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

Multiomics analysis of microbial succession and flavor formation mechanism during the fermentation process of Maotai-flavour Baijiu

Metagenomics analysis reveals flavors formation and AFB1 metabolic network of doubanjiang-meju microbiota in rotary drum fermenter of Unsteady Temperature Field

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.

ABSTRACTCurrent challenges in controlling phytopathogenic bacteria lie in widespread chemical resistance, biosafety concerns, and the scarcity of novel biomacromolecule targets. While transposable elements have emerged as critical drivers of genetic variability and virulence in plant pathogens, their potential as druggable targets remains unexplored. Here, we report the first discovery of ISXoo15 transposase in Xanthomonas oryzae pv. oryzae (Xoo) as the bactericidal receptor for J9, a pyrimidine‐substituted pleuromutilin derivative. In vitro assays demonstrate J9's superior anti‐Xoo activity, with an EC50 of 0.12 mg/L—significantly lower than commercial agents thiodiazole copper (86.39 mg/L) and zinc thiazole (26.15 mg/L). In vivo pot trials reveal enhanced curative and protective efficacy of J9 against rice bacterial leaf blight compared to these metal‐based controls. A photoaffinity probe, P‐J9, is synthesised and coupled with activity‐based protein profiling to unequivocally identify ISXoo15 transposase (encoded by PXO_03433) as J9's specific target. Reverse transcription‐quantitative PCR confirmed significant downregulation of PXO_03433 expression in J9‐treated Xoo. Physiological and virulence‐related functional analyses of a homologous recombination‐mediated PXO_03433‐knockout strain (ΔPXO_03433) showed markedly attenuated virulence and impaired pathogenicity. Conversely, PXO_03433‐complemented strain CΔPXO_03433 possessed substantial restoration of pathogenicity‐related traits. Proteomic profiling revealed significant downregulation of pathways associated with DNA repair, recombination and binding proteins in both J9‐treated and mutant strains. ISXoo15 transposase may serve as a key regulator in enabling the homeostasis of the DNA metabolic network in the bacteria. This study provides pioneering evidence for targeting bacterial transposases as a novel antibacterial strategy, establishing a foundation for effective management of phytopathogenic bacteria.

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.

Application of a thiophene-based conjugated polymer in the photocatalytic eradication of multidrug-resistant bacteria: Synergistic inactivation of MRSA via carboxyl capture and multi-pathway regulation.

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.

Linking soil organic carbon mineralization to microbial interactions during alpine grassland degradation.

Metabolic network divergence: polyamine and ethylene biosynthesis dynamics in Arabidopsis thaliana and Solanum lycopersicum

Integrated multi-omics unravels bioactive metabolic networks during red chicory heading.