.3390/metabo12050371.
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Neurodevelopmental disorders are associated with metabolic pathway imbalances; however, most metabolic measurements are made peripherally, leaving central metabolic disturbances under-investigated. Cerebrospinal fluid obtained intraoperatively from children with autism spectrum disorder (ASD, n = 34), developmental delays (DD, n = 20), and those without known DD/ASD (n = 34) was analyzed using large-scale targeted mass spectrometry. Eighteen also had epilepsy (EPI). Metabolites significantly related to ASD, DD and EPI were identified by linear models and entered into metabolite–metabolite network pathway analysis. Common disrupted pathways were analyzed for each group of interest. Central metabolites most involved in metabolic pathways were L-cysteine, adenine, and dodecanoic acid for ASD; nicotinamide adenine dinucleotide phosphate, L-aspartic acid, and glycine for EPI; and adenosine triphosphate, L-glutamine, ornithine, L-arginine, L-lysine, citrulline, and L-homoserine for DD. Amino acid and energy metabolism pathways were most disrupted in all disorders, but the source of the disruption was different for each disorder. Disruption in vitamin and one-carbon metabolism was associated with DD and EPI, lipid pathway disruption was associated with EPI and redox metabolism disruption was related to ASD. Two microbiome metabolites were also detected in the CSF: shikimic and cis-cis-muconic acid. Overall, this study provides increased insight into unique metabolic disruptions in distinct but overlapping neurodevelopmental disorders.
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Lee, Yu Ra, Bark Lynn Lew, Woo Young Sim, Jongki Hong, and Bong Chul Chung. "Alterations in Pattern Baldness According to Sex: Hair Metabolomics Approach." Metabolites 11, no. 3 (2021): 178. http://dx.doi.org/10.3390/metabo11030178.
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Pattern baldness has been associated with the male hormone, dihydrotestosterone. In this study, we tried to determine how the overall metabolic pathways of pattern baldness differ in patients and in normal controls. Our study aimed to identify alterations in hair metabolomic profiles in order to identify possible markers of pattern baldness according to sex. Untargeted metabolomics profiling in pattern baldness patients and control subjects was conducted using ultra-performance liquid chromatography-mass spectrometry. To identify significantly altered metabolic pathways, partial least squares discriminant analysis was performed. Our analysis indicated differences in steroid biosynthesis pathway in both males and females. However, there was a remarkable difference in the androgen metabolic pathway in males, and the estrogen metabolic and arachidonic acid pathways in females. For the first time, we were able to confirm the metabolic pathway in pattern baldness patients using hair samples. Our finding improves understanding of pattern baldness and highlights the need to link pattern baldness and sex-related differences.
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DE, RAJAT K., and NAMRATA TOMAR. "MODELING THE OPTIMAL CENTRAL CARBON METABOLIC PATHWAYS UNDER FEEDBACK INHIBITION USING FLUX BALANCE ANALYSIS." Journal of Bioinformatics and Computational Biology 10, no. 06 (2012): 1250019. http://dx.doi.org/10.1142/s0219720012500199.
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Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.
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Young, Michael R., and David L. Craft. "Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data." Cancer Informatics 15 (January 2016): CIN.S40088. http://dx.doi.org/10.4137/cin.s40088.
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We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into subtypes. Variants of the method allow it to be used on datasets that do and do not contain noncancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate; for pancreatic cancer, signaling and metabolic pathways dominate; and for melanoma, immune system pathways are the most useful. This work suggests the utility of pathway-level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.
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Schuster, Stefan, Luís F. de Figueiredo, and Christoph Kaleta. "Predicting novel pathways in genome-scale metabolic networks." Biochemical Society Transactions 38, no. 5 (2010): 1202–5. http://dx.doi.org/10.1042/bst0381202.
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Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.
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Meyers, Jeremy, Raul Castro-Portuguez, Luis Espejo, and George Sutphin. "Genomic Analysis Of NAD+ Synthesis Pathways Involved In Aging and Cancer." Innovation in Aging 5, Supplement_1 (2021): 665. http://dx.doi.org/10.1093/geroni/igab046.2510.
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Abstract Cancer cells have elevated energy demands to sustain continuous growth and other malignant processes and undergo extensive metabolic reprogramming to meet these demands. One element of this reprogramming in many cancer subtypes is elevated synthesis of nicotinamide adenine dinucleotide (NAD+), a critical co-enzyme that supports energy production through both glycolysis and the TCA cycle. The kynurenine metabolic pathway is the evolutionarily conserved means by which cells produce NAD+ de novo from tryptophan. NAD+ levels drop with age, a contributing factor to many forms of age-related disease. While interventions that increase NAD+ have been shown to extend lifespan, previous work from our lab demonstrates that knockdown of several kynurenine pathway enzymes, thus decreasing de novo NAD+ production, results in increased longevity of Caenorhabditis elegans by 20-30%. To address this apparent contradiction, we propose that kynurenine pathway inhibition may produce metabolic feedback that results in upregulation of NAD+ recycling. Eukaryotic cells recycle NAD+ from nicotinamide (NAM) through one of two pathways: the Salvage pathway in mammalian cells and the Preiss-Handler pathway in C. elegans and related invertebrates species. We are using tools in C. elegans and human cell culture to examine the interaction between kynurenine/de novo NAD+ synthesis and NAD+ recycling through Salvage and Preiss-Handler. In particular, we are interested in how combining interventions between these pathways will influence activity throughout the NAD+ metabolic networks (measured via mass spectrometry), physiological phenotypes, and transcriptomic changes (via RNA sequence data) involved in aging and age-associated disease.
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Agarwal, Divyansh, Tina Bharani, and Somabha Mukherjee. "Abstract 2042: Graph-based pathway analysis of T cell populations in hepatocellular carcinoma reveals novel metabolic regulators of tumor-infiltration lymphocyte activity." Cancer Research 83, no. 7_Supplement (2023): 2042. http://dx.doi.org/10.1158/1538-7445.am2023-2042.
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Abstract With advances in single cell RNA sequencing (scRNAseq), accurate detection of perturbed pathways between conditions or cell types of interest becomes a critical analytical step. The ability to model alterations in a set of genes corresponding of a biological function is particularly useful when comparing cells between healthy and tumor tissues. Yet, few tools exist to detect changes in the multivariate distribution of genes corresponding to a given pathway. We developed a novel, graph-based statistical framework based on optimal matching for testing differential distribution of biological pathways in scRNAseq. We applied our method to data generated from &gt;4,000 T cells isolated from six individuals with hepatocellular carcinoma (HCC). The T cell populations were purified from three tissue locations: peripheral blood, tumor-infiltrating immune cells (TIICs), and normal tissue adjacent to the tumor. We examined the distribution of gene sets that belong to a particular metabolic pathway across T cell subtypes to address the following questions: (1) Which pathways have a similar distribution across T cell subtypes in a given tissue? (2) Are there pathways that have a stable distribution across T cell subtypes in a normal/healthy tissue, but a perturbed distribution in HCC? (3) For pathways that have a disparate distribution across the T cell subtypes, which subtypes show the most distinct distribution? Of the 86 metabolic pathways compared, 41, 63 and 76 metabolic pathways were indistinguishable across T cell subtypes in the tumor, peripheral blood and adjacent normal tissues, respectively. We observed that most metabolic pathways do not show evidence for dissimilar distribution across cell types, suggesting that T cell subtypes might be more similar than previously appreciated in terms of how they regulate their basic metabolic machinery. Further, for each pair of tissue locations, we computed the overlap in perturbed pathways, and found that the concordance was substantially higher for blood and adjacent normal, than what either of these tissues had with the tumor tissue. Interestingly, five metabolic pathways were differentially distributed across the T cell subtypes in each tissue examined in HCC: glycolysis, purine metabolism, glycosphingolipid biosynthesis, pyruvate metabolism, and glycerophospholipid metabolism. Our model also found that CD4+ regulatory T cells were the strongest contributors, driving the differential distribution of these pathways between T cell subtypes in HCC. Altogether, our approach allows for a systems level characterization of pathway activity across multiple cell types with a variety of applications in single cell pathway analysis in oncology. Our work here also highlights unexpected regulatory mechanisms of regulatory T cells that might play in role in the immunobiology of HCC. Citation Format: Divyansh Agarwal, Tina Bharani, Somabha Mukherjee. Graph-based pathway analysis of T cell populations in hepatocellular carcinoma reveals novel metabolic regulators of tumor-infiltration lymphocyte activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2042.
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Smith, Thomas Brendan, Kamlesh Patel, Haydn Munford, et al. "High-Speed Tracer Analysis of Metabolism (HS-TrAM)." Wellcome Open Research 3 (August 22, 2018): 5. http://dx.doi.org/10.12688/wellcomeopenres.13387.2.
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Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) spectroscopy offers high resolution data in terms of resolving metabolic pathway utilisation. Despite this, NMR spectroscopy is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting of 13C signals due to homonuclear 13C,13C or heteronuclear 13C,15N J-coupling in 1H,13C-HSQC NMR spectra. Together, these allow the rapid collection of NMR spectroscopy data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways in vivo. We show how these techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ. In addition, we show how the use of multiple nutrients, differentially labelled with 13C and 15N, can be used to provide additional information with which to profile metabolic pathways.
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Nam, Seungyoon, and Yongmin Lee. "Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer." Cancers 14, no. 9 (2022): 2340. http://dx.doi.org/10.3390/cancers14092340.
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Gastric cancer (GC) is one of the most lethal cancers worldwide; it has a high mortality rate, particularly in East Asia. Recently, genetic events (e.g., mutations and copy number alterations) and molecular signaling associated with histologically different GC subtypes (diffuse and intestinal) have been elucidated. However, metabolic differences among the histological GC subtypes have not been studied systematically. In this study, we utilized transcriptome-based genome-scale metabolic models (GEMs) to identify differential metabolic pathways between Lauren diffuse and intestinal subtypes. We found that diverse metabolic pathways, including cholesterol homeostasis, xenobiotic metabolism, fatty acid metabolism, the MTORC1 pathway, and glycolysis, were dysregulated between the diffuse and intestinal subtypes. Our study provides an overview of the metabolic differences between the two subtypes, possibly leading to an understanding of metabolism in GC heterogeneity.
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Haj, Amelia K., Haytham Hasan, and Thomas J. Raife. "Heritability of Protein and Metabolite Biomarkers Associated with COVID-19 Severity: A Metabolomics and Proteomics Analysis." Biomolecules 13, no. 1 (2022): 46. http://dx.doi.org/10.3390/biom13010046.
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Objectives: Prior studies have characterized protein and metabolite changes associated with SARS-CoV-2 infection; we hypothesized that these biomarkers may be part of heritable metabolic pathways in erythrocytes. Methods: Using a twin study of erythrocyte protein and metabolite levels, we describe the heritability of, and correlations among, previously identified biomarkers that correlate with COVID-19 severity. We used gene ontology and pathway enrichment analysis tools to identify pathways and biological processes enriched among these biomarkers. Results: Many COVID-19 biomarkers are highly heritable in erythrocytes. Among heritable metabolites downregulated in COVID-19, metabolites involved in amino acid metabolism and biosynthesis are enriched. Specific amino acid metabolism pathways (valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and arginine biosynthesis) are heritable in erythrocytes. Conclusions: Metabolic pathways downregulated in COVID-19, particularly amino acid biosynthesis and metabolism pathways, are heritable in erythrocytes. This finding suggests that a component of the variation in COVID-19 severity may be the result of phenotypic variation in heritable metabolic pathways; future studies will be necessary to determine whether individual variation in amino acid metabolism pathways correlates with heritable outcomes of COVID-19.
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Chen, Wenbo, Xin Chen, Zhenyu Zhao, et al. "Pan-Cancer Identification of Prognostic-Associated Metabolic Pathways." Biology 12, no. 8 (2023): 1129. http://dx.doi.org/10.3390/biology12081129.
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Metabolic dysregulation has been reported involving in the clinical outcomes of multiple cancers. However, systematical identification of the impact of metabolic pathways on cancer prognosis is still lacking. Here, we performed a pan-cancer analysis of popular metabolic checkpoint genes and pathways with cancer prognosis by integrating information of clinical survival with gene expression and pathway activity in multiple cancer patients. By discarding the effects of age and sex, we revealed extensive and significant associations between the survival of cancer patients and the expression of metabolic checkpoint genes, as well as the activities of three primary metabolic pathways: amino acid metabolism, carbohydrate metabolism, lipid metabolism, and eight nonprimary metabolic pathways. Among multiple cancers, we found the survival of kidney renal clear cell carcinoma and low-grade glioma exhibit high metabolic dependence. Our work systematically assesses the impact of metabolic checkpoint genes and pathways on cancer prognosis, providing clues for further study of cancer diagnosis and therapy.
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Kan, N. E., Z. V. Khachatryan, V. V. Chagovets, et al. "Analysis of metabolic pathways in intrauterine growth restriction." Biomeditsinskaya Khimiya 66, no. 2 (2020): 174–80. http://dx.doi.org/10.18097/pbmc20206602174.
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Objective was to analyze metabolic pathways based on a study of the metabolomic profile of pregnant women with intrauterine growth restriction. The metabolic profile of pregnant women with fetal growth restriction has been analyzed using liquid chromatography-mass spectrometry. At the second stage pathways were identified using SMPDB and MetaboAnalyst databases to clarify the relationship between metabolites. Biological networks allow to determine the effect of proteins on the metabolic pathways involved in pathogenesis of IUGR and determine the epigenetic mechanisms of its formation.
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Mitchell, Sabrina L., Chunyu Ma, William K. Scott, et al. "Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients." Cells 10, no. 11 (2021): 3141. http://dx.doi.org/10.3390/cells10113141.
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To characterize metabolites and metabolic pathways altered in intermediate and neovascular age-related macular degeneration (IAMD and NVAMD), high resolution untargeted metabolomics was performed via liquid chromatography-mass spectrometry on plasma samples obtained from 91 IAMD patients, 100 NVAMD patients, and 195 controls. Plasma metabolite levels were compared between: AMD patients and controls, IAMD patients and controls, and NVAMD and IAMD patients. Partial least-squares discriminant analysis and linear regression were used to identify discriminatory metabolites. Pathway analysis was performed to determine metabolic pathways altered in AMD. Among the comparisons, we identified 435 unique discriminatory metabolic features. Using computational methods and tandem mass spectrometry, we identified 11 metabolic features whose molecular identities had been previously verified and confirmed the molecular identities of three additional discriminatory features. Included among the discriminatory metabolites were acylcarnitines, phospholipids, amino acids, and steroid metabolites. Pathway analysis revealed that lipid, amino acid, and vitamin metabolism pathways were altered in NVAMD, IAMD, or AMD in general, including the carnitine shuttle pathway which was significantly altered in all comparisons. Finally, few discriminatory features were identified between IAMD patients and controls, suggesting that plasma metabolic profiles of IAMD patients are more similar to controls than to NVAMD patients.
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Lindroos, H., and S. G. E. Andersson. "Visualizing metabolic pathways: comparative genomics and expression analysis." Proceedings of the IEEE 90, no. 11 (2002): 1793–802. http://dx.doi.org/10.1109/jproc.2002.804687.
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Kan, N. E., Z. V. Khachatryan, V. V. Chagovets, et al. "Analysis of Metabolic Pathways in Intrauterine Growth Restriction." Biochemistry (Moscow), Supplement Series B: Biomedical Chemistry 14, no. 4 (2020): 356–62. http://dx.doi.org/10.1134/s1990750820040071.
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Dersch, Lisa Maria, Veronique Beckers, and Christoph Wittmann. "Green pathways: Metabolic network analysis of plant systems." Metabolic Engineering 34 (March 2016): 1–24. http://dx.doi.org/10.1016/j.ymben.2015.12.001.
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Sen, A. K. "Application of electrical analogues for control analysis of simple metabolic pathways." Biochemical Journal 272, no. 1 (1990): 65–70. http://dx.doi.org/10.1042/bj2720065.
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I have used electrical analogues for calculating the Flux Control Coefficients of metabolic pathways. An analogue circuit consists of resistances that are connected in series (or parallel) with a voltage (or current) source. In constructing the analogues, each of the enzymes in the pathway is associated with a resistance whose magnitude depends on the Elasticity Coefficients of the enzymes. These circuits can be designed in a heuristic fashion directly from the configuration of the pathway, without the necessity of writing down the governing equations with the use of Summation and Connectivity Theorems. The Flux Control Coefficients of the enzymes are represented by voltages across (or currents through) the resistances and are determined by an application of Ohm's Law. Results are given for (a) a simple linear pathway without feedback or feedforward regulation, and (b) a linear pathway with feedback inhibition. The analogue circuits are also convenient for assessing the relative importance of the various enzymes in flux control, and for simplifying the structure of a given pathway.
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Wang, Yujue, Fredric E. Wondisford, Chi Song, Teng Zhang, and Xiaoyang Su. "Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes." Metabolites 10, no. 11 (2020): 447. http://dx.doi.org/10.3390/metabo10110447.
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Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.
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Ge, Kai, and Zhaoyu Geng. "Proteomic analysis of the liver regulating lipid metabolism in Chaohu ducks using two-dimensional electrophoresis." Open Life Sciences 17, no. 1 (2022): 960–72. http://dx.doi.org/10.1515/biol-2022-0101.
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Abstract In this study, we aimed to characterize the liver protein profile of Chaohu ducks using two-dimensional electrophoresis and proteomics. The livers were quickly collected from 120 healthy, 84-day-old Chaohu ducks. The intramuscular fat (IMF) content of the left pectoralis muscle was determined using the Soxhlet extraction method. The total protein of liver tissues from the high and low IMF groups was extracted for proteomics. Functional enrichment analysis of the differentially expressed proteins (DEPs) was conducted using gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG). In total, 43 DEPs were identified. Functional enrichment analysis indicated that these DEPs were significantly related to four lipid metabolic processes: carboxylic acid metabolic process, ATP metabolic process, oxoacid metabolic process, and organic acid metabolic process. Three pathways correlated with lipid metabolism were identified using KEGG analysis: glycolysis/gluconeogenesis, pentose phosphate pathway, fructose, and mannose metabolism. Eight key proteins associated with lipid metabolism were identified: ALDOB, GAPDH, ENO1, RGN, TPI1, HSPA9, PRDX1, and GPX1. Protein–protein interaction analysis revealed that the glycolysis/gluconeogenesis pathway mediated the interaction relationship. Key proteins and metabolic pathways were closely related to lipid metabolism and showed a strong interaction in Chaohu ducks.
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Lu, Heng, Yi Chen, and Linlin Li. "Metabolic Pathway Genes Associated with Susceptibility Genes to Coronary Artery Disease." International Journal of Genomics 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/9025841.
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Coronary artery disease (CAD) is one of the leading threats to global health. Previous research has proven that metabolic pathway disorders, such as high blood lipids and diabetes, are one of the risk factors that mostly cause CAD. However, the crosstalk between metabolic pathways and CAD was mostly studied on physiology processes by analyzing a single gene function. A canonical correlation analysis was used to identify the metabolic pathways, which were integrated as a unit to coexpress with CAD susceptibility genes, and to resolve additional metabolic factors that are related to CAD. Seven pathways, including citrate cycle, ubiquinone, terpenoid quinone biosynthesis, and N-glycan biosynthesis, were identified as an integrated unit coexpressed with CAD genes. These pathways could not be revealed as a coexpressed pathway through traditional methods as each single gene has weak correlation. Furthermore, sets of genes in these pathways were candidate markers for diagnosis and detection from patients’ serum.
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Tsoi, Ryan, Feilun Wu, Carolyn Zhang, Sharon Bewick, David Karig, and Lingchong You. "Metabolic division of labor in microbial systems." Proceedings of the National Academy of Sciences 115, no. 10 (2018): 2526–31. http://dx.doi.org/10.1073/pnas.1716888115.
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Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.
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Lei, Jinzhou, Wei Zhang, Fangwei Yu, et al. "Integrated Analysis of Transcriptome and Metabolome Reveals Differential Responses to Alternaria brassicicola Infection in Cabbage (Brassica oleracea var. capitata)." Genes 15, no. 5 (2024): 545. http://dx.doi.org/10.3390/genes15050545.
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Black spot, caused by Alternaria brassicicola (Ab), poses a serious threat to crucifer production, and knowledge of how plants respond to Ab infection is essential for black spot management. In the current study, combined transcriptomic and metabolic analysis was employed to investigate the response to Ab infection in two cabbage (Brassica oleracea var. capitata) genotypes, Bo257 (resistant to Ab) and Bo190 (susceptible to Ab). A total of 1100 and 7490 differentially expressed genes were identified in Bo257 (R_mock vs. R_Ab) and Bo190 (S_mock vs. S_Ab), respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that “metabolic pathways”, “biosynthesis of secondary metabolites”, and “glucosinolate biosynthesis” were the top three enriched KEGG pathways in Bo257, while “metabolic pathways”, “biosynthesis of secondary metabolites”, and “carbon metabolism” were the top three enriched KEGG pathways in Bo190. Further analysis showed that genes involved in extracellular reactive oxygen species (ROS) production, jasmonic acid signaling pathway, and indolic glucosinolate biosynthesis pathway were differentially expressed in response to Ab infection. Notably, when infected with Ab, genes involved in extracellular ROS production were largely unchanged in Bo257, whereas most of these genes were upregulated in Bo190. Metabolic profiling revealed 24 and 56 differentially accumulated metabolites in Bo257 and Bo190, respectively, with the majority being primary metabolites. Further analysis revealed that dramatic accumulation of succinate was observed in Bo257 and Bo190, which may provide energy for resistance responses against Ab infection via the tricarboxylic acid cycle pathway. Collectively, this study provides comprehensive insights into the Ab–cabbage interactions and helps uncover targets for breeding Ab-resistant varieties in cabbage.
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Li, Xi-Lian, Pei-Jing Shen, Wen-Ping Jiang, Ji-Lun Meng, Hai-Hua Cheng, and Qiang Gao. "Metabonomic Analysis of Macrobrachium rosenbergii with Iron Prawn Syndrome (IPS)." Fishes 8, no. 4 (2023): 196. http://dx.doi.org/10.3390/fishes8040196.
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We previously reported on the comparison of proteomic data between seven tissue types of a novel “iron prawn” species. However, no transcripts or metabolic information are available for this species. We therefore performed shotgun LC–MS/MS metabonomic and RNA-seq analyses of the total protein from “iron prawns”. KEGG analysis revealed that the largest group consisted of a total of 114 KEGG pathway proteins, comparing the “iron prawns” with the normal prawns. A total of 423 peptides, corresponding to metabolic pathways, ABC transporters, starch and sucrose metabolism, insulin resistance/secretion, fatty digestion and absorption, and lipid metabolism, were identified. The pathways of carbohydrate and amino acid metabolism decreased in female iron prawns, while organic acid and its derivatives increased. However, the pathway of organic acid and its derivatives decreased and lipid metabolism increased in the male iron prawns. The pathways of choline metabolism in cancer and glycerophospholipid/histidine/propanoate metabolism have been significantly affected in iron prawns. Our work provides insight into the understanding of the formation mechanism of the “iron prawn”.
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Martin, D. Brand, and R. Keira Curtis. "Simplifying metabolic complexity." Biochemical Society Transactions 30, no. 2 (2002): 25–30. http://dx.doi.org/10.1042/bst0300025.
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A complete description of the regulation of metabolism in even a single cell will be very hard to achieve, enormous and indigestible. However, there are two powerful ways to simplify the complexity. Firstly, related processes and intermediates can be grouped into a small number of modules, and the regulation of the simplified system can be studied. Secondly, control analysis can be used. With these simplifications to illuminate the important regulatory features, even a full description could be made intellectually and experimentally accessible without distorting the essential regulatory features. Modular control analysis is powerful because it can quantify the relative importance of different flows of regulatory information through any metabolic, physiological, signalling or transcriptional network. It can answer global questions about the importance of different pathways mediating any change to a system. It has been used to analyse how cadmium, a poison with multiple effects, changes oxidative phosphorylation in isolated mitochondria, and to quantify the regulation of energy metabolism in hepatocytes. It has been used to measure how energy metabolism is regulated during mitogen stimulation of thymocytes, quantifying the relative importance of different signalling pathways and how each pathway contributes to the activation of energy metabolism. Recently, we have applied modular control analysis to modern DNA micro-array expression profiling to measure the importance of different groups of mRNA transcripts in mediating physiological responses.
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Wu, Chunyan, Ying Zhu, and Mengqian Yu. "Serum Metabonomics Analysis of Liver Failure Treated by Nonbioartificial Liver Support Systems." Canadian Journal of Gastroenterology and Hepatology 2018 (July 4, 2018): 1–10. http://dx.doi.org/10.1155/2018/2586052.
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Objective. To analyze the small molecular metabolic compounds of nonbioartificial liver for treatment of hepatic failure and make further efforts to study the clinical efficacy, mechanism of action, and pathogenesis of hepatic failure. Methods. 52 patients who met the standard of artificial liver treatment for liver failure were enrolled; these patients included 6 cases of acute liver failure (11.54%), 3 cases of subacute liver failure (5.77%), acute-on-chronic liver failure in 10 cases (19.23%), and 33 cases of chronic liver failure (63.46%). Treatment modes included plasma exchange in 34 patients (65.38%), bilirubin adsorption in 9 patients (17.31%), and hemofiltration in 9 patients (17.31%). The clinical efficacy of artificial liver was assessed by monitoring the effects in the near future. Significant changes in metabolic compounds of liver failure in the treatment before and after artificial liver were screened by using Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). Related metabolic pathways were analyzed by MetaboAnalyst. Results. After artificial liver treatment, the liver function and coagulation function of liver failure patients were significantly improved (P < 0.01), the Meld score was lower than that before treatment, and the difference was statistically significant (P < 0.05). Serum metabolomics identified 29 small metabolic compounds and 12 metabolic pathways with variable projection importance (VIP) greater than 1 before and after artificial liver treatment. There were 11 metabolic compounds of VIP over 1 and 7 metabolic pathways in the different modes of artificial liver treatment for chronic liver failure. Among them, bile acid metabolism, fatty acid metabolism, and amino acid metabolism are the main sources. Conclusion. Artificial liver treatment can effectively improve liver function and blood coagulation function and Meld score, clinical symptoms and signs in patients with liver failure; the curative effect of artificial liver was verified, which reflected the clinical value of artificial liver in the treatment of liver failure. Artificial liver treatment of liver failure on fatty acids and primary bile acid synthesis pathway was the most significant. The difference of fatty acid, primary bile acid synthesis pathway, and phenylalanine metabolic pathway in different artificial liver patterns of chronic liver failure was the most significant. This provides a new basis for understanding the mechanism of hepatic failure and the mechanism of liver failure by artificial liver treatment.
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Tan, Cheng, Xiaoyang Liu, and Jiajun Chen. "Microarray Analysis of the Molecular Mechanism Involved in Parkinson’s Disease." Parkinson's Disease 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1590465.
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Purpose. This study aimed to investigate the underlying molecular mechanisms of Parkinson’s disease (PD) by bioinformatics.Methods. Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed.Results. Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1) was the hub node in the PPI network; hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain-containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD.Conclusions. “rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD.ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development.
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Sen, A. K. "Metabolic control analysis. An application of signal flow graphs." Biochemical Journal 269, no. 1 (1990): 141–47. http://dx.doi.org/10.1042/bj2690141.
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In this paper the method of signal-flow graphs is used for calculating the Control Coefficients of metabolic pathways in terms of enzyme elasticities. The method is applied to an unbranched pathway (a) without feedback or feedforward regulation and (b) with feedback inhibition of the first enzyme by the last variable metabolite. It is shown that, by using a signal-flow graph, the control structure of a metabolic pathway can be represented in a graphical manner directly from the configuration of the pathway, without the necessity of writing the governing equations in a matrix form. From a signal-flow graph the various Control Coefficients can be evaluated in an easy and straightforward fashion without recourse to matrix inversion or other algebraic techniques. A signal-flow graph also provides a visual framework for analysing the cause-effect relationships of the individual enzymes.
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Liu, Gang, Douglas P. Lee, Eckhardt Schmidt, and GL Prasad. "Pathway Analysis of Global Metabolomic Profiles Identified Enrichment of Caffeine, Energy, and Arginine Metabolism in Smokers but Not Moist Snuff Consumers." Bioinformatics and Biology Insights 13 (January 2019): 117793221988296. http://dx.doi.org/10.1177/1177932219882961.
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Existing US epidemiological data demonstrate that consumption of smokeless tobacco, particularly moist snuff, is less harmful than cigarette smoking. However, the molecular and biochemical changes due to moist snuff consumption relative to smoking remain incompletely understood. We previously reported that smokers (SMK) exhibit elevated oxidative stress and inflammation relative to moist snuff consumers (MSC) and non-tobacco consumers (NTC), based on metabolomic profiling data of saliva, plasma, and urine from MSC, SMK, and NTC. In this study, we investigated the effects of tobacco consumption on additional metabolic pathways using pathway-based analysis tools. To this end, metabolic pathway enrichment analysis and topology analysis were performed through pair-wise comparisons of global metabolomic profiles of SMK, MSC, and NTC. The analyses identified >8 significantly perturbed metabolic pathways in SMK compared with NTC and MSC in all 3 matrices. Among these differentially enriched pathways, perturbations of caffeine metabolism, energy metabolism, and arginine metabolism were mostly observed. In comparison, fewer enriched metabolic pathways were identified in MSC compared with NTC (5 in plasma, none in urine and saliva). This is consistent with our transcriptomics profiling results that show no significant differences in peripheral blood mononuclear cell gene expression between MSC and NTC. These findings, taken together with our previous biochemical, metabolomic, and transcriptomic analysis results, provide a better understanding of the relative changes in healthy tobacco consumers, and demonstrate that chronic cigarette smoking, relative to the use of smokeless tobacco, results in more pronounced biological changes, which could culminate in smoking-related diseases.
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PUIGJANER, J., M. CASCANTE, and A. SORRIBAS. "ASSESSING OPTIMAL DESIGNS IN METABOLIC PATHWAYS." Journal of Biological Systems 03, no. 01 (1995): 197–206. http://dx.doi.org/10.1142/s0218339095000198.
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The evolution of metabolic pathways is characterized by the search of the optimum reaction network, both as for chemical transformations and for the associated pattern of regulation. Understanding this process requires the evaluation of alternative designs for a given function. After this evaluation, we would be in a good situation for drawing general conclusions on the evolution of the considered system. This goal can be undertaken by means of different complementary approaches. The method of Control Comparisons, first developed within Biochemical Systems Analysis, has produced some valuable insights on this kind of problems. In this contribution, we present this method within the context of Metabolic Control Analysis.