Metabolic Networks Research Articles

The dynamic regulatory network of phosphatidic acid metabolism: a spotlight on substrate cycling between phosphatidic acid and diacylglycerol.

Mammalian cells utilize over 1000 different lipid species to maintain cell and organelle membrane properties, control cell signaling and processes, and store energy. Lipid synthesis and metabolism are mediated by highly interconnected and spatiotemporally regulated networks of lipid-metabolizing enzymes and supported by vesicle trafficking and lipid-transfer at membrane contact sites. However, the regulatory mechanisms that achieve lipid homeostasis are largely unknown. Phosphatidic acid (PA) serves as the central hub for phospholipid biosynthesis, acting as a key intermediate in both the Kennedy pathway and the CDP-DAG pathway. Additionally, PA is a potent signaling molecule involved in various cellular processes. This dual role of PA, both as a critical intermediate in lipid biosynthesis and as a significant signaling molecule, suggests that it is tightly regulated within cells. This minireview will summarize the functional diversity of PA molecules based on their acyl tail structures and subcellular localization, highlighting recent tools and findings that shed light on how the physical, chemical, and spatial properties of PA species contribute to their differential metabolic fates and functions. Dysfunctional effects of altered PA metabolism as well as the strategies cells employ to maintain PA regulation and homeostasis will also be discussed. Furthermore, this review will explore the differential regulation of PA metabolism across distinct subcellular membranes. Our recent proximity labeling studies highlight the possibility that substrate cycling between PA and DAG may be location-dependent and have functional significance in cell signaling and lipid homeostasis.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

Proteomics Reveals Divergent Cardiac Inflammatory and Metabolic Responses After Inhalation of Ambient Particulate Matter With or Without Ozone.

Inhalation of ambient particulate matter (PM) and ozone (O3) has been associated with increased cardiovascular morbidity and mortality. However, the interactive effects of PM and O3 on cardiac dysfunction and disease have not been thoroughly examined, especially at a proteomic level. The purpose of this study was to identify and compare proteome changes in spontaneously hypertensive (SH) rats co-exposed to concentrated ambient particulates (CAPs) and O3, with a focus on investigating inflammatory and metabolic pathways, which are the two major ones implicated in the pathophysiology of cardiac dysfunction. For this, we measured and compared changes in expression status of 9 critical pro- and anti-inflammatory cytokines using multiplexed ELISA and 450 metabolic proteins involved in ATP production, oxidative phosphorylation, cytoskeletal organization, and stress response using two-dimensional electrophoresis (2-DE) and mass spectrometry (MS) in cardiac tissue of SH rats exposed to CAPs alone, O3 alone, and CAPs + O3. Proteomic expression profiling revealed that CAPs alone, O3 alone, and CAPs + O3 differentially altered protein expression patterns, and utilized divergent mechanisms to affect inflammatory and metabolic pathways and responses. Ingenuity Pathway Analysis (IPA) of the proteomic data demonstrated that the metabolic protein network centered by gap junction alpha-1 protein (GJA 1) was interconnected with the inflammatory cytokine network centered by nuclear factor kappa beta (NF-kB) potentially suggesting inflammation-induced alterations in metabolic pathways, or vice versa, collectively contributing to the development of cardiac dysfunction in response to CAPs and O3 exposure. These findings may enhance understanding of the pathophysiology of cardiac dysfunction induced by air pollution and provide testable hypotheses regarding mechanisms of action.

Spatial multiomics atlas reveals smooth muscle phenotypic transformation and metabolic reprogramming in diabetic macroangiopathy

BackgroundDiabetic macroangiopathy has been the main cause of death and disability in diabetic patients. The mechanisms underlying smooth muscle cell transformation and metabolic reprogramming other than abnormal glucose and lipid metabolism remain to be further explored.MethodSingle-cell transcriptome, spatial transcriptome and spatial metabolome sequencing were performed on anterior tibial artery from 11 diabetic patients with amputation. Multi-omics integration, cell communication analysis, time series analysis, network analysis, enrichment analysis, and gene expression analysis were performed to elucidate the potential molecular features.ResultWe constructed a spatial multiomics map of diabetic blood vessels based on multiomics integration, indicating single-cell and spatial landscape of transcriptome and spatial landscape of metabolome. At the same time, the characteristics of cell composition and biological function of calcified regions were obtained by integrating spatial omics and single cell omics. On this basis, our study provides favorable evidence for the cellular fate of smooth muscle cells, which can be transformed into pro-inflammatory chemotactic smooth muscle cells, macrophage-like smooth muscle cells/foam-like smooth muscle cells, and fibroblast/chondroblast smooth muscle cells in the anterior tibial artery of diabetic patients. The smooth muscle cell phenotypic transformation is driven by transcription factors net including KDM5B, DDIT3, etc. In addition, in order to focus on metabolic reprogramming apart from abnormal glucose and lipid metabolism, we constructed a metabolic network of diabetic vascular activation, and found that HNMT and CYP27A1 participate in diabetic vascular metabolic reprogramming by combining public data.ConclusionThis study constructs the spatial gene-metabolism map of the whole anterior tibial artery for the first time and reveals the characteristics of vascular calcification, the phenotypic transformation trend of SMCs, and the transcriptional driving network of SMCs phenotypic transformation of diabetic macrovascular disease. In the perspective of combining the transcriptome and metabolome, the study demonstrates the activated metabolic pathways in diabetic blood vessels and the key genes involved in diabetic metabolic reprogramming.Graphical abstract

An automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Identification of key gene networks controlling organic acid and sugar metabolism during star fruit (Averrhoa carambola) development

The sugar and organic acid content significantly impacts the flavor quality of star fruit, and it undergoes dynamic changes during development. However, the metabolic network and molecular mechanisms governing the formation of sugar and organic acid in star fruit remain unclear. In this study, 23 of 743 components were detected by metabonomic analysis. The highest metabolites contents were organic acids and derivatives. The highest sugar content in the fruit was fructose and glucose, followed by sucrose, which proved that A. carambola is a hexose accumulation type fruit. Genome identification preliminarily screened 141 genes related to glucose metabolism and 67 genes related to acid metabolism. A total of 7,881 unigenes were found in transcriptome data, 6,124 differentially expressed genes were screened, with more up-regulated than down-regulated genes. Transcriptome and metabolome association analysis screened seven core candidate genes related to glucose metabolism and 17 core genes highly related to organic acid pathway, and eight differentially expressed sugar and acid genes were selected for qRT-PCR verification. In addition, 29 bHLHs and eight bZIPs transcription factors were predicted in the glucose metabolism pathway, and 23 MYBs, nine C2H2s transcription factors and one GRAS transcription factor was predicted in the acid metabolism pathway, and transcription factors have both positive and negative regulatory effects on sugar and acid structure genes. This study increased our understanding of A. carambola fruit flavor and provided basic information for further exploring the ornamental and edible values of star fruit.

Unravelling the effects of drying techniques on Porphyra yezoensis: Morphology, rehydration properties, metabolomic profile, and taste formation

This study explored the impact of sun drying (SD), freeze drying (FD), oven drying (OD), and microwave drying (MD) on the morphology, rehydration properties, metabolomic profile, and taste formation of Porphyra yezoensis. FD elicited a brighter colour, smooth surface, porous microstructure, and strong rehydration properties in P. yezoensis, while dramatically maintaining the umami, sweetness, and saltiness. OD and MD decreased structural openness owing to tissue collapse and affected water absorption. Metabolomic analysis revealed 1030 metabolites, among which taste-related compounds, especially free amino acids, nucleotides, organic acids, and their derivatives, were the main biomarkers for distinguishing the different drying methods. Their related metabolic pathways, such as taurine and hypotaurine metabolism; purine metabolism; glyoxylate and dicarboxylate metabolism; and citrate cycle, were the most active. A metabolic pathway network of the main taste compounds was built to provide novel insights into the mechanisms underlying the taste profile changes associated with different drying methods.

Modulation of the Arabidopsis Starch Metabolic Network by the Cytosolic Acetyl-CoA Pathway in the Context of the Diurnal Illumination Cycle

The starch metabolic network was investigated in relation to other metabolic processes by examining a mutant with altered single-gene expression of ATP citrate lyase (ACL), an enzyme responsible for generating cytosolic acetyl-CoA pool from citrate. Previous research has shown that transgenic antisense plants with reduced ACL activity accumulate abnormally enlarged starch granules. In this study, we explored the underlying molecular mechanisms linking cytosolic acetyl-CoA generation and starch metabolism under short-day photoperiods. We performed transcriptome and quantification of starch accumulation in the leaves of wild-type and antisense seedlings with reduced ACL activity. The antisense-ACLA mutant accumulated more starch than the wild type under short-day conditions. Zymogram analyses were conducted to compare the activities of starch-metabolizing enzymes with transcriptomic changes in the seedling. Differential expression between wild-type and antisense-ACLA plants was detected in genes implicated in starch and acetyl-CoA metabolism, and cell wall metabolism. These analyses revealed a strong correlation between the transcript levels of genes responsible for starch synthesis and degradation, reflecting coordinated regulation at the transcriptomic level. Furthermore, our data provide novel insights into the regulatory links between cytosolic acetyl-CoA metabolism and starch metabolic pathways.

A Systems-Level Analysis of 2-[18F]FDG PET in Pigs Reveals Complex Skeletal Metabolism Networks in vivo which are altered with Osteomyelitis

Our understanding of complex tissue interactions at a systems level remains rudimentary, limiting our ability to dissect mechanisms underlying diseases and develop novel therapeutics. Skeletal research has focused on the pathogenicity of obesity and diabetes mellitus on the skeleton; however, it has been recently revealed that the skeleton is not merely an endocrine target but also a secretory organ, modulating systemic energy homeostasis1,2. We recently discovered that different bones within the murine skeleton have a unique glucose metabolism and form a complex metabolic network3. Thus, we hypothesised that different bones within the porcine skeleton would have unique molecular signatures and form a distinct metabolic network, and these networks would be altered by osteomyelitis, an inflammatory condition of the bone. Five healthy mini and three Danish Landrace-Yorkshire pigs (inoculated with S. aureus to induce osteomyelitis) underwent static 2-[18F]FDG PET/CT and were analysed to measure bone and bone marrow adipose tissue (BMAT) glucose metabolism. Analyze was used for image analysis and processing. Bone and bone marrow volumes of interest were segmented in CT images using Hounsfield Unit (HU) > 300 (bone) and HU -200 to 115 (BMAT; Fig1). PET images were used to extract average standard uptake values. Network analysis was performed using Graphia. Osteomyelitis pigs had a lower bone density and increased BMAT volume compared to healthy controls. There was an increased incorporation of 2-[18F]FDG into the bone and BMAT of osteomyelitis pigs. Similar to what has previously been observed in the mouse3 and human4, there was an increased incorporation of 2-[18F]FDG in the axial skeleton compared to the appendicular skeleton. Network analysis indicated a shift in bone metabolic profiles between healthy and osteomyelitis pigs. Please click on the 'PDF' for the full abstract!

The marine environmental microbiome mediates physiological outcomes in host nematodes.

Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown. RESULTS: Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5'-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe-nematodes framework for microbiome mediated effects on host animal fitness.

Microalgal Metabolomes and Recent Biotechnological Advances for Their Industrial Application

In recent decades, microalgae have emerged as new sustainable resources for the production of various bioactive and biochemical compounds. These organisms are photosynthetic, use CO2 as their sole carbon source, and grow rapidly under different environmental conditions. This makes microalgae a promising source of feedstock for many bio-commodities. However, the economic viability for large-scale production through microalgal cells is limited to certain organisms. Recent advances in omics technologies, along with high-throughput approaches, have provided new insights for understanding microalgal metabolites. With the improvement in metabolomic technology, sensitivity for the detection of cellular metabolites has increased, and it has directly enhanced our understanding of cellular metabolism and the corresponding metabolic pathways. Moreover, these metabolic techniques, along with other omics technologies, help us to characterize the changes in the cellular functioning of the different cells under environmental conditions. Metabolomic research on microalgae explores new metabolites and the understanding of their biosynthesis at the metabolic level. In this review, we discuss how these metabolomics techniques are applied to microalgae to study their metabolic networks. Additionally, we also discuss how integrating other tools of systems biology can improve the productivity of microalgal cells, especially for industrially important commodities.

Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks.

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

Metabolic modelling reveals distinct roles of sugars and carboxylic acids in stomatal opening and uncovers unexpected carbon fluxes.

Guard cell metabolism is crucial for stomatal dynamics, but a full understanding of its role is hampered by experimental limitations and the flexible nature of the metabolic network. To tackle this challenge, we constructed a time-resolved stoichiometric model of guard cell metabolism that accounts for energy and osmolyte requirements and which is integrated with the mesophyll. The model resolved distinct roles for starch, sugars, and malate in guard cell metabolism and revealed several unexpected flux patterns in central metabolism. During blue light-mediated stomatal opening, starch breakdown was the most efficient way to generate osmolytes with downregulation of glycolysis allowing starch-derived glucose to accumulate as a cytosolic osmolyte. Maltose couldalso accumulate as a cytosolic osmoticum, although this made the metabolic system marginally less efficient. The metabolic energy for stomatal opening was predicted to be derived independently of starch, using nocturnally accumulated citrate which was metabolised in the tricarboxylic acid cycle to malate to provide mitochondrial reducing power for ATP synthesis. In white light-mediated stomatal opening, malate transferred reducing equivalents from guard cell photosynthesis to mitochondria for ATP production. Depending on the capacity for guard cell photosynthesis, glycolysis showed little flux during the day but was crucial for energy metabolism at night. In summary, our analyses have corroborated recent findings in Arabidopsis guard cell research, resolved conflicting observations by highlighting the flexibility of guard cell metabolism, and proposed new metabolic flux modes for further experimental testing.

Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups.

As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. Bacillus subtilis strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 B. subtilis strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.

Functional Characterization of the Gibberellin (GA) Receptor ScGID1 in Sugarcane.

Sugarcane smut caused by Sporisorium scitamineum represents the most destructive disease in the sugarcane industry, causing host hormone disruption and producing a black whip-like sorus in the apex of the stalk. In this study, the gibberellin metabolic pathway was found to respond to S. scitamineum infection, and the contents of bioactive gibberellins were significantly reduced in the leaves of diseased plants. The gibberellin receptor gene ScGID1 was identified and significantly downregulated. ScGID1 localized in both the nucleus and cytoplasm and had the highest expression level in the leaves. Eight proteins that interact with ScGID1 were screened out using a yeast two-hybrid assay. Novel DELLA proteins named ScGAI1a and ScGA20ox2, key enzymes in GA biosynthesis, were both found to interact with ScGID1 in a gibberellin-independent manner. Transcription factor trapping with a yeast one-hybrid system identified 50 proteins that interacted with the promoter of ScGID1, among which ScS1FA and ScPLATZ inhibited ScGID1 transcription, while ScGDSL promoted transcription. Overexpression of ScGID1 in transgenic Nicotiana benthamiana plants could increase plant height and promote flowering. These results not only contribute to improving our understanding of the metabolic regulatory network of sugarcane gibberellin but also expand our knowledge of the interaction between sugarcane and pathogens.

Evolution of gene networks underlying adaptation to drought stress in the wild tomato Solanum chilense.

Drought stress is a key limitation for plant growth and colonization of arid habitats. We study the evolution of gene expression response to drought stress in a wild tomato, Solanum chilense, naturally occurring in dry habitats in South America. We conduct a transcriptome analysis under standard and drought experimental conditions to identify drought-responsive gene networks and estimate the age of the involved genes. We identify two main regulatory networks corresponding to two typical drought-responsive strategies: cell cycle and fundamental metabolic processes. The metabolic network exhibits a more recent evolutionary origin and a more variable transcriptome response than the cell cycle network (with ancestral origin and higher conservation of the transcriptional response). We also integrate population genomics analyses to reveal positive selection signals acting at the genes of both networks, revealing that genes exhibiting selective sweeps of older age also exhibit greater connectivity in the networks. These findings suggest that adaptive changes first occur at core genes of drought response networks, driving significant network re-wiring, which likely underpins species divergence and further spread into drier habitats. Combining transcriptomics and population genomics approaches, we decipher the timing of gene network evolution for drought stress response in arid habitats.

Engineering Escherichia coli for l-Threonine Hyperproduction Based on Multidimensional Optimization Strategies.

Exploring effective remodeling strategies to further improve the productivity of high-yield strains is the goal of biomanufacturing. However, the lack of insight into host-specific metabolic networks prevents timely identification of useful engineering targets. Here, multidimensional engineering strategies were implemented to optimize the global metabolic network for improving l-threonine production. First, the metabolic bottleneck for l-threonine synthesis was eliminated by synergistic utilization of NADH and an enhanced ATP supply. Carbon fluxes were redistributed into the TCA cycle by rationally regulating the GltA activity. Subsequently, the stress global response regulator UspA was identified to enhance l-threonine production by a transcriptomic analysis. Then, l-threonine productivity was improved by enhancing the host's stress resistance and releasing the inhibitory reaction of glucose utilization. Eventually, the l-threonine yield of THRH16 reached 170.3 g/L and 3.78 g/L/h in a 5 L bioreactor, which is the highest production index reported. This study provides rational guidance for increasing the productivity of other chemicals.

Unravelling the molecular regulation network of carbon metabolism and lipid metabolism during seed development in Akebia trifoliata via integrated multi-omics analysis

Akebia trifoliata is a medicinal plant with high oil content and broad pharmacological effects. To investigate the regulatory mechanisms of key metabolic pathways during seed development, we conducted an integrated multi-omics analysis, including transcriptomics, proteomics, and metabolomics, exploring the dynamic changes in carbon and lipid metabolism. Metabolomics analysis revealded that glucose and sucrose levels decreased, while glycolytic intermediate phosphoenolpyruvate and fatty acids increased with seed development, indicating a shift in carbon flux towards fatty acid synthesis. Integrated transcriptomic and proteomic analyses showed that 70 days after flowering, the expression levels of genes and proteins associated with carbon and fatty acid metabolism were upregulated, suggesting an increased energy demand. Additionally, LEC2, LEC1, WRI1, FUS3, and ABI3 were identified as vital regulators of lipid synthesis. By constructing a multi-omics co-expression network, we identified hub genes such as aroE, GAPDH, KCS, TPS, and hub proteins like PGM, PDH, ENO, PFK, PK, ACCase, SAD, PLC, and OGDH that play critical regulatory roles in seed lipid synthesis. This study provides new ideas for the molecular basis of lipid synthesis in Akebia trifoliata seeds and can facilitate future research on the genetic improvement through molecular-assisted breeding.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease.

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

Formalizing the law of diminishing returns in metabolic networks using an electrical analogy.

The way biological systems respond to changes in parameter values caused by mutations is a key issue in evolution and quantitative genetics, as it affects fundamental aspects such as adaptation, selective neutrality, robustness, optimality, evolutionary equilibria, etc. We address this question using the enzyme-flux relationship in a metabolic network as a model of the genotype-phenotype relationship. The lack of a suitable mathematical tool from biochemical theory to investigate this relationship led us to use an analogy between electrical circuits and metabolic networks with uni-uni reactions. We show that a behaviour of diminishing returns, which is commonly observed at various phenotypic levels, is inevitable, irrespective of the complexity of the system. We also present a possible generalization to metabolic networks with both uni-uni and bi-bi reactions.