Active Cells, Biochemistry & Physiology Chemistry

Systems Lipidomics tools and resources for biomedical research; LIPID MAPS.

In plain English

AI plain-English summary

Fats in human tissues are constantly shifting in response to diet, infection, drug treatment, and illness, and this project will build the databases and software tools needed to track those changes systematically. The problem is that scientists can now measure thousands of different fat molecules in a single blood sample using mass spectrometry, but they lack the curated databases and computational tools to connect those fat measurements to the genes, enzymes, and proteins that produce and transform them. Without high-quality expert-curated resources, researchers risk generating poor-quality data and wasting time and money. This project establishes a new partnership to reconfigure the existing LIPID MAPS Structure Database, linking each lipid entry to reaction, protein, and gene network information from collaborating resources. New tools will allow researchers to integrate fat data with other molecular data—such as gene activity readouts—to predict the regulatory networks controlling lipid metabolism. If successful, this infrastructure will support both fundamental biomedical research and applied clinical diagnostics, biomarker discovery, and precision medicine. The work is primarily about building research infrastructure rather than delivering immediate practical applications, but it will enable other scientists to ask—and answer—questions about how fats respond to environment, lifestyle, and treatment.

View original technical description
Lipids (fats) represent the majority of metabolites in human tissues. They are ubiquitous molecules that play essential roles in disease, as well in the related areas of vaccine development, pharmaceuticals and nutrition. They are metabolically transformed by enzymes/proteins, in turn encoded by genes. Dynamic changes in lipids reflect both genetic and environmental impacts and their accurate analysis is of major interest for clinical diagnostics, biomarker discovery and precision medicine. This is because unlike genes, lipids respond to environment, lifestyle, illness, infection, drug treatment and other challenges. The analysis of lipids at scale using mass spectrometry (lipidomics), is rapidly expanding in biomedical research. Along with this, there is a growing need to interrogate lipidomics in combination with complex datasets from other omic domains, for example, transcriptomics and proteomics. Termed Systems Lipidomics, the aim is to bring together multidimensional data to develop a holistic view of the system. This type of analysis brings enormous challenges. Specifically, there is first a need for highly-curated databases, that bring together lipids with their respective reactions/enzymes/proteins/genes, and following this, appropriate informatics tools are needed to rigorously exploit the large datasets generated from both untargeted and targeted mass spectrometry and then to combine it with multidimensional omic data. Systems Lipidomics is in its infancy, however without high quality expert curated databases, poor quality data will be generated, leading to significant wastage of time and resources. To address this, we will establish a new partnership, which will focus on generating tools and resources to support this new area. A leadership team will be established that includes basic and applied biomedical and biochemical researchers and IT specialists, based in Cardiff University, Babraham Institute, Swansea University, and University of Edinburgh. This will be supported by a wider group of global collaborators, and an industry project partner, Cayman Chemical. The infrastructure will be hosted within the long established LIPID MAPS platform, which has led the field in lipid nomenclature and classification since 2003. There are two primary objectives. (i) Provide systems biology resources and new databases for the lipidomics research community, and (ii) Expand lipid structure curation, classification/nomenclature, data sharing and training activities. These will be achieved through re-configuring our flagship database LIPID MAPS Structure Database (LMSD) with data from other collaborating resources (Rhea, Reactome) and a community biocuration project that has recently been initiated with WikiPathways and ELIXIR. This will draw in reaction, protein, and gene network information to directly link lipid structure entries, backed up by expert curation. New tools will expose the database information for data analysis/reuse, access to pathway and network information, and later, integration of other omics datasets (e.g., transcriptomics) with lipidomics for prediction of gene/protein regulatory networks involved in lipid metabolism. The new partnership will directly support the emerging area of Systems Lipidomics as applied to both fundamental and applied biomedical and clinical research. LIPID MAPS is the appropriate host for this new partnership, since it is globally used by the biomedical community, including by many MRC-funded groups and large scale initiatives, in the UK and worldwide. Recent data from Google Analytics shows >72K users and >1.9M pageviews annually, with LMSD downloaded >4.6K times per year. LIPID MAPS became an ELIXIR-UK resource in 2020.

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Researchers

Dominic Campopiano (Co-Investigator)Edward Dennis (Co-Investigator)Len Stephens (Co-Investigator)Phillip Hawkins (Co-Investigator)Robert Andrews (Co-Investigator)Ruth Andrew (Co-Investigator)Simon Andrews (Co-Investigator)Valerie O'Donnell (Principal Investigator)William Griffiths (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

LIPID MAPS Resource and Database
Mass Spectrometry Based Lipidomics and Metabolomics to Drive Bioscience Discovery
MRC Cambridge Lipidomics Biomarker Research Initiative (CLBRI)
Funding for a QTrap Mass spectrometer for the Cardiff Lipidomic Group.
UK Consortium for MetAbolic Phenotyping (MAP UK)

Original classification

Research Grant

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