Fidele Tugizimana

Cancer remains a major global health challenge, and despite advances in chemotherapy, radiotherapy, immunotherapy, and targeted therapy, treatment outcomes are still limited by systemic toxicity, poor tumor selectivity, multidrug resistance, and inadequate efficacy against advanced disease. Nanotechnology has emerged as a promising strategy to address these limitations by improving drug delivery, imaging, radiosensitization, and immunomodulation. Among nanomaterials, phytochemical-mediated metal and metal oxide nanoparticles have attracted increasing interest as sustainable and potentially biocompatible alternatives to conventionally synthesized systems. However, their development remains constrained by poor reproducibility, lack of mechanistic understanding, and limited clinical translational progression, mainly because of the chemical complexity of plant extracts. In this review, we critically examine the role of metal nanoparticles in oncology, and we compare their anticancer mechanisms. We further evaluate phytochemical-mediated nanoparticle synthesis by discussing how plant-derived metabolites influence reduction, nucleation, capping, and the resulting physicochemical properties that influence biological activity. We argue that metabolomics offers a critical framework for overcoming one of the major limitations of green nanotechnology by enabling the comprehensive chemical characterization of plant extracts, identification of metabolites associated with nanoparticle formation, and correlation of metabolite profiles with nanoparticle physicochemical properties and anticancer activity. The integration of metabolomics with nanoparticle characterization could contribute to the advancement of green synthesis from empirical extract-based fabrication, toward a more predictive, standardized, and mechanistically informed nanoparticle design. Finally, we discuss the major barriers to clinical translation, including batch-to-batch variability, bio-corona effects, biodistribution, long-term toxicity, and regulatory compliance. Overall, the integration of metabolomics with phytochemical-mediated nanoparticle synthesis represents an emerging strategy for improving chemical characterization, mechanistic understanding, and reproducibility, with potential future relevance to the development of more rationally designed nanomedicines for precision oncology.

Introduction : Plant metabolomes encode biochemical information underlying species diversity, ecological adaptation, and pharmacological potential. Within the genus Plectranthus , diterpenoids are widely regarded as both the primary bioactive constituents and species-specific chemical signatures; however, the extent to which other bioactive metabolite classes contribute to species-specific chemical signatures remains poorly understood. Objective : To characterize and compare the leaf metabolomes of Plectranthus hadiensis and Plectranthus ambiguus , two medicinally important species used in South African ethnopharmacology. Methods: Methanolic leaf extracts of P. hadiensis and P. ambiguus were analysed usinguntargeted LC–MS/MS platform. The spectral data were mined and annotated using feature-based molecular networking followed by comparative metabolomics and GNPS library matching, MS2Query; and MS2LDA 2.0-enabled structure and substructure-level annotations. Results : The findings revealed extensive metabolic diversity beyond diterpenoids. While the prenol lipid chemical class, consisting of diterpenoids, was confirmed as a major discriminatory chemical class, flavonoids emerged as key drivers of species-level chemical differentiation. Distinct structural patterns were observed, with glycosylated flavones predominating in P. hadiensis , whereas methylated flavones and glycosylated flavonols were enriched in P. ambiguus . These compositional differences reflect underlying variation in flavonoid biosynthetic modifications, suggesting divergent metabolic regulation between species. Conclusions : Computational metabolomics revealed that flavonoid structural diversity constitutes a previously underappreciated layer of chemical specialization in Plectranthus plants and contributes substantially to species-specific chemical identity beyond the emphasized diterpenoids. By moving beyond targeted phytochemical screening, this work establishes a scalable framework for resolving species-specific metabolic architectures in medicinal plants. These findings have implications for chemotaxonomy, natural product discovery, and the rational exploration of plant-derived bioactives, contributing to a deeper understanding of the molecular basis of ethnomedicinal efficacy.

Introduction: Microbial biostimulants comprise beneficial microorganisms, such as Bacillus and Trichoderma , that enhance plant growth, nutrient uptake, and stress tolerance through biological mechanisms. The combined use of Bacillus and Trichoderma is increasingly recognised as an important strategy for enhanced crop productivity and sustainable agriculture. However, the compatibility of microorganisms is constrained by their distinct ecological niche and metabolic activities, which may limit the stability of consortia. Objectives: To characterize the biochemical landscape of Trichoderma strains and evaluate their metabolic compatibility with the Bacillus consortium. Methods: Metabolites were extracted from each Trichoderma strain using LC–MS grade quality methanol. The methanolic extracts were analysed on a UPLC–MS system. Principal component analysis and orthogonal partial least squares discriminant analysis were performed to assess sample clustering and overall metabolic variation among Trichoderma strains. Feature-based molecular networking was used for metabolite annotation of Trichoderma ’s chemical space. Functional analysis was used to map annotated metabolites in the metabolic pathways of Trichoderma strains. Results: The findings revealed distinct metabolic markers, including primary and secondary metabolites. The sphingolipid metabolism was significantly impacted, among others. The in vitro studies revealed a positive interaction with no evidence of antagonism between Trichoderma strains and the Bacillus consortium. Conclusion: The major constraint of biostimulant application is the lack of standardisation in product composition. Therefore, these findings provide insights into the molecular composition and functional dynamics of microbial networks. Such knowledge is crucial and essential for the development of cutting-edge innovations in the field of biostimulants.

The application of microbial consortia as biostimulant formulations for improving plant growth and productivity has gained momentum as a promising approach to agricultural sustainability. However, the underlying biochemical and molecular mechanisms that govern the modes of action of microbial biostimulants, as well as microbial interactions in a consortium, remain an area of ongoing research. This knowledge gap limits the design and implementation of microbial biostimulants for sustainable agriculture. Thus, this computational metabolomics study aimed to comprehensively characterize the metabolomes of three microbial consortia formulated from different combinations of Bacillus licheniformis , B. laterosporus, B. amyloliquefaciens, B. subtilis, B. pumilus, and B. megaterium. Three consortia were cultured in liquid media, and metabolites were extracted from the extracellular and intracellular media at different stages of bacterial growth. The samples were analyzed using a liquid chromatography-mass spectrometry (LC–MS/MS) system. Molecular networking and machine learning methods were employed to mine and interpret the acquired spectral data. The results revealed differential metabolite profiles that define the chemical space of the 3 microbial consortia. The annotated metabolome was characterized by diverse molecular families, including amino acids and peptides, antimicrobials, phytohormones, lipids, organic acids, carbohydrates, and pyrimidines. Each consortium was characterized by differential metabolite profiles at different growth stages. Thus, the findings of this study provide valuable insights into the differential metabolomic landscapes of Bacillus consortia, charting a chemical lexicon of Bacillus consortium metabolism with potential relevance to microbial interactions and plant-associated functions. These findings provide a biochemical framework that may inform the rational design and subsequent experimental validation of microbial biostimulant formulations. As such, this work supports the realization of the United Nations Sustainable Development Goals, particularly zero hunger (SDG 2) and climate action (SDG 13).

Microbial biostimulants are known to promote plant growth, nutrient use efficiency, increase yield and nutritional value, and enhance plant resilience against environmental stresses. However, the exact mechanisms of action underlying the effects of microbial biostimulants on plants under field conditions are still not fully understood. Hence, a field trial study was conducted, where untargeted metabolomics was applied to interrogate the metabolism of maize plants treated with a microbial biostimulant. Complement to metabolomics analyses, agronomic traits assessments were performed and showed improved plant biomass, root growth, increased yield and protein content in the cob and enhanced antioxidant capacity. Metabolomics analyses revealed biostimulant-induced metabolic alterations underlying the observed agronomic traits which included increased levels of glycosylated flavonoids, sucrose and oxylipins, and decreased levels of hydroxycinnamic acid and galactolipids, at the transition stage of maize growth. This metabolomic reprogramming is associated with enhanced antioxidant capacity, membrane remodeling, cell wall strengthening, defense potentiation, improved plant growth, grain yield and quality. Insights derived from this field trial provide a foundation for the rational design of novel biostimulant formulations and application strategies. This knowledge-driven approach supports the systematic integration of biostimulants into conventional agronomic practices.

Livestock production in Africa occurs across highly heterogeneous agroecological and management environments, ranging from extensive pastoral and mixed crop–livestock systems to intensive enterprises. These systems are characterized by seasonal and spatial variation in feed resources, reliance on locally available forage and agricultural by-products, climatic stress, endemic diseases, and the use of indigenous and locally adapted breeds. Such conditions create distinctive microbiome–host interactions that remain poorly represented in global livestock omics research. Although the gut microbiome is central to nutrient utilization, immune function, metabolic homeostasis, and resilience, the functional mechanisms linking microbial communities, diet, host physiology, and productivity in African livestock remain insufficiently characterized. African systems are particularly underrepresented in integrated microbiome–metabolomics datasets, longitudinal studies, and artificial intelligence (AI)-enabled predictive models, limiting the development of context-specific precision nutrition strategies. This review examines the integration of metabolomics and AI with microbiome and host data to advance precision livestock nutrition within an African and One Health context. It identifies both substantial constraints and strategic opportunities. Limited research infrastructure, high-quality regional datasets, computational capacity, and specialized expertise remain major barriers. Conversely, Africa’s diversity of livestock breeds, feed resources, agroecological conditions, and naturally occurring resilience phenotypes provides an important opportunity to identify microbiome–metabolite signatures associated with feed efficiency, disease resilience, climate adaptation, and product quality. Emerging metabolomics and computational capacity, particularly in South Africa, could support regional research networks and continental data infrastructures. Furthermore, the review proposes an Africa-specific approach that develops locally grounded, scalable, and resource-sensitive precision nutrition strategies, strengthening antimicrobial stewardship, animal health, food safety, climate resilience, sustainable livestock production, and broader One Health objectives.

Cancer remains a major global health challenge, and despite advances in chemotherapy, radiotherapy, immunotherapy, and targeted therapy, treatment outcomes are still limited by systemic toxicity, poor tumor selectivity, multidrug resistance, and inadequate efficacy against advanced disease. Nanotechnology has emerged as a promising strategy to address these limitations by improving drug delivery, imaging, radiosensitization, and immunomodulation. Among nanomaterials, phytochemical-mediated metal nanoparticles have attracted increasing interest as sustainable and potentially biocompatible alternatives to conventionally synthesized systems. However, their development remains constrained by poor reproducibility, lack of mechanistic understanding, and limited clinical translational progression, mainly because of the chemical complexity of plant extracts. In this review, we critically examine the role of metal nanoparticles in oncology, with emphasis on gold, silver, and zinc oxide, and we compare their anticancer mechanisms. We further evaluate phytochemical-mediated nanoparticle synthesis by discussing how plant-derived metabolites influence reduction, nucleation, capping, and the resulting physicochemical properties that influence biological activity. We argue that metabolomics offers a critical framework for overcoming one of the major limitations of green nanotechnology by enabling the comprehensive chemical characterization of plant extracts, identification of metabolites associated with nanoparticle formation, and correlation of metabolite profiles with nanoparticle physicochemical properties and anticancer activity. The integration of metabolomics with nanoparticle characterization could contribute to the advancement of green synthesis from empirical extract-based fabrication, toward a more predictive, standardized, and mechanistically informed nanoparticle design. Finally, we discuss the major barriers to clinical translation, including batch-to-batch variability, bio-corona effects, biodistribution, long-term toxicity, and regulatory compliance. Overall, metabolomics-guided phytochemical holds considerable promise for advancing reproducible and clinically relevant metal nanoparticles.

Cassava and cocoyam are major dietary staples in sub-Saharan Africa, commonly processed by natural fermentation before consumption. Although fermentation reduces antinutritional compounds and improves food quality, its molecular effects remain poorly characterized. We used untargeted mass spectrometry-based metabolomics with a computational annotation pipeline to compare fermentation-induced molecular remodeling in the two tubers, which showed distinct responses. In cassava, 718 of 773 significant features (92.9%) were depleted, indicating a predominantly catabolic process. In cocoyam, the response was more balanced, with 385 of 1,013 features (38.0%) enriched, including di- and tripeptides consistent with proteolytic processing. Class analysis, molecular networking, and pathway enrichment revealed tuber-specific signatures: cassava was dominated by purine metabolism, whereas cocoyam showed stronger enrichment of amino acid pathways. Cyanogenic glycoside-related features were depleted, consistent with detoxification. Biotransformation prediction also suggested putative fermentation products absent from current databases, highlighting the under-characterized chemistry of these tubers.

Microbial metabolites play a critical role in regulating ecosystems, including the human body and its microbiota. However, understanding the physiologically relevant role of these molecules, especially through liquid chromatography tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics, poses significant challenges and often requires manual parsing of a large amount of literature, databases, and webpages. To address this gap, we established the Collaborative Microbial Metabolite Center knowledgebase (CMMC-KB), a platform that fosters collaborative efforts within the scientific community to curate knowledge about microbial metabolites. The CMMC-KB aims to collect comprehensive information about microbial molecules originating from microbial biosynthesis, drug metabolism, exposure-related molecules, food, host-derived molecules, and, whenever available, their known activities. Molecules from other sources, including host-produced, dietary, and pharmaceutical compounds, are also included. By enabling direct integration of this knowledgebase with downstream analytical tools, including molecular networking, we can deepen insights into microbiota and their metabolites, ultimately advancing our understanding of microbial ecosystems.