The Tugizimana Research Group is based in the Department of Biochemistry at the University of Johannesburg, South Africa. Led by Fidele Tugizimana, this dynamic, metabolomics-focused lab comprises over 15 members, including postdoctoral research fellows and postgraduates both BSc honours, master’s, and doctoral candidates. The lab utilizes cutting-edge technology to advance the field of metabolomics and its applications across various domains.

The Tugizimana Lab’s research focuses on understanding crop plant-environment interactions, discovering health-beneficial natural products from African medicinal plants, and elucidating the modes of action of plant biostimulants for innovative and sustainable farming practices. The Tugizimana Lab fosters an environment of continuous growth and development. As we live in the Digital Age, we embrace and leverage disruptive technological advancements and artificial intelligence (AI) ecosystems. The spectrum of our core values encompasses agility, authenticity, integrity, creativity, ethics, inclusivity, adaptability, excellence, hard work, flexibility, expertness, innovation, helpfulness, harmony, diversity, and wisdom.

Vision Statement

The Tugizimana Metabolomics Group envisions driving and pioneering innovation and sustainable solutions in agriculture and healthcare through cutting-edge science. We strive to advance knowledge and practical applications, enhancing crop resilience, human health, and environmental sustainability. Our vision aligns with the UN Sustainable Development Goals, the Africa Agenda 2063, and South Africa’s National Development Plan, addressing critical global, regional, and local challenges through innovative research.

Mission Statement

Our mission is to conduct transformative research in metabolomics, employing advanced technologies to explore plant-environment interactions, discover natural health products, and develop innovative biostimulant formulations.

We are dedicated to the development and exploration of emerging computational strategies to mine and interpret metabolomics spectral data, the application of machine learning and deep learning methods for analysing metabolomics big data, and the creation of methodologies and protocols for data mining and interpretation. We foster a collaborative research environment to nurture emerging scientists and translate our findings into practical solutions for sustainable agriculture and improved health outcomes.

We combine a multidisciplinary approach at the interface of mass spectrometry-based (computational) metabolomics, natural product research, plant biostimulants, big data analytics, and biochemistry, as well as omics integration. The main research areas include:

Research Area I

Biostimulant-plant
interactions

Plant–Biostimulant Interactions: elucidation of modes of action of biostimulants.

The research pillar of biostimulant-plant interactions is pivotal for elucidating the molecular mechanisms governing the modes of action of biostimulants. This research is significant as it lays the groundwork for designing innovative biostimulant formulations and application programs, advancing sustainable agriculture. We look at both non-microbial and microbial biostimulants, and biostimulants 2.0. The pillar encompasses two main research focuses. The first focus is on studying microbial biostimulant formulations by characterizing the metabolomic atlas of microorganisms. This helps decode the chemical lexicon used by microorganisms in their intercommunications with plants and other microorganisms. The second focus investigates the biochemistry defining plant responses to biostimulant applications under varying conditions, covering both below- and aboveground ecosystems. By applying computational metabolomics, with big data analytics, machine learning, and deep learning tools and methodologies, and integration with other omics layers, the research generates actionable insights explaining the interactions between biostimulants and crop plants. These models demonstrate growth promotion and enhanced defenses in plants. Furthermore, these research focuses explore the universe of plant microbiome. Thus, our work in this research pillar, feeds impactfully into both the biostimulant and agricultural industries, contributing significantly to the realization of sustainable and precision agriculture. Ultimately, this work supports improved local and global food security.

Recent contributions under this research pillar:

Plant–Biostimulant Interactions: elucidation of modes of action of biostimulants.

The research pillar of biostimulant-plant interactions is pivotal for elucidating the molecular mechanisms governing the modes of action of biostimulants. This research is significant as it lays the groundwork for designing innovative biostimulant formulations and application programs, advancing sustainable agriculture. We look at both non-microbial and microbial biostimulants, and biostimulants 2.0. The pillar encompasses two main research focuses. The first focus is on studying microbial biostimulant formulations by characterizing the metabolomic atlas of microorganisms. This helps decode the chemical lexicon used by microorganisms in their intercommunications with plants and other microorganisms. The second focus investigates the biochemistry defining plant responses to biostimulant applications under varying conditions, covering both below- and aboveground ecosystems. By applying computational metabolomics, with big data analytics, machine learning, and deep learning tools and methodologies, and integration with other omics layers, the research generates actionable insights explaining the interactions between biostimulants and crop plants. These models demonstrate growth promotion and enhanced defenses in plants. Furthermore, these research focuses explore the universe of plant microbiome. Thus, our work in this research pillar, feeds impactfully into both the biostimulant and agricultural industries, contributing significantly to the realization of sustainable and precision agriculture. Ultimately, this work supports improved local and global food security.

Recent contributions under this research pillar:

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Research Area II

Natural product research for drug discovery

Medicinal Plants: natural product research for drug discovery

The research pillar of natural product research for drug discovery focuses on exploring medicinal plants and microorganisms for potential "hit" compounds with significant biological activity. These compounds, often hidden within biochemically complex extracts, are akin to finding a "needle in a haystack." Utilizing state-of-the-art mass spectrometry platforms, advanced computational and bioinformatics strategies, machine learning and deep learning-based workflows, this research characterizes the metabolomic landscapes of medicinal plants and microorganisms. Integrating these approaches with computational methods such as network pharmacology and molecular docking enables the screening of potential compounds before conducting in vitro cell culture-based assays or chemical modifications, thereby accelerating the drug discovery process. Additionally, using mass spectrometry and emerging computational strategies, this research investigates the connection between biosynthetic gene clusters and the small molecule metabolites they encode. This approach is central to discovering and characterizing new metabolic pathways with ecological and pharmacological potential, significantly advancing drug discovery.

Recent contributions under this research pillar:

Medicinal Plants: natural product research for drug discovery

The research pillar of natural product research for drug discovery focuses on exploring medicinal plants and microorganisms for potential "hit" compounds with significant biological activity. These compounds, often hidden within biochemically complex extracts, are akin to finding a "needle in a haystack." Utilizing state-of-the-art mass spectrometry platforms, advanced computational and bioinformatics strategies, machine learning and deep learning-based workflows, this research characterizes the metabolomic landscapes of medicinal plants and microorganisms. Integrating these approaches with computational methods such as network pharmacology and molecular docking enables the screening of potential compounds before conducting in vitro cell culture-based assays or chemical modifications, thereby accelerating the drug discovery process. Additionally, using mass spectrometry and emerging computational strategies, this research investigates the connection between biosynthetic gene clusters and the small molecule metabolites they encode. This approach is central to discovering and characterizing new metabolic pathways with ecological and pharmacological potential, significantly advancing drug discovery.

Recent contributions under this research pillar:

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Research Area III

Methodology and workflows in metabolomics

Metabolomics Workflow Methodologies and Computational Metabolomics

Our research pillar of methodology and workflows in metabolomics is dedicated to advancing this continuously evolving omics science. We address various bottlenecks in metabolomics workflows through community-driven efforts, innovatively designing strategies to maximize biological insights from metabolomics studies. We contribute to the development and exploration of methodologies, strategies, and workflows, with a primary focus on data acquisition, data mining, and interpretation. For data acquisition, we concentrate on mass spectrometry systems, developing advanced methods to enhance metabolome coverage. In data mining and interpretation, we utilize and explore machine learning and deep learning algorithms for comprehensive data analysis. We also contribute to the development of computational strategies for mining, interpreting, and visualizing spectral data.

Recent contributions under this research pillar:

Metabolomics Workflow Methodologies and Computational Metabolomics

Our research pillar of methodology and workflows in metabolomics is dedicated to advancing this continuously evolving omics science. We address various bottlenecks in metabolomics workflows through community-driven efforts, innovatively designing strategies to maximize biological insights from metabolomics studies. We contribute to the development and exploration of methodologies, strategies, and workflows, with a primary focus on data acquisition, data mining, and interpretation. For data acquisition, we concentrate on mass spectrometry systems, developing advanced methods to enhance metabolome coverage. In data mining and interpretation, we utilize and explore machine learning and deep learning algorithms for comprehensive data analysis. We also contribute to the development of computational strategies for mining, interpreting, and visualizing spectral data.

Recent contributions under this research pillar:

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Fundings

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