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Article ; Online: Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies.

Olshansky, Gavriel / Giles, Corey / Salim, Agus / Meikle, Peter J

2022 Volume 87, Page(s) 101177

Abstract: Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple ...

Abstract	Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort size and complexity have led to new challenges in sample collection, handling, storage, and preparation. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.
MeSH term(s)	Humans ; Lipidomics ; Metabolomics/methods ; Reproducibility of Results
Language	English
Publishing date	2022-06-30
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	282560-0
ISSN	1873-2194 ; 0079-6832 ; 0163-7827
ISSN (online)	1873-2194
ISSN	0079-6832 ; 0163-7827
DOI	10.1016/j.plipres.2022.101177
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Article ; Online: Comprehensive Targeted Lipidomic Profiling for Research and Clinical Applications.

Huynh, Kevin / Duong, Thy / Mellett, Natalie A / Cinel, Michelle / Giles, Corey / Meikle, Peter J

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2628, Page(s) 489–504

Abstract: Mass spectrometry remains one of the gold standard approaches in examining the lipidome in biological samples. Recently, advancements in chromatography and mass spectrometry approaches have enabled broad coverage of the lipidome. However, many ... ...

Abstract	Mass spectrometry remains one of the gold standard approaches in examining the lipidome in biological samples. Recently, advancements in chromatography and mass spectrometry approaches have enabled broad coverage of the lipidome. However, many limitations still exist, and lipidomic analysis often requires a fine balance between coverage of the lipidome, structural detail, and sample throughput. For biomedical and clinical research using human samples, the diversity and natural variation between different individuals necessitate larger sample numbers to identify significant associations with clinical outcomes and account for potential confounding factors. Here we describe a targeted lipidomics workflow that enables reproducible profiling of thousands of plasma samples in a systematic manner, while maintaining good structural detail and high coverage of the lipidome.
MeSH term(s)	Humans ; Lipidomics ; Lipids/chemistry ; Mass Spectrometry/methods ; Workflow
Chemical Substances	Lipids
Language	English
Publishing date	2023-02-13
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2978-9_29
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Article ; Online: Clinical lipidomics: realizing the potential of lipid profiling.

Meikle, Thomas G / Huynh, Kevin / Giles, Corey / Meikle, Peter J

Journal of lipid research

2021 Volume 62, Page(s) 100127

Abstract: Dysregulation of lipid metabolism plays a major role in the etiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic ... ...

Abstract	Dysregulation of lipid metabolism plays a major role in the etiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic profiling of clinically relevant biological samples, enabling researchers to associate lipid species and metabolic pathways with disease onset and progression. The resulting data serve not only to advance our fundamental knowledge of the underlying disease process but also to develop risk assessment models to assist in the diagnosis and management of disease. Currently, clinical applications of in-depth lipidomic profiling are largely limited to the use of research-based protocols in the analysis of population or clinical sample sets. However, we foresee the development of purpose-built clinical platforms designed for continuous operation and clinical integration-assisting health care providers with disease risk assessment, diagnosis, and monitoring. Herein, we review the current state of clinical lipidomics, including the use of research-based techniques and platforms in the analysis of clinical samples as well as assays already available to clinicians. With a primary focus on MS-based strategies, we examine instrumentation, analysis techniques, statistical models, prospective design of clinical platforms, and the possible pathways toward implementation of clinical lipidomics.
MeSH term(s)	Humans ; Lipid Metabolism ; Lipidomics ; Lipids/chemistry ; Neoplasms/metabolism
Chemical Substances	Lipids
Language	English
Publishing date	2021-09-25
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	80154-9
ISSN	1539-7262 ; 0022-2275
ISSN (online)	1539-7262
ISSN	0022-2275
DOI	10.1016/j.jlr.2021.100127
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Article ; Online: The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases.

Jurrjens, Aaron W / Seldin, Marcus M / Giles, Corey / Meikle, Peter J / Drew, Brian G / Calkin, Anna C

eLife

2023 Volume 12

Abstract: Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are ... ...

Abstract	Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
MeSH term(s)	Animals ; Humans ; Mice ; Cardiovascular Diseases/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Obesity/genetics ; Phenotype ; Risk Factors
Language	English
Publishing date	2023-03-31
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.86139
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Article ; Online: Plasma Lipidomic Profiling Identifies Elevated Triglycerides as Potential Risk Factor in Chemotherapy-Induced Peripheral Neuropathy.

Yeung, Nicole / Li, Tiffany / Lin, Hui-Ming / Timmins, Hannah C / Goldstein, David / Harrison, Michelle / Friedlander, Michael / Mahon, Kate L / Giles, Corey / Meikle, Peter J / Park, Susanna B / Horvath, Lisa G

JCO precision oncology

2024 Volume 8, Page(s) e2300690

Abstract: Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) is a dose-limiting side effect of cytotoxic cancer treatment, often necessitating dose reduction (DR) or chemotherapy discontinuation (CD). Studies on peripheral neuropathy related to ... ...

Abstract	Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) is a dose-limiting side effect of cytotoxic cancer treatment, often necessitating dose reduction (DR) or chemotherapy discontinuation (CD). Studies on peripheral neuropathy related to chemotherapy, obesity, and diabetes have implicated lipid metabolism. This study examined the association between circulating lipids and CIPN. Methods: Lipidomic analysis was performed on plasma samples from 137 patients receiving taxane-based treatment. CIPN was graded using Total Neuropathy Score-clinical version (TNSc) and patient-reported outcome measure European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-CIPN (EORTC-QLQ-CIPN20). Results: A significant proportion of elevated baseline lipids were associated with high-grade CIPN defined by TNSc and EORTC-QLQ-CIPN20 including triacylglycerols (TGs). Multivariable Cox regression on lipid species, adjusting for BMI, age, and diabetes, showed several elevated baseline TG associated with shorter time to DR/CD. Latent class analysis identified two baseline lipid profiles with differences in risk of CIPN (hazard ratio, 2.80 [95% CI, 1.50 to 5.23]; Conclusion: Elevated baseline plasma TG is associated with an increased risk of CIPN development and warrants further validation in other cohorts. Ultimately, this may enable therapeutic intervention.
MeSH term(s)	Humans ; Peripheral Nervous System Diseases/chemically induced ; Peripheral Nervous System Diseases/blood ; Female ; Male ; Lipidomics ; Middle Aged ; Triglycerides/blood ; Risk Factors ; Aged ; Antineoplastic Agents/adverse effects ; Antineoplastic Agents/therapeutic use ; Adult ; Taxoids/adverse effects ; Taxoids/therapeutic use ; Bridged-Ring Compounds
Chemical Substances	Triglycerides ; Antineoplastic Agents ; Taxoids ; taxane (1605-68-1) ; Bridged-Ring Compounds
Language	English
Publishing date	2024-05-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2473-4284
ISSN (online)	2473-4284
DOI	10.1200/PO.23.00690
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Article ; Online: Imputation of plasma lipid species to facilitate integration of lipidomic datasets.

Dakic, Aleksandar / Wu, Jingqin / Wang, Tingting / Huynh, Kevin / Mellett, Natalie / Duong, Thy / Beyene, Habtamu B / Magliano, Dianna J / Shaw, Jonathan E / Carrington, Melinda J / Inouye, Michael / Yang, Jean Y / Figtree, Gemma A / Curran, Joanne E / Blangero, John / Simes, John / Giles, Corey / Meikle, Peter J

Nature communications

2024 Volume 15, Issue 1, Page(s) 1540

Abstract: Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made ... ...

Abstract	Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.
MeSH term(s)	Humans ; Lipidomics ; Mass Spectrometry ; Plasma ; Lipids
Chemical Substances	Lipids
Language	English
Publishing date	2024-02-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-45838-3
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Article ; Online: Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

Jo, Taeho / Kim, Junpyo / Bice, Paula / Huynh, Kevin / Wang, Tingting / Arnold, Matthias / Meikle, Peter J / Giles, Corey / Kaddurah-Daouk, Rima / Saykin, Andrew J / Nho, Kwangsik

EBioMedicine

2023 Volume 97, Page(s) 104820

Abstract: Background: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. ...

Abstract	Background: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. Methods: The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. Findings: The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). Interpretation: Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. Funding: The specific funding of this article is provided in the acknowledgements section.
MeSH term(s)	Humans ; Aged ; Magnetic Resonance Imaging/methods ; Deep Learning ; Alzheimer Disease/diagnosis ; Neuroimaging/methods ; Metabolome ; Lipids ; Cognitive Dysfunction
Chemical Substances	Lipids
Language	English
Publishing date	2023-10-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2851331-9
ISSN	2352-3964
ISSN (online)	2352-3964
DOI	10.1016/j.ebiom.2023.104820
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Article: The Impact of Simvastatin on Lipidomic Markers of Cardiovascular Risk in Human Liver Cells Is Secondary to the Modulation of Intracellular Cholesterol.

Schooneveldt, Yvette L / Giles, Corey / Keating, Michael F / Mellett, Natalie A / Jurrjens, Aaron W / Paul, Sudip / Calkin, Anna C / Meikle, Peter J

Metabolites

2021 Volume 11, Issue 6

Abstract: Statins are the first-line lipid-lowering therapy for reducing cardiovascular disease (CVD) risk. A plasma lipid ratio of two phospholipids, PI(36:2) and PC(18:0_20:4), was previously identified to explain 58% of the relative CVD risk reduction ... ...

Abstract	Statins are the first-line lipid-lowering therapy for reducing cardiovascular disease (CVD) risk. A plasma lipid ratio of two phospholipids, PI(36:2) and PC(18:0_20:4), was previously identified to explain 58% of the relative CVD risk reduction associated with pravastatin, independent of a change in low-density lipoprotein-cholesterol. This ratio may be a potential biomarker for the treatment effect of statins; however, the underlying mechanisms linking this ratio to CVD risk remain unclear. In this study, we investigated the effect of altered cholesterol conditions on the lipidome of cultured human liver cells (Hep3B). Hep3B cells were treated with simvastatin (5 μM), cyclodextrin (20 mg/mL) or cholesterol-loaded cyclodextrin (20 mg/mL) for 48 hours and their lipidomes were examined. Induction of a low-cholesterol environment via simvastatin or cyclodextrin was associated with elevated levels of lipids containing arachidonic acid and decreases in phosphatidylinositol species and the PI(36:2)/PC(18:0_20:4) ratio. Conversely, increasing cholesterol levels via cholesterol-loaded cyclodextrin resulted in reciprocal regulation of these lipid parameters. Expression of genes involved in cholesterol and fatty acid synthesis supported the lipidomics data. These findings demonstrate that the PI(36:2)/PC(18:0_20:4) ratio responds to changes in intracellular cholesterol abundance per se, likely through a flux of the n-6 fatty acid pathway and altered phosphatidylinositol synthesis. These findings support this ratio as a potential marker for CVD risk reduction and may be useful in monitoring treatment response.
Language	English
Publishing date	2021-05-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo11060340
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Article ; Online: A lipid atlas of human and mouse immune cells provides insights into ferroptosis susceptibility.

Morgan, Pooranee K / Pernes, Gerard / Huynh, Kevin / Giles, Corey / Paul, Sudip / Smith, Adam Alexander T / Mellett, Natalie A / Liang, Amy / van Buuren-Milne, Tilly / Veiga, Camilla Bertuzzo / Collins, Thomas J C / Xu, Yangsong / Lee, Man K S / De Silva, T Michael / Meikle, Peter J / Lancaster, Graeme I / Murphy, Andrew J

Nature cell biology

2024 Volume 26, Issue 4, Page(s) 645–659

Abstract: The cellular lipidome comprises thousands of unique lipid species. Here, using mass spectrometry-based targeted lipidomics, we characterize the lipid landscape of human and mouse immune cells ( www.cellularlipidatlas.com ). Using this resource, we show ... ...

Abstract	The cellular lipidome comprises thousands of unique lipid species. Here, using mass spectrometry-based targeted lipidomics, we characterize the lipid landscape of human and mouse immune cells ( www.cellularlipidatlas.com ). Using this resource, we show that immune cells have unique lipidomic signatures and that processes such as activation, maturation and development impact immune cell lipid composition. To demonstrate the potential of this resource to provide insights into immune cell biology, we determine how a cell-specific lipid trait-differences in the abundance of polyunsaturated fatty acid-containing glycerophospholipids (PUFA-PLs)-influences immune cell biology. First, we show that differences in PUFA-PL content underpin the differential susceptibility of immune cells to ferroptosis. Second, we show that low PUFA-PL content promotes resistance to ferroptosis in activated neutrophils. In summary, we show that the lipid landscape is a defining feature of immune cell identity and that cell-specific lipid phenotypes underpin aspects of immune cell physiology.
MeSH term(s)	Humans ; Animals ; Mice ; Ferroptosis ; Fatty Acids, Unsaturated
Chemical Substances	Fatty Acids, Unsaturated
Language	English
Publishing date	2024-04-08
Publishing country	England
Document type	Journal Article
ZDB-ID	1474722-4
ISSN	1476-4679 ; 1465-7392
ISSN (online)	1476-4679
ISSN	1465-7392
DOI	10.1038/s41556-024-01377-z
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Article ; Online: Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts.

Beyene, Habtamu B / Giles, Corey / Huynh, Kevin / Wang, Tingting / Cinel, Michelle / Mellett, Natalie A / Olshansky, Gavriel / Meikle, Thomas G / Watts, Gerald F / Hung, Joseph / Hui, Jennie / Cadby, Gemma / Beilby, John / Blangero, John / Moses, Eric K / Shaw, Jonathan E / Magliano, Dianna J / Meikle, Peter J

Nature communications

2023 Volume 14, Issue 1, Page(s) 6280

Abstract: Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic ... ...

Abstract	Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/epidemiology ; Diabetes Mellitus, Type 2/complications ; Body Mass Index ; Obesity/complications ; Obesity/epidemiology ; Obesity/metabolism ; Risk Factors ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/complications ; Metabolic Syndrome/epidemiology ; Metabolic Syndrome/complications
Language	English
Publishing date	2023-10-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-41963-7
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