Metabolic Networks Research Articles

Cardiovascular disease (CVD) and cancer are the leading causes of morbidity and mortality in the U.S. Diet is a significant risk factor for both CVD and cancer and has been shown to influence survival and treatment response. Importantly, dietary interventions exacerbate chemotherapy-related cardiotoxicities. To effectively integrate dietary interventions into treatment recommendations, it is critical to understand the complex interactions between nutrients and metabolic changes in the heart. We used the Periodic Table of Food Initiative (PTFI) dataset within the American Heart Association Precision Medicine 2024 Data Challenge. Mass spectrometry analysis of molecular compositions from 500 food products was integrated into flux balance analysis using the mammalian network of cardiac metabolism, CardioNet. We developed an algorithm to compare the metabolic efficacy of PTFI food products and their combinations as diets in computational simulations. We simulated physiological health conditions, oncometabolic stress, and anthracycline chemotherapy treatment by integrating proteomics datasets. In total, over 600,000 simulations mimicking diets were conducted. Our analysis revealed that the availability and composition of food sources directly impact cardiac metabolism, depending on a patient’s health status. Simulations of cancer patients showed an overall reduction in metabolic efficacy for most food products and their combinations. Our analysis revealed that cancer patients require distinct food compositions to ensure cardio-metabolic health. In anthracycline treatment simulations, we identified food combinations that impaired cardiac metabolism by (1) decreased energy provision, (2) significant increases in oxidative stress, reflecting increased beta-oxidation of saturated long-chain fatty acids, and (3) rapid reduction in biomass provision. Using network analysis, we identified food pairings that enable the optimization of cardiac metabolic efficacy. These networks are a starting point for further mechanistic studies and clinical validation. Our findings directly impact cancer patients by developing data-driven recommendations for improved food and meal plans during the different disease stages and treatment. Our results highlight the potential of exploring food interactions for improving cardiometabolic health. Computational workflow simulating food and diets to evaluate metabolic efficacy in the heart during cancer and cancer-related treatment.

Alcoholic liver disease (ALD) remains a major global health burden, with over 283million individuals affected by alcohol use disorder (AUD). Early-stage ALD is characterized by gut microbiota dysbiosis, intestinal barrier dysfunction, and endotoxemia. Traditional therapies focusing solely hepatoprotection or lipid reduction often show limited efficacy due to their inability to restore the gut-liver axis balance. This study aimed to evaluate the adjuvant efficacy of a multi-strain probiotic formulation (including Weizmannia coagulans BC99, Lacticaseibacillus rhamnosus LRa05, Bifidobacterium animalis subsp. lactis BLa80, and Weizmannia coagulans BC179) combined with polyene phosphatidylcholine (Essentale®) in improving liver function and metabolic profiles in ALD patients.A randomized, single-blind, placebo-controlled clinical trial was conducted in 42 ALD patients. Participants received either Essentale® plus probiotics or Essentale® plus placebo for 30 days. Liver function tests, serum lipids, fecal microbiota (16S rRNA sequencing), and fecal metabolites (GC-MS) were assessed at baseline, day 15, and day 30.Compared to placebo, the probiotic group showed significant reductions in ALT, AST, GGT, and TG, along with increased HDL-C levels. Probiotics promoted the enrichment of Bifidobacterium, Faecalibacterium, and Akkermansia, and improved microbial diversity. Metabolomic profiling revealed upregulation of anti-inflammatory and antioxidant metabolites (e.g., EGCG, S-methylglutathione) and downregulation of pro-inflammatory lipotoxic intermediates. Spearman analysis confirmed correlations between key bacterial genera and liver/metabolic biomarkers.Multi-strain probiotics effectively modulate the gut-liver axis by reshaping gut microbiota and metabolic networks, thereby enhancing the therapeutic efficacy of conventional hepatoprotective drugs in ALD. These findings support their clinical potential as a safe and complementary strategy for managing ALD.

Exploring the metabolic characteristics of different plant organs and tissues at a spatial level can help us to better understand the functional mechanisms of biological tissues and cells. Mass spectrometry imaging (MSI) provides a reliable tool for this purpose. However, its application for high-resolution metabolic mapping across various plant organs remains a significant challenge due to the intrinsic biological properties of plant samples and unfavorable analysis conditions. This study aimed to develop a novel MSI platform that can expand more diverse plant samples in spatial metabolomics research and enhance the detection efficiency of plant metabolites. The platform (AMG-LDI-MSI) based on an Au nanoparticles-loaded MoS2 and doped graphene oxide (Au@MoS2/GO) flexible film substrate combined with laser desorption/ionization (LDI)-MSI was established to enhance the detection and visualization of metabolites in various plant tissues. It has a non-sectioning, matrix-free, dual-ion mode imaging strategy, enabling high-throughput detection of metabolites and high-resolution molecular imaging within a micrometer scale. The Au@MoS2/GO as a new substrate can offer high sensitivity and molecular coverage for diverse plant metabolites (10 classes) under the positive and negative ion modes. Moreover, the AMG-LDI-MSI platform overcomes the limitations of plant tissues (e.g., fragile leaf, water-rich fruit, or lignified roots) for in situ imaging. We successfully applied the platform to map the metabolite spatial dynamics in different types of fresh tissues (rhizome, main root, branch root, fruit, leaf, and root nodule) from medicinal plants, obtained the high-quality mass spectral imaging data, and demonstrated the universality and applicability of the platform to multiple plant tissues. These results demonstrate the significant advantages of enhancing the detection of multiple tissue metabolites in plants and their high-resolution imaging. It has overcome the limitations of previously reported MSI methods, suggesting that it could become a widely used tool for deciphering metabolic networks in plant biology.

Unraveling Qu-aroma variation between inner and outer layers of medium-temperature Daqu: A multi-omics and sensory approach.

Genome-wide identification of cytochrome c oxidase genes in cotton and functional characterization of GhCOX11 in drought and cold stress.

Light-specific effects of myo-inositol on carbohydrate partitioning and metabolic networks in Chinese kale (Brassica oleracea) sprouts

Metabolic network remodeling and AI-driven precision diagnostics in geriatric Parkinson's disease: Advances in multimodal imaging.

Algal nutraceuticals have emerged as the valuable bioresources due to their various chemical compositions and potential health benefits. Algae contain many bioactive compounds, including polyphenols, polysaccharides, omega-3 fatty acids, pigments, and vitamins, which are vital for the various biological processes in the human body. Understanding these complex metabolites is essential for their application in functional foods, dietary supplements, and pharmaceuticals. In this context, metabolomics provides a comprehensive approach for analyzing algal metabolic profiles and their nutritional and medicinal values. This review explores the role of metabolomics in the evaluation and development of algal nutraceuticals. It focuses particularly on the identification and characterization of small-molecule metabolites in algae, offering insights into their functional properties and bioactivities. This review also discusses the integration of metabolomics with other omics technologies to achieve a holistic understanding of the metabolism of algae. Metabolomic studies have successfully explored a wide range of bioactive compounds in algae with antioxidant, anti-inflammatory, anticancer, antibacterial, and neuroprotective activities. Techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have advanced the detection and quantification of metabolites with high sensitivity and resolution, respectively. Additionally, metabolomics aids to determine the quality biomarkers and the assessment of algal nutritional content. Integrating metabolomics with genomics, proteomics, and transcriptomics will further elucidate the metabolic pathways and regulatory networks in algae. This review highlights the critical role of metabolomics in maximizing the utilization of algae for health benefits.

Characterization of pivotal metabolites influencing the production of milk components in dairy goats.

Using control bias to identify initial targets for bioproduction improvement.

Background: Graph neural networks’ (GNNs) explainability, especially the explanation of edges and interactions among vertices in GNNs, is demanding mainly owing to dynamics and groupings between vertices. The existing graph explainability methods ignore the analysis of the following tasks weights over subgraphs but instead analyze solely sample-level explainability. Such sample-level explainability decreases their generalizability since it directly searches the explaining behaviour in the input dataset. Objective: In this study, we come up with a novel Orbit-based GNN explainer (OExplainer), which integrates both sample-level and method-level approaches over a predetermined set of subgraphs. As part of such analysis of subgraphs, our goal is to interpret graphs more comprehensively and intelligibly while providing each vertex’s explainability score for a particular graph instance. Methods: Our OExplainer decomposes the following graph neural network weights into explaining subgraph bases while identifying and characterizing particular predictions. By such characterization, we can carefully and accurately interpret the predetermined graph orbit’s role in vertex representation determination. In this characterization, we can also clarify the method’s behaviour generally for the whole input dataset. Moreover, we come up with novel vertex-specific scores in our subgraphbased approach over nonisomorphic graphlets. Such vertex-specific score encourages sample-level vertex improvement, and such improvement is related to the graph neural network’s vertex classification task. Results: Our experiments over simulated datasets confirm the importance and criticality of method weights in vertex classification explanation. In this case, method weight decomposition also has criticality. Our detailed experiments over multiple real protein-protein interaction datasets and metabolic interaction networks also exhibit enhanced performance in vertex classification. Conclusion: In both simulated and biological protein-protein interaction datasets, our approach outperforms the competing explanation approaches

Human immunodeficiency virus (HIV) infection alters gut microbiota composition and function, but the impact of geography and antiretroviral therapy remains unclear. Here we determined gut microbiome alterations linked to HIV infection and antiretroviral treatment in 327 individuals with HIV and 260 control participants in cohorts from Uganda, Botswana and the USA via faecal metagenomics. We found that while HIV-associated taxonomic differences were mostly site specific, changes in microbial functional pathways were broadly consistent across the cohorts and exacerbated in individuals with acquired immunodeficiency syndrome. Microbiome perturbations associated with antiretroviral medications were also geography dependent. In Botswana and Uganda, use of the non-nucleoside reverse transcriptase inhibitor efavirenz was linked to depletion of Prevotella, disruption of interspecies metabolic networks, exacerbation of systemic inflammation and atherosclerosis. Efavirenz-associated Prevotella depletion may occur through cross-inhibition of prokaryotic reverse transcriptases involved in antiphage defences, as shown by computational and in vitro experiments. These observations could inform future geography-specific and microbiome-guided therapy.

Machine learning classification of steatogenic compounds using toxicogenomics profiles.

A novel preparation method for black rice wine (beer, Huangjiu and sweet wine) and its association with a core nutrient-metabolite network.

Characterization of the flavor profile of Huangjiu brewed with Polygonatum sibiricum and Broomcorn millet using HS-SPME-GC×GC-TOF-MS, GC-IMS, intelligent sensory and molecular docking approaches.

Chronic intracellular bacterial infections persist within host cells by evading immune clearance, imposing prolonged metabolic stress on the host. In response, the immune system undergoes metabolic reprogramming to sustain prolonged defense. A key feature of this reprogramming is the shift from oxidative phosphorylation (OXPHOS) to aerobic glycolysis, which enhances pro-inflammatory and antimicrobial responses. Concurrently, fatty acid and amino acid catabolism provide additional metabolic support. Beyond shaping immune function, these metabolic shifts also influence the trajectory of infection by altering the host-pathogen metabolic interplay. In this review, we focus primarily on Mycobacterium tuberculosis (Mtb) infection and integrate quantitative flux analyses of carbon and nitrogen distribution, emphasizing how these metabolic changes connect to epigenetic regulation. We also explore metabolic reprogramming in five representative immune cell types-comprising both innate and adaptive immune cells-to elucidate how their distinct metabolic profiles influence host defense mechanisms and disease progression. Building on these foundations, we propose an innovative metabolic competition model between host and pathogen, offering new insights into the intricate interplay of metabolic networks in chronic intracellular infections.

Elucidating microbial succession dynamics and flavor metabolite formation in korean style spicy cabbage fermentation: Integration of flavoromics, amplicon sequencing, and metagenomics.

Integrative insights into benzo[a]pyrene degradation mechanisms by Aspergillus fumigatus Z5: Spectroscopic, transcriptomic, and computational biological analyses.

Different nitrogen sources influence docosahexaenoic acid biosynthesis in marine heterotrophic protist Aurantiochytrium sp. PKU#SW8 by modulating central metabolic pathways.

Understanding and targeting erythroid cell metabolism.